MétaCan
Menu
Back to cohort
Record W2104283537 · doi:10.1128/aem.02066-07

Autoinducer-2 Triggers the Oxidative Stress Response in <i>Mycobacterium avium</i> , Leading to Biofilm Formation

2008· article· en· W2104283537 on OpenAlexaff
Henriette Geier, Serge Mostowy, Gerard A. Cangelosi, Marcel A. Behr, Tim Ford

Bibliographic record

VenueApplied and Environmental Microbiology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsMcGill University Health Centre
FundersNational Center for Research Resources
KeywordsBiofilmQuorum sensingMicrobiologyOxidative stressHydrogen peroxideBiologyMycobacteriumAutoinducerChemistryBacteriaBiochemistryGenetics

Abstract

fetched live from OpenAlex

Mycobacterium avium is an environmental organism and opportunistic pathogen with inherent resistance to drugs, environmental stresses, and the host immune response. To adapt to these disparate conditions, M. avium must control its transcriptional response to environmental cues. M. avium forms biofilms in various environmental settings, including drinking water pipes and potable water reservoirs. In this study, we investigated the role of the universal signaling molecule autoinducer-2 (AI-2) in biofilm formation by M. avium. The addition of the compound to planktonic M. avium cultures resulted in increased biofilm formation. Microarray and reverse transcriptase PCR studies revealed an upregulation of the oxidative stress response upon addition of AI-2. This suggests that the response to AI-2 might be related to oxidative stress, rather than quorum sensing. Consistent with this model, addition of hydrogen peroxide, a known stimulus of the oxidative stress response, to M. avium cultures resulted in elevated biofilm formation. These results suggest that AI-2 does not act as a quorum-sensing signal in M. avium. Instead, biofilm formation is triggered by environmental stresses of biotic and abiotic origins and AI-2 may exert effects on that level.

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 score0.029
Threshold uncertainty score0.523

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 head0.193
Teacher spread0.186 · 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

Citations92
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueApplied and Environmental MicrobiologySame topicBacterial biofilms and quorum sensingFrench-language works237,207