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Record W3107332736 · doi:10.1093/jas/skaa278.474

PSI-8 Effect of breed on the abundance and expression of Shiga toxin in Escherichia coli from the recto-anal junction of feedlot beef cattle

2020· article· en· W3107332736 on OpenAlexaff
Zhe Pan, Yanhong Chen, Michael Gaenzle, Tim A. McAllister, Leluo Guan

Bibliographic record

VenueJournal of Animal Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
Fundersnot available
KeywordsBiologyBeef cattleShiga toxinSTX2Escherichia coliMicrobiologyBreedAnimal scienceFeedlotImmune systemCalprotectinVeterinary medicineFood scienceGeneImmunologyGeneticsInternal medicineMedicine

Abstract

fetched live from OpenAlex

Abstract Shiga toxin (Stx) is the main virulence factor of Shiga toxin-producing E. coli (STEC), and ruminants including cattle are the main reservoir of STEC. This study aimed to assess whether cattle breed affects the abundance and expression of Stx and to determine whether the expression of host immune genes can serve as markers of STEC colonization. In total, 143 rectal tissue and content samples were collected from feedlot beef steers in 2014 (n = 71) and 2015 (n = 72) composed of three breeds (Angus, Charolais and Kinsella Composite) with differing feed efficiency. The abundance and expression of Stx1 and Stx2 by STEC associated with rectal tissue was quantified by qPCR and reverse-transcription-qPCR, respectively. Four immune genes (MS4A1, CCL21, CD19, and LTB), previously reported to be down-regulated in super-shedder cattle (i.e., > 104 cfu g-1) were selected and their expression was evaluated using qPCR. The abundance of stx1 and stx2 differed (P < 0.001) among breeds in rectal content samples collected in 2014, while no such difference was detected for 2015 samples. Correlation analysis showed that the expression of stx2 was negatively correlated with MS4A1 (R = -0.56, P = 0.05) and positively associated with LTB (R = 0.60, P = 0.05). The random forest model revealed that the expression of selected immune genes could be used as indicators of Stx2 expression and potential STEC colonization with prediction accuracy of MS4A1 >LTB >CCL21 >CD19. Our results indicate that the abundance and expression of Stx could be affected by cattle breed and the year of sampling, suggesting that host genetics and environment influence STEC colonization. The relationship between the expression of host genes associated with immunity and Stx by STEC expression suggests a role of host in STEC colonization, but further validation is needed to confirm the predictiveness of identified markers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.036
GPT teacher head0.253
Teacher spread0.217 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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