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Record W2956606079 · doi:10.1007/s10295-019-02198-y

Advances in microbial culturing conditions to activate silent biosynthetic gene clusters for novel metabolite production

2019· review· en· W2956606079 on OpenAlexafffund
Hailey Tomm, Lorena Ucciferri, Avena C. Ross

Bibliographic record

VenueJournal of Industrial Microbiology & Biotechnology · 2019
Typereview
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsGeneBiologyBacteriaComputational biologyGenomeMetaboliteSecondary metaboliteEnzymeBiochemistryBiotechnologyGenetics

Abstract

fetched live from OpenAlex

Natural products (NPs) produced by bacteria and fungi are often used as therapeutic agents due to their complex structures and wide range of bioactivities. Enzymes that build NPs are encoded by co-localized biosynthetic gene clusters (BGCs), and genome sequencing has recently revealed that many BGCs are "silent" under standard laboratory conditions. There are numerous methods used to activate "silent" BGCs that rely either upon altering culture conditions or genetic modification. In this review, we discuss several recent microbial cultivation methods that have been used to expand the scope of NPs accessible in the laboratory. These approaches are divided into three categories: addition of a physical scaffold, addition of small molecule elicitors, and co-cultivation with another microbe.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.002
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 head0.067
GPT teacher head0.334
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
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

Citations78
Published2019
Admission routes2
Has abstractyes

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