Multi-tier signaling and epigenomic reprogramming orchestrate microglial inflammatory states and functions associated with demyelination
Bibliographic record
Abstract
Summary The extensive heterogeneity of microglia inflammatory states accompanying neurodegenerative diseases underscores the complex molecular mechanisms that regulate these cells. Here, we report on transcriptional mechanisms that control microglial inflammatory polarization associated with brain demyelination in mice. Using flow cytometry, microscopy and RNA-seq, we identified two dominant, functionally distinct states inflammatory microglia: Clec7a+CD229+CD11c-microglia that are prone to proliferation and that transcribe high levels of extracellular matrix-associated genes, and Clec7a+CD229+CD11c+ microglia characterized by inflammatory tissue-remodeling and antigen presentation gene signatures. Epigenomic analyses implicated genome-wide nucleosome remodeling to the polarization process, driven by state-associated hierarchal stimulation of pro-inflammatory transcription factors, as well as re-calibration of Mef2 homeostatic input. Mechanistically, we confirmed relevance for Trem2, Mef2a and Egr2 to the microglial inflammatory polarization and demyelinating processes. Therefore, distinct configurations of signaling input cooperate with epigenetic mechanisms to reprogram the transcriptional output of microglia to enable their inflammatory functions.
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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".