Quantifying gene expression and network regulation in single cells to reveal the consequences of stress on the immune response

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PROJECT SUMMARY Human health is profoundly affected by genetics and the environment. The social environment mediates changes in physiology, gene regulation, and immune signaling, resulting in significant differences in disease susceptibility and mortality. Chronic social stress and social inequity are major drivers of disease, in part due to effects on the immune response to infectious pathogens. However, the molecular mechanisms underlying these effects are poorly understood. In order to address this gap, robust experimental models for uncovering social environmental effects on the immune system are needed. Rhesus macaques are primates closely related to humans. In social groups, they maintain stable hierarchies of social dominance, and these hierarchies can be experimentally manipulated through social group rearrangement. Thus, they provide a unique resource to study causal social rank effects on disease. Although significant and substantial gene expression changes in peripheral blood have been found to be associated with social rank, the cell types underlying these signatures are not well-characterized. Single-cell RNA-sequencing enables the discovery of effects of the social environment on individual cells of many kinds, which can help with the targeting of clinically-relevant cell types that are responsible for immune dysregulation. Therefore, this work proposes to study rank effects on gene regulation in individual peripheral blood cells, at baseline and after immune stimulation, to uncover cell-type-specific social stress effects on immunity. The basic research question driving this proposal is: How does the social environment affect gene expression across diverse peripheral blood immune cells? To answer this question, the approach of this proposal is to use a well-established model of social adversity in captive female rhesus macaques, and to apply next- generation single-cell RNA-seq technology to deeply characterize cells isolated from 50 individuals across a social gradient. From this data, effects of social rank will be examined for: (1) immune composition and LPS- induced polarization, (2) gene expression robustness across cell types, and (3) gene regulatory networks and co-expression modules. Through this work, clinically-relevant genes and pathways will be identified that are rewired by social environment effects, in order to design targeted therapeutics to improve immune responses relevant to cancer and infectious disease.