Alzheimer’s disease-linked risk alleles elevate microglial cGAS-associated senescence and neurodegeneration in a tauopathy model
Bibliographic record
Abstract
SUMMARY The strongest risk factors for Alzheimer’s disease (AD) include the χ4 allele of apolipoprotein E (APOE), the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2), and female sex. Here, we combine APOE4 and TREM2R47H ( R47H ) in female P301S tauopathy mice to identify the pathways activated when AD risk is the strongest, thereby highlighting disease-causing mechanisms. We find that the R47H variant induces neurodegeneration in female APOE4 mice without impacting hippocampal tau load. The combination of APOE4 and R47H amplified tauopathy-induced cell-autonomous microglial cGAS-STING signaling and type-I interferon response, and interferon signaling converged across glial cell types in the hippocampus. APOE4-R47H microglia displayed cGAS- and BAX-dependent upregulation of senescence, showing association between neurotoxic signatures and implicating mitochondrial permeabilization in pathogenesis. By uncovering pathways enhanced by the strongest AD risk factors, our study points to cGAS-STING signaling and associated microglial senescence as potential drivers of AD risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".