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Record W2618734322 · doi:10.25148/etd.fidc001220

Elucidating The Role of MifS-MifR Two-Component System in Regulating Pseudomonas aeruginosa Pathogenicity

2016· dissertation· en· W2618734322 on OpenAlexfundno aff
Gorakh Tatke

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyNational Institute of General Medical SciencesLeonard M. Miller School of MedicineUniversité de LausanneFlorida International UniversityUniversity of MiamiConcordia UniversityNational Institutes of HealthNational Science Foundation
KeywordsVirulencePseudomonas aeruginosaOperonBiologyMicrobiologyResponse regulatorQuorum sensingPathogenicity islandHistidine kinaseAntibiotic resistanceGeneTwo-component regulatory systemGeneticsMutantAntibioticsBacteria

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa is a Gram-negative, metabolically versatile, opportunistic pathogen that exhibits a multitude of virulence factors, and is extraordinarily resistant to a gamut of clinically significant antibiotics. This ability is in part mediated by two-component systems (TCS) that play a crucial role in regulating virulence mechanisms, metabolism and antibiotic resistance. Our sequence analysis of the P. aeruginosa PAO1 genome revealed the presence of two open reading frames, mifS and mifR, which encodes putative TCS proteins, a histidine sensor kinase MifS and a response regulator MifR, respectively. This two-gene operon was found immediately upstream of the poxAB operon, where poxB encodes a chromosomal ß-lactamase, hinting at the role of MifSR TCS in regulating antibiotic resistance. However, loss of mifSR had no effect on the antibiotic resistance profile when compared to P. aeruginosa parent PAO1 strain. Subsequently, our phenotypic microarray data (BioLOG) and growth profile studies indicated the inability of mifSR mutants to grow in α-ketoglutarate (α-KG), a key tricarboxylic acid (TCA) cycle intermediate, as a sole carbon source. To date, very little is known about the physiology of P. aeruginosa when provided with α-KG as its sole carbon source and the role of MifS and MifR TCS in virulence. Importantly, in the recent years, α-KG has gained notoriety for its newly identified role as a signaling molecule in addition to its conventional role in metabolism. This led us to hypothesize that MifSR TCS is involved in α-KG utilization and virulence in P. aeruginosa. Using mifS, mifR and mifSR clean in-frame deletion strains, our study demonstrates that the MifSR TCS modulates the expression P. aeruginosa kgtP (PA5530) and pcaT (PA0229) genes encoding putative α-KG permeases. In addition, our study shows that the MifSR-regulation of these transporters requires functional sigma factor RpoN (σ54). Loss of mifSR in the presence of α-KG, resulted in differential regulation of P. aeruginosa key virulence determinants including biofilm formation, motility, cell cytoxicity and the production of pyocyanin and pyoverdine. Involvement of multiple regulators and transporters suggests the presence of an intricate circuitry in the transport of α-KG and its importance in P. aeruginosa survival. This is further supported by the α-KG-dependent MifSR regulation of multiple virulence mechanisms. Simultaneous regulation of multiple mechanisms involved in P. aeruginosa pathogenesis suggests a complex mechanism of MifSR action. Understanding the physiological cues and regulation would provide a better stratagem to fight often indomitable P. aeruginosa infections.

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 score0.001
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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
Published2016
Admission routes1
Has abstractyes

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