<i>ACTN3</i> genotype influences androgen response in skeletal muscle
Bibliographic record
Abstract
Abstract Androgens are vital for the maintenance of muscle mass and their anabolic effects are primarily exerted through the androgen receptor (AR). Accumulating evidence in humans and mice suggests that circulating androgens, AR and androgen response are influenced by ACTN3 ( α- actinin-3), also known as “the gene for speed”. One in 5 people worldwide are α-actinin-3 deficient due to homozygous inheritance of a common null polymorphism (577X) in ACTN3 . In this study, we show that α-actinin-3 deficiency decreases baseline AR in skeletal muscles of mice and humans, in both males and females, and that AR expression directly correlates with ACTN3 in a dosage dependent manner. We further demonstrate in Actn3 knockout mice that α- actinin-3 deficiency increases muscle wasting induced by androgen deprivation and reduces the muscle hypertrophic response to dihydrotestosterone and this is mediated by differential activation of pathways regulating amino acid metabolism, intracellular transport, MAPK signalling, autophagy, mitochondrial activity and calcineurin signalling. Gene set enrichment and protein analyses indicate that the absence of α-actinin-3 results in a failure to coactivate many of these pathways in response to changes in androgens, and relies on leveraging mitochondrial remodelling and calcineurin signalling to restore muscle homeostasis. We further identified 7 genes that are androgen sensitive and α-actinin-3-dependent in expression, and whose functions correspond to these processes. Our results highlight the pivotal role of α- actinin-3 in various processes associated with the regulation of protein turnover and muscle mass, and suggest that ACTN3 genotype is a genetic modifier of androgen action in skeletal muscle.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".