1. Home
  2. Organizations
  3. Embed
  4. Sex-specific ...

Sex-specific Transcriptional Signatures in Human Depression

gse102556

Description

Rational: Major depressive disorder (MDD) is a leading cause of disease burden worldwide. While the incidence, symptoms and treatment of MDD all point toward major sex differences, the molecular mechanisms underlying this sexual dimorphism remain largely unknown.; Methods: Here, combining differential expression and gene coexpression network analyses, we provide a comprehensive characterization of male and female transcriptional profiles associated with MDD across 6 brain regions. We overlap our human profiles with those from a mouse model of chronic variable stress and capitalize on converging pathways to define molecular and physiological mechanisms underlying the expression of stress susceptibility in males and females.; Results: Our results show a major rearrangement of transcriptional patterns in MDD, with limited overlap between males and females, an effect seen in depressed humans and in stressed mice. We identify key regulators of sex-specific gene networks underlying MDD and confirm their sex-specific impact as mediators of stress susceptibility. For example, downregulation of the female-specific hub gene DUSP6 in prefrontal cortex mimics stress susceptibility in females only by increasing ERK signaling and pyramidal neuron excitability. Such DUSP6 downregulation also recapitulates the transcriptional remodeling that occurs in PFC of depressed females.; Conclusions: Together, our findings reveal dramatic sexual dimorphism at the transcriptional level in MDD and highlight the importance of studying sex-specific treatments for this disorder.

Overall Design

RNA sequencing data from (1) 6 human postmortem brain regions in males and females with and without major depression and (2) 2 equivalent brain regions in males and female mice with and without 21 days of chronic variable stress (CVS)

Histogram

Data and Resources

Raw Files [281]

Additional Info

Field Value
Source https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102556
Type of Data

Expression profiling by high throughput sequencing

Technology

RNA Sequencing

GSE Submission Date 11/08/2017
GSE Authors Benoit,,Labonte; Olivia Engmann; Immanuel Purushothaman; Caroline Ménard; Junshi Wang; Chunfeng Tan; Joseph,R,Scarpa; Gregory Moy; Yong-Hwee,E,Loh; Michael Cahill; Zachary,S,Lorsch; Peter,J,Hamilton; Erin,S,Calipari; Georgia,E,Hodes; Orna Issler; Hope Kr
Dataset Last Updated November 29, 2019, 13:43 (UTC)
Dataset Created November 29, 2019, 13:01 (UTC)