DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data

A. B Canelas

A. M Wentzell

A. S Leonardson

Andre Levchenko

B Futcher

B Gonzalez

B. D Bennett

C Bornaes

C Jiang

C. T Ferrara

D Altshuler

D. a. Y Madigan

D. C Kulp

E. A Winzeler

E. E Schadt

E. J Foss

E. N Smith

E. Y Xu

Eric E. Schadt

Ethan Y. Xu

G Schwarz

G Yvert

H Zhong

Heather Vu

J Pearl

J Ronald

J Zhu

J. G Bundy

J. H Dwyer

J. S Witte

J. Y Sze

Jun Zhu

K Kitamoto

K. D MacIsaac

Kenneth M. Dombek

L Kruglyak

L Nagarajan

M Kanehisa

M. J Khoury

M. J Shin

P. H Bradley

P. Y Lum

Pavel Sova

Q Xu

Qiuwei Xu

R Albert

R Korstanje

R. B Brem

R. J Ogg

R. M Anderson

R. R Gordon

Rachel B. Brem

Roger E. Bumgarner

S Doss

S. I Lee

S. K Sieberts

S. P Gygi

T Babak

T Mashimo

T Satyanarayana

T Sekito

T. H Ehrich

T. I Lee

U Guldener

V Denis

V Emilsson

W Zhang

W. C Willett

W. H Mager

W. J Ball Jr

Y Chen

Y. H Hsu

Zhidong Tu

English

PubMed

Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA–protein binding, protein–metabolite interaction, and protein–protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated know

CiteSeerX

Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol

Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA-protein binding, protein-metabolite interaction, and protein-protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots

Kenneth M Dombek

Ethan Y Xu

Rachel B Brem

Roger E Bumgarner

Eric E Schadt

Directory of Open Access Journals

PLoS Biology

Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation.

<div><p>Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA–protein binding, protein–metabolite interaction, and protein–protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.</p> </div

Jun Zhu (84054)

Pavel Sova (13422)

Qiuwei Xu (173697)

Kenneth M. Dombek (173704)

Ethan Y. Xu (173710)

Heather Vu (173715)

Zhidong Tu (15201)

Rachel B. Brem (114263)

Roger E. Bumgarner (173719)

Eric E. Schadt (50182)

The Francis Crick Institute

Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

Zhu Jun

Sova Pavel

Xu Qiuwei

Dombek Kenneth M.

Xu Ethan Y.

Vu Heather

Tu Zhidong

Brem Rachel B.

Bumgarner Roger E.

Schadt Eric E.

Public Library of Science (PLOS)

Crossref

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Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

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