PRMT1 is required for the generation of MHC-associated microglia and remyelination in the central nervous system
Bibliographic record
Abstract
Remyelination failure in multiple sclerosis leads to progressive demyelination and inflammation, resulting in neurodegeneration and clinical decline. Microglia are innate immune cells that can acquire a regenerative phenotype to promote remyelination, yet little is known about the regulators controlling the regenerative microglia activation. Herein, using a cuprizone (CPZ)-diet induced de- and remyelination mice model, we identify PRMT1 as a driver for MHC-associated microglia population required for remyelination in the central nervous system. The loss of PRMT1, but not PRMT5, in microglia resulted in impairment of the remyelination with a reduction of oligoprogenitor cell number and prolonged microgliosis and astrogliosis. Using single-cell RNA sequencing, we found eight distinct microglial clusters during the CPZ diet, and PRMT1 depleted microglia hindered the formation of the MHC-associated cluster, expressing MHCII and CD11c. Mechanistically, PRMT1-KO microglia displayed reduced the H3K27ac peaks at the promoter regions of the MHC- and IFN-associated genes and further suppressed gene expression during CPZ diet. Overall, our findings demonstrate that PRMT1 is a critical regulator of the MHC- and IFN-associated microglia, necessary for central nervous system remyelination.
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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".