Cosmic silence and viral noise: transcriptomic crosstalk in <i>Caenorhabditis elegans</i> under simulated space conditions
Bibliographic record
Abstract
ABSTRACT Spaceflight environments pose unique physiological challenges due to altered gravity and radia-tion exposure. To investigate how these abiotic stressors interact with viral infection, we analyzed the transcriptomic response of Caenorhabditis elegans acclimated to simulated microgravity (µG) and below-background muon radiation flux (BBR), upon infection with Orsay virus (OrV). Using RNA-seq, we characterized gene expression profiles across single and combined stress condi-tions. Both µG and BBR elicited distinct stress responses, including modulation of oxidative stress, lipid metabolism, and immune pathways. OrV infection alone induced robust transcrip-tional changes, but its impact was significantly attenuated when combined with either abiotic stress, suggesting antagonistic interactions. Notably, proviral genes such as drl-1 , fat-7 and hipr-1 were downregulated under BBR and µG, potentially impairing viral replication. Gene ontology analyses revealed enrichment in immune effectors, RNA metabolism, and proteostasis-related pathways, particularly under BBR. Viral load and RNA2/RNA1 ratios were reduced in both stress conditions, indicating a shift in viral replication dynamics. Moreover, genomic diversity and de-fective viral genome formation were differentially affected, with increased genetic diversity and structural variation under stress. These findings suggest that acclimation to off-Earth conditions primes the host for a dampened response to an acute viral infection, potentially through resource reallocation and transcriptional attenuation. This study provides transcriptomic insight into viral infection under space-relevant conditions, highlighting complex stress interactions and their im-plications for host-pathogen dynamics in extraterrestrial environments.
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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".