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Record W2416033144 · doi:10.1093/femsle/fnv177

Spontaneous and on point: Do spontaneous mutations used for laboratory experiments cause pleiotropic effects that might confound bacterial infection and evolution assays?

2015· review· en· W2416033144 on OpenAlexafffund
Lucas Robinson, Andrew D. S. Cameron, John Stavrinides

Bibliographic record

VenueFEMS Microbiology Letters · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Regina
FundersCanadian Institutes of Health Research
KeywordsPoint mutationBiologyGeneticsPhenotypeSelectable markerExperimental evolutionMutationAntibiotic resistanceAntibioticsMutantMutagenesisStreptomycinComputational biologyGenePlasmid

Abstract

fetched live from OpenAlex

Many selectable phenotypes in microbial systems, including antibiotic resistance, can be conferred by single point mutations. This is frequently exploited in research, where the selection and use of microbial mutants that are spontaneously resistant to antibiotics like rifampicin and streptomycin facilitate the recovery and/or quantification of a target microbe. Such mutations are commonly employed as genetic markers for in vitro and in vivo experiments, often with little consideration as to the ultimate system-level impact of these single nucleotide mutations on the physiology of the microbe. There is substantial literature on the pleiotropic effects of point mutations conferring antibiotic resistance; yet, it is unclear whether this work is considered by the research communities outside of the discipline. This review examines some of the known pleiotropic effects of point mutations that provide selectable resistance markers, and how these mutations may impact general physiology and growth in host and non-host environments.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.999
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.023
GPT teacher head0.307
Teacher spread0.284 · 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.

Study designSimulation or modeling
DomainMethods
GenreReview

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

Citations11
Published2015
Admission routes2
Has abstractyes

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