Microexon alternative splicing of small <scp>GTPase</scp> regulators: Implication in central nervous system diseases
Bibliographic record
Abstract
Microexons are small sized (≤51 bp) exons which undergo extensive alternative splicing in neurons, microglia, embryonic stem cells, and cancer cells, giving rise to cell type specific protein isoforms. Due to their small sizes, microexons provide a unique challenge for the splicing machinery. They frequently lack exon splicer enhancers/repressors and require specialized neighboring trans-regulatory and cis-regulatory elements bound by RNA binding proteins (RBPs) for their inclusion. The functional consequences of including microexons within mRNAs have been extensively documented in the central nervous system (CNS) and aberrations in their inclusion have been observed to lead to abnormal processes. Despite the increasing evidence for microexons impacting cellular physiology within CNS, mechanistic details illustrating their functional importance in diseases of the CNS is still limited. In this review, we discuss the unique characteristics of microexons, and how RBPs participate in regulating their inclusion and exclusion during splicing. We consider recent findings of microexon alternative splicing and their implication for regulating the function of small GTPases in the context of the microglia, and we extrapolate these findings to what is known in neurons. We further discuss the emerging evidence for dysregulation of the Rho GTPase pathway in CNS diseases and the consequences contributed by the mis-splicing of microexons. This article is categorized under: RNA Processing > Splicing Mechanisms RNA Processing > Splicing Regulation/Alternative Splicing RNA in Disease and Development > RNA in Disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".