Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry

In order to accelerate the understanding of pathophysiological mechanisms and clinical biomarker discovery and in psychiatry, approaches that integrate multiple -omics platforms are needed. We introduce a workflow that investigates a narrowly defined psychiatric phenotype, makes use of the potent and cost-effective discovery technology of gene expression microarrays, applies Weighted Gene Co-Expression Network Analysis (WGCNA) to better capture complex and polygenic traits, and finally explores gene expression findings on the proteomic level using targeted mass-spectrometry (MS) technologies. To illustrate the effectiveness of the workflow, we present a proteomic analysis of peripheral blood plasma from patient's remitted major depressive disorder (MDD) who experience ongoing cognitive deficits. We show that co-expression patterns previous detected on the transcript level could be replicated for plasma proteins, as could the module eigengene correlation with cognitive performance. Further, we demonstrate that functional analysis of multi-omics data has the potential to point to cellular mechanisms and candidate biomarkers for cognitive dysfunction in MDD, implicating cell cycle regulation by cyclin D3 (CCND3), regulation of protein processing in the endoplasmatic reticulum by Thioredoxin domain-containing protein 5 (TXND5), and modulation of inflammatory cytokines by Tripartite Motif Containing 26 (TRI26).

SIGNIFICANCE

This paper discusses how data from multiple -omics platforms can be integrated to accelerate biomarker discovery in psychiatry. Using the phenotype of cognitive impairment in remitted major depressive disorder (MDD) as an example, we show that the application of a systems biology approach - weighted gene co-expression network analysis (WGCNA) - in the discovery phase, and targeted proteomic follow-up of results, provides a structured avenue towards uncovering novel candidate markers and pathways for personalized clinical psychiatry.

Biosignature Discovery for Substance Use Disorders Using Statistical Learning

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts.

The Landscape of Small Non-Coding RNAs in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an operational term for breast cancers lacking targetable estrogen receptor expression and HER2 amplifications. TNBC is, therefore, inherently heterogeneous, and is associated with worse prognosis, greater rates of metastasis, and earlier onset. TNBC displays mutational and transcriptional diversity, and distinct mRNA transcriptional subtypes exhibiting unique biology. High-throughput sequencing has extended cancer research far beyond protein coding regions that include non-coding small RNAs, such as miRNA, isomiR, tRNA, snoRNAs, snRNA, yRNA, 7SL, and 7SK. In this study, we performed small RNA profiling of 26 TNBC cell lines, and compared the abundance of non-coding RNAs among the transcriptional subtypes of triple negative breast cancer. We also examined their co-expression pattern with corresponding mRNAs. This study provides a detailed description of small RNA expression in triple-negative breast cancer cell lines that can aid in the development of future biomarker and novel targeted therapies.

Long-term ethanol exposure: Temporal pattern of microRNA expression and associated mRNA gene networks in mouse brain

Long-term alcohol use can result in lasting changes in brain function, ultimately leading to alcohol dependence. These functional alterations arise from dysregulation of complex gene networks, and growing evidence implicates microRNAs as key regulators of these networks. We examined time- and brain region-dependent changes in microRNA expression after chronic intermittent ethanol (CIE) exposure in C57BL/6J mice. Animals were sacrificed at 0, 8, and 120h following the last exposure to four weekly cycles of CIE vapor and we measured microRNA expression in prefrontal cortex (PFC), nucleus accumbens (NAC), and amygdala (AMY). The number of detected (395–419) and differentially expressed (DE, 42–47) microRNAs was similar within each brain region. However, the DE microRNAs were distinct among brain regions and across time within each brain region. DE microRNAs were linked with their DE mRNA targets across each brain region. In all brain regions, the greatest number of DE mRNA targets occurred at the 0 or 8h time points and these changes were associated with microRNAs DE at 0 or 8h. Two separate approaches (discrete temporal association and hierarchical clustering) were combined with pathway analysis to further characterize the temporal relationships between DE microRNAs and their 120h DE targets. We focused on targets dysregulated at 120h as this time point represents a state of protracted withdrawal known to promote an increase in subsequent ethanol consumption. Discrete temporal association analysis identified networks with highly connected genes including ERK1/2 (mouse equivalent Mapk3, Mapk1), Bcl2 (in AMY networks) and Srf (in PFC networks). Similarly, the cluster-based analysis identified hub genes that include Bcl2 (in AMY networks) and Srf in PFC networks, demonstrating robust microRNA-mRNA network alterations in response to CIE exposure. In contrast, datasets utilizing targets from 0 and 8h microRNAs identified NF-kB-centered networks (in NAC and PFC), and Smad3-centered networks (in AMY). These results demonstrate that CIE exposure results in dynamic and complex temporal changes in microRNA-mRNA gene network structure.

FoxO1, A2M, and TGF-β1: three novel genes predicting depression in gene X environment interactions are identified using cross-species and cross-tissues transcriptomic and miRNomic analyses

To date, gene-environment (GxE) interaction studies in depression have been limited to hypothesis-based candidate genes, since genome-wide (GWAS)-based GxE interaction studies would require enormous datasets with genetics, environmental, and clinical variables. We used a novel, cross-species and cross-tissues "omics" approach to identify genes predicting depression in response to stress in GxE interactions. We integrated the transcriptome and miRNome profiles from the hippocampus of adult rats exposed to prenatal stress (PNS) with transcriptome data obtained from blood mRNA of adult humans exposed to early life trauma, using a stringent statistical analyses pathway. Network analysis of the integrated gene lists identified the Forkhead box protein O1 (FoxO1), Alpha-2-Macroglobulin (A2M), and Transforming Growth Factor Beta 1 (TGF-β1) as candidates to be tested for GxE interactions, in two GWAS samples of adults either with a range of childhood traumatic experiences (Grady Study Project, Atlanta, USA) or with separation from parents in childhood only (Helsinki Birth Cohort Study, Finland). After correction for multiple testing, a meta-analysis across both samples confirmed six FoxO1 SNPs showing significant GxE interactions with early life emotional stress in predicting depressive symptoms. Moreover, in vitro experiments in a human hippocampal progenitor cell line confirmed a functional role of FoxO1 in stress responsivity. In secondary analyses, A2M and TGF-β1 showed significant GxE interactions with emotional, physical, and sexual abuse in the Grady Study. We therefore provide a successful 'hypothesis-free' approach for the identification and prioritization of candidate genes for GxE interaction studies that can be investigated in GWAS datasets.

Transcriptomic profiling of the human brain reveals that altered synaptic gene expression is associated with chronological aging

Aging is a biologically universal event, and yet the key events that drive aging are still poorly understood. One approach to generate new hypotheses about aging is to use unbiased methods to look at change across lifespan. Here, we have examined gene expression in the human dorsolateral frontal cortex using RNA- Seq to populate a whole gene co-expression network analysis. We show that modules of co-expressed genes enriched for those encoding synaptic proteins are liable to change with age. We extensively validate these age-dependent changes in gene expression across several datasets including the publically available GTEx resource which demonstrated that gene expression associations with aging vary between brain regions. We also estimated the extent to which changes in cellular composition account for age associations and find that there are independent signals for cellularity and aging. Overall, these results demonstrate that there are robust age-related alterations in gene expression in the human brain and that genes encoding for neuronal synaptic function may be particularly sensitive to the aging process.

Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model

In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.

Emerging roles for ncRNAs in alcohol use disorders

Chronic alcohol exposure produces widespread neuroadaptations and alterations in gene expression in human alcoholics and animal models. Technological advances in the past decade have increasingly highlighted the role of non-protein-coding RNAs (ncRNAs) in the regulation of gene expression and function. These recently characterized molecules were discovered to mediate diverse processes in the central nervous system, from normal development and physiology to regulation of disease, including alcoholism and other psychiatric disorders. This review will investigate the recent studies in human alcoholics and rodent models that have profiled different classes of ncRNAs and their dynamic alcohol-dependent regulation in brain.

Gene expression profiling in the human alcoholic brain

Long-term alcohol use causes widespread changes in gene expression in the human brain. Aberrant gene expression changes likely contribute to the progression from occasional alcohol use to alcohol use disorder (including alcohol dependence). Transcriptome studies have identified individual gene candidates that are linked to alcohol-dependence phenotypes. The use of bioinformatics techniques to examine expression datasets has provided novel systems-level approaches to transcriptome profiling in human postmortem brain. These analytical advances, along with recent developments in next-generation sequencing technology, have been instrumental in detecting both known and novel coding and non-coding RNAs, alternative splicing events, and cell-type specific changes that may contribute to alcohol-related pathologies. This review offers an integrated perspective on alcohol-responsive transcriptional changes in the human brain underlying the regulatory gene networks that contribute to alcohol dependence. This article is part of the Special Issue entitled "Alcoholism".

Genetic studies of alcohol dependence in the context of the addiction cycle

Family, twin and adoption studies demonstrate clearly that alcohol dependence and alcohol use disorders are phenotypically complex and heritable. The heritability of alcohol use disorders is estimated at approximately 50-60% of the total phenotypic variability. Vulnerability to alcohol use disorders can be due to multiple genetic or environmental factors or their interaction which gives rise to extensive and daunting heterogeneity. This heterogeneity makes it a significant challenge in mapping and identifying the specific genes that influence alcohol use disorders. Genetic linkage and (candidate gene) association studies have been used now for decades to map and characterize genomic loci and genes that underlie the genetic vulnerability to alcohol use disorders. These approaches have been moderately successful in identifying several genes that contribute to the complexity of alcohol use disorders. Recently, genome-wide association studies have become one of the major tools for identifying genes for alcohol use disorders by examining correlations between millions of common single-nucleotide polymorphisms with diagnosis status. Genome-wide association studies are just beginning to uncover novel biology; however, the functional significance of results remains a matter of extensive debate and uncertainty. In this review, we present a select group of genome-wide association studies of alcohol dependence, as one example of a way to generate functional hypotheses, within the addiction cycle framework. This analysis may provide novel directions for validating the functional significance of alcohol dependence candidate genes. This article is part of the Special Issue entitled "Alcoholism".

Strong correlation between ASPM gene expression and HCV cirrhosis progression identified by co-expression analysis

Hepatitis C virus (HCV) cirrhosis is at a high risk of hepatocellular carcinoma (HCC), and its progression is influenced by a complex network of gene interactions. A weighted gene co-expression network was constructed to identify gene modules associated with the seven-stage disease progression from HCV cirrhosis to HCV-related HCC (n=65). In the significant module (R²=0.86), a total of 25 network hub genes were identified, half of which were also hub nodes in the protein-protein interaction network of the module genes. In validation, most hub genes showed a moderate correlation with the disease progression, and only ASPM was highly correlated (R²=0.801). In the test set (n=63), ASPM was also more highly expressed in HCV cirrhosis with concomitant HCC than in those without HCC (P=0.0054). Gene set enrichment analysis (GSEA) demonstrated that the gene set of "regulation of protein amino acid phosphorylation" (n=20) was enriched in HCV cirrhosis samples with ASPM highly expressed (false discovery rate (FDR)=0.049). In gene ontology (GO) analysis, genes in the enriched set were associated with liver neoplasms and other neoplastic diseases. In conclusion, through co-expression analysis, ASPM was identified and validated in association with the progression of HCV cirrhosis probably by regulating tumor-related phosphorylation.

MicroRNAs and Drinking: Association between the Pre-miR-27a rs895819 Polymorphism and Alcohol Consumption in a Mediterranean Population

Recently, microRNAs (miRNA) have been proposed as regulators in the different processes involved in alcohol intake, and differences have been found in the miRNA expression profile in alcoholics. However, no study has focused on analyzing polymorphisms in genes encoding miRNAs and daily alcohol consumption at the population level. Our aim was to investigate the association between a functional polymorphism in the pre-miR-27a (rs895819 A>G) gene and alcohol consumption in an elderly population. We undertook a cross-sectional study of PREvención con DIeta MEDiterránea (PREDIMED)-Valencia participants (n = 1007, including men and women aged 67 ± 7 years) and measured their alcohol consumption (total and alcoholic beverages) through a validated questionnaire. We found a strong association between the pre-miR-27a polymorphism and total alcohol intake, this being higher in GG subjects (5.2 ± 0.4 in AA, 5.9 ± 0.5 in AG and 9.1 ± 1.8 g/day in GG; padjusted = 0.019). We also found a statistically-significant association of the pre-miR-27a polymorphism with the risk of having a high alcohol intake (>2 drinks/day in men and >1 in women): 5.9% in AA versus 17.5% in GG; padjusted < 0.001. In the sensitivity analysis, this association was homogeneous for sex, obesity and Mediterranean diet adherence. In conclusion, we report for the first time a significant association between a miRNA polymorphism (rs895819) and daily alcohol consumption.

Regulatory mechanisms of microRNA expression

MicroRNAs (miRs, miRNAs) are small molecules of 18-22 nucleotides that serve as important regulators of gene expression at the post-transcriptional level. One of the mechanisms through which miRNAs regulate gene expression involves the interaction of their "seed" sequences primarily with 3'-end and more rarely with 5'-end, of mRNA transcribed from target genes. Numerous studies over the past decade have been devoted to quantitative and qualitative assessment of miRNAs expression and have shown remarkable changes in miRNA expression profiles in various diseases. Thus, profiling of miRNA expression can be an important tool for diagnostics and treatment of disease. However, less attention has been paid towards understanding the underlying reasons for changes in miRNA expression, especially in cancer cells. The purpose of this review is to analyze and systematize current data that explains reasons for changes in the expression of miRNAs. The review will cover both transcriptional (changes in gene expression and promoter hypermethylation) and post-transcriptional (changes in miRNA processing) mechanisms of regulation of miRNA expression, as well as effects of endogenous (hormones, cytokines) and exogenous (xenobiotics) compounds on the miRNA expression. The review will summarize the complex multilevel regulation of miRNA expression, in relation to cell type, physiological state of the body and various external factors.