Temperature Sensitivity Conferred by <i>ligA</i> Alleles from Psychrophilic Bacteria upon Substitution in Mesophilic Bacteria and a Yeast Species
Bibliographic record
Abstract
We have assembled a collection of 13 psychrophilic ligA alleles that can serve as genetic elements for engineering mesophiles to a temperature-sensitive (TS) phenotype. When these ligA alleles were substituted into Francisella novicida, they conferred a TS phenotype with restrictive temperatures between 33 and 39°C. When the F. novicida ligA hybrid strains were plated above their restrictive temperatures, eight of them generated temperature-resistant variants. For two alleles, the mutations that led to temperature resistance clustered near the 5' end of the gene, and the mutations increased the predicted strength of the ribosome binding site at least 3-fold. Four F. novicida ligA hybrid strains generated no temperature-resistant variants at a detectable level. These results suggest that multiple mutations are needed to create temperature-resistant variants of these ligA gene products. One ligA allele was isolated from a Colwellia species that has a maximal growth temperature of 12°C, and this allele supported growth of F. novicida only as a hybrid between the psychrophilic and the F. novicida ligA genes. However, the full psychrophilic gene alone supported the growth of Salmonella enterica, imparting a restrictive temperature of 27°C. We also tested two ligA alleles from two Pseudoalteromonas strains for their ability to support the viability of a Saccharomyces cerevisiae strain that lacked its essential gene, CDC9, encoding an ATP-dependent DNA ligase. In both cases, the psychrophilic bacterial alleles supported yeast viability and their expression generated TS phenotypes. This collection of ligA alleles should be useful in engineering bacteria, and possibly eukaryotic microbes, to predictable TS phenotypes.
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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 | 0.001 |
| Research integrity | 0.000 | 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".