Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series
作者： Dumas Marc-Emmanuel , Domange Céline , Calderari Sophie , Martinez A , Rodriguez
DOI: 10.1186/S13073-016-0352-6
关键词: Quantitative trait locus 、 Genetic architecture 、 Biology 、 Topology 、 Genetics 、 Expression quantitative trait loci 、 Gene mapping 、 Gene expression profiling 、 Metabolomics 、 Congenic 、 Candidate gene
摘要: Background: The 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 resistance. Genome- wide gene expression profiling systems can dissect the contributions metabolome transcriptome regulations. integrative analysis multiple traits together with their underlying remains a challenge. Here, we introduce genetics approach based on topological combined molecular network made genes metabolites identified metabotype quantitative trait locus mapping eQTL mQTL) prioritise biological characterisation candidate traits. Methods: We used systematic metabotyping by H-1 NMR spectroscopy genome-wide white adipose tissue map genomic blocks associated obesity series rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) normoglycemic Brown-Norway (BN) rats. implemented biology strategy visualize shortest paths between significantly each block. Results: Despite strong similarities (95-99 %) among congenics, strain exhibited specific patterns metabotypes, reflecting consequences linked polymorphisms intervals. subsequently panel loci mQTLs eQTLs. Variation key like glucose, succinate, lactate, or 3-hydroxybutyrate second messenger precursors inositol was several independent intervals, indicating functional redundancy these regions. To navigate complexity association mapped onto pathways path highlight potential mechanistic links transcripts at colocalized Minimizing length drove prioritization validations silencing. Conclusions: These results underline importance network-based integration multilevel datasets improve understanding architecture transcriptomic characterize novel roles for determining tissue-specific metabolism.
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