TNFα and IFNγ cooperate for efficient pro- to anti-inflammatory transition of macrophages during muscle regeneration
Bibliographic record
Abstract
IFNγ is traditionally known as a proinflammatory cytokine with diverse roles in antimicrobial and antitumor immunity. Yet, findings regarding its sources and functions during the regeneration process following a sterile injury are conflicting. Here, we show that natural killer (NK) cells are the main source of IFNγ in regenerating muscle. Beyond this cell population, IFNγ production is limited to a small population of T cells. We further show that NK cells do not play a major role in muscle regeneration following an acute injury or in dystrophic mice. Surprisingly, the absence of IFNγ per se also has no effect on muscle regeneration following an acute injury. However, the role of IFNγ is partially unmasked when TNFα is also neutralized, suggesting a compensatory mechanism. Using transgenic mice, we showed that conditional inhibition of IFNGR1 signaling in muscle stem cells or fibro-adipogenic progenitors does not play a major role in muscle regeneration. In contrast to common belief, we found that IFNγ is not present in the early inflammatory phase of the regeneration process but rather peaks when macrophages are acquiring an anti-inflammatory phenotype. Further transcriptomic analysis suggests that IFNγ cooperates with TNFα to regulate the transition of macrophages from pro- to anti-inflammatory states. The absence of the cooperative effect of these cytokines on macrophages, however, does not result in significant regeneration impairment likely due to the presence of other compensatory mechanisms. Our findings support the arising view of IFNγ as a pleiotropic inflammatory regulator rather than an inducer of the inflammatory response.
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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 | 0.001 |
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".