Identification of a Novel Antisepsis Pathway: Sectm1a Enhances Macrophage Phagocytosis of Bacteria through Activating GITR
Bibliographic record
Abstract
The inability to effectively control invading bacteria or other pathogens is a major cause of multiple organ dysfunction and death in sepsis. As the first-line defense of the immune system, macrophages play a crucial role in the removal of pathogens during sepsis. In this study, we define secreted and transmembrane 1A (Sectm1a) as a novel ligand of glucocorticoid-induced TNFR (GITR) that greatly boosts macrophage phagocytosis and bactericidal capacity. Using a global Sectm1a knockout (KO) mouse model, we observed that Sectm1a deficiency significantly suppressed phagocytosis and bactericidal activity in both recruited macrophages and tissue-resident macrophages, which consequently aggravated bacterial burden in the blood and multiple organs and further increased systemic inflammation, leading to multiple organ injury and increased mortality during polymicrobial sepsis. By contrast, treatment of septic mice with recombinant Sectm1a protein (rSectm1a) not only promoted macrophage phagocytosis and bactericidal activity but also significantly improved survival outcome. Mechanistically, we identified that Sectm1a could bind to GITR in the surface of macrophages and thereby activate its downstream PI3K-Akt pathway. Accordingly, rSectm1a-mediated phagocytosis and bacterial killing were abolished in macrophages by either KO of GITR or pharmacological inhibition of the PI3K-Akt pathway. In addition, rSectm1a-induced therapeutic effects on sepsis injury were negated in GITR KO mice. Taken together, these results uncover that Sectm1a may represent a novel target for drug development to control bacterial dissemination during sepsis or other infectious diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".