SARM Regulates CCL5 Production in Macrophages by Promoting the Recruitment of Transcription Factors and RNA Polymerase II to the <i>Ccl5</i> Promoter
Bibliographic record
Abstract
The four Toll/IL-1R domain-containing adaptor proteins MyD88, MAL, TRIF, and TRAM are well established as essential mediators of TLR signaling and gene induction following microbial detection. In contrast, the function of the fifth, most evolutionarily conserved Toll/IL-1R adaptor, sterile α and HEAT/Armadillo motif-containing protein (SARM), has remained more elusive. Recent studies of Sarm(-/-) mice have highlighted a role for SARM in stress-induced neuronal cell death and immune responses in the CNS. However, whether SARM has a role in immune responses in peripheral myeloid immune cells is less clear. Thus, we characterized TLR-induced cytokine responses in SARM-deficient murine macrophages and discovered a requirement for SARM in CCL5 production, whereas gene induction of TNF, IL-1β, CCL2, and CXCL10 were SARM-independent. SARM was not required for TLR-induced activation of MAPKs or of transcription factors implicated in CCL5 induction, namely NF-κB and IFN regulatory factors, nor for Ccl5 mRNA stability or splicing. However, SARM was critical for the recruitment of transcription factors and of RNA polymerase II to the Ccl5 promoter. Strikingly, the requirement of SARM for CCL5 induction was not restricted to TLR pathways, as it was also apparent in cytosolic RNA and DNA responses. Thus, this study identifies a new role for SARM in CCL5 expression in macrophages.
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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".