Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose

Identifying novel gene dysregulation associated with opioid overdose death: A meta-analysis of differential gene expression in human prefrontal cortex

Only recently have human postmortem brain studies of differential gene expression (DGE) associated with opioid overdose death (OOD) been published; sample sizes from these studies have been modest (N = 40-153). To increase statistical power to identify OOD-associated genes, we leveraged human prefrontal cortex RNAseq data from four independent OOD studies and conducted a transcriptome-wide DGE meta-analysis (N = 285). Using a unified gene expression data processing and analysis framework across studies, we meta-analyzed 20 098 genes and found 335 significant differentially expressed genes (DEGs) by OOD status (false discovery rate < 0.05). Of these, 66 DEGs were among the list of 303 genes reported as OOD-associated in prior prefrontal cortex molecular studies, including genes/gene families (e.g., OPRK1, NPAS4, DUSP, EGR). The remaining 269 DEGs were not previously reported (e.g., NR4A2, SYT1, HCRTR2, BDNF). There was little evidence of genetic drivers for the observed differences in gene expression between opioid addiction cases and controls. Enrichment analyses for the DEGs across molecular pathway and biological process databases highlight an interconnected set of genes and pathways from orexin and tyrosine kinase receptors through MEK/ERK/MAPK signaling to affect neuronal plasticity.

Novel method of isolating nuclei of human oligodendrocyte precursor cells reveals substantial developmental changes in gene expression and H3K27ac histone modification

Oligodendrocyte precursor cells (OPCs) generate differentiated mature oligodendrocytes (MOs) during development. In adult brain, OPCs replenish MOs in adaptive plasticity, neurodegenerative disorders, and after trauma. The ability of OPCs to differentiate to MOs decreases with age and is compromised in disease. Here we explored the cell specific and age-dependent differences in gene expression and H3K27ac histone mark in these two cell types. H3K27ac is indicative of active promoters and enhancers. We developed a novel flow-cytometry-based approach to isolate OPC and MO nuclei from human postmortem brain and profiled gene expression and H3K27ac in adult and infant OPCs and MOs genome-wide. In adult brain, we detected extensive H3K27ac differences between the two cell types with high concordance between gene expression and epigenetic changes. Notably, the expression of genes that distinguish MOs from OPCs appears to be under a strong regulatory control by the H3K27ac modification in MOs but not in OPCs. Comparison of gene expression and H3K27ac between infants and adults uncovered numerous developmental changes in each cell type, which were linked to several biological processes, including cell proliferation and glutamate signaling. A striking example was a subset of histone genes that were highly active in infant samples but fully lost activity in adult brain. Our findings demonstrate a considerable rearrangement of the H3K27ac landscape that occurs during the differentiation of OPCs to MOs and during postnatal development of these cell types, which aligned with changes in gene expression. The uncovered regulatory changes justify further in-depth epigenetic studies of OPCs and MOs in development and disease.

Single nucleus transcriptomics of ventral midbrain identifies glial activation associated with chronic opioid use disorder

Dynamic interactions of neurons and glia in the ventral midbrain mediate reward and addiction behavior. We studied gene expression in 212,713 ventral midbrain single nuclei from 95 individuals with history of opioid misuse, and individuals without drug exposure. Chronic exposure to opioids was not associated with change in proportions of glial and neuronal subtypes, however glial transcriptomes were broadly altered, involving 9.5 - 6.2% of expressed genes within microglia, oligodendrocytes, and astrocytes. Genes associated with activation of the immune response including interferon, NFkB signaling, and cell motility pathways were upregulated, contrasting with down-regulated expression of synaptic signaling and plasticity genes in ventral midbrain non-dopaminergic neurons. Ventral midbrain transcriptomic reprogramming in the context of chronic opioid exposure included 325 genes that previous genome-wide studies had linked to risk of substance use traits in the broader population, thereby pointing to heritable risk architectures in the genomic organization of the brain's reward circuitry.

Molecular and long-term behavioral consequences of neonatal opioid exposure and withdrawal in mice

Introduction

Infants exposed to opioids in utero are at high risk of exhibiting Neonatal Opioid Withdrawal Syndrome (NOWS), a combination of somatic withdrawal symptoms including high pitched crying, sleeplessness, irritability, gastrointestinal distress, and in the worst cases, seizures. The heterogeneity of in utero opioid exposure, particularly exposure to polypharmacy, makes it difficult to investigate the underlying molecular mechanisms that could inform early diagnosis and treatment of NOWS, and challenging to investigate consequences later in life.

Methods

To address these issues, we developed a mouse model of NOWS that includes gestational and post-natal morphine exposure that encompasses the developmental equivalent of all three human trimesters and assessed both behavior and transcriptome alterations.

Results

Opioid exposure throughout all three human equivalent trimesters delayed developmental milestones and produced acute withdrawal phenotypes in mice reminiscent of those observed in infants. We also uncovered different patterns of gene expression depending on the duration and timing of opioid exposure (3-trimesters, in utero only, or the last trimester equivalent only). Opioid exposure and subsequent withdrawal affected social behavior and sleep in adulthood in a sex-dependent manner but did not affect adult behaviors related to anxiety, depression, or opioid response.

Discussion

Despite marked withdrawal and delays in development, long-term deficits in behaviors typically associated with substance use disorders were modest. Remarkably, transcriptomic analysis revealed an enrichment for genes with altered expression in published datasets for Autism Spectrum Disorders, which correlate well with the deficits in social affiliation seen in our model. The number of differentially expressed genes between the NOWS and saline groups varied markedly based on exposure protocol and sex, but common pathways included synapse development, the GABAergic and myelin systems, and mitochondrial function.

Machine learning for predicting opioid use disorder from healthcare data: A systematic review

INTRODUCTION

The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD). The review is split into two parts. The first part summarizes the current research in OUD prediction with ML. The second part evaluates how ML techniques and processes were used to achieve these results and suggests improvements to refine further attempts to use ML for OUD prediction.

METHODS

The review includes peer-reviewed journal papers published on or after 2012 that use healthcare data to predict OUD. We searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore, and Science.gov in September of 2022. Data extracted includes the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models.

RESULTS

The review analyzed 16 papers. Three papers created their dataset, five used a publicly available dataset, and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to over half a million. Six papers used one type of ML model, and the remaining ten used up to five different ML models. The reported ROC AUC was higher than 0.8 for all but one of the papers. Five papers used only non-interpretable models, and the other 11 used interpretable models exclusively or in combination with non-interpretable ones. The interpretable models were the highest or second-highest ROC AUC values. Most papers did not sufficiently describe the ML techniques and tools used to produce their results. Only three papers published their source code.

CONCLUSIONS

We found that while there are indications that ML methods applied to OUD prediction may be valuable, the lack of details and transparency in creating the ML models limits their usefulness. We end the review with recommendations to improve studies on this critical healthcare subject.

Single Nucleus Transcriptomics Reveals Pervasive Glial Activation in Opioid Overdose Cases

Dynamic interactions of neurons and glia in the ventral midbrain (VM) mediate reward and addiction behavior. We studied gene expression in 212,713 VM single nuclei from 95 human opioid overdose cases and drug-free controls. Chronic exposure to opioids left numerical proportions of VM glial and neuronal subtypes unaltered, while broadly affecting glial transcriptomes, involving 9.5 - 6.2% of expressed genes within microglia, oligodendrocytes, and astrocytes, with prominent activation of the immune response including interferon, NFkB signaling, and cell motility pathways, sharply contrasting with down-regulated expression of synaptic signaling and plasticity genes in VM non-dopaminergic neurons. VM transcriptomic reprogramming in the context of opioid exposure and overdose included 325 genes with genetic variation linked to substance use traits in the broader population, thereby pointing to heritable risk architectures in the genomic organization of the brain's reward circuitry.