Imperial College London
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Dr Andrea Rodriguez Martinez

Faculty of Medicine, School of Public Health

Honorary Research Associate
 
 
 
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Contact

 

andrea.rodriguez-martinez13

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dumas:2016:10.1186/s13073-016-0352-6,
author = {Dumas, ME and Domange, C and Calderari, S and Rodriguez, Martinez A and ayala, R and Wilder, S and Suárez-Zamorano, N and Collins, S and Wallis, R and Gu, Q and wang, Y and Hue, C and Otto, GW and Argoud, K and Navratil, V and Mitchell, S and Lindon, JC and Holmes, E and Cazier, JB and Nicholson, JK and Gauguier, D},
doi = {10.1186/s13073-016-0352-6},
journal = {Genome Medicine},
title = {Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series},
url = {http://dx.doi.org/10.1186/s13073-016-0352-6},
volume = {8},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 f
AU  - Dumas,ME
AU  - Domange,C
AU  - Calderari,S
AU  - Rodriguez,Martinez A
AU  - ayala,R
AU  - Wilder,S
AU  - Suárez-Zamorano,N
AU  - Collins,S
AU  - Wallis,R
AU  - Gu,Q
AU  - wang,Y
AU  - Hue,C
AU  - Otto,GW
AU  - Argoud,K
AU  - Navratil,V
AU  - Mitchell,S
AU  - Lindon,JC
AU  - Holmes,E
AU  - Cazier,JB
AU  - Nicholson,JK
AU  - Gauguier,D
DO  - 10.1186/s13073-016-0352-6
PY  - 2016///
SN  - 1756-994X
TI  - Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series
T2  - Genome Medicine
UR  - http://dx.doi.org/10.1186/s13073-016-0352-6
UR  - http://hdl.handle.net/10044/1/39858
VL  - 8
ER  -
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