The neurorepellent SLIT2 inhibits LPS-induced proinflammatory signaling in macrophages
Bibliographic record
Abstract
Macrophages are important mediators of immune responses with critical roles in the recognition and clearance of pathogens, as well as in the resolution of inflammation and wound healing. The neuronal guidance cue SLIT2 has been widely studied for its effects on immune cell functions, most notably directional cell migration. Recently, SLIT2 has been shown to directly enhance bacterial killing by macrophages, but the effects of SLIT2 on inflammatory activation of macrophages are less known. Using RNA sequencing analysis, quantitative polymerase chain reaction, and enzyme-linked immunosorbent assay, we determined that in murine bone marrow-derived macrophages challenged with the potent proinflammatory mediator lipopolysaccharide (LPS), exposure to the bioactive N-terminal fragment of SLIT2 (NSLIT2) suppressed production of proinflammatory cytokines interleukin (IL)-6 and IL-12 and concurrently increased the anti-inflammatory cytokine IL-10. We found that NSLIT2 inhibited LPS-induced MyD88- and TRIF-mediated signaling cascades and did not inhibit LPS-induced internalization of Toll-like receptor 4 (TLR4), but instead inhibited LPS-induced upregulation of macropinocytosis. Inhibition of macropinocytosis in macrophages attenuated LPS-induced production of proinflammatory IL-6 and IL-12 and concurrently enhanced anti-inflammatory IL-10. Taken together, our results indicate that SLIT2 can selectively modulate macrophage response to potent proinflammatory stimuli, such as LPS, by attenuating proinflammatory activation and simultaneously enhancing anti-inflammatory activity. Our results highlight the role of macropinocytosis in proinflammatory activation of macrophages exposed to LPS. Given that LPS-producing bacteria cause host illness through synergistic direct bacterial infection and excessive LPS-induced systemic inflammation, our work suggests a novel therapeutic role for SLIT2 in combatting the significant morbidity and mortality of patients with Gram-negative bacterial sepsis.
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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) | 0.001 | 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".