Age, sex, and frailty modify the expression of common reference genes in skeletal muscle from ageing mice
Bibliographic record
Abstract
Changes in gene expression with age are typically normalised to constitutively expressed reference genes (RGs). However, RG expression may be affected by age or overall health and most studies use only male animals. We investigated whether expression of common RGs (Gapdh, Gusb, Rplp0, B2m, Tubb5, Rpl7l1, Hprt, Rer1) was affected by age, sex and/or overall health (frailty index) in skeletal muscle from young (4-mos) and aged (25–26-mos) mice. Standard RG selection programs recommended Gapdh (RefFinder/Genorm/NormFinder) or Rpl7l1 (BestKeeper) without considering age and sex. Analysis of raw Cq values showed only Rplp0 was stable in both sexes at both ages. When qPCR data were normalised to Rplp0, age affected RG expression, especially in females. For example, Hprt expression declined with age (Hprt=9.8 ×10-2 ± 4.7 ×10-2 vs. 6.5 ×10-3 ± 8.8 ×10-4; mean±SEM), while Gusb expression increased (6.0 ×10-4 ± 5.5 ×10-5 vs. 1.7 ×10-3 ± 3.1 ×10-4; n = 5/group; p < 0.05). These effects were not seen in males. Tubb5 and Gapdh were not affected by age or sex when normalised to Rplp0. Similar results were seen with normalisation by Gapdh or the Rplp0/Gapdh pair. Interestingly, RG expression was graded not only by age but by frailty. These data demonstrate that age, sex, and frailty of animals must be carefully considered when selecting RGs to normalise mRNA abundance data.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".