Clinical and experimental literature have rapidly converged to demonstrate that sleep and circadian dysfunction play an integral role in the onset and maintenance of a broad spectrum of chronic diseases. Sleep dysfunction is especially common among patients with neuropsychiatric disorders, which is the leading contributor of disease burden in the United States, more than twice as much as cardiovascular disease. Epidemiological data demonstrates that sleep disruption precedes and often predisposes people to anxiety, depression, and PTSD, suggesting that these complex traits are highly intertwined. Addressing the complexity of sleep and stress phenotypes requires novel approaches that leverage and integrate multiple forms of data. My proposal focuses on identifying causal molecular networks common to sleep and stress traits in order to investigate novel disease mechanisms and therapeutic strategies relevant to neuropsychiatric disorders. With an exhaustive phenotypic assay (479 sleep and stress traits) and an integrated multi-scale computational approach, we will leverage the complexity of these traits to probe how molecular pathways naturally interact as a coordinated unit, rather than how they react when they are artificially manipulated. By integrating genetic, gene expression, and co-expression data from F2 mouse populations (>100 mice), we can use Bayesian reconstruction to identify molecular subnetworks that act as causal regulators of stress and sleep phenotypes. Because of the complexity of sleep and stress traits, it is imperative that we understand individual genes in the context of polygenic networks and treat phenotypes as emergent properties of these networks. By understanding how these molecular networks act as sensors and drivers of phenotypes, we can then appropriately consider targets for pharmacological interventions and utilize novel computational strategies for repurposing drugs. We anticipate that this work will provide the foundation for future in vivo studies related to depression, anxiety, stress susceptibility (PTSD), and sleep.

Public Health Relevance

Sleep disruption predisposes people to neuropsychiatric disorders - the leading contributor of disease burden in the United States. The focus of this proposal is to study causal molecular networks common to sleep and stress traits, in order to investigate novel disease mechanisms and therapeutic strategies relevant to neuropsychiatric disease. Our results will provide the foundation for future in vivo studies related to depression, anxiety, stress susceptibility (PTSD), and sleep dysfunction.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
5F30MH106293-04
Application #
9503010
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Driscoll, Jamie
Project Start
2014-09-03
Project End
2018-10-23
Budget Start
2018-06-03
Budget End
2018-10-23
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Icahn School of Medicine at Mount Sinai
Department
Genetics
Type
Schools of Medicine
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Ayata, Pinar; Badimon, Ana; Strasburger, Hayley J et al. (2018) Epigenetic regulation of brain region-specific microglia clearance activity. Nat Neurosci 21:1049-1060
Scarpa, Joseph R; Jiang, Peng; Gao, Vance D et al. (2018) Cross-species systems analysis identifies gene networks differentially altered by sleep loss and depression. Sci Adv 4:eaat1294
Baum, Aaron; Scarpa, Joseph; Bruzelius, Emilie et al. (2017) Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. Lancet Diabetes Endocrinol 5:808-815
Ribeiro, Efrain A; Scarpa, Joseph R; Garamszegi, Susanna P et al. (2017) Gene Network Dysregulation in Dorsolateral Prefrontal Cortex Neurons of Humans with Cocaine Use Disorder. Sci Rep 7:5412
Labonté, Benoit; Engmann, Olivia; Purushothaman, Immanuel et al. (2017) Sex-specific transcriptional signatures in human depression. Nat Med 23:1102-1111
Cohain, Ariella; Divaraniya, Aparna A; Zhu, Kuixi et al. (2017) EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS. Pac Symp Biocomput 22:120-131
Scarpa, Joseph R; Jiang, Peng; Losic, Bojan et al. (2016) Systems Genetic Analyses Highlight a TGF?-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease. PLoS Genet 12:e1006137
Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie et al. (2015) A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders. Cell Rep 11:835-48