Diverse types of genomic evidence converge on alcohol use disorder risk genes

Potential Molecular Mechanisms of Alcohol Use Disorder with Non-Coding RNAs and Gut Microbiota for the Development of Superior Therapeutic Application

Many investigations have evaluated the expression of noncoding RNAs (ncRNAs) as well as their related molecular functions and biological machineries in individuals with alcohol dependence. Alcohol dependence may be one of the most prevailing psychological disorders globally, and its pathogenesis is intricate and inadequately comprehended. There is substantial evidence indicating significant links between multiple genetic factors and the development of alcohol dependence. In particular, the critical roles of ncRNAs have been emphasized in the pathology of mental illnesses, probably including alcohol dependence. In the comprehension of the action of ncRNAs and their machineries of modification, furthermore, they have emerged as therapeutic targets for a variety of psychiatric illnesses, including alcohol dependence. It is worth mentioning that the dysregulated expression of ncRNAs has been regularly detected in individuals with alcohol dependence. An in-depth knowledge of the roles of ncRNAs and m6A modification may be valuable for the development of a novel treatment against alcohol dependence. In general, a more profound understanding of the practical roles of ncRNAs might make important contributions to the precise diagnosis and/or actual management of alcohol dependence. Here, in this review, we mostly focused on up-to-date knowledge regarding alterations and/or modifications in the expression of ncRNAs in individuals with alcohol dependence. Then, we present prospects for future research and therapeutic applications with a novel concept of the engram system.

Genetic and environmental influence on alcohol intent and alcohol sips among U.S. children-Effects across sex, race, and ethnicity

INTRODUCTION

Alcohol intent (the susceptibility to initiating alcohol use) and alcohol sips (the initiation of alcohol) in youth are a multifactorial puzzle with many components. This research aims to examine the connection between genetic and environmental factors across sex, race and ethnicity.

METHODS

Data was obtained from the twin hub of the Adolescent Brain Cognitive Development (ABCD) study at baseline (2016-2018). Variance component models were conducted to dissect the additive genetic (A), common (C) and unique environmental (E) effects on alcohol traits. The proportion of the total alcohol phenotypic variation attributable to additive genetic factors is reported as heritability (h2).

RESULTS

The sample (n = 1,772) included an approximately equal male-female distribution. The 886 same-sex twin pairs were 60.4% dizygotic (DZ), 39.6% monozygotic (MZ), 65.4% non-Hispanic Whites, 13.9% non-Hispanic Blacks, 10.8% of Hispanics with a mean age of 121.2 months. Overall, genetic predisposition was moderate for alcohol intent (h2 = 28%, p = .006) and low for alcohol initiation (h2 = 4%, p = 0.83). Hispanics (h2 = 53%, p < .0001) and Blacks (h2 = 48%, p < .0001) demonstrated higher alcohol intent due to additive genetic factors than Whites (h2 = 34%, p < .0001). Common environmental factors explained more variation in alcohol sips in females (c2 = 63%, p = .001) than in males (c2 = 55%, p = .003). Unique environmental factors largely attributed to alcohol intent, while common environmental factors explained the substantial variation in alcohol initiation.

CONCLUSION

Sex and racial/ethnic disparities in genetic and environmental risk factors for susceptibility to alcohol initiation can lead to significant health disparities. Certain populations may be at greater risk for alcohol use due to their genetic and ecological factors at an early age.

Modeling Brain Gene Expression in Alcohol Use Disorder with Genetic Animal Models

Animal genetic models have and will continue to provide important new information about the behavioral and physiological adaptations associated with alcohol use disorder (AUD). This chapter focuses on two models, ethanol preference and drinking in the dark (DID), their usefulness in interrogating brain gene expression data and the relevance of the data obtained to interpret AUD-related GWAS and TWAS studies. Both the animal and human data point to the importance for AUD of changes in synaptic transmission (particularly glutamate and GABA transmission), of changes in the extracellular matrix (specifically including collagens, cadherins and protocadherins) and of changes in neuroimmune processes. The implementation of new technologies (e.g., cell type-specific gene expression) is expected to further enhance the value of genetic animal models in understanding AUD.

RNA m6A Modification Changes in Postmortem Nucleus Accumbens of Subjects with Alcohol Use Disorder: A Pilot Study

BACKGROUND

The nucleus accumbens (NAc) is a key brain structure mediating the rewarding effect of alcohol and drug abuse. Chronic alcohol consumption may alter RNA methylome (or epitranscriptome) in the NAc, leading to altered gene expression and thus behavioral neuroadaptation to alcohol.

METHODS

This pilot study profiled the epitranscriptomes of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in postmortem NAc of three male Caucasian subjects with alcohol use disorder (AUD) and three matched male Caucasian control subjects using Arraystar's m6A-mRNA&lncRNA Epitranscriptomic Microarray assay. Differentially methylated (DM) RNAs and the function of DM RNAs were analyzed by biostatistics and bioinformatics programs.

RESULTS

26 mRNAs were hypermethylated and three mRNAs were hypomethylated in the NAc of AUD subjects (≥2-fold changes and p ≤ 0.05). Most of these 29 DM mRNAs are involved in immune-related pathways (e.g., IL-17 signaling). Moreover, four lncRNAs were hypermethylated and one lncRNA was hypomethylated in the NAc of AUD subjects (≥2-fold changes and p ≤ 0.05). Additionally, three miRNAs were hypermethylated in the NAc of AUD subjects (≥2-fold changes and p ≤ 0.05).

CONCLUSIONS

This study revealed RNA methylomic changes in the NAc of AUD subjects, suggesting that chronic alcohol consumption may lead to AUD through epitranscriptomic RNA modifications. Our findings need to be replicated in a larger sample.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment

BACKGROUND

Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness.

RESULTS

We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways.

CONCLUSIONS

In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.

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.

Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8⁺ T (T_RM) cells, we found a 2.24-fold decrease of cell proportion among CD8⁺ T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the T_RM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung T_RM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis.

Association of CXCR6 with COVID-19 severity: Delineating the host genetic factors in transcriptomic regulation

Background:

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~80% asymptomatic or mild cases and ~5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms.

Methods:

We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients.

Results:

We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8⁺ T (T_RM) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in T_RM cells trajectory from moderate to severe patients.

Conclusions:

CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung T_RM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and T_RM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.

CSEA-DB: an omnibus for human complex trait and cell type associations

During the past decade, genome-wide association studies (GWAS) have identified many genetic variants with susceptibility to several thousands of complex diseases or traits. The genetic regulation of gene expression is highly tissue-specific and cell type-specific. Recently, single-cell technology has paved the way to dissect cellular heterogeneity in human tissues. Here, we present a reference database for GWAS trait-associated cell type-specificity, named Cell type-Specific Enrichment Analysis DataBase (CSEA-DB, available at https://bioinfo.uth.edu/CSEADB/). Specifically, we curated total of 5120 GWAS summary statistics data for a wide range of human traits and diseases followed by rigorous quality control. We further collected >900 000 cells from the leading consortia such as Human Cell Landscape, Human Cell Atlas, and extensive literature mining, including 752 tissue cell types from 71 adult and fetal tissues across 11 human organ systems. The tissues and cell types were annotated with Uberon and Cell Ontology. By applying our deTS algorithm, we conducted 10 250 480 times of trait-cell type associations, reporting a total of 598 (11.68%) GWAS traits with at least one significantly associated cell type. In summary, CSEA-DB could serve as a repository of association map for human complex traits and their underlying cell types, manually curated GWAS, and single-cell transcriptome resources.

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.