Activin A–Expressing Polymorphonuclear Myeloid-Derived Suppressor Cells Infiltrate Skeletal and Cardiac Muscle and Promote Cancer Cachexia
Bibliographic record
Abstract
Cachexia is a major cause of death in cancer and leads to wasting of cardiac and skeletal muscle, as well as adipose tissue. Various cellular and soluble mediators have been postulated in driving cachexia; however, the specific mechanisms behind this muscle wasting remain poorly understood. In this study, we found polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) to be critical for the development of cancer-associated cachexia. Significant expansion of PMN-MDSCs was observed in the cardiac and skeletal muscles of cachectic murine models. Importantly, the depletion of this cell subset, using depleting anti-Ly6G Abs, attenuated this cachectic phenotype. To elucidate the mechanistic involvement of PMN-MDSCs in cachexia, we examined major mediators, that is, IL-6, TNF-α, and arginase 1. By employing a PMN-MDSC-specific Cre-recombinase mouse model, we showed that PMN-MDSCs were not maintained by IL-6 signaling. In addition, PMN-MDSC-mediated cardiac and skeletal muscle loss was not abrogated by deficiency in TNF-α or arginase 1. Alternatively, we found PMN-MDSCs to be critical producers of activin A in cachexia, which was noticeably elevated in cachectic murine serum. Moreover, inhibition of the activin A signaling pathway completely protected against cardiac and skeletal muscle loss. Collectively, we demonstrate that PMN-MDSCs are active producers of activin A, which in turn induces cachectic muscle loss. Targeting this immune/hormonal axis will allow the development of novel therapeutic interventions for patients afflicted with this debilitating syndrome.
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".