Context‐dependent transcriptional regulation of microglial proliferation
Bibliographic record
Abstract
Abstract Microglia proliferate during brain development and brain lesions, but how this is coordinated at the transcriptional level is not well understood. Here, we investigated fundamental aspects of the transcriptional process associated with proliferation of mouse microglia during postnatal development and in adults in a model of induced microglial depletion‐repopulation. While each proliferative subset displayed globally a distinct signature of gene expression, they also co‐expressed a subgroup of 1370 genes at higher levels than quiescent microglia. Expression of these may be coordinated by one of two mechanisms of regulation with distinct properties. A first mechanism augments expression of genes already expressed in quiescent microglia and is subject to regulation by Klf/Sp, Nfy, and Ets transcription factors. Alternatively, a second mechanism enables de novo transcription of cell cycle genes and requires additional regulatory input from Lin54 and E2f transcription factors. Of note, transcriptional upregulation of E2f1 and E2f2 family members may represent a critical regulatory checkpoint to enable microglia to achieve efficient cell cycling. Furthermore, analysis of the activity profile of the repertoire of promoter‐distal genomic regulatory elements suggests a relatively restricted role for these elements in coordinating cell cycle gene expression in microglia. Overall, proliferating microglia integrates regulation of cell cycle gene expression with their broader, context‐dependent, transcriptional landscape.
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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".