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Record W2999992106 · doi:10.1038/s41467-019-14042-z

Virulent coliphages in 1-year-old children fecal samples are fewer, but more infectious than temperate coliphages

2020· article· en· W2999992106 on OpenAlexaff
Aurélie Mathieu, Moïra B. Dion, Ling Deng, Denise M. Tremblay, Elisabeth Moncaut, Shiraz A. Shah, Jakob Stokholm, Karen A. Krogfelt, Susanne Schjørring, Hans Bisgaard, Dennis Sandris Nielsen, Sylvain Moineau, Marie‐Agnès Petit

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité Laval
FundersNovo Nordisk Fonden
KeywordsVirulenceFecesBiologyBacteriophageHuman viromeMicrobiologyTemperate climateInfectivityHost (biology)TemperatenessSiphoviridaeVirologyVirusEscherichia coliEcologyGeneticsMetagenomicsGene

Abstract

fetched live from OpenAlex

Bacteriophages constitute an important part of the human gut microbiota, but their impact on this community is largely unknown. Here, we cultivate temperate phages produced by 900 E. coli strains isolated from 648 fecal samples from 1-year-old children and obtain coliphages directly from the viral fraction of the same fecal samples. We find that 63% of strains hosted phages, while 24% of the viromes contain phages targeting E. coli. 150 of these phages, half recovered from strain supernatants, half from virome (73% temperate and 27% virulent) were tested for their host range on 75 E. coli strains isolated from the same cohort. Temperate phages barely infected the gut strains, whereas virulent phages killed up to 68% of them. We conclude that in fecal samples from children, temperate coliphages dominate, while virulent ones have greater infectivity and broader host range, likely playing a role in gut microbiota dynamics.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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 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

Citations90
Published2020
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicBacteriophages and microbial interactionsFrench-language works237,207