Fidelity of a Bacterial DNA Polymerase in Microgravity, a Model for Human Health in Space
Bibliographic record
Abstract
Long-term space missions will expose crew members, their cells as well as their microbiomes to prolonged periods of microgravity and ionizing radiation, environmental stressors for which almost no earth-based organisms have evolved to survive. Despite the importance of maintaining genomic integrity, the impact of these stresses on DNA polymerase-mediated replication and repair has not been fully explored. DNA polymerase fidelity and replication rates were assayed under conditions of microgravity generated by parabolic flight and compared to earth-like gravity. Upon commencement of a parabolic arc, primed synthetic single-stranded DNA was used as a template for one of two enzymes (Klenow fragment exonuclease+/−; with and without proofreading exonuclease activity, respectively) and were quenched immediately following the 20 s microgravitational period. DNA polymerase error rates were determined with an algorithm developed to identify experimental mutations. In microgravity Klenow exonuclease+ showed a median 1.1-fold per-base decrease in polymerization fidelity for base substitutions when compared to earth-like gravity (p = 0.02), but in the absence of proofreading activity, a 2.4-fold decrease was observed (p = 1.98 × 10−11). Similarly, 1.1-fold and 1.5-fold increases in deletion frequencies in the presence or absence of exonuclease activity (p = 1.51 × 10−7 and p = 8.74 × 10−13), respectively, were observed in microgravity compared to controls. The development of this flexible semi-autonomous payload system coupled with genetic and bioinformatic approaches serves as a proof-of-concept for future space health research.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".