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Record W2765154940 · doi:10.1128/aem.01738-17

Interactions of the Hindgut Mucosa-Associated Microbiome with Its Host Regulate Shedding of Escherichia coli O157:H7 by Cattle

2017· article· en· W2765154940 on OpenAlexafffund
Ou Wang, Tim A. McAllister, Graham Plastow, Kim Stanford, L. Brent Selinger, Le Luo Guan

Bibliographic record

VenueApplied and Environmental Microbiology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of LethbridgeUniversity of Alberta
FundersAlberta InnovatesAlberta Innovates Bio SolutionsNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsHindgutMicrobiomeEscherichia coliBiologyHost (biology)MicrobiologyBacteriaEnterobacteriaceaeGeneticsEcologyGeneMidgut

Abstract

fetched live from OpenAlex

ABSTRACT Cattle are the primary carrier of Escherichia coli O157:H7, a foodborne human pathogen, and those shedding >10 4 CFU/gram of feces of E. coli O157:H7 are defined as supershedders (SS). This study investigated the rectoanal junction (RAJ) mucosa-associated microbiota and its relationship with host gene expression in SS and in cattle from which E. coli O157:H7 was not detected (nonshedders [NS]), aiming to elucidate the mechanisms involved in supershedding. In total, 14 phyla, 66 families, and 101 genera of RAJ mucosa-associated bacteria were identified and Firmicutes (61.5 ± 7.5%), Bacteroidetes (27.9 ± 6.4%), and Proteobacteria (5.5 ± 2.1%) were the predominant phyla. Differential abundance analysis of operational taxonomic units (OTUs) identified 2 OTUs unique to SS which were members of Bacteroides and Clostridium and 7 OTUs unique to NS which were members of Coprococcus , Prevotella , Clostridium , and Paludibacter . Differential abundance analysis of predicted microbial functions (using PICRUSt [phylogenetic investigation of communities by reconstruction of unobserved states]) revealed that 3 pathways had higher abundance (log 2 fold change, 0.10 to 0.23) whereas 12 pathways had lower abundance (log 2 fold change, −0.36 to −0.20) in SS. In addition, we identified significant correlations between expression of 19 differentially expressed genes and the relative abundance of predicted microbial functions, including nucleic acid polymerization and carbohydrate and amino acid metabolism. Our findings suggest that differences in RAJ microbiota at both the compositional and functional levels may be associated with E. coli O157:H7 supershedding and that certain microbial groups and microbial functions may influence RAJ physiology of SS by affecting host gene expression. IMPORTANCE Cattle with fecal E. coli O157:H7 at >10 4 CFU per gram of feces have been defined as the supershedders, and they are responsible for the most of the E. coli O157:H7 spread into farm environment. Currently, no method is available for beef producers to eliminate shedding of E. coli O157:H7 in cattle, and the lack of information about the mechanisms of supershedding greatly impedes the development of effective methods. This study investigated the role of the rectoanal junction (RAJ) mucosa-associated microbiome in E. coli O157:H7 shedding, and our results indicated that the compositions and functions of RAJ microbiota differed between supershedders and nonshedders. The identified relationship between the differentially abundant microbes and 19 previously identified differentially expressed genes suggests the role of host-microbial interactions involved in E. coli O157:H7 supershedding. Our findings provide a fundamental understanding of the supershedding phenomenon which is essential for the development of strategies, such as the use of directly fed microbials, to reduce E. coli O157:H7 shedding in cattle.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.004
GPT teacher head0.203
Teacher spread0.198 · 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 designBench or experimental
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

Citations35
Published2017
Admission routes2
Has abstractyes

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