Metabolic reprogramming of skeletal muscle by resident macrophages points to CSF1R inhibitors as muscular dystrophy therapeutics
Bibliographic record
Abstract
The role of tissue-resident macrophages during tissue regeneration or fibrosis is not well understood, mainly due to the lack of a specific marker for their identification. Here, we identified three populations of skeletal muscle–resident myelomonocytic cells: a population of macrophages positive for lymphatic vessel endothelial receptor 1 (LYVE1) and T cell membrane protein 4 (TIM4 or TIMD4), a population of LYVE1 − TIM4 − macrophages, and a population of cells likely representing dendritic cells that were positive for CD11C and major histocompatibility complex class II (MHCII). Using a combination of parabiosis and lineage-tracing experiments, we found that, at steady state, TIM4 − macrophages were replenished from the blood, whereas TIM4 + macrophages locally self-renewed [self-renewing resident macrophages (SRRMs)]. We further showed that Timd4 could be reliably used to distinguish SRRMs from damage-induced infiltrating macrophages. Using a colony-stimulating factor 1 receptor (CSF1R) inhibition/withdrawal approach to specifically deplete SRRMs, we found that SRRMs provided a nonredundant function in clearing damage-induced apoptotic cells early after extensive acute injury. In contrast, in chronic mild injury as seen in a mouse model of Duchenne muscular dystrophy, depletion of both TIM4 − - and TIM4 + -resident macrophage populations through long-term CSF1R inhibition changed muscle fiber composition from damage-sensitive glycolytic fibers toward damage-resistant glycolytic-oxidative fibers, thereby protecting muscle against contraction-induced injury both ex vivo and in vivo. This work reveals a previously unidentified role for resident macrophages in modulating tissue metabolism and may have therapeutic potential given the ongoing clinical testing of CSF1R inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".