bioRxivNeurosciencePreprint
Translational approach to social isolation during a global pandemic: Hippocampal somatic mutation
Bomee LeeiD, Yuri Seo, Sohee JungiD, Soojung ImiD, Hyung Jun Choi, Jae Nam BaeiD, Yangsik KimiD
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has attributed to stress not only by the infection itself but also by social isolation owing to self-quarantine and social distancing. Stress has adverse effects on the mental health and chronic medical diseases; the potential of stress-induced somatic mutations in the brain to cause psychiatric disorders is being studied. Here we conducted behavioral studies, protein expression studies, single-nucleus sequencing (snRNAseq), and whole-genome sequencing (WGS) of the hippocampus of mice that underwent early maternal separation and social isolation, and a demographic study of community populations who had been self-quarantined owing to COVID-19 exposure to investigate the link between somatic mutations and stress due to social isolation. The demographic study demonstrated more negative mental health findings among individuals who live alone or are single. Mice subjected to early maternal separation and social isolation demonstrated increased anxiety-like behaviors and stress-related corticotropin-releasing hormone receptor 1, and neurogenesis-related sex-determining region Y-box 2 and doublecortin expression. In snRNA-seq, differences, such as transthyretin increase, were observed in the maternal separation group, and somatic mutations, including insertion in the intron site of Tmem267, were observed in the social isolation group on WGS. The results of this study suggest that stress, such as social isolation, can cause changes at the genetic level, as well as behavioral and brain protein changes.
- organism: mouse
- assay: bulk RNA-seq
- assay: whole-genome sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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