Research

Research in our lab is directed at determining the molecular pathologies underlying stress disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and suicidal behavior. We identify therapeutic and biomarker targets relevant to those disorders by utilizing human brain-derived data from frozen postmortem tissue. We conduct research focused on the connection between stress and brain functions.

Molecular Technologies in the Girgenti Lab

Recent large genome-wide association studies (GWAS) have confirmed significant heritability in both sexes and identified reliable PTSD-associated loci that have not yet been linked with causal biological mechanisms. Brain molecular phenotypes, such as gene expression and chromatin assembly and modification, can be used to translate psychiatric GWAS loci into meaningful biological mechanisms and identify causal disease pathways.

RNA-sequencing and whole genome sequencing

RNA-seq is a giant leap ahead of microarray-based platforms to measure gene expression. In our lab, we perform whole genome and transcriptome (RNA) sequencing from brain tissue in-house. We are equipped to fully capture the functional diversity of the human brain transcriptome at various levels of gene feature resolution: whole transcript, exon and exon junction as published (Girgenti MJ et al, 2021).

Whole genome bisulfite (DNA methylation) sequencing

Since PTSD mechanisms derive from genes and the environment, it is critical to evaluate epigenetic marks such as DNA methylation in correlation with gene expression studies. We are performing whole genome bisulfite sequencing approaches to quantify DNA methylation with extensive genome-wide coverage.

Single Cell Multi-omics

PTSD arises from differences at various levels of gene regulation in diverse brain cell types that converge on specific pathways (e.g. glucocorticoid and GABAergic signaling) harboring clinical significance. However, the identity of the cell types and their individual contribution to the molecular pathology of PTSD is a significant research gap whose understanding will facilitate the development of diagnostics and personalized therapeutics. We are isolating nuclei from PTSD postmortem brains and perfroming single nuclei RNA and ATAC to generate single-cell type atlases of gene regulatory networks in PTSD primary brain regions.

Patient-derived cell lines

For nearly 10% of the postmortem brains collected at the NPBB (and all donors moving forward), we have viable dura mater derived fibroblast cell lines, thus permitting molecular characterization of targets for drug screening and development. The patient-derived PTSD cellular model best replicates the genetic risk architecture of PTSD patients.

Research Projects

Cell-type specific genomic atlas of stress disorders

We are currently profiling two molecular modalities (transcriptome and epigenome) by snRNA-seq and snATAC-seq in primary postmortem brain regions across several neuropsychiatric disorders.

Functional characterization of PTSD risk variants

We are expanding upon the rich transcriptomic data generated from our PTSD and MDD postmortem brain tissue by incorporating genome-scale DNA methylation (DNAm) data, alternative splicing, non-coding RNAs and proteomics with genetic data to better determine how genetic risk for PTSD and MDD manifests in the human brain.

Animal models of traumatic and chronic stress

We are validating our molecular findings using a broad array of mouse models and techniques (viral vectors and genetic mutants) in females and males to characterize the behavioral and molecular mechanisms of stress-related disorders.

Molecular pathology of suicide

We are identifying cell types, networks, and genes with causal roles in suicide by comparing transcriptomic signatures of PTSD and MDD subjects with or without death by suicide.

iPSC Functional Validation of PTSD causal genes

We are applying hiPSC-based approaches to manipulate the genotype and/or expression levels of putative causal risk genes identified in large GWAS and postmortem datasets.