ArticleGenome medicine2016
Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series.
Article in Genome medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Transcriptomics reveals dynamic changes in the "gene profiles" of rat supraspinatus tendon at three different time points after diabetes induction.BMC medical genomics · 2024Article
- Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results.iScience · 2023Article
- The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats.Nucleic acids research · 2022Article
- Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome.Comprehensive Physiology · 2021Article
- A large-scale genome-lipid association map guides lipid identification.Nature metabolism · 2020Article
- CMap analysis identifies Atractyloside as a potential drug candidate for type 2 diabetes based on integration of metabolomics and transcriptomics.Journal of cellular and molecular medicine · 2020Article
- Systems genetics of theGenome research · 2020Article
- Fatty Acid Synthase Correlates With Prognosis-Related Abdominal Adipose Distribution and Metabolic Disorders of Clear Cell Renal Cell Carcinoma.Frontiers in molecular biosciences · 2020Article
- Metabolomics technology and bioinformatics for precision medicine.Briefings in bioinformatics · 2019Review
- STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.Scientific data · 2019Article
- Conserved properties of genetic architecture of renal and fat transcriptomes in rat models of insulin resistance.Disease models & mechanisms · 2019Article
- MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways.Bioinformatics (Oxford, England) · 2017Article
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Authors and funding
21 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundThe genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits.
methodsWe used systematic metabotyping by
resultsDespite strong genomic similarities (95-99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing.
conclusionsThese results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.
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