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Record W2944645675 · doi:10.3389/fmicb.2019.01020

Whole Genome Sequencing and Metabolomic Study of Cave Streptomyces Isolates ICC1 and ICC4

2019· article· en· W2944645675 on OpenAlexafffundabout
Jessica T. Gosse, Soumya Ghosh, Amanda Sproule, David P. Overy, Naowarat Cheeptham, Christopher N. Boddy

Bibliographic record

VenueFrontiers in Microbiology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsAgriculture and Agri-Food CanadaThompson Rivers UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSecondary metaboliteBiologyGenomeStreptomycesMetabolomeMetaboliteMetabolomicsWhole genome sequencingGenomicsSecondary metabolismGenePolyketideComputational biologyBacteriaGeneticsBioinformaticsBiochemistryBiosynthesis

Abstract

fetched live from OpenAlex

The terrestrial subsurface microbiome has gained considerable amount of interests in the recent years because of its rich potential resource for biomining novel genes possessing antimicrobial activities. In our previous study, we identified two Streptomyces isolates, designated as ICC1 and ICC4, from the Iron Curtain Cave, Chilliwack, Canada that exhibited antagonistic activities against the multidrug resistant (MDR) strains of Escherichia coli. In this study, whole genome sequences of the two isolates were sequenced by Illumina MiSeq and their genomes assembled and annotated. The genes associated with secondary metabolite production was identified and annotated using the bioinformatics platforms antiSMASH and BAGEL. ICC1 and ICC4 where then cultivated and the metabolome characterized by LC-ESI-HRMS. GNPS was used to identify metabolites from the extracts. ICC1 and ICC4 show a high level of identity with the terrestrial bacteria Streptomyces lavendulae, however they possess a greater secondary metabolite potential. This enhanced secondary metabolite potential may contribute to their enhanced fitness in the Iron Curtain Cave.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.561

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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 designObservational
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

Citations50
Published2019
Admission routes3
Has abstractyes

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