Exploration of alcohol use disorder-associated brain miRNA–mRNA regulatory networks
Bibliographic record
Abstract
Transcriptomic changes in specific brain regions can influence the risk of alcohol use disorder (AUD), 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 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 European Australians. 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 tissue samples (Set 2) from six of the above eight brain regions of eight AUD and eight control European Australians. Some of the AUD-associated miRNA-mRNA regulatory networks were confirmed. In addition, miRNA and mRNA transcriptomes were analyzed in hESC-derived cortical interneurons with or 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".