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Record W2047673499 · doi:10.1016/j.celrep.2013.12.019

Rapid Evolution of Culture-Impaired Bacteria during Adaptation to Biofilm Growth

2014· article· en· W2047673499 on OpenAlexafffund
Jon Penterman, Dao Nguyen, Erin M. Anderson, Benjamin J. Staudinger, E. Peter Greenberg, Joseph S. Lam, Pradeep K. Singh

Bibliographic record

VenueCell Reports · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsUniversity of GuelphMcGill University
FundersNational Institute of General Medical SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsBiofilmBacteriaPseudomonas aeruginosaMicrobiologyBiologyPhenotypeLipopolysaccharideMultidrug toleranceBacterial growthAdaptation (eye)Experimental evolutionBacterial outer membraneGeneGeneticsEscherichia coliImmunology

Abstract

fetched live from OpenAlex

Biofilm growth increases the fitness of bacteria in harsh conditions. However, bacteria from clinical and environmental biofilms can exhibit impaired growth in culture, even when the species involved are readily culturable and permissive conditions are used. Here, we show that culture-impaired variants of Pseudomonas aeruginosa arise rapidly and become abundant in laboratory biofilms. The culture-impaired phenotype is caused by mutations that alter the outer-membrane lipopolysaccharide structure. Within biofilms, the lipopolysaccharide mutations markedly increase bacterial fitness. However, outside the protected biofilm environment, the mutations sensitize the variants to killing by a self-produced antimicrobial agent. Thus, a biofilm-mediated adaptation produces a stark fitness trade-off that compromises bacterial survival in culture. Trade-offs like this could limit the ability of bacteria to transition between biofilm growth and the free-living state and produce bacterial populations that escape detection by culture-based sampling.

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)0.000
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 score0.023
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations74
Published2014
Admission routes2
Has abstractyes

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