Genomic analysis of laboratory-evolved, heat-adapted <i>Escherichia coli</i> strains
Bibliographic record
Abstract
ABSTRACT Adaptive laboratory evolution to high incubation temperatures represents a complex evolutionary problem, and each study to date performed in Escherichia coli has resulted in a different set of mutations. We performed adaptive laboratory evolution of E. coli to heat by passaging a culture at elevated temperatures for 150 days. Throughout the adaptive evolution we expressed a set of genes that induce hyper-mutagenesis. These growth conditions yielded a strain with a maximum growth temperature approximately 2 °C above that of the parental strain. We preserved evolved isolates weekly and obtained and analyzed whole-genome sequencing data for three isolates from different time points. We identified hundreds of mutations, including mutations in components of the RNA polymerase (RpoB, RpoC and RpoD), Rho, and the heat shock proteins GroES, GroEL, DnaK, ClpB, IbpA and HslU. We compared the proteomes of the starting strain and final strain grown at 37 °C and 42.5 °C and identified changes in abundance between samples for GroESL, HslVU, DnaK, ClpB and other important proteins. This study details a distinct evolutionary route towards enhanced thermotolerance, contributes to our understanding of adaptation to heat in Escherichia coli and may provide insights into heat adaptation in other organisms.
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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 | 0.000 | 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".