Angiogenic gene networks are dysregulated in opioid use disorder: evidence from multi-omics and imaging of postmortem human brain.

Opioid use disorder (OUD) is a public health crisis in the U.S. that causes over 50 thousand deaths annually due to overdose. Using next-generation RNA sequencing and proteomics techniques, we identified 394 differentially expressed (DE) coding and long noncoding (lnc) RNAs as well as 213 DE proteins in Brodmann Area 9 of OUD subjects. The RNA and protein changes converged on pro-angiogenic gene networks and cytokine signaling pathways. Four genes (LGALS3, SLC2A1, PCLD1, and VAMP1) were dysregulated in both RNA and protein. Dissecting these DE genes and networks, we found cell type-specific effects with enrichment in astrocyte, endothelial, and microglia correlated genes. Weighted-genome correlation network analysis (WGCNA) revealed cell-type correlated networks including an astrocytic/endothelial/microglia network involved in angiogenic cytokine signaling as well as a neuronal network involved in synaptic vesicle formation. In addition, using ex vivo magnetic resonance imaging, we identified increased vascularization in postmortem brains from a subset of subjects with OUD. This is the first study integrating dysregulation of angiogenic gene networks in OUD with qualitative imaging evidence of hypervascularization in postmortem brain. Understanding the neurovascular effects of OUD is critical in this time of widespread opioid use.

Genome-Wide Correlation of DNA Methylation and Gene Expression in Postmortem Brain Tissues of Opioid Use Disorder Patients.

BACKGROUND: Opioid use disorder (OUD) affects millions of people, causing nearly 50 000 deaths annually in the United States. While opioid exposure and OUD are known to cause widespread transcriptomic and epigenetic changes, few studies in human samples have been conducted. Understanding how OUD affects the brain at the molecular level could help decipher disease pathogenesis and shed light on OUD treatment. METHODS: We generated genome-wide transcriptomic and DNA methylation profiles of 22 OUD subjects and 19 non-psychiatric controls. We applied weighted gene co-expression network analysis to identify genetic markers consistently associated with OUD at both transcriptomic and methylomic levels. We then performed functional enrichment for biological interpretation. We employed cross-omics analysis to uncover OUD-specific regulatory networks. RESULTS: We found 6 OUD-associated co-expression gene modules and 6 co-methylation modules (false discovery rate <0.1). Genes in these modules are involved in astrocyte and glial cell differentiation, gliogenesis, response to organic substance, and response to cytokine (false discovery rate <0.05). Cross-omics analysis revealed immune-related transcription regulators, suggesting the role of transcription factor-targeted regulatory networks in OUD pathogenesis. CONCLUSIONS: Our integrative analysis of multi-omics data in OUD postmortem brain samples suggested complex gene regulatory mechanisms involved in OUD-associated expression patterns. Candidate genes and their upstream regulators revealed in astrocyte, and glial cells could provide new insights into OUD treatment development.