Expression profiling by high throughput sequencing
Summary
Depression is a complex and heterogeneous disorder and a leading contributor to the global burden of of disease. Most previous research has focused on individual brain regions and individual genes that contribute to depression. However, emerging evidence in both humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here we use a bioinformatics approach intersecting differential expression analysis with weighted gene co-expression network analysis to identify transcriptional networks that regulate susceptibility to depressive-like symptoms in mice. We performed RNA-sequencing on multiple brain regions from control animals and those either susceptible or resilient to chronic social defeat stress (CSDS) at multiple time points after defeat. We bioinformatically identified several transcriptional networks that regulate depression susceptibility, and in vivo manipulations of these networks confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our findings reveal novel transcriptional networks that control stress susceptibility and offer fundamentally new leads for antidepressant drug discovery.
Overall design
RNA-seq samples were generated from 4 brain regions (nucleus accumbens (NAC), prefrontal cortex (PFC), amygdala (AMY) and ventral hippocampus (VHIP) ) at 3 time-points (48h, 28d, 28d +1h stress) after chronic social defeat stress in control, susceptible and resilient mice. Additionally, RNA-seq samples were generated from virally infected VHIP tissue (HSV-GFP or HSV-Dkkl1) after an accelerated social defeat to assess the effect of Dkkl1 over-expression.
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