An Alzheimer’s disease pathway uncovered by functional omics: the risk gene <i>CELF1</i> regulates <i>KLC1</i> splice variant E expression, which drives Aβ pathology
Bibliographic record
Abstract
Abstract In an era when numerous disease-associated genes have been identified, determining the molecular mechanisms of complex diseases is still difficult. The CELF1 region was identified by genome-wide association studies as an Alzheimer’s disease (AD) risk locus. Using transcriptomics and cross-linking and immunoprecipitation sequencing (CLIP-seq), we found that CELF1, an RNA-binding protein, binds to KLC1 RNA and regulates its splicing. Analysis of two brain banks revealed that CELF1 expression is correlated with inclusion of KLC1 exons downstream of the CELF1-binding region identified by CLIP-seq. In AD, low CELF1 levels result in high levels of KLC1 splice variant E ( KLC1_vE ), an amyloid-β (Aβ) pathology-driving gene product. Cell culture experiments confirmed regulation of KLC1_vE by CELF1. Analysis of mouse strains with different propensities for Aβ accumulation confirmed that Klc1_vE drives Aβ pathology. Using omics methods, we revealedthe molecular pathway of a complex disease supported by human and mouse genetics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".