A Novel Alternative Splicing Mechanism That Enhances Human 5-HT1A Receptor RNA Stability Is Altered in Major Depression
Bibliographic record
Abstract
The serotonin-1A (5-HT1A) receptor is a key regulator of serotonergic activity and is implicated in mood and emotion. However, its post-transcriptional regulation has never been studied in humans. In the present study, we show that the “intronless” human 5-HT1A gene (HTR1A) is alternatively spliced in its 3′-UTR, yielding two novel splice variants. These variants lack a ∼1.6 kb intron, which contains an microRNA-135 (miR135) target site. Unlike the humanHTR1A, the mouseHTR1Alacks the splice donor/accepter sites. Thus, in the mouseHTR1A, splicing was not detected. The two spliced mRNAs are extremely stable, are resistant to miR135-induced downregulation, and have greater translational output than the unspliced variant. Moreover, alternativeHTR1ARNA splicing is oppositely regulated by the splice factors PTBP1 and nSR100, which inhibit or enhance its splicing, respectively. In postmortem human brain tissue from both sexes,HTR1AmRNA splicing was prevalent and region-specific. UnsplicedHTR1Awas expressed more strongly in the hippocampus and midbrain versus the prefrontal cortex (PFC), and correlated with reduced levels of nSR100. Importantly,HTR1ARNA splicing and nSR100 levels were reduced in the PFC of individuals with major depression compared with controls. Our unexpected findings uncover a novel mechanism to regulateHTR1Agene expression through alternative splicing of microRNA sites. Altered levels of splice factors could contribute to changes in regional and depression-related gene expression through alternative splicing. SIGNIFICANCE STATEMENTAlternative splicing, which is prevalent in brain tissue, increases gene diversity. The serotonin-1A receptor gene (HTR1A) is a regulator of serotonin, which is implicated in mood and emotion. Here we show that humanHTR1ARNA is alternately spliced. Splicing removes a microRNA site to generate ultrastable RNA and increaseHTR1Aexpression. This splicing varies in different brain regions and is reduced in major depression. We also identify specific splice factors forHTR1ARNA, showing they are also reduced in depression. Thus, we describe a novel mechanism to regulate gene expression through splicing. Altered levels of splice factors could contribute to depression by changing gene expression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".