Selection of low‐temperature resistance in bacteria and potential applications
Bibliographic record
Abstract
Microbial consortia may harbour an array of resistance mechanisms that facilitate survival under harsh conditions, including antifreeze and ice-nucleation proteins. Antifreeze proteins lower freezing points as well as inhibit the growth of large, potentially damaging ice crystals from small ice embryos. In contrast, ice-nucleation proteins prevent supercooling and allow ice formation at high, sub-zero temperatures. Psychrophiles and psychrotolerant microbes are typically sought in extremely cold environments. However, given that geography is unlikely to present an insurmountable barrier to microbial dispersal, we reasoned that species with low-temperature adaptations should also be present, although rare, in more temperate environments. In consequence, the challenge then becomes one of selecting for rare microbes present in a larger community. Following the introductory commentary, we demonstrate that both freeze-thaw survival and ice-affinity selection can be used to identify microbes, which demonstrate low-temperature resistance, from enrichments derived from temperate environments. Selection resulted in a drastic decrease in cell abundance and diversity, allowing the isolation of a subset of resistant microbes. Depending on the origin of the consortia, these resistant microbes demonstrated cross-tolerance to osmotic stress, or a high proportion of antifreeze and/or ice-nucleation protein activities. Both types of ice-associating proteins presumably facilitate microbial survival at low temperatures. These proteins, as well as molecules that maintain osmotic balance, are also of commercial interest, with applications in the food, energy and medical industries. In addition, the resistant phenotypes described here provide a glimpse into the breadth of strategies microbes use to survive and thrive at low temperatures.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".