Exploration of alcohol use disorder-associated brain miRNA-mRNA regulatory networks

Alcohol use disorder (AUD) is due to gene expression changes in specific brain regions, but the underlying mechanism is not fully understood. We investigated AUD-associated miRNA-mRNA regulatory networks in multiple brain regions by analyzing transcriptomic changes in two sets of postmortem brain tissue samples and ethanol-exposed human embryonic stem cell (hESC)-derived cortical interneurons. miRNA and mRNA transcriptomes were profiled in 192 postmortem tissue samples (Set 1) from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control Caucasians. Nineteen differentially expressed miRNAs (fold-change>2.0 & P<0.05) and 97 differentially expressed mRNAs (fold-change>2.0 & P<0.001) were identified in one or multiple brain regions of AUD subjects. AUD-associated miRNA-mRNA regulatory networks in each brain region were constructed using differentially expressed and negatively correlated miRNA-mRNA pairs. AUD-relevant pathways (including CREB Signaling, IL-8 Signaling, and Axonal Guidance Signaling) were potentially regulated by AUD-associated brain miRNA-mRNA pairs. Moreover, miRNA and mRNA transcriptomes were mapped in additional 96 postmortem tissue samples (Set 2) from six of the above eight brain regions of eight AUD and eight control Caucasians, and some of the AUD-associated miRNA-mRNA regulatory networks were confirmed. Additionally, miRNA and mRNA transcriptomes were analyzed in hESC-derived cortical interneurons with and without ethanol exposure, and ethanol-influenced miRNA-mRNA regulatory networks were constructed. This study provided evidence that alcohol could induce concerted miRNA and mRNA expression changes in reward-related or alcohol-responsive brain regions. We concluded that altered brain miRNA-mRNA regulatory networks might contribute to AUD development.


Introduction
Alcohol use disorder (AUD) is characterized by uncontrolled alcohol drinking due to physical and psychological dependence on alcohol. According to the 2019 National Survey on Drug Use and Health (NSDUH), AUD affects 14.1 million (4.2%) adult Americans (8.9 million men and 5.2 million women) [1]. Mounting evidence suggests that AUD is a complex genetic disorder, with an estimated heritability of about 50% [2]. Besides genetic variation, chronic alcohol consumption can lead to neuroadaptive phenomena, such as alcohol tolerance, dependence, and withdrawal [3]. The underlying molecular mechanisms of alcohol-induced neuroadaptations has not been fully explored, but it is believed that gene expression changes in specific brain regions are associated with AUD development.
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The copyright holder for this preprint this version posted July 2, 2021. ; https://doi.org/10.1101/2021.06.27.21259397 doi: medRxiv preprint 5 may participate in synaptic transmission [5,8], vesicle formation and cell architecture [5], transcription and lipid metabolism [14], and oxidative phosphorylation, mitochondrial dysfunction and cytokine signaling [15]. Only one study is known to have examined mRNA transcriptomic changes in postmortem VTA of AUD subjects, and the identified differentially expressed coding genes likely contribute to neurotransmission and signal transduction [8]. These findings suggest that altered expression of coding genes or mRNAs in reward-related brain regions may underlie alcohol-induced neuroadaptations.  [16]. The function of miRNAs implies an additional layer of gene expression regulation besides genetic variation. Accumulating evidence suggests that alcohol could induce miRNA expression changes, leading to altered cellular functions. Expression changes in miRNAs and their target mRNAs have been demonstrated as a consequence of exposure of alcohol to cultured cells [17,18] as well as mouse [19] and rat [20-23] brains. miRNA transcriptomic changes have also been observed in postmortem PFC [13,24] and NAc [15] of AUD subjects by microarray-based transcriptome analysis.
Given that AUD is a genetically heterogeneous disorder, it is commonly agreed that multiple genes (including both coding and noncoding genes) and the interactions among them contribute All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. to the etiology of AUD. Studies have shown that a single miRNA can target hundreds of mRNA transcripts while a single mRNA transcript can be simultaneously regulated by distinct miRNAs [25]. The particular role of miRNAs in posttranscriptional regulation implies that miRNAs finetune the expression of numerous genes involved in a variety of cellular functions and thus coordinate multiple cross-communicating pathways. To date, no studies are known to have explored AUD-associated miRNA-mRNA regulatory networks.
Here, we report the first network analysis of AUD-associated brain miRNAs and mRNAs.
Specifically, we examined AUD-associated miRNA and mRNA transcriptomic changes in multiple brain regions of AUD subjects. We also performed miRNA-mRNA pairing analysis and constructed AUD-associated and brain region-specific miRNA-mRNA regulatory networks. To understand whether miRNA and mRNA expression changes in postmortem brains of AUD subject are due to alcohol consumption, we differentiated human embryonic stem cells (hESCs) into cortical interneurons and then used hESC-derived cortical interneurons as in vitro cellular models to examine ethanol-induced miRNA and mRNA transcriptomic changes. The convergence of multiple brain region transcriptome analysis and neuronal modeling could facilitate our understanding of the neuroadaptive mechanisms of AUD.

Human postmortem brain tissues
Two sets of freshly-frozen autopsy brain tissue samples were obtained from the New South Wales Brain Tissue Resource Centre (NSWBTRC) in Australia. Set  (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 2, 2021. Isolation and selection of brain tissue RNA samples for miRNA and mRNA transcriptome analysis Total RNAs were isolated from 10-50 mg of postmortem brain tissue samples using the miRNeasy Mini Kit (QIAGEN, Valencia, CA, USA). RNA integrity number (RIN) and concentration were measured using the Agilent 2100 Bioanalyser with the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, USA). From the 480 Set 1 RNA samples, we selected 192 [from 8 brain regions of 12 AUD cases (6 males and 6 females) and 12 controls (6 males and 6 females)] with larger RINs (mean±SD: 6.6±1.3) for miRNA and mRNA transcriptome analysis. From the 360 Set 2 RNA samples, we selected 96 (from 6 brain regions of 8 male AUD cases and 8 male controls) with larger RINs (mean±SD: 5.9±1.4) for miRNA and mRNA transcriptome analysis. In both sets of selected RNA samples, cases and controls were matched by sex, age, RINs, and postmortem internals (PMIs). Characteristics (including the amount of daily alcohol use, sex, age, PMIs, RINs, brain weight, brain pH, cerebral hemispheres, All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. RNA-seq analysis of miRNA and mRNA transcriptomic changes in eight brain regions of AUD subjects (192 Set 1 RNA samples) miRNA and mRNA expression profiles of the 192 selected Set 1 RNA samples were analyzed respectively by small RNA-seq and ribosome RNA (rRNA) depletion RNA-seq. Small RNA-seq was conducted as described in our previous study [27]. Briefly, small RNA libraries were miRNA Sequencing Data (CAP-miRseq) workflow [28] was used for raw reads (in fastq files) pre-processing, alignment, mature/precursor/novel miRNA qualification and prediction. The mean total number of reads per sample was 16,591,602, and the mean mapping rate (aligned reads/reads sent to Aligner) was 73.2%. Principal component analysis (PCA) of miRNA transcriptome data of these 192 samples (from 8 brain regions) showed clustered CRB and VTA samples, but samples from six other brain regions could not be separated by brain regions using the miRNA expression data (Supplementary Fig. S1a). The small RNA-seq fastq files and normalized read counts are available for downloading from the NCBI Gene Expression Omnibus (GEO) database (accession number: Pending). All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  [29] was utilized to quantitate gene and isoform expression. The mean total number of reads per sample was 38,758,477, and the mean mapping rate (aligned reads/reads sent to Aligner) was 84.6%. PCA plotting of the mRNA-seq data showed similar sample clustering patterns as above using the miRNA-seq data (Supplementary Fig. S1b). The rRNA depletion RNA-seq fastq files and normalized read counts are available for downloading from the NCBI GEO database (accession number: Pending).

Microarray analysis of miRNA and mRNA transcriptomic changes in six brain regions of AUD subjects (96 Set 2 RNA samples)
For miRNA transcriptome analysis, the Affymetrix GeneChip TM miRNA4.0 array (Affymetrix, Santa Clara, CA, USA) was used following the manufacturer's instructions. This array was designed to detect all miRNAs in miRBase Release 20 [30]. It contains 30,424 probe sets for mature miRNAs of 203 species including 2,578 human mature miRNA probe sets, 2,025 human pre-miRNA probe sets, and 1,996 human snoRNA and scaRNA probe sets. Probe cell intensity files (or CEL files) for small noncoding RNAs (including miRNAs) were generated using the Affymetrix® GeneChip™ Command Console (AGCC) software. Small noncoding RNA CEL All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted July 2, 2021. ; https://doi.org/10.1101/2021.06.27.21259397 doi: medRxiv preprint files were processed using the Affymetrix Expression Console (EC) software (v1.4.1) with the "MicroRNA Arrays -RMA (robust multi-array average) +DABG (detection above Background)-Human only" workflow as the default analysis for background adjustment and signal normalization as well as log2 transformation to create probe level summarization files (or CHP files). Quality control (QC) analysis of the CHP files was performed within the EC software, and the quality of the miRNA expression array data was visualized using box plots ( Supplementary Fig. S2a). The CHP files for case and control samples were further analyzed by statistical programs to identify differentially expressed miRNAs and other small noncoding RNAs. The Affymetrix miRNA expression data has been deposited in the NCBI GEO database (accession number: Pending).
For mRNA transcriptome analysis, the Affymetrix Clariom TM D human array (Affymetrix, Santa Clara, CA, USA) was used following the manufacturer's instructions. This array allows interrogating more than 540,000 transcripts (including coding and long non-coding genes, exons, and alternative splicing events as well as rare transcripts) using over 6.7 million probes. Probe cell intensity files (or CEL files) for transcripts were generated using the AGCC software. They were then analyzed using the Affymetrix EC software (v1.4.1) with the "Gene Level -RMA-Sketch (robust multi-array average with sketch quantile normalization)" workflow as the default analysis for background adjustment and signal normalization as well as log2 transformation to create probe level summarization files (or CHP files). Quality control (QC) analysis of the CHP files was performed within the EC software, and the quality of the mRNA expression array data was visualized using box plots (Supplementary Fig. S2b). The CHP files for case and control samples were further analyzed by statistical programs to identify differentially expressed All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted July 2, 2021. Louis, MO, USA), and the medium was changed every four days. After six weeks of maturation (totally 62 days in vitro differentiation), the H1 hESC-derived cortical interneurons were All rights reserved. No reuse allowed without permission.
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hESC-derived cortical interneurons were then cultured in the neuronal maturation media containing ethanol at a concentration of around 50-100 mM (equivalent to blood alcohol levels of heavy or intoxicated drinkers) for 7 days. The ethanol-containing neuronal maturation medium was changed every other day. After additional 24-hr culture without ethanol exposure, the cells were collected for total RNA isolation. Cell treatment experiments (exposed or unexposed to ethanol for 7 days) were performed in duplicate. Extra wells of cells treated with or without ethanol were fixed with 4% paraformaldehyde for cell morphology assay. Ethanolexposed cells did not show apparent morphological changes (Supplementary Fig. S4).
miRNA transcriptomes of hESC-derived neurons (exposed or unexposed to ethanol) were profiled by small RNA-seq and the raw data obtained from small RNA-seq was processed by CAP-miRseq [28], as described above. The mean total number of reads per sample was 29,145,212, and the mean mapping rate (aligned reads/reads sent to Aligner) was 85.0%. The quality of the miRNA-seq data was visualized using box plots (Supplementary Fig. S5a). The miRNA-seq data has been deposited in the NCBI GEO database (accession number: Pending). mRNA-seq raw data was processed by Pipeliner [29], as described above. The mean total number of reads per sample was 25,524,900 and the mean mapping rate (aligned reads/reads sent All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. to Aligner) was 75.1%. %. The quality of the mRNA-seq data was visualized using box plots ( Supplementary Fig. S5b). The mRNA-seq data has been deposited in the NCBI GEO database (accession number: Pending).

Statistical analyses
Differential expression analysis was performed to identify differentially expressed miRNAs and mRNAs in each brain region of AUD subjects (given that gene expression is tissue-specific) and ethanol-exposed hESC-derived cortical interneurons. For RNA-seq count data from brain tissue samples, the differential expression analysis was performed using limma-voom [33], with a number of confounding factors being considered as covariates. We did principal component analysis (PCA) to extract the first three PCs for both technical (batch, RIN, and PMI) and biological (sex, age, brain weight, brain pH, left-right brain, smoking, and liver disease) confounding variables, and the obtained PC1, PC2, and PC3 were then used as covariates in the model matrix design for differential expression analysis.

Bioinformatics analysis
The function of differentially expressed miRNAs was annotated using DIANA TOOLS -mirPath v. 3 [34]. The gene ontology (GO) analysis of molecular functions (MF), biological processes (BP), and cellular components (CC) overrepresented in differentially expressed mRNAs was conducted using DAVID v6. 8 [35]. Additionally, AUD-associated miRNA-mRNA pairs and their associated canonical pathways in each brain region were analyzed using the miRNA Target Filter function in Ingenuity Pathway Analysis (IPA, Ingenuity Systems, http://www.ingenuity.com). First, the differential expression analysis results [including folder changes (FC) and P values] of differentially expressed miRNAs identified in each brain region All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. peer-reviewed literature. Second, the differential expression analysis results (including FC and P values) of differentially expressed mRNAs identified in the same brain region were added, and the Expression Pairing function of the IPA miRNA Target Filter was applied to obtain miRNA-mRNA pairs in which their expression levels were negatively-correlated (i.e., upregulated miRNA-downregulated mRNA pairs or downregulated miRNA-upregulated mRNA pairs).
Third, the obtained miRNA-mRNA pairs were used to construct miRNA-mRNA interaction networks. Finally, AUD-related canonical pathways were added to miRNA-mRNA networks to display miRNA-mRNA-pathway relationships.

Results
Differentially expressed miRNAs in multiple brain regions of AUD subjects and ethanol-exposed hESC-derived cortical interneurons (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 2, 2021.  Fig. S7 and Table S4). The expression of miR-412-5p, which was downregulated in multiple brain regions of AUD subjects, was on a decreasing trend (1.3-fold decrease & P=0.263) in ethanol-exposed hESC-derived cortical interneurons.
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The copyright holder for this preprint this version posted July 2, 2021. We also examined ethanol-induced mRNA transcriptome changes in hESC-derived cortical interneurons by mRNA-seq. A 7-day ethanol exposure did not cause coding gene expression changes at the above significance level (absolute FC>2.0 & P<0.001) (Supplementary Fig.   S10). When the significance level was set at FC>2.0 & P<0.01, 19 coding genes showed All rights reserved. No reuse allowed without permission.
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Venn diagrams were used to show the number of differentially expressed mRNAs (P<0.05) shared between eight brain regions of AUD subjects (Set 1 and Set 2) and ethanol-exposed hESC-derived cortical interneurons (Supplementary Fig. S11). The in vitro cellular model study confirmed a number of AUD-associated mRNAs, including 15 mRNAs in the AMY, 15 mRNAs in the CN, five mRNAs in the CRB, five mRNAs in the HIP, seven mRNAs in the NAc, eight mRNAs in the PFC, 10 mRNAs in the PUT, and four mRNAs in the VTA. Among them, 13 AUD-associated coding genes identified in multiple brain regions were found differentially expressed in ethanol-exposed hESC-derived cortical interneurons (Supplementary Fig. S11). and Amphetamine Addiction (P = 1.0×10 -4 ; 15 miRNAs), were associated with mRNAs potentially targeted by these 19 miRNAs (Fig. 3) (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Functional annotations of miRNAs and mRNAs differentially expressed in the brain of AUD
The copyright holder for this preprint this version posted July 2, 2021. ; https://doi.org/10.1101/2021.06.27.21259397 doi: medRxiv preprint Myelination), and cellular components (CC; such as Integral Component of Membrane) were enriched in these 97 differentially expressed mRNAs. GO terms (MF, BP, and CC) overrepresented (P<0.05) for this gene set are displayed in Supplementary Fig. S12.

AUD-associated brain miRNA-mRNA regulatory networks
Differentially expressed and negatively correlated miRNA-mRNA pairs were included in brain region-specific IPA network analysis. The differential expression analysis P value was set at < 0.05 and the absolute FC was set at > 1. Signaling) (Fig. 4b). Within the CRB, seven miRNAs (2 upregulated and 5 downregulated) and nine paired mRNAs (8 upregulated and 1 downregulated) could regulate three pathways (Synaptogenesis Signaling, CREB Signaling in Neurons, and Neuroinflammatory Signaling) (Fig. 4c). Within the HIP, 21 miRNAs (13 upregulated and 8 downregulated) and 15 paired mRNAs (9 upregulated and 6 downregulated) could regulate four pathways (G-Protein Coupled Receptor Signaling, CREB Signaling in Neurons, Synaptogenesis Signaling, and Axonal Guidance Signaling) (Fig. 4d). Moreover, within the NAc, two upregulated miRNAs and two paired down-regulated mRNAs could regulate one pathway (i.e., CREB Signaling in Neurons) All rights reserved. No reuse allowed without permission.
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& P<0.05)
and negatively correlated miRNA-mRNA pairs identified in six of the above eight brain regions of AUD subjects (the Set 2 sample) (Supplementary Fig. S13 and Fig. S14).
The analysis of miRNA-mRNA regulatory networks using differentially expressed (absolute  (Supplementary Fig. S15). In total, 17 canonical pathways potentially regulated by differentially expressed and negatively correlated miRNA-mRNA pairs were identified in eight brain region of AUD subjects and ethanol-exposed hESC-derived cortical interneurons (Supplementary Table S8). The top three pathways potentially regulated by AUD-associated and negatively correlated miRNA-mRNA pairs in multiple brain regions included CREB Signaling in Neurons, IL-8 Signaling, and Axonal Guidance Signaling. All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  Table S8).

Discussion
In this study, we observed miRNA and mRNA transcriptomic changes in multiple reward-related or alcohol-responsive brain regions of AUD subjects. We also discovered that miRNA-mRNA interactions in specific brain regions potentially contributed to the biological pathways important for AUD risk. Through the in vitro cellular model study, we validated that alcohol exposure could alter miRNA and mRNA expression profiles and miRNA-mRNA regulatory networks. To our knowledge, this is the first study that investigated the relationship of AUD and brain miRNA-mRNA regulatory networks.
First, RNA-seq and microarray analyses of transcriptomic changes in multiple brain regions of AUD subjects suggest that several cortical and subcortical regions (or components of the reward circuit) are essential for the rewarding effect of alcohol. We observed miRNA and mRNA transcriptomic changes in eight reward-related or alcohol-responsive brain regions of AUD subjects. The reason that we chose these eight brain regions for this study is that they participate in brain functions such as motivation, memory, and pleasure as well as balance and locomotion [39][40][41]. Certainly, we cannot exclude the possibility that other brain regions also mediate the rewarding effect of ethanol or be in involved in AUD-related pathways.
Second, a brain region with more AUD-associated miRNAs and mRNAs may be more responsive to alcohol stimulation or play a more important role in alcohol-induced neuroadaptations. As shown in Figure 1, the PFC had the largest number of AUD-associated All rights reserved. No reuse allowed without permission.
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Correspondingly, more mRNAs were significantly downregulated in the PFC of AUD subjects than in other brain regions of AUD subjects (Figure 2). Given the role of the PFC in higher cognitive functions, alcohol-induced expression changes of miRNAs and their target mRNAs in the PFC may lead to cognitive deficits and compromised working memory. Moreover, differentially expressed miRNAs and mRNAs were also observed in seven other brain regions of AUD subjects, and some AUD-associated miRNAs and mRNAs were shared among multiple brain regions of AUD subjects ( Fig. 1 and Fig. 2). These findings provided insight into the coordinated role of multiple brain regions in AUD development and also suggested coordinated expression changes of miRNAs and mRNAs in the brains of AUD subjects.
Third, the findings that AUD-associated brain miRNAs potentially target genes involved in addiction-linked pathways suggest that these miRNAs play a critical role in AUD development.
Through miRNA target gene prediction and pathway enrichment analyses by DIANA-mirPath, we found that the majority of the 19 differentially expressed miRNAs (Supplementary Table   S2) identified in one or more of the eight brain regions could target coding genes (or mRNAs) that participate in neurobiological processes of drug reward or addiction (Fig. 3). Among the top 14 pathways, four were related to drug addiction (Morphine Addition, Retrograde Endocannabinoid Signaling, Cocaine Addiction, and Amphetamine Addiction) and two were related to synaptic functions (GABAergic Synapse and Glutamatergic Synapse). Although alcohol and drugs of abuse (e.g., morphine and cocaine) possess diverse neuropharmacological potentials, their reinforcing effects are mediated by common pathways (such as dopaminergic and glutamatergic pathways) via the activation of the mesocorticolimbic system that are mainly comprised of the AMY, the NAc, the PFC, and the VTA [42]. That is to say, the above pathways All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted July 2, 2021. ; https://doi.org/10.1101/2021.06.27.21259397 doi: medRxiv preprint 22 for drug addiction or synaptic function can also mediate the rewarding effect of alcohol or are essential for neuroadaptive processes triggered by alcohol. Accordingly, AUD-associated miRNAs identified in the above eight brain regions are expected to regulate the expression of genes that are important for alcohol-induced neuroadaptations.
Additionally, the present study provided evidence that brain miRNA-mRNA regulatory networks consisting of dysregulated and negatively correlated miRNA-mRNA pairs contribute to the risk of AUD. We identified at least 17 canonical pathways that were likely influenced by dysregulated and negatively correlated miRNA-mRNA pairs in the brains of AUD subjects (Supplementary Table S8). The top three pathways potentially regulated by dysregulated and negatively correlated miRNA-mRNA pairs in multiple brain regions of AUD subjects included CREB Signaling in Neurons, IL-8 Signaling, and Axonal Guidance Signaling. The CREB Signaling was found to be a central amygdaloid signaling pathway involved in high anxiety-like and excessive alcohol drinking behaviors [43]. We found that the CREB Signaling pathway could be regulated by dysregulated and negatively correlated miRNA-miRNA pairs in seven of the eight brain regions (except PUT) of AUD subjects ( Fig. 4 and Fig. 5). Regarding the relationship of the IL-8 Signaling pathway and AUD, there is emerging evidence that alcohol use can stimulate immune cells to secrete peripheral pro-and anti-inflammatory cytokines (such as IL-8) [44,45], thus supporting the role of the immune system in the pathophysiology of AUD.
We observed that the IL-8 Signaling pathway could be regulated by dysregulated and negatively correlated miRNA-miRNA pairs in four (AMY, PFC, PUT, and VTA) of the eight brain regions of AUD subjects ( Fig. 4 and Fig. 5). The Axon Guidance Signaling pathway can regulate axon guidance, synaptogenesis, and cell migration. Studies have shown that ethanol disrupted axon outgrowth by influencing the Axon Guidance Signaling pathway [46]. We noticed that the Axon All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Guidance Signaling pathway could be regulated by dysregulated and negatively correlated miRNA-miRNA pairs in four (AMY, CN, HIP, and PFC) of the eight brain regions of AUD subjects ( Fig. 4 and Fig. 5). These three top pathways were validated in the Set 2 brain tissue sample by the network analysis of dysregulated and negatively correlated miRNA-mRNA pairs in six of the eight brain regions of AUD subjects (Supplementary Fig. S13 and S14). Although these three top pathways were not found to be regulated by differentially expressed and negatively correlated miRNA-mRNA pairs identified in ethanol-exposed hESC-derived cortical interneurons, four other pathways [Sirtuin Signaling, Opioid Signaling, NRF2-mediated Oxidative Stress Response, and interleukin-1 (IL-1) Signaling] were uncovered (Supplementary Table S15). Except the IL-1 Signaling pathway, three other pathways were also identified by the analysis of Set 1 and Set 2 samples. Similar to the IL-8 Signaling pathway, the IL-1 Signaling pathway can also regulate immune response or inflammation caused by alcohol [47]. Therefore, multiple addiction-linked pathways influenced by AUD-associated miRNA-mRNA regulatory networks could contribute to the occurrence of AUD.
Some limitations of this study should be noted. First, bulk RNA-seq cannot evaluate the functional relevance of miRNA-mRNA pairing at the cellular level. Since RNA samples for the transcriptome analysis were extracted from homogenized brain tissues, AUD-associated miRNA and mRNA expression changes may not occur in the same type of cells. To identified AUDassociated and cell type-specific miRNA-mRNA pairs, single-cell (or nucleus) RNA-seq can be applied to map miRNA and mRNA transcriptomes at the individual cell level. Second, the functional role of AUD-associated miRNA-mRNA networks in regulating neuronal function was not investigated. We only predicted by bioinformatics programs or based on published studies that a number of AUD-related pathways were regulated by AUD-associated and negatively All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted July 2, 2021. ; https://doi.org/10.1101/2021.06.27.21259397 doi: medRxiv preprint correlated miRNA-mRNAs pairs. Animal model studies can be conducted to determine the influence of miRNA-mRNA interactions on neuronal function and addiction-related behaviors.
Third, the transcriptome analysis of postmortem brain tissues cannot determine whether the dysregulation of brain miRNAs and mRNAs was due to pre-existing vulnerability factors (such as genetic variants and/or environmental insults) or long-term alcohol consumption. We intended to verify AUD-associated brain miRNA and mRNA changes using ethanol-exposed hESCderived cortical interneurons as models. However, not many AUD-associated brain miRNAs and mRNAs were validated by the in vitro cellular model study. To confirm whether AUDassociated brain miRNA and mRNA expression changes were indeed due to alcohol use and occur in a certain type of brain neuronal or glial cells, we could use controlled animal model studies and single-cell (or nucleus) RNA-seq. Additionally, we did not analyze other types of AUD-associated noncoding RNAs, such as long noncoding RNAs (lncRNAs). In the follow-up study, we will further analyze AUD-associated miRNA-mRNA-lncRNA regulatory networks.
In conclusion, the concerted expression changes of brain miRNAs and their target mRNAs as well as the interaction of them may govern alcohol-induced neuroplasticity, thus contributing to the development of AUD. To understand the mechanisms of the transition of alcohol use to abuse or dependence, the spatial and temporal expression of brain miRNAs and their target mRNAs need to be investigated. All rights reserved. No reuse allowed without permission.
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20.
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