Splicing to keep splicing: A feedback system for cellular homeostasis and state transition
Bibliographic record
Abstract
BACKGROUND: Alternative splicing (AS) plays a crucial role in regulating gene expression and governing proteomic diversity by generating multiple protein isoforms from a single gene. Increasing evidence has highlighted the regulation for pre-mRNA splicing of the splicing factors (SFs). This review aims to examine featured mechanisms and examples of SF regulation by AS, focusing on paradigmatic feedback loops and their biological implications. MAIN BODY OF THE ABSTRACT: We specifically focus on the autoregulation and inter-regulation of SFs through AS machinery. These interactions give rise to a feedback system, where the negative feedback loops aid in maintaining cellular homeostasis, and the positive feedback loops play roles in triggering cellular state transitions. We examine the growing evidence highlighting the specific mechanisms employed by SFs to autoregulate their own splicing, including AS-coupled nonsense-mediated mRNA decay (AS-NMD), nuclear retention, and alternative 3'UTR regulation. We showcase the influence of AS feedback in amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and cancer. Furthermore, we discuss how master splicing factors can dominantly orchestrate splicing cascades, leading to widespread impacts in cellular processes. We also discuss how non-coding RNAs, particularly circular RNAs and microRNAs, engage in the splicing regulatory networks. Lastly, we showcase how negative and positive feedback loops can collaboratively achieve remarkable biological functions during the cell fate decision. SHORT CONCLUSION: This review highlights the regulation of SFs by AS, providing enriched information for future investigations that aim at deciphering the intricate interplay within splicing regulatory networks. KEY POINTS: Negative feedback of alternative splicing maintains cellular homeostasis. Positive feedback of alternative splicing triggers cellular state transitions. Alternative splicing forms integrated feedback networks with circRNAs and microRNAs to reciprocally regulate their expression and function. The coordinated interplay of distinct splicing feedback mechanisms orchestrates precise cell fate transitions. Future directions and therapeutic possibilities that could transform alternative splicing research into treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".