Senolytic treatment depletes microglia and decreases severity of experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
Abstract The role of senescence in disease contexts is complex, however there is considerable evidence that depletion of senescent cells improves outcomes in a variety of contexts particularly related to aging, cognition, and neurodegeneration. Here, the effect of a bioinformatically-rationalized senolytic was tested in the experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis (MS). Single-cell analysis from brain tissue isolated from mice subjected to EAE identified microglia with a strong senescence signature including the presence of BCL2-family member transcripts. Cells expressing Bcl2l1 had higher expression of pro-inflammatory and senescence genes than their negative counterparts in EAE, suggesting they may exacerbate inflammation. Notably, in human single-nucleus sequencing from MS, BCL2L1 positive microglia were strongly enriched in lesions with active inflammatory pathology, and likewise demonstrated increased expression of immune related genes suggesting they may contribute to the active lesion pathology and tissue damage in chronic active lesions. Employing a small molecule BCL2 inhibitor, Navitoclax (ABT-263), significantly reduced the presence of microglia in the EAE spinal cord, suggesting that these cells can be targeted by senolytic treatment. ABT-263 treatment had a profound effect on EAE mice, decreasing motor symptom severity, improving visual acuity, promoting neuronal survival, and decreasing white matter inflammation. Together, these results provide evidence to support the idea that senescent glia may exacerbate inflammation resulting in negative outcomes in neuroinflammatory disease and that removing them may ameliorate 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) | 0.000 | 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 | 0.001 |
| 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".