Transcriptomic Adaptation of Legionella pneumophila to Transient Heat Shock
Bibliographic record
Abstract
A natural inhabitant of freshwater microbial ecology, Legionella pneumophila is an opportunistically pathogenic bacteria that has found a niche in hot water distribution systems (HWDS) where it challenges hospitals with the spread of nosocomial infection. Superheat-and-flush is a high-temperature pasteurization which can be temporarily effective, but systems often test positive for contamination soon after pasteurization. Because of the centrality of pasteurization in HWDS sanitation schemes, an understanding of the L. pneumophila intrinsic heat shock response may help improve the strategies used to counter its proliferation and ability to trigger nosocomial outbreaks. We tested a group of strains to assess differences in heat shock tolerance between clinical, laboratory, and environmental strains of different origin. The transcriptome of a model Philadelphia-1 L. pneumophila strain in response to heat shock was determined using microarray as an exploratory analysis of the heat shock response: 401 genes were induced, including genes involved in determining protein fate and ribosome biosynthesis while 43 genes were downregulated. Mutants of 3 individual genes were tested to find their direct effects on heat shock tolerance. Deletions of dksA or rpoS , genes involved in the regulation of life cycle switching and important for surviving long-term nutrient deprivation in freshwater lowered heat shock tolerance, suggesting an overlap in the pathways required to tolerate these stressors. Surprisingly, the deletion of htpG , the 90-kilodalton heat shock protein, was found to increase the ability to survive under transient heat shock. Taken altogether, our results show that L. pneumophila exhibits most components of the conserved bacterial heat shock response. Based on this exploratory transcriptomic study, we have provided data that can act as a platform for the research of L. pneumophila 's survival to pasteurization in hot water systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".