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

Science.gov (United States)

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.

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

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.

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

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...

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

Novel TIA biomarkers identified by mass spectrometry-based proteomics.

Science.gov (United States)

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

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.

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.

Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application

Energy Technology Data Exchange (ETDEWEB)

Smith, Richard D.

2012-03-01

It is really only in the last ten years that mass spectrometry (MS) has had a truly significant (but still small) impact on biomedical research. Much of this impact can be attributed to proteomics and its more basic applications. Early biomedical applications have included a number of efforts aimed at developing new biomarkers; however, the success of these endeavors to date have been quite modest - essentially confined to preclinical applications - and have often suffered from combinations of immature technology and hubris. Now that MS-based proteomics is reaching adolescence, it is appropriate to ask if and when biomarker-related applications will extend to the clinical realm, and what developments will be essential for this transition. Biomarker development can be described as a multistage process consisting of discovery, qualification, verification, research assay optimization, validation, and commercialization (1). From a MS perspective, it is possible to 'bin' measurements into 1 of 2 categories - those aimed at discovering potential protein biomarkers and those seeking to verify and validate biomarkers. Approaches in both categories generally involve digesting proteins (e.g., with trypsin) as a first step to yield peptides that can be effectively detected and identified with MS. Discovery-based approaches use broad 'unbiased' or 'undirected' measurements that attempt to cover as many proteins as possible in the hope of revealing promising biomarker candidates. A key challenge with this approach stems from the extremely large dynamic range (i.e., relative stoichiometry) of proteins of potential interest in biofluids such as plasma and the expectation that biomarker proteins of the greatest clinical value for many diseases may very well be present at low relative abundances (2). Protein concentrations in plasma extend from approximately 10{sup 10} pg/mL for albumin to approximately 10 pg/mL and below for interleukins and other

Mass Spectrometry-Based N-Glycomics of Colorectal Cancer

Directory of Open Access Journals (Sweden)

Manveen K. Sethi

2015-12-01

Full Text Available Colorectal cancer (CRC is one of the most prevalent cancers worldwide. An increased molecular understanding of the CRC pathology is warranted to gain insights into the underlying molecular and cellular mechanisms of the disease. Altered protein glycosylation patterns are associated with most diseases including malignant transformation. Recent advances in mass spectrometry and bioinformatics have accelerated glycomics research and present a new paradigm for cancer biomarker discovery. Mass spectrometry (MS-based glycoproteomics and glycomics, therefore, hold considerable promise to improve the discovery of novel biomarkers with utility in disease diagnosis and therapy. This review focuses on the emerging field of glycomics to present a comprehensive review of advances in technologies and their application in studies aimed at discovering novel glycan-based biomarkers. We will also discuss some of the challenges associated with using glycans as biomarkers.

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.

Integration of Proteomics, Bioinformatics, and Systems Biology in Traumatic Brain Injury Biomarker Discovery

Science.gov (United States)

Guingab-Cagmat, J.D.; Cagmat, E.B.; Hayes, R.L.; Anagli, J.

2013-01-01

Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed. PMID:23750150

Cancer Biomarker Discovery: Lectin-Based Strategies Targeting Glycoproteins

Directory of Open Access Journals (Sweden)

David Clark

2012-01-01

Full Text Available Biomarker discovery can identify molecular markers in various cancers that can be used for detection, screening, diagnosis, and monitoring of disease progression. Lectin-affinity is a technique that can be used for the enrichment of glycoproteins from a complex sample, facilitating the discovery of novel cancer biomarkers associated with a disease state.

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

Science.gov (United States)

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

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

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.

Automated Sample Preparation Platform for Mass Spectrometry-Based Plasma Proteomics and Biomarker Discovery

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Vilém Guryča

2014-03-01

Full Text Available The identification of novel biomarkers from human plasma remains a critical need in order to develop and monitor drug therapies for nearly all disease areas. The discovery of novel plasma biomarkers is, however, significantly hampered by the complexity and dynamic range of proteins within plasma, as well as the inherent variability in composition from patient to patient. In addition, it is widely accepted that most soluble plasma biomarkers for diseases such as cancer will be represented by tissue leakage products, circulating in plasma at low levels. It is therefore necessary to find approaches with the prerequisite level of sensitivity in such a complex biological matrix. Strategies for fractionating the plasma proteome have been suggested, but improvements in sensitivity are often negated by the resultant process variability. Here we describe an approach using multidimensional chromatography and on-line protein derivatization, which allows for higher sensitivity, whilst minimizing the process variability. In order to evaluate this automated process fully, we demonstrate three levels of processing and compare sensitivity, throughput and reproducibility. We demonstrate that high sensitivity analysis of the human plasma proteome is possible down to the low ng/mL or even high pg/mL level with a high degree of technical reproducibility.

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...

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...

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

biological fluids with varying immunoreactivity which can present bioanalytical challenges when first discovered. The potential success of these efforts is greatly enhanced by recent advances in two closely linked technologies, toxicoproteomics and targeted, quantitative mass spectrometry. This review focuses on the examination of the current status of these technologies as they relate to the discovery and development of novel preclinical biomarkers of hepatotoxicity. A critical assessment of the current literature reveals two distinct lines of safety biomarker investigation, (1) peripheral fluid biomarkers of organ toxicity and (2) tissue or cell-based toxicity signatures. Improved peripheral fluid biomarkers should allow the sensitive detection of potential organ toxicity prior to the onset of overt organ pathology. Advancements in tissue or cell-based toxicity biomarkers will provide sensitive in vitro or ex vivo screening systems based on toxicity pathway markers. An examination of the current practices in clinical pathology and the critical evaluation of some recently proposed biomarker candidates in comparison to the desired characteristics of an ideal toxicity biomarker lead this author to conclude that a combination of selected biomarkers will be more informative if not predictive of potential animal organ toxicity than any single biomarker, new or old. For the practical assessment of combinations of conventional and/or novel toxicity biomarkers in rodent and large animal preclinical species, mass spectrometry has emerged as the premier analytical tool compared to specific immunoassays or functional assays. Selected and multiple reaction monitoring mass spectrometry applications make it possible for this same basic technology to be used in the progressive stages of biomarker discovery, development, and more importantly, routine study applications without the use of specific antibody reagents. This technology combined with other 'omics' technologies can provide added

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.

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.

Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?

Science.gov (United States)

Chen, Jiaqing; Zhang, Pei; Lv, Mengying; Guo, Huimin; Huang, Yin; Zhang, Zunjian; Xu, Fengguo

2017-05-16

Data reduction techniques in gas chromatography-mass spectrometry-based untargeted metabolomics has made the following workflow of data analysis more lucid. However, the normalization process still perplexes researchers, and its effects are always ignored. In order to reveal the influences of normalization method, five representative normalization methods (mass spectrometry total useful signal, median, probabilistic quotient normalization, remove unwanted variation-random, and systematic ratio normalization) were compared in three real data sets with different types. First, data reduction techniques were used to refine the original data. Then, quality control samples and relative log abundance plots were utilized to evaluate the unwanted variations and the efficiencies of normalization process. Furthermore, the potential biomarkers which were screened out by the Mann-Whitney U test, receiver operating characteristic curve analysis, random forest, and feature selection algorithm Boruta in different normalized data sets were compared. The results indicated the determination of the normalization method was difficult because the commonly accepted rules were easy to fulfill but different normalization methods had unforeseen influences on both the kind and number of potential biomarkers. Lastly, an integrated strategy for normalization method selection was recommended.

Proteomic and metabolomic approaches to biomarker discovery

CERN Document Server

Issaq, Haleem J

2013-01-01

Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution.  The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis...

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.

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

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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.

Systemic sclerosis biomarkers discovered using mass-spectrometry-based proteomics: a systematic review.

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Bălănescu, Paul; Lădaru, Anca; Bălănescu, Eugenia; Băicuş, Cristian; Dan, Gheorghe Andrei

2014-08-01

Systemic sclerosis (SSc) is an autoimmune disease with incompletely known physiopathology. There is a great challenge to predict its course and therapeutic response using biomarkers. To critically review proteomic biomarkers discovered from biological specimens from systemic sclerosis patients using mass spectrometry technologies. Medline and Embase databases were searched in February 2014. Out of the 199 records retrieved, a total of 20 records were included, identifying 116 candidate proteomic biomarkers. Research in SSc proteomic biomarkers should focus on biomarker validation, as there are valuable mass-spectrometry proteomics studies in the literature.

Native Mass Spectrometry in Fragment-Based Drug Discovery

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Liliana Pedro

2016-07-01

Full Text Available The advent of native mass spectrometry (MS in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein–ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD. Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

Native Mass Spectrometry in Fragment-Based Drug Discovery.

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Pedro, Liliana; Quinn, Ronald J

2016-07-28

The advent of native mass spectrometry (MS) in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein-ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD). Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

Biomarkers of systemic lupus erythematosus identified using mass spectrometry-based proteomics: a systematic review.

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Nicolaou, Orthodoxia; Kousios, Andreas; Hadjisavvas, Andreas; Lauwerys, Bernard; Sokratous, Kleitos; Kyriacou, Kyriacos

2017-05-01

Advances in mass spectrometry technologies have created new opportunities for discovering novel protein biomarkers in systemic lupus erythematosus (SLE). We performed a systematic review of published reports on proteomic biomarkers identified in SLE patients using mass spectrometry-based proteomics and highlight their potential disease association and clinical utility. Two electronic databases, MEDLINE and EMBASE, were systematically searched up to July 2015. The methodological quality of studies included in the review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Twenty-five studies were included in the review, identifying 241 SLE candidate proteomic biomarkers related to various aspects of the disease including disease diagnosis and activity or pinpointing specific organ involvement. Furthermore, 13 of the 25 studies validated their results for a selected number of biomarkers in an independent cohort, resulting in the validation of 28 candidate biomarkers. It is noteworthy that 11 candidate biomarkers were identified in more than one study. A significant number of potential proteomic biomarkers that are related to a number of aspects of SLE have been identified using mass spectrometry proteomic approaches. However, further studies are required to assess the utility of these biomarkers in routine clinical practice. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

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

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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

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

Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

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Federica Villanova

Full Text Available Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid flow cytometry platform (CFP and a unique lyoplate-based flow cytometry platform (LFP in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10 and activation markers (Foxp3 and CD25. Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

Science.gov (United States)

Villanova, Federica; Di Meglio, Paola; Inokuma, Margaret; Aghaeepour, Nima; Perucha, Esperanza; Mollon, Jennifer; Nomura, Laurel; Hernandez-Fuentes, Maria; Cope, Andrew; Prevost, A Toby; Heck, Susanne; Maino, Vernon; Lord, Graham; Brinkman, Ryan R; Nestle, Frank O

2013-01-01

Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

Emerging Concepts and Methodologies in Cancer Biomarker Discovery.

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Lu, Meixia; Zhang, Jinxiang; Zhang, Lanjing

2017-01-01

Cancer biomarker discovery is a critical part of cancer prevention and treatment. Despite the decades of effort, only a small number of cancer biomarkers have been identified for and validated in clinical settings. Conceptual and methodological breakthroughs may help accelerate the discovery of additional cancer biomarkers, particularly their use for diagnostics. In this review, we have attempted to review the emerging concepts in cancer biomarker discovery, including real-world evidence, open access data, and data paucity in rare or uncommon cancers. We have also summarized the recent methodological progress in cancer biomarker discovery, such as high-throughput sequencing, liquid biopsy, big data, artificial intelligence (AI), and deep learning and neural networks. Much attention has been given to the methodological details and comparison of the methodologies. Notably, these concepts and methodologies interact with each other and will likely lead to synergistic effects when carefully combined. Newer, more innovative concepts and methodologies are emerging as the current emerging ones became mainstream and widely applied to the field. Some future challenges are also discussed. This review contributes to the development of future theoretical frameworks and technologies in cancer biomarker discovery and will contribute to the discovery of more useful cancer biomarkers.

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.

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.

Science.gov (United States)

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.

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.

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.

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.

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

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...

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective.

Science.gov (United States)

Findeisen, Peter; Neumaier, Michael

2009-01-01

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

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

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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.

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)

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

Untargeted mass spectrometry-based metabolomic profiling of pleural effusions: fatty acids as novel cancer biomarkers for malignant pleural effusions.

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Lam, Ching-Wan; Law, Chun-Yiu

2014-09-05

Untargeted mass spectrometry-based metabolomic profiling is a powerful analytical method used for broad-spectrum identification and quantification of metabolites in biofluids in human health and disease states. In this study, we exploit metabolomic profiling for cancer biomarker discovery for diagnosis of malignant pleural effusions. We envisage the result will be clinically useful since currently there are no cancer biomarkers that are accurate enough for the diagnosis of malignant pleural effusions. Metabolomes of 32 malignant pleural effusions from lung cancer patients and 18 benign effusions from patients with pulmonary tuberculosis were analyzed using reversed-phase liquid chromatography tandem mass spectrometry (LC-MS/MS) using AB SCIEX TripleTOF 5600. MS spectra were analyzed using XCMS, PeakView, and LipidView. Metabolome-Wide Association Study (MWAS) was performed by Receiver Operating Characteristic Curve Explorer and Tester (ROCCET). Insignificant markers were filtered out using a metabolome-wide significance level (MWSL) with p-value pleural effusions. Using a ratio of FFA 18:1-to-ceramide (d18:1/16:0), the area-under-ROC was further increased to 0.99 (95% CI = 0.91-1.00) with sensitivity 93.8% and specificity 100.0%. Using untargeted metabolomic profiling, the diagnostic cancer biomarker with the largest area-under-ROC can be determined objectively. This lipogenic phenotype could be explained by overexpression of fatty acid synthase (FASN) in cancer cells. The diagnostic performance of FFA 18:1-to-ceramide (d18:1/16:0) ratio supports its use for diagnosis of malignant pleural effusions.

Discovery based and targeted Mass Spectrometry in farm animal proteomics

DEFF Research Database (Denmark)

Bendixen, Emøke

2013-01-01

for investigating farm animal biology. SRM is particularly important for validation biomarker candidates This talk will introduce the use of different mass spectrometry approaches through examples related to food quality and animal welfare, including studies of gut health in pigs, host pathogen interactions...

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

Science.gov (United States)

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.

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...

Mass Spectrometry-Based Proteomics for Pre-Eclampsia and Preterm Birth

Directory of Open Access Journals (Sweden)

Kai P. Law

2015-05-01

Full Text Available Pregnancy-related complications such as pre-eclampsia and preterm birth now represent a notable burden of adverse health. Pre-eclampsia is a hypertensive disorder unique to pregnancy. It is an important cause of maternal death worldwide and a leading cause of fetal growth restriction and iatrogenic prematurity. Fifteen million infants are born preterm each year globally, but more than one million of those do not survive their first month of life. Currently there are no predictive tests available for diagnosis of these pregnancy-related complications and the biological mechanisms of the diseases have not been fully elucidated. Mass spectrometry-based proteomics have all the necessary attributes to provide the needed breakthrough in understanding the pathophysiology of complex human diseases thorough the discovery of biomarkers. The mass spectrometry methodologies employed in the studies for pregnancy-related complications are evaluated in this article. Top-down proteomic and peptidomic profiling by laser mass spectrometry, liquid chromatography or capillary electrophoresis coupled to mass spectrometry, and bottom-up quantitative proteomics and targeted proteomics by liquid chromatography mass spectrometry have been applied to elucidate protein biomarkers and biological mechanism of pregnancy-related complications. The proteomes of serum, urine, amniotic fluid, cervical-vaginal fluid, placental tissue, and cytotrophoblastic cells have all been investigated. Numerous biomarkers or biomarker candidates that could distinguish complicated pregnancies from healthy controls have been proposed. Nevertheless, questions as to the clinically utility and the capacity to elucidate the pathogenesis of the pre-eclampsia and preterm birth remain to be answered.

Mass Spectrometry-Based Proteomics for Pre-Eclampsia and Preterm Birth

Science.gov (United States)

Law, Kai P.; Han, Ting-Li; Tong, Chao; Baker, Philip N.

2015-01-01

Pregnancy-related complications such as pre-eclampsia and preterm birth now represent a notable burden of adverse health. Pre-eclampsia is a hypertensive disorder unique to pregnancy. It is an important cause of maternal death worldwide and a leading cause of fetal growth restriction and iatrogenic prematurity. Fifteen million infants are born preterm each year globally, but more than one million of those do not survive their first month of life. Currently there are no predictive tests available for diagnosis of these pregnancy-related complications and the biological mechanisms of the diseases have not been fully elucidated. Mass spectrometry-based proteomics have all the necessary attributes to provide the needed breakthrough in understanding the pathophysiology of complex human diseases thorough the discovery of biomarkers. The mass spectrometry methodologies employed in the studies for pregnancy-related complications are evaluated in this article. Top-down proteomic and peptidomic profiling by laser mass spectrometry, liquid chromatography or capillary electrophoresis coupled to mass spectrometry, and bottom-up quantitative proteomics and targeted proteomics by liquid chromatography mass spectrometry have been applied to elucidate protein biomarkers and biological mechanism of pregnancy-related complications. The proteomes of serum, urine, amniotic fluid, cervical-vaginal fluid, placental tissue, and cytotrophoblastic cells have all been investigated. Numerous biomarkers or biomarker candidates that could distinguish complicated pregnancies from healthy controls have been proposed. Nevertheless, questions as to the clinically utility and the capacity to elucidate the pathogenesis of the pre-eclampsia and preterm birth remain to be answered. PMID:26006232

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.

Glycoscience aids in biomarker discovery

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Serenus Hua1,2 & Hyun Joo An1,2,*

2012-06-01

Full Text Available The glycome consists of all glycans (or carbohydrates within abiological system, and modulates a wide range of important biologicalactivities, from protein folding to cellular communications.The mining of the glycome for disease markers representsa new paradigm for biomarker discovery; however, this effortis severely complicated by the vast complexity and structuraldiversity of glycans. This review summarizes recent developmentsin analytical technology and methodology as applied tothe fields of glycomics and glycoproteomics. Mass spectrometricstrategies for glycan compositional profiling are described, as arepotential refinements which allow structure-specific profiling.Analytical methods that can discern protein glycosylation at aspecific site of modification are also discussed in detail.Biomarker discovery applications are shown at each level ofanalysis, highlighting the key role that glycoscience can play inhelping scientists understand disease biology.

Mass Spectrometry-based Assay for High Throughput and High Sensitivity Biomarker Verification

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Guo, Xuejiang; Tang, Keqi

2017-06-14

Searching for disease specific biomarkers has become a major undertaking in the biomedical research field as the effective diagnosis, prognosis and treatment of many complex human diseases are largely determined by the availability and the quality of the biomarkers. A successful biomarker as an indicator to a specific biological or pathological process is usually selected from a large group of candidates by a strict verification and validation process. To be clinically useful, the validated biomarkers must be detectable and quantifiable by the selected testing techniques in their related tissues or body fluids. Due to its easy accessibility, protein biomarkers would ideally be identified in blood plasma or serum. However, most disease related protein biomarkers in blood exist at very low concentrations (<1ng/mL) and are “masked” by many none significant species at orders of magnitude higher concentrations. The extreme requirements of measurement sensitivity, dynamic range and specificity make the method development extremely challenging. The current clinical protein biomarker measurement primarily relies on antibody based immunoassays, such as ELISA. Although the technique is sensitive and highly specific, the development of high quality protein antibody is both expensive and time consuming. The limited capability of assay multiplexing also makes the measurement an extremely low throughput one rendering it impractical when hundreds to thousands potential biomarkers need to be quantitatively measured across multiple samples. Mass spectrometry (MS)-based assays have recently shown to be a viable alternative for high throughput and quantitative candidate protein biomarker verification. Among them, the triple quadrupole MS based assay is the most promising one. When it is coupled with liquid chromatography (LC) separation and electrospray ionization (ESI) source, a triple quadrupole mass spectrometer operating in a special selected reaction monitoring (SRM) mode

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.

Mass spectrometry-based metabolomics: applications to biomarker and metabolic pathway research.

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

2016-01-01

Mass spectrometry-based metabolomics has become increasingly popular in molecular medicine. High-definition mass spectrometry (MS), coupled with pattern recognition methods, have been carried out to obtain comprehensive metabolite profiling and metabolic pathway of large biological datasets. This sets the scene for a new and powerful diagnostic approach. Analysis of the key metabolites in body fluids has become an important part of improving disease diagnosis. With technological advances in analytical techniques, the ability to measure low-molecular-weight metabolites in bio-samples provides a powerful platform for identifying metabolites that are uniquely correlated with a specific human disease. MS-based metabolomics can lead to enhanced understanding of disease mechanisms and to new diagnostic markers and has a strong potential to contribute to improving early diagnosis of diseases. This review will highlight the importance and benefit with certain characteristic examples of MS-metabolomics for identifying metabolic pathways and metabolites that accurately screen for potential diagnostic biomarkers of diseases. Copyright © 2015 John Wiley & Sons, Ltd.

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

Sparse Mbplsr for Metabolomics Data and Biomarker Discovery

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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...

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.

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

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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.

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).

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

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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

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

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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

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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.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology

DEFF Research Database (Denmark)

Lind, Mads Vendelbo; Savolainen, Otto I; Ross, Alastair B

2016-01-01

measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many...... biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses...

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

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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).

A Ligand-observed Mass Spectrometry Approach Integrated into the Fragment Based Lead Discovery Pipeline

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Chen, Xin; Qin, Shanshan; Chen, Shuai; Li, Jinlong; Li, Lixin; Wang, Zhongling; Wang, Quan; Lin, Jianping; Yang, Cheng; Shui, Wenqing

2015-01-01

In fragment-based lead discovery (FBLD), a cascade combining multiple orthogonal technologies is required for reliable detection and characterization of fragment binding to the target. Given the limitations of the mainstream screening techniques, we presented a ligand-observed mass spectrometry approach to expand the toolkits and increase the flexibility of building a FBLD pipeline especially for tough targets. In this study, this approach was integrated into a FBLD program targeting the HCV RNA polymerase NS5B. Our ligand-observed mass spectrometry analysis resulted in the discovery of 10 hits from a 384-member fragment library through two independent screens of complex cocktails and a follow-up validation assay. Moreover, this MS-based approach enabled quantitative measurement of weak binding affinities of fragments which was in general consistent with SPR analysis. Five out of the ten hits were then successfully translated to X-ray structures of fragment-bound complexes to lay a foundation for structure-based inhibitor design. With distinctive strengths in terms of high capacity and speed, minimal method development, easy sample preparation, low material consumption and quantitative capability, this MS-based assay is anticipated to be a valuable addition to the repertoire of current fragment screening techniques. PMID:25666181

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

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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.

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...

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.

Metabolomics for Biomarker Discovery: Moving to the Clinic

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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

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.

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

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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.

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

Directory of Open Access Journals (Sweden)

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

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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

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

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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.

Identification and localization of trauma-related biomarkers using matrix assisted laser desorption/ionization imaging mass spectrometry

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Jones, Kirstin; Reilly, Matthew A.; Glickman, Randolph D.

2017-02-01

Current treatments for ocular and optic nerve trauma are largely ineffective and may have adverse side effects; therefore, new approaches are needed to understand trauma mechanisms. Identification of trauma-related biomarkers may yield insights into the molecular aspects of tissue trauma that can contribute to the development of better diagnostics and treatments. The conventional approach for protein biomarker measurement largely relies on immunoaffinity methods that typically can only be applied to analytes for which antibodies or other targeting means are available. Matrix assisted laser-assisted desorption/ionization imaging mass spectrometry (MALDI-IMS) is a specialized application of mass spectrometry that not only is well suited to the discovery of novel or unanticipated biomarkers, but also provides information about the spatial localization of biomarkers in tissue. We have been using MALDI-IMS to find traumarelated protein biomarkers in retina and optic nerve tissue from animal models subjected to ocular injury produced by either blast overpressure or mechanical torsion. Work to date by our group, using MALDI-IMS, found that the pattern of protein expression is modified in the injured ocular tissue as soon as 24 hr post-injury, compared to controls. Specific proteins may be up- or down-regulated by trauma, suggesting different tissue responses to a given injury. Ongoing work is directed at identifying the proteins affected and mapping their expression in the ocular tissue, anticipating that systematic analysis can be used to identify targets for prospective therapies for ocular trauma.

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

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

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples.

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Lind, Mads V; Savolainen, Otto I; Ross, Alastair B

2016-08-01

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

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

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

Two combinatorial optimization problems for SNP discovery using base-specific cleavage and mass spectrometry.

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Chen, Xin; Wu, Qiong; Sun, Ruimin; Zhang, Louxin

2012-01-01

The discovery of single-nucleotide polymorphisms (SNPs) has important implications in a variety of genetic studies on human diseases and biological functions. One valuable approach proposed for SNP discovery is based on base-specific cleavage and mass spectrometry. However, it is still very challenging to achieve the full potential of this SNP discovery approach. In this study, we formulate two new combinatorial optimization problems. While both problems are aimed at reconstructing the sample sequence that would attain the minimum number of SNPs, they search over different candidate sequence spaces. The first problem, denoted as SNP - MSP, limits its search to sequences whose in silico predicted mass spectra have all their signals contained in the measured mass spectra. In contrast, the second problem, denoted as SNP - MSQ, limits its search to sequences whose in silico predicted mass spectra instead contain all the signals of the measured mass spectra. We present an exact dynamic programming algorithm for solving the SNP - MSP problem and also show that the SNP - MSQ problem is NP-hard by a reduction from a restricted variation of the 3-partition problem. We believe that an efficient solution to either problem above could offer a seamless integration of information in four complementary base-specific cleavage reactions, thereby improving the capability of the underlying biotechnology for sensitive and accurate SNP discovery.

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...

Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review.

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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.

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

Exhaled Breath Condensate for Proteomic Biomarker Discovery

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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.

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

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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...

Proteomic Approaches in Biomarker Discovery: New Perspectives in Cancer Diagnostics

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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

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.

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.

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

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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.

Mass Spectrometry for Translational Proteomics: Progress and Clinical Implications

Energy Technology Data Exchange (ETDEWEB)

Baker, Erin Shammel; Liu, Tao; Petyuk, Vladislav A.; Burnum-Johnson, Kristin E.; Ibrahim, Yehia M.; Anderson, Gordon A.; Smith, Richard D.

2012-08-31

Mass spectrometry (MS)-based proteomics measurements have become increasingly utilized in a wide range of biological and biomedical applications, and have significantly enhanced the understanding of the complex and dynamic nature of the proteome and its connections to biology and diseases. While some MS techniques such as those for targeted analysis are increasingly applied with great success, others such as global quantitative analysis (for e.g. biomarker discovery) are more challenging and continue to be developed and refined to provide the desired throughput, sensitivity and/ or specificity. New MS capabilities and proteomics-based pipelines/strategies also keep enhancing for the advancement of clinical proteomics applications such as protein biomarker discovery and validation. Herein, we provide a brief review to summarize the current state of MS-based proteomics with respect to its advantages and present limitations, while highlighting its potential in future clinical applications.

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.

Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry.

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Tsutsui, Haruhito; Maeda, Toshio; Min, Jun Zhe; Inagaki, Shinsuke; Higashi, Tatsuya; Kagawa, Yoshiyuki; Toyo'oka, Toshimasa

2011-05-12

The number of diabetic patients has recently been increasing worldwide. Diabetes is a multifactorial disorder based on environmental factors and genetic background. In many cases, diabetes is asymptomatic for a long period and the patient is not aware of the disease. Therefore, the potential biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, are strongly required. However, the diagnosis of the prediabetic state in humans is a very difficult issue, because the lifestyle is variable in each person. Although the development of a diagnosis method in humans is the goal of our research, the extraction and structural identification of biomarker candidates in several biological specimens (i.e., plasma, hair, liver and kidney) of ddY strain mice, which undergo naturally occurring diabetes along with aging, were carried out based upon a metabolite profiling study. The low-molecular-mass compounds including metabolites in the biological specimens of diabetic mice (ddY-H) and normal mice (ddY-L) were globally separated by ultra-performance liquid chromatography (UPLC) using different reversed-phase columns (i.e., T3-C18 and HS-F5) and detected by electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS). The biomarker candidates related to diabetes mellitus were extracted from a multivariate statistical analysis, such as an orthogonal partial least-squares-discriminant analysis (OPLS-DA), followed by a database search, such as ChemSpider, KEGG and HMDB. Many metabolites and unknown compounds in each biological specimen were detected as the biomarker candidates related to diabetic mellitus. Among them, the elucidation of the chemical structures of several possible metabolites, including more than two biological specimens, was carried out along with the comparison of the tandem MS/MS analyses using authentic compounds. One metabolite was clearly identified as N-acetyl-L-leucine based upon the MS/MS spectra and the retention time on

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

Biomarker discovery for colon cancer using a 761 gene RT-PCR assay

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Hackett James R

2007-08-01

Full Text Available Abstract Background Reverse transcription PCR (RT-PCR is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan® RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX™ assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis and the likelihood of tumor response to standard chemotherapy regimens (prediction. We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application. Results RNA was extracted from formalin fixed paraffin embedded (FPE tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan® reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery. Conclusion We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of

Detection of Radiation-Exposure Biomarkers by Differential Mobility Prefiltered Mass Spectrometry (DMS-MS).

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Coy, Stephen L; Krylov, Evgeny V; Schneider, Bradley B; Covey, Thomas R; Brenner, David J; Tyburski, John B; Patterson, Andrew D; Krausz, Kris W; Fornace, Albert J; Nazarov, Erkinjon G

2010-04-15

Technology to enable rapid screening for radiation exposure has been identified as an important need, and, as a part of a NIH / NIAD effort in this direction, metabolomic biomarkers for radiation exposure have been identified in a recent series of papers. To reduce the time necessary to detect and measure these biomarkers, differential mobility spectrometry - mass spectrometry (DMS-MS) systems have been developed and tested. Differential mobility ion filters preselect specific ions and also suppress chemical noise created in typical atmospheric-pressure ionization sources (ESI, MALDI, and others). Differential-mobility-based ion selection is based on the field dependence of ion mobility, which, in turn, depends on ion characteristics that include conformation, charge distribution, molecular polarizability, and other properties, and on the transport gas composition which can be modified to enhance resolution. DMS-MS is able to resolve small-molecule biomarkers from nearly-isobaric interferences, and suppresses chemical noise generated in the ion source and in the mass spectrometer, improving selectivity and quantitative accuracy. Our planar DMS design is rapid, operating in a few milliseconds, and analyzes ions before fragmentation. Depending on MS inlet conditions, DMS-selected ions can be dissociated in the MS inlet expansion, before mass analysis, providing a capability similar to MS/MS with simpler instrumentation. This report presents selected DMS-MS experimental results, including resolution of complex test mixtures of isobaric compounds, separation of charge states, separation of isobaric biomarkers (citrate and isocitrate), and separation of nearly-isobaric biomarker anions in direct analysis of a bio-fluid sample from the radiation-treated group of a mouse-model study. These uses of DMS combined with moderate resolution MS instrumentation indicate the feasibility of field-deployable instrumentation for biomarker evaluation.

Database-augmented Mass Spectrometry Analysis of Exosomes Identifies Claudin 3 as a Putative Prostate Cancer Biomarker.

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Worst, Thomas Stefan; von Hardenberg, Jost; Gross, Julia Christina; Erben, Philipp; Schnölzer, Martina; Hausser, Ingrid; Bugert, Peter; Michel, Maurice Stephan; Boutros, Michael

2017-06-01

In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer.Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry data sets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized ( n = 58; 42 with Gleason score 6-7, 16 with Gleason score ≥8) and metastatic prostate cancer ( n = 11) compared with patients with benign prostatic hyperplasia ( n = 15) and healthy individuals ( n = 15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups ( p = 0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared with patients with benign prostatic hyperplasia ( p = 0.012) and Gleason 6-7 tumors ( p = 0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC = 0.705; p = 0.016) and did not correlate with serum PSA.By using the described workflow claudin 3 was identified and validated as a

Novel ageing-biomarker discovery using data-intensive technologies.

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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.

Identification of urinary biomarkers of exposure to di-(2-propylheptyl) phthalate using high-resolution mass spectrometry and two data-screening approaches.

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Shih, Chia-Lung; Liao, Pao-Mei; Hsu, Jen-Yi; Chung, Yi-Ning; Zgoda, Victor G; Liao, Pao-Chi

2018-02-01

Di-(2-propylheptyl) phthalate (DPHP) is a plasticizer used in polyvinyl chloride and vinyl chloride copolymer that has been suggested to be a toxicant in rats and may affect human health. Because the use of DPHP is increasing, the general German population is being exposed to DPHP. Toxicant metabolism is important for human toxicant exposure assessments. To date, the knowledge regarding DPHP metabolism has been limited, and only four metabolites have been identified in human urine. Ultra-performance liquid chromatography was coupled with Orbitrap high-resolution mass spectrometry (MS) and two data-screening approaches-the signal mining algorithm with isotope tracing (SMAIT) and the mass defect filter (MDF)-for DPHP metabolite candidate discovery. In total, 13 and 104 metabolite candidates were identified by the two approaches, respectively, in in vitro DPHP incubation samples. Of these candidates, 17 were validated as tentative exposure biomarkers using a rat model, 13 of which have not been reported in the literature. The two approaches generated rather different tentative DPHP exposure biomarkers, indicating that these approaches are complementary for discovering exposure biomarkers. Compared with the four previously reported DPHP metabolites, the three tentative novel biomarkers had higher peak intensity ratios, and two were confirmed as DPHP hydroxyl metabolites based on their MS/MS product ion profiles. These three tentative novel biomarkers should be further investigated for potential application in human exposure assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using direct infusion mass spectrometry for serum metabolomics in Alzheimer's disease.

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González-Domínguez, R; García-Barrera, T; Gómez-Ariza, J L

2014-11-01

Currently, there is no cure for Alzheimer's disease and early diagnosis is very difficult, since no biomarkers have been established with the necessary reliability and specificity. For the discovery of new biomarkers, the application of omics is emerging, especially metabolomics based on the use of mass spectrometry. In this work, an analytical approach based on direct infusion electrospray mass spectrometry was applied for the first time to blood serum samples in order to elucidate discriminant metabolites. Complementary methodologies of extraction and mass spectrometry analysis were employed for comprehensive metabolic fingerprinting. Finally, the application of multivariate statistical tools allowed us to discriminate Alzheimer patients and healthy controls, and identify some compounds as potential markers of disease. This approach provided a global vision of disease, given that some important metabolic pathways could be studied, such as membrane destabilization processes, oxidative stress, hypometabolism, or neurotransmission alterations. Most remarkable results are the high levels of phospholipids containing saturated fatty acids, respectively, polyunsaturated ones and the high concentration of whole free fatty acids in Alzheimer's serum samples. Thus, these results represent an interesting approximation to understand the pathogenesis of disease and the identification of potential biomarkers.

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.

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

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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.

Metabolome analysis for discovering biomarkers of gastroenterological cancer.

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Suzuki, Makoto; Nishiumi, Shin; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

2014-09-01

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

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

Developing novel blood-based biomarkers for Alzheimer's disease

DEFF Research Database (Denmark)

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...

Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

International Nuclear Information System (INIS)

Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

2009-01-01

Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

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

Multi-dimensional discovery of biomarker and phenotype complexes

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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.

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

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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

[Application of Imaging Mass Spectrometry for Drug Discovery].

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Hayasaka, Takahiro

2016-01-01

Imaging mass spectrometry (IMS) can reveal the distribution of biomolecules on tissue sections. In this process, the biomolecules are directly ionized within tissue sections using matrix-assisted laser desorption/ionization, and then their distribution is visualized by pseudo-color based on the relative signal intensity. The biomolecules, such as fatty acids, phospholipids, glycolipids, peptides, proteins, and neurotransmitters, have been analyzed at a spatial resolution of 5 μm. A special instrument for IMS analysis was developed by Shimadzu. The IMS analysis does not require the labeling of biomolecules and is capable of analyzing all the ionized biomolecules. Interest in this method has expanded to many research fields, including biology, agriculture, medicine, and pharmacology. The technique is especially relevant to the drug discovery process. As practiced currently, drug discovery is expensive and time consuming, requiring the preparation of probes for each drug and its metabolites, followed by systematic probe tracking in animal models. The IMS technique is expected to overcome these drawbacks by revealing the distribution of drugs and their metabolites using only a single analysis. In this symposium, I introduced the methodology and applications of IMS and discussed the feasibility of its application to drug discovery in the near future.

A Pilot Proteomic Analysis of Salivary Biomarkers in Autism Spectrum Disorder.

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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.

Mass spectrometry imaging of biomarker lipids for phagocytosis and signalling during focal cerebral ischaemia

DEFF Research Database (Denmark)

Nielsen, Mette M B; Lambertsen, Kate L; Clausen, Bettina H

2016-01-01

biomarker CD11b, and probably with cholesteryl ester. Mass spectrometry imaging can visualize spatiotemporal changes in the lipidome during the progression and resolution of focal cerebral inflammation and suggests that BMP(22:6/22:6) and N-acyl-phosphatidylethanolamines can be used as biomarkers......Focal cerebral ischaemia has an initial phase of inflammation and tissue injury followed by a later phase of resolution and repair. Mass spectrometry imaging (desorption electrospray ionization and matrix assisted laser desorption ionization) was applied on brain sections from mice 2 h, 24 h, 5d, 7...

Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection.

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van de Ven, Stephanie M W Y; Bemis, Kyle D; Lau, Kenneth; Adusumilli, Ravali; Kota, Uma; Stolowitz, Mark; Vitek, Olga; Mallick, Parag; Gambhir, Sanjiv S

2016-06-01

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were biomarkers and a new benchmarking strategy that can be used for comparing diverse MALDI-MSI workflows. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identifying Predictors of Taxane-Induced Peripheral Neuropathy Using Mass Spectrometry-Based Proteomics Technology.

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Emily I Chen

Full Text Available Major advances in early detection and therapy have significantly increased the survival of breast cancer patients. Unfortunately, most cancer therapies are known to carry a substantial risk of adverse long-term treatment-related effects. Little is known about patient susceptibility to severe side effects after chemotherapy. Chemotherapy-induced peripheral neuropathy (CIPN is a common side effect of taxanes. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. On the other hand, recent advances in proteomic technologies incorporating mass spectrometry (MS for biomarker discovery show great promise to provide clinically relevant protein biomarkers. In this study, we evaluated the association between protein content in serum exosomes and severity of CIPN. Women with early stage breast cancer receiving adjuvant taxane chemotherapy were assessed with the FACT-Ntx score and serum was collected before and after the taxane treatment. Based on the change in FACT-Ntx score from baseline to 12 month follow-up, we separated patients into two groups: those who had no change (Group 1, N = 9 and those who had a ≥20% worsening (Group 1, N = 8. MS-based proteomics technology was used to identify proteins present in serum exosomes to determine potential biomarkers. Mann-Whitney-Wilcoxon analysis was applied and maximum FDR was controlled at 20%. From the serum exosomes derived from this cohort, we identified over 700 proteins known to be in different subcellular locations and have different functions. Statistical analysis revealed a 12-protein signature that resulted in a distinct separation between baseline serum samples of both groups (q<0.2 suggesting that the baseline samples can predict subsequent neurotoxicity. These toxicity

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

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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.

Rapid and High-Throughput Detection and Quantitation of Radiation Biomarkers in Human and Nonhuman Primates by Differential Mobility Spectrometry-Mass Spectrometry

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Chen, Zhidan; Coy, Stephen L.; Pannkuk, Evan L.; Laiakis, Evagelia C.; Hall, Adam B.; Fornace, Albert J.; Vouros, Paul

2016-10-01

Radiation exposure is an important public health issue due to a range of accidental and intentional threats. Prompt and effective large-scale screening and appropriate use of medical countermeasures (MCM) to mitigate radiation injury requires rapid methods for determining the radiation dose. In a number of studies, metabolomics has identified small-molecule biomarkers responding to the radiation dose. Differential mobility spectrometry-mass spectrometry (DMS-MS) has been used for similar compounds for high-throughput small-molecule detection and quantitation. In this study, we show that DMS-MS can detect and quantify two radiation biomarkers, trimethyl-L-lysine (TML) and hypoxanthine. Hypoxanthine is a human and nonhuman primate (NHP) radiation biomarker and metabolic intermediate, whereas TML is a radiation biomarker in humans but not in NHP, which is involved in carnitine synthesis. They have been analyzed by DMS-MS from urine samples after a simple strong cation exchange-solid phase extraction (SCX-SPE). The dramatic suppression of background and chemical noise provided by DMS-MS results in an approximately 10-fold reduction in time, including sample pretreatment time, compared with liquid chromatography-mass spectrometry (LC-MS). DMS-MS quantitation accuracy has been verified by validation testing for each biomarker. Human samples are not yet available, but for hypoxanthine, selected NHP urine samples (pre- and 7-d-post 10 Gy exposure) were analyzed, resulting in a mean change in concentration essentially identical to that obtained by LC-MS (fold-change 2.76 versus 2.59). These results confirm the potential of DMS-MS for field or clinical first-level rapid screening for radiation exposure.

Metabolomic study of lipids in serum for biomarker discovery in Alzheimer's disease using direct infusion mass spectrometry.

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González-Domínguez, R; García-Barrera, T; Gómez-Ariza, J L

2014-09-01

In this study, we demonstrated the potential of direct infusion mass spectrometry for the lipidomic characterization of Alzheimer's disease. Serum samples were extracted for lipids recovery, and directly analyzed using an electrospray source. Metabolomic fingerprints were subjected to multivariate analysis in order to discriminate between groups of patients and healthy controls, and then some key-compounds were identified as possible markers of Alzheimer's disease. Major differences were found in lipids, although some low molecular weight metabolites also showed significant changes. Thus, important metabolic pathways involved in neurodegeneration could be studied on the basis of these perturbations, such as membrane breakdown (phospholipids and diacylglycerols), oxidative stress (prostaglandins, imidazole and histidine), alterations in neurotransmission systems (oleamide and putrescine) and hyperammonaemia (guanidine and arginine). Moreover, it is noteworthy that some of these potential biomarkers have not been previously described for Alzheimer's disease. Copyright © 2014 Elsevier B.V. All rights reserved.

Defining Diagnostic Biomarkers Using Shotgun Proteomics and MALDI-TOF Mass Spectrometry.

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Armengaud, Jean

2017-01-01

Whole-cell MALDI-TOF has become a robust and widely used tool to quickly identify any pathogen. In addition to being routinely used in hospitals, it is also useful for low cost dereplication in large scale screening procedures of new environmental isolates for environmental biotechnology or taxonomical applications. Here, I describe how specific biomarkers can be defined using shotgun proteomics and whole-cell MALDI-TOF mass spectrometry. Based on MALDI-TOF spectra recorded on a given set of pathogens with internal calibrants, m/z values of interest are extracted. The proteins which contribute to these peaks are deduced from label-free shotgun proteomics measurements carried out on the same sample. Quantitative information based on the spectral count approach allows ranking the most probable candidates. Proteogenomic approaches help to define whether these proteins give the same m/z values along the whole taxon under consideration or result in heterogeneous lists. These specific biomarkers nicely complement conventional profiling approaches and may help to better define groups of organisms, for example at the subspecies level.

Random glycopeptide bead libraries for seromic biomarker discovery

DEFF Research Database (Denmark)

Kracun, Stjepan Kresimir; Cló, Emiliano; Clausen, Henrik

2010-01-01

have developed a random glycopeptide bead library screening platform for detection of autoantibodies and other binding proteins. Libraries were build on biocompatible PEGA beads including a safety-catch C-terminal amide linker (SCAL) that allowed mild cleavage conditions (I(2)/NaBH(4) and TFA...... to other tumor glycoforms by on-bead enzymatic glycosylation reactions with recombinant glycosyltransferases. Hence, we have developed a high-throughput flexible platform for rapid discovery of O-glycopeptide biomarkers and the method has applicability in other types of assays such as lectin...

Application of alpha spectrometry to the discovery of new elements by heavy-ion-beam bombardment

International Nuclear Information System (INIS)

Nitschke, J.M.

1983-05-01

Starting with polonium in 1898, α-spectrometry has played a decisive role in the discovery of new, heavy elements. For even-even nuclei, α-spectra have proved simple to interpret and exhibit systematic trends that allow extrapolation to unknown isotopes. The early discovery of the natural α-decay series led to the very powerful method of genetically linking the decay of new elements to the well-established α-emission of daughter and granddaughter nuclei. This technique has been used for all recent discoveries of new elements including Z = 109. Up to mendelevium (Z = 101), thin samples suitable for α-spectrometry were prepared by chemical methods. With the advent of heavy-ion accelerators new sample preparation methods emerged. These were based on the large momentum transfer associated with heavy-ion reactions, which produced energetic target recoils that, when ejected from the target, could be thermalized in He gas. Subsequent electrical deposition or a He-jet technique yielded samples that were not only thin enough for α-spectroscopy, but also for α- and #betta#-recoil experiments. Many variations of these methods have been developed and are discussed. For the synthesis of element 106 an aerosol-based recoil transport technique was devised. In the most recent experiments, α-spectrometry has been coupled with the magnetic analysis of the recoils. The time from production to analysis of an isotope has thereby been reduced to 10 - 6 s; while it was 10 - 1 to 10 0 s for He-jets and 10 1 to 10 3 s for rapid chemical separations. Experiments are now in progress to synthesize super heavy elements (SHE) and to analyze them with these latest techniques. Again, α-spectrometry will play a major role since the expected signature for the decay of a SHE is a sequence of α-decays followed by spontaneous fission

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

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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

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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

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

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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.

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging for Peptide and Protein Analyses: A Critical Review of On-Tissue Digestion

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Cillero-Pastor, B.; Heeren, R.M.A.

2013-01-01

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has established itself among the plethora of mass spectrometry applications. In the biomedical field, MALDI-MSI is being more frequently recognized as a new method for the discovery of biomarkers and targets of

Urinary Metabolomic Profiling to Identify Potential Biomarkers for the Diagnosis of Behcet’s Disease by Gas Chromatography/Time-of-Flight−Mass Spectrometry

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Joong Kyong Ahn

2017-11-01

Full Text Available Diagnosing Behcet’s disease (BD is challenging because of the lack of a diagnostic biomarker. The purposes of this study were to investigate distinctive metabolic changes in urine samples of BD patients and to identify urinary metabolic biomarkers for diagnosis of BD using gas chromatography/time-of-flight–mass spectrometry (GC/TOF−MS. Metabolomic profiling of urine samples from 44 BD patients and 41 healthy controls (HC were assessed using GC/TOF−MS, in conjunction with multivariate statistical analysis. A total of 110 urinary metabolites were identified. The urine metabolite profiles obtained from GC/TOF−MS analysis could distinguish BD patients from the HC group in the discovery set. The parameter values of the orthogonal partial least squared-discrimination analysis (OPLS-DA model were R2X of 0.231, R2Y of 0.804, and Q2 of 0.598. A biomarker panel composed of guanine, pyrrole-2-carboxylate, 3-hydroxypyridine, mannose, l-citrulline, galactonate, isothreonate, sedoheptuloses, hypoxanthine, and gluconic acid lactone were selected and adequately validated as putative biomarkers of BD (sensitivity 96.7%, specificity 93.3%, area under the curve 0.974. OPLS-DA showed clear discrimination of BD and HC groups by a biomarker panel of ten metabolites in the independent set (accuracy 88%. We demonstrated characteristic urinary metabolic profiles and potential urinary metabolite biomarkers that have clinical value in the diagnosis of BD using GC/TOF−MS.

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

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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.

Cloud-based solution to identify statistically significant MS peaks differentiating sample categories.

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Ji, Jun; Ling, Jeffrey; Jiang, Helen; Wen, Qiaojun; Whitin, John C; Tian, Lu; Cohen, Harvey J; Ling, Xuefeng B

2013-03-23

Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). "Turnkey" solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. Here we present an efficient and effective solution, which provides experimental biologists easy access to "cloud" computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/. Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS.

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

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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.

Allergic asthma biomarkers using systems approaches

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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.

The expression profile of phosphatidylinositol in high spatial resolution imaging mass spectrometry as a potential biomarker for prostate cancer.

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Takayuki Goto

Full Text Available High-resolution matrix-assisted laser desorption/ionization imaging mass spectrometry (HR-MALDI-IMS is an emerging application for the comprehensive and detailed analysis of the spatial distribution of ionized molecules in situ on tissue slides. HR-MALDI-IMS in negative mode in a mass range of m/z 500-1000 was performed on optimal cutting temperature (OCT compound-embedded human prostate tissue samples obtained from patients with prostate cancer at the time of radical prostatectomy. HR-MALDI-IMS analysis of the 14 samples in the discovery set identified 26 molecules as highly expressed in the prostate. Tandem mass spectrometry (MS/MS showed that these molecules included 14 phosphatidylinositols (PIs, 3 phosphatidylethanolamines (PEs and 3 phosphatidic acids (PAs. Among the PIs, the expression of PI(18:0/18:1, PI(18:0/20:3 and PI(18:0/20:2 were significantly higher in cancer tissue than in benign epithelium. A biomarker algorithm for prostate cancer was formulated by analyzing the expression profiles of PIs in cancer tissue and benign epithelium of the discovery set using orthogonal partial least squares discriminant analysis (OPLS-DA. The sensitivity and specificity of this algorithm for prostate cancer diagnosis in the 24 validation set samples were 87.5 and 91.7%, respectively. In conclusion, HR-MALDI-IMS identified several PIs as being more highly expressed in prostate cancer than benign prostate epithelium. These differences in PI expression profiles may serve as a novel diagnostic tool for prostate cancer.

Identification of proteomic biomarkers of preeclampsia using protein microarray and tandem mass spectrometry

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Karol Charkiewicz

2015-05-01

Full Text Available Preeclampsia (PE is the leading cause of death of the fetus and the mother. The exact pathomechanism has not so far been clarified. PE coexists with many other diseases, but it is often difficult to explain the association between them and find a clear reason for their occurrence. There are many predictive factors, but none are highly specific in preeclampsia. The diagnosis of preeclampsia seems to be very complex, which is another argument for the exploration of knowledge on this subject. Although many of the discoveries have hitherto been made in the field of proteomics, still no single specific biomarker of preeclampsia has been discovered. Research at the genome level is important because it can help us understand the genetic predisposition of patients affected by this disease. Nevertheless, researchers have recently become more interested in the pathophysiology of PE, and they are trying to answer the question: what is the real, direct cause of preeclampsia? Thus, the discovery of a protein that is a good predictor of preeclampsia development would significantly accelerate the medical care of pregnant women, and consequently reduce the risk of occurrence of HELLP syndrome and fetal death. Apart from the predictive and diagnostic function, such a discovery would help us to better understand the pathogenesis of preeclampsia and to find in the future a medical drug to suppress this disease. In order to make a breakthrough in this field, scientists need to use the most modern methods of proteomics, which allow for the analysis of small amounts of biological material in the shortest possible time, thereby giving a lot of information about existing proteins in the sample. Such optimization allows two methods, most commonly used by researchers: tandem mass spectrometry and protein microarray technique.

Biomarker Gene Signature Discovery Integrating Network Knowledge

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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.

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

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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.

Serum metabonomics of NAFLD plus T2DM based on liquid chromatography-mass spectrometry.

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Chen, Yang; Li, Chunlong; Liu, Liyan; Guo, Fuchuan; Li, Songtao; Huang, Lina; Sun, Changhao; Feng, Rennan

2016-09-01

Nonalcoholic fatty liver disease (NAFLD), a main liver disease around the world, is closely associated with insulin resistance, type 2 diabetes mellitus (T2DM) and other metabolic diseases. The objective of this study is to identify distinct metabolites of NAFLD patients with or without T2DM. We used a biomarker-discovery population to find distinct metabolites of NAFLD patients with or without T2DM. Then, a validation population was applied to test the model of the biomarker-discovery population. All the individuals received anthropometric and common biochemical measurements. The metabolic data were analyzed by multivariable statistical analyses using ultra-high-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry. There were 7, 7, 2 metabolites in the positive electrospray ionization (ESI(+)) mode, which were identified between groups from both the biomarker-discovery and validation population. The NAFLD group showed higher concentrations of oleamide, l-phenylalanine, l-proline, bilirubin, l-palmitoylcarnitine, and PC (20:5) and a lower concentration of Lyso-PAF C-18 than those of control. Compared with the control group, the NAFLD+T2DM group displayed higher oleamide, l-leucine, LysoPC (14:0), bilirubin, tetradecenoylcarnitine, linoleyl carnitine, and tetradecadiencarnitine in serum. Tetradecenoylcarnitine and tetradecadiencarnitine were more elevated in patients with NAFLD+T2DM than in the NAFLD group. Serum metabonomic analyses displayed great metabolic changes in patients with NAFLD and NAFLD plus T2DM. Our study is beneficial in providing a further view into the pathogenesis and pathophysiology of NAFLD and NAFLD plus T2DM, which might be useful for the prevention and therapy of NAFLD and NAFLD plus T2DM. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Nonylphenol Toxicity Evaluation and Discovery of Biomarkers in Rat Urine by a Metabolomics Strategy through HPLC-QTOF-MS

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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.

Mass spectrometry techniques in the survey of steroid metabolites as potential disease biomarkers: a review.

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Gouveia, Maria João; Brindley, Paul J; Santos, Lúcio Lara; Correia da Costa, José Manuel; Gomes, Paula; Vale, Nuno

2013-09-01

Mass spectrometric approaches have been fundamental to the identification of metabolites associated with steroid hormones, yet this topic has not been reviewed in depth in recent years. To this end, and given the increasing relevance of liquid chromatography-mass spectrometry (LC-MS) studies on steroid hormones and their metabolites, the present review addresses this subject. This review provides a timely summary of the use of various mass spectrometry-based analytical techniques during the evaluation of steroidal biomarkers in a range of human disease settings. The sensitivity and specificity of these technologies are clearly providing valuable new insights into breast cancer and cardiovascular disease. We aim to contribute to an enhanced understanding of steroid metabolism and how it can be profiled by LC-MS techniques. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Engineered gold nanoparticles for identification of novel ovarian biomarkers

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Giri, Karuna

Ovarian cancer is a leading cause of cancer related death among women in the US and worldwide. The disease has a high mortality rate due to limited tools available that can diagnose ovarian cancer at an early stage and the lack of effective treatments for disease free survival at late stages. Identification of proteins specifically expressed/overexpressed in ovarian cancer could lead to identification of novel diagnostic biomarkers and therapeutic targets that improve patient outcomes. In this regard, mass spectrometry is a powerful tool to probe the proteome of a cancer cell. It can aid discovery of proteins important for the pathophysiology of ovarian cancer. These proteins in turn could serve as diagnostic and treatment biomarkers of the disease. However, a limitation of mass spectrometry based proteomic analyses is that the technique lacks sensitivity and is biased against detection of low abundance proteins. With current approaches to biomarker discovery, we may therefore be overlooking candidate proteins that are important for ovarian cancer. This study presents a new approach to enrich low abundance proteins and subsequently detect them with mass spectrometry. Gold nanoparticles (AuNPs) and functionalization of their surfaces provide an excellent opportunity to capture and enrich low abundance proteins. First, the study focused on conducting an extensive investigation of the time evolution of nanoparticle-protein interaction and understanding drivers of protein attachment on nanoparticle surface. The adsorption of proteins to AuNPs was found to be highly dynamic with multiple attachment and detachment events which decreased over time. Initially, electrostatic forces played an important role in protein binding and structurally flexible proteins such as those involved in RNA processing were more likely to bind to AuNPs. More importantly, the feasibility and success of protein enrichment by AuNPs was evaluated. The AuNPs based approach was able to detect

Use of ribosomal proteins as biomarkers for identification of Flavobacterium psychrophilum by MALDI-TOF mass spectrometry.

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Fernández-Álvarez, Clara; Torres-Corral, Yolanda; Santos, Ysabel

2018-01-06

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) is a rapid methodology for identification of bacteria that is increasingly used in diagnostic laboratories. This work aimed at evaluating the potential of MALDI-TOF-MS for identification of the main serotypes of Flavobacterium psychrophilum isolated from salmonids, and its discrimination from closely related Flavobacterium spp. A mass spectra library was constructed by analysing 70 F. psychrophilum strains representing the serotypes O1, O2a, O2b and O3, including reference and clinical isolates. Peak mass lists were examined using the Mass-Up software for the detection of potential biomarkers, similarity and cluster analysis. Fourteen species-identifying biomarkers were detected in all the F. psychrophilum isolates tested, moreover, sets of serotype-identifying biomarkers ions were selected. F. psychrophilum-specific biomarkers were identified as ribosomal proteins by matching with protein databases. Furthermore, sequence variation corresponding to amino acid exchanges in several biomarker proteins were tentatively assigned. Closely related Flavobacterium species (F. flevense, F. succinicans, F. columnare, F. branchiophilum and F. johnsoniae) could be differentiated from F. psychrophilum by defining species identifying biomarkers and hierarchical cluster analysis. These results demonstrated that MALDI-TOF spectrometry represents a powerful tool for an accurate identification of the fish pathogen F. psychrophilum as well as for epidemiological studies. The results obtained in this study demonstrated that MALDI-TOF mass spectrometry represents a powerful tool that can be used by diagnostic laboratories for rapid identification of the fish pathogen Flavobacterium psychrophilum and its differentiation from other Flavobacterium-related species. Analysis of mass peak lists revealed the potential of the MALDI-TOF technique to identify epidemiologically important serotypes affecting

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

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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.

Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics.

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Gika, Helen G; Theodoridis, Georgios A; Plumb, Robert S; Wilson, Ian D

2014-01-01

Based on publication and citation numbers liquid chromatography (LC-MS) has become the major analytical technology in the field of global metabolite profiling. This dominance reflects significant investments from both the research community and instrument manufacturers. Here an overview of the approaches taken for LC-MS-based metabolomics research is given, describing critical steps in the realisation of such studies: study design and its needs, specific technological problems to be addressed and major obstacles in data treatment and biomarker identification. The current state of the art for LC-MS-based analysis in metabonomics/metabolomics is described including recent developments in liquid chromatography, mass spectrometry and data treatment as these are applied in metabolomics underlining the challenges, limitations and prospects for metabolomics research. Examples of the application of metabolite profiling in the life sciences focusing on disease biomarker discovery are highlighted. In addition, new developments and future prospects are described. Copyright © 2013 Elsevier B.V. All rights reserved.

Perspectives on bioanalytical mass spectrometry and automation in drug discovery.

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Janiszewski, John S; Liston, Theodore E; Cole, Mark J

2008-11-01

The use of high speed synthesis technologies has resulted in a steady increase in the number of new chemical entities active in the drug discovery research stream. Large organizations can have thousands of chemical entities in various stages of testing and evaluation across numerous projects on a weekly basis. Qualitative and quantitative measurements made using LC/MS are integrated throughout this process from early stage lead generation through candidate nomination. Nearly all analytical processes and procedures in modern research organizations are automated to some degree. This includes both hardware and software automation. In this review we discuss bioanalytical mass spectrometry and automation as components of the analytical chemistry infrastructure in pharma. Analytical chemists are presented as members of distinct groups with similar skillsets that build automated systems, manage test compounds, assays and reagents, and deliver data to project teams. The ADME-screening process in drug discovery is used as a model to highlight the relationships between analytical tasks in drug discovery. Emerging software and process automation tools are described that can potentially address gaps and link analytical chemistry related tasks. The role of analytical chemists and groups in modern 'industrialized' drug discovery is also discussed.

Metabolic profiling of potential lung cancer biomarkers using bronchoalveolar lavage fluid and the integrated direct infusion/ gas chromatography mass spectrometry platform.

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Callejón-Leblic, Belén; García-Barrera, Tamara; Grávalos-Guzmán, Jesús; Pereira-Vega, Antonio; Gómez-Ariza, José Luis

2016-08-11

Lung cancer is one of the ten most common causes of death worldwide, so that the search for early diagnosis biomarkers is a very challenging task. Bronchoalveolar lavage fluid (BALF) provides information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and no previous metabolomic studies have been performed with BALF samples from patients with lung cancer. Therefore, this fluid has been explored looking for new contributions in lung cancer metabolism. In this way, two complementary metabolomics techniques based on direct infusion high resolution mass spectrometry (DI-ESI-QTOF-MS) and gas chromatography mass spectrometry (GC-MS) have been applied to compare statistically differences between lung cancer (LC) and control (C) BALF samples, using partial least square discriminant analysis (PLS-DA) in order to find and identify potential biomarkers of the disease. A total of 42 altered metabolites were found in BALF from LC. The metabolic pathway analysis showed that glutamate and glutamine metabolism pathway was mainly altered by this disease. In addition, we assessed the biomarker specificity and sensitivity according to the area under the receiver operator characteristic (ROC) curves, indicating that glycerol and phosphoric acid were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. The search for early diagnosis of lung cancer is a very challenging task because of the high mortality associated to this disease and its critical linkage to the initiation of treatment. Bronchoalveolar lavage fluid provides information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and no previous metabolomic studies have been performed with BALF samples from patients with lung cancer. Since BALF is in close interaction with lung tissue it is a more representative sample of lung status than other peripheral biofluids as blood or urine studied in previous works

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

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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.

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data

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Bean, Heather D.; Hill, Jane E.; Dimandja, Jean-Marie D.

2015-01-01

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly- resolved peaks, especially those at the extremes of the detector linear range, and no influence on well- chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. PMID:25857541

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data.

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Bean, Heather D; Hill, Jane E; Dimandja, Jean-Marie D

2015-05-15

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. Copyright © 2015 Elsevier B.V. All rights reserved.

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

Discovery of Novel Biomarkers for Alzheimer’s Disease from Blood

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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%.

BioSunMS: a plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry

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Zhang Xuemin

2009-02-01

Full Text Available Abstract Background With wide applications of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS, statistical comparison of serum peptide profiles and management of patients information play an important role in clinical studies, such as early diagnosis, personalized medicine and biomarker discovery. However, current available software tools mainly focused on data analysis rather than providing a flexible platform for both the management of patients information and mass spectrometry (MS data analysis. Results Here we presented a plug-in-based software, BioSunMS, for both the management of patients information and serum peptide profiles-based statistical analysis. By integrating all functions into a user-friendly desktop application, BioSunMS provided a comprehensive solution for clinical researchers without any knowledge in programming, as well as a plug-in architecture platform with the possibility for developers to add or modify functions without need to recompile the entire application. Conclusion BioSunMS provides a plug-in-based solution for managing, analyzing, and sharing high volumes of MALDI-TOF or SELDI-TOF MS data. The software is freely distributed under GNU General Public License (GPL and can be downloaded from http://sourceforge.net/projects/biosunms/.

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

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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 ).

Biomarkers for predicting complete debulking in ovarian cancer

DEFF Research Database (Denmark)

Fagö-Olsen, Carsten Lindberg; Ottesen, Bent; Christensen, Ib Jarle

2014-01-01

AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients. PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were...... used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new.......64. CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer....

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

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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

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.

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

LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data.

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Koelmel, Jeremy P; Kroeger, Nicholas M; Ulmer, Candice Z; Bowden, John A; Patterson, Rainey E; Cochran, Jason A; Beecher, Christopher W W; Garrett, Timothy J; Yost, Richard A

2017-07-10

Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry

[Search for potential gastric cancer biomarkers using low molecular weight blood plasma proteome profiling by mass spectrometry].

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Shevchenko, V E; Arnotskaia, N E; Ogorodnikova, E V; Davydov, M M; Ibraev, M A; Turkin, I N; Davydov, M I

2014-01-01

Gastric cancer, one of the most widespread malignant tumors, still lacks reliable serum/plasma biomarkers of its early detection. In this study we have developed, unified, and tested a new methodology for search of gastric cancer biomarkers based on profiling of low molecular weight proteome (LMWP) (1-17 kDa). This approach included three main components: sample pre-fractionation, matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), data analysis by a bioinformatics software package. Applicability and perspectives of the developed approach for detection of potential gastric cancer markers during LMWP analysis have been demonstrated using 69 plasma samples from patients with gastric cancer (stages I-IV) and 238 control samples. The study revealed peptides/polypeptides, which may be potentially used for detection of this pathology.

Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging

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Lou, Sha; Balluff, Benjamin; Cleven, Arjen H. G.; Bovée, Judith V. M. G.; McDonnell, Liam A.

2017-02-01

Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival ( m/z 180.9436 and 241.0118) and metastasis-free survival ( m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine.

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

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

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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.

Zooming into Molecular Biomarker Distribution through Spatially Resolved Mass Spectrometry on Intact Sediment Sections

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Wörmer, L.; Fuchser, J.; Alfken, S.; Elvert, M.; Schimmelmann, A.; Hinrichs, K. U.

2016-02-01

Marine microorganisms adapt to their habitat by structural modification of their membrane lipids. After sedimentation, and due to their persistence in the sedimentary record, the information archived in them remains available on geological time-scales. Thereby sedimentary lipid biomarkers become important informants of past environments. Conventional biomarker analysis is labor-intensive and requires cm-sized samples, temporal resolution is consequently low. We here present an approach, based on laser desorption ionization (LDI) coupled to ultra high resolution mass spectrometry, that avoids wet-chemical sample preparation and enables analysis directly on sediment sections at sub-mm spatial resolution. Our initial study targeted archaeal glycerol dialkyl glycerol tetraethers (GDGTs). GDGTS are ubiquitous and persistent components in marine sediments, and used in several, widely recognized paleoenvironmental proxies. Applied to an Eastern Mediterranean Sapropel layer, GDGT-profiles with previously unachieved temporal resolution were obtained, and pointed to a strong influence of high frequency cycles on sea-surface temperature and planktonic archaeal ecology. Spatial information furthermore revealed a new view on the fine-scale patchiness of lipid distribution. Following these pioneering studies, major developments are under way. A dedicated facility has been set up at MARUM/University of Bremen, which combines lipid biomarker and elemental analysis at sub-mm resolution (down to 50 µm). We present methods for other comprehensive lipid biomarkers (e.g. alkenones or sterols) that are currently being targeted; and the application of spatially resolved biomarker analysis to recent laminated sediments (Santa Barbara Basin), yielding informative profiles with subannual resolution. We also discuss criteria for analyte and sample selection, as well as the main potentialities and constraints of this new approach.

Mass Spectrometry-Based Methods for Identifying Oxidized Proteins in Disease: Advances and Challenges

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Ivan Verrastro

2015-04-01

Full Text Available Many inflammatory diseases have an oxidative aetiology, which leads to oxidative damage to biomolecules, including proteins. It is now increasingly recognized that oxidative post-translational modifications (oxPTMs of proteins affect cell signalling and behaviour, and can contribute to pathology. Moreover, oxidized proteins have potential as biomarkers for inflammatory diseases. Although many assays for generic protein oxidation and breakdown products of protein oxidation are available, only advanced tandem mass spectrometry approaches have the power to localize specific oxPTMs in identified proteins. While much work has been carried out using untargeted or discovery mass spectrometry approaches, identification of oxPTMs in disease has benefitted from the development of sophisticated targeted or semi-targeted scanning routines, combined with chemical labeling and enrichment approaches. Nevertheless, many potential pitfalls exist which can result in incorrect identifications. This review explains the limitations, advantages and challenges of all of these approaches to detecting oxidatively modified proteins, and provides an update on recent literature in which they have been used to detect and quantify protein oxidation in disease.

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

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

Evaluation of mass spectrometry of urinary proteins and peptides as biomarkers for cats at risk of developing azotemia.

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Jepson, Rosanne E; Coulton, Gary R; Cowan, Matthew L; Markwell, Peter; Syme, Harriet M; Elliott, Jonathan

2013-02-01

To evaluate proteomic delineation of feline urine by mass spectrometry as a method for identifying biomarkers in cats at risk of developing azotemia. Urine samples from geriatric cats (> 9 years old) with chronic kidney disease and nonazotemic cats that either remained nonazotemic (n = 10) or developed azotemia (10) within 1 year. Optimization studies with pooled urine were performed to facilitate the use of surface enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF-MS) for analysis of the urinary proteome of cats. Urine samples from nonazotemic cats at entry to the study were analyzed via SELDI-TOF-MS with weak cation exchange and strong anion exchange arrays. Spectral data were compared to identify biomarkers for development of azotemia. Low protein concentration in feline urine precluded direct application to array surfaces, and a buffer exchange and concentration step was required prior to SELDI-TOF-MS analysis. Three preparation conditions by use of weak cation and strong anion exchange arrays were selected on the basis of optimization studies for detection of biomarkers. Eight potential biomarkers with an m/z of 2,822, 9,886, 10,033, 10,151, 10,234, 11,653, 4,421, and 9,505 were delineated. SELDI-TOF-MS can be used to detect urinary low-molecular weight peptides and proteins that may represent biomarkers for early detection of renal damage. Further study is required to purify and identify potential biomarkers before their use in a clinical setting.

New method for the discovery of adulterated cognacs and brandies based on solid-phase microextraction and gas chromatography - mass spectrometry

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Darya Mozhayeva

2014-10-01

Full Text Available The article represents new method for discovery of adulterated cognacs and brandies based on solidphase microextraction (SPME in combination with gas chromatography – mass spectrometry (GC-MS. The work comprised optimization of SPME parameters (extraction temperature and time, concentration of added salt with subsequent analysis of authentic samples and comparison of the obtained chromatograms using principal component analysis (PCA. According to the obtained results, increase of extraction temperature resulted in an increase of response of the most volatile brandy constituents. To avoid chemical transformations and/or degradation of the samples, the extraction temperature must be limited to 30!C. Increase of the extraction time lead to higher total peak area, but longer extraction times (>10 min for 100 µm polydimethylsiloxane and >2 min for divinylbenzene/Carboxen/polydimethylsiloxane fibers caused displacement of analytes. Salt addition increased total response of analytes, but caused problems with reproducibility. The developed method was successfully applied for discovery of adulterated samples of brandy, cognac, whisky and whiskey sold in Kazakhstan. The obtained data was analyzed applying principal component analysis (PCA. Five adulterated brandy and whisky samples were discovered and confirmed. The developed method is recommended for application in forensic laboratories.

Integrative analysis to select cancer candidate biomarkers to targeted validation

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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

Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis

Directory of Open Access Journals (Sweden)

Kaur Parminder

2012-08-01

Full Text Available Abstract Background An approach to molecular classification based on the comparative expression of protein pairs is presented. The method overcomes some of the present limitations in using peptide intensity data for class prediction for problems such as the detection of a disease, disease prognosis, or for predicting treatment response. Data analysis is particularly challenging in these situations due to sample size (typically tens being much smaller than the large number of peptides (typically thousands. Methods based upon high dimensional statistical models, machine learning or other complex classifiers generate decisions which may be very accurate but can be complex and difficult to interpret in simple or biologically meaningful terms. A classification scheme, called ProtPair, is presented that generates simple decision rules leading to accurate classification which is based on measurement of very few proteins and requires only relative expression values, providing specific targeted hypotheses suitable for straightforward validation. Results ProtPair has been tested against clinical data from 21 patients following a bone marrow transplant, 13 of which progress to idiopathic pneumonia syndrome (IPS. The approach combines multiple peptide pairs originating from the same set of proteins, with each unique peptide pair providing an independent measure of discriminatory power. The prediction rate of the ProtPair for IPS study as measured by leave-one-out CV is 69.1%, which can be very beneficial for clinical diagnosis as it may flag patients in need of closer monitoring. The “top ranked” proteins provided by ProtPair are known to be associated with the biological processes and pathways intimately associated with known IPS biology based on mouse models. Conclusions An approach to biomarker discovery, called ProtPair, is presented. ProtPair is based on the differential expression of pairs of peptides and the associated proteins. Using mass

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...

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.

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

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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.

MALDI-TOF mass spectrometry analysis of small molecular weight compounds (under 10 KDa) as biomarkers of rat hearts undergoing arecoline challenge.

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Chen, Tung-Sheng; Chang, Mu-Hsin; Kuo, Wei-Wen; Lin, Yueh-Min; Yeh, Yu-Lan; Day, Cecilia Hsuan; Lin, Chien-Chung; Tsai, Fuu-Jen; Tsai, Chang-Hai; Huang, Chih-Yang

2013-04-01

Statistical and clinical reports indicate that betel nut chewing is strongly associated with progression of oral cancer because some ingredients in betel nuts are potential cancer promoters, especially arecoline. Early diagnosis for cancer biomarkers is the best strategy for prevention of cancer progression. Several methods are suggested for investigating cancer biomarkers. Among these methods, gel-based proteomics approach is the most powerful and recommended tool for investigating biomarkers due to its high-throughput. However, this proteomics approach is not suitable for screening biomarkers with molecular weight under 10 KDa because of the characteristics of gel electrophoresis. This study investigated biomarkers with molecular weight under 10 KDa in rats with arecoline challenge. The centrifuging vials with membrane (10 KDa molecular weight cut-off) played a crucial role in this study. After centrifuging, the filtrate (containing compounds with molecular weight under 10 KDa) was collected and spotted on a sample plate for MALDI-TOF mass spectrometry analysis. Compared to control, three extra peaks (m/z values were 1553.1611, 1668.2097 and 1740.1832, respectively) were found in sera and two extra peaks were found in heart tissue samples (408.9719 and 524.9961, respectively). These small compounds should play important roles and may be potential biomarker candidates in rats with arecoline. This study successfully reports a mass-based method for investigating biomarker candidates with small molecular weight in different types of sample (including serum and tissue). In addition, this reported method is more time-efficient (1 working day) than gel-based proteomics approach (5~7 working days).

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

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

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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.

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

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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.

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...

Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry.

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Hmmier, Abduladim; O'Brien, Michael Emmet; Lynch, Vincent; Clynes, Martin; Morgan, Ross; Dowling, Paul

2017-06-01

Lung cancer is the leading cause of cancer-related mortality in both men and women throughout the world. The need to detect lung cancer at an early, potentially curable stage, is essential and may reduce mortality by 20%. The aim of this study was to identify distinct proteomic profiles in bronchoalveolar fluid (BALF) and plasma that are able to discriminate individuals with benign disease from those with non-small cell lung cancer (NSCLC). Using label-free mass spectrometry analysis of BALF during discovery-phase analysis, a significant number of proteins were found to have different abundance levels when comparing control to adenocarcinoma (AD) or squamous cell lung carcinoma (SqCC). Validation of candidate biomarkers identified in BALF was performed in a larger cohort of plasma samples by detection with enzyme-linked immunoassay. Four proteins (Cystatin-C, TIMP-1, Lipocalin-2 and HSP70/HSPA1A) were selected as a representative group from discovery phase mass spectrometry BALF analysis. Plasma levels of TIMP-1, Lipocalin-2 and Cystatin-C were found to be significantly elevated in AD and SqCC compared to control. The results presented in this study indicate that BALF is an important proximal biofluid for the discovery and identification of candidate lung cancer biomarkers. There is good correlation between the trend of protein abundance levels in BALF and that of plasma which validates this approach to develop a blood biomarker to aid lung cancer diagnosis, particularly in the era of lung cancer screening. The protein signatures identified also provide insight into the molecular mechanisms associated with lung malignancy.

Serum Biomarker Identification by Mass Spectrometry in Acute Aortic Dissection

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Yong Ren

2017-12-01

Full Text Available Background/Aims: Aortic dissection (AD is also known as intramural hematoma. This study aimed to screen peripheral blood biomarkers of small molecule metabolites for AD using high-performance liquid chromatography-mass spectrometry (HPLC-MS. Methods: Sera from 25 healthy subjects, 25 patients with well-established AD, and 25 patients with well-established hypertension were investigated by HPLC-MS to detect metabolites, screen differentially expressed metabolites, and analyze metabolic pathways. Results: Twenty-six and four metabolites were significantly up- and down-regulated in the hypertensive patients compared with the healthy subjects; 165 metabolites were significantly up-regulated and 109 significantly down-regulated in the AD patients compared with the hypertensive patients. Of these metabolites, 35 were up-regulated and 105 down-regulated only in AD patients. The metabolites that were differentially expressed in AD are mainly involved in tryptophan, histidine, glycerophospholipid, ether lipid, and choline metabolic pathways. As AD alters the peripheral blood metabolome, analysis of peripheral blood metabolites can be used in auxiliary diagnosis of AD. Conclusion: Eight metabolites are potential biomarkers for AD, 3 of which were differentially expressed and can be used for auxiliary diagnosis of AD and evaluation of treatment effectiveness.

Potential of Mass Spectrometry in Developing Clinical Laboratory Biomarkers of Nonvolatiles in Exhaled Breath.

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Beck, Olof; Olin, Anna-Carin; Mirgorodskaya, Ekaterina

2016-01-01

Exhaled breath contains nonvolatile substances that are part of aerosol particles of submicrometer size. These particles are formed and exhaled as a result of normal breathing and contain material from distal airways of the respiratory system. Exhaled breath can be used to monitor biomarkers of both endogenous and exogenous origin and constitutes an attractive specimen for medical investigations. This review summarizes the present status regarding potential biomarkers of nonvolatile compounds in exhaled breath. The field of exhaled breath condensate is briefly reviewed, together with more recent work on more selective collection procedures for exhaled particles. The relation of these particles to the surfactant in the terminal parts of the respiratory system is described. The literature on potential endogenous low molecular weight compounds as well as protein biomarkers is reviewed. The possibility to measure exposure to therapeutic and abused drugs is demonstrated. Finally, the potential future role and importance of mass spectrometry is discussed. Nonvolatile compounds exit the lung as aerosol particles that can be sampled easily and selectively. The clinical applications of potential biomarkers in exhaled breath comprise diagnosis of disease, monitoring of disease progress, monitoring of drug therapy, and toxicological investigations. © 2015 American Association for Clinical Chemistry.

Opportunities and Challenges of Proteomics in Pediatric Patients: Circulating Biomarkers After Hematopoietic Stem Cell Transplantation As a Successful Example

Science.gov (United States)

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

Low molecular weight protein enrichment on mesoporous silica thin films for biomarker discovery.

Science.gov (United States)

Fan, Jia; Gallagher, James W; Wu, Hung-Jen; Landry, Matthew G; Sakamoto, Jason; Ferrari, Mauro; Hu, Ye

2012-04-17

The identification of circulating biomarkers holds great potential for non invasive approaches in early diagnosis and prognosis, as well as for the monitoring of therapeutic efficiency.(1-3) The circulating low molecular weight proteome (LMWP) composed of small proteins shed from tissues and cells or peptide fragments derived from the proteolytic degradation of larger proteins, has been associated with the pathological condition in patients and likely reflects the state of disease.(4,5) Despite these potential clinical applications, the use of Mass Spectrometry (MS) to profile the LMWP from biological fluids has proven to be very challenging due to the large dynamic range of protein and peptide concentrations in serum.(6) Without sample pre-treatment, some of the more highly abundant proteins obscure the detection of low-abundance species in serum/plasma. Current proteomic-based approaches, such as two-dimensional polyacrylamide gel-electrophoresis (2D-PAGE) and shotgun proteomics methods are labor-intensive, low throughput and offer limited suitability for clinical applications.(7-9) Therefore, a more effective strategy is needed to isolate LMWP from blood and allow the high throughput screening of clinical samples. Here, we present a fast, efficient and reliable multi-fractionation system based on mesoporous silica chips to specifically target and enrich LMWP.(10,11) Mesoporous silica (MPS) thin films with tunable features at the nanoscale were fabricated using the triblock copolymer template pathway. Using different polymer templates and polymer concentrations in the precursor solution, various pore size distributions, pore structures, connectivity and surface properties were determined and applied for selective recovery of low mass proteins. The selective parsing of the enriched peptides into different subclasses according to their physicochemical properties will enhance the efficiency of recovery and detection of low abundance species. In combination with mass

Mass spectrometry applied to the identification of Mycobacterium tuberculosis and biomarker discovery.

Science.gov (United States)

López-Hernández, Y; Patiño-Rodríguez, O; García-Orta, S T; Pinos-Rodríguez, J M

2016-12-01

An adequate and effective tuberculosis (TB) diagnosis system has been identified by the World Health Organization as a priority in the fight against this disease. Over the years, several methods have been developed to identify the bacillus, but bacterial culture remains one of the most affordable methods for most countries. For rapid and accurate identification, however, it is more feasible to implement molecular techniques, taking advantage of the availability of public databases containing protein sequences. Mass spectrometry (MS) has become an interesting technique for the identification of TB. Here, we review some of the most widely employed methods for identifying Mycobacterium tuberculosis and present an update on MS applied for the identification of mycobacterial species. © 2016 The Society for Applied Microbiology.

Comparative Analysis of Mass Spectral Similarity Measures on Peak Alignment for Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry

Science.gov (United States)

2013-01-01

Peak alignment is a critical procedure in mass spectrometry-based biomarker discovery in metabolomics. One of peak alignment approaches to comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) data is peak matching-based alignment. A key to the peak matching-based alignment is the calculation of mass spectral similarity scores. Various mass spectral similarity measures have been developed mainly for compound identification, but the effect of these spectral similarity measures on the performance of peak matching-based alignment still remains unknown. Therefore, we selected five mass spectral similarity measures, cosine correlation, Pearson's correlation, Spearman's correlation, partial correlation, and part correlation, and examined their effects on peak alignment using two sets of experimental GC×GC-MS data. The results show that the spectral similarity measure does not affect the alignment accuracy significantly in analysis of data from less complex samples, while the partial correlation performs much better than other spectral similarity measures when analyzing experimental data acquired from complex biological samples. PMID:24151524

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

Science.gov (United States)

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

Disease Classification and Biomarker Discovery Using ECG Data

Directory of Open Access Journals (Sweden)

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.

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

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.

Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry.

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Planatscher, Hannes; Supper, Jochen; Poetz, Oliver; Stoll, Dieter; Joos, Thomas; Templin, Markus F; Zell, Andreas

2010-06-25

Mass spectrometry (MS) based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency. We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties. For small datasets (a few hundred proteins) it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics.

The Role of Mass Spectrometry-Based Metabolomics in Medical Countermeasures Against Radiation

Science.gov (United States)

Patterson, Andrew D.; Lanz, Christian; Gonzalez, Frank J.; Idle, Jeffrey R.

2013-01-01

Radiation metabolomics can be defined as the global profiling of biological fluids to uncover latent, endogenous small molecules whose concentrations change in a dose-response manner following exposure to ionizing radiation. In response to the potential threat of nuclear or radiological terrorism, the Center for High-Throughput Minimally Invasive Radiation Biodosimetry (CMCR) was established to develop field-deployable biodosimeters based, in principle, on rapid analysis by mass spectrometry of readily and easily obtainable biofluids. In this review, we briefly summarize radiation biology and key events related to actual and potential nuclear disasters, discuss the important contributions the field of mass spectrometry has made to the field of radiation metabolomics, and summarize current discovery efforts to use mass spectrometry-based metabolomics to identify dose-responsive urinary constituents, and ultimately to build and deploy a noninvasive high-throughput biodosimeter. PMID:19890938

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.

Proteogenomic biomarkers for identification of Francisella species and subspecies by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry.

Science.gov (United States)

Durighello, Emie; Bellanger, Laurent; Ezan, Eric; Armengaud, Jean

2014-10-07

Francisella tularensis is the causative agent of tularemia. Because some Francisella strains are very virulent, this species is considered by the Centers for Disease Control and Prevention to be a potential category A bioweapon. A mass spectrometry method to quickly and robustly distinguish between virulent and nonvirulent Francisella strains is desirable. A combination of shotgun proteomics and whole-cell matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry on the Francisella tularensis subsp. holarctica LVS defined three protein biomarkers that allow such discrimination: the histone-like protein HU form B, the 10 kDa chaperonin Cpn10, and the 50S ribosomal protein L24. We established that their combined detection by whole-cell MALDI-TOF spectrum could enable (i) the identification of Francisella species, and (ii) the prediction of their virulence level, i.e., gain of a taxonomical level with the identification of Francisella tularensis subspecies. The detection of these biomarkers by MALDI-TOF mass spectrometry is straightforward because of their abundance and the absence of other abundant protein species closely related in terms of m/z. The predicted molecular weights for the three biomarkers and their presence as intense peaks were confirmed with MALDI-TOF/MS spectra acquired on Francisella philomiragia ATCC 25015 and on Francisella tularensis subsp. tularensis CCUG 2112, the most virulent Francisella subspecies.

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

Science.gov (United States)

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

Serum Biomarker Identification by Mass Spectrometry in Acute Aortic Dissection.

Science.gov (United States)

Ren, Yong; Tang, Qizhu; Liu, Wenwei; Tang, Yongqian; Zhu, Rui; Li, Bin

2017-01-01

Aortic dissection (AD) is also known as intramural hematoma. This study aimed to screen peripheral blood biomarkers of small molecule metabolites for AD using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Sera from 25 healthy subjects, 25 patients with well-established AD, and 25 patients with well-established hypertension were investigated by HPLC-MS to detect metabolites, screen differentially expressed metabolites, and analyze metabolic pathways. Twenty-six and four metabolites were significantly up- and down-regulated in the hypertensive patients compared with the healthy subjects; 165 metabolites were significantly up-regulated and 109 significantly down-regulated in the AD patients compared with the hypertensive patients. Of these metabolites, 35 were up-regulated and 105 down-regulated only in AD patients. The metabolites that were differentially expressed in AD are mainly involved in tryptophan, histidine, glycerophospholipid, ether lipid, and choline metabolic pathways. As AD alters the peripheral blood metabolome, analysis of peripheral blood metabolites can be used in auxiliary diagnosis of AD. Eight metabolites are potential biomarkers for AD, 3 of which were differentially expressed and can be used for auxiliary diagnosis of AD and evaluation of treatment effectiveness. © 2017 The Author(s). Published by S. Karger AG, Basel.

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.

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

Science.gov (United States)

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...

Mass spectrometry-based metabolic profiling of gemcitabine-sensitive and gemcitabine-resistant pancreatic cancer cells.

Science.gov (United States)

Fujimura, Yoshinori; Ikenaga, Naoki; Ohuchida, Kenoki; Setoyama, Daiki; Irie, Miho; Miura, Daisuke; Wariishi, Hiroyuki; Murata, Masaharu; Mizumoto, Kazuhiro; Hashizume, Makoto; Tanaka, Masao

2014-03-01

Gemcitabine resistance (GR) is one of the critical issues for therapy for pancreatic cancer, but the mechanism still remains unclear. Our aim was to increase the understanding of GR by metabolic profiling approach. To establish GR cells, 2 human pancreatic cancer cell lines, SUIT-2 and CAPAN-1, were exposed to increasing concentration of gemcitabine. Both parental and chemoresistant cells obtained by this treatment were subjected to metabolic profiling based on liquid chromatography-mass spectrometry. Multivariate statistical analyses, both principal component analysis and orthogonal partial least squares discriminant analysis, distinguished metabolic signature of responsiveness and resistance to gemcitabine in both SUIT-2 and CAPAN-1 cells. Among significantly different (P metabolic pathways such as amino acid, nucleotide, energy, cofactor, and vitamin pathways. Decreases in glutamine and proline levels as well as increases in aspartate, hydroxyproline, creatine, and creatinine levels were observed in chemoresistant cells from both cell lines. These results suggest that metabolic profiling can isolate distinct features of pancreatic cancer in the metabolome of gemcitabine-sensitive and GR cells. These findings may contribute to the biomarker discovery and an enhanced understanding of GR in pancreatic cancer.

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

Biomarker identification and pathway analysis of preeclampsia based on serum metabolomics.

Science.gov (United States)

Chen, Tingting; He, Ping; Tan, Yong; Xu, Dongying

2017-03-25

Preeclampsia presents serious risk of both maternal and fetal morbidity and mortality. Biomarkers for the detection of preeclampsia are critical for risk assessment and targeted intervention. The goal of this study is to screen potential biomarkers for the diagnosis of preeclampsia and to illuminate the pathogenesis of preeclampsia development based on the differential expression network. Two groups of subjects, including healthy pregnant women, subjects with preeclampsia, were recruited for this study. The metabolic profiles of all of the subjects' serum were obtained by liquid chromatography quadruple time-of-flight mass spectrometry. Correlation between metabolites was analyzed by bioinformatics technique. Results showed that the PC(14:0/00), proline betaine and proline were potential sensitive and specific biomarkers for preeclampsia diagnosis and prognosis. Perturbation of corresponding biological pathways, such as iNOS signaling, nitric oxide signaling in the cardiovascular system, mitochondrial dysfunction were responsible for the pathogenesis of preeclampsia. This study indicated that the metabolic profiling had a good clinical significance in the diagnosis of preeclampsia as well as in the study of its pathogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Assuring Consistent Performance of an Insulin-Like Growth Factor 1 MALDImmunoassay by Monitoring Measurement Quality Indicators

NARCIS (Netherlands)

Klont, Frank; ten Hacken, Nick H. T.; Horvatovich, Peter; Bakker, Stephan J. L.; Bischoff, Rainer

2017-01-01

Analytical methods based on mass spectrometry (MS) have been successfully applied in biomarker discovery studies, while the role of MS in translating biomarker candidates to clinical diagnostics is less pronounced. MALDImnmunoassays methods that combine immunoaffinity enrichment with matrix-assisted

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.

Differential Mobility Spectrometry-Mass Spectrometry (DMS-MS) in Radiation Biodosimetry: Rapid and High-Throughput Quantitation of Multiple Radiation Biomarkers in Nonhuman Primate Urine

Science.gov (United States)

Chen, Zhidan; Coy, Stephen L.; Pannkuk, Evan L.; Laiakis, Evagelia C.; Fornace, Albert J.; Vouros, Paul

2018-05-01

High-throughput methods to assess radiation exposure are a priority due to concerns that include nuclear power accidents, the spread of nuclear weapon capability, and the risk of terrorist attacks. Metabolomics, the assessment of small molecules in an easily accessible sample, is the most recent method to be applied for the identification of biomarkers of the biological radiation response with a useful dose-response profile. Profiling for biomarker identification is frequently done using an LC-MS platform which has limited throughput due to the time-consuming nature of chromatography. We present here a chromatography-free simplified method for quantitative analysis of seven metabolites in urine with radiation dose-response using urine samples provided from the Pannkuk et al. (2015) study of long-term (7-day) radiation response in nonhuman primates (NHP). The stable isotope dilution (SID) analytical method consists of sample preparation by strong cation exchange-solid phase extraction (SCX-SPE) to remove interferences and concentrate the metabolites of interest, followed by differential mobility spectrometry (DMS) ion filtration to select the ion of interest and reduce chemical background, followed by mass spectrometry (overall SID-SPE-DMS-MS). Since no chromatography is used, calibration curves were prepared rapidly, in under 2 h (including SPE) for six simultaneously analyzed radiation biomarkers. The seventh, creatinine, was measured separately after 2500× dilution. Creatinine plays a dual role, measuring kidney glomerular filtration rate (GFR), and indicating kidney damage at high doses. The current quantitative method using SID-SPE-DMS-MS provides throughput which is 7.5 to 30 times higher than that of LC-MS and provides a path to pre-clinical radiation dose estimation. [Figure not available: see fulltext.

Differential Mobility Spectrometry-Mass Spectrometry (DMS-MS) in Radiation Biodosimetry: Rapid and High-Throughput Quantitation of Multiple Radiation Biomarkers in Nonhuman Primate Urine.

Science.gov (United States)

Chen, Zhidan; Coy, Stephen L; Pannkuk, Evan L; Laiakis, Evagelia C; Fornace, Albert J; Vouros, Paul

2018-05-07

High-throughput methods to assess radiation exposure are a priority due to concerns that include nuclear power accidents, the spread of nuclear weapon capability, and the risk of terrorist attacks. Metabolomics, the assessment of small molecules in an easily accessible sample, is the most recent method to be applied for the identification of biomarkers of the biological radiation response with a useful dose-response profile. Profiling for biomarker identification is frequently done using an LC-MS platform which has limited throughput due to the time-consuming nature of chromatography. We present here a chromatography-free simplified method for quantitative analysis of seven metabolites in urine with radiation dose-response using urine samples provided from the Pannkuk et al. (2015) study of long-term (7-day) radiation response in nonhuman primates (NHP). The stable isotope dilution (SID) analytical method consists of sample preparation by strong cation exchange-solid phase extraction (SCX-SPE) to remove interferences and concentrate the metabolites of interest, followed by differential mobility spectrometry (DMS) ion filtration to select the ion of interest and reduce chemical background, followed by mass spectrometry (overall SID-SPE-DMS-MS). Since no chromatography is used, calibration curves were prepared rapidly, in under 2 h (including SPE) for six simultaneously analyzed radiation biomarkers. The seventh, creatinine, was measured separately after 2500× dilution. Creatinine plays a dual role, measuring kidney glomerular filtration rate (GFR), and indicating kidney damage at high doses. The current quantitative method using SID-SPE-DMS-MS provides throughput which is 7.5 to 30 times higher than that of LC-MS and provides a path to pre-clinical radiation dose estimation. Graphical Abstract.

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

Science.gov (United States)

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.

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.

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.

Haptoglobin is a serological biomarker for adenocarcinoma lung cancer by using the ProteomeLab PF2D combined with mass spectrometry.

Science.gov (United States)

Chang, You-Kang; Lai, Yu-Heng; Chu, Yen; Lee, Ming-Cheng; Huang, Chun-Yao; Wu, Semon

2016-01-01

Identification of serological biomarker is urgently needed for cancer screening, monitoring cancer progression, treatment response, and surveillance for recurrence in lung cancer. Therefore, we try to find new serological biomarker that has more specificity and sensitivity for lung cancer diagnostics. In this study, the 2-D liquid phase fractionation system (PF2D) and mass spectrometry approach has been used for comparison the serum profiles between lung cancer patients and healthy individuals. Eight proteins were identified form PF2D and subsequently by mass spectrometry. Among these proteins, haptoglobin (HP) and apolipoprotein AI (APOA1) were chosen and validated with turbidimetric assay. We found that HP levels were significantly higher and APOA1 levels were significantly lower in lung cancer patients. However, after the participants were stratified by gender, the expression trends of HP and APOA1 in lung cancer patients existed only in men, which is gender specific phenomenon. HP, APOA1 and carcinoembryonic antigen (CEA), used for distinguishing lung adenocarcinoma, had a sensitivity of 64%, 64% and 79%, respectively. Area under the ROC curve (AUC) of HP, APOA1 and CEA were 0.768, 0.761 and 0.884, respectively. When restricted to male subjects, HP, APOA1 and CEA showed sensitivity of 89%, 73% and 100%, respectively. AUC of HP, APOA1 and CEA were 0.929, 0.840 and 0.877, respectively. Therefore, our results showed that combined with PF2D system and mass spectrometry, this is a promising novel approach to identify new serological biomarkers for lung cancer research. In addition, HP may be a potential serological biomarker for lung adenocarcinoma diagnostics, especially in male subjects.

Direct molecular analysis of whole-body animal tissue sections by MALDI imaging mass spectrometry.

Science.gov (United States)

Reyzer, Michelle L; Chaurand, Pierre; Angel, Peggi M; Caprioli, Richard M

2010-01-01

The determination of the localization of various compounds in a whole animal is valuable for many applications, including pharmaceutical absorption, distribution, metabolism, and excretion (ADME) studies and biomarker discovery. Imaging mass spectrometry is a powerful tool for localizing compounds of biological interest with molecular specificity and relatively high resolution. Utilizing imaging mass spectrometry for whole-body animal sections offers considerable analytical advantages compared to traditional methods, such as whole-body autoradiography, but the experiment is not straightforward. This chapter addresses the advantages and unique challenges that the application of imaging mass spectrometry to whole-body animal sections entails, including discussions of sample preparation, matrix application, signal normalization, and image generation. Lipid and protein images obtained from whole-body tissue sections of mouse pups are presented along with detailed protocols for the experiments.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications.

Science.gov (United States)

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.

MALDI TOF imaging mass spectrometry in clinical pathology: a valuable tool for cancer diagnostics (review).

Science.gov (United States)

Kriegsmann, Jörg; Kriegsmann, Mark; Casadonte, Rita

2015-03-01

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) is an evolving technique in cancer diagnostics and combines the advantages of mass spectrometry (proteomics), detection of numerous molecules, and spatial resolution in histological tissue sections and cytological preparations. This method allows the detection of proteins, peptides, lipids, carbohydrates or glycoconjugates and small molecules.Formalin-fixed paraffin-embedded tissue can also be investigated by IMS, thus, this method seems to be an ideal tool for cancer diagnostics and biomarker discovery. It may add information to the identification of tumor margins and tumor heterogeneity. The technique allows tumor typing, especially identification of the tumor of origin in metastatic tissue, as well as grading and may provide prognostic information. IMS is a valuable method for the identification of biomarkers and can complement histology, immunohistology and molecular pathology in various fields of histopathological diagnostics, especially with regard to identification and grading of tumors.

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.

Ocular Proteomics with Emphasis on Two-Dimensional Gel Electrophoresis and Mass Spectrometry

Directory of Open Access Journals (Sweden)

Honoré Bent

2010-01-01

Full Text Available Abstract The intention of this review is to provide an overview of current methodologies employed in the rapidly developing field of ocular proteomics with emphasis on sample preparation, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE and mass spectrometry (MS. Appropriate sample preparation for the diverse range of cells and tissues of the eye is essential to ensure reliable results. Current methods of protein staining for 2D-PAGE, protein labelling for two-dimensional difference gel electrophoresis, gel-based expression analysis and protein identification by MS are summarised. The uses of gel-free MS-based strategies (MuDPIT, iTRAQ, ICAT and SILAC are also discussed. Proteomic technologies promise to shed new light onto ocular disease processes that could lead to the discovery of strong novel biomarkers and therapeutic targets useful in many ophthalmic conditions.

Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry

Directory of Open Access Journals (Sweden)

Joos Thomas

2010-06-01

Full Text Available Abstract Background Mass spectrometry (MS based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency. Results We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties. Conclusions For small datasets (a few hundred proteins it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics.

Lipidomics in translational research and the clinical significance of lipid-based biomarkers.

Science.gov (United States)

Stephenson, Daniel J; Hoeferlin, L Alexis; Chalfant, Charles E

2017-11-01

Lipidomics is a rapidly developing field of study that focuses on the identification and quantitation of various lipid species in the lipidome. Lipidomics has now emerged in the forefront of scientific research due to the importance of lipids in metabolism, cancer, and disease. Using both targeted and untargeted mass spectrometry as a tool for analysis, progress in the field has rapidly progressed in the last decade. Having the ability to assess these small molecules in vivo has led to better understanding of several lipid-driven mechanisms and the identification of lipid-based biomarkers in neurodegenerative disease, cancer, sepsis, wound healing, and pre-eclampsia. Biomarker identification and mechanistic understanding of specific lipid pathways linked to a disease's pathologies can form the foundation in the development of novel therapeutics in hopes of curing human disease. Published by Elsevier Inc.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers.

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Yamada, Takashi; Hashimoto, Ryu-Ichiro; Yahata, Noriaki; Ichikawa, Naho; Yoshihara, Yujiro; Okamoto, Yasumasa; Kato, Nobumasa; Takahashi, Hidehiko; Kawato, Mitsuo

2017-10-01

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use. © The Author 2017. Published by Oxford University Press on behalf of CINP.

Early diagnostic protein biomarkers for breast cancer: how far have we come?

NARCIS (Netherlands)

Opstal - van Winden, A.W.J.; Vermeulen, R.C.H.; Peeters, P.H.M.; Beijnen, J.H.; van Gils, C.H.

2012-01-01

Many studies have used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry or matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to search for blood-based proteins that are related to the presence of breast cancer. We review the biomarkers

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.

Identification of specific bovine blood biomarkers with a non-targeted approach using HPLC ESI tandem mass spectrometry.

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Lecrenier, M C; Marbaix, H; Dieu, M; Veys, P; Saegerman, C; Raes, M; Baeten, V

2016-12-15

Animal by-products are valuable protein sources in animal nutrition. Among them are blood products and blood meal, which are used as high-quality material for their beneficial effects on growth and health. Within the framework of the feed ban relaxation, the development of complementary methods in order to refine the identification of processed animal proteins remains challenging. The aim of this study was to identify specific biomarkers that would allow the detection of bovine blood products and processed animal proteins using tandem mass spectrometry. Seventeen biomarkers were identified: nine peptides for bovine plasma powder; seven peptides for bovine haemoglobin powder, including six peptides for bovine blood meal; and one peptide for porcine blood. They were not detected in several commercial compound feed or feed materials, such as blood by-products of other animal origins, milk-derived products and fish meal. These biomarkers could be used for developing a species-specific and blood-specific detection method. Copyright © 2016 Elsevier Ltd. All rights reserved.

Widely-targeted quantitative lipidomics methodology by supercritical fluid chromatography coupled with fast-scanning triple quadrupole mass spectrometry.

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Takeda, Hiroaki; Izumi, Yoshihiro; Takahashi, Masatomo; Paxton, Thanai; Tamura, Shohei; Koike, Tomonari; Yu, Ying; Kato, Noriko; Nagase, Katsutoshi; Shiomi, Masashi; Bamba, Takeshi

2018-05-03

Lipidomics, the mass spectrometry-based comprehensive analysis of lipids, has attracted attention as an analytical approach to provide novel insight into lipid metabolism and to search for biomarkers. However, an ideal method for both comprehensive and quantitative analysis of lipids has not been fully developed. Herein, we have proposed a practical methodology for widely-targeted quantitative lipidome analysis using supercritical fluid chromatography fast-scanning triple-quadrupole mass spectrometry (SFC/QqQMS) and theoretically calculated a comprehensive lipid multiple reaction monitoring (MRM) library. Lipid classes can be separated by SFC with a normal phase diethylamine-bonded silica column with high-resolution, high-throughput, and good repeatability. Structural isomers of phospholipids can be monitored by mass spectrometric separation with fatty acyl-based MRM transitions. SFC/QqQMS analysis with an internal standard-dilution method offers quantitative information for both lipid class and individual lipid molecular species in the same lipid class. Additionally, data acquired using this method has advantages including reduction of misidentification and acceleration of data analysis. Using the SFC/QqQMS system, alteration of plasma lipid levels in myocardial infarction-prone rabbits to the supplementation of eicosapentaenoic acid was first observed. Our developed SFC/QqQMS method represents a potentially useful tool for in-depth studies focused on complex lipid metabolism and biomarker discovery. Published under license by The American Society for Biochemistry and Molecular Biology, Inc.

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

Rapid label-free profiling of oral cancer biomarker proteins using nano-UPLC-Q-TOF ion mobility mass spectrometry.

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Nassar, Ala F; Williams, Brad J; Yaworksy, Dustin C; Patel, Vyomesh; Rusling, James F

2016-03-01

It has become quite clear that single cancer biomarkers cannot in general provide high sensitivity and specificity for reliable clinical cancer diagnostics. This paper explores the feasibility of rapid detection of multiple biomarker proteins in model oral cancer samples using label-free protein relative quantitation. MS-based label-free quantitative proteomics offer a rapid alternative that bypasses the need for stable isotope containing compounds to chemically bind and label proteins. Total protein content in oral cancer cell culture conditioned media was precipitated, subjected to proteolytic digestion, and then analyzed using a nano-UPLC (where UPLC is ultra-performance liquid chromatography) coupled to a hybrid Q-Tof ion-mobility mass spectrometry (MS). Rapid, simultaneous identification and quantification of multiple possible cancer biomarker proteins was achieved. In a comparative study between cancer and noncancer samples, approximately 952 proteins were identified using a high-throughput 1D ion mobility assisted data independent acquisition (IM-DIA) approach. As we previously demonstrated that interleukin-8 (IL-8) and vascular endothelial growth factor A (VEGF-A) were readily detected in oral cancer cell conditioned media(1), we targeted these biomarker proteins to validate our approach. Target biomarker protein IL-8 was found between 3.5 and 8.8 fmol, while VEGF-A was found at 1.45 fmol in the cancer cell media. Overall, our data suggest that the nano-UPLC-IM-DIA bioassay is a feasible approach to identify and quantify proteins in complex samples without the need for stable isotope labeling. These results have significant implications for rapid tumor diagnostics and prognostics by monitoring proteins such as IL-8 and VEGF-A implicated in cancer development and progression. The analysis in tissue or plasma is not possible at this time, but the subsequent work would be needed for validation. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Use of on-line supercritical fluid extraction-supercritical fluid chromatography/tandem mass spectrometry to analyze disease biomarkers in dried serum spots compared with serum analysis using liquid chromatography/tandem mass spectrometry.

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Suzuki, Makoto; Nishiumi, Shin; Kobayashi, Takashi; Sakai, Arata; Iwata, Yosuke; Uchikata, Takato; Izumi, Yoshihiro; Azuma, Takeshi; Bamba, Takeshi; Yoshida, Masaru

2017-05-30

The analytical stability and throughput of biomarker assays based on dried serum spots (DSS) are strongly dependent on the extraction process and determination method. In the present study, an on-line system based on supercritical fluid extraction-supercritical fluid chromatography coupled with tandem mass spectrometry (SFE-SFC/MS/MS) was established for analyzing the levels of disease biomarkers in DSS. The chromatographic conditions were investigated using the ODS-EP, diol, and SIL-100A columns. Then, we optimized the SFE-SFC/MS/MS method using the diol column, focusing on candidate biomarkers of oral, colorectal, and pancreatic cancer that were identified using liquid chromatography (LC)/MS/MS. By using this system, four hydrophilic metabolites and 17 hydrophobic metabolites were simultaneously detected within 15 min. In an experiment involving clinical samples, PC 16:0-18:2/16:1-18:1 exhibited 93.8% sensitivity and 64.3% specificity, whereas PC 17:1-18:1/17:0-18:2 showed 81.3% sensitivity and 92.9% specificity for detecting oral cancer. In addition, assessments of the creatine levels demonstrated 92.3% sensitivity and 78.6% specificity for detecting colorectal cancer. The results of this study indicate that our method has great potential for clinical diagnosis and would be suitable for large-scale screening. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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

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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.

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.

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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.

PODCAST: From Lost in Translation to Paradise Found: Enabling Protein Biomarker Method Transfer by Mass Spectrometry | Office of Cancer Clinical Proteomics Research

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Translation of novel biomarkers into clinical care for the evaluation of therapeutic safety and efficacy has been slow, partly attributable to the cost and complexity of immunoassay development.  The potential for liquid chromatography-tandem mass spectrometry (LC-MS/MS) to streamline the translation of novel protein biomarkers is profound.  Drs. Henry Rodriguez and Andrew Hoofnagle discuss what the future may be for clinical proteomics. This is an American Association for Clinical Chemistry (AACC) podcast.

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

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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.

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.

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.

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.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer.

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Miah, Sayem; Banks, Charles A S; Adams, Mark K; Florens, Laurence; Lukong, Kiven E; Washburn, Michael P

2016-12-20

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

Early Detection of Cancer by Affinity Mass Spectrometry-Set Aside funds — EDRN Public Portal

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A.   RATIONALE The recent introduction of multiple reaction monitoring capabilities offers unprecedented capability to the research arsenal available to protein based biomarker discovery. Specific to the discovery process this technology offers an ability to monitor specific protein changes in concentration and/or post-translational modification. The ability to accurately confirm specific biomarkers in a sensitive and reproducible manner is critical to the confirmation and pre-validation process. We are proposing two collaborative studies that promise to develop Multiple Reaction Monitoring (MRM) work flows for the biomarker scientific community and specifically for EDRN. B.   GOALS The overall goal for this proposal is the identification of protein biomarkers that can be associated with prostate cancer detection. The underlying goal is the application of a novel technological approach aided by MRM toward biomarker discovery. An additional goal will be the dissemination of knowledge gained from these studies EDRN wide.

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

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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.

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

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

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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

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data

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Trede Dennis

2012-03-01

Full Text Available In the last decade, matrix-assisted laser desorption/ionization (MALDI imaging mass spectrometry (IMS, also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies.

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

Multiplexed mass spectrometry monitoring of biomarker candidates for osteoarthritis.

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Fernández-Puente, Patricia; Calamia, Valentina; González-Rodríguez, Lucía; Lourido, Lucía; Camacho-Encina, María; Oreiro, Natividad; Ruiz-Romero, Cristina; Blanco, Francisco J

2017-01-30

The methods currently available for the diagnosis and monitoring of osteoarthritis (OA) are very limited and lack sensitivity. Being the most prevalent rheumatic disease, one of the most disabling pathologies worldwide and currently untreatable, there is a considerable interest pointed in the verification of specific biological markers for improving its diagnosis and disease progression studies. Considering the remarkable development of targeted proteomics methodologies in the frame of the Human Proteome Project, the aim of this work was to develop and apply a MRM-based method for the multiplexed analysis of a panel of 6 biomarker candidates for OA encoded by the Chromosome 16, and another 8 proteins identified in previous shotgun studies as related with this pathology, in specimens derived from the human joint and serum. The method, targeting 35 different peptides, was applied to samples from human articular chondrocytes, healthy and osteoarthritic cartilage, synovial fluid and serum. Subsequently, a verification analysis of the biomarker value of these proteins was performed by single point measurements on a set of 116 serum samples, leading to the identification of increased amounts of Haptoglobin and von Willebrand Factor in OA patients. Altogether, the present work provides a tool for the multiplexed monitoring of 14 biomarker candidates for OA, and verifies for the first time the increased amount of two of these circulating markers in patients diagnosed with this disease. We have developed an MRM method for the identification and relative quantification of a panel of 14 protein biomarker candidates for osteoarthritis. This method has been applied to analyze human articular chondrocytes, articular cartilage, synovial fluid, and finally a collection of 116 serum samples from healthy controls and patients suffering different degrees of osteoarthritis, in order to verify the biomarker usefulness of the candidates. HPT and VWF were validated as increased in OA

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

Science.gov (United States)

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.

Ribosomal proteins as biomarkers for bacterial identification by mass spectrometry in the clinical microbiology laboratory.

Science.gov (United States)

Suarez, Stéphanie; Ferroni, Agnès; Lotz, Aurélie; Jolley, Keith A; Guérin, Philippe; Leto, Julie; Dauphin, Brunhilde; Jamet, Anne; Maiden, Martin C J; Nassif, Xavier; Armengaud, Jean

2013-09-01

Whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a rapid method for identification of microorganisms that is increasingly used in microbiology laboratories. This identification is based on the comparison of the tested isolate mass spectrum with reference databases. Using Neisseria meningitidis as a model organism, we showed that in one of the available databases, the Andromas database, 10 of the 13 species-specific biomarkers correspond to ribosomal proteins. Remarkably, one biomarker, ribosomal protein L32, was subject to inter-strain variability. The analysis of the ribosomal protein patterns of 100 isolates for which whole genome sequences were available, confirmed the presence of inter-strain variability in the molecular weight of 29 ribosomal proteins, thus establishing a correlation between the sequence type (ST) and/or clonal complex (CC) of each strain and its ribosomal protein pattern. Since the molecular weight of three of the variable ribosomal proteins (L30, L31 and L32) was included in the spectral window observed by MALDI-TOF MS in clinical microbiology, i.e., 3640-12000 m/z, we were able by analyzing the molecular weight of these three ribosomal proteins to classify each strain in one of six subgroups, each of these subgroups corresponding to specific STs and/or CCs. Their detection by MALDI-TOF allows therefore a quick typing of N. meningitidis isolates. © 2013 Elsevier B.V. All rights reserved.

Biomarker- and similarity coefficient-based approaches to bacterial mixture characterization using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).

Science.gov (United States)

Zhang, Lin; Smart, Sonja; Sandrin, Todd R

2015-11-05

MALDI-TOF MS profiling has been shown to be a rapid and reliable method to characterize pure cultures of bacteria. Currently, there is keen interest in using this technique to identify bacteria in mixtures. Promising results have been reported with two- or three-isolate model systems using biomarker-based approaches. In this work, we applied MALDI-TOF MS-based methods to a more complex model mixture containing six bacteria. We employed: 1) a biomarker-based approach that has previously been shown to be useful in identification of individual bacteria in pure cultures and simple mixtures and 2) a similarity coefficient-based approach that is routinely and nearly exclusively applied to identification of individual bacteria in pure cultures. Both strategies were developed and evaluated using blind-coded mixtures. With regard to the biomarker-based approach, results showed that most peaks in mixture spectra could be assigned to those found in spectra of each component bacterium; however, peaks shared by two isolates as well as peaks that could not be assigned to any individual component isolate were observed. For two-isolate blind-coded samples, bacteria were correctly identified using both similarity coefficient- and biomarker-based strategies, while for blind-coded samples containing more than two isolates, bacteria were more effectively identified using a biomarker-based strategy.

The application of cell cultures, body fluids and tissues in oncoproteomics

Directory of Open Access Journals (Sweden)

Kamila Duś-Szachniewicz

2014-11-01

Full Text Available Mass spectrometry (MS-based proteomics is a rapidly developing technology for the large scale analysis of proteins, their interactions and subcellular localization. In recent years proteomics has attracted much attention in medicine. Since a single biomarker might not have sufficient sensitivity and specificity in clinical practice, the identification of biomarker panels that comprise several proteins would improve the detection and clinical management of cancer patients. Additionally, the characteristics of protein profiles of most severe human malignancies certainly contribute to the understanding of the biology of cancer and fill the gap in our knowledge of carcinogenesis. This knowledge also is likely to result in the discovery of novel potential cancer markers and targets for molecular therapeutics. It is believed that the novel biomarkers will help in the development of personalized therapy tailored to the individual patient and will thereby reduce the mortality rate from cancer. In this review, the use of different types of human clinical samples (cell cultures, tissues and body fluids in oncoproteomics is explained and the latest advances in mass spectrometry-based proteomics biomarker discovery are discussed.

Tandem mass spectrometry-based newborn screening strategy could be used to facilitate rapid and sensitive lung cancer diagnosis

Directory of Open Access Journals (Sweden)

Huang T

2016-04-01

Full Text Available Ting Huang,1,* Yunfeng Cao,1,* Jia Zeng,1 Jun Dong,2 Xiaoyu Sun,2 Jianxing Chen,1 Peng Gao2,3 1Key Laboratory of Contraceptives and Devices Research (NPFPC, Shanghai Engineer and Technology Research Center of Reproductive Health Drug and Devices, Shanghai Institute of Planned Parenthood Research, Shanghai, 2Clinical Laboratory, Dalian Sixth People’s Hospital, 3CASKey Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, People’s Republic of China *These authors contributed equally to this work Objective: Newborn screening (NBS helps in the early detection of inborn errors of metabolism (IEM. The most effective NBS strategy prevailing in clinics is tandem mass spectrometry (MS/MS analysis using dried blood spot (DBS samples. Taking lung cancer (LC as an example, this study tried to explore if this technique could be of any assistance for the discovery of tumor metabolite markers.Materials and methods: Twenty-six acylcarnitines and 23 amino acids, which are commonly used in IEM screening, were quantified using DBS samples from 222 LC patients, 118 benign lung disease (LD patients, and 96 healthy volunteers (CONT. Forty-four calculated ratios based on the abovementioned metabolites were also included using MS/MS quantification results.Results: This pilot study led to the findings of 65 significantly changed amino acids, acylcarnitines, and some of their ratios for the LC, LD, and CONT groups. Among the differential parameters, 12 items showed reverse changing trends between the LC and LD groups compared to the CONT group. Regression analysis demonstrated that six of them – Arg, Pro, C10:1, Arg/Orn, Cit/Arg, and C5-OH/C0 – could be used to diagnose LC with a sensitivity of 91.3% and a specificity of 92.7%.Conclusion: This study demonstrated the DBS-based MS/MS strategy was a promising tool for the discovery of tumor metabolite markers. Remarkably, this MS

A Selected Reaction Monitoring Mass Spectrometry Protocol for Validation of Proteomic Biomarker Candidates in Studies of Psychiatric Disorders.

Science.gov (United States)

Reis-de-Oliveira, Guilherme; Garcia, Sheila; Guest, Paul C; Cassoli, Juliana S; Martins-de-Souza, Daniel

2017-01-01

Most biomarker candidates arising from proteomic studies of psychiatric disorders have not progressed for use in clinical studies due to insufficient validation steps. Here we describe a selective reaction monitoring mass spectrometry (SRM-MS) approach that could be used as a follow-up validation tool of proteins identified in blood serum or plasma. This protocol specifically covers the stages of peptide selection and optimization. The increasing application of SRM-MS should enable fast, sensitive, and robust methods with the potential for use in clinical studies involving sampling of serum or plasma. Understanding the molecular mechanisms and identifying potential biomarkers for risk assessment, diagnosis, prognosis, and prediction of drug response goes toward the implementation of translational medicine strategies for improved treatment of patients with psychiatric disorders and other debilitating diseases.

Headspace-programmed temperature vaporization-mass spectrometry for the rapid determination of possible volatile biomarkers of lung cancer in urine.

Science.gov (United States)

Pérez Antón, Ana; Ramos, Álvaro García; Del Nogal Sánchez, Miguel; Pavón, José Luis Pérez; Cordero, Bernardo Moreno; Pozas, Ángel Pedro Crisolino

2016-07-01

We propose a new method for the rapid determination of five volatile compounds described in the literature as possible biomarkers of lung cancer in urine samples. The method is based on the coupling of a headspace sampler, a programmed temperature vaporizer in solvent-vent injection mode, and a mass spectrometer (HS-PTV-MS). This configuration is known as an electronic nose based on mass spectrometry. Once the method was developed, it was used for the analysis of urine samples from lung cancer patients and healthy individuals. Multivariate calibration models were employed to quantify the biomarker concentrations in the samples. The detection limits ranged between 0.16 and 21 μg/L. For the assignment of the samples to the patient group or the healthy individuals, the Wilcoxon signed-rank test was used, comparing the concentrations obtained with the median of a reference set of healthy individuals. To date, this is the first time that multivariate calibration and non-parametric methods have been combined to classify biological samples from profile signals obtained with an electronic nose. When significant differences in the concentration of one or more biomarkers were found with respect to the reference set, the sample is considered as a positive one and a new analysis was performed using a chromatographic method (HS-PTV-GC/MS) to confirm the result. The main advantage of the proposed HS-PTV-MS methodology is that no prior chromatographic separation and no sample manipulation are required, which allows an increase of the number of samples analyzed per hour and restricts the use of time-consuming techniques to only when necessary. Graphical abstract Schematic diagram of the developed methodology.

Increasing the predictive accuracy of amyloid-β blood-borne biomarkers in Alzheimer's disease.

Science.gov (United States)

Watt, Andrew D; Perez, Keyla A; Faux, Noel G; Pike, Kerryn E; Rowe, Christopher C; Bourgeat, Pierrick; Salvado, Olivier; Masters, Colin L; Villemagne, Victor L; Barnham, Kevin J

2011-01-01

Diagnostic measures for Alzheimer's disease (AD) commonly rely on evaluating the levels of amyloid-β (Aβ) peptides within the cerebrospinal fluid (CSF) of affected individuals. These levels are often combined with levels of an additional non-Aβ marker to increase predictive accuracy. Recent efforts to overcome the invasive nature of CSF collection led to the observation of Aβ species within the blood cellular fraction, however, little is known of what additional biomarkers may be found in this membranous fraction. The current study aimed to undertake a discovery-based proteomic investigation of the blood cellular fraction from AD patients (n = 18) and healthy controls (HC; n = 15) using copper immobilized metal affinity capture and Surface Enhanced Laser Desorption/Ionisation Time-Of-Flight Mass Spectrometry. Three candidate biomarkers were observed which could differentiate AD patients from HC (ROC AUC > 0.8). Bivariate pairwise comparisons revealed significant correlations between these markers and measures of AD severity including; MMSE, composite memory, brain amyloid burden, and hippocampal volume. A partial least squares regression model was generated using the three candidate markers along with blood levels of Aβ. This model was able to distinguish AD from HC with high specificity (90%) and sensitivity (77%) and was able to separate individuals with mild cognitive impairment (MCI) who converted to AD from MCI non-converters. While requiring further characterization, these candidate biomarkers reaffirm the potential efficacy of blood-based investigations into neurodegenerative conditions. Furthermore, the findings indicate that the incorporation of non-amyloid markers into predictive models, function to increase the accuracy of the diagnostic potential of Aβ.

Fluorescence-based Western blotting for quantitation of protein biomarkers in clinical samples.

Science.gov (United States)

Zellner, Maria; Babeluk, Rita; Diestinger, Michael; Pirchegger, Petra; Skeledzic, Senada; Oehler, Rudolf

2008-09-01

Since most high throughput techniques used in biomarker discovery are very time and cost intensive, highly specific and quantitative analytical alternative application methods are needed for the routine analysis. Conventional Western blotting allows detection of specific proteins to the level of single isotypes while its quantitative accuracy is rather limited. We report a novel and improved quantitative Western blotting method. The use of fluorescently labelled secondary antibodies strongly extends the dynamic range of the quantitation and improves the correlation with the protein amount (r=0.997). By an additional fluorescent staining of all proteins immediately after their transfer to the blot membrane, it is possible to visualise simultaneously the antibody binding and the total protein profile. This allows for an accurate correction for protein load. Applying this normalisation it could be demonstrated that fluorescence-based Western blotting is able to reproduce a quantitative analysis of two specific proteins in blood platelet samples from 44 subjects with different diseases as initially conducted by 2D-DIGE. These results show that the proposed fluorescence-based Western blotting is an adequate application technique for biomarker quantitation and suggest possibilities of employment that go far beyond.

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.

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

Science.gov (United States)

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.

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

A SIMPLE AND RAPID MATRIX-ASSISTED LASER DESORPTION/IONIZATION TIME OF FLIGHT MASS SPECTROMETRY METHOD TO SCREEN FISH PLASMA SAMPLES FOR ESTROGEN-RESPONSIVE BIOMARKERS

Science.gov (United States)

In this study, we describe and evaluate the performance of a simple and rapid mass spectral method for screening fish plasma for estrogen-responsive biomarkers using matrix assisted laster desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) couopled with a short...

Rapid isolation of biomarkers for compound specific radiocarbon dating using high-performance liquid chromatography and flow injection analysis-atmospheric pressure chemical ionisation mass spectrometry

NARCIS (Netherlands)

Sinninghe Damsté, J.S.; Smittenberg, R.H.; Hopmans, E.C.; Schouten, S.

2002-01-01

Repeated semi-preparative normal-phase HPLC was performed to isolate selected biomarkers from sediment extracts for radiocarbon analysis. Flow injection analysis mass spectrometry was used for rapid analysis of collected fractions to evaluate the separation procedure, taking only 1 min per fraction.

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...

Untargeted saliva metabonomics study of breast cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations.

Science.gov (United States)

Zhong, Liping; Cheng, Fei; Lu, Xiaoyong; Duan, Yixiang; Wang, Xiaodong

2016-09-01

Breast cancer (BC) is not only the most frequently diagnosed cancer, but also the leading cause of cancer death among women worldwide. This study aimed to screen the potential salivary biomarkers for breast cancer diagnosis, staging, and biomarker discovery. For the first time, a UPLC-MS based method along with multivariate data analysis, was proposed for the global saliva metabonomics analysis and diagnosis of BC, which used both hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) separations and operated in both positive (ESI+) and negative (ESI-) ionization modes. On account of different polarities of endogenous metabolites, this method was established to overcome the boundedness of a single chromatographic approach. As a result, 18 potential metabolites for diagnosing BC were identified. A nonparametric Mann-Whitney U test, heat map, and the receiver operating characteristic (ROC) were exploited to analyze the data with the purpose of evaluating the predictive power of the 18 biomarkers. Significant differences (Pmetabonomics analysis in human saliva for identifying potential biomarkers to diagnose and stage BC was successfully eastablished, which was non-invasive, reliable, low-cost, and simple. The HILIC was demonstrated to be essential for a comprehensive saliva metabonomics profiling as well as RPLC separation. This saliva metabonomics technique may provide new insight into the discovery and identification of diagnostic biomarkers for BC. Copyright © 2016. Published by Elsevier B.V.

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...

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis.

LENUS (Irish Health Repository)

Mc Ardle, Angela

2015-01-01

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate

Hydrogen deuterium exchange mass spectrometry in biopharmaceutical discovery and development – A review

International Nuclear Information System (INIS)

Deng, Bin; Lento, Cristina; Wilson, Derek J.

2016-01-01

Protein therapeutics have emerged as a major class of biopharmaceuticals over the past several decades, a trend that has motivated the advancement of bioanalytical technologies for protein therapeutic characterization. Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful and sensitive technique that can probe the higher order structure of proteins and has been used in the assessment and development of monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs) and biosimilar antibodies. It has also been used to quantify protein-ligand, protein-receptor and other protein-protein interactions involved in signaling pathways. In manufacturing and development, HDX-MS can validate storage formulations and manufacturing processes for various biotherapeutics. Currently, HDX-MS is being refined to provide additional coverage, sensitivity and structural specificity and implemented on the millisecond timescale to reveal residual structure and dynamics in disordered domains and intrinsically disordered proteins. - Highlights: • The pharmaceuticals industry is increasingly shifting to protein therapeutics. • Hydrogen deuterium exchange mass spectrometry is uniquely well suited to support biopharmaceutical development. • Applications for hydrogen deuterium exchange span drug discovery, development and manufacturing. • Future developments will allow improved sensitivity, structural resolution and a broader range of dynamics to be monitored.

Hydrogen deuterium exchange mass spectrometry in biopharmaceutical discovery and development – A review

Energy Technology Data Exchange (ETDEWEB)

Deng, Bin, E-mail: dengbin@yorku.ca [Chemistry Department, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 (Canada); The Centre for Research in Mass Spectrometry, York University, Toronto, ON, M3J1P3 (Canada); Lento, Cristina, E-mail: clento@yorku.ca [Chemistry Department, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 (Canada); The Centre for Research in Mass Spectrometry, York University, Toronto, ON, M3J1P3 (Canada); Wilson, Derek J., E-mail: dkwilson@yorku.ca [Chemistry Department, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 (Canada); The Centre for Research in Mass Spectrometry, York University, Toronto, ON, M3J1P3 (Canada)

2016-10-12

Protein therapeutics have emerged as a major class of biopharmaceuticals over the past several decades, a trend that has motivated the advancement of bioanalytical technologies for protein therapeutic characterization. Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful and sensitive technique that can probe the higher order structure of proteins and has been used in the assessment and development of monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs) and biosimilar antibodies. It has also been used to quantify protein-ligand, protein-receptor and other protein-protein interactions involved in signaling pathways. In manufacturing and development, HDX-MS can validate storage formulations and manufacturing processes for various biotherapeutics. Currently, HDX-MS is being refined to provide additional coverage, sensitivity and structural specificity and implemented on the millisecond timescale to reveal residual structure and dynamics in disordered domains and intrinsically disordered proteins. - Highlights: • The pharmaceuticals industry is increasingly shifting to protein therapeutics. • Hydrogen deuterium exchange mass spectrometry is uniquely well suited to support biopharmaceutical development. • Applications for hydrogen deuterium exchange span drug discovery, development and manufacturing. • Future developments will allow improved sensitivity, structural resolution and a broader range of dynamics to be monitored.

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.

Studying Protein-Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry.

Science.gov (United States)

Xie, Zhongqiu; Jia, Yuemeng; Li, Hui

2017-01-01

The study of protein-protein interactions represents a key aspect of biological research. Identifying unknown protein binding partners using mass spectrometry (MS)-based proteomics has evolved into an indispensable strategy in drug discovery. The classic approach of immunoprecipitation with specific antibodies against the proteins of interest has limitations, such as the need for immunoprecipitation-qualified antibody. The biotin AP-tag pull-down system has the advantage of high specificity, ease of use, and no requirement for antibody. It is based on the high specificity, high affinity interaction between biotin and streptavidin. After pulldown, in-gel tryptic digestion and tandem mass spectrometry (MS/MS) analysis of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein bands can be performed. In this work, we provide protocols that can be used for the identification of proteins that interact with FOXM1, a protein that has recently emerged as a potential biomarker and drug target in oncotherapy, as an example. We focus on the pull-down procedure and assess the efficacy of the pulldown with known FOXM1 interactors such as β-catenin. We use a high performance LTQ Orbitrap MSn system that combines rapid LTQ ion trap data acquisition with high mass accuracy Orbitrap analysis to identify the interacting proteins.

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.

Identification of protein biomarkers in Dupuytren's contracture using surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS).

Science.gov (United States)

O'Gorman, David; Howard, Jeffrey C; Varallo, Vincenzo M; Cadieux, Peter; Bowley, Erin; McLean, Kris; Pak, Brian J; Gan, Bing Siang

2006-06-01

To study the protein expression profiles associated with Dupuytren's contracture (DC) to identify potential disease protein biomarkers (PBM) using a proteomic technology--Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). Normal and disease palmar fascia from DC patients were analyzed using Ciphergen's SELDI-TOF-MS Protein Biological System II (PBSII) ProteinChip reader. Analysis of the resulting SELDI-TOF spectra was carried out using the peak cluster analysis program (BioMarker Wizard, Ciphergen). Common peak clusters were then filtered using a bootstrap algorithm called SAM (Significant Analysis of Microarrays) for increased fidelity in our analysis. Several differentially expressed low molecular weight (mass standard deviation for both methods of biomarker-rich low molecular weight region of the human proteome. Application of such novel technology may help clinicians to focus on specific molecular abnormalities in diseases with no known molecular pathogenesis, and uncover therapeutic and/or diagnostic targets.

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.

Environmentally Friendly Procedure Based on Supercritical Fluid Chromatography and Tandem Mass Spectrometry Molecular Networking for the Discovery of Potent Antiviral Compounds from Euphorbia semiperfoliata.

Science.gov (United States)

Nothias, Louis-Félix; Boutet-Mercey, Stéphanie; Cachet, Xavier; De La Torre, Erick; Laboureur, Laurent; Gallard, Jean-François; Retailleau, Pascal; Brunelle, Alain; Dorrestein, Pieter C; Costa, Jean; Bedoya, Luis M; Roussi, Fanny; Leyssen, Pieter; Alcami, José; Paolini, Julien; Litaudon, Marc; Touboul, David

2017-10-27

A supercritical fluid chromatography-based targeted purification procedure using tandem mass spectrometry and molecular networking was developed to analyze, annotate, and isolate secondary metabolites from complex plant extract mixture. This approach was applied for the targeted isolation of new antiviral diterpene esters from Euphorbia semiperfoliata whole plant extract. The analysis of bioactive fractions revealed that unknown diterpene esters, including jatrophane esters and phorbol esters, were present in the samples. The purification procedure using semipreparative supercritical fluid chromatography led to the isolation and identification of two new jatrophane esters (13 and 14) and one known (15) and three new 4-deoxyphorbol esters (16-18). The structure and absolute configuration of compound 16 were confirmed by X-ray crystallography. This compound was found to display antiviral activity against Chikungunya virus (EC 50 = 0.45 μM), while compound 15 proved to be a potent and selective inhibitor of HIV-1 replication in a recombinant virus assay (EC 50 = 13 nM). This study showed that a supercritical fluid chromatography-based protocol and molecular networking can facilitate and accelerate the discovery of bioactive small molecules by targeting molecules of interest, while minimizing the use of toxic solvents.

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.

Glutathione transferase (GST) as a candidate molecular-based biomarker for soil toxin exposure in the earthworm Lumbricus rubellus

International Nuclear Information System (INIS)

LaCourse, E. James; Hernandez-Viadel, Mariluz; Jefferies, James R.; Svendsen, Claus; Spurgeon, David J.; Barrett, John; John Morgan, A.; Kille, Peter; Brophy, Peter M.

2009-01-01

The earthworm Lumbricus rubellus (Hoffmeister, 1843) is a terrestrial pollution sentinel. Enzyme activity and transcription of phase II detoxification superfamily glutathione transferases (GST) is known to respond in earthworms after soil toxin exposure, suggesting GST as a candidate molecular-based pollution biomarker. This study combined sub-proteomics, bioinformatics and biochemical assay to characterise the L. rubellus GST complement as pre-requisite to initialise assessment of the applicability of GST as a biomarker. L. rubellus possesses a range of GSTs related to known classes, with evidence of tissue-specific synthesis. Two affinity-purified GSTs dominating GST protein synthesis (Sigma and Pi class) were cloned, expressed and characterised for enzyme activity with various substrates. Electrospray ionisation mass spectrometry (ESI-MS) and tandem mass spectrometry (MS/MS) following SDS-PAGE were superior in retaining subunit stability relative to two-dimensional gel electrophoresis (2-DE). This study provides greater understanding of Phase II detoxification GST superfamily status of an important environmental pollution sentinel organism. - This study currently provides the most comprehensive view of the Phase II detoxification enzyme superfamily of glutathione transferases within the important environmental pollution sentinel earthworm Lumbricus rubellus.

Glutathione transferase (GST) as a candidate molecular-based biomarker for soil toxin exposure in the earthworm Lumbricus rubellus

Energy Technology Data Exchange (ETDEWEB)

LaCourse, E. James, E-mail: james.la-course@liverpool.ac.u [Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA (United Kingdom); Hernandez-Viadel, Mariluz; Jefferies, James R. [Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA (United Kingdom); Svendsen, Claus; Spurgeon, David J. [Centre for Ecology and Hydrology, Huntingdon PE28 2LS (United Kingdom); Barrett, John [Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA (United Kingdom); John Morgan, A.; Kille, Peter [Biosciences, University of Cardiff, Cardiff CF10 3TL (United Kingdom); Brophy, Peter M. [Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA (United Kingdom)

2009-08-15

The earthworm Lumbricus rubellus (Hoffmeister, 1843) is a terrestrial pollution sentinel. Enzyme activity and transcription of phase II detoxification superfamily glutathione transferases (GST) is known to respond in earthworms after soil toxin exposure, suggesting GST as a candidate molecular-based pollution biomarker. This study combined sub-proteomics, bioinformatics and biochemical assay to characterise the L. rubellus GST complement as pre-requisite to initialise assessment of the applicability of GST as a biomarker. L. rubellus possesses a range of GSTs related to known classes, with evidence of tissue-specific synthesis. Two affinity-purified GSTs dominating GST protein synthesis (Sigma and Pi class) were cloned, expressed and characterised for enzyme activity with various substrates. Electrospray ionisation mass spectrometry (ESI-MS) and tandem mass spectrometry (MS/MS) following SDS-PAGE were superior in retaining subunit stability relative to two-dimensional gel electrophoresis (2-DE). This study provides greater understanding of Phase II detoxification GST superfamily status of an important environmental pollution sentinel organism. - This study currently provides the most comprehensive view of the Phase II detoxification enzyme superfamily of glutathione transferases within the important environmental pollution sentinel earthworm Lumbricus rubellus.

Analysis of Serum Metabolic Profile by Ultra-performance Liquid Chromatography-mass Spectrometry for Biomarkers Discovery: Application in a Pilot Study to Discriminate Patients with Tuberculosis

Directory of Open Access Journals (Sweden)

Shuang Feng

2015-01-01

metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Biomarker candidate discovery in Atlantic cod (Gadus morhua) continuously exposed to North Sea produced water from egg to fry

DEFF Research Database (Denmark)

Bohne-Kjersem, Anneli; Bache, Nicolai; Meier, Sonnich

2010-01-01

were able to compare the induced changes by PW to the mode of action of oestrogens. Changes in the proteome in response to exposure in whole cod fry (approximately 80 days post-hatching, dph) were detected by two-dimensional gel electrophoresis and image analysis and identified by MALDI-ToF-ToF mass...... spectrometry, using a newly developed cod EST database and the NCBI database. Many of the protein changes occurred at low levels (0.01% and 0.1% PW) of exposure, indicating putative biological responses at lower levels than previously detected. Using discriminant analysis, we identified a set of protein...... changes that may be useful as biomarker candidates of produced water (PW) and oestradiol exposure in Atlantic cod fry. The biomarker candidates discovered in this study may, following validation, prove effective as diagnostic tools in monitoring exposure and effects of discharges from the petroleum...

Cancer biomarker discovery: the entropic hallmark.

Science.gov (United States)

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

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

Energy Technology Data Exchange (ETDEWEB)

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.

Mass spectrometry-based metabolomics: Targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders.

Science.gov (United States)

Luan, Hemi; Wang, Xian; Cai, Zongwei

2017-11-12

Metabolomics seeks to take a "snapshot" in a time of the levels, activities, regulation and interactions of all small molecule metabolites in response to a biological system with genetic or environmental changes. The emerging development in mass spectrometry technologies has shown promise in the discovery and quantitation of neuroactive small molecule metabolites associated with gut microbiota and brain. Significant progress has been made recently in the characterization of intermediate role of small molecule metabolites linked to neural development and neurodegenerative disorder, showing its potential in understanding the crosstalk between gut microbiota and the host brain. More evidence reveals that small molecule metabolites may play a critical role in mediating microbial effects on neurotransmission and disease development. Mass spectrometry-based metabolomics is uniquely suitable for obtaining the metabolic signals in bidirectional communication between gut microbiota and brain. In this review, we summarized major mass spectrometry technologies including liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and imaging mass spectrometry for metabolomics studies of neurodegenerative disorders. We also reviewed the recent advances in the identification of new metabolites by mass spectrometry and metabolic pathways involved in the connection of intestinal microbiota and brain. These metabolic pathways allowed the microbiota to impact the regular function of the brain, which can in turn affect the composition of microbiota via the neurotransmitter substances. The dysfunctional interaction of this crosstalk connects neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease and Huntington's disease. The mass spectrometry-based metabolomics analysis provides information for targeting dysfunctional pathways of small molecule metabolites in the development of the neurodegenerative diseases, which may be valuable for the

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...

Preliminary characterizations of a serum biomarker for sarcoidosis by comparative proteomic approach with tandem-mass spectrometry in ethnic Han Chinese patients.

Science.gov (United States)

Zhang, Yuan; Chen, Xianqiu; Hu, Yang; Du, Shanshan; Shen, Li; He, Yifan; Zhang, Yuxuan; Zhang, Xia; Li, Huiping; Yung, Rex C

2013-02-11

The diagnosis of sarcoidosis is still a significant challenge in China because of the need to exclude other diseases including granulomatous infections and malignancies that may be clinically and radiographically similar. The specific aim of the study is to search for serum protein biomarkers of sarcoidosis and to validate their clinical usefulness in differential diagnosis. Serum samples were collected from patients with sarcoidosis (n = 37), and compared to those from patients with tuberculosis (n = 20), other pulmonary diseases (n = 20), and healthy volunteers (n = 20) for determination of sarcoidosis-specific or -associated protein expression profiles. The first part of this study focused on proteomic analysis of serum from patients with sarcoidosis to identify a pattern of peptides capable of differentiating the studied populations using the ClinProt profiling technology based on mass spectrometry. Enzyme Linked Immunosorbent Assay (ELISA) was then used to verify corresponding elevation of the serum protein concentration of the potential biomarkers in the same patients sets. Receiver operating characteristic curve (ROC) analyses was performed to determine the optimal cutoff value for diagnosis. Immunohistochemistry was carried out to further confirm the protein expression patterns of the biomarkers in lung tissue. An unique protein peak of M/Z 3,210 Daltons (Da) was found to be differentially expressed between the sarcoidosis and control groups and was identified as the N-terminal peptide of 29 amino acids (94-122) of serum amyloid A (SAA). ELISA confirmed that the serum SAA level was significantly higher in the sarcoidosis group than that of the other 3 control groups (p biomarker for ruling-out the diagnosis of sarcoidosis in Chinese subjects.

Resource Discovery in Activity-Based Sensor Networks

DEFF Research Database (Denmark)

Bucur, Doina; Bardram, Jakob

This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networ....... ABSN enhances the generic Extended Zone Routing Protocol with logical sensor grouping and greatly lowers network overhead during the process of discovery, while keeping discovery latency close to optimal.......This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

Identification of organic acids as potential biomarkers in the urine of autistic children using gas chromatography/mass spectrometry.

Science.gov (United States)

Kałużna-Czaplińska, Joanna; Żurawicz, Ewa; Struck, Wiktoria; Markuszewski, Michał

2014-09-01

There is a need to identify metabolic phenotypes in autism as they might each require unique approaches to prevention. Biological markers can help define autism subtypes and reveal potential therapeutic targets. The aim of the study was to identify alterations of small molecular weight compounds and to find potential biomarkers. Gas chromatography/mass spectrometry was employed to evaluate major metabolic changes in low molecular weight urine metabolites of 14 children with autism spectrum disorders vs. 10 non-autistic subjects. The results prove the usefulness of an identified set of 21 endogenous compounds (including 14 organic acids), whose levels are changed in diseased children. Gas chromatography/mass spectrometry method combined with multivariate statistical analysis techniques provide an efficient way of depicting metabolic perturbations of diseases, and may potentially be applicable as a novel strategy for the noninvasive diagnosis and treatment of autism. Copyright © 2014 Elsevier B.V. All rights reserved.

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...

Solution NMR Spectroscopy in Target-Based Drug Discovery.

Science.gov (United States)

Li, Yan; Kang, Congbao

2017-08-23

Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.

Biomarkers for personalized oncology: recent advances and future challenges.

Science.gov (United States)

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

Functional principles of registry-based service discovery

NARCIS (Netherlands)

Sundramoorthy, V.; Tan, C.; Hartel, P.H.; Hartog, den J.I.; Scholten, J.

2005-01-01

As Service Discovery Protocols (SDP) are becoming increasingly important for ubiquitous computing, they must behave according to predefined principles. We present the functional Principles of Service Discovery for robust, registry-based service discovery. A methodology to guarantee adherence to

Proteomic profiling of renal allograft rejection in serum using magnetic bead-based sample fractionation and MALDI-TOF MS.

Science.gov (United States)

Sui, Weiguo; Huang, Liling; Dai, Yong; Chen, Jiejing; Yan, Qiang; Huang, He

2010-12-01

Proteomics is one of the emerging techniques for biomarker discovery. Biomarkers can be used for early noninvasive diagnosis and prognosis of diseases and treatment efficacy evaluation. In the present study, the well-established research systems of ClinProt Micro solution incorporated unique magnetic bead sample preparation technology, which, based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), have become very successful in bioinformatics due to its outstanding performance and reproducibility for discovery disease-related biomarker. We collected fasting blood samples from patients with biopsy-confirmed acute renal allograft rejection (n = 12), chronic rejection (n = 12), stable graft function (n = 12) and also from healthy volunteers (n = 13) to study serum peptidome patterns. Specimens were purified with magnetic bead-based weak cation exchange chromatography and analyzed with a MALDI-TOF mass spectrometer. The results indicated that 18 differential peptide peaks were selected as potential biomarkers of acute renal allograft rejection, and 6 differential peptide peaks were selected as potential biomarkers of chronic rejection. A Quick Classifier Algorithm was used to set up the classification models for acute and chronic renal allograft rejection. The algorithm models recognize 82.64% of acute rejection and 98.96% of chronic rejection episodes, respectively. We were able to identify serum protein fingerprints in small sample sizes of recipients with renal allograft rejection and establish the models for diagnosis of renal allograft rejection. This preliminary study demonstrated that proteomics is an emerging tool for early diagnosis of renal allograft rejection and helps us to better understand the pathogenesis of disease process.

Quantitative mass spectrometry: an overview

Science.gov (United States)

Urban, Pawel L.

2016-10-01

Mass spectrometry (MS) is a mainstream chemical analysis technique in the twenty-first century. It has contributed to numerous discoveries in chemistry, physics and biochemistry. Hundreds of research laboratories scattered all over the world use MS every day to investigate fundamental phenomena on the molecular level. MS is also widely used by industry-especially in drug discovery, quality control and food safety protocols. In some cases, mass spectrometers are indispensable and irreplaceable by any other metrological tools. The uniqueness of MS is due to the fact that it enables direct identification of molecules based on the mass-to-charge ratios as well as fragmentation patterns. Thus, for several decades now, MS has been used in qualitative chemical analysis. To address the pressing need for quantitative molecular measurements, a number of laboratories focused on technological and methodological improvements that could render MS a fully quantitative metrological platform. In this theme issue, the experts working for some of those laboratories share their knowledge and enthusiasm about quantitative MS. I hope this theme issue will benefit readers, and foster fundamental and applied research based on quantitative MS measurements. This article is part of the themed issue 'Quantitative mass spectrometry'.

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

Assuring Consistent Performance of an Insulin-Like Growth Factor 1 MALDImmunoassay by Monitoring Measurement Quality Indicators

OpenAIRE

Klont, Frank; ten Hacken, Nick H. T.; Horvatovich, P?ter; Bakker, Stephan J. L.; Bischoff, Rainer

2017-01-01

Analytical methods based on mass spectrometry (MS) have been successfully applied in biomarker discovery studies, while the role of MS in translating biomarker candidates to clinical diagnostics is less pronounced. MALDImnmunoassays methods that combine immunoaffinity enrichment with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometric detection are attractive analytical approaches for large-scale sample analysis by virtue of their ease of operation and hi...

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.

Meeting Report--NASA Radiation Biomarker Workshop

Energy Technology Data Exchange (ETDEWEB)

Straume, Tore; Amundson, Sally A,; Blakely, William F.; Burns, Frederic J.; Chen, Allen; Dainiak, Nicholas; Franklin, Stephen; Leary, Julie A.; Loftus, David J.; Morgan, William F.; Pellmar, Terry C.; Stolc, Viktor; Turteltaub, Kenneth W.; Vaughan, Andrew T.; Vijayakumar, Srinivasan; Wyrobek, Andrew J.

2008-05-01

A summary is provided of presentations and discussions from the NASA Radiation Biomarker Workshop held September 27-28, 2007, at NASA Ames Research Center in Mountain View, California. Invited speakers were distinguished scientists representing key sectors of the radiation research community. Speakers addressed recent developments in the biomarker and biotechnology fields that may provide new opportunities for health-related assessment of radiation-exposed individuals, including for long-duration space travel. Topics discussed include the space radiation environment, biomarkers of radiation sensitivity and individual susceptibility, molecular signatures of low-dose responses, multivariate analysis of gene expression, biomarkers in biodefense, biomarkers in radiation oncology, biomarkers and triage following large-scale radiological incidents, integrated and multiple biomarker approaches, advances in whole-genome tiling arrays, advances in mass-spectrometry proteomics, radiation biodosimetry for estimation of cancer risk in a rat skin model, and confounding factors. Summary conclusions are provided at the end of the report.

Mass Spectrometry-Based Quantitative Metabolomics Revealed a Distinct Lipid Profile in Breast Cancer Patients

Directory of Open Access Journals (Sweden)

Yun Yen

2013-04-01

Full Text Available Breast cancer accounts for the largest number of newly diagnosed cases in female cancer patients. Although mammography is a powerful screening tool, about 20% of breast cancer cases cannot be detected by this method. New diagnostic biomarkers for breast cancer are necessary. Here, we used a mass spectrometry-based quantitative metabolomics method to analyze plasma samples from 55 breast cancer patients and 25 healthy controls. A number of 30 patients and 20 age-matched healthy controls were used as a training dataset to establish a diagnostic model and to identify potential biomarkers. The remaining samples were used as a validation dataset to evaluate the predictive accuracy for the established model. Distinct separation was obtained from an orthogonal partial least squares-discriminant analysis (OPLS-DA model with good prediction accuracy. Based on this analysis, 39 differentiating metabolites were identified, including significantly lower levels of lysophosphatidylcholines and higher levels of sphingomyelins in the plasma samples obtained from breast cancer patients compared with healthy controls. Using logical regression, a diagnostic equation based on three metabolites (lysoPC a C16:0, PC ae C42:5 and PC aa C34:2 successfully differentiated breast cancer patients from healthy controls, with a sensitivity of 98.1% and a specificity of 96.0%.

Discovery and characterization of antibody variants using mass spectrometry-based comparative analysis for biosimilar candidates of monoclonal antibody drugs.

Science.gov (United States)

Li, Wenhua; Yang, Bin; Zhou, Dongmei; Xu, Jun; Ke, Zhi; Suen, Wen-Chen

2016-07-01

Liquid chromatography mass spectrometry (LC-MS) is the most commonly used technique for the characterization of antibody variants. MAb-X and mAb-Y are two approved IgG1 subtype monoclonal antibody drugs recombinantly produced in Chinese hamster ovary (CHO) cells. We report here that two unexpected and rare antibody variants have been discovered during cell culture process development of biosimilars for these two approved drugs through intact mass analysis. We then used comprehensive mass spectrometry-based comparative analysis including reduced light, heavy chains, and domain-specific mass as well as peptide mapping analysis to fully characterize the observed antibody variants. The "middle-up" mass comparative analysis demonstrated that the antibody variant from mAb-X biosimilar candidate was caused by mass variation of antibody crystalline fragment (Fc), whereas a different variant with mass variation in antibody antigen-binding fragment (Fab) from mAb-Y biosimilar candidate was identified. Endoproteinase Lys-C digested peptide mapping and tandem mass spectrometry analysis further revealed that a leucine to glutamine change in N-terminal 402 site of heavy chain was responsible for the generation of mAb-X antibody variant. Lys-C and trypsin coupled non-reduced and reduced peptide mapping comparative analysis showed that the formation of the light-heavy interchain trisulfide bond resulted in the mAb-Y antibody variant. These two cases confirmed that mass spectrometry-based comparative analysis plays a critical role for the characterization of monoclonal antibody variants, and biosimilar developers should start with a comprehensive structural assessment and comparative analysis to decrease the risk of the process development for biosimilars. Copyright © 2016 Elsevier B.V. All rights reserved.

A Parallel Reaction Monitoring Mass Spectrometric Method for Analysis of Potential CSF Biomarkers for Alzheimer's Disease

DEFF Research Database (Denmark)

Brinkmalm, Gunnar; Sjödin, Simon; Simonsen, Anja Hviid

2018-01-01

SCOPE: The aim of this study was to develop and evaluate a parallel reaction monitoring mass spectrometry (PRM-MS) assay consisting of a panel of potential protein biomarkers in cerebrospinal fluid (CSF). EXPERIMENTAL DESIGN: Thirteen proteins were selected based on their association with neurode......SCOPE: The aim of this study was to develop and evaluate a parallel reaction monitoring mass spectrometry (PRM-MS) assay consisting of a panel of potential protein biomarkers in cerebrospinal fluid (CSF). EXPERIMENTAL DESIGN: Thirteen proteins were selected based on their association...... with neurodegenerative diseases and involvement in synaptic function, secretory vesicle function, or innate immune system. CSF samples were digested and two to three peptides per protein were quantified using stable isotope-labeled peptide standards. RESULTS: Coefficients of variation were generally below 15%. Clinical...

Salivary proteomic and genomic biomarkers for primary Sjogren's syndrome

NARCIS (Netherlands)

Hu, Shen; Wang, Jianghua; Leong, Sonya; Xie, Yongming; Yu, Tianwei; Zhou, Hui; Henry, Sharon; Vissink, Arjan; Pijpe, Justin; Kallenberg, Cees; Elashoff, David; Loo, Joseph A.; Wong, David T.

2007-01-01

Objective. To identify a panel of protein and messenger RNA (mRNA) biomarkers in human whole saliva (WS) that may be used in the detection of primary Sjogren's syndrome (SS). Methods. Mass spectrometry and expression microarray profiling were used to identify candidate protein and mRNA, biomarkers

Salivary proteomic and genomic biomarkers for primary Sjogren's syndrome

NARCIS (Netherlands)

Hu, Shen; Wang, Jianghua; Leong, Sonya; Xie, Yongming; Yu, Tianwei; Zhou, Hui; Henry, Sharon; Vissink, Arjan; Pijpe, Justin; Kallenberg, Cees; Elashoff, David; Loo, Joseph A.; Wong, David T.

Objective. To identify a panel of protein and messenger RNA (mRNA) biomarkers in human whole saliva (WS) that may be used in the detection of primary Sjogren's syndrome (SS). Methods. Mass spectrometry and expression microarray profiling were used to identify candidate protein and mRNA, biomarkers

De Novo Identification of Biomarker Proteins Using Tandem Mass Spectrometry

Science.gov (United States)

Many studies have shown that biological fluids contain an important number of biomarkers associated with various pathologies. For instance, there has been extensive research to identify effective biomarkers as prognostic indicators of breast cancer. An effective approach for biom...

Resource Discovery in Activity-Based Sensor Networks

DEFF Research Database (Denmark)

Bucur, Doina; Bardram, Jakob

This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

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.

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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.

Mass spectrometry imaging: a novel technology in rheumatology.

Science.gov (United States)

Rocha, Beatriz; Ruiz-Romero, Cristina; Blanco, Francisco J

2017-01-01

Mass spectrometry imaging (MSI) is used to determine the relative abundance and spatial distribution of biomolecules such as peptides, proteins, lipids and other organic compounds in tissue sections by their molecular masses. This technique provides a sensitive and label-free approach for high-resolution imaging, and is currently used in an increasing number of biomedical applications such as biomarker discovery, tissue classification and drug monitoring. Owing to technological advances in the past 5 years in diverse MSI strategies, this technology is expected to become a standard tool in clinical practice and provides information complementary to that obtained using existing methods. Given that MSI is able to extract mass-spectral signatures from pathological tissue samples, this technique provides a novel platform to study joint-related tissues affected by rheumatic diseases. In rheumatology, MSI has been performed on articular cartilage, synovium and bone to increase the understanding of articular destruction and to characterize diagnostic and prognostic biomarkers for osteoarthritis, rheumatoid arthritis and osteoporosis. In this Review, we provide an overview of MSI technology and of the studies in which joint tissues have been analysed by use of this methodology. This approach might increase knowledge of rheumatic pathologies and ultimately prompt the development of targeted strategies for their management.

The potential of pathological protein fragmentation in blood-based biomarker development for dementia – with emphasis on Alzheimer’s disease

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Dilek eInekci

2015-05-01

Full Text Available The diagnosis of dementia is challenging and early stages are rarely detected limiting the possibilities for early interven-tion. Another challenge is the overlap in the clinical features across the different dementia types leading to difficulties in the differential diagnosis. Identifying biomarkers that can detect the pre-dementia stage and allow differential diagnosis could provide an opportunity for timely and optimal intervention strategies. Also, such biomarkers could help in selection and inclusion of the right patients in clinical trials of both Alzheimer’s disease and other dementia treatment candidates.The cerebrospinal fluid (CSF has been the most investigated source of biomarkers and several candidate proteins have been identified. However, looking solely at protein levels is too simplistic to provide enough detailed information to differentiate between dementias, as there is a significant crossover between the proteins involved in the different types of dementia. Additionally, CSF sampling makes these biomarkers challenging for presymptomatic identification. We need to focus on disease-specific protein fragmentation to find a fragment pattern unique for each separate dementia type – a form of protein fragmentology. Targeting protein fragments generated by disease-specific combinations of proteins and proteases opposed to detecting the intact protein could reduce the overlap between diagnostic groups as the extent of processing as well as which proteins and proteases constitute the major hallmark of each dementia type differ. In addition, the fragments could be detectable in blood as they may be able to cross the blood-brain-barrier due to their smaller size. In this review, the potential of the fragment-based biomarker discovery for dementia diagnosis and prognosis is discussed, especially highlighting how the knowledge from CSF protein biomarkers can be used to guide blood-based biomarker development.

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

A hybrid approach to protein differential expression in mass spectrometry-based proteomics

KAUST Repository

Wang, X.

2012-04-19

MOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein\\'s associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. RESULTS: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of \\'presence/absence,\\' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. \\'one-state\\' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate. AVAILABILITY: All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.

Introduction to fragment-based drug discovery.

Science.gov (United States)

Erlanson, Daniel A

2012-01-01

Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.

Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis

Science.gov (United States)

Zhang, Hui; Li, Tangxin; Zheng, Linqing

2017-01-01

Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms. PMID:29098014

Verification of protein biomarker specificity for the identification of biological stains by quadrupole time-of-flight mass spectrometry.

Science.gov (United States)

Legg, Kevin M; Powell, Roger; Reisdorph, Nichole; Reisdorph, Rick; Danielson, Phillip B

2017-03-01

Advances in proteomics technology over the past decade offer forensic serologists a greatly improved opportunity to accurately characterize the tissue source from which a DNA profile has been developed. Such information can provide critical context to evidence and can help to prioritize downstream DNA analyses. Previous proteome studies compiled panels of "candidate biomarkers" specific to each of five body fluids (i.e., peripheral blood, vaginal/menstrual fluid, seminal fluid, urine, and saliva). Here, a multiplex quadrupole time-of-flight mass spectrometry assay has been developed in order to verify the tissue/body fluid specificity the 23 protein biomarkers that comprise these panels and the consistency with which they can be detected across a sample population of 50 humans. Single-source samples of these human body fluids were accurately identified by the detection of one or more high-specificity biomarkers. Recovery of body fluid samples from a variety of substrates did not impede accurate characterization and, of the potential inhibitors assayed, only chewing tobacco juice appeared to preclude the identification of a target body fluid. Using a series of 2-component mixtures of human body fluids, the multiplex assay accurately identified both components in a single-pass. Only in the case of saliva and peripheral blood did matrix effects appear to impede the detection of salivary proteins. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rapid bacterial antibiotic susceptibility test based on simple surface-enhanced Raman spectroscopic biomarkers

Science.gov (United States)

Liu, Chia-Ying; Han, Yin-Yi; Shih, Po-Han; Lian, Wei-Nan; Wang, Huai-Hsien; Lin, Chi-Hung; Hsueh, Po-Ren; Wang, Juen-Kai; Wang, Yuh-Lin

2016-03-01

Rapid bacterial antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) measurement are important to help reduce the widespread misuse of antibiotics and alleviate the growing drug-resistance problem. We discovered that, when a susceptible strain of Staphylococcus aureus or Escherichia coli is exposed to an antibiotic, the intensity of specific biomarkers in its surface-enhanced Raman scattering (SERS) spectra drops evidently in two hours. The discovery has been exploited for rapid AST and MIC determination of methicillin-susceptible S. aureus and wild-type E. coli as well as clinical isolates. The results obtained by this SERS-AST method were consistent with that by the standard incubation-based method, indicating its high potential to supplement or replace existing time-consuming methods and help mitigate the challenge of drug resistance in clinical microbiology.

Preliminary characterizations of a serum biomarker for sarcoidosis by comparative proteomic approach with tandem-mass spectrometry in ethnic Han Chinese patients

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Zhang Yuan

2013-02-01

Full Text Available Abstract Background The diagnosis of sarcoidosis is still a significant challenge in China because of the need to exclude other diseases including granulomatous infections and malignancies that may be clinically and radiographically similar. The specific aim of the study is to search for serum protein biomarkers of sarcoidosis and to validate their clinical usefulness in differential diagnosis. Methods Serum samples were collected from patients with sarcoidosis (n = 37, and compared to those from patients with tuberculosis (n = 20, other pulmonary diseases (n = 20, and healthy volunteers (n = 20 for determination of sarcoidosis-specific or -associated protein expression profiles. The first part of this study focused on proteomic analysis of serum from patients with sarcoidosis to identify a pattern of peptides capable of differentiating the studied populations using the ClinProt profiling technology based on mass spectrometry. Enzyme Linked Immunosorbent Assay (ELISA was then used to verify corresponding elevation of the serum protein concentration of the potential biomarkers in the same patients sets. Receiver operating characteristic curve (ROC analyses was performed to determine the optimal cutoff value for diagnosis. Immunohistochemistry was carried out to further confirm the protein expression patterns of the biomarkers in lung tissue. Results An unique protein peak of M/Z 3,210 Daltons (Da was found to be differentially expressed between the sarcoidosis and control groups and was identified as the N-terminal peptide of 29 amino acids (94-122 of serum amyloid A (SAA. ELISA confirmed that the serum SAA level was significantly higher in the sarcoidosis group than that of the other 3 control groups (p p  Conclusion This is the first study to investigate serum protein markers in Chinese subjects with sarcoidosis. This study shows that the serum SAA expression profiles were different between the sarcoidosis and non

Using concepts in literature-based discovery : Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries

NARCIS (Netherlands)

Weeber, M; Klein, Henny; de Jong-van den Berg, LTW; Vos, R

Literature-based discovery has resulted in new knowledge. In the biomedical context, Don R. Swanson has generated several literature-based hypotheses that have been corroborated experimentally and clinically. In this paper, we propose a two-step model of the discovery process in which hypotheses are

Gas chromatography-mass spectrometry of ethyl palmitate calibration and resolution with ethyl oleate as biomarker ethanol sub acute in urine application study

Science.gov (United States)

Suaniti, Ni Made; Manurung, Manuntun

2016-03-01

Gas Chromatography-Mass Spectrometry is used to separate two and more compounds and identify fragment ion specific of biomarker ethanol such as palmitic acid ethyl ester (PAEE), as one of the fatty acid ethyl esters as early detection through conyugated reaction. This study aims to calibrate ethyl palmitate and develop analysis with oleate acid. This methode can be used analysis ethanol and its chemistry biomarker in ethanol sub-acute consumption as analytical forensic toxicology. The result show that ethanol level in urine rats Wistar were 9.21 and decreased 6.59 ppm after 48 hours consumption. Calibration curve of ethyl palmitate was y = 0.2035 x + 1.0465 and R2 = 0.9886. Resolution between ethyl palmitate and oleate were >1.5 as good separation with fragment ion specific was 88 and the retention time was 18 minutes.

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

Directory of Open Access Journals (Sweden)

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

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)...

Pharmacological screening technologies for venom peptide discovery.

Science.gov (United States)

Prashanth, Jutty Rajan; Hasaballah, Nojod; Vetter, Irina

2017-12-01

Venomous animals occupy one of the most successful evolutionary niches and occur on nearly every continent. They deliver venoms via biting and stinging apparatuses with the aim to rapidly incapacitate prey and deter predators. This has led to the evolution of venom components that act at a number of biological targets - including ion channels, G-protein coupled receptors, transporters and enzymes - with exquisite selectivity and potency, making venom-derived components attractive pharmacological tool compounds and drug leads. In recent years, plate-based pharmacological screening approaches have been introduced to accelerate venom-derived drug discovery. A range of assays are amenable to this purpose, including high-throughput electrophysiology, fluorescence-based functional and binding assays. However, despite these technological advances, the traditional activity-guided fractionation approach is time-consuming and resource-intensive. The combination of screening techniques suitable for miniaturization with sequence-based discovery approaches - supported by advanced proteomics, mass spectrometry, chromatography as well as synthesis and expression techniques - promises to further improve venom peptide discovery. Here, we discuss practical aspects of establishing a pipeline for venom peptide drug discovery with a particular emphasis on pharmacology and pharmacological screening approaches. This article is part of the Special Issue entitled 'Venom-derived Peptides as Pharmacological Tools.' Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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.

Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

Directory of Open Access Journals (Sweden)

Stobiecki Maciej

2009-07-01

.0003 increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity. Conclusion MALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Biomarkers identified by urinary metabonomics for noninvasive diagnosis of nutritional rickets.

Science.gov (United States)

Wang, Maoqing; Yang, Xue; Ren, Lihong; Li, Songtao; He, Xuan; Wu, Xiaoyan; Liu, Tingting; Lin, Liqun; Li, Ying; Sun, Changhao

2014-09-05

Nutritional rickets is a worldwide public health problem; however, the current diagnostic methods retain shortcomings for accurate diagnosis of nutritional rickets. To identify urinary biomarkers associated with nutritional rickets and establish a noninvasive diagnosis method, urinary metabonomics analysis by ultra-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis were employed to investigate the metabolic alterations associated with nutritional rickets in 200 children with or without nutritional rickets. The pathophysiological changes and pathogenesis of nutritional rickets were illustrated by the identified biomarkers. By urinary metabolic profiling, 31 biomarkers of nutritional rickets were identified and five candidate biomarkers for clinical diagnosis were screened and identified by quantitative analysis and receiver operating curve analysis. Urinary levels of five candidate biomarkers were measured using mass spectrometry or commercial kits. In the validation step, the combination of phosphate and sebacic acid was able to give a noninvasive and accurate diagnostic with high sensitivity (94.0%) and specificity (71.2%). Furthermore, on the basis of the pathway analysis of biomarkers, our urinary metabonomics analysis gives new insight into the pathogenesis and pathophysiology of nutritional rickets.

Fingerprinting Deepwater Horizon Oil in the northern Gulf of Mexico using biomarkers and Gas Chromatography-Triple Quadrupole Mass Spectrometry (GC/MS/MS)

Science.gov (United States)

Adhikari, P. L.; Overton, E. B.; Maiti, K.; Wong, R. L.

2016-02-01

Petroleum biomarkers such as hopanes, steranes, and triaromatic steroids are more persistent than alkanes and aromatic compounds. Thus, they are often used to track spilled oil in the environments and as a proxy for weathering processes. The present study utilizes water samples, suspended and sinking particles, and seafloor sediments collected during 2011-2013 from various locations of the northern Gulf of Mexico with wide range of contaminated oil for Deepwater Horizon (DWH) oil fingerprinting. The MC252 source oil along with the samples collected in this study were analyzed using a gas chromatography coupled with a triple quadrupole mass spectrometry (GC/MS/MS) in Multiple Reaction Monitoring (MRM) mode and the results were compared with results from commonly used GC/MS selective ion monitoring (SIM) method. The results indicate that the MRM method separates interfering ions from interfering compounds and can be a powerful analytical strategy for a reliable identification and determination of trace levels of biomarkers in complex matrices. Source indicators such as the MRM fragment ion chromatograms of the biomarkers and their diagnostic ratios in samples were compared with the MC252 source oil. The preliminary results show that the biomarkers were below detection limits in dissolved samples. However, in few particulate and seafloor sediment samples, primarily from the immediate vicinity of the Macondo wellhead, contained their patterns. The results also illustrate that these biomarker compounds have been weathered within 1-3 years following the oil spill, and their DWH oil signature in some of these samples reflects this weathering.

Seminal plasma as a source of prostate cancer peptide biomarker candidates for detection of indolent and advanced disease.

Directory of Open Access Journals (Sweden)

Jochen Neuhaus

Full Text Available BACKGROUND: Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease. METHODOLOGY/PRINCIPAL FINDINGS: In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls were enrolled in 3 centres. Biomarker panels a for PCa diagnosis (comparison of PCa patients versus benign controls and b for advanced disease (comparison of patients with post surgery Gleason score 7 were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase, prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study. CONCLUSIONS/SIGNIFICANCE: Seminal plasma represents a robust source of potential peptide makers

Differential blood-based biomarkers of psychopathological dimensions of schizophrenia.

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Garcia-Alvarez, Leticia; Garcia-Portilla, Maria Paz; Gonzalez-Blanco, Leticia; Saiz Martinez, Pilar Alejandra; de la Fuente-Tomas, Lorena; Menendez-Miranda, Isabel; Iglesias, Celso; Bobes, Julio

Symptomatology of schizophrenia is heterogeneous, there is not any pathognomonic symptom. Moreover, the diagnosis is difficult, since it is based on subjective information, instead of markers. The purpose of this study is to provide a review of the current status of blood-based biomarkers of psychopathological dimensions of schizophrenia. Inflammatory, hormonal or metabolic dysfunctions have been identified in patients with schizophrenia and it has attempted to establish biomarkers responsible for these dysfunctions. The identification of these biomarkers could contribute to the diagnosis and treatment of schizophrenia. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

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...

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.

CURRENT APPROACHES FOR RESEARCH OF MULTIPLE SCLEROSIS BIOMARKERS

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Kolyada T.I

2016-12-01

severity, progression, pathogenetic type and treatment efficacy are based on transcriptomics, proteomics and metabolomics technologies. Transcriptomics includes genome-wide research of RNA sequences based on the results obtained with comparative genomic hybridization on biochips, massive parallel RNA sequencing, and measuring the amount of mRNA by real-time PCR. This technology is actively used in studies of gene expression profile of peripheral blood mononuclear cells from MS patients aimed at identifying molecular markers of disease status suitable for clinical use. Proteomics is a large-scale expression and protein distribution studies in patients with MS based on the results obtained via microarray and mass spectrometry, liquid and gas chromatography methods. In recent years, a growing number of MS proteomic studies using 2DE-MS method (two-dimensional electrophoresis coupled with mass spectrometry. Metabolomics studies of low-molecular-weight metabolic profiles based on the results obtained by mass spectrometry, liquid and gas chromatography, nuclear magnetic resonance. However, unlike other «-omics»-technologies, in metabolomics microarray-techniques are not used. Conclusion. Search, verification and clinical application of biomarkers for multiple sclerosis are one of the most challenging medical and biological problems. Its solution requires an interdisciplinary approach, organization of large-scale research and engagement of new research methods. In recent years, a significant amount of data received allowed to reveal hundreds of candidate biomarkers. Some of these biomarkers have significant potential for the monitoring of disease activity and assessment of therapy efficiency. However, the verification is required for a widespread clinical application; it implies further large-scale studies in different countries. The development of personalized medicine in Ukraine, the application of its principles to the management of multiple sclerosis patients, along with

Discovery of urinary biomarkers to discriminate between exogenous and semi-endogenous thiouracil in cattle: A parallel-like randomized design.

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Van Meulebroek, Lieven; Wauters, Jella; Pomian, Beata; Vanden Bussche, Julie; Delahaut, Philippe; Fichant, Eric; Vanhaecke, Lynn

2018-01-01

In the European Union, the use of thyreostats for animal fattening purposes has been banned and monitoring plans have been established to detect potential abuse. However, this is not always straightforward as thyreostats such as thiouracil may also have a semi-endogenous origin. Therefore, this study aimed at defining urinary metabolites, which may aid in defining the origin of detected thiouracil. Hereto, a parallel-like randomized in vivo study was conducted in which calves (n = 8) and cows (n = 8) were subjected to either a control treatment, rapeseed-enriched diet to induce semi-endogenous formation, or thiouracil treatment. Urine samples (n = 330) were assessed through metabolic fingerprinting, employing liquid-chromatography and Q-ExactiveTM Orbitrap mass spectrometry. Urinary fingerprints comprised up to 40,000 features whereby multivariate discriminant analysis was able to point out significant metabolome differences between treatments (Q2(Y) ≥ 0.873). Using the validated models, a total of twelve metabolites (including thiouracil) were assigned marker potential. Combining these markers into age-dependent biomarker panels rendered a tool by which sample classification could be improved in comparison with thiouracil-based thresholds, and this during on-going thiouracil treatment (specificities ≥ 95.2% and sensitivities ≥ 85.7%), post-treatment (sensitivities ≥ 80% for ≥ 24 h after last administration), and simulated low-dose thiouracil treatment (exogenous thiouracil below 30 ng μL-1). Moreover, the metabolic relevance of revealed markers was supported by the suggested identities, for which a structural link with thiouracil could be determined in most cases. The proposed biomarker panels may contribute to a more justified decision-making in monitoring thiouracil abuse.

Identification and characterization of N-glycosylated proteins using proteomics

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Selby, David S; Larsen, Martin R; Calvano, Cosima Damiana

2008-01-01

and analysis of glycoproteins and glycopeptides. Combinations of affinity-enrichment techniques, chemical and biochemical protocols, and advanced mass spectrometry facilitate detailed glycoprotein analysis in proteomics, from fundamental biological studies to biomarker discovery in biomedicine....... is a complex task and is currently achieved by mass spectrometry-based methods that enable identification of glycoproteins and localization, classification, and analysis of individual glycan structures on proteins. In this chapter we briefly introduce a range of analytical technologies for recovery...

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

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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 ...

DNA methylation based biomarkers: Practical considerations and applications

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Nielsen, Helene Myrtue; How Kit, Alexandre; Tost, Jorg

2012-01-01

of biochemical molecules such as proteins, DNA, RNA or lipids, whereby protein biomarkers have been the most extensively studied and used, notably in blood-based protein quantification tests or immunohistochemistry. The rise of interest in epigenetic mechanisms has allowed the identification of a new type...... of biomarker, DNA methylation, which is of great potential for many applications. This stable and heritable covalent modification mostly affects cytosines in the context of a CpG dinucleotide in humans. It can be detected and quantified by a number of technologies including genome-wide screening methods...... as well as locus- or gene-specific high-resolution analysis in different types of samples such as frozen tissues and FFPE samples, but also in body fluids such as urine, plasma, and serum obtained through non-invasive procedures. In some cases, DNA methylation based biomarkers have proven to be more...

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.

Lead discovery for mammalian elongation of long chain fatty acids family 6 using a combination of high-throughput fluorescent-based assay and RapidFire mass spectrometry assay

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Takamiya, Mari; Sakurai, Masaaki; Teranishi, Fumie; Ikeda, Tomoko; Kamiyama, Tsutomu; Asai, Akira

2016-01-01

A high-throughput RapidFire mass spectrometry assay is described for elongation of very long-chain fatty acids family 6 (Elovl6). Elovl6 is a microsomal enzyme that regulates the elongation of C12-16 saturated and monounsaturated fatty acids. Elovl6 may be a new therapeutic target for fat metabolism disorders such as obesity, type 2 diabetes, and nonalcoholic steatohepatitis. To identify new Elovl6 inhibitors, we developed a high-throughput fluorescence screening assay in 1536-well format. However, a number of false positives caused by fluorescent interference have been identified. To pick up the real active compounds among the primary hits from the fluorescence assay, we developed a RapidFire mass spectrometry assay and a conventional radioisotope assay. These assays have the advantage of detecting the main products directly without using fluorescent-labeled substrates. As a result, 276 compounds (30%) of the primary hits (921 compounds) in a fluorescence ultra-high-throughput screening method were identified as common active compounds in these two assays. It is concluded that both methods are very effective to eliminate false positives. Compared with the radioisotope method using an expensive 14 C-labeled substrate, the RapidFire mass spectrometry method using unlabeled substrates is a high-accuracy, high-throughput method. In addition, some of the hit compounds selected from the screening inhibited cellular fatty acid elongation in HEK293 cells expressing Elovl6 transiently. This result suggests that these compounds may be promising lead candidates for therapeutic drugs. Ultra-high-throughput fluorescence screening followed by a RapidFire mass spectrometry assay was a suitable strategy for lead discovery against Elovl6. - Highlights: • A novel assay for elongation of very-long-chain fatty acids 6 (Elovl6) is proposed. • RapidFire mass spectrometry (RF-MS) assay is useful to select real screening hits. • RF-MS assay is proved to be beneficial because of

Application of a new procedure for liquid chromatography/mass spectrometry profiling of plasma amino acid-related metabolites and untargeted shotgun proteomics to identify mechanisms and biomarkers of calcific aortic stenosis.

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Olkowicz, Mariola; Debski, Janusz; Jablonska, Patrycja; Dadlez, Michal; Smolenski, Ryszard T

2017-09-29

Calcific aortic valve stenosis (CAS) increasingly affects our ageing population, but the mechanisms of the disease and its biomarkers are not well established. Recently, plasma amino acid-related metabolite (AA) profiling has attracted attention in studies on pathology and development of biomarkers of cardiovascular diseases, but has not been studied in CAS. To evaluate the potential relationship between CAS and AA metabolome, a new ion-pairing reversed-phase liquid chromatography-tandem mass spectrometry (IP-RPLC-MS/MS) method has been developed and validated for simultaneous determination of 43 AAs in plasma of stenotic patients and age-matched control subjects. Furthermore, untargeted mass spectrometry-based proteomic analysis and confirmatory ELISA assays were performed. The method developed offered high accuracy (intra-assay imprecision averaged 4.4% for all compounds) and sensitivity (LOQ within 0.01-0.5μM). We found that 22 AAs and three AA ratios significantly changed in the CAS group as compared to control. The most pronounced differences were observed in urea cycle-related AAs and branched-chain AA (BCAA)-related AAs. The contents of asymmetric dimethylarginine (ADMA) and its monomethylated derivative (NMMA) were increased by 30-64% with CAS. The arginine/ADMA and Fischer's ratios as well as arginine, homoarginine, ADMA, symmetric dimethylarginine, hydroxyproline, betaine and 3-methylhistidine correlated with cardiac function-related parameters and concomitant systemic factors in the CAS patients. The results of proteomic analysis were consistent with involvement of inflammation, lipid abnormalities, hemostasis and extracellular matrix remodeling in CAS. In conclusion, changes in plasma AA profile and protein pattern that we identified in CAS provide information relevant to pathomechanisms and may deliver new biomarkers of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

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).

Plasma metabonomics study of the patients with acute anterior uveitis based on ultra-performance liquid chromatography-mass spectrometry.

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Guo, Junguo; Yan, Tingqin; Bi, Hongsheng; Xie, Xiaofeng; Wang, Xingrong; Guo, Dadong; Jiang, Haiqiang

2014-06-01

The identification of the biomarkers of patients with acute anterior uveitis (AAU) may allow for a less invasive and more accurate diagnosis, as well as serving as a predictor in AAU progression and treatment response. The aim of this study was to identify the potential biomarkers and the metabolic pathways from plasma in patients with AAU. Both plasma metabolic biomarkers and metabolic pathways in the AAU patients versus healthy volunteers were investigated using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and a metabonomics approach. The principal component analysis (PCA) was used to separate AAU patients from healthy volunteers as well as to identify the different biomarkers between the two groups. Metabolic compounds were matched to the KEGG, METLIN, and HMDB databases, and metabolic pathways associated with AAU were identified. The PCA for UPLC-MS data shows that the metabolites in AAU patients were significantly different from those of healthy volunteers. Of the 4,396 total features detected by UPLC-MS, 102 features were significantly different between AAU patients and healthy volunteers according to the variable importance plot (VIP) values (greater than two) of partial least squares discriminate analysis (PLS-DA). Thirty-three metabolic compounds were identified and were considered as potential biomarkers. Meanwhile, ten metabolic pathways were found that were related to the AAU according to the identified biomarkers. These data suggest that metabolomics study can identify potential metabolites that differ between AAU patients and healthy volunteers. Based on the PCA, PLS-DA, several potential metabolic biomarkers and pathways in AAU patients were found and identified. In addition, the UPLC-MS technique combined with metabonomics could be a suitable systematic biology tool in research in clinical problems in ophthalmology, and can provide further insight into the pathophysiology of AAU.

Glycosylation-Based Serum Biomarkers for Cancer Diagnostics and Prognostics.

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Kirwan, Alan; Utratna, Marta; O'Dwyer, Michael E; Joshi, Lokesh; Kilcoyne, Michelle

2015-01-01

Cancer is the second most common cause of death in developed countries with approximately 14 million newly diagnosed individuals and over 6 million cancer-related deaths in 2012. Many cancers are discovered at a more advanced stage but better survival rates are correlated with earlier detection. Current clinically approved cancer biomarkers are most effective when applied to patients with widespread cancer. Single biomarkers with satisfactory sensitivity and specificity have not been identified for the most common cancers and some biomarkers are ineffective for the detection of early stage cancers. Thus, novel biomarkers with better diagnostic and prognostic performance are required. Aberrant protein glycosylation is well known hallmark of cancer and represents a promising source of potential biomarkers. Glycoproteins enter circulation from tissues or blood cells through active secretion or leakage and patient serum is an attractive option as a source for biomarkers from a clinical and diagnostic perspective. A plethora of technical approaches have been developed to address the challenges of glycosylation structure detection and determination. This review summarises currently utilised glycoprotein biomarkers and novel glycosylation-based biomarkers from the serum glycoproteome under investigation as cancer diagnostics and for monitoring and prognostics and includes details of recent high throughput and other emerging glycoanalytical techniques.

Current status and future prospects for enabling chemistry technology in the drug discovery process.

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Djuric, Stevan W; Hutchins, Charles W; Talaty, Nari N

2016-01-01

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

Urinary proteomic biomarkers for diagnosis and risk stratification of autosomal dominant polycystic kidney disease: a multicentric study.

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Andreas D Kistler

Full Text Available Treatment options for autosomal dominant polycystic kidney disease (ADPKD will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS, we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI. The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001 with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD.

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

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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...

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

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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

Biomarker research for moyamoya disease in cerebrospinal fluid using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

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Maruwaka, Mikio; Yoshikawa, Kazuhiro; Okamoto, Sho; Araki, Yoshio; Sumitomo, Masaki; Kawamura, Akino; Yokoyama, Kinya; Wakabayashi, Toshihiko

2015-01-01

Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by steno-occlusive change in bilateral internal carotid arteries with unknown etiology. To discover biomarker candidates in cerebrospinal fluid from MMD patients, proteome analysis was performed by the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Three peptides, 4473Da, 4475Da, and 6253Da, were significantly elevated in MMD group. A positive correlation between 4473Da peptide and postoperative angiogenesis was determined. Twenty MMD patients were enrolled in this pilot study, including 11 pediatric cases less than 18 years of age (mean age, 8.67 years) and 9 adult MMD patients (mean age, 38.1 years). This study also includes 17 control cases with the mean age of 27.9 years old. In conclusion, 4473Da peptide is supposed to be a reliable biomarker of MMD. 4473Da peptide showed higher intensity peaks especially in younger MMD patients, and it was proved to be highly related to postoperative angiogenesis. Further study is needed to show how 4473Da peptide is involved with the etiology and the onset of MMD. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

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.

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.

A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

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Chen Bor-Sen

2011-01-01

Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

Mining biomarker information in biomedical literature

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Younesi Erfan

2012-12-01

Full Text Available Abstract Background For selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives. Methods A biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases. Results The current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http

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.

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

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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.

The utility of biomarkers in hepatocellular carcinoma: review of urine-based 1H-NMR studies – what the clinician needs to know

Directory of Open Access Journals (Sweden)

Cartlidge CR

2017-11-01

Full Text Available Caroline R Cartlidge,1 M R Abellona U,2 Alzhraa M A Alkhatib,2 Simon D Taylor-Robinson1 1Department of Surgery and Cancer, Liver Unit, Division of Digestive Health, 2Department of Surgery and Cancer, Division of Computational and Systems Medicine, Faculty of Medicine, Imperial College London, London, UK Abstract: Hepatocellular carcinoma (HCC is the fifth most common malignancy, the third most common cause of cancer death, and the most common primary liver cancer. Overall, there is a need for more reliable biomarkers for HCC, as those currently available lack sensitivity and specificity. For example, the current gold-standard biomarker, serum alpha-fetoprotein, has a sensitivity of roughly only 70%. Cancer cells have different characteristic metabolic signatures in biofluids, compared to healthy cells; therefore, metabolite analysis in blood or urine should lead to the detection of suitable candidates for the detection of HCC. With the advent of metabonomics, this has increased the potential for new biomarker discovery. In this article, we look at approaches used to identify biomarkers of HCC using proton nuclear magnetic resonance (1H-NMR spectroscopy of urine samples. The various multivariate statistical analysis techniques used are explained, and the process of biomarker identification is discussed, with a view to simplifying the knowledge base for the average clinician. Keywords: hepatocellular carcinoma, biomarkers, metabonomics, urine, proton nuclear magnetic resonance spectroscopy, 1H-NMR 

Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

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Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu

2013-01-04

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

Current status and future prospects for enabling chemistry technology in the drug discovery process

Science.gov (United States)

Djuric, Stevan W.; Hutchins, Charles W.; Talaty, Nari N.

2016-01-01

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities. PMID:27781094

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....

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.

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.

Rapid isolation of biomarkers for compound specific radiocarbon dating using high-performance liquid chromatography and flow injection analysis-atmospheric pressure chemical ionisation mass spectrometry.

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Smittenberg, Rienk H; Hopmans, Ellen C; Schouten, Stefan; Sinninghe Damsté, Jaap S

2002-11-29

Repeated semi-preparative normal-phase HPLC was performed to isolate selected biomarkers from sediment extracts for radiocarbon analysis. Flow injection analysis-mass spectrometry was used for rapid analysis of collected fractions to evaluate the separation procedure, taking only 1 min per fraction. In this way 100-1000 microg of glycerol dialkyl glycerol tetraethers, sterol fractions and chlorophyll-derived phytol were isolated from typically 100 g of marine sediment, i.e., in sufficient quantities for radiocarbon analysis, without significant carbon isotopic fractionation or contamination.

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...

Nanomaterials based biosensors for cancer biomarker detection

International Nuclear Information System (INIS)

Malhotra, Bansi D; Kumar, Saurabh; Pandey, Chandra Mouli

2016-01-01

Biosensors have enormous potential to contribute to the evolution of new molecular diagnostic techniques for patients suffering with cancerous diseases. A major obstacle preventing faster development of biosensors pertains to the fact that cancer is a highly complex set of diseases. The oncologists currently rely on a few biomarkers and histological characterization of tumors. Some of the signatures include epigenetic and genetic markers, protein profiles, changes in gene expression, and post-translational modifications of proteins. These molecular signatures offer new opportunities for development of biosensors for cancer detection. In this context, conducting paper has recently been found to play an important role towards the fabrication of a biosensor for cancer biomarker detection. In this paper we will focus on results of some of the recent studies obtained in our laboratories relating to fabrication and application of nanomaterial modified paper based biosensors for cancer biomarker detection. (paper)

Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals.

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Zeng, Tao; Zhang, Wanwei; Yu, Xiangtian; Liu, Xiaoping; Li, Meiyi; Chen, Luonan

2016-07-01

Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression

Development, validation and application of a micro-liquid chromatography-tandem mass spectrometry based method for simultaneous quantification of selected protein biomarkers of endothelial dysfunction in murine plasma.

Science.gov (United States)

Suraj, Joanna; Kurpińska, Anna; Olkowicz, Mariola; Niedzielska-Andres, Ewa; Smolik, Magdalena; Zakrzewska, Agnieszka; Jasztal, Agnieszka; Sitek, Barbara; Chlopicki, Stefan; Walczak, Maria

2018-02-05

The objective of this study was to develop and validate the method based on micro-liquid chromatography-tandem mass spectrometry (microLC/MS-MRM) for simultaneous determination of adiponectin (ADN), von Willebrand factor (vWF), soluble form of vascular cell adhesion molecule 1 (sVCAM-1), soluble form of intercellular adhesion molecule 1 (sICAM-1) and syndecan-1 (SDC-1) in mouse plasma. The calibration range was established from 2.5pmol/mL to 5000pmol/mL for ADN; 5pmol/mL to 5000pmol/mL for vWF; 0.375pmol/mL to 250pmol/mL for sVCAM-1 and sICAM-1; and 0.25pmol/mL to 250pmol/mL for SDC-1. The method was applied to measure the plasma concentration of selected proteins in mice fed high-fat diet (HFD), and revealed the pro-thrombotic status by increased concentration of vWF (1.31±0.17 nmol/mL (Control) vs 1.98±0.09 nmol/mL (HFD), p <0.05) and the dysregulation of adipose tissue metabolism by decreased concentration of ADN (0.62±0.08 nmol/mL (Control) vs 0.37±0.06 nmol/mL (HFD), p <0.05). In conclusion, the microLC/MS-MRM-based method allows for reliable measurements of selected protein biomarkers of endothelial dysfunction in mouse plasma. Copyright © 2017 Elsevier B.V. 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.

Glyco-centric lectin magnetic bead array (LeMBA − proteomics dataset of human serum samples from healthy, Barrett׳s esophagus and esophageal adenocarcinoma individuals

Directory of Open Access Journals (Sweden)

Alok K. Shah

2016-06-01

Full Text Available This data article describes serum glycoprotein biomarker discovery and qualification datasets generated using lectin magnetic bead array (LeMBA – mass spectrometry techniques, “Serum glycoprotein biomarker discovery and qualification pipeline reveals novel diagnostic biomarker candidates for esophageal adenocarcinoma” [1]. Serum samples collected from healthy, metaplastic Barrett׳s esophagus (BE and esophageal adenocarcinoma (EAC individuals were profiled for glycoprotein subsets via differential lectin binding. The biomarker discovery proteomics dataset consisting of 20 individual lectin pull-downs for 29 serum samples with a spiked-in internal standard chicken ovalbumin protein has been deposited in the PRIDE partner repository of the ProteomeXchange Consortium with the data set identifier PRIDE: http://www.ebi.ac.uk/pride/archive/projects/PXD002442. Annotated MS/MS spectra for the peptide identifications can be viewed using MS-Viewer (〈http://prospector2.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer〉 using search key “jn7qafftux”. The qualification dataset contained 6-lectin pulldown-coupled multiple reaction monitoring-mass spectrometry (MRM-MS data for 41 protein candidates, from 60 serum samples. This dataset is available as a supplemental files with the original publication [1].

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.

Metabolic profiling study on potential toxicity and immunotoxicity-biomarker discovery in rats treated with cyclophosphamide using HPLC-ESI-IT-TOF-MS.

Science.gov (United States)

Li, Jing; Lin, Wensi; Lin, Weiwei; Xu, Peng; Zhang, Jianmei; Yang, Haisong; Ling, Xiaomei

2015-05-01

Despite the recent advances in understanding toxicity mechanism of cyclophosphamide (CTX), the development of biomarkers is still essential. CTX-induced immunotoxicity in rats by a metabonomics approach was investigated using high-performance liquid chromatography coupled with ion trap time-of-flight mass spectrometry (HPLC-ESI-IT-TOF-MS). The rats were orally administered CTX (30 mg/kg/day) for five consecutive days, and on the fifth day samples of urine, thymus and spleen were collected and analyzed. A significant difference in metabolic profiling was observed between the CTX-treated group and the control group by partial least squares-discriminant analysis (PLS-DA), which indicated that metabolic disturbances of immunotoxicity in CTX-treated rats had occurred. One potential biomarker in spleen, three in urine and three in thymus were identified. It is suggested that the CTX-toxicity mechanism may involve the modulation of tryptophan metabolism, phospholipid metabolism and energy metabolism. This research can help to elucidate the CTX-influenced pathways at a low dose and can further help to indicate the patients' pathological status at earlier stages of toxicological progression after drug administration. Copyright © 2014 John Wiley & Sons, Ltd.

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.

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

Science.gov (United States)

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.

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.

Dithiothreitol-based protein equalization technology to unravel biomarkers for bladder cancer.

Science.gov (United States)

Araújo, J E; López-Fernández, H; Diniz, M S; Baltazar, Pedro M; Pinheiro, Luís Campos; da Silva, Fernando Calais; Carrascal, Mylène; Videira, Paula; Santos, H M; Capelo, J L

2018-04-01

This study aimed to assess the benefits of dithiothreitol (DTT)-based sample treatment for protein equalization to assess potential biomarkers for bladder cancer. The proteome of plasma samples of patients with bladder carcinoma, patients with lower urinary tract symptoms (LUTS) and healthy volunteers, was equalized with dithiothreitol (DTT) and compared. The equalized proteomes were interrogated using two-dimensional gel electrophoresis and matrix assisted laser desorption ionization time of flight mass spectrometry. Six proteins, namely serum albumin, gelsolin, fibrinogen gamma chain, Ig alpha-1 chain C region, Ig alpha-2 chain C region and haptoglobin, were found dysregulated in at least 70% of bladder cancer patients when compared with a pool of healthy individuals. One protein, serum albumin, was found overexpressed in 70% of the patients when the equalized proteome of the healthy pool was compared with the equalized proteome of the LUTS patients. The pathways modified by the proteins differentially expressed were analyzed using Cytoscape. The method here presented is fast, cheap, of easy application and it matches the analytical minimalism rules as outlined by Halls. Orthogonal validation was done using western-blot. Overall, DTT-based protein equalization is a promising methodology in bladder cancer research. Copyright © 2017 Elsevier B.V. All rights reserved.

Discrimination of multilocus sequence typing-based Campylobacter jejuni subgroups by MALDI-TOF mass spectrometry.

Science.gov (United States)

Zautner, Andreas Erich; Masanta, Wycliffe Omurwa; Tareen, Abdul Malik; Weig, Michael; Lugert, Raimond; Groß, Uwe; Bader, Oliver

2013-11-07

Campylobacter jejuni, the most common bacterial pathogen causing gastroenteritis, shows a wide genetic diversity. Previously, we demonstrated by the combination of multi locus sequence typing (MLST)-based UPGMA-clustering and analysis of 16 genetic markers that twelve different C. jejuni subgroups can be distinguished. Among these are two prominent subgroups. The first subgroup contains the majority of hyperinvasive strains and is characterized by a dimeric form of the chemotaxis-receptor Tlp7(m+c). The second has an extended amino acid metabolism and is characterized by the presence of a periplasmic asparaginase (ansB) and gamma-glutamyl-transpeptidase (ggt). Phyloproteomic principal component analysis (PCA) hierarchical clustering of MALDI-TOF based intact cell mass spectrometry (ICMS) spectra was able to group particular C. jejuni subgroups of phylogenetic related isolates in distinct clusters. Especially the aforementioned Tlp7(m+c)(+) and ansB+/ ggt+ subgroups could be discriminated by PCA. Overlay of ICMS spectra of all isolates led to the identification of characteristic biomarker ions for these specific C. jejuni subgroups. Thus, mass peak shifts can be used to identify the C. jejuni subgroup with an extended amino acid metabolism. Although the PCA hierarchical clustering of ICMS-spectra groups the tested isolates into a different order as compared to MLST-based UPGMA-clustering, the isolates of the indicator-groups form predominantly coherent clusters. These clusters reflect phenotypic aspects better than phylogenetic clustering, indicating that the genes corresponding to the biomarker ions are phylogenetically coupled to the tested marker genes. Thus, PCA clustering could be an additional tool for analyzing the relatedness of bacterial isolates.

Establishing Drug Resistance in Microorganisms by Mass Spectrometry

Science.gov (United States)

Demirev, Plamen A.; Hagan, Nathan S.; Antoine, Miquel D.; Lin, Jeffrey S.; Feldman, Andrew B.

2013-08-01

A rapid method to determine drug resistance in bacteria based on mass spectrometry is presented. In it, a mass spectrum of an intact microorganism grown in drug-containing stable isotope-labeled media is compared with a mass spectrum of the intact microorganism grown in non-labeled media without the drug present. Drug resistance is determined by predicting characteristic mass shifts of one or more microorganism biomarkers using bioinformatics algorithms. Observing such characteristic mass shifts indicates that the microorganism is viable even in the presence of the drug, thus incorporating the isotopic label into characteristic biomarker molecules. The performance of the method is illustrated on the example of intact E. coli, grown in control (unlabeled) and 13C-labeled media, and analyzed by MALDI TOF MS. Algorithms for data analysis are presented as well.

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

Science.gov (United States)

Rantalainen, Mattias; Klevebring, Daniel; Lindberg, Johan; Ivansson, Emma; Rosin, Gustaf; Kis, Lorand; Celebioglu, Fuat; Fredriksson, Irma; Czene, Kamila; Frisell, Jan; Hartman, Johan; Bergh, Jonas; Grönberg, Henrik

2016-11-30

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Application of Mass Spectrometry for the Analysis of Vitellogenin, a Unique Biomarker for Xenobiotic Compounds

Science.gov (United States)

Cohen, Alejandro M.; Banoub, Joseph H.

Vitellogenin is a complex phosphoglycolipoprotein that is secreted into the bloodstream of sexually mature, female, oviparous animals in response to circulating estrogens. It is then incorporated into the ovaries by receptor mediated endocytosis, where it is further cleaved to form the major constituents of the egg yolk proteins. It is generally accepted that these protein and peptide products serve as the main nutritional reserve for the developing embryo. Quantification of vitellogenin in blood is useful for different purposes. The reproductive status and degree of sexual maturation of oviparous animals can be assessed according to the levels of vitellogenin in plasma. The expression of this protein can also be induced in males under the effect of estrogenic compounds. Relying on this observation, vitellogenin has been used as a unique biomarker of environmental endocrine disruption in many species. In this respect, vitellogenin levels could potentially be used to assess the use of chemical warefare compounds with estrogenic activity. In this paper we review a technique developed for measuring vitellogenin plasma levels of different fish species using high performance liquid chromatography coupled to tandem mass spectrometry.

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,

Optimization and evaluation of surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry for protein profiling of cerebrospinal fluid

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Gomez-Mancilla Baltazar

2006-04-01

Full Text Available Abstract Cerebrospinal fluid (CSF potentially carries an archive of peptides and small proteins relevant to pathological processes in the central nervous system (CNS and surrounding brain tissue. Proteomics is especially well suited for the discovery of biomarkers of diagnostic potential in CSF for early diagnosis and discrimination of several neurodegenerative diseases. ProteinChip surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS is one such approach which offers a unique platform for high throughput profiling of peptides and small proteins in CSF. In this study, we evaluated methodologies for the retention of CSF proteins m/z we found a high degree of overlap between the tested array surfaces. The combination of CM10 and IMAC30 arrays was sufficient to represent between 80–90% of all assigned peaks when using either sinapinic acid or α-Cyano-4-hydroxycinnamic acid as the energy absorbing matrices. Moreover, arrays processed with SPA consistently showed better peak resolution and higher peak number across all surfaces within the measured mass range. We intend to use CM10 and IMAC30 arrays prepared in sinapinic acid as a fast and cost-effective approach to drive decisions on sample selection prior to more in-depth discovery of diagnostic biomarkers in CSF using alternative but complementary proteomic strategies.

Rapid quantification of biomarkers during kerogen microscale pyrolysis

Energy Technology Data Exchange (ETDEWEB)

Stott, A.W.; Abbott, G.D. [Fossil Fuels and Environmental Geochemistry NRG, The University, Newcastle-upon-Tyne (United Kingdom)

1995-02-01

A rapid, reproducible method incorporating closed system microscale pyrolysis and thermal desorption-gas chromatography/mass spectrometry has been developed and applied to the quantification of sterane biomarkers released during pyrolysis of the Messel oil shale kerogen under confined conditions. This method allows a substantial experimental concentration-time data set to be collected at accurately controlled temperatures, due to the low thermal inertia of the microscale borosilicate glass reaction vessels, which facilitates kinetic studies of biomarker reactions during kerogen microscale pyrolysis

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

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Nichola C Garbett

Full Text Available Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN and extent of spread of invasive carcinomas of the cervix (IC are needed. Differential scanning calorimetry (DSC has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

Science.gov (United States)

Garbett, Nichola C; Merchant, Michael L; Helm, C William; Jenson, Alfred B; Klein, Jon B; Chaires, Jonathan B

2014-01-01

Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms) were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS) analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate cervical cancer

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.

A magnetic bead-based ligand binding assay to facilitate human kynurenine 3-monooxygenase drug discovery.

Science.gov (United States)

Wilson, Kris; Mole, Damian J; Homer, Natalie Z M; Iredale, John P; Auer, Manfred; Webster, Scott P

2015-02-01

Human kynurenine 3-monooxygenase (KMO) is emerging as an important drug target enzyme in a number of inflammatory and neurodegenerative disease states. Recombinant protein production of KMO, and therefore discovery of KMO ligands, is challenging due to a large membrane targeting domain at the C-terminus of the enzyme that causes stability, solubility, and purification difficulties. The purpose of our investigation was to develop a suitable screening method for targeting human KMO and other similarly challenging drug targets. Here, we report the development of a magnetic bead-based binding assay using mass spectrometry detection for human KMO protein. The assay incorporates isolation of FLAG-tagged KMO enzyme on protein A magnetic beads. The protein-bound beads are incubated with potential binding compounds before specific cleavage of the protein-compound complexes from the beads. Mass spectrometry analysis is used to identify the compounds that demonstrate specific binding affinity for the target protein. The technique was validated using known inhibitors of KMO. This assay is a robust alternative to traditional ligand-binding assays for challenging protein targets, and it overcomes specific difficulties associated with isolating human KMO. © 2014 Society for Laboratory Automation and Screening.

Data from a targeted proteomics approach to discover biomarkers in saliva for the clinical diagnosis of periodontitis

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V. Orti

2018-06-01

Full Text Available This study focused on the search for new biomarkers based on liquid chromatography-multiple reaction monitoring (LC-MRM proteomics profiling of whole saliva from patients with periodontitis compared to healthy subjects. The LC-MRM profiling approach is a new and innovative method that has already been validated for the absolute and multiplexed quantification of biomarkers in several diseases. The dataset for this study was produced using LC-MRM to monitor protein levels in a multiplex assay, it provides clinical information on salivary biomarkers of periodontitis. The data presented here is an extension of our recently published research article (Mertens et al., 2017 [1]. Keywords: Clinical chemistry, Mass spectrometry, Proteomics, Saliva biochemistry, Oral disease, Periodontitis

[Latest development in mass spectrometry for clinical application].

Science.gov (United States)

Takino, Masahiko

2013-09-01

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has seen enormous growth in special clinical chemistry laboratories. It significantly increases the analytic potential in clinical chemistry, especially in the field of low molecular weight biomarker analysis. This review summarizes the state of the art in mass spectrometry and related techniques for clinical application with a main focus on recent developments in LC-MS. Current trends in ionization techniques, automated online sample preparation techniques coupled with LC-MS, and ion mobility spectrometry are discussed. Emerging mass spectrometric approaches complementary to LC-MS are discussed as well.

Imaging mass spectrometry statistical analysis.

Science.gov (United States)

Jones, Emrys A; Deininger, Sören-Oliver; Hogendoorn, Pancras C W; Deelder, André M; McDonnell, Liam A

2012-08-30

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research. Copyright © 2012 Elsevier B.V. All rights reserved.

Statistical methods for mass spectrometry-based clinical proteomics

NARCIS (Netherlands)

Kakourou, A.

2018-01-01

The work presented in this thesis focuses on methods for the construction of diagnostic rules based on clinical mass spectrometry proteomic data. Mass spectrometry has become one of the key technologies for jointly measuring the expression of thousands of proteins in biological samples.

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

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.

Liquid chromatography with mass spectrometry and NMR spectroscopy based discovery of cytotoxic principles from Daphne tangutica Maxim.

Science.gov (United States)

Yang, Xinzhou; Huang, Mi; Zheng, Sijian; Ma, Xinhua; Wan, Dingrong; Feng, Yunjiang

2016-06-01

An ethyl acetate extract from the barks of the ethnic Chinese medicine Daphne tangutica Maxim. exhibited antihepatocellular carcinoma activity against HepG2 and Hep3B cell lines. By using high-performance liquid chromatography based activity profiling in combination with offline liquid chromatography with mass spectrometry and NMR analysis, we rapidly identified ten major components of the extract, including seven active principles, coumarins (1-4) and biscoumarins (7, 8, 10), along with three inactive flavonoids (5, 6, 9). This study demonstrated that our combined protocol can be used as an important strategy for chemical profiling, dereplication as well as the identification of bioactive compounds from herbal medicines. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Label-assisted mass spectrometry for the acceleration of reaction discovery and optimization

Science.gov (United States)

Cabrera-Pardo, Jaime R.; Chai, David I.; Liu, Song; Mrksich, Milan; Kozmin, Sergey A.

2013-05-01

The identification of new reactions expands our knowledge of chemical reactivity and enables new synthetic applications. Accelerating the pace of this discovery process remains challenging. We describe a highly effective and simple platform for screening a large number of potential chemical reactions in order to discover and optimize previously unknown catalytic transformations, thereby revealing new chemical reactivity. Our strategy is based on labelling one of the reactants with a polyaromatic chemical tag, which selectively undergoes a photoionization/desorption process upon laser irradiation, without the assistance of an external matrix, and enables rapid mass spectrometric detection of any products originating from such labelled reactants in complex reaction mixtures without any chromatographic separation. This method was successfully used for high-throughput discovery and subsequent optimization of two previously unknown benzannulation reactions.

Development of decision tree software and protein profiling using surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) in papillary thyroid cancer

International Nuclear Information System (INIS)

Yoon, Joon Kee; An, Young Sil; Park, Bok Nam; Yoon, Seok Nam; Lee, Jun

2007-01-01

The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (ρ < 0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer

On-Chip Spyhole Nanoelectrospray Ionization Mass Spectrometry for Sensitive Biomarker Detection in Small Volumes

Science.gov (United States)

Zhong, Xiaoqin; Qiao, Liang; Stauffer, Géraldine; Liu, Baohong; Girault, Hubert H.

2018-03-01

A polyimide microfluidic chip with a microhole emitter (Ø 10-12 μm) created on top of a microchannel by scanning laser ablation has been designed for nanoelectrospray ionization (spyhole-nanoESI) to couple microfluidics with mass spectrometry. The spyhole-nanoESI showed higher sensitivity compared to standard ESI and microESI from the end of the microchannel. The limits of detection (LOD) for peptide with the spyhole-nanoESI MS reached 50 pM, which was 600 times lower than that with standard ESI. The present microchip emitter allows the analysis of small volumes of samples. As an example, a small cell lung cancer biomarker, neuron-specific enolase (NSE), was detected by monitoring the transition of its unique peptide with the spyhole-nanoESI MS/MS. NSE at 0.2 nM could be well identified with a signal to noise ratio (S/N) of 50, and thereby its LOD was estimated to be 12 pM. The potential application of the spyhole-nanoESI MS/MS in cancer diagnosis was further demonstrated with the successful detection of 2 nM NSE from 1 μL of human serum. Before the detection, the serum sample spiked with NSE was first depleted with immune spin column, then desalted by centrifugal filter device, and finally digested by trypsin, without any other complicated preparation steps. The concentration matched the real condition of clinical samples. In addition, the microchips can be disposable to avoid any cross contamination. The present technique provides a highly efficient way to couple microfluidics with MS, which brings additional values to various microfluidics and MS-based analysis.

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

Multiple reaction monitoring assay based on conventional liquid chromatography and electrospray ionization for simultaneous monitoring of multiple cerebrospinal fluid biomarker candidates for Alzheimer's disease.

Science.gov (United States)

Choi, Yong Seok; Lee, Kelvin H

2016-03-01

Alzheimer's disease (AD) is the most common type of dementia, but early and accurate diagnosis remains challenging. Previously, a panel of cerebrospinal fluid (CSF) biomarker candidates distinguishing AD and non-AD CSF accurately (>90 %) was reported. Furthermore, a multiple reaction monitoring (MRM) assay based on nano liquid chromatography tandem mass spectrometry (nLC-MS/MS) was developed to help validate putative AD CSF biomarker candidates including proteins from the panel. Despite the good performance of the MRM assay, wide acceptance may be challenging because of limited availability of nLC-MS/MS systems in laboratories. Thus, here, a new MRM assay based on conventional LC-MS/MS is presented. This method monitors 16 peptides representing 16 (of 23) biomarker candidates that belonged to the previous AD CSF panel. A 30-times more concentrated sample than the sample used for the previous study was loaded onto a high capacity trap column, and all 16 MRM transitions showed good linearity (average R(2) = 0.966), intra-day reproducibility (average coefficient of variance (CV) = 4.78 %), and inter-day reproducibility (average CV = 9.85 %). The present method has several advantages such as a shorter analysis time, no possibility of target variability, and no need for an internal standard.

Accelerator-based ultrasensitive mass spectrometry

International Nuclear Information System (INIS)

Gove, H.E.

1985-01-01