Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence.
- Authors
- Mamdani, Mohammed; Williamson, Vernell; McMichael, Gowon O; Blevins, Tana; Aliev, Fazil; Adkins, Amy; Hack, Laura; Bigdeli, Tim; van der Vaart, Andrew D; Web, Bradley Todd; Bacanu, Silviu-Alin; Kalsi, Gursharan; COGA Consortium; Kendler, Kenneth S; Miles, Michael F; Dick, Danielle; Riley, Brien P; Dumur, Catherine; Vladimirov, Vladimir I
- Year
- 2015
- Journal
- PloS one
- PMID
- 26381263
- DOI
- 10.1371/journal.pone.0137671
- PMCID
- PMC4575063
Alcohol consumption is known to lead to gene expression changes in the brain. After performing weighted gene co-expression network analyses (WGCNA) on genome-wide mRNA and microRNA (miRNA) expression in Nucleus Accumbens (NAc) of subjects with alcohol dependence (AD; N = 18) and of matched controls (N = 18), six mRNA and three miRNA modules significantly correlated with AD were identified (Bonferoni-adj. pβ€ 0.05). Cell-type-specific transcriptome analyses revealed two of the mRNA modules to be enriched for neuronal specific marker genes and downregulated in AD, whereas the remaining four mRNA modules were enriched for astrocyte and microglial specific marker genes and upregulated in AD. Gene set enrichment analysis demonstrated that neuronal specific modules were enriched for genes involved in oxidative phosphorylation, mitochondrial dysfunction and MAPK signaling. Glial-specific modules were predominantly enriched for genes involved in processes related to immune functions, i.e. cytokine signaling (all adj. pβ€ 0.05). In mRNA and miRNA modules, 461 and 25 candidate hub genes were identified, respectively. In contrast to the expected biological functions of miRNAs, correlation analyses between mRNA and miRNA hub genes revealed a higher number of positive than negative correlations (Ο2 test pβ€ 0.0001). Integration of hub gene expression with genome-wide genotypic data resulted in 591 mRNA cis-eQTLs and 62 miRNA cis-eQTLs. mRNA cis-eQTLs were significantly enriched for AD diagnosis and AD symptom counts (adj. p = 0.014 and p = 0.024, respectively) in AD GWAS signals in a large, independent genetic sample from the Collaborative Study on Genetics of Alcohol (COGA). In conclusion, our study identified putative gene network hubs coordinating mRNA and miRNA co-expression changes in the NAc of AD subjects, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.
(A) Cluster dendrogram and module assignment for mRNA modules from WGCNA. Topological overlap dissimilarity measure is clustered by average linkage hierarchical clustering and module assignments (dynamic hybrid algorithm) are denoted in the color bar (bottom). 4571 transcripts were assigned to one of 24 modules including Mgrey. (B). Following the same outline, 240 miRNAs are assigned to one of 12 modules indicated by color (including Mgrey).
LLM interpretation
This figure consists of two cluster dendrograms (A and B) showing Weighted Gene Co-expression Network Analysis (WGCNA) for mRNA and miRNA, respectively. Both panels feature a vertical axis labeled "Height" and a corresponding horizontal color bar at the bottom indicating module assignments. Panel A shows 4,571 transcripts partitioned into 24 modules, while Panel B shows 240 miRNAs partitioned into 12 modules.
Module-trait relationships.(A) mRNA module MEs are correlated (Pearson) to AD case-status (Class), brain pH, PMI, Age, RIN and subject smoking status to assess for confounding. P-values shown are unadjusted for multiple testing. After adjusting for number of modules tested, MEturquoise, MEyellow, MEgrey60, MEpink, MEgreen and MEsalmon are significantly correlated with AD case-status (Class). (B). Similarly, after adjusting p-values for number of modules tested, miRNAs MEyellow, MEblue and MEbrown modules are significantly correlated with AD case-status (Class).
LLM interpretation
This figure consists of two heatmaps (A and B) showing Pearson correlation coefficients ($r$) and p-values between gene module eigengenes (MEs) and various clinical traits. The x-axes list traits including AD case-status (Class), brain pH, PMI, Age, RIN, and smoking status, while the y-axes list specific mRNA (A) and miRNA (B) modules. Color coding indicates the strength and direction of the correlation, with red representing positive and blue representing negative correlations.
(A). mRNA modules significantly correlated with AD case-status. Each point represents an individual transcript within each module, which are plotted by the absolute value of their expression correlation to AD case-status (Gene Significance) on the y-axis and module eigengene (Module Membership) on the x-axis. The regression line, correlation value and p-value is shown for each plot, which indicates that GS of transcripts increases with increasing intramodular connectivity (MM). (B). miRNA modules significantly correlated with AD case-status. Each point represents an individual miRNA transcript within each module, which are plotted by the absolute value of their expression correlation to AD case-status (Gene Significance) on the y-axis and module eigengene (Module Membership) on the x-axis. Similarly to mRNA modules, GS of miRNA transcripts increases with increasing intramodular connectivity (MM).
LLM interpretation
This figure consists of two sets of scatter plots (A and B) showing the relationship between Module Membership (x-axis) and Gene Significance (y-axis) for various mRNA and miRNA modules correlated with AD case-status. Each plot includes a linear regression line, a correlation coefficient (cor), and a p-value, indicating a positive correlation between intramodular connectivity and expression correlation to AD status. Panel A displays six mRNA modules (Green, Grey60, Salmon, Yellow, Turquoise, Pink), while Panel B displays three miRNA modules (Yellow, Brown, Blue).
miRNA:mRNA Module relationships: Significant mRNA module MEs (rows) are correlated (Pearson) to significant miRNA MEs for blue, brown and yellow modules (columns).Strongest correlations is positive between miRNA MEbrown and mRNA MEgrey60 (r = 0.75) and strongest negative correlation is between miRNA MEbrown and mRNA MEturquoise (r = -0.56) and miRNA MEblue and mRNA MEgrey60 (r = -0.56). miRNA MEblue and MEyellow are negatively correlated with glial cell-associated mRNA modules and miRNA MEbrown is negatively with neuron-associated mRNA modules.
LLM interpretation
This is a heatmap showing Pearson correlation coefficients ($r$) and p-values between significant mRNA module eigengenes (rows: METurquoise, MEyellow, MEgrey60, MEpink, MEgreen, MEsalmon) and miRNA module eigengenes (columns: blue, brown, yellow). The color scale ranges from -1 (blue, negative correlation) to 1 (red, positive correlation). The strongest positive correlation is observed between miRNA MEbrown and mRNA MEgrey60 ($r = 0.75, p = 2.6\text{e-}07$), while strong negative correlations are visible between miRNA MEblue and mRNA MEgrey60 ($r = -0.56$) and miRNA MEbrown and mRNA METurquoise ($r = -0.56$).
eQTL (chr2:170783092:D) effect on the expression of the glutamate decarboxylase (GAD1) gene.The bar plot depicts the differential expression of GAD1 among homozygote for the major (11, red), heterozygote (12, green) and homozygote for the minor alleles (22, blue) subjects.
LLM interpretation
This box plot shows the expression levels of the GAD1 gene across three genotypes: homozygote major (11, red), heterozygote (12, green), and homozygote minor (22, blue). The y-axis represents expression values, ranging from approximately 9.5 to 12.5. Expression is highest and most consistent in the 11 and 12 groups, while the 22 group shows a marked decrease in median expression and greater variability.
(A). rs11626307 effect on the hsa-miR-134-5p (A) and hsa-miR-370-3p (B) expressions.
LLM interpretation
This figure consists of two box plots (A and B) showing the expression levels of hsa-miR-134-5p and hsa-miR-370-3p across three genotypes of rs11626307 (11, 21, and 22). In both plots, expression levels decrease progressively from genotype 11 (red) to 21 (green) to 22 (blue). The y-axes represent expression values, while the x-axes label the specific genotypes.
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In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Epigenetic Insights into Substance Use Disorder and Associated Psychiatric Conditions. | Ngo AL et al. | β | 2025 | β |
| Noncoding RNA and Alcohol Use Disorder: A Scoping Review of Current Research and Knowledge Gaps. | Upreti D et al. | β | 2025 | β |
| Adaptations in glutathione-based redox protein signaling pathways and alcohol drinking across species. | Womersley JS et al. | β | 2024 | β |
| Space radiation damage rescued by inhibition of key spaceflight associated miRNAs. | McDonald JT et al. | β | 2024 | β |
| Addictive drugs modify neurogenesis, synaptogenesis and synaptic plasticity to impair memory formation through neurotransmitter imbalances and signaling dysfunction. | Rezayof A et al. | β | 2023 | β |
| Analysis of the brain transcriptome for substance-associated genes: An update on large-scale genome-wide association studies. | Zhao Y et al. | β | 2023 | β |
| Functional roles, regulatory mechanisms and theranostics applications of ncRNAs in alcohol use disorder. | Wang JQ et al. | β | 2023 | β |
| Mapping microRNA expression quantitative trait loci in the prenatal human brain implicates miR-1908-5p expression in bipolar disorder and other brain-related traits. | Toste CC et al. | β | 2023 | β |
| The Effects of Transcranial Focused Ultrasound Stimulation of Nucleus Accumbens on Neuronal Gene Expression and Brain Tissue in High Alcohol-Preferring Rats. | Deveci E et al. | β | 2023 | β |
| Integrative analysis of microRNAs and mRNAs reveals the regulatory networks of triterpenoid saponin metabolism in Soapberry (<i>Sapindus mukorossi</i> Gaertn.). | Xu Y et al. | β | 2022 | β |
| MicroRNAs and Synaptic Plasticity: From Their Molecular Roles to Response to Therapy. | Mohammadi AH et al. | β | 2022 | β |
| Noncoding RNA therapeutics for substance use disorder. | Seyednejad SA et al. | β | 2022 | β |
| Proteomics and weighted gene correlated network analysis reveal glutamatergic synapse signaling in diazepam treatment of alcohol withdrawal. | Kong W et al. | β | 2022 | β |
| An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning. | Zhuang Y et al. | β | 2021 | β |
| An integrated analysis of mRNAs, lncRNAs, and miRNAs based on weighted gene co-expression network analysis involved in bovine endometritis. | Sheybani N et al. | β | 2021 | β |
| Brain-derived neurotrophic factor predominantly regulates the expression of synapse-related genes in the striatum: Insights from in vitro transcriptomics. | Koshimizu H et al. | β | 2021 | β |
| Chronic Alcohol Use Induces Molecular Genetic Changes in the Dorsomedial Thalamus of People with Alcohol-Related Disorders. | Hade AC et al. | β | 2021 | β |
| Effect of chronic ethanol consumption in rhesus macaques on the nucleus accumbens core transcriptome. | Walter N et al. | β | 2021 | β |
| Epigenetic modification in alcohol use disorder and alcoholic cardiomyopathy: From pathophysiology to therapeutic opportunities. | Wu L et al. | β | 2021 | β |
| Exploration of alcohol use disorder-associated brain miRNA-mRNA regulatory networks. | Lim Y et al. | β | 2021 | β |
| Exploration of the Role of Serine Proteinase Inhibitor A3 in Alcohol Dependence Using Gene Expression Omnibus Database. | Zhang B et al. | β | 2021 | β |
| Gene Expression Differences Between Young Adults Based on Trauma History and Post-traumatic Stress Disorder. | Bountress KE et al. | β | 2021 | β |
| Genetics of substance use disorders in the era of big data. | Gelernter J et al. | β | 2021 | β |
| Identifying a novel biological mechanism for alcohol addiction associated with circRNA networks acting as potential miRNA sponges. | Vornholt E et al. | β | 2021 | β |
| PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes. | Carpenter CM et al. | β | 2021 | β |
| Role of microRNAs in the pathophysiology of addiction. | Gowen AM et al. | β | 2021 | β |
| Targeting Epigenetic Mechanisms to Treat Alcohol Use Disorders (AUD). | Rodriguez FD | β | 2021 | β |
| Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models. | Zuo Y et al. | β | 2021 | β |
| Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence. | Drake J et al. | β | 2020 | β |
| Chronic Voluntary Ethanol Drinking in Cynomolgus Macaques Elicits Gene Expression Changes in Prefrontal Cortical Area 46. | Walter NAR et al. | β | 2020 | β |
| Network preservation reveals shared and unique biological processes associated with chronic alcohol abuse in NAc and PFC. | Vornholt E et al. | β | 2020 | β |
| RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models. | Kozell LB et al. | β | 2020 | β |
| Suicide and suicide behaviors: A review of transcriptomics and multiomics studies in psychiatric disorders. | Zhou Y et al. | β | 2020 | β |
| Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism. | Kapoor M et al. | β | 2019 | β |
| A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder. | Ferguson LB et al. | β | 2019 | β |
| Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder. | Salvatore JE et al. | β | 2019 | β |
| Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data. | Mignogna KM et al. | β | 2019 | β |
| microRNAs: Novel Markers in Diagnostics and Therapeutics of Celiac Disease. | Chamani E et al. | β | 2019 | β |
| miRNA-373 promotes urinary bladder cancer cell proliferation, migration and invasion through upregulating epidermal growth factor receptor. | Wang Y et al. | β | 2019 | β |
| Molecular windows into the human brain for psychiatric disorders. | Egervari G et al. | β | 2019 | β |
| Postmortem brain tissue as an underutilized resource to study the molecular pathology of neuropsychiatric disorders across different ethnic populations. | Vornholt E et al. | β | 2019 | β |
| Silencing synaptic MicroRNA-411 reduces voluntary alcohol consumption in mice. | Most D et al. | β | 2019 | β |
| Unsupervised discovery of phenotype-specific multi-omics networks. | Shi WJ et al. | β | 2019 | β |
| An impaired hepatic clock system effects lipid metabolism in rats with nephropathy. | Chen P et al. | β | 2018 | β |
| An integrative transcriptome analysis indicates regulatory mRNA-miRNA networks for residual feed intake in Nelore cattle. | De Oliveira PSN et al. | β | 2018 | β |
| Biosignature Discovery for Substance Use Disorders Using Statistical Learning. | Baurley JW et al. | β | 2018 | β |
| Cerebral white matter sex dimorphism in alcoholism: a diffusion tensor imaging study. | Sawyer KS et al. | β | 2018 | β |
| Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data | Mignogna KM et al. | β | 2018 | β |
| Deep sequencing and miRNA profiles in alcohol-induced neuroinflammation and the TLR4 response in mice cerebral cortex. | UreΓ±a-Peralta JR et al. | β | 2018 | β |
| FoxO1, A2M, and TGF-Ξ²1: three novel genes predicting depression in gene X environment interactions are identified using cross-species and cross-tissues transcriptomic and miRNomic analyses. | Cattaneo A et al. | β | 2018 | β |
| Genetics of Alcohol Use Disorder: A Role for Induced Pluripotent Stem Cells? | Prytkova I et al. | β | 2018 | β |
| Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses. | Luo J et al. | β | 2018 | β |
| Long-term ethanol exposure: Temporal pattern of microRNA expression and associated mRNA gene networks in mouse brain. | Osterndorff-Kahanek EA et al. | β | 2018 | β |
| miRNA-regulated transcription associated with mouse strains predisposed to hypnotic effects of ethanol. | Vestal B et al. | β | 2018 | β |
| The Landscape of Small Non-Coding RNAs in Triple-Negative Breast Cancer. | Guo Y et al. | β | 2018 | β |
| Transcriptome analysis of alcohol-treated microglia reveals downregulation of beta amyloid phagocytosis. | Kalinin S et al. | β | 2018 | β |
| Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model. | Nalluri JJ et al. | β | 2017 | β |
| Emerging roles for ncRNAs in alcohol use disorders. | Mayfield RD | β | 2017 | β |
| Gene expression profiling in the human alcoholic brain. | Warden AS et al. | β | 2017 | β |
| Genetic studies of alcohol dependence in the context of the addiction cycle. | Reilly MT et al. | β | 2017 | β |
| Strong correlation between ASPM gene expression and HCV cirrhosis progression identified by co-expression analysis. | Wang F et al. | β | 2017 | β |
| Transcriptomic profiling of the human brain reveals that altered synaptic gene expression is associated with chronological aging. | Dillman AA et al. | β | 2017 | β |
| MicroRNAs and Drinking: Association between the Pre-miR-27a rs895819 Polymorphism and Alcohol Consumption in a Mediterranean Population. | BarragΓ‘n R et al. | β | 2016 | β |
| Regulatory mechanisms of microRNA expression. | Gulyaeva LF et al. | β | 2016 | β |