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

Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction

BACKGROUND

Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.

METHODS

To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals.

RESULTS

We identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression.

CONCLUSIONS

Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.

An emerging multi-omic understanding of the genetics of opioid addiction

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

Solving the Global Opioid Crisis: Incorporating Genetic Addiction Risk Assessment with Personalized Dopaminergic Homeostatic Therapy and Awareness Integration Therapy

Objectives

The opioid crisis in the last few decades has mounted to a global level, impacting all areas of socioeconomic, demographic, geographic, and cultural boundaries. Traditional treatments have not been deemed to show the degree of efficacy necessary to address the crisis. The authors of this review paper have set forth an unprecedented and in-depth look into multi-factorial determinants that have contributed to the opioid crisis becoming global and multi-faceted.

Methods

For this narrative review/opinion article, we searched PsychINFO, PubMed, Google Scholar, and Web of Science databases to identify relevant articles on topics including the "opioid crisis," "opioid mechanisms," "genetics and epigenetics," "neuropharmacology," and "clinical aspects of opioid treatment and prevention." Since this was not a systematic review the articles selected could represent unitential bias.

Results

Despite some success achieved through Opioid Substitution Therapy (OST) in harm reduction, the annual mortality toll in the US alone surpasses 106,699 individuals, a figure expected to climb to 165,000 by 2025. Data from the Substance Abuse and Mental Health Services Administration's (SAMHSA) National Survey on Drug Abuse and Health (NSDUH) reveals that approximately 21.4% of individuals in the US engaged in illicit drug use in 2020, with 40.3 million individuals aged 12 or older experiencing a Substance Use Disorder (SUD). Provisional figures from the Centers for Disease Control and Prevention (CDC) indicate a troubling 15% increase in overdose deaths in 2021, rising from 93,655 in 2020 to 107,622, with opioids accounting for roughly 80,816 of these deaths.

Conclusions

We advocate reevaluating the "standard of care" and shifting towards inducing dopamine homeostasis by manipulating key neurotransmitter systems within the brain's reward cascade. We propose a paradigm shift towards a novel "standard of care" that begins with incorporating Genetic Addiction Risk Severity (GARS) testing to assess pre-addiction risk and vulnerability to opioid-induced addiction; emphasis should be placed on inducing dopamine homeostasis through safe and non-addictive alternatives like KB220, and comprehensive treatment approaches that address psychological, spiritual, and societal aspects of addiction through Awareness Integration Therapy (AIT).

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.