Gpr18 agonist dampens inflammation, enhances myogenesis, and restores muscle function in models of Duchenne muscular dystrophy
Bibliographic record
Abstract
Introduction: Muscle wasting in Duchenne Muscular Dystrophy is caused by myofiber fragility and poor regeneration that lead to chronic inflammation and muscle replacement by fibrofatty tissue. Our recent findings demonstrated that Resolvin-D2, a bioactive lipid derived from omega-3 fatty acids, has the capacity to dampen inflammation and stimulate muscle regeneration to alleviate disease progression. This therapeutic avenue has many advantages compared to glucocorticoids, the current gold-standard treatment for Duchenne Muscular Dystrophy. However, the use of bioactive lipids as therapeutic drugs also faces many technical challenges such as their instability and poor oral bioavailability. Methods: Here, we explored the potential of PSB-KD107, a synthetic agonist of the resolvin-D2 receptor Gpr18, as a therapeutic alternative for Duchenne Muscular Dystrophy. Results and discussion: We showed that PSB-KD107 can stimulate the myogenic capacity of patient iPSC-derived myoblasts in vitro. RNAseq analysis revealed an enrichment in biological processes related to fatty acid metabolism, lipid biosynthesis, small molecule biosynthesis, and steroid-related processes in PSB-KD107-treated mdx myoblasts, as well as signaling pathways such as Peroxisome proliferator-activated receptors, AMP-activated protein kinase, mammalian target of rapamycin, and sphingolipid signaling pathways. In vivo, the treatment of dystrophic mdx mice with PSB-KD107 resulted in reduced inflammation, enhanced myogenesis, and improved muscle function. The positive impact of PSB-KD107 on muscle function is similar to the one of Resolvin-D2. Overall, our findings provide a proof-of concept that synthetic analogs of bioactive lipid receptors hold therapeutic potential for the treatment of Duchenne Muscular Dystrophy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".