Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation

BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data

Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools

Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D

Six Tissue Transcriptomics Reveals Specific Immune Suppression in Spleen by Dietary Polyunsaturated Fatty Acids

Dietary polyunsaturated fatty acids (PUFA) are suggested to modulate immune function, but the effects of dietary fatty acids composition on gene expression patterns in immune organs have not been fully characterized. In the current study we investigated how dietary fatty acids composition affects the total transcriptome profile, and especially, immune related genes in two immune organs, spleen (SPL) and bone marrow cells (BMC). Four tissues with metabolic function, skeletal muscle (SKM), white adipose tissue (WAT), brown adipose tissue (BAT), and liver (LIV), were investigated as a comparison. Following 8 weeks on low fat diet (LFD), high fat diet (HFD) rich in saturated fatty acids (HFD-S), or HFD rich in PUFA (HFD-P), tissue transcriptomics were analyzed by microarray and metabolic health assessed by fasting blood glucose level, HOMA-IR index, oral glucose tolerance test as well as quantification of crown-like structures in WAT. HFD-P corrected the metabolic phenotype induced by HFD-S. Interestingly, SKM and BMC were relatively inert to the diets, whereas the two adipose tissues (WAT and BAT) were mainly affected by HFD per se (both HFD-S and HFD-P). In particular, WAT gene expression was driven closer to that of the immune organs SPL and BMC by HFDs. The LIV exhibited different responses to both of the HFDs. Surprisingly, the spleen showed a major response to HFD-P (82 genes differed from LFD, mostly immune genes), while it was not affected at all by HFD-S (0 genes differed from LFD). In conclusion, the quantity and composition of dietary fatty acids affected the transcriptome in distinct manners in different organs. Remarkably, dietary PUFA, but not saturated fat, prompted a specific regulation of immune related genes in the spleen, opening the possibility that PUFA can regulate immune function by influencing gene expression in this organ

Succinate dehydrogenase inhibition leads to epithelial-mesenchymal transition and reprogrammed carbon metabolism

Succinate dehydrogenase (SDH) is a mitochondrial metabolic enzyme complex involved in both the electron transport chain and the citric acid cycle. SDH mutations resulting in enzymatic dysfunction have been found to be a predisposing factor in various hereditary cancers. Therefore, SDH has been implicated as a tumor suppressor. We identified that dysregulation of SDH components also occurs in serous ovarian cancer, particularly the SDH subunit SDHB. Targeted knockdown of Sdhb in mouse ovarian cancer cells resulted in enhanced proliferation and an epithelial-to-mesenchymal transition (EMT). Bioinformatics analysis revealed that decreased SDHB expression leads to a transcriptional upregulation of genes involved in metabolic networks affecting histone methylation. We confirmed that Sdhb knockdown leads to a hypermethylated epigenome that is sufficient to promote EMT. Metabolically, the loss of Sdhb resulted in reprogrammed carbon source utilization and mitochondrial dysfunction. This altered metabolic state of Sdhb knockdown cells rendered them hypersensitive to energy stress. These data illustrate how SDH dysfunction alters the epigenetic and metabolic landscape in ovarian cancer. By analyzing the involvement of this enzyme in transcriptional and metabolic networks, we find a metabolic Achilles' heel that can be exploited therapeutically. Analyses of this type provide an understanding how specific perturbations in cancer metabolism may lead to novel anticancer strategies.Foundation for Women’s Cancer (Lynette Medlin-Brumfield/Mary-Jane Welker Ovarian Cancer Early Detection Research Grant)Alex's Lemonade Stand FoundationMargaret E. Early Medical Research TrustSandy Rollman FoundationUnited States. Dept. of Defense (Ovarian Cancer Clinical Translational Leverage Award (W81XWH-13-OCRP-TLA))American Cancer Society (RSG-10-252-01-TBG)National Institutes of Health (U.S.) (grant P01 HL28481)National Center for Research Resources (U.S.) (NCRR S10RR026744)Ovarian Cancer Research Fund (Liz Tilberis Scholarship)United States. Defense Advanced Research Projects Agency (CDMRP Visionary Postdoctoral Award number W81XWH-12-1-0466

SysBioChalmers/RAVEN: v2.8.5

<ul> <li>feat:<ul> <li>Distribute SoPlex binary for Windows with RAVEN, as another open-source alternative, slower than glpk, but suitable for MILP. Installation instructions are <a href="https://github.com/SysBioChalmers/RAVEN/wiki/Installation#solvers">updated</a>.</li> <li><code>readYAMLmodel</code> should always make model.c-field, even if no objective function is specified (solves #509).</li> <li><code>standardizeGrRules</code> should throw error if no grRules field is found.</li> </ul> </li> <li>refactor:<ul> <li><code>addRxnsGenesMets</code> more detailed error message if reactions cannot be found.</li> <li><code>getAllowedBounds</code> use progressbar for reporting status.</li> </ul> </li> </ul&gt

SysBioChalmers/RAVEN: v2.8.6

<ul> <li>fix:<ul> <li><code>checkInstallation</code> (via <code>solverTests</code>) will not report soplex as <code>fail</code> on Windows when using RAVEN-provided binary (solves #513).</li> </ul> </li> </ul&gt