Metabolomics for Biomarker Discovery: Moving to the Clinic

Science.gov (United States)

Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

2015-01-01

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases. PMID:26090402

Improving the quality of biomarker discovery research: the right samples and enough of them.

Science.gov (United States)

Pepe, Margaret S; Li, Christopher I; Feng, Ziding

2015-06-01

Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs. The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE). We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions. Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified. The scientific rigor of discovery research should be improved. ©2015 American Association for Cancer Research.

Using Aptamers for Cancer Biomarker Discovery

Directory of Open Access Journals (Sweden)

Yun Min Chang

2013-01-01

Full Text Available Aptamers are single-stranded synthetic DNA- or RNA-based oligonucleotides that fold into various shapes to bind to a specific target, which includes proteins, metals, and molecules. Aptamers have high affinity and high specificity that are comparable to that of antibodies. They are obtained using iterative method, called (Systematic Evolution of Ligands by Exponential Enrichment SELEX and cell-based SELEX (cell-SELEX. Aptamers can be paired with recent advances in nanotechnology, microarray, microfluidics, and other technologies for applications in clinical medicine. One particular area that aptamers can shed a light on is biomarker discovery. Biomarkers are important in diagnosis and treatment of cancer. In this paper, we will describe ways in which aptamers can be used to discover biomarkers for cancer diagnosis and therapeutics.

Multiple inflammatory biomarker detection in a prospective cohort study: a cross-validation between well-established single-biomarker techniques and an electrochemiluminescense-based multi-array platform.

Directory of Open Access Journals (Sweden)

Bas C T van Bussel

Full Text Available BACKGROUND: In terms of time, effort and quality, multiplex technology is an attractive alternative for well-established single-biomarker measurements in clinical studies. However, limited data comparing these methods are available. METHODS: We measured, in a large ongoing cohort study (n = 574, by means of both a 4-plex multi-array biomarker assay developed by MesoScaleDiscovery (MSD and single-biomarker techniques (ELISA or immunoturbidimetric assay, the following biomarkers of low-grade inflammation: C-reactive protein (CRP, serum amyloid A (SAA, soluble intercellular adhesion molecule 1 (sICAM-1 and soluble vascular cell adhesion molecule 1 (sVCAM-1. These measures were realigned by weighted Deming regression and compared across a wide spectrum of subjects' cardiovascular risk factors by ANOVA. RESULTS: Despite that both methods ranked individuals' levels of biomarkers very similarly (Pearson's r all≥0.755 absolute concentrations of all biomarkers differed significantly between methods. Equations retrieved by the Deming regression enabled proper realignment of the data to overcome these differences, such that intra-class correlation coefficients were then 0.996 (CRP, 0.711 (SAA, 0.895 (sICAM-1 and 0.858 (sVCAM-1. Additionally, individual biomarkers differed across categories of glucose metabolism, weight, metabolic syndrome and smoking status to a similar extent by either method. CONCLUSIONS: Multiple low-grade inflammatory biomarker data obtained by the 4-plex multi-array platform of MSD or by well-established single-biomarker methods are comparable after proper realignment of differences in absolute concentrations, and are equally associated with cardiovascular risk factors, regardless of such differences. Given its greater efficiency, the MSD platform is a potential tool for the quantification of multiple biomarkers of low-grade inflammation in large ongoing and future clinical studies.

Aptamer-based multiplexed proteomic technology for biomarker discovery.

Directory of Open Access Journals (Sweden)

Larry Gold

2010-12-01

Full Text Available The interrogation of proteomes ("proteomics" in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma. Our current assay measures 813 proteins with low limits of detection (1 pM median, 7 logs of overall dynamic range (~100 fM-1 µM, and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD. We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

Aptamer-based multiplexed proteomic technology for biomarker discovery.

Science.gov (United States)

Gold, Larry; Ayers, Deborah; Bertino, Jennifer; Bock, Christopher; Bock, Ashley; Brody, Edward N; Carter, Jeff; Dalby, Andrew B; Eaton, Bruce E; Fitzwater, Tim; Flather, Dylan; Forbes, Ashley; Foreman, Trudi; Fowler, Cate; Gawande, Bharat; Goss, Meredith; Gunn, Magda; Gupta, Shashi; Halladay, Dennis; Heil, Jim; Heilig, Joe; Hicke, Brian; Husar, Gregory; Janjic, Nebojsa; Jarvis, Thale; Jennings, Susan; Katilius, Evaldas; Keeney, Tracy R; Kim, Nancy; Koch, Tad H; Kraemer, Stephan; Kroiss, Luke; Le, Ngan; Levine, Daniel; Lindsey, Wes; Lollo, Bridget; Mayfield, Wes; Mehan, Mike; Mehler, Robert; Nelson, Sally K; Nelson, Michele; Nieuwlandt, Dan; Nikrad, Malti; Ochsner, Urs; Ostroff, Rachel M; Otis, Matt; Parker, Thomas; Pietrasiewicz, Steve; Resnicow, Daniel I; Rohloff, John; Sanders, Glenn; Sattin, Sarah; Schneider, Daniel; Singer, Britta; Stanton, Martin; Sterkel, Alana; Stewart, Alex; Stratford, Suzanne; Vaught, Jonathan D; Vrkljan, Mike; Walker, Jeffrey J; Watrobka, Mike; Waugh, Sheela; Weiss, Allison; Wilcox, Sheri K; Wolfson, Alexey; Wolk, Steven K; Zhang, Chi; Zichi, Dom

2010-12-07

The interrogation of proteomes ("proteomics") in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (~100 fM-1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states. We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

Bioinformatics and biomarker discovery "Omic" data analysis for personalized medicine

CERN Document Server

Azuaje, Francisco

2010-01-01

This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided w

The path from biomarker discovery to regulatory qualification

CERN Document Server

Goodsaid, Federico

2013-01-01

The Path from Biomarker Discovery to Regulatory Qualification is a unique guide that focuses on biomarker qualification, its history and current regulatory settings in both the US and abroad. This multi-contributed book provides a detailed look at the next step to developing biomarkers for clinical use and covers overall concepts, challenges, strategies and solutions based on the experiences of regulatory authorities and scientists. Members of the regulatory, pharmaceutical and biomarker development communities will benefit the most from using this book-it is a complete and practical guide to biomarker qualification, providing valuable insight to an ever-evolving and important area of regulatory science. For complimentary access to chapter 13, 'Classic' Biomarkers of Liver Injury, by John R. Senior, Associate Director for Science, Food and Drug Administration, Silver Spring, Maryland, USA, please visit the following site:  http://tinyurl.com/ClassicBiomarkers Contains a collection of experiences of different...

Biomarker detection for disease diagnosis using cost-effective microfluidic platforms.

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Sanjay, Sharma T; Fu, Guanglei; Dou, Maowei; Xu, Feng; Liu, Rutao; Qi, Hao; Li, XiuJun

2015-11-07

Early and timely detection of disease biomarkers can prevent the spread of infectious diseases, and drastically decrease the death rate of people suffering from different diseases such as cancer and infectious diseases. Because conventional diagnostic methods have limited application in low-resource settings due to the use of bulky and expensive instrumentation, simple and low-cost point-of-care diagnostic devices for timely and early biomarker diagnosis is the need of the hour, especially in rural areas and developing nations. The microfluidics technology possesses remarkable features for simple, low-cost, and rapid disease diagnosis. There have been significant advances in the development of microfluidic platforms for biomarker detection of diseases. This article reviews recent advances in biomarker detection using cost-effective microfluidic devices for disease diagnosis, with the emphasis on infectious disease and cancer diagnosis in low-resource settings. This review first introduces different microfluidic platforms (e.g. polymer and paper-based microfluidics) used for disease diagnosis, with a brief description of their common fabrication techniques. Then, it highlights various detection strategies for disease biomarker detection using microfluidic platforms, including colorimetric, fluorescence, chemiluminescence, electrochemiluminescence (ECL), and electrochemical detection. Finally, it discusses the current limitations of microfluidic devices for disease biomarker detection and future prospects.

Proteome analysis of body fluids for amyotrophic lateral sclerosis biomarker discovery.

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Krüger, Thomas; Lautenschläger, Janin; Grosskreutz, Julian; Rhode, Heidrun

2013-01-01

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder of motor neurons leading to death of the patients, mostly within 2-5 years after disease onset. The pathomechanism of motor neuron degeneration is only partially understood and therapeutic strategies based on mechanistic insights are largely ineffective. The discovery of reliable biomarkers of disease diagnosis and progression is the sine qua non of both the revelation of insights into the ALS pathomechanism and the assessment of treatment efficacies. Proteomic approaches are an important pillar in ALS biomarker discovery. Cerebrospinal fluid is the most promising body fluid for differential proteome analyses, followed by blood (serum, plasma), and even urine and saliva. The present study provides an overview about reported peptide/protein biomarker candidates that showed significantly altered levels in certain body fluids of ALS patients. These findings have to be discussed according to proposed pathomechanisms to identify modifiers of disease progression and to pave the way for the development of potential therapeutic strategies. Furthermore, limitations and advantages of proteomic approaches for ALS biomarker discovery in different body fluids and reliable validation of biomarker candidates have been addressed. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

Science.gov (United States)

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Computational and Experimental Approaches to Cancer Biomarker Discovery

DEFF Research Database (Denmark)

Krzystanek, Marcin

of a patient’s response to a particular treatment, thus helping to avoid unnecessary treatment and unwanted side effects in non-responding individuals.Currently biomarker discovery is facilitated by recent advances in high-throughput technologies when association between a given biological phenotype...... and the state or level of a large number of molecular entities is investigated. Such associative analysis could be confounded by several factors, leading to false discoveries. For example, it is assumed that with the exception of the true biomarkers most molecular entities such as gene expression levels show...... random distribution in a given cohort. However, gene expression levels may also be affected by technical bias when the actual measurement technology or sample handling may introduce a systematic error. If the distribution of systematic errors correlates with the biological phenotype then the risk...

Approach to cerebrospinal fluid (CSF) biomarker discovery and evaluation in HIV infection.

Science.gov (United States)

Price, Richard W; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena; Smith, Richard D; Jacobs, Jon M; Brown, Joseph N; Gisslen, Magnus

2013-12-01

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previously-defined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

Energy Technology Data Exchange (ETDEWEB)

Price, Richard W.; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E.; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena S.; Smith, Richard D.; Jacobs, Jon M.; Brown, Joseph N.; Gisslen, Magnus

2013-12-13

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

International Nuclear Information System (INIS)

Smolinska, Agnieszka; Blanchet, Lionel; Buydens, Lutgarde M.C.; Wijmenga, Sybren S.

2012-01-01

Highlights: ► Procedures for acquisition of different biofluids by NMR. ► Recent developments in metabolic profiling of different biofluids by NMR are presented. ► The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. ► Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

Proteomic Approaches in Biomarker Discovery: New Perspectives in Cancer Diagnostics

Science.gov (United States)

Kocevar, Nina; Komel, Radovan

2014-01-01

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies. PMID:24550697

Metabolomics in cancer biomarker discovery: current trends and future perspectives.

Science.gov (United States)

Armitage, Emily G; Barbas, Coral

2014-01-01

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted. Copyright © 2013 Elsevier B.V. All rights reserved.

Systems biology and biomarker discovery

Energy Technology Data Exchange (ETDEWEB)

Rodland, Karin D.

2010-12-01

Medical practitioners have always relied on surrogate markers of inaccessible biological processes to make their diagnosis, whether it was the pallor of shock, the flush of inflammation, or the jaundice of liver failure. Obviously, the current implementation of biomarkers for disease is far more sophisticated, relying on highly reproducible, quantitative measurements of molecules that are often mechanistically associated with the disease in question, as in glycated hemoglobin for the diagnosis of diabetes [1] or the presence of cardiac troponins in the blood for confirmation of myocardial infarcts [2]. In cancer, where the initial symptoms are often subtle and the consequences of delayed diagnosis often drastic for disease management, the impetus to discover readily accessible, reliable, and accurate biomarkers for early detection is compelling. Yet despite years of intense activity, the stable of clinically validated, cost-effective biomarkers for early detection of cancer is pathetically small and still dominated by a handful of markers (CA-125, CEA, PSA) first discovered decades ago. It is time, one could argue, for a fresh approach to the discovery and validation of disease biomarkers, one that takes full advantage of the revolution in genomic technologies and in the development of computational tools for the analysis of large complex datasets. This issue of Disease Markers is dedicated to one such new approach, loosely termed the 'Systems Biology of Biomarkers'. What sets the Systems Biology approach apart from other, more traditional approaches, is both the types of data used, and the tools used for data analysis - and both reflect the revolution in high throughput analytical methods and high throughput computing that has characterized the start of the twenty first century.

Mass spectrometry imaging enriches biomarker discovery approaches with candidate mapping.

Science.gov (United States)

Scott, Alison J; Jones, Jace W; Orschell, Christie M; MacVittie, Thomas J; Kane, Maureen A; Ernst, Robert K

2014-01-01

Integral to the characterization of radiation-induced tissue damage is the identification of unique biomarkers. Biomarker discovery is a challenging and complex endeavor requiring both sophisticated experimental design and accessible technology. The resources within the National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Consortium, Medical Countermeasures Against Radiological Threats (MCART), allow for leveraging robust animal models with novel molecular imaging techniques. One such imaging technique, MALDI (matrix-assisted laser desorption ionization) mass spectrometry imaging (MSI), allows for the direct spatial visualization of lipids, proteins, small molecules, and drugs/drug metabolites-or biomarkers-in an unbiased manner. MALDI-MSI acquires mass spectra directly from an intact tissue slice in discrete locations across an x, y grid that are then rendered into a spatial distribution map composed of ion mass and intensity. The unique mass signals can be plotted to generate a spatial map of biomarkers that reflects pathology and molecular events. The crucial unanswered questions that can be addressed with MALDI-MSI include identification of biomarkers for radiation damage that reflect the response to radiation dose over time and the efficacy of therapeutic interventions. Techniques in MALDI-MSI also enable integration of biomarker identification among diverse animal models. Analysis of early, sublethally irradiated tissue injury samples from diverse mouse tissues (lung and ileum) shows membrane phospholipid signatures correlated with histological features of these unique tissues. This paper will discuss the application of MALDI-MSI for use in a larger biomarker discovery pipeline.

Application of mass spectrometry-based proteomics for biomarker discovery in neurological disorders

Directory of Open Access Journals (Sweden)

Venugopal Abhilash

2009-01-01

Full Text Available Mass spectrometry-based quantitative proteomics has emerged as a powerful approach that has the potential to accelerate biomarker discovery, both for diagnostic as well as therapeutic purposes. Proteomics has traditionally been synonymous with 2D gels but is increasingly shifting to the use of gel-free systems and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS. Quantitative proteomic approaches have already been applied to investigate various neurological disorders, especially in the context of identifying biomarkers from cerebrospinal fluid and serum. This review highlights the scope of different applications of quantitative proteomics in understanding neurological disorders with special emphasis on biomarker discovery.

Proteomics for discovery of candidate colorectal cancer biomarkers

Science.gov (United States)

Álvarez-Chaver, Paula; Otero-Estévez, Olalla; Páez de la Cadena, María; Rodríguez-Berrocal, Francisco J; Martínez-Zorzano, Vicenta S

2014-01-01

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers. PMID:24744574

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Energy Technology Data Exchange (ETDEWEB)

Smolinska, Agnieszka, E-mail: A.Smolinska@science.ru.nl [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Blanchet, Lionel [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Department of Biochemistry, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen (Netherlands); Buydens, Lutgarde M.C.; Wijmenga, Sybren S. [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands)

2012-10-31

Highlights: Black-Right-Pointing-Pointer Procedures for acquisition of different biofluids by NMR. Black-Right-Pointing-Pointer Recent developments in metabolic profiling of different biofluids by NMR are presented. Black-Right-Pointing-Pointer The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. Black-Right-Pointing-Pointer Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

Mass Spectrometry-Based Proteomics in Molecular Diagnostics: Discovery of Cancer Biomarkers Using Tissue Culture

Directory of Open Access Journals (Sweden)

Debasish Paul

2013-01-01

Full Text Available Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

Mass Spectrometry-Based Proteomics in Molecular Diagnostics: Discovery of Cancer Biomarkers Using Tissue Culture

Science.gov (United States)

Paul, Debasish; Kumar, Avinash; Gajbhiye, Akshada; Santra, Manas K.; Srikanth, Rapole

2013-01-01

Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches. PMID:23586059

Biomarker Discovery in Gulf War Veterans: Development of a War Illness Diagnostic Panel

Science.gov (United States)

2016-12-01

Award Number: W81XWH-12-1-0382 TITLE: Biomarker Discovery in Gulf War Veterans: Development of a War Illness Diagnostic Panel PRINCIPAL...SUBTITLE Biomarker Discovery in Gulf War Veterans: Development of a War Illness Diagnostic Panel 5a. CONTRACT NUMBER W81XWH-12-1-0382 5b. GRANT...of the 1990-1991 Gulf War are affected by Gulf War illness (GWI), the chronic condition currently defined only by veterans’ self-reported symptoms

Secreted proteins as a fundamental source for biomarker discovery

Czech Academy of Sciences Publication Activity Database

Šťastná, Miroslava; Van Eyk, J.E.

2012-01-01

Roč. 12, 4-5 (2012), s. 722-735 ISSN 1615-9853 Institutional research plan: CEZ:AV0Z40310501 Keywords : conditioned media * secreted proteins * proteomics * biomarker discovery Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 4.132, year: 2012

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery

Directory of Open Access Journals (Sweden)

Astrid Wachter

2015-11-01

Full Text Available Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

Science.gov (United States)

Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

2012-01-01

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

Energy Technology Data Exchange (ETDEWEB)

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar; Baker, Erin M.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

2018-05-01

Abstract. In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150–250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMSCID- TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Energy Technology Data Exchange (ETDEWEB)

Wang, Hui; Shi, Tujin; Qian, Wei-Jun; Liu, Tao; Kagan, Jacob; Srivastava, Sudhir; Smith, Richard D.; Rodland, Karin D.; Camp, David G.

2015-12-04

Mass spectrometry-based proteomics has become an indispensable tool in biomedical research with broad applications ranging from fundamental biology, systems biology, and biomarker discovery. Recent advances in LC-MS have made it become a major technology in clinical applications, especially in cancer biomarker discovery and verification. To overcome the challenges associated with the analysis of clinical samples, such as extremely wide dynamic range of protein concentrations in biofluids and the need to perform high throughput and accurate quantification, significant efforts have been devoted to improve the overall performance of LC-MS bases clinical proteomics. In this review, we summarize the recent advances in LC-MS in the aspect of cancer biomarker discovery and quantification, and discuss its potentials, limitations, and future perspectives.

Novel ageing-biomarker discovery using data-intensive technologies.

Science.gov (United States)

Griffiths, H R; Augustyniak, E M; Bennett, S J; Debacq-Chainiaux, F; Dunston, C R; Kristensen, P; Melchjorsen, C J; Navarrete, Santos A; Simm, A; Toussaint, O

2015-11-01

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for biomarker discovery; (1) microarray analyses and/or proteomics in cell systems e.g. endothelial progenitor cells or T cell ageing including a stress model; and (2) investigation of cellular material and plasma directly from tightly-defined proband subsets of different ages using proteomic, transcriptomic and miR array. The first approach provided longitudinal insight into endothelial progenitor and T cell ageing. This review describes the strategy and use of hypothesis-free, data-intensive approaches to explore cellular proteins, miR, mRNA and plasma proteins as healthy ageing biomarkers, using ageing models and directly within samples from adults of different ages. It considers the challenges associated with integrating multiple models and pilot studies as rational biomarkers for a large cohort study. From this approach, a number of high-throughput methods were developed to evaluate novel, putative biomarkers of ageing in the MARK-AGE cohort. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

Biomarker discovery in high grade sarcomas by mass spectrometry imaging

OpenAIRE

Lou, S.

2017-01-01

This thesis demonstrates a detailed biomarker discovery Mass Spectrometry Imaging workflow for histologically heterogeneous high grade sarcomas. Panels of protein and metabolite signatures were discovered either distinguishing different histological subtypes or stratifying high risk patients with poor survival.

Sparse Mbplsr for Metabolomics Data and Biomarker Discovery

DEFF Research Database (Denmark)

Karaman, İbrahim

2014-01-01

the link between high throughput metabolomics data generated on different analytical platforms, discover important metabolites deriving from the digestion processes in the gut, and automate metabolic pathway discovery from mass spectrometry. PLS (partial least squares) based chemometric methods were...

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

Science.gov (United States)

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.; Baker, Erin S.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

2017-09-01

In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150-250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10-200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins. [Figure not available: see fulltext.

Advances in fragment-based drug discovery platforms.

Science.gov (United States)

Orita, Masaya; Warizaya, Masaichi; Amano, Yasushi; Ohno, Kazuki; Niimi, Tatsuya

2009-11-01

Fragment-based drug discovery (FBDD) has been established as a powerful alternative and complement to traditional high-throughput screening techniques for identifying drug leads. At present, this technique is widely used among academic groups as well as small biotech and large pharmaceutical companies. In recent years, > 10 new compounds developed with FBDD have entered clinical development, and more and more attention in the drug discovery field is being focused on this technique. Under the FBDD approach, a fragment library of relatively small compounds (molecular mass = 100 - 300 Da) is screened by various methods and the identified fragment hits which normally weakly bind to the target are used as starting points to generate more potent drug leads. Because FBDD is still a relatively new drug discovery technology, further developments and optimizations in screening platforms and fragment exploitation can be expected. This review summarizes recent advances in FBDD platforms and discusses the factors important for the successful application of this technique. Under the FBDD approach, both identifying the starting fragment hit to be developed and generating the drug lead from that starting fragment hit are important. Integration of various techniques, such as computational technology, X-ray crystallography, NMR, surface plasmon resonance, isothermal titration calorimetry, mass spectrometry and high-concentration screening, must be applied in a situation-appropriate manner.

State of the Art in Tumor Antigen and Biomarker Discovery

International Nuclear Information System (INIS)

Even-Desrumeaux, Klervi; Baty, Daniel; Chames, Patrick

2011-01-01

Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology

Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry

DEFF Research Database (Denmark)

Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John

2016-01-01

automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked...

Automation on an Open-Access Platform of Alzheimer's Disease Biomarker Immunoassays.

Science.gov (United States)

Gille, Benjamin; Dedeene, Lieselot; Stoops, Erik; Demeyer, Leentje; Francois, Cindy; Lefever, Stefanie; De Schaepdryver, Maxim; Brix, Britta; Vandenberghe, Rik; Tournoy, Jos; Vanderstichele, Hugo; Poesen, Koen

2018-04-01

The lack of (inter-)laboratory standardization has hampered the application of universal cutoff values for Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers and their transfer to general clinical practice. The automation of the AD biomarker immunoassays is suggested to generate more robust results than using manual testing. Open-access platforms will facilitate the integration of automation for novel biomarkers, allowing the introduction of the protein profiling concept. A feasibility study was performed on an automated open-access platform of the commercial immunoassays for the 42-amino-acid isoform of amyloid-β (Aβ 1-42 ), Aβ 1-40 , and total tau in CSF. Automated Aβ 1-42 , Aβ 1-40 , and tau immunoassays were performed within predefined acceptance criteria for bias and imprecision. Similar accuracy was obtained for ready-to-use calibrators as for reconstituted lyophilized kit calibrators. When compared with the addition of a standard curve in each test run, the use of a master calibrator curve, determined before and applied to each batch analysis as the standard curve, yielded an acceptable overall bias of -2.6% and -0.9% for Aβ 1-42 and Aβ 1-40 , respectively, with an imprecision profile of 6.2% and 8.4%, respectively. Our findings show that transfer of commercial manual immunoassays to fully automated open-access platforms is feasible, as it performs according to universal acceptance criteria.

Implementation of proteomic biomarkers: making it work

Science.gov (United States)

Mischak, Harald; Ioannidis, John PA; Argiles, Angel; Attwood, Teresa K; Bongcam-Rudloff, Erik; Broenstrup, Mark; Charonis, Aristidis; Chrousos, George P; Delles, Christian; Dominiczak, Anna; Dylag, Tomasz; Ehrich, Jochen; Egido, Jesus; Findeisen, Peter; Jankowski, Joachim; Johnson, Robert W; Julien, Bruce A; Lankisch, Tim; Leung, Hing Y; Maahs, David; Magni, Fulvio; Manns, Michael P; Manolis, Efthymios; Mayer, Gert; Navis, Gerjan; Novak, Jan; Ortiz, Alberto; Persson, Frederik; Peter, Karlheinz; Riese, Hans H; Rossing, Peter; Sattar, Naveed; Spasovski, Goce; Thongboonkerd, Visith; Vanholder, Raymond; Schanstra, Joost P; Vlahou, Antonia

2012-01-01

While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare. PMID:22519700

Biomarkers as drug development tools: discovery, validation, qualification and use.

Science.gov (United States)

Kraus, Virginia B

2018-06-01

The 21st Century Cures Act, approved in the USA in December 2016, has encouraged the establishment of the national Precision Medicine Initiative and the augmentation of efforts to address disease prevention, diagnosis and treatment on the basis of a molecular understanding of disease. The Act adopts into law the formal process, developed by the FDA, of qualification of drug development tools, including biomarkers and clinical outcome assessments, to increase the efficiency of clinical trials and encourage an era of molecular medicine. The FDA and European Medicines Agency (EMA) have developed similar processes for the qualification of biomarkers intended for use as companion diagnostics or for development and regulatory approval of a drug or therapeutic. Biomarkers that are used exclusively for the diagnosis, monitoring or stratification of patients in clinical trials are not subject to regulatory approval, although their qualification can facilitate the conduct of a trial. In this Review, the salient features of biomarker discovery, analytical validation, clinical qualification and utilization are described in order to provide an understanding of the process of biomarker development and, through this understanding, convey an appreciation of their potential advantages and limitations.

Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

Science.gov (United States)

Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

2014-07-01

Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain- (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

Yeast surface display platform for rapid discovery of conformationally selective nanobodies

DEFF Research Database (Denmark)

McMahon, Conor; Baier, Alexander S.; Pascolutti, Roberta

2018-01-01

this problem, we report a fully in vitro platform for nanobody discovery based on yeast surface display. We provide a blueprint for identifying nanobodies, demonstrate the utility of the library by crystallizing a nanobody with its antigen, and most importantly, we utilize the platform to discover...... conformationally selective nanobodies to two distinct human GPCRs. To facilitate broad deployment of this platform, the library and associated protocols are freely available for nonprofit research....

Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry.

Science.gov (United States)

Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John; Oller Moreno, Sergio; Irincheeva, Irina; Valsesia, Armand; Astrup, Arne; Saris, Wim H M; Hager, Jörg; Kussmann, Martin; Dayon, Loïc

2016-02-05

The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.

The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort

Science.gov (United States)

Goldman, Jennifer G.; Alcalay, Roy N.; Xie, Tao; Tuite, Paul; Henchcliffe, Claire; Hogarth, Penelope; Amara, Amy W.; Frank, Samuel; Rudolph, Alice; Casaceli, Cynthia; Andrews, Howard; Gwinn, Katrina; Sutherland, Margaret; Kopil, Catherine; Vincent, Lona; Frasier, Mark

2016-01-01

ABSTRACT Background Identifying PD‐specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well‐characterized, clinically typical, moderate to advanced PD cohorts is critically needed. Methods BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross‐sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites. Results We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls. Conclusion Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society PMID:27113479

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

Science.gov (United States)

Tahara, Hideaki; Sato, Marimo; Thurin, Magdalena; Wang, Ena; Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Wigginton, Jon M; Ambs, Stefan; Akutsu, Yasunori; Chaussabel, Damien; Doki, Yuichiro; Eremin, Oleg; Fridman, Wolf Hervé; Hirohashi, Yoshihiko; Imai, Kohzoh; Jacobson, James; Jinushi, Masahisa; Kanamoto, Akira; Kashani-Sabet, Mohammed; Kato, Kazunori; Kawakami, Yutaka; Kirkwood, John M; Kleen, Thomas O; Lehmann, Paul V; Liotta, Lance; Lotze, Michael T; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Matsubara, Hisahiro; Mayrand-Chung, Shawmarie; Nakamura, Kiminori; Nishikawa, Hiroyoshi; Palucka, A Karolina; Petricoin, Emanuel F; Pos, Zoltan; Ribas, Antoni; Rivoltini, Licia; Sato, Noriyuki; Shiku, Hiroshi; Slingluff, Craig L; Streicher, Howard; Stroncek, David F; Takeuchi, Hiroya; Toyota, Minoru; Wada, Hisashi; Wu, Xifeng; Wulfkuhle, Julia; Yaguchi, Tomonori; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Marincola, Francesco M

2009-01-01

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

Directory of Open Access Journals (Sweden)

Rivoltini Licia

2009-06-01

Full Text Available Abstract Supported by the Office of International Affairs, National Cancer Institute (NCI, the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc and the United States Food and Drug Administration (FDA to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers

A POCT platform for sepsis biomarkers (Conference Presentation)

Science.gov (United States)

Baldini, Francesco; Adinolfi, Barbara; Berneschi, Simone; Bernini, Romeo; Giannetti, Ambra; Grimaldi, Immacolata Angelica; Persichetti, Gianluca; Testa, Genni; Tombelli, Sara; Trono, Cosimo

2017-06-01

Infectious diseases and sepsis, as a severe and potential medical condition in which the immune system overreacts and finally turns against itself, are a worldwide problem. As a matter of fact, it is considered the main cause of mortality in intensive care. For such a pathology, a timely diagnosis is essential, since it has been shown that each hour of delay in the administration of an effective pharmacological treatment increases the mortality rate of 7%. Therefore, the advent of a POCT platform for sepsis is highly requested by physicians. Biomarkers have gained importance for the diagnosis and treatment monitoring of septic patients, since biomarkers can indicate the severity of sepsis and can differentiate bacterial from viral and fungal infection, and systemic sepsis from local infection. The present paper deals with the development of fluorescence-based bioassays for the sepsis biomarkers and their integration on a multianalyte chip. Among the different biomarker candidates, the attention was focused on procalcitonin (PCT), C-reactive protein (CRP) and interleukine-6 (IL-6) as well as on soluble urokinase plasminogen activator receptor (suPAR) recently proposed as a very effective inflammatory marker, potentially capable of acting also as a prognostic biomarker. Starting point of this new setup was an already developed fluorescence-based optical platform, which makes use of multichannel polymethylmetacrylate chips for the detection of different bioanalytes, and the serial interrogation of the microfluidic channels of the chip. The novel proposed optical setup makes use of a suitable fluorescence excitation and detection scheme, capable of performing the simultaneous interrogation of all the channels. For the excitation part of the optical setup, a diffractive optical element is used which generates a pattern of parallel lines, for the simultaneous excitation of all the channels and for the optimization of the optical power distribution. For the detection part

Implementation of proteomic biomarkers: making it work.

Science.gov (United States)

Mischak, Harald; Ioannidis, John P A; Argiles, Angel; Attwood, Teresa K; Bongcam-Rudloff, Erik; Broenstrup, Mark; Charonis, Aristidis; Chrousos, George P; Delles, Christian; Dominiczak, Anna; Dylag, Tomasz; Ehrich, Jochen; Egido, Jesus; Findeisen, Peter; Jankowski, Joachim; Johnson, Robert W; Julien, Bruce A; Lankisch, Tim; Leung, Hing Y; Maahs, David; Magni, Fulvio; Manns, Michael P; Manolis, Efthymios; Mayer, Gert; Navis, Gerjan; Novak, Jan; Ortiz, Alberto; Persson, Frederik; Peter, Karlheinz; Riese, Hans H; Rossing, Peter; Sattar, Naveed; Spasovski, Goce; Thongboonkerd, Visith; Vanholder, Raymond; Schanstra, Joost P; Vlahou, Antonia

2012-09-01

While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare. © 2012 The Authors. European Journal of Clinical Investigation © 2012 Stichting European Society for Clinical Investigation Journal Foundation.

The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity

International Nuclear Information System (INIS)

Amacher, David E.

2010-01-01

Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in

atBioNet– an integrated network analysis tool for genomics and biomarker discovery

Directory of Open Access Journals (Sweden)

Ding Yijun

2012-07-01

Full Text Available Abstract Background Large amounts of mammalian protein-protein interaction (PPI data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. Results atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks. The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. Conclusion atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

Science.gov (United States)

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Open discovery: An integrated live Linux platform of Bioinformatics tools.

Science.gov (United States)

Vetrivel, Umashankar; Pilla, Kalabharath

2008-01-01

Historically, live linux distributions for Bioinformatics have paved way for portability of Bioinformatics workbench in a platform independent manner. Moreover, most of the existing live Linux distributions limit their usage to sequence analysis and basic molecular visualization programs and are devoid of data persistence. Hence, open discovery - a live linux distribution has been developed with the capability to perform complex tasks like molecular modeling, docking and molecular dynamics in a swift manner. Furthermore, it is also equipped with complete sequence analysis environment and is capable of running windows executable programs in Linux environment. Open discovery portrays the advanced customizable configuration of fedora, with data persistency accessible via USB drive or DVD. The Open Discovery is distributed free under Academic Free License (AFL) and can be downloaded from http://www.OpenDiscovery.org.in.

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

Science.gov (United States)

Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

2016-03-10

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.

A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

Directory of Open Access Journals (Sweden)

Jianwen She

2013-09-01

Full Text Available Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC, reversed-phase liquid chromatography (RP–LC, and gas chromatography (GC. All three techniques are coupled to a mass spectrometer (MS in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.

Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction

DEFF Research Database (Denmark)

Rossing, Kasper; Bosselmann, Helle Skovmand; Gustafsson, Finn

2016-01-01

and Results Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFr......Background Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF. Methods.......6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin. Conclusion CE-MS based urine proteome analysis served as a sensitive tool...

An integrative multi-platform analysis for discovering biomarkers of osteosarcoma

International Nuclear Information System (INIS)

Li, Guodong; Zhang, Wenjuan; Zeng, Huazong; Chen, Lei; Wang, Wenjing; Liu, Jilong; Zhang, Zhiyu; Cai, Zhengdong

2009-01-01

SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (m/z) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an integrative workflow combining data sets from both SELDI-TOF-MS and gene microarray analysis. After extracting the information for potential biomarkers from SELDI data and microarray analysis, their associations were further inferred by link-test to identify biomarkers that could likely be used for diagnosis. Immuno-blot analysis was then performed to examine whether the expression of the putative biomarkers were indeed altered in serum from patients with osteosarcoma. Six differentially expressed protein peaks with strong statistical significances were detected by SELDI-TOF-MS. Four of the proteins were up-regulated and two of them were down-regulated. Microarray analysis showed that, compared with an osteoblastic cell line, the expression of 653 genes was changed more than 2 folds in three osteosarcoma cell lines. While expression of 310 genes was increased, expression of the other 343 genes was decreased. The two sets of biomarkers candidates were combined by the link-test statistics, indicating that 13 genes were potential biomarkers for early diagnosis of osteosarcoma. Among these genes, cytochrome c1 (CYC-1) was selected for further experimental validation. Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis can accelerate the identification of tumor biomarkers. The result confirmed that CYC-1 may be a promising biomarker for early diagnosis of osteosarcoma

The use of time-resolved fluorescence in gel-based proteomics for improved biomarker discovery

Science.gov (United States)

Sandberg, AnnSofi; Buschmann, Volker; Kapusta, Peter; Erdmann, Rainer; Wheelock, Åsa M.

2010-02-01

This paper describes a new platform for quantitative intact proteomics, entitled Cumulative Time-resolved Emission 2-Dimensional Gel Electrophoresis (CuTEDGE). The CuTEDGE technology utilizes differences in fluorescent lifetimes to subtract the confounding background fluorescence during in-gel detection and quantification of proteins, resulting in a drastic improvement in both sensitivity and dynamic range compared to existing technology. The platform is primarily designed for image acquisition in 2-dimensional gel electrophoresis (2-DE), but is also applicable to 1-dimensional gel electrophoresis (1-DE), and proteins electroblotted to membranes. In a set of proof-of-principle measurements, we have evaluated the performance of the novel technology using the MicroTime 100 instrument (PicoQuant GmbH) in conjunction with the CyDye minimal labeling fluorochromes (GE Healthcare, Uppsala, Sweden) to perform differential gel electrophoresis (DIGE) analyses. The results indicate that the CuTEDGE technology provides an improvement in the dynamic range and sensitivity of detection of 3 orders of magnitude as compared to current state-of-the-art image acquisition instrumentation available for 2-DE (Typhoon 9410, GE Healthcare). Given the potential dynamic range of 7-8 orders of magnitude and sensitivities in the attomol range, the described invention represents a technological leap in detection of low abundance cellular proteins, which is desperately needed in the field of biomarker discovery.

Role of proteomics in the discovery of autism biomarkers

Energy Technology Data Exchange (ETDEWEB)

Ayadhi, L. A.; Halepoto, D. M. [King Saud Univ., Riyadh (Saudi Arabia). Dept. of Physiology

2013-02-15

The epidemiology of autism is continuously increasing all over the world with social, behavioural and economical burdens. Autism is considered as a multi-factorial disorder, influenced by genetic, neurological, environmental and immunological aspects. Autism is still believed to be incurable disorder with little information about the role of proteins patterns in the diagnosis of the disease. Knowing the applications of proteomic tools, it is possible to identify quantitative and qualitative protein patterns in a wide variety of tissues and body fluids such as blood, urine, saliva and cerebrospinal fluid in order to establish specific diagnostic and prognostic biomarkers. The aim of this review is to provide an overview of the various protocols available for proteomics by using mass spectrometry analysis, discuss reports in which these techniques have been previously applied in biomarker discovery for the diagnosis of autism, and consider the future development of this area of research. (author)

Role of proteomics in the discovery of autism biomarkers

International Nuclear Information System (INIS)

Ayadhi, L.A.; Halepoto, D.M.

2013-01-01

The epidemiology of autism is continuously increasing all over the world with social, behavioural and economical burdens. Autism is considered as a multi-factorial disorder, influenced by genetic, neurological, environmental and immunological aspects. Autism is still believed to be incurable disorder with little information about the role of proteins patterns in the diagnosis of the disease. Knowing the applications of proteomic tools, it is possible to identify quantitative and qualitative protein patterns in a wide variety of tissues and body fluids such as blood, urine, saliva and cerebrospinal fluid in order to establish specific diagnostic and prognostic biomarkers. The aim of this review is to provide an overview of the various protocols available for proteomics by using mass spectrometry analysis, discuss reports in which these techniques have been previously applied in biomarker discovery for the diagnosis of autism, and consider the future development of this area of research. (author)

Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

Directory of Open Access Journals (Sweden)

Ana Rita Lima

2016-08-01

Full Text Available Prostate cancer (PCa is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.

Candidate biomarker discovery and selection for ‘Granny Smith' superficial scald risk management and diagnosis, poster board

Science.gov (United States)

Discovery of candidate biomarkers for superficial scald, a peel disorder that develops during storage of susceptible apple cultivars, is part of a larger project aimed at developing biomarker-based risk-management and diagnostic tools for multiple apple postharvest disorders (http://www.tfrec.wsu.ed...

Informatics-guided procurement of patient samples for biomarker discovery projects in cancer research.

Science.gov (United States)

Suh, K Stephen; Remache, Yvonne K; Patel, Jalpa S; Chen, Steve H; Haystrand, Russell; Ford, Peggy; Shaikh, Anadil M; Wang, Jian; Goy, Andre H

2009-02-01

Modern cancer research for biomarker discovery program requires solving several tasks that are directly involved with patient sample procurement. One requirement is to construct a highly efficient workflow on the clinical side for the procurement to generate a consistent supply of high quality samples for research. This undertaking needs a network of interdepartmental collaborations and participations at various levels, including physical human interactions, information technology implementations and a bioinformatics tool that is highly effective and user-friendly to busy clinicians and researchers associated with the sample procurement. Collegial participation that is sequential but continual from one department to another demands dedicated bioinformatics software coordinating between the institutional clinic and the tissue repository facility. Participants in the process include admissions, consenting process, phlebotomy, surgery center and pathology. During this multiple step procedures, clinical data are collected for detailed analytical endpoints to supplement logistics of defining and validating the discovery of biomarkers.

Biomarker Gene Signature Discovery Integrating Network Knowledge

Directory of Open Access Journals (Sweden)

Holger Fröhlich

2012-02-01

Full Text Available Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.

Shotgun Proteomics and Biomarker Discovery

Directory of Open Access Journals (Sweden)

W. Hayes McDonald

2002-01-01

Full Text Available Coupling large-scale sequencing projects with the amino acid sequence information that can be gleaned from tandem mass spectrometry (MS/MS has made it much easier to analyze complex mixtures of proteins. The limits of this “shotgun” approach, in which the protein mixture is proteolytically digested before separation, can be further expanded by separating the resulting mixture of peptides prior to MS/MS analysis. Both single dimensional high pressure liquid chromatography (LC and multidimensional LC (LC/LC can be directly interfaced with the mass spectrometer to allow for automated collection of tremendous quantities of data. While there is no single technique that addresses all proteomic challenges, the shotgun approaches, especially LC/LC-MS/MS-based techniques such as MudPIT (multidimensional protein identification technology, show advantages over gel-based techniques in speed, sensitivity, scope of analysis, and dynamic range. Advances in the ability to quantitate differences between samples and to detect for an array of post-translational modifications allow for the discovery of classes of protein biomarkers that were previously unassailable.

Allergic asthma biomarkers using systems approaches

Directory of Open Access Journals (Sweden)

Gaurab eSircar

2014-01-01

Full Text Available Asthma is characterized by lung inflammation caused by complex interaction between the immune system and environmental factors such as allergens and inorganic pollutants. Recent research in this field is focused on discovering new biomarkers associated with asthma pathogenesis. This review illustrates updated research associating biomarkers of allergic asthma and their potential use in systems biology of the disease. We focus on biomolecules with altered expression, which may serve as inflammatory, diagnostic and therapeutic biomarkers of asthma discovered in human or experimental asthma model using genomic, proteomic and epigenomic approaches for gene and protein expression profiling. These include high-throughput technologies such as state of the art microarray and proteomics Mass Spectrometry (MS platforms. Emerging concepts of molecular interactions and pathways may provide new insights in searching potential clinical biomarkers. We summarized certain pathways with significant linkage to asthma pathophysiology by analyzing the compiled biomarkers. Systems approaches with this data can identify the regulating networks, which will eventually identify the key biomarkers to be used for diagnostics and drug discovery.

Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays.

Science.gov (United States)

Parker, Carol E; Borchers, Christoph H

2014-06-01

In its early years, mass spectrometry (MS)-based proteomics focused on the cataloging of proteins found in different species or different tissues. By 2005, proteomics was being used for protein quantitation, typically based on "proteotypic" peptides which act as surrogates for the parent proteins. Biomarker discovery is usually done by non-targeted "shotgun" proteomics, using relative quantitation methods to determine protein expression changes that correlate with disease (output given as "up-or-down regulation" or "fold-increases"). MS-based techniques can also perform "absolute" quantitation which is required for clinical applications (output given as protein concentrations). Here we describe the differences between these methods, factors that affect the precision and accuracy of the results, and some examples of recent studies using MS-based proteomics to verify cancer-related biomarkers. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery.

Science.gov (United States)

Quanico, Jusal; Franck, Julien; Wisztorski, Maxence; Salzet, Michel; Fournier, Isabelle

2017-01-01

Mapping provides a direct means to assess the impact of protein biomarkers and puts into context their relevance in the type of cancer being examined. To this end, mass spectrometry imaging (MSI) was developed to provide the needed spatial information which is missing in traditional liquid-based mass spectrometric proteomics approaches. Aptly described as a "molecular histology" technique, MSI gives an additional dimension in characterizing tumor biopsies, allowing for mapping of hundreds of molecules in a single analysis. A decade of developments focused on improving and standardizing MSI so that the technique can be translated into the clinical setting. This review describes the progress made in addressing the technological development that allows to bridge local protein detection by MSI to its identification and to illustrate its potential in studying various aspects of cancer biomarker discovery.

Plasma biomarker discovery in preeclampsia using a novel differential isolation technology for circulating extracellular vesicles.

Science.gov (United States)

Tan, Kok Hian; Tan, Soon Sim; Sze, Siu Kwan; Lee, Wai Kheong Ryan; Ng, Mor Jack; Lim, Sai Kiang

2014-10-01

To circumvent the complex protein milieu of plasma and discover robust predictive biomarkers for preeclampsia (PE), we investigate if phospholipid-binding ligands can reduce the milieu complexity by extracting plasma extracellular vesicles for biomarker discovery. Cholera toxin B chain (CTB) and annexin V (AV) which respectively binds GM1 ganglioside and phosphatidylserine were used to isolate extracellular vesicles from plasma of PE patients and healthy pregnant women. The proteins in the vesicles were identified using enzyme-linked immunosorbent assay, antibody array, and mass spectrometry. CTB and AV were found to bind 2 distinct groups of extracellular vesicles. Antibody array and enzyme-linked immunosorbent assay revealed that PE patients had elevated levels of CD105, interleukin-6, placental growth factor, tissue inhibitor of metallopeptidase 1, and atrial natriuretic peptide in cholera toxin B- but not AV-vesicles, and elevated levels of plasminogen activator inhibitor-1, pro-calcitonin, S100b, tumor growth factor β, vascular endothelial growth factor receptor 1, brain natriuretic peptide, and placental growth factor in both cholera toxin B- and AV-vesicles. CD9 level was elevated in cholera toxin B-vesicles but reduced in AV vesicles of PE patients. Proteome analysis revealed that in cholera toxin B-vesicles, 87 and 222 proteins were present only in PE patients and healthy pregnant women respectively while in AV-vesicles, 104 and 157 proteins were present only in PE and healthy pregnant women, respectively. This study demonstrated for the first time that CTB and AV bind unique extracellular vesicles, and their protein cargo reflects the disease state of the patient. The successful use of these 2 ligands to isolate circulating plasma extracellular vesicles for biomarker discovery in PE represents a novel technology for biomarker discovery that can be applied to other specialties. Copyright © 2014 Elsevier Inc. All rights reserved.

The EGS Data Collaboration Platform: Enabling Scientific Discovery

Energy Technology Data Exchange (ETDEWEB)

Weers, Jonathan D [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Johnston, Henry [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Huggins, Jay V [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2018-02-14

Collaboration in the digital age has been stifled in recent years. Reasonable responses to legitimate security concerns have created a virtual landscape of silos and fortified castles incapable of sharing information efficiently. This trend is unfortunately opposed to the geothermal scientific community's migration toward larger, more collaborative projects. To facilitate efficient sharing of information between team members from multiple national labs, universities, and private organizations, the 'EGS Collab' team has developed a universally accessible, secure data collaboration platform and has fully integrated it with the U.S. Department of Energy's (DOE) Geothermal Data Repository (GDR) and the National Geothermal Data System (NGDS). This paper will explore some of the challenges of collaboration in the modern digital age, highlight strategies for active data management, and discuss the integration of the EGS Collab data management platform with the GDR to enable scientific discovery through the timely dissemination of information.

Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis.

Science.gov (United States)

Bouchal, Pavel; Roumeliotis, Theodoros; Hrstka, Roman; Nenutil, Rudolf; Vojtesek, Borivoj; Garbis, Spiros D

2009-01-01

The present pilot study constitutes a proof-of-principle in the use of a quantitative LC-MS/MS based proteomic method for the comparative analysis of representative low-grade breast primary tumor tissues with and without metastases and metastasis in lymph node relative to the nonmetastatic tumor type. The study method incorporated iTRAQ stable isotope labeling, two-dimensional liquid chromatography, nanoelectrospray ionization and high resolution tandem mass spectrometry using the hybrid QqTOF platform (iTRAQ-2DLC-MS/MS). The principal aims of this study were (1) to define the protein spectrum obtainable using this approach, and (2) to highlight potential candidates for verification and validation studies focused on biomarkers involved in metastatic processes in breast cancer. The study resulted in the reproducible identification of 605 nonredundant proteins (p biomarker discovery program.

Molecular Elucidation of Disease Biomarkers at the Interface of Chemistry and Biology.

Science.gov (United States)

Zhang, Liqin; Wan, Shuo; Jiang, Ying; Wang, Yanyue; Fu, Ting; Liu, Qiaoling; Cao, Zhijuan; Qiu, Liping; Tan, Weihong

2017-02-22

Disease-related biomarkers are objectively measurable molecular signatures of physiological status that can serve as disease indicators or drug targets in clinical diagnosis and therapy, thus acting as a tool in support of personalized medicine. For example, the prostate-specific antigen (PSA) biomarker is now widely used to screen patients for prostate cancer. However, few such biomarkers are currently available, and the process of biomarker identification and validation is prolonged and complicated by inefficient methods of discovery and few reliable analytical platforms. Therefore, in this Perspective, we look at the advanced chemistry of aptamer molecules and their significant role as molecular probes in biomarker studies. As a special class of functional nucleic acids evolved from an iterative technology termed Systematic Evolution of Ligands by Exponential Enrichment (SELEX), these single-stranded oligonucleotides can recognize their respective targets with selectivity and affinity comparable to those of protein antibodies. Because of their fast turnaround time and exceptional chemical properties, aptamer probes can serve as novel molecular tools for biomarker investigations, particularly in assisting identification of new disease-related biomarkers. More importantly, aptamers are able to recognize biomarkers from complex biological environments such as blood serum and cell surfaces, which can provide direct evidence for further clinical applications. This Perspective highlights several major advancements of aptamer-based biomarker discovery strategies and their potential contribution to the practice of precision medicine.

Schizophrenia genomics and proteomics: are we any closer to biomarker discovery?

Directory of Open Access Journals (Sweden)

Kramer Alon

2009-01-01

Full Text Available Abstract The field of proteomics has made leaps and bounds in the last 10 years particularly in the fields of oncology and cardiovascular medicine. In comparison, neuroproteomics is still playing catch up mainly due to the relative complexity of neurological disorders. Schizophrenia is one such disorder, believed to be the results of multiple factors both genetic and environmental. Affecting over 2 million people in the US alone, it has become a major clinical and public health concern worldwide. This paper gives an update of schizophrenia biomarker research as reviewed by Lakhan in 2006 and gives us a rundown of the progress made during the last two years. Several studies demonstrate the potential of cerebrospinal fluid as a source of neuro-specific biomarkers. Genetic association studies are making headway in identifying candidate genes for schizophrenia. In addition, metabonomics, bioinformatics, and neuroimaging techniques are aiming to complete the picture by filling in knowledge gaps. International cooperation in the form of genomics and protein databases and brain banks is facilitating research efforts. While none of the recent developments described here in qualifies as biomarker discovery, many are likely to be stepping stones towards that goal.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

Science.gov (United States)

Zha, Haihong; Cai, Yuping; Yin, Yandong; Wang, Zhuozhong; Li, Kang; Zhu, Zheng-Jiang

2018-03-20

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS 2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

Implementation of a protein profiling platform developed as an academic-pharmaceutical industry collaborative effort.

Science.gov (United States)

Végvári, Akos; Magnusson, Mattias; Wallman, Lars; Ekström, Simon; Bolmsjö, Gunnar; Nilsson, Johan; Miliotis, Tasso; Ostling, Jörgen; Kjellström, Sven; Ottervald, Jan; Franzén, Bo; Hultberg, Hans; Marko-Varga, György; Laurell, Thomas

2008-06-01

As much attention has devoted to the proteome research during the last few years, biomarker discovery has become an increasingly hot area, potentially enabling the development of new assays for diagnosis and prognosis of severe diseases. This is the field of research interest where efforts originating from both academic and industrial groups should jointly work on solutions. In this paper, we would like to demonstrate the fruitful combination of both research domains where the scientific crossroads sprout fresh ideas from the basic research domain and how these are refined and tethered to industrial standards. We will present an approach that is based on novel microfluidic devices, utilizing their benefits in processing small-volume samples. Our biomarker discovery strategy, built around this platform, involves optimized samples processing (based on SPE and sample enrichment) and fast MALDI-MS readout. The identification of novel biomarkers at low-abundance level has been achieved by the utilization of a miniaturized sample handling platform, which offers clean-up and enrichment of proteins in one step. Complete automation has been realized in the form of a unique robotic instrumentation that is able to extract and transfer 96 samples onto standard MALDI target plates with high throughput. The developed platform was operated with a 60 sample turnaround per hour allowing sensitivities in femtomol regions of medium- and low-abundant target proteins from clinical studies on samples of multiple sclerosis and gastroesophageal reflux disease. Several proteins have been identified as new biomarkers from cerebrospinal fluid and esophagus epithelial cells.

Identification and Quantitation of Biomarkers for Radiation-Induced Injury via Mass Spectrometry

Science.gov (United States)

Jones, Jace W.; Scott, Alison J.; Tudor, Gregory; Xu, Pu-Ting; Jackson, Isabel L.; Vujaskovic, Zeljko; Booth, Catherine; MacVittie, Thomas J.; Ernst, Robert K.; Kane, Maureen A.

2013-01-01

Biomarker identification and validation for radiation exposure is a rapidly expanding field encompassing the need for well-defined animal models and advanced analytical techniques. The resources within the consortium, Medical Countermeasures Against Radiological Threats (MCART), provide a unique opportunity for accessing well-defined animal models that simulate the key sequelae of the acute radiation syndrome and the delayed effects of acute radiation exposure. Likewise, the use of mass spectrometry-based analytical techniques for biomarker discovery and validation enables a robust analytical platform that is amenable to a variety of sample matrices and considered the benchmark for bio-molecular identification and quantitation. Herein, we demonstrate the use of two targeted mass spectrometry approaches to link established MCART animal models to identified metabolite biomarkers. Circulating citrulline concentration was correlated to gross histological gastrointestinal tissue damage and retinoic acid production in lung tissue was established to be reduced at early and late time points post high dose irradiation. Going forward, the use of mass spectrometry-based metabolomics coupled to well-defined animal models provides the unique opportunity for comprehensive biomarker discovery. PMID:24276554

Multi-dimensional discovery of biomarker and phenotype complexes

Directory of Open Access Journals (Sweden)

Huang Kun

2010-10-01

Full Text Available Abstract Background Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature. Results In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI funded Chronic Lymphocytic Leukemia Research Consortium. Conclusions Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

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Serkova, Natalie J.; Standiford, Theodore J.

2011-01-01

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a “snapshot” reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis. PMID:21680948

Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers.

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Ping Yip

Full Text Available FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933 and CA-125 (AUC=0.907 were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800. To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912. Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the

Biomarker discovery and applications for foods and beverages: proteomics to nanoproteomics.

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Agrawal, Ganesh Kumar; Timperio, Anna Maria; Zolla, Lello; Bansal, Vipul; Shukla, Ravi; Rakwal, Randeep

2013-11-20

Foods and beverages have been at the heart of our society for centuries, sustaining humankind - health, life, and the pleasures that go with it. The more we grow and develop as a civilization, the more we feel the need to know about the food we eat and beverages we drink. Moreover, with an ever increasing demand for food due to the growing human population food security remains a major concern. Food safety is another growing concern as the consumers prefer varied foods and beverages that are not only traded nationally but also globally. The 21st century science and technology is at a new high, especially in the field of biological sciences. The availability of genome sequences and associated high-throughput sensitive technologies means that foods are being analyzed at various levels. For example and in particular, high-throughput omics approaches are being applied to develop suitable biomarkers for foods and beverages and their applications in addressing quality, technology, authenticity, and safety issues. Proteomics are one of those technologies that are increasingly being utilized to profile expressed proteins in different foods and beverages. Acquired knowledge and protein information have now been translated to address safety of foods and beverages. Very recently, the power of proteomic technology has been integrated with another highly sensitive and miniaturized technology called nanotechnology, yielding a new term nanoproteomics. Nanoproteomics offer a real-time multiplexed analysis performed in a miniaturized assay, with low-sample consumption and high sensitivity. To name a few, nanomaterials - quantum dots, gold nanoparticles, carbon nanotubes, and nanowires - have demonstrated potential to overcome the challenges of sensitivity faced by proteomics for biomarker detection, discovery, and application. In this review, we will discuss the importance of biomarker discovery and applications for foods and beverages, the contribution of proteomic technology in

WATER TEMPERATURE and other data from DISCOVERY, UNKNOWN PLATFORMS and other platforms from 1988-07-29 to 1991-07-26 (NODC Accession 9200096)

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National Oceanic and Atmospheric Administration, Department of Commerce — The water depth and temperature data were collected from UK ships CHALLENGER, DISCOVERY, DARWIN, FLEETWOOD, LIMA, OCEAN WEATHER STATION, and UNKNOWN PLATFORMS...

Sample preparation and fractionation for proteome analysis and cancer biomarker discovery by mass spectrometry.

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Ahmed, Farid E

2009-03-01

Sample preparation and fractionation technologies are one of the most crucial processes in proteomic analysis and biomarker discovery in solubilized samples. Chromatographic or electrophoretic proteomic technologies are also available for separation of cellular protein components. There are, however, considerable limitations in currently available proteomic technologies as none of them allows for the analysis of the entire proteome in a simple step because of the large number of peptides, and because of the wide concentration dynamic range of the proteome in clinical blood samples. The results of any undertaken experiment depend on the condition of the starting material. Therefore, proper experimental design and pertinent sample preparation is essential to obtain meaningful results, particularly in comparative clinical proteomics in which one is looking for minor differences between experimental (diseased) and control (nondiseased) samples. This review discusses problems associated with general and specialized strategies of sample preparation and fractionation, dealing with samples that are solution or suspension, in a frozen tissue state, or formalin-preserved tissue archival samples, and illustrates how sample processing might influence detection with mass spectrometric techniques. Strategies that dramatically improve the potential for cancer biomarker discovery in minimally invasive, blood-collected human samples are also presented.

Molecular correlates of trait anxiety: expanding biomarker discovery from protein expression to turnover

OpenAIRE

Zhang, Yaoyang

2010-01-01

Depression and anxiety disorders affect a great number of people in the world. Although remarkable efforts have been devoted to understanding the clinical and biological basis of these disorders, progress has been relatively slow. Furthermore, no laboratory test currently is available for diagnosis of anxiety and depression. These disorders are mainly diagnosed empirically on the basis of a doctor’s personal observations and experiences. Hence, discovery of biomarkers for these psychiatric di...

Genome-scale metabolic models as platforms for strain design and biological discovery.

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Mienda, Bashir Sajo

2017-07-01

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems' metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.

Multiplexed homogeneous proximity ligation assays for high throughput protein biomarker research in serological material

DEFF Research Database (Denmark)

Lundberg, Martin; Thorsen, Stine Buch; Assarsson, Erika

2011-01-01

A high throughput protein biomarker discovery tool has been developed based on multiplexed proximity ligation assays (PLA) in a homogeneous format in the sense of no washing steps. The platform consists of four 24-plex panels profiling 74 putative biomarkers with sub pM sensitivity each consuming...... sequences are united by DNA ligation upon simultaneous target binding forming a PCR amplicon. Multiplex PLA thereby converts multiple target analytes into real-time PCR amplicons that are individually quantificatied using microfluidic high capacity qPCR in nano liter volumes. The assay shows excellent...

Lung Cancer Serum Biomarker Discovery Using Label Free LC-MS/MS

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Zeng, Xuemei; Hood, Brian L.; Zhao, Ting; Conrads, Thomas P.; Sun, Mai; Gopalakrishnan, Vanathi; Grover, Himanshu; Day, Roger S.; Weissfeld, Joel L.; Wilson, David O.; Siegfried, Jill M.; Bigbee, William L.

2011-01-01

Introduction Lung cancer remains the leading cause of cancer-related death with poor survival due to the late stage at which lung cancer is typically diagnosed. Given the clinical burden from lung cancer, and the relatively favorable survival associated with early stage lung cancer, biomarkers for early detection of lung cancer are of important potential clinical benefit. Methods We performed a global lung cancer serum biomarker discovery study using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a set of pooled non-small cell lung cancer (NSCLC) case sera and matched controls. Immunoaffinity subtraction was used to deplete the top most abundant serum proteins; the remaining serum proteins were subjected to trypsin digestion and analyzed in triplicate by LC-MS/MS. The tandem mass spectrum data were searched against the human proteome database and the resultant spectral counting data were used to estimate the relative abundance of proteins across the case/control serum pools. The spectral counting derived abundances of some candidate biomarker proteins were confirmed with multiple reaction monitoring MS assays. Results A list of 49 differentially abundant candidate proteins was compiled by applying a negative binomial regression model to the spectral counting data (pbiomarkers with statistically significant differential abundance across the lung cancer case/control pools which, when validated, could improve lung cancer early detection. PMID:21304412

Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction.

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Kasper Rossing

Full Text Available Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF.Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972 discriminated between HFrEF patients (N = 94, sensitivity = 93.6% and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%. Interestingly, HFrEF103 showed low sensitivity (12.6% in individuals with diastolic left ventricular dysfunction (N = 176. The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin.CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

Using MALDI-IMS and MRM to stablish a pipeline for discovery and validation of tumor neovasculature biomarker candidates. — EDRN Public Portal

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In an effort to circumvent the limitations associated with biomarker discovery workflows involving cell lines and cell cultures, histology-directed MALDI protein profiling and imaging mass spectrometry will be used for identification of vascular endothelial biomarkers suitable for early prostate cancer detection by CEUS targeted molecular imaging

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

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Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

2016-08-18

A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed

Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification

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Book Chapter 18, titled Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification, will be published in the book titled High Performance Liquid Chromatography in Pesticide Residue Analysis (Part of the C...

Disease Classification and Biomarker Discovery Using ECG Data

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Rong Huang

2015-01-01

Full Text Available In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA, SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively.

GWATCH: a web platform for automated gene association discovery analysis

Science.gov (United States)

2014-01-01

Background As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations. Findings Here we present a dynamic web-based platform – GWATCH – that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis. Conclusions GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH. PMID:25374661

Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab.

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Arvidsson, Per I; Sandberg, Kristian; Sakariassen, Kjell S

2017-06-01

The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.

Analytical Pipeline for Discovery and Verification of Glycoproteins from Plasma-Derived Extracellular Vesicles as Breast Cancer Biomarkers.

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Chen, I-Hsuan; Aguilar, Hillary Andaluz; Paez Paez, J Sebastian; Wu, Xiaofeng; Pan, Li; Wendt, Michael K; Iliuk, Anton B; Zhang, Ying; Tao, W Andy

2018-05-15

Glycoproteins comprise more than half of current FDA-approved protein cancer markers, but the development of new glycoproteins as disease biomarkers has been stagnant. Here we present a pipeline to develop glycoproteins from extracellular vesicles (EVs) through integrating quantitative glycoproteomics with a novel reverse phase glycoprotein array and then apply it to identify novel biomarkers for breast cancer. EV glycoproteomics show promise in circumventing the problems plaguing current serum/plasma glycoproteomics and allowed us to identify hundreds of glycoproteins that have not been identified in blood. We identified 1,453 unique glycopeptides representing 556 glycoproteins in EVs, among which 20 were verified significantly higher in individual breast cancer patients. We further applied a novel glyco-specific reverse phase protein array to quantify a subset of the candidates. Together, this study demonstrates the great potential of this integrated pipeline for biomarker discovery.

From the endometrium physiology to a comprehensive strategy for the discovery of ovarian cancer biomarkers

OpenAIRE

Janos L. Tanyi; Nathalie Scholler

2011-01-01

The development of comprehensive strategies for biomarker discovery of gynecological cancers is needed. The unique physiology of the female genital track revolves around ovulatory cycles ending by the proteolysis of the endometrium triggered by progesterone decline during the last part of the luteal phase. Building on the known link between incessant ovulation and ovarian cancer, we hypothesize that life-long menstruations could damage neighboring organs such as fallopian tubes, ovaries and p...

Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms.

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Jinfeng Zou

Full Text Available BACKGROUND: There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. RESULTS: In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR control approach and that the reproducibility of DE peak detection could thereby be increased. CONCLUSIONS: Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.

A semiparametric modeling framework for potential biomarker discovery and the development of metabonomic profiles

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Dey Dipak K

2008-01-01

Full Text Available Abstract Background The discovery of biomarkers is an important step towards the development of criteria for early diagnosis of disease status. Recently electrospray ionization (ESI and matrix assisted laser desorption (MALDI time-of-flight (TOF mass spectrometry have been used to identify biomarkers both in proteomics and metabonomics studies. Data sets generated from such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information. Most recent attempts to process these types of data model each compound's intensity either discretely by positional (mass to charge ratio clustering or through each compounds' own intensity distribution. Traditionally data processing steps such as noise removal, background elimination and m/z alignment, are generally carried out separately resulting in unsatisfactory propagation of signals in the final model. Results In the present study a novel semi-parametric approach has been developed to distinguish urinary metabolic profiles in a group of traumatic patients from those of a control group consisting of normal individuals. Data sets obtained from the replicates of a single subject were used to develop a functional profile through Dirichlet mixture of beta distribution. This functional profile is flexible enough to accommodate variability of the instrument and the inherent variability of each individual, thus simultaneously addressing different sources of systematic error. To address instrument variability, all data sets were analyzed in replicate, an important issue ignored by most studies in the past. Different model comparisons were performed to select the best model for each subject. The m/z values in the window of the irregular pattern are then further recommended for possible biomarker discovery. Conclusion To the best of our knowledge this is the very first attempt to model the physical process behind the time-of flight mass

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health.

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Wilson, Jennifer L; Altman, Russ B

2018-02-01

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

Crowdsourcing Disease Biomarker Discovery Research: The IP4IC Study.

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Chancellor, Michael B; Bartolone, Sarah N; Veerecke, Andrew; Lamb, Laura E

2018-05-01

Biomarker discovery is limited by readily assessable, cost efficient human samples available in large numbers that represent the entire heterogeneity of the disease. We developed a novel, active participation crowdsourcing method to determine BP-RS (Bladder Permeability Defect Risk Score). It is based on noninvasive urinary cytokines to discriminate patients with interstitial cystitis/bladder pain syndrome who had Hunner lesions from controls and patients with interstitial cystitis/bladder pain syndrome but without Hunner lesions. We performed a national crowdsourcing study in cooperation with the Interstitial Cystitis Association. Patients answered demographic, symptom severity and urinary frequency questionnaires on a HIPAA (Health Insurance Portability and Accountability Act) compliant website. Urine samples were collected at home, stabilized with a preservative and sent to Beaumont Hospital for analysis. The expression of 3 urinary cytokines was used in a machine learning algorithm to develop BP-RS. The IP4IC study collected a total of 448 urine samples, representing 153 patients (147 females and 6 males) with interstitial cystitis/bladder pain syndrome, of whom 54 (50 females and 4 males) had Hunner lesions. A total of 159 female and 136 male controls also participated, who were age matched. A defined BP-RS was calculated to predict interstitial cystitis/bladder pain syndrome with Hunner lesions or a bladder permeability defect etiology with 89% validity. In this novel participation crowdsourcing study we obtained a large number of urine samples from 46 states, which were collected at home, shipped and stored at room temperature. Using a machine learning algorithm we developed BP-RS to quantify the risk of interstitial cystitis/bladder pain syndrome with Hunner lesions, which is indicative of a bladder permeability defect etiology. To our knowledge BP-RS is the first validated urine biomarker assay for interstitial cystitis/bladder pain syndrome and one of the

Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.

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Richard J Perrin

Full Text Available Biomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS for CSF biomarker discovery by (1 identifying reparable sources of technical variability, (2 assessing subject variance and residual technical variability for numerous CSF proteins, and (3 testing its ability to segregate samples on the basis of desired biomarker characteristics.Fourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i incomplete MAF and keratins; ii globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5% whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and

Unraveling the molecular repertoire of tears as a source of biomarkers: beyond ocular diseases.

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Pieragostino, Damiana; D'Alessandro, Michele; di Ioia, Maria; Di Ilio, Carmine; Sacchetta, Paolo; Del Boccio, Piero

2015-02-01

Proteomics and metabolomics investigations of body fluids present several challenges for biomarker discovery of several diseases. The search for biomarkers is actually conducted in different body fluids, even if the ideal biomarker can be found in an easily accessible biological fluid, because, if validated, the biomarker could be sought in the healthy population. In this regard, tears could be considered an optimum material obtained by noninvasive procedures. In the past years, the scientific community has become more interested in the study of tears for the research of new biomarkers not only for ocular diseases. In this review, we provide a discussion on the current state of biomarkers research in tears and their relevance for clinical practice, and report the main results of clinical proteomics studies on systemic and eye diseases. We summarize the main methods for tear samples analyses and report recent advances in "omics" platforms for tears investigations. Moreover, we want to take stock of the emerging field of metabolomics and lipidomics as a new and integrated approach to study protein-metabolites interplay for biomarkers research, where tears represent a still unexplored and attractive field. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children.

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Paul R West

Full Text Available The diagnosis of autism spectrum disorder (ASD at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age.To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment.Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD.A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set.This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1 determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2 gaining new insight into the biochemical mechanisms of various subtypes of ASD 3 identifying biomolecular targets for new modes of therapy, and 4 providing the basis for individualized treatment recommendations.

Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children

Science.gov (United States)

West, Paul R.; Amaral, David G.; Bais, Preeti; Smith, Alan M.; Egnash, Laura A.; Ross, Mark E.; Palmer, Jessica A.; Fontaine, Burr R.; Conard, Kevin R.; Corbett, Blythe A.; Cezar, Gabriela G.; Donley, Elizabeth L. R.; Burrier, Robert E.

2014-01-01

Background The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. Objectives To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. Methods Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. Results A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. Conclusions This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment

Mass spectrometry-driven drug discovery for development of herbal medicine.

Science.gov (United States)

Zhang, Aihua; Sun, Hui; Wang, Xijun

2018-05-01

Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes. © 2016 Wiley Periodicals, Inc.

Absolute Quantification of Toxicological Biomarkers via Mass Spectrometry.

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Lau, Thomas Y K; Collins, Ben C; Stone, Peter; Tang, Ning; Gallagher, William M; Pennington, Stephen R

2017-01-01

With the advent of "-omics" technologies there has been an explosion of data generation in the field of toxicology, as well as many others. As new candidate biomarkers of toxicity are being regularly discovered, the next challenge is to validate these observations in a targeted manner. Traditionally, these validation experiments have been conducted using antibody-based technologies such as Western blotting, ELISA, and immunohistochemistry. However, this often produces a significant bottleneck as the time, cost, and development of successful antibodies are often far outpaced by the generation of targets of interest. In response to this, there recently have been several developments in the use of triple quadrupole (QQQ) mass spectrometry (MS) as a platform to provide quantification of proteins. This technology does not require antibodies; it is typically less expensive and quicker to develop assays and has the opportunity for more accessible multiplexing. The speed of these experiments combined with their flexibility and ability to multiplex assays makes the technique a valuable strategy to validate biomarker discovery.

Biomarkers Discovery for Colorectal Cancer: A Review on Tumor Endothelial Markers as Perspective Candidates

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Łukasz Pietrzyk

2016-01-01

Full Text Available Colorectal cancer (CRC is the third most common cancer in the world. The early detection of CRC, during the promotion/progression stages, is an enormous challenge for a successful outcome and remains a fundamental problem in clinical approach. Despite the continuous advancement in diagnostic and therapeutic methods, there is a need for discovery of sensitive and specific, noninvasive biomarkers. Tumor endothelial markers (TEMs are associated with tumor-specific angiogenesis and are potentially useful to discriminate between tumor and normal endothelium. The most promising TEMs for oncogenic signaling in CRC appeared to be the TEM1, TEM5, TEM7, and TEM8. Overexpression of TEMs especially TEM1, TEM7, and TEM8 in colorectal tumor tissue compared to healthy tissue suggests their role in tumor blood vessels formation. Thus TEMs appear to be perspective candidates for early detection, monitoring, and treatment of CRC patients. This review provides an update on recent data on tumor endothelial markers and their possible use as biomarkers for screening, diagnosis, and therapy of colorectal cancer patients.

Biomarkers Discovery for Colorectal Cancer: A Review on Tumor Endothelial Markers as Perspective Candidates.

Science.gov (United States)

Pietrzyk, Łukasz

2016-01-01

Colorectal cancer (CRC) is the third most common cancer in the world. The early detection of CRC, during the promotion/progression stages, is an enormous challenge for a successful outcome and remains a fundamental problem in clinical approach. Despite the continuous advancement in diagnostic and therapeutic methods, there is a need for discovery of sensitive and specific, noninvasive biomarkers. Tumor endothelial markers (TEMs) are associated with tumor-specific angiogenesis and are potentially useful to discriminate between tumor and normal endothelium. The most promising TEMs for oncogenic signaling in CRC appeared to be the TEM1, TEM5, TEM7, and TEM8. Overexpression of TEMs especially TEM1, TEM7, and TEM8 in colorectal tumor tissue compared to healthy tissue suggests their role in tumor blood vessels formation. Thus TEMs appear to be perspective candidates for early detection, monitoring, and treatment of CRC patients. This review provides an update on recent data on tumor endothelial markers and their possible use as biomarkers for screening, diagnosis, and therapy of colorectal cancer patients.

A pilot study to evaluate the application of a generic protein standard panel for quality control of biomarker detection technologies

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Valdivia Hernan J

2011-08-01

Full Text Available Abstract Background Protein biomarker studies are currently hampered by a lack of measurement standards to demonstrate quality, reliability and comparability across multiple assay platforms. This is especially pertinent for immunoassays where multiple formats for detecting target analytes are commonly used. Findings In this pilot study a generic panel of six non-human protein standards (50 - 10^7 pg/mL of varying abundance was prepared as a quality control (QC material. Simulated "normal" and "diseased" panels of proteins were prepared in pooled human plasma and incorporated into immunoassays using the Meso Scale Discovery® (MSD® platform to illustrate reliable detection of the component proteins. The protein panel was also evaluated as a spike-in material for a model immunoassay involving detection of ovarian cancer biomarkers within individual human plasma samples. Our selected platform could discriminate between two panels of the proteins exhibiting small differences in abundance. Across distinct experiments, all component proteins exhibited reproducible signal outputs in pooled human plasma. When individual donor samples were used, half the proteins produced signals independent of matrix effects. These proteins may serve as a generic indicator of platform reliability. Each of the remaining proteins exhibit differential signals across the distinct samples, indicative of sample matrix effects, with the three proteins following the same trend. This subset of proteins may be useful for characterising the degree of matrix effects associated with the sample which may impact on the reliability of quantifying target diagnostic biomarkers. Conclusions We have demonstrated the potential utility of this panel of standards to act as a generic QC tool for evaluating the reproducibility of the platform for protein biomarker detection independent of serum matrix effects.

A Standardized and Reproducible Urine Preparation Protocol for Cancer Biomarkers Discovery

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Julia Beretov

2014-01-01

Full Text Available A suitable and standardized protein purification technique is essential to maintain consistency and to allow data comparison between proteomic studies for urine biomarker discovery. Ultimately, efforts should be made to standardize urine preparation protocols. The aim of this study was to develop an optimal analytical protocol to achieve maximal protein yield and to ensure that this method was applicable to examine urine protein patterns that distinguish disease and disease-free states. In this pilot study, we compared seven different urine sample preparation methods to remove salts, and to precipitate and isolate urinary proteins. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE profiles showed that the sequential preparation of urinary proteins by combining acetone and trichloroacetic acid (TCA alongside high speed centrifugation (HSC provided the best separation, and retained the most urinary proteins. Therefore, this approach is the preferred method for all further urine protein analysis.

High-throughput platform assay technology for the discovery of pre-microrna-selective small molecule probes.

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Lorenz, Daniel A; Song, James M; Garner, Amanda L

2015-01-21

MicroRNAs (miRNA) play critical roles in human development and disease. As such, the targeting of miRNAs is considered attractive as a novel therapeutic strategy. A major bottleneck toward this goal, however, has been the identification of small molecule probes that are specific for select RNAs and methods that will facilitate such discovery efforts. Using pre-microRNAs as proof-of-concept, herein we report a conceptually new and innovative approach for assaying RNA-small molecule interactions. Through this platform assay technology, which we term catalytic enzyme-linked click chemistry assay or cat-ELCCA, we have designed a method that can be implemented in high throughput, is virtually free of false readouts, and is general for all nucleic acids. Through cat-ELCCA, we envision the discovery of selective small molecule ligands for disease-relevant miRNAs to promote the field of RNA-targeted drug discovery and further our understanding of the role of miRNAs in cellular biology.

Custom database development and biomarker discovery methods for MALDI-TOF mass spectrometry-based identification of high-consequence bacterial pathogens.

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Tracz, Dobryan M; Tyler, Andrea D; Cunningham, Ian; Antonation, Kym S; Corbett, Cindi R

2017-03-01

A high-quality custom database of MALDI-TOF mass spectral profiles was developed with the goal of improving clinical diagnostic identification of high-consequence bacterial pathogens. A biomarker discovery method is presented for identifying and evaluating MALDI-TOF MS spectra to potentially differentiate biothreat bacteria from less-pathogenic near-neighbour species. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Mass spectrometry for biomarker development

Energy Technology Data Exchange (ETDEWEB)

Wu, Chaochao; Liu, Tao; Baker, Erin Shammel; Rodland, Karin D.; Smith, Richard D.

2015-06-19

Biomarkers potentially play a crucial role in early disease diagnosis, prognosis and targeted therapy. In the past decade, mass spectrometry based proteomics has become increasingly important in biomarker development due to large advances in technology and associated methods. This chapter mainly focuses on the application of broad (e.g. shotgun) proteomics in biomarker discovery and the utility of targeted proteomics in biomarker verification and validation. A range of mass spectrometry methodologies are discussed emphasizing their efficacy in the different stages in biomarker development, with a particular emphasis on blood biomarker development.

Integration of Antibody Array Technology into Drug Discovery and Development.

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Huang, Wei; Whittaker, Kelly; Zhang, Huihua; Wu, Jian; Zhu, Si-Wei; Huang, Ruo-Pan

Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.

Nutrition and biomarkers in psychiatry : research on micronutrient deficiencies in schizophrenia, the role of the intestine in the hyperserotonemia of autism, and a method for non-hypothesis driven discovery of biomarkers in urine

NARCIS (Netherlands)

Kemperman, Ramses Franciscus Jacobus

2007-01-01

This thesis describes the study of markers of nutrition and intestinal motility in mental disorders with a focus on schizophrenia and autism, and the development, evaluation and application of a biomarker discovery method for urine. The aim of the thesis is to investigate the role of long-chain

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers.

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Irigoyen, Antonio; Jimenez-Luna, Cristina; Benavides, Manuel; Caba, Octavio; Gallego, Javier; Ortuño, Francisco Manuel; Guillen-Ponce, Carmen; Rojas, Ignacio; Aranda, Enrique; Torres, Carolina; Prados, Jose

2018-01-01

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Top-down proteomics with mass spectrometry imaging: a pilot study towards discovery of biomarkers for neurodevelopmental disorders.

Directory of Open Access Journals (Sweden)

Hui Ye

Full Text Available In the developing mammalian brain, inhibition of NMDA receptor can induce widespread neuroapoptosis, inhibit neurogenesis and cause impairment of learning and memory. Although some mechanistic insights into adverse neurological actions of these NMDA receptor antagonists exist, our understanding of the full spectrum of developmental events affected by early exposure to these chemical agents in the brain is still limited. Here we attempt to gain insights into the impact of pharmacologically induced excitatory/inhibitory imbalance in infancy on the brain proteome using mass spectrometric imaging (MSI. Our goal was to study changes in protein expression in postnatal day 10 (P10 rat brains following neonatal exposure to the NMDA receptor antagonist dizocilpine (MK801. Analysis of rat brains exposed to vehicle or MK801 and comparison of their MALDI MS images revealed differential relative abundances of several proteins. We then identified these markers such as ubiquitin, purkinje cell protein 4 (PEP-19, cytochrome c oxidase subunits and calmodulin, by a combination of reversed-phase (RP HPLC fractionation and top-down tandem MS platform. More in-depth large scale study along with validation experiments will be carried out in the future. Overall, our findings indicate that a brief neonatal exposure to a compound that alters excitatory/inhibitory balance in the brain has a long term effect on protein expression patterns during subsequent development, highlighting the utility of MALDI-MSI as a discovery tool for potential biomarkers.

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma.

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Fujii, Kiyonaga; Nakamura, Haruhiko; Nishimura, Toshihide

2017-04-01

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.

From the endometrium physiology to a comprehensive strategy for the discovery of ovarian cancer biomarkers

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Janos L. Tanyi

2011-12-01

Full Text Available The development of comprehensive strategies for biomarker discovery of gynecological cancers is needed. The unique physiology of the female genital track revolves around ovulatory cycles ending by the proteolysis of the endometrium triggered by progesterone decline during the last part of the luteal phase. Building on the known link between incessant ovulation and ovarian cancer, we hypothesize that life-long menstruations could damage neighboring organs such as fallopian tubes, ovaries and peritoneum via endometrial secretions, and thus endometrium neighboring structures may have developed highly efficient protective strategies that could, in turn, be hijacked by cancer cells for survival and invasion. After literature review, we could classify the molecules involved in ovulation and menstruation pathways in three main categories: proteases, proteases inhibitors and cell-surface protectors. Strikingly, all validated biomarkers for ovarian cancers belong to at least one of these categories. We thus propose the development of comprehensive methods for identification of early diagnostic markers for gynecological cancers using systematical mapping and characterization of surface or soluble molecules belonging to physiological pathways linked to menstruation and differently expressed during luteal cycles.

Association of SNCA with Parkinson: replication in the Harvard NeuroDiscovery Center Biomarker Study

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Ding, Hongliu; Sarokhan, Alison K.; Roderick, Sarah S.; Bakshi, Rachit; Maher, Nancy E.; Ashourian, Paymon; Kan, Caroline G.; Chang, Sunny; Santarlasci, Andrea; Swords, Kyleen E.; Ravina, Bernard M.; Hayes, Michael T.; Sohur, U. Shivraj; Wills, Anne-Marie; Flaherty, Alice W.; Unni, Vivek K.; Hung, Albert Y.; Selkoe, Dennis J.; Schwarzschild, Michael A.; Schlossmacher, Michael G.; Sudarsky, Lewis R.; Growdon, John H.; Ivinson, Adrian J.; Hyman, Bradley T.; Scherzer, Clemens R.

2011-01-01

Background Mutations in the α-synuclein gene (SNCA) cause autosomal dominant forms of Parkinson’s disease, but the substantial risk conferred by this locus to the common sporadic disease has only recently emerged from genome-wide association studies. Methods Here we genotyped a prioritized non-coding variant in SNCA intron-4 in 344 patients with Parkinson’s and 275 controls from the longitudinal Harvard NeuroDiscovery Center Biomarker Study. Results The common minor allele of rs2736990 was associated with elevated disease susceptibility (odds ratio = 1.40, P value = 0.0032). Conclusions This result increases confidence in the notion that in many clinically well-characterized patients genetic variation in SNCA contributes to “sporadic” disease. PMID:21953863

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

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Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Mass spectrometric based approaches in urine metabolomics and biomarker discovery.

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Khamis, Mona M; Adamko, Darryl J; El-Aneed, Anas

2017-03-01

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing

MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides

International Nuclear Information System (INIS)

Yang, Xu; Lazar, Iulia M

2009-01-01

The discovery of novel protein biomarkers is essential in the clinical setting to enable early disease diagnosis and increase survivability rates. To facilitate differential expression analysis and biomarker discovery, a variety of tandem mass spectrometry (MS/MS)-based protein profiling techniques have been developed. For achieving sensitive detection and accurate quantitation, targeted MS screening approaches, such as multiple reaction monitoring (MRM), have been implemented. MCF-7 breast cancer protein cellular extracts were analyzed by 2D-strong cation exchange (SCX)/reversed phase liquid chromatography (RPLC) separations interfaced to linear ion trap MS detection. MS data were interpreted with the Sequest-based Bioworks software (Thermo Electron). In-house developed Perl-scripts were used to calculate the spectral counts and the representative fragment ions for each peptide. In this work, we report on the generation of a library of 9,677 peptides (p < 0.001), representing ~1,572 proteins from human breast cancer cells, that can be used for MRM/MS-based biomarker screening studies. For each protein, the library provides the number and sequence of detectable peptides, the charge state, the spectral count, the molecular weight, the parameters that characterize the quality of the tandem mass spectrum (p-value, DeltaM, Xcorr, DeltaCn, Sp, no. of matching a, b, y ions in the spectrum), the retention time, and the top 10 most intense product ions that correspond to a given peptide. Only proteins identified by at least two spectral counts are listed. The experimental distribution of protein frequencies, as a function of molecular weight, closely matched the theoretical distribution of proteins in the human proteome, as provided in the SwissProt database. The amino acid sequence coverage of the identified proteins ranged from 0.04% to 98.3%. The highest-abundance proteins in the cellular extract had a molecular weight (MW)<50,000. Preliminary experiments have

Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

Science.gov (United States)

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.

Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

Directory of Open Access Journals (Sweden)

Sandra Page

Full Text Available The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1 NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2 biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.

Metabolic profiling of an Echinostoma caproni infection in the mouse for biomarker discovery.

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Jasmina Saric

Full Text Available BACKGROUND: Metabolic profiling holds promise with regard to deepening our understanding of infection biology and disease states. The objectives of our study were to assess the global metabolic responses to an Echinostoma caproni infection in the mouse, and to compare the biomarkers extracted from different biofluids (plasma, stool, and urine in terms of characterizing acute and chronic stages of this intestinal fluke infection. METHODOLOGY/PRINCIPAL FINDINGS: Twelve female NMRI mice were infected with 30 E. caproni metacercariae each. Plasma, stool, and urine samples were collected at 7 time points up to day 33 post-infection. Samples were also obtained from non-infected control mice at the same time points and measured using (1H nuclear magnetic resonance (NMR spectroscopy. Spectral data were subjected to multivariate statistical analyses. In plasma and urine, an altered metabolic profile was already evident 1 day post-infection, characterized by reduced levels of plasma choline, acetate, formate, and lactate, coupled with increased levels of plasma glucose, and relatively lower concentrations of urinary creatine. The main changes in the urine metabolic profile started at day 8 post-infection, characterized by increased relative concentrations of trimethylamine and phenylacetylglycine and lower levels of 2-ketoisocaproate and showed differentiation over the course of the infection. CONCLUSION/SIGNIFICANCE: The current investigation is part of a broader NMR-based metabonomics profiling strategy and confirms the utility of this approach for biomarker discovery. In the case of E. caproni, a diagnosis based on all three biofluids would deliver the most comprehensive fingerprint of an infection. For practical purposes, however, future diagnosis might aim at a single biofluid, in which case urine would be chosen for further investigation, based on quantity of biomarkers, ease of sampling, and the degree of differentiation from the non

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

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Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

2017-03-01

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

Novel ageing-biomarker discovery using data-intensive technologies

OpenAIRE

Griffiths, H.R.; Augustyniak, E.M.; Bennett, S.J.; Debacq-Chainiaux, F.; Dunston, C.R.; Kristensen, P.; Melchjorsen, C.J.; Navarrete, Santos A.; Simm, A.; Toussaint, O.

2015-01-01

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for bioma...

Probing the O-glycoproteome of Gastric Cancer Cell Lines for Biomarker Discovery

DEFF Research Database (Denmark)

Vieira Campos, Diana Alexandra; Freitas, Daniela; Gomes, Joana

2015-01-01

biomarker assays. However, the current knowledge of secreted and circulating O-glycoproteins is limited. Here, we used the COSMC KO "SimpleCell" (SC) strategy to characterize the O-glycoproteome of two gastric cancer SC lines (AGS, MKN45) as well as a gastric cell line (KATO III) which naturally expresses...... at least partially truncated O-glycans. Overall we identified 499 O-glycoproteins and 1,236 O-glycosites in gastric cancer SCs, and a total 47 O-glycoproteins and 73 O-glycosites in the KATO III cell line. We next modified the glycoproteomic strategy to apply it to pools of sera from gastric cancer...... with the STn glycoform were further validated as being expressed in gastric cancer tissue. A proximity ligation assay was used to demonstrate that CD44 was expressed with the STn glycoform in gastric cancer tissues. The study provides a discovery strategy for aberrantly glycosylated O-glycoproteins and a set...

Mass spectrometry-based proteomic quest for diabetes biomarkers.

Science.gov (United States)

Shao, Shiying; Guo, Tiannan; Aebersold, Ruedi

2015-06-01

Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia, which affects hundreds of millions of individuals worldwide. Early diagnosis and complication prevention of DM are helpful for disease treatment. However, currently available DM diagnostic markers fail to achieve the goals. Identification of new diabetic biomarkers assisted by mass spectrometry (MS)-based proteomics may offer solution for the clinical challenges. Here, we review the current status of biomarker discovery in DM, and describe the pressure cycling technology (PCT)-Sequential Window Acquisition of all Theoretical fragment-ion (SWATH) workflow for sample-processing, biomarker discovery and validation, which may accelerate the current quest for DM biomarkers. This article is part of a Special Issue entitled: Medical Proteomics. Copyright © 2014 Elsevier B.V. All rights reserved.

Potential Peripheral Biomarkers for the Diagnosis of Alzheimer's Disease

Directory of Open Access Journals (Sweden)

Seema Patel

2011-01-01

Full Text Available Advances in the discovery of a peripheral biomarker for the diagnosis of Alzheimer's would provide a way to better detect the onset of this debilitating disease in a manner that is both noninvasive and universally available. This paper examines the current approaches that are being used to discover potential biomarker candidates available in the periphery. The search for a peripheral biomarker that could be utilized diagnostically has resulted in an extensive amount of studies that employ several biological approaches, including the assessment of tissues, genomics, proteomics, epigenetics, and metabolomics. Although a definitive biomarker has yet to be confirmed, advances in the understanding of the mechanisms of the disease and major susceptibility factors have been uncovered and reveal promising possibilities for the future discovery of a useful biomarker.

Adapting mass spectrometry-based platforms for clinical proteomics applications: The capillary electrophoresis coupled mass spectrometry paradigm

Science.gov (United States)

Metzger, Jochen; Luppa, Peter B.; Good, David M.; Mischak, Harald

2018-01-01

Single biomarker detection is common in clinical laboratories due to the currently available method spectrum. For various diseases, however, no specific single biomarker could be identified. A strategy to overcome this diagnostic void is to shift from single analyte detection to multiplexed biomarker profiling. Mass spectrometric methods were employed for biomarker discovery in body fluids. The enormous complexity of biofluidic proteome compartments implies upstream fractionation. For this reason, mass spectrometry (MS) was coupled to two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization, or capillary electrophoresis (CE). Differences in performance and operating characteristics make them differentially suited for routine laboratory applications. Progress in the field of clinical proteomics relies not only on the use of an adequate technological platform, but also on a fast and efficient proteomic workflow including standardized sample preparation, proteomic data processing, statistical validation of biomarker selection, and sample classification. Based on CE-MS analysis, we describe how proteomic technology can be implemented in a clinical laboratory environment. In the last part of this review, we give an overview of CE-MS-based clinical studies and present information on identity and biological significance of the identified peptide biomarkers providing evidence of disease-induced changes in proteolytic processing and posttranslational modification. PMID:19404829

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization.

Science.gov (United States)

Amur, S; LaVange, L; Zineh, I; Buckman-Garner, S; Woodcock, J

2015-07-01

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration. © 2015 American Society for Clinical Pharmacology and Therapeutics.

Discovery of Novel Biomarkers for Alzheimer’s Disease from Blood

Directory of Open Access Journals (Sweden)

Jintao Long

2016-01-01

Full Text Available Blood-based biomarkers for Alzheimer’s disease would be very valuable because blood is a more accessible biofluid and is suitable for repeated sampling. However, currently there are no robust and reliable blood-based biomarkers for practical diagnosis. In this study we used a knowledge-based protein feature pool and two novel support vector machine embedded feature selection methods to find panels consisting of two and three biomarkers. We validated these biomarker sets using another serum cohort and an RNA profile cohort from the brain. Our panels included the proteins ECH1, NHLRC2, HOXB7, FN1, ERBB2, and SLC6A13 and demonstrated promising sensitivity (>87%, specificity (>91%, and accuracy (>89%.

Towards Precision Medicine in the Clinic: From Biomarker Discovery to Novel Therapeutics.

Science.gov (United States)

Collins, Dearbhaile C; Sundar, Raghav; Lim, Joline S J; Yap, Timothy A

2017-01-01

Precision medicine continues to be the benchmark to which we strive in cancer research. Seeking out actionable aberrations that can be selectively targeted by drug compounds promises to optimize treatment efficacy and minimize toxicity. Utilizing these different targeted agents in combination or in sequence may further delay resistance to treatments and prolong antitumor responses. Remarkable progress in the field of immunotherapy adds another layer of complexity to the management of cancer patients. Corresponding advances in companion biomarker development, novel methods of serial tumor assessments, and innovative trial designs act synergistically to further precision medicine. Ongoing hurdles such as clonal evolution, intra- and intertumor heterogeneity, and varied mechanisms of drug resistance continue to be challenges to overcome. Large-scale data-sharing and collaborative networks using next-generation sequencing (NGS) platforms promise to take us further into the cancer 'ome' than ever before, with the goal of achieving successful precision medicine. Copyright © 2016 Elsevier Ltd. All rights reserved.

Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease.

Science.gov (United States)

Barallobre-Barreiro, Javier; Chung, Yuen-Li; Mayr, Manuel

2013-08-01

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Overview of Biomarkers and Surrogate Endpoints in Drug Development

Directory of Open Access Journals (Sweden)

John A. Wagner

2002-01-01

Full Text Available There are numerous factors that recommend the use of biomarkers in drug development including the ability to provide a rational basis for selection of lead compounds, as an aid in determining or refining mechanism of action or pathophysiology, and the ability to work towards qualification and use of a biomarker as a surrogate endpoint. Examples of biomarkers come from many different means of clinical and laboratory measurement. Total cholesterol is an example of a clinically useful biomarker that was successfully qualified for use as a surrogate endpoint. Biomarkers require validation in most circumstances. Validation of biomarker assays is a necessary component to delivery of high-quality research data necessary for effective use of biomarkers. Qualification is necessary for use of a biomarker as a surrogate endpoint. Putative biomarkers are typically identified because of a relationship to known or hypothetical steps in a pathophysiologic cascade. Biomarker discovery can also be effected by expression profiling experiment using a variety of array technologies and related methods. For example, expression profiling experiments enabled the discovery of adipocyte related complement protein of 30 kD (Acrp30 or adiponectin as a biomarker for in vivo activation of peroxisome proliferator-activated receptors (PPAR γ activity.

Cellular Models for Environmental Toxicant Biomarker Discovery

National Research Council Canada - National Science Library

Halverson, Kelly M; Lewsis, John A; Jackson, David A; Dennis, William; Brennan, Linda; Krakaner, Teresa

2006-01-01

...) is the development of biomarkers of exposure, effect, and susceptibility. As exposure monitoring using environmental sampling equipment can be impractical and doesn't account for differences in individual responses, new methodologies must be sought...

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

LENUS (Irish Health Repository)

Dakna, Mohammed

2010-12-10

Abstract Background The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School. Results We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential. Conclusions Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma

Science.gov (United States)

Ueda, Koji; Saichi, Naomi; Takami, Sachiko; Kang, Daechun; Toyama, Atsuhiko; Daigo, Yataro; Ishikawa, Nobuhisa; Kohno, Nobuoki; Tamura, Kenji; Shuin, Taro; Nakayama, Masato; Sato, Taka-Aki; Nakamura, Yusuke; Nakagawa, Hidewaki

2011-01-01

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000–5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273–283, FIBA 5–16, and LBN 306–313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens. PMID:21533267

A computational platform for MALDI-TOF mass spectrometry data: application to serum and plasma samples.

Science.gov (United States)

Mantini, Dante; Petrucci, Francesca; Pieragostino, Damiana; Del Boccio, Piero; Sacchetta, Paolo; Candiano, Giovanni; Ghiggeri, Gian Marco; Lugaresi, Alessandra; Federici, Giorgio; Di Ilio, Carmine; Urbani, Andrea

2010-01-03

Mass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data. MASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population. The analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at http://www.unich.it/proteomica/bioinf. (c) 2009 Elsevier B.V. All rights reserved.

Integrative analysis to select cancer candidate biomarkers to targeted validation

Science.gov (United States)

Heberle, Henry; Domingues, Romênia R.; Granato, Daniela C.; Yokoo, Sami; Canevarolo, Rafael R.; Winck, Flavia V.; Ribeiro, Ana Carolina P.; Brandão, Thaís Bianca; Filgueiras, Paulo R.; Cruz, Karen S. P.; Barbuto, José Alexandre; Poppi, Ronei J.; Minghim, Rosane; Telles, Guilherme P.; Fonseca, Felipe Paiva; Fox, Jay W.; Santos-Silva, Alan R.; Coletta, Ricardo D.; Sherman, Nicholas E.; Paes Leme, Adriana F.

2015-01-01

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS. PMID:26540631

Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach

International Nuclear Information System (INIS)

Kumar, Bhowmik Salil; Lee, Young-Joo; Yi, Hong Jae; Chung, Bong Chul; Jung, Byung Hwa

2010-01-01

In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg -1 day -1 or 250 mg kg -1 day -1 for a period of 7 days (n = 4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers.

Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach

Energy Technology Data Exchange (ETDEWEB)

Kumar, Bhowmik Salil [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); University of Science and Technology, (305-333) 113 Gwahangno, Yuseong-gu, Daejeon (Korea, Republic of); Lee, Young-Joo; Yi, Hong Jae [College of Pharmacy, Kyung Hee University, Hoegi-dong, Dongdaemun-gu, Seoul 130-791 (Korea, Republic of); Chung, Bong Chul [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); Jung, Byung Hwa, E-mail: jbhluck@kist.re.kr [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); University of Science and Technology, (305-333) 113 Gwahangno, Yuseong-gu, Daejeon (Korea, Republic of)

2010-02-19

In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg{sup -1} day{sup -1} or 250 mg kg{sup -1} day{sup -1} for a period of 7 days (n = 4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers.

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort.

Science.gov (United States)

Lam, Raymond W; Milev, Roumen; Rotzinger, Susan; Andreazza, Ana C; Blier, Pierre; Brenner, Colleen; Daskalakis, Zafiris J; Dharsee, Moyez; Downar, Jonathan; Evans, Kenneth R; Farzan, Faranak; Foster, Jane A; Frey, Benicio N; Geraci, Joseph; Giacobbe, Peter; Feilotter, Harriet E; Hall, Geoffrey B; Harkness, Kate L; Hassel, Stefanie; Ismail, Zahinoor; Leri, Francesco; Liotti, Mario; MacQueen, Glenda M; McAndrews, Mary Pat; Minuzzi, Luciano; Müller, Daniel J; Parikh, Sagar V; Placenza, Franca M; Quilty, Lena C; Ravindran, Arun V; Salomons, Tim V; Soares, Claudio N; Strother, Stephen C; Turecki, Gustavo; Vaccarino, Anthony L; Vila-Rodriguez, Fidel; Kennedy, Sidney H

2016-04-16

Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants

A novel compact mass detection platform for the open access (OA) environment in drug discovery and early development.

Science.gov (United States)

Gao, Junling; Ceglia, Scott S; Jones, Michael D; Simeone, Jennifer; Antwerp, John Van; Zhang, Li-Kang; Ross, Charles W; Helmy, Roy

2016-04-15

A new 'compact mass detector' co-developed with an instrument manufacturer (Waters Corporation) as an interface for liquid chromatography (LC), specifically Ultra-high performance LC(®) (UPLC(®) or UHPLC) analysis was evaluated as a potential new Open Access (OA) LC-MS platform in the Drug Discovery and Early Development space. This new compact mass detector based platform was envisioned to provide increased reliability and speed while exhibiting significant cost, noise, and footprint reductions. The new detector was evaluated in batch mode (typically 1-3 samples per run) to monitor reactions and check purity, as well as in High Throughput Screening (HTS) mode to run 24, 48, and 96 well plates. The latter workflows focused on screening catalysis conditions, process optimization, and library work. The objective of this investigation was to assess the performance, reliability, and flexibility of the compact mass detector in the OA setting for a variety of applications. The compact mass detector results were compared to those obtained by current OA LC-MS systems, and the capabilities and benefits of the compact mass detector in the open access setting for chemists in the drug discovery and development space are demonstrated. Copyright © 2016 Elsevier B.V. All rights reserved.

Revisiting biomarker discovery by plasma proteomics

DEFF Research Database (Denmark)

Geyer, Philipp E; Holdt, Lesca M; Teupser, Daniel

2017-01-01

slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker...

A Supramolecular Sensing Platform for Phosphate Anions and an Anthrax Biomarker in a Microfluidic Device

Directory of Open Access Journals (Sweden)

Jurriaan Huskens

2011-10-01

Full Text Available A supramolecular platform based on self-assembled monolayers (SAMs has been implemented in a microfluidic device. The system has been applied for the sensing of two different analyte types: biologically relevant phosphate anions and aromatic carboxylic acids, which are important for anthrax detection. A Eu(III-EDTA complex was bound to β-cyclodextrin monolayers via orthogonal supramolecular host-guest interactions. The self-assembly of the Eu(III-EDTA conjugate and naphthalene β-diketone as an antenna resulted in the formation of a highly luminescent lanthanide complex on the microchannel surface. Detection of different phosphate anions and aromatic carboxylic acids was demonstrated by monitoring the decrease in red emission following displacement of the antenna by the analyte. Among these analytes, adenosine triphosphate (ATP and pyrophosphate, as well as dipicolinic acid (DPA which is a biomarker for anthrax, showed a strong response. Parallel fabrication of five sensing SAMs in a single multichannel chip was performed, as a first demonstration of phosphate and carboxylic acid screening in a multiplexed format that allows a general detection platform for both analyte systems in a single test run with µM and nM detection sensitivity for ATP and DPA, respectively.

Metabolomics as a tool in the identification of dietary biomarkers.

Science.gov (United States)

Gibbons, Helena; Brennan, Lorraine

2017-02-01

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Science.gov (United States)

Phan, John H.; Moffitt, Richard A.; Stokes, Todd H.; Liu, Jian; Young, Andrew N.; Nie, Shuming; Wang, May D.

2013-01-01

Recent advances in biomarker discovery, biocomputing, and nanotechnology have raised new opportunities for the emerging field of personalized medicine in which disease detection, diagnosis, and therapy are tailored to each individual’s molecular profile, and also for predictive medicine that uses genetic/molecular information to predict disease development, progression, and clinical outcome. Here we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, together with prospects and challenges in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases. PMID:19409634

Averaged differential expression for the discovery of biomarkers in the blood of patients with prostate cancer.

Directory of Open Access Journals (Sweden)

V Uma Bai

Full Text Available The identification of a blood-based diagnostic marker is a goal in many areas of medicine, including the early diagnosis of prostate cancer. We describe the use of averaged differential display as an efficient mechanism for biomarker discovery in whole blood RNA. The process of averaging reduces the problem of clinical heterogeneity while simultaneously minimizing sample handling.RNA was isolated from the blood of prostate cancer patients and healthy controls. Samples were pooled and subjected to the averaged differential display process. Transcripts present at different levels between patients and controls were purified and sequenced for identification. Transcript levels in the blood of prostate cancer patients and controls were verified by quantitative RT-PCR. Means were compared using a t-test and a receiver-operating curve was generated. The Ring finger protein 19A (RNF19A transcript was identified as having higher levels in prostate cancer patients compared to healthy men through the averaged differential display process. Quantitative RT-PCR analysis confirmed a more than 2-fold higher level of RNF19A mRNA levels in the blood of patients with prostate cancer than in healthy controls (p = 0.0066. The accuracy of distinguishing cancer patients from healthy men using RNF19A mRNA levels in blood as determined by the area under the receiving operator curve was 0.727.Averaged differential display offers a simplified approach for the comprehensive screening of body fluids, such as blood, to identify biomarkers in patients with prostate cancer. Furthermore, this proof-of-concept study warrants further analysis of RNF19A as a clinically relevant biomarker for prostate cancer detection.

Concepts and principles of photodynamic therapy as an alternative antifungal discovery platform

Directory of Open Access Journals (Sweden)

George eTegos

2012-04-01

Full Text Available Opportunistic fungal pathogens may cause superficial or serious invasive infections, especially in immunocompromised and debilitated patients. Invasive mycoses represent an exponentially growing threat for human health due to a combination of slow diagnosis and the existence of relatively few classes of available and effective antifungal drugs. Therefore systemic fungal infections result in high attributable mortality. There is an urgent need to pursue and deploy novel and effective alternative anti-fungal countermeasures. Photodynamic therapy was established as a successful modality for malignancies and age-related macular degeneration but photodynamic inactivation has only recently been intensively investigated as an alternative antimicrobial discovery and development platform. The concept of photodynamic inactivation requires microbial exposure to either exogenous or endogenous photosensitizer molecules, followed by visible light energy, typically wavelengths in the red/near infrared region that cause the excitation of the photosensitizers resulting in the production of singlet oxygen and other reactive oxygen species that react with intracellular components, and consequently produce cell inactivation and death. Anti-fungal photodynamic therapy is an area of increasing interest, as research is advancing i to identify the photochemical and photophysical mechanisms involved in photoinactivation; ii to develop potent and clinically compatible photosensitizers; iii to understand how photoinactivation is affected by key microbial phenotypic elements multidrug resistance and efflux, virulence and pathogenesis determinants, and formation of biofilms; iv to explore novel photosensitizer delivery platforms and v to identify photoinactivation applications beyond the clinical setting such as environmental disinfectants.

Glycoblotting method allows for rapid and efficient glycome profiling of human Alzheimer's disease brain, serum and cerebrospinal fluid towards potential biomarker discovery.

Science.gov (United States)

Gizaw, Solomon T; Ohashi, Tetsu; Tanaka, Masakazu; Hinou, Hiroshi; Nishimura, Shin-Ichiro

2016-08-01

Understanding of the significance of posttranslational glycosylation in Alzheimer's disease (AD) is of growing importance for the investigation of the pathogenesis of AD as well as discovery research of the disease-specific serum biomarkers. We designed a standard protocol for the glycoblotting combined with MALDI-TOFMS to perform rapid and quantitative profiling of the glycan parts of glycoproteins (N-glycans) and glycosphingolipids (GSLs) using human AD's post-mortem samples such as brain tissues (dissected cerebral cortices such as frontal, parietal, occipital, and temporal domains), serum and cerebrospinal fluid (CSF). The structural profiles of the major N-glycans released from glycoproteins and the total expression levels of the glycans were found to be mostly similar between the brain tissues of the AD patients and those of the normal control group. In contrast, the expression levels of the serum and CSF protein N-glycans such as bisect-type and multiply branched glycoforms were increased significantly in AD patient group. In addition, the levels of some gangliosides such as GM1, GM2 and GM3 appeared to alter in the AD patient brain and serum samples when compared with the normal control groups. Alteration of the expression levels of major N- and GSL-glycans in human brain tissues, serum and CSF of AD patients can be monitored quantitatively by means of the glycoblotting-based standard protocols. The changes in the expression levels of the glycans derived from the human post-mortem samples uncovered by the standardized glycoblotting method provides potential serum biomarkers in central nervous system disorders and can contribute to the insight into the molecular mechanisms in the pathogenesis of neurodegenerative diseases and future drug discovery. Most importantly, the present preliminary trials using human post-mortem samples of AD patients suggest that large-scale serum glycomics cohort by means of various-types of human AD patients as well as the normal

ALS Biomarkers for Therapy Development: State of the Field & Future Directions

Science.gov (United States)

Benatar, Michael; Boylan, Kevin; Jeromin, Andreas; Rutkove, Seward B.; Berry, James; Atassi, Nazem; Bruijn, Lucie

2015-01-01

Biomarkers have become the focus of intense research in the field of amyotrophic lateral sclerosis (ALS), with the hope that they might aid therapy development efforts. Notwithstanding the discovery of many candidate biomarkers, none have yet emerged as validated tools for drug development. In this review we present a nuanced view of biomarkers based on the perspective of the FDA; highlight the distinction between discovery and validation; describe existing and emerging resources; review leading biological fluid-based, electrophysiological and neuroimaging candidates relevant to therapy development efforts; discuss lessons learned from biomarker initiatives in related neurodegenerative diseases; and outline specific steps that we, as a field, might take in order to hasten the development and validation of biomarkers that will prove useful in enhancing efforts to develop effective treatments for ALS patients. Most important among these perhaps is the proposal to establish a federated ALS Biomarker Consortium (ABC) in which all interested and willing stakeholders may participate with equal opportunity to contribute to the broader mission of biomarker development and validation. PMID:26574709

Radiation Biomarker Research Using Mass Spectrometry

National Research Council Canada - National Science Library

Bach, Stephan B; Hubert, Walter

2007-01-01

.... This review is intended to give an overview of mass spectrometry and its application to biological systems and biomarker discovery and how that might relate to relevant radiation dosimetry studies...

Discovery of prognostic biomarker candidates of lacunar infarction by quantitative proteomics of microvesicles enriched plasma.

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Arnab Datta

Full Text Available Lacunar infarction (LACI is a subtype of acute ischemic stroke affecting around 25% of all ischemic stroke cases. Despite having an excellent recovery during acute phase, certain LACI patients have poor mid- to long-term prognosis due to the recurrence of vascular events or a decline in cognitive functions. Hence, blood-based biomarkers could be complementary prognostic and research tools.Plasma was collected from forty five patients following a non-disabling LACI along with seventeen matched control subjects. The LACI patients were monitored prospectively for up to five years for the occurrence of adverse outcomes and grouped accordingly (i.e., LACI-no adverse outcome, LACI-recurrent vascular event, and LACI-cognitive decline without any recurrence of vascular events. Microvesicles-enriched fractions isolated from the pooled plasma of four groups were profiled by an iTRAQ-guided discovery approach to quantify the differential proteome. The data have been deposited to the ProteomeXchange with identifier PXD000748. Bioinformatics analysis and data mining revealed up-regulation of brain-specific proteins including myelin basic protein, proteins of coagulation cascade (e.g., fibrinogen alpha chain, fibrinogen beta chain and focal adhesion (e.g., integrin alpha-IIb, talin-1, and filamin-A while albumin was down-regulated in both groups of patients with adverse outcome.This data set may offer important insight into the mechanisms of poor prognosis and provide candidate prognostic biomarkers for validation on larger cohort of individual LACI patients.

Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography-mass spectrometry.

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Lee, Jung-Eun; Lee, Yu Ho; Kim, Se-Yun; Kim, Yang Gyun; Moon, Ju-Young; Jeong, Kyung-Hwan; Lee, Tae Won; Ihm, Chun-Gyoo; Kim, Sooah; Kim, Kyoung Heon; Kim, Dong Ki; Kim, Yon Su; Kim, Chan-Duck; Park, Cheol Whee; Lee, Do Yup; Lee, Sang-Ho

2016-07-01

The goal of this study is to identify systematic biomarker panel for primary nephrotic syndromes from urine samples by applying a non-target metabolite profiling, and to validate their utility in independent sampling and analysis by multiplex statistical approaches. Nephrotic syndrome (NS) is a nonspecific kidney disorder, which is mostly represented by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous glomerulonephritis (MGN). Since urine metabolites may mirror disease-specific functional perturbations in kidney injury, we examined urine samples for distinctive metabolic changes to identify biomarkers for clinical applications. We developed unbiased multi-component covarianced models from a discovery set with 48 samples (12 healthy controls, 12 MCD, 12 FSGS, and 12 MGN). To extensively validate their diagnostic potential, new batch from 54 patients with primary NS were independently examined a year after. In the independent validation set, the model including citric acid, pyruvic acid, fructose, ethanolamine, and cysteine effectively discriminated each NS using receiver operating characteristic (ROC) analysis except MCD-MGN comparison; nonetheless an additional metabolite multi-composite greatly improved the discrimination power between MCD and MGN. Finally, we proposed the re-constructed metabolic network distinctively dysregulated by the different NSs that may deepen comprehensive understanding of the disease mechanistic, and help the enhanced identification of NS and therapeutic plans for future. Copyright © 2016 Elsevier B.V. All rights reserved.

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

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Mino-Kenudson, Mari

2017-10-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

Mining PubMed for Biomarker-Disease Associations to Guide Discovery

OpenAIRE

Jessen, Walter; Landschulz, Katherine; Turi, Thomas; Reams, Rachel

2012-01-01

Biomedical knowledge is growing exponentially; however, meta-knowledge around the data is often lacking. PubMed is a database comprising more than 21 million citations for biomedical literature from MEDLINE and additional life science journals dating back to the 1950s. To explore the use and frequency of biomarkers across human disease, we mined PubMed for biomarker-disease associations. We then ranked the top 100 linked diseases by relevance and mapped them to medical subject headings (MeSH)...

Use of the local false discovery rate for identification of metabolic biomarkers in rat urine following Genkwa Flos-induced hepatotoxicity.

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Zuojing Li

Full Text Available Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR and HPLC/MS (high-performance liquid chromatography with mass spectrometry. Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA, LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.

What is a biomarker? Research investments and lack of clinical integration necessitate a review of biomarker terminology and validation schema.

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Ptolemy, Adam S; Rifai, Nader

2010-01-01

A continual trend of annual growth can be seen within research devoted to the discovery and validation of disease biomarkers within both the natural and clinical sciences. This expansion of intellectual endeavours was quantified through database searches of (a) research grant awards provided by the various branches of the National Institutes of Health (NIH) and (b) academic publications. A search of awards presented between 1986 and 2009 revealed a total of 28,856 grants awarded by the NIH containing the term "biomarker". The total funds for these awards in 2008 and 2009 alone were over $2.5 billion. During the same respective time frames, searches of "biomarker" and either "discovery", "genomics", "proteomics" or "metabolomics" yielded a total of 4,928 NIH grants whose combined funding exceeded $1.2 billion. The derived trend in NIH awards paralleled the annual expansion in "biomarker" literature. A PubMed search for the term, between 1990 and 2009, revealed a total of 441,510 published articles, with 38,457 published in 2008. These enormous investments and academic outputs however have not translated into the expected integration of new biomarkers for patient care. For example no proteomics derived biomarkers are currently being utilized in routine clinical management. This translational chasm necessitates a review of the previously proposed biomarker definitions and evaluation schema. A subsequent discussion of both the analytical and pre-analytical considerations for such research is also presented within. This required knowledge should aid scientists in their pursuit and validation of new biological markers of disease.

An In Vivo Platform for Rapid High-Throughput Antitubercular Drug Discovery

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Kevin Takaki

2012-07-01

Full Text Available Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model’s utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

DEFF Research Database (Denmark)

Hickey, John M.; Chiurugwi, Tinashe; Mackay, Ian

2017-01-01

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human...... that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying...... use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform...

Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer.

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Rachel M Ostroff

Full Text Available BACKGROUND: Lung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥ 10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC. CONCLUSIONS/SIGNIFICANCE: This study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels

Miocene climate variations in the Moesian Platform sediments based on sedimentology and biomarkers

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Butiseaca, Geanina; Vasiliev, Iuliana; Rabagia, Traian; Dinu, Corneliu; Mulch, Andreas

2017-04-01

During the Miocene the Moesian Platform (southern Romania and northern Bulgaria) had a complicated flexural behavior due to the mobility of the nearby orogens. The different behavior induced varying sediment charges, sediment distribution and sediment types. The northern part of the study area (on which the Dacian Basin is overlaid) is characterized by siliciclastic units with dominantly deep facieses, while the southern part is characterized by carbonate production in shallower basin waters. Since the Miocene, the Dacian and Black Sea basins have been highly sensitive to fluctuations in the hydrological cycle. To establish the dynamic evolution of the basin and the climate variations during the Miocene, we have sampled both northern and southern margins of the basin. To discriminate between the tectonic imprint and the eustatic influence over the sedimentation rate we have chosen a multidisciplinary approach including sedimentology, tectonics and organic geochemistry based reconstructions. The sedimentary succession is interrupted by few unconformities correspondent with the main phases of orogeny (in the Carpathian Foredeep) while the southern part seems to have been exposed more often expressed in the geological record by a higher number of unconformities and paleo-soils levels. The n-alkanes distribution recovered from the lipids extracted from the sedimentary rocks indicates a mixture of terrestrial and marine input in the northern, Romanian, closer to Carpathians, part of the Dacian Basin. Surprisingly, the southern, Bulgarian side, showed a more predominant terrestrial input (with higher contribution of the long chain n-alkanes) at least for the Sarmatian (arround 10 Ma). The estimated paleotemperatures based on branched GDGT's indicate much warmer conditions than present day, up to a value of 20 C mean annual temperatures. We will further investigate the paleoenvironmental changes during the latest Miocene of the Dacian basin, using the biomarker approach

Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery.

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Peng, Jun; Chen, Yi-Ting; Chen, Chien-Lun; Li, Liang

2014-07-01

Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and

A Sol-gel Integrated Dual-readout Microarray Platform for Quantification and Identification of Prostate-specific Antigen.

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Lee, SangWook; Lee, Jong Hyun; Kwon, Hyuck Gi; Laurell, Thomas; Jeong, Ok Chan; Kim, Soyoun

2018-01-01

Here, we report a sol-gel integrated affinity microarray for on-chip matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) that enables capture and identification of prostate?specific antigen (PSA) in samples. An anti-PSA antibody (H117) was mixed with a sol?gel, and the mixture was spotted onto a porous silicon (pSi) surface without additional surface modifications. The antibody easily penetrates the sol-gel macropore fluidic network structure, making possible high affinities. To assess the capture affinity of the platform, we performed a direct assay using fluorescein isothiocyanate-labeled PSA. Pure PSA was subjected to on-chip MALDI-TOF-MS analysis, yielding three clear mass peptide peaks (m/z = 1272, 1407, and 1872). The sol-gel microarray platform enables dual readout of PSA both fluorometric and MALDI-TOF MS analysis in biological samples. Here we report a useful method for a means for discovery of biomarkers in complex body fluids.

Magnetic Bead-Based Biosensing on an Automated & Integrated Lab-on-a-Disc Platform

DEFF Research Database (Denmark)

Uddin, Rokon

-biomarker for inflammatory diseases; and mononuclear white blood cell count – a biomarker for the prognosis of different medical conditions. Furthermore, the concept of the agglutination assay was utilized for a biomarker discovery application by investigating the mechanism of action of a T2D drug - metformin through...

Biomarker discovery in neurological diseases: a metabolomic approach

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Afaf El-Ansary

2009-12-01

Full Text Available Afaf El-Ansary, Nouf Al-Afaleg, Yousra Al-YafaeeBiochemistry Department, Science College, King Saud University, Riyadh, Saudi ArabiaAbstract: Biomarkers are pharmacological and physiological measurements or specific biochemicals in the body that have a particular molecular feature that makes them useful for measuring the progress of disease or the effects of treatment. Due to the complexity of neurological disorders, it is very difficult to have perfect markers. Brain diseases require plenty of markers to reflect the metabolic impairment of different brain cells. The recent introduction of the metabolomic approach helps the study of neurological diseases based on profiling a multitude of biochemical components related to brain metabolism. This review is a trial to elucidate the possibility to use this approach to identify plasma metabolic markers related to neurological disorders. Previous trials using different metabolomic analyses including nuclear magnetic resonance spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, and capillary electrophoresis will be traced.Keywords: metabolic biomarkers, neurological disorders. metabolome, nuclear magnetic resonance, mass spectrometry, chromatography

Bioinformatics in translational drug discovery.

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Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

2017-08-31

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

Urine Proteomics in the Era of Mass Spectrometry

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Ashley Beasley-Green

2016-11-01

Full Text Available With the technological advances of mass spectrometry (MS-based platforms, clinical proteomics is one of the most rapidly growing areas in biomedical research. Urine proteomics has become a popular subdiscipline of clinical proteomics because it is an ideal source for the discovery of noninvasive disease biomarkers. The urine proteome offers a comprehensive view of the local and systemic physiology since the proteome is primarily composed of proteins/peptides from the kidneys and plasma. The emergence of MS-based proteomic platforms as prominent bioanalytical tools in clinical applications has enhanced the identification of protein-based urinary biomarkers. This review highlights the characteristics of urine that make it an attractive biofluid for biomarker discovery and the impact of MS-based technologies on the clinical assessment of urinary protein biomarkers.

Plasma proteomics to identify biomarkers - Application to cardiovascular diseases

DEFF Research Database (Denmark)

Beck, Hans Christian; Overgaard, Martin; Melholt Rasmussen, Lars

2015-01-01

There is an unmet need for new cardiovascular biomarkers. Despite this only few biomarkers for the diagnosis or screening of cardiovascular diseases have been implemented in the clinic. Thousands of proteins can be analysed in plasma by mass spectrometry-based proteomics technologies. Therefore......, this technology may therefore identify new biomarkers that previously have not been associated with cardiovascular diseases. In this review, we summarize the key challenges and considerations, including strategies, recent discoveries and clinical applications in cardiovascular proteomics that may lead...

Biomarkers in Alzheimer’s Disease Analysis by Mass Spectrometry-Based Proteomics

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Yahui Liu

2014-05-01

Full Text Available Alzheimer’s disease (AD is a common chronic and destructive disease. The early diagnosis of AD is difficult, thus the need for clinically applicable biomarkers development is growing rapidly. There are many methods to biomarker discovery and identification. In this review, we aim to summarize Mass spectrometry (MS-based proteomics studies on AD and discuss thoroughly the methods to identify candidate biomarkers in cerebrospinal fluid (CSF and blood. This review will also discuss the potential research areas on biomarkers.

Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation.

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Bhosale, Santosh D; Moulder, Robert; Kouvonen, Petri; Lahesmaa, Riitta; Goodlett, David R

2017-01-01

Blood protein measurements are used frequently in the clinic in the assessment of patient health. Nevertheless, there remains the need for new biomarkers with better diagnostic specificities. With the advent of improved technology for bioanalysis and the growth of biobanks including collections from specific disease risk cohorts, the plasma proteome has remained a target of proteomics research toward the characterization of disease-related biomarkers. The following protocol presents a workflow for serum/plasma proteomics including details of sample preparation both with and without immunoaffinity depletion of the most abundant plasma proteins and methodology for selected reaction monitoring mass spectrometry validation.

Predictive Power Estimation Algorithm (PPEA--a new algorithm to reduce overfitting for genomic biomarker discovery.

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Jiangang Liu

Full Text Available Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA, which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1 PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2 the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3 using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4 more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

Biomarkers in psoriasis and psoriatic arthritis.

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Villanova, Federica; Di Meglio, Paola; Nestle, Frank O

2013-04-01

Psoriasis is a common immune-mediated disease of the skin, which associates in 20-30% of patients with psoriatic arthritis (PsA). The immunopathogenesis of both conditions is not fully understood as it is the result of a complex interaction between genetic, environmental and immunological factors. At present there is no cure for psoriasis and there are no specific markers that can accurately predict disease progression and therapeutic response. Therefore, biomarkers for disease prognosis and response to treatment are urgently needed to help clinicians with objective indications to improve patient management and outcomes. Although many efforts have been made to identify psoriasis/PsA biomarkers none of them has yet been translated into routine clinical practice. In this review we summarise the different classes of possible biomarkers explored in psoriasis and PsA so far and discuss novel strategies for biomarker discovery.

Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay.

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Ludwig, Susann K J; Zhu, Hongying; Phillips, Stephen; Shiledar, Ashutosh; Feng, Steve; Tseng, Derek; van Ginkel, Leendert A; Nielen, Michel W F; Ozcan, Aydogan

2014-11-01

Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99% of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed. Such a prescreening can be performed using a small attachment on a cellphone. To this end, a cellphone-based imaging platform for a microsphere fluorescence immunoassay that detects the presence of anti-recombinant bovine somatotropin (rbST) antibodies in milk extracts was developed. RbST administration to cows increases their milk production, but is illegal in the EU and a public health concern in the USA. The cellphone monitors the presence of anti-rbST antibodies (rbST biomarker), which are endogenously produced upon administration of rbST and excreted in milk. The rbST biomarker present in milk extracts was captured by rbST covalently coupled to paramagnetic microspheres and labeled by quantum dot (QD)-coupled detection antibodies. The emitted fluorescence light from these captured QDs was then imaged using the cellphone camera. Additionally, a dark-field image was taken in which all microspheres present were visible. The fluorescence and dark-field microimages were analyzed using a custom-developed Android application running on the same cellphone. With this setup, the microsphere fluorescence immunoassay and cellphone-based detection were successfully applied to milk sample extracts from rbST-treated and untreated cows. An 80% true-positive rate and 95% true-negative rate were achieved using this setup. Next, the cellphone-based detection platform was benchmarked against a newly developed planar imaging array alternative and found to be equally performing versus the much more sophisticated alternative. Using cellphone-based on-site analysis in

Biomarkers of Pediatric Brain Tumors

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Mark D Russell

2013-03-01

Full Text Available Background and Need for Novel Biomarkers: Brain tumors are the leading cause of death by solid tumors in children. Although improvements have been made in their radiological detection and treatment, our capacity to promptly diagnose pediatric brain tumors in their early stages remains limited. This contrasts several other cancers where serum biomarkers such as CA 19-9 and CA 125 facilitate early diagnosis and treatment. Aim: The aim of this article is to review the latest literature and highlight biomarkers which may be of clinical use in the common types of primary pediatric brain tumor. Methods: A PubMed search was performed to identify studies reporting biomarkers in the bodily fluids of pediatric patients with brain tumors. Details regarding the sample type (serum, cerebrospinal fluid or urine, biomarkers analyzed, methodology, tumor type and statistical significance were recorded. Results: A total of 12 manuscripts reporting 19 biomarkers in 367 patients vs. 397 controls were identified in the literature. Of the 19 biomarkers identified, 12 were isolated from cerebrospinal fluid, 2 from serum, 3 from urine, and 2 from multiple bodily fluids. All but one study reported statistically significant differences in biomarker expression between patient and control groups.Conclusions: This review identifies a panel of novel biomarkers for pediatric brain tumors. It provides a platform for the further studies necessary to validate these biomarkers and, in addition, highlights several techniques through which new biomarkers can be discovered.

Cancer biomarker discovery: the entropic hallmark.

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Berretta, Regina; Moscato, Pablo

2010-08-18

It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles

Gaucher disease: a model disorder for biomarker discovery

DEFF Research Database (Denmark)

Boot, Rolf G; van Breemen, Mariëlle J; Wegdam, Wouter

2009-01-01

Gaucher disease is an inherited lysosomal storage disorder, characterized by massive accumulation of glucosylceramide-laden macrophages in the spleen, liver and bone marrow as a consequence of deficient activity of glucocerebrosidase. Gaucher disease has been the playground to develop new therape...... in clinical management of Gaucher patients are discussed. Moreover, the use of several modern proteomic technologies for the identification of Gaucher biomarkers is reviewed....

DNA Methylation Biomarkers: Cancer and Beyond

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Thomas Mikeska

2014-09-01

Full Text Available Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient’s response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease.

Biomarkers of tolerance: searching for the hidden phenotype.

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Perucha, Esperanza; Rebollo-Mesa, Irene; Sagoo, Pervinder; Hernandez-Fuentes, Maria P

2011-08-01

Induction of transplantation tolerance remains the ideal long-term clinical and logistic solution to the current challenges facing the management of renal allograft recipients. In this review, we describe the recent studies and advances made in identifying biomarkers of renal transplant tolerance, from study inceptions, to the lessons learned and their implications for current and future studies with the same goal. With the age of biomarker discovery entering a new dimension of high-throughput technologies, here we also review the current approaches, developments, and pitfalls faced in the subsequent statistical analysis required to identify valid biomarker candidates.

Cell and small animal models for phenotypic drug discovery

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Szabo M

2017-06-01

Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

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Hickey, John M; Chiurugwi, Tinashe; Mackay, Ian; Powell, Wayne

2017-08-30

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.

Exhaled Breath Condensate for Proteomic Biomarker Discovery

Directory of Open Access Journals (Sweden)

Sean W. Harshman

2014-07-01

Full Text Available Exhaled breath condensate (EBC has been established as a potential source of respiratory biomarkers. Compared to the numerous small molecules identified, the protein content of EBC has remained relatively unstudied due to the methodological and technical difficulties surrounding EBC analysis. In this review, we discuss the proteins identified in EBC, by mass spectrometry, focusing on the significance of those proteins identified. We will also review the limitations surrounding mass spectral EBC protein analysis emphasizing recommendations to enhance EBC protein identifications by mass spectrometry. Finally, we will provide insight into the future directions of the EBC proteomics field.

Quantitative proteomic analysis of microdissected oral epithelium for cancer biomarker discovery.

Science.gov (United States)

Xiao, Hua; Langerman, Alexander; Zhang, Yan; Khalid, Omar; Hu, Shen; Cao, Cheng-Xi; Lingen, Mark W; Wong, David T W

2015-11-01

Specific biomarkers are urgently needed for the detection and progression of oral cancer. The objective of this study was to discover cancer biomarkers from oral epithelium through utilizing high throughput quantitative proteomics approaches. Morphologically malignant, epithelial dysplasia, and adjacent normal epithelial tissues were laser capture microdissected (LCM) from 19 patients and used for proteomics analysis. Total proteins from each group were extracted, digested and then labelled with corresponding isobaric tags for relative and absolute quantitation (iTRAQ). Labelled peptides from each sample were combined and analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) for protein identification and quantification. In total, 500 proteins were identified and 425 of them were quantified. When compared with adjacent normal oral epithelium, 17 and 15 proteins were consistently up-regulated or down-regulated in malignant and epithelial dysplasia, respectively. Half of these candidate biomarkers were discovered for oral cancer for the first time. Cornulin was initially confirmed in tissue protein extracts and was further validated in tissue microarray. Its presence in the saliva of oral cancer patients was also explored. Myoglobin and S100A8 were pre-validated by tissue microarray. These data demonstrated that the proteomic biomarkers discovered through this strategy are potential targets for oral cancer detection and salivary diagnostics. Copyright © 2015 Elsevier Ltd. All rights reserved.

Urine Exosomes: An Emerging Trove of Biomarkers.

Science.gov (United States)

Street, J M; Koritzinsky, E H; Glispie, D M; Star, R A; Yuen, P S T

Exosomes are released by most cells and can be isolated from all biofluids including urine. Exosomes are small vesicles formed as part of the endosomal pathway that contain cellular material surrounded by a lipid bilayer that can be traced to the plasma membrane. Exosomes are potentially a more targeted source of material for biomarker discovery than unfractionated urine, and provide diagnostic and pathophysiological information without an invasive tissue biopsy. Cytoplasmic contents including protein, mRNA, miRNA, and lipids have all been studied within the exosomal fraction. Many prospective urinary exosomal biomarkers have been successfully identified for a variety of kidney or genitourinary tract conditions; detection of systemic conditions may also be possible. Isolation and analysis of exosomes can be achieved by several approaches, although many require specialized equipment or involve lengthy protocols. The need for timely analysis in the clinical setting has driven considerable innovation with several promising options recently emerging. Consensus on exosome isolation, characterization, and normalization procedures would resolve critical clinical translational bottlenecks for existing candidate exosomal biomarkers and provide a template for additional discovery studies. 2017 Published by Elsevier Inc.

Biomarkers: refining diagnosis and expediting drug development - reality, aspiration and the role of open innovation.

Science.gov (United States)

Salter, H; Holland, R

2014-09-01

In the last decade, there have been intensive efforts to invent, qualify and use novel biomarkers as a means to improve success rates in drug discovery and development. The biomarkers field is maturing and this article considers whether these research efforts have brought about the expected benefits. The characteristics of a clinically useful biomarker are described and the impact this area of research has had is evaluated by reviewing a few, key examples of emerging biomarkers. There is evidence that the impact has been genuine and is increasing in both the drug and the diagnostic discovery and development processes. Beneficial impact on patient health outcomes seems relatively limited thus far, with the greatest impact in oncology (again, both in terms of novel drugs and in terms of more refined diagnoses and therefore more individualized treatment). However, the momentum of research would indicate that patient benefits are likely to increase substantially and to broaden across multiple therapeutic areas. Even though this research was originally driven by a desire to improve the drug discovery and development process, and was therefore funded with this aim in mind, it seems likely that the largest impact may actually come from more refined diagnosis. Refined diagnosis will facilitate both better allocation of healthcare resources and the use of treatment regimens which are optimized for the individual patient. This article also briefly reviews emerging technological approaches and how they relate to the challenges inherent in biomarker discovery and validation, and discusses the role of public/private partnerships in innovative biomarker research. © 2014 The Association for the Publication of the Journal of Internal Medicine.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine.

Science.gov (United States)

Pecak, Matija; Korošec, Peter; Kunej, Tanja

2018-06-01

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

KAUST Repository

Kaur, Mandeep; MacPherson, Cameron R; Schmeier, Sebastian; Narasimhan, Kothandaraman; Choolani, Mahesh; Bajic, Vladimir B.

2011-01-01

Background: Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.Results: The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.Conclusions: We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors. 2011 Kaur et al; licensee BioMed Central Ltd.

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

KAUST Repository

Kaur, Mandeep

2011-09-19

Background: Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.Results: The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.Conclusions: We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors. 2011 Kaur et al; licensee BioMed Central Ltd.

Biomarkers in cancer screening: a public health perspective.

Science.gov (United States)

Srivastava, Sudhir; Gopal-Srivastava, Rashmi

2002-08-01

definitive technology, such as CT scan. The National Cancer Institute's Early Detection Research Network (EDRN) has begun an innovative, investigator-initiated project to improve methods for detecting the biomarkers of cancer cells. The EDRN is a consortium of more than 32 institutions to link discovery of biomarkers to the next steps in the process of developing early detection tests. These discoveries will lead to early clinical validation of tests with improved accuracy and reliability.

Guidelines for Biomarker of Food Intake Reviews (BFIRev: how to conduct an extensive literature search for biomarker of food intake discovery

Directory of Open Access Journals (Sweden)

Giulia Praticò

2018-02-01

Full Text Available Abstract Identification of new biomarkers of food and nutrient intake has developed fast over the past two decades and could potentially provide important new tools for compliance monitoring and dietary intake assessment in nutrition and health science. In recent years, metabolomics has played an important role in identifying a large number of putative biomarkers of food intake (BFIs. However, the large body of scientific literature on potential BFIs outside the metabolomics area should also be taken into account. In particular, we believe that extensive literature reviews should be conducted and that the quality of all suggested biomarkers should be systematically evaluated. In order to cover the literature on BFIs in the most appropriate and consistent manner, there is a need for appropriate guidelines on this topic. These guidelines should build upon guidelines in related areas of science while targeting the special needs of biomarker methodology. This document provides a guideline for conducting an extensive literature search on BFIs, which will provide the basis to systematically validate BFIs. This procedure will help to prioritize future work on the identification of new potential biomarkers and on validating these as well as other biomarker candidates, thereby providing better tools for future studies in nutrition and health.

Guidelines for Biomarker of Food Intake Reviews (BFIRev): how to conduct an extensive literature search for biomarker of food intake discovery.

Science.gov (United States)

Praticò, Giulia; Gao, Qian; Scalbert, Augustin; Vergères, Guy; Kolehmainen, Marjukka; Manach, Claudine; Brennan, Lorraine; Pedapati, Sri Harsha; Afman, Lydia A; Wishart, David S; Vázquez-Fresno, Rosa; Lacueva, Cristina Andres; Garcia-Aloy, Mar; Verhagen, Hans; Feskens, Edith J M; Dragsted, Lars O

2018-01-01

Identification of new biomarkers of food and nutrient intake has developed fast over the past two decades and could potentially provide important new tools for compliance monitoring and dietary intake assessment in nutrition and health science. In recent years, metabolomics has played an important role in identifying a large number of putative biomarkers of food intake (BFIs). However, the large body of scientific literature on potential BFIs outside the metabolomics area should also be taken into account. In particular, we believe that extensive literature reviews should be conducted and that the quality of all suggested biomarkers should be systematically evaluated. In order to cover the literature on BFIs in the most appropriate and consistent manner, there is a need for appropriate guidelines on this topic. These guidelines should build upon guidelines in related areas of science while targeting the special needs of biomarker methodology. This document provides a guideline for conducting an extensive literature search on BFIs, which will provide the basis to systematically validate BFIs. This procedure will help to prioritize future work on the identification of new potential biomarkers and on validating these as well as other biomarker candidates, thereby providing better tools for future studies in nutrition and health.

Proteomic profiling of exosomes leads to the identification of novel biomarkers for prostate cancer

NARCIS (Netherlands)

D. Duijvesz (Diederick); K.E. Burnum-Johnson (Kristin); M.A. Gritsenko (Marina); A.M. Hoogland (Marije); M.S. Vredenbregt-van den Berg (Mirella); R. Willemsen (Rob); T.M. Luider (Theo); L. Paša-Tolić (Ljiljana); G.W. Jenster (Guido)

2013-01-01

textabstractBackground: Current markers for prostate cancer, such as PSA lack specificity. Therefore, novel biomarkers are needed. Unfortunately, the complexity of body fluids often hampers biomarker discovery. An attractive alternative approach is the isolation of small vesicles, i.e. exosomes,

Common Subcluster Mining in Microarray Data for Molecular Biomarker Discovery.

Science.gov (United States)

Sadhu, Arnab; Bhattacharyya, Balaram

2017-10-11

Molecular biomarkers can be potential facilitators for detection of cancer at early stage which is otherwise difficult through conventional biomarkers. Gene expression data from microarray experiments on both normal and diseased cell samples provide enormous scope to explore genetic relations of disease using computational techniques. Varied patterns of expressions of thousands of genes at different cell conditions along with inherent experimental error make the task of isolating disease related genes challenging. In this paper, we present a data mining method, common subcluster mining (CSM), to discover highly perturbed genes under diseased condition from differential expression patterns. The method builds heap through superposing near centroid clusters from gene expression data of normal samples and extracts its core part. It, thus, isolates genes exhibiting the most stable state across normal samples and constitute a reference set for each centroid. It performs the same operation on datasets from corresponding diseased samples and isolates the genes showing drastic changes in their expression patterns. The method thus finds the disease-sensitive genesets when applied to datasets of lung cancer, prostrate cancer, pancreatic cancer, breast cancer, leukemia and pulmonary arterial hypertension. In majority of the cases, few new genes are found over and above some previously reported ones. Genes with distinct deviations in diseased samples are prospective candidates for molecular biomarkers of the respective disease.

microRNA Biomarker Discovery and High-Throughput DNA Sequencing Are Possible Using Long-term Archived Serum Samples.

Science.gov (United States)

Rounge, Trine B; Lauritzen, Marianne; Langseth, Hilde; Enerly, Espen; Lyle, Robert; Gislefoss, Randi E

2015-09-01

The impacts of long-term storage and varying preanalytical factors on the quality and quantity of DNA and miRNA from archived serum have not been fully assessed. Preanalytical and analytical variations and degradation may introduce bias in representation of DNA and miRNA and may result in loss or corruption of quantitative data. We have evaluated DNA and miRNA quantity, quality, and variability in samples stored up to 40 years using one of the oldest prospective serum collections in the world, the Janus Serumbank, a biorepository dedicated to cancer research. miRNAs are present and stable in archived serum samples frozen at -25°C for at least 40 years. Long-time storage did not reduce miRNA yields; however, varying preanalytical conditions had a significant effect and should be taken into consideration during project design. Of note, 500 μL serum yielded sufficient miRNA for qPCR and small RNA sequencing and on average 650 unique miRNAs were detected in samples from presumably healthy donors. Of note, 500 μL serum yielded sufficient DNA for whole-genome sequencing and subsequent SNP calling, giving a uniform representation of the genomes. DNA and miRNA are stable during long-term storage, making large prospectively collected serum repositories an invaluable source for miRNA and DNA biomarker discovery. Large-scale biomarker studies with long follow-up time are possible utilizing biorepositories with archived serum and state-of-the-art technology. ©2015 American Association for Cancer Research.

An Integrative Clinical Database and Diagnostics Platform for Biomarker Identification and Analysis in Ion Mobility Spectra of Human Exhaled Air

Directory of Open Access Journals (Sweden)

Schneider Till

2013-06-01

Full Text Available Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS, is a well known technology for detecting volatile organic compounds (VOCs in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute- value (EAV model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform’s functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at http://imsdb.mpi-inf.mpg.de.

A practical platform for blood biomarker study by using global gene expression profiling of peripheral whole blood.

Directory of Open Access Journals (Sweden)

Ze Tian

Full Text Available Although microarray technology has become the most common method for studying global gene expression, a plethora of technical factors across the experiment contribute to the variable of genome gene expression profiling using peripheral whole blood. A practical platform needs to be established in order to obtain reliable and reproducible data to meet clinical requirements for biomarker study.We applied peripheral whole blood samples with globin reduction and performed genome-wide transcriptome analysis using Illumina BeadChips. Real-time PCR was subsequently used to evaluate the quality of array data and elucidate the mode in which hemoglobin interferes in gene expression profiling. We demonstrated that, when applied in the context of standard microarray processing procedures, globin reduction results in a consistent and significant increase in the quality of beadarray data. When compared to their pre-globin reduction counterparts, post-globin reduction samples show improved detection statistics, lowered variance and increased sensitivity. More importantly, gender gene separation is remarkably clearer in post-globin reduction samples than in pre-globin reduction samples. Our study suggests that the poor data obtained from pre-globin reduction samples is the result of the high concentration of hemoglobin derived from red blood cells either interfering with target mRNA binding or giving the pseudo binding background signal.We therefore recommend the combination of performing globin mRNA reduction in peripheral whole blood samples and hybridizing on Illumina BeadChips as the practical approach for biomarker study.

Identification of biomarkers for lung cancer in never smokers — EDRN Public Portal

Science.gov (United States)

The overall goal of this project is to identify, verify and apply biomarkers for the early diagnosis or risk assessment of lung cancer in never smokers. The first year will be regarded as a year of discovery. After successful demonstration of the feasibility of the approach for novel marker discovery, funding will be applied for to perform confirmation and preclinical studies on the biomarkers and validation studies (specific aims 2 and 3, to be performed in years two and three). Year two can be regarded as the year of confirmation and year three as the year of validation.

Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

Directory of Open Access Journals (Sweden)

Cui Ziyou

2009-03-01

Full Text Available Abstract Background Acute lymphoblastic leukemia (ALL is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL. Methods Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML patients. Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS. A classification model was established by Biomarker Pattern Software (BPS. Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays. Results A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4 and pro-platelet basic protein precursor (PBP. Two other candidate protein peaks (8137 and 8937 m/z were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a. Conclusion Platelet factor (PF4, connective tissue activating peptide III (CTAP-III and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with

Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

Science.gov (United States)

Shi, Linan; Zhang, Jun; Wu, Peng; Feng, Kai; Li, Jing; Xie, Zhensheng; Xue, Peng; Cai, Tanxi; Cui, Ziyou; Chen, Xiulan; Hou, Junjie; Zhang, Jianzhong; Yang, Fuquan

2009-01-01

Background Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL. Methods Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays. Results A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a). Conclusion Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional

Human cervicovaginal fluid biomarkers to predict term and preterm labor

Science.gov (United States)

Heng, Yujing J.; Liong, Stella; Permezel, Michael; Rice, Gregory E.; Di Quinzio, Megan K. W.; Georgiou, Harry M.

2015-01-01

Preterm birth (PTB; birth before 37 completed weeks of gestation) remains the major cause of neonatal morbidity and mortality. The current generation of biomarkers predictive of PTB have limited utility. In pregnancy, the human cervicovaginal fluid (CVF) proteome is a reflection of the local biochemical milieu and is influenced by the physical changes occurring in the vagina, cervix and adjacent overlying fetal membranes. Term and preterm labor (PTL) share common pathways of cervical ripening, myometrial activation and fetal membranes rupture leading to birth. We therefore hypothesize that CVF biomarkers predictive of labor may be similar in both the term and preterm labor setting. In this review, we summarize some of the existing published literature as well as our team's breadth of work utilizing the CVF for the discovery and validation of putative CVF biomarkers predictive of human labor. Our team established an efficient method for collecting serial CVF samples for optimal 2-dimensional gel electrophoresis resolution and analysis. We first embarked on CVF biomarker discovery for the prediction of spontaneous onset of term labor using 2D-electrophoresis and solution array multiple analyte profiling. 2D-electrophoretic analyses were subsequently performed on CVF samples associated with PTB. Several proteins have been successfully validated and demonstrate that these biomarkers are associated with term and PTL and may be predictive of both term and PTL. In addition, the measurement of these putative biomarkers was found to be robust to the influences of vaginal microflora and/or semen. The future development of a multiple biomarker bed-side test would help improve the prediction of PTB and the clinical management of patients. PMID:26029118

NETL's Energy Data Exchange (EDX) - a coordination, collaboration, and data resource discovery platform for energy science

Science.gov (United States)

Rose, K.; Rowan, C.; Rager, D.; Dehlin, M.; Baker, D. V.; McIntyre, D.

2015-12-01

Multi-organizational research teams working jointly on projects often encounter problems with discovery, access to relevant existing resources, and data sharing due to large file sizes, inappropriate file formats, or other inefficient options that make collaboration difficult. The Energy Data eXchange (EDX) from Department of Energy's (DOE) National Energy Technology Laboratory (NETL) is an evolving online research environment designed to overcome these challenges in support of DOE's fossil energy goals while offering improved access to data driven products of fossil energy R&D such as datasets, tools, and web applications. In 2011, development of NETL's Energy Data eXchange (EDX) was initiated and offers i) a means for better preserving of NETL's research and development products for future access and re-use, ii) efficient, discoverable access to authoritative, relevant, external resources, and iii) an improved approach and tools to support secure, private collaboration and coordination between multi-organizational teams to meet DOE mission and goals. EDX presently supports fossil energy and SubTER Crosscut research activities, with an ever-growing user base. EDX is built on a heavily customized instance of the open source platform, Comprehensive Knowledge Archive Network (CKAN). EDX connects users to externally relevant data and tools through connecting to external data repositories built on different platforms and other CKAN platforms (e.g. Data.gov). EDX does not download and repost data or tools that already have an online presence. This leads to redundancy and even error. If a relevant resource already has an online instance, is hosted by another online entity, EDX will point users to that external host either using web services, inventorying URLs and other methods. EDX offers users the ability to leverage private-secure capabilities custom built into the system. The team is presently working on version 3 of EDX which will incorporate big data analytical

Scientific workflows as productivity tools for drug discovery.

Science.gov (United States)

Shon, John; Ohkawa, Hitomi; Hammer, Juergen

2008-05-01

Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.

Metabolomics-based promising candidate biomarkers and pathways in Alzheimer's disease.

Science.gov (United States)

Kang, Jian; Lu, Jingli; Zhang, Xiaojian

2015-05-01

Pathologically, loss of synapses and neurons, extracellular senile plaques and intracellular neurofibrillary tangles (NFTs) are observed in the brains of patients with Alzheimer's disease (AD). These features are associated with changes Aβ (amyloid β) 40, Aβ42, total tau and phosphorylated tau (p-tau), which are as definitely biomarkers for severe AD state. However, biomarkers for effectively diagnosing AD in the pre-clinical state for directing therapeutic strategies are lacking. Metabolic profiling as a powerful tool to identify new biomarkers is receiving increasing attention in AD. This review will focus on metabolomics-based detection of promising candidate biomarkers and pathways in AD to facilitate the discovery of new medicines and disease pathways.

Biomarkers for Ectopic Pregnancy and Pregnancy of Unknown Location

OpenAIRE

Senapati, Suneeta; Barnhart, Kurt T.

2013-01-01

Early pregnancy failure is the most common complication of pregnancy, and 1–2% of all pregnancies will be ectopic. As one of the leading causes of maternal morbidity and mortality, diagnosing ectopic pregnancy and determining the fate of a pregnancy of unknown location are of great clinical concern. Several serum and plasma biomarkers for ectopic pregnancy have been investigated independently and in combination. The following is a review of the state of biomarker discovery and development for...

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

Science.gov (United States)

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Evaluating biomarkers for prognostic enrichment of clinical trials.

Science.gov (United States)

Kerr, Kathleen F; Roth, Jeremy; Zhu, Kehao; Thiessen-Philbrook, Heather; Meisner, Allison; Wilson, Francis Perry; Coca, Steven; Parikh, Chirag R

2017-12-01

A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

A high-throughput O-glycopeptide discovery platform for seromic profiling

DEFF Research Database (Denmark)

Blixt, Ola; Cló, Emiliano; Nudelman, Aaron Samuel

2010-01-01

Biomarker microarrays are becoming valuable tools for serological screening of disease-associated autoantibodies. Post-translational modifications (PTMs) such as glycosylation extend the range of protein function, and a variety of glycosylated proteins are known to be altered in disease progressi...

mySearch changed my life – a resource discovery journey

OpenAIRE

Crowley, Emma J.

2013-01-01

mySearch: the federated years mySearch: choosing a new platform mySearch: EBSCO Discovery Service (EDS) Implementing a new system Technical challenges Has resource discovery enhanced experiences at BU? Ongoing challenges Implications for library management systems Implications for information literacy Questions

Protein biomarker discovery and fast monitoring for the identification and detection of Anisakids by parallel reaction monitoring (PRM) mass spectrometry.

Science.gov (United States)

Carrera, Mónica; Gallardo, José M; Pascual, Santiago; González, Ángel F; Medina, Isabel

2016-06-16

Anisakids are fish-borne parasites that are responsible for a large number of human infections and allergic reactions around the world. World health organizations and food safety authorities aim to control and prevent this emerging health problem. In the present work, a new method for the fast monitoring of these parasites is described. The strategy is divided in three steps: (i) purification of thermostable proteins from fish-borne parasites (Anisakids), (ii) in-solution HIFU trypsin digestion and (iii) monitoring of several peptide markers by parallel reaction monitoring (PRM) mass spectrometry. This methodology allows the fast detection of Anisakids in Biomarker Discovery and the Fast Monitoring for the identification and detection of Anisakids in fishery products. The strategy is based on the purification of thermostable proteins, the use of accelerated in-solution trypsin digestions under an ultrasonic field provided by High-Intensity Focused Ultrasound (HIFU) and the monitoring of several peptide biomarkers by Parallel Reaction Monitoring (PRM) Mass Spectrometry in a linear ion trap mass spectrometer. The workflow allows the unequivocal detection of Anisakids, in <2h. The present strategy constitutes the fastest method for Anisakids detection, whose application in the food quality control area, could provide to the authorities an effective and rapid method to guarantee the safety to the consumers. Copyright © 2016 Elsevier B.V. All rights reserved.

COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry.

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Janice M Leung

Full Text Available Acute exacerbations of chronic obstructive pulmonary disease (AECOPD result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD.We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72. Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate 1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109 using leave-pair-out cross-validation methods.Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p<0.001. The receiver operating characteristic cross-validation area under the curve (CV-AUC statistic was 0.73 in Cohort A, while in the replication cohorts the CV-AUC was 0.77 for Cohort B and 0.79 for Cohort C.A panel of five biomarkers shows promise in distinguishing AECOPD from convalescence and may provide the basis for a clinical blood test to diagnose AECOPD. Further validation in larger cohorts is necessary for future clinical translation.

Enhanced Biosensor Platforms for Detecting the Atherosclerotic Biomarker VCAM1 Based on Bioconjugation with Uniformly Oriented VCAM1-Targeting Nanobodies

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Duy Tien Ta

2016-07-01

Full Text Available Surface bioconjugation of biomolecules has gained enormous attention for developing advanced biomaterials including biosensors. While conventional immobilization (by physisorption or covalent couplings using the functional groups of the endogenous amino acids usually results in surfaces with low activity, reproducibility and reusability, the application of methods that allow for a covalent and uniformly oriented coupling can circumvent these limitations. In this study, the nanobody targeting Vascular Cell Adhesion Molecule-1 (NbVCAM1, an atherosclerotic biomarker, is engineered with a C-terminal alkyne function via Expressed Protein Ligation (EPL. Conjugation of this nanobody to azidified silicon wafers and Biacore™ C1 sensor chips is achieved via Copper(I-catalyzed azide-alkyne cycloaddition (CuAAC “click” chemistry to detect VCAM1 binding via ellipsometry and surface plasmon resonance (SPR, respectively. The resulting surfaces, covered with uniformly oriented nanobodies, clearly show an increased antigen binding affinity, sensitivity, detection limit, quantitation limit and reusability as compared to surfaces prepared by random conjugation. These findings demonstrate the added value of a combined EPL and CuAAC approach as it results in strong control over the surface orientation of the nanobodies and an improved detecting power of their targets—a must for the development of advanced miniaturized, multi-biomarker biosensor platforms.

Developing novel blood-based biomarkers for Alzheimer's disease

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Snyder, Heather M; Carrillo, Maria C; Grodstein, Francine

2014-01-01

Alzheimer's disease is the public health crisis of the 21st century. There is a clear need for a widely available, inexpensive and reliable method to diagnosis Alzheimer's disease in the earliest stages, track disease progression, and accelerate clinical development of new therapeutics. One avenue...... of research being explored is blood based biomarkers. In April 2012, the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation convened top scientists from around the world to discuss the state of blood based biomarker development. This manuscript summarizes the meeting and the resultant...

Biomarker Discovery Using New Metabolomics Software for Automated Processing of High Resolution LC-MS Data

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Hnatyshyn, S.; Reily, M.; Shipkova, P.; McClure, T.; Sanders, M.; Peake, D.

2011-01-01

Robust biomarkers of target engagement and efficacy are required in different stages of drug discovery. Liquid chromatography coupled to high resolution mass spectrometry provides sensitivity, accuracy and wide dynamic range required for identification of endogenous metabolites in biological matrices. LCMS is widely-used tool for biomarker identification and validation. Typical high resolution LCMS profiles from biological samples may contain greater than a million mass spectral peaks corresponding to several thousand endogenous metabolites. Reduction of the total number of peaks, component identification and statistical comparison across sample groups remains to be a difficult and time consuming challenge. Blood samples from four groups of rats (male vs. female, fully satiated and food deprived) were analyzed using high resolution accurate mass (HRAM) LCMS. All samples were separated using a 15 minute reversed-phase C18 LC gradient and analyzed in both positive and negative ion modes. Data was acquired using 15K resolution and 5ppm mass measurement accuracy. The entire data set was analyzed using software developed in collaboration between Bristol Meyers Squibb and Thermo Fisher Scientific to determine the metabolic effects of food deprivation on rats. Metabolomic LC-MS data files are extraordinarily complex and appropriate reduction of the number of spectral peaks via identification of related peaks and background removal is essential. A single component such as hippuric acid generates more than 20 related peaks including isotopic clusters, adducts and dimers. Plasma and urine may contain 500-1500 unique quantifiable metabolites. Noise filtering approaches including blank subtraction were used to reduce the number of irrelevant peaks. By grouping related signals such as isotopic peaks and alkali adducts, data processing was greatly simplified by reducing the total number of components by 10-fold. The software processes 48 samples in under 60minutes. Principle

Urine biomarkers in the early stages of diseases: current status and perspective.

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Jing, Jian; Gao, Youhe

2018-02-01

As a noninvasive and easily available biological fluid, the urine is becoming an important source for disease biomarker study. Change is essential for the usefulness of a biomarker. Without homeostasis mechanisms, urine can accommodate more changes, especially in the early stages of diseases. In this review, we summarize current status and discuss perspectives on the discovery of urine biomarkers in the early stages of diseases. We emphasize the advantages of urine biomarkers compared to plasma biomarkers for the diagnosis of diseases at early stages, propose a urine biomarker research roadmap, and highlight a novel membrane storage technique that enables large-scale urine sample collection and storage efficiently and economically. It is anticipated that urine biomarker studies will greatly promote early diagnosis, prevention, treatment, and prognosis of a variety of diseases, and provide strong support for translational and precision medicine.

Impact of biomarker development on drug safety assessment

International Nuclear Information System (INIS)

Marrer, Estelle; Dieterle, Frank

2010-01-01

Drug safety has always been a key aspect of drug development. Recently, the Vioxx case and several cases of serious adverse events being linked to high-profile products have increased the importance of drug safety, especially in the eyes of drug development companies and global regulatory agencies. Safety biomarkers are increasingly being seen as helping to provide the clarity, predictability, and certainty needed to gain confidence in decision making: early-stage projects can be stopped quicker, late-stage projects become less risky. Public and private organizations are investing heavily in terms of time, money and manpower on safety biomarker development. An illustrative and 'door opening' safety biomarker success story is the recent recognition of kidney safety biomarkers for pre-clinical and limited translational contexts by FDA and EMEA. This milestone achieved for kidney biomarkers and the 'know how' acquired is being transferred to other organ toxicities, namely liver, heart, vascular system. New technologies and molecular-based approaches, i.e., molecular pathology as a complement to the classical toolbox, allow promising discoveries in the safety biomarker field. This review will focus on the utility and use of safety biomarkers all along drug development, highlighting the present gaps and opportunities identified in organ toxicity monitoring. A last part will be dedicated to safety biomarker development in general, from identification to diagnostic tests, using the kidney safety biomarkers success as an illustrative example.

Candidate proteins, metabolites and transcripts in the Biomarkers for Spinal Muscular Atrophy (BforSMA clinical study.

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Richard S Finkel

Full Text Available Spinal Muscular Atrophy (SMA is a neurodegenerative motor neuron disorder resulting from a homozygous mutation of the survival of motor neuron 1 (SMN1 gene. The gene product, SMN protein, functions in RNA biosynthesis in all tissues. In humans, a nearly identical gene, SMN2, rescues an otherwise lethal phenotype by producing a small amount of full-length SMN protein. SMN2 copy number inversely correlates with disease severity. Identifying other novel biomarkers could inform clinical trial design and identify novel therapeutic targets.To identify novel candidate biomarkers associated with disease severity in SMA using unbiased proteomic, metabolomic and transcriptomic approaches.A cross-sectional single evaluation was performed in 108 children with genetically confirmed SMA, aged 2-12 years, manifesting a broad range of disease severity and selected to distinguish factors associated with SMA type and present functional ability independent of age. Blood and urine specimens from these and 22 age-matched healthy controls were interrogated using proteomic, metabolomic and transcriptomic discovery platforms. Analyte associations were evaluated against a primary measure of disease severity, the Modified Hammersmith Functional Motor Scale (MHFMS and to a number of secondary clinical measures.A total of 200 candidate biomarkers correlate with MHFMS scores: 97 plasma proteins, 59 plasma metabolites (9 amino acids, 10 free fatty acids, 12 lipids and 28 GC/MS metabolites and 44 urine metabolites. No transcripts correlated with MHFMS.In this cross-sectional study, "BforSMA" (Biomarkers for SMA, candidate protein and metabolite markers were identified. No transcript biomarker candidates were identified. Additional mining of this rich dataset may yield important insights into relevant SMA-related pathophysiology and biological network associations. Additional prospective studies are needed to confirm these findings, demonstrate sensitivity to change with

Synthetic biology platform technologies for antimicrobial applications.

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Braff, Dana; Shis, David; Collins, James J

2016-10-01

The growing prevalence of antibiotic resistance calls for new approaches in the development of antimicrobial therapeutics. Likewise, improved diagnostic measures are essential in guiding the application of targeted therapies and preventing the evolution of therapeutic resistance. Discovery platforms are also needed to form new treatment strategies and identify novel antimicrobial agents. By applying engineering principles to molecular biology, synthetic biologists have developed platforms that improve upon, supplement, and will perhaps supplant traditional broad-spectrum antibiotics. Efforts in engineering bacteriophages and synthetic probiotics demonstrate targeted antimicrobial approaches that can be fine-tuned using synthetic biology-derived principles. Further, the development of paper-based, cell-free expression systems holds promise in promoting the clinical translation of molecular biology tools for diagnostic purposes. In this review, we highlight emerging synthetic biology platform technologies that are geared toward the generation of new antimicrobial therapies, diagnostics, and discovery channels. Copyright © 2016 Elsevier B.V. All rights reserved.

Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

DEFF Research Database (Denmark)

Swanton, C.; Larkin, J.M.; Gerlinger, M.

2010-01-01

-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway...

Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery.

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Therrien, Eric; Englebienne, Pablo; Arrowsmith, Andrew G; Mendoza-Sanchez, Rodrigo; Corbeil, Christopher R; Weill, Nathanael; Campagna-Slater, Valérie; Moitessier, Nicolas

2012-01-23

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

Biomarkers for personalized oncology: recent advances and future challenges.

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Kalia, Madhu

2015-03-01

Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells and oncology is a branch of medicine that deals with tumors. The last decade has seen significant advances in the development of biomarkers in oncology that play a critical role in understanding molecular and cellular mechanisms which drive tumor initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical-pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and

Qualitative and quantitative characterization of plasma proteins when incorporating traveling wave ion mobility into a liquid chromatography-mass spectrometry workflow for biomarker discovery: use of product ion quantitation as an alternative data analysis tool for label free quantitation.

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Daly, Charlotte E; Ng, Leong L; Hakimi, Amirmansoor; Willingale, Richard; Jones, Donald J L

2014-02-18

Discovery of protein biomarkers in clinical samples necessitates significant prefractionation prior to liquid chromatography-mass spectrometry (LC-MS) analysis. Integrating traveling wave ion mobility spectrometry (TWIMS) enables in-line gas phase separation which when coupled with nanoflow liquid chromatography and data independent acquisition tandem mass spectrometry, confers significant advantages to the discovery of protein biomarkers by improving separation and inherent sensitivity. Incorporation of TWIMS leads to a packet of concentrated ions which ultimately provides a significant improvement in sensitivity. As a consequence of ion packeting, when present at high concentrations, accurate quantitation of proteins can be affected due to detector saturation effects. Human plasma was analyzed in triplicate using liquid-chromatography data independent acquisition mass spectrometry (LC-DIA-MS) and using liquid-chromatography ion-mobility data independent acquisition mass spectrometry (LC-IM-DIA-MS). The inclusion of TWIMS was assessed for the effect on sample throughput, data integrity, confidence of protein and peptide identification, and dynamic range. The number of identified proteins is significantly increased by an average of 84% while both the precursor and product mass accuracies are maintained between the modalities. Sample dynamic range is also maintained while quantitation is achieved for all but the most abundant proteins by incorporating a novel data interpretation method that allows accurate quantitation to occur. This additional separation is all achieved within a workflow with no discernible deleterious effect on throughput. Consequently, TWIMS greatly enhances proteome coverage and can be reliably used for quantification when using an alternative product ion quantification strategy. Using TWIMS in biomarker discovery in human plasma is thus recommended.

Multi-platform ’Omics Analysis of Human Ebola Virus Disease Pathogenesis

Energy Technology Data Exchange (ETDEWEB)

Eisfeld, Amie J.; Halfmann, Peter J.; Wendler, Jason P.; Kyle, Jennifer E.; Burnum-Johnson, Kristin E.; Peralta, Zuleyma; Maemura, Tadashi; Walters, Kevin B.; Watanabe, Tokiko; Fukuyama, Satoshi; Yamashita, Makoto; Jacobs, Jon M.; Kim, Young-Mo; Casey, Cameron P.; Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M.; Gritsenko, Marina A.; Monroe, Matthew E.; Weitz, Karl K.; Shukla, Anil K.; Tian, Mingyuan; Neumann, Gabriele; Reed, Jennifer L.; van Bakel, Harm; Metz, Thomas O.; Smith, Richard D.; Waters, Katrina M.; N' jai, Alhaji; Sahr, Foday; Kawaoka, Yoshihiro

2017-12-01

The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform ’omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. This work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.

The NINDS Parkinson's disease biomarkers program: The Ninds Parkinson's Disease Biomarkers Program

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Rosenthal, Liana S. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Drake, Daniel [Department of Biostatistics, Columbia University, New York New York USA; Alcalay, Roy N. [Department of Neurology, Columbia University, New York New York USA; Babcock, Debra [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Bowman, F. DuBois [Department of Biostatistics, Columbia University, New York New York USA; Chen-Plotkin, Alice [Department of Neurology, University of Pennsylvania, Philadelphia Pennsylvania USA; Dawson, Ted M. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Solomon H. Snyder Department of Neuroscience, Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Dewey, Richard B. [Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas USA; German, Dwight C. [Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas USA; Huang, Xuemei [Department of Neurology, Penn State Hershey Medical Center, Hershey Pennsylvania USA; Landin, Barry [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; McAuliffe, Matthew [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; Petyuk, Vladislav A. [Biological Sciences Division, Pacific Northwest National Laboratory, Richland Washington USA; Scherzer, Clemens R. [Department of Neurology, Brigham & Women' s Hospital, Harvard Medical School, Cambridge Massachusetts USA; Hillaire-Clarke, Coryse St. [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sieber, Beth-Anne [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sutherland, Margaret [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Tarn, Chi [Coriell Institute for Medical Research, Camden New Jersey USA; West, Andrew [Department of Neurology, University of Alabama at Birmingham, Birmingham USA; Vaillancourt, David [Department of Applied Physiology and Kinesiology, University of Florida, Gainesville Florida USA; Zhang, Jing [Department of Pathology, University of Washington, Seattle Washington USA; Gwinn, Katrina [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA

2015-10-07

Background: Neuroprotection for Parkinson Disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke (NINDS) has therefore established the Parkinson’s Disease Biomarkers Program (PDBP) to promote discovery of biomarkers for use in phase II-III clinical trials in PD. Methods: The PDBP facilitates biomarker development to improve neuroprotective clinical trial design, essential for advancing therapeutics for PD. To date, eleven consortium projects in the PDBP are focused on the development of clinical and laboratory-based PD biomarkers for diagnosis, progression tracking, and/or the prediction of prognosis. Seven of these projects also provide detailed longitudinal data and biospecimens from PD patients and controls, as a resource for all PD researchers. Standardized operating procedures and pooled reference samples have been created in order to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the entire PD research community for additional biomarker projects. Results: Here we describe the PDBP, highlight standard operating procedures for the collection of biospecimens and data, and provide an interim report with quality control analysis on the first 1082 participants and 1033 samples with quality control analysis collected as of October 2014. Conclusions: By making samples and data available to academics and industry, encouraging the adoption of existing standards, and providing a resource which complements existing programs, the PDBP will accelerate the pace of PD biomarker research, with the goal of improving diagnostic methods and treatment.

Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

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Si, Qian

Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

Clinical proteomics: Applications for prostate cancer biomarker discovery and detection.

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Petricoin, Emanuel F; Ornstein, David K; Liotta, Lance A

2004-01-01

The science of proteomics comprises much more than simply generating lists of proteins that change in expression as a cause of or consequence of pathophysiology. The goal of proteomics should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. Serum proteomic pattern diagnostics is a new type of proteomic concept in which patterns of ion signatures generated from high dimensional mass spectrometry data are used as diagnostic classifiers. This recent approach has exciting potential for clinical utility of diagnostic patterns because low molecular weight metabolites, peptides, and protein fragments may have higher accuracy than traditional biomarkers of cancer detection. Intriguingly, we now have discovered that this diagnostic information exists in a bound state, complexed with circulating highly abundant carrier proteins. These diagnostic fragments may one day be harvested by circulating nanoparticles, designed to absorb, enrich, and amplify the repertoire of diagnostic biomarkers generated-even at the critical, initial stages of carcinogenesis. Copyright 2004 Elsevier Inc.

Use of biomarkers in ALS drug development and clinical trials.

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Bakkar, Nadine; Boehringer, Ashley; Bowser, Robert

2015-05-14

The past decade has seen a dramatic increase in the discovery of candidate biomarkers for ALS. These biomarkers typically can either differentiate ALS from control subjects or predict disease course (slow versus fast progression). At the same time, late-stage clinical trials for ALS have failed to generate improved drug treatments for ALS patients. Incorporation of biomarkers into the ALS drug development pipeline and the use of biologic and/or imaging biomarkers in early- and late-stage ALS clinical trials have been absent and only recently pursued in early-phase clinical trials. Further clinical research studies are needed to validate biomarkers for disease progression and develop biomarkers that can help determine that a drug has reached its target within the central nervous system. In this review we summarize recent progress in biomarkers across ALS model systems and patient population, and highlight continued research directions for biomarkers that stratify the patient population to enrich for patients that may best respond to a drug candidate, monitor disease progression and track drug responses in clinical trials. It is crucial that we further develop and validate ALS biomarkers and incorporate these biomarkers into the ALS drug development process. This article is part of a Special Issue entitled ALS complex pathogenesis. Copyright © 2014 Elsevier B.V. All rights reserved.

Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity

International Nuclear Information System (INIS)

Beger, Richard D.; Sun, Jinchun; Schnackenberg, Laura K.

2010-01-01

Hepatotoxicity and nephrotoxicity are two major reasons that drugs are withdrawn post-market, and hence it is of major concern to both the FDA and pharmaceutical companies. The number of cases of serious adverse effects (SAEs) in marketed drugs has climbed faster than the number of total drug prescriptions issued. In some cases, preclinical animal studies fail to identify the potential toxicity of a new chemical entity (NCE) under development. The current clinical chemistry biomarkers of liver and kidney injury are inadequate in terms of sensitivity and/or specificity, prompting the need to discover new translational specific biomarkers of organ injury. Metabolomics along with genomics and proteomics technologies have the capability of providing translational diagnostic and prognostic biomarkers specific for early stages of liver and kidney injury. Metabolomics has several advantages over the other omics platforms such as ease of sample preparation, data acquisition and use of biofluids collected through minimally invasive procedures in preclinical and clinical studies. The metabolomics platform is reviewed with particular emphasis on applications involving drug-induced hepatotoxicity and nephrotoxicity. Analytical platforms for metabolomics, chemometrics for mining metabolomics data and the applications of the metabolomics technologies are covered in detail with emphasis on recent work in the field.

Other biomarkers for detecting prostate cancer.

Science.gov (United States)

Nogueira, Lucas; Corradi, Renato; Eastham, James A

2010-01-01

Prostate-specific antigen (PSA) has been used for detecting prostate cancer since 1994. Although it is the best cancer biomarker available, PSA is not perfect. It lacks both the sensitivity and specificity to accurately detect the presence of prostate cancer. None of the PSA thresholds currently in use consistently identify patients with prostate cancer and exclude patients without cancer. Novel approaches to improve our ability to detect prostate cancer and predict the course of the disease are needed. Additional methods for detecting prostate cancer have been evaluated. Despite the discovery of many new biomarkers, only a few have shown some clinical value. These markers include human kallikrein 2, urokinase-type plasminogen activator receptor, prostate-specific membrane antigen, early prostate cancer antigen, PCA3, alpha-methylacyl-CoA racemase and glutathione S-transferase pi hypermethylation. We review the reports on biomarkers for prostate cancer detection, and their possible role in the clinical practice.

Microfluidic-Based Multi-Organ Platforms for Drug Discovery

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Ahmad Rezaei Kolahchi

2016-09-01

Full Text Available Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

Biology and Biomarkers for Wound Healing

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Lindley, Linsey E.; Stojadinovic, Olivera; Pastar, Irena; Tomic-Canic, Marjana

2016-01-01

Background As the population grows older, the incidence and prevalence of conditions which lead to a predisposition for poor wound healing also increases. Ultimately, this increase in non-healing wounds has led to significant morbidity and mortality with subsequent huge economic ramifications. Therefore, understanding specific molecular mechanisms underlying aberrant wound healing is of great importance. It has, and will continue to be the leading pathway to the discovery of therapeutic targets as well as diagnostic molecular biomarkers. Biomarkers may help identify and stratify subsets of non-healing patients for whom biomarker-guided approaches may aid in healing. Methods A series of literature searches were performed using Medline, PubMed, Cochrane Library, and Internet searches. Results Currently, biomarkers are being identified using biomaterials sourced locally, from human wounds and/or systemically using systematic high-throughput “omics” modalities (genomic, proteomic, lipidomic, metabolomic analysis). In this review we highlight the current status of clinically applicable biomarkers and propose multiple steps in validation and implementation spectrum including those measured in tissue specimens e.g. β-catenin and c-myc, wound fluid e.g. MMP’s and interleukins, swabs e.g. wound microbiota and serum e.g. procalcitonin and MMP’s. Conclusions Identification of numerous potential biomarkers utilizing different avenues of sample collection and molecular approaches is currently underway. A focus on simplicity, and consistent implementation of these biomarkers as well as an emphasis on efficacious follow-up therapeutics is necessary for transition of this technology to clinically feasible point-of-care applications. PMID:27556760

Classification of genes and putative biomarker identification using distribution metrics on expression profiles.

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Hung-Chung Huang

Full Text Available BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic, and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

Biomarkers in the diagnosis of lysosomal storage disorders: proteins, lipids, and inhibodies.

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Aerts, Johannes M F G; Kallemeijn, Wouter W; Wegdam, Wouter; Joao Ferraz, Maria; van Breemen, Marielle J; Dekker, Nick; Kramer, Gertjan; Poorthuis, Ben J; Groener, Johanna E M; Cox-Brinkman, Josanne; Rombach, Saskia M; Hollak, Carla E M; Linthorst, Gabor E; Witte, Martin D; Gold, Henrik; van der Marel, Gijs A; Overkleeft, Herman S; Boot, Rolf G

2011-06-01

A biomarker is an analyte indicating the presence of a biological process linked to the clinical manifestations and outcome of a particular disease. In the case of lysosomal storage disorders (LSDs), primary and secondary accumulating metabolites or proteins specifically secreted by storage cells are good candidates for biomarkers. Clinical applications of biomarkers are found in improved diagnosis, monitoring disease progression, and assessing therapeutic correction. These are illustrated by reviewing the discovery and use of biomarkers for Gaucher disease and Fabry disease. In addition, recently developed chemical tools allowing specific visualization of enzymatically active lysosomal glucocerebrosidase are described. Such probes, coined inhibodies, offer entirely new possibilities for more sophisticated molecular diagnosis, enzyme replacement therapy monitoring, and fundamental research.

Accounting for control mislabeling in case-control biomarker studies.

Science.gov (United States)

Rantalainen, Mattias; Holmes, Chris C

2011-12-02

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.

Pilot Study on the Investigation of Tear Fluid Biomarkers as an Indicator of Ocular, Neurological, and Immunological Health in Astronauts

Science.gov (United States)

Morton, Stephen; Crucian, Brian; Hagan, Suzanne; Satyamitra, Merriline; Daily, Anna

2018-01-01

The purpose of this pilot study is to investigate the collection, preparation, and analysis of tear biomarkers as a means of assessing ocular, neurological, and immunological health. At present, no published data exists on the cytokine profiles of tears from astronauts exposed to long periods of microgravity and space irradiations. In addition, no published data exist on cytokine (biomarker) profiles of tears that have been collected from irradiated non-human biological systems (primates and other animal models). A goal for the proposed pilot study is to discover novel tear biomarkers which can help inform researchers, clinicians, epidemiologist and healthcare providers about the health status of a living biological system, as well as informing them when a disease state is triggered. This would be done via analysis of the onset of expression of pro-inflammatory cytokines, leading up to the full progression of a disease (i.e. cancer, loss of vision, radiation-induced oxidative stress, cardiovascular disorders, fibrosis in major organs, bone loss). Another goal of this pilot study is to investigate the state of disease against proposed medical countermeasures, in order to determine whether the countermeasures are efficacious in preventing or mitigating these injuries. An example of an up and coming tear biomarker technology, Ascendant Dx, a clinical stage diagnostic company, is developing a screening test to detect breast cancer using proteins from tears. The team utilized Liquid Chromatography -Mass Spectrometry with Mass analysis (LC MS/MS) as a discovery platform followed by validation with ELISA to come up with a panel of protein biomarkers that can differentiate breast cancer samples from control ("cancer free") samples with results far surpassing the results of imaging techniques in use today. Continued research into additional proteins is underway to increase the sensitivity and specificity of the test and development efforts are on the way to transfer the

A study of a self diagnostic platform for the detection of A2 biomarker for Leishmania donovani

Science.gov (United States)

Roche, Philip J. R.; Cheung, Maurice C.; Najih, Mohamed; McCall, Laura-Isobel; Fakih, Ibrahim; Chodavarapu, Vamsy P.; Ward, Brian; Ndao, Momar; Kirk, Andrew G.

2012-03-01

Visceral leishmaniasis (L.donovani) is a protozoan infection that attacks mononuclear phagocytes and causes the liver and spleen damage that can cause death. The investigation presented is a proof of concept development applying a plasmonic diagnostic platform with simple microfluidic sample delivery and optical readout. An immune-assay method is applied to the quantification of A2 protein, a highly immunogenic biomarker for the pathogen. Quantification of A2 was performed in the ng/ml range, analysis by ELISA suggested that a limit of 0.1ng/ml of A2 is approximate to 1 pathogen per ml and the sensing system shows the potential to deliver a similar level of quantification. Significant reduction in assay complexity as further enzyme linked enhancement is not required when applying a plasmonic methodology to an immunoassay. The basic instrumentation required for a portable device and potential dual optical readout where both plasmonic and photoluminescent response are assessed and investigated including consideration of the application of the device to testing where non-literate communication of results is considered and issues of performance are addressed.

In search of biomarkers for autism: scientific, social and ethical challenges.

Science.gov (United States)

Walsh, Pat; Elsabbagh, Mayada; Bolton, Patrick; Singh, Ilina

2011-09-20

There is widespread hope that the discovery of valid biomarkers for autism will both reveal the causes of autism and enable earlier and more targeted methods for diagnosis and intervention. However, growing enthusiasm about recent advances in this area of autism research needs to be tempered by an awareness of the major scientific challenges and the important social and ethical concerns arising from the development of biomarkers and their clinical application. Collaborative approaches involving scientists and other stakeholders must combine the search for valid, clinically useful autism biomarkers with efforts to ensure that individuals with autism and their families are treated with respect and understanding.

Quantification of Protein Biomarker Using SERS Nano-Stress Sensing with Peak Intensity Ratiometry

Science.gov (United States)

Goh, Douglas; Kong, Kien Voon; Jayakumar, Perumal; Gong, Tianxun; Dinish, U. S.; Olivo, Malini

We report a surface enhanced Raman spectroscopy (SERS) ratiometry method based on peak intensity coupled in a nano-stress sensing platform to detect and quantify biological molecules. Herein, we employed an antibody-conjugated p-aminothiophenol (ATP) functionalized on a bimetallic-film-over-nanosphere (BMFON) substrate as a sensitive SERS platform to detect human haptoglobin (Hp) protein, which is an acute phase protein and a biomarker for various cancers. Correlation between change in the ATP spectral characteristics and concentration of Hp protein was established by examining the peak intensity ratio at 1572cm-1 and 1592cm-1 that reflects the degree of stress experienced by the aromatic ring of ATP during Hp protein-antibody interaction. Development of this platform shows the potential in developing a low-cost and sensitive SERS sensor for the pre-screening of various biomarkers.

Assessment of cisplatin-induced kidney injury using an integrated rodent platform

Energy Technology Data Exchange (ETDEWEB)

Chen, Yafei [Global Safety Assessment, AstraZeneca R and D Waltham, MA 02451 (United States); Brott, David [Patient Safety, AstraZeneca R and D Wilmington, DE 19850 (United States); Luo, Wenli [Discovery Statistics, AstraZeneca R and D Waltham, MA 02451 (United States); Gangl, Eric [DMPK, AstraZeneca R and D Waltham, MA 02451 (United States); Kamendi, Harriet; Barthlow, Herbert; Lengel, David; Fikes, James; Kinter, Lewis [Global Safety Assessment, AstraZeneca R and D Waltham, MA 02451 (United States); Valentin, Jean-Pierre [Global Safety Assessment, AstraZeneca R and D Alderley Park, Macclesfield, SK10 4TG (United Kingdom); Bialecki, Russell, E-mail: russell.bialecki@astrazeneca.com [Global Safety Assessment, AstraZeneca R and D Waltham, MA 02451 (United States)

2013-05-01

Current diagnosis of drug-induced kidney injury (DIKI) primarily relies on detection of elevated plasma creatinine (Cr) or blood urea nitrogen (BUN) levels; however, both are indices of overall kidney function and changes are delayed with respect to onset of nephron injury. Our aim was to investigate whether early changes in new urinary DIKI biomarkers predict plasma Cr, BUN, renal hemodynamic and kidney morphological changes associated with kidney injury following a single dose of cisplatin (CDDP) using an integrated platform in rodent. Conscious surgically prepared male Han Wistar rats were given a single intraperitoneal dose of CDDP (15 mg/kg). Glomerular filtration rate (GFR), effective renal plasma flow (ERPF), urinalysis, DIKI biomarkers, CDDP pharmacokinetics, blood pressures, heart rate, body temperature and electroencephalogram (EEG) were measured in the same vehicle- or CDDP-treated animals over 72 h. Plasma chemistry (including Cr and BUN) and renal tissues were examined at study termination. Cisplatin caused progressive reductions of GFR, ERPF, heart rate and body temperature from day 1 (0–24 h). DIKI biomarkers including alpha-glutathione S-transferase (α-GST) significantly increased as early as 6 h post-dose, which preceded significant declines of GFR and ERPF (24 h), increased plasma Cr and BUN (72 h), and associated with renal acute tubular necrosis at 72 h post-dose. The present study adds to the current understanding of CDDP action by demonstrating that early increases in urinary excretion of α-GST predict DIKI risk following acute exposure to CDDP in rats, before changes in traditional DIKI markers are evident. - Highlights: ► CDDP causes direct damage to kidneys without affecting EEG or CVS function. ► α-GST and albumin detect DIKI earlier when compared with traditional indices. ► Integrated “cardiovascular-EEG-renal” model to better understand DIKI mechanisms ► Promotes 3R's principles in drug discovery and development.

Assessment of cisplatin-induced kidney injury using an integrated rodent platform

International Nuclear Information System (INIS)

Chen, Yafei; Brott, David; Luo, Wenli; Gangl, Eric; Kamendi, Harriet; Barthlow, Herbert; Lengel, David; Fikes, James; Kinter, Lewis; Valentin, Jean-Pierre; Bialecki, Russell

2013-01-01

Current diagnosis of drug-induced kidney injury (DIKI) primarily relies on detection of elevated plasma creatinine (Cr) or blood urea nitrogen (BUN) levels; however, both are indices of overall kidney function and changes are delayed with respect to onset of nephron injury. Our aim was to investigate whether early changes in new urinary DIKI biomarkers predict plasma Cr, BUN, renal hemodynamic and kidney morphological changes associated with kidney injury following a single dose of cisplatin (CDDP) using an integrated platform in rodent. Conscious surgically prepared male Han Wistar rats were given a single intraperitoneal dose of CDDP (15 mg/kg). Glomerular filtration rate (GFR), effective renal plasma flow (ERPF), urinalysis, DIKI biomarkers, CDDP pharmacokinetics, blood pressures, heart rate, body temperature and electroencephalogram (EEG) were measured in the same vehicle- or CDDP-treated animals over 72 h. Plasma chemistry (including Cr and BUN) and renal tissues were examined at study termination. Cisplatin caused progressive reductions of GFR, ERPF, heart rate and body temperature from day 1 (0–24 h). DIKI biomarkers including alpha-glutathione S-transferase (α-GST) significantly increased as early as 6 h post-dose, which preceded significant declines of GFR and ERPF (24 h), increased plasma Cr and BUN (72 h), and associated with renal acute tubular necrosis at 72 h post-dose. The present study adds to the current understanding of CDDP action by demonstrating that early increases in urinary excretion of α-GST predict DIKI risk following acute exposure to CDDP in rats, before changes in traditional DIKI markers are evident. - Highlights: ► CDDP causes direct damage to kidneys without affecting EEG or CVS function. ► α-GST and albumin detect DIKI earlier when compared with traditional indices. ► Integrated “cardiovascular-EEG-renal” model to better understand DIKI mechanisms ► Promotes 3R's principles in drug discovery and development

Metabolic profiling of yeast culture using gas chromatography coupled with orthogonal acceleration accurate mass time-of-flight mass spectrometry: application to biomarker discovery.

Science.gov (United States)

Kondo, Elsuida; Marriott, Philip J; Parker, Rhiannon M; Kouremenos, Konstantinos A; Morrison, Paul; Adams, Mike

2014-01-07

Yeast and yeast cultures are frequently used as additives in diets of dairy cows. Beneficial effects from the inclusion of yeast culture in diets for dairy mammals have been reported, and the aim of this study was to develop a comprehensive analytical method for the accurate mass identification of the 'global' metabolites in order to differentiate a variety of yeasts at varying growth stages (Diamond V XP, Yea-Sacc and Levucell). Microwave-assisted derivatization for metabolic profiling is demonstrated through the analysis of differing yeast samples developed for cattle feed, which include a wide range of metabolites of interest covering a large range of compound classes. Accurate identification of the components was undertaken using GC-oa-ToFMS (gas chromatography-orthogonal acceleration-time-of-flight mass spectrometry), followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) for data reduction and biomarker discovery. Semi-quantification (fold changes in relative peak areas) was reported for metabolites identified as possible discriminative biomarkers (p-value 2), including D-ribose (four fold decrease), myo-inositol (five fold increase), L-phenylalanine (three fold increase), glucopyranoside (two fold increase), fructose (three fold increase) and threitol (three fold increase) respectively. Copyright © 2013 Elsevier B.V. All rights reserved.

Hepcidin- A Burgeoning Biomarker

Directory of Open Access Journals (Sweden)

Hemkant Manikrao Deshmukh

2017-10-01

Full Text Available The discovery of hepcidin has triggered a virtual ignition of studies on iron metabolism and related disorders. The peptide hormone hepcidin is a key homeostatic regulator of iron metabolism. The synthesis of hepcidin is induced by systemic iron levels and by inflammatory stimuli. Several human diseases are associated with variations in hepcidin concentrations. The evaluation of hepcidin in biological fluids is therefore a promising device in the diagnosis and management of medical situations in which iron metabolism is affected. Thus, it made us to recapitulate role of hepcidin as biomarker.

Cancer therapy trials employing level-of-evidence-1 disease forecast cancer biomarkers uPA and its inhibitor PAI-1

DEFF Research Database (Denmark)

Schmitt, Manfred; Harbeck, Nadia; Brünner, Nils

2011-01-01

Clinical research on cancer biomarkers is essential in understanding recent discoveries in cancer biology and heterogeneity of the cancer disease. However, there are only a few examples of clinically useful studies that have identified cancer biomarkers with clinical benefit. Urokinase-type plasm...

Lab-on-a-chip platform for high throughput drug discovery with DNA-encoded chemical libraries

Science.gov (United States)

Grünzner, S.; Reddavide, F. V.; Steinfelder, C.; Cui, M.; Busek, M.; Klotzbach, U.; Zhang, Y.; Sonntag, F.

2017-02-01

The fast development of DNA-encoded chemical libraries (DECL) in the past 10 years has received great attention from pharmaceutical industries. It applies the selection approach for small molecular drug discovery. Because of the limited choices of DNA-compatible chemical reactions, most DNA-encoded chemical libraries have a narrow structural diversity and low synthetic yield. There is also a poor correlation between the ranking of compounds resulted from analyzing the sequencing data and the affinity measured through biochemical assays. By combining DECL with dynamical chemical library, the resulting DNA-encoded dynamic library (EDCCL) explores the thermodynamic equilibrium of reversible reactions as well as the advantages of DNA encoded compounds for manipulation/detection, thus leads to enhanced signal-to-noise ratio of the selection process and higher library quality. However, the library dynamics are caused by the weak interactions between the DNA strands, which also result in relatively low affinity of the bidentate interaction, as compared to a stable DNA duplex. To take advantage of both stably assembled dual-pharmacophore libraries and EDCCLs, we extended the concept of EDCCLs to heat-induced EDCCLs (hi-EDCCLs), in which the heat-induced recombination process of stable DNA duplexes and affinity capture are carried out separately. To replace the extremely laborious and repetitive manual process, a fully automated device will facilitate the use of DECL in drug discovery. Herein we describe a novel lab-on-a-chip platform for high throughput drug discovery with hi-EDCCL. A microfluidic system with integrated actuation was designed which is able to provide a continuous sample circulation by reducing the volume to a minimum. It consists of a cooled and a heated chamber for constant circulation. The system is capable to generate stable temperatures above 75 °C in the heated chamber to melt the double strands of the DNA and less than 15 °C in the cooled chamber

Mining biomarker information in biomedical literature

Directory of Open Access Journals (Sweden)

Younesi Erfan

2012-12-01

://www.scaiview.com/scaiview-academia.html. Conclusions The approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

DEFF Research Database (Denmark)

Olsen, Lars Rønn; Campos, Benito; Winther, Ole

2014-01-01

directly linked to the hallmarks of cancer. The results found by proteogenomic analysis of the 32 tumor antigens studied here, capture largely the same pathway irregularities as those elucidated from large-scale screening of genomics analyses, where several thousands of genes are often found......Background: The majority of genetic biomarkers for human cancers are defined by statistical screening of high-throughput genomics data. While a large number of genetic biomarkers have been proposed for diagnostic and prognostic applications, only a small number have been applied in the clinic....... Similarly, the use of proteomics methods for the discovery of cancer biomarkers is increasing. The emerging field of proteogenomics seeks to enrich the value of genomics and proteomics approaches by studying the intersection of genomics and proteomics data. This task is challenging due to the complex nature...

Sputum-Based Molecular Biomarkers for the Early Detection of Lung Cancer: Limitations and Promise

Energy Technology Data Exchange (ETDEWEB)

Kim, Connie E. [Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine. 462 First Avenue, NBV 7N24, New York, NY 10016 (United States); Tchou-Wong, Kam-Meng; Rom, William N., E-mail: william.rom@nyumc.org [Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine. 462 First Avenue, NBV 7N24, New York, NY 10016 (United States); Department of Environmental Medicine, New York University School of Medicine, 57 Old Forge Road, Tuxedo, NY 10987 (United States)

2011-07-19

Lung cancer is the leading cause of cancer deaths, with an overall survival of 15% at five years. Biomarkers that can sensitively and specifically detect lung cancer at early stage are crucial for improving this poor survival rate. Sputum has been the target for the discovery of non-invasive biomarkers for lung cancer because it contains airway epithelial cells, and molecular alterations identified in sputum are most likely to reflect tumor-associated changes or field cancerization caused by smoking in the lung. Sputum-based molecular biomarkers include morphology, allelic imbalance, promoter hypermethylation, gene mutations and, recently, differential miRNA expression. To improve the sensitivity and reproducibility of sputum-based biomarkers, we recommend standardization of processing protocols, bronchial epithelial cell enrichment, and identification of field cancerization biomarkers.

CBD: a biomarker database for colorectal cancer.

Science.gov (United States)

Zhang, Xueli; Sun, Xiao-Feng; Cao, Yang; Ye, Benchen; Peng, Qiliang; Liu, Xingyun; Shen, Bairong; Zhang, Hong

2018-01-01

Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.Database URL: http://sysbio.suda.edu.cn/CBD/.

Lessons for tumor biomarker trials: vicious cycles, scientific method & developing guidelines.

Science.gov (United States)

Hayes, Daniel; Raison, Claire

2015-02-01

Interview with Daniel Hayes, by Claire Raison (Commissioning Editor) Daniel F Hayes, M.D. is the Stuart A Padnos Professor of Breast Cancer Research and co-Director of the Breast Oncology Program at the University of Michigan Comprehensive Cancer Center (Ann Arbor, MI, USA). Dr Hayes has extensive experience in clinical and translational breast cancer biomarker research, and in drug development and clinical trials. Around 30 years ago, he led the discovery of the circulating breast tumor biomarker, CA15-3, which started his career into further tumor biomarker work. The main thrust of his work since then has been in clinical trials, tumor biomarkers and trying to integrate the two. Dr Hayes is Chair of the Correlative Sciences Committee of the North American Breast Cancer Group (now called the Breast Cancer Steering Committee), and co-chairs the Expert Panel for Tumor Biomarker Practice Guidelines for the American Society of Clinical Oncology.

Biomarkers of Hypoxic Ischemic Encephalopathy in Newborns

Directory of Open Access Journals (Sweden)

Martha V. Douglas-Escobar

2012-11-01

Full Text Available As neonatal intensive care has evolved, the focus has shifted from improving mortality alone to an effort to improve both mortality and morbidity. The most frequent source of neonatal brain injury occurs as a result of hypoxic-ischemic injury. Hypoxic-ischemic injury occurs in about 2 of 1,000 full-term infants and severe injured infants will have lifetime disabilities and neurodevelopmental delays. Most recently, remarkable efforts toward neuroprotection have been started with the advent of therapeutic hypothermia and a key step in the evolution of neonatal neuroprotection is the discovery of biomarkers that enable the clinician-scientist to screen infants for brain injury, monitor progression of disease, identify injured brain regions, and assess efficacy of neuroprotective clinical trials. Lastly, biomarkers offer great hope identifying when an injury occurred shedding light on the potential pathophysiology and the most effective therapy. In this article, we will review biomarkers of HIE including S100b, neuron specific enolase, umbilical cord IL-6, CK-BB, GFAP, myelin basic protein, UCHL-1, and pNF-H. We hope to contribute to the awareness, validation and clinical use of established as well as novel neonatal brain injury biomarkers.

Biomarker Analysis of Stored Blood Products: Emphasis on Pre-Analytical Issues

Science.gov (United States)

Delobel, Julien; Rubin, Olivier; Prudent, Michel; Crettaz, David; Tissot, Jean-Daniel; Lion, Niels

2010-01-01

Millions of blood products are transfused every year; many lives are thus directly concerned by transfusion. The three main labile blood products used in transfusion are erythrocyte concentrates, platelet concentrates and fresh frozen plasma. Each of these products has to be stored according to its particular components. However, during storage, modifications or degradation of those components may occur, and are known as storage lesions. Thus, biomarker discovery of in vivo blood aging as well as in vitro labile blood products storage lesions is of high interest for the transfusion medicine community. Pre-analytical issues are of major importance in analyzing the various blood products during storage conditions as well as according to various protocols that are currently used in blood banks for their preparations. This paper will review key elements that have to be taken into account in the context of proteomic-based biomarker discovery applied to blood banking. PMID:21151459

Potentials of single-cell biology in identification and validation of disease biomarkers.

Science.gov (United States)

Niu, Furong; Wang, Diane C; Lu, Jiapei; Wu, Wei; Wang, Xiangdong

2016-09-01

Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Update on Biomarkers for the Detection of Endometriosis

Science.gov (United States)

Fassbender, Amelie; Burney, Richard O.; O, Dorien F.; D'Hooghe, Thomas; Giudice, Linda

2015-01-01

Endometriosis is histologically characterized by the displacement of endometrial tissue to extrauterine locations including the pelvic peritoneum, ovaries, and bowel. An important cause of infertility and pelvic pain, the individual and global socioeconomic burden of endometriosis is significant. Laparoscopy remains the gold standard for the diagnosis of the condition. However, the invasive nature of surgery, coupled with the lack of a laboratory biomarker for the disease, results in a mean latency of 7–11 years from onset of symptoms to definitive diagnosis. Unfortunately, the delay in diagnosis may have significant consequences in terms of disease progression. The discovery of a sufficiently sensitive and specific biomarker for the nonsurgical detection of endometriosis promises earlier diagnosis and prevention of deleterious sequelae and represents a clear research priority. In this review, we describe and discuss the current status of biomarkers of endometriosis in plasma, urine, and endometrium. PMID:26240814

Update on Biomarkers for the Detection of Endometriosis

Directory of Open Access Journals (Sweden)

Amelie Fassbender

2015-01-01

Full Text Available Endometriosis is histologically characterized by the displacement of endometrial tissue to extrauterine locations including the pelvic peritoneum, ovaries, and bowel. An important cause of infertility and pelvic pain, the individual and global socioeconomic burden of endometriosis is significant. Laparoscopy remains the gold standard for the diagnosis of the condition. However, the invasive nature of surgery, coupled with the lack of a laboratory biomarker for the disease, results in a mean latency of 7–11 years from onset of symptoms to definitive diagnosis. Unfortunately, the delay in diagnosis may have significant consequences in terms of disease progression. The discovery of a sufficiently sensitive and specific biomarker for the nonsurgical detection of endometriosis promises earlier diagnosis and prevention of deleterious sequelae and represents a clear research priority. In this review, we describe and discuss the current status of biomarkers of endometriosis in plasma, urine, and endometrium.

Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma

DEFF Research Database (Denmark)

Kristiansen, Troels Zakarias; Harsha, H C; Grønborg, Mads

2008-01-01

Quantitative proteomic methodologies allow profiling of hundreds to thousands of proteins in a high-throughput fashion. This approach is increasingly applied to cancer biomarker discovery to identify proteins that are differentially regulated in cancers. Fractionation of protein samples based...

Tissue- and Serum-Associated Biomarkers of Hepatocellular Carcinoma

Science.gov (United States)

Chauhan, Ranjit; Lahiri, Nivedita

2016-01-01

Hepatocellular carcinoma (HCC), one of the leading causes of cancer deaths in the world, is offering a challenge to human beings, with the current modes of treatment being a palliative approach. Lack of proper curative or preventive treatment methods encouraged extensive research around the world with an aim to detect a vaccine or therapeutic target biomolecule that could lead to development of a drug or vaccine against HCC. Biomarkers or biological disease markers have emerged as a potential tool as drug/vaccine targets, as they can accurately diagnose, predict, and even prevent the diseases. Biomarker expression in tissue, serum, plasma, or urine can detect tumor in very early stages of its development and monitor the cancer progression and also the effect of therapeutic interventions. Biomarker discoveries are driven by advanced techniques, such as proteomics, transcriptomics, whole genome sequencing, micro- and micro-RNA arrays, and translational clinics. In this review, an overview of the potential of tissue- and serum-associated HCC biomarkers as diagnostic, prognostic, and therapeutic targets for drug development is presented. In addition, we highlight recently developed micro-RNA, long noncoding RNA biomarkers, and single-nucleotide changes, which may be used independently or as complementary biomarkers. These active investigations going on around the world aimed at conquering HCC might show a bright light in the near future. PMID:27398029

Tissue- and Serum-Associated Biomarkers of Hepatocellular Carcinoma

Directory of Open Access Journals (Sweden)

Ranjit Chauhan

2016-01-01

Full Text Available Hepatocellular carcinoma (HCC, one of the leading causes of cancer deaths in the world, is offering a challenge to human beings, with the current modes of treatment being a palliative approach. Lack of proper curative or preventive treatment methods encouraged extensive research around the world with an aim to detect a vaccine or therapeutic target biomolecule that could lead to development of a drug or vaccine against HCC. Biomarkers or biological disease markers have emerged as a potential tool as drug/vaccine targets, as they can accurately diagnose, predict, and even prevent the diseases. Biomarker expression in tissue, serum, plasma, or urine can detect tumor in very early stages of its development and monitor the cancer progression and also the effect of therapeutic interventions. Biomarker discoveries are driven by advanced techniques, such as proteomics, transcriptomics, whole genome sequencing, micro- and micro-RNA arrays, and translational clinics. In this review, an overview of the potential of tissue- and serum-associated HCC biomarkers as diagnostic, prognostic, and therapeutic targets for drug development is presented. In addition, we highlight recently developed micro-RNA, long noncoding RNA biomarkers, and single-nucleotide changes, which may be used independently or as complementary biomarkers. These active investigations going on around the world aimed at conquering HCC might show a bright light in the near future.

Tectonic resemblance of the Indian Platform, Pakistan with the Moesian Platform, Romania and strategy for exploration of hydrocarbons

International Nuclear Information System (INIS)

Memon, A.D.

1994-01-01

There is a remarkable tectonic resemblance between the indian Platform (Pakistan) and the Moesian Platform (Romania). As viewed in global tectonic perspective Moeslan and Indian Plates have played important role in Alpine Himalayan Orogeny; Moesian and Indian Platforms are extension of these respective plates. Characteristics features of both the platforms are block faulting which has effected not only the general tectonic framework but has also played important role in oil accumulation. Main producing rocks in the Moesian platform are Jurassic sandstones and cretaceous limestones while in the indian platform cretaceous sandstones are important reservoirs. The average geothermal gradient in the indian platform is 2.45 C/100m with the higher gradients in the central gas producing region. Geothermal gradients in the Moesian platform have an average value of 3 C/100m with higher gradients in the northern in the northern part. Some of the producing structures in both the platforms are remarkably similar, traps associated with normal faults are very important. Extensive exploration carried in the Moesian Platform makes it very important oil producing region of Romania. After the discovery of oil lower Sindh, serious exploration is being carried in the Indian platform. The paper deals with the similarities between these two important platforms. In the light of the studies of the Moesian platform, strategies or exploration of oil and gas in the Indian Platform are suggested. (author)

Sea Level Rise Data Discovery

Science.gov (United States)

Quach, N.; Huang, T.; Boening, C.; Gill, K. M.

2016-12-01

Research related to sea level rise crosses multiple disciplines from sea ice to land hydrology. The NASA Sea Level Change Portal (SLCP) is a one-stop source for current sea level change information and data, including interactive tools for accessing and viewing regional data, a virtual dashboard of sea level indicators, and ongoing updates through a suite of editorial products that include content articles, graphics, videos, and animations. The architecture behind the SLCP makes it possible to integrate web content and data relevant to sea level change that are archived across various data centers as well as new data generated by sea level change principal investigators. The Extensible Data Gateway Environment (EDGE) is incorporated into the SLCP architecture to provide a unified platform for web content and science data discovery. EDGE is a data integration platform designed to facilitate high-performance geospatial data discovery and access with the ability to support multi-metadata standard specifications. EDGE has the capability to retrieve data from one or more sources and package the resulting sets into a single response to the requestor. With this unified endpoint, the Data Analysis Tool that is available on the SLCP can retrieve dataset and granule level metadata as well as perform geospatial search on the data. This talk focuses on the architecture that makes it possible to seamlessly integrate and enable discovery of disparate data relevant to sea level rise.

The next generation of targeted mutation discovery

NARCIS (Netherlands)

Harakalova, M.

2013-01-01

Sequencing technologies (NGS) now allows efficient analysis of the complete protein-coding regions of genomes (exomes) for multiple samples in a single sequencing run. In Chapter 2, we present our results with a genomic DNA pooling strategy for rare variant discovery on a NGS platform. The high

Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue

NARCIS (Netherlands)

N.Q. Liu (Ning Qing); R.B.H. Braakman (René); C. Stingl (Christoph); T.M. Luider (Theo); J.W.M. Martens (John); J.A. Foekens (John); A. Umar (Arzu)

2012-01-01

textabstractMass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while

Discovery of Biochemical Biomarkers for Aggression: A Role for Metabolomics in Psychiatry

NARCIS (Netherlands)

Hagenbeek, F.A.; Kluft, C.; Hankemeier, T.; Bartels, M.; Draisma, H.H.M.; Middeldorp, C.M.; Berger, R.; Noto, A.; Lussu, M.; Pool, R.; Fanos, V.; Boomsma, D.I.

2016-01-01

Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published

Application of data mining and artificial intelligence techniques to mass spectrometry data for knowledge discovery

Directory of Open Access Journals (Sweden)

Hugo López-Fernández

2016-05-01

Full Text Available Mass spectrometry using matrix assisted laser desorption ionization coupled to time of flight analyzers (MALDI-TOF MS has become popular during the last decade due to its high speed, sensitivity and robustness for detecting proteins and peptides. This allows quickly analyzing large sets of samples are in one single batch and doing high-throughput proteomics. In this scenario, bioinformatics methods and computational tools play a key role in MALDI-TOF data analysis, as they are able handle the large amounts of raw data generated in order to extract new knowledge and useful conclusions. A typical MALDI-TOF MS data analysis workflow has three main stages: data acquisition, preprocessing and analysis. Although the most popular use of this technology is to identify proteins through their peptides, analyses that make use of artificial intelligence (AI, machine learning (ML, and statistical methods can be also carried out in order to perform biomarker discovery, automatic diagnosis, and knowledge discovery. In this research work, this workflow is deeply explored and new solutions based on the application of AI, ML, and statistical methods are proposed. In addition, an integrated software platform that supports the full MALDI-TOF MS data analysis workflow that facilitate the work of proteomics researchers without advanced bioinformatics skills has been developed and released to the scientific community.

Toward Precision Medicine: A Cancer Molecular Subtyping Nano-Strategy for RNA Biomarkers in Tumor and Urine.

Science.gov (United States)

Koo, Kevin M; Wee, Eugene J H; Mainwaring, Paul N; Wang, Yuling; Trau, Matt

2016-12-01

Cancer is a heterogeneous disease which manifests as different molecular subtypes due to the complex nature of tumor initiation, progression, and metastasis. The concept of precision medicine aims to exploit this cancer heterogeneity by incorporating diagnostic technology to characterize each cancer patient's molecular subtype for tailored treatments. To characterize cancer molecular subtypes accurately, a suite of multiplexed bioassays have currently been developed to detect multiple oncogenic biomarkers. Despite the reliability of current multiplexed detection techniques, novel strategies are still needed to resolve limitations such as long assay time, complex protocols, and difficulty in interpreting broad overlapping spectral peaks of conventional fluorescence readouts. Herein a rapid (80 min) multiplexed platform strategy for subtyping prostate cancer tumor and urine samples based on their RNA biomarker profiles is presented. This is achieved by combining rapid multiplexed isothermal reverse transcription-recombinase polymerase amplification (RT-RPA) of target RNA biomarkers with surface-enhanced Raman spectroscopy (SERS) nanotags for "one-pot" readout. This is the first translational application of a RT-RPA/SERS-based platform for multiplexed cancer biomarker detection to address a clinical need. With excellent sensitivity of 200 zmol (100 copies) and specificity, we believed that this platform methodology could be a useful tool for rapid multiplexed subtyping of cancers. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Matchmaker Exchange: a platform for rare disease gene discovery

NARCIS (Netherlands)

Philippakis, A.A.; Azzariti, D.R.; Beltran, S.; Brookes, A.J.; Brownstein, C.A.; Brudno, M.; Brunner, H.G.; Buske, O.J.; Carey, K.; Doll, C.; Dumitriu, S.; Dyke, S.O.M.; Dunnen, J.T. den; Firth, H.V.; Gibbs, R.A.; Girdea, M.; Gonzalez, M.; Haendel, M.A.; Hamosh, A.; Holm, I.A.; Huang, L.; Hurles, M.E.; Hutton, B.; Krier, J.B.; Misyura, A.; Mungall, C.J.; Paschall, J.; Paten, B.; Robinson, P.N.; Schiettecatte, F.; Sobreira, N.L.; Swaminathan, G.J.; Taschner, P.E.M.; Terry, S.F.; Washington, N.L.; Zuchner, S.; Boycott, K.M.; Rehm, H.L.

2015-01-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of

Biomarkers in Sports and Exercise: Tracking Health, Performance, and Recovery in Athletes.

Science.gov (United States)

Lee, Elaine C; Fragala, Maren S; Kavouras, Stavros A; Queen, Robin M; Pryor, John Luke; Casa, Douglas J

2017-10-01

Biomarker discovery and validation is a critical aim of the medical and scientific community. Research into exercise and diet-related biomarkers aims to improve health, performance, and recovery in military personnel, athletes, and lay persons. Exercise physiology research has identified individual biomarkers for assessing health, performance, and recovery during exercise training. However, there are few recommendations for biomarker panels for tracking changes in individuals participating in physical activity and exercise training programs. Our approach was to review the current literature and recommend a collection of validated biomarkers in key categories of health, performance, and recovery that could be used for this purpose. We determined that a comprehensive performance set of biomarkers should include key markers of (a) nutrition and metabolic health, (b) hydration status, (c) muscle status, (d) endurance performance, (e) injury status and risk, and (f) inflammation. Our review will help coaches, clinical sport professionals, researchers, and athletes better understand how to comprehensively monitor physiologic changes, as they design training cycles that elicit maximal improvements in performance while minimizing overtraining and injury risk.

"Seeing is believing": perspectives of applying imaging technology in discovery toxicology.

Science.gov (United States)

Xu, Jinghai James; Dunn, Margaret Condon; Smith, Arthur Russell

2009-11-01

Efficiency and accuracy in addressing drug safety issues proactively are critical in minimizing late-stage drug attritions. Discovery toxicology has become a specialty subdivision of toxicology seeking to effectively provide early predictions and safety assessment in the drug discovery process. Among the many technologies utilized to select safer compounds for further development, in vitro imaging technology is one of the best characterized and validated to provide translatable biomarkers towards clinically-relevant outcomes of drug safety. By carefully applying imaging technologies in genetic, hepatic, and cardiac toxicology, and integrating them with the rest of the drug discovery processes, it was possible to demonstrate significant impact of imaging technology on drug research and development and substantial returns on investment.

Numeric computation and statistical data analysis on the Java platform

CERN Document Server

Chekanov, Sergei V

2016-01-01

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis ...

Targeted proteomics guided by label-free global proteome analysis in saliva reveal transition signatures from health to periodontal disease.

Science.gov (United States)

Bostanci, Nagihan; Selevsek, Nathalie; Wolski, Witold; Grossmann, Jonas; Bao, Kai; Wahlander, Asa; Trachsel, Christian; Schlapbach, Ralph; Özturk, Veli Özgen; Afacan, Beral; Emingil, Gulnur; Belibasakis, Georgios N

2018-04-02

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. We carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n=67, health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n=82). The LFQ platform led to the discovery of 119 proteins with at least two-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1), with maximum area under the receiver operating curve >0.97. This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum

Assessment of the real-time PCR and different digital PCR platforms for DNA quantification.

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Pavšič, Jernej; Žel, Jana; Milavec, Mojca

2016-01-01

Digital PCR (dPCR) is beginning to supersede real-time PCR (qPCR) for quantification of nucleic acids in many different applications. Several analytical properties of the two most commonly used dPCR platforms, namely the QX100 system (Bio-Rad) and the 12.765 array of the Biomark system (Fluidigm), have already been evaluated and compared with those of qPCR. However, to the best of our knowledge, direct comparison between the three of these platforms using the same DNA material has not been done, and the 37 K array on the Biomark system has also not been evaluated in terms of linearity, analytical sensitivity and limit of quantification. Here, a first assessment of qPCR, the QX100 system and both arrays of the Biomark system was performed with plasmid and genomic DNA from human cytomegalovirus. With use of PCR components that alter the efficiency of qPCR, each dPCR platform demonstrated consistent copy-number estimations, which indicates the high resilience of dPCR. Two approaches, one considering the total reaction volume and the other considering the effective reaction size, were used to assess linearity, analytical sensitivity and variability. When the total reaction volume was considered, the best performance was observed with qPCR, followed by the QX100 system and the Biomark system. In contrast, when the effective reaction size was considered, all three platforms showed almost equal limits of detection and variability. Although dPCR might not always be more appropriate than qPCR for quantification of low copy numbers, dPCR is a suitable method for robust and reproducible quantification of viral DNA, and a promising technology for the higher-order reference measurement method.

PROFILEing idiopathic pulmonary fibrosis: rethinking biomarker discovery

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Toby M. Maher

2013-06-01

Full Text Available Despite major advances in the understanding of the pathogenesis of idiopathic pulmonary fibrosis (IPF, diagnosis and management of the condition continue to pose significant challenges. Clinical management of IPF remains unsatisfactory due to limited availability of effective drug therapies, a lack of accurate indicators of disease progression, and an absence of simple short-term measures of therapeutic response. The identification of more accurate predictors of prognosis and survival in IPF would facilitate counseling of patients and their families, aid communication among clinicians, and would guide optimal timing of referral for transplantation. Improvements in molecular techniques have led to the identification of new disease pathways and a more targeted approach to the development of novel anti-fibrotic agents. However, despite an increased interest in biomarkers of IPF disease progression there are a lack of measures that can be used in early phase clinical trials. Careful longitudinal phenotyping of individuals with IPF together with the application of novel omics-based technology should provide important insights into disease pathogenesis and should address some of the major issues holding back drug development in IPF. The PROFILE (Prospective Observation of Fibrosis in the Lung Clinical Endpoints study is a currently enrolling, prospective cohort study designed to tackle these issues.

Discovery of putative salivary biomarkers for Sjögren's syndrome using high resolution mass spectrometry and bioinformatics.

Science.gov (United States)

Zoukhri, Driss; Rawe, Ian; Singh, Mabi; Brown, Ashley; Kublin, Claire L; Dawson, Kevin; Haddon, William F; White, Earl L; Hanley, Kathleen M; Tusé, Daniel; Malyj, Wasyl; Papas, Athena

2012-03-01

The purpose of the current study was to determine if saliva contains biomarkers that can be used as diagnostic tools for Sjögren's syndrome (SjS). Twenty seven SjS patients and 27 age-matched healthy controls were recruited for these studies. Unstimulated glandular saliva was collected from the Wharton's duct using a suction device. Two µl of salvia were processed for mass spectrometry analyses on a prOTOF 2000 matrix-assisted laser desorption/ionization orthogonal time of flight (MALDI O-TOF) mass spectrometer. Raw data were analyzed using bioinformatic tools to identify biomarkers. MALDI O-TOF MS analyses of saliva samples were highly reproducible and the mass spectra generated were very rich in peptides and peptide fragments in the 750-7,500 Da range. Data analysis using bioinformatic tools resulted in several classification models being built and several biomarkers identified. One model based on 7 putative biomarkers yielded a sensitivity of 97.5%, specificity of 97.8% and an accuracy of 97.6%. One biomarker was present only in SjS samples and was identified as a proteolytic peptide originating from human basic salivary proline-rich protein 3 precursor. We conclude that salivary biomarkers detected by high-resolution mass spectrometry coupled with powerful bioinformatic tools offer the potential to serve as diagnostic/prognostic tools for SjS.

Evaluation of external RNA controls for the standardisation of gene expression biomarker measurements

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Elaswarapu Ramnath

2010-11-01

Full Text Available Abstract Background Gene expression profiling is an important approach for detecting diagnostic and prognostic biomarkers, and predicting drug safety. The development of a wide range of technologies and platforms for measuring mRNA expression makes the evaluation and standardization of transcriptomic data problematic due to differences in protocols, data processing and analysis methods. Thus, universal RNA standards, such as those developed by the External RNA Controls Consortium (ERCC, are proposed to aid validation of research findings from diverse platforms such as microarrays and RT-qPCR, and play a role in quality control (QC processes as transcriptomic profiling becomes more commonplace in the clinical setting. Results Panels of ERCC RNA standards were constructed in order to test the utility of these reference materials (RMs for performance characterization of two selected gene expression platforms, and for discrimination of biomarker profiles between groups. The linear range, limits of detection and reproducibility of microarray and RT-qPCR measurements were evaluated using panels of RNA standards. Transcripts of low abundance (≤ 10 copies/ng total RNA showed more than double the technical variability compared to higher copy number transcripts on both platforms. Microarray profiling of two simulated 'normal' and 'disease' panels, each consisting of eight different RNA standards, yielded robust discrimination between the panels and between standards with varying fold change ratios, showing no systematic effects due to different labelling and hybridization runs. Also, comparison of microarray and RT-qPCR data for fold changes showed agreement for the two platforms. Conclusions ERCC RNA standards provide a generic means of evaluating different aspects of platform performance, and can provide information on the technical variation associated with quantification of biomarkers expressed at different levels of physiological abundance

A new strategy for faster urinary biomarkers identification by Nano-LC-MALDI-TOF/TOF mass spectrometry

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Le Meur Y

2008-11-01

Full Text Available Abstract Background LC-MALDI-TOF/TOF analysis is a potent tool in biomarkers discovery characterized by its high sensitivity and high throughput capacity. However, methods based on MALDI-TOF/TOF for biomarkers discovery still need optimization, in particular to reduce analysis time and to evaluate their reproducibility for peak intensities measurement. The aims of this methodological study were: (i to optimize and critically evaluate each step of urine biomarker discovery method based on Nano-LC coupled off-line to MALDI-TOF/TOF, taking full advantage of the dual decoupling between Nano-LC, MS and MS/MS to reduce the overall analysis time; (ii to evaluate the quantitative performance and reproducibility of nano-LC-MALDI analysis in biomarker discovery; and (iii to evaluate the robustness of biomarkers selection. Results A pool of urine sample spiked at increasing concentrations with a mixture of standard peptides was used as a specimen for biological samples with or without biomarkers. Extraction and nano-LC-MS variabilities were estimated by analyzing in triplicates and hexaplicates, respectively. The stability of chromatographic fractions immobilised with MALDI matrix on MALDI plates was evaluated by successive MS acquisitions after different storage times at different temperatures. Low coefficient of variation (CV%: 10–22% and high correlation (R2 > 0.96 values were obtained for the quantification of the spiked peptides, allowing quantification of these peptides in the low fentomole range, correct group discrimination and selection of "specific" markers using principal component analysis. Excellent peptide integrity and stable signal intensity were found when MALDI plates were stored for periods of up to 2 months at +4°C. This allowed storage of MALDI plates between LC separation and MS acquisition (first decoupling, and between MS and MSMS acquisitions while the selection of inter-group discriminative ions is done (second decoupling

A new strategy for faster urinary biomarkers identification by Nano-LC-MALDI-TOF/TOF mass spectrometry

Science.gov (United States)

Benkali, K; Marquet, P; Rérolle, JP; Le Meur, Y; Gastinel, LN

2008-01-01

Background LC-MALDI-TOF/TOF analysis is a potent tool in biomarkers discovery characterized by its high sensitivity and high throughput capacity. However, methods based on MALDI-TOF/TOF for biomarkers discovery still need optimization, in particular to reduce analysis time and to evaluate their reproducibility for peak intensities measurement. The aims of this methodological study were: (i) to optimize and critically evaluate each step of urine biomarker discovery method based on Nano-LC coupled off-line to MALDI-TOF/TOF, taking full advantage of the dual decoupling between Nano-LC, MS and MS/MS to reduce the overall analysis time; (ii) to evaluate the quantitative performance and reproducibility of nano-LC-MALDI analysis in biomarker discovery; and (iii) to evaluate the robustness of biomarkers selection. Results A pool of urine sample spiked at increasing concentrations with a mixture of standard peptides was used as a specimen for biological samples with or without biomarkers. Extraction and nano-LC-MS variabilities were estimated by analyzing in triplicates and hexaplicates, respectively. The stability of chromatographic fractions immobilised with MALDI matrix on MALDI plates was evaluated by successive MS acquisitions after different storage times at different temperatures. Low coefficient of variation (CV%: 10–22%) and high correlation (R2 > 0.96) values were obtained for the quantification of the spiked peptides, allowing quantification of these peptides in the low fentomole range, correct group discrimination and selection of "specific" markers using principal component analysis. Excellent peptide integrity and stable signal intensity were found when MALDI plates were stored for periods of up to 2 months at +4°C. This allowed storage of MALDI plates between LC separation and MS acquisition (first decoupling), and between MS and MSMS acquisitions while the selection of inter-group discriminative ions is done (second decoupling). Finally the recording of

Isolation and characterization of urinary extracellular vesicles: implications for biomarker discovery

NARCIS (Netherlands)

Merchant, M.L.; Rood, I.M.; Deegens, J.K.J.; Klein, J.B.

2017-01-01

Urine is a valuable diagnostic medium and, with the discovery of urinary extracellular vesicles, is viewed as a dynamic bioactive fluid. Extracellular vesicles are lipid-enclosed structures that can be classified into three categories: exosomes, microvesicles (or ectosomes) and apoptotic bodies.

An E-government Interoperability Platform Supporting Personal Data Protection Regulations

OpenAIRE

González, Laura; Echevarría, Andrés; Morales, Dahiana; Ruggia, Raúl

2016-01-01

Public agencies are increasingly required to collaborate with each other in order to provide high-quality e-government services. This collaboration is usually based on the service-oriented approach and supported by interoperability platforms. Such platforms are specialized middleware-based infrastructures enabling the provision, discovery and invocation of interoperable software services. In turn, given that personal data handled by governments are often very sensitive, most governments have ...

Use of Tethered Enzymes as a Platform Technology for Rapid Analyte Detection.

Directory of Open Access Journals (Sweden)

Roy Cohen

Full Text Available Rapid diagnosis for time-sensitive illnesses such as stroke, cardiac arrest, and septic shock is essential for successful treatment. Much attention has therefore focused on new strategies for rapid and objective diagnosis, such as Point-of-Care Tests (PoCT for blood biomarkers. Here we use a biomimicry-based approach to demonstrate a new diagnostic platform, based on enzymes tethered to nanoparticles (NPs. As proof of principle, we use oriented immobilization of pyruvate kinase (PK and luciferase (Luc on silica NPs to achieve rapid and sensitive detection of neuron-specific enolase (NSE, a clinically relevant biomarker for multiple diseases ranging from acute brain injuries to lung cancer. We hypothesize that an approach capitalizing on the speed and catalytic nature of enzymatic reactions would enable fast and sensitive biomarker detection, suitable for PoCT devices.We performed in-vitro, animal model, and human subject studies. First, the efficiency of coupled enzyme activities when tethered to NPs versus when in solution was tested, demonstrating a highly sensitive and rapid detection of physiological and pathological concentrations of NSE. Next, in rat stroke models the enzyme-based assay was able in minutes to show a statistically significant increase in NSE levels in samples taken 1 hour before and 0, 1, 3 and 6 hours after occlusion of the distal middle cerebral artery. Finally, using the tethered enzyme assay for detection of NSE in samples from 20 geriatric human patients, we show that our data match well (r = 0.815 with the current gold standard for biomarker detection, ELISA-with a major difference being that we achieve detection in 10 minutes as opposed to the several hours required for traditional ELISA.Oriented enzyme immobilization conferred more efficient coupled activity, and thus higher assay sensitivity, than non-tethered enzymes. Together, our findings provide proof of concept for using oriented immobilization of active

Blood-Based Biomarker Candidates of Cerebral Amyloid Using PiB PET in Non-Demented Elderly

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Westwood, Sarah; Leoni, Emanuela; Hye, Abdul; Lynham, Steven; Khondoker, Mizanur R.; Ashton, Nicholas J.; Kiddle, Steven J.; Baird, Alison L.; Sainz-Fuertes, Ricardo; Leung, Rufina; Graf, John; Hehir, Cristina Tan; Baker, David; Cereda, Cristina; Bazenet, Chantal; Ward, Malcolm; Thambisetty, Madhav; Lovestone, Simon

2018-01-01

Increasingly, clinical trials for Alzheimer’s disease (AD) are being conducted earlier in the disease phase and with biomarker confirmation using in vivo amyloid PET imaging or CSF tau and Aβ measures to quantify pathology. However, making such a pre-clinical AD diagnosis is relatively costly and the screening failure rate is likely to be high. Having a blood-based marker that would reduce such costs and accelerate clinical trials through identifying potential participants with likely pre-clinical AD would be a substantial advance. In order to seek such a candidate biomarker, discovery phase proteomic analyses using 2DGE and gel-free LC-MS/MS for high and low molecular weight analytes were conducted on longitudinal plasma samples collected over a 12-year period from non-demented older individuals who exhibited a range of 11C-PiB PET measures of amyloid load. We then sought to extend our discovery findings by investigating whether our candidate biomarkers were also associated with brain amyloid burden in disease, in an independent cohort. Seven plasma proteins, including A2M, Apo-A1, and multiple complement proteins, were identified as pre-clinical biomarkers of amyloid burden and were consistent across three time points (p biomarker signature indicative of AD pathology at a stage long before the onset of clinical disease manifestation. As in previous studies, acute phase reactants and inflammatory markers dominate this signature. PMID:27031486

Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry.

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Keshishian, Hasmik; Burgess, Michael W; Specht, Harrison; Wallace, Luke; Clauser, Karl R; Gillette, Michael A; Carr, Steven A

2017-08-01

Proteomic characterization of blood plasma is of central importance to clinical proteomics and particularly to biomarker discovery studies. The vast dynamic range and high complexity of the plasma proteome have, however, proven to be serious challenges and have often led to unacceptable tradeoffs between depth of coverage and sample throughput. We present an optimized sample-processing pipeline for analysis of the human plasma proteome that provides greatly increased depth of detection, improved quantitative precision and much higher sample analysis throughput as compared with prior methods. The process includes abundant protein depletion, isobaric labeling at the peptide level for multiplexed relative quantification and ultra-high-performance liquid chromatography coupled to accurate-mass, high-resolution tandem mass spectrometry analysis of peptides fractionated off-line by basic pH reversed-phase (bRP) chromatography. The overall reproducibility of the process, including immunoaffinity depletion, is high, with a process replicate coefficient of variation (CV) of 4,500 proteins are detected and quantified per patient sample on average, with two or more peptides per protein and starting from as little as 200 μl of plasma. The approach can be multiplexed up to 10-plex using tandem mass tags (TMT) reagents, further increasing throughput, albeit with some decrease in the number of proteins quantified. In addition, we provide a rapid protocol for analysis of nonfractionated depleted plasma samples analyzed in 10-plex. This provides ∼600 quantified proteins for each of the ten samples in ∼5 h of instrument time.

Potential biomarker panels in overall breast cancer management: advancements by multilevel diagnostics.

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Girotra, Shantanu; Yeghiazaryan, Kristina; Golubnitschaja, Olga

2016-09-01

Breast cancer (BC) prevalence has reached an epidemic scale with half a million deaths annually. Current deficits in BC management include predictive and preventive approaches, optimized screening programs, individualized patient profiling, highly sensitive detection technologies for more precise diagnostics and therapy monitoring, individualized prediction and effective treatment of BC metastatic disease. To advance BC management, paradigm shift from delayed to predictive, preventive and personalized medical services is essential. Corresponding step forwards requires innovative multilevel diagnostics procuring specific panels of validated biomarkers. Here, we discuss current instrumental advancements including genomics, proteomics, epigenetics, miRNA, metabolomics, circulating tumor cells and cancer stem cells with a focus on biomarker discovery and multilevel diagnostic panels. A list of the recommended biomarker candidates is provided.

Metabolomics: beyond biomarkers and towards mechanisms

Science.gov (United States)

Johnson, Caroline H.; Ivanisevic, Julijana; Siuzdak, Gary

2017-01-01

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases. PMID:26979502

Biomarkers of Nutrition for Development—Iodine Review1234

Science.gov (United States)

Rohner, Fabian; Zimmermann, Michael; Jooste, Pieter; Pandav, Chandrakant; Caldwell, Kathleen; Raghavan, Ramkripa; Raiten, Daniel J.

2014-01-01

The objective of the Biomarkers of Nutrition for Development (BOND) project is to provide state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. Specifically, the BOND project seeks to develop consensus on accurate assessment methodologies that are applicable to researchers (laboratory/clinical/surveillance), clinicians, programmers, and policy makers (data consumers). The BOND project is also intended to develop targeted research agendas to support the discovery and development of biomarkers through improved understanding of nutrient biology within relevant biologic systems. In phase I of the BOND project, 6 nutrients (iodine, vitamin A, iron, zinc, folate, and vitamin B-12) were selected for their high public health importance because they typify the challenges faced by users in the selection, use, and interpretation of biomarkers. For each nutrient, an expert panel was constituted and charged with the development of a comprehensive review covering the respective nutrient’s biology, existing biomarkers, and specific issues of use with particular reference to the needs of the individual user groups. In addition to the publication of these reviews, materials from each will be extracted to support the BOND interactive Web site (http://www.nichd.nih.gov/global_nutrition/programs/bond/pages/index.aspx). This review represents the first in the series of reviews and covers all relevant aspects of iodine biology and biomarkers. The article is organized to provide the reader with a full appreciation of iodine’s background history as a public health issue, its biology, and an overview of available biomarkers and specific considerations for the use and interpretation of iodine biomarkers across a range of clinical and population-based uses. The review also includes a detailed research agenda to address priority gaps in our understanding of iodine biology and assessment

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

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Parida Shreemanta K

2010-01-01

Full Text Available Abstract Background For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

NARCIS (Netherlands)

Peters, S.; Janssen, H.-G.; Vivó-Truyols, G.

2010-01-01

A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical

Distinguishing prognostic and predictive biomarkers: An information theoretic approach.

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Sechidis, Konstantinos; Papangelou, Konstantinos; Metcalfe, Paul D; Svensson, David; Weatherall, James; Brown, Gavin

2018-05-02

The identification of biomarkers to support decision-making is central to personalised medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework. Our contribution is a novel procedure, INFO+, which naturally distinguishes the prognostic vs predictive role of each biomarker and handles higher order interactions. In a comprehensive empirical evaluation INFO+ outperforms more complex methods, most notably when noise factors dominate, and biomarkers are likely to be falsely identified as predictive, when in fact they are just strongly prognostic. Furthermore, we show that our methods can be 1-3 orders of magnitude faster than competitors, making it useful for biomarker discovery in 'big data' scenarios. Finally, we apply our methods to identify predictive biomarkers on two real clinical trials, and introduce a new graphical representation that provides greater insight into the prognostic and predictive strength of each biomarker. R implementations of the suggested methods are available at https://github.com/sechidis. konstantinos.sechidis@manchester.ac.uk. Supplementary data are available at Bioinformatics online.

SpirPep: an in silico digestion-based platform to assist bioactive peptides discovery from a genome-wide database.

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Anekthanakul, Krittima; Hongsthong, Apiradee; Senachak, Jittisak; Ruengjitchatchawalya, Marasri

2018-04-20

web and database server are available at http://spirpepapp.sbi.kmutt.ac.th . SpirPep, a web-based bioactive peptide discovery application, is an in silico-based tool with an overview of the results. The platform is a one-stop analysis and visualization facility; and offers advantages over the currently available tools. This tool may be useful for further bioactivity analysis and the quantitative discovery of desirable peptides.

Circulating exosomes and exosomal microRNAs as biomarkers in gastrointestinal cancer.

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Nedaeinia, R; Manian, M; Jazayeri, M H; Ranjbar, M; Salehi, R; Sharifi, M; Mohaghegh, F; Goli, M; Jahednia, S H; Avan, A; Ghayour-Mobarhan, M

2017-02-01

The most important biological function of exosomes is their possible use as biomarkers in clinical diagnosis. Compared with biomarkers identified in conventional specimens such as serum or urine, exosomal biomarkers provide the highest amount of sensitivity and specificity, which can be attributed to their excellent stability. Exosomes, which harbor different types of proteins, nucleic acids and lipids, are present in almost all bodily fluids. The molecular constituents of exosomes, especially exosomal proteins and microRNAs (miRNAs), are promising as biomarkers in clinical diagnosis. This discovery that exosomes also contain messenger RNAs and miRNAs shows that they could be carriers of genetic information. Although the majority of RNAs found in exosomes are degraded RNA fragments with a length of exosomal miRNAs have been found to be associated with certain diseases. Several studies have pointed out miRNA contents of circulating exosomes that are similar to those of originating cancer cells. In this review, the recent advances in circulating exosomal miRNAs as biomarkers in gastrointestinal cancers are discussed. These studies indicated that miRNAs can be detected in exosomes isolated from body fluids such as saliva, which suggests potential advantages of using exosomal miRNAs as noninvasive novel biomarkers.

Discovery of dachshund 2 protein as a novel biomarker of poor prognosis in epithelial ovarian cancer

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Nodin Björn

2012-01-01

Full Text Available Abstract Background The Dachshund homolog 2 (DACH2 gene has been implicated in development of the female genital tract in mouse models and premature ovarian failure syndrome, but to date, its expression in human normal and cancerous tissue remains unexplored. Using the Human Protein Atlas as a tool for cancer biomarker discovery, DACH2 protein was found to be differentially expressed in epithelial ovarian cancer (EOC. Here, the expression and prognostic significance of DACH2 was further evaluated in ovarian cancer cell lines and human EOC samples. Methods Immunohistochemical expression of DACH2 was examined in tissue microarrays with 143 incident EOC cases from two prospective, population-based cohorts, including a subset of benign-appearing fallopian tubes (n = 32. A nuclear score (NS, i.e. multiplier of staining fraction and intensity, was calculated. For survival analyses, cases were dichotomized into low (NS 3 using classification and regression tree analysis. Kaplan Meier analysis and Cox proportional hazards modelling were used to assess the impact of DACH2 expression on survival. DACH2 expression was analysed in the cisplatin sensitive ovarian cancer cell line A2780 and its cisplatin resistant derivative A2780-Cp70. The specificity of the DACH2 antibody was tested using siRNA-mediated silencing of DACH2 in A2780-Cp70 cells. Results DACH2 expression was considerably higher in the cisplatin resistant A2780-Cp70 cells compared to the cisplatin-sensitive A2780 cells. While present in all sampled fallopian tubes, DACH2 expression ranged from negative to strong in EOC. In EOC, DACH2 expression correlated with several proteins involved in DNA integrity and repair, and proliferation. DACH2 expression was significantly higher in carcinoma of the serous subtype compared to non-serous carcinoma. In the full cohort, high DACH2 expression was significantly associated with poor prognosis in univariable analysis, and in carcinoma of the serous subtype

Urine Metabonomics Reveals Early Biomarkers in Diabetic Cognitive Dysfunction.

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Song, Lili; Zhuang, Pengwei; Lin, Mengya; Kang, Mingqin; Liu, Hongyue; Zhang, Yuping; Yang, Zhen; Chen, Yunlong; Zhang, Yanjun

2017-09-01

Recently, increasing attention has been paid to diabetic encephalopathy, which is a frequent diabetic complication and affects nearly 30% of diabetics. Because cognitive dysfunction from diabetic encephalopathy might develop into irreversible dementia, early diagnosis and detection of this disease is of great significance for its prevention and treatment. This study is to investigate the early specific metabolites biomarkers in urine prior to the onset of diabetic cognitive dysfunction (DCD) by using metabolomics technology. An ultra-high performance liquid-chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) platform was used to analyze the urine samples from diabetic mice that were associated with mild cognitive impairment (MCI) and nonassociated with MCI in the stage of diabetes (prior to the onset of DCD). We then screened and validated the early biomarkers using OPLS-DA model and support vector machine (SVM) method. Following multivariate statistical and integration analysis, we found that seven metabolites could be accepted as early biomarkers of DCD, and the SVM results showed that the prediction accuracy is as high as 91.66%. The identities of four biomarkers were determined by mass spectrometry. The identified biomarkers were largely involved in nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism, and sphingolipid metabolism. The present study first revealed reliable biomarkers for early diagnosis of DCD. It provides new insight and strategy for the early diagnosis and treatment of DCD.

High-throughput screening platform for natural product-based drug discovery against 3 neglected tropical diseases: human African trypanosomiasis, leishmaniasis, and Chagas disease.

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Annang, F; Pérez-Moreno, G; García-Hernández, R; Cordon-Obras, C; Martín, J; Tormo, J R; Rodríguez, L; de Pedro, N; Gómez-Pérez, V; Valente, M; Reyes, F; Genilloud, O; Vicente, F; Castanys, S; Ruiz-Pérez, L M; Navarro, M; Gamarro, F; González-Pacanowska, D

2015-01-01

African trypanosomiasis, leishmaniasis, and Chagas disease are 3 neglected tropical diseases for which current therapeutic interventions are inadequate or toxic. There is an urgent need to find new lead compounds against these diseases. Most drug discovery strategies rely on high-throughput screening (HTS) of synthetic chemical libraries using phenotypic and target-based approaches. Combinatorial chemistry libraries contain hundreds of thousands of compounds; however, they lack the structural diversity required to find entirely novel chemotypes. Natural products, in contrast, are a highly underexplored pool of unique chemical diversity that can serve as excellent templates for the synthesis of novel, biologically active molecules. We report here a validated HTS platform for the screening of microbial extracts against the 3 diseases. We have used this platform in a pilot project to screen a subset (5976) of microbial extracts from the MEDINA Natural Products library. Tandem liquid chromatography-mass spectrometry showed that 48 extracts contain potentially new compounds that are currently undergoing de-replication for future isolation and characterization. Known active components included actinomycin D, bafilomycin B1, chromomycin A3, echinomycin, hygrolidin, and nonactins, among others. The report here is, to our knowledge, the first HTS of microbial natural product extracts against the above-mentioned kinetoplastid parasites. © 2014 Society for Laboratory Automation and Screening.

Use of biomarkers in the discovery of novel anti-schizophrenia drugs

DEFF Research Database (Denmark)

Mikkelsen, Jens D; Thomsen, Morten S; Hansen, Henrik H

2010-01-01

Schizophrenia is characterized by a diverse symptomatology that often includes positive, cognitive and negative symptoms. Current anti-schizophrenic drugs act at multiple receptors, but little is known about how each of these receptors contributes to their mechanisms of action. Screening of novel...... anti-schizophrenic drug candidates targeting single receptors will be based on biomarker assays that measure signalling pathways, transcriptional factors, epigenetic mechanisms and synaptic function and translate these effects to behavioural effects in animals and humans. This review discusses current...

Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study.

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Simina M Boca

Full Text Available Serum metabolite profiling in Duchenne muscular dystrophy (DMD may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future.

Three-Dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation.

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Pan, Li; Aguilar, Hillary Andaluz; Wang, Linna; Iliuk, Anton; Tao, W Andy

2016-11-30

Glycoproteins have vast structural diversity that plays an important role in many biological processes and have great potential as disease biomarkers. Here, we report a novel functionalized reverse phase protein array (RPPA), termed polymer-based reverse phase glycoprotein array (polyGPA), to capture and profile glycoproteomes specifically, and validate glycoproteins. Nitrocellulose membrane functionalized with globular hydroxyaminodendrimers was used to covalently capture preoxidized glycans on glycoproteins from complex protein samples such as biofluids. The captured glycoproteins were subsequently detected using the same validated antibodies as in RPPA. We demonstrated the outstanding specificity, sensitivity, and quantitative capabilities of polyGPA by capturing and detecting purified as well as endogenous α-1-acid glycoprotein (AGP) in human plasma. We further applied quantitative N-glycoproteomics and the strategy to validate a panel of glycoproteins identified as potential biomarkers for bladder cancer by analyzing urine glycoproteins from bladder cancer patients or matched healthy individuals.

Identification of potential biomarkers from microarray experiments using multiple criteria optimization

International Nuclear Information System (INIS)

Sánchez-Peña, Matilde L; Isaza, Clara E; Pérez-Morales, Jaileene; Rodríguez-Padilla, Cristina; Castro, José M; Cabrera-Ríos, Mauricio

2013-01-01

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Institute of Scientific and Technical Information of China (English)

Dongyun Li; Hans-Otto Karnath; Xiu Xu

2017-01-01

Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD).Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure,function,maturation,connectivity,and metabolism of the brain of children with ASD.Here,we review the more recent MRI studies in young children with ASD,aiming to provide candidate biomarkers for the diagnosis of childhood ASD.The review covers structural imaging methods,diffusion tensor imaging,resting-state functional MRI,and magnetic reso nance spectroscopy.Future advances in neuroimaging techniques,as well as cross-disciplinary studies and largescale collaborations will be needed for an integrated approach linking neuroimaging,genetics,and phenotypic data to allow the discovery of new,effective biomarkers.

Assessed and Emerging Biomarkers in Stroke and Training-Mediated Stroke Recovery: State of the Art

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Marialuisa Gandolfi

2017-01-01

Full Text Available Since the increasing update of the biomolecular scientific literature, biomarkers in stroke have reached an outstanding and remarkable revision in the very recent years. Besides the diagnostic and prognostic role of some inflammatory markers, many further molecules and biological factors have been added to the list, including tissue derived cytokines, growth factor-like molecules, hormones, and microRNAs. The literatures on brain derived growth factor and other neuroimmune mediators, bone-skeletal muscle biomarkers, cellular and immunity biomarkers, and the role of microRNAs in stroke recovery were reviewed. To date, biomarkers represent a possible challenge in the diagnostic and prognostic evaluation of stroke onset, pathogenesis, and recovery. Many molecules are still under investigation and may become promising and encouraging biomarkers. Experimental and clinical research should increase this list and promote new discoveries in this field, to improve stroke diagnosis and treatment.

The Matchmaker Exchange: a platform for rare disease gene discovery.

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Philippakis, Anthony A; Azzariti, Danielle R; Beltran, Sergi; Brookes, Anthony J; Brownstein, Catherine A; Brudno, Michael; Brunner, Han G; Buske, Orion J; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O M; den Dunnen, Johan T; Firth, Helen V; Gibbs, Richard A; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A; Hamosh, Ada; Holm, Ingrid A; Huang, Lijia; Hurles, Matthew E; Hutton, Ben; Krier, Joel B; Misyura, Andriy; Mungall, Christopher J; Paschall, Justin; Paten, Benedict; Robinson, Peter N; Schiettecatte, François; Sobreira, Nara L; Swaminathan, Ganesh J; Taschner, Peter E; Terry, Sharon F; Washington, Nicole L; Züchner, Stephan; Boycott, Kym M; Rehm, Heidi L

2015-10-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. © 2015 WILEY PERIODICALS, INC.

Differential profiling of volatile organic compound biomarker signatures utilizing a logical statistical filter-set and novel hybrid evolutionary classifiers

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Grigsby, Claude C.; Zmuda, Michael A.; Boone, Derek W.; Highlander, Tyler C.; Kramer, Ryan M.; Rizki, Mateen M.

2012-06-01

A growing body of discoveries in molecular signatures has revealed that volatile organic compounds (VOCs), the small molecules associated with an individual's odor and breath, can be monitored to reveal the identity and presence of a unique individual, as well their overall physiological status. Given the analysis requirements for differential VOC profiling via gas chromatography/mass spectrometry, our group has developed a novel informatics platform, Metabolite Differentiation and Discovery Lab (MeDDL). In its current version, MeDDL is a comprehensive tool for time-series spectral registration and alignment, visualization, comparative analysis, and machine learning to facilitate the efficient analysis of multiple, large-scale biomarker discovery studies. The MeDDL toolset can therefore identify a large differential subset of registered peaks, where their corresponding intensities can be used as features for classification. This initial screening of peaks yields results sets that are typically too large for incorporation into a portable, electronic nose based system in addition to including VOCs that are not amenable to classification; consequently, it is also important to identify an optimal subset of these peaks to increase classification accuracy and to decrease the cost of the final system. MeDDL's learning tools include a classifier similar to a K-nearest neighbor classifier used in conjunction with a genetic algorithm (GA) that simultaneously optimizes the classifier and subset of features. The GA uses ROC curves to produce classifiers having maximal area under their ROC curve. Experimental results on over a dozen recognition problems show many examples of classifiers and feature sets that produce perfect ROC curves.

In Vivo Imaging Biomarkers in Mouse Models of Alzheimer's Disease: Are We Lost in Translation or Breaking Through?

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Benoît Delatour

2010-01-01

Full Text Available Identification of biomarkers of Alzheimer's Disease (AD is a critical priority to efficiently diagnose the patients, to stage the progression of neurodegeneration in living subjects, and to assess the effects of disease-modifier treatments. This paper addresses the development and usefulness of preclinical neuroimaging biomarkers of AD. It is today possible to image in vivo the brain of small rodents at high resolution and to detect the occurrence of macroscopic/microscopic lesions in these species, as well as of functional alterations reminiscent of AD pathology. We will outline three different types of imaging biomarkers that can be used in AD mouse models: biomarkers with clear translational potential, biomarkers that can serve as in vivo readouts (in particular in the context of drug discovery exclusively for preclinical research, and finally biomarkers that constitute new tools for fundamental research on AD physiopathogeny.

Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment.

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Levey, D F; Niculescu, E M; Le-Niculescu, H; Dainton, H L; Phalen, P L; Ladd, T B; Weber, H; Belanger, E; Graham, D L; Khan, F N; Vanipenta, N P; Stage, E C; Ballew, A; Yard, M; Gelbart, T; Shekhar, A; Schork, N J; Kurian, S M; Sandusky, G E; Salomon, D R; Niculescu, A B

2016-06-01

Women are under-represented in research on suicidality to date. Although women have a lower rate of suicide completion than men, due in part to the less-violent methods used, they have a higher rate of suicide attempts. Our group has previously identified genomic (blood gene expression biomarkers) and clinical information (apps) predictors for suicidality in men. We now describe pilot studies in women. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation (no SI) and high suicidal ideation (high SI) states (n=12 participants out of a cohort of 51 women psychiatric participants followed longitudinally, with diagnoses of bipolar disorder, depression, schizoaffective disorder and schizophrenia). We then used a Convergent Functional Genomics (CFG) approach to prioritize the candidate biomarkers identified in the discovery step by using all the prior evidence in the field. Next, we validated for suicidal behavior the top-ranked biomarkers for SI, in a demographically matched cohort of women suicide completers from the coroner's office (n=6), by assessing which markers were stepwise changed from no SI to high SI to suicide completers. We then tested the 50 biomarkers that survived Bonferroni correction in the validation step, as well as top increased and decreased biomarkers from the discovery and prioritization steps, in a completely independent test cohort of women psychiatric disorder participants for prediction of SI (n=33) and in a future follow-up cohort of psychiatric disorder participants for prediction of psychiatric hospitalizations due to suicidality (n=24). Additionally, we examined how two clinical instruments in the form of apps, Convergent Functional Information for Suicidality (CFI-S) and Simplified Affective State Scale (SASS), previously tested in men, perform in women. The top CFI-S item distinguishing high SI from no SI states was the chronic stress of social isolation. We

Biomarkers in systemic lupus erythematosus: challenges and prospects for the future

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Kao, Amy H.; Manzi, Susan; Ahearn, Joseph M.

2013-01-01

The search for lupus biomarkers to diagnose, monitor, stratify, and predict individual response to therapy is currently more intense than ever before. This effort is essential for several reasons. First, epidemic overdiagnosis and underdiagnosis of lupus, even by certified rheumatologists, leads to errors in therapy with concomitant side effects which may be more serious than the disease itself. Second, identification of lupus flares remains as much an art as it is a science. Third, the capacity to stratify patients so as to predict those who will develop specific patterns of organ involvement is not currently possible but would potentially lead to preventive therapeutic strategies. Fourth, only one new drug for the treatment of lupus has been approved by the US Food and Drug Administration in over 50 years. A major obstacle in this pipeline is the dearth of biomarkers available to prove a patient has responded to an experimental therapeutic intervention. This review will summarize the challenges faced in the discovery and validation of lupus biomarkers, the most promising lupus biomarkers identified to date, and the promise of future directions. PMID:23904865

Clinical usefulness of a biomarker-based diagnostic test for acute stroke: the Biomarker Rapid Assessment in Ischemic Injury (BRAIN) study.

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Laskowitz, Daniel T; Kasner, Scott E; Saver, Jeffrey; Remmel, Kerri S; Jauch, Edward C

2009-01-01

One of the significant limitations in the evaluation and management of patients with suspected acute cerebral ischemia is the absence of a widely available, rapid, and sensitive diagnostic test. The objective of the current study was to assess whether a test using a panel of biomarkers might provide useful diagnostic information in the early evaluation of stroke by differentiating patients with cerebral ischemia from other causes of acute neurological deficit. A total of 1146 patients presenting with neurological symptoms consistent with possible stroke were prospectively enrolled at 17 different sites. Timed blood samples were assayed for matrix metalloproteinase 9, brain natriuretic factor, d-dimer, and protein S100beta. A separate cohort of 343 patients was independently enrolled to validate the multiple biomarker model approach. A diagnostic tool incorporating the values of matrix metalloproteinase 9, brain natriuretic factor, d-dimer, and S-100beta into a composite score was sensitive for acute cerebral ischemia. The multivariate model demonstrated modest discriminative capabilities with an area under the receiver operating characteristic curve of 0.76 for hemorrhagic stroke and 0.69 for all stroke (likelihood test P<0.001). When the threshold for the logistic model was set at the first quartile, this resulted in a sensitivity of 86% for detecting all stroke and a sensitivity of 94% for detecting hemorrhagic stroke. Moreover, results were reproducible in a separate cohort tested on a point-of-care platform. These results suggest that a biomarker panel may add valuable and time-sensitive diagnostic information in the early evaluation of stroke. Such an approach is feasible on a point-of-care platform. The rapid identification of patients with suspected stroke would expand the availability of time-limited treatment strategies. Although the diagnostic accuracy of the current panel is clearly imperfect, this study demonstrates the feasibility of incorporating a

Application of Fluorescence Two-Dimensional Difference In-Gel Electrophoresis as a Proteomic Biomarker Discovery Tool in Muscular Dystrophy Research

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Carberry, Steven; Zweyer, Margit; Swandulla, Dieter; Ohlendieck, Kay

2013-01-01

In this article, we illustrate the application of difference in-gel electrophoresis for the proteomic analysis of dystrophic skeletal muscle. The mdx diaphragm was used as a tissue model of dystrophinopathy. Two-dimensional gel electrophoresis is a widely employed protein separation method in proteomic investigations. Although two-dimensional gels usually underestimate the cellular presence of very high molecular mass proteins, integral membrane proteins and low copy number proteins, this method is extremely powerful in the comprehensive analysis of contractile proteins, metabolic enzymes, structural proteins and molecular chaperones. This gives rise to two-dimensional gel electrophoretic separation as the method of choice for studying contractile tissues in health and disease. For comparative studies, fluorescence difference in-gel electrophoresis has been shown to provide an excellent biomarker discovery tool. Since aged diaphragm fibres from the mdx mouse model of Duchenne muscular dystrophy closely resemble the human pathology, we have carried out a mass spectrometry-based comparison of the naturally aged diaphragm versus the senescent dystrophic diaphragm. The proteomic comparison of wild type versus mdx diaphragm resulted in the identification of 84 altered protein species. Novel molecular insights into dystrophic changes suggest increased cellular stress, impaired calcium buffering, cytostructural alterations and disturbances of mitochondrial metabolism in dystrophin-deficient muscle tissue. PMID:24833232

Predicting Developmental Toxicity of ToxCast Phase I Chemicals Using Human Embryonic Stem Cells and Metabolomics

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EPA’s ToxRefDB contains prenatal guideline study data from rats and rabbits for over 240 chemicals that overlap with the ToxCast in vitro high throughput screening project. A subset of these compounds were tested in Stemina Biomarker Discovery's developmental toxicity platform, a...

Acquisition of docetaxel resistance in breast cancer cells reveals upregulation of ABCB1 expression as a key mediator of resistance accompanied by discrete upregulation of other specific genes and pathways

DEFF Research Database (Denmark)

Ninel Hansen, Stine; Westergaard, David; Borg Houlberg Thomsen, Mathilde

2015-01-01

The microtubule-targeting taxanes are important in breast cancer therapy, but no predictive biomarkers have yet been identified with sufficient scientific evidence to allow clinical routine use. The purposes of the present study were to develop a cell-culture-based discovery platform for docetaxel...

Catch and measure-mass spectrometry-based immunoassays in biomarker research.

Science.gov (United States)

Weiß, Frederik; van den Berg, Bart H J; Planatscher, Hannes; Pynn, Christopher J; Joos, Thomas O; Poetz, Oliver

2014-05-01

Mass spectrometry-based (MS) methods are effective tools for discovering protein biomarker candidates that can differentiate between physiological and pathophysiological states. Promising candidates are validated in studies comprising large patient cohorts. Here, targeted protein analytics are used to increase sample throughput. Methods involving antibodies, such as sandwich immunoassays or Western blots, are commonly applied at this stage. Highly-specific and sensitive mass spectrometry-based immunoassays that have been established in recent years offer a suitable alternative to sandwich immunoassays for quantifying proteins. Mass Spectrometric ImmunoAssays (MSIA) and Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA/iMALDI) are two prominent types of MS-based immunoassays in which the capture is done either at the protein or the peptide level. We present an overview of these emerging types of immunoassays and discuss their suitability for the discovery and validation of protein biomarkers. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge. © 2013.

Can the Growth/Differentiation Factor-15 Be a Surrogate Target in Chronic Heart Failure Biomarker-Guided Therapy?

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Alexander E. Berezin

2017-03-01

Full Text Available Heart failure (HF biomarker-guided therapy is a promising method, which directs to the improvement of clinical status, attenuation of admission/readmission to the hospital and reduction in mortality rate. Many biological markers, like inflammatory cytokines, are under consideration as a surrogate target for HF treatment, while there are known biomarkers with established predictive value, such as natriuretic peptides. However, discovery of new biomarkers reflecting various underlying mechanisms of HF and appearing to be surrogate targets for biomarker-guided therapy is fairly promising. Nowadays, growth/differentiation factor 15 (GDF-15 is suggested a target biomarker for HF treatment. Although elevated level of GDF-15 is associated with HF development, progression, and prognosis, there is no represented evidence regarding the direct comparison of this biomarker with other clinical risk predictors and biomarkers. Moreover, GDF-15 might serve as a contributor to endothelial progenitor cells (EPC dysfunction by inducing EPC death/autophagy and limiting their response to angiopoetic and reparative effects. The short communication was discussed whether GDF-15 is good molecular target for HF biomarker-guided therapy.

The Present and Future of Biomarkers in Prostate Cancer: Proteomics, Genomics, and Immunology Advancements

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Pierre-Olivier Gaudreau

2016-01-01

Full Text Available Prostate cancer (PC is the second most common form of cancer in men worldwide. Biomarkers have emerged as essential tools for treatment and assessment since the variability of disease behavior, the cost and diversity of treatments, and the related impairment of quality of life have given rise to a need for a personalized approach. High-throughput technology platforms in proteomics and genomics have accelerated the development of biomarkers. Furthermore, recent successes of several new agents in PC, including immunotherapy, have stimulated the search for predictors of response and resistance and have improved the understanding of the biological mechanisms at work. This review provides an overview of currently established biomarkers in PC, as well as a selection of the most promising biomarkers within these particular fields of development.

Harvest: a web-based biomedical data discovery and reporting application development platform.

Science.gov (United States)

Italia, Michael J; Pennington, Jeffrey W; Ruth, Byron; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; Miller, Jeffrey; White, Peter S

2013-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible. This need is increasingly acute as investigators seek opportunities for discovery amidst an exponential growth in the volume and complexity of laboratory and clinical data. To address this need, we developed Harvest, an open source framework that provides a set of modular components to aid the rapid development and deployment of custom data discovery software applications. Harvest incorporates visual representations of multidimensional data types in an intuitive, web-based interface that promotes a real-time, iterative approach to exploring complex clinical and experimental data. The Harvest architecture capitalizes on standards-based, open source technologies to address multiple functional needs critical to a research and development environment, including domain-specific data modeling, abstraction of complex data models, and a customizable web client.

Creating a Discovery Platform for Confined-Space Chemistry and Materials: Metal-Organic Frameworks.

Energy Technology Data Exchange (ETDEWEB)

Allendorf, Mark D.; Greathouse, Jeffery A.; Simmons, Blake

2008-09-01

Metal organic frameworks (MOF) are a recently discovered class of nanoporous, defect-free crystalline materials that enable rational design and exploration of porous materials at the molecular level. MOFs have tunable monolithic pore sizes and cavity environments due to their crystalline nature, yielding properties exceeding those of most other porous materials. These include: the lowest known density (91% free space); highest surface area; tunable photoluminescence; selective molecular adsorption; and methane sorption rivaling gas cylinders. These properties are achieved by coupling inorganic metal complexes such as ZnO4 with tunable organic ligands that serve as struts, allowing facile manipulation of pore size and surface area through reactant selection. MOFs thus provide a discovery platform for generating both new understanding of chemistry in confined spaces and novel sensors and devices based on their unique properties. At the outset of this project in FY06, virtually nothing was known about how to couple MOFs to substrates and the science of MOF properties and how to tune them was in its infancy. An integrated approach was needed to establish the required knowledge base for nanoscale design and develop methodologies integrate MOFs with other materials. This report summarizes the key accomplishments of this project, which include creation of a new class of radiation detection materials based on MOFs, luminescent MOFs for chemical detection, use of MOFs as templates to create nanoparticles of hydrogen storage materials, MOF coatings for stress-based chemical detection using microcantilevers, and "flexible" force fields that account for structural changes in MOFs that occur upon molecular adsorption/desorption. Eight journal articles, twenty presentations at scientific conferences, and two patent applications resulted from the work. The project created a basis for continuing development of MOFs for many Sandia applications and succeeded in securing $2.75 M in

Strategies to design clinical studies to identify predictive biomarkers in cancer research.

Science.gov (United States)

Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

2017-02-01

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Use of Tethered Enzymes as a Platform Technology for Rapid Analyte Detection

Science.gov (United States)

Cohen, Roy; Lata, James P.; Lee, Yurim; Hernández, Jean C. Cruz; Nishimura, Nozomi; Schaffer, Chris B.; Mukai, Chinatsu; Nelson, Jacquelyn L.; Brangman, Sharon A.; Agrawal, Yash; Travis, Alexander J.

2015-01-01

Background Rapid diagnosis for time-sensitive illnesses such as stroke, cardiac arrest, and septic shock is essential for successful treatment. Much attention has therefore focused on new strategies for rapid and objective diagnosis, such as Point-of-Care Tests (PoCT) for blood biomarkers. Here we use a biomimicry-based approach to demonstrate a new diagnostic platform, based on enzymes tethered to nanoparticles (NPs). As proof of principle, we use oriented immobilization of pyruvate kinase (PK) and luciferase (Luc) on silica NPs to achieve rapid and sensitive detection of neuron-specific enolase (NSE), a clinically relevant biomarker for multiple diseases ranging from acute brain injuries to lung cancer. We hypothesize that an approach capitalizing on the speed and catalytic nature of enzymatic reactions would enable fast and sensitive biomarker detection, suitable for PoCT devices. Methods and findings We performed in-vitro, animal model, and human subject studies. First, the efficiency of coupled enzyme activities when tethered to NPs versus when in solution was tested, demonstrating a highly sensitive and rapid detection of physiological and pathological concentrations of NSE. Next, in rat stroke models the enzyme-based assay was able in minutes to show a statistically significant increase in NSE levels in samples taken 1 hour before and 0, 1, 3 and 6 hours after occlusion of the distal middle cerebral artery. Finally, using the tethered enzyme assay for detection of NSE in samples from 20 geriatric human patients, we show that our data match well (r = 0.815) with the current gold standard for biomarker detection, ELISA—with a major difference being that we achieve detection in 10 minutes as opposed to the several hours required for traditional ELISA. Conclusions Oriented enzyme immobilization conferred more efficient coupled activity, and thus higher assay sensitivity, than non-tethered enzymes. Together, our findings provide proof of concept for using

Current status of fluid biomarkers in mild traumatic brain injury

Science.gov (United States)

Kulbe, Jacqueline R.; Geddes, James W.

2015-01-01

Mild traumatic brain injury (mTBI) affects millions of people annually and is difficult to diagnose. Mild injury is insensitive to conventional imaging techniques and diagnoses are often made using subjective criteria such as self-reported symptoms. Many people who sustain a mTBI develop persistent post-concussive symptoms. Athletes and military personnel are at great risk for repeat injury which can result in second impact syndrome or chronic traumatic encephalopathy. An objective and quantifiable measure, such as a serum biomarker, is needed to aid in mTBI diagnosis, prognosis, return to play/duty assessments, and would further elucidate mTBI pathophysiology. The majority of TBI biomarker research focuses on severe TBI with few studies specific to mild injury. Most studies use a hypothesis-driven approach, screening biofluids for markers known to be associated with TBI pathophysiology. This approach has yielded limited success in identifying markers that can be used clinically, additional candidate biomarkers are needed. Innovative and unbiased methods such as proteomics, microRNA arrays, urinary screens, autoantibody identification and phage display would complement more traditional approaches to aid in the discovery of novel mTBI biomarkers. PMID:25981889

Onchocerca volvulus-neurotransmitter tyramine is a biomarker for river blindness.

Science.gov (United States)

Globisch, Daniel; Moreno, Amira Y; Hixon, Mark S; Nunes, Ashlee A K; Denery, Judith R; Specht, Sabine; Hoerauf, Achim; Janda, Kim D

2013-03-12

Onchocerciasis, also known as "river blindness", is a neglected tropical disease infecting millions of people mainly in Africa and the Middle East but also in South America and Central America. Disease infectivity initiates from the filarial parasitic nematode Onchocerca volvulus, which is transmitted by the blackfly vector Simulium sp. carrying infectious third-stage larvae. Ivermectin has controlled transmission of microfilariae, with an African Program elimination target date of 2025. However, there is currently no point-of-care diagnostic that can distinguish the burden of infection--including active and/or past infection--and enable the elimination program to be effectively monitored. Here, we describe how liquid chromatography-MS-based urine metabolome analysis can be exploited for the identification of a unique biomarker, N-acetyltyramine-O,β-glucuronide (NATOG), a neurotransmitter-derived secretion metabolite from O. volvulus. The regulation of this tyramine neurotransmitter was found to be linked to patient disease infection, including the controversial antibiotic doxycycline treatment that has been shown to both sterilize and kill adult female worms. Further clues to its regulation have been elucidated through biosynthetic pathway determination within the nematode and its human host. Our results demonstrate that NATOG tracks O. volvulus metabolism in both worms and humans, and thus can be considered a host-specific biomarker for onchocerciasis progression. Liquid chromatography-MS-based urine metabolome analysis discovery of NATOG not only has broad implications for a noninvasive host-specific onchocerciasis diagnostic but provides a basis for the metabolome mining of other neglected tropical diseases for the discovery of distinct biomarkers and monitoring of disease progression.

Key drivers of biomedical innovation in cancer drug discovery

OpenAIRE

Huber, Margit A; Kraut, Norbert

2014-01-01

Discovery and translational research has led to the identification of a series of ?cancer drivers??genes that, when mutated or otherwise misregulated, can drive malignancy. An increasing number of drugs that directly target such drivers have demonstrated activity in clinical trials and are shaping a new landscape for molecularly targeted cancer therapies. Such therapies rely on molecular and genetic diagnostic tests to detect the presence of a biomarker that predicts response. Here, we highli...

Stem cells: a model for screening, discovery and development of drugs.

Science.gov (United States)

Kitambi, Satish Srinivas; Chandrasekar, Gayathri

2011-01-01

The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.

Proteomic Profiling of Exosomes Leads to the Identification of Novel Biomarkers for Prostate Cancer

Energy Technology Data Exchange (ETDEWEB)

Duijvesz, Diederick; Burnum-Johnson, Kristin E.; Gritsenko, Marina A.; Hoogland, Marije; Vredenbregt-van den Berg, Mirella S.; Willemsen, Rob; Luider, Theo N.; Pasa-Tolic, Ljiljana; Jenster, Guido

2013-12-31

Introduction: Current markers for prostate cancer, such as PSA lack specificity. Therefore, novel biomarkers are needed. Unfortunately, biomarker discovery from body fluids is often hampered by the high abundance of many proteins unrelated to disease. An attractive alternative biomarker discovery approach is the isolation of small vesicles (exosomes, ~100 nm). They contain proteins that are specific to the tissue from which they are derived and therefore can be considered as treasure chests for disease-specific marker discovery. Profiling prostate cancer-derived exosomes could reveal new markers for this malignancy. Materials and Methods: Exosomes were isolated from 2 immortalized primary prostate epithelial cells (PNT2C2 and RWPE-1) and 2 PCa cell lines (PC346C and VCaP) by ultracentrifugation. Proteomic analyses utilized a nanoLC coupled with an LTQ-Orbitrap operated in tandem MS (MS/MS) mode, followed by the Accurate Mass and Time (AMT) tag approach. Exosomal proteins were validated by Western blotting. A Tissue Micro Array, containing 481 different PCa samples (radical prostatectomy), was used to correlate candidate markers with several clinical-pathological parameters such as PSA, Gleason score, biochemical recurrence, and (PCa-related) death. Results: Proteomic characterization resulted in the identification of 263 proteins by at least 2 peptides. Specifically analysis of exosomes from PNT2C2, RWPE-1, PC346C, and VCaP identified 248, 233, 169, and 216 proteins, respectively. Statistical analyses revealed 52 proteins differently expressed between PCa and control cells, 9 of which were more abundant in PCa. Validation by Western blotting confirmed a higher abundance of FASN, XPO1 and PDCD6IP (ALIX) in PCa exosomes. The Tissue Micro 4 Array showed strong correlation of higher Gleason scores and local recurrence with increased cytoplasmic XPO1 (P<0.001). Conclusions: Differentially abundant proteins of cell line-derived exosomes make a clear subdivision between

A translational platform for prototyping closed-loop neuromodulation systems

Science.gov (United States)

Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim

2013-01-01

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders. PMID:23346048

Method and platform standardization in MRM-based quantitative plasma proteomics.

Science.gov (United States)

Percy, Andrew J; Chambers, Andrew G; Yang, Juncong; Jackson, Angela M; Domanski, Dominik; Burkhart, Julia; Sickmann, Albert; Borchers, Christoph H

2013-12-16

There exists a growing demand in the proteomics community to standardize experimental methods and liquid chromatography-mass spectrometry (LC/MS) platforms in order to enable the acquisition of more precise and accurate quantitative data. This necessity is heightened by the evolving trend of verifying and validating candidate disease biomarkers in complex biofluids, such as blood plasma, through targeted multiple reaction monitoring (MRM)-based approaches with stable isotope-labeled standards (SIS). Considering the lack of performance standards for quantitative plasma proteomics, we previously developed two reference kits to evaluate the MRM with SIS peptide approach using undepleted and non-enriched human plasma. The first kit tests the effectiveness of the LC/MRM-MS platform (kit #1), while the second evaluates the performance of an entire analytical workflow (kit #2). Here, these kits have been refined for practical use and then evaluated through intra- and inter-laboratory testing on 6 common LC/MS platforms. For an identical panel of 22 plasma proteins, similar concentrations were determined, regardless of the kit, instrument platform, and laboratory of analysis. These results demonstrate the value of the kit and reinforce the utility of standardized methods and protocols. The proteomics community needs standardized experimental protocols and quality control methods in order to improve the reproducibility of MS-based quantitative data. This need is heightened by the evolving trend for MRM-based validation of proposed disease biomarkers in complex biofluids such as blood plasma. We have developed two kits to assist in the inter- and intra-laboratory quality control of MRM experiments: the first kit tests the effectiveness of the LC/MRM-MS platform (kit #1), while the second evaluates the performance of an entire analytical workflow (kit #2). In this paper, we report the use of these kits in intra- and inter-laboratory testing on 6 common LC/MS platforms. This

Embroidered electrochemical sensors on gauze for rapid quantification of wound biomarkers.

Science.gov (United States)

Liu, Xiyuan; Lillehoj, Peter B

2017-12-15

Electrochemical sensors are an attractive platform for analytical measurements due to their high sensitivity, portability and fast response time. These attributes also make electrochemical sensors well suited for wearable applications which require excellent flexibility and durability. Towards this end, we have developed a robust electrochemical sensor on gauze via a unique embroidery fabrication process for quantitative measurements of wound biomarkers. For proof of principle, this biosensor was used to detect uric acid, a biomarker for wound severity and healing, in simulated wound fluid which exhibits high specificity, good linearly from 0 to 800µM, and excellent reproducibility. Continuous sensing of uric acid was also performed using this biosensor which reveals that it can generate consistent and accurate measurements for up to 7h. Experiments to evaluate the robustness of the embroidered gauze sensor demonstrate that it offers excellent resilience against mechanical stress and deformation, making it a promising wearable platform for assessing and monitoring wound status in situ. Copyright © 2017 Elsevier B.V. All rights reserved.

Cancer oriented biobanks: a comprehensive review

OpenAIRE

Shankargouda Patil; Barnali Majumdar; Kamran Habib Awan; Gargi S. Sarode; Sachin C. Sarode; Amol R. Gadbail; Shailesh Gondivkar

2018-01-01

Biobanks provide a platform for innovative biomedical research and has improvised translational and personalized medicine to a great extent. Time 2009 published “10 ideas changing the world right now” with biobanks on the list emphasizing its role in discovery and development of new therapeutic drugs. They form the cornerstone, providing resources for future investigations and biomarker discovery to understand the effects of genetic, environmental and lifestyle factors on human morbidity, mor...

Proxying UPnP service discovery and access to a non-IP Bluetooth network on a mobile phone

NARCIS (Netherlands)

Delphinanto, A.; Koonen, A.M.J.; Hartog, den F.T.H.; Peeters, M.E.

2007-01-01

The current service-and device discovery protocols are not platform-and network independent. Therefore, proxy servers will be needed to extend the range of IP-based discovery protocols to non-IP domains. We developed an architecture of a proxy that enables Universal Plug and Play (UPnP) devices and

Evaluation of pathogen-specific biomarkers for the diagnosis of tuberculosis in white-tailed deer (Odocoileus virginianus)

Science.gov (United States)

Objective - To develop a noninvasive biomarker based Mycobacterium bovis specific detection system to track infection in domestic and wild animals. Design – Experimental longitudinal study for discovery and cross sectional design for validation Animals - Yearling white-tailed deer fawns (n=8) were ...

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma

Science.gov (United States)

Addona, Terri A; Abbatiello, Susan E; Schilling, Birgit; Skates, Steven J; Mani, D R; Bunk, David M; Spiegelman, Clifford H; Zimmerman, Lisa J; Ham, Amy-Joan L; Keshishian, Hasmik; Hall, Steven C; Allen, Simon; Blackman, Ronald K; Borchers, Christoph H; Buck, Charles; Cardasis, Helene L; Cusack, Michael P; Dodder, Nathan G; Gibson, Bradford W; Held, Jason M; Hiltke, Tara; Jackson, Angela; Johansen, Eric B; Kinsinger, Christopher R; Li, Jing; Mesri, Mehdi; Neubert, Thomas A; Niles, Richard K; Pulsipher, Trenton C; Ransohoff, David; Rodriguez, Henry; Rudnick, Paul A; Smith, Derek; Tabb, David L; Tegeler, Tony J; Variyath, Asokan M; Vega-Montoto, Lorenzo J; Wahlander, Åsa; Waldemarson, Sofia; Wang, Mu; Whiteaker, Jeffrey R; Zhao, Lei; Anderson, N Leigh; Fisher, Susan J; Liebler, Daniel C; Paulovich, Amanda G; Regnier, Fred E; Tempst, Paul; Carr, Steven A

2010-01-01

Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low µg/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma. PMID:19561596

2-Aminoadipic acid is a biomarker for diabetes risk

OpenAIRE

Wang, Thomas J.; Ngo, Debby; Psychogios, Nikolaos; Dejam, Andre; Larson, Martin G.; Vasan, Ramachandran S.; Ghorbani, Anahita; O’Sullivan, John; Cheng, Susan; Rhee, Eugene P.; Sinha, Sumita; McCabe, Elizabeth; Fox, Caroline S.; O’Donnell, Christopher J.; Ho, Jennifer E.

2013-01-01

Improvements in metabolite-profiling techniques are providing increased breadth of coverage of the human metabolome and may highlight biomarkers and pathways in common diseases such as diabetes. Using a metabolomics platform that analyzes intermediary organic acids, purines, pyrimidines, and other compounds, we performed a nested case-control study of 188 individuals who developed diabetes and 188 propensity-matched controls from 2,422 normoglycemic participants followed for 12 years in the F...

Prognostic Biomarkers Used for Localised Prostate Cancer Management: A Systematic Review.

Science.gov (United States)

Lamy, Pierre-Jean; Allory, Yves; Gauchez, Anne-Sophie; Asselain, Bernard; Beuzeboc, Philippe; de Cremoux, Patricia; Fontugne, Jacqueline; Georges, Agnès; Hennequin, Christophe; Lehmann-Che, Jacqueline; Massard, Christophe; Millet, Ingrid; Murez, Thibaut; Schlageter, Marie-Hélène; Rouvière, Olivier; Kassab-Chahmi, Diana; Rozet, François; Descotes, Jean-Luc; Rébillard, Xavier

2017-03-07

Prostate cancer stratification is based on tumour size, pretreatment PSA level, and Gleason score, but it remains imperfect. Current research focuses on the discovery and validation of novel prognostic biomarkers to improve the identification of patients at risk of aggressive cancer or of tumour relapse. This systematic review by the Intergroupe Coopérateur Francophone de Recherche en Onco-urologie (ICFuro) analysed new evidence on the analytical validity and clinical validity and utility of six prognostic biomarkers (PHI, 4Kscore, MiPS, GPS, Prolaris, Decipher). All available data for the six biomarkers published between January 2002 and April 2015 were systematically searched and reviewed. The main endpoints were aggressive prostate cancer prediction, additional value compared to classical prognostic parameters, and clinical benefit for patients with localised prostate cancer. The preanalytical and analytical validations were heterogeneous for all tests and often not adequate for the molecular signatures. Each biomarker was studied for specific indications (candidates for a first or second biopsy, and potential candidates for active surveillance, radical prostatectomy, or adjuvant treatment) for which the level of evidence (LOE) was variable. PHI and 4Kscore were the biomarkers with the highest LOE for discriminating aggressive and indolent tumours in different indications. Blood biomarkers (PHI and 4Kscore) have the highest LOE for the prediction of more aggressive prostate cancer and could help clinicians to manage patients with localised prostate cancer. The other biomarkers show a potential prognostic value; however, they should be evaluated in additional studies to confirm their clinical validity. We reviewed studies assessing the value of six prognostic biomarkers for prostate cancer. On the basis of the available evidence, some biomarkers could help in discriminating between aggressive and non-aggressive tumours with an additional value compared to the

PDTCM: a systems pharmacology platform of traditional Chinese medicine for psoriasis.

Science.gov (United States)

Wang, Dongmei; Gu, Jiangyong; Zhu, Wei; Luo, Fang; Chen, Lirong; Xu, Xiaojie; Lu, Chuanjian

2017-12-01

Psoriasis is a refractory skin disorder, and usually requires a lifetime control. Traditional Chinese medicine (TCM) is effective and safe for this disease. However, the cellular and molecular mechanisms of TCM remedies for psoriasis are still not fully understood. TCM contains numerous natural products. Natural products have historically been invaluable as a resource of therapeutic agents. Yet, there is no integrated information about active compounds of TCM for psoriasis. We use systems pharmacology methods to develop the Psoriasis Database of Traditional Chinese Medicine (PDTCM). The database covered a number of psoriasis-related information (formulas, TCM, compounds, target proteins, diseases and biomarkers). With these data information, an online platform was constructed Results: PDTCM comprises 38 empirical therapeutic formulas, 34373 compounds from 1424 medicinal plants, 44 psoriasis-related proteins and 76 biomarkers from 111 related diseases. On this platform, users can screen active compounds for a psoriasis-related target and explore molecular mechanisms of TCM. Accordingly, users can also download the retrieved structures and data information with a defined value set. In addition, it helps to get a better understanding of Chinese prescriptions in disease treatment. With the systems pharmacology-based data, PDTCM would become a valuable resource for TCM in psoriasis-related research. Key messages PDTCM platform comprises a great deal of data on TCM and psoriasis. On this platform, users can retrieve and get needed information with systems pharmacology methods, such as active compounds screening, target prediction and molecular mechanisms exploration. It is a tool for psoriasis-related research on natural drugs systematically.

Discovery and validation of DNA hypomethylation biomarkers for liver cancer using HRM-specific probes.

Directory of Open Access Journals (Sweden)

Barbara Stefanska

Full Text Available Poor prognosis of hepatocellular carcinoma (HCC associated with late diagnosis necessitates the development of early diagnostic biomarkers. We have previously delineated the landscape of DNA methylation in HCC patients unraveling the importance of promoter hypomethylation in activation of cancer- and metastasis-driving genes. The purpose of the present study was to test the feasibility that genes that are hypomethylated in HCC could serve as candidate diagnostic markers. We use high resolution melting analysis (HRM as a simple translatable PCR-based method to define methylation states in clinical samples. We tested seven regions selected from the shortlist of genes hypomethylated in HCC and showed that HRM analysis of several of them distinguishes methylation states in liver cancer specimens from normal adjacent liver and chronic hepatitis in the Shanghai area. Such regions were identified within promoters of neuronal membrane glycoprotein M6-B (GPM6B and melanoma antigen family A12 (MAGEA12 genes. Differences in HRM in the immunoglobulin superfamily Fc receptor (FCRL1 separated invasive tumors from less invasive HCC. The identified biomarkers differentiated HCC from chronic hepatitis in another set of samples from Dhaka. Although the main thrust in DNA methylation diagnostics in cancer is on hypermethylated genes, our study for the first time illustrates the potential use of hypomethylated genes as markers for solid tumors. After further validation in a larger cohort, the identified DNA hypomethylated regions can become important candidate biomarkers for liver cancer diagnosis and prognosis, especially in populations with high risk for HCC development.

Searching for new biomarkers in ovarian cancer patients

DEFF Research Database (Denmark)

Hentze, Julie L.; Høgdall, Claus; Kjær, Susanne K.

2017-01-01

, will be examined. Relevant microRNAs and DNA methylation patterns will be investigated using array technology. Patient exomes will be fully sequenced, and identified genetic variations will be validated with Next Generation Sequencing. In all cases, data will be correlated with clinical information on the patient...... of cancer and the discovery of new drugs. Moreover, biomarkers are a prerequisite for the development of precision medicine. This study will attack the ovarian cancer problem from several angles, thereby increasing the chance of successfully contributing to saving lives....

Biomarkers of World Trade Center Particulate Matter Exposure: Physiology of distal airway and blood biomarkers that predict FEV1 decline

Science.gov (United States)

Weiden, Michael D.; Kwon, Sophia; Caraher, Erin; Berger, Kenneth I.; Reibman, Joan; Rom, William N.; Prezant, David J.; Nolan, Anna

2016-01-01

Biomarkers can be important predictors of disease severity and progression. The intense exposure to particulates and other toxins from the destruction of the World Trade Center (WTC) overwhelmed the lung’s normal protective barriers. The Fire Department of New York (FDNY) cohort not only had baseline pre-exposure lung function measures but also had serum samples banked soon after their WTC exposure. This well phenotyped group of highly exposed first responders is an ideal cohort for biomarker discovery and eventual validation. Disease progression was heterogeneous in this group in that some individuals subsequently developed abnormal lung function while others recovered. Airflow obstruction predominated in WTC exposed patients who were symptomatic. Multiple independent disease pathways may cause this abnormal FEV1 after irritant exposure. WTC exposure activates one or more of these pathways causing abnormal FEV1 in an individual. Our hypothesis was that serum biomarkers expressed within 6 months after World Trade Center (WTC) exposure reflect active disease pathways and predict subsequent development or protection from abnormal FEV1biomarkers of WTC-LI. We have identified biomarkers of Inflammation, metabolic derangement, protease/antiprotease balance and vascular injury expressed in serum within 6 months of WTC exposure that were predictive of their FEV1 up to 7 years after their WTC exposure. Predicting future risk of airway injury after particulate exposures can focus monitoring and early treatment on a subset of patients in greatest need of these services. PMID:26024341

Dampening Enthusiasm for Circulating MicroRNA in Breast Cancer

OpenAIRE

Leidner, Rom S.; Li, Li; Thompson, Cheryl L.

2013-01-01

Genome-wide platforms for high-throughput profiling of circulating miRNA (oligoarray or miR-Seq) offer enormous promise for agnostic discovery of circulating miRNA biomarkers as a pathway for development in breast cancer detection. By harmonizing data from 15 previous reports, we found widespread inconsistencies across prior studies. Whether this arises from differences in study design, such as sample source or profiling platform, is unclear. As a reproducibility experiment, we generated a ge...

VOLTTRON™: An Agent Platform for Integrating Electric Vehicles and Smart Grid

Energy Technology Data Exchange (ETDEWEB)

Haack, Jereme N.; Akyol, Bora A.; Tenney, Nathan D.; Carpenter, Brandon J.; Pratt, Richard M.; Carroll, Thomas E.

2013-12-06

The VOLTTRON™ platform provides a secure environment for the deployment of intelligent applications in the smart grid. VOLTTRON design is based on the needs of control applications running on small form factor devices, namely security and resource guarantees. Services such as resource discovery, secure agent mobility, and interacting with smart and legacy devices are provided by the platform to ease the development of control applications and accelerate their deployment. VOLTTRON platform has been demonstrated in several different domains that influenced and enhanced its capabilities. This paper will discuss the features of VOLTTRON and highlight its usage to coordinate electric vehicle charging with home energy usage

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

Science.gov (United States)

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Three-dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation

OpenAIRE

Pan, Li; Aguilar, Hillary Andaluz; Wang, Linna; Iliuk, Anton; Tao, W. Andy

2016-01-01

Glycoproteins have vast structural diversity which plays an important role in many biological processes and have great potential as disease biomarkers. Here we report a novel functionalized reverse phase protein array (RPPA), termed polymer-based reverse phase GlycoProtein Array (polyGPA), to specifically capture and profile glycoproteomes, and validate glycoproteins. Nitrocellulose membrane functionalized with globular hydroxyaminodendrimers was used to covalently capture pre-oxidized glycan...

Metabolomic Discovery of Novel Urinary Galabiosylceramide Analogs as Fabry Disease Biomarkers

Science.gov (United States)

Boutin, Michel; Auray-Blais, Christiane

2015-03-01

Fabry disease is an X-linked, complex, multisystemic lysosomal storage disorder presenting marked phenotypic and genotypic variability among affected male and female patients. Glycosphingolipids, mainly globotriaosylceramide (Gb3) isoforms/analogs, globotriaosylsphingosine (lyso-Gb3) and analogs, as well as galabiosylceramide (Ga2) isoforms/analogs accumulate in the vascular endothelium, nerves, cardiomyocytes, renal glomerular and tubular epithelial cells, and biological fluids. The search for biomarkers reflecting disease severity and progression is still on-going. A metabolomic study using quadrupole time-of-flight mass spectrometry has revealed 22 galabiosylceramide isoforms/analogs in urine of untreated Fabry patients classified in seven groups according to their chemical structure: (1) Saturated fatty acid; (2) one extra double bond; (3) two extra double bonds; (4) hydroxylated saturated fatty acid; (5) hydroxylated fatty acid and one extra double bond; (6) hydrated sphingosine and hydroxylated fatty acid; (7) methylated amide linkage. Relative quantification of both Ga2 and Gb3 isoforms/analogs was performed. All these biomarkers are significantly more abundant in urine samples from untreated Fabry males compared with healthy male controls. A significant amount of Ga2 isoforms/analogs, accounting for 18% of all glycosphingolipids analyzed (Ga2 + Gb3 and respective isoforms/analogs), were present in urine of Fabry patients. Gb3 isoforms containing saturated fatty acids are the most abundant (60.9%) compared with 26.3% for Ga2. A comparison between Ga2 isoforms/analogs and their Gb3 counterparts also showed that the proportion of analogs with hydroxylated fatty acids is significantly greater for Ga2 (35.8%) compared with Gb3 (1.9%). These results suggest different biological pathways involved in the synthesis and/or degradation of Gb3 and Ga2 metabolites.

Testes sanguíneos de biomarcadores para diagnóstico e tratamento de desordens mentais: foco em esquizofrenia Biomarker blood tests for diagnosis and management of mental disorders: focus on schizophrenia

Directory of Open Access Journals (Sweden)

Sabine Bahn

2012-01-01

Full Text Available A descoberta e a aplicação clínica de biomarcadores para desordens mentais são confrontadas com muitos desafios. Em geral, os atuais métodos de descoberta e validação de biomarcadores não produziram os resultados que foram inicialmente aguardados depois da finalização do Projeto Genoma Humano. Isso se deve principalmente à falta de processos padronizados conectando a descoberta de marcadores com tecnologias para a validação e a tradução para uma plataforma que ofereça precisão e fácil uso em clínica. Como consequência, a maior parte dos psiquiatras e praticantes em geral são relutantes em aceitar que testes de biomarcadores pode suplementar ou substituir os métodos de diagnóstico utilizados baseados em entrevista. Apesar disso, agências regulatórias concordam agora que melhoras nos correntes métodos são essenciais. Além disso, essas agências estipularam que biomarcadores são importantes para o desenvolvimento de futuras drogas e iniciaram esforços no sentido de modernizar métodos e técnicas para suportar esses esforços. Aqui revisamos os desafios encontrados por essa tentativa do ponto de vista de psiquiatras, praticantes em geral, agências reguladoras e cientistas de biomarcadores. Também descrevemos o desenvolvimento de um novo teste sanguíneo molecular para esquizofrenia como um primeiro passo a esse objetivo.The discovery and clinical application of biomarkers for mental disorders is faced with many challenges. In general, the current methods for discovery and validation of biomarkers have not produced the results which were first anticipated after completion of the human genome project. This is mostly due to the lack of a standardized pipeline connecting marker discovery with technologies for validation and translation to a platform that offers accuracy and ease of use in a clinical setting. As a consequence, most psychiatrists and general practitioners are still reluctant to accept that biomarker tests

Testes sanguíneos de biomarcadores para diagnóstico e tratamento de desordens mentais: foco em esquizofrenia Biomarker blood tests for diagnosis and management of mental disorders: focus on schizophrenia

Directory of Open Access Journals (Sweden)

Sabine Bahn

2013-01-01

Full Text Available A descoberta e a aplicação clínica de biomarcadores para desordens mentais são confrontadas com muitos desafios. Em geral, os atuais métodos de descoberta e validação de biomarcadores não produziram os resultados que foram inicialmente aguardados depois da finalização do Projeto Genoma Humano. Isso se deve principalmente à falta de processos padronizados conectando a descoberta de marcadores com tecnologias para a validação e a tradução para uma plataforma que ofereça precisão e fácil uso em clínica. Como consequência, a maior parte dos psiquiatras e praticantes em geral são relutantes em aceitar que testes de biomarcadores pode suplementar ou substituir os métodos de diagnóstico utilizados baseados em entrevista. Apesar disso, agências regulatórias concordam agora que melhoras nos correntes métodos são essenciais. Além disso, essas agências estipularam que biomarcadores são importantes para o desenvolvimento de futuras drogas e iniciaram esforços no sentido de modernizar métodos e técnicas para suportar esses esforços. Aqui revisamos os desafios encontrados por essa tentativa do ponto de vista de psiquiatras, praticantes em geral, agências reguladoras e cientistas de biomarcadores. Também descrevemos o desenvolvimento de um novo teste sanguíneo molecular para esquizofrenia como um primeiro passo a esse objetivo.The discovery and clinical application of biomarkers for mental disorders is faced with many challenges. In general, the current methods for discovery and validation of biomarkers have not produced the results which were first anticipated after completion of the human genome project. This is mostly due to the lack of a standardized pipeline connecting marker discovery with technologies for validation and translation to a platform that offers accuracy and ease of use in a clinical setting. As a consequence, most psychiatrists and general practitioners are still reluctant to accept that biomarker tests

Image Biomarkers and Precision Medicine: need for validation

International Nuclear Information System (INIS)

Marti-Bonmati, L.; Alberich-Bayarri, A.; Garcia Castro, F.

2016-01-01

Personalized medicine aims to improve the diagnosis, classification and the best treatment for a particular patient. Today, radiologists are challenged to translate new biological discoveries, the different mechanisms of disease and advances in preclinical research, into a clinical reality through patients, images and their associated parameters. In this article we show how digital medical imaging and computational data processing extract numerous quantitative parameters from the obtained images as virtual biopsies. To be implemented in clinical practice, biomarkers should provide useful and relevant information, improving processes diagnostic, therapeutic and monitoring, for the benefit of patients. (Author)

New developments and concepts related to biomarker application to vaccines

Science.gov (United States)

Ahmed, S. Sohail; Black, Steve; Ulmer, Jeffrey

2012-01-01

Summary This minireview will provide a perspective on new developments and concepts related to biomarker applications for vaccines. In the context of preventive vaccines, biomarkers have the potential to predict adverse events in select subjects due to differences in genetic make‐up/underlying medical conditions or to predict effectiveness (good versus poor response). When expanding them to therapeutic vaccines, their utility in identification of patients most likely to respond favourably (or avoid potentially negative effects of treatment) becomes self‐explanatory. Despite the progress made so far on dissection of various pathways of biological significance in humans, there is still plenty to unravel about the mysteries related to the quantitative and qualitative aspects of the human host response. This review will provide a focused overview of new concepts and developments in the field of vaccine biomarkers including (i) vaccine‐dependent signatures predicting subject response and safety, (ii) predicting therapeutic vaccine efficacy in chronic diseases, (iii) exploring the genetic make‐up of the host that may modulate subject‐specific adverse events or affect the quality of immune responses, and (iv) the topic of volunteer stratification as a result of biomarker screening (e.g. for therapeutic vaccines but also potentially for preventive vaccines) or as a reflection of an effort to compare select groups (e.g. vaccinated subjects versus patients recovering from infection) to enable the discovery of clinically relevant biomarkers for preventive vaccines. PMID:21895991

Omics techniques and biobanks to find new biomarkers for the early detection of acute lymphoblastic leukemia in middle-income countries: a perspective from Mexico.

Science.gov (United States)

Aguirre-Guillén, William Alejandro; Angeles-Floriano, Tania; López-Martínez, Briceida; Reyes-Morales, Hortensia; Zlotnik, Albert; Valle-Rios, Ricardo

Acute lymphoblastic leukemia (ALL) affects the quality of life of many children in the world and particularly in Mexico, where a high incidence has been reported. With a proper financial investment and with well-organized institutions caring for those patients, together with solid platforms to perform high-throughput analyses, we propose the creation of a Mexican repository system of serum and cells from bone marrow and blood samples derived from tissues of pediatric patients with ALL diagnosis. This resource, in combination with omics technologies, particularly proteomics and metabolomics, would allow longitudinal studies, offering an opportunity to design and apply personalized ALL treatments. Importantly, it would accelerate the development of translational science and will lead us to further discoveries, including the identification of new biomarkers for the early detection of leukemia. Copyright © 2017 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.

Pathways to new drug discovery in neuropsychiatry

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Berk Michael

2012-11-01

Full Text Available Abstract There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success. See related article http://www.biomedcentral.com/1741-7015/10/150

Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

Energy Technology Data Exchange (ETDEWEB)

Gu, Yi; Jiang, Huaiguang; Zhang, Yingchen; Zhang, Jun Jason; Gao, Tianlu; Muljadi, Eduard

2016-11-21

In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmit the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.

Executive summary—Biomarkers of Nutrition for Development: Building a Consensus123

Science.gov (United States)

Namasté, Sorrel; Brabin, Bernard; Combs, Gerald; L'Abbe, Mary R; Wasantwisut, Emorn; Darnton-Hill, Ian

2011-01-01

The ability to develop evidence-based clinical guidance and effective programs and policies to achieve global health promotion and disease prevention goals depends on the availability of valid and reliable data. With specific regard to the role of food and nutrition in achieving those goals, relevant data are developed with the use of biomarkers that reflect nutrient exposure, status, and functional effect. A need exists to promote the discovery, development, and use of biomarkers across a range of applications. In addition, a process is needed to harmonize the global health community's decision making about what biomarkers are best suited for a given use under specific conditions and settings. To address these needs, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, organized a conference entitled “Biomarkers of Nutrition for Development: Building a Consensus,” which was hosted by the International Atomic Energy Agency. Partners included key multilateral, US agencies and public and private organizations. The assembly endorsed the utility of this initiative and the need for the BOND (Biomarkers of Nutrition for Development) project to continue. A consensus was reached on the requirement to develop a process to inform the community about the relative strengths or weaknesses and specific applications of various biomarkers under defined conditions. The articles in this supplement summarize the deliberations of the 4 working groups: research, clinical, policy, and programmatic. Also described are content presentations on the harmonization processes, the evidence base for biomarkers for 5 case-study micronutrients, and new frontiers in science and technology. PMID:21733880

Chromosome 1q21.3 amplification is a trackable biomarker and actionable target for breast cancer recurrence

DEFF Research Database (Denmark)

Goh, Jian Yuan; Feng, Min; Wang, Wenyu

2017-01-01

Tumor recurrence remains the main reason for breast cancer-associated mortality, and there are unmet clinical demands for the discovery of new biomarkers and development of treatment solutions to benefit patients with breast cancer at high risk of recurrence. Here we report the identification of ...

Establishment of a novel whole animal HTS technology platform for melioidosis drug discovery.

Science.gov (United States)

Lakshmanan, Umayal; Yap, Amelia; Fulwood, Justina; Yichun, Li; Hoon, Sim Siew; Lim, Jolander; Ting, Audrey; Sem, Xiao Hui; Kreisberg, Jason F; Tan, Patrick; Tan, Gladys; Flotow, Horst

2014-01-01

Melioidosis is a serious emerging endemic infectious disease caused by Burkholderia pseudomallei, a gram-negative pathogen. Septicemic melioidosis has a mortality rate of 50% even with treatment. Like other gram-negative bacteria, B. pseudomallei is resistant to a number of antibiotics and multi-drug resistant B. pseudomallei is beginning to be encountered in hospitals. There is a clear medical need to develop new treatment options to manage this disease. We used Burkholderia thailandensis (a BSL-2 class organism) to infect Caenorhabditis elegans and set up a surrogate whole animal infection model of melioidosis that we could run in a 384 microtitre plate and establish a whole animal HTS assay. We have optimized and validated this assay in a fluorescence-based format that can be run on our automated screening platforms. This assay has now been used to screen over 300,000 compounds from our small molecule library and we are in the process of characterizing the hits obtained and select compounds for further studies. We have thus established a biologically relevant assay technology platform to screen for antibacterial compounds and used this platform to identify new compounds that may find application in treating melioidosis infections.

Interactomic approach for evaluating nucleophosmin-binding proteins as biomarkers for Ewing's sarcoma.

Science.gov (United States)

Haga, Ayako; Ogawara, Yoko; Kubota, Daisuke; Kitabayashi, Issay; Murakami, Yasufumi; Kondo, Tadashi

2013-06-01

Nucleophosmin (NPM) is a novel prognostic biomarker for Ewing's sarcoma. To evaluate the prognostic utility of NPM, we conducted an interactomic approach to characterize the NPM protein complex in Ewing's sarcoma cells. A gene suppression assay revealed that NPM promoted cell proliferation and the invasive properties of Ewing's sarcoma cells. FLAG-tag-based affinity purification coupled with liquid chromatography-tandem mass spectrometry identified 106 proteins in the NPM protein complex. The functional classification suggested that the NPM complex participates in critical biological events, including ribosome biogenesis, regulation of transcription and translation, and protein folding, that are mediated by these proteins. In addition to JAK1, a candidate prognostic biomarker for Ewing's sarcoma, the NPM complex, includes 11 proteins known as prognostic biomarkers for other malignancies. Meta-analysis of gene expression profiles of 32 patients with Ewing's sarcoma revealed that 6 of 106 were significantly and independently associated with survival period. These observations suggest a functional role as well as prognostic value of these NPM complex proteins in Ewing's sarcoma. Further, our study suggests the potential applications of interactomics in conjunction with meta-analysis for biomarker discovery. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Urinary metabonomics study on toxicity biomarker discovery in rats treated with Xanthii Fructus.

Science.gov (United States)

Lu, Fang; Cao, Min; Wu, Bin; Li, Xu-zhao; Liu, Hong-yu; Chen, Da-zhong; Liu, Shu-min

2013-08-26

Xanthii Fructus (XF) is commonly called "Cang-Erzi" in traditional Chinese medicine (TCM) and widely used for the treatment of sinusitis, headache, rheumatism, and skin itching. However, the clinical utilization of XF is relatively restricted owing to its toxicity. To discover the characteristic potential biomarkers in rats treated with XF by urinary metabonomics. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was applied in the study. The total ion chromatograms obtained from control and different dosage groups were distinguishable by a multivariate statistical analysis method. The greatest difference in metabolic profile was observed between high dosage group and control group, and the metabolic characters in rats treated with XF were perturbed in a dose-dependent manner. The metabolic changes in response for XF treatment were observed in urinary samples, which were revealed by orthogonal projection to latent structures discriminate analysis (OPLS-DA), and 10 metabolites could be served as the potential toxicity biomarkers. In addition, the mechanism associated with the damages of lipid per-oxidation and the metabolic disturbances of fatty acid oxidation were investigated. These results indicate that metabonomics analysis in urinary samples may be useful for predicting the toxicity induced by XF. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Neuropathological biomarker candidates in brain tumors: key issues for translational efficiency.

Science.gov (United States)

Hainfellner, J A; Heinzl, H

2010-01-01

Brain tumors comprise a large spectrum of rare malignancies in children and adults that are often associated with severe neurological symptoms and fatal outcome. Neuropathological tumor typing provides both prognostic and predictive tissue information which is the basis for optimal postoperative patient management and therapy. Molecular biomarkers may extend and refine prognostic and predictive information in a brain tumor case, providing more individualized and optimized treatment options. In the recent past a few neuropathological brain tumor biomarkers have translated smoothly into clinical use whereas many candidates show protracted translation. We investigated the causes of protracted translation of candidate brain tumor biomarkers. Considering the research environment from personal, social and systemic perspectives we identified eight determinants of translational success: methodology, funding, statistics, organization, phases of research, cooperation, self-reflection, and scientific progeny. Smoothly translating biomarkers are associated with low degrees of translational complexity whereas biomarkers with protracted translation are associated with high degrees. Key issues for translational efficiency of neuropathological brain tumor biomarker research seem to be related to (i) the strict orientation to the mission of medical research, that is the improval of medical practice as primordial purpose of research, (ii) definition of research priorities according to clinical needs, and (iii) absorption of translational complexities by means of operatively beneficial standards. To this end, concrete actions should comprise adequate scientific education of young investigators, and shaping of integrative diagnostics and therapy research both on the local level and the level of influential international brain tumor research platforms.

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Science.gov (United States)

Picard de Muller, Gaël; Ait-Belkacem, Rima; Bonnel, David; Longuespée, Rémi; Stauber, Jonathan

2017-12-01

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. [Figure not available: see fulltext.

Bringing your tools to CyVerse Discovery Environment using Docker.

Science.gov (United States)

Devisetty, Upendra Kumar; Kennedy, Kathleen; Sarando, Paul; Merchant, Nirav; Lyons, Eric

2016-01-01

Docker has become a very popular container-based virtualization platform for software distribution that has revolutionized the way in which scientific software and software dependencies (software stacks) can be packaged, distributed, and deployed. Docker makes the complex and time-consuming installation procedures needed for scientific software a one-time process. Because it enables platform-independent installation, versioning of software environments, and easy redeployment and reproducibility, Docker is an ideal candidate for the deployment of identical software stacks on different compute environments such as XSEDE and Amazon AWS. CyVerse's Discovery Environment also uses Docker for integrating its powerful, community-recommended software tools into CyVerse's production environment for public use. This paper will help users bring their tools into CyVerse Discovery Environment (DE) which will not only allows users to integrate their tools with relative ease compared to the earlier method of tool deployment in DE but will also help users to share their apps with collaborators and release them for public use.

Profiling of circulating microRNAs for prostate cancer biomarker discovery

DEFF Research Database (Denmark)

Haldrup, Christa; Kosaka, Nobuyoshi; Ochiya, Takahiro

2014-01-01

Prostate cancer (PC) is the most frequent cancer in men in the Western world. Currently, serum prostate-specific antigen levels and digital rectal examinations are used to indicate the need for diagnostic prostate biopsy, but lack in specificity and sensitivity. Thus, many men undergo unnecessary...... performed genome-wide miRNA profiling of serum samples from 13 benign prostatic hyperplasia (BPH) control patients and 31 PC patients. Furthermore, we carefully reviewed the literature on circulating miRNA biomarkers for PC. Our results confirmed the de-regulation of miR-141 and miR-375, two of the most...... well-documented candidate miRNA markers for PC. Moreover, we identified several new potential serum miRNA markers for PC and developed three novel and highly specific (100 %) miRNA candidate marker panels able to identify 84 % of all PC patients (miR-562/miR-210/miR-501-3p/miR-375/miR-551b), 80...

Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project

Directory of Open Access Journals (Sweden)

Julie L. Hentze

2017-12-01

A thorough investigation of biomarkers in ovarian cancer, including large numbers of different markers, has never been done before. Besides from improving diagnosis and treatment, other outcomes could be markers for screening, knowledge of the molecular aspects of cancer and the discovery of new drugs. Moreover, biomarkers are a prerequisite for the development of precision medicine. This study will attack the ovarian cancer problem from several angles, thereby increasing the chance of successfully contributing to saving lives.

Recommendations and Standardization of Biomarker Quantification Using NMR-based Metabolomics with Particular Focus on Urinary Analysis

KAUST Repository

Emwas, Abdul-Hamid M.

2016-01-08

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to non-destructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Indeed, precise metabolite quantification is a necessary prerequisite to move any chemical biomarker or biomarker panel from the lab into the clinic. Among the many biofluids (urine, serum, plasma, cerebrospinal fluid and saliva) commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, easily obtained, needs little sample preparation and does not require any invasive medical procedures for collection. Furthermore, urine captures and concentrates many “unwanted” or “undesirable” compounds throughout the body, thereby providing a rich source of potentially useful disease biomarkers. However, the incredible variation in urine chemical concentrations due to effects such as gender, age, diet, life style, health conditions, and physical activity make the analysis of urine and the identification of useful urinary biomarkers by NMR quite challenging. In this review, we discuss a number of the most significant issues regarding NMR-based urinary metabolomics with a specific emphasis on metabolite quantification for disease biomarker applications. We also propose a number of data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, as well as recommendations regarding sample preparation and biomarker assessment.

Recommendations and Standardization of Biomarker Quantification Using NMR-based Metabolomics with Particular Focus on Urinary Analysis

KAUST Repository

Emwas, Abdul-Hamid M.; Roy, Raja; McKay, Ryan T.; Ryan, Danielle; Brennan, Lorraine; Tenori, Leonardo; Luchinat, Claudio; Gao, Xin; Zeri, Ana Carolina; Gowda, G. A. Nagana; Raftery, Daniel; Steinbeck, Christoph; Salek, Reza M; Wishart, David S.

2016-01-01

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to non-destructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Indeed, precise metabolite quantification is a necessary prerequisite to move any chemical biomarker or biomarker panel from the lab into the clinic. Among the many biofluids (urine, serum, plasma, cerebrospinal fluid and saliva) commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, easily obtained, needs little sample preparation and does not require any invasive medical procedures for collection. Furthermore, urine captures and concentrates many “unwanted” or “undesirable” compounds throughout the body, thereby providing a rich source of potentially useful disease biomarkers. However, the incredible variation in urine chemical concentrations due to effects such as gender, age, diet, life style, health conditions, and physical activity make the analysis of urine and the identification of useful urinary biomarkers by NMR quite challenging. In this review, we discuss a number of the most significant issues regarding NMR-based urinary metabolomics with a specific emphasis on metabolite quantification for disease biomarker applications. We also propose a number of data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, as well as recommendations regarding sample preparation and biomarker assessment.

Neuroproteomics and Systems Biology Approach to Identify Temporal Biomarker Changes Post Experimental Traumatic Brain Injury in Rats

Directory of Open Access Journals (Sweden)

Firas H Kobeissy

2016-11-01

Full Text Available Traumatic brain injury (TBI represents a critical health problem of which diagnosis, management and treatment remain challenging. TBI is a contributing factor in approximately 1/3 of all injury-related deaths in the United States. The Centers for Disease Control and Prevention (CDC estimate that 1.7 million TBI people suffer a TBI in the United States annually. Efforts continue to focus on elucidating the complex molecular mechanisms underlying TBI pathophysiology and defining sensitive and specific biomarkers that can aid in improving patient management and care. Recently, the area of neuroproteomics-systems biology is proving to be a prominent tool in biomarker discovery for central nervous system (CNS injury and other neurological diseases. In this work, we employed the controlled cortical impact (CCI model of experimental TBI in rat model to assess the temporal-global proteome changes after acute (1 day and for the first time, subacute (7 days, post-injury time frame using the established CAX-PAGE LC-MS/MS platform for protein separation combined with discrete systems biology analyses to identify temporal biomarker changes related to this rat TBI model. Rather than focusing on any one individual molecular entities, we used in silico systems biology approach to understand the global dynamics that govern proteins that are differentially altered post-injury. In addition, gene ontology analysis of the proteomic data was conducted in order to categorize the proteins by molecular function, biological process, and cellular localization. Results show alterations in several proteins related to inflammatory responses and oxidative stress in both acute (1 day and subacute (7 days periods post TBI. Moreover, results suggest a differential upregulation of neuroprotective proteins at 7-days post-CCI involved in cellular functions such as neurite growth, regeneration, and axonal guidance. Our study is amongst the first to assess temporal neuroproteome

A Review of the “Omics” Approach to Biomarkers of Oxidative Stress in Oryza sativa

Directory of Open Access Journals (Sweden)

Su Shiung Lam

2013-04-01

Full Text Available Physiological and ecological constraints that cause the slow growth and depleted production of crops have raised a major concern in the agriculture industry as they represent a possible threat of short food supply in the future. The key feature that regulates the stress signaling pathway is always related to the reactive oxygen species (ROS. The accumulation of ROS in plant cells would leave traces of biomarkers at the genome, proteome, and metabolome levels, which could be identified with the recent technological breakthrough coupled with improved performance of bioinformatics. This review highlights the recent breakthrough in molecular strategies (comprising transcriptomics, proteomics, and metabolomics in identifying oxidative stress biomarkers and the arising opportunities and obstacles observed in research on biomarkers in rice. The major issue in incorporating bioinformatics to validate the biomarkers from different omic platforms for the use of rice-breeding programs is also discussed. The development of powerful techniques for identification of oxidative stress-related biomarkers and the integration of data from different disciplines shed light on the oxidative response pathways in plants.

Biomarkers of brain injury in the premature infant

Directory of Open Access Journals (Sweden)

Martha V. Douglas-Escobar

2013-01-01

Full Text Available The term encephalopathy of prematurity encompasses not only the acute brain injury (such as intraventricular hemorrhage but also complex disturbance on the infant’s subsequent brain development. In premature infants, the most frequent recognized source of brain injury is intraventricular hemorrhage (IVH and periventricular leukomalacia (PVL. Furthermore 20-25% infants with birth weigh less than 1,500 g will have IVH and that proportion increases to 45% if the birth weight is less than 500-750 g. In addition, nearly 60% of very low birth weight newborns will have hypoxic-ischemic injury. Therefore permanent lifetime neurodevelopmental disabilities are frequent in premature infants. Innovative approach to prevent or decrease brain injury in preterm infants requires discovery of biomarkers able to discriminate infants at risk for injury, monitor the progression of the injury and assess efficacy of neuroprotective clinical trials. In this article, we will review biomarkers studied in premature infants with IVH, Post-hemorrhagic ventricular dilation (PHVD and PVL including: S100b, Activin A, erythropoietin, chemokine CCL 18, GFAP and NFL will also be examined. Some of the most promising biomarkers for IVH are S100β and Activin. The concentrations of TGF-β1, MMP-9 and PAI-1 in cerebrospinal fluid could be used to discriminate patients that will require shunt after post-hemorrhagic ventricular dilation. Neonatal brain injury is frequent in premature infants admitted to the neonatal intensive care and we hope to contribute to the awareness and interest in clinical validation of established as well as novel neonatal brain injury biomarkers.

A Pilot Proteomic Analysis of Salivary Biomarkers in Autism Spectrum Disorder.

Science.gov (United States)

Ngounou Wetie, Armand G; Wormwood, Kelly L; Russell, Stefanie; Ryan, Jeanne P; Darie, Costel C; Woods, Alisa G

2015-06-01

Autism spectrum disorder (ASD) prevalence is increasing, with current estimates at 1/68-1/50 individuals diagnosed with an ASD. Diagnosis is based on behavioral assessments. Early diagnosis and intervention is known to greatly improve functional outcomes in people with ASD. Diagnosis, treatment monitoring and prognosis of ASD symptoms could be facilitated with biomarkers to complement behavioral assessments. Mass spectrometry (MS) based proteomics may help reveal biomarkers for ASD. In this pilot study, we have analyzed the salivary proteome in individuals with ASD compared to neurotypical control subjects, using MS-based proteomics. Our goal is to optimize methods for salivary proteomic biomarker discovery and to identify initial putative biomarkers in people with ASDs. The salivary proteome is virtually unstudied in ASD, and saliva could provide an easily accessible biomaterial for analysis. Using nano liquid chromatography-tandem mass spectrometry, we found statistically significant differences in several salivary proteins, including elevated prolactin-inducible protein, lactotransferrin, Ig kappa chain C region, Ig gamma-1 chain C region, Ig lambda-2 chain C regions, neutrophil elastase, polymeric immunoglobulin receptor and deleted in malignant brain tumors 1. Our results indicate that this is an effective method for identification of salivary protein biomarkers, support the concept that immune system and gastrointestinal disturbances may be present in individuals with ASDs and point toward the need for larger studies in behaviorally-characterized individuals. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review.

Science.gov (United States)

Lădaru, Anca; Bălănescu, Paul; Stan, Mihaela; Codreanu, Ioana; Anca, Ioana Alina

2016-01-01

Non-alcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation in the liver which is accompanied by a series of metabolic deregulations. There are sustained research efforts focusing upon biomarker discovery for NAFLD diagnosis and its prognosis in order investigate and follow-up patients as minimally invasive as possible. The objective of this study is to critically review proteomic studies that used mass spectrometry techniques and summarize relevant proteomic NAFLD candidate biomarkers. Medline and Embase databases were searched from inception to December 2014. A final number of 22 records were included that identified 251 candidate proteomic biomarkers. Thirty-three biomarkers were confirmed - 14 were found in liver samples, 21 in serum samples, and two from both serum and liver samples. Some of the biomarkers identified have already been extensively studied regarding their diagnostic and prognostic capacity. However, there are also more potential biomarkers that still need to be addressed in future studies.

Novel Biomarkers of Physical Activity Maintenance in Midlife Women: Preliminary Investigation

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Kelly A. Bosak

2018-04-01

Full Text Available The precision health initiative is leading the discovery of novel biomarkers as important indicators of biological processes or responses to behavior, such as physical activity. Neural biomarkers identified by magnetic resonance imaging (MRI hold promise to inform future research, and ultimately, for transfer to the clinical setting to optimize health outcomes. This study investigated resting-state and functional brain biomarkers between midlife women who were maintaining physical activity in accordance with the current national guidelines and previously acquired age-matched sedentary controls. Approval was obtained from the Human Subjects Committee. Participants included nondiabetic, healthy weight to overweight (body mass index 19–29.9 kg/m2 women (n = 12 aged 40–64 years. Control group data were used from participants enrolled in our previous functional MRI study and baseline resting-state MRI data from a subset of sedentary (<500 kcal of physical activity per week midlife women who were enrolled in a 9-month exercise intervention conducted in our imaging center. Differential activation of the inferior frontal gyrus (IFG and greater connectivity with the dorsolateral prefrontal cortex (dlPFC was identified between physically active women and sedentary controls. After correcting for multiple comparisons, these differences in biomarkers of physical activity maintenance did not reach statistical significance. Preliminary evidence in this small sample suggests that neural biomarkers of physical activity maintenance involve activations in the brain region associated with areas involved in implementing goal-directed behavior. Specifically, activation of the IFG and connectivity with the dlPFC is identified as a neural biomarker to explain and predict long-term physical activity maintenance for healthy aging. Future studies should evaluate these biomarker links with relevant clinical correlations.

Multiple reaction monitoring (MRM)-profiling for biomarker discovery applied to human polycystic ovarian syndrome.

Science.gov (United States)

Cordeiro, Fernanda B; Ferreira, Christina R; Sobreira, Tiago Jose P; Yannell, Karen E; Jarmusch, Alan K; Cedenho, Agnaldo P; Lo Turco, Edson G; Cooks, R Graham

2017-09-15

We describe multiple reaction monitoring (MRM)-profiling, which provides accelerated discovery of discriminating molecular features, and its application to human polycystic ovary syndrome (PCOS) diagnosis. The discovery phase of the MRM-profiling seeks molecular features based on some prior knowledge of the chemical functional groups likely to be present in the sample. It does this through use of a limited number of pre-chosen and chemically specific neutral loss and/or precursor ion MS/MS scans. The output of the discovery phase is a set of precursor/product transitions. In the screening phase these MRM transitions are used to interrogate multiple samples (hence the name MRM-profiling). MRM-profiling was applied to follicular fluid samples of 22 controls and 29 clinically diagnosed PCOS patients. Representative samples were delivered by flow injection to a triple quadrupole mass spectrometer set to perform a number of pre-chosen and chemically specific neutral loss and/or precursor ion MS/MS scans. The output of this discovery phase was a set of 1012 precursor/product transitions. In the screening phase each individual sample was interrogated for these MRM transitions. Principal component analysis (PCA) and receiver operating characteristic (ROC) curves were used for statistical analysis. To evaluate the method's performance, half the samples were used to build a classification model (testing set) and half were blinded (validation set). Twenty transitions were used for the classification of the blind samples, most of them (N = 19) showed lower abundances in the PCOS group and corresponded to phosphatidylethanolamine (PE) and phosphatidylserine (PS) lipids. Agreement of 73% with clinical diagnosis was found when classifying the 26 blind samples. MRM-profiling is a supervised method characterized by its simplicity, speed and the absence of chromatographic separation. It can be used to rapidly isolate discriminating molecules in healthy/disease conditions by

Maximizing biomarker discovery by minimizing gene signatures

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Chang Chang

2011-12-01

Full Text Available Abstract Background The use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II, trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications. Results We analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II. Conclusions Our results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the

Effects of dietary restriction on adipose mass and biomarkers of healthy aging in human.

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Lettieri-Barbato, Daniele; Giovannetti, Esmeralda; Aquilano, Katia

2016-11-29

In developing countries the rise of obesity and obesity-related metabolic disorders, such as cardiovascular diseases and type 2 diabetes, reflects the changes in lifestyle habits and wrong dietary choices. Dietary restriction (DR) regimens have been shown to extend health span and lifespan in many animal models including primates. Identifying biomarkers predictive of clinical benefits of treatment is one of the primary goals of precision medicine. To monitor the clinical outcomes of DR interventions in humans, several biomarkers are commonly adopted. However, a validated link between the behaviors of such biomarkers and DR effects is lacking at present time. Through a systematic analysis of human intervention studies, we evaluated the effect size of DR (i.e. calorie restriction, very low calorie diet, intermittent fasting, alternate day fasting) on health-related biomarkers. We found that DR is effective in reducing total and visceral adipose mass and improving inflammatory cytokines profile and adiponectin/leptin ratio. By analysing the levels of canonical biomarkers of healthy aging, we also validated the changes of insulin, IGF-1 and IGFBP-1,2 to monitor DR effects. Collectively, we developed a useful platform to evaluate the human responses to dietary regimens low in calories.

Integrated Detection of Pathogens and Host Biomarkers for Wounds

Energy Technology Data Exchange (ETDEWEB)

Jaing, C

2012-03-19

The increasing incidence and complications arising from combat wounds has necessitated a reassessment of methods for effective treatment. Infection, excessive inflammation, and incidence of drug-resistant organisms all contribute toward negative outcomes for afflicted individuals. The organisms and host processes involved in wound progression, however, are incompletely understood. We therefore set out, using our unique technical resources, to construct a profile of combat wounds which did or did not successfully resolve. We employed the Lawrence Livermore Microbial Detection Array and identified a number of nosocomial pathogens present in wound samples. Some of these identities corresponded with bacterial isolates previously cultured, while others were not obtained via standard microbiology. Further, we optimized proteomics protocols for the identification of host biomarkers indicative of various stages in wound progression. In combination with our pathogen data, our biomarker discovery efforts will provide a profile corresponding to wound complications, and will assist significantly in treatment of these complex cases.

Optimized acoustic biochip integrated with microfluidics for biomarkers detection in molecular diagnostics.

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Papadakis, G; Friedt, J M; Eck, M; Rabus, D; Jobst, G; Gizeli, E

2017-09-01

The development of integrated platforms incorporating an acoustic device as the detection element requires addressing simultaneously several challenges of technological and scientific nature. The present work was focused on the design of a microfluidic module, which, combined with a dual or array type Love wave acoustic chip could be applied to biomedical applications and molecular diagnostics. Based on a systematic study we optimized the mechanics of the flow cell attachment and the sealing material so that fluidic interfacing/encapsulation would impose minimal losses to the acoustic wave. We have also investigated combinations of operating frequencies with waveguide materials and thicknesses for maximum sensitivity during the detection of protein and DNA biomarkers. Within our investigations neutravidin was used as a model protein biomarker and unpurified PCR amplified Salmonella DNA as the model genetic target. Our results clearly indicate the need for experimental verification of the optimum engineering and analytical parameters, in order to develop commercially viable systems for integrated analysis. The good reproducibility of the signal together with the ability of the array biochip to detect multiple samples hold promise for the future use of the integrated system in a Lab-on-a-Chip platform for application to molecular diagnostics.

An E-government Interoperability Platform Supporting Personal Data Protection Regulations

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Laura González

2016-08-01

Full Text Available Public agencies are increasingly required to collaborate with each other in order to provide high-quality e-government services. This collaboration is usually based on the service-oriented approach and supported by interoperability platforms. Such platforms are specialized middleware-based infrastructures enabling the provision, discovery and invocation of interoperable software services. In turn, given that personal data handled by governments are often very sensitive, most governments have developed some sort of legislation focusing on data protection. This paper proposes solutions for monitoring and enforcing data protection laws within an E-government Interoperability Platform. In particular, the proposal addresses requirements posed by the Uruguayan Data Protection Law and the Uruguayan E-government Platform, although it can also be applied in similar scenarios. The solutions are based on well-known integration mechanisms (e.g. Enterprise Service Bus as well as recognized security standards (e.g. eXtensible Access Control Markup Language and were completely prototyped leveraging the SwitchYard ESB product.

Circulating microRNAs as Potential Biomarkers of Infectious Disease

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Correia, Carolina N.; Nalpas, Nicolas C.; McLoughlin, Kirsten E.; Browne, John A.; Gordon, Stephen V.; MacHugh, David E.; Shaughnessy, Ronan G.

2017-01-01

microRNAs (miRNAs) are a class of small non-coding endogenous RNA molecules that regulate a wide range of biological processes by post-transcriptionally regulating gene expression. Thousands of these molecules have been discovered to date, and multiple miRNAs have been shown to coordinately fine-tune cellular processes key to organismal development, homeostasis, neurobiology, immunobiology, and control of infection. The fundamental regulatory role of miRNAs in a variety of biological processes suggests that differential expression of these transcripts may be exploited as a novel source of molecular biomarkers for many different disease pathologies or abnormalities. This has been emphasized by the recent discovery of remarkably stable miRNAs in mammalian biofluids, which may originate from intracellular processes elsewhere in the body. The potential of circulating miRNAs as biomarkers of disease has mainly been demonstrated for various types of cancer. More recently, however, attention has focused on the use of circulating miRNAs as diagnostic/prognostic biomarkers of infectious disease; for example, human tuberculosis caused by infection with Mycobacterium tuberculosis, sepsis caused by multiple infectious agents, and viral hepatitis. Here, we review these developments and discuss prospects and challenges for translating circulating miRNA into novel diagnostics for infectious disease. PMID:28261201

Urinary biomarker investigation in children with Fabry disease using tandem mass spectrometry.

Science.gov (United States)

Auray-Blais, Christiane; Blais, Catherine-Marie; Ramaswami, Uma; Boutin, Michel; Germain, Dominique P; Dyack, Sarah; Bodamer, Olaf; Pintos-Morell, Guillem; Clarke, Joe T R; Bichet, Daniel G; Warnock, David G; Echevarria, Lucia; West, Michael L; Lavoie, Pamela

2015-01-01

Fabry disease is an X-linked lysosomal storage disorder affecting both males and females with tremendous genotypic/phenotypic variability. Concentrations of globotriaosylceramide (Gb3), globotriaosylsphingosine (lyso-Gb3)/related analogues were investigated in pediatric and adult Fabry cohorts. The aims of this study were to transfer and validate an HPLC-MS/MS methodology on a UPLC-MS/MS new generation platform, using an HPLC column, for urine analysis of treated and untreated pediatric and adult Fabry patients, to establish correlations between the excretion of Fabry biomarkers with gender, treatment, types of mutations, and to evaluate the biomarker reliability for early detection of pediatric Fabry patients. A UPLC-MS/MS was used for biomarker analysis. Reference values are presented for all biomarkers. Results show that gender strongly influences the excretion of each biomarker in the pediatric Fabry cohort, with females having lower urinary levels of all biomarkers. Urinary distribution of lyso-Gb3/related analogues in treated Fabry males was similar to the untreated and treated Fabry female groups in both children and adult cohorts. Children with the late-onset p.N215S mutation had normal urinary levels of Gb3, and lyso-Gb3 but abnormal levels of related analogues. In this study, Fabry males and most Fabry females would have been diagnosed using the urinary lyso-Gb3/related analogue profile. Copyright © 2014 Elsevier B.V. All rights reserved.

Opportunities and Challenges of Proteomics in Pediatric Patients: Circulating Biomarkers After Hematopoietic Stem Cell Transplantation As a Successful Example

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Paczesny, Sophie; Duncan, Christine; Jacobsohn, David; Krance, Robert; Leung, Kathryn; Carpenter, Paul; Bollard, Catherine; Renbarger, Jamie; Cooke, Kenneth

2015-01-01

Biomarkers have the potential to improve diagnosis and prognosis, facilitate targeted treatment, and reduce health care costs. Thus, there is great hope that biomarkers will be integrated in all clinical decisions in the near future. A decade ago, the biomarker field was launched with great enthusiasm because mass spectrometry revealed that blood contains a rich library of candidate biomarkers. However, biomarker research has not yet delivered on its promise due to several limitations: (i) improper sample handling and tracking as well as limited sample availability in the pediatric population, (ii) omission of appropriate controls in original study designs, (iii) lability and low abundance of interesting biomarkers in blood, and (iv) the inability to mechanistically tie biomarker presence to disease biology. These limitations as well as successful strategies to overcome them are discussed in this review. Several advances in biomarker discovery and validation have been made in hematopoietic stem cell transplantation, the current most effective tumor immunotherapy, and these could serve as examples for other conditions. This review provides fresh optimism that biomarkers clinically relevant in pediatrics are closer to being realized based on: (i) a uniform protocol for low-volume blood collection and preservation, (ii) inclusion of well-controlled independent cohorts, (iii) novel technologies and instrumentation with low analytical sensitivity, and (iv) integrated animal models for exploring potential biomarkers and targeted therapies. PMID:25196024

Recommendations for adaptation and validation of commercial kits for biomarker quantification in drug development.

Science.gov (United States)

Khan, Masood U; Bowsher, Ronald R; Cameron, Mark; Devanarayan, Viswanath; Keller, Steve; King, Lindsay; Lee, Jean; Morimoto, Alyssa; Rhyne, Paul; Stephen, Laurie; Wu, Yuling; Wyant, Timothy; Lachno, D Richard

2015-01-01

Increasingly, commercial immunoassay kits are used to support drug discovery and development. Longitudinally consistent kit performance is crucial, but the degree to which kits and reagents are characterized by manufacturers is not standardized, nor are the approaches by users to adapt them and evaluate their performance through validation prior to use. These factors can negatively impact data quality. This paper offers a systematic approach to assessment, method adaptation and validation of commercial immunoassay kits for quantification of biomarkers in drug development, expanding upon previous publications and guidance. These recommendations aim to standardize and harmonize user practices, contributing to reliable biomarker data from commercial immunoassays, thus, enabling properly informed decisions during drug development.

An integrated platform for biomolecule interaction analysis

Science.gov (United States)

Jan, Chia-Ming; Tsai, Pei-I.; Chou, Shin-Ting; Lee, Shu-Sheng; Lee, Chih-Kung

2013-02-01

We developed a new metrology platform which can detect real-time changes in both a phase-interrogation mode and intensity mode of a SPR (surface plasmon resonance). We integrated a SPR and ellipsometer to a biosensor chip platform to create a new biomolecular interaction measurement mechanism. We adopted a conductive ITO (indium-tinoxide) film to the bio-sensor platform chip to expand the dynamic range and improve measurement accuracy. The thickness of the conductive film and the suitable voltage constants were found to enhance performance. A circularly polarized ellipsometry configuration was incorporated into the newly developed platform to measure the label-free interactions of recombinant human C-reactive protein (CRP) with immobilized biomolecule target monoclonal human CRP antibody at various concentrations. CRP was chosen as it is a cardiovascular risk biomarker and is an acute phase reactant as well as a specific prognostic indicator for inflammation. We found that the sensitivity of a phaseinterrogation SPR is predominantly dependent on the optimization of the sample incidence angle. The effect of the ITO layer effective index under DC and AC effects as well as an optimal modulation were experimentally performed and discussed. Our experimental results showed that the modulated dynamic range for phase detection was 10E-2 RIU based on a current effect and 10E-4 RIU based on a potential effect of which a 0.55 (°/RIU) measurement was found by angular-interrogation. The performance of our newly developed metrology platform was characterized to have a higher sensitivity and less dynamic range when compared to a traditional full-field measurement system.

Stem cells: a model for screening, discovery and development of drugs

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Kitambi SS

2011-09-01

Full Text Available Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.Keywords: therapeutics, stem cells, cancer stem cells, screening models, drug development, high throughput screening

MycoCAP - Mycobacterium Comparative Analysis Platform.

Science.gov (United States)

Choo, Siew Woh; Ang, Mia Yang; Dutta, Avirup; Tan, Shi Yang; Siow, Cheuk Chuen; Heydari, Hamed; Mutha, Naresh V R; Wee, Wei Yee; Wong, Guat Jah

2015-12-15

Mycobacterium spp. are renowned for being the causative agent of diseases like leprosy, Buruli ulcer and tuberculosis in human beings. With more and more mycobacterial genomes being sequenced, any knowledge generated from comparative genomic analysis would provide better insights into the biology, evolution, phylogeny and pathogenicity of this genus, thus helping in better management of diseases caused by Mycobacterium spp.With this motivation, we constructed MycoCAP, a new comparative analysis platform dedicated to the important genus Mycobacterium. This platform currently provides information of 2108 genome sequences of at least 55 Mycobacterium spp. A number of intuitive web-based tools have been integrated in MycoCAP particularly for comparative analysis including the PGC tool for comparison between two genomes, PathoProT for comparing the virulence genes among the Mycobacterium strains and the SuperClassification tool for the phylogenic classification of the Mycobacterium strains and a specialized classification system for strains of Mycobacterium abscessus. We hope the broad range of functions and easy-to-use tools provided in MycoCAP makes it an invaluable analysis platform to speed up the research discovery on mycobacteria for researchers. Database URL: http://mycobacterium.um.edu.my.

Health-e-Child a grid platform for european paediatrics

CERN Document Server

Skaburskas, K; Shade, J; Manset, D; Revillard, J; Rios, A; Anjum, A; Branson, A; Bloodsworth, P; Hauer, T; McClatchey, R; Rogulin, D

2008-01-01

The Health-e-Child (HeC) project [1], [2] is an EC Framework Programme 6 Integrated Project that aims to develop a grid-based integrated healthcare platform for paediatrics. Using this platform biomedical informaticians will integrate heterogeneous data and perform epidemiological studies across Europe. The resulting Grid enabled biomedical information platform will be supported by robust search, optimization and matching techniques for information collected in hospitals across Europe. In particular, paediatricians will be provided with decision support, knowledge discovery and disease modelling applications that will access data in hospitals in the UK, Italy and France, integrated via the Grid. For economy of scale, reusability, extensibility, and maintainability, HeC is being developed on top of an EGEE/gLite [3] based infrastructure that provides all the common data and computation management services required by the applications. This paper discusses some of the major challenges in bio-medical data integr...

A GMM-IG framework for selecting genes as expression panel biomarkers.

Science.gov (United States)

Wang, Mingyi; Chen, Jake Y

2010-01-01

The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker genes from publicly available functional genomics studies. We developed a new framework, Gaussian Mixture Modeling-Coupled Information Gain (GMM-IG). In this framework, we first apply a two-component Gaussian mixture model (GMM) to estimate the conditional probability distributions of gene expression data between two different types of samples, for example, normal versus cancer. An expectation-maximization algorithm is then used to estimate the maximum likelihood parameters of a mixture of two Gaussian models in the feature space and determine the underlying expression levels of genes. Gene expression results from different studies are discretized, based on GMM estimations and then unified. Significantly differentially-expressed genes are filtered and assessed with information gain (IG) measures. DNA microarray experimental data for lung cancers from three different prior studies was processed using the new GMM-IG method. Target gene markers from a gene expression panel were selected and compared with several conventional computational biomarker data analysis methods. GMM-IG showed consistently high accuracy for several classification assessments. A high reproducibility of gene selection results was also determined from statistical validations. Our study shows that the GMM-IG framework can overcome poor reliability issues from single-study DNA microarray experiment while maintaining high accuracies by combining true signals from multiple studies. We present a conceptually simple framework that enables reliable integration of true differential gene expression signals from multiple

Building a Community-Focused Data Platform

Energy Technology Data Exchange (ETDEWEB)

Turk, Matthew J. [Univ. of Illinois, Urbana-Champaign, IL (United States). National Center for Supercomputing Applications (NCSA); Warren, Michael S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Skillman, Samuel W. [Stanford Univ., CA (United States)

2014-06-10

The National Data Service (NDS) presents a broad-reaching vision of integrating discovery, publishing, and sharing of data. Metcalfe's Law states that the value of a network increases with the number of connections in the network, not the number of nodes. The NDS must develop and steward connections not only between data, as is in its vision statement, but between individuals as well. We propose that this be accomplished by explicitly developing it as a platform on which data exploration can be constructed by individual researchers.

Multiple inflammatory biomarker detection in a prospective cohort study: a cross-validation between well-established single-biomarker techniques and electrochemiluminescense-based multi-array platform

NARCIS (Netherlands)

Bussel, van B.C.T.; Ferreira, I.; Waarenburg, M.P.H.; Greevenbroek, van M.M.J.; Kallen, van der C.J.H.; Henry, R.M.A.; Feskens, E.J.M.; Stehouwer, C.D.A.; Schalkwijk, C.G.

2013-01-01

Background - In terms of time, effort and quality, multiplex technology is an attractive alternative for well-established single-biomarker measurements in clinical studies. However, limited data comparing these methods are available. Methods - We measured, in a large ongoing cohort study (n = 574),

Screening Preoperative Peptide Biomarkers for Predicting Postoperative Myocardial Infarction after Coronary Artery Bypass Grafting

Science.gov (United States)

Jiang, Zhibin; Hu, Ping; Liu, Jianxin; Wang, Dianjun; Jin, Longyu; Hong, Chao

2014-01-01

Postoperative myocardial infarction (PMI) is one of the most serious complications of cardiac surgeries. No preoperative biomarker is currently available for predicting PMI after cardiac surgeries. In the present study, we used a phage display peptide library to screen potential preoperative peptide biomarkers for predicting PMI after coronary artery bypass grafting (CABG) surgery. Twenty patients who developed PMI after CABG and 20 age-, sex-, and body mass index-matched patients without PMI after CABG were enrolled as a discovery cohort. Another 50 patients who developed PMI after CABG and 50 randomly selected patients without PMI after CABG were enrolled as a validation cohort to validate the potential peptide biomarkers identified in the discovery cohort. Fifty randomly selected healthy volunteers were also enrolled in the validation phase as a healthy control group. In the discovery/screening phase, 17 out of 20 randomly selected phage clones exhibited specific reaction with purified sera IgG from the PMI group, among which 11 came from the same phage clone with inserted peptide sequence GVIMVIAVSCVF (named PMI-1). In the validation phase, phage ELISA showed that serum IgG from 90% of patients in the PMI group had a positive reaction with PMI-1; in contrast, only 14% and 6% of patients in the non-PMI group and the healthy control group had a positive reaction with PMI-1, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the PMI-1 phage clone to preoperatively identify patients who would develop PMI after CABG were 90.0%, 86.0%, 86.5, 89.5% and 88.0%, respectively. The absorbance value of the PMI-1 phage clone showed statistically significant correlation with the peak postoperative serum cardiac troponin I level (r = 0.349, p = 0.012) in the PMI group. In conclusion, we for the first time identified a mimic peptide (PMI-1) with high validity in preoperative prediction of PMI after CABG. PMID

G-DOC Plus - an integrative bioinformatics platform for precision medicine.

Science.gov (United States)

Bhuvaneshwar, Krithika; Belouali, Anas; Singh, Varun; Johnson, Robert M; Song, Lei; Alaoui, Adil; Harris, Michael A; Clarke, Robert; Weiner, Louis M; Gusev, Yuriy; Madhavan, Subha

2016-04-30

G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability. G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available

Nonylphenol Toxicity Evaluation and Discovery of Biomarkers in Rat Urine by a Metabolomics Strategy through HPLC-QTOF-MS

Directory of Open Access Journals (Sweden)

Yan-Xin Zhang

2016-05-01

Full Text Available Nonylphenol (NP was quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS in the urine and plasma of rats treated with 0, 50, and 250 mg/kg/day of NP for four consecutive days. A urinary metabolomic strategy was originally implemented by high performance liquid chromatography time of flight mass spectrometry (HPLC-QTOF-MS to explore the toxicological effects of NP and determine the overall alterations in the metabolite profiles so as to find potential biomarkers. It is essential to point out that from the observation, the metabolic data were clearly clustered and separated for the three groups. To further identify differentiated metabolites, multivariate analysis, including principal component analysis (PCA, orthogonal partial least-squares discriminant analysis (OPLS-DA, high-resolution MS/MS analysis, as well as searches of Metlin and Massbank databases, were conducted on a series of metabolites between the control and dose groups. Finally, five metabolites, including glycine, glycerophosphocholine, 5-hydroxytryptamine, malonaldehyde (showing an upward trend, and tryptophan (showing a downward trend, were identified as the potential urinary biomarkers of NP-induced toxicity. In order to validate the reliability of these potential biomarkers, an independent validation was performed by using the multiple reaction monitoring (MRM-based targeted approach. The oxidative stress reflected by urinary 8-oxo-deoxyguanosine (8-oxodG levels was elevated in individuals highly exposed to NP, supporting the hypothesis that mitochondrial dysfunction was a result of xenoestrogen accumulation. This study reveals a promising approach to find biomarkers to assist researchers in monitoring NP.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

Science.gov (United States)

Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org. © The Author(s) 2016. Published by Oxford University Press.

Automated microfluidic devices integrating solid-phase extraction, fluorescent labeling, and microchip electrophoresis for preterm birth biomarker analysis.

Science.gov (United States)

Sahore, Vishal; Sonker, Mukul; Nielsen, Anna V; Knob, Radim; Kumar, Suresh; Woolley, Adam T

2018-01-01

We have developed multichannel integrated microfluidic devices for automated preconcentration, labeling, purification, and separation of preterm birth (PTB) biomarkers. We fabricated multilayer poly(dimethylsiloxane)-cyclic olefin copolymer (PDMS-COC) devices that perform solid-phase extraction (SPE) and microchip electrophoresis (μCE) for automated PTB biomarker analysis. The PDMS control layer had a peristaltic pump and pneumatic valves for flow control, while the PDMS fluidic layer had five input reservoirs connected to microchannels and a μCE system. The COC layers had a reversed-phase octyl methacrylate porous polymer monolith for SPE and fluorescent labeling of PTB biomarkers. We determined μCE conditions for two PTB biomarkers, ferritin (Fer) and corticotropin-releasing factor (CRF). We used these integrated microfluidic devices to preconcentrate and purify off-chip-labeled Fer and CRF in an automated fashion. Finally, we performed a fully automated on-chip analysis of unlabeled PTB biomarkers, involving SPE, labeling, and μCE separation with 1 h total analysis time. These integrated systems have strong potential to be combined with upstream immunoaffinity extraction, offering a compact sample-to-answer biomarker analysis platform. Graphical abstract Pressure-actuated integrated microfluidic devices have been developed for automated solid-phase extraction, fluorescent labeling, and microchip electrophoresis of preterm birth biomarkers.

Sparse multi-block PLSR for biomarker discovery when integrating data from LC-MS and NMR metabolomics

DEFF Research Database (Denmark)

Karaman, Ibrahim; Nørskov, Natalja; Yde, Christian Clement

2015-01-01

The objective of this study was to implement a multivariate method which analyzes multi-block metabolomics data and performs variable selection in order to discover potential biomarkers, simultaneously. We call this method sparse multi-block partial least squares regression (Sparse MBPLSR). To ac...

Ratiometric fluorescence transduction by hybridization after isothermal amplification for determination of zeptomole quantities of oligonucleotide biomarkers with a paper-based platform and camera-based detection

Energy Technology Data Exchange (ETDEWEB)

Noor, M. Omair; Hrovat, David [Chemical Sensors Group, Department of Chemical and Physical Sciences, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6 (Canada); Moazami-Goudarzi, Maryam [Department of Cell and Systems Biology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6 (Canada); Espie, George S. [Department of Cell and Systems Biology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6 (Canada); Department of Biology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6 (Canada); Krull, Ulrich J., E-mail: ulrich.krull@utoronto.ca [Chemical Sensors Group, Department of Chemical and Physical Sciences, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6 (Canada)

2015-07-23

Highlights: • Solid-phase QD-FRET transduction of isothermal tHDA amplicons on paper substrates. • Ratiometric QD-FRET transduction improves assay precision and lowers the detection limit. • Zeptomole detection limit by an iPad camera after isothermal amplification. • Tunable assay sensitivity by immobilizing different amounts of QD–probe bioconjugates. - Abstract: Paper is a promising platform for the development of decentralized diagnostic assays owing to the low cost and ease of use of paper-based analytical devices (PADs). It can be challenging to detect on PADs very low concentrations of nucleic acid biomarkers of lengths as used in clinical assays. Herein we report the use of thermophilic helicase-dependent amplification (tHDA) in combination with a paper-based platform for fluorescence detection of probe-target hybridization. Paper substrates were patterned using wax printing. The cellulosic fibers were chemically derivatized with imidazole groups for the assembly of the transduction interface that consisted of immobilized quantum dot (QD)–probe oligonucleotide conjugates. Green-emitting QDs (gQDs) served as donors with Cy3 as the acceptor dye in a fluorescence resonance energy transfer (FRET)-based transduction method. After probe-target hybridization, a further hybridization event with a reporter sequence brought the Cy3 acceptor dye in close proximity to the surface of immobilized gQDs, triggering a FRET sensitized emission that served as an analytical signal. Ratiometric detection was evaluated using both an epifluorescence microscope and a low-cost iPad camera as detectors. Addition of the tHDA method for target amplification to produce sequences of ∼100 base length allowed for the detection of zmol quantities of nucleic acid targets using the two detection platforms. The ratiometric QD-FRET transduction method not only offered improved assay precision, but also lowered the limit of detection of the assay when compared with the non

Ratiometric fluorescence transduction by hybridization after isothermal amplification for determination of zeptomole quantities of oligonucleotide biomarkers with a paper-based platform and camera-based detection

International Nuclear Information System (INIS)

Noor, M. Omair; Hrovat, David; Moazami-Goudarzi, Maryam; Espie, George S.; Krull, Ulrich J.

2015-01-01

Highlights: • Solid-phase QD-FRET transduction of isothermal tHDA amplicons on paper substrates. • Ratiometric QD-FRET transduction improves assay precision and lowers the detection limit. • Zeptomole detection limit by an iPad camera after isothermal amplification. • Tunable assay sensitivity by immobilizing different amounts of QD–probe bioconjugates. - Abstract: Paper is a promising platform for the development of decentralized diagnostic assays owing to the low cost and ease of use of paper-based analytical devices (PADs). It can be challenging to detect on PADs very low concentrations of nucleic acid biomarkers of lengths as used in clinical assays. Herein we report the use of thermophilic helicase-dependent amplification (tHDA) in combination with a paper-based platform for fluorescence detection of probe-target hybridization. Paper substrates were patterned using wax printing. The cellulosic fibers were chemically derivatized with imidazole groups for the assembly of the transduction interface that consisted of immobilized quantum dot (QD)–probe oligonucleotide conjugates. Green-emitting QDs (gQDs) served as donors with Cy3 as the acceptor dye in a fluorescence resonance energy transfer (FRET)-based transduction method. After probe-target hybridization, a further hybridization event with a reporter sequence brought the Cy3 acceptor dye in close proximity to the surface of immobilized gQDs, triggering a FRET sensitized emission that served as an analytical signal. Ratiometric detection was evaluated using both an epifluorescence microscope and a low-cost iPad camera as detectors. Addition of the tHDA method for target amplification to produce sequences of ∼100 base length allowed for the detection of zmol quantities of nucleic acid targets using the two detection platforms. The ratiometric QD-FRET transduction method not only offered improved assay precision, but also lowered the limit of detection of the assay when compared with the non

Cloud Based Earth Observation Data Exploitation Platforms

Science.gov (United States)

Romeo, A.; Pinto, S.; Loekken, S.; Marin, A.

2017-12-01

In the last few years data produced daily by several private and public Earth Observation (EO) satellites reached the order of tens of Terabytes, representing for scientists and commercial application developers both a big opportunity for their exploitation and a challenge for their management. New IT technologies, such as Big Data and cloud computing, enable the creation of web-accessible data exploitation platforms, which offer to scientists and application developers the means to access and use EO data in a quick and cost effective way. RHEA Group is particularly active in this sector, supporting the European Space Agency (ESA) in the Exploitation Platforms (EP) initiative, developing technology to build multi cloud platforms for the processing and analysis of Earth Observation data, and collaborating with larger European initiatives such as the European Plate Observing System (EPOS) and the European Open Science Cloud (EOSC). An EP is a virtual workspace, providing a user community with access to (i) large volume of data, (ii) algorithm development and integration environment, (iii) processing software and services (e.g. toolboxes, visualization routines), (iv) computing resources, (v) collaboration tools (e.g. forums, wiki, etc.). When an EP is dedicated to a specific Theme, it becomes a Thematic Exploitation Platform (TEP). Currently, ESA has seven TEPs in a pre-operational phase dedicated to geo-hazards monitoring and prevention, costal zones, forestry areas, hydrology, polar regions, urban areas and food security. On the technology development side, solutions like the multi cloud EO data processing platform provides the technology to integrate ICT resources and EO data from different vendors in a single platform. In particular it offers (i) Multi-cloud data discovery, (ii) Multi-cloud data management and access and (iii) Multi-cloud application deployment. This platform has been demonstrated with the EGI Federated Cloud, Innovation Platform Testbed Poland

Autoantibody profiling on human proteome microarray for biomarker discovery in cerebrospinal fluid and sera of neuropsychiatric lupus.

Directory of Open Access Journals (Sweden)

Chaojun Hu

Full Text Available Autoantibodies in cerebrospinal fluid (CSF from patients with neuropsychiatric systemic lupus erythematosus (NPSLE may be potential biomarkers for prediction, diagnosis, or prognosis of NPSLE. We used a human proteome microarray with~17,000 unique full-length human proteins to investigate autoantibodies associated with NPSLE. Twenty-nine CSF specimens from 12 NPSLE, 7 non-NPSLE, and 10 control (non-systemic lupus erythematosuspatients were screened for NPSLE-associated autoantibodies with proteome microarrays. A focused autoantigen microarray of candidate NPSLE autoantigens was applied to profile a larger cohort of CSF with patient-matched sera. We identified 137 autoantigens associated with NPSLE. Ingenuity Pathway Analysis revealed that these autoantigens were enriched for functions involved in neurological diseases (score = 43.Anti-proliferating cell nuclear antigen (PCNA was found in the CSF of NPSLE and non-NPSLE patients. The positive rates of 4 autoantibodies in CSF specimens were significantly different between the SLE (i.e., NPSLE and non-NPSLE and control groups: anti-ribosomal protein RPLP0, anti-RPLP1, anti-RPLP2, and anti-TROVE2 (also known as anti-Ro/SS-A. The positive rate for anti-SS-A associated with NPSLE was higher than that for non-NPSLE (31.11% cf. 10.71%; P = 0.045.Further analysis showed that anti-SS-A in CSF specimens was related to neuropsychiatric syndromes of the central nervous system in SLE (P = 0.009. Analysis with Spearman's rank correlation coefficient indicated that the titers of anti-RPLP2 and anti-SS-A in paired CSF and serum specimens significantly correlated. Human proteome microarrays offer a powerful platform to discover novel autoantibodies in CSF samples. Anti-SS-A autoantibodies may be potential CSF markers for NPSLE.

Microbial metabolomics in open microscale platforms

Science.gov (United States)

Barkal, Layla J.; Theberge, Ashleigh B.; Guo, Chun-Jun; Spraker, Joe; Rappert, Lucas; Berthier, Jean; Brakke, Kenneth A.; Wang, Clay C. C.; Beebe, David J.; Keller, Nancy P.; Berthier, Erwin

2016-01-01

The microbial secondary metabolome encompasses great synthetic diversity, empowering microbes to tune their chemical responses to changing microenvironments. Traditional metabolomics methods are ill-equipped to probe a wide variety of environments or environmental dynamics. Here we introduce a class of microscale culture platforms to analyse chemical diversity of fungal and bacterial secondary metabolomes. By leveraging stable biphasic interfaces to integrate microculture with small molecule isolation via liquid–liquid extraction, we enable metabolomics-scale analysis using mass spectrometry. This platform facilitates exploration of culture microenvironments (including rare media typically inaccessible using established methods), unusual organic solvents for metabolite isolation and microbial mutants. Utilizing Aspergillus, a fungal genus known for its rich secondary metabolism, we characterize the effects of culture geometry and growth matrix on secondary metabolism, highlighting the potential use of microscale systems to unlock unknown or cryptic secondary metabolites for natural products discovery. Finally, we demonstrate the potential for this class of microfluidic systems to study interkingdom communication between fungi and bacteria. PMID:26842393

Adipokines: a treasure trove for the discovery of biomarkers for metabolic disorders.

Science.gov (United States)

Lehr, Stefan; Hartwig, Sonja; Sell, Henrike

2012-01-01

Adipose tissue is a major endocrine organ, releasing signaling and mediator proteins, termed adipokines, via which adipose tissue communicates with other organs. Expansion of adipose tissue in obesity alters adipokine secretion which may contribute to the development of metabolic diseases. Consequently, this correlation has emphasized the importance to further characterize the adipocyte secretion profile, and several attempts have been made to characterize the complex nature of the adipose tissue secretome by utilizing diverse proteomic profiling approaches. Although the entirety of human adipokines is still incompletely characterized, to date more than 600 potentially secretory proteins were identified providing a rich source to identify putative novel biomarkers associated with metabolic diseases. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Novel automated blood separations validate whole cell biomarkers.

Directory of Open Access Journals (Sweden)

Douglas E Burger

Full Text Available Progress in clinical trials in infectious disease, autoimmunity, and cancer is stymied by a dearth of successful whole cell biomarkers for peripheral blood lymphocytes (PBLs. Successful biomarkers could help to track drug effects at early time points in clinical trials to prevent costly trial failures late in development. One major obstacle is the inaccuracy of Ficoll density centrifugation, the decades-old method of separating PBLs from the abundant red blood cells (RBCs of fresh blood samples.To replace the Ficoll method, we developed and studied a novel blood-based magnetic separation method. The magnetic method strikingly surpassed Ficoll in viability, purity and yield of PBLs. To reduce labor, we developed an automated platform and compared two magnet configurations for cell separations. These more accurate and labor-saving magnet configurations allowed the lymphocytes to be tested in bioassays for rare antigen-specific T cells. The automated method succeeded at identifying 79% of patients with the rare PBLs of interest as compared with Ficoll's uniform failure. We validated improved upfront blood processing and show accurate detection of rare antigen-specific lymphocytes.Improving, automating and standardizing lymphocyte detections from whole blood may facilitate development of new cell-based biomarkers for human diseases. Improved upfront blood processes may lead to broad improvements in monitoring early trial outcome measurements in human clinical trials.

Novel automated blood separations validate whole cell biomarkers.

Science.gov (United States)

Burger, Douglas E; Wang, Limei; Ban, Liqin; Okubo, Yoshiaki; Kühtreiber, Willem M; Leichliter, Ashley K; Faustman, Denise L

2011-01-01

Progress in clinical trials in infectious disease, autoimmunity, and cancer is stymied by a dearth of successful whole cell biomarkers for peripheral blood lymphocytes (PBLs). Successful biomarkers could help to track drug effects at early time points in clinical trials to prevent costly trial failures late in development. One major obstacle is the inaccuracy of Ficoll density centrifugation, the decades-old method of separating PBLs from the abundant red blood cells (RBCs) of fresh blood samples. To replace the Ficoll method, we developed and studied a novel blood-based magnetic separation method. The magnetic method strikingly surpassed Ficoll in viability, purity and yield of PBLs. To reduce labor, we developed an automated platform and compared two magnet configurations for cell separations. These more accurate and labor-saving magnet configurations allowed the lymphocytes to be tested in bioassays for rare antigen-specific T cells. The automated method succeeded at identifying 79% of patients with the rare PBLs of interest as compared with Ficoll's uniform failure. We validated improved upfront blood processing and show accurate detection of rare antigen-specific lymphocytes. Improving, automating and standardizing lymphocyte detections from whole blood may facilitate development of new cell-based biomarkers for human diseases. Improved upfront blood processes may lead to broad improvements in monitoring early trial outcome measurements in human clinical trials.

The role of pancreatic cancer-derived exosomes in cancer progress and their potential application as biomarkers.

Science.gov (United States)

Jin, H; Wu, Y; Tan, X

2017-08-01

Pancreatic cancer is one of the most deadly cancers, with dismal prognosis due to its poor early detection rate and high metastatic rate. Thus, elucidation of the molecular mechanisms accounting for its metastasis and discovery of competent biomarkers is required. Exosomes are multivesicular body-derived small extracellular vesicles released by various cell types that serve as important message carriers during intercellular communication. They are also known to play critical roles during cancer-genesis, cancer-related immune reactions, and metastasis. They also possess promising potential as novel biomarkers for cancer early detection. Therefore, extensive studies on pancreatic cancer-derived exosomes are currently being performed because they hold the promising potential of elevating the overall survival rate of patients with pancreatic cancer. In the present review, we focus on the role of exosomes in pancreatic cancer-related immune reactions, metastasis, and complications, and on their potential application as pancreatic cancer biomarkers.

A High-Throughput Screening Platform of Microbial Natural Products for the Discovery of Molecules with Antibiofilm Properties against Salmonella

Science.gov (United States)

Paytubi, Sonia; de La Cruz, Mercedes; Tormo, Jose R.; Martín, Jesús; González, Ignacio; González-Menendez, Victor; Genilloud, Olga; Reyes, Fernando; Vicente, Francisca; Madrid, Cristina; Balsalobre, Carlos

2017-01-01

In this report, we describe a High-Throughput Screening (HTS) to identify compounds that inhibit biofilm formation or cause the disintegration of an already formed biofilm using the Salmonella Enteritidis 3934 strain. Initially, we developed a new methodology for growing Salmonella biofilms suitable for HTS platforms. The biomass associated with biofilm at the solid-liquid interface was quantified by staining both with resazurin and crystal violet, to detect living cells and total biofilm mass, respectively. For a pilot project, a subset of 1120 extracts from the Fundación MEDINA's collection was examined to identify molecules with antibiofilm activity. This is the first validated HTS assay of microbial natural product extracts which allows for the detection of four types of activities which are not mutually exclusive: inhibition of biofilm formation, detachment of the preformed biofilm and antimicrobial activity against planktonic cells or biofilm embedded cells. Currently, several extracts have been selected for further fractionation and purification of the active compounds. In one of the natural extracts patulin has been identified as a potent molecule with antimicrobial activity against both, planktonic cells and cells within the biofilm. These findings provide a proof of concept that the developed HTS can lead to the discovery of new natural compounds with antibiofilm activity against Salmonella and its possible use as an alternative to antimicrobial therapies and traditional disinfectants. PMID:28303128

Definitions and validation criteria for biomarkers and surrogate endpoints: development and testing of a quantitative hierarchical levels of evidence schema

DEFF Research Database (Denmark)

Lassere, Marissa N; Johnson, Kent R; Boers, Maarten

2007-01-01

endpoints, and leading indicators, a quantitative surrogate validation schema was developed and subsequently evaluated at a stakeholder workshop. RESULTS: The search identified several classification schema and definitions. Components of these were incorporated into a new quantitative surrogate validation...... level of evidence schema that evaluates biomarkers along 4 domains: Target, Study Design, Statistical Strength, and Penalties. Scores derived from 3 domains the Target that the marker is being substituted for, the Design of the (best) evidence, and the Statistical strength are additive. Penalties...... of the National Institutes of Health definitions of biomarker, surrogate endpoint, and clinical endpoint was useful. CONCLUSION: Further development and application of this schema provides incentives and guidance for effective biomarker and surrogate endpoint research, and more efficient drug discovery...

Simultaneous capture and sequential detection of two malarial biomarkers on magnetic microparticles.

Science.gov (United States)

Markwalter, Christine F; Ricks, Keersten M; Bitting, Anna L; Mudenda, Lwiindi; Wright, David W

2016-12-01

We have developed a rapid magnetic microparticle-based detection strategy for malarial biomarkers Plasmodium lactate dehydrogenase (pLDH) and Plasmodium falciparum histidine-rich protein II (PfHRPII). In this assay, magnetic particles functionalized with antibodies specific for pLDH and PfHRPII as well as detection antibodies with distinct enzymes for each biomarker are added to parasitized lysed blood samples. Sandwich complexes for pLDH and PfHRPII form on the surface of the magnetic beads, which are washed and sequentially re-suspended in detection enzyme substrate for each antigen. The developed simultaneous capture and sequential detection (SCSD) assay detects both biomarkers in samples as low as 2.0parasites/µl, an order of magnitude below commercially available ELISA kits, has a total incubation time of 35min, and was found to be reproducible between users over time. This assay provides a simple and efficient alternative to traditional 96-well plate ELISAs, which take 5-8h to complete and are limited to one analyte. Further, the modularity of the magnetic bead-based SCSD ELISA format could serve as a platform for application to other diseases for which multi-biomarker detection is advantageous. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome

International Nuclear Information System (INIS)

Mannello, Ferdinando; Ligi, Daniela

2013-01-01

One of the major goals in cancer research is to find and evaluate the early presence of biomarkers in human fluids and tissues. To resolve the complex cell heterogeneity of a tumor mass, it will be useful to characterize the intricate biomolecular composition of tumor microenvironment (the so called cancer secretome), validating secreted proteins as early biomarkers of cancer initiation and progression. This approach is not broadly applicable because of the paucity of well validated and FDA-approved biomarkers and because most of the candidate biomarkers are mainly organ-specific rather than tumor-specific. For these reasons, there is an urgent need to identify and validate a panel of biomarker combinations for early detection of human tumors. This is especially important for breast cancer, the cancer spread most worldwide among women. It is well known that patients with early diagnosed breast cancer live longer, require less extensive treatment and fare better than patients with more aggressive and/or advanced disease. In the frame of searching breast cancer biomarkers (especially using nipple aspirate fluid mirroring breast microenvironment), studies have highlighted an optimal combination of well-known biomarkers: uPA + PAI-1 + TF. When individually investigated they did not show perfect accuracy in predicting the presence of breast cancer, whereas the triple combination has been demonstrated to be highly predictive of pre-cancer and/or cancerous conditions, approaching 97-100% accuracy. Despite the heterogeneous composition of breast cancer and the difficulties to find specific breast cancer biomolecules, the noninvasive analysis of the nipple aspirate fluid secretome may significantly improve the discovery of promising biomarkers, helping also the differentiation among benign and invasive breast diseases, opening new frontiers in early oncoproteomics

Authentic Astronomical Discovery in Planetariums: Bringing Data to Domes

Science.gov (United States)

Wyatt, Ryan Jason; Subbarao, Mark; Christensen, Lars; Emmons, Ben; Hurt, Robert

2018-01-01

Planetariums offer a unique opportunity to disseminate astronomical discoveries using data visualization at all levels of complexity: the technical infrastructure to display data and a sizeable cohort of enthusiastic educators to interpret results. “Data to Dome” is an initiative the International Planetarium Society to develop our community’s capacity to integrate data in fulldome planetarium systems—including via open source software platforms such as WorldWide Telescope and OpenSpace. We are cultivating a network of planetarium professionals who integrate data into their presentations and share their content with others. Furthermore, we propose to shorten the delay between discovery and dissemination in planetariums. Currently, the “latest science” is often presented days or weeks after discoveries are announced, and we can shorten this to hours or even minutes. The Data2Dome (D2D) initiative, led by the European Southern Observatory, proposes technical infrastructure and data standards that will streamline content flow from research institutions to planetariums, offering audiences a unique opportunity to access to the latest astronomical data in near real time.

Strategies, models and biomarkers in experimental non-alcoholic fatty liver disease research

Science.gov (United States)

Willebrords, Joost; Pereira, Isabel Veloso Alves; Maes, Michaël; Yanguas, Sara Crespo; Colle, Isabelle; Van Den Bossche, Bert; Da silva, Tereza Cristina; Oliveira, Cláudia P; Andraus, Wellington; Alves, Venâncio Avancini Ferreira; Cogliati, Bruno; Vinken, Mathieu

2015-01-01

Non-alcoholic fatty liver disease encompasses a spectrum of liver diseases, including simple steatosis, steatohepatitis, liver fibrosis and cirrhosis and hepatocellular carcinoma. Non-alcoholic fatty liver disease is currently the most dominant chronic liver disease in Western countries due to the fact that hepatic steatosis is associated with insulin resistance, type 2 diabetes mellitus, obesity, metabolic syndrome and drug-induced injury. A variety of chemicals, mainly drugs, and diets is known to cause hepatic steatosis in humans and rodents. Experimental non-alcoholic fatty liver disease models rely on the application of a diet or the administration of drugs to laboratory animals or the exposure of hepatic cell lines to these drugs. More recently, genetically modified rodents or zebrafish have been introduced as non-alcoholic fatty liver disease models. Considerable interest now lies in the discovery and development of novel non-invasive biomarkers of non-alcoholic fatty liver disease, with specific focus on hepatic steatosis. Experimental diagnostic biomarkers of non-alcoholic fatty liver disease, such as (epi)genetic parameters and ‘-omics’-based read-outs are still in their infancy, but show great promise. . In this paper, the array of tools and models for the study of liver steatosis is discussed. Furthermore, the current state-of-art regarding experimental biomarkers such as epigenetic, genetic, transcriptomic, proteomic and metabonomic biomarkers will be reviewed. PMID:26073454

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry.

Science.gov (United States)

Zhao, Ying-Yong; Cheng, Xian-Long; Vaziri, Nosratola D; Liu, Shuman; Lin, Rui-Chao

2014-10-01

Metabonomics is a powerful and promising analytic tool that allows assessment of global low-molecular-weight metabolites in biological systems. It has a great potential for identifying useful biomarkers for early diagnosis, prognosis and assessment of therapeutic interventions in clinical practice. The aim of this review is to provide a brief summary of the recent advances in UPLC-based metabonomic approach for biomarker discovery in a variety of diseases, and to discuss their significance in clinical chemistry. All the available information on UPLC-based metabonomic applications for discovering biomarkers of diseases were collected via a library and electronic search (using Web of Science, Pubmed, ScienceDirect, Springer, Google Scholar, etc.). Metabonomics has been used in clinical chemistry to identify and evaluate potential biomarkers and therapeutic targets in various diseases affecting the liver (hepatocarcinoma and liver cirrhosis), lung (lung cancer and pneumonia), gastrointestinal tract (colorectal cancer) and urogenital tract (prostate cancer, ovarian cancer and chronic kidney disease), as well as metabolic diseases (diabetes) and neuropsychiatric disorders (Alzheimer's disease and schizophrenia), etc. The information provided highlights the potential value of determination of endogenous low-molecular-weight metabolites and the advantages and potential drawbacks of the application of UPLC-based metabonomics in clinical setting. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Multifunctional Dendrimer-templated Antibody Presentation on Biosensor Surfaces for Improved Biomarker Detection.

Science.gov (United States)

Han, Hye Jung; Kannan, Rangaramanujam M; Wang, Sunxi; Mao, Guangzhao; Kusanovic, Juan Pedro; Romero, Roberto

2010-02-08

Dendrimers, with their well-defined globular shape and a high density of functional groups, are ideal nanoscale materials for templating sensor surfaces. This work exploits dendrimers as a versatile platform for capturing biomarkers with improved sensitivity and specificity. Synthesis, characterization, fabrication, and functional validation of the dendrimer-based assay platform are described. Bifunctional hydroxyl/thiol functionalized G4-polyamidoamine (PAMAM) dendrimer is synthesized and immobilized on to the polyethylene-glycol (PEG)-functionalized assay plate by coupling PEG-maleimide and dendrimer thiol groups. Simultaneously, part of the dendrimer thiol groups are converted to hydrazide functionalities. The resulting dendrimer-modified surface is coupled to the capture antibody in the Fc region of the oxidized antibody. This preserves the orientation flexibility of the antigen binding region (Fv) of the antibody. To validate the approach, the fabricated plates are further used as a solid phase for developing a sandwich type ELISA to detect IL-6 and IL-1β, important biomarkers for early stages of chorioamnionitis. The dendrimer-modified plate provides assays with significantly enhanced sensitivity, lower nonspecific adsorption, and a detection limit of 0.13 pg ml -1 for IL-6 luminol detection and 1.15 pg ml -1 for IL-1β TMB detection, which are significantly better than those for the traditional ELISA. The assays were validated in human serum samples from normal (non-pregnant) woman and pregnant women with pyelonephritis. The specificity and the improved sensitivity of the dendrimer-based capture strategy could have significant implications for the detection of a wide range of cytokines and biomarkers since the capture strategy could be applied to multiplex microbead assays, conductometric immunosensors and field effect biosensors.

Comparison of peripheral and central schizophrenia biomarker profiles.

Directory of Open Access Journals (Sweden)

Laura W Harris

Full Text Available We have recently shown that a molecular biomarker signature comprised of inflammatory, hormonal and growth factors occurs in the blood serum from first onset schizophrenia patients. Here, we use the same platform to investigate post mortem brain tissue (Brodmann area 10 from schizophrenia patients who were mainly chronically ill and drug treated. Twenty-one analytes are differentially expressed in post-mortem brain tissue. Comparison with our previous mRNA profiling studies of the same patient samples in another frontal cortical area showed that 9 of these molecules were also altered at the transcriptional level. Furthermore, 9 of the molecules were also altered in serum from living first onset schizophrenia patients compared to controls. We propose a model in which the brain and periphery are coordinated through hormones and other regulatory molecules released into the blood via the diffuse neuroendocrine system. These findings provide further evidence for the systemic nature of schizophrenia and give added validity to the concept that schizophrenia can be investigated through studies of blood-based biomarkers.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer.

LENUS (Irish Health Repository)

Tonry, Claire L

2016-07-18

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and\\/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Directory of Open Access Journals (Sweden)

Claire L. Tonry

2016-07-01

Full Text Available Prostate Cancer (PCa is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i might best receive no treatment (active surveillance of the disease; (ii would benefit from existing treatments; or (iii those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Sex-specific associations between particulate matter exposure and gene expression in independent discovery and validation cohorts of middle-aged men and women

DEFF Research Database (Denmark)

Vrijens, Karen; Winckelmans, Ellen; Tsamou, Maria

2017-01-01

Background: Particulate matter (PM) exposure leads to premature death, mainly due to respiratory and cardiovascular diseases. Objectives: Identification of transcriptomic biomarkers of air pollution exposure and effect in a healthy adult population. Methods: Microarray analyses were performed in 98...... healthy volunteers (48 men, 50 women). The expression of eight sex-specific candidate biomarker genes (significantly associated with PM10 in the discovery cohort and with a reported link to air pollution-related disease) was measured with qPCR in an independent validation cohort (75 men, 94 women...

Bio-mining for biomarkers with a multi-resolution block chain

Science.gov (United States)

Jenkins, Jeffrey; Kopf, Jarad; Tran, Binh Q.; Frenchi, Christopher; Szu, Harold

2015-05-01

In this paper, we discuss a framework for bridging the gap between security and medical Large Data Analysis (LDA) with functional- biomarkers. Unsupervised Learning for individual e-IQ & IQ relying on memory eliciting (i.e. scent, grandmother images) and IQ baseline profiles could further enhance the ability to uniquely identify and properly diagnose individuals. Sub-threshold changes in a common/probable biomedical biomarker (disorders) means that an individual remains healthy, while a martingale would require further investigation and more measurements taken to determine credibility. Empirical measurements of human actions can discover anomalies hidden in data, which point to biomarkers revealed through stimulus response. We review the approach for forming a single-user baseline having 1-d devices and a scale-invariant representation for N users each (i) having N*d(i) total devices. Such a fractal representation of human-centric data provides self-similar levels information and relationships which are useful for diagnosis and identification causality anywhere from a mental disorder to a DNA match. Biomarkers from biomedical devices offer a robust way to collect data. Biometrics could be envisioned as enhanced and personalized biomedical devices (e.g. typing fist), but used for security. As long as the devices have a shared context origin, useful information can be found by coupling the sensors. In the case of the electroencephalogram (EEG), known patterns have emerged in low frequency Delta Theta Alpha Beta-Gamma (DTAB-G) waves when an individual views a familiar picture in the visual cortex which is shown on EEGs as a sharp peak. Using brainwaves as a functional biomarker for security can lead the industry to create more secure sessions by allowing not only passwords but also visual stimuli and/or keystrokes coupled with EEG to capture and stay informed about real time user e-IQ/IQ data changes. This holistic Computer Science (CS) Knowledge Discovery in

Real-time multi-parameter cell-based analysis platform

DEFF Research Database (Denmark)

Caviglia, Claudia

biomedical diagnostic techniques, drug discovery and screening. My project focused on the further development, improvement and exploration of the EXCELL microfluidic platform with particular interest in drug kinetic monitoring and neurotransmitter detection. The aim was to perform multi-parameter real...... of protocols and procedures for performing different cellular assays. (2) Electrochemical impedance spectroscopy (EIS) applied for drug screening and drug delivery in cancer research and wound healing studies. (3) Amperometry for monitoring of neurotransmitter exocytosis, relevant in research on Parkinson...

New biomarkers for sepsis

Directory of Open Access Journals (Sweden)

Li-xin XIE

2013-01-01

Full Text Available There is a higher sepsis rate in the intensive care unit (ICU patients, which is one of the most important causes for patient death, but the sepsis lacks specific clinical manifestations. Exploring sensitive and specific molecular markers for infection that accurately reflect infection severity and prognosis is very clinically important. In this article, based on our previous study, we introduce some new biomarkers with high sensitivity and specificity for the diagnosis and predicting the prognosis and severity of sepsis. Increase of serum soluble(s triggering receptor expressed on myeloid cells-1 (sTREM-1 suggests a poor prognosis of septic patients, and changes of locus rs2234237 of sTREM-1 may be the one of important mechanisms. Additionally, urine sTREM-1 can provide an early warning of possible secondary acute kidney injury (AKI in sepsis patients. Serum sCD163 level was found to be a more important factor than procalcitonin (PCT and C-reactive protein (CRP in prognosis of sepsis, especially severe sepsis. Moreover, urine sCD163 also shows excellent performance in the diagnosis of sepsis and sepsis-associated AKI. Circulating microRNAs, such as miR-150, miR-297, miR-574-5p, miR -146a , miR-223, miR -15a and miR-16, also play important roles in the evaluation of status of septic patients. In the foreseeable future, newly-emerging technologies, including proteomics, metabonomics and trans-omics, may exert profound effects on the discovery of valuable biomarkers for sepsis.

Biomarker selection for determining bone biocompatibility of pure magnesium processed by equal channel angular pressing (ECAP) using immunohistochemistry

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Handayani, Lisa; Sulistyani, Lilies Dwi; Supriadi, Sugeng; Priosoeryanto, Bambang Pontjo; Latief, Benny Syariefsyah

2018-02-01

Since grain refinement is proved to be favorable to improve mechanical properties and corrosion resistance, a new conceptual metal forming process, equal channel angular pressing (ECAP), has been carried out on magnesium, a very promising biodegradable material in the field of oral and maxillofacial surgery. The popularity of immunohisto-chemistry (IHC) has been rising following the discovery of biomarker. In the meantime, more antibodies being produced for research have been continuously rising and becoming more varied. This review provides a conceptual framework to understand the roles of IHC on determination of bone biocompatibility to ECAP magnesium by selecting biomarker and point needed to either select or make an antibody to the target. From the review, it has been concluded that the most suitable biomarkers for biocompatibility test of bone implanted with ECAP magnesium are collagen-1, osteocalcin, smooth muscle actin, and CD68.

An integrated sample pretreatment platform for quantitative N-glycoproteome analysis with combination of on-line glycopeptide enrichment, deglycosylation and dimethyl labeling

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Weng, Yejing; Qu, Yanyan; Jiang, Hao; Wu, Qi [National Chromatographic Research and Analysis Center, Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023 (China); University of the Chinese Academy of Sciences, Beijing 100039 (China); Zhang, Lihua, E-mail: lihuazhang@dicp.ac.cn [National Chromatographic Research and Analysis Center, Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023 (China); Yuan, Huiming [National Chromatographic Research and Analysis Center, Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023 (China); Zhou, Yuan [National Chromatographic Research and Analysis Center, Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023 (China); University of the Chinese Academy of Sciences, Beijing 100039 (China); Zhang, Xiaodan; Zhang, Yukui [National Chromatographic Research and Analysis Center, Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023 (China)

2014-06-23

Highlights: • An integrated platform for quantitative N-glycoproteome analysis was established. • On-line enrichment, deglycosylation and labeling could be achieved within 160 min. • A N{sub 2}-assisted interface was applied to improve the compatibility of the platform. • The platform exhibited improved quantification accuracy, precision and throughput. - Abstract: Relative quantification of N-glycoproteomes shows great promise for the discovery of candidate biomarkers and therapeutic targets. The traditional protocol for quantitative analysis of glycoproteomes is usually off-line performed, and suffers from long sample preparation time, and the risk of sample loss or contamination due to manual manipulation. In this study, a novel integrated sample preparation platform for quantitative N-glycoproteome analysis was established, with combination of online N-glycopeptide capture by a HILIC column, sample buffer exchange by a N{sub 2}-assisted HILIC–RPLC interface, deglycosylation by a hydrophilic PNGase F immobilized enzymatic reactor (hIMER) and solid dimethyl labeling on a C18 precolumn. To evaluate the performance of such a platform, two equal aliquots of immunoglobulin G (IgG) digests were sequentially pretreated, followed by MALDI-TOF MS analysis. The signal intensity ratio of heavy/light (H/L) labeled deglycosylated peptides with the equal aliquots was 1.00 (RSD = 6.2%, n = 3), much better than those obtained by the offline protocol, with H/L ratio as 0.76 (RSD = 11.6%, n = 3). Additionally, the total on-line sample preparation time was greatly shortened to 160 min, much faster than that of offline approach (24 h). Furthermore, such an integrated pretreatment platform was successfully applied to analyze the two kinds of hepatocarcinoma ascites syngeneic cell lines with high (Hca-F) and low (Hca-P) lymph node metastasis rates. For H/L labeled Hca-P lysates with the equal aliquots, 99.6% of log 2 ratios (H/L) of quantified glycopeptides ranged from −1

Intelligent Tools for Building a Scientific Information Platform

CERN Document Server

Skonieczny, Lukasz; Rybiński, Henryk; Niezgodka, Marek

2012-01-01

This book is a selection of results obtained within one year of research performed under SYNAT - a nation-wide scientific project aiming to create an infrastructure for scientific content storage and sharing for academia, education and open knowledge society in Poland. The selection refers to the research in artificial intelligence, knowledge discovery and data mining, information retrieval and natural language processing, addressing the problems of implementing intelligent tools for building a scientific information platform. The idea of this book is based on the very successful SYNAT Project Conference and the SYNAT Workshop accompanying the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011). The papers included in this book present an overview and insight into such topics as architecture of scientific information platforms, semantic clustering, ontology-based systems, as well as, multimedia data processing.

Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids

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2014-01-01

Background Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids. Methodology A positive set of abstracts was defined by the terms ‘breast cancer’ and ‘lung cancer’ in conjunction with 14 separate ‘biofluids’ (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms ‘(biofluid) NOT breast cancer’ or ‘(biofluid) NOT lung cancer.’ More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method’s performance. Results Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI’s On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI’s Genes & Disease, NCI’s Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

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Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

The conifer biomarkers dehydroabietic and abietic acids are widespread in Cyanobacteria

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Costa, Maria Sofia; Rego, Adriana; Ramos, Vitor; Afonso, Tiago B.; Freitas, Sara; Preto, Marco; Lopes, Viviana; Vasconcelos, Vitor; Magalhães, Catarina; Leão, Pedro N.

2016-01-01

Terpenes, a large family of natural products with important applications, are commonly associated with plants and fungi. The diterpenoids dehydroabietic and abietic acids are defense metabolites abundant in resin, and are used as biomarkers for conifer plants. We report here for the first time that the two diterpenoid acids are produced by members of several genera of cyanobacteria. Dehydroabietic acid was isolated from two cyanobacterial strains and its identity was confirmed spectroscopically. One or both of the diterpenoids were detected in the cells of phylogenetically diverse cyanobacteria belonging to four cyanobacterial ‘botanical orders’, from marine, estuarine and inland environments. Dehydroabietic acid was additionally found in culture supernatants. We investigated the natural role of the two resin acids in cyanobacteria using ecologically-relevant bioassays and found that the compounds inhibited the growth of a small coccoid cyanobacterium. The unexpected discovery of dehydroabietic and abietic acids in a wide range of cyanobacteria has implications for their use as plant biomarkers. PMID:26996104

Company Profile: Selventa, Inc.

Science.gov (United States)

Fryburg, David A; Latino, Louis J; Tagliamonte, John; Kenney, Renee D; Song, Diane H; Levine, Arnold J; de Graaf, David

2012-08-01

Selventa, Inc. (MA, USA) is a biomarker discovery company that enables personalized healthcare. Originally founded as Genstruct, Inc., Selventa has undergone significant evolution from a technology-based service provider to an active partner in the development of diagnostic tests, functioning as a molecular dashboard of disease activity using a unique platform. As part of that evolution, approximately 2 years ago the company was rebranded as Selventa to reflect its new identity and mission. The contributions to biomedical research by Selventa are based on in silico, reverse-engineering methods to determine biological causality. That is, given a set of in vitro or in vivo biological observations, which biological mechanisms can explain the measured results? Facilitated by a large and carefully curated knowledge base, these in silico methods generated new insights into the mechanisms driving a disease. As Selventa's methods would enable biomarker discovery and be directly applicable to generating novel diagnostics, the scientists at Selventa have focused on the development of predictive biomarkers of response in autoimmune and oncologic diseases. Selventa is presently building a portfolio of independent, as well as partnered, biomarker projects with the intention to create diagnostic tests that predict response to therapy.

Differential Proteomics Identification of HSP90 as Potential Serum Biomarker in Hepatocellular Carcinoma by Two-dimensional Electrophoresis and Mass Spectrometry

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Yiyi Sun

2010-03-01

Full Text Available The aim of the current study is to identify the potential biomarkers involved in Hepatocellular carcinoma (HCC carcinogenesis. A comparative proteomics approach was utilized to identify the differentially expressed proteins in the serum of 10 HCC patients and 10 controls. A total of 12 significantly altered proteins were identified by mass spectrometry. Of the 12 proteins identified, HSP90 was one of the most significantly altered proteins and its over-expression in the serum of 20 HCC patients was confirmed using ELISA analysis. The observations suggest that HSP90 might be a potential biomarker for early diagnosis, prognosis, and monitoring in the therapy of HCC. This work demonstrates that a comprehensive strategy of proteomic identification combined with further validation should be adopted in the field of cancer biomarker discovery.

Integrated Microfluidic Lectin Barcode Platform for High-Performance Focused Glycomic Profiling

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Shang, Yuqin; Zeng, Yun; Zeng, Yong

2016-02-01

Protein glycosylation is one of the key processes that play essential roles in biological functions and dysfunctions. However, progress in glycomics has considerably lagged behind genomics and proteomics, due in part to the enormous challenges in analysis of glycans. Here we present a new integrated and automated microfluidic lectin barcode platform to substantially improve the performance of lectin array for focused glycomic profiling. The chip design and flow control were optimized to promote the lectin-glycan binding kinetics and speed of lectin microarray. Moreover, we established an on-chip lectin assay which employs a very simple blocking method to effectively suppress the undesired background due to lectin binding of antibodies. Using this technology, we demonstrated focused differential profiling of tissue-specific glycosylation changes of a biomarker, CA125 protein purified from ovarian cancer cell line and different tissues from ovarian cancer patients in a fast, reproducible, and high-throughput fashion. Highly sensitive CA125 detection was also demonstrated with a detection limit much lower than the clinical cutoff value for cancer diagnosis. This microfluidic platform holds the potential to integrate with sample preparation functions to construct a fully integrated “sample-to-answer” microsystem for focused differential glycomic analysis. Thus, our technology should present a powerful tool in support of rapid advance in glycobiology and glyco-biomarker development.

Discovery prepares to land after successful mission STS-95

Science.gov (United States)

1998-01-01

Orbiter Discovery prepares to land on runway 33 at the Shuttle Landing Facility. Discovery returns to Earth with its crew of seven after successfully completing mission STS-95, lasting nearly nine days and 3.6 million miles. The crew members are Mission Commander Curtis L. Brown Jr., Pilot Steven W. Lindsey, Mission Specialist Scott E. Parazynski, Mission Specialist Stephen K. Robinson, Payload Specialist John H. Glenn Jr., senator from Ohio, Mission Specialist Pedro Duque, with the European Space Agency (ESA), and Payload Specialist Chiaki Mukai, with the National Space Development Agency of Japan (NASDA). The mission included research payloads such as the Spartan solar- observing deployable spacecraft, the Hubble Space Telescope Orbital Systems Test Platform, the International Extreme Ultraviolet Hitchhiker, as well as the SPACEHAB single module with experiments on space flight and the aging process.

HIP2: An online database of human plasma proteins from healthy individuals

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Shen Changyu

2008-04-01

Full Text Available Abstract Background With the introduction of increasingly powerful mass spectrometry (MS techniques for clinical research, several recent large-scale MS proteomics studies have sought to characterize the entire human plasma proteome with a general objective for identifying thousands of proteins leaked from tissues in the circulating blood. Understanding the basic constituents, diversity, and variability of the human plasma proteome is essential to the development of sensitive molecular diagnosis and treatment monitoring solutions for future biomedical applications. Biomedical researchers today, however, do not have an integrated online resource in which they can search for plasma proteins collected from different mass spectrometry platforms, experimental protocols, and search software for healthy individuals. The lack of such a resource for comparisons has made it difficult to interpret proteomics profile changes in patients' plasma and to design protein biomarker discovery experiments. Description To aid future protein biomarker studies of disease and health from human plasma, we developed an online database, HIP2 (Healthy Human Individual's Integrated Plasma Proteome. The current version contains 12,787 protein entries linked to 86,831 peptide entries identified using different MS platforms. Conclusion This web-based database will be useful to biomedical researchers involved in biomarker discovery research. This database has been developed to be the comprehensive collection of healthy human plasma proteins, and has protein data captured in a relational database schema built to contain mappings of supporting peptide evidence from several high-quality and high-throughput mass-spectrometry (MS experimental data sets. Users can search for plasma protein/peptide annotations, peptide/protein alignments, and experimental/sample conditions with options for filter-based retrieval to achieve greater analytical power for discovery and validation.

A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma

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Liwei Zhuang

2014-01-01

Full Text Available In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC.

An open source platform for multi-scale spatially distributed simulations of microbial ecosystems

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Segre, Daniel [Boston Univ., MA (United States)

2014-08-14

The goal of this project was to develop a tool for facilitating simulation, validation and discovery of multiscale dynamical processes in microbial ecosystems. This led to the development of an open-source software platform for Computation Of Microbial Ecosystems in Time and Space (COMETS). COMETS performs spatially distributed time-dependent flux balance based simulations of microbial metabolism. Our plan involved building the software platform itself, calibrating and testing it through comparison with experimental data, and integrating simulations and experiments to address important open questions on the evolution and dynamics of cross-feeding interactions between microbial species.

Novel TIA biomarkers identified by mass spectrometry-based proteomics.

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George, Paul M; Mlynash, Michael; Adams, Christopher M; Kuo, Calvin J; Albers, Gregory W; Olivot, Jean-Marc

2015-12-01

Transient ischemic attacks remain a clinical diagnosis with significant variability between physicians. Finding reliable biomarkers to identify transient ischemic attacks would improve patient care and optimize treatment. Our aim is to identify novel serum TIA biomarkers through the use of mass spectroscopy-based proteomics. Patients with transient neurologic symptoms were prospectively enrolled. Mass spectrometry-based proteomics, an unbiased method to identify candidate proteins, was used to test the serum of the patients for biomarkers of cerebral ischemia. Three candidate proteins were found, and serum concentrations of these proteins were measured by enzyme-linked immunosorbent assay in a second cohort of prospectively enrolled patients. The Student's t-test was used for comparison. The Benjamini-Hochberg false discovery rate controlling procedure for multiple comparison adjustments determined significance for the proteomic screen. Patients with transient ischemic attacks (n = 20), minor strokes (n = 15), and controls (i.e. migraine, seizure, n = 12) were enrolled in the first cohort. Ceruloplasmin, complement component C8 gamma (C8γ), and platelet basic protein were significantly different between the ischemic group (transient ischemic attack and minor stroke) and the controls (P = 0·0001, P = 0·00027, P = 0·00105, respectively). A second cohort of patients with transient ischemic attack (n = 22), minor stroke (n = 20), and controls' (n = 12) serum was enrolled. Platelet basic protein serum concentrations were increased in the ischemic samples compared with control (for transient ischemic attack alone, P = 0·019, for the ischemic group, P = 0·046). Ceruloplasmin trended towards increased concentrations in the ischemic group (P = 0·127); no significant difference in C8γ (P = 0·44) was found. Utilizing mass spectrometry-based proteomics, platelet basic protein has been identified as a candidate serum

Changing the Scale and Efficiency of Chemical Warfare Countermeasure Discovery Using the Zebrafish

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Peterson, Randall T.; MacRae, Calum A.

2013-01-01

As the scope of potential chemical warfare agents grows rapidly and as the diversity of potential threat scenarios expands with non-state actors, so a need for innovative approaches to countermeasure development has emerged. In the last few years, the utility of the zebrafish as a model organism that is amenable to high-throughput screening has become apparent and this system has been applied to the unbiased discovery of chemical warfare countermeasures. This review summarizes the in vivo screening approach that has been pioneered in the countermeasure discovery arena, and highlights the successes to date as well as the potential challenges in moving the field forward. Importantly, the establishment of a zebrafish platform for countermeasure discovery would offer a rapid response system for the development of antidotes to the continuous stream of new potential chemical warfare agents. PMID:24273586

OSIRIS, an entirely in-house developed drug discovery informatics system.

Science.gov (United States)

Sander, Thomas; Freyss, Joel; von Korff, Modest; Reich, Jacqueline Renée; Rufener, Christian

2009-02-01

We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.

Pharmacogenomic Biomarkers

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Sandra C. Kirkwood

2002-01-01

Full Text Available Pharmacogenomic biomarkers hold great promise for the future of medicine and have been touted as a means to personalize prescriptions. Genetic biomarkers for disease susceptibility including both Mendelian and complex disease promise to result in improved understanding of the pathophysiology of disease, identification of new potential therapeutic targets, and improved molecular classification of disease. However essential to fulfilling the promise of individualized therapeutic intervention is the identification of drug activity biomarkers that stratify individuals based on likely response to a particular therapeutic, both positive response, efficacy, and negative response, development of side effect or toxicity. Prior to the widespread clinical application of a genetic biomarker multiple scientific studies must be completed to identify the genetic variants and delineate their functional significance in the pathophysiology of a carefully defined phenotype. The applicability of the genetic biomarker in the human population must then be verified through both retrospective studies utilizing stored or clinical trial samples, and through clinical trials prospectively stratifying patients based on the biomarker. The risk conferred by the polymorphism and the applicability in the general population must be clearly understood. Thus, the development and widespread application of a pharmacogenomic biomarker is an involved process and for most disease states we are just at the beginning of the journey towards individualized therapy and improved clinical outcome.

Development and Characterization of a Human and Mouse Intestinal Epithelial Cell Monolayer Platform

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Kenji Kozuka

2017-12-01

Full Text Available Summary: We describe the development and characterization of a mouse and human epithelial cell monolayer platform of the small and large intestines, with a broad range of potential applications including the discovery and development of minimally systemic drug candidates. Culture conditions for each intestinal segment were optimized by correlating monolayer global gene expression with the corresponding tissue segment. The monolayers polarized, formed tight junctions, and contained a diversity of intestinal epithelial cell lineages. Ion transport phenotypes of monolayers from the proximal and distal colon and small intestine matched the known and unique physiology of these intestinal segments. The cultures secreted serotonin, GLP-1, and FGF19 and upregulated the epithelial sodium channel in response to known biologically active agents, suggesting intact secretory and absorptive functions. A screen of over 2,000 pharmacologically active compounds for inhibition of potassium ion transport in the mouse distal colon cultures led to the identification of a tool compound. : Siegel and colleagues describe their development of a human and mouse intestinal epithelial cell monolayer platform that maintains the cellular, molecular, and functional characteristics of tissue for each intestinal segment. They demonstrate the platform's application to drug discovery by screening a library of over 2,000 compounds to identify an inhibitor of potassium ion transport in the mouse distal colon. Keywords: intestinal epithelium, organoids, monolayer, colon, small intestine, phenotype screening assays, enteroid, colonoid

CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics.

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Gai, Xiaowu; Perin, Juan C; Murphy, Kevin; O'Hara, Ryan; D'arcy, Monica; Wenocur, Adam; Xie, Hongbo M; Rappaport, Eric F; Shaikh, Tamim H; White, Peter S

2010-02-04

Recent studies have shown that copy number variations (CNVs) are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist. We developed a suite of software tools and resources (CNV Workshop) for automated, genome-wide CNV detection from a variety of SNP array platforms. CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. CNV detection utilizes a robust and genotype-specific extension of the Circular Binary Segmentation algorithm, and the use of additional detection algorithms is supported. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. To assist with determination of pathogenicity, detected CNVs are also annotated automatically for gene content, known disease loci, and gene-based literature references. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data, integration with the UCSC Genome Browser, and tabular displays of genomic attributes for each CNV. To our knowledge, CNV Workshop represents the first cohesive and convenient platform for detection, annotation, and assessment of the biological and clinical significance of structural variants. CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and is an ideal platform for coordinating multiple associated

CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics

Directory of Open Access Journals (Sweden)

Rappaport Eric F

2010-02-01

Full Text Available Abstract Background Recent studies have shown that copy number variations (CNVs are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist. Results We developed a suite of software tools and resources (CNV Workshop for automated, genome-wide CNV detection from a variety of SNP array platforms. CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. CNV detection utilizes a robust and genotype-specific extension of the Circular Binary Segmentation algorithm, and the use of additional detection algorithms is supported. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. To assist with determination of pathogenicity, detected CNVs are also annotated automatically for gene content, known disease loci, and gene-based literature references. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data, integration with the UCSC Genome Browser, and tabular displays of genomic attributes for each CNV. Conclusions To our knowledge, CNV Workshop represents the first cohesive and convenient platform for detection, annotation, and assessment of the biological and clinical significance of structural variants. CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and

Biomarkers for Early Detection of Clinically Relevant Prostate Cancer: A Multi-Institutional Validation Trial

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2015-10-01

provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently ...biomarker platforms in our multi-center, prospectively accrued prostate cancer active surveillance cohort – the Canary Prostate Active Surveillance...prostate cancers currently diagnosed are low risk tumors for which there is substantial evidence that the cancer will not cause harm if left untreated

Biomarkers as Common Data Elements for Symptom and Self-Management Science.

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Page, Gayle G; Corwin, Elizabeth J; Dorsey, Susan G; Redeker, Nancy S; McCloskey, Donna Jo; Austin, Joan K; Guthrie, Barbara J; Moore, Shirley M; Barton, Debra; Kim, Miyong T; Docherty, Sharron L; Waldrop-Valverde, Drenna; Bailey, Donald E; Schiffman, Rachel F; Starkweather, Angela; Ward, Teresa M; Bakken, Suzanne; Hickey, Kathleen T; Renn, Cynthia L; Grady, Patricia

2018-05-01

Biomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a "minimum set" of biomarkers for consideration as CDEs in symptom and self-management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance. From May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and self-management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and self-management science are proposed along with implications for future research and use of CDEs in these areas. The recommended minimum set of biomarker CDEs include pro- and anti-inflammatory cytokines, a hypothalamic-pituitary-adrenal axis marker, cortisol, the

Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

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Oguzhan Begik

2017-07-01

Full Text Available Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA, a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

Serum biomarkers predictive of depressive episodes in panic disorder.

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Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2016-02-01

Panic disorder with or without comorbid agoraphobia (PD/PDA) has been linked to an increased risk to develop subsequent depressive episodes, yet the underlying pathophysiology of these disorders remains poorly understood. We aimed to identify a biomarker panel predictive for the development of a depressive disorder (major depressive disorder and/or dysthymia) within a 2-year-follow-up period. Blood serum concentrations of 165 analytes were evaluated in 120 PD/PDA patients without depressive disorder baseline diagnosis (6-month-recency) in the Netherlands Study of Depression and Anxiety (NESDA). We assessed the predictive performance of serum biomarkers, clinical, and self-report variables using receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC). False-discovery-rate corrected logistic regression model selection of serum analytes and covariates identified an optimal predictive panel comprised of tetranectin and creatine kinase MB along with patient gender and scores from the Inventory of Depressive Symptomatology (IDS) rating scale. Combined, an AUC of 0.87 was reached for identifying the PD/PDA patients who developed a depressive disorder within 2 years (n = 44). The addition of biomarkers represented a significant (p = 0.010) improvement over using gender and IDS alone as predictors (AUC = 0.78). For the first time, we report on a combination of biological serum markers, clinical variables and self-report inventories that can detect PD/PDA patients at increased risk of developing subsequent depressive disorders with good predictive performance in a naturalistic cohort design. After an independent validation our proposed biomarkers could prove useful in the detection of at-risk PD/PDA patients, allowing for early therapeutic interventions and improving clinical outcome. Copyright © 2015 Elsevier Ltd. All rights reserved.

Personalized Herbal Medicine? A Roadmap for Convergence of Herbal and Precision Medicine Biomarker Innovations.

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Thomford, Nicholas Ekow; Dzobo, Kevin; Chimusa, Emile; Andrae-Marobela, Kerstin; Chirikure, Shadreck; Wonkam, Ambroise; Dandara, Collet

2018-06-01

While drugs remain the cornerstone of medicine, herbal medicine is an important comedication worldwide. Thus, precision medicine ought to face this clinical reality and develop "companion diagnostics" for drugs as well as herbal medicines. Yet, many are in denial with respect to the extent of use of traditional/herbal medicines, overlooking that a considerable number of contemporary therapeutic drugs trace their discovery from herbal medicines. This expert review underscores that absent such appropriate attention on both classical drug therapy and herbal medicines, precision medicine biomarkers will likely not stand the full test of clinical practice while patients continue to use both drugs and herbal medicines and, yet the biomarker research and applications focus only (or mostly) on drug therapy. This asymmetry in biomarker innovation strategy needs urgent attention from a wide range of innovation actors worldwide, including governments, research funders, scientists, community leaders, civil society organizations, herbal, pharmaceutical, and insurance industries, policymakers, and social/political scientists. We discuss the various dimensions of a future convergence map between herbal and conventional medicine, and conclude with a set of concrete strategies on how best to integrate biomarker research in a realm of both herbal and drug treatment. Africa, by virtue of its vast experience and exposure in herbal medicine and a "pregnant" life sciences innovation ecosystem, could play a game-changing role for the "birth" of biomarker-informed personalized herbal medicine in the near future. At this critical juncture when precision medicine initiatives are being rolled out worldwide, precision/personalized herbal medicine is both timely and essential for modern therapeutics, not to mention biomarker innovations that stand the test of real-life practices and implementation in the clinic and society.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications.

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Sajic, Tatjana; Liu, Yansheng; Aebersold, Ruedi

2015-04-01

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Recent technical and biological development in the analysis of biomarker N-deoxyguanosine-C8-4-aminobiphenyl.

Science.gov (United States)

Chen, Zhidan; Zhang, Yuesheng; Vouros, Paul

2018-06-15

4-Aminobiphenyl (4-ABP) which is primarily formed during tobacco combustion and overheated meat is a major carcinogen responsible for various cancers. Its adducted form, N-deoxyguanosine-C8-4-aminobiphenyl (dG-C8-4-ABP), has long been employed as a biomarker for assessment of the risk for cancer. In this review, the metabolism and carcinogenisity of 4-ABP will be discussed, followed by a discussion of the current common approaches of analyzing dG-C8-4-ABP. The major part of this review will be on the history and recent development of key methods for detection and quantitation of dG-C8-4-ABP in complex biological samples and their biological applications, from the traditional 2 P-postlabelling and immunoassay methods to modern liquid chromatography-mass spectrometry (LC-MS) with the latter as the focus. Many vital biological discoveries based on dG-C8-4-ABP have been published by using the nanoLC-MS with column switching platform in our laboratory, which has also been adopted and further improved by many other researchers. We hope this review can provide a perspective of the challenges that had to be addressed in reaching our present goals and possibly bring new ideas for those who are still working on the frontline of DNA adducts area. Copyright © 2018 Elsevier B.V. All rights reserved.

The "BIOmarkers associated with Sarcopenia and PHysical frailty in EldeRly pErsons" (BIOSPHERE) study: Rationale, design and methods.