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Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series

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Title: Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series
Authors: Dumas, ME
Domange, C
Calderari, S
Rodriguez Martinez, A
Ayala, R
Wilder, S
Suárez-Zamorano, N
Collins, S
Wallis, R
Gu, Q
Wang, Y
Hue, C
Otto, GW
Argoud, K
Navratil, V
Mitchell, S
Lindon, JC
Holmes, E
Cazier, JB
Nicholson, JK
Gauguier, D
Item Type: Journal Article
Abstract: Background: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus is caused by complex organ-specific cellular mechanisms contributing to impaired insulin secretion and insulin resistance. Methods: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualise shortest paths between metabolites and genes significantly associated with each genomic block. Results: Despite strong genomic similarities (95-99%) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting 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 metabotypes (mQTL) and genome-wide expression traits (eQTL). 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 shortest path length drove prioritization of biological validations by gene silencing. Conclusions: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulations and to characterize novel functional roles for genes determining tissue-specific metabolism.
Issue Date: 30-Sep-2016
Date of Acceptance: 6-Sep-2016
URI: http://hdl.handle.net/10044/1/39858
DOI: https://dx.doi.org/10.1186/s13073-016-0352-6
ISSN: 1756-994X
Publisher: BioMed Central
Journal / Book Title: Genome Medicine
Volume: 8
Copyright Statement: © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Sponsor/Funder: Nestec SA
Commission of the European Communities
Funder's Grant Number: Contract Ref:RDLS015375
241504
Keywords: 0604 Genetics
1103 Clinical Sciences
Publication Status: Published
Article Number: 101
Appears in Collections:Division of Surgery
Department of Medicine
Faculty of Medicine



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