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To kill a bacterium, you need to think like a bacterium

2017· article· en· W4389024284 on OpenAlexafffundabout
Eric D. Brown

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBacteriaBiologyGeneChemically defined mediumMicrobial metabolismAmino acidBacterial growthBiochemistryEscherichia coliGenetics

Abstract

fetched live from OpenAlex

When bacteria are grown in media containing only carbon, nitrogen and essential salts, they shift in their metabolic activities to include the synthesis of essential amino acids, vitamins and other cofactors. Only 303 genes, for example, are essential for growth of E. coli on rich media and some 119 additional genes are required for growth on nutrient‐limited media. Hence compounds that target bacteria under nutrient‐limited conditions could serve as leads for novel antibacterial drugs. In fact, nutrient‐limited media probably provide a better proxy for the host environment. There have been many reports of impaired growth and attenuated virulence in pathogens due to mutations in vitamin, nucleobase and amino acid biosynthetic genes. Nevertheless, systematic searches for antibacterial chemicals have overwhelmingly emphasized rich media conditions. Thus, there is a considerable gap in antibacterial chemical space surveyed to date. In the Brown laboratory, we are trying to understand the potential of nutrient biosynthesis as a new and tractable target in drug‐resistant Gram‐negative pathogens. To this end, we have been screening libraries of structurally diverse synthetic compounds and natural products to find inhibitors of bacterial growth in minimal media. Compounds and extracts active in primary screens are subject to the addition of an array of key metabolites and pools thereof to identify suppressors of growth inhibition and provide hypotheses for physiological, genetic and biochemical experiments to elaborate mechanism of action. More recently, we are developing chemical and genomic platforms to understand the interaction of the nutrient biosynthesis apparatus with all aspects of bacterial physiology using systems approaches. The ultimate goal of these studies is to contribute fresh directions for new antibacterial therapies. Support or Funding Information I acknowledge operating funding from the Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada as well as a salary support from the Canada Research Chairs Program.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.008
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0240.027

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 head0.090
GPT teacher head0.407
Teacher spread0.317 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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
Published2017
Admission routes3
Has abstractyes

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