Transcriptome analyses of mouse cardiac myocytes and non-cardiomyocytes: postmitotic vs. proliferative cells
Bibliographic record
Abstract
Abstract Adult heart mostly contains long-lived postmitotic cardiomyocytes and non-cardiomyocytes that have proliferative potential. Here, we isolated cardiomyocytes and non-cardiomyocytes from young and aged mouse heart, and performed transcriptome analyses by RNA sequencing to understand the differences of gene expression in postmitotic and proliferative cells. Gene ontology analyses revealed that genes associated with inflammatory response were upregulated in aged cardiac myocytes, whereas genes including two ATP synthases in mitochondrial respiratory complex V ( Atp5l and Atp5J2 ) and two NADH dehydrogenases in complex I ( Ndufa11 and Ndufv3 ) were significantly downregulated. In aged non-cardiomyocytes, genes related to inflammatory responses were also upregulated, while genes involved in cell cycle and DNA replication process were downregulated. We also found that the expression levels of some small nucleolar RNAs (snoRNAs) are decreased cardiomyocytes with aging. snoRNAs are deeply involved in RNA modification such as pseudouridylation stabilizing ribosomal RNA (rRNA) and mRNA splicing. Therefore, the age-related reduction in snoRNA expression may lead to the destabilization of rRNA, splicing dysfunction, and ultimately a decrease in protein synthesis capacity. A comparison with transcriptome results obtained for non-cardiomyocytes suggests that the decline in the expression of mitochondria-related genes and snoRNAs accompanying aging is specific to cardiomyocytes, implying their potential utility as one of novel aging markers in postmitotic cells.
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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 | 0.000 | 0.000 |
| 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".