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Record W4286560385 · doi:10.3389/fmicb.2022.853703

Prophage Diversity Across Salmonella and Verotoxin-Producing Escherichia coli in Agricultural Niches of British Columbia, Canada

2022· article· en· W4286560385 on OpenAlexafffundabout
Karen Fong, Yu Tong Lu, Thomas Brenner, Justin Falardeau, Siyun Wang

Bibliographic record

VenueFrontiers in Microbiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProphageBiologyGeneticsSalmonella entericaSalmonellaMicrobiologyPlasmidEscherichia coliGeneVirologyBacteriophageBacteria

Abstract

fetched live from OpenAlex

Prophages have long been regarded as an important contributor to the evolution of Salmonella and Verotoxin-producing E. coli (VTEC), members of the Enterobacteriaceae that cause millions of cases of foodborne illness in North America. In S . Typhimurium, prophages provide many of the genes required for invasion; similarly, in VTEC, the Verotoxin-encoding genes are located in cryptic prophages. The ability of prophages to quickly acquire and lose genes have driven their rapid evolution, leading to highly diversified populations of phages that can infect distantly-related bacterial hosts. To defend against foreign genetic materials (i.e., phages), bacteria have evolved Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) immunity, consisting of variable spacer regions that match short nucleic acid sequences of invaders previously encountered. The number of spacer regions varies widely amongst Enterobacteriaceae , and there is currently no clear consensus if the accumulation of spacers is linked to genomic prophage abundance. Given the immense prophage diversity and contribution to bacterial host phenotypes, we analyzed the prophage sequences within 118 strains of Salmonella and VTEC, 117 of which are of agricultural origin. Overall, 130 unique prophage sequences were identified and they were found to be remarkably diverse with <50% nucleotide similarity, particularly with the Gifsy-1 group which was identified in several Salmonella serovars and interestingly, a strain of VTEC. Additionally, we identified a novel plasmid-like phage that carried antibiotic resistance and bacteriocin resistance genes. The strains analyzed carried at least six distinct spacers which did not possess homology to prophages identified in the same genome. In fact, only a fraction of all identified spacers (14%) possessed significant homology to known prophages. Regression models did not discern a correlation between spacer and prophage abundance in our strains, although the relatively high number of spacers in our strains (an average of 27 in Salmonella and 19 in VTEC) suggest that high rates of infection may occur in agricultural niches and be a contributing driver in bacterial evolution. Cumulatively, these results shed insight into prophage diversity of Salmonella and VTEC, which will have further implications when informing development of phage therapies against these foodborne pathogens.

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 score0.028
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.007
Science and technology studies0.0060.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations12
Published2022
Admission routes3
Has abstractyes

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