Genetic determinants of heat resistance in Escherichia coli
Bibliographic record
Abstract
Escherichia coli AW1.7 is a heat resistant food isolate and the occurrence of pathogenic strains with comparable heat resistance may pose a risk to food safety. To identify the genetic determinants of heat resistance, 29 strains of E. coli that differed in their of heat resistance were analyzed by comparative genomics. Strains were classified as highly heat resistant strains, exhibiting a D60-value of more than 6 min; moderately heat resistant strains, exhibiting a D60-value of more than 1 min; or as heat sensitive. A ~14 kb genomic island containing 16 predicted open reading frames encoding putative heat shock proteins and proteases was identified only in highly heat resistant strains. The genomic island was termed the locus of heat resistance (LHR). This putative operon is flanked by mobile elements and possesses >99% sequence identity to genomic islands contributing to heat resistance in Cronobacter sakazakii and Klebsiella pneumoniae. An additional 41 LHR sequences with >87% sequence identity were identified in 11 different species of β- and γ-proteobacteria. Cloning of the full length LHR conferred high heat resistance to the heat sensitive E. coli AW1.7ΔpHR1 and DH5α. The presence of the LHR correlates perfectly to heat resistance in several species of Enterobacteriaceae and occurs at a frequency of 2% of all E. coli genomes, including pathogenic strains. This study suggests the LHR has been laterally exchanged among the β- and γ-proteobacteria and is a reliable indicator of high heat resistance in E. coli.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".