Publications by authors named "Michiel E Adriaens"

27 Publications

  • Page 1 of 1

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge.

PLoS Comput Biol 2021 Mar 31;17(3):e1008852. Epub 2021 Mar 31.

Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.
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http://dx.doi.org/10.1371/journal.pcbi.1008852DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011733PMC
March 2021

Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences.

Eur Heart J 2021 01;42(2):162-174

Department of Cardiology, Cardiovascular Research Institute (CARIM), Maastricht University Medical Center, PO Box 5800, 6202 AZ Maastricht, The Netherlands.

Aims: The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups.

Methods And Results: We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping.

Conclusion: The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach.
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http://dx.doi.org/10.1093/eurheartj/ehaa841DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813623PMC
January 2021

Distinct Cardiac Transcriptomic Clustering in Titin and Lamin A/C-Associated Dilated Cardiomyopathy Patients.

Circulation 2020 Sep 21;142(12):1230-1232. Epub 2020 Sep 21.

Department of Cardiology, Cardiovascular Research Institute (CARIM) (J.A.J.V., M.R.H., E.L.R., S.R.B.H.), Maastricht University Medical Center, The Netherlands.

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http://dx.doi.org/10.1161/CIRCULATIONAHA.119.045118DOI Listing
September 2020

EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models.

Metabolites 2020 Feb 12;10(2). Epub 2020 Feb 12.

Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands.

Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.
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http://dx.doi.org/10.3390/metabo10020066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074156PMC
February 2020

Stratifying cellular metabolism during weight loss: an interplay of metabolism, metabolic flexibility and inflammation.

Sci Rep 2020 02 3;10(1):1651. Epub 2020 Feb 3.

Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.

Obesity is a global epidemic, contributing significantly to chronic non-communicable diseases, such as type 2 diabetes mellitus, cardiovascular diseases and metabolic syndrome. Metabolic flexibility, the ability of organisms to switch between metabolic substrates, is found to be impaired in obesity, possibly contributing to the development of chronic illnesses. Several studies have shown the improvement of metabolic flexibility after weight loss. In this study, we have mapped the cellular metabolism of the adipose tissue from a weight loss study to stratify the cellular metabolic processes and metabolic flexibility during weight loss. We have found that for a majority of the individuals, cellular metabolism was downregulated during weight loss, with gene expression of all major cellular metabolic processes (such as glycolysis, fatty acid β-oxidation etc.) being lowered during weight loss and weight maintenance. Parallel to this, the gene expression of immune system related processes involving interferons and interleukins increased. Previously, studies have indicated both negative and positive effects of post-weight loss inflammation in the adipose tissue with regards to weight loss or obesity and its co-morbidities; however, mechanistic links need to be constructed in order to determine the effects further. Our study contributes towards this goal by mapping the changes in gene expression across the weight loss study and indicates possible cross-talk between cellular metabolism and inflammation.
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http://dx.doi.org/10.1038/s41598-020-58358-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997359PMC
February 2020

Logical modelling reveals the PDC-PDK interaction as the regulatory switch driving metabolic flexibility at the cellular level.

Genes Nutr 2019 9;14:27. Epub 2019 Sep 9.

1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.

Background: Metabolic flexibility is the ability of an organism to switch between substrates for energy metabolism, in response to the changing nutritional state and needs of the organism. On the cellular level, metabolic flexibility revolves around the tricarboxylic acid cycle by switching acetyl coenzyme A production from glucose to fatty acids and vice versa. In this study, we modelled cellular metabolic flexibility by constructing a logical model connecting glycolysis, fatty acid oxidation, fatty acid synthesis and the tricarboxylic acid cycle, and then using network analysis to study the behaviours of the model.

Results: We observed that the substrate switching usually occurs through the inhibition of pyruvate dehydrogenase complex (PDC) by pyruvate dehydrogenase kinases (PDK), which moves the metabolism from glycolysis to fatty acid oxidation. Furthermore, we were able to verify four different regulatory models of PDK to contain known biological observations, leading to the biological plausibility of all four models across different cells and conditions.

Conclusion: These results suggest that the cellular metabolic flexibility depends upon the PDC-PDK regulatory interaction as a key regulatory switch for changing metabolic substrates.
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http://dx.doi.org/10.1186/s12263-019-0647-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6734263PMC
September 2019

Systems Genetics Approaches in Rat Identify Novel Genes and Gene Networks Associated With Cardiac Conduction.

J Am Heart Assoc 2018 11;7(21):e009243

1 Department of Experimental Cardiology Heart Centre Academic Medical Center Amsterdam Amsterdam The Netherlands.

Background Electrocardiographic ( ECG ) parameters are regarded as intermediate phenotypes of cardiac arrhythmias. Insight into the genetic underpinnings of these parameters is expected to contribute to the understanding of cardiac arrhythmia mechanisms. Here we used HXB / BXH recombinant inbred rat strains to uncover genetic loci and candidate genes modulating ECG parameters. Methods and Results RR interval, PR interval, QRS duration, and QT c interval were measured from ECG s obtained in 6 male rats from each of the 29 available HXB / BXH recombinant inbred strains. Genes at loci displaying significant quantitative trait loci (QTL) effects were prioritized by assessing the presence of protein-altering variants, and by assessment of cis expression QTL ( eQTL ) effects and correlation of transcript abundance to the respective trait in the heart. Cardiac RNA -seq data were additionally used to generate gene co-expression networks. QTL analysis of ECG parameters identified 2 QTL for PR interval, respectively, on chromosomes 10 and 17. At the chromosome 10 QTL , cis- eQTL effects were identified for Acbd4, Cd300lg, Fam171a2, and Arhgap27; the transcript abundance in the heart of these 4 genes was correlated with PR interval. At the chromosome 17 QTL , a cis- eQTL was uncovered for Nhlrc1 candidate gene; the transcript abundance of this gene was also correlated with PR interval. Co-expression analysis furthermore identified 50 gene networks, 6 of which were correlated with PR interval or QRS duration, both parameters of cardiac conduction. Conclusions These newly identified genetic loci and gene networks associated with the ECG parameters of cardiac conduction provide a starting point for future studies with the potential of identifying novel mechanisms underlying cardiac electrical function.
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http://dx.doi.org/10.1161/JAHA.118.009243DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404199PMC
November 2018

Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression.

Genes (Basel) 2018 Oct 29;9(11). Epub 2018 Oct 29.

Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.
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http://dx.doi.org/10.3390/genes9110525DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6266822PMC
October 2018

Exploring the cellular network of metabolic flexibility in the adipose tissue.

Genes Nutr 2018 5;13:17. Epub 2018 Jul 5.

1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands.

Background: Metabolic flexibility is the ability of cells to change substrates for energy production based on the nutrient availability and energy requirement. It has been shown that metabolic flexibility is impaired in obesity and chronic diseases such as type 2 diabetes mellitus, cardiovascular diseases, and metabolic syndrome, although, whether it is a cause or an effect of these conditions remains to be elucidated.

Main Body: In this paper, we have reviewed the literature on metabolic flexibility and curated pathways and processes resulting in a network resource to investigate the interplay between these processes in the subcutaneous adipose tissue. The adipose tissue has been shown to be responsible, not only for energy storage but also for maintaining energy homeostasis through oxidation of glucose and fatty acids. We highlight the role of pyruvate dehydrogenase complex-pyruvate dehydrogenase kinase () interaction as a regulatory switch which is primarily responsible for changing substrates in energy metabolism from glucose to fatty acids and back. Baseline gene expression of the subcutaneous adipose tissue, along with a publicly available obesity data set, are visualised on the cellular network of metabolic flexibility to highlight the genes that are expressed and which are differentially affected in obesity.

Conclusion: We have constructed an abstracted network covering glucose and fatty acid oxidation, as well as the regulatory switch. In addition, we have shown how the network can be used for data visualisation and as a resource for follow-up studies.
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http://dx.doi.org/10.1186/s12263-018-0609-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6034334PMC
July 2018

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results.

PLoS One 2018 4;13(4):e0193515. Epub 2018 Apr 4.

Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands.

Genome-wide association studies (GWAS) have become a common method for discovery of gene-disease relationships, in particular for complex diseases like Type 2 Diabetes Mellitus (T2DM). The experience with GWAS analysis has revealed that the genetic risk for complex diseases involves cumulative, small effects of many genes and only some genes with a moderate effect. In order to explore the complexity of the relationships between T2DM genes and their potential function at the process level as effected by polymorphism effects, a secondary analysis of a GWAS meta-analysis is presented. Network analysis, pathway information and integration of different types of biological information such as eQTLs and gene-environment interactions are used to elucidate the biological context of the genetic variants and to perform an analysis based on data visualization. We selected a T2DM dataset from a GWAS meta-analysis, and extracted 1,971 SNPs associated with T2DM. We mapped 580 SNPs to 360 genes, and then selected 460 pathways containing these genes from the curated collection of WikiPathways. We then created and analyzed SNP-gene and SNP-gene-pathway network modules in Cytoscape. A focus on genes with robust connections to pathways permitted identification of many T2DM pertinent pathways. However, numerous genes lack literature evidence of association with T2DM. We also speculate on the genes in specific network structures obtained in the SNP-gene network, such as gene-SNP-gene modules. Finally, we selected genes relevant to T2DM from our SNP-gene-pathway network, using different sources that reveal gene-environment interactions and eQTLs. We confirmed functions relevant to T2DM for many genes and have identified some-LPL and APOB-that require further validation to clarify their involvement in T2DM.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193515PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884486PMC
July 2018

Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy.

Genome Biol 2017 09 14;18(1):170. Epub 2017 Sep 14.

Department of Clinical and Experimental Cardiology, Heart Center, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands.

Background: Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits, including cardiovascular diseases.

Results: Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 97 patients with dilated cardiomyopathy and 108 non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage, and allele-specific expression. Systematic annotation of genome-wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome-wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome-wide association signals in two independent cohorts.

Conclusions: RNA transcription, splicing, and allele-specific expression are each important determinants of the dilated cardiomyopathy phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics.
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http://dx.doi.org/10.1186/s13059-017-1286-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5598015PMC
September 2017

Quantitative analysis of ChIP-seq data uncovers dynamic and sustained H3K4me3 and H3K27me3 modulation in cancer cells under hypoxia.

Epigenetics Chromatin 2016 1;9:48. Epub 2016 Nov 1.

Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands.

Background: A comprehensive assessment of the epigenetic dynamics in cancer cells is the key to understanding the molecular mechanisms underlying cancer and to improving cancer diagnostics, prognostics and treatment. By combining genome-wide ChIP-seq epigenomics and microarray transcriptomics, we studied the effects of oxygen deprivation and subsequent reoxygenation on histone 3 trimethylation of lysine 4 (H3K4me3) and lysine 27 (H3K27me3) in a breast cancer cell line, serving as a model for abnormal oxygenation in solid tumors. A priori, epigenetic markings and gene expression levels not only are expected to vary greatly between hypoxic and normoxic conditions, but also display a large degree of heterogeneity across the cell population. Where traditionally ChIP-seq data are often treated as dichotomous data, the model and experiment here necessitate a quantitative, data-driven analysis of both datasets.

Results: We first identified genomic regions with sustained epigenetic markings, which provided a sample-specific reference enabling quantitative ChIP-seq data analysis. Sustained H3K27me3 marking was located around centromeres and intergenic regions, while sustained H3K4me3 marking is associated with genes involved in RNA binding, translation and protein transport and localization. Dynamic marking with both H3K4me3 and H3K27me3 (hypoxia-induced bivalency) was found in CpG-rich regions at loci encoding factors that control developmental processes, congruent with observations in embryonic stem cells.

Conclusions: -identified epigenetically sustained and dynamic genomic regions were confirmed through ChIP-PCR in vitro, and obtained results are corroborated by published data and current insights regarding epigenetic regulation.
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http://dx.doi.org/10.1186/s13072-016-0090-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5090954PMC
January 2018

Hypoxia increases genome-wide bivalent epigenetic marking by specific gain of H3K27me3.

Epigenetics Chromatin 2016 26;9:46. Epub 2016 Oct 26.

Department of Molecular Genetics, Maastricht University Medical Centre, Maastricht, The Netherlands.

Background: Trimethylation at histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) controls gene activity during development and differentiation. Whether H3K4me3 and H3K27me3 changes dynamically in response to altered microenvironmental conditions, including low-oxygen conditions commonly present in solid tumors, is relatively unknown. Demethylation of H3K4me3 and H3K27me3 is mediated by oxygen and 2-oxoglutarate dioxygenases enzymes, suggesting that oxygen deprivation (hypoxia) may influence histone trimethylation. Using the MCF7 breast epithelial adenocarcinoma cell model, we have determined the relationship between epigenomic and transcriptomic reprogramming as a function of fluctuating oxygen tension.

Results: We find that in MCF7, H3K4me3 and H3K27me3 marks rapidly increase at specific locations throughout the genome and are largely reversed upon reoxygenation. Whereas dynamic changes are relatively highest for H3K27me3 marking under hypoxic conditions, H3K4me3 occupation is identified as the defining epigenetic marker of transcriptional control. In agreement with the global increase of H3K27 trimethylation, we provide direct evidence that the histone H3K27me3 demethylase KDM6B/JMJD3 is inactivated by limited oxygen. In situ immunohistochemical analysis confirms a marked rise of histone trimethylation in hypoxic tumor areas. Acquisition of H3K27me3 at H3K4me3-marked loci results in a striking increase in "bivalent" epigenetic marking. Hypoxia-induced bivalency substantially overlaps with embryonal stem cell-associated genic bivalency and is retained at numerous loci upon reoxygenation. Transcriptional activity is selectively and progressively dampened at bivalently marked loci upon repeated exposure to hypoxia, indicating that this subset of genes uniquely maintains the potential for epigenetic regulation by KDM activity.

Conclusions: These data suggest that dynamic regulation of the epigenetic state within the tumor environment may have important consequences for tumor plasticity and biology.
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http://dx.doi.org/10.1186/s13072-016-0086-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080723PMC
January 2018

52 Genetic Loci Influencing Myocardial Mass.

J Am Coll Cardiol 2016 09;68(13):1435-1448

Department of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands.

Background: Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death.

Objectives: This meta-analysis sought to gain insights into the genetic determinants of myocardial mass.

Methods: We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment.

Results: We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo.

Conclusions: Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets.
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http://dx.doi.org/10.1016/j.jacc.2016.07.729DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478167PMC
September 2016

Genome-Wide Polyadenylation Maps Reveal Dynamic mRNA 3'-End Formation in the Failing Human Heart.

Circ Res 2016 Feb 15;118(3):433-8. Epub 2015 Dec 15.

From the Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands (E.E.C., A.B., H.W.M.D., I.M., N.E.G., M.E.A., C.R.B., Y.M.P.); Division of Biological Stress Response, The Netherlands Cancer Institute, Amsterdam, The Netherlands (A.P.U., R.E., R.A.); National Heart and Lung Institute, Imperial College, London, United Kingdom (S.A.C.); Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany (N.H.); and Department of Physiology, VU University Medical Center, Amsterdam, The Netherlands (J.V.).

Rationale: Alternative cleavage and polyadenylation (APA) of mRNA represents a layer of gene regulation that to date has remained unexplored in the heart. This phenomenon may be relevant, as the positioning of the poly(A) tail in mRNAs influences the length of the 3'-untranslated region (UTR), a critical determinant of gene expression.

Objective: To investigate whether the 3'UTR length is regulated by APA in the human heart and whether this changes in the failing heart.

Methods And Results: We used 3'end RNA sequencing (e3'-Seq) to directly measure global patterns of APA in healthy and failing human heart specimens. By monitoring polyadenylation profiles in these hearts, we identified disease-specific APA signatures in numerous genes. Interestingly, many of the genes with shortened 3'UTRs in heart failure were enriched for functional groups such as RNA binding, whereas genes with longer 3'UTRs were enriched for cytoskeletal organization and actin binding. RNA sequencing in a larger series of human hearts revealed that these APA candidates are often differentially expressed in failing hearts, with an inverse correlation between 3'UTR length and the level of gene expression. Protein levels of the APA regulator, poly(A)-binding protein nuclear-1 were substantially downregulated in failing hearts.

Conclusions: We provide genome-wide, high-resolution polyadenylation maps of the human heart and show that the 3'end formation of mRNA is dynamic in heart failure, suggesting that APA-mediated 3'UTR length modulation represents an additional layer of gene regulation in failing hearts.
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http://dx.doi.org/10.1161/CIRCRESAHA.115.307082DOI Listing
February 2016

A user-friendly workflow for analysis of Illumina gene expression bead array data available at the arrayanalysis.org portal.

BMC Genomics 2015 Jun 30;16:482. Epub 2015 Jun 30.

TNO, Research Group Microbiology & Systems Biology, Utrechtseweg 48, 3704 HE, Zeist, The Netherlands.

Background: Illumina whole-genome expression bead arrays are a widely used platform for transcriptomics. Most of the tools available for the analysis of the resulting data are not easily applicable by less experienced users. ArrayAnalysis.org provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows.

Results: To enable data analysis of Illumina bead arrays for a broad user community, we have developed a module for ArrayAnalysis.org that provides a free and user-friendly web interface for quality control and pre-processing for these arrays. This module can be used together with existing modules for statistical and pathway analysis to provide a full workflow for Illumina gene expression data analysis. The module accepts data exported from Illumina's GenomeStudio, and provides the user with quality control plots and normalized data. The outputs are directly linked to the existing statistics module of ArrayAnalysis.org, but can also be downloaded for further downstream analysis in third-party tools.

Conclusions: The Illumina bead arrays analysis module is available at http://www.arrayanalysis.org . A user guide, a tutorial demonstrating the analysis of an example dataset, and R scripts are available. The module can be used as a starting point for statistical evaluation and pathway analysis provided on the website or to generate processed input data for a broad range of applications in life sciences research.
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http://dx.doi.org/10.1186/s12864-015-1689-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4486126PMC
June 2015

Integrative genomic approach identifies multiple genes involved in cardiac collagen deposition.

Circ Cardiovasc Genet 2014 Dec 12;7(6):790-8. Epub 2014 Sep 12.

From the Department of Experimental Cardiology (E.M.L., B.P.S., L.B., M.E.A., C.R.B.), and Department of Clinical Epidemiology, Biostatistics and Bioinformatics (P.D.M., M.W.T.T.), University of Amsterdam, Academic Medical Center, Amsterdam, Netherlands; and Groningen Bioinformatics Centre, University of Groningen, Groningen, Netherlands (D.A.).

Background: With aging and in cardiac disease, fibrosis caused by collagen deposition is increased, impairing contractility and providing a substrate for arrhythmia. In this study, we set out to identify genetic modifiers of collagen deposition in the heart by exploiting the genetic variability among F2 progeny of 129P2 and FVBN/J mice carrying the Scn5a(tm1Care/+) mutation.

Methods And Results: Relative amounts of collagen were determined in left ventricular myocardium of 65 F2-mice and combined with genome-wide genotypic and cardiac expression data to identify collagen quantitative trait loci (QTLs) and overlapping expression QTLs (eQTLs). A significant collagen QTL was identified on mouse Chr8; an additional collagen QTL was identified on mouse Chr2 after correction for a genetic covariate uncovered on Chr18 using multiple QTL mapping. Of the 24 eQTLs colocalizing with the Chr8-collagen QTL, 6 transcripts were correlated with relative collagen amount. Similarly, of the 7 eQTLs colocalizing with the Chr2-collagen QTL, 1 transcript, Gpr158, correlated with relative collagen. Of the 12 transcripts with an eQTLs in the Chr18-covariate region, only Fgf1 correlated with relative collagen amount. Furthermore, 2 of the transcripts, Pdlim3 (Chr8) and Itga6 (Chr2), with eQTLs overlapping with collagen QTLs, had a genetic covariate on Chr18 that coincided with the Chr18 collagen covariate locus. Application of recombinant human FGF1 to isolated cardiac fibroblasts in culture affected the level of expression of Pdlim3, Itga6, and Gpr158.

Conclusions: We here mapped a possible novel genetic network modulating collagen deposition in mouse left ventricular myocardium.
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http://dx.doi.org/10.1161/CIRCGENETICS.114.000537DOI Listing
December 2014

Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart.

PLoS One 2014 20;9(5):e97380. Epub 2014 May 20.

Department of Experimental Cardiology, Heart Failure Research Centre, Academic Medical Center, Amsterdam, The Netherlands.

In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. The underlying trait-associated variants likely reside in regulatory regions and exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying these cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. For a total of 129 left ventricular samples that were collected from non-diseased human donor hearts, genome-wide transcript abundance and genotyping was determined using microarrays. Each of the 18,402 transcripts and 897,683 SNP genotypes that remained after pre-processing and stringent quality control were tested for eQTL effects. We identified 771 eQTLs, regulating 429 unique transcripts. Overlaying these eQTLs with cardiac GWAS loci identified novel candidates for studies aimed at elucidating the functional and transcriptional impact of these loci. Thus, this work provides for the first time a comprehensive eQTL map of human heart: a powerful and unique resource that enables systems genetics approaches for the study of cardiac traits.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0097380PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4028258PMC
February 2015

A common genetic variant within SCN10A modulates cardiac SCN5A expression.

J Clin Invest 2014 Apr 18;124(4):1844-52. Epub 2014 Mar 18.

Variants in SCN10A, which encodes a voltage-gated sodium channel, are associated with alterations of cardiac conduction parameters and the cardiac rhythm disorder Brugada syndrome; however, it is unclear how SCN10A variants promote dysfunctional cardiac conduction. Here we showed by high-resolution 4C-seq analysis of the Scn10a-Scn5a locus in murine heart tissue that a cardiac enhancer located in Scn10a, encompassing SCN10A functional variant rs6801957, interacts with the promoter of Scn5a, a sodium channel-encoding gene that is critical for cardiac conduction. We observed that SCN5A transcript levels were several orders of magnitude higher than SCN10A transcript levels in both adult human and mouse heart tissue. Analysis of BAC transgenic mouse strains harboring an engineered deletion of the enhancer within Scn10a revealed that the enhancer was essential for Scn5a expression in cardiac tissue. Furthermore, the common SCN10A variant rs6801957 modulated Scn5a expression in the heart. In humans, the SCN10A variant rs6801957, which correlated with slowed conduction, was associated with reduced SCN5A expression. These observations establish a genomic mechanism for how a common genetic variation at SCN10A influences cardiac physiology and predisposes to arrhythmia.
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http://dx.doi.org/10.1172/JCI73140DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973109PMC
April 2014

Coxsackie and adenovirus receptor is a modifier of cardiac conduction and arrhythmia vulnerability in the setting of myocardial ischemia.

J Am Coll Cardiol 2014 Feb 27;63(6):549-59. Epub 2013 Nov 27.

Heart Failure Research Center, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, the Netherlands. Electronic address:

Objectives: The aim of this study was to investigate the modulatory effect of the coxsackie and adenovirus receptor (CAR) on ventricular conduction and arrhythmia vulnerability in the setting of myocardial ischemia.

Background: A heritable component in the risk of ventricular fibrillation during myocardial infarction has been well established. A recent genome-wide association study of ventricular fibrillation during acute myocardial infarction led to the identification of a locus on chromosome 21q21 (rs2824292) in the vicinity of the CXADR gene. CXADR encodes the CAR, a cell adhesion molecule predominantly located at the intercalated disks of the cardiomyocyte.

Methods: The correlation between CAR transcript levels and rs2824292 genotype was investigated in human left ventricular samples. Electrophysiological studies and molecular analyses were performed using CAR haploinsufficient (CAR⁺/⁻) mice.

Results: In human left ventricular samples, the risk allele at the chr21q21 genome-wide association study locus was associated with lower CXADR messenger ribonucleic acid levels, suggesting that decreased cardiac levels of CAR predispose to ischemia-induced ventricular fibrillation. Hearts from CAR⁺/⁻ mice displayed slowing of ventricular conduction in addition to an earlier onset of ventricular arrhythmias during the early phase of acute myocardial ischemia after ligation of the left anterior descending artery. Expression and distribution of connexin 43 were unaffected, but CAR⁺/⁻ hearts displayed increased arrhythmia susceptibility on pharmacological electrical uncoupling. Patch-clamp analysis of isolated CAR⁺/⁻ myocytes showed reduced sodium current magnitude specifically at the intercalated disk. Moreover, CAR coprecipitated with NaV1.5 in vitro, suggesting that CAR affects sodium channel function through a physical interaction with NaV1.5.

Conclusions: CAR is a novel modifier of ventricular conduction and arrhythmia vulnerability in the setting of myocardial ischemia. Genetic determinants of arrhythmia susceptibility (such as CAR) may constitute future targets for risk stratification of potentially lethal ventricular arrhythmias in patients with coronary artery disease.
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http://dx.doi.org/10.1016/j.jacc.2013.10.062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3926969PMC
February 2014

Genome-wide association study of multiple congenital heart disease phenotypes identifies a susceptibility locus for atrial septal defect at chromosome 4p16.

Nat Genet 2013 Jul 26;45(7):822-4. Epub 2013 May 26.

Institute of Genetic Medicine, Newcastle University, Newcastle-upon-Tyne, UK.

We carried out a genome-wide association study (GWAS) of congenital heart disease (CHD). Our discovery cohort comprised 1,995 CHD cases and 5,159 controls and included affected individuals from each of the 3 major clinical CHD categories (with septal, obstructive and cyanotic defects). When all CHD phenotypes were considered together, no region achieved genome-wide significant association. However, a region on chromosome 4p16, adjacent to the MSX1 and STX18 genes, was associated (P = 9.5 × 10⁻⁷) with the risk of ostium secundum atrial septal defect (ASD) in the discovery cohort (N = 340 cases), and this association was replicated in a further 417 ASD cases and 2,520 controls (replication P = 5.0 × 10⁻⁵; odds ratio (OR) in replication cohort = 1.40, 95% confidence interval (CI) = 1.19-1.65; combined P = 2.6 × 10⁻¹⁰). Genotype accounted for ~9% of the population-attributable risk of ASD.
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http://dx.doi.org/10.1038/ng.2637DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3793630PMC
July 2013

User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org.

Nucleic Acids Res 2013 Jul 24;41(Web Server issue):W71-6. Epub 2013 Apr 24.

Department of Bioinformatics-BiGCaT, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.

Quality control (QC) is crucial for any scientific method producing data. Applying adequate QC introduces new challenges in the genomics field where large amounts of data are produced with complex technologies. For DNA microarrays, specific algorithms for QC and pre-processing including normalization have been developed by the scientific community, especially for expression chips of the Affymetrix platform. Many of these have been implemented in the statistical scripting language R and are available from the Bioconductor repository. However, application is hampered by lack of integrative tools that can be used by users of any experience level. To fill this gap, we developed a freely available tool for QC and pre-processing of Affymetrix gene expression results, extending, integrating and harmonizing functionality of Bioconductor packages. The tool can be easily accessed through a wizard-like web portal at http://www.arrayanalysis.org or downloaded for local use in R. The portal provides extensive documentation, including user guides, interpretation help with real output illustrations and detailed technical documentation. It assists newcomers to the field in performing state-of-the-art QC and pre-processing while offering data analysts an integral open-source package. Providing the scientific community with this easily accessible tool will allow improving data quality and reuse and adoption of standards.
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http://dx.doi.org/10.1093/nar/gkt293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692049PMC
July 2013

Molecular pathways involved in prostate carcinogenesis: insights from public microarray datasets.

PLoS One 2012 20;7(11):e49831. Epub 2012 Nov 20.

Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands.

Background: Prostate cancer is currently the most frequently diagnosed malignancy in men and the second leading cause of cancer-related deaths in industrialized countries. Worldwide, an increase in prostate cancer incidence is expected due to an increased life-expectancy, aging of the population and improved diagnosis. Although the specific underlying mechanisms of prostate carcinogenesis remain unknown, prostate cancer is thought to result from a combination of genetic and environmental factors altering key cellular processes. To elucidate these complex interactions and to contribute to the understanding of prostate cancer progression and metastasis, analysis of large scale gene expression studies using bioinformatics approaches is used to decipher regulation of core processes.

Methodology/principal Findings: In this study, a standardized quality control procedure and statistical analysis (http://www.arrayanalysis.org/) were applied to multiple prostate cancer datasets retrieved from the ArrayExpress data repository and pathway analysis using PathVisio (http://www.pathvisio.org/) was performed. The results led to the identification of three core biological processes that are strongly affected during prostate carcinogenesis: cholesterol biosynthesis, the process of epithelial-to-mesenchymal transition and an increased metabolic activity.

Conclusions: This study illustrates how a standardized bioinformatics evaluation of existing microarray data and subsequent pathway analysis can quickly and cost-effectively provide essential information about important molecular pathways and cellular processes involved in prostate cancer development and disease progression. The presented results may assist in biomarker profiling and the development of novel treatment approaches.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0049831PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3502280PMC
May 2013

MK3 controls Polycomb target gene expression via negative feedback on ERK.

Epigenetics Chromatin 2012 Aug 7;5(1):12. Epub 2012 Aug 7.

Department of Molecular Genetics, GROW School for Oncology and Developmental Biology, Maastricht University, Universiteitssingel 50, 6229ER, Maastricht, The Netherlands.

Unlabelled:

Background: Gene-environment interactions are mediated by epigenetic mechanisms. Polycomb Group proteins constitute part of an epigenetic cellular transcriptional memory system that is subject to dynamic modulation during differentiation. Molecular insight in processes that control dynamic chromatin association and dissociation of Polycomb repressive complexes during and beyond development is limited. We recently showed that MK3 interacts with Polycomb repressive complex 1 (PRC1). The functional relevance of this interaction, however, remained poorly understood. MK3 is activated downstream of mitogen- and stress-activated protein kinases (M/SAPKs), all of which fulfill crucial roles during development. We here use activation of the immediate-early response gene ATF3, a bona fide PRC1 target gene, as a model to study how MK3 and its effector kinases MAPK/ERK and SAPK/P38 are involved in regulation of PRC1-dependent ATF3 transcription.

Results: Our current data show that mitogenic signaling through ERK, P38 and MK3 regulates ATF3 expression by PRC1/chromatin dissociation and epigenetic modulation. Mitogenic stimulation results in transient P38-dependent H3S28 phosphorylation and ERK-driven PRC1/chromatin dissociation at PRC1 targets. H3S28 phosphorylation by itself appears not sufficient to induce PRC1/chromatin dissociation, nor ATF3 transcription, as inhibition of MEK/ERK signaling blocks BMI1/chromatin dissociation and ATF3 expression, despite induced H3S28 phosphorylation. In addition, we establish that concomitant loss of local H3K27me3 promoter marking is not required for ATF3 activation. We identify pERK as a novel signaling-induced binding partner of PRC1, and provide evidence that MK3 controls ATF3 expression in cultured cells via negative regulatory feedback on M/SAPKs. Dramatically increased ectopic wing vein formation in the absence of Drosophila MK in a Drosophila ERK gain-of-function wing vein patterning model, supports the existence of MK-mediated negative feedback regulation on pERK.

Conclusion: We here identify and characterize important actors in a PRC1-dependent epigenetic signal/response mechanism, some of which appear to be nonspecific global responses, whereas others provide modular specificity. Our findings provide novel insight into a Polycomb-mediated epigenetic mechanism that dynamically controls gene transcription and support a direct link between PRC1 and cellular responses to changes in the microenvironment.
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http://dx.doi.org/10.1186/1756-8935-5-12DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499388PMC
August 2012

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies.

BMC Genomics 2012 Jan 25;13:42. Epub 2012 Jan 25.

Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, The Netherlands.

Background: The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate.

Results: We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures.

Conclusion: T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.
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http://dx.doi.org/10.1186/1471-2164-13-42DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3293711PMC
January 2012

Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway.

BMC Syst Biol 2009 Oct 5;3:100. Epub 2009 Oct 5.

Institute of Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Glasgow, UK.

Background: The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway.

Results: We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors.

Conclusion: Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.
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http://dx.doi.org/10.1186/1752-0509-3-100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764635PMC
October 2009

The public road to high-quality curated biological pathways.

Drug Discov Today 2008 Oct 27;13(19-20):856-62. Epub 2008 Aug 27.

Department of Bioinformatics-BiGCaT, Maastricht University, Universiteitssingel 40, 6229ER Maastricht, The Netherlands.

Biological pathways are abstract and functional visual representations of existing biological knowledge. By mapping high-throughput data on these representations, changes and patterns in biological systems on the genetic, metabolic and protein level are instantly assessable. Many public domain repositories exist for storing biological pathways, each applying its own conventions and storage format. A pathway-based content review of these repositories reveals that none of them are comprehensive. To address this issue, we apply a general workflow to create curated biological pathways, in which we combine three content sources: public domain databases, literature and experts. In this workflow all content of a particular biological pathway is manually retrieved from biological pathway databases and literature, after which this content is compared, combined and subsequently curated by experts. From the curated content, new biological pathways can be created for a pathway analysis tool of choice and distributed among its user base. We applied this procedure to construct high-quality curated biological pathways involved in human fatty acid metabolism.
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http://dx.doi.org/10.1016/j.drudis.2008.06.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3646620PMC
October 2008