Research & Projects

Our research mission is to understand genetic and epigenetic contributions to addictive behavior and to translate the genomic findings to potential clinical applications. Specifically:

A. Stress-immune mechanisms for people living with HIV, CUD and Depression
Psychosocial stress profoundly affects neuroendocrine (hypothalamic-pituitary-adrenal, HPA) and immune function in both central and peripheral cells. People with HIV (PWH) show heightened distress and increased inflammatory burden, leading to greater complex morbidity including high prevalence of cannabis use disorder and major depressive disorders. Epigenetic aberrant of the “stress genome” has been shown to disrupt HPA function and stress adaptation. In the HIV infected host genome, the epigenome landscape is profoundly altered by HIV-1, including genes that are part of the stress genome, highlighting the interactive role of the stress and immune genomes in development of complex morbidity in PWH. But the epigenetically regulated gene expression of stress-immune effects in PWH with major depressive disorder and cannabis use disorder has not been studied. This project aims to address these research gaps using a powerful and novel cross-diagnostic approach with multiple complementary approaches using a combined human experimental approach with prospective longitudinal assessment of daily distress, and substance use symptoms as well as assessment of chronic stress and resilience in the experimental cohort with corroboration in a population-based analysis of a well-established large cohort of PWH.

B. In vivo study of THC-induced immunogenome changes at single cell resolution in HIV-infected humans
In this clinical trial, we hypothesize that the principal cannabinoid constituent of cannabis, Δ-9 tetrahydrocannabinol (THC), alters the host immunogenome in peripheral mononuclear cells (PBMC) in a cell type-specific fashion via epigenetic regulatory mechanisms and that gene expression changes differ between HIV+ and HIV uninfected host genomes. We aim to define the epigenetic mechanisms responsible for THC-modulated gene expression by conducting single cell Transposase-Accessible Chromatin followed by sequencing (scATAC-seq) and DNA methylome capture sequencing (MC-seq), and an integrative multiple omic analysis at individual cell type. We expect the results will permit us to define the specific cellular effects of THC in the setting of HIV-infection, which can be tested for their ability to predict clinical outcomes (e.g., cardiometabolic disease) in the future given the well-established role of inflammation in both accelerated aging and end organ disease.

C. Cell-type based epigenomic analysis to identify druggable genes for people living with HIV infection and using cannabis
Previous studies on the epigenetic effects of HIV and cannabis use have identified DNA methylation sites in bulk peripheral blood mononuclear cells (PBMC), which only provides an average view of DNA methylation (DNAm) changes across various cell types. We hypothesize that cannabis use alters DNAm in a cell type-specific manner within the HIV-infected host, with these changes potentially modulated by methylation quantitative trait loci (meQTLs) specific to each cell type. Our study aims to dissect the epigenomic landscape of CB and HIV interaction by conducting cell-type based genome-wide DNAm and meQTL analyses in two large cohorts, assessing their potential for druggable targets. This will involve comprehensive profiling of DNAm across five different cell types isolated from PBMCs, alongside functional validation studies both in vivo and in vitro. The functional validation involves transcriptome-wide association analyses, single-cell RNA and ATAC sequencing to elucidate the functional impact of specific epigenetic modifications and their relevance to drug development.

1. Identifying genomic signatures as biomarkers for substance use disorder (e.g., alcohol and cannabis use) and related medication consequences.
   Substance misuse is a significant public health problem in the United States, and diagnosis and treatment are limited due to a lack of robust biomarkers. Using computational approaches, our projects involve selecting biologically meaningful features from the human genome, epigenome, and transcriptome for substance use disorder. These projects provide powerful predictive models not only for substance misuse but predicting substance use-related medical outcomes, such as HIV and HCV infection, disease prognosis, and mortality.
2. Understanding the genetic architecture of smoking behavior.
   The project includes identifying associated genetic variations, detecting relevant functional tissue and cell types, and estimating genetic correlations with other complex traits and diseases. We are also interested in the potential sex and ancestral effects on mediating the genetic risks of smoking behavior.
3. Machine learning model prediction.
   We are currently developing DNA methylation prediction machine learning models for frailty among people living with HIV (PLWH) and conducting analysis of candidate DNA methylation sites on cocaine use affecting HIV outcomes.
4. Methodology development.
   Due to increasing evidence illustrating that pleiotropy is a widespread phenomenon in complex disease states, we are also developing powerful statistical tests for detecting association using multivariate phenotypes (e.g., smoking status and alcohol consumption) for genome-wide association studies in order to identify the underlying genetic mechanism in these diseases. There is an increasing need to develop and apply these tests for epigenome-wide association studies. The results of our research will have potential clinical applications. In addition, we developed a new computational model to deconvolute cell-type specific meQTLs.

Our lab has actively participated in multiple open competitions in DREAM challenges. We were ranked joint third in Subchallenge 2 of the Disease Module Identification Challenge in 2016, won a travel award in the Parkinson’s Diseases Digital Biomarker Challenge in 2017, and won the Best Performance Team with travel award in Single Cell Transcriptomics Challenge in Subchallenge 2 and 3 in 2018. Our lab was listed as co-authors in multiple DREAM challenge papers.