Whole-genome sequencing analysis of two heat-evolved Escherichia coli strains
Bibliographic record
Abstract
BACKGROUND: High temperatures cause a suite of problems for cells, including protein unfolding and aggregation; increased membrane fluidity; and changes in DNA supercoiling, RNA stability, transcription and translation. Consequently, enhanced thermotolerance can evolve through an unknown number of genetic mechanisms even in the simple model bacterium Escherichia coli. To date, each E. coli study exploring this question resulted in a different set of mutations. To understand the changes that can arise when an organism evolves to grow at higher temperatures, we sequenced and analyzed two previously described E. coli strains, BM28 and BM28 ΔlysU, that have been laboratory adapted to the highest E. coli growth temperature reported to date. RESULTS: We found three large deletions in the BM28 and BM28 ΔlysU strains of 123, 15 and 8.5 kb in length and an expansion of IS10 elements. We found that BM28 and BM28 ΔlysU have considerably different genomes, suggesting that the BM28 culture that gave rise to BM28 and BM28 ΔlysU was a mixed population of genetically different cells. Consistent with published findings of high GroESL expression in BM28, we found that BM28 inexplicitly carries the groESL bearing plasmid pOF39 that was maintained simply by high-temperature selection pressure. We identified over 200 smaller insertions, deletions, single nucleotide polymorphisms and other mutations, including changes in master regulators such as the RNA polymerase and the transcriptional termination factor Rho. Importantly, this genome analysis demonstrates that the commonly cited findings that LysU plays a crucial role in thermotolerance and that GroESL hyper-expression is brought about by chromosomal mutations are based on a previous misinterpretation of the genotype of BM28. CONCLUSIONS: This whole-genome sequencing study describes genetically distinct mechanisms of thermotolerance evolution from those found in other heat-evolved E. coli strains. Studying adaptive laboratory evolution to heat in simple model organisms is important in the context of climate change. It is important to better understand genetic mechanisms of enhancing thermotolerance in bacteria and other organisms, both in terms of optimizing laboratory evolution methods for various organisms and in terms of potential genetic engineering of organisms most at risk or most important to our societies and ecosystems.
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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".