High-methionine diet in skeletal muscle remodeling: epigenetic mechanism of homocysteine-mediated growth retardation
Bibliographic record
Abstract
Epigenetic DNA methylation (1-carbon metabolism) is crucial for gene imprinting/off-printing that ensures epigenetic memory but also generates a copious amount of homocysteine (Hcy), unequivocally. That is why during pregnancy, expectant mothers are recommended “folic acid” preemptively to avoid birth defects in the young ones because of elevated Hcy levels (i.e., hyperhomocysteinemia (HHcy)). As we know, children born with HHcy have several musculoskeletal abnormalities, including growth retardation. Here, we focus on the gut-dysbiotic microbiome implication(s) that we believe instigates the “1-carbon metabolism” and HHcy causing growth retardation along with skeletal muscle abnormalities. We test our hypothesis whether high-methionine diet (HMD) (an amino acid that is high in red meat), a substrate for Hcy, can cause skeletal muscle and growth retardation, and treatment with probiotics (PB) to mitigate skeletal muscle dysfunction. To test this, we employed cystathionine β-synthase, CBS deficient mouse (CBS+/–) fed with/without HMD and with/without a probiotic (Lactobacillus rhamnosus) in drinking water for 16 weeks. Matrix metalloproteinase (MMP) activity, a hallmark of remodeling, was measured by zymography. Muscle functions were scored via electric stimulation. Our results suggest that compared to the wild-type, CBS+/– mice exhibited reduced growth phenotype. MMP-2 activity was robust in CBS+/– and HMD effects were successfully attenuated by PB intervention. Electrical stimulation magnitude was decreased in CBS+/– and CBS+/– treated with HMD. Interestingly; PB mitigated skeletal muscle growth retardation and atrophy. Collectively, results imply that individuals with mild/moderate HHcy seem more prone to skeletal muscle injury and its dysfunction.
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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 | 0.001 | 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".