Sectm1a Facilitates Protection against Inflammation-Induced Organ Damage through Promoting TRM Self-Renewal
Bibliographic record
Abstract
Tissue-resident macrophages (TRMs) are sentinel cells for maintaining tissue homeostasis and organ function. In this study, we discovered that lipopolysaccharide (LPS) administration dramatically reduced TRM populations and suppressed their self-renewal capacities in multiple organs. Using loss- and gain-of-function approaches, we define Sectm1a as a novel regulator of TRM self-renewal. Specifically, at the earlier stage of endotoxemia, Sectm1a deficiency exaggerated acute inflammation-induced reduction of TRM numbers in multiple organs by suppressing their proliferation, which was associated with more infiltrations of inflammatory monocytes/neutrophils and more serious organ damage. By contrast, administration of recombinant Sectm1a enhanced TRM populations and improved animal survival upon endotoxin challenge. Mechanistically, we identified that Sectm1a-induced upregulation in the self-renewal capacity of TRM is dependent on GITR-activated T helper cell expansion and cytokine production. Meanwhile, we found that TRMs may play an important role in protecting local vascular integrity during endotoxemia. Our study demonstrates that Sectm1a contributes to stabling TRM populations through maintaining their self-renewal capacities, which benefits the host immune response to acute inflammation. Therefore, Sectm1a may serve as a new therapeutic agent for the treatment of inflammatory 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".