A systems biology approach for pathway level analysis

OpenAIRE

Draghici, Sorin; Khatri, Purvesh; Tarca, Adi Laurentiu; Amin, Kashyap; Done, Arina; Voichita, Calin; Georgescu, Constantin; Romero, Roberto

2007-01-01

A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By usin...

Using a multi-method, user centred, prospective hazard analysis to assess care quality and patient safety in a care pathway

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Escoto Kamisha

2007-06-01

Full Text Available Abstract Background Care pathways can be complex, often involving multiple care providers and as such are recognised as containing multiple opportunities for error. Prospective hazard analysis methods may be useful for evaluating care provided across primary and secondary care pathway boundaries. These methods take into account the views of users (staff and patients when determining where potential hazards may lie. The aim of this study is to evaluate the feasibility of prospective hazard analysis methods when assessing quality and safety in care pathways that lie across primary and secondary care boundaries. Methods Development of a process map of the care pathway for patients entering into a Chronic Obstructive Pulmonary Disease (COPD supported discharge programme. Triangulation of information from: care process mapping, semi-structured interviews with COPD patients, semi-structured interviews with COPD staff, two round modified Delphi study and review of prioritised quality and safety challenges by health care staff. Results Interview themes emerged under the headings of quality of care and patient safety. Quality and safety concerns were mostly raised in relation to communication, for example, communication with other hospital teams. The three highest ranked safety concerns from the modified Delphi review were: difficulties in accessing hospital records, information transfer to primary care and failure to communicate medication changes to primary care. Conclusion This study has demonstrated the feasibility of using mixed methods to review the quality and safety of care in a care pathway. By using multiple research methods it was possible to get a clear picture of service quality variations and also to demonstrate which points in the care pathway had real potential for patient safety incidents or system failures to occur. By using these methods to analyse one condition specific care pathway it was possible to uncover a number of hospital

Machine learning methods for metabolic pathway prediction

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Karp Peter D

2010-01-01

Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

Machine learning methods for metabolic pathway prediction

Science.gov (United States)

2010-01-01

Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis.

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Zhang, Fan; Drabier, Renee

2012-01-01

multiple available data sources.IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study. IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies.

A novel bi-level meta-analysis approach: applied to biological pathway analysis.

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Nguyen, Tin; Tagett, Rebecca; Donato, Michele; Mitrea, Cristina; Draghici, Sorin

2016-02-01

The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. The R scripts are available on demand from the authors. sorin@wayne.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e

An ensemble method to predict target genes and pathways in uveal melanoma

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Wei Chao

2018-04-01

Full Text Available This work proposes to predict target genes and pathways for uveal melanoma (UM based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC, a causal inference method (IDA and a regression method (Lasso utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.

SNP-based pathway enrichment analysis for genome-wide association studies

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Potkin Steven G

2011-04-01

Full Text Available Abstract Background Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs, have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. Results We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1 for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2 ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one

Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways.

Science.gov (United States)

Kong, Wei; Mou, Xiaoyang; Di, Benteng; Deng, Jin; Zhong, Ruxing; Wang, Shuaiqun

2017-11-20

Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Pathway Distiller - multisource biological pathway consolidation.

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Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow

PyPathway: Python Package for Biological Network Analysis and Visualization.

Science.gov (United States)

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Methods of analysis of the membrane trafficking pathway from recycling endosomes to lysosomes.

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Matsui, Takahide; Fukuda, Mitsunori

2014-01-01

The transferrin receptor (TfR) is responsible for iron uptake through its trafficking between the plasma membrane and recycling endosomes, and as a result it has become a well-known marker for recycling endosomes. Although the molecular basis of the TfR recycling pathway has been thoroughly investigated, the TfR degradation mechanism has been poorly understood. Exposure of cultured cells to two drugs, the protein synthesis inhibitor cycloheximide and the V-ATPase inhibitor bafilomycin A1, recently showed that TfR is not only recycled back to the plasma membrane after endocytosis but is constitutively transported to lysosomes for degradation. The results of genome-wide screening of mouse Rab small GTPases (common regulators of membrane trafficking in all eukaryotes) have indicated that Rab12 regulates TfR trafficking to lysosomes independently of the known membrane trafficking pathways, for example, the conventional endocytic pathway and recycling pathway. This chapter summarizes the methods that the authors used to analyze the membrane trafficking pathway from recycling endosomes to lysosomes that is specifically regulated by Rab12. © 2014 Elsevier Inc. All rights reserved.

PathNet: a tool for pathway analysis using topological information

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Dutta Bhaskar

2012-09-01

Full Text Available Abstract Background Identification of canonical pathways through enrichment of differentially expressed genes in a given pathway is a widely used method for interpreting gene lists generated from high-throughput experimental studies. However, most algorithms treat pathways as sets of genes, disregarding any inter- and intra-pathway connectivity information, and do not provide insights beyond identifying lists of pathways. Results We developed an algorithm (PathNet that utilizes the connectivity information in canonical pathway descriptions to help identify study-relevant pathways and characterize non-obvious dependencies and connections among pathways using gene expression data. PathNet considers both the differential expression of genes and their pathway neighbors to strengthen the evidence that a pathway is implicated in the biological conditions characterizing the experiment. As an adjunct to this analysis, PathNet uses the connectivity of the differentially expressed genes among all pathways to score pathway contextual associations and statistically identify biological relations among pathways. In this study, we used PathNet to identify biologically relevant results in two Alzheimer’s disease microarray datasets, and compared its performance with existing methods. Importantly, PathNet identified de-regulation of the ubiquitin-mediated proteolysis pathway as an important component in Alzheimer’s disease progression, despite the absence of this pathway in the standard enrichment analyses. Conclusions PathNet is a novel method for identifying enrichment and association between canonical pathways in the context of gene expression data. It takes into account topological information present in pathways to reveal biological information. PathNet is available as an R workspace image from http://www.bhsai.org/downloads/pathnet/.

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

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Liu, Chun-Han; Liu, Lian

2017-05-08

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

Method for determining heterologous biosynthesis pathways

KAUST Repository

Gao, Xin

2017-08-10

The present invention relates to a method and system for dynamically analyzing, determining, predicting and displaying ranked suitable heterologous biosynthesis pathways for a specified host. The present invention addresses the problem of finding suitable pathways for the endogenous metabolism of a host organism because the efficacy of heterologous biosynthesis is affected by competing endogenous pathways. The present invention is called MRE (Metabolic Route Explorer), and it was conceived and developed to systematically and dynamically search for, determine, analyze, and display promising heterologous pathways while considering competing endogenous reactions in a given host organism.

Pathway analysis of IMC

DEFF Research Database (Denmark)

Skrypnyuk, Nataliya; Nielson, Flemming; Pilegaard, Henrik

2009-01-01

We present the ongoing work on the pathway analysis of a stochastic calculus. Firstly we present a particular stochastic calculus that we have chosen for our modeling - the Interactive Markov Chains calculus, IMC for short. After that we specify a few restrictions that we have introduced into the...... into the syntax of IMC in order to make our analysis feasible. Finally we describe the analysis itself together with several theoretical results that we have proved for it.......We present the ongoing work on the pathway analysis of a stochastic calculus. Firstly we present a particular stochastic calculus that we have chosen for our modeling - the Interactive Markov Chains calculus, IMC for short. After that we specify a few restrictions that we have introduced...

A novel approach to select differential pathways associated with hypertrophic cardiomyopathy based on gene co‑expression analysis.

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Chen, Xiao-Min; Feng, Ming-Jun; Shen, Cai-Jie; He, Bin; Du, Xian-Feng; Yu, Yi-Bo; Liu, Jing; Chu, Hui-Min

2017-07-01

The present study was designed to develop a novel method for identifying significant pathways associated with human hypertrophic cardiomyopathy (HCM), based on gene co‑expression analysis. The microarray dataset associated with HCM (E‑GEOD‑36961) was obtained from the European Molecular Biology Laboratory‑European Bioinformatics Institute database. Informative pathways were selected based on the Reactome pathway database and screening treatments. An empirical Bayes method was utilized to construct co‑expression networks for informative pathways, and a weight value was assigned to each pathway. Differential pathways were extracted based on weight threshold, which was calculated using a random model. In order to assess whether the co‑expression method was feasible, it was compared with traditional pathway enrichment analysis of differentially expressed genes, which were identified using the significance analysis of microarrays package. A total of 1,074 informative pathways were screened out for subsequent investigations and their weight values were also obtained. According to the threshold of weight value of 0.01057, 447 differential pathways, including folding of actin by chaperonin containing T‑complex protein 1 (CCT)/T‑complex protein 1 ring complex (TRiC), purine ribonucleoside monophosphate biosynthesis and ubiquinol biosynthesis, were obtained. Compared with traditional pathway enrichment analysis, the number of pathways obtained from the co‑expression approach was increased. The results of the present study demonstrated that this method may be useful to predict marker pathways for HCM. The pathways of folding of actin by CCT/TRiC and purine ribonucleoside monophosphate biosynthesis may provide evidence of the underlying molecular mechanisms of HCM, and offer novel therapeutic directions for HCM.

Effects of clinical pathways in the joint replacement: a meta-analysis

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Faggiano F

2009-07-01

Full Text Available Abstract Background A meta-analysis was performed to evaluate the use of clinical pathways for hip and knee joint replacements when compared with standard medical care. The impact of clinical pathways was evaluated assessing the major outcomes of in-hospital hip and knee joint replacement processes: postoperative complications, number of patients discharged at home, length of in-hospital stay and direct costs. Methods Medline, Cinahl, Embase and the Cochrane Central Register of Controlled Trials were searched. The search was performed from 1975 to 2007. Each study was assessed independently by two reviewers. The assessment of methodological quality of the included studies was based on the Jadad methodological approach and on the New Castle Ottawa Scale. Data analysis abided by the guidelines set out by The Cochrane Collaboration regarding statistical methods. Meta-analyses were performed using RevMan software, version 4.2. Results Twenty-two studies met the study inclusion criteria and were included in the meta-analysis for a total sample of 6,316 patients. The aggregate overall results showed significantly fewer patients suffering postoperative complications in the clinical pathways group when compared with the standard care group. A shorter length of stay in the clinical pathway group was also observed and lower costs during hospital stay were associated with the use of the clinical pathways. No significant differences were found in the rates of discharge to home. Conclusion The results of this meta-analysis show that clinical pathways can significantly improve the quality of care even if it is not possible to conclude that the implementation of clinical pathways is a cost-effective process, because none of the included studies analysed the cost of the development and implementation of the pathways. Based on the results we assume that pathways have impact on the organisation of care if the care process is structured in a standardised way

Methods of assessing total doses integrated across pathways

International Nuclear Information System (INIS)

Grzechnik, M.; Camplin, W.; Clyne, F.; Allott, R.; Webbe-Wood, D.

2006-01-01

Calculated doses for comparison with limits resulting from discharges into the environment should be summed across all relevant pathways and food groups to ensure adequate protection. Current methodology for assessments used in the radioactivity in Food and the Environment (R.I.F.E.) reports separate doses from pathways related to liquid discharges of radioactivity to the environment from those due to gaseous releases. Surveys of local inhabitant food consumption and occupancy rates are conducted in the vicinity of nuclear sites. Information has been recorded in an integrated way, such that the data for each individual is recorded for all pathways of interest. These can include consumption of foods, such as fish, crustaceans, molluscs, fruit and vegetables, milk and meats. Occupancy times over beach sediments and time spent in close proximity to the site is also recorded for inclusion of external and inhalation radiation dose pathways. The integrated habits survey data may be combined with monitored environmental radionuclide concentrations to calculate total dose. The criteria for successful adoption of a method for this calculation were: Reproducibility can others easily use the approach and reassess doses? Rigour and realism how good is the match with reality?Transparency a measure of the ease with which others can understand how the calculations are performed and what they mean. Homogeneity is the group receiving the dose relatively homogeneous with respect to age, diet and those aspects that affect the dose received? Five methods of total dose calculation were compared and ranked according to their suitability. Each method was labelled (A to E) and given a short, relevant name for identification. The methods are described below; A) Individual doses to individuals are calculated and critical group selection is dependent on dose received. B) Individual Plus As in A, but consumption and occupancy rates for high dose is used to derive rates for application in

Application of the critical pathway and integrated case teaching method to nursing orientation.

Science.gov (United States)

Goodman, D

1997-01-01

Nursing staff development programs must be responsive to current changes in healthcare. New nursing staff must be prepared to manage continuous change and to function competently in clinical practice. The orientation pathway, based on a case management model, is used as a structure for the orientation phase of staff development. The integrated case is incorporated as a teaching strategy in orientation. The integrated case method is based on discussion and analysis of patient situations with emphasis on role modeling and integration of theory and skill. The orientation pathway and integrated case teaching method provide a useful framework for orientation of new staff. Educators, preceptors and orientees find the structure provided by the orientation pathway very useful. Orientation that is developed, implemented and evaluated based on a case management model with the use of an orientation pathway and incorporation of an integrated case teaching method provides a standardized structure for orientation of new staff. This approach is designed for the adult learner, promotes conceptual reasoning, and encourages the social and contextual basis for continued learning.

Constraint-based modeling and kinetic analysis of the Smad dependent TGF-beta signaling pathway.

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Zhike Zi

Full Text Available BACKGROUND: Investigation of dynamics and regulation of the TGF-beta signaling pathway is central to the understanding of complex cellular processes such as growth, apoptosis, and differentiation. In this study, we aim at using systems biology approach to provide dynamic analysis on this pathway. METHODOLOGY/PRINCIPAL FINDINGS: We proposed a constraint-based modeling method to build a comprehensive mathematical model for the Smad dependent TGF-beta signaling pathway by fitting the experimental data and incorporating the qualitative constraints from the experimental analysis. The performance of the model generated by constraint-based modeling method is significantly improved compared to the model obtained by only fitting the quantitative data. The model agrees well with the experimental analysis of TGF-beta pathway, such as the time course of nuclear phosphorylated Smad, the subcellular location of Smad and signal response of Smad phosphorylation to different doses of TGF-beta. CONCLUSIONS/SIGNIFICANCE: The simulation results indicate that the signal response to TGF-beta is regulated by the balance between clathrin dependent endocytosis and non-clathrin mediated endocytosis. This model is useful to be built upon as new precise experimental data are emerging. The constraint-based modeling method can also be applied to quantitative modeling of other signaling pathways.

Similar judgment method of brain neural pathway using DT-MRI

International Nuclear Information System (INIS)

Watashiba, Yasuhiro; Sakamoto, Naohisa; Sakai, Koji; Koyamada, Koji; Kanazawa, Masanori; Doi, Akio

2008-01-01

Nowadays, the visualization of brain neural pathway extracted by the tractography technology is thought as a useful effective tool for the detection of involved area and the analysis of sick cause by comparison of difference of normal and patient's nerve fiber configurations and for the support of the surgery planning and the forecast of progress after an operation. So far, for the observation of the brain neural pathway, the method of the user's subjectively judging the 3D shape of them displayed in the image has been used. However, in this kind of subjective observation, verification of the propriety for the diagnostic result is difficult, in addition it cannot obtain sufficient reliability. Therefore, we think that the system to compare the shape based on a quantitative evaluation is necessary. To resolve this problem, we propose the system that enables the shape of the brain neural pathway extracted by the tractography technology to be compared quantitatively. The proposed system realized to calculate similarity between two neural pathways, and to display the difference area according to the similarity. (author)

Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

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Wilson, Paul; Larminie, Christopher; Smith, Rona

2016-01-01

To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

Pathway analysis concepts for radiological impact assessment

International Nuclear Information System (INIS)

Moroney, J.R.

1992-06-01

The concepts underlying exposure pathways analysis are outlined with reference to the features of the two broad types of radionuclide transport models now in use - dynamic and steady-state - and the methods for constructing and developing them. By way of illustration, representative radiation doses are estimated for the four main exposure pathways likely to be involved in the land application of effluent water from Retention Pond 2 of Ranger Uranium Mines. These include: external irradiation by 226 Ra and natural uranium (Un at) in soil, ingestion of 226 Ra and Un at in food, inhalation of 222 Rn daughter products from 226 Rn in soil, and inhalation of 226 Ra and Un at in airborne dust resuspended from soil. Consideration has been given to local residents pursuing a traditional lifestyle on conclusion of the land application program. Because of the possible importance of the contribution from resuspended dust, currently available data are explored in refining the methodology for the pathway and developing a more appropriate model for it. 37 refs., 9 tabs., 6 figs

Safety assessment for deep underground disposal vault-pathways analysis

International Nuclear Information System (INIS)

Lyon, R.B.; Rosinger, E.L.J.

1980-01-01

The concept verification phase of the Canadian programme for the disposal of nuclear fuel waste encompasses a period of about three years before the start of site selection. During this time, the methodology for Environmental and Safety Assessment studies is being developed by focusing on a model site. Pathways analysis is an important component of these studies. It involves the prediction of the rate at which radionuclides might be released from a disposal vault and travel through the geosphere and biosphere to reach man. The pathways analysis studies cover three major topics: geosphere pathways analysis, biosphere pathways analysis and potentially-disruptive-phenomena analysis. Geosphere pathways analysis includes a total systems analysis, using the computer program GARD2, vault analysis, which considers container failure and waste leaching, hydrogeological modelling and geochemical modelling. Biosphere pathways analysis incorporates a compartmental modelling approach using the computer program RAMM, and a food chain analysis using the computer program FOOD II. Potentially-disruptive-phenomena analysis involves the estimation of the probability and consequences of events such as earthquakes which might reduce the effectiveness of the barriers preventing the release of radionuclides. The current stage of development of the required methodology and data is discussed in each of the three areas and preliminary results are presented. (author)

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

Science.gov (United States)

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

Science.gov (United States)

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

Science.gov (United States)

Li, Qike; Schissler, A Grant; Gardeux, Vincent; Achour, Ikbel; Kenost, Colleen; Berghout, Joanne; Li, Haiquan; Zhang, Hao Helen; Lussier, Yves A

2017-05-24

Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

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Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-06

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

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Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome.

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Campbell, Malcolm G; Kohane, Isaac S; Kong, Sek Won

2013-09-24

Decades of research strongly suggest that the genetic etiology of autism spectrum disorders (ASDs) is heterogeneous. However, most published studies focus on group differences between cases and controls. In contrast, we hypothesized that the heterogeneity of the disorder could be characterized by identifying pathways for which individuals are outliers rather than pathways representative of shared group differences of the ASD diagnosis. Two previously published blood gene expression data sets--the Translational Genetics Research Institute (TGen) dataset (70 cases and 60 unrelated controls) and the Simons Simplex Consortium (Simons) dataset (221 probands and 191 unaffected family members)--were analyzed. All individuals of each dataset were projected to biological pathways, and each sample's Mahalanobis distance from a pooled centroid was calculated to compare the number of case and control outliers for each pathway. Analysis of a set of blood gene expression profiles from 70 ASD and 60 unrelated controls revealed three pathways whose outliers were significantly overrepresented in the ASD cases: neuron development including axonogenesis and neurite development (29% of ASD, 3% of control), nitric oxide signaling (29%, 3%), and skeletal development (27%, 3%). Overall, 50% of cases and 8% of controls were outliers in one of these three pathways, which could not be identified using group comparison or gene-level outlier methods. In an independently collected data set consisting of 221 ASD and 191 unaffected family members, outliers in the neurogenesis pathway were heavily biased towards cases (20.8% of ASD, 12.0% of control). Interestingly, neurogenesis outliers were more common among unaffected family members (Simons) than unrelated controls (TGen), but the statistical significance of this effect was marginal (Chi squared P < 0.09). Unlike group difference approaches, our analysis identified the samples within the case and control groups that manifested each expression

Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

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Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-03-09

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold ([Formula: see text]). These phenotypes are more heritable ([Formula: see text]) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. Copyright © 2015 Brown et al.

Primary Metabolic Pathways and Metabolic Flux Analysis

DEFF Research Database (Denmark)

Villadsen, John

2015-01-01

his chapter introduces the metabolic flux analysis (MFA) or stoichiometry-based MFA, and describes the quantitative basis for MFA. It discusses the catabolic pathways in which free energy is produced to drive the cell-building anabolic pathways. An overview of these primary pathways provides...... the reader who is primarily trained in the engineering sciences with atleast a preliminary introduction to biochemistry and also shows how carbon is drained off the catabolic pathways to provide precursors for cell mass building and sometimes for important industrial products. The primary pathways...... to be examined in the following are: glycolysis, primarily by the EMP pathway, but other glycolytic pathways is also mentioned; fermentative pathways in which the redox generated in the glycolytic reactions are consumed; reactions in the tricarboxylic acid (TCA) cycle, which produce biomass precursors and redox...

A cross-study gene set enrichment analysis identifies critical pathways in endometriosis

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Bai Chunyan

2009-09-01

Full Text Available Abstract Background Endometriosis is an enigmatic disease. Gene expression profiling of endometriosis has been used in several studies, but few studies went further to classify subtypes of endometriosis based on expression patterns and to identify possible pathways involved in endometriosis. Some of the observed pathways are more inconsistent between the studies, and these candidate pathways presumably only represent a fraction of the pathways involved in endometriosis. Methods We applied a standardised microarray preprocessing and gene set enrichment analysis to six independent studies, and demonstrated increased concordance between these gene datasets. Results We find 16 up-regulated and 19 down-regulated pathways common in ovarian endometriosis data sets, 22 up-regulated and one down-regulated pathway common in peritoneal endometriosis data sets. Among them, 12 up-regulated and 1 down-regulated were found consistent between ovarian and peritoneal endometriosis. The main canonical pathways identified are related to immunological and inflammatory disease. Early secretory phase has the most over-represented pathways in the three uterine cycle phases. There are no overlapping significant pathways between the dataset from human endometrial endothelial cells and the datasets from ovarian endometriosis which used whole tissues. Conclusion The study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. By standardised microarray preprocessing and GSEA, we have increased the concordance in identifying many biological mechanisms involved in endometriosis. The identified gene pathways will shed light on the understanding of endometriosis and promote the development of novel therapies.

Heading in the right direction: thermodynamics-based network analysis and pathway engineering.

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Ataman, Meric; Hatzimanikatis, Vassily

2015-12-01

Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Final report on the Pathway Analysis Task

International Nuclear Information System (INIS)

Whicker, F.W.; Kirchner, T.B.

1993-04-01

The Pathway Analysis Task constituted one of several multi-laboratory efforts to estimate radiation doses to people, considering all important pathways of exposure, from the testing of nuclear devices at the Nevada Test Site (NTS). The primary goal of the Pathway Analysis Task was to predict radionuclide ingestion by residents of Utah, Nevada, and portions of seven other adjoining western states following radioactive fallout deposition from individual events at the NTS. This report provides comprehensive documentation of the activities and accomplishments of Colorado State University's Pathway Analysis Task during the entire period of support (1979--91). The history of the project will be summarized, indicating the principal dates and milestones, personnel involved, subcontractors, and budget information. Accomplishments, both primary and auxiliary, will be summarized with general results rather than technical details being emphasized. This will also serve as a guide to the reports and open literature publications produced, where the methodological details and specific results are documented. Selected examples of results on internal dose estimates are provided in this report because the data have not been published elsewhere

Final report on the Pathway Analysis Task

Energy Technology Data Exchange (ETDEWEB)

Whicker, F.W.; Kirchner, T.B. [Colorado State Univ., Fort Collins, CO (United States)

1993-04-01

The Pathway Analysis Task constituted one of several multi-laboratory efforts to estimate radiation doses to people, considering all important pathways of exposure, from the testing of nuclear devices at the Nevada Test Site (NTS). The primary goal of the Pathway Analysis Task was to predict radionuclide ingestion by residents of Utah, Nevada, and portions of seven other adjoining western states following radioactive fallout deposition from individual events at the NTS. This report provides comprehensive documentation of the activities and accomplishments of Colorado State University`s Pathway Analysis Task during the entire period of support (1979--91). The history of the project will be summarized, indicating the principal dates and milestones, personnel involved, subcontractors, and budget information. Accomplishments, both primary and auxiliary, will be summarized with general results rather than technical details being emphasized. This will also serve as a guide to the reports and open literature publications produced, where the methodological details and specific results are documented. Selected examples of results on internal dose estimates are provided in this report because the data have not been published elsewhere.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Directory of Open Access Journals (Sweden)

Rosa Aghdam

2017-12-01

Full Text Available Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.

Triple phase boundary specific pathway analysis for quantitative characterization of solid oxide cell electrode microstructure

DEFF Research Database (Denmark)

Jørgensen, Peter Stanley; Ebbehøj, Søren Lyng; Hauch, Anne

2015-01-01

of the pathways through which they can be reached. New methods for performing TPB specific pathway analysis on 3D image data are introduced, analyzing the pathway properties of each TPB site in the electrode structure. The methods seek to provide additional information beyond whether the TPB sites are percolating......The density and percolation of Triple phase boundary sites are important quantities in analyzing microstructures of solid oxide fuel cell electrodes from tomography data. However, these measures do not provide descriptions of the quality of the TPB sites in terms of the length and radius...... or not by also analyzing the pathway length to the TPB sites and the bottleneck radius of the pathway. We show how these methods can be utilized in quantifying and relating the TPB specific results to cell test data of an electrode reduction protocol study for Ni/Scandia-and-Yttria-doped-Zirconia (Ni...

The future use of pathway analysis in IAEA safeguards

International Nuclear Information System (INIS)

Budlong Sylvester, Kory; Pilat, J.; Murphy, Chantell

2013-01-01

Pathway analysis has the potential to play an important role in the development of a safeguards system that is more information driven, leveraging all the information available to the International Atomic Energy Agency (IAEA). Pathway analysis should be seen as an extension of traditional hypothesis testing used by the Agency in the past. The most attractive pathways based on the assessed capabilities of a given state can be identified and used in the development of state-level safeguards approaches. This ranking of pathways can be revised based on evidence of pathway use, or preparations for use, allowing limited safeguards resources to flow to the areas of highest concern. The possible uses of pathway analysis in the implementation of the IAEA's state-level concept are described along with implementation issues that will likely arise. The paper is followed by the slides of the presentation. (authors)

Computational Chemical Synthesis Analysis and Pathway Design

Directory of Open Access Journals (Sweden)

Fan Feng

2018-06-01

Full Text Available With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules. Programs like ARChem Route Designer replaced hand-coded reaction modes with automatically-extracted rules, and programs like Chematica changed traditional designing into network searching. Afterward, with the help of machine learning, two-step models which combine reaction rules and statistical methods became the main stream. Recently, fully data-driven learning methods using deep neural networks which even do not require any prior knowledge, were applied into this field. Up to now, however, these methods still cannot replace experienced human organic chemists due to their relatively low accuracies. Future new algorithms with the aid of powerful computational hardware will make this topic promising and with good prospects.

Separate enrichment analysis of pathways for up- and downregulated genes.

Science.gov (United States)

Hong, Guini; Zhang, Wenjing; Li, Hongdong; Shen, Xiaopei; Guo, Zheng

2014-03-06

Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.

Analysis of Membrane Protein Topology in the Plant Secretory Pathway.

Science.gov (United States)

Guo, Jinya; Miao, Yansong; Cai, Yi

2017-01-01

Topology of membrane proteins provides important information for the understanding of protein function and intermolecular associations. Integrate membrane proteins are generally transported from endoplasmic reticulum (ER) to Golgi and downstream compartments in the plant secretory pathway. Here, we describe a simple method to study membrane protein topology along the plant secretory pathway by transiently coexpressing a fluorescent protein (XFP)-tagged membrane protein and an ER export inhibitor protein, ARF1 (T31N), in tobacco BY-2 protoplast. By fractionation, microsome isolation, and trypsin digestion, membrane protein topology could be easily detected by either direct confocal microscopy imaging or western-blot analysis using specific XFP antibodies. A similar strategy in determining membrane protein topology could be widely adopted and applied to protein analysis in a broad range of eukaryotic systems, including yeast cells and mammalian cells.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.

Science.gov (United States)

Aghdam, Rosa; Baghfalaki, Taban; Khosravi, Pegah; Saberi Ansari, Elnaz

2017-12-01

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/. Copyright © 2017. Production and hosting by Elsevier B.V.

minepath.org: a free interactive pathway analysis web server.

Science.gov (United States)

Koumakis, Lefteris; Roussos, Panos; Potamias, George

2017-07-03

( www.minepath.org ) is a web-based platform that elaborates on, and radically extends the identification of differentially expressed sub-paths in molecular pathways. Besides the network topology, the underlying MinePath algorithmic processes exploit exact gene-gene molecular relationships (e.g. activation, inhibition) and are able to identify differentially expressed pathway parts. Each pathway is decomposed into all its constituent sub-paths, which in turn are matched with corresponding gene expression profiles. The highly ranked, and phenotype inclined sub-paths are kept. Apart from the pathway analysis algorithm, the fundamental innovation of the MinePath web-server concerns its advanced visualization and interactive capabilities. To our knowledge, this is the first pathway analysis server that introduces and offers visualization of the underlying and active pathway regulatory mechanisms instead of genes. Other features include live interaction, immediate visualization of functional sub-paths per phenotype and dynamic linked annotations for the engaged genes and molecular relations. The user can download not only the results but also the corresponding web viewer framework of the performed analysis. This feature provides the flexibility to immediately publish results without publishing source/expression data, and get all the functionality of a web based pathway analysis viewer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts.

Science.gov (United States)

Liggi, Sonia; Drakakis, Georgios; Koutsoukas, Alexios; Cortes-Ciriano, Isidro; Martínez-Alonso, Patricia; Malliavin, Thérèse E; Velazquez-Campoy, Adrian; Brewerton, Suzanne C; Bodkin, Michael J; Evans, David A; Glen, Robert C; Carrodeguas, José Alberto; Bender, Andreas

2014-01-01

An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.

Data driven linear algebraic methods for analysis of molecular pathways: application to disease progression in shock/trauma.

Science.gov (United States)

McGuire, Mary F; Sriram Iyengar, M; Mercer, David W

2012-04-01

Although trauma is the leading cause of death for those below 45years of age, there is a dearth of information about the temporal behavior of the underlying biological mechanisms in those who survive the initial trauma only to later suffer from syndromes such as multiple organ failure. Levels of serum cytokines potentially affect the clinical outcomes of trauma; understanding how cytokine levels modulate intra-cellular signaling pathways can yield insights into molecular mechanisms of disease progression and help to identify targeted therapies. However, developing such analyses is challenging since it necessitates the integration and interpretation of large amounts of heterogeneous, quantitative and qualitative data. Here we present the Pathway Semantics Algorithm (PSA), an algebraic process of node and edge analyses of evoked biological pathways over time for in silico discovery of biomedical hypotheses, using data from a prospective controlled clinical study of the role of cytokines in multiple organ failure (MOF) at a major US trauma center. A matrix algebra approach was used in both the PSA node and PSA edge analyses with different matrix configurations and computations based on the biomedical questions to be examined. In the edge analysis, a percentage measure of crosstalk called XTALK was also developed to assess cross-pathway interference. In the node/molecular analysis of the first 24h from trauma, PSA uncovered seven molecules evoked computationally that differentiated outcomes of MOF or non-MOF (NMOF), of which three molecules had not been previously associated with any shock/trauma syndrome. In the edge/molecular interaction analysis, PSA examined four categories of functional molecular interaction relationships--activation, expression, inhibition, and transcription--and found that the interaction patterns and crosstalk changed over time and outcome. The PSA edge analysis suggests that a diagnosis, prognosis or therapy based on molecular interaction

Pathway-based Analysis of the Hidden Genetic Heterogeneities in Cancers

Directory of Open Access Journals (Sweden)

Xiaolei Zhao

2014-02-01

Full Text Available Many cancers apparently showing similar phenotypes are actually distinct at the molecular level, leading to very different responses to the same treatment. It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers. Nevertheless, it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers. Therefore, we aimed to test this possibility in the present study. First, we used a NCI60 dataset to validate the ability of pathways to correctly partition samples. Next, we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma (DLBCL. Finally, the clinical significance of the identified subtypes was verified using survival analysis. For the NCI60 dataset, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Subsequently, for a DLBCL dataset, we identified three hidden subtypes that showed very different 10-year overall survival rates (90%, 46% and 20% and were highly significantly (P = 0.008 correlated with the clinical survival rate. This study demonstrated that the pathway-based approach is promising for unveiling genetic heterogeneities in complex human diseases.

PathwaySplice: An R package for unbiased pathway analysis of alternative splicing in RNA-Seq data.

Science.gov (United States)

Yan, Aimin; Ban, Yuguang; Gao, Zhen; Chen, Xi; Wang, Lily

2018-04-24

Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the "significant" gene list in alternative splicing. We present PathwaySplice, an R package that (1) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (2) Visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (3) Supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (4) Identifies the significant genes driving pathway significance and (5) Organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph. https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html. lily.wangg@gmail.com, xi.steven.chen@gmail.com.

Pan-cancer analysis of TCGA data reveals notable signaling pathways

International Nuclear Information System (INIS)

Neapolitan, Richard; Horvath, Curt M.; Jiang, Xia

2015-01-01

A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition. We present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types. In each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring. We obtained 37 notable findings concerning 18 pathways. Some of them appear to be

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

Science.gov (United States)

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K; Mathé, Ewy A

2018-02-22

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be

miR2Pathway: A Novel Analytical Method to Discover MicroRNA-mediated Dysregulated Pathways Involved in Hepatocellular Carcinoma.

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Li, Chaoxing; Dinu, Valentin

2018-03-22

MicroRNAs (miRNAs) are small, non-coding RNAs involved in the regulation of gene expression at a post-transcriptional level. Recent studies have shown miRNAs as key regulators of a variety of biological processes, such as proliferation, differentiation, apoptosis, metabolism, etc. Aberrantly expressed miRNAs influence individual gene expression level, but rewired miRNA-mRNA connections can influence the activity of biological pathways. Here, we define rewired miRNA-mRNA connections as the differential (rewiring) effects on the activity of biological pathways between hepatocellular carcinoma (HCC) and normal phenotypes. Our work presented here uses a PageRank-based approach to measure the degree of miRNA-mediated dysregulation of biological pathways between HCC and normal samples based on rewired miRNA-mRNA connections. In our study, we regard the degree of miRNA-mediated dysregulation of biological pathways as disease risk of biological pathways. Therefore, we propose a new method, miR2Pathway, to measure and rank the degree of miRNA-mediated dysregulation of biological pathways by measuring the total differential influence of miRNAs on the activity of pathways between HCC and normal states. miR2Pathway proposed here systematically shows the first evidence for a mechanism of biological pathways being dysregulated by rewired miRNA-mRNA connections, and provides new insight into exploring mechanisms behind HCC. Thus, miR2Pathway is a novel method to identify and rank miRNA-dysregulated pathways in HCC. Copyright © 2018. Published by Elsevier Inc.

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

Science.gov (United States)

Song, Xian-Dong; Song, Xian-Xu; Liu, Gui-Bo; Ren, Chun-Hui; Sun, Yuan-Bo; Liu, Ke-Xin; Liu, Bo; Liang, Shuang; Zhu, Zhu

2018-03-01

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.

Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

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Wu Xiaogang

2012-06-01

Full Text Available In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease, and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI database, and pathway enrichment from the human pathway database (HPD. We use a recently-published microarray dataset (GSE24215 related to insulin resistance and type 2 diabetes (T2D as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

Pathway-based analyses.

Science.gov (United States)

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases

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Karp Peter D

2004-06-01

Full Text Available Abstract Background The PathoLogic program constructs Pathway/Genome databases by using a genome's annotation to predict the set of metabolic pathways present in an organism. PathoLogic determines the set of reactions composing those pathways from the enzymes annotated in the organism's genome. Most annotation efforts fail to assign function to 40–60% of sequences. In addition, large numbers of sequences may have non-specific annotations (e.g., thiolase family protein. Pathway holes occur when a genome appears to lack the enzymes needed to catalyze reactions in a pathway. If a protein has not been assigned a specific function during the annotation process, any reaction catalyzed by that protein will appear as a missing enzyme or pathway hole in a Pathway/Genome database. Results We have developed a method that efficiently combines homology and pathway-based evidence to identify candidates for filling pathway holes in Pathway/Genome databases. Our program not only identifies potential candidate sequences for pathway holes, but combines data from multiple, heterogeneous sources to assess the likelihood that a candidate has the required function. Our algorithm emulates the manual sequence annotation process, considering not only evidence from homology searches, but also considering evidence from genomic context (i.e., is the gene part of an operon? and functional context (e.g., are there functionally-related genes nearby in the genome? to determine the posterior belief that a candidate has the required function. The method can be applied across an entire metabolic pathway network and is generally applicable to any pathway database. The program uses a set of sequences encoding the required activity in other genomes to identify candidate proteins in the genome of interest, and then evaluates each candidate by using a simple Bayes classifier to determine the probability that the candidate has the desired function. We achieved 71% precision at a

Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia.

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van Uitert, Miranda; Moerland, Perry D; Enquobahrie, Daniel A; Laivuori, Hannele; van der Post, Joris A M; Ris-Stalpers, Carrie; Afink, Gijs B

2015-01-01

Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite) and protein-protein associations (STRING). This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome). The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300) and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.

Hanford Site Composite Analysis Technical Approach Description: Groundwater Pathway Dose Calculation.

Energy Technology Data Exchange (ETDEWEB)

Morgans, D. L. [CH2M Hill Plateau Remediation Company, Richland, WA (United States); Lindberg, S. L. [Intera Inc., Austin, TX (United States)

2017-09-20

The purpose of this technical approach document (TAD) is to document the assumptions, equations, and methods used to perform the groundwater pathway radiological dose calculations for the revised Hanford Site Composite Analysis (CA). DOE M 435.1-1, states, “The composite analysis results shall be used for planning, radiation protection activities, and future use commitments to minimize the likelihood that current low-level waste disposal activities will result in the need for future corrective or remedial actions to adequately protect the public and the environment.”

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

Energy Technology Data Exchange (ETDEWEB)

Ovacik, Meric A. [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Androulakis, Ioannis P., E-mail: yannis@rci.rutgers.edu [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Biomedical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States)

2013-09-15

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy.

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

International Nuclear Information System (INIS)

Ovacik, Meric A.; Androulakis, Ioannis P.

2013-01-01

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy

Pathways to Medical Home Recognition: A Qualitative Comparative Analysis of the PCMH Transformation Process.

Science.gov (United States)

Mendel, Peter; Chen, Emily K; Green, Harold D; Armstrong, Courtney; Timbie, Justin W; Kress, Amii M; Friedberg, Mark W; Kahn, Katherine L

2017-12-15

To understand the process of practice transformation by identifying pathways for attaining patient-centered medical home (PCMH) recognition. The CMS Federally Qualified Health Center (FQHC) Advanced Primary Care Practice Demonstration was designed to help FQHCs achieve NCQA Level 3 PCMH recognition and improve patient outcomes. We used a stratified random sample of 20 (out of 503) participating sites for this analysis. We developed a conceptual model of structural, cultural, and implementation factors affecting PCMH transformation based on literature and initial qualitative interview themes. We then used conventional cross-case analysis, followed by qualitative comparative analysis (QCA), a cross-case method based on Boolean logic algorithms, to systematically identify pathways (i.e., combinations of factors) associated with attaining-or not attaining-Level 3 recognition. Site-level indicators were derived from semistructured interviews with site leaders at two points in time (mid- and late-implementation) and administrative data collected prior to and during the demonstration period. The QCA results identified five distinct pathways to attaining PCMH recognition and four distinct pathways to not attaining recognition by the end of the demonstration. Across these pathways, one condition (change leader capacity) was common to all pathways for attaining recognition, and another (previous improvement or recognition experience) was absent in all pathways for not attaining recognition. In general, sites could compensate for deficiencies in one factor with capacity in others, but they needed a threshold of strengths in cultural and implementation factors to attain PCMH recognition. Future efforts at primary care transformation should take into account multiple pathways sites may pursue. Sites should be assessed on key cultural and implementation factors, in addition to structural components, in order to differentiate interventions and technical assistance. © Health

Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia.

Directory of Open Access Journals (Sweden)

Miranda van Uitert

Full Text Available Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite and protein-protein associations (STRING. This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome. The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300 and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.

Identifying novel glioma associated pathways based on systems biology level meta-analysis.

Science.gov (United States)

Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong

2013-01-01

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

An overview of bioinformatics methods for modeling biological pathways in yeast.

Science.gov (United States)

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao; Cheng, Jianlin

2016-03-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways.

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Lee, Yun; Lafontaine Rivera, Jimmy G; Liao, James C

2014-09-01

Metabolic pathways in cells must be sufficiently robust to tolerate fluctuations in expression levels and changes in environmental conditions. Perturbations in expression levels may lead to system failure due to the disappearance of a stable steady state. Increasing evidence has suggested that biological networks have evolved such that they are intrinsically robust in their network structure. In this article, we presented Ensemble Modeling for Robustness Analysis (EMRA), which combines a continuation method with the Ensemble Modeling approach, for investigating the robustness issue of non-native pathways. EMRA investigates a large ensemble of reference models with different parameters, and determines the effects of parameter drifting until a bifurcation point, beyond which a stable steady state disappears and system failure occurs. A pathway is considered to have high bifurcational robustness if the probability of system failure is low in the ensemble. To demonstrate the utility of EMRA, we investigate the bifurcational robustness of two synthetic central metabolic pathways that achieve carbon conservation: non-oxidative glycolysis and reverse glyoxylate cycle. With EMRA, we determined the probability of system failure of each design and demonstrated that alternative designs of these pathways indeed display varying degrees of bifurcational robustness. Furthermore, we demonstrated that target selection for flux improvement should consider the trade-offs between robustness and performance. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

International Nuclear Information System (INIS)

Thomassen, Mads; Tan, Qihua; Kruse, Torben A

2008-01-01

Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent studies. Besides classification of outcome, these global expression patterns may reflect biological mechanisms involved in metastasis of breast cancer. Our purpose has been to investigate pathways and transcription factors involved in metastasis by use of gene expression data sets. We have analyzed 8 publicly available gene expression data sets. A global approach, 'gene set enrichment analysis' as well as an approach focusing on a subset of significantly differently regulated genes, GenMAPP, has been applied to rank pathway gene sets according to differential regulation in metastasizing tumors compared to non-metastasizing tumors. Meta-analysis has been used to determine overrepresentation of pathways and transcription factors targets, concordant deregulated in metastasizing breast tumors, in several data sets. The major findings are up-regulation of cell cycle pathways and a metabolic shift towards glucose metabolism reflected in several pathways in metastasizing tumors. Growth factor pathways seem to play dual roles; EGF and PDGF pathways are decreased, while VEGF and sex-hormone pathways are increased in tumors that metastasize. Furthermore, migration, proteasome, immune system, angiogenesis, DNA repair and several signal transduction pathways are associated to metastasis. Finally several transcription factors e.g. E2F, NFY, and YY1 are identified as being involved in metastasis. By pathway meta-analysis many biological mechanisms beyond major characteristics such as proliferation are identified. Transcription factor analysis identifies a number of key factors that support central pathways. Several previously proposed treatment targets are identified and several new pathways that may

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.

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Yu-Hang Zhang

Full Text Available A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347, monoamine transmembrane transporter activity (GO:0008504, negative regulation of synaptic transmission (GO:0050805, neuroactive ligand-receptor interaction (hsa04080, serotonergic synapse (hsa04726, and linoleic acid metabolism (hsa00591, among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

Identification of Key Pathways and Genes in Advanced Coronary Atherosclerosis Using Bioinformatics Analysis

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Xiaowen Tan

2017-01-01

Full Text Available Background. Coronary artery atherosclerosis is a chronic inflammatory disease. This study aimed to identify the key changes of gene expression between early and advanced carotid atherosclerotic plaque in human. Methods. Gene expression dataset GSE28829 was downloaded from Gene Expression Omnibus (GEO, including 16 advanced and 13 early stage atherosclerotic plaque samples from human carotid. Differentially expressed genes (DEGs were analyzed. Results. 42,450 genes were obtained from the dataset. Top 100 up- and downregulated DEGs were listed. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG identification were performed. The result of functional and pathway enrichment analysis indicted that the immune system process played a critical role in the progression of carotid atherosclerotic plaque. Protein-protein interaction (PPI networks were performed either. Top 10 hub genes were identified from PPI network and top 6 modules were inferred. These genes were mainly involved in chemokine signaling pathway, cell cycle, B cell receptor signaling pathway, focal adhesion, and regulation of actin cytoskeleton. Conclusion. The present study indicated that analysis of DEGs would make a deeper understanding of the molecular mechanisms of atherosclerosis development and they might be used as molecular targets and diagnostic biomarkers for the treatment of atherosclerosis.

GenMAPP 2: new features and resources for pathway analysis

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Dahlquist Kam D

2007-06-01

Full Text Available Abstract Background Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses. Results To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP, a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP, and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages. Conclusion GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms.

Pathway Analysis and Metabolites Identification by Metabolomics of Etiolation Substrate from Fresh-Cut Chinese Water Chestnut (Eleocharis tuberosa

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Yi-Xiao Li

2016-12-01

Full Text Available Fresh-cut Chinese water chestnuts (CWC turn yellow after being peeled, reducing their shelf life and commercial value. Metabolomics, the systematic study of the full complement of small molecular metabolites, was useful for clarifying the mechanism of fresh-cut CWC etiolation and developing methods to inhibit yellowing. In this study, metabolic alterations associated with etiolation at different growth stages (0 day, 2 days, 3 days, 4 days, 5 days from fresh-cut CWC were investigated using LC–MS and analyzed by pattern recognition methods (principal component analysis (PCA, partial least squares-discriminant analysis (PLS-DA, and orthogonal projection to latent structures-discriminant analysis (OPLS-DA. The metabolic pathways of the etiolation molecules were elucidated. The main metabolic pathway appears to be the conversion of phenylalanine to p-coumaroyl-CoA, followed by conversion to naringenin chalcone, to naringenin, and naringenin then following different pathways. Firstly, it can transform into apigenin and its derivatives; secondly, it can produce eriodictyol and its derivatives; and thirdly it can produce dihydrokaempferol, quercetin, and myricetin. The eriodictyol can be further transformed to luteolin, cyanidin, dihydroquercetin, dihydrotricetin, and others. This is the first reported use of metabolomics to study the metabolic pathways of the etiolation of fresh-cut CWC.

In silico tools for the analysis of antibiotic biosynthetic pathways

DEFF Research Database (Denmark)

Weber, Tilmann

2014-01-01

Natural products of bacteria and fungi are the most important source for antimicrobial drug leads. For decades, such compounds were exclusively found by chemical/bioactivity-guided screening approaches. The rapid progress in sequencing technologies only recently allowed the development of novel...... screening methods based on the genome sequences of potential producing organisms. The basic principle of such genome mining approaches is to identify genes, which are involved in the biosynthesis of such molecules, and to predict the products of the identified pathways. Thus, bioinformatics methods...... and tools are crucial for genome mining. In this review, a comprehensive overview is given on programs and databases for the identification and analysis of antibiotic biosynthesis gene clusters in genomic data....

Mapping subsurface pathways for contaminant migration at a proposed low level waste disposal site using electromagnetic methods

International Nuclear Information System (INIS)

Pin, F.G.; Ketelle, R.H.

1984-01-01

Electromagnetic methods have been used to measure apparent terrain conductivity in the downstream portion of a watershed in which a waste disposal site is proposed. At that site, the pathways for waste migration in ground water are controlled by subsurface channels. The channels are identified using isocurves of measured apparent conductivity. Two upstream channel branches are found to merge into a single downstream channel which constitutes the main drainage path out of the watershed. The identification and mapping of the ground water pathways is an important contribution to the site characterization study and the pathways analysis. The direct applications of terrain conductivity mapping to the planning of the monitoring program, the hydrogeological testing, and the modeling study are demonstrated. 7 references, 4 figures

Comparison of methods for the analysis of relatively simple mediation models

NARCIS (Netherlands)

Rijnhart, Judith J.M.; Twisk, Jos W.R.; Chinapaw, Mai J.M.; de Boer, Michiel R.; Heymans, Martijn W.

2017-01-01

Background/aims Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural

Integrated In Silico Analysis of Pathway Designs for Synthetic Photo-Electro-Autotrophy.

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Michael Volpers

Full Text Available The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems. A variety of carbon fixation pathways and several types of photosystems and other energy uptake systems can be chosen and, potentially, modularly combined to design synthetic autotrophic metabolism. Prior to implementation, these designs can be evaluated by the combination of several computational pathway analysis techniques. Here we present a systematic, integrated in silico analysis of photo-electro-autotrophic pathway designs, consisting of natural and synthetic carbon fixation pathways, a proton-pumping rhodopsin photosystem for ATP regeneration and an electron uptake pathway. We integrated Flux Balance Analysis of the heterotrophic chassis Escherichia coli with kinetic pathway analysis and thermodynamic pathway analysis (Max-min Driving Force. The photo-electro-autotrophic designs are predicted to have a limited potential for anaerobic, autotrophic growth of E. coli, given the relatively low ATP regenerating capacity of the proton pumping rhodopsin photosystems and the high ATP maintenance of E. coli. If these factors can be tackled, our analysis indicates the highest growth potential for the natural reductive tricarboxylic acid cycle and the synthetic pyruvate synthase-pyruvate carboxylate -glyoxylate bicycle. Both carbon fixation cycles are very ATP efficient, while maintaining fast kinetics, which also results in relatively low estimated protein costs for these pathways. Furthermore, the synthetic bicycles are highly

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

Science.gov (United States)

Lee, Hyeonjeong; Shin, Miyoung

2017-01-01

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into

Modelling and Analysis of Biochemical Signalling Pathway Cross-talk

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Robin Donaldson

2010-02-01

Full Text Available Signalling pathways are abstractions that help life scientists structure the coordination of cellular activity. Cross-talk between pathways accounts for many of the complex behaviours exhibited by signalling pathways and is often critical in producing the correct signal-response relationship. Formal models of signalling pathways and cross-talk in particular can aid understanding and drive experimentation. We define an approach to modelling based on the concept that a pathway is the (synchronising parallel composition of instances of generic modules (with internal and external labels. Pathways are then composed by (synchronising parallel composition and renaming; different types of cross-talk result from different combinations of synchronisation and renaming. We define a number of generic modules in PRISM and five types of cross-talk: signal flow, substrate availability, receptor function, gene expression and intracellular communication. We show that Continuous Stochastic Logic properties can both detect and distinguish the types of cross-talk. The approach is illustrated with small examples and an analysis of the cross-talk between the TGF-b/BMP, WNT and MAPK pathways.

Genetic variants in two pathways influence serum urate levels and gout risk: a systematic pathway analysis.

Science.gov (United States)

Dong, Zheng; Zhou, Jingru; Xu, Xia; Jiang, Shuai; Li, Yuan; Zhao, Dongbao; Yang, Chengde; Ma, Yanyun; Wang, Yi; He, Hongjun; Ji, Hengdong; Zhang, Juan; Yuan, Ziyu; Yang, Yajun; Wang, Xiaofeng; Pang, Yafei; Jin, Li; Zou, Hejian; Wang, Jiucun

2018-03-01

The aims of this study were to identify candidate pathways associated with serum urate and to explore the genetic effect of those pathways on the risk of gout. Pathway analysis of the loci identified in genome-wide association studies (GWASs) showed that the ion transmembrane transporter activity pathway (GO: 0015075) and the secondary active transmembrane transporter activity pathway (GO: 0015291) were both associated with serum urate concentrations, with P FDR values of 0.004 and 0.007, respectively. In a Chinese population of 4,332 individuals, the two pathways were also found to be associated with serum urate (P FDR  = 1.88E-05 and 3.44E-04, separately). In addition, these two pathways were further associated with the pathogenesis of gout (P FDR  = 1.08E-08 and 2.66E-03, respectively) in the Chinese population and a novel gout-associated gene, SLC17A2, was identified (OR = 0.83, P FDR  = 0.017). The mRNA expression of candidate genes also showed significant differences among different groups at pathway level. The present study identified two transmembrane transporter activity pathways (GO: 0015075 and GO: 0015291) were associations with serum urate concentrations and the risk of gout. SLC17A2 was identified as a novel gene that influenced the risk of gout.

Analysis of acyl CoA ester intermediates of the mevalonate pathway in Saccharomyces cerevisiae

DEFF Research Database (Denmark)

Seker, Tamay; Møller, Kasper; Nielsen, Jens

2005-01-01

The mevalonate pathway plays an important role in providing the cell with a number of essential precursors for the synthesis of biomass constituents. With respect to their chemical structure, the metabolites of this pathway can be divided into two groups: acyl esters [acetoacetyl CoA, acetyl Co......A, hydroxymethylglutaryl (HMG) CoA] and phosphorylated metabolites (isopentenyl pyrophosphate, dimethylallyl pyrophosphate, geranyl pyrophosphate, farnesyl pyrophosphate). In this study, we developed a method for the precise analysis of the intracellular concentration of acetoacetyl CoA, acetyl CoA and HMG CoA; and we...... used this method for quantification of these metabolites in Saccharomyces cerevisiae, both during batch growth on glucose and on galactose and in glucose-limited chemostat cultures operated at three different dilution rates. The level of the metabolites changed depending on the growth phase...

Biomarker Identification and Pathway Analysis by Serum Metabolomics of Lung Cancer

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Yingrong Chen

2015-01-01

Full Text Available Lung cancer is one of the most common causes of cancer death, for which no validated tumor biomarker is sufficiently accurate to be useful for diagnosis. Additionally, the metabolic alterations associated with the disease are unclear. In this study, we investigated the construction, interaction, and pathways of potential lung cancer biomarkers using metabolomics pathway analysis based on the Kyoto Encyclopedia of Genes and Genomes database and the Human Metabolome Database to identify the top altered pathways for analysis and visualization. We constructed a diagnostic model using potential serum biomarkers from patients with lung cancer. We assessed their specificity and sensitivity according to the area under the curve of the receiver operator characteristic (ROC curves, which could be used to distinguish patients with lung cancer from normal subjects. The pathway analysis indicated that sphingolipid metabolism was the top altered pathway in lung cancer. ROC curve analysis indicated that glycerophospho-N-arachidonoyl ethanolamine (GpAEA and sphingosine were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. Compared with the traditional lung cancer diagnostic biomarkers carcinoembryonic antigen and cytokeratin 19 fragment, GpAEA and sphingosine were as good or more appropriate for detecting lung cancer. We report our identification of potential metabolic diagnostic and prognostic biomarkers of lung cancer and clarify the metabolic alterations in lung cancer.

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

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Zhongqi Ge

2018-04-01

Full Text Available Summary: Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer Genome Atlas, we perform comprehensive molecular characterization of 929 ubiquitin-related genes and 95 deubiquitinase genes. Among them, we systematically identify top somatic driver candidates, including mutated FBXW7 with cancer-type-specific patterns and amplified MDM2 showing a mutually exclusive pattern with BRAF mutations. Ubiquitin pathway genes tend to be upregulated in cancer mediated by diverse mechanisms. By integrating pan-cancer multiomic data, we identify a group of tumor samples that exhibit worse prognosis. These samples are consistently associated with the upregulation of cell-cycle and DNA repair pathways, characterized by mutated TP53, MYC/TERT amplification, and APC/PTEN deletion. Our analysis highlights the importance of the ubiquitin pathway in cancer development and lays a foundation for developing relevant therapeutic strategies. : Ge et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to provide a comprehensive characterization of the ubiquitin pathway. They detect somatic driver candidates in the ubiquitin pathway and identify a cluster of patients with poor survival, highlighting the importance of this pathway in cancer development. Keywords: ubiquitin pathway, pan-cancer analysis, The Cancer Genome Atlas, tumor subtype, cancer prognosis, therapeutic targets, biomarker, FBXW7

Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts.

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Matt Silver

2013-11-01

Full Text Available Standard approaches to data analysis in genome-wide association studies (GWAS ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK

Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts

Science.gov (United States)

Silver, Matt; Chen, Peng; Li, Ruoying; Cheng, Ching-Yu; Wong, Tien-Yin; Tai, E-Shyong; Teo, Yik-Ying; Montana, Giovanni

2013-01-01

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune

Microarray analysis reveals key genes and pathways in Tetralogy of Fallot

Science.gov (United States)

He, Yue-E; Qiu, Hui-Xian; Jiang, Jian-Bing; Wu, Rong-Zhou; Xiang, Ru-Lian; Zhang, Yuan-Hai

2017-01-01

The aim of the present study was to identify key genes that may be involved in the pathogenesis of Tetralogy of Fallot (TOF) using bioinformatics methods. The GSE26125 microarray dataset, which includes cardiovascular tissue samples derived from 16 children with TOF and five healthy age-matched control infants, was downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed between TOF and control samples to identify differentially expressed genes (DEGs) using Student's t-test, and the R/limma package, with a log2 fold-change of >2 and a false discovery rate of <0.01 set as thresholds. The biological functions of DEGs were analyzed using the ToppGene database. The ReactomeFIViz application was used to construct functional interaction (FI) networks, and the genes in each module were subjected to pathway enrichment analysis. The iRegulon plugin was used to identify transcription factors predicted to regulate the DEGs in the FI network, and the gene-transcription factor pairs were then visualized using Cytoscape software. A total of 878 DEGs were identified, including 848 upregulated genes and 30 downregulated genes. The gene FI network contained seven function modules, which were all comprised of upregulated genes. Genes enriched in Module 1 were enriched in the following three neurological disorder-associated signaling pathways: Parkinson's disease, Alzheimer's disease and Huntington's disease. Genes in Modules 0, 3 and 5 were dominantly enriched in pathways associated with ribosomes and protein translation. The Xbox binding protein 1 transcription factor was demonstrated to be involved in the regulation of genes encoding the subunits of cytoplasmic and mitochondrial ribosomes, as well as genes involved in neurodegenerative disorders. Therefore, dysfunction of genes involved in signaling pathways associated with neurodegenerative disorders, ribosome function and protein translation may contribute to the pathogenesis of TOF

PathNet: A Tool for Pathway Analysis Using Topological Information

Science.gov (United States)

2012-09-24

potentiation (4720) 0.00 [31,32] 4 Neurotrophin signal. . . (4722) 0.01 [31,48] 4 Oocyte meiosis (4114) 0.00 NA 4 Pathways in cancer (5200) 0.71 [35-37...McKeel D, Morris JC, et al: Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol...Background: Identification of canonical pathways through enrichment of differentially expressed genes in a given pathway is a widely used method for

DMPD: The Toll-like receptors: analysis by forward genetic methods. [Dynamic Macrophage Pathway CSML Database

Lifescience Database Archive (English)

Full Text Available 16001129 The Toll-like receptors: analysis by forward genetic methods. Beutler B. I...mmunogenetics. 2005 Jul;57(6):385-92. (.png) (.svg) (.html) (.csml) Show The Toll-like receptors: analysis by forwar...d genetic methods. PubmedID 16001129 Title The Toll-like receptors: analysis by forward genetic meth

Pathway Analysis of miR-181a in Hypopharyngeal Squamous Cell ...

African Journals Online (AJOL)

syakima

2012-03-15

Mar 15, 2012 ... Key words: MicroRNA, head and neck cancer, miR-181a, pathway analysis, luciferase assay, FaDu cell line, transfection ..... prostate carcinoma and glioblastoma cells. Nucleic Acids .... WNT pathway in oral cancer: epigenetic.

CRITICAL RADIONUCLIDE AND PATHWAY ANALYSIS FOR THE SAVANNAH RIVER SITE

Energy Technology Data Exchange (ETDEWEB)

Jannik, T.

2011-08-30

This report is an update to the analysis, Assessment of SRS Radiological Liquid and Airborne Contaminants and Pathways, that was performed in 1997. An electronic version of this large original report is included in the attached CD to this report. During the operational history (1954 to the present) of the Savannah River Site (SRS), many different radionuclides have been released to the environment from the various production facilities. However, as will be shown by this updated radiological critical contaminant/critical pathway analysis, only a small number of the released radionuclides have been significant contributors to potential doses and risks to offsite people. The analysis covers radiological releases to the atmosphere and to surface waters, the principal media that carry contaminants offsite. These releases potentially result in exposure to offsite people. The groundwater monitoring performed at the site shows that an estimated 5 to 10% of SRS has been contaminated by radionuclides, no evidence exists from the extensive monitoring performed that groundwater contaminated with these constituents has migrated off the site (SRS 2011). Therefore, with the notable exception of radiological source terms originating from shallow surface water migration into site streams, onsite groundwater was not considered as a potential exposure pathway to offsite people. In addition, in response to the Department of Energy's (DOE) Order 435.1, several Performance Assessments (WSRC 2008; LWO 2009; SRR 2010; SRR 2011) and a Comprehensive SRS Composite Analysis (SRNO 2010) have recently been completed at SRS. The critical radionuclides and pathways identified in these extensive reports are discussed and, where applicable, included in this analysis.

Energy pathway analysis - a hydrogen fuel cycle framework for system studies

International Nuclear Information System (INIS)

Badin, J.S.; Tagore, S.

1997-01-01

An analytical framework has been developed that can be used to estimate a range of life-cycle costs and impacts that result from the incremental production, storage, transport, and use of different fuels or energy carriers, such as hydrogen, electricity, natural gas, and gasoline. This information is used in a comparative analysis of energy pathways. The pathways provide the U.S. Department of Energy (DOE) with an indication of near-, mid-, and long-term technologies that have the greatest potential for advancement and can meet the cost goals. The methodology and conceptual issues are discussed. Also presented are results for selected pathways from the E3 (Energy, Economics, Emissions) Pathway Analysis Model. This model will be expanded to consider networks of pathways and to be compatible with a linear programming optimization processor. Scenarios and sets of constraints (energy demands, sources, emissions) will be defined so the effects on energy transformation activities included in the solution and on the total optimized system cost can be investigated. This evaluation will be used as a guide to eliminate technically feasible pathways if they are not cost effective or do not meet the threshold requirements for the market acceptance. (Author)

Method for determining heterologous biosynthesis pathways

KAUST Repository

Gao, Xin; Kuwahara, Hiroyuki; Alazmi, Meshari Saud; Cui, Xuefeng

2017-01-01

suitable pathways for the endogenous metabolism of a host organism because the efficacy of heterologous biosynthesis is affected by competing endogenous pathways. The present invention is called MRE (Metabolic Route Explorer), and it was conceived

Pathway analysis of kidney cancer using proteomics and metabolic profiling

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Fiehn Oliver

2006-11-01

Full Text Available Abstract Background Renal cell carcinoma (RCC is the sixth leading cause of cancer death and is responsible for 11,000 deaths per year in the US. Approximately one-third of patients present with disease which is already metastatic and for which there is currently no adequate treatment, and no biofluid screening tests exist for RCC. In this study, we have undertaken a comprehensive proteomic analysis and subsequently a pathway and network approach to identify biological processes involved in clear cell RCC (ccRCC. We have used these data to investigate urinary markers of RCC which could be applied to high-risk patients, or to those being followed for recurrence, for early diagnosis and treatment, thereby substantially reducing mortality of this disease. Results Using 2-dimensional electrophoresis and mass spectrometric analysis, we identified 31 proteins which were differentially expressed with a high degree of significance in ccRCC as compared to adjacent non-malignant tissue, and we confirmed some of these by immunoblotting, immunohistochemistry, and comparison to published transcriptomic data. When evaluated by several pathway and biological process analysis programs, these proteins are demonstrated to be involved with a high degree of confidence (p values Conclusion Extensive pathway and network analysis allowed for the discovery of highly significant pathways from a set of clear cell RCC samples. Knowledge of activation of these processes will lead to novel assays identifying their proteomic and/or metabolomic signatures in biofluids of patient at high risk for this disease; we provide pilot data for such a urinary bioassay. Furthermore, we demonstrate how the knowledge of networks, processes, and pathways altered in kidney cancer may be used to influence the choice of optimal therapy.

Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways

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Q. Wang

Full Text Available Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC based on the functional dependency among pathways. Protein-protein interaction (PPI information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN, where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

Science.gov (United States)

Kammen, Alexandra; Law, Meng; Tjan, Bosco S; Toga, Arthur W; Shi, Yonggang

2016-01-15

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic

Inference of miRNA targets using evolutionary conservation and pathway analysis

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van Nimwegen Erik

2007-03-01

assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server.

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

Science.gov (United States)

Lai, En-Yu; Chen, Yi-Hau; Wu, Kun-Pin

2017-06-01

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https

Understanding alternative fluxes/effluxes through comparative metabolic pathway analysis of phylum actinobacteria using a simplified approach.

Science.gov (United States)

Verma, Mansi; Lal, Devi; Saxena, Anjali; Anand, Shailly; Kaur, Jasvinder; Kaur, Jaspreet; Lal, Rup

2013-12-01

Actinobacteria are known for their diverse metabolism and physiology. Some are dreadful human pathogens whereas some constitute the natural flora for human gut. Therefore, the understanding of metabolic pathways is a key feature for targeting the pathogenic bacteria without disturbing the symbiotic ones. A big challenge faced today is multiple drug resistance by Mycobacterium and other pathogens that utilize alternative fluxes/effluxes. With the availability of genome sequence, it is now feasible to conduct the comparative in silico analysis. Here we present a simplified approach to compare metabolic pathways so that the species specific enzyme may be traced and engineered for future therapeutics. The analyses of four key carbohydrate metabolic pathways, i.e., glycolysis, pyruvate metabolism, tri carboxylic acid cycle and pentose phosphate pathway suggest the presence of alternative fluxes. It was found that the upper pathway of glycolysis was highly variable in the actinobacterial genomes whereas lower glycolytic pathway was highly conserved. Likewise, pentose phosphate pathway was well conserved in contradiction to TCA cycle, which was found to be incomplete in majority of actinobacteria. The clustering based on presence and absence of genes of these metabolic pathways clearly revealed that members of different genera shared identical pathways and, therefore, provided an easy method to identify the metabolic similarities/differences between pathogenic and symbiotic organisms. The analyses could identify isoenzymes and some key enzymes that were found to be missing in some pathogenic actinobacteria. The present work defines a simple approach to explore the effluxes in four metabolic pathways within the phylum actinobacteria. The analysis clearly reflects that actinobacteria exhibit diverse routes for metabolizing substrates. The pathway comparison can help in finding the enzymes that can be used as drug targets for pathogens without effecting symbiotic organisms

Effect of curcumin on aged Drosophila melanogaster: a pathway prediction analysis.

Science.gov (United States)

Zhang, Zhi-guo; Niu, Xu-yan; Lu, Ai-ping; Xiao, Gary Guishan

2015-02-01

To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

Pathway-based analysis of a melanoma genome-wide association study: analysis of genes related to tumour-immunosuppression.

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Nils Schoof

Full Text Available Systemic immunosuppression is a risk factor for melanoma, and sunburn-induced immunosuppression is thought to be causal. Genes in immunosuppression pathways are therefore candidate melanoma-susceptibility genes. If variants within these genes individually have a small effect on disease risk, the association may be undetected in genome-wide association (GWA studies due to low power to reach a high significance level. Pathway-based approaches have been suggested as a method of incorporating a priori knowledge into the analysis of GWA studies. In this study, the association of 1113 single nucleotide polymorphisms (SNPs in 43 genes (39 genomic regions related to immunosuppression have been analysed using a gene-set approach in 1539 melanoma cases and 3917 controls from the GenoMEL consortium GWA study. The association between melanoma susceptibility and the whole set of tumour-immunosuppression genes, and also predefined functional subgroups of genes, was considered. The analysis was based on a measure formed by summing the evidence from the most significant SNP in each gene, and significance was evaluated empirically by case-control label permutation. An association was found between melanoma and the complete set of genes (p(emp=0.002, as well as the subgroups related to the generation of tolerogenic dendritic cells (p(emp=0.006 and secretion of suppressive factors (p(emp=0.0004, thus providing preliminary evidence of involvement of tumour-immunosuppression gene polymorphisms in melanoma susceptibility. The analysis was repeated on a second phase of the GenoMEL study, which showed no evidence of an association. As one of the first attempts to replicate a pathway-level association, our results suggest that low power and heterogeneity may present challenges.

Critical Radionuclide and Pathway Analysis for the Savannah River Site, 2016 Update

Energy Technology Data Exchange (ETDEWEB)

Jannik, Tim [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Hartman, Larry [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2016-09-08

During the operational history of Savannah River Site, many different radionuclides have been released from site facilities. However, as shown in this analysis, only a relatively small number of the released radionuclides have been significant contributors to doses to the offsite public. This report is an update to the 2011 analysis, Critical Radionuclide and Pathway Analysis for the Savannah River Site. SRS-based Performance Assessments for E-Area, Saltstone, F-Tank Farm, H-Tank Farm, and a Comprehensive SRS Composite Analysis have been completed. The critical radionuclides and pathways identified in those extensive reports are also detailed and included in this analysis.

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

Science.gov (United States)

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

Directory of Open Access Journals (Sweden)

Gaora Peadar Ó

2010-10-01

Full Text Available Abstract Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of

Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

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Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

2018-04-01

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

A Pathway-Centered Analysis of Pig Domestication and Breeding in Eurasia

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Jordi Leno-Colorado

2017-07-01

Full Text Available Ascertaining the molecular and physiological basis of domestication and breeding is an active area of research. Due to the current wide distribution of its wild ancestor, the wild boar, the pig (Sus scrofa is an excellent model to study these processes, which occurred independently in East Asia and Europe ca. 9000 yr ago. Analyzing genome variability patterns in terms of metabolic pathways is attractive since it considers the impact of interrelated functions of genes, in contrast to genome-wide scans that treat genes or genome windows in isolation. To that end, we studied 40 wild boars and 123 domestic pig genomes from Asia and Europe when metabolic pathway was the unit of analysis. We computed statistical significance for differentiation (Fst and linkage disequilibrium (nSL statistics at the pathway level. In terms of Fst, we found 21 and 12 pathways significantly differentiated at a q-value 10 significant pathways (in terms of Fst, comprising genes involved in the transduction of a large number of signals, like phospholipase PCLB1, which is expressed in the brain, or ITPR3, which has an important role in taste transduction. In terms of nSL, significant pathways were mainly related to reproductive performance (ovarian steroidogenesis, a similarly important target trait during domestication and modern animal breeding. Different levels of recombination cannot explain these results, since we found no correlation between Fst and recombination rate. However, we did find an increased ratio of deleterious mutations in domestic vs. wild populations, suggesting a relaxed functional constraint associated with the domestication and breeding processes. Purifying selection was, nevertheless, stronger in significantly differentiated pathways than in random pathways, mainly in Europe. We conclude that pathway analysis facilitates the biological interpretation of genome-wide studies. Notably, in the case of pig, behavior played an important role, among other

A Pathway-Centered Analysis of Pig Domestication and Breeding in Eurasia.

Science.gov (United States)

Leno-Colorado, Jordi; Hudson, Nick J; Reverter, Antonio; Pérez-Enciso, Miguel

2017-07-05

Ascertaining the molecular and physiological basis of domestication and breeding is an active area of research. Due to the current wide distribution of its wild ancestor, the wild boar, the pig ( Sus scrofa ) is an excellent model to study these processes, which occurred independently in East Asia and Europe ca. 9000 yr ago. Analyzing genome variability patterns in terms of metabolic pathways is attractive since it considers the impact of interrelated functions of genes, in contrast to genome-wide scans that treat genes or genome windows in isolation. To that end, we studied 40 wild boars and 123 domestic pig genomes from Asia and Europe when metabolic pathway was the unit of analysis. We computed statistical significance for differentiation (Fst) and linkage disequilibrium (nSL) statistics at the pathway level. In terms of Fst, we found 21 and 12 pathways significantly differentiated at a q -value 10 significant pathways (in terms of Fst), comprising genes involved in the transduction of a large number of signals, like phospholipase PCLB1, which is expressed in the brain, or ITPR3, which has an important role in taste transduction. In terms of nSL, significant pathways were mainly related to reproductive performance (ovarian steroidogenesis), a similarly important target trait during domestication and modern animal breeding. Different levels of recombination cannot explain these results, since we found no correlation between Fst and recombination rate. However, we did find an increased ratio of deleterious mutations in domestic vs. wild populations, suggesting a relaxed functional constraint associated with the domestication and breeding processes. Purifying selection was, nevertheless, stronger in significantly differentiated pathways than in random pathways, mainly in Europe. We conclude that pathway analysis facilitates the biological interpretation of genome-wide studies. Notably, in the case of pig, behavior played an important role, among other physiological

Endocrine-disrupting Chemicals: Review of Toxicological Mechanisms Using Molecular Pathway Analysis

Science.gov (United States)

Yang, Oneyeol; Kim, Hye Lim; Weon, Jong-Il; Seo, Young Rok

2015-01-01

Endocrine disruptors are known to cause harmful effects to human through various exposure routes. These chemicals mainly appear to interfere with the endocrine or hormone systems. As importantly, numerous studies have demonstrated that the accumulation of endocrine disruptors can induce fatal disorders including obesity and cancer. Using diverse biological tools, the potential molecular mechanisms related with these diseases by exposure of endocrine disruptors. Recently, pathway analysis, a bioinformatics tool, is being widely used to predict the potential mechanism or biological network of certain chemicals. In this review, we initially summarize the major molecular mechanisms involved in the induction of the above mentioned diseases by endocrine disruptors. Additionally, we provide the potential markers and signaling mechanisms discovered via pathway analysis under exposure to representative endocrine disruptors, bisphenol, diethylhexylphthalate, and nonylphenol. The review emphasizes the importance of pathway analysis using bioinformatics to finding the specific mechanisms of toxic chemicals, including endocrine disruptors. PMID:25853100

Life Cycle Greenhouse Gas Analysis of Multiple Vehicle Fuel Pathways in China

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Tianduo Peng

2017-11-01

Full Text Available The Tsinghua University Life Cycle Analysis Model (TLCAM is applied to calculate the life cycle fossil energy consumption and greenhouse gas (GHG emissions for more than 20 vehicle fuel pathways in China. In addition to conventional gasoline and diesel, these include coal- and gas-based vehicle fuels, and electric vehicle (EV pathways. The results indicate the following. (1 China’s current dependence on coal and relative low-efficiency processes limits the potential for most alternative fuel pathways to decrease energy consumption and emissions; (2 Future low-carbon electricity pathways offer more obvious advantages, with coal-based pathways needing to adopt carbon dioxide capture and storage technology to compete; (3 A well-to-wheels analysis of the fossil energy consumption of vehicles fueled by compressed natural gas and liquefied natural gas (LNG showed that they are comparable to conventional gasoline vehicles. However, importing rather than domestically producing LNG for vehicle use can decrease domestic GHG emissions by 35% and 31% compared with those of conventional gasoline and diesel vehicles, respectively; (4 The manufacturing and recovery of battery and vehicle in the EV analysis has significant impact on the overall ability of EVs to decrease fossil energy consumption and GHG emissions from ICEVs.

Analysis of Geothermal Pathway in the Metamorphic Area, Northeastern Taiwan

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Wang, C.; Wu, M. Y.; Song, S. R.; Lo, W.

2016-12-01

A quantitative measure by play fairway analysis in geothermal energy development is an important tool that can present the probability map of potential resources through the uncertainty studies in geology for early phase decision making purpose in the related industries. While source, pathway, and fluid are the three main geologic factors in traditional geothermal systems, identifying the heat paths is critical to reduce drilling cost. Taiwan is in East Asia and the western edge of Pacific Ocean, locating on the convergent boundary of Eurasian Plate and Philippine Sea Plate with many earthquake activities. This study chooses a metamorphic area in the western corner of Yi-Lan plain in northeastern Taiwan with high geothermal potential and several existing exploration sites. Having high subsurface temperature gradient from the mountain belts, and plenty hydrologic systems through thousands of millimeters annual precipitation that would bring up heats closer to the surface, current geothermal conceptual model indicates the importance of pathway distribution which affects the possible concentration of extractable heat location. The study conducts surface lineation analysis using analytic hierarchy process to determine weights among various fracture types for their roles in geothermal pathways, based on the information of remote sensing data, published geologic maps and field work measurements, to produce regional fracture distribution probability map. The results display how the spatial distribution of pathways through various fractures could affect geothermal systems, identify the geothermal plays using statistical data analysis, and compare against the existing drilling data.

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

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Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

Screening key candidate genes and pathways involved in insulinoma by microarray analysis.

Science.gov (United States)

Zhou, Wuhua; Gong, Li; Li, Xuefeng; Wan, Yunyan; Wang, Xiangfei; Li, Huili; Jiang, Bin

2018-06-01

Insulinoma is a rare type tumor and its genetic features remain largely unknown. This study aimed to search for potential key genes and relevant enriched pathways of insulinoma.The gene expression data from GSE73338 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified between insulinoma tissues and normal pancreas tissues, followed by pathway enrichment analysis, protein-protein interaction (PPI) network construction, and module analysis. The expressions of candidate key genes were validated by quantitative real-time polymerase chain reaction (RT-PCR) in insulinoma tissues.A total of 1632 DEGs were obtained, including 1117 upregulated genes and 514 downregulated genes. Pathway enrichment results showed that upregulated DEGs were significantly implicated in insulin secretion, and downregulated DEGs were mainly enriched in pancreatic secretion. PPI network analysis revealed 7 hub genes with degrees more than 10, including GCG (glucagon), GCGR (glucagon receptor), PLCB1 (phospholipase C, beta 1), CASR (calcium sensing receptor), F2R (coagulation factor II thrombin receptor), GRM1 (glutamate metabotropic receptor 1), and GRM5 (glutamate metabotropic receptor 5). DEGs involved in the significant modules were enriched in calcium signaling pathway, protein ubiquitination, and platelet degranulation. Quantitative RT-PCR data confirmed that the expression trends of these hub genes were similar to the results of bioinformatic analysis.The present study demonstrated that candidate DEGs and enriched pathways were the potential critical molecule events involved in the development of insulinoma, and these findings were useful for better understanding of insulinoma genesis.

Pathway analyses implicate glial cells in schizophrenia.

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Laramie E Duncan

Full Text Available The quest to understand the neurobiology of schizophrenia and bipolar disorder is ongoing with multiple lines of evidence indicating abnormalities of glia, mitochondria, and glutamate in both disorders. Despite high heritability estimates of 81% for schizophrenia and 75% for bipolar disorder, compelling links between findings from neurobiological studies, and findings from large-scale genetic analyses, are only beginning to emerge.Ten publically available gene sets (pathways related to glia, mitochondria, and glutamate were tested for association to schizophrenia and bipolar disorder using MAGENTA as the primary analysis method. To determine the robustness of associations, secondary analyses were performed with: ALIGATOR, INRICH, and Set Screen. Data from the Psychiatric Genomics Consortium (PGC were used for all analyses. There were 1,068,286 SNP-level p-values for schizophrenia (9,394 cases/12,462 controls, and 2,088,878 SNP-level p-values for bipolar disorder (7,481 cases/9,250 controls.The Glia-Oligodendrocyte pathway was associated with schizophrenia, after correction for multiple tests, according to primary analysis (MAGENTA p = 0.0005, 75% requirement for individual gene significance and also achieved nominal levels of significance with INRICH (p = 0.0057 and ALIGATOR (p = 0.022. For bipolar disorder, Set Screen yielded nominally and method-wide significant associations to all three glial pathways, with strongest association to the Glia-Astrocyte pathway (p = 0.002.Consistent with findings of white matter abnormalities in schizophrenia by other methods of study, the Glia-Oligodendrocyte pathway was associated with schizophrenia in our genomic study. These findings suggest that the abnormalities of myelination observed in schizophrenia are at least in part due to inherited factors, contrasted with the alternative of purely environmental causes (e.g. medication effects or lifestyle. While not the primary purpose of our study

Analysis of Chlorogenic Acid Oxidation Pathway in Simulated ...

African Journals Online (AJOL)

Purpose: To investigate the pathways involved in the oxidation of chlorogenic acid (CA) and phenol metabolism in honeysuckle buds. Methods: A model that mimics CA oxidation by honeysuckle polyphenol oxidase (PPO) by controlling the reaction temperature or reaction duration was employed, and the resulting products ...

Environmental-pathways analysis for evaluation of a low-level waste disposal site

International Nuclear Information System (INIS)

Lee, D.W.; Ketelle, R.H.; Pin, F.G.; Hill, G.S.

1983-01-01

The suitability of a site for the shallow land burial of low-level waste is evaluated by an environmental-pathways analysis. The environmental-pathways analysis considers the probable paths for the transport of contamination to man and models the long-term transport of contamination to determine the resulting dose-to-man. The model of the long-term transport of contamination is developed for a proposed site using data obtained from a comprehensive laboratory and field investigation. The proposed site is located at the US Department of Energy Portsmouth Reservation, Piketon, Ohio and is planned to accept low-level radioactive waste generated by the enrichment of uranium. Laboratory studies were performed to characterize the waste and determine the wastes' leaching and retardation characteristics with site soils and groundwater. Comprehensive drilling, sampling and laboratory investigations were performed to provide the necessary information for interpreting the site's geology and hydrology. Field tests were performed to further quantify the site's hydrology. The pathway of greatest concern is the migration of contaminated groundwater and subsequent consumption by man. This pathway was modeled using a numerical simulation of the long-term transport of contamination. Conservative scenarios were developed for leachate generation and migration through the geohydrologic system. The dose-to-man determined from the pathways analysis formed the basis for evaluating site acceptability and providing recommendations for site design and development

Environmental pathways analysis for evaluation of a low-level waste disposal site

International Nuclear Information System (INIS)

Lee, D.W.; Ketelle, R.H.; Pin, F.G.; Hill, G.S.

1984-01-01

The suitability of a site for the shallow land burial of low-level waste is evaluated by an environmental pathways analysis. The environmental pathways analysis considers the probable paths for the transport of contamination to man and models the long-term transport of contamination to determine the resulting dose to man. The model of the long-term transport of contamination is developed for a proposed site using data obtained from a comprehensive laboratory and field investigation. The proposed site is located at the US Department of Energy Portsmouth Reservation, Piketon, Ohio, and is planned to accept low-level radioactive waste generated by the enrichment of uranium. Laboratory studies were performed to characterize the waste and determine the wastes' leaching and retardation characteristics with site soils and groundwater. Comprehensive drilling, sampling and laboratory investigations were performed to provide the necessary information for interpreting the site's geology and hydrology. Field tests were performed to further quantify the site's hydrology. The pathway of greatest concern is the migration of contaminated groundwater and subsequent consumption by man. This pathway was modelled using a numerical simulation of the long-term transport of contamination. Conservative scenarios were developed for leachate generation and migration through the geohydrologic system. The dose to man determined from the pathways analysis formed the basis for evaluating site acceptability and providing recommendations for site design and development. (author)

A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

Science.gov (United States)

Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-01

Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer.

Science.gov (United States)

Wang, Yuzhi; Zhang, Yi; Huang, Qian; Li, Chengwen

2018-04-19

Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non‑tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t‑tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein‑protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in 'cell division', the 'proteinaceous extracellular matrix (ECM)', 'ECM structural constituents' and 'ECM‑receptor interaction', whereas downregulated genes were mainly enriched in 'response to drugs', 'extracellular space', 'transcriptional activator activity' and the 'peroxisome proliferator‑activated receptor signaling pathway'. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2‑a, baculoviral inhibitor of apoptosis repeat‑containing protein 5, cyclin‑dependent kinase 1, G2/mitotic‑specific cyclin‑B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in 'mitotic nuclear division', 'mid‑body', 'protein binding' and 'cell cycle'. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide

A techno-economic analysis of EU renewable electricity policy pathways in 2030

International Nuclear Information System (INIS)

Río, Pablo del; Resch, Gustav; Ortner, Andre; Liebmann, Lukas; Busch, Sebastian; Panzer, Christian

2017-01-01

The aim of this paper is to assess several pathways of a harmonised European policy framework for supporting renewable electricity (RES-E) in a 2030 horizon according to different criteria. The pathways combine two main dimensions: degrees of harmonisation and instruments and design elements. A quantitative model-based analysis with the Green-X model is provided. The results of the simulations show that there are small differences between the evaluated cases regarding effectiveness. All the policy pathways score similarly with respect to RES-E deployment, i.e., with different degrees of harmonisation and whether using a feed-in tariff, a feed-in premium, a quota system with banding or a quota without banding scheme. In contrast, the policy costs clearly differ across the pathways, but the differences can mostly be attributed to the instruments rather than to the degrees of harmonisation. This is also the case with other criteria (static and dynamic efficiency and the socioeconomic and environmental benefits in terms of CO2 emissions and fossil fuels avoided). Both the degree of harmonisation and the choice of instrument influence the distribution of support costs across countries. Finally, our findings suggest that keeping strengthened national support leads to similar results to other policy pathways. - Highlights: • Pathways of a harmonised European policy framework for renewable electricity in 2030. • Two main dimensions: degrees of harmonisation and instruments. • A quantitative model-based analysis based on the Green-X model. • Small differences between the pathways regarding the effectiveness criterion. • Important differences between pathways regarding other assessment criteria.

A systematic model identification method for chemical transformation pathways – the case of heroin biomarkers in wastewater

DEFF Research Database (Denmark)

Ramin, Pedram; Valverde Pérez, Borja; Polesel, Fabio

2017-01-01

This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained...... at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method....... Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify...

Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

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Pingzhao Hu

2006-01-01

Full Text Available Background: Microarray technology has been previously used to identify genes that are differentially expressed between tumour and normal samples in a single study, as well as in syntheses involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies summarized probeset-level data, which may potentially lead to a loss of information available at the probe-level. In this paper, we present an approach for integrating results across studies while taking probe-level data into account. Additionally, we follow a new direction in the analysis of microarray expression data, namely to focus on the variation of expression phenotypes in predefined gene sets, such as pathways. This targeted approach can be helpful for revealing information that is not easily visible from the changes in the individual genes. Results: We used a recently developed method to integrate Affymetrix expression data across studies. The idea is based on a probe-level based test statistic developed for testing for differentially expressed genes in individual studies. We incorporated this test statistic into a classic random-effects model for integrating data across studies. Subsequently, we used a gene set enrichment test to evaluate the significance of enriched biological pathways in the differentially expressed genes identified from the integrative analysis. We compared statistical and biological significance of the prognostic gene expression signatures and pathways identified in the probe-level model (PLM with those in the probeset-level model (PSLM. Our integrative analysis of Affymetrix microarray data from 110 prostate cancer samples obtained from three studies reveals thousands of genes significantly correlated with tumour cell differentiation. The bioinformatics analysis, mapping these genes to the publicly available KEGG database, reveals evidence that tumour cell differentiation is significantly associated with many

Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.

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Lieven P C Verbeke

Full Text Available The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad

Degenerative Pathways of Lumbar Motion Segments

DEFF Research Database (Denmark)

Jensen, Rikke K.; Kjaer, Per; Jensen, Tue S.

2016-01-01

pathways of degeneration based on scientific knowledge of disco-vertebral degeneration, and (iii) compare these clusters and degenerative pathways between samples. METHODS: We performed a secondary cross-sectional analysis on two dissimilar MRI samples collected in a hospital department: (1) data from...... pathways of degeneration. RESULTS: Six clusters of MRI findings were identified in each of the two samples. The content of the clusters in the two samples displayed some differences but had the same overall pattern of MRI findings. Although the hypothetical degenerative pathways identified in the two...... samples were not identical, the overall pattern of increasing degeneration within the pathways was the same. CONCLUSIONS: It was expected that different clusters could emerge from different samples, however, when organised into hypothetical pathways of degeneration, the overall pattern of increasing...

Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases.

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Hariklia Eleftherohorinou

2009-11-01

Full Text Available Although the introduction of genome-wide association studies (GWAS have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn's disease (CD, rheumatoid arthritis (RA and type 1 diabetes (T1D with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC. The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10(-3-10(-20 with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes for T1D, 350 SNPs (189 genes for RA and 493 SNPs (277 genes for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC and RA (85% AUC, and weakly predictive of CD (60% AUC. The predictive ability of the T1D model (without any parameter refitting had good predictive ability (79% AUC in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.

Parameter-free methods distinguish Wnt pathway models and guide design of experiments

KAUST Repository

MacLean, Adam L.

2015-02-17

The canonical Wnt signaling pathway, mediated by β-catenin, is crucially involved in development, adult stem cell tissue maintenance, and a host of diseases including cancer. We analyze existing mathematical models of Wnt and compare them to a new Wnt signaling model that targets spatial localization; our aim is to distinguish between the models and distill biological insight from them. Using Bayesian methods we infer parameters for each model from mammalian Wnt signaling data and find that all models can fit this time course. We appeal to algebraic methods (concepts from chemical reaction network theory and matroid theory) to analyze the models without recourse to specific parameter values. These approaches provide insight into aspects of Wnt regulation: the new model, via control of shuttling and degradation parameters, permits multiple stable steady states corresponding to stem-like vs. committed cell states in the differentiation hierarchy. Our analysis also identifies groups of variables that should be measured to fully characterize and discriminate between competing models, and thus serves as a guide for performing minimal experiments for model comparison.

Metabolic-flux analysis of hydrogen production pathway in Citrobacter amalonaticus Y19

Energy Technology Data Exchange (ETDEWEB)

Oh, You-Kwan; Kim, Mi-Sun [Bioenergy Research Center, Korea Institute of Energy Research, Daejeon 305-343 (Korea); Kim, Heung-Joo; Park, Sunghoon [Department of Chemical and Biochemical Engineering and Institute for Environmental Technology and Industry, Pusan National University, Busan 609-735 (Korea); Ryu, Dewey D.Y. [Biochemical Engineering Program, Department of Chemical Engineering and Material Science, University of California, Davis, CA 95616 (United States)

2008-03-15

For the newly isolated chemoheterotrophic bacterium Citrobacter amalonaticus Y19, anaerobic glucose metabolism and hydrogen (H{sub 2}) production pathway were studied using batch cultivation and an in silico metabolic-flux analysis. Batch cultivation was conducted under varying initial glucose concentration between 1.5 and 9.5 g/L with quantitative measurement of major metabolites to obtain accurate carbon material balance. The metabolic flux of Y19 was analyzed using a metabolic-pathway model which was constructed from 81 biochemical reactions. The linear optimization program MetaFluxNet was employed for the analysis. When the specific growth rate of cells was chosen as an objective function, the model described the batch culture characteristics of Ci. amalonaticus Y19 reasonably well. When the specific H{sub 2} production rate was selected as an objective function, on the other hand, the achievable maximal H{sub 2} production yield (8.7molH{sub 2}/mol glucose) and the metabolic pathway enabling the high H{sub 2} yield were identified. The pathway involved non-native NAD(P)-linked hydrogenase and H{sub 2} production from NAD(P)H which were supplied at a high rate from glucose degradation through the pentose phosphate pathway. (author)

Systematic enrichment analysis of gene expression profiling studies identifies consensus pathways implicated in colorectal cancer development

Directory of Open Access Journals (Sweden)

Jesús Lascorz

2011-01-01

Full Text Available Background: A large number of gene expression profiling (GEP studies on colorectal carcinogenesis have been performed but no reliable gene signature has been identified so far due to the lack of reproducibility in the reported genes. There is growing evidence that functionally related genes, rather than individual genes, contribute to the etiology of complex traits. We used, as a novel approach, pathway enrichment tools to define functionally related genes that are consistently up- or down-regulated in colorectal carcinogenesis. Materials and Methods: We started the analysis with 242 unique annotated genes that had been reported by any of three recent meta-analyses covering GEP studies on genes differentially expressed in carcinoma vs normal mucosa. Most of these genes (218, 91.9% had been reported in at least three GEP studies. These 242 genes were submitted to bioinformatic analysis using a total of nine tools to detect enrichment of Gene Ontology (GO categories or Kyoto Encyclopedia of Genes and Genomes (KEGG pathways. As a final consistency criterion the pathway categories had to be enriched by several tools to be taken into consideration. Results: Our pathway-based enrichment analysis identified the categories of ribosomal protein constituents, extracellular matrix receptor interaction, carbonic anhydrase isozymes, and a general category related to inflammation and cellular response as significantly and consistently overrepresented entities. Conclusions: We triaged the genes covered by the published GEP literature on colorectal carcinogenesis and subjected them to multiple enrichment tools in order to identify the consistently enriched gene categories. These turned out to have known functional relationships to cancer development and thus deserve further investigation.

Screening possible solid electrolytes by calculating the conduction pathways using Bond Valence method

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Gao, Jian; Chu, Geng; He, Meng; Zhang, Shu; Xiao, RuiJuan; Li, Hong; Chen, LiQuan

2014-08-01

Inorganic solid electrolytes have distinguished advantages in terms of safety and stability, and are promising to substitute for conventional organic liquid electrolytes. However, low ionic conductivity of typical candidates is the key problem. As connective diffusion path is the prerequisite for high performance, we screen for possible solid electrolytes from the 2004 International Centre for Diffraction Data (ICDD) database by calculating conduction pathways using Bond Valence (BV) method. There are 109846 inorganic crystals in the 2004 ICDD database, and 5295 of them contain lithium. Except for those with toxic, radioactive, rare, or variable valence elements, 1380 materials are candidates for solid electrolytes. The rationality of the BV method is approved by comparing the existing solid electrolytes' conduction pathways we had calculated with those from experiments or first principle calculations. The implication for doping and substitution, two important ways to improve the conductivity, is also discussed. Among them Li2CO3 is selected for a detailed comparison, and the pathway is reproduced well with that based on the density functional studies. To reveal the correlation between connectivity of pathways and conductivity, α/ γ-LiAlO2 and Li2CO3 are investigated by the impedance spectrum as an example, and many experimental and theoretical studies are in process to indicate the relationship between property and structure. The BV method can calculate one material within a few minutes, providing an efficient way to lock onto targets from abundant data, and to investigate the structure-property relationship systematically.

GEP analysis validates high risk MDS and acute myeloid leukemia post MDS mice models and highlights novel dysregulated pathways

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Laura Guerenne

2016-01-01

Full Text Available Abstract Background In spite of the recent discovery of genetic mutations in most myelodysplasic (MDS patients, the pathophysiology of these disorders still remains poorly understood, and only few in vivo models are available to help unravel the disease. Methods We performed global specific gene expression profiling and functional pathway analysis in purified Sca1+ cells of two MDS transgenic mouse models that mimic human high-risk MDS (HR-MDS and acute myeloid leukemia (AML post MDS, with NRASD12 and BCL2 transgenes under the control of different promoters MRP8NRASD12/tethBCL-2 or MRP8[NRASD12/hBCL-2], respectively. Results Analysis of dysregulated genes that were unique to the diseased HR-MDS and AML post MDS mice and not their founder mice pointed first to pathways that had previously been reported in MDS patients, including DNA replication/damage/repair, cell cycle, apoptosis, immune responses, and canonical Wnt pathways, further validating these models at the gene expression level. Interestingly, pathways not previously reported in MDS were discovered. These included dysregulated genes of noncanonical Wnt pathways and energy and lipid metabolisms. These dysregulated genes were not only confirmed in a different independent set of BM and spleen Sca1+ cells from the MDS mice but also in MDS CD34+ BM patient samples. Conclusions These two MDS models may thus provide useful preclinical models to target pathways previously identified in MDS patients and to unravel novel pathways highlighted by this study.

Survival associated pathway identification with group Lp penalized global AUC maximization

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Liu Zhenqiu

2010-08-01

Full Text Available Abstract It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group Lp penalized global AUC summary maximization. Unlike LASSO, Lp (p 1. We first extend Lp for individual gene identification to group Lp penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS. This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.

Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

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Hagedorn Peter H

2011-02-01

Full Text Available Abstract Background Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA. Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes. Results Microrray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708 with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY. Conclusions In this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.

Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE Cohorts

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Unjin Shim

2014-12-01

Full Text Available Metabolic syndrome (MetS is a complex disorder related to insulin resistance, obesity, and inflammation. Genetic and environmental factors also contribute to the development of MetS, and through genome-wide association studies (GWASs, important susceptibility loci have been identified. However, GWASs focus more on individual single-nucleotide polymorphisms (SNPs, explaining only a small portion of genetic heritability. To overcome this limitation, pathway analyses are being applied to GWAS datasets. The aim of this study is to elucidate the biological pathways involved in the pathogenesis of MetS through pathway analysis. Cohort data from the Korea Associated Resource (KARE was used for analysis, which include 8,842 individuals (age, 52.2 ± 8.9 years; body mass index, 24.6 ± 3.2 kg/m2. A total of 312,121 autosomal SNPs were obtained after quality control. Pathway analysis was conducted using Meta-analysis Gene-Set Enrichment of Variant Associations (MAGENTA to discover the biological pathways associated with MetS. In the discovery phase, SNPs from chromosome 12, including rs11066280, rs2074356, and rs12229654, were associated with MetS (p < 5 × 10-6, and rs11066280 satisfied the Bonferroni-corrected cutoff (unadjusted p < 1.38 × 10-7, Bonferroni-adjusted p < 0.05. Through pathway analysis, biological pathways, including electron carrier activity, signaling by platelet-derived growth factor (PDGF, the mitogen-activated protein kinase kinase kinase cascade, PDGF binding, peroxisome proliferator-activated receptor (PPAR signaling, and DNA repair, were associated with MetS. Through pathway analysis of MetS, pathways related with PDGF, mitogen-activated protein kinase, and PPAR signaling, as well as nucleic acid binding, protein secretion, and DNA repair, were identified. Further studies will be needed to clarify the genetic pathogenesis leading to MetS.

The combination of four analytical methods to explore skeletal muscle metabolomics: Better coverage of metabolic pathways or a marketing argument?

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Bruno, C; Patin, F; Bocca, C; Nadal-Desbarats, L; Bonnier, F; Reynier, P; Emond, P; Vourc'h, P; Joseph-Delafont, K; Corcia, P; Andres, C R; Blasco, H

2018-01-30

Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering. We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained. Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed

A functional genomics approach using metabolomics and in silico pathway analysis

DEFF Research Database (Denmark)

Förster, Jochen; Gombert, Andreas Karoly; Nielsen, Jens

2002-01-01

analysis techniques and changes in the genotype will in many cases lead to different metabolite profiles. Here, a theoretical framework that may be applied to identify the function of orphan genes is presented. The approach is based on a combination of metabolome analysis combined with in silico pathway...

Genome wide expression analysis in HPV16 Cervical Cancer: identification of altered metabolic pathways

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Salcedo Mauricio

2007-09-01

Full Text Available Abstract Background Cervical carcinoma (CC is a leading cause of death among women worldwide. Human papilloma virus (HPV is a major etiological factor in CC and HPV 16 is the more frequent viral type present. Our aim was to characterize metabolic pathways altered in HPV 16 tumor samples by means of transcriptome wide analysis and bioinformatics tools for visualizing expression data in the context of KEGG biological pathways. Results We found 2,067 genes significantly up or down-modulated (at least 2-fold in tumor clinical samples compared to normal tissues, representing ~3.7% of analyzed genes. Cervical carcinoma was associated with an important up-regulation of Wnt signaling pathway, which was validated by in situ hybridization in clinical samples. Other up-regulated pathways were those of calcium signaling and MAPK signaling, as well as cell cycle-related genes. There was down-regulation of focal adhesion, TGF-β signaling, among other metabolic pathways. Conclusion This analysis of HPV 16 tumors transcriptome could be useful for the identification of genes and molecular pathways involved in the pathogenesis of cervical carcinoma. Understanding the possible role of these proteins in the pathogenesis of CC deserves further studies.

Proliferation Resistance: Acquisition/Diversion Pathway Analysis for the DUPIC Fuel Cycle

International Nuclear Information System (INIS)

Ko, Won Il; Chang, Hong Lae; Song, Dae Yong; Lee, Ho Hee; Kwon, Eun Ha; Jeong, Chang Joon; Kim, Ho Dong

2009-07-01

Within the International Project on Innovative Nuclear Reactors and Fuel Cycles (INPRO), a methodology for evaluating proliferation resistance (INPRO PR methodology) has been developed. However, it remains to develop the methodology to evaluate User Requirements (UR) 4 regarding multiplicity and robustness of barriers against proliferation - innovative nuclear energy systems should incorporate multiple proliferation resistance features and measures. Since this requires an acquisition/diversion pathway analysis, this report describes a systematic approach developed for the identification and analysis of pathways for the acquisition of weapons-usable nuclear material using the DUPIC fuel cycle system. At the first step, the objectives of the proliferation were identified, including the quality and quantity of the material, the time required to acquire the material for the proliferation, thr capability of the potential proliferant country, etc. At the second step, the possible strategies, which the potential proliferant country could adopt, were identified: undeclared removal of nuclear material from the fuel cycle facilities; and further treatment of the diverted nuclear materials needed to acquire weapons-usable materials. At the final step, a systematic approach to select the plausible pathways for the acquisition/diversion of nuclear material during the whole fuel cycle has been developed. The coarse material diversion pathways for the DUPIC fuel cycle and the approach developed was reviewed and discussed at the experts meeting at the IAEA for its appropriateness and comprehensiveness

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

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Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

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Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Genome-Wide Gene Set Analysis for Identification of Pathways Associated with Alcohol Dependence

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Biernacka, Joanna M.; Geske, Jennifer; Jenkins, Gregory D.; Colby, Colin; Rider, David N.; Karpyak, Victor M.; Choi, Doo-Sup; Fridley, Brooke L.

2013-01-01

It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the “Synthesis and Degradation of Ketone Bodies” pathway. Our results also support the potential involvement of the “Neuroactive Ligand Receptor Interaction” pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence. PMID:22717047

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

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Mary Qu Yang

Full Text Available Clear cell renal cell carcinoma (ccRCC is the most common and most aggressive form of renal cell cancer (RCC. The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways. Keywords: ccRCC, Causative mutation, Pathways, Protein-protein interaction, Gene module, eQTL

Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia.

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Wu, Chong; Pan, Wei

2018-04-01

Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS. © 2018 WILEY PERIODICALS, INC.

Comparison of pathways associated with hepatitis B- and C-infected hepatocellular carcinoma using pathway-based class discrimination method.

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Lee, Sun Young; Song, Kwang Hoon; Koo, Imhoi; Lee, Kee-Ho; Suh, Kyung-Suk; Kim, Bu-Yeo

2012-06-01

Molecular signatures causing hepatocellular carcinoma (HCC) from chronic infection of hepatitis B virus (HBV) or hepatitis C virus (HCV) are not clearly known. Using microarray datasets composed of HCV-positive HCC or HBV-positive HCC, pathways that could discriminate tumor tissue from adjacent non-tumor liver tissue were selected by implementing nearest shrunken centroid algorithm. Cancer-related signaling pathways and lipid metabolism-related pathways were predominantly enriched in HCV-positive HCC, whereas functionally diverse pathways including immune-related pathways, cell cycle pathways, and RNA metabolism pathways were mainly enriched in HBV-positive HCC. In addition to differentially involved pathways, signaling pathways such as TGF-β, MAPK, and p53 pathways were commonly significant in both HCCs, suggesting the presence of common hepatocarcinogenesis process. The pathway clustering also verified segregation of pathways into the functional subgroups in both HCCs. This study indicates the functional distinction and similarity on the pathways implicated in the development of HCV- and/or HBV-positive HCC. Copyright © 2012 Elsevier Inc. All rights reserved.

Microarray Study of Pathway Analysis Expression Profile Associated with MicroRNA-29a with Regard to Murine Cholestatic Liver Injuries

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Sung-Chou Li

2016-03-01

Full Text Available Accumulating evidence demonstrates that microRNA-29 (miR-29 expression is prominently decreased in patients with hepatic fibrosis, which consequently stimulates hepatic stellate cells’ (HSCs activation. We used a cDNA microarray study to gain a more comprehensive understanding of genome-wide gene expressions by adjusting miR-29a expression in a bile duct-ligation (BDL animal model. Methods: Using miR-29a transgenic mice and wild-type littermates and applying the BDL mouse model, we characterized the function of miR-29a with regard to cholestatic liver fibrosis. Pathway enrichment analysis and/or specific validation were performed for differentially expressed genes found within the comparisons. Results: Analysis of the microarray data identified a number of differentially expressed genes due to the miR-29a transgene, BDL, or both. Additional pathway enrichment analysis revealed that TGF-β signaling had a significantly differential activated pathway depending on the occurrence of miR-29a overexpression or the lack thereof. Furthermore, overexpression was found to elicit changes in Wnt/β-catenin after BDL. Conclusion: This study verified that an elevated miR-29a level could alleviate liver fibrosis caused by cholestasis. Furthermore, the protective effects of miR-29a correlate with the downregulation of TGF-β and associated with Wnt/β-catenin signal pathway following BDL.

Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways.

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Wexler, Eric M; Rosen, Ezra; Lu, Daning; Osborn, Gregory E; Martin, Elizabeth; Raybould, Helen; Geschwind, Daniel H

2011-10-04

Wnt proteins are critical to mammalian brain development and function. The canonical Wnt signaling pathway involves the stabilization and nuclear translocation of β-catenin; however, Wnt also signals through alternative, noncanonical pathways. To gain a systems-level, genome-wide view of Wnt signaling, we analyzed Wnt1-stimulated changes in gene expression by transcriptional microarray analysis in cultured human neural progenitor (hNP) cells at multiple time points over a 72-hour time course. We observed a widespread oscillatory-like pattern of changes in gene expression, involving components of both the canonical and the noncanonical Wnt signaling pathways. A higher-order, systems-level analysis that combined independent component analysis, waveform analysis, and mutual information-based network construction revealed effects on pathways related to cell death and neurodegenerative disease. Wnt effectors were tightly clustered with presenilin1 (PSEN1) and granulin (GRN), which cause dominantly inherited forms of Alzheimer's disease and frontotemporal dementia (FTD), respectively. We further explored a potential link between Wnt1 and GRN and found that Wnt1 decreased GRN expression by hNPs. Conversely, GRN knockdown increased WNT1 expression, demonstrating that Wnt and GRN reciprocally regulate each other. Finally, we provided in vivo validation of the in vitro findings by analyzing gene expression data from individuals with FTD. These unbiased and genome-wide analyses provide evidence for a connection between Wnt signaling and the transcriptional regulation of neurodegenerative disease genes.

Analysis of Stomatal Patterning in Selected Mutants of MAPK Pathways

KAUST Repository

Felemban, Abrar

2016-05-01

Stomata are cellular valves in plants that play an essential role in the regulation of gas exchange and are distributed in the epidermis of aerial organs. In Arabidopsis thaliana, stomatal production and development are coordinated by the mitogen-activated protein kinase (MAPK) signalling pathway, which modulates a variety of other processes, including cell proliferation, regulation of cytokinesis, programed cell death, and response to abiotic and biotic stress. The environment also plays a role in stomatal development, by influencing the frequency at which stomata develop in leaves. This thesis presents an analysis of stomatal development in Arabidopsis mutants in two MAPK pathways: MEKK1-MKK1/MKK2-MPK4, and MAP3K17/18-MKK3. Obtained results demonstrate the effect of stress conditions on stomatal development and specify the involvement of analysed MAPK in stomatal patterning. First, both analysed pathways modulate stomatal patterning in Arabidopsis cotyledons. Second, plant growth-promoting bacteria tested enhance stomatal density and affect guard cell morphology. Third, the sucrose or mannitol treatment increases defects in stomatal patterning. Finally, salt stress or high temperature can suppress stomatal defects in mutants of the MEKK1-MKK1/MKK2-MPK4 pathway.

Predicting metabolic pathways by sub-network extraction.

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Faust, Karoline; van Helden, Jacques

2012-01-01

Various methods result in groups of functionally related genes obtained from genomes (operons, regulons, syntheny groups, and phylogenetic profiles), transcriptomes (co-expression groups) and proteomes (modules of interacting proteins). When such groups contain two or more enzyme-coding genes, graph analysis methods can be applied to extract a metabolic pathway that interconnects them. We describe here the way to use the Pathway extraction tool available on the NeAT Web server ( http://rsat.ulb.ac.be/neat/ ) to piece together the metabolic pathway from a group of associated, enzyme-coding genes. The tool identifies the reactions that can be catalyzed by the products of the query genes (seed reactions), and applies sub-graph extraction algorithms to extract from a metabolic network a sub-network that connects the seed reactions. This sub-network represents the predicted metabolic pathway. We describe here the pathway prediction process in a step-by-step way, give hints about the main parametric choices, and illustrate how this tool can be used to extract metabolic pathways from bacterial genomes, on the basis of two study cases: the isoleucine-valine operon in Escherichia coli and a predicted operon in Cupriavidus (Ralstonia) metallidurans.

Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway

Science.gov (United States)

2011-01-01

Background Sporadic amyotrophic lateral sclerosis (sALS) is a motor neuron disease with poorly understood etiology. Results of gene expression profiling studies of whole blood from ALS patients have not been validated and are difficult to relate to ALS pathogenesis because gene expression profiles depend on the relative abundance of the different cell types present in whole blood. We conducted microarray analyses using Agilent Human Whole Genome 4 × 44k Arrays on a more homogeneous cell population, namely purified peripheral blood lymphocytes (PBLs), from ALS patients and healthy controls to identify molecular signatures possibly relevant to ALS pathogenesis. Methods Differentially expressed genes were determined by LIMMA (Linear Models for MicroArray) and SAM (Significance Analysis of Microarrays) analyses. The SAFE (Significance Analysis of Function and Expression) procedure was used to identify molecular pathway perturbations. Proteasome inhibition assays were conducted on cultured peripheral blood mononuclear cells (PBMCs) from ALS patients to confirm alteration of the Ubiquitin/Proteasome System (UPS). Results For the first time, using SAFE in a global gene ontology analysis (gene set size 5-100), we show significant perturbation of the KEGG (Kyoto Encyclopedia of Genes and Genomes) ALS pathway of motor neuron degeneration in PBLs from ALS patients. This was the only KEGG disease pathway significantly upregulated among 25, and contributing genes, including SOD1, represented 54% of the encoded proteins or protein complexes of the KEGG ALS pathway. Further SAFE analysis, including gene set sizes >100, showed that only neurodegenerative diseases (4 out of 34 disease pathways) including ALS were significantly upregulated. Changes in UBR2 expression correlated inversely with time since onset of disease and directly with ALSFRS-R, implying that UBR2 was increased early in the course of ALS. Cultured PBMCs from ALS patients accumulated more ubiquitinated proteins

Identification of altered pathways in breast cancer based on individualized pathway aberrance score.

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Shi, Sheng-Hong; Zhang, Wei; Jiang, Jing; Sun, Long

2017-08-01

The objective of the present study was to identify altered pathways in breast cancer based on the individualized pathway aberrance score (iPAS) method combined with the normal reference (nRef). There were 4 steps to identify altered pathways using the iPAS method: Data preprocessing conducted by the robust multi-array average (RMA) algorithm; gene-level statistics based on average Z ; pathway-level statistics according to iPAS; and a significance test dependent on 1 sample Wilcoxon test. The altered pathways were validated by calculating the changed percentage of each pathway in tumor samples and comparing them with pathways from differentially expressed genes (DEGs). A total of 688 altered pathways with Ppathways were involved in the total 688 altered pathways, which may validate the present results. In addition, there were 324 DEGs and 155 common genes between DEGs and pathway genes. DEGs and common genes were enriched in the same 9 significant terms, which also were members of altered pathways. The iPAS method was suitable for identifying altered pathways in breast cancer. Altered pathways (such as KIF and PLK mediated events) were important for understanding breast cancer mechanisms and for the future application of customized therapeutic decisions.

Radionuclide migration pathways analysis for the Oak Ridge Central Waste Disposal Facility on the West Chestnut Ridge site

International Nuclear Information System (INIS)

Pin, F.G.; Witherspoon, J.P.; Lee, D.W.; Cannon, J.B.; Ketelle, R.H.

1984-10-01

A dose-to-man pathways analysis is performed for disposal of low-level radioactive waste at the Central Waste Disposal Facility on the West Chestnut Ridge Site. Both shallow land burial (trench) and aboveground (tumulus) disposal methods are considered. The waste volumes, characteristics, and radionuclide concentrations are those of waste streams anticipated from the Oak Ridge National Laboratory, the Y-12 Plant, and the Oak Ridge Gaseous Diffusion Plant. The site capacity for the waste streams is determined on the basis of the pathways analysis. The exposure pathways examined include (1) migration and transport of leachate from the waste disposal units to the Clinch River (via the groundwater medium for trench disposal and Ish Creek for tumulus disposal) and (2) those potentially associated with inadvertent intrusion following a 100-year period of institutional control: an individual resides on the site, inhales suspended particles of contaminated dust, ingests vegetables grown on the plot, consumes contaminated water from either an on-site well or from a nearby surface stream, and receives direct exposure from the contaminated soil. It is found that either disposal method would provide effective containment and isolation for the anticipated waste inventory. However, the proposed trench disposal method would provide more effective containment than tumuli because of sorption of some radionuclides in the soil. Persons outside the site boundary would receive radiation doses well below regulatory limits if they were to ingest water from the Clinch River. An inadvertent intruder could receive doses that approach regulatory limits; however, the likelihood of such intrusions and subsequent exposures is remote. 33 references, 31 figures, 28 tables

Analysis of culture media screening data by projection to latent pathways: The case of Pichia pastoris X-33.

Science.gov (United States)

Isidro, Inês A; Ferreira, Ana R; Clemente, João J; Cunha, António E; Oliveira, Rui

2016-01-10

Cell culture media formulations contain hundreds of individual components in water solutions which have complex interactions with metabolic pathways. The currently used statistical design methods are empirical and very limited to explore such a large design space. In a previous work we developed a computational method called projection to latent pathways (PLP), which was conceived to maximize covariance between envirome and fluxome data under the constraint of metabolic network elementary flux modes (EFM). More specifically, PLP identifies a minimal set of EFMs (i.e., pathways) with the highest possible correlation with envirome and fluxome measurements. In this paper we extend the concept for the analysis of culture media screening data to investigate how culture medium components up-regulate or down-regulate key metabolic pathways. A Pichia pastoris X-33 strain was cultivated in 26 shake flask experiments with variations in trace elements concentrations and basal medium dilution, based on the standard BSM+PTM1 medium. PLP identified 3 EFMs (growth, maintenance and by-product formation) describing 98.8% of the variance in observed fluxes. Furthermore, PLP presented an overall predictive power comparable to that of PLS regression. Our results show iron and manganese at concentrations close to the PTM1 standard inhibit overall metabolic activity, while the main salts concentration (BSM) affected mainly energy expenditures for cellular maintenance. Copyright © 2015 Elsevier B.V. All rights reserved.

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome.

Science.gov (United States)

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-11-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS.

Penalized differential pathway analysis of integrative oncogenomics studies.

Science.gov (United States)

van Wieringen, Wessel N; van de Wiel, Mark A

2014-04-01

Through integration of genomic data from multiple sources, we may obtain a more accurate and complete picture of the molecular mechanisms underlying tumorigenesis. We discuss the integration of DNA copy number and mRNA gene expression data from an observational integrative genomics study involving cancer patients. The two molecular levels involved are linked through the central dogma of molecular biology. DNA copy number aberrations abound in the cancer cell. Here we investigate how these aberrations affect gene expression levels within a pathway using observational integrative genomics data of cancer patients. In particular, we aim to identify differential edges between regulatory networks of two groups involving these molecular levels. Motivated by the rate equations, the regulatory mechanism between DNA copy number aberrations and gene expression levels within a pathway is modeled by a simultaneous-equations model, for the one- and two-group case. The latter facilitates the identification of differential interactions between the two groups. Model parameters are estimated by penalized least squares using the lasso (L1) penalty to obtain a sparse pathway topology. Simulations show that the inclusion of DNA copy number data benefits the discovery of gene-gene interactions. In addition, the simulations reveal that cis-effects tend to be over-estimated in a univariate (single gene) analysis. In the application to real data from integrative oncogenomic studies we show that inclusion of prior information on the regulatory network architecture benefits the reproducibility of all edges. Furthermore, analyses of the TP53 and TGFb signaling pathways between ER+ and ER- samples from an integrative genomics breast cancer study identify reproducible differential regulatory patterns that corroborate with existing literature.

Large-scale transcriptome analysis reveals arabidopsis metabolic pathways are frequently influenced by different pathogens.

Science.gov (United States)

Jiang, Zhenhong; He, Fei; Zhang, Ziding

2017-07-01

Through large-scale transcriptional data analyses, we highlighted the importance of plant metabolism in plant immunity and identified 26 metabolic pathways that were frequently influenced by the infection of 14 different pathogens. Reprogramming of plant metabolism is a common phenomenon in plant defense responses. Currently, a large number of transcriptional profiles of infected tissues in Arabidopsis (Arabidopsis thaliana) have been deposited in public databases, which provides a great opportunity to understand the expression patterns of metabolic pathways during plant defense responses at the systems level. Here, we performed a large-scale transcriptome analysis based on 135 previously published expression samples, including 14 different pathogens, to explore the expression pattern of Arabidopsis metabolic pathways. Overall, metabolic genes are significantly changed in expression during plant defense responses. Upregulated metabolic genes are enriched on defense responses, and downregulated genes are enriched on photosynthesis, fatty acid and lipid metabolic processes. Gene set enrichment analysis (GSEA) identifies 26 frequently differentially expressed metabolic pathways (FreDE_Paths) that are differentially expressed in more than 60% of infected samples. These pathways are involved in the generation of energy, fatty acid and lipid metabolism as well as secondary metabolite biosynthesis. Clustering analysis based on the expression levels of these 26 metabolic pathways clearly distinguishes infected and control samples, further suggesting the importance of these metabolic pathways in plant defense responses. By comparing with FreDE_Paths from abiotic stresses, we find that the expression patterns of 26 FreDE_Paths from biotic stresses are more consistent across different infected samples. By investigating the expression correlation between transcriptional factors (TFs) and FreDE_Paths, we identify several notable relationships. Collectively, the current study

Integrating computational methods to retrofit enzymes to synthetic pathways.

Science.gov (United States)

Brunk, Elizabeth; Neri, Marilisa; Tavernelli, Ivano; Hatzimanikatis, Vassily; Rothlisberger, Ursula

2012-02-01

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large-scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi-layer challenge, our strategy merges knowledge-based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate. Copyright © 2011 Wiley Periodicals, Inc.

Transcriptome Analysis of Manganese-deficient Chlamydomonas reinhardtii Provides Insight on the Chlorophyll Biosynthesis Pathway

Energy Technology Data Exchange (ETDEWEB)

Lockhart, Ainsley; Zvenigorodsky, Natasha; Pedraza, Mary Ann; Lindquist, Erika

2011-08-11

The biosynthesis of chlorophyll and other tetrapyrroles is a vital but poorly understood process. Recent genomic advances with the unicellular green algae Chlamydomonas reinhardtii have created opportunity to more closely examine the mechanisms of the chlorophyll biosynthesis pathway via transcriptome analysis. Manganese is a nutrient of interest for complex reactions because of its multiple stable oxidation states and role in molecular oxygen coordination. C. reinhardtii was cultured in Manganese-deplete Tris-acetate-phosphate (TAP) media for 24 hours and used to create cDNA libraries for sequencing using Illumina TruSeq technology. Transcriptome analysis provided intriguing insight on possible regulatory mechanisms in the pathway. Evidence supports similarities of GTR (Glutamyl-tRNA synthase) to its Chlorella vulgaris homolog in terms of Mn requirements. Data was also suggestive of Mn-related compensatory up-regulation for pathway proteins CHLH1 (Manganese Chelatase), GUN4 (Magnesium chelatase activating protein), and POR1 (Light-dependent protochlorophyllide reductase). Intriguingly, data suggests possible reciprocal expression of oxygen dependent CPX1 (coproporphyrinogen III oxidase) and oxygen independent CPX2. Further analysis using RT-PCR could provide compelling evidence for several novel regulatory mechanisms in the chlorophyll biosynthesis pathway.

Reconstructing biochemical pathways from time course data.

Science.gov (United States)

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Effect of the absolute statistic on gene-sampling gene-set analysis methods.

Science.gov (United States)

Nam, Dougu

2017-06-01

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

Pathways analysis and radiation-dose estimates for radioactive residues at formerly utilized MED/AEC sites

Energy Technology Data Exchange (ETDEWEB)

Gilbert, T.L.; Chee, P.C.; Knight, M.J.; Peterson, J.M.; Roberts, C.J.; Robinson, J.E.; Tsai, S.Y.H.; Yuan, Y.C.

1983-03-01

Methods of analysis are developed for estimating the largest individual radiation dose that could result from residual radioactivity at sites identified by the Formerly Utilized Sites Remedial Action Program (FUSRAP) of the US Department of Energy. Two unique aspects of the methods are (1) a systematic structuring of the radiation pathways analysis into source terms, source-to-exposure analysis, and exposure-to-dose analysis, and (2) the systematic use of data on the average concentrations of naturally occurring radionuclides in soil, food, and the human body in order to assess the validity of model calculations and obtain more realistic values. The methods are applied to a typical FUSRAP site in order to obtain generic source-to-dose (D/S) conversion factors for estimating the radiation dose to the maximally exposed individual from a known concentration of radionuclides in the soil. The D/S factors are used to derive soil guidelines, i.e., the limiting concentrations of radionuclides at a typical FUSRAP site that are unlikely to result in individual dose limits that exceed generally accepted radiation protection standards. The results lead to the conclusion that the soil guidelines should not exceed 17, 75, and 300 pCi/g for Ra-226, U-238, and Th-230, respectively.

Pathways analysis and radiation-dose estimates for radioactive residues at formerly utilized MED/AEC sites

International Nuclear Information System (INIS)

Gilbert, T.L.; Chee, P.C.; Knight, M.J.; Peterson, J.M.; Roberts, C.J.; Robinson, J.E.; Tsai, S.Y.H.; Yuan, Y.C.

1983-03-01

Methods of analysis are developed for estimating the largest individual radiation dose that could result from residual radioactivity at sites identified by the Formerly Utilized Sites Remedial Action Program (FUSRAP) of the US Department of Energy. Two unique aspects of the methods are (1) a systematic structuring of the radiation pathways analysis into source terms, source-to-exposure analysis, and exposure-to-dose analysis, and (2) the systematic use of data on the average concentrations of naturally occurring radionuclides in soil, food, and the human body in order to assess the validity of model calculations and obtain more realistic values. The methods are applied to a typical FUSRAP site in order to obtain generic source-to-dose (D/S) conversion factors for estimating the radiation dose to the maximally exposed individual from a known concentration of radionuclides in the soil. The D/S factors are used to derive soil guidelines, i.e., the limiting concentrations of radionuclides at a typical FUSRAP site that are unlikely to result in individual dose limits that exceed generally accepted radiation protection standards. The results lead to the conclusion that the soil guidelines should not exceed 17, 75, and 300 pCi/g for Ra-226, U-238, and Th-230, respectively

Aligning Event Logs to Task-Time Matrix Clinical Pathways in BPMN for Variance Analysis.

Science.gov (United States)

Yan, Hui; Van Gorp, Pieter; Kaymak, Uzay; Lu, Xudong; Ji, Lei; Chiau, Choo Chiap; Korsten, Hendrikus H M; Duan, Huilong

2018-03-01

Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement.

Atmospheric Pathway Screening Analysis for Saltstone Disposal Facility Vault 4

International Nuclear Information System (INIS)

COOK, JAMES

2004-01-01

A sequential screening process using a methodology developed by the National Council on Radiation Protection and Measurements, professional judgment and process knowledge has been used to produce a list of radionuclides requiring detailed analysis to derive disposal limits for the Saltstone Disposal Facility based on the atmospheric pathway

Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA.

Science.gov (United States)

Yang, Xian; Han, Rui; Guo, Yike; Bradley, Jeremy; Cox, Benita; Dickinson, Robert; Kitney, Richard

2012-01-01

Hospitals nowadays have to serve numerous patients with limited medical staff and equipment while maintaining healthcare quality. Clinical pathway informatics is regarded as an efficient way to solve a series of hospital challenges. To date, conventional research lacks a mathematical model to describe clinical pathways. Existing vague descriptions cannot fully capture the complexities accurately in clinical pathways and hinders the effective management and further optimization of clinical pathways. Given this motivation, this paper presents a clinical pathway management platform, the Imperial Clinical Pathway Analyzer (ICPA). By extending the stochastic model performance evaluation process algebra (PEPA), ICPA introduces a clinical-pathway-specific model: clinical pathway PEPA (CPP). ICPA can simulate stochastic behaviours of a clinical pathway by extracting information from public clinical databases and other related documents using CPP. Thus, the performance of this clinical pathway, including its throughput, resource utilisation and passage time can be quantitatively analysed. A typical clinical pathway on stroke extracted from a UK hospital is used to illustrate the effectiveness of ICPA. Three application scenarios are tested using ICPA: 1) redundant resources are identified and removed, thus the number of patients being served is maintained with less cost; 2) the patient passage time is estimated, providing the likelihood that patients can leave hospital within a specific period; 3) the maximum number of input patients are found, helping hospitals to decide whether they can serve more patients with the existing resource allocation. ICPA is an effective platform for clinical pathway management: 1) ICPA can describe a variety of components (state, activity, resource and constraints) in a clinical pathway, thus facilitating the proper understanding of complexities involved in it; 2) ICPA supports the performance analysis of clinical pathway, thereby assisting

Enhancement of Nucleoside Production in Hirsutella sinensis Based on Biosynthetic Pathway Analysis

Science.gov (United States)

Liu, Zhi-Qiang; Zhang, Bo; Lin, Shan; Baker, Peter James; Chen, Mao-Sheng; Xue, Ya-Ping; Wu, Hui; Xu, Feng; Yuan, Shui-Jin; Teng, Yi; Wu, Ling-Fang

2017-01-01

To enhance nucleoside production in Hirsutella sinensis, the biosynthetic pathways of purine and pyrimidine nucleosides were constructed and verified. The differential expression analysis showed that purine nucleoside phosphorylase, inosine monophosphate dehydrogenase, and guanosine monophosphate synthase genes involved in purine nucleotide biosynthesis were significantly upregulated 16.56-fold, 8-fold, and 5.43-fold, respectively. Moreover, dihydroorotate dehydrogenase, uridine nucleosidase, uridine/cytidine monophosphate kinase, and inosine triphosphate pyrophosphatase genes participating in pyrimidine nucleoside biosynthesis were upregulated 4.53-fold, 10.63-fold, 4.26-fold, and 5.98-fold, respectively. To enhance the nucleoside production, precursors for synthesis of nucleosides were added based on the analysis of biosynthetic pathways. Uridine and cytidine contents, respectively, reached 5.04 mg/g and 3.54 mg/g when adding 2 mg/mL of ribose, resulting in an increase of 28.6% and 296% compared with the control, respectively. Meanwhile, uridine and cytidine contents, respectively, reached 10.83 mg/g 2.12 mg/g when adding 0.3 mg/mL of uracil, leading to an increase of 176.3% and 137.1%, respectively. This report indicated that fermentation regulation was an effective way to enhance the nucleoside production in H. sinensis based on biosynthetic pathway analysis. PMID:29333435

Microarray analysis reveals genetic pathways modulated by tipifarnib in acute myeloid leukemia

International Nuclear Information System (INIS)

Raponi, Mitch; Belly, Robert T; Karp, Judith E; Lancet, Jeffrey E; Atkins, David; Wang, Yixin

2004-01-01

Farnesyl protein transferase inhibitors (FTIs) were originally developed to inhibit oncogenic ras, however it is now clear that there are several other potential targets for this drug class. The FTI tipifarnib (ZARNESTRA™, R115777) has recently demonstrated clinical responses in adults with refractory and relapsed acute leukemias. This study was conducted to identify genetic markers and pathways that are regulated by tipifarnib in acute myeloid leukemia (AML). Tipifarnib-mediated gene expression changes in 3 AML cell lines and bone marrow samples from two patients with AML were analyzed on a cDNA microarray containing approximately 7000 human genes. Pathways associated with these expression changes were identified using the Ingenuity Pathway Analysis tool. The expression analysis identified a common set of genes that were regulated by tipifarnib in three leukemic cell lines and in leukemic blast cells isolated from two patients who had been treated with tipifarnib. Association of modulated genes with biological functional groups identified several pathways affected by tipifarnib including cell signaling, cytoskeletal organization, immunity, and apoptosis. Gene expression changes were verified in a subset of genes using real time RT-PCR. Additionally, regulation of apoptotic genes was found to correlate with increased Annexin V staining in the THP-1 cell line but not in the HL-60 cell line. The genetic networks derived from these studies illuminate some of the biological pathways affected by FTI treatment while providing a proof of principle for identifying candidate genes that might be used as surrogate biomarkers of drug activity

Final Report: Hydrogen Production Pathways Cost Analysis (2013 – 2016)

Energy Technology Data Exchange (ETDEWEB)

James, Brian David [Strategic Analysis Inc., Arlington, VA (United States); DeSantis, Daniel Allan [Strategic Analysis Inc., Arlington, VA (United States); Saur, Genevieve [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2016-09-30

This report summarizes work conducted under a three year Department of Energy (DOE) funded project to Strategic Analysis, Inc. (SA) to analyze multiple hydrogen (H₂) production technologies and project their corresponding levelized production cost of H₂. The analysis was conducted using the H2A Hydrogen Analysis Tool developed by the DOE and National Renewable Energy Laboratory (NREL). The project was led by SA but conducted in close collaboration with the NREL and Argonne National Laboratory (ANL). In-depth techno-economic analysis (TEA) of five different H₂ production methods was conducted. These TEAs developed projections for capital costs, fuel/feedstock usage, energy usage, indirect capital costs, land usage, labor requirements, and other parameters, for each H₂ production pathway, and use the resulting cost and system parameters as inputs into the H2A discounted cash flow model to project the production cost of H₂ ($/kgH₂). Five technologies were analyzed as part of the project and are summarized in this report: Proton Exchange Membrane technology (PEM), High temperature solid oxide electrolysis cell technology (SOEC), Dark fermentation of biomass for H₂ production, H₂ production via Monolithic Piston-Type Reactors with rapid swing reforming and regeneration reactions, and Reformer-Electrolyzer-Purifier (REP) technology developed by Fuel Cell Energy, Inc. (FCE).

Interleukin-2 signaling pathway analysis by quantitative phosphoproteomics

DEFF Research Database (Denmark)

Osinalde, Nerea; Moss, Helle; Arrizabalaga, Onetsine

2011-01-01

among which 79 were found with increased abundance in the tyrosine-phosphorylated complexes, including several previously not reported IL-2 downstream effectors. Combinatorial site-specific phosphoproteomic analysis resulted in identification of 99 phosphorylated sites mapping to the identified proteins...... with increased abundance in the tyrosine-phosphorylated complexes, of which 34 were not previously described. In addition, chemical inhibition of the identified IL-2-mediated JAK, PI3K and MAPK signaling pathways, resulted in distinct alteration on the IL-2 dependent proliferation....

Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer

Directory of Open Access Journals (Sweden)

Chung Jae

2009-06-01

Full Text Available Abstract Background Cisplatin and carboplatin are the primary first-line therapies for the treatment of ovarian cancer. However, resistance to these platinum-based drugs occurs in the large majority of initially responsive tumors, resulting in fully chemoresistant, fatal disease. Although the precise mechanism(s underlying the development of platinum resistance in late-stage ovarian cancer patients currently remains unknown, CpG-island (CGI methylation, a phenomenon strongly associated with aberrant gene silencing and ovarian tumorigenesis, may contribute to this devastating condition. Methods To model the onset of drug resistance, and investigate DNA methylation and gene expression alterations associated with platinum resistance, we treated clonally derived, drug-sensitive A2780 epithelial ovarian cancer cells with increasing concentrations of cisplatin. After several cycles of drug selection, the isogenic drug-sensitive and -resistant pairs were subjected to global CGI methylation and mRNA expression microarray analyses. To identify chemoresistance-associated, biological pathways likely impacted by DNA methylation, promoter CGI methylation and mRNA expression profiles were integrated and subjected to pathway enrichment analysis. Results Promoter CGI methylation revealed a positive association (Spearman correlation of 0.99 between the total number of hypermethylated CGIs and GI50 values (i.e., increased drug resistance following successive cisplatin treatment cycles. In accord with that result, chemoresistance was reversible by DNA methylation inhibitors. Pathway enrichment analysis revealed hypermethylation-mediated repression of cell adhesion and tight junction pathways and hypomethylation-mediated activation of the cell growth-promoting pathways PI3K/Akt, TGF-beta, and cell cycle progression, which may contribute to the onset of chemoresistance in ovarian cancer cells. Conclusion Selective epigenetic disruption of distinct biological

Hepatic Proteomic Analysis Revealed Altered Metabolic Pathways in Insulin Resistant Akt1+/-/Akt2-/-Mice

Science.gov (United States)

Pedersen, Brian A; Wang, Weiwen; Taylor, Jared F; Khattab, Omar S; Chen, Yu-Han; Edwards, Robert A; Yazdi, Puya G; Wang, Ping H

2015-01-01

Objective The aim of this study was to identify liver proteome changes in a mouse model of severe insulin resistance and markedly decreased leptin levels. Methods Two-dimensional differential gel electrophoresis was utilized to identify liver proteome changes in AKT1+/-/AKT2-/- mice. Proteins with altered levels were identified with tandem mass spectrometry. Ingenuity Pathway analysis was performed for the interpretation of the biological significance of the observed proteomic changes. Results 11 proteins were identified from 2 biological replicates to be differentially expressed by a ratio of at least 1.3 between age-matched insulin resistant (Akt1+/-/Akt2-/-) and wild type mice. Albumin and mitochondrial ornithine aminotransferase were detected from multiple spots, which suggest post-translational modifications. Enzymes of the urea cycle were common members of top regulated pathways. Conclusion Our results help to unveil the regulation of the liver proteome underlying altered metabolism in an animal model of severe insulin resistance. PMID:26455965

Cloning and Expression Analysis of MEP Pathway Enzyme-encoding Genes in Osmanthus fragrans

Directory of Open Access Journals (Sweden)

Chen Xu

2016-09-01

Full Text Available The 2-C-methyl-d-erythritol 4-phosphate (MEP pathway is responsible for the biosynthesis of many crucial secondary metabolites, such as carotenoids, monoterpenes, plastoquinone, and tocopherols. In this study, we isolated and identified 10 MEP pathway genes in the important aromatic plant sweet osmanthus (Osmanthus fragrans. Multiple sequence alignments revealed that 10 MEP pathway genes shared high identities with other reported proteins. The genes showed distinctive expression profiles in various tissues, or at different flower stages and diel time points. The qRT-PCR results demonstrated that these genes were highly expressed in inflorescences, which suggested a tissue-specific transcript pattern. Our results also showed that OfDXS1, OfDXS2, and OfHDR1 had a clear diurnal oscillation pattern. The isolation and expression analysis provides a strong foundation for further research on the MEP pathway involved in gene function and molecular evolution, and improves our understanding of the molecular mechanism underlying this pathway in plants.

Metabolic pathway analysis and kinetic studies for production of nattokinase in Bacillus subtilis.

Science.gov (United States)

Unrean, Pornkamol; Nguyen, Nhung H A

2013-01-01

We have constructed a reaction network model of Bacillus subtilis. The model was analyzed using a pathway analysis tool called elementary mode analysis (EMA). The analysis tool was used to study the network capabilities and the possible effects of altered culturing conditions on the production of a fibrinolytic enzyme, nattokinase (NK) by B. subtilis. Based on all existing metabolic pathways, the maximum theoretical yield for NK synthesis in B. subtilis under different substrates and oxygen availability was predicted and the optimal culturing condition for NK production was identified. To confirm model predictions, experiments were conducted by testing these culture conditions for their influence on NK activity. The optimal culturing conditions were then applied to batch fermentation, resulting in high NK activity. The EMA approach was also applied for engineering B. subtilis metabolism towards the most efficient pathway for NK synthesis by identifying target genes for deletion and overexpression that enable the cell to produce NK at the maximum theoretical yield. The consistency between experiments and model predictions proves the feasibility of EMA being used to rationally design culture conditions and genetic manipulations for the efficient production of desired products.

Association genetics and transcriptome analysis reveal a gibberellin-responsive pathway involved in regulating photosynthesis.

Science.gov (United States)

Xie, Jianbo; Tian, Jiaxing; Du, Qingzhang; Chen, Jinhui; Li, Ying; Yang, Xiaohui; Li, Bailian; Zhang, Deqiang

2016-05-01

Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights

A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia.

LENUS (Irish Health Repository)

Jia, Peilin

2012-02-01

After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases.

Integrated bioinformatic analysis unveils significant genes and pathways in the pathogenesis of supratentorial primitive neuroectodermal tumor

Directory of Open Access Journals (Sweden)

Wang G

2018-04-01

Full Text Available Guang-Yu Wang,1,* Ling Li,2,* Bo Liu,1 Xiao Han,1 Chun-Hua Wang,1 Ji-Wen Wang3 1Department of Neurosurgery, 2Department of Pediatrics, Qilu Children’s Hospital of Shandong University, Jinan, Shandong, 3Department of Neurology, Shanghai Children’s Medical Center, Shanghai Jiaotong University School of Medicine, Pudong New District, Shanghai, People’s Republic of China *These authors contributed equally to this work Purpose: This study aimed to explore significant genes and pathways involved in the pathogenesis of supratentorial primitive neuroectodermal tumor (sPNET. Materials and methods: Gene expression profile of GSE14295 was downloaded from publicly available Gene Expression Omnibus (GEO database. Differentially expressed genes (DEGs were screened out in primary sPNET samples compared with normal fetal and adult brain reference samples (sPNET vs fetal brain and sPNET vs adult brain. Pathway enrichment analysis of these DEGs was conducted, followed by protein–protein interaction (PPI network construction and significant module selection. Additionally, transcription factors (TFs regulating the common DEGs in the two comparison groups were identified, and the regulatory network was constructed. Results: In total, 526 DEGs (99 up- and 427 downregulated in sPNET vs fetal brain and 815 DEGs (200 up- and 615 downregulated in sPNET vs adult brain were identified. DEGs in sPNET vs fetal brain and sPNET vs adult brain were associated with calcium signaling pathway, cell cycle, and p53 signaling pathway. CDK1, CDC20, BUB1B, and BUB1 were hub nodes in the PPI networks of DEGs in sPNET vs fetal brain and sPNET vs adult brain. Significant modules were extracted from the PPI networks. In addition, 64 upregulated and 200 downregulated overlapping DEGs were identified in both sPNET vs fetal brain and sPNET vs adult brain. The genes involved in the regulatory network upon overlapping DEGs and the TFs were correlated with calcium signaling pathway

User Interface Requirements for Web-Based Integrated Care Pathways: Evidence from the Evaluation of an Online Care Pathway Investigation Tool.

Science.gov (United States)

Balatsoukas, Panos; Williams, Richard; Davies, Colin; Ainsworth, John; Buchan, Iain

2015-11-01

Integrated care pathways (ICPs) define a chronological sequence of steps, most commonly diagnostic or treatment, to be followed in providing care for patients. Care pathways help to ensure quality standards are met and to reduce variation in practice. Although research on the computerisation of ICP progresses, there is still little knowledge on what are the requirements for designing user-friendly and usable electronic care pathways, or how users (normally health care professionals) interact with interfaces that support design, analysis and visualisation of ICPs. The purpose of the study reported in this paper was to address this gap by evaluating the usability of a novel web-based tool called COCPIT (Collaborative Online Care Pathway Investigation Tool). COCPIT supports the design, analysis and visualisation of ICPs at the population level. In order to address the aim of this study, an evaluation methodology was designed based on heuristic evaluations and a mixed method usability test. The results showed that modular visualisation and direct manipulation of information related to the design and analysis of ICPs is useful for engaging and stimulating users. However, designers should pay attention to issues related to the visibility of the system status and the match between the system and the real world, especially in relation to the display of statistical information about care pathways and the editing of clinical information within a care pathway. The paper concludes with recommendations for interface design.

Radioresistance-related signaling pathways in nasopharyngeal carcinoma cells

International Nuclear Information System (INIS)

Guo Ya; Zhu Xiaodong; Qu Song; Su Fang; Wang Qi; Zhang Wei

2011-01-01

Objective: To study the difference of gene expression profile between the radioresistant human nasopharyngeal carcinoma cell line CNE-2R and CNE-2, and to screen the signaling pathway associated with radioresistance of nasopharyngeal carcinoma. Methods: The radioresistant nasopharyngeal carcinoma cell line CNE-2R was constructed from the original cell line CNE-2. CNE-2R and CNE-2 cells were cultured and administered with 60 Co γ-ray irradiation at the dose of 400 cGy for 15 times. Human-6v 3.0 whole genome expression profile was used to screen the differentially expressed genes. Bioinformatic analysis was used to identify the pathways related to radioresistance. Results: The number of the differentially expressed genes that were found in these 2 experiments was 374. The Kegg pathway and Biocarta pathway analysis of the differentially expressed genes showed the biological importance of Toll-like receptor signaling pathway and IL-1 R-mediated signal transduction pathway to the radioresistance of the CNE-2R cells and the significant differences of 13 genes in these 2 pathways,including JUN, MYD88, CCL5, CXCL10, STAT1, LY96, FOS, CCL3, IL-6, IL-8, IL-1α, IL-1β, and IRAK2 (t=13.47-66.57, P<0.05). Conclusions: Toll-like receptor signaling pathway and IL-1R-mediated signal transduction pathway might be related to the occurrence of radioresistance. (authors)

Phase analysis in gated blood pool tomography. Detection of accessory conduction pathway

Energy Technology Data Exchange (ETDEWEB)

Nakajima, Kenichi; Bunko, Hisashi; Tada, Akira; Taki, Junichi; Nanbu, Ichiro (Kanazawa Univ. (Japan). School of Medicine)

1984-02-01

Phase analysis of gated blood pool study has been applied to detect the site of accessory conduction pathway (ACP) in the Wolff-Parkinson-White (WPW) syndrome; however, there was a limitation to detect the precise location of ACP by phase analysis alone. In this study, we applied phase analysis to gated blood pool tomography using seven pin hole tomography (7PT) and gated emission computed tomography (GECT) in 21 patients with WPW syndrome and 3 normal subjects. In 17 patients, the sites of ACPs were confirmed by epicardial mapping and the result of the surgical division of ACP. In 7PT, the site of ACP grossly agreed to the abnormal initial phase in phase image in 5 out of 6 patients with left cardiac type. In GECT, phase images were generated in short axial, vertical and horizontal long axial sections. In 8 out of 9 patients, the site of ACP was correctly identified by phase images, and in a patient who had two ACPs, initial phase corresponded to one of the two locations. Phase analysis of gated blood pool tomography has advantages for avoiding overlap of blood pools and for estimating three-dimensional propagation of the contraction, and can be a good adjunctive method in patients with WPW syndrome.

Normal mode-guided transition pathway generation in proteins.

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Byung Ho Lee

Full Text Available The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

Radical SAM, A Novel Protein Superfamily Linking Unresolved Steps in Familiar Biosynthetic Pathways with Radical Mechanisms: Functional Characterization Using New Analysis and Information Visualization Methods

Energy Technology Data Exchange (ETDEWEB)

Sofia, Heidi J.; Chen, Guang; Hetzler, Elizabeth G.; Reyes Spindola, Jorge F.; Miller, Nancy E.

2001-03-01

A large protein superfamily with over 500 members has been discovered and analyzed using powerful new bioinformatics and information visualization methods. Evidence exists that these proteins generate a 5?-deoxyadenosyl radical by reductive cleavage of S-adenosylmethionine (SAM) through an unusual Fe-S center. Radical SAM superfamily proteins function in DNA precursor, vitamin, cofactor, antibiotic, and herbicide biosynthesis in a collection of basic and familiar pathways. One of the members is interferon-inducible and is considered a candidate drug target for osteoporosis. The identification of this superfamily suggests that radical-based catalysis is important in a number of previously well-studied but unresolved biochemical pathways.

Comparative expression pathway analysis of human and canine mammary tumors

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Marconato Laura

2009-03-01

Full Text Available Abstract Background Spontaneous tumors in dog have been demonstrated to share many features with their human counterparts, including relevant molecular targets, histological appearance, genetics, biological behavior and response to conventional treatments. Mammary tumors in dog therefore provide an attractive alternative to more classical mouse models, such as transgenics or xenografts, where the tumour is artificially induced. To assess the extent to which dog tumors represent clinically significant human phenotypes, we performed the first genome-wide comparative analysis of transcriptional changes occurring in mammary tumors of the two species, with particular focus on the molecular pathways involved. Results We analyzed human and dog gene expression data derived from both tumor and normal mammary samples. By analyzing the expression levels of about ten thousand dog/human orthologous genes we observed a significant overlap of genes deregulated in the mammary tumor samples, as compared to their normal counterparts. Pathway analysis of gene expression data revealed a great degree of similarity in the perturbation of many cancer-related pathways, including the 'PI3K/AKT', 'KRAS', 'PTEN', 'WNT-beta catenin' and 'MAPK cascade'. Moreover, we show that the transcriptional relationships between different gene signatures observed in human breast cancer are largely maintained in the canine model, suggesting a close interspecies similarity in the network of cancer signalling circuitries. Conclusion Our data confirm and further strengthen the value of the canine mammary cancer model and open up new perspectives for the evaluation of novel cancer therapeutics and the development of prognostic and diagnostic biomarkers to be used in clinical studies.

An evaluation of the implementation of maternal obesity pathways of care: a mixed methods study with data integration.

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Nicola Heslehurst

Full Text Available Maternal obesity has multiple associated risks and requires substantial intervention. This research evaluated the implementation of maternal obesity care pathways from multiple stakeholder perspectives.A simultaneous mixed methods model with data integration was used. Three component studies were given equal priority. 1: Semi-structured qualitative interviews explored obese pregnant women's experiences of being on the pathways. 2: A quantitative and qualitative postal survey explored healthcare professionals' experiences of delivering the pathways. 3: A case note audit quantitatively assessed pathway compliance. Data were integrated using following a thread and convergence coding matrix methods to search for agreement and disagreement between studies.Study 1: Four themes were identified: women's overall (positive and negative views of the pathways; knowledge and understanding of the pathways; views on clinical and weight management advice and support; and views on the information leaflet. Key results included positive views of receiving additional clinical care, negative experiences of risk communication, and weight management support was considered a priority. Study 2: Healthcare professionals felt the pathways were worthwhile, facilitated good practice, and increased confidence. Training was consistently identified as being required. Healthcare professionals predominantly focussed on women's response to sensitive obesity communication. Study 3: There was good compliance with antenatal clinical interventions. However, there was poor compliance with public health and postnatal interventions. There were some strong areas of agreement between component studies which can inform future development of the pathways. However, disagreement between studies included a lack of shared priorities between healthcare professionals and women, different perspectives on communication issues, and different perspectives on women's prioritisation of weight

An Evaluation of the Implementation of Maternal Obesity Pathways of Care: A Mixed Methods Study with Data Integration

Science.gov (United States)

Heslehurst, Nicola; Dinsdale, Sarah; Sedgewick, Gillian; Simpson, Helen; Sen, Seema; Summerbell, Carolyn Dawn; Rankin, Judith

2015-01-01

Objectives Maternal obesity has multiple associated risks and requires substantial intervention. This research evaluated the implementation of maternal obesity care pathways from multiple stakeholder perspectives. Study Design A simultaneous mixed methods model with data integration was used. Three component studies were given equal priority. 1: Semi-structured qualitative interviews explored obese pregnant women’s experiences of being on the pathways. 2: A quantitative and qualitative postal survey explored healthcare professionals’ experiences of delivering the pathways. 3: A case note audit quantitatively assessed pathway compliance. Data were integrated using following a thread and convergence coding matrix methods to search for agreement and disagreement between studies. Results Study 1: Four themes were identified: women’s overall (positive and negative) views of the pathways; knowledge and understanding of the pathways; views on clinical and weight management advice and support; and views on the information leaflet. Key results included positive views of receiving additional clinical care, negative experiences of risk communication, and weight management support was considered a priority. Study 2: Healthcare professionals felt the pathways were worthwhile, facilitated good practice, and increased confidence. Training was consistently identified as being required. Healthcare professionals predominantly focussed on women’s response to sensitive obesity communication. Study 3: There was good compliance with antenatal clinical interventions. However, there was poor compliance with public health and postnatal interventions. There were some strong areas of agreement between component studies which can inform future development of the pathways. However, disagreement between studies included a lack of shared priorities between healthcare professionals and women, different perspectives on communication issues, and different perspectives on women�

Ontology modeling for generation of clinical pathways

Directory of Open Access Journals (Sweden)

Jasmine Tehrani

2012-12-01

Full Text Available Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the

Using Formal Concept Analysis to Create Pathways through Museum Collections

DEFF Research Database (Denmark)

Wray, Tim; Eklund, Peter

2014-01-01

This paper presents A Place for Art - an iPad app that allows users to explore an art collection via semantically linked pathways that are generated using Formal Concept Analysis. The app embraces the information seeking approach of exploration and is based on the idea that showing context...... and relationships among objects in a museum collection augments an interpretive experience. The fundamental interaction metaphor inherent in A Place for Art relies on Formal Concept Analysis so the interface has embedded within it the semantic clustering features of machine learning in artificial intelligence....

Thermodynamics of metabolic pathways for penicillin production: Analysis of thermodynamic feasibility and free energy changes during fed-batch cultivation

DEFF Research Database (Denmark)

Pissarra, P.D.; Nielsen, Jens Bredal

1997-01-01

This paper describes the thermodynamic analysis of pathways related to penicillin production in Penicillium chrysogenum. First a thermodynamic feasibility analysis is performed of the L-lysine pathway of which one of the precursors for penicillin biosynthesis (alpha-aminoadipic acid......) is an intermediate. It is found that the L-lysine pathway in P. chrysogenum is thermodynamically feasible and that the calculated standard Gibbs free energy values of the two enzymes controlling the pathway flux indicate that they operate far from equilibrium. It is therefore proposed that the regulation of alpha......-aminoadipate reductase by lysine is important to maintain a high concentration of alpha-aminoadipate in order to direct the carbon flux to penicillin production. Secondly the changes in Gibbs free energy in the penicillin biosynthetic pathway during fed-batch cultivation were studied. The analysis showed that all...

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles.

Science.gov (United States)

Kitayama, Tomoya; Kinoshita, Ayako; Sugimoto, Masahiro; Nakayama, Yoichi; Tomita, Masaru

2006-07-17

In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems. The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations. The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles.

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles

Directory of Open Access Journals (Sweden)

Sugimoto Masahiro

2006-07-01

Full Text Available Abstract Background In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems. Results The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations. Conclusion The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles.

Life cycle analysis of transportation fuel pathways

Energy Technology Data Exchange (ETDEWEB)

NONE

2012-02-24

The purpose of this work is to improve the understanding of the concept of life cycle analysis (LCA) of transportation fuels and some of its pertinent issues among non-technical people, senior managers, and policy makers. This work should provide some guidance to nations considering LCA-based policies and to people who are affected by existing policies or those being developed. While the concept of employing LCA to evaluate fuel options is simple and straightforward, the act of putting the concept into practice is complex and fraught with issues. Policy makers need to understand the limitations inherent in carrying out LCA work for transportation fuel systems. For many systems, even those that have been employed for a 100 years, there is a lack of sound data on the performance of those systems. Comparisons between systems should ideally be made using the same tool, so that differences caused by system boundaries, allocation processes, and temporal issues can be minimized (although probably not eliminated). Comparing the results for fuel pathway 1 from tool A to those of fuel system 2 from tool B introduces significant uncertainty into the results. There is also the question of the scale of system changes. LCA will give more reliable estimates when it is used to examine small changes in transportation fuel pathways than when used to estimate large scale changes that replace current pathways with completely new pathways. Some LCA tools have been developed recently primarily for regulatory purposes. These tools may deviate from ISO principles in order to facilitate simplicity and ease of use. In a regulatory environment, simplicity and ease of use are worthy objectives and in most cases there is nothing inherently wrong with this approach, particularly for assessing relative performance. However, the results of these tools should not be confused with, or compared to, the results that are obtained from a more complex and rigorous ISO compliant LCA. It should be

Integrated pathway clusters with coherent biological themes for target prioritisation.

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Yi-An Chen

Full Text Available Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

Assembly of inflammation-related genes for pathway-focused genetic analysis.

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Matthew J Loza

2007-10-01

Full Text Available Recent identifications of associations between novel variants in inflammation-related genes and several common diseases emphasize the need for systematic evaluations of these genes in disease susceptibility. Considering that many genes are involved in the complex inflammation responses and many genetic variants in these genes have the potential to alter the functions and expression of these genes, we assembled a list of key inflammation-related genes to facilitate the identification of genetic associations of diseases with an inflammation-related etiology. We first reviewed various phases of inflammation responses, including the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. Assisted by the Ingenuity Pathway Analysis, we then identified 17 functional sub-pathways that are involved in one or multiple phases. This organization would greatly increase the chance of detecting gene-gene interactions by hierarchical clustering of genes with their functional closeness in a pathway. Finally, as an example application, we have developed tagging single nucleotide polymorphism (tSNP arrays for populations of European and African descent to capture all the common variants of these key inflammation-related genes. Assays of these tSNPs have been designed and assembled into two Affymetrix ParAllele customized chips, one each for European (12,011 SNPs and African (21,542 SNPs populations. These tSNPs have greater coverage for these inflammation-related genes compared to the existing genome-wide arrays, particularly in the African population. These tSNP arrays can facilitate systematic evaluation of inflammation pathways in disease susceptibility. For additional applications, other genotyping platforms could also be employed. For existing genome-wide association data, this list of key inflammation-related genes and

Sensitivity analysis of intracellular signaling pathway kinetics predicts targets for stem cell fate control.

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Alborz Mahdavi

2007-07-01

Full Text Available Directing stem cell fate requires knowledge of how signaling networks integrate temporally and spatially segregated stimuli. We developed and validated a computational model of signal transducer and activator of transcription-3 (Stat3 pathway kinetics, a signaling network involved in embryonic stem cell (ESC self-renewal. Our analysis identified novel pathway responses; for example, overexpression of the receptor glycoprotein-130 results in reduced pathway activation and increased ESC differentiation. We used a systematic in silico screen to identify novel targets and protein interactions involved in Stat3 activation. Our analysis demonstrates that signaling activation and desensitization (the inability to respond to ligand restimulation is regulated by balancing the activation state of a distributed set of parameters including nuclear export of Stat3, nuclear phosphatase activity, inhibition by suppressor of cytokine signaling, and receptor trafficking. This knowledge was used to devise a temporally modulated ligand delivery strategy that maximizes signaling activation and leads to enhanced ESC self-renewal.

Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genes

DEFF Research Database (Denmark)

Pers, Tune H; Timshel, Pascal; Ripke, Stephan

2016-01-01

Over 100 associated genetic loci have been robustly associated with schizophrenia. Gene prioritization and pathway analysis have focused on a priori hypotheses and thus may have been unduly influenced by prior assumptions and missed important causal genes and pathways. Using a data-driven approac...

CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures.

Science.gov (United States)

Chovancova, Eva; Pavelka, Antonin; Benes, Petr; Strnad, Ondrej; Brezovsky, Jan; Kozlikova, Barbora; Gora, Artur; Sustr, Vilem; Klvana, Martin; Medek, Petr; Biedermannova, Lada; Sochor, Jiri; Damborsky, Jiri

2012-01-01

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.

CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures.

Directory of Open Access Journals (Sweden)

Eva Chovancova

Full Text Available Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.

CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures

Science.gov (United States)

Strnad, Ondrej; Brezovsky, Jan; Kozlikova, Barbora; Gora, Artur; Sustr, Vilem; Klvana, Martin; Medek, Petr; Biedermannova, Lada; Sochor, Jiri; Damborsky, Jiri

2012-01-01

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz. PMID:23093919

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

DEFF Research Database (Denmark)

Huang, Sijia; Chong, Nicole; Lewis, Nathan

2016-01-01

diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study...... metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease...

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies

KAUST Repository

Cannistraci, Carlo

2013-02-14

Background: Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent. Methods. Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0). Results. The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies. Conclusions: The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The

An optimization model for metabolic pathways.

Science.gov (United States)

Planes, F J; Beasley, J E

2009-10-15

Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

Science.gov (United States)

Yang, Yunlai; Arouri, Khaled

2016-03-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways.

Science.gov (United States)

Yang, Yunlai; Arouri, Khaled

2016-03-11

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

Directory of Open Access Journals (Sweden)

Yang X

2018-03-01

Full Text Available Xiao Yang,1 Shaoming Zhu,2 Li Li,3 Li Zhang,1 Shu Xian,1 Yanqing Wang,1 Yanxiang Cheng1 1Department of Obstetrics and Gynecology, 2Department of Urology, Renmin Hospital of Wuhan University, 3Department of Pharmacology, Wuhan University Health Science Center, Wuhan, Hubei, People’s Republic of China Background: The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms. Results: The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs and were deeply analyzed by bioinformatics methods. The gene ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein–protein interaction (PPI networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the

A new method for finding the minimum free energy pathway of ions and small molecule transportation through protein based on 3D-RISM theory and the string method

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Yoshida, Norio

2018-05-01

A new method for finding the minimum free energy pathway (MFEP) of ions and small molecule transportation through a protein based on the three-dimensional reference interaction site model (3D-RISM) theory combined with the string method has been proposed. The 3D-RISM theory produces the distribution function, or the potential of mean force (PMF), for transporting substances around the given protein structures. By applying the string method to the PMF surface, one can readily determine the MFEP on the PMF surface. The method has been applied to consider the Na+ conduction pathway of channelrhodopsin as an example.

Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility.

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Yen Ling Low

2010-07-01

Full Text Available Despite the central role of estrogen exposure in breast and endometrial cancer development and numerous studies of genes in the estrogen metabolic pathway, polymorphisms within the pathway have not been consistently associated with these cancers. We posit that this is due to the complexity of multiple weak genetic effects within the metabolic pathway that can only be effectively detected through multi-variant analysis. We conducted a comprehensive association analysis of the estrogen metabolic pathway by interrogating 239 tagSNPs within 35 genes of the pathway in three tumor samples. The discovery sample consisted of 1,596 breast cancer cases, 719 endometrial cancer cases, and 1,730 controls from Sweden; and the validation sample included 2,245 breast cancer cases and 1,287 controls from Finland. We performed admixture maximum likelihood (AML-based global tests to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three sub-pathways for androgen synthesis, androgen-to-estrogen conversion, and estrogen removal. In the discovery sample, although no single polymorphism was significant after correction for multiple testing, the pathway-based AML global test suggested association with both breast (p(global = 0.034 and endometrial (p(global = 0.052 cancers. Further testing revealed the association to be focused on polymorphisms within the androgen-to-estrogen conversion sub-pathway, for both breast (p(global = 0.008 and endometrial cancer (p(global = 0.014. The sub-pathway association was validated in the Finnish sample of breast cancer (p(global = 0.015. Further tumor subtype analysis demonstrated that the association of the androgen-to-estrogen conversion sub-pathway was confined to postmenopausal women with sporadic estrogen receptor positive tumors (p(global = 0.0003. Gene-based AML analysis suggested CYP19A1 and UGT2B4 to be the major players within the sub-pathway. Our study indicates that the composite

Network features and pathway analyses of a signal transduction cascade

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Ryoji Yanashima

2009-05-01

Full Text Available The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

Analysis of PIK3CA Mutations and Activation Pathways in Triple Negative Breast Cancer.

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Paolo Cossu-Rocca

Full Text Available Triple Negative Breast Cancer (TNBC accounts for 12-24% of all breast carcinomas, and shows worse prognosis compared to other breast cancer subtypes. Molecular studies demonstrated that TNBCs are a heterogeneous group of tumors with different clinical and pathologic features, prognosis, genetic-molecular alterations and treatment responsivity. The PI3K/AKT is a major pathway involved in the regulation of cell survival and proliferation, and is the most frequently altered pathway in breast cancer, apparently with different biologic impact on specific cancer subtypes. The most common genetic abnormality is represented by PIK3CA gene activating mutations, with an overall frequency of 20-40%. The aims of our study were to investigate PIK3CA gene mutations on a large series of TNBC, to perform a wider analysis on genetic alterations involving PI3K/AKT and BRAF/RAS/MAPK pathways and to correlate the results with clinical-pathologic data.PIK3CA mutation analysis was performed by using cobas® PIK3CA Mutation Test. EGFR, AKT1, BRAF, and KRAS genes were analyzed by sequencing. Immunohistochemistry was carried out to identify PTEN loss and to investigate for PI3K/AKT pathways components.PIK3CA mutations were detected in 23.7% of TNBC, whereas no mutations were identified in EGFR, AKT1, BRAF, and KRAS genes. Moreover, we observed PTEN loss in 11.3% of tumors. Deregulation of PI3K/AKT pathways was revealed by consistent activation of pAKT and p-p44/42 MAPK in all PIK3CA mutated TNBC.Our data shows that PIK3CA mutations and PI3K/AKT pathway activation are common events in TNBC. A deeper investigation on specific TNBC genomic abnormalities might be helpful in order to select patients who would benefit from current targeted therapy strategies.

Analysis of PIK3CA Mutations and Activation Pathways in Triple Negative Breast Cancer.

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Cossu-Rocca, Paolo; Orrù, Sandra; Muroni, Maria Rosaria; Sanges, Francesca; Sotgiu, Giovanni; Ena, Sara; Pira, Giovanna; Murgia, Luciano; Manca, Alessandra; Uras, Maria Gabriela; Sarobba, Maria Giuseppina; Urru, Silvana; De Miglio, Maria Rosaria

2015-01-01

Triple Negative Breast Cancer (TNBC) accounts for 12-24% of all breast carcinomas, and shows worse prognosis compared to other breast cancer subtypes. Molecular studies demonstrated that TNBCs are a heterogeneous group of tumors with different clinical and pathologic features, prognosis, genetic-molecular alterations and treatment responsivity. The PI3K/AKT is a major pathway involved in the regulation of cell survival and proliferation, and is the most frequently altered pathway in breast cancer, apparently with different biologic impact on specific cancer subtypes. The most common genetic abnormality is represented by PIK3CA gene activating mutations, with an overall frequency of 20-40%. The aims of our study were to investigate PIK3CA gene mutations on a large series of TNBC, to perform a wider analysis on genetic alterations involving PI3K/AKT and BRAF/RAS/MAPK pathways and to correlate the results with clinical-pathologic data. PIK3CA mutation analysis was performed by using cobas® PIK3CA Mutation Test. EGFR, AKT1, BRAF, and KRAS genes were analyzed by sequencing. Immunohistochemistry was carried out to identify PTEN loss and to investigate for PI3K/AKT pathways components. PIK3CA mutations were detected in 23.7% of TNBC, whereas no mutations were identified in EGFR, AKT1, BRAF, and KRAS genes. Moreover, we observed PTEN loss in 11.3% of tumors. Deregulation of PI3K/AKT pathways was revealed by consistent activation of pAKT and p-p44/42 MAPK in all PIK3CA mutated TNBC. Our data shows that PIK3CA mutations and PI3K/AKT pathway activation are common events in TNBC. A deeper investigation on specific TNBC genomic abnormalities might be helpful in order to select patients who would benefit from current targeted therapy strategies.

Parallel imaging of Drosophila embryos for quantitative analysis of genetic perturbations of the Ras pathway

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Yogesh Goyal

2017-07-01

Full Text Available The Ras pathway patterns the poles of the Drosophila embryo by downregulating the levels and activity of a DNA-binding transcriptional repressor Capicua (Cic. We demonstrate that the spatiotemporal pattern of Cic during this signaling event can be harnessed for functional studies of mutations in the Ras pathway in human diseases. Our approach relies on a new microfluidic device that enables parallel imaging of Cic dynamics in dozens of live embryos. We found that although the pattern of Cic in early embryos is complex, it can be accurately approximated by a product of one spatial profile and one time-dependent amplitude. Analysis of these functions of space and time alone reveals the differential effects of mutations within the Ras pathway. Given the highly conserved nature of Ras-dependent control of Cic, our approach provides new opportunities for functional analysis of multiple sequence variants from developmental abnormalities and cancers.

A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility

DEFF Research Database (Denmark)

Damotte, V; Guillot-Noel, L; Patsopoulos, N A

2014-01-01

adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes...... in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell...... belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted...

Automated tool for virtual screening and pharmacology-based pathway prediction and analysis

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Sugandh Kumar

2017-10-01

Full Text Available The virtual screening is an effective tool for the lead identification in drug discovery. However, there are limited numbers of crystal structures available as compared to the number of biological sequences which makes (Structure Based Drug Discovery SBDD a difficult choice. The current tool is an attempt to automate the protein structure modelling and automatic virtual screening followed by pharmacology-based prediction and analysis. Starting from sequence(s, this tool automates protein structure modelling, binding site identification, automated docking, ligand preparation, post docking analysis and identification of hits in the biological pathways that can be modulated by a group of ligands. This automation helps in the characterization of ligands selectivity and action of ligands on a complex biological molecular network as well as on individual receptor. The judicial combination of the ligands binding different receptors can be used to inhibit selective biological pathways in a disease. This tool also allows the user to systemically investigate network-dependent effects of a drug or drug candidate.

Functional analysis of the MAPK pathways in fungi.

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Martínez-Soto, Domingo; Ruiz-Herrera, José

The Mitogen-Activated Protein Kinase (MAPK) signaling pathways constitute one of the most important and evolutionarily conserved mechanisms for the perception of extracellular information in all the eukaryotic organisms. The MAPK pathways are involved in the transfer to the cell of the information perceived from extracellular stimuli, with the final outcome of activation of different transcription factors that regulate gene expression in response to them. In all species of fungi, the MAPK pathways have important roles in their physiology and development; e.g. cell cycle control, mating, morphogenesis, response to different stresses, resistance to UV radiation and to temperature changes, cell wall assembly and integrity, degradation of cellular organelles, virulence, cell-cell signaling, fungus-plant interaction, and response to damage-associated molecular patterns (DAMPs). Considering the importance of the phylogenetically conserved MAPK pathways in fungi, an updated review of the knowledge on them is discussed in this article. This information reveals their importance, their distribution in fungal species evolutionarily distant and with different lifestyles, their organization and function, and the interactions occurring between different MAPK pathways, and with other signaling pathways, for the regulation of the most complex cellular processes. Copyright © 2017 Asociación Española de Micología. Publicado por Elsevier España, S.L.U. All rights reserved.

The Cardiopulmonary Effects of Ambient Air Pollution and Mechanistic Pathways: A Comparative Hierarchical Pathway Analysis

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Thomas, Duncan C.; Zhang, Junfeng; Kipen, Howard M.; Rich, David Q.; Zhu, Tong; Huang, Wei; Hu, Min; Wang, Guangfa; Wang, Yuedan; Zhu, Ping; Lu, Shou-En; Ohman-Strickland, Pamela; Diehl, Scott R.; Eckel, Sandrah P.

2014-01-01

Previous studies have investigated the associations between exposure to ambient air pollution and biomarkers of physiological pathways, yet little has been done on the comparison across biomarkers of different pathways to establish the temporal pattern of biological response. In the current study, we aim to compare the relative temporal patterns in responses of candidate pathways to different pollutants. Four biomarkers of pulmonary inflammation and oxidative stress, five biomarkers of systemic inflammation and oxidative stress, ten parameters of autonomic function, and three biomarkers of hemostasis were repeatedly measured in 125 young adults, along with daily concentrations of ambient CO, PM2.5, NO2, SO2, EC, OC, and sulfate, before, during, and after the Beijing Olympics. We used a two-stage modeling approach, including Stage I models to estimate the association between each biomarker and pollutant over each of 7 lags, and Stage II mixed-effect models to describe temporal patterns in the associations when grouping the biomarkers into the four physiological pathways. Our results show that candidate pathway groupings of biomarkers explained a significant amount of variation in the associations for each pollutant, and the temporal patterns of the biomarker-pollutant-lag associations varied across candidate pathways (p<0.0001) and were not linear (from lag 0 to lag 3: p = 0.0629, from lag 3 to lag 6: p = 0.0005). These findings suggest that, among this healthy young adult population, the pulmonary inflammation and oxidative stress pathway is the first to respond to ambient air pollution exposure (within 24 hours) and the hemostasis pathway responds gradually over a 2–3 day period. The initial pulmonary response may contribute to the more gradual systemic changes that likely ultimately involve the cardiovascular system. PMID:25502951

The cardiopulmonary effects of ambient air pollution and mechanistic pathways: a comparative hierarchical pathway analysis.

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Ananya Roy

Full Text Available Previous studies have investigated the associations between exposure to ambient air pollution and biomarkers of physiological pathways, yet little has been done on the comparison across biomarkers of different pathways to establish the temporal pattern of biological response. In the current study, we aim to compare the relative temporal patterns in responses of candidate pathways to different pollutants. Four biomarkers of pulmonary inflammation and oxidative stress, five biomarkers of systemic inflammation and oxidative stress, ten parameters of autonomic function, and three biomarkers of hemostasis were repeatedly measured in 125 young adults, along with daily concentrations of ambient CO, PM2.5, NO2, SO2, EC, OC, and sulfate, before, during, and after the Beijing Olympics. We used a two-stage modeling approach, including Stage I models to estimate the association between each biomarker and pollutant over each of 7 lags, and Stage II mixed-effect models to describe temporal patterns in the associations when grouping the biomarkers into the four physiological pathways. Our results show that candidate pathway groupings of biomarkers explained a significant amount of variation in the associations for each pollutant, and the temporal patterns of the biomarker-pollutant-lag associations varied across candidate pathways (p<0.0001 and were not linear (from lag 0 to lag 3: p = 0.0629, from lag 3 to lag 6: p = 0.0005. These findings suggest that, among this healthy young adult population, the pulmonary inflammation and oxidative stress pathway is the first to respond to ambient air pollution exposure (within 24 hours and the hemostasis pathway responds gradually over a 2-3 day period. The initial pulmonary response may contribute to the more gradual systemic changes that likely ultimately involve the cardiovascular system.

Disease-specific clinical pathways - are they feasible in primary care? A mixed-methods study.

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Grimsmo, Anders; Løhre, Audhild; Røsstad, Tove; Gjerde, Ingunn; Heiberg, Ina; Steinsbekk, Aslak

2018-04-12

To explore the feasibility of disease-specific clinical pathways when used in primary care. A mixed-method sequential exploratory design was used. First, merging and exploring quality interview data across two cases of collaboration between the specialist care and primary care on the introduction of clinical pathways for four selected chronic diseases. Secondly, using quantitative data covering a population of 214,700 to validate and test hypothesis derived from the qualitative findings. Primary care and specialist care collaborating to manage care coordination. Primary-care representatives expressed that their patients often have complex health and social needs that clinical pathways guidelines seldom consider. The representatives experienced that COPD, heart failure, stroke and hip fracture, frequently seen in hospitals, appear in low numbers in primary care. The quantitative study confirmed the extensive complexity among home healthcare nursing patients and demonstrated that, for each of the four selected diagnoses, a homecare nurse on average is responsible for preparing reception of the patient at home after discharge from hospital, less often than every other year. The feasibility of disease-specific pathways in primary care is limited, both from a clinical and organisational perspective, for patients with complex needs. The low prevalence in primary care of patients with important chronic conditions, needing coordinated care after hospital discharge, constricts transferring tasks from specialist care. Generic clinical pathways are likely to be more feasible and efficient for patients in this setting. Key points Clinical pathways in hospitals apply to single-disease guidelines, while more than 90% of the patients discharged to community health care for follow-up have multimorbidity. Primary care has to manage the health care of the patient holistically, with all his or her complex needs. Patients most frequently admitted to hospitals, i.e. patients with COPD

Metabolic pathway alignment between species using a comprehensive and flexible similarity measure

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de Ridder Dick

2008-12-01

Full Text Available Abstract Background Comparative analysis of metabolic networks in multiple species yields important information on their evolution, and has great practical value in metabolic engineering, human disease analysis, drug design etc. In this work, we aim to systematically search for conserved pathways in two species, quantify their similarities, and focus on the variations between them. Results We present an efficient framework, Metabolic Pathway Alignment and Scoring (M-PAS, for identifying and ranking conserved metabolic pathways. M-PAS aligns all reactions in entire metabolic networks of two species and assembles them into pathways, taking mismatches, gaps and crossovers into account. It uses a comprehensive scoring function, which quantifies pathway similarity such that we can focus on different pathways given different biological motivations. Using M-PAS, we detected 1198 length-four pathways fully conserved between Saccharomyces cerevisiae and Escherichia coli, and also revealed 1399 cases of a species using a unique route in otherwise highly conserved pathways. Conclusion Our method efficiently automates the process of exploring reaction arrangement possibilities, both between species and within species, to find conserved pathways. We not only reconstruct conventional pathways such as those found in KEGG, but also discover new pathway possibilities. Our results can help to generate hypotheses on missing reactions and manifest differences in highly conserved pathways, which is useful for biology and life science applications.

Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.

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Shchetynsky, Klementy; Diaz-Gallo, Lina-Marcella; Folkersen, Lasse; Hensvold, Aase Haj; Catrina, Anca Irinel; Berg, Louise; Klareskog, Lars; Padyukov, Leonid

2017-02-02

Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA). RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls. There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples. Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.

Boundary conditions for pathways, safety analysis and basic criteria for low-level radiation waste site selection

International Nuclear Information System (INIS)

Saverot, P.

1994-01-01

There are three successive periods in the life of a disposal facility: the operating period, the institutional control period and the unrestricted site access period. The purpose of safety analysis of the disposal facility is to ensure that the radiological impacts for each period in the life of the facility are acceptable under all circumstances. Founded on a deterministic approach, this analysis leads to a determination of the maximum quantity of each radionuclide present in the facility at the beginning of the institutional control period in order for the impacts to be considered acceptable. Safety analysis involves the calculation of the radiological impacts of a given radiological inventory under a selected scenario, from all plausible scenarios of radionuclide migration to the environment in both normal and accident conditions, and taking into account other specified variables. The calculation itself involves an assessment of the quantities of radionuclides that could be released to the environment under the specific scenario selected and following identified pathways, and a determination of the resultant exposure, both internal and external, to the public. An iterative approach is used in the performance of pathways analyses. If the pathways analyses result in unacceptable radiological impacts, either the radiological inventory of the site is reduced or barrier characteristics not previously factored into the analysis are taken into account. New pathways analyses are then performed until the results are within the acceptable range. Once accepted by the safety authorities, the radiological inventory becomes the radiological capacity, which is the approved quantities of specific radionuclides that may be disposed of at the site. The following elaborates on the boundary conditions used in safety analyses and describes the types of pathways analyses performed for a LLW disposal facility

In Silico Analysis of Putrefaction Pathways in Bacteria and Its Implication in Colorectal Cancer

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Harrisham Kaur

2017-11-01

Full Text Available Fermentation of undigested proteins in human gastrointestinal tract (gut by the resident microbiota, a process called bacterial putrefaction, can sometimes disrupt the gut homeostasis. In this process, essential amino acids (e.g., histidine, tryptophan, etc. that are required by the host may be utilized by the gut microbes. In addition, some of the products of putrefaction, like ammonia, putrescine, cresol, indole, phenol, etc., have been implicated in the disease pathogenesis of colorectal cancer (CRC. We have investigated bacterial putrefaction pathways that are known to be associated with such metabolites. Results of the comprehensive in silico analysis of the selected putrefaction pathways across bacterial genomes revealed presence of these pathways in limited bacterial groups. Majority of these bacteria are commonly found in human gut. These include Bacillus, Clostridium, Enterobacter, Escherichia, Fusobacterium, Salmonella, etc. Interestingly, while pathogens utilize almost all the analyzed pathways, commensals prefer putrescine and H2S production pathways for metabolizing the undigested proteins. Further, comparison of the putrefaction pathways in the gut microbiomes of healthy, carcinoma and adenoma datasets indicate higher abundances of putrefying bacteria in the carcinoma stage of CRC. The insights obtained from the present study indicate utilization of possible microbiome-based therapies to minimize the adverse effects of gut microbiome in enteric diseases.

Sensitivity and uncertainty analysis of the PATHWAY radionuclide transport model

International Nuclear Information System (INIS)

Otis, M.D.

1983-01-01

Procedures were developed for the uncertainty and sensitivity analysis of a dynamic model of radionuclide transport through human food chains. Uncertainty in model predictions was estimated by propagation of parameter uncertainties using a Monte Carlo simulation technique. Sensitivity of model predictions to individual parameters was investigated using the partial correlation coefficient of each parameter with model output. Random values produced for the uncertainty analysis were used in the correlation analysis for sensitivity. These procedures were applied to the PATHWAY model which predicts concentrations of radionuclides in foods grown in Nevada and Utah and exposed to fallout during the period of atmospheric nuclear weapons testing in Nevada. Concentrations and time-integrated concentrations of iodine-131, cesium-136, and cesium-137 in milk and other foods were investigated. 9 figs., 13 tabs

Identification of key genes and pathways associated with neuropathic pain in uninjured dorsal root ganglion by using bioinformatic analysis

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Chen CJ

2017-11-01

Full Text Available Chao-Jin Chen,* De-Zhao Liu,* Wei-Feng Yao, Yu Gu, Fei Huang, Zi-Qing Hei, Xiang Li Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China *These authors contributed equally to this work Purpose: Neuropathic pain is a complex chronic condition occurring post-nervous system damage. The transcriptional reprogramming of injured dorsal root ganglia (DRGs drives neuropathic pain. However, few comparative analyses using high-throughput platforms have investigated uninjured DRG in neuropathic pain, and potential interactions among differentially expressed genes (DEGs and pathways were not taken into consideration. The aim of this study was to identify changes in genes and pathways associated with neuropathic pain in uninjured L4 DRG after L5 spinal nerve ligation (SNL by using bioinformatic analysis.Materials and methods: The microarray profile GSE24982 was downloaded from the Gene Expression Omnibus database to identify DEGs between DRGs in SNL and sham rats. The prioritization for these DEGs was performed using the Toppgene database followed by gene ontology and pathway enrichment analyses. The relationships among DEGs from the protein interactive perspective were analyzed using protein–protein interaction (PPI network and module analysis. Real-time polymerase chain reaction (PCR and Western blotting were used to confirm the expression of DEGs in the rodent neuropathic pain model.Results: A total of 206 DEGs that might play a role in neuropathic pain were identified in L4 DRG, of which 75 were upregulated and 131 were downregulated. The upregulated DEGs were enriched in biological processes related to transcription regulation and molecular functions such as DNA binding, cell cycle, and the FoxO signaling pathway. Ctnnb1 protein had the highest connectivity degrees in the PPI network. The in vivo studies also validated that mRNA and protein levels of Ctnnb1 were

Groundwater flow analysis using mixed hybrid finite element method for radioactive waste disposal facilities

International Nuclear Information System (INIS)

Aoki, Hiroomi; Shimomura, Masanori; Kawakami, Hiroto; Suzuki, Shunichi

2011-01-01

In safety assessments of radioactive waste disposal facilities, ground water flow analysis are used for calculating the radionuclide transport pathway and the infiltration flow rate of groundwater into the disposal facilities. For this type of calculations, the mixed hybrid finite element method has been used and discussed about the accuracy of ones in Europe. This paper puts great emphasis on the infiltration flow rate of groundwater into the disposal facilities, and describes the accuracy of results obtained from mixed hybrid finite element method by comparing of local water mass conservation and the reliability of the element breakdown numbers among the mixed hybrid finite element method, finite volume method and nondegenerated finite element method. (author)

RNAseq analysis reveals pathways and candidate genes associated with salinity tolerance in a spaceflight-induced wheat mutant.

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Xiong, Hongchun; Guo, Huijun; Xie, Yongdun; Zhao, Linshu; Gu, Jiayu; Zhao, Shirong; Li, Junhui; Liu, Luxiang

2017-06-02

Salinity stress has become an increasing threat to food security worldwide and elucidation of the mechanism for salinity tolerance is of great significance. Induced mutation, especially spaceflight mutagenesis, is one important method for crop breeding. In this study, we show that a spaceflight-induced wheat mutant, named salinity tolerance 1 (st1), is a salinity-tolerant line. We report the characteristics of transcriptomic sequence variation induced by spaceflight, and show that mutations in genes associated with sodium ion transport may directly contribute to salinity tolerance in st1. Furthermore, GO and KEGG enrichment analysis of differentially expressed genes (DEGs) between salinity-treated st1 and wild type suggested that the homeostasis of oxidation-reduction process is important for salt tolerance in st1. Through KEGG pathway analysis, "Butanoate metabolism" was identified as a new pathway for salinity responses. Additionally, key genes for salinity tolerance, such as genes encoding arginine decarboxylase, polyamine oxidase, hormones-related, were not only salt-induced in st1 but also showed higher expression in salt-treated st1 compared with salt-treated WT, indicating that these genes may play important roles in salinity tolerance in st1. This study presents valuable genetic resources for studies on transcriptome variation caused by induced mutation and the identification of salt tolerance genes in crops.

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways.

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Beltrame, Luca; Calura, Enrica; Popovici, Razvan R; Rizzetto, Lisa; Guedez, Damariz Rivero; Donato, Michele; Romualdi, Chiara; Draghici, Sorin; Cavalieri, Duccio

2011-08-01

Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists. The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.

Data mining and pathway analysis of glucose-6-phosphate dehydrogenase with natural language processing

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Chen, Long; Zhang, Chunhua; Wang, Yanling; Li, Yuqian; Han, Qiaoqiao; Yang, Huixin; Zhu, Yuechun

2017-01-01

Human glucose-6-phosphate dehydrogenase (G6PD) is a crucial enzyme in the pentose phosphate pathway, and serves an important role in biosynthesis and the redox balance. G6PD deficiency is a major cause of neonatal jaundice and acute hemolyticanemia, and recently, G6PD has been associated with diseases including inflammation and cancer. The aim of the present study was to conduct a search of the National Center for Biotechnology Information PubMed library for articles discussing G6PD. Genes that were identified to be associated with G6PD were recorded, and the frequency at which each gene appeared was calculated. Gene ontology (GO), pathway and network analyses were then performed. A total of 98 G6PD-associated genes and 33 microRNAs (miRNAs) that potentially regulate G6PD were identified. The 98 G6PD-associated genes were then sub-classified into three functional groups by GO analysis, followed by analysis of function, pathway, network, and disease association. Out of the 47 signaling pathways identified, seven were significantly correlated with G6PD-associated genes. At least two out of four independent programs identified the 33 miRNAs that were predicted to target G6PD. miR-1207-5P, miR-1 and miR-125a-5p were predicted by all four software programs to target G6PD. The results of the present study revealed that dysregulation of G6PD was associated with cancer, autoimmune diseases, and oxidative stress-induced disorders. These results revealed the potential roles of G6PD-regulated signaling and metabolic pathways in the etiology of these diseases. PMID:28627690

Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

Directory of Open Access Journals (Sweden)

Cheng-Yu Yeh

2012-01-01

Full Text Available With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system.

Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology.

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Vivar, Juan C; Pemu, Priscilla; McPherson, Ruth; Ghosh, Sujoy

2013-08-01

Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association

Genome-Based Construction of the Metabolic Pathways of Orientia tsutsugamushi and Comparative Analysis within the Rickettsiales Order

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Chan-Ki Min

2008-01-01

Full Text Available Orientia tsutsugamushi, the causative agent of scrub typhus, is an obligate intracellular bacterium that belongs to the order of Rickettsiales. Recently, we have reported that O. tsutsugamushi has a unique genomic structure, consisting of highly repetitive sequences, and suggested that it may provide valuable insight into the evolution of intracellular bacteria. Here, we have used genomic information to construct the major metabolic pathways of O. tsutsugamushi and performed a comparative analysis of the metabolic genes and pathways of O. tsutsugamushi with other members of the Rickettsiales order. While O. tsutsugamushi has the largest genome among the members of this order, mainly due to the presence of repeated sequences, its metabolic pathways have been highly streamlined. Overall, the metabolic pathways of O. tsutsugamushi were similar to Rickettsia but there were notable differences in several pathways including carbohydrate metabolism, the TCA cycle, and the synthesis of cell wall components as well as in the transport systems. Our results will provide a useful guide to the postgenomic analysis of O. tsutsugamushi and lead to a better understanding of the virulence and physiology of this intracellular pathogen.

Diagnosis of accessory conduction pathway using ECG-gated emission CT analysis

International Nuclear Information System (INIS)

Misaki, Takuro; Mukai, Keiichi; Tsubota, Makoto; Iwa, Takashi; Nakajima, Ken-ichi; Hisada, Kin-ichi

1987-01-01

Pinpointing the location of accessory conduction pathway (ACP) is of great importance in the surgical treatment for Wolff-Parkinson-White (WPW) syndrome. For this purpose, this study explored the usefulness of ECG-gated emission computed tomography (Gated-ECT) in 30 patients who preoperatively underwent Gated-ECT. The site of earliest contraction at level of atrioventicular valves, obtained on tomographic phase analysis, was compared with the site of earliest activation, obtained on epicardial mapping during surgery. The concordance rate of the two methods was 94 % (28/30). Among them, one patient was found to have the association of corrected transposition of great arteries on Gated-ECT. Gated-ECT was, however, of limited value in differentiating right posterior ACP from right postseptal ACP. The discordance between the sites of earliest contraction and activation, which was observed in the two others, was likely due to decreased wall motion resulting from myocardial disturbance. Gated-ECT may have a diagnostic potential for the location of ACP, especially in view of providing images that corresponded to the surgical anatomy. (Namekawa, K.)

Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

DEFF Research Database (Denmark)

Thomassen, Mads; Tan, Qihua; Kruse, Torben

2008-01-01

ABSTRACT: BACKGROUND: Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent stud...

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

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Li, Chunquan; Han, Junwei; Yao, Qianlan; Zou, Chendan; Xu, Yanjun; Zhang, Chunlong; Shang, Desi; Zhou, Lingyun; Zou, Chaoxia; Sun, Zeguo; Li, Jing; Zhang, Yunpeng; Yang, Haixiu; Gao, Xu; Li, Xia

2013-05-01

Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Cost-effectiveness analysis of a postoperative clinical care pathway in head and neck surgery with microvascular reconstruction

OpenAIRE

Dautremont, Jonathan F; Rudmik, Luke R; Yeung, Justin; Asante, Tiffany; Nakoneshny, Steve C; Hoy, Monica; Lui, Amanda; Chandarana, Shamir P; Matthews, Thomas W; Schrag, Christiaan; Dort, Joseph C

2013-01-01

Background The objective of this study is to evaluate the cost-effectiveness of a postoperative clinical care pathway for patients undergoing major head and neck oncologic surgery with microvascular reconstruction. Methods This is a comparative trial of a prospective treatment group managed on a postoperative clinical care pathway and a historical group managed prior to pathway implementation. Effectiveness outcomes evaluated were total hospital days, return to OR, readmission to ICU and rate...

Understanding sharps injuries in home healthcare: The Safe Home Care qualitative methods study to identify pathways for injury prevention.

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Markkanen, Pia; Galligan, Catherine; Laramie, Angela; Fisher, June; Sama, Susan; Quinn, Margaret

2015-04-11

Home healthcare is one of the fastest growing sectors in the United States. Percutaneous injuries from sharp medical devices (sharps) are a source of bloodborne pathogen infections among home healthcare workers and community members. Sharps use and disposal practices in the home are highly variable and there is no comprehensive analysis of the system of sharps procurement, use and disposal in home healthcare. This gap is a barrier to effective public health interventions. The objectives of this study were to i) identify the full range of pathways by which sharps enter and exit the home, stakeholders involved, and barriers for using sharps with injury prevention features; and ii) assess the leverage points for preventive interventions. This study employed qualitative research methods to develop two systems maps of the use of sharps and prevention of sharps injuries in home healthcare. Twenty-six in-depth interview sessions were conducted including home healthcare agency clinicians, public health practitioners, sharps device manufacturers, injury prevention advocates, pharmacists and others. Interview transcripts were audio-recorded and analyzed thematically using NVIVO qualitative research analysis software. Analysis of supporting archival material also was conducted. All findings guided development of the two maps. Sharps enter the home via multiple complex pathways involving home healthcare providers and home users. The providers reported using sharps with injury prevention features. However, home users' sharps seldom had injury prevention features and sharps were commonly re-used for convenience and cost-savings. Improperly discarded sharps present hazards to caregivers, waste handlers, and community members. The most effective intervention potential exists at the beginning of the sharps systems maps where interventions can eliminate or minimize sharps injuries, in particular with needleless treatment methods and sharps with injury prevention features

Signaling pathway networks mined from human pituitary adenoma proteomics data

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Zhan Xianquan

2010-04-01

Full Text Available Abstract Background We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins, comparative proteomic data (56 differentially expressed proteins, and nitroproteomic data (17 nitroproteins. There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system. Methods The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses. Results For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a

Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets.

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Agarwal, Rahul; Narayan, Jitendra; Bhattacharyya, Amitava; Saraswat, Mayank; Tomar, Anil Kumar

2017-10-01

A very low 5-year survival rate among hepatocellular carcinoma (HCC) patients is mainly due to lack of early stage diagnosis, distant metastasis and high risk of postoperative recurrence. Hence ascertaining novel biomarkers for early diagnosis and patient specific therapeutics is crucial and urgent. Here, we have performed a comprehensive analysis of the expression data of 423 HCC patients (373 tumors and 50 controls) downloaded from The Cancer Genome Atlas (TCGA) followed by pathway enrichment by gene ontology annotations, subtype classification and overall survival analysis. The differential gene expression analysis using non-parametric Wilcoxon test revealed a total of 479 up-regulated and 91 down-regulated genes in HCC compared to controls. The list of top differentially expressed genes mainly consists of tumor/cancer associated genes, such as AFP, THBS4, LCN2, GPC3, NUF2, etc. The genes over-expressed in HCC were mainly associated with cell cycle pathways. In total, 59 kinases associated genes were found over-expressed in HCC, including TTK, MELK, BUB1, NEK2, BUB1B, AURKB, PLK1, CDK1, PKMYT1, PBK, etc. Overall four distinct HCC subtypes were predicted using consensus clustering method. Each subtype was unique in terms of gene expression, pathway enrichment and median survival. Conclusively, this study has exposed a number of interesting genes which can be exploited in future as potential markers of HCC, diagnostic as well as prognostic and subtype classification may guide for improved and specific therapy. Copyright © 2017 Elsevier Inc. All rights reserved.

Unbiased proteomics analysis demonstrates significant variability in mucosal immune factor expression depending on the site and method of collection.

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Kenzie M Birse

Full Text Available Female genital tract secretions are commonly sampled by lavage of the ectocervix and vaginal vault or via a sponge inserted into the endocervix for evaluating inflammation status and immune factors critical for HIV microbicide and vaccine studies. This study uses a proteomics approach to comprehensively compare the efficacy of these methods, which sample from different compartments of the female genital tract, for the collection of immune factors. Matching sponge and lavage samples were collected from 10 healthy women and were analyzed by tandem mass spectrometry. Data was analyzed by a combination of differential protein expression analysis, hierarchical clustering and pathway analysis. Of the 385 proteins identified, endocervical sponge samples collected nearly twice as many unique proteins as cervicovaginal lavage (111 vs. 61 with 55% of proteins common to both (213. Each method/site identified 73 unique proteins that have roles in host immunity according to their gene ontology. Sponge samples enriched for specific inflammation pathways including acute phase response proteins (p = 3.37×10(-24 and LXR/RXR immune activation pathways (p = 8.82×10(-22 while the role IL-17A in psoriasis pathway (p = 5.98×10(-4 and the complement system pathway (p = 3.91×10(-3 were enriched in lavage samples. Many host defense factors were differentially enriched (p<0.05 between sites including known/potential antimicrobial factors (n = 21, S100 proteins (n = 9, and immune regulatory factors such as serpins (n = 7. Immunoglobulins (n = 6 were collected at comparable levels in abundance in each site although 25% of those identified were unique to sponge samples. This study demonstrates significant differences in types and quantities of immune factors and inflammation pathways collected by each sampling technique. Therefore, clinical studies that measure mucosal immune activation or factors assessing HIV transmission should utilize

Analysis of aromatic catabolic pathways in Pseudomonas putida KT 2440 using a combined proteomic approach: 2-DE/MS and cleavable isotope-coded affinity tag analysis.

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Kim, Young Hwan; Cho, Kun; Yun, Sung-Ho; Kim, Jin Young; Kwon, Kyung-Hoon; Yoo, Jong Shin; Kim, Seung Il

2006-02-01

Proteomic analysis of Pseudomonas putida KT2440 cultured in monocyclic aromatic compounds was performed using 2-DE/MS and cleavable isotope-coded affinity tag (ICAT) to determine whether proteins involved in aromatic compound degradation pathways were altered as predicted by genomic analysis (Jiménez et al., Environ Microbiol. 2002, 4, 824-841). Eighty unique proteins were identified by 2-DE/MS or MS/MS analysis from P. putida KT2440 cultured in the presence of six different organic compounds. Benzoate dioxygenase (BenA, BenD) and catechol 1,2-dioxygenase (CatA) were induced by benzoate. Protocatechuate 3,4-dixoygenase (PcaGH) was induced by p-hydroxybenzoate and vanilline. beta-Ketoadipyl CoA thiolase (PcaF) and 3-oxoadipate enol-lactone hydrolase (PcaD) were induced by benzoate, p-hydroxybenzoate and vanilline, suggesting that benzoate, p-hydroxybenzoate and vanilline were degraded by different dioxygenases and then converged in the same beta-ketoadipate degradation pathway. An additional 110 proteins, including 19 proteins from 2-DE analysis, were identified by cleavable ICAT analysis for benzoate-induced proteomes, which complemented the 2-DE results. Phenylethylamine exposure induced beta-ketoacyl CoA thiolase (PhaD) and ring-opening enzyme (PhaL), both enzymes of the phenylacetate (pha) biodegradation pathway. Phenylalanine induced 4-hydroxyphenyl-pyruvate dioxygenase (Hpd) and homogentisate 1,2-dioxygenase (HmgA), key enzymes in the homogentisate degradation pathway. Alkyl hydroperoxide reductase (AphC) was induced under all aromatic compounds conditions. These results suggest that proteome analysis complements and supports predictive information obtained by genomic sequence analysis.

Environmental, health, and safety decision making for naturally occurring radioactive materials in producing operations using pathway exposure analysis

International Nuclear Information System (INIS)

Miller, H.T.; Cook, L.M.

1991-01-01

A number of health and safety issues have arisen because of the occurrence of NORM, naturally occurring radioactive materials of the 226 radium and 228 radium decay chains, in production operations. Issues such as risk to workers or the general public, disposal of contaminated production fluids, disposal of NORM removed in cleaning equipment and tubing, and procedures to follow in well rework, equipment decontamination and other types of maintenance must be addressed. This paper describes the application of a procedural aid to decision making known as pathway exposure analysis to these issues. The procedure examines the radiation exposure of individuals and population groups by calculating the dose from each exposure route and pathway. The sum of these is used to calculate the overall risk to the individual or the group. This method can be used to examine management and procedural options to identify the option offering the smallest risk. Risk information coupled with cost estimates then permits management maximum utilization of its available resources

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

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Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-01-01

Background Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Methods Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. Results A ‘Rich Picture’ was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. Conclusions When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further

Note of the methodological flaws in the paper entitled "Polymorphisms in IL-4/IL-13 pathway genes and glioma risk: an updated meta-analysis".

Science.gov (United States)

Wang, Ting-Ting; Li, Jin-Mei; Zhou, Dong

2016-01-01

With great interest, we read the paper "Polymorphisms in IL-4/IL-13 pathway genes and glioma risk: an updated meta-analysis" (by Chen PQ et al.) [1], which has reached important conclusions about the relationship between polymorphisms in interleukin (IL)-4/IL-13 pathway genes and glioma risk. Through quantitative analysis, the meta-analysis found no association between IL-4/IL-13 pathway genetic polymorphisms and glioma risk (Chen et al. in Tumor Biol 36:121-127, 2015). The meta-analysis is the most comprehensive study of polymorphisms in the IL-4/IL-13 pathway and glioma risk. Nevertheless, some deficiencies still exist in this meta-analysis that we would like to raise.

From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints

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Klamt, Steffen; Gerstl, Matthias P.; Jungreuthmayer, Christian; Mahadevan, Radhakrishnan; Müller, Stefan

2017-01-01

Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks. PMID:28406903

Connectome imaging for mapping human brain pathways.

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Shi, Y; Toga, A W

2017-09-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.

A single-run liquid chromatography mass spectrometry method to quantify neuroactive kynurenine pathway metabolites in rat plasma.

Science.gov (United States)

Orsatti, Laura; Speziale, Roberto; Orsale, Maria Vittoria; Caretti, Fulvia; Veneziano, Maria; Zini, Matteo; Monteagudo, Edith; Lyons, Kathryn; Beconi, Maria; Chan, Kelvin; Herbst, Todd; Toledo-Sherman, Leticia; Munoz-Sanjuan, Ignacio; Bonelli, Fabio; Dominguez, Celia

2015-03-25

Neuroactive metabolites in the kynurenine pathway of tryptophan catabolism are associated with neurodegenerative disorders. Tryptophan is transported across the blood-brain barrier and converted via the kynurenine pathway to N-formyl-L-kynurenine, which is further degraded to L-kynurenine. This metabolite can then generate a group of metabolites called kynurenines, most of which have neuroactive properties. The association of tryptophan catabolic pathway alterations with various central nervous system (CNS) pathologies has raised interest in analytical methods to accurately quantify kynurenines in body fluids. We here describe a rapid and sensitive reverse-phase HPLC-MS/MS method to quantify L-kynurenine (KYN), kynurenic acid (KYNA), 3-hydroxy-L-kynurenine (3HK) and anthranilic acid (AA) in rat plasma. Our goal was to quantify these metabolites in a single run; given their different physico-chemical properties, major efforts were devoted to develop a chromatography suitable for all metabolites that involves plasma protein precipitation with acetonitrile followed by chromatographic separation by C18 RP chromatography, detected by electrospray mass spectrometry. Quantitation range was 0.098-100 ng/ml for 3HK, 9.8-20,000 ng/ml for KYN, 0.49-1000 ng/ml for KYNA and AA. The method was linear (r>0.9963) and validation parameters were within acceptance range (calibration standards and QC accuracy within ±30%). Copyright © 2015 Elsevier B.V. All rights reserved.

Dose-volume complication analysis for visual pathway structures of patients with advanced paranasal sinus tumors

International Nuclear Information System (INIS)

Martel, Mary Kaye; Sandler, Howard M.; Cornblath, Wayne T.; Marsh, Lon H.; Hazuka, Mark B.; Roa, Wilson H.; Fraass, Benedict A.; Lichter, Allen S.

1997-01-01

Purpose: The purpose of the present work was to relate dose and volume information to complication data for visual pathway structures in patients with advanced paranasal sinus tumors. Methods and Materials: Three-dimensional (3D) dose distributions for chiasm, optic nerve, and retina were calculated and analyzed for 20 patients with advanced paranasal sinus malignant tumors. 3D treatment planning with beam's eye view capability was used to design beam and block arrangements, striving to spare the contralateral orbit (to lessen the chance of unilateral blindness) and frequently the ipsilateral orbit (to help prevent bilateral blindness). Point doses, dose-volume histogram analysis, and normal tissue complication probability (NTCP) calculations were performed. Published tolerance doses that indicate significant risk of complications were used as guidelines for analysis of the 3D dose distributions. Results: Point doses, percent volume exceeding a specified published tolerance dose, and NTCP calculations are given in detail for patients with complications versus patients without complications. Two optic nerves receiving maximum doses below the published tolerance dose sustained damage (mild vision loss). Three patients (of 13) without optic nerve sparing and/or chiasm sparing had moderate or severe vision loss. Complication data, including individual patient analysis to estimate overall risk for loss of vision, are given. Conclusion: 3D treatment planning techniques were used successfully to provide bilateral sparing of the globe for most patients. It was more difficult to spare the optic nerves, especially on the ipsilateral side, when prescription dose exceeded the normal tissue tolerance doses. NTCP calculations may be useful in assessing complication risk better than point dose tolerance criteria for the chiasm, optic nerve, and retina. It is important to assess the overall risk of blindness for the patient in addition to the risk for individual visual pathway

Discriminating response groups in metabolic and regulatory pathway networks.

Science.gov (United States)

Van Hemert, John L; Dickerson, Julie A

2012-04-01

Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. julied@iastate.edu Supplementary data are available at Bioinformatics online.

Consortium analysis of gene and gene–folate interactions in purine and pyrimidine metabolism pathways with ovarian carcinoma risk

DEFF Research Database (Denmark)

Kelemen, Linda E; Terry, Kathryn L; Goodman, Marc T

2014-01-01

SCOPE: We reevaluated previously reported associations between variants in pathways of one-carbon (1-C) (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake. METHODS AND RESULTS: Odds rat...

Branching points for transition pathways: assessing responses of actors to challenges on pathways to a low carbon future

International Nuclear Information System (INIS)

Foxon, Timothy J.; Pearson, Peter J.G.; Arapostathis, Stathis; Carlsson-Hyslop, Anna; Thornton, Judith

2013-01-01

This paper describes initial analysis of branching points on a set of transition pathways to a UK low carbon electricity future by 2050. As described in other papers in this special issue, we are exploring and analysing a set of core transition pathways, based on alternative governance patterns in which the ‘logics’ of market actors, government actors and civil society actors, respectively dominate. This core pathway analysis is enhanced by analyses of branching points within and across the pathways, which informs how competition between different logics plays out at key decision points. Branching points are defined as key decision points at which choices made by actors, in response to internal or external stresses or triggers, determine whether and in what ways the pathway is followed. A set of initial branching points for our three core transition pathways is identified through project and stakeholder workshops, and drawing on analysis of actors’ choices and responses at past branching points in energy system transitions. The potential responses of the actors are identified at these branching points, and risk mitigation strategies are formulated for the dominant actors to reinforce that pathway, as well as opportunities for actors to move away from the pathway. - Highlights: Transition Pathways is analysing three potential pathways to a low carbon future. ► Stresses lead to branching points, where actors make choices, creating pathways. ► These choices may lead to path-dependency. ► Differences in governance logics within transition pathways are also analysed. ► Studying branching points adds theoretical understanding and policy relevance to TP.

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

International Nuclear Information System (INIS)

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.; Gaido, Kevin W.; Ierapetritou, Marianthi G.; Androulakis, Ioannis P.

2013-01-01

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

Energy Technology Data Exchange (ETDEWEB)

Ovacik, Meric A. [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Sen, Banalata [National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27709 (United States); Euling, Susan Y. [National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC 20460 (United States); Gaido, Kevin W. [U.S. Food and Drug Administration, Center for Veterinary Medicine, Office of New Animal Drug Evaluation, Division of Human Food Safety, Rockville, MD 20855 (United States); Ierapetritou, Marianthi G. [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Androulakis, Ioannis P., E-mail: yannis@rci.rutgers.edu [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Biomedical Engineering Department, Rutgers University, NJ 08854 (United States)

2013-09-15

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.

A Hypothesis for Using Pathway Genetic Load Analysis for Understanding Complex Outcomes in Bilirubin Encephalopathy

Science.gov (United States)

Riordan, Sean M.; Bittel, Douglas C.; Le Pichon, Jean-Baptiste; Gazzin, Silvia; Tiribelli, Claudio; Watchko, Jon F.; Wennberg, Richard P.; Shapiro, Steven M.

2016-01-01

Genetic-based susceptibility to bilirubin neurotoxicity and chronic bilirubin encephalopathy (kernicterus) is still poorly understood. Neonatal jaundice affects 60–80% of newborns, and considerable effort goes into preventing this relatively benign condition from escalating into the development of kernicterus making the incidence of this potentially devastating condition very rare in more developed countries. The current understanding of the genetic background of kernicterus is largely comprised of mutations related to alterations of bilirubin production, elimination, or both. Less is known about mutations that may predispose or protect against CNS bilirubin neurotoxicity. The lack of a monogenetic source for this risk of bilirubin neurotoxicity suggests that disease progression is dependent upon an overall decrease in the functionality of one or more essential genetically controlled metabolic pathways. In other words, a “load” is placed on key pathways in the form of multiple genetic variants that combine to create a vulnerable phenotype. The idea of epistatic interactions creating a pathway genetic load (PGL) that affects the response to a specific insult has been previously reported as a PGL score. We hypothesize that the PGL score can be used to investigate whether increased susceptibility to bilirubin-induced CNS damage in neonates is due to a mutational load being placed on key genetic pathways important to the central nervous system's response to bilirubin neurotoxicity. We propose a modification of the PGL score method that replaces the use of a canonical pathway with custom gene lists organized into three tiers with descending levels of evidence combined with the utilization of single nucleotide polymorphism (SNP) causality prediction methods. The PGL score has the potential to explain the genetic background of complex bilirubin induced neurological disorders (BIND) such as kernicterus and could be the key to understanding ranges of outcome severity

Pathways of early fatherhood, marriage, and employment: a latent class growth analysis.

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Dariotis, Jacinda K; Pleck, Joseph H; Astone, Nan M; Sonenstein, Freya L

2011-05-01

In the National Longitudinal Survey of Youth 1979 (NLSY79), young fathers include heterogeneous subgroups with varying early life pathways in terms of fatherhood timing, the timing of first marriage, and holding full-time employment. Using latent class growth analysis with 10 observations between ages 18 and 37, we derived five latent classes with median ages of first fatherhood below the cohort median (26.4), constituting distinct early fatherhood pathways representing 32.4% of NLSY men: (A) Young Married Fathers, (B) Teen Married Fathers, (C) Young Underemployed Married Fathers, (D) Young Underemployed Single Fathers, and (E) Young Later-Marrying Fathers. A sixth latent class of men who become fathers around the cohort median, following full-time employment and marriage (On-Time On-Sequence Fathers), is the comparison group. With sociodemographic background controlled, all early fatherhood pathways show disadvantage in at least some later-life circumstances (earnings, educational attainment, marital status, and incarceration). The extent of disadvantage is greater when early fatherhood occurs at relatively younger ages (before age 20), occurs outside marriage, or occurs outside full-time employment. The relative disadvantage associated with early fatherhood, unlike early motherhood, increases over the life course.

Novel degradation pathway and kinetic analysis for buprofezin removal by newly isolated Bacillus sp.

Science.gov (United States)

Wang, Guangli; Xu, Dayong; Xiong, Minghua; Zhang, Hui; Li, Feng; Liu, Yuan

2016-09-15

Given the intensive and widespread application of the pesticide, buprofezin, its environmental residues potentially pose a problem; yet little is known about buprofezin's kinetic and metabolic behaviors. In this study, a novel gram-positive strain, designated BF-5, isolated from aerobic activated sludge, was found to be capable of metabolizing buprofezin as its sole energy, carbon, and nitrogen source. Based on its physiological and biochemical characteristics, other aspects of its phenotype, and a phylogenetic analysis, strain BF-5 was identified as Bacillus sp. This study investigated the effect of culture conditions on bacterial growth and substrate degradation, such as pH, temperature, initial concentration, different nitrogen source, and additional nitrogen sources as co-substrates. The degradation rate parameters, qmax, Ks, Ki and Sm were determined to be 0.6918 h(-1), 105.4 mg L(-1), 210.5 mg L(-1), and 148.95 mg L(-1) respectively. The capture of unpublished potential metabolites by gas chromatography-mass spectrometry (GC-MS) analysis has led to the proposal of a novel degradation pathway. Taken together, our results clarify buprofezin's biodegradation pathway(s) and highlight the promising potential of strain BF-5 in bioremediation of buprofezin-contaminated environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal structural inference of signaling pathways from unordered and overlapping gene sets.

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Acharya, Lipi R; Judeh, Thair; Wang, Guangdi; Zhu, Dongxiao

2012-02-15

A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and

Point Analysis in Java applied to histological images of the perforant pathway: A user’s account

OpenAIRE

Scorcioni, Ruggero; Wright, Susan N.; Card, J. Patrick; Ascoli, Giorgio A.; Barrionuevo, Germán

2008-01-01

The freeware Java tool PAJ, created to perform 3D point analysis, was tested in an independent laboratory setting. The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions. The low magnification set (2× objective) comprised the entire perforant pathway, while the high magnification set (100× objective) allowed the identification of individual fibers. A preliminary stereologi...

Text mining in cancer gene and pathway prioritization.

Science.gov (United States)

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Adipose tissue gene expression analysis reveals changes in inflammatory, mitochondrial respiratory and lipid metabolic pathways in obese insulin-resistant subjects

Directory of Open Access Journals (Sweden)

Soronen Jarkko

2012-04-01

Full Text Available Abstract Background To get insight into molecular mechanisms underlying insulin resistance, we compared acute in vivo effects of insulin on adipose tissue transcriptional profiles between obese insulin-resistant and lean insulin-sensitive women. Methods Subcutaneous adipose tissue biopsies were obtained before and after 3 and 6 hours of intravenously maintained euglycemic hyperinsulinemia from 9 insulin-resistant and 11 insulin-sensitive females. Gene expression was measured using Affymetrix HG U133 Plus 2 microarrays and qRT-PCR. Microarray data and pathway analyses were performed with Chipster v1.4.2 and by using in-house developed nonparametric pathway analysis software. Results The most prominent difference in gene expression of the insulin-resistant group during hyperinsulinemia was reduced transcription of nuclear genes involved in mitochondrial respiration (mitochondrial respiratory chain, GO:0001934. Inflammatory pathways with complement components (inflammatory response, GO:0006954 and cytokines (chemotaxis, GO:0042330 were strongly up-regulated in insulin-resistant as compared to insulin-sensitive subjects both before and during hyperinsulinemia. Furthermore, differences were observed in genes contributing to fatty acid, cholesterol and triglyceride metabolism (FATP2, ELOVL6, PNPLA3, SREBF1 and in genes involved in regulating lipolysis (ANGPTL4 between the insulin-resistant and -sensitive subjects especially during hyperinsulinemia. Conclusions The major finding of this study was lower expression of mitochondrial respiratory pathway and defective induction of lipid metabolism pathways by insulin in insulin-resistant subjects. Moreover, the study reveals several novel genes whose aberrant regulation is associated with the obese insulin-resistant phenotype.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

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Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

Exploring the Leishmania Hydrophilic Acylated Surface Protein B (HASPB) Export Pathway by Live Cell Imaging Methods.

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MacLean, Lorna; Price, Helen; O'Toole, Peter

2016-01-01

Leishmania major is a human-infective protozoan parasite transmitted by the bite of the female phlebotomine sand fly. The L. major hydrophilic acylated surface protein B (HASPB) is only expressed in infective parasite stages suggesting a role in parasite virulence. HASPB is a "nonclassically" secreted protein that lacks a conventional signal peptide, reaching the cell surface by an alternative route to the classical ER-Golgi pathway. Instead HASPB trafficking to and exposure on the parasite plasma membrane requires dual N-terminal acylation. Here, we use live cell imaging methods to further explore this pathway allowing visualization of key events in real time at the individual cell level. These methods include live cell imaging using fluorescent reporters to determine the subcellular localization of wild type and acylation site mutation HASPB18-GFP fusion proteins, fluorescence recovery after photobleaching (FRAP) to analyze the dynamics of HASPB in live cells, and live antibody staining to detect surface exposure of HASPB by confocal microscopy.

Uncertainty and sensitivity analysis of food pathway results with the MACCS Reactor Accident Consequence Model

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; Rollstin, J.A.; Shiver, A.W.; Sprung, J.L.

1995-01-01

Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the food pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 87 imprecisely-known input variables on the following reactor accident consequences are studied: crop growing season dose, crop long-term dose, milk growing season dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, area dependent cost, crop disposal cost, milk disposal cost, condemnation area, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: fraction of cesium deposition on grain fields that is retained on plant surfaces and transferred directly to grain, maximum allowable ground concentrations of Cs-137 and Sr-90 for production of crops, ground concentrations of Cs-134, Cs-137 and I-131 at which the disposal of milk will be initiated due to accidents that occur during the growing season, ground concentrations of Cs-134, I-131 and Sr-90 at which the disposal of crops will be initiated due to accidents that occur during the growing season, rate of depletion of Cs-137 and Sr-90 from the root zone, transfer of Sr-90 from soil to legumes, transfer of Cs-137 from soil to pasture, transfer of cesium from animal feed to meat, and the transfer of cesium, iodine and strontium from animal feed to milk

Uncertainty and sensitivity analysis of food pathway results with the MACCS reactor accident consequence model

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; Rollstin, J.A.; Shiver, A.W.; Sprung, J.L.

1995-01-01

Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the food pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 87 imprecisely-known input variables on the following reactor accident consequences are studied: crop growing-season dose, crop long-term dose, milk growing-season dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, area dependent cost, crop disposal cost, milk disposal cost, condemnation area, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: fraction of cesium deposition on grain fields that is retained on plant surfaces and transferred directly to grain, maximum allowable ground concentrations of Cs-137 and Sr-90 for production of crops, ground concentrations of Cs-134, Cs-137 and I-131 at which the disposal of milk will be initiated due to accidents that occur during the growing season, ground concentrations of Cs-134, I-131 and Sr-90 at which the disposal of crops will be initiated due to accidents that occur during the growing season, rate of depletion of Cs-137 and Sr-90 from the root zone, transfer of Sr-90 from soil to legumes, transfer of Cs-137 from soil to pasture, transfer of cesium from animal feed to meat, and the transfer of cesium, iodine and strontium from animal feed to milk

Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

Science.gov (United States)

West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

2011-09-27

Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research.

Science.gov (United States)

Slenter, Denise N; Kutmon, Martina; Hanspers, Kristina; Riutta, Anders; Windsor, Jacob; Nunes, Nuno; Mélius, Jonathan; Cirillo, Elisa; Coort, Susan L; Digles, Daniela; Ehrhart, Friederike; Giesbertz, Pieter; Kalafati, Marianthi; Martens, Marvin; Miller, Ryan; Nishida, Kozo; Rieswijk, Linda; Waagmeester, Andra; Eijssen, Lars M T; Evelo, Chris T; Pico, Alexander R; Willighagen, Egon L

2018-01-04

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Spectral analysis methods for vehicle interior vibro-acoustics identification

Science.gov (United States)

Hosseini Fouladi, Mohammad; Nor, Mohd. Jailani Mohd.; Ariffin, Ahmad Kamal

2009-02-01

Noise has various effects on comfort, performance and health of human. Sound are analysed by human brain based on the frequencies and amplitudes. In a dynamic system, transmission of sound and vibrations depend on frequency and direction of the input motion and characteristics of the output. It is imperative that automotive manufacturers invest a lot of effort and money to improve and enhance the vibro-acoustics performance of their products. The enhancement effort may be very difficult and time-consuming if one relies only on 'trial and error' method without prior knowledge about the sources itself. Complex noise inside a vehicle cabin originated from various sources and travel through many pathways. First stage of sound quality refinement is to find the source. It is vital for automotive engineers to identify the dominant noise sources such as engine noise, exhaust noise and noise due to vibration transmission inside of vehicle. The purpose of this paper is to find the vibro-acoustical sources of noise in a passenger vehicle compartment. The implementation of spectral analysis method is much faster than the 'trial and error' methods in which, parts should be separated to measure the transfer functions. Also by using spectral analysis method, signals can be recorded in real operational conditions which conduce to more consistent results. A multi-channel analyser is utilised to measure and record the vibro-acoustical signals. Computational algorithms are also employed to identify contribution of various sources towards the measured interior signal. These achievements can be utilised to detect, control and optimise interior noise performance of road transport vehicles.

On the deduction of chemical reaction pathways from measurements of time series of concentrations.

Science.gov (United States)

Samoilov, Michael; Arkin, Adam; Ross, John

2001-03-01

We discuss the deduction of reaction pathways in complex chemical systems from measurements of time series of chemical concentrations of reacting species. First we review a technique called correlation metric construction (CMC) and show the construction of a reaction pathway from measurements on a part of glycolysis. Then we present two new improved methods for the analysis of time series of concentrations, entropy metric construction (EMC), and entropy reduction method (ERM), and illustrate (EMC) with calculations on a model reaction system. (c) 2001 American Institute of Physics.

Well-to-wheels analysis of fast pyrolysis pathways with the GREET model.

Energy Technology Data Exchange (ETDEWEB)

Han, J.; Elgowainy, A.; Palou-Rivera, I.; Dunn, J.B.; Wang, M.Q. (Energy Systems)

2011-12-01

The pyrolysis of biomass can help produce liquid transportation fuels with properties similar to those of petroleum gasoline and diesel fuel. Argonne National Laboratory conducted a life-cycle (i.e., well-to-wheels [WTW]) analysis of various pyrolysis pathways by expanding and employing the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The WTW energy use and greenhouse gas (GHG) emissions from the pyrolysis pathways were compared with those from the baseline petroleum gasoline and diesel pathways. Various pyrolysis pathway scenarios with a wide variety of possible hydrogen sources, liquid fuel yields, and co-product application and treatment methods were considered. At one extreme, when hydrogen is produced from natural gas and when bio-char is used for process energy needs, the pyrolysis-based liquid fuel yield is high (32% of the dry mass of biomass input). The reductions in WTW fossil energy use and GHG emissions relative to those that occur when baseline petroleum fuels are used, however, is modest, at 50% and 51%, respectively, on a per unit of fuel energy basis. At the other extreme, when hydrogen is produced internally via reforming of pyrolysis oil and when bio-char is sequestered in soil applications, the pyrolysis-based liquid fuel yield is low (15% of the dry mass of biomass input), but the reductions in WTW fossil energy use and GHG emissions are large, at 79% and 96%, respectively, relative to those that occur when baseline petroleum fuels are used. The petroleum energy use in all scenarios was restricted to biomass collection and transportation activities, which resulted in a reduction in WTW petroleum energy use of 92-95% relative to that found when baseline petroleum fuels are used. Internal hydrogen production (i.e., via reforming of pyrolysis oil) significantly reduces fossil fuel use and GHG emissions because the hydrogen from fuel gas or pyrolysis oil (renewable sources) displaces that from fossil fuel

Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae.

Science.gov (United States)

Damte, Dereje; Suh, Joo-Won; Lee, Seung-Jin; Yohannes, Sileshi Belew; Hossain, Md Akil; Park, Seung-Chun

2013-07-01

In the present study, a computational comparative and subtractive genomic/proteomic analysis aimed at the identification of putative therapeutic target and vaccine candidate proteins from Kyoto Encyclopedia of Genes and Genomes (KEGG) annotated metabolic pathways of Mycoplasma hyopneumoniae was performed for drug design and vaccine production pipelines against M.hyopneumoniae. The employed comparative genomic and metabolic pathway analysis with a predefined computational systemic workflow extracted a total of 41 annotated metabolic pathways from KEGG among which five were unique to M. hyopneumoniae. A total of 234 proteins were identified to be involved in these metabolic pathways. Although 125 non homologous and predicted essential proteins were found from the total that could serve as potential drug targets and vaccine candidates, additional prioritizing parameters characterize 21 proteins as vaccine candidate while druggability of each of the identified proteins evaluated by the DrugBank database prioritized 42 proteins suitable for drug targets. Copyright © 2013 Elsevier Inc. All rights reserved.

Leading coordinate analysis of reaction pathways in proton chain transfer: Application to a two-proton transfer model for the green fluorescent protein

International Nuclear Information System (INIS)

Wang Sufan; Smith, Sean C.

2006-01-01

The 'leading coordinate' approach to computing an approximate reaction pathway, with subsequent determination of the true minimum energy profile, is applied to a two-proton chain transfer model based on the chromophore and its surrounding moieties within the green fluorescent protein (GFP). Using an ab initio quantum chemical method, a number of different relaxed energy profiles are found for several plausible guesses at leading coordinates. The results obtained for different trial leading coordinates are rationalized through the calculation of a two-dimensional relaxed potential energy surface (PES) for the system. Analysis of the 2-D relaxed PES reveals that two of the trial pathways are entirely spurious, while two others contain useful information and can be used to furnish starting points for successful saddle-point searches. Implications for selection of trial leading coordinates in this class of proton chain transfer reactions are discussed, and a simple diagnostic function is proposed for revealing whether or not a relaxed pathway based on a trial leading coordinate is likely to furnish useful information

Aligning ontologies and integrating textual evidence for pathway analysis of microarray data

Energy Technology Data Exchange (ETDEWEB)

Gopalan, Banu; Posse, Christian; Sanfilippo, Antonio P.; Stenzel-Poore, Mary; Stevens, S.L.; Castano, Jose; Beagley, Nathaniel; Riensche, Roderick M.; Baddeley, Bob; Simon, R.P.; Pustejovsky, James

2006-10-08

Expression arrays are introducing a paradigmatic change in biology by shifting experimental approaches from single gene studies to genome-level analysis, monitoring the ex-pression levels of several thousands of genes in parallel. The massive amounts of data obtained from the microarray data needs to be integrated and interpreted to infer biological meaning within the context of information-rich pathways. In this paper, we present a methodology that integrates textual information with annotations from cross-referenced ontolo-gies to map genes to pathways in a semi-automated way. We illustrate this approach and compare it favorably to other tools by analyzing the gene expression changes underlying the biological phenomena related to stroke. Stroke is the third leading cause of death and a major disabler in the United States. Through years of study, researchers have amassed a significant knowledge base about stroke, and this knowledge, coupled with new technologies, is providing a wealth of new scientific opportunities. The potential for neu-roprotective stroke therapy is enormous. However, the roles of neurogenesis, angiogenesis, and other proliferative re-sponses in the recovery process following ischemia and the molecular mechanisms that lead to these processes still need to be uncovered. Improved annotation of genomic and pro-teomic data, including annotation of pathways in which genes and proteins are involved, is required to facilitate their interpretation and clinical application. While our approach is not aimed at replacing existing curated pathway databases, it reveals multiple hidden relationships that are not evident with the way these databases analyze functional groupings of genes from the Gene Ontology.

GEP analysis validates high risk MDS and acute myeloid leukemia post MDS mice models and highlights novel dysregulated pathways.

Science.gov (United States)

Guerenne, Laura; Beurlet, Stéphanie; Said, Mohamed; Gorombei, Petra; Le Pogam, Carole; Guidez, Fabien; de la Grange, Pierre; Omidvar, Nader; Vanneaux, Valérie; Mills, Ken; Mufti, Ghulam J; Sarda-Mantel, Laure; Noguera, Maria Elena; Pla, Marika; Fenaux, Pierre; Padua, Rose Ann; Chomienne, Christine; Krief, Patricia

2016-01-27

In spite of the recent discovery of genetic mutations in most myelodysplasic (MDS) patients, the pathophysiology of these disorders still remains poorly understood, and only few in vivo models are available to help unravel the disease. We performed global specific gene expression profiling and functional pathway analysis in purified Sca1+ cells of two MDS transgenic mouse models that mimic human high-risk MDS (HR-MDS) and acute myeloid leukemia (AML) post MDS, with NRASD12 and BCL2 transgenes under the control of different promoters MRP8NRASD12/tethBCL-2 or MRP8[NRASD12/hBCL-2], respectively. Analysis of dysregulated genes that were unique to the diseased HR-MDS and AML post MDS mice and not their founder mice pointed first to pathways that had previously been reported in MDS patients, including DNA replication/damage/repair, cell cycle, apoptosis, immune responses, and canonical Wnt pathways, further validating these models at the gene expression level. Interestingly, pathways not previously reported in MDS were discovered. These included dysregulated genes of noncanonical Wnt pathways and energy and lipid metabolisms. These dysregulated genes were not only confirmed in a different independent set of BM and spleen Sca1+ cells from the MDS mice but also in MDS CD34+ BM patient samples. These two MDS models may thus provide useful preclinical models to target pathways previously identified in MDS patients and to unravel novel pathways highlighted by this study.

New force replica exchange method and protein folding pathways probed by force-clamp technique.

Science.gov (United States)

Kouza, Maksim; Hu, Chin-Kun; Li, Mai Suan

2008-01-28

We have developed a new extended replica exchange method to study thermodynamics of a system in the presence of external force. Our idea is based on the exchange between different force replicas to accelerate the equilibrium process. This new approach was applied to obtain the force-temperature phase diagram and other thermodynamical quantities of the three-domain ubiquitin. Using the C(alpha)-Go model and the Langevin dynamics, we have shown that the refolding pathways of single ubiquitin depend on which terminus is fixed. If the N end is fixed then the folding pathways are different compared to the case when both termini are free, but fixing the C terminal does not change them. Surprisingly, we have found that the anchoring terminal does not affect the pathways of individual secondary structures of three-domain ubiquitin, indicating the important role of the multidomain construction. Therefore, force-clamp experiments, in which one end of a protein is kept fixed, can probe the refolding pathways of a single free-end ubiquitin if one uses either the polyubiquitin or a single domain with the C terminus anchored. However, it is shown that anchoring one end does not affect refolding pathways of the titin domain I27, and the force-clamp spectroscopy is always capable to predict folding sequencing of this protein. We have obtained the reasonable estimate for unfolding barrier of ubiquitin, using the microscopic theory for the dependence of unfolding time on the external force. The linkage between residue Lys48 and the C terminal of ubiquitin is found to have the dramatic effect on the location of the transition state along the end-to-end distance reaction coordinate, but the multidomain construction leaves the transition state almost unchanged. We have found that the maximum force in the force-extension profile from constant velocity force pulling simulations depends on temperature nonlinearly. However, for some narrow temperature interval this dependence becomes

Pathway discovery in metabolic networks by subgraph extraction.

Science.gov (United States)

Faust, Karoline; Dupont, Pierre; Callut, Jérôme; van Helden, Jacques

2010-05-01

Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well. We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from Saccharomyces cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of approximately 77%. Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license.

DEGAS: de novo discovery of dysregulated pathways in human diseases.

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Igor Ulitsky

Full Text Available BACKGROUND: Molecular studies of the human disease transcriptome typically involve a search for genes whose expression is significantly dysregulated in sick individuals compared to healthy controls. Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals. However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals. While a pathway is usually defined as a set of genes known to share a specific function, pathway boundaries are frequently difficult to assign, and methods that rely on such definition cannot discover novel pathways. Protein interaction networks can potentially be used to overcome these problems. METHODOLOGY/PRINCIPAL FINDINGS: We present DEGAS (DysrEgulated Gene set Analysis via Subnetworks, a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package (http://acgt.cs.tau.ac.il/matisse. CONCLUSIONS/SIGNIFICANCE: The subnetworks identified by DEGAS can provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention.

Altered expression of circulating microRNA in plasma of patients with primary osteoarthritis and in silico analysis of their pathways.

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Verónica M Borgonio Cuadra

Full Text Available OBJECTIVE: To analyze a set of circulating microRNA (miRNA in plasma from patients with primary Osteoarthritis (OA and describe the biological significance of altered miRNA in OA based on an in silico analysis of their target genes. METHODS: miRNA expression was analyzed using TaqMan Low Density Arrays and independent assays. The search for potential messenger RNA (mRNA targets of the differentially expressed miRNA was performed by means of the miRWalk and miRecords database; we conducted the biological relevance of the predicted miRNA targets by pathway analysis with the Reactome and DAVID databases. RESULTS: We measured the expression of 380 miRNA in OA; 12 miRNA were overexpressed under the OA condition (p value, ≤0.05; fold change, >2. These results were validated by the detection of some selected miRNA by quantitative PCR (qPCR. In silico analysis showed that target messenger RNA (mRNA were potentially regulated by these miRNA, including genes such as SMAD1, IL-1B, COL3A, VEGFA, and FGFR1, important in chondrocyte maintenance and differentiation. Some metabolic pathways affected by the miRNA: mRNA ratio are signaling Bone morphogenetic proteins (BMP, Platelet-derived growth factor (PDGF, and Nerve growth factor (NGF, these latter two involved in the process of pain. CONCLUSIONS: We identified 12 miRNA in the plasma of patients with primary OA. Specific miRNA that are altered in the disease could be released into plasma, either due to cartilage damage or to an inherent cellular mechanism. Several miRNA could regulate genes and pathways related with development of the disease; eight of these circulating miRNA are described, to our knowledge, for first time in OA.

GWAS-based pathway analysis differentiates between fluid and crystallized intelligence.

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Christoforou, A; Espeseth, T; Davies, G; Fernandes, C P D; Giddaluru, S; Mattheisen, M; Tenesa, A; Harris, S E; Liewald, D C; Payton, A; Ollier, W; Horan, M; Pendleton, N; Haggarty, P; Djurovic, S; Herms, S; Hoffman, P; Cichon, S; Starr, J M; Lundervold, A; Reinvang, I; Steen, V M; Deary, I J; Le Hellard, S

2014-09-01

Cognitive abilities vary among people. About 40-50% of this variability is due to general intelligence (g), which reflects the positive correlation among individuals' scores on diverse cognitive ability tests. g is positively correlated with many life outcomes, such as education, occupational status and health, motivating the investigation of its underlying biology. In psychometric research, a distinction is made between general fluid intelligence (gF) - the ability to reason in novel situations - and general crystallized intelligence (gC) - the ability to apply acquired knowledge. This distinction is supported by developmental and cognitive neuroscience studies. Classical epidemiological studies and recent genome-wide association studies (GWASs) have established that these cognitive traits have a large genetic component. However, no robust genetic associations have been published thus far due largely to the known polygenic nature of these traits and insufficient sample sizes. Here, using two GWAS datasets, in which the polygenicity of gF and gC traits was previously confirmed, a gene- and pathway-based approach was undertaken with the aim of characterizing and differentiating their genetic architecture. Pathway analysis, using genes selected on the basis of relaxed criteria, revealed notable differences between these two traits. gF appeared to be characterized by genes affecting the quantity and quality of neurons and therefore neuronal efficiency, whereas long-term depression (LTD) seemed to underlie gC. Thus, this study supports the gF-gC distinction at the genetic level and identifies functional annotations and pathways worthy of further investigation. © 2014 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes, and Cytokine Signaling Genes in Crohn's Disease

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Carbonetto, Peter; Stephens, Matthew

2013-01-01

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14

KeyPathwayMinerWeb

DEFF Research Database (Denmark)

List, Markus; Alcaraz, Nicolas; Dissing-Hansen, Martin

2016-01-01

, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles. The web interface at (http://keypathwayminer.compbio.sdu.dk) implements all core functionalities of the KeyPathwayMiner tool set......We present KeyPathwayMinerWeb, the first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein-protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression...... such as data integration, input of background knowledge, batch runs for parameter optimization and visualization of extracted pathways. In addition to an intuitive web interface, we also implemented a RESTful API that now enables other online developers to integrate network enrichment as a web service...

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings.

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Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-08-01

Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. A 'Rich Picture' was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration. Published by the BMJ Publishing Group

Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis.

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Tong Wu

Full Text Available Breast cancer is a highly heterogeneous disease that is clinically classified into several subtypes. Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC, and these two groups are generally studied together as a single entity. Differences in the molecular makeup of breast cancers can result in different treatment strategies and prognoses for patients with different breast cancer subtypes. Compared with other subtypes, basal-like and other ER+ breast cancer subtypes exhibit marked differences in etiologic factors, clinical characteristics and therapeutic potential. Anthracycline drugs are typically used as the first-line clinical treatment for basal-like breast cancer subtypes. However, certain patients develop drug resistance following chemotherapy, which can lead to disease relapse and death. Even among patients with basal-like breast cancer, there can be significant molecular differences, and it is difficult to identify specific drug resistance proteins in any given patient using conventional variance testing methods. Therefore, we designed a new method for identifying drug resistance genes. Subgroups, personalized biomarkers, and therapy targets were identified using cluster analysis of differentially expressed genes. We found that basal-like breast cancer could be further divided into at least four distinct subgroups, including two groups at risk for drug resistance and two groups characterized by sensitivity to pharmacotherapy. Based on functional differences among these subgroups, we identified nine biomarkers related to drug resistance: SYK, LCK, GAB2, PAWR, PPARG, MDFI, ZAP70, CIITA and ACTA1. Finally, based on the deviation scores of the examined pathways, 16 pathways were shown to exhibit varying degrees of abnormality in the various subgroups, indicating that patients with different subtypes of basal-like breast cancer can be characterized by differences in the functional status of

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

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Caspi, Ron; Altman, Tomer; Dale, Joseph M.; Dreher, Kate; Fulcher, Carol A.; Gilham, Fred; Kaipa, Pallavi; Karthikeyan, Athikkattuvalasu S.; Kothari, Anamika; Krummenacker, Markus; Latendresse, Mario; Mueller, Lukas A.; Paley, Suzanne; Popescu, Liviu; Pujar, Anuradha; Shearer, Alexander G.; Zhang, Peifen; Karp, Peter D.

2010-01-01

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism. PMID:19850718

Transcriptome analysis of bitter acid biosynthesis and precursor pathways in hop (Humulus lupulus

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Clark Shawn M

2013-01-01

Full Text Available Abstract Background Bitter acids (e.g. humulone are prenylated polyketides synthesized in lupulin glands of the hop plant (Humulus lupulus which are important contributors to the bitter flavour and stability of beer. Bitter acids are formed from acyl-CoA precursors derived from branched-chain amino acid (BCAA degradation and C5 prenyl diphosphates from the methyl-D-erythritol 4-phosphate (MEP pathway. We used RNA sequencing (RNA-seq to obtain the transcriptomes of isolated lupulin glands, cones with glands removed and leaves from high α-acid hop cultivars, and analyzed these datasets for genes involved in bitter acid biosynthesis including the supply of major precursors. We also measured the levels of BCAAs, acyl-CoA intermediates, and bitter acids in glands, cones and leaves. Results Transcripts encoding all the enzymes of BCAA metabolism were significantly more abundant in lupulin glands, indicating that BCAA biosynthesis and subsequent degradation occurs in these specialized cells. Branched-chain acyl-CoAs and bitter acids were present at higher levels in glands compared with leaves and cones. RNA-seq analysis showed the gland-specific expression of the MEP pathway, enzymes of sucrose degradation and several transcription factors that may regulate bitter acid biosynthesis in glands. Two branched-chain aminotransferase (BCAT enzymes, HlBCAT1 and HlBCAT2, were abundant, with gene expression quantification by RNA-seq and qRT-PCR indicating that HlBCAT1 was specific to glands while HlBCAT2 was present in glands, cones and leaves. Recombinant HlBCAT1 and HlBCAT2 catalyzed forward (biosynthetic and reverse (catabolic reactions with similar kinetic parameters. HlBCAT1 is targeted to mitochondria where it likely plays a role in BCAA catabolism. HlBCAT2 is a plastidial enzyme likely involved in BCAA biosynthesis. Phylogenetic analysis of the hop BCATs and those from other plants showed that they group into distinct biosynthetic (plastidial and

Nonradiological chemical pathway analysis and identification of chemicals of concern for environmental monitoring at the Hanford Site

International Nuclear Information System (INIS)

Blanton, M.L.; Cooper, A.T.; Castleton, K.J.

1995-11-01

Pacific Northwest's Surface Environmental Surveillance Project (SESP) is an ongoing effort tot design, review, and conducted monitoring on and off the Hanford site. Chemicals of concern that were selected are listed. Using modeled exposure pathways, the offsite cancer incidence and hazard quotient were calculated and a retrospective pathway analysis performed to estimate what onsite concentrations would be required in the soil for each chemical of concern and other detected chemicals that would be required to obtain an estimated offsite human-health risk of 1.0E-06 cancer incidence or 1.0 hazard quotient. This analysis indicates that current nonradiological chemical contamination occurring on the site does not pose a significant offsite human-health risk; the highest cancer incidence to the offsite maximally exposed individual was from arsenic (1.76E-10); the highest hazard quotient was chromium(VI) (1.48E-04). The most sensitive pathways of exposure were surfacewater and aquatic food consumption. Combined total offsite excess cancer incidence was 2.09E-10 and estimated hazard quotient was 2.40E-04. Of the 17 identified chemicals of concern, the SESP does not currently (routinely) monitor arsenic, benzo(a)pyrene, bis(2- ethylhexyl)phthalate (BEHP), and chrysene. Only 3 of the chemicals of concern (arsenic, BEHP, chloroform) could actually occur in onsite soil at concern high enough to cause a 1.0E-06 excess cancer incidence or a 1.0 hazard index for a given offsite exposure pathway. During the retrospective analysis, 20 other chemicals were also evaluated; only vinyl chloride and thallium could reach targeted offsite risk values

[Analysis on "component-target-pathway" of Paeonia lactiflora in treating cardiac diseases based on data mining].

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Liu, Yang; Zhang, Fang-Bo; Tang, Shi-Huan; Wang, Ping; Li, Sen; Su, Jin; Zhou, Rong-Rong; Zhang, Jia-Qi; Sun, Hui-Feng

2018-04-01

Based on the literature review and modern application of Paeonia lactiflora in heart diseases, this article would predict the target of drug and disease by intergrative pharmacology platform of traditional Chinese medicine (TCMIP, http://www.tcmip.cn), and then explore the molecular mechanism of P. lactiflora in treatment of heart disease, providing theoretical basis and method for further studies on P. lactiflora. According to the ancient books, P. lactiflora with functions of "removing the vascular obstruction, removing the lumps, relieving pain, diuretic, nutrient qi" and other effects, have been used for many times to treat heart disease. Some prescriptions are also favored by the modern physicians nowadays. With the development of science, the chemical components that play a role in heart disease and the interrelation between these components and the body become the research hotspot. In order to further reveal the pharmacological substance base and molecular mechanism of P. lactiflora for the treatment of such diseases, TCM-IP was used to obtain multiple molecular targets and signaling pathways in treatment of heart disease. ATP1A1, a common target of drug and disease, was related to energy, and HDAC2 mainly regulated cardiomyocyte hypertrophy gene and cardiomyocyte expression. Other main drug targets such as GCK, CHUK and PRKAA2 indirectly regulated heart disease through many pathways; multiple disease-associated signaling pathways interfered with various heart diseases including coronary heart disease, myocardial ischemia and myocardial hypertrophy through influencing energy metabolism, enzyme activity and gene expression. In conclusion, P. lactiflora plays a role in protecting heart function by regulating the gene expression of cardiomyocytes directly. Meanwhile, it can indirectly intervene in other pathways of heart function, and thus participate in the treatment of heart disease. In this paper, the molecular mechanism of P. lactiflora for treatment of

Synergy between methylerythritol phosphate pathway and mevalonate pathway for isoprene production in Escherichia coli.

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Yang, Chen; Gao, Xiang; Jiang, Yu; Sun, Bingbing; Gao, Fang; Yang, Sheng

2016-09-01

Isoprene, a key building block of synthetic rubber, is currently produced entirely from petrochemical sources. In this work, we engineered both the methylerythritol phosphate (MEP) pathway and the mevalonate (MVA) pathway for isoprene production in E. coli. The synergy between the MEP pathway and the MVA pathway was demonstrated by the production experiment, in which overexpression of both pathways improved the isoprene yield about 20-fold and 3-fold, respectively, compared to overexpression of the MEP pathway or the MVA pathway alone. The (13)C metabolic flux analysis revealed that simultaneous utilization of the two pathways resulted in a 4.8-fold increase in the MEP pathway flux and a 1.5-fold increase in the MVA pathway flux. The synergy of the dual pathway was further verified by quantifying intracellular flux responses of the MEP pathway and the MVA pathway to fosmidomycin treatment and mevalonate supplementation. Our results strongly suggest that coupling of the complementary reducing equivalent demand and ATP requirement plays an important role in the synergy of the dual pathway. Fed-batch cultivation of the engineered strain overexpressing the dual pathway resulted in production of 24.0g/L isoprene with a yield of 0.267g/g of glucose. The synergy of the MEP pathway and the MVA pathway also successfully increased the lycopene productivity in E. coli, which demonstrates that it can be used to improve the production of a broad range of terpenoids in microorganisms. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Functional pathway analysis of genes associated with response to treatment for chronic hepatitis C.

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Birerdinc, A; Afendy, A; Stepanova, M; Younossi, I; Manyam, G; Baranova, A; Younossi, Z M

2010-10-01

Chronic hepatitis C (CH-C) is among the most common causes of chronic liver disease. Approximately 50% of patients with CH-C treated with pegylated interferon-α and ribavirin (PEG-IFN-α + RBV) achieve a sustained virological response (SVR). Several factors such as genotype 1, African American (AA) race, obesity and the absence of an early virological response (EVR) are associated with low SVR. This study elucidates molecular pathways deregulated in patients with CH-C with negative predictors of response to antiviral therapy. Sixty-eight patients with CH-C who underwent a full course of treatment with PEG-IFN-α + RBV were included in the study. Pretreatment blood samples were collected in PAXgene™ RNA tubes. EVR, complete EVR (cEVR), and SVR rates were 76%, 57% and 41%, respectively. Total RNA was extracted from pretreatment peripheral blood mononuclear cells, quantified and used for one-step RT-PCR to profile 154 mRNAs. The expression of mRNAs was normalized with six 'housekeeping' genes. Differentially expressed genes were separated into up and downregulated gene lists according to the presence or absence of a risk factor and subjected to KEGG Pathway Painter which allows high-throughput visualization of the pathway-specific changes in expression profiles. The genes were consolidated into the networks associated with known predictors of response. Before treatment, various genes associated with core components of the JAK/STAT pathway were activated in the cohorts least likely to achieve SVR. Genes related to focal adhesion and TGF-β pathways were activated in some patients with negative predictors of response. Pathway-centred analysis of gene expression profiles from treated patients with CH-C points to the Janus kinase-signal transducers and activators of transcription signalling cascade as the major pathogenetic component responsible for not achieving SVR. In addition, focal adhesion and TGF-β pathways are associated with some predictors of response. �

From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.

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Chen, Yunshun; Lun, Aaron T L; Smyth, Gordon K

2016-01-01

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.

Plasma metabolomics reveal the correlation of metabolic pathways and Prakritis of humans

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Amey Shirolkar

2018-04-01

Full Text Available Background: Ayurveda, an ancient Indian medicinal system, has categorized human body constitutions in three broad constitutional types (prakritis i.e. Vata, Pitta and Kapha. Objectives: Analysis of plasma metabolites and related pathways to classify Prakriti specific dominant marker metabolites and metabolic pathways. Materials and methods: 38 healthy male individuals were assessed for dominant Prakritis and their fasting blood samples were collected. The processed plasma samples were subjected to rapid resolution liquid chromatography–electrospray ionization–quadrupole time of flight mass spectrometry (RRLC–ESI–QTOFMS. Mass profiles were aligned and subjected to multivariate analysis. Results: Partial least square discriminant analysis (PLS-DA model showed 97.87% recognition capability. List of PLS-DA metabolites was subjected to permutative Benjamini–Hochberg false discovery rate (FDR correction and final list of 76 metabolites with p  2.0 was identified. Pathway analysis using metascape and JEPETTO plugins in Cytoscape revealed that steroidal hormone biosynthesis, amino acid, and arachidonic acid metabolism are major pathways varying with different constitution. Biological Go processes analysis showed that aromatic amino acids, sphingolipids, and pyrimidine nucleotides metabolic processes were dominant in kapha type of body constitution. Fat soluble vitamins, cellular amino acid, and androgen biosynthesis process along with branched chain amino acid and glycerolipid catabolic processes were dominant in pitta type individuals. Vata Prakriti was found to have dominant catecholamine, arachidonic acid and hydrogen peroxide metabolomics processes. Conclusion: The neurotransmission and oxidative stress in vata, BCAA catabolic, androgen, xenobiotics metabolic processes in pitta, and aromatic amino acids, sphingolipid, and pyrimidine metabolic process in kapha Prakriti were the dominant marker pathways. Keywords: Ayurveda, Prakriti, Human

Oxygen entry through multiple pathways in T-state human hemoglobin.

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Takayanagi, Masayoshi; Kurisaki, Ikuo; Nagaoka, Masataka

2013-05-23

The heme oxygen (O2) binding site of human hemoglobin (HbA) is buried in the interior of the protein, and there is a debate over the O2 entry pathways from solvent to the binding site. As a first step to understand HbA O2 binding process at the atomic level, we detected all significant multiple O2 entry pathways from solvent to the binding site in the α and β subunits of the T-state tetramer HbA by utilizing ensemble molecular dynamics (MD) simulation. By executing 128 independent 8 ns MD trajectories in O2-rich aqueous solvent, we simulated the O2 entry processes and obtained 141 and 425 O2 entry events in the α and β subunits of HbA, respectively. We developed the intrinsic pathway identification by clustering method to achieve a persuasive visualization of the multiple entry pathways including both the shapes and relative importance of each pathway. The rate constants of O2 entry estimated from the MD simulations correspond to the experimentally observed values, suggesting that O2 ligands enter the binding site through multiple pathways. The obtained multiple pathway map can be utilized for future detailed analysis of HbA O2 binding process.

Identification and pathway analysis of microRNAs with no previous involvement in breast cancer.

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Sandra Romero-Cordoba

Full Text Available microRNA expression signatures can differentiate normal and breast cancer tissues and can define specific clinico-pathological phenotypes in breast tumors. In order to further evaluate the microRNA expression profile in breast cancer, we analyzed the expression of 667 microRNAs in 29 tumors and 21 adjacent normal tissues using TaqMan Low-density arrays. 130 miRNAs showed significant differential expression (adjusted P value = 0.05, Fold Change = 2 in breast tumors compared to the normal adjacent tissue. Importantly, the role of 43 of these microRNAs has not been previously reported in breast cancer, including several evolutionary conserved microRNA*, showing similar expression rates to that of their corresponding leading strand. The expression of 14 microRNAs was replicated in an independent set of 55 tumors. Bioinformatic analysis of mRNA targets of the altered miRNAs, identified oncogenes like ERBB2, YY1, several MAP kinases, and known tumor-suppressors like FOXA1 and SMAD4. Pathway analysis identified that some biological process which are important in breast carcinogenesis are affected by the altered microRNA expression, including signaling through MAP kinases and TP53 pathways, as well as biological processes like cell death and communication, focal adhesion and ERBB2-ERBB3 signaling. Our data identified the altered expression of several microRNAs whose aberrant expression might have an important impact on cancer-related cellular pathways and whose role in breast cancer has not been previously described.

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways.

Science.gov (United States)

Gu, Xiang; Liu, Cong-Jian; Wei, Jian-Jie

2017-11-13

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

Tomographic phase analysis to detect the site of accessory conduction pathway in Wolff-Parkinson-White syndrome

International Nuclear Information System (INIS)

Nakajima, K.; Bunko, H.; Tada, A.; Tonami, N.; Taki, J.; Nanbu, I.; Hisada, K.; Misaki, T.; Iwa, T.

1984-01-01

Phase analysis has been applied to Wolff-Parkinson-White syndrome (WPW) to detect the site of accessory conduction pathway (ACP); however, there was a limitation to estimate the precise location of ACP by planar phase analysis. In this study, the authors applied phase analysis to gated blood pool tomography. Twelve patients with WPW who underwent epicardial mapping and surgical division of ACP were studied by both of gated emission computed tomography (GECT) and routine gated blood pool study (GBPS). The GBPS was performed with Tc-99m red blood cells in multiple projections; modified left anterior oblique, right anterior oblique and/or left lateral views. In GECT, short axial, horizontal and vertical long axial blood pool images were reconstructed. Phase analysis was performed using fundamental frequency of the Fourier transform in both GECT and GBPS images, and abnormal initial contractions on both the planar and tomographic phase analysis were compared with the location of surgically confirmed ACPs. In planar phase analysis, abnormal initial phase was identified in 7 out of 12 (58%) patients, while in tomographic phase analysis, the localization of ACP was predicted in 11 out of 12 (92%) patients. Tomographic phase analysis is superior to planar phase images in 8 out of 12 patients to estimate the location of ACP. Phase analysis by GECT can avoid overlap of blood pool in cardiac chambers and has advantage to identify the propagation of phase three-dimensionally. Tomographic phase analysis is a good adjunctive method for patients with WPW to estimate the site of ACP

Tomographic phase analysis to detect the site of accessory conduction pathway in Wolff-Parkinson-White syndrome

Energy Technology Data Exchange (ETDEWEB)

Nakajima, K.; Bunko, H.; Tada, A.; Tonami, N.; Taki, J.; Nanbu, I.; Hisada, K.; Misaki, T.; Iwa, T.

1984-01-01

Phase analysis has been applied to Wolff-Parkinson-White syndrome (WPW) to detect the site of accessory conduction pathway (ACP); however, there was a limitation to estimate the precise location of ACP by planar phase analysis. In this study, the authors applied phase analysis to gated blood pool tomography. Twelve patients with WPW who underwent epicardial mapping and surgical division of ACP were studied by both of gated emission computed tomography (GECT) and routine gated blood pool study (GBPS). The GBPS was performed with Tc-99m red blood cells in multiple projections; modified left anterior oblique, right anterior oblique and/or left lateral views. In GECT, short axial, horizontal and vertical long axial blood pool images were reconstructed. Phase analysis was performed using fundamental frequency of the Fourier transform in both GECT and GBPS images, and abnormal initial contractions on both the planar and tomographic phase analysis were compared with the location of surgically confirmed ACPs. In planar phase analysis, abnormal initial phase was identified in 7 out of 12 (58%) patients, while in tomographic phase analysis, the localization of ACP was predicted in 11 out of 12 (92%) patients. Tomographic phase analysis is superior to planar phase images in 8 out of 12 patients to estimate the location of ACP. Phase analysis by GECT can avoid overlap of blood pool in cardiac chambers and has advantage to identify the propagation of phase three-dimensionally. Tomographic phase analysis is a good adjunctive method for patients with WPW to estimate the site of ACP.

Pathways to Lean Software Development: An Analysis of Effective Methods of Change

Science.gov (United States)

Hanson, Richard D.

2014-01-01

This qualitative Delphi study explored the challenges that exist in delivering software on time, within budget, and with the original scope identified. The literature review identified many attempts over the past several decades to reform the methods used to develop software. These attempts found that the classical waterfall method, which is…

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

Science.gov (United States)

Koumakis, Lefteris; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Vassou, Despoina; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-01-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the

COMPUTER METHODS OF GENETIC ANALYSIS.

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A. L. Osipov

2017-02-01

Full Text Available The basic statistical methods used in conducting the genetic analysis of human traits. We studied by segregation analysis, linkage analysis and allelic associations. Developed software for the implementation of these methods support.

Evolution of the TOR Pathway.

NARCIS (Netherlands)

Dam, T.J.P. van; Zwartkruis, F.J.; Bos, J.L.; Snel, B.

2011-01-01

The TOR kinase is a major regulator of growth in eukaryotes. Many components of the TOR pathway are implicated in cancer and metabolic diseases in humans. Analysis of the evolution of TOR and its pathway may provide fundamental insight into the evolution of growth regulation in eukaryotes and

The surface analysis methods

International Nuclear Information System (INIS)

Deville, J.P.

1998-01-01

Nowadays, there are a lot of surfaces analysis methods, each having its specificity, its qualities, its constraints (for instance vacuum) and its limits. Expensive in time and in investment, these methods have to be used deliberately. This article appeals to non specialists. It gives some elements of choice according to the studied information, the sensitivity, the use constraints or the answer to a precise question. After having recalled the fundamental principles which govern these analysis methods, based on the interaction between radiations (ultraviolet, X) or particles (ions, electrons) with matter, two methods will be more particularly described: the Auger electron spectroscopy (AES) and x-rays photoemission spectroscopy (ESCA or XPS). Indeed, they are the most widespread methods in laboratories, the easier for use and probably the most productive for the analysis of surface of industrial materials or samples submitted to treatments in aggressive media. (O.M.)

STOCHASTIC METHODS IN RISK ANALYSIS

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Vladimíra OSADSKÁ

2017-06-01

Full Text Available In this paper, we review basic stochastic methods which can be used to extend state-of-the-art deterministic analytical methods for risk analysis. We can conclude that the standard deterministic analytical methods highly depend on the practical experience and knowledge of the evaluator and therefore, the stochastic methods should be introduced. The new risk analysis methods should consider the uncertainties in input values. We present how large is the impact on the results of the analysis solving practical example of FMECA with uncertainties modelled using Monte Carlo sampling.

Integrative analysis of circRNAs acting as ceRNAs involved in ethylene pathway in tomato.

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Wang, Yunxiang; Wang, Qing; Gao, Lipu; Zhu, Benzhong; Luo, Yunbo; Deng, Zhiping; Zuo, Jinhua

2017-11-01

Circular RNAs (circRNAs) are a large class of non-coding endogenous RNAs that could act as competing endogenous RNAs (ceRNAs) to terminate the mRNA targets' suppression of miRNAs. To elucidate the intricate regulatory roles of circRNAs in the ethylene pathway in tomato fruit, deep sequencing and bioinformatics methods were performed. After strict screening, a total of 318 circRNAs were identified. Among these circRNAs, 282 were significantly differentially expressed among wild-type and sense-/antisense-LeERF1 transgenic tomato fruits. Besides, 1254 target genes were identified and a large amount of them were found to be involved in ethylene pathway. In addition, a sophisticated regulatory model consisting of circRNAs, target genes and ethylene was set up. Importantly, 61 circRNAs were found to be potential ceRNAs to combine with miRNAs and some of the miRNAs had been revealed to participate in the ethylene signaling pathway. This research further raised the possibility that the ethylene pathway in tomato fruit may be under the regulation of various circRNAs and provided a new perspective of the roles of circRNAs. © 2017 Scandinavian Plant Physiology Society.

Supply Chain Sustainability Analysis of Three Biofuel Pathways

Energy Technology Data Exchange (ETDEWEB)

Jacob J. Jacobson; Erin Searcy; Kara Cafferty; Jennifer B. Dunn; Michael Johnson; Zhichao Wang; Michael Wang; Mary Biddy; Abhijit Dutta; Daniel Inman; Eric Tan; Sue Jones; Lesley Snowden-Swan

2013-11-01

The Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) collaborates with industrial, agricultural, and non-profit partners to develop and deploy biofuels and other biologically-derived products. As part of this effort, BETO and its national laboratory teams conduct in-depth techno-economic assessments (TEA) of technologies to produce biofuels as part state of technology (SOT) analyses. An SOT assesses progress within and across relevant technology areas based on actual experimental results relative to technical targets and cost goals from design cases and includes technical, economic, and environmental criteria as available. Overall assessments of biofuel pathways begin with feedstock production and the logistics of transporting the feedstock from the farm or plantation to the conversion facility or biorefinery. The conversion process itself is modeled in detail as part of the SOT analysis. The teams then develop an estimate of the biofuel minimum selling price (MSP) and assess the cost competitiveness of the biofuel with conventional fuels such as gasoline.

Dual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data

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Fernandez Pedro L

2005-08-01

Full Text Available Abstract Background We use an approach based on Factor Analysis to analyze datasets generated for transcriptional profiling. The method groups samples into biologically relevant categories, and enables the identification of genes and pathways most significantly associated to each phenotypic group, while allowing for the participation of a given gene in more than one cluster. Genes assigned to each cluster are used for the detection of pathways predominantly activated in that cluster by finding statistically significant associated GO terms. We tested the approach with a published dataset of microarray experiments in yeast. Upon validation with the yeast dataset, we applied the technique to a prostate cancer dataset. Results Two major pathways are shown to be activated in organ-confined, non-metastatic prostate cancer: those regulated by the androgen receptor and by receptor tyrosine kinases. A number of gene markers (HER3, IQGAP2 and POR1 highlighted by the software and related to the later pathway have been validated experimentally a posteriori on independent samples. Conclusion Using a new microarray analysis tool followed by a posteriori experimental validation of the results, we have confirmed several putative markers of malignancy associated with peptide growth factor signalling in prostate cancer and revealed others, most notably ERRB3 (HER3. Our study suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than in receptor complexes involving HER2, could play an important role in the biology of these tumors. These results provide new evidence for the role of receptor tyrosine kinases in the establishment and progression of prostate cancer.

Targetome Analysis Revealed Involvement of MiR-126 in Neurotrophin Signaling Pathway: A Possible Role in Prevention of Glioma Development.

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Rouigari, Maedeh; Dehbashi, Moein; Ghaedi, Kamran; Pourhossein, Meraj

2018-07-01

For the first time, we used molecular signaling pathway enrichment analysis to determine possible involvement of miR-126 and IRS-1 in neurotrophin pathway. In this prospective study, Validated and predicted targets (targetome) of miR-126 were collected following searching miRtarbase (http://mirtarbase.mbc.nctu.edu.tw/) and miRWalk 2.0 databases, respectively. Then, approximate expression of miR-126 targeting in Glioma tissue was examined using UniGene database (http://www.ncbi. nlm.nih.gov/unigene). In silico molecular pathway enrichment analysis was carried out by DAVID 6.7 database (http://david. abcc.ncifcrf.gov/) to explore which signaling pathway is related to miR-126 targeting and how miR-126 attributes to glioma development. MiR-126 exerts a variety of functions in cancer pathogenesis via suppression of expression of target gene including PI3K, KRAS, EGFL7, IRS-1 and VEGF. Our bioinformatic studies implementing DAVID database, showed the involvement of miR-126 target genes in several signaling pathways including cancer pathogenesis, neurotrophin functions, Glioma formation, insulin function, focal adhesion production, chemokine synthesis and secretion and regulation of the actin cytoskeleton. Taken together, we concluded that miR-126 enhances the formation of glioma cancer stem cell probably via down regulation of IRS-1 in neurotrophin signaling pathway. Copyright© by Royan Institute. All rights reserved.

Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method.

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Gao, Jun; Che, Dongsheng; Zheng, Vincent W; Zhu, Ruixin; Liu, Qi

2012-07-31

The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have proposed several

Comparison of methods for the analysis of relatively simple mediation models.

Science.gov (United States)

Rijnhart, Judith J M; Twisk, Jos W R; Chinapaw, Mai J M; de Boer, Michiel R; Heymans, Martijn W

2017-09-01

Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

Point Analysis in Java applied to histological images of the perforant pathway: a user's account.

Science.gov (United States)

Scorcioni, Ruggero; Wright, Susan N; Patrick Card, J; Ascoli, Giorgio A; Barrionuevo, Germán

2008-01-01

The freeware Java tool Point Analysis in Java (PAJ), created to perform 3D point analysis, was tested in an independent laboratory setting. The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions. The low magnification set (x2 objective) comprised the entire perforant pathway, while the high magnification set (x100 objective) allowed the identification of individual fibers. A preliminary stereological study revealed a striking linear relationship between the fiber count at high magnification and the optical density at low magnification. PAJ enabled fast analysis for down-sampled data sets and a friendly interface with automated plot drawings. Noted strengths included the multi-platform support as well as the free availability of the source code, conducive to a broad user base and maximum flexibility for ad hoc requirements. PAJ has great potential to extend its usability by (a) improving its graphical user interface, (b) increasing its input size limit, (c) improving response time for large data sets, and (d) potentially being integrated with other Java graphical tools such as ImageJ.

Allowable residual contamination levels of radionuclides in soil from pathway analysis

International Nuclear Information System (INIS)

Nyquist, J.E.; Baes, C.F. III

1987-01-01

The Remedial Action Program (RAP) at Oak Ridge National Laboratory will include well drilling, facility upgrades, and other waste management operations likely to involve soils contaminated with radionuclides. A preliminary protocol and generalized criteria for handling contaminated soils is needed to coordinate and plan RAP activities, but there exists only limited information on contaminate nature and distribution at ORNL RAP sites. Furthermore, projections of long-term decommissioning and closure options for these sites are preliminary. They have adapted a pathway analysis model, DECOM, to quantify risks to human health from radionuclides in soil and used it to outline preliminary criteria for determining the fate of contaminated soil produced during RAP activities. They assumed that the site could be available for unrestricted use immediately upon decontamination. The pathways considered are consumption of food grown on the contaminated soil, including direct ingestion of soil from poorly washed vegetables, direct radiation from the ground surface, inhalation of resuspended radioactive soil, and drinking water from a well drilled through or near the contaminated soil. We will discuss the assumptions and simplifications implicit in DECOM, the site-specific data required, and the results of initial calculations for the Oak Ridge Reservation

Thermodynamic analysis of computed pathways integrated into the metabolic networks of E. coli and Synechocystis reveals contrasting expansion potential.

Science.gov (United States)

Asplund-Samuelsson, Johannes; Janasch, Markus; Hudson, Elton P

2018-01-01

Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Bioinformatic evaluation of L-arginine catabolic pathways in 24 cyanobacteria and transcriptional analysis of genes encoding enzymes of L-arginine catabolism in the cyanobacterium Synechocystis sp. PCC 6803

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Pistorius Elfriede K

2007-11-01

Full Text Available Abstract Background So far very limited knowledge exists on L-arginine catabolism in cyanobacteria, although six major L-arginine-degrading pathways have been described for prokaryotes. Thus, we have performed a bioinformatic analysis of possible L-arginine-degrading pathways in cyanobacteria. Further, we chose Synechocystis sp. PCC 6803 for a more detailed bioinformatic analysis and for validation of the bioinformatic predictions on L-arginine catabolism with a transcript analysis. Results We have evaluated 24 cyanobacterial genomes of freshwater or marine strains for the presence of putative L-arginine-degrading enzymes. We identified an L-arginine decarboxylase pathway in all 24 strains. In addition, cyanobacteria have one or two further pathways representing either an arginase pathway or L-arginine deiminase pathway or an L-arginine oxidase/dehydrogenase pathway. An L-arginine amidinotransferase pathway as a major L-arginine-degrading pathway is not likely but can not be entirely excluded. A rather unusual finding was that the cyanobacterial L-arginine deiminases are substantially larger than the enzymes in non-photosynthetic bacteria and that they are membrane-bound. A more detailed bioinformatic analysis of Synechocystis sp. PCC 6803 revealed that three different L-arginine-degrading pathways may in principle be functional in this cyanobacterium. These are (i an L-arginine decarboxylase pathway, (ii an L-arginine deiminase pathway, and (iii an L-arginine oxidase/dehydrogenase pathway. A transcript analysis of cells grown either with nitrate or L-arginine as sole N-source and with an illumination of 50 μmol photons m-2 s-1 showed that the transcripts for the first enzyme(s of all three pathways were present, but that the transcript levels for the L-arginine deiminase and the L-arginine oxidase/dehydrogenase were substantially higher than that of the three isoenzymes of L-arginine decarboxylase. Conclusion The evaluation of 24

Pathway analysis for intracellular Porphyromonas gingivalis using a strain ATCC 33277 specific database

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Wang Tiansong

2009-09-01

Full Text Available Abstract Background Porphyromonas gingivalis is a Gram-negative intracellular pathogen associated with periodontal disease. We have previously reported on whole-cell quantitative proteomic analyses to investigate the differential expression of virulence factors as the organism transitions from an extracellular to intracellular lifestyle. The original results with the invasive strain P. gingivalis ATCC 33277 were obtained using the genome sequence available at the time, strain W83 [GenBank: AE015924]. We present here a re-processed dataset using the recently published genome annotation specific for strain ATCC 33277 [GenBank: AP009380] and an analysis of differential abundance based on metabolic pathways rather than individual proteins. Results Qualitative detection was observed for 1266 proteins using the strain ATCC 33277 annotation for 18 hour internalized P. gingivalis within human gingival epithelial cells and controls exposed to gingival cell culture medium, an improvement of 7% over the W83 annotation. Internalized cells showed increased abundance of proteins in the energy pathway from asparagine/aspartate amino acids to ATP. The pathway producing one short chain fatty acid, propionate, showed increased abundance, while that of another, butyrate, trended towards decreased abundance. The translational machinery, including ribosomal proteins and tRNA synthetases, showed a significant increase in protein relative abundance, as did proteins responsible for transcription. Conclusion Use of the ATCC 33277 specific genome annotation resulted in improved proteome coverage with respect to the number of proteins observed both qualitatively in terms of protein identifications and quantitatively in terms of the number of calculated abundance ratios. Pathway analysis showed a significant increase in overall protein synthetic and transcriptional machinery in the absence of significant growth. These results suggest that the interior of host cells

KEGGParser: parsing and editing KEGG pathway maps in Matlab.

Science.gov (United States)

Arakelyan, Arsen; Nersisyan, Lilit

2013-02-15

KEGG pathway database is a collection of manually drawn pathway maps accompanied with KGML format files intended for use in automatic analysis. KGML files, however, do not contain the required information for complete reproduction of all the events indicated in the static image of a pathway map. Several parsers and editors of KEGG pathways exist for processing KGML files. We introduce KEGGParser-a MATLAB based tool for KEGG pathway parsing, semiautomatic fixing, editing, visualization and analysis in MATLAB environment. It also works with Scilab. The source code is available at http://www.mathworks.com/matlabcentral/fileexchange/37561.

Multi-criteria decision analysis of energy system transformation pathways: A case study for Switzerland

International Nuclear Information System (INIS)

Volkart, Kathrin; Weidmann, Nicolas; Bauer, Christian; Hirschberg, Stefan

2017-01-01

Two recent political decisions are expected to frame the development of the Swiss energy system in the coming decades: the nuclear phase-out and the greenhouse gas (GHG) emission reduction target. To accomplish both of them, low-carbon technologies based on renewable energy and Carbon Capture and Storage (CCS) are expected to gain importance. The objective of the present work is to support prospective Swiss energy policy-making by providing a detailed sustainability analysis of possible energy system transformation pathways. For this purpose, the results of the scenario quantification with an energy system model are coupled with multi-criteria sustainability analysis. Two climate protection and one reference scenario are addressed, and the trade-offs between the scenarios are analysed based on a set of 12 interdisciplinary indicators. Implementing a stringent climate policy in Switzerland is associated with co-benefits such as less fossil resource use, less fatalities in severe accidents in the energy sector, less societal conflicts and higher resource autonomy. The availability and implementation of CCS allows for achieving the GHG emission reduction target at lower costs, but at the expense of a more fossil fuel-based energy system. - Highlights: • Three energy system transformation pathways for Switzerland are analysed. • A set of policy-relevant sustainability indicators are quantified for each pathway. • Implementing a stringent climate policy in Switzerland is associated with co-benefits. • In the CCS scenario fossil fuel use increases, but the total system costs are lower. • Fossil-fuelled transport substantially contributes to most of the addressed criteria.

Cotton transformation via pollen tube pathway.

Science.gov (United States)

Wang, Min; Zhang, Baohong; Wang, Qinglian

2013-01-01

Although many gene transfer methods have been employed for successfully obtaining transgenic cotton, the major constraint in cotton improvement is the limitation of genotype because the majority of transgenic methods require plant regeneration from a single transformed cell which is limited by cotton tissue culture. Comparing with other plant species, it is difficult to induce plant regeneration from cotton; currently, only a limited number of cotton cultivars can be cultured for obtaining regenerated plants. Thus, development of a simple and genotype-independent genetic transformation method is particularly important for cotton community. In this chapter, we present a simple, cost-efficient, and genotype-independent cotton transformation method-pollen tube pathway-mediated transformation. This method uses pollen tube pathway to deliver transgene into cotton embryo sacs and then insert foreign genes into cotton genome. There are three major steps for pollen tube pathway-mediated genetic transformation, which include injection of -foreign genes into pollen tube, integration of foreign genes into plant genome, and selection of transgenic plants.

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors

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Paweletz Cloud

2010-06-01

Full Text Available Abstract Background Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. Methods We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. Results The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90% sensitivity but relatively low (50% specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. Conclusions These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical

Global developmental gene expression and pathway analysis of normal brain development and mouse models of human neuronal migration defects.

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Tiziano Pramparo

2011-03-01

Full Text Available Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε, and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can

Metabolic pathways for the whole community.

Science.gov (United States)

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Enriched pathways for major depressive disorder identified from a genome-wide association study.

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Kao, Chung-Feng; Jia, Peilin; Zhao, Zhongming; Kuo, Po-Hsiu

2012-11-01

Major depressive disorder (MDD) has caused a substantial burden of disease worldwide with moderate heritability. Despite efforts through conducting numerous association studies and now, genome-wide association (GWA) studies, the success of identifying susceptibility loci for MDD has been limited, which is partially attributed to the complex nature of depression pathogenesis. A pathway-based analytic strategy to investigate the joint effects of various genes within specific biological pathways has emerged as a powerful tool for complex traits. The present study aimed to identify enriched pathways for depression using a GWA dataset for MDD. For each gene, we estimated its gene-wise p value using combined and minimum p value, separately. Canonical pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta were used. We employed four pathway-based analytic approaches (gene set enrichment analysis, hypergeometric test, sum-square statistic, sum-statistic). We adjusted for multiple testing using Benjamini & Hochberg's method to report significant pathways. We found 17 significantly enriched pathways for depression, which presented low-to-intermediate crosstalk. The top four pathways were long-term depression (p⩽1×10-5), calcium signalling (p⩽6×10-5), arrhythmogenic right ventricular cardiomyopathy (p⩽1.6×10-4) and cell adhesion molecules (p⩽2.2×10-4). In conclusion, our comprehensive pathway analyses identified promising pathways for depression that are related to neurotransmitter and neuronal systems, immune system and inflammatory response, which may be involved in the pathophysiological mechanisms underlying depression. We demonstrated that pathway enrichment analysis is promising to facilitate our understanding of complex traits through a deeper interpretation of GWA data. Application of this comprehensive analytic strategy in upcoming GWA data for depression could validate the findings reported in this study.

Pathways to lean software development: An analysis of effective methods of change

Science.gov (United States)

Hanson, Richard D.

This qualitative Delphi study explored the challenges that exist in delivering software on time, within budget, and with the original scope identified. The literature review identified many attempts over the past several decades to reform the methods used to develop software. These attempts found that the classical waterfall method, which is firmly entrenched in American business today was to blame for this difficulty (Chatterjee, 2010). Each of these proponents of new methods sought to remove waste, lighten out the process, and implement lean principles in software development. Through this study, the experts evaluated the barriers to effective development principles and defined leadership qualities necessary to overcome these barriers. The barriers identified were issues of resistance to change, risk and reward issues, and management buy-in. Thirty experts in software development from several Fortune 500 companies across the United States explored each of these issues in detail. The conclusion reached by these experts was that visionary leadership is necessary to overcome these challenges.

A path flux analysis method for the reduction of detailed chemical kinetic mechanisms

Energy Technology Data Exchange (ETDEWEB)

Sun, Wenting; Ju, Yiguang [Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 (United States); Chen, Zheng [State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871 (China); Gou, Xiaolong [School of Power Engineering, Chongqing University, Chongqing 400044 (China)

2010-07-15

A direct path flux analysis (PFA) method for kinetic mechanism reduction is proposed and validated by using high temperature ignition, perfect stirred reactors, and steady and unsteady flame propagations of n-heptane and n-decane/air mixtures. The formation and consumption fluxes of each species at multiple reaction path generations are analyzed and used to identify the important reaction pathways and the associated species. The formation and consumption path fluxes used in this method retain flux conservation information and are used to define the path indexes for the first and the second generation reaction paths related to a targeted species. Based on the indexes of each reaction path for the first and second generations, different sized reduced chemical mechanisms which contain different number of species are generated. The reduced mechanisms of n-heptane and n-decane obtained by using the present method are compared to those generated by the direct relation graph (DRG) method. The reaction path analysis for n-decane is conducted to demonstrate the validity of the present method. The comparisons of the ignition delay times, flame propagation speeds, flame structures, and unsteady spherical flame propagation processes showed that with either the same or significantly less number of species, the reduced mechanisms generated by the present PFA are more accurate than that of DRG in a broad range of initial pressures and temperatures. The method is also integrated with the dynamic multi-timescale method and a further increase of computation efficiency is achieved. (author)

Metabolomics coupled with multivariate data and pathway analysis on potential biomarkers in cholestasis and intervention effect of Paeonia lactiflora Pall.

Directory of Open Access Journals (Sweden)

Xiao eMa

2016-02-01

Full Text Available Background: The dried root of Paeonia lactiflora Pall. (PLP is a classical Chinese herbal medicine that has been used to treat hepatic disease for thousands of years. Our previous work suggested that PLP can be used to treat hepatitis with severe cholestasis. This study explored the mechanism by which PLP affects ANIT-induced cholestasis in rats using a metabolomics approach.Methods: The effects of PLP on serum indices (TBIL, DBIL, AST, ALT, ALP and TBA and the histopathology of the liver were analyzed. Moreover, UHPLC-Q-TOF was performed to identify the possible effect of PLP on metabolites. The pathway analysis was conducted to illustrate the pathways and network by which PLP treats cholestasis. Result: High-dose PLP remarkably down-regulated the serum indices and alleviated histological damage to the liver. Metabolomics analyses showed that the therapeutic effect of high-dose PLP is mainly associated with the regulation of several metabolites, such as glycocholic acid, taurocholic acid, glycochenodeoxycholic acid, L(D-arginine and L-tryptophan. A pathway analysis showed that the metabolites were related to bile acid secretion and amino acid metabolism. In addition, the significant changes in bile acid transporters also indicated that bile acid metabolism might be involved in the therapeutic effect of PLP on cholestasis. Moreover, a principal component analysis indicated that the metabolites in the high-dose PLP group were closer to those of the control, whereas those of the moderate dose or low-dose PLP group were closer to those of the ANIT group. This finding indicated that metabolites may be responsible for the differences between the effects of low-dose and moderate-dose PLP. Conclusions: The therapeutic effect of high-dose PLP on cholestasis is possibly related to regulation of bile acid secretion and amino acid metabolism. Moreover, these findings may help better understand the mechanisms of disease and provide a potential therapy for

In response to 'Can sugars be produced from fatty acids? A test case for pathway analysis tools'.

Science.gov (United States)

Faust, Karoline; Croes, Didier; van Helden, Jacques

2009-12-01

In their article entitled 'Can sugars be produced from fatty acids? A test case for pathway analysis tools' de Figueiredo and co-authors assess the performance of three pathway prediction tools (METATOOL, PathFinding and Pathway Hunter Tool) using the synthesis of glucose-6-phosphate (G6P) from acetyl-CoA in humans as a test case. We think that this article is biased for three reasons: (i) the metabolic networks used as input for the respective tools were of very different sizes; (ii) the 'assessment' is restricted to two study cases; (iii) developers are inherently more skilled to use their own tools than those developed by other people. We extended the analyses led by de Figueiredo and clearly show that the apparent superior performance of their tool (METATOOL) is partly due to the differences in input network sizes. We also see a conceptual problem in the comparison of tools that serve different purposes. In our opinion, metabolic path finding and elementary mode analysis are answering different biological questions, and should be considered as complementary rather than competitive approaches. Supplementary data are available at Bioinformatics online.

iTRAQ proteomics analysis reveals that PI3K is highly associated with bupivacaine-induced neurotoxicity pathways.

Science.gov (United States)

Zhao, Wei; Liu, Zhongjie; Yu, Xujiao; Lai, Luying; Li, Haobo; Liu, Zipeng; Li, Le; Jiang, Shan; Xia, Zhengyuan; Xu, Shi-yuan

2016-02-01

Bupivacaine, a commonly used local anesthetic, has potential neurotoxicity through diverse signaling pathways. However, the key mechanism of bupivacaine-induced neurotoxicity remains unclear. Cultured human SH-SY5Y neuroblastoma cells were treated (bupivacaine) or untreated (control) with bupivacaine for 24 h. Compared to the control group, bupivacaine significantly increased cyto-inhibition, cellular reactive oxygen species, DNA damage, mitochondrial injury, apoptosis (increased TUNEL-positive cells, cleaved caspase 3, and Bcl-2/Bax), and activated autophagy (enhanced LC3II/LC3I ratio). To explore changes in protein expression and intercommunication among the pathways involved in bupivacaine-induced neurotoxicity, an 8-plex iTRAQ proteomic technique and bioinformatics analysis were performed. Compared to the control group, 241 differentially expressed proteins were identified, of which, 145 were up-regulated and 96 were down-regulated. Bioinformatics analysis of the cross-talk between the significant proteins with altered expression in bupivacaine-induced neurotoxicity indicated that phosphatidyl-3-kinase (PI3K) was the most frequently targeted protein in each of the interactions. We further confirmed these results by determining the downstream targets of the identified signaling pathways (PI3K, Akt, FoxO1, Erk, and JNK). In conclusion, our study demonstrated that PI3K may play a central role in contacting and regulating the signaling pathways that contribute to bupivacaine-induced neurotoxicity. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Model of an Integrated Immune System Pathway in Homo sapiens and Its Interaction with Superantigen Producing Expression Regulatory Pathway in Staphylococcus aureus: Comparing Behavior of Pathogen Perturbed and Unperturbed Pathway

Science.gov (United States)

Tomar, Namrata; De, Rajat K.

2013-01-01

Response of an immune system to a pathogen attack depends on the balance between the host immune defense and the virulence of the pathogen. Investigation of molecular interactions between the proteins of a host and a pathogen helps in identifying the pathogenic proteins. It is necessary to understand the dynamics of a normally behaved host system to evaluate the capacity of its immune system upon pathogen attack. In this study, we have compared the behavior of an unperturbed and pathogen perturbed host system. Moreover, we have developed a formalism under Flux Balance Analysis (FBA) for the optimization of conflicting objective functions. We have constructed an integrated pathway system, which includes Staphylococcal Superantigen (SAg) expression regulatory pathway and TCR signaling pathway of Homo sapiens. We have implemented the method on this pathway system and observed the behavior of host signaling molecules upon pathogen attack. The entire study has been divided into six different cases, based on the perturbed/unperturbed conditions. In other words, we have investigated unperturbed and pathogen perturbed human TCR signaling pathway, with different combinations of optimization of concentrations of regulatory and signaling molecules. One of these cases has aimed at finding out whether minimization of the toxin production in a pathogen leads to the change in the concentration levels of the proteins coded by TCR signaling pathway genes in the infected host. Based on the computed results, we have hypothesized that the balance between TCR signaling inhibitory and stimulatory molecules can keep TCR signaling system into resting/stimulating state, depending upon the perturbation. The proposed integrated host-pathogen interaction pathway model has accurately reflected the experimental evidences, which we have used for validation purpose. The significance of this kind of investigation lies in revealing the susceptible interaction points that can take back the

Variant allele frequency enrichment analysis in vitro reveals sonic hedgehog pathway to impede sustained temozolomide response in GBM.

Science.gov (United States)

Biswas, Nidhan K; Chandra, Vikas; Sarkar-Roy, Neeta; Das, Tapojyoti; Bhattacharya, Rabindra N; Tripathy, Laxmi N; Basu, Sunandan K; Kumar, Shantanu; Das, Subrata; Chatterjee, Ankita; Mukherjee, Ankur; Basu, Pryiadarshi; Maitra, Arindam; Chattopadhyay, Ansuman; Basu, Analabha; Dhara, Surajit

2015-01-21

Neoplastic cells of Glioblastoma multiforme (GBM) may or may not show sustained response to temozolomide (TMZ) chemotherapy. We hypothesize that TMZ chemotherapy response in GBM is predetermined in its neoplastic clones via a specific set of mutations that alter relevant pathways. We describe exome-wide enrichment of variant allele frequencies (VAFs) in neurospheres displaying contrasting phenotypes of sustained versus reversible TMZ-responses in vitro. Enrichment of VAFs was found on genes ST5, RP6KA1 and PRKDC in cells showing sustained TMZ-effect whereas on genes FREM2, AASDH and STK36, in cells showing reversible TMZ-effect. Ingenuity pathway analysis (IPA) revealed that these genes alter cell-cycle, G2/M-checkpoint-regulation and NHEJ pathways in sustained TMZ-effect cells whereas the lysine-II&V/phenylalanine degradation and sonic hedgehog (Hh) pathways in reversible TMZ-effect cells. Next, we validated the likely involvement of the Hh-pathway in TMZ-response on additional GBM neurospheres as well as on GBM patients, by extracting RNA-sequencing-based gene expression data from the TCGA-GBM database. Finally, we demonstrated TMZ-sensitization of a TMZ non-responder neurosphere in vitro by treating them with the FDA-approved pharmacological Hh-pathway inhibitor vismodegib. Altogether, our results indicate that the Hh-pathway impedes sustained TMZ-response in GBM and could be a potential therapeutic target to enhance TMZ-response in this malignancy.

Coloured Petri Nets: Basic Concepts, Analysis Methods and Practical Use. Vol. 2, Analysis Methods

DEFF Research Database (Denmark)

Jensen, Kurt

ideas behind the analysis methods are described as well as the mathematics on which they are based and also how the methods are supported by computer tools. Some parts of the volume are theoretical while others are application oriented. The purpose of the volume is to teach the reader how to use......This three-volume work presents a coherent description of the theoretical and practical aspects of coloured Petri nets (CP-nets). The second volume contains a detailed presentation of the analysis methods for CP-nets. They allow the modeller to investigate dynamic properties of CP-nets. The main...... the formal analysis methods, which does not require a deep understanding of the underlying mathematical theory....

Savannah River Laboratory DOSTOMAN code: a compartmental pathways computer model of contaminant transport

International Nuclear Information System (INIS)

King, C.M.; Wilhite, E.L.; Root, R.W. Jr.

1985-01-01

The Savannah River Laboratory DOSTOMAN code has been used since 1978 for environmental pathway analysis of potential migration of radionuclides and hazardous chemicals. The DOSTOMAN work is reviewed including a summary of historical use of compartmental models, the mathematical basis for the DOSTOMAN code, examples of exact analytical solutions for simple matrices, methods for numerical solution of complex matrices, and mathematical validation/calibration of the SRL code. The review includes the methodology for application to nuclear and hazardous chemical waste disposal, examples of use of the model in contaminant transport and pathway analysis, a user's guide for computer implementation, peer review of the code, and use of DOSTOMAN at other Department of Energy sites. 22 refs., 3 figs

Transcriptomic analysis in the developing zebrafish embryo after compound exposure: Individual gene expression and pathway regulation

Energy Technology Data Exchange (ETDEWEB)

Hermsen, Sanne A.B., E-mail: Sanne.Hermsen@rivm.nl [Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven (Netherlands); Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht (Netherlands); Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80.178, 3508 TD, Utrecht (Netherlands); Pronk, Tessa E. [Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven (Netherlands); Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht (Netherlands); Brandhof, Evert-Jan van den [Centre for Environmental Quality, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven (Netherlands); Ven, Leo T.M. van der [Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven (Netherlands); Piersma, Aldert H. [Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven (Netherlands); Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80.178, 3508 TD, Utrecht (Netherlands)

2013-10-01

The zebrafish embryotoxicity test is a promising alternative assay for developmental toxicity. Classically, morphological assessment of the embryos is applied to evaluate the effects of compound exposure. However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased. For defining gene expression signatures of developmental toxicity, we explored the possibility of using gene expression signatures of compound exposures based on commonly expressed individual genes as well as based on regulated gene pathways. Four developmental toxic compounds were tested in concentration-response design, caffeine, carbamazepine, retinoic acid and valproic acid, and two non-embryotoxic compounds, D-mannitol and saccharin, were included. With transcriptomic analyses we were able to identify commonly expressed genes, which were mostly development related, after exposure to the embryotoxicants. We also identified gene pathways regulated by the embryotoxicants, suggestive of their modes of action. Furthermore, whereas pathways may be regulated by all compounds, individual gene expression within these pathways can differ for each compound. Overall, the present study suggests that the use of individual gene expression signatures as well as pathway regulation may be useful starting points for defining gene biomarkers for predicting embryotoxicity. - Highlights: • The zebrafish embryotoxicity test in combination with transcriptomics was used. • We explored two approaches of defining gene biomarkers for developmental toxicity. • Four compounds in concentration-response design were tested. • We identified commonly expressed individual genes as well as regulated gene pathways. • Both approaches seem suitable starting points for defining gene biomarkers.

Well-to-wheels analysis of hydrogen based fuel-cell vehicle pathways in Shanghai

International Nuclear Information System (INIS)

Huang Zhijia; Zhang Xu

2006-01-01

Due to high energy efficiency and zero emissions, some believe fuel cell vehicles (FCVs) could revolutionize the automobile industry by replacing internal combustion engine technology, and first boom in China. However, hydrogen infrastructure is one of the major barriers. Because different H 2 pathways have very different energy and emissions effects, the well-to-wheels (WTW) analyses are necessary for adequately evaluating fuel/vehicle systems. The pathways used to supply H 2 for FCVs must be carefully examined by their WTW energy use, greenhouse gases (GHGs) emissions, total criteria pollutions emissions, and urban criteria pollutions emissions. Ten hydrogen pathways in Shanghai have been simulated. The results include well-to-wheels energy use, GHGs emissions, total criteria pollutions and urban criteria pollutions. A fuel-cycle model developed at Argonne National Laboratory-called the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model-was used to evaluate well-to-wheels energy and emissions impacts of hydrogen pathways in this study. Because the initial GREET model had no coal and naphtha-based hydrogen pathways, four hydrogen pathway computer programs were added to GREET in the research. To analyze uncertain impacts, commercial software, Crystal Ball(TM) was used to conduct Monte Carlo simulations. Hence, instead of point estimates, the results of this study were probability distributions. Through the research of H 2 pathways in Shanghai, the following conclusions were achieved:(1)All the pathways have significant reductions in WTW petroleum use, except two H 2 pathways from naphtha, which achieve about 20% reductions in WTW petroleum. (2)All the pathways have significant reductions in WTW urban criteria pollutions emissions, except two H 2 pathways from coal, which result in significant increases in WTW urban SO X emissions. (3)The NG-based H 2 pathways have the best WTW energy efficiencies, and the electrolysis H 2 pathways

Methods of Multivariate Analysis

CERN Document Server

Rencher, Alvin C

2012-01-01

Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

Multivariate analysis: models and method

International Nuclear Information System (INIS)

Sanz Perucha, J.

1990-01-01

Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

TSLP signaling pathway map: a platform for analysis of TSLP-mediated signaling.

Science.gov (United States)

Zhong, Jun; Sharma, Jyoti; Raju, Rajesh; Palapetta, Shyam Mohan; Prasad, T S Keshava; Huang, Tai-Chung; Yoda, Akinori; Tyner, Jeffrey W; van Bodegom, Diederik; Weinstock, David M; Ziegler, Steven F; Pandey, Akhilesh

2014-01-01

Thymic stromal lymphopoietin (TSLP) is a four-helix bundle cytokine that plays a critical role in the regulation of immune responses and in the differentiation of hematopoietic cells. TSLP signals through a heterodimeric receptor complex consisting of an interleukin-7 receptor α chain and a unique TSLP receptor (TSLPR) [also known as cytokine receptor-like factor 2 (CRLF2)]. Cellular targets of TSLP include dendritic cells, B cells, mast cells, regulatory T (Treg) cells and CD4+ and CD8+ T cells. The TSLP/TSLPR axis can activate multiple signaling transduction pathways including the JAK/STAT pathway and the PI-3 kinase pathway. Aberrant TSLP/TSLPR signaling has been associated with a variety of human diseases including asthma, atopic dermatitis, nasal polyposis, inflammatory bowel disease, eosinophilic eosophagitis and, most recently, acute lymphoblastic leukemia. A centralized resource of the TSLP signaling pathway cataloging signaling events is not yet available. In this study, we present a literature-annotated resource of reactions in the TSLP signaling pathway. This pathway map is publicly available through NetPath (http://www.netpath.org/), an open access signal transduction pathway resource developed previously by our group. This map includes 236 molecules and 252 reactions that are involved in TSLP/TSLPR signaling pathway. We expect that the TSLP signaling pathway map will provide a rich resource to study the biology of this important cytokine as well as to identify novel therapeutic targets for diseases associated with dysregulated TSLP/TSLPR signaling. Database URL: http://www.netpath.org/pathways?path_id=NetPath_24.

Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

Directory of Open Access Journals (Sweden)

Ram Prahlad T

2008-08-01

Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models

Integrative analysis of RUNX1 downstream pathways and target genes

Directory of Open Access Journals (Sweden)

Liu Marjorie

2008-07-01

Full Text Available Abstract Background The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML. The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia. Results Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1 cell lines with RUNX1 mutations from FPD-AML patients, 2 over-expression of RUNX1 and CBFβ, and 3 Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFβ. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes. Conclusion This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease

Evaluating the effect of clinical care pathways on quality of cancer care: analysis of breast, colon and rectal cancer pathways.

Science.gov (United States)

Bao, Han; Yang, Fengjuan; Su, Shaofei; Wang, Xinyu; Zhang, Meiqi; Xiao, Yaming; Jiang, Hao; Wang, Jiaying; Liu, Meina

2016-05-01

Substantial gaps exist between clinical practice and evidence-based cancer care, potentially leading to adverse clinical outcomes and decreased quality of life for cancer patients. This study aimed to evaluate the usefulness of clinical pathways as a tool for improving quality of cancer care, using breast, colon, and rectal cancer pathways as demonstrations. Newly diagnosed patients with invasive breast, colon, and rectal cancer were enrolled as pre-pathway groups, while patients with the same diagnoses treated according to clinical pathways were recruited for post-pathway groups. Compliance with preoperative core biopsy or fine-needle aspiration, utilization of sentinel lymph node biopsy, and proportion of patients whose tumor hormone receptor status was stated in pathology report were significantly increased after implementation of clinical pathway for breast cancer. For colon cancer, compliance with two care processes was significantly improved: surgical resection with anastomosis and resection of at least 12 lymph nodes. Regarding rectal cancer, there was a significant increase in compliance with preoperative evaluation of depth of tumor invasion, total mesorectal excision treatment of middle- or low-position rectal cancer, and proportion of patients who had undergone rectal cancer surgery whose pathology report included margin status. Moreover, total length of hospital stay was decreased remarkably for all three cancer types, and postoperative complications remained unchanged following implementation of the clinical pathways. Clinical pathways can improve compliance with standard care by implementing evidence-based quality indicators in daily practice, which could serve as a useful tool for narrowing the gap between clinical practice and evidence-based care.

Pathway-based analysis of genome-wide siRNA screens reveals the regulatory landscape of APP processing.

Directory of Open Access Journals (Sweden)

Luiz Miguel Camargo

Full Text Available The progressive aggregation of Amyloid-β (Aβ in the brain is a major trait of Alzheimer's Disease (AD. Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP. Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes (CLU, BIN1, CR1, PICALM, TREM2, SORL1, MEF2C, DSG2, EPH1A recently implicated with AD through genome wide association studies (GWAS and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the "regulatory landscape" of APP.

Pathway analysis for a contaminated landfill in Middlesex, New Jersey

International Nuclear Information System (INIS)

Yu, C.; Merry-Libby, P.; Yang, J.Y.

1985-01-01

Under the Formerly Utilized Sites Remedial Action Program, the US Department of Energy began excavating contaminated materials from the Middlesex Municipal landfill in 1984. A total of 16,000 m 3 of landfill materials covering a 0.2-ha area was excavated, of which 11,000 m 3 was contaminated and has been transported to the nearby sampling plant site for interim storage. Based on the pathway analysis for the onsite and near-site resident scenarios, the radiation dose rates and radionuclide concentrations in groundwater would be below the regulatory requirements for both the short-term and long-term scenarios. Hence, the potential health risks to maximally exposed individuals due to radioactive releases from the Middlesex landfill would be insignificant

Survival-related profile, pathways, and transcription factors in ovarian cancer.

Directory of Open Access Journals (Sweden)

Anne P G Crijns

2009-02-01

Full Text Available BACKGROUND: Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1 to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2 to assess the association of pathways and transcription factors with overall survival, and (3 to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers. METHODS AND FINDINGS: According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using approximately 35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19

Analysis apparatus and method of analysis

International Nuclear Information System (INIS)

1976-01-01

A continuous streaming method developed for the excution of immunoassays is described in this patent. In addition, a suitable apparatus for the method was developed whereby magnetic particles are automatically employed for the consecutive analysis of a series of liquid samples via the RIA technique

Attention to impact pathways in EISs of large dam projects

International Nuclear Information System (INIS)

Brismar, Anna

2004-01-01

The importance of addressing cumulative environmental impacts in Environmental Impact Statements (EISs) of large development projects is increasingly underlined. However, cumulative impacts are generated through complex impact pathways, involving multiple root causes and lower and higher order effects, interlinked by cause-effect relationships. Consideration to potential impact pathways may thus be difficult without appropriate analytical methods, expertise, and supportive Environmental Impact Assessment guidelines and terms-of-references (TOR). This paper presents the results of an analysis of six EISs prepared for large dam projects between 1994 and 2001. The objective was to analyze if, how, and to what extent potential impact pathways involved in the generation of dam-related cumulative impacts have been addressed in the analyzed material. For this purpose, a theoretical framework was developed, which identifies four key root causes, their potential effects, and associated cause-effect relationships. The analysis revealed various shortcomings. Important imbalances were found in the degree of attention given to effects of different categories. Lower order effects received greater attention than higher order, and the potential effects of reservoir filling were more extensively attended to than those of flow blockage, storage, and regulation. Most importantly, little effort was made to carefully explain the potential impact pathways involved; root causes were often referred to in general terms only, and potential pathways leading up to an anticipated higher order effect or following upon an expected lower order effect were often inadequately addressed or ignored. Probable reasons for the discovered shortcomings are discussed and recommendations are presented for improving the World Bank EIA guidelines for large dam projects

Assessing invasion process through pathway and vector analysis: case of saltcedar (Tamarix spp.

Directory of Open Access Journals (Sweden)

Evangelina Natale

2012-12-01

Full Text Available Biological invasions are one of the most pervasive environmental threats to native ecosystems worldwide. The spontaneous spread ofsaltcedar is a particular threat to biodiversity conservation in arid and semiarid environments. In Argentina, three species belonging to this genus have been recognized as invaders. The aim of the present study was to identify main dispersal vectors and pathways to refine risk analysis and increase our ability to predict new areas at risk of Tamarix establishment. We surveyed and categorized 223 populations, 39% as invasive, 26% as established, 21% as contained and 14% as detected in nature. Dispersion of saltcedar was found to be associated with watercourses and human-driven disturbances; in addition roads were found to be relevant for the introduction of propagules in newenvironments. Considering the potential impact of saltcedar invasion and that it is an easily wind-dispersed invasive, it is necessary toimplement strategies to monitor dispersal pathways and take actions to eliminate invasion foci, particularly in vulnerable and highconservation value areas.

RAMM: a system of computer programs for radionuclide pathway analysis calculations

International Nuclear Information System (INIS)

Lyon, R.B.

1976-09-01

A generalized system of computer programs, designated RAMM (Radioactive Materials Management) system, has been developed to assist in the analysis of the movement of radionuclides through the environment to man. RAMM incorporates the GASP IV continuous/discrete simulation system. A nodal approach is used whereby the system to be analyzed is split up into parts small enough that the distribution of nuclides within the node may be taken to be homogeneous. Pathways are defined between nodes, and appropriate transfer coefficients are input or generated. Output includes the time dependent contents of the nodes and dose rates, integrated doses and dose commitments of selected nodes. (author)

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

KAUST Repository

MacLean, Adam L.

2015-12-16

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality.

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Jiang, Yue; Xiong, Xuejian; Danska, Jayne; Parkinson, John

2016-01-12

Metatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function. Here, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76% of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions. The application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of

The early identification of risk factors on the pathway to school dropout in the SIODO study: a sequential mixed-methods study

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Theunissen Marie-José

2012-11-01

Full Text Available Abstract Background School dropout is a persisting problem with major socioeconomic consequences. Although poor health probably contributes to pathways leading to school dropout and health is likely negatively affected by dropout, these issues are relatively absent on the public health agenda. This emphasises the importance of integrative research aimed at identifying children at risk for school dropout at an early stage, discovering how socioeconomic status and gender affect health-related pathways that lead to dropout and developing a prevention tool that can be used in public health services for youth. Methods/design The SIODO study is a sequential mixed-methods study. A case–control study will be conducted among 18 to 24 year olds in the south of the Netherlands (n = 580. Data are currently being collected from compulsory education departments at municipalities (dropout data, regional public health services (developmental data from birth onwards and an additional questionnaire has been sent to participants (e.g. personality data. Advanced analyses, including cluster and factor analyses, will be used to identify children at risk at an early stage. Using the quantitative data, we have planned individual interviews with participants and focus groups with important stakeholders such as parents, teachers and public health professionals. A thematic content analysis will be used to analyse the qualitative data. Discussion The SIODO study will use a life-course perspective, the ICF-CY model to group the determinants and a mixed-methods design. In this respect, the SIODO study is innovative because it both broadens and deepens the study of health-related determinants of school dropout. It examines how these determinants contribute to socioeconomic and gender differences in health and contributes to the development of a tool that can be used in public health practice to tackle the problem of school dropout at its roots.

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

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2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

Path analysis suggests phytoene accumulation is the key step limiting the carotenoid pathway in white carrot roots

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Carlos Antonio Fernandes Santos

2005-01-01

Full Text Available Two F2 carrot (Daucus carota L. populations (orange rooted Brasilia x very dark orange rooted High Carotene Mass - HCM cross and the dark orange rooted cultivated variety B493 x white rooted wild carrot Queen Anne's Lace - QAL cross with very unrelated genetic backgrounds were used to investigate intrinsic factors limiting carotenoid accumulation in carrots by applying phenotypic correlation and path analysis to study the relationships between major root carotenes, root color and several other morphological traits. Most of the correlations between traits were close and agreed in sign between the two populations. Root weight had a moderate to highly significant positive correlation with leaf length, root length and top and middle root diameter. Although phenotypic correlations failed to identify the order of the substrates and products in the carotenoid pathway the correct order of substrates and products (phytoene -> zeta-carotene -> lycopene was identified in the causal diagram of beta-carotene for the Brasilia x HCM population. Path analysis of beta-carotene synthesis in the B493 x QAL population suggested that selection for root carotenes had little effect on plant morphological traits. Causal model of beta-carotene and lycopene in the B493 x QAL population suggested that phytoene synthesis is the key step limiting the carotenoid pathway in white carrots. Path analysis, first presented by Sewall Wright to study quantitative traits, appears to be a powerful statistical approach for the identification of key compounds in complex pathways.

Info-Gap robustness pathway method for transitioning of urban drainage systems under deep uncertainties.

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Zischg, Jonatan; Goncalves, Mariana L R; Bacchin, Taneha Kuzniecow; Leonhardt, Günther; Viklander, Maria; van Timmeren, Arjan; Rauch, Wolfgang; Sitzenfrei, Robert

2017-09-01

In the urban water cycle, there are different ways of handling stormwater runoff. Traditional systems mainly rely on underground piped, sometimes named 'gray' infrastructure. New and so-called 'green/blue' ambitions aim for treating and conveying the runoff at the surface. Such concepts are mainly based on ground infiltration and temporal storage. In this work a methodology to create and compare different planning alternatives for stormwater handling on their pathways to a desired system state is presented. Investigations are made to assess the system performance and robustness when facing the deeply uncertain spatial and temporal developments in the future urban fabric, including impacts caused by climate change, urbanization and other disruptive events, like shifts in the network layout and interactions of 'gray' and 'green/blue' structures. With the Info-Gap robustness pathway method, three planning alternatives are evaluated to identify critical performance levels at different stages over time. This novel methodology is applied to a real case study problem where a city relocation process takes place during the upcoming decades. In this case study it is shown that hybrid systems including green infrastructures are more robust with respect to future uncertainties, compared to traditional network design.

Multivariate analysis methods in physics

International Nuclear Information System (INIS)

Wolter, M.

2007-01-01

A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru

Evaluating between-pathway models with expression data.

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Hescott, B J; Leiserson, M D M; Cowen, L J; Slonim, D K

2010-03-01

Between-pathway models (BPMs) are network motifs consisting of pairs of putative redundant pathways. In this article, we show how adding another source of high-throughput data--microarray gene expression data from knockout experiments--allows us to identify a compensatory functional relationship between genes from the two BPM pathways. We evaluate the quality of the BPMs from four different studies, and we describe how our methods might be extended to refine pathways.