Mechanisms Underlying the Selection and Function of Macrophage-Specific Enhancers
Bibliographic record
Abstract
Macrophages populate every tissue of the body and play vital roles in homeostasis, pathogen elimination, and tissue healing. These cells possess the ability to adapt to a multitude of abruptly changing and complex environments. Furthermore, different populations of resident tissue macrophages each show their own defining gene signatures. The enhancer repertoire of these cells underlies both the cellular identity of a given subset of resident macrophage population and their ability to dynamically alter, in an efficient manner, their gene expression programs in response to internal and external signals. Notably, transcription is pervasive at active enhancers and enhancer RNAs, or eRNAs, are tightly correlated to regulated transcription of protein-coding genes. Furthermore, selection and establishment of enhancers is a dynamic and plastic process in which activation of intracellular signaling pathways by factors present in a macrophage's environment play a determining role. Here, we review recent studies providing insights into the distinct mechanisms that contribute to the selection and function of enhancers in macrophages and the relevance of studying these mechanisms to gain a better understanding of complex human diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".