Gene-gene functional relationships in Alzheimer's disease: CELF1 regulates KLC1 alternative splicing
Bibliographic record
Abstract
The causes of Alzheimer's disease (AD) are poorly understood, although many genes are known to be involved in this pathology. To gain insights into the underlying molecular mechanisms, it is essential to identify the relationships between individual AD genes. Previous work has shown that the splice variant E of KLC1 (KLC1_vE) promotes AD, and that the CELF1 gene, which encodes an RNA-binding protein involved in splicing regulation, is at a risk locus for AD. Here, we identified a functional link between CELF1 and KLC1 in AD pathogenesis. Transcriptomic data from human samples from different ethnic groups revealed that CELF1 mRNA levels are low in AD brains, and the splicing pattern of KLC1 is strongly correlated with CELF1 expression levels. Specifically, KLC1_vE is negatively correlated with CELF1. Depletion and overexpression experiments in cultured cells demonstrated that the CELF1 protein down-regulates KLC1_vE. In a cross-linking and immunoprecipitation sequencing (CLIP-seq) database, CELF1 directly binds to KLC1 RNA, following which it likely modulates terminal exon usage, hence KLC1_vE formation. These findings reveal a new pathogenic pathway where a risk allele of CELF1 is associated with reduced CELF1 expression, which up-regulates KLC1_vE to promote AD.
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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".