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Record W3027413366 · doi:10.1101/2020.05.23.112573

The stringent stress response controls proteases and global regulators under optimal growth conditions in <i>Pseudomonas aeruginosa</i>

2020· preprint· en· W3027413366 on OpenAlexafffund
Daniel Pletzer, Travis M. Blimkie, Heidi Wolfmeier, Yicong Li, Arjun Baghela, Amy Huei‐Yi Lee, Reza Falsafi, Robert E. W. Hancock

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersKillam TrustsMichael Smith Health Research BCCystic Fibrosis Canada
KeywordsStringent responseMutantProteasesPseudomonas aeruginosaBiologyGuanosineTranscriptomeSerineIntracellularCell biologyTranscriptional regulationVirulenceGeneGene knockdownBiochemistryGene expressionGeneticsPhosphorylationBacteriaEnzyme

Abstract

fetched live from OpenAlex

Abstract The bacterial stringent stress response, mediated by the signaling molecule guanosine tetra-phosphate, ppGpp, has recently gained attention as being important during normal cellular growth and as potential new therapeutic target, which warrants detailed mechanistic understanding. Here, we used intracellular protein tracking in Pseudomonas aeruginosa PAO1, which indicated that RelA was bound to the ribosome, while SpoT localized at the cell poles. RNA-Seq was used to investigate the transcriptome of a ppGpp-deficient strain under non-stressful nutrient-rich broth conditions where the mutant grew at the same rate as the parent strain. In exponential growth phase, the lack of ppGpp led to >1,600 transcriptional changes (fold-change cut-off ±1.5), providing further novel insights into the normal physiological role of ppGpp. The stringent response was linked to gene expression of various proteases and secretion systems including aprA , PA0277, impA , and clpP2 . The previously observed reduction in cytotoxicity towards red blood cells, in a stringent response mutant, appeared to be due to aprA . Investigation of an aprA mutant in a murine skin infection model, showed increased survival rates of the aprA mutant consistent with previous observations that stringent-response mutants have reduced virulence. In addition, the overexpression of relA , but not induction of ppGpp with serine hydroxamate, dysregulated global transcriptional regulators as well as >30% of the regulatory networks controlled by AlgR, OxyR, LasR, and AmrZ. Together these data expand our knowledge about ppGpp and its regulatory network and role in environmental adaptation. It also confirms its important role throughout the normal growth cycle of bacteria. Significance Statement Microorganisms need to adapt rapidly to survive harsh environmental changes. Here, we showed the broad influence of the highly studied bacterial stringent stress response under non-stressful conditions that indicate its general physiological importance and might reflect the readiness of bacteria to respond to and activate acute stress responses. Using RNA-Seq to investigate the transcriptional network of Pseudomonas aeruginosa cells revealed that >30% of all genes changed expression in a stringent-response mutant under optimal growth conditions. This included genes regulated by global transcriptional regulators and novel downstream effectors. Our results help to understand the importance of this stress regulator in bacterial lifestyle under relatively unstressed conditions. As such it draws attention to the consequences of targeting this ubiquitous bacterial signaling molecule.

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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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