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Record W2910786482 · doi:10.1101/523126

Ethanol stimulates trehalose production through a SpoT-DksA-AlgU dependent pathway in <i>Pseudomonas aeruginosa</i>

2019· preprint· en· W2910786482 on OpenAlexafffund
Colleen E. Harty, Dorival Martins, Georgia Doing, Dallas L. Mould, Michelle E. Clay, Dao Nguyen, Deborah A. Hogan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute of Allergy and Infectious DiseasesAudrey and Theodor Geisel School of Medicine at DartmouthUniversity of California, San DiegoNational Institutes of HealthOhio State UniversityDirectorate for Biological SciencesSeoul National UniversityDartmouth CollegeNational Science FoundationCanadian Institutes of Health ResearchSchool of Medicine, Virginia Commonwealth UniversityVirginia Commonwealth University
KeywordsTrehaloseQuorum sensingSigma factorBiologyBiofilmPseudomonas aeruginosaBiochemistryGeneCell biologyGene expressionMicrobiologyBacteriaGeneticsPromoterVirulence

Abstract

fetched live from OpenAlex

Abstract Pseudomonas aeruginosa frequently resides among ethanol-producing microbes, making its response to these microbially-produced concentrations of ethanol relevant to understanding its biology. Our ranscriptome analysis found that the genes involved in trehalose metabolism were induced by low concentrations of ethanol, and levels of intracellular trehalose increased significantly upon growth with ethanol. The increase in trehalose was dependent on the TreYZ pathway, but not other trehalose metabolic enzymes TreS or TreA. The sigma factor AlgU (AlgT), a homolog of RpoE in other species, was required for increased expression of the treZ gene and trehalose levels, but induction was not controlled by the well-characterized proteolysis of its antisigma factor MucA. Growth with ethanol led to increased SpoT-dependent (p)ppGpp accumulation, which stimulates AlgU-dependent transcription of treZ and other AlgU-regulated genes through DksA, a (p)ppGpp and RNA polymerase binding protein. Ethanol stimulation of trehalose also required acylhomoserine lactone (AHL)-mediated quorum sensing, as induction was not observed in a Δ lasR Δ rhlR strain. A network analysis using a model, eADAGE, built from publicly available P. aeruginosa transcriptome datasets (1) provided strong support for our model that treZ and co-regulated genes are controlled by both AlgU and AHL-mediated QS (QS). Consistent with (p)ppGpp and AHL-mediated quorum sensing regulation, ethanol, even when added at the time of culture inoculation, stimulated treZ transcript levels and trehalose production in cells from post-exponential phase cultures but not from exponential phase cultures. These data highlight the integration of growth and cell density cues in the P. aeruginosa transcriptional response to ethanol. Importance Pseudomonas aeruginosa is often found with bacteria and fungi that produce fermentation products including ethanol. At concentrations similar to those produced by environmental microbes, we found that ethanol stimulated expression of trehalose biosynthetic genes and cellular levels of trehalose, a disaccharide that protects against environmental stresses. The induction of trehalose by ethanol required the alternative sigma factor AlgU through DksA and SpoT-dependent (p)ppGpp. Trehalose accumulation also required AHL quorum sensing and only occurred in post-exponential phase cultures. This work highlights how cells integrate cell-density and growth cues in their responses to products made by other microbes and a reveals a new role for (p)ppGpp in the regulation of AlgU activity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.219
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes2
Has abstractyes

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