https://doi.org/10.1016/j.ynstr.2023.100525

Genetics and epigenetics of stress: New avenues for an old concept

Matthew J. Friedman and the VA National PTSD Brain Bank: New Transcriptomic Insight into PTSD Pathophysiology

Introduction
Posttraumatic stress disorder (PTSD) is a complex multifactorial disorder influenced by the interaction of genetic and environmental factors. Analyses of epigenomic and transcriptomic modifications may help to dissect the biological factors underlying the gene-environment interplay in PTSD. To date, most human PTSD epigenetics studies have used peripheral tissue, and these findings have complex and poorly understood relationships to brain alterations. Studies examining brain tissue may help characterize the brain-specific transcriptomic and epigenomic profiles of PTSD. In this review, we compiled and integrated brain-specific molecular findings of PTSD from humans and animals.


Methods
A systematic literature search according to the PRISMA criteria was performed to identify transcriptomic and epigenomic studies of PTSD, focusing on brain tissue from human postmortem samples or animal-stress paradigms.


Results
Gene- and pathway-level convergence analyses revealed PTSD-dysregulated genes and biological pathways across brain regions and species. A total of 243 genes converged across species, with 17 of them significantly enriched for PTSD. Chemical synaptic transmission and signaling by G-protein-coupled receptors were consistently enriched across omics and species.


Discussion
Our findings point out dysregulated genes highly replicated across PTSD studies in humans and animal models and suggest a potential role for the corticotropin-releasing hormone/orexin pathway in PTSD's pathophysiology. Further, we highlight current knowledge gaps and limitations and recommend future directions to address them.

https://www.karger.com/Article/Pdf/529536

Cross-Species Convergence of Brain Transcriptomic and Epigenomic Findings in Posttraumatic Stress Disorder: A Systematic Review.

In recent years, postmortem brain studies have revealed that some molecular, cellular, and circuit changes associated with suicide, have an independent or additive effect on depression. The aim of the present study is to identify potential phenotypic, tissue, and sex-specific novel targets and pathways to distinguish depression or suicide from major depressive disorder (MDD) comorbid with suicide. The mRNA expression profiling datasets from two previous independent postmortem brain studies of suicide and depression (GSE102556 and GSE101521) were retrieved from the GEO database. Machine learning analysis was used to differentiate three regrouped gene expression profiles, i.e., MDD with suicide, MDD without suicide, and suicide without depression. Weighted correlation network analysis (WGCNA) was further conducted to identify the key modules and hub genes significantly associated with each of these three sub-phenotypes. TissueEnrich approaches were used to find the essential brain tissues and the difference of tissue enriched genes between depression with or without suicide. Dysregulated gene expression cross two variables, including phenotypes and tissues, were determined by global analysis with Vegan. RRHO analysis was applied to examine the difference in global expression pattern between male and female groups. Using the optimized machine learning model, several ncRNAs and mRNAs with higher AUC and MeanDecreaseGini, including GCNT1P1 and AC092745.1, etc., were identified as potential molecular targets to distinguish suicide with, or without MDD and depression without suicide. WGCNA analysis identified some key modules significantly associated with these three phenotypes, and the gene biological functions of the key modules mainly relate to ncRNA and miRNA processing, as well as oxidoreductase and dehydrogenase activity. Hub genes such as RP11-349A22.5, C20orf196, MAPK8IP3 and RP11-697N18.2 were found in these key modules. TissueEnrich analysis showed that nucleus accumbens and subiculum were significantly changed among the 6 brain regions studied. Global analysis with Vegan and RRHO identified PRS26, ARNT and SYN3 as the most significantly differentially expressed genes across phenotype and tissues, and there was little overlap between the male and female groups. In this study, we have identified novel gene targets, as well as annotated functions of co-expression patterns and hub genes that are significantly distinctive between depression with suicide, depression without suicide, and suicide without depression. Moreover, global analysis across three phenotypes and tissues confirmed the evidence of sex difference in mood disorders. 

/pdf/identification-of-novel-targets-and-pathways-to-distinguish-3i2ndgwb.pdf

Identification of novel targets and pathways to distinguish suicide dependent or independent on depression diagnosis

Transcriptomics of the depressed and PTSD brain.

Single Cell Genomic Analysis Reveals Cell Type-Specific Molecular Signatures in the Human PTSD Prefrontal Cortex

Recent advances in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) have allowed simultaneous epigenetic profiling over thousands of individual cells to dissect the cellular heterogeneity and elucidate regulatory mechanisms at the finest possible resolution. However, scATAC-seq is challenging to model computationally due to the ultra-high dimensionality, low signal-to-noise ratio, complex feature interactions, and high vulnerability to various confounding factors. In this study, we present Translator, an efficient transfer learning approach to capture generalizable chromatin interactions from high-quality (HQ) reference scATAC-seq data to obtain robust cell representations in low-to-moderate quality target scATAC-seq data. We applied Translator on various simulated and real scATAC-seq datasets and demonstrated that Translator could learn more biologically meaningful cell representations than other methods by incorporating information learned from the reference data, thus facilitating various downstream analyses such as clustering and motif enrichment measurements. Moreover, Translator's block-wise deep learning framework can handle nonlinear relationships with restricted connections using fewer parameters to boost computational efficiency through Graphics Processing Unit (GPU) parallelism. Finally, we have implemented Translator as a free software package available for the community to leverage large-scale, HQ reference data to study target scATAC-seq data.

Jing Zhang

Papers

Transcriptomics of the depressed and PTSD brain.

Single Cell Genomic Analysis Reveals Cell Type-Specific Molecular Signatures in the Human PTSD Prefrontal Cortex

Translator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset