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MOESM1 of Using biological networks to integrate, visualize and analyze genomics data

Additional file 1: Table S1. Case study gene expression data. Description: 514 genes from Lawless et al. [30] that were found to be significantly up-regulated more than threefold in monocytes isolated from milk at either 36 or 48 h post-infection (hpi) with the pathogen Streptococcus uberis that causes mastitis in cattle

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package

Transcriptional and metabolic rewiring of colorectal cancer cells expressing the oncogenic KRASG13D mutation

Background: Activating mutations in KRAS frequently occur in colorectal cancer (CRC) patients, leading to resistance to EGFR-targeted therapies. Methods: To better understand the cellular reprogramming which occurs in mutant KRAS cells, we have undertaken a systems-level analysis of four CRC cell lines which express either wild type (wt) KRAS or the oncogenic KRAS allele (mtKRAS). Results: RNAseq revealed that genes involved in ribosome biogenesis, mRNA translation and metabolism were significantly upregulated in mtKRAS cells. Consistent with the transcriptional data, protein synthesis and cell proliferation were significantly higher in the mtKRAS cells. Targeted metabolomics analysis also confirmed the metabolic reprogramming in mtKRAS cells. Interestingly, mtKRAS cells were highly transcriptionally responsive to EGFR activation by TGFα stimulation, which was associated with an unexpected downregulation of genes involved in a range of anabolic processes. While TGFα treatment strongly activated protein synthesis in wtKRAS cells, protein synthesis was not activated above basal levels in the TGFα-treated mtKRAS cells. This was likely due to the defective activation of the mTORC1 and other pathways by TGFα in mtKRAS cells, which was associated with impaired activation of PKB signalling and a transient induction of AMPK signalling. Conclusions: We have found that mtKRAS cells are substantially rewired at the transcriptional, translational and metabolic levels and that this rewiring may reveal new vulnerabilities in oncogenic KRAS CRC cells that could be exploited in future

Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRAS G13D

Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.European Commission Horizon 2020European Commission - Seventh Framework Programme (FP7)European Research CouncilScience Foundation IrelandTeagascCanada Research Chair ProgramKrembil FoundationOntario Research FundNatural Sciences Research CouncilCanada Foundation for InnovationIBMEMBL AustraliaCanadian Cancer Society Research InstituteGenome Canada via Ontario GenomicsOntario Research fundConsortium Québécois sur la Découverte du MédicamentBrain CanadaCQDM ExploreOCETistou & Charlotte Kerstan Stiftun