Microgravity Impacts the Expression of Aging-Associated Candidate Gene Targets in the p53 Regulatory Network
Bibliographic record
Abstract
The extreme space environment accelerates aging and compromises human health. NASA has named five main hazards in space, including gravity changes. However, the contribution of each factor to the overall impact on biomolecular and cellular processes is not always clear. We aimed to explore the effects of microgravity on the transcriptomes of healthy volunteers, with a focus on gene expression in p53 pathways. Ten healthy men were exposed to dry immersion simulated microgravity (DI-SMG) for three weeks and blood samples were collected at five timepoints before, during and after the course of DI-SMG. T cells were purified from the peripheral blood samples and total RNA was isolated and sequenced followed by a bioinformatics analysis of the volunteers’ global transcriptomes. A differential expression of p53 network genes was observed. The expression of 30 genes involved in the p53 gene network was affected during a 3-week course of DI-SMG including classic p53 downstream target genes involved in cellular senescence: GADD45, p21, PUMA, IGF1 and other target genes. For the first time, the p53-associated cell signaling pathways and gene networks in human T cells were reported to be affected in vivo by DI-SMG. It is evident that the relatively mild effects of simulated weightlessness on the human body are sufficient to activate these pathways. Identified transcriptomic changes point toward a potential molecular overlap with aging and cellular senescence. These findings could contribute to a broader research landscape that may lead to the discovery of a new class of drugs—MG-senolytics.
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 | 0.000 | 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".