Sex and cell-specific gene expression in corticolimbic brain regions associated with psychiatric disorders revealed by bulk and single-nuclei RNA sequencing.

BACKGROUND: There are sex-specific differences in the prevalence, symptomology and course of psychiatric disorders. However, preclinical models have primarily used males, such that the molecular mechanisms underlying sex-specific differences in psychiatric disorders are not well established. METHODS: In this study, we compared transcriptome-wide gene expression profiles in male and female rats within the corticolimbic system, including the cingulate cortex, nucleus accumbens medial shell (NAcS), ventral dentate gyrus and the basolateral amygdala (n = 22-24 per group/region). FINDINGS: We found over 3000 differentially expressed genes (DEGs) in the NAcS between males and females. Of these DEGs in the NAcS, 303 showed sex-dependent conservation DEGs in humans and were significantly enriched for gene ontology terms related to blood vessel morphogenesis and regulation of cell migration. Single nuclei RNA sequencing in the NAcS of male and female rats identified widespread sex-dependent expression, with genes upregulated in females showing a notable enrichment for synaptic function. Female upregulated genes in astrocytes, Drd3+MSNs and oligodendrocyte were also enriched in several psychiatric genome-wide association studies (GWAS). INTERPRETATION: Our data provide comprehensive evidence of sex- and cell-specific molecular profiles in the NAcS. Importantly these differences associate with anxiety, bipolar disorder, schizophrenia, and cross-disorder, suggesting an intrinsic molecular basis for sex-based differences in psychiatric disorders that strongly implicates the NAcS. FUNDING: This work was supported by funding from the Hope for Depression Research Foundation (MJM).

Genomic modules and intramodular network concordance in susceptible and resilient male mice across models of stress.

The multifactorial etiology of stress-related disorders necessitates a constant interrogation of the molecular convergences in preclinical models of stress that use disparate paradigms as stressors spanning from environmental challenges to genetic predisposition to hormonal signaling. Using RNA-sequencing, we investigated the genomic signatures in the ventral hippocampus common to mouse models of stress. Chronic oral corticosterone (CORT) induced increased anxiety- and depression-like behavior in wild-type male mice and male mice heterozygous for the gene coding for brain-derived neurotrophic factor Val66Met, a variant associated with genetic susceptibility to stress. In a separate set of male mice, chronic social defeat stress (CSDS) led to a susceptible or a resilient population, whose proportion was dependent on housing conditions, namely standard housing or enriched environment. Rank-rank-hypergeometric overlap (RRHO), a threshold-free approach that ranks genes by their p value and effect size direction, was used to identify genes from a continuous gradient of significancy that were concordant across groups. In mice treated with CORT and in standard-housed susceptible mice, differentially expressed genes (DEGs) were concordant for gene networks involved in neurotransmission, cytoskeleton function, and vascularization. Weighted gene co-expression analysis generated 54 gene hub modules and revealed two modules in which both CORT and CSDS-induced enrichment in DEGs, whose function was concordant with the RRHO predictions, and correlated with behavioral resilience or susceptibility. These data showed transcriptional concordance across models in which the stress coping depends upon hormonal, environmental, or genetic factors revealing common genomic drivers that embody the multifaceted nature of stress-related disorders.

Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies.

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html.