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Record W2806672822 · doi:10.1038/s41598-018-24263-9

The LetA/S two-component system regulates transcriptomic changes that are essential for the culturability of Legionella pneumophila in water

2018· article· en· W2806672822 on OpenAlexafffund
Nilmini Mendis, Peter McBride, Joseph Saoud, Thangadurai Mani, Sébastien P. Faucher

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTranscriptomerpoSBiologyCell biologyMicrobiologyGeneticsGeneGene expressionPromoter

Abstract

fetched live from OpenAlex

Surviving the nutrient-poor aquatic environment for extended periods of time is important for the transmission of various water-borne pathogens, including Legionella pneumophila (Lp). Previous work concluded that the stringent response and the sigma factor RpoS are essential for the survival of Lp in water. In the present study, we investigated the role of the LetA/S two-component signal transduction system in the successful survival of Lp in water. In addition to cell size reduction in the post-exponential phase, LetS also contributes to cell size reduction when Lp is exposed to water. Importantly, absence of the sensor kinase results in a significantly lower survival as measured by CFUs in water at various temperatures and an increased sensitivity to heat shock. According to the transcriptomic analysis, LetA/S orchestrates a general transcriptomic downshift of major metabolic pathways upon exposure to water leading to better culturability, and likely survival, suggesting a potential link with the stringent response. However, the expression of the LetA/S regulated small regulatory RNAs, RsmY and RsmZ, is not changed in a relAspoT mutant, which indicates that the stringent response and the LetA/S response are two distinct regulatory systems contributing to the survival of Lp in water.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.266
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2018
Admission routes2
Has abstractyes

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