Systemic Genome Correlation Loss as a Central Characteristic of Spaceflight
Bibliographic record
Abstract
ABSTRACT Spaceflight exposes the human body to a unique combination of stressors—microgravity, radiation, and confinement—that induces multisystemic physiological dysregulation. Traditional transcriptomic analyses have focused on differential expression to identify key genes, yet this approach fails to explain why astronauts experience systemic fragility despite often subtle changes in gene abundance. Here, we present a comprehensive meta-analysis of 10 independent transcriptomic and genomic datasets ( N = 136) from the NASA Open Science Data Repository. By shifting focus from gene abundance to gene-gene correlation topology, we identify Systemic Genome Correlation Loss as a central biosignature of spaceflight. We show that the regulatory architecture of the transcriptome undergoes a profound decoherence in microgravity, shifting the global correlation distribution toward stochasticity ( p < 10 −15 ). This phenomenon is universal across tissues and independent of gene variance. We identify a massive population of 760 genes that maintain stable expression levels but lose over 500 regulatory connections each, outnumbering canonical differentially expressed genes by three-to-one. Finally, we demonstrate that cells preserve the connectivity of survival-critical DNA repair networks preferentially while allowing mitochondrial and synaptic networks to shatter. These findings suggest that astronaut health risks are driven by the entropic decay of regulatory synchronization, proposing a new paradigm for countermeasure development focused on network stabilization rather than pathway inhibition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".