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Record W2624713630 · doi:10.1139/cjm-2017-0244

Hyperbiofilm phenotype of <i>Pseudomonas aeruginosa</i> defective for the PlcB and PlcN secreted phospholipases

2017· article· en· W2624713630 on OpenAlexaffvenue
Shawn Lewenza, Laetitia Charron‐Mazenod, Shirin Afroj, Erik van Tilburg Bernardes

Bibliographic record

VenueCanadian Journal of Microbiology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsAthabasca UniversityUniversity of Calgary
Fundersnot available
KeywordsBiofilmPseudomonas aeruginosaMicrobiologyPhospholipaseSecretionBacterial outer membraneBiologyBacteriaPseudomonadaceaeMutantEnzymePhenotypePseudomonadalesBiochemistryGeneGeneticsEscherichia coli

Abstract

fetched live from OpenAlex

Biofilms are dense communities of bacteria enmeshed in a protective extracellular matrix composed mainly of exopolysaccharides, extracellular DNA, proteins, and outer membrane vesicles (OMVs). Given the role of biofilms in antibiotic-tolerant and chronic infections, novel strategies are needed to block, disperse, or degrade biofilms. Enzymes that degrade the biofilm matrix are a promising new therapy. We screened mutants in many of the enzymes secreted by the type II secretion system (T2SS) and determined that the T2SS, and specifically phospholipases, play a role in biofilm formation. Mutations in the xcp secretion system and in the plcB and plcN phospholipases all resulted in hyperbiofilm phenotypes. PlcB has activity against many phospholipids, including the common bacterial membrane lipid phosphatidylethanolamine, and may degrade cell membrane debris or OMVs in the biofilm matrix. Exogenous phospholipase was shown to reduce aggregation and biofilm formation, suggesting its potential role as a novel enzymatic treatment to dissolve biofilms.

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 score0.005
Threshold uncertainty score0.010

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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