Individuals differ greatly in their responses to potentially stressful events. While stress responses permit the organism to respond to threats, stress responses that are stimulus-inappropriate or prolonged by stressor persistence or increased individual susceptibility to stress are maladaptive. Differences in stress responsiveness are in part genetically determined. The social and physical environments where an individual lives interact with his or her genetic make-up, early developmental history, adult experiences, life style, and life experiences to determine his or her ability to cope with stress. The clinical literature suggests that individual differences in stress responses and ability to cope with stressful situations are factors in the likelihood of developing cardiovascular and other illness as well as substance abuse. Psychological distress is also a challenge in the management of chronic diseases like cancer and HIV/AIDS. The hypothesis under test in this proposal is that complex phenotypes like individual differences in stress responsiveness are regulated by specific genetic modules within networks of interacting molecular pathways. To test this hypothesis, two collaborative teams headed by two PIs are proposed: The Scripps Research Institute (TSRI) team with expertise in behavioral and molecular neurobiology (PI: Pietro P. Sanna) and the Columbia University team of the National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) with expertise in systems biology strategies (PI: Andrea Califano). To model individual differences in stress responses, the TSRI team has selectively bred two replicate lines of rats for high (HSR) and low (LSR) stress responses that diverge for their anxiogenic-like responses to stressful environments. We propose to use reverse engineering strategies recently introduced by the Columbia team to identify the neurobiological bases of differential susceptibility to stress using these rat lines. Specifically, the present proposal will use the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) and the Modulator Inference by Network DYnamics (MINDy) developed by Dr. Califano at MAGNet. ARACNe is specifically designed to identify gene regulatory networks in large microarray expression datasets and has the ability to scale up to the complexity of regulatory networks in mammalian systems. MINDY is designed to identify post-translational interactions. A map of transcriptional and post-translational interactions that regulate the central stress system processes from HSR- LSR gene expression profile data (Central Stress System Interactome) will be assembled. Biochemical and behavioral approaches will then be used to validate the role of the candidate genetic modules identified in determining differential responsiveness to stress. The present research will deepen our understanding of the neurobiology of differential stress responsiveness and will inform new pathogenetic hypotheses of individual differences in stress vulnerability in humans that will be tested in an eventual funding period. The elucidation of the neurobiological bases of individual vulnerability to stress will require a quantitative understanding of the molecular networks that govern the neuronal pathways involved in the central regulation of the stress system. To investigate such a multi-scale system the present collaborative project will leverage animal models, behavioral, and other neurobiological expertise of the TSRI team in conjunction with the bioinformatics capabilities of the MAGNet at Columbia University to bring to bear a unique integrated approach. Ultimately, it is expected that the results of the present study, by elucidating genes that regulate divergent stress responsiveness and the gene networks in which they are active, will identify novel therapeutic and diagnostic targets for the prevention and treatment of the detrimental effects of excessive stress on human health.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM085659-03
Application #
7858097
Study Section
Special Emphasis Panel (ZGM1-GDB-2 (CP))
Program Officer
Maas, Stefan
Project Start
2008-08-01
Project End
2012-05-31
Budget Start
2010-06-01
Budget End
2011-05-31
Support Year
3
Fiscal Year
2010
Total Cost
$447,419
Indirect Cost
Name
Scripps Research Institute
Department
Type
DUNS #
781613492
City
La Jolla
State
CA
Country
United States
Zip Code
92037
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