Transcriptomics

Dataset Information

0

Impact of Sex Chromosomes and Gonad Type in Stress Susceptibility in Corticostriatal Brain Regions


ABSTRACT: Major depressive disorder (MDD) is characterized by symptom heterogeneity and sex differences in prevalence. To gain insight into these sex differences, we utilized the subchronic variable stress paradigm (SCVS) in which female mice are susceptible, while males are behaviorally resilient. We used the Four Core Genotypes mice to differentiate between sex chromosome complement (XX vs. XY) and gonad type (ovaries vs. testes) in stress susceptibility, uncoupling these factors by removing the testes-determining SRY gene from the Y chromosome. Mice were subjected to SCVS followed by assays to assess anxiety-/depressive-like behaviors. Regardless of gonads, XX mice were stress susceptible, while XY mice were stress resilient, underscoring the importance of sex chromosome effects to sex differences in susceptibility to SCVS, independent of gonad type. We then performed RNA-sequencing of the prefrontal cortex (PFC) and nucleus accumbens (NAc). Consistent with human MDD and previous rodent SCVS findings, there was little overlap in genes altered by SCVS across sex. In stress susceptible XX mice, stress exposure altered pathways related to immune function, while in resilient XY mice, stress exposure altered pathways involved in neuronal function. There was brain region specificity to how sex chromosome complement and gonad type contributed to sex-specific stress effects. In the NAc, sex chromosome complement was the primary contributor to stress-induced gene expression, while in the PFC, both sex chromosome complement and gonad type contributed. These findings underscore a complex relationship between sex chromosome complement and gonadal factors in shaping stress vulnerability, partly through immune-related pathways.

ORGANISM(S): Mus musculus

PROVIDER: GSE330309 | GEO | 2026/05/07

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2018-07-22 | GSE102143 | GEO
2016-11-07 | GSE81580 | GEO
2023-12-06 | GSE244866 | GEO
2021-12-01 | GSE184153 | GEO
2005-11-24 | GSE3650 | GEO
2022-01-31 | GSE184098 | GEO
2022-01-31 | GSE184013 | GEO
2024-11-29 | GSE280999 | GEO
2016-08-04 | E-GEOD-85136 | biostudies-arrayexpress
2022-11-24 | GSE145118 | GEO