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

paper Cited Public

Authors: Osterndorff-Kahanek, Elizabeth A; Tiwari, Gayatri R; Lopez, Marcelo F; Becker, Howard C; Harris, R Adron; Mayfield, R Dayne
Year: 2018
Journal: PloS one
PMID: 29315347
DOI: 10.1371/journal.pone.0190841
PMCID: PMC5760035

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.

Fig 1

Overview of procedure used to pair DE microRNAs with DE targets and analyses performed on resulting datasets.

Fig 2

Overlap of probesets among brain regions.Data from all time points were combined for each brain region. Panel A shows overlap of all probes detected within each brain region. Panel B shows overlap of all probes DE (p ≤ 0.05) within each brain region.

Fig 3

Numbers of microRNAs and mRNAs (from[26]) DE at each time point.Circles represent microRNAs, squares represent genes. Genes at 0 and 8h were considered DE at an FDR of 0.05. MicroRNAs and 120h mRNAs were considered DE at a nominal value of 0.05.

Fig 4

Number of microRNA targets DE at each time point.Bars indicate the number of DE genes that are targets of DE (p < 0.05) microRNAs at the given time point. Gene targets were considered DE at an FDR of 0.05 (0 and 8h targets) or at a nominal value of 0.05 (120h targets).

Fig 5

Temporal profiles of hierarchically clustered, DE (p < 0.05) microRNAs in amygdala.Profiles are based on hierarchical clustering of centered and scaled expression log ratios of microRNAs DE in at least one time point. Average expression is plotted in red, and each individual microRNA is plotted in gray. Blue text indicates the total number of microRNAs in each cluster.

Fig 6

Temporal profiles of hierarchically clustered, DE (p < 0.05) microRNAs in nucleus accumbens.Profiles are based on hierarchical clustering of centered and scaled expression log ratios of microRNAs DE in at least one time point. Average expression is plotted in red, and each individual microRNA is plotted in gray. Blue text indicates the total number of microRNAs in each cluster.

Fig 7

Temporal profiles of hierarchically clustered, DE (p < 0.05) microRNAs in prefrontal cortex.Profiles are based on hierarchical clustering of centered and scaled expression log ratios of microRNAs DE in at least one time point. Average expression is plotted in red, and each individual microRNA is plotted in gray. Blue text indicates the total number of microRNAs in each cluster.

Fig 8

Critical network genes (mRNAs) DE at 120h and their potential microRNA regulators DE at 0 or 8h.Only genes with an associated microRNA are listed. Critical network genes were dysregulated at 120hr and present in at least one of the top three IPA networks derived from the time-point based network analysis. Blue text indicates that RNAs were also identified in the cluster sharing the greatest overlap with the list of critical network genes: AMY—cluster 4; NAC—cluster 3; PFC: cluster 4. Inset shows temporal expression pattern for the designated cluster. See S5 Fig for a full version of the inset figures. Red upward pointing arrow indicates up-regulation; Green downward pointing arrow indicates down-regulation. Bracketed letters denote membership in cell-type specific gene lists (See [26]) enriched in the analyzed datasets [a = astrocyte, i = immune related, m = microglia, n = neuron, o = oligodendrocyte.] (Cell specific gene lists were pre-loaded into IPA and automatically scored against all datasets submitted to Core Analysis. Significant enrichment was determined by p value < 0.05 using Fisher’s exact test.) A single asterisk (*) indicates the microRNA was uniquely detected in the indicated brain-region.

#	Section	Preview
0	Introduction	Excessive, chronic alcohol use can evoke persistent alterations in brain function that result in…
1	Introduction	Studies have implicated microRNAs in both human [10–13] and animal models of addiction [7, 9,…
2	Introduction	The chronic intermittent ethanol vapor (CIE) paradigm is known to increase voluntary ethanol…
3	Introduction	We report microRNA expression changes in three brain regions (AMY, NAC and PFC) at three time points…
4	Materials and methods — Ethics statement	All procedures were approved by the Medical University of South Carolina Institutional Animal Care…
5	Materials and methods — Animals and chronic ethanol inhalation procedure	Adult male C57BL/6 mice, purchased from Jackson Laboratories (Bar Harbor, ME), were used in this…
6	Materials and methods — Animals and chronic ethanol inhalation procedure	Chronic ethanol inhalation procedures, tissue harvest, and brain dissection methods were as…
7	Materials and methods — Animals and chronic ethanol inhalation procedure	drinking weeks). Animals were sacrificed at 3 time points: 0-, 8- and 120-hours following the last…
8	Materials and methods — MicroRNA array analysis	Total RNAs from amygdala (AMY), nucleus accumbens (combined core and shell, NAC) and prefrontal…
9	Materials and methods — Statistics and bioinformatics	Data were analyzed using open source software packages from Bioconductor (http://bioconductor.org)…
10	Materials and methods — Statistics and bioinformatics	moderated t-statistics from the Bioconductor package limma [33] to compare treated and control mice.…
11	Materials and methods — Statistics and bioinformatics	Two separate approaches (discrete temporal association, hierarchical clustering) were used to…
12	Materials and methods — Statistics and bioinformatics	In the first discrete temporal association analysis, differentially expressed microRNAs from each…
13	Materials and methods — Statistics and bioinformatics	we reasoned that it would allow us to utilize all of our data while maintaining as much rigor in the…
14	Materials and methods — Statistics and bioinformatics	An alternative approach, hierarchical clustering, used centered and scaled log ratios of DE…
15	Results — MicroRNA descriptive statistics	MicroRNAs were profiled in AMY, NAC and PFC at three different time points (0, 8 and 120h) after…
16	Results — Temporal patterns of DE microRNAs and associated gene targets	DE gene targets of the ethanol-responsive microRNAs were identified for each time point. Both…
17	Results — Functional annotation of paired DE microRNAs with 120h DE targets	To evaluate the role of microRNAs in the persistent (120h) changes in gene expression, paired DE…
18	Results — Hierarchical clustering of DE microRNAs	For each brain region, DE microRNAs were clustered based on expression log ratios. This resulted in…
19	Results — Time point-based network analysis	As part of the "Core Analysis", IPA uses "network eligible molecules" (those molecules in the…

Name	Type
0h local	cohort
120h local	cohort
8h local	cohort
addiction	phenotype
Affymetrix GCS3000 scanner local	drug
Air local	drug
alcohol	phenotype
Alcohol consumption preference local	phenotype
alcohol dehydrogenase	gene
alcohol dependence	phenotype
alcohol-dependent rats	cohort
Alcohol Exposure	phenotype
Alcohol preferring mice local	cohort
alcohol withdrawal	phenotype
Amy	anatomy
amygdala	anatomy
animal models	cohort
animals	cohort
Antianxiety effects local	phenotype
apoptosis	phenotype
Bcl2	gene
Bdnf	gene
binge drinking	phenotype
blood ethanol concentration	phenotype
brain	anatomy
Brain region-specific transcriptional profiles local	anatomy
brain tissue	anatomy
C57BL/6J	cohort
Cellular reward mechanisms local	phenotype
Cellular tolerance to alcohol local	phenotype
cerebellum	anatomy
chronic alcoholism	phenotype
chronic ethanol exposure	phenotype
CIE	drug
CIE paradigm local	drug
CIE-treated mice local	cohort
CNS cell death local	phenotype
cocaine	phenotype
cognition	phenotype
Conditioned-place preference local	phenotype
cortex	anatomy
DE genes	gene
dependence	phenotype
DRD1	gene
ERK1/2	gene
ethanol consumption	phenotype
ethanol dependence	phenotype
Ethanol-dependent mice local	cohort
ethanol withdrawal	phenotype
extended withdrawal local	phenotype
FlashTag Biotin HSR RNA Labeling Kit local	drug
Fndc3b local	gene
Formation of cellular protrusions local	phenotype
Formation of lymphocytes local	phenotype
frontal cortex	anatomy
gene	gene
GeneChip miRNA 3.0 array local	drug
Growth of neurites local	phenotype
GSE60676 local	cohort
GSE90608 local	cohort
Hub genes local	gene
human alcoholics	phenotype
Ingenuity Pathway Analysis (IPA) local	drug
intoxication	phenotype
learning	phenotype
liver cancer	phenotype
Liver carcinoma local	phenotype
Liver hyperplasia/proliferation local	phenotype
Mapk1	gene
MAPK3	gene
Mapt	gene
MCL1 local	gene
memory	phenotype
mice	cohort
microglial activation	phenotype
microRNA	drug
MicroRNA array data local	drug
microRNA families local	gene
microRNAs local	gene
miR-146a-5p local	drug
miR-16-5p local	drug
miR-17-5p local	drug
miR-181a-5p local	drug
miR-335-5p local	drug
miR-34a-5p local	drug
Moffitt Cancer Center local	cohort
mRNA	drug
NAc	anatomy
NCBI Gene Expression Omnibus local	cohort
NCBI Gene Expression Omnibus (GEO) local	cohort
NFKB1	gene
NF-κB	gene
nucleus accumbens	anatomy
Organization of actin cytoskeleton local	phenotype
PFC	anatomy
prefrontal cortex	anatomy
protracted abstinence	phenotype
protracted withdrawal	phenotype
Pyrazole local	drug
RIT1 local	gene
Rodent drinking models local	cohort
rodents	cohort
SMAD3 local	gene
Srf local	gene
SRF local	gene
synaptic plasticity	phenotype
TargetScan local	drug
Teklad rodent diet local	drug
TGF-β	drug
voluntary ethanol consumption	phenotype
wild-type mice	cohort
withdrawal	phenotype
β local	drug

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In this knowledge base

Title	Year	PMID
Alcohol reverses the effects of KCNJ6 (GIRK2) noncoding variants on excitability of human glutamatergic neurons.	2023	36207584
Alcohol use disorder causes global changes in splicing in the human brain.	2021	33414398
Transcriptome analysis of alcohol-treated microglia reveals downregulation of beta amyloid phagocytosis.	2018	29759078

External

Title	Authors	Journal	Year	Link
Systemic Interplay of BDNF and Serotonin Pathways Defines Behavioral and Molecular Responses to Midbrain 5-HT7 Overexpression and Chronic Ethanol Consumption.	Rodnyy A et al.	—	2026	→
Alcohol Use Disorder Causes Profound Global Changes in Gene Expression and Splicing in the Liver	Wahlestedt C et al.	—	2025	—
Noncoding RNA and Alcohol Use Disorder: A Scoping Review of Current Research and Knowledge Gaps.	Upreti D et al.	—	2025	→
Neuron enriched extracellular vesicles' MicroRNA expression profiles as a marker of early life alcohol consumption.	Yakovlev V et al.	—	2024	→
Alcohol reverses the effects of KCNJ6 (GIRK2) noncoding variants on excitability of human glutamatergic neurons.	Popova D et al.	—	2023	→
Functional roles, regulatory mechanisms and theranostics applications of ncRNAs in alcohol use disorder.	Wang JQ et al.	—	2023	→
Molecular mechanisms of alcohol's effects on the human body: A review and update.	Renu K et al.	—	2023	→
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.	—	2023	→
Acetylsalicylic Acid Suppresses Alcoholism-Induced Cognitive Impairment Associated with Atorvastatin Intake by Targeting Cerebral miRNA155 and NLRP3: In Vivo, and In Silico Study.	Mohamed DI et al.	—	2022	→
Hippocampal ceRNA networks from chronic intermittent ethanol vapor-exposed male mice and functional analysis of top-ranked lncRNA genes for ethanol drinking phenotypes.	Plasil SL et al.	—	2022	→
Regulation of alcohol drinking by ventral striatum and extended amygdala circuitry.	Borrego MB et al.	—	2022	→
Alcohol induced impairment/abnormalities in brain: Role of MicroRNAs.	Sushma et al.	—	2021	→
Alcohol use disorder causes global changes in splicing in the human brain.	Van Booven D et al.	—	2021	→
High ethanol preference and dissociated memory are co-occurring phenotypes associated with hippocampal GABA<sub>A</sub>R-δ receptor levels.	Jovasevic V et al.	—	2021	→
Non-Invasive microRNA Profiling in Saliva can Serve as a Biomarker of Alcohol Exposure and Its Effects in Humans.	Mead EA et al.	—	2021	→
Chronic Voluntary Ethanol Drinking in Cynomolgus Macaques Elicits Gene Expression Changes in Prefrontal Cortical Area 46.	Walter NAR et al.	—	2020	→
Differential expression of microRNAs in the hippocampi of male and female rodents after chronic alcohol administration.	Choi MR et al.	—	2020	→
Effects of chronic intermittent ethanol exposure and withdrawal on neuroblastoma cell transcriptome.	McClintick JN et al.	—	2020	→
Network Analysis of miRNA and mRNA Changes in the Prelimbic Cortex of Rats With Chronic Neuropathic Pain: Pointing to Inflammation.	Cai G et al.	—	2020	→
Acute Ethanol Produces Ataxia and Induces Fmr1 Expression via Histone Modifications in the Rat Cerebellum.	Dulman RS et al.	—	2019	→
Alterations in sperm-inherited noncoding RNAs associate with late-term fetal growth restriction induced by preconception paternal alcohol use.	Bedi Y et al.	—	2019	→
Bioinformatic and biological avenues for understanding alcohol use disorder.	Grantham EK et al.	—	2019	→
Brain Regional and Temporal Changes in BDNF mRNA and microRNA-206 Expression in Mice Exposed to Repeated Cycles of Chronic Intermittent Ethanol and Forced Swim Stress.	Solomon MG et al.	—	2019	→
Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data.	Mignogna KM et al.	—	2019	→
Music-performance regulates microRNAs in professional musicians.	Nair PS et al.	—	2019	→
Deep sequencing and miRNA profiles in alcohol-induced neuroinflammation and the TLR4 response in mice cerebral cortex.	Ureña-Peralta JR et al.	—	2018	→
Sex-specific deficits in biochemical but not behavioral responses to delay fear conditioning in prenatal alcohol exposure mice.	Caldwell KK et al.	—	2018	→
Transcriptome analysis of alcohol-treated microglia reveals downregulation of beta amyloid phagocytosis.	Kalinin S et al.	—	2018	→