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Identification of <i>Streptomyces coelicolor</i> Proteins That Are Differentially Expressed in the Presence of Plant Material

2003· article· en· W2106950540 on OpenAlexafffund
Patrick Langlois, Sylvie Bourassa, Guy G. Poirier, Carole Beaulieu

Bibliographic record

VenueApplied and Environmental Microbiology · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsCentre hospitalier de l'Université LavalUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStreptomyces coelicolorBiochemistryPeptide mass fingerprintingProteomeBiologyBacteriaPentose phosphate pathwayLemna minorChemistryProteomicsMetabolismGlycolysisGeneMutantGenetics

Abstract

fetched live from OpenAlex

Streptomyces coelicolor and Lemna minor were used as a model to study the modulation of bacterial gene expression during plant-streptomycete interactions. S. coelicolor was grown in minimal medium with and without L. minor fronds. Bacterial proteomes were analyzed by two-dimensional gel electrophoresis, and a comparison of the two culture conditions resulted in identification of 31 proteins that were induced or repressed by the presence of plant material. One-half of these proteins were identified by peptide mass fingerprinting by using matrix-assisted laser desorption ionization-time of flight mass spectrometry. The induced proteins were involved in energetic metabolism (glycolysis, pentose phosphate pathway, oxidative phosphorylation), protein synthesis, degradation of amino acids, alkenes, or cellulose, tellurite resistance, and growth under general physiological or oxidative stress conditions. The repressed proteins were proteins synthesized under starvation stress conditions. These results suggest that root exudates provide additional carbon sources to the bacteria and that physiological adaptations are required for efficient bacterial growth in the presence of plants.

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 score0.090
Threshold uncertainty score0.128

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 head0.009
GPT teacher head0.172
Teacher spread0.163 · 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

Citations38
Published2003
Admission routes2
Has abstractyes

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