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Record W7064624710

Characterization of the interactions between mammary pathogenic Escherichia coli and bovine microbiome

2025· dissertation· en· W7064624710 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicElectrical and Electromagnetic Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEscherichia coliPathogenic bacteriaPathogenic Escherichia coliMicrobiomeBacteria
DOInot available

Abstract

fetched live from OpenAlex

Bovine mastitis is one of the most common and costliest diseases in the dairy industry, causing an economic loss of $665 million CAD annually in Canada.Mammary pathogenic Escherichia coli (MPEC) is one of the most common bovine mastitis pathogens.Despite improved hygiene on dairy farms, the prevention of MPEC mastitis remains challenging.Persistent clinical mastitis (CM) caused by MPEC is treated with antibiotics, but antimicrobial resistant MPEC have been reported, raising concerns for both animal welfare and the sustainability of the dairy industry.This thesis characterizes the interaction between MPEC and the bovine microbiome through three approaches: a comparative genomic analysis to identify genotypic markers for MPEC, an analysis of the raw milk microbiome associated with CM to identify commensals that negatively correlate with the pathogen, and a proof-of-principle study on the ability of the CRISPR-Cas9 system to target and remove antimicrobial resistance genes (ARGs) on a plasmid housed in E. coli in the bovine microbiome.We conducted a genomic analysis of MPEC isolates from CM cases and environmental isolates from dairy farms.Both MPEC and environmental isolates formed phylogenomic clades based on sequence types and H-and O-antigens but did not cluster based on mammary pathogenicity.The genes of the ferric dicitrate uptake system, Fec, fecI, fecR, and fecA were softcore genes of MPEC while they were present as shell genes in environmental E. coli isolates.This suggests that the Fec system provides a competitive advantage to E. coli in the iron-poor mammary gland environment.Thus, fec operon could be a diagnostic marker or therapeutic target for MPEC mastitis.vi Next, we characterized the bacterial community composition in raw milk from healthy and diseased quarters of Holstein dairy cows using 16S rRNA gene amplicon and metagenomic sequencing.We found that MPEC displaces other microbiome members, causing a significant decrease in microbial diversity in diseased quarters, followed by recovery post-infection.Two genera, Staphylococcus and Aerococcus, and the family Oscillospiraceae, were more abundant in healthy quarters.Among them, S. auricularis, S. haemolyticus, and A. urinaeequi were identified with genes related to antagonism against E. coli, such as lactic acid and bacteriocins.These species could optimize the microbiome to resist colonization and CM without triggering inflammation.Lastly, we adapted a conjugative CRISPR-Cas9 system from the plasmid eB-TP114::Kill1 to target the plasmid pDJBC01, which carries chloramphenicol and cefotaxime resistance genes, cat and blaCMY-2, respectively, in E. coli in the bovine microbiome.We transformed a commensal strain isolated from the feces of a healthy Holstein cow to the CRISPR-Cas9 plasmid donor and recipient strains.In vitro, conjugation assays showed that the system could eliminate up to 95.82% of the target plasmid.In vivo, Holstein bull calves colonized by the recipient strain and treated with CRISPR-Cas9 donor showed zero colony forming units (CFUs) on target selective agar within 3-5 days, compared to control groups, which maintained logCFU between 1.37 and 3.61 on the selective agar.This proof-of-principle demonstrates the high precision of the CRISPR-Cas9 system in eliminating AMR plasmids and its potential as a microbiome modulation strategy to reduce AMR associated with dairy production.Collectively, the findings offer insights into new diagnostic, prophylactic, and therapeutic strategies to reduce CM in dairy cattle, supporting the sustainability of the Canadian dairy vii industry.From a One Health perspective, this thesis contributes to a holistic understanding of how microbiome modulation can help reduce infection and AMR.viii RÉSUMÉ La mammite bovine, l'une des maladies les plus fréquentes et coûteuses dans l'industrie laitière, génère une perte économique annuelle de 665 millions de dollars canadiens au Canada.L'Escherichia coli pathogène mammaire (ECPM) est l'un des principaux agents pathogènes de la maladie.Bien que l'hygiène des fermes se soit améliorée, prévenir la mammite à ECPM reste difficile.Les cas persistants de mammite clinique causés par ECPM sont traités par antibiotiques, mais des souches résistantes aux antimicrobiens ont été observées, posant des risques pour le bien-être animal et la durabilité de l'industrie laitière.Cette thèse explore l'interaction entre ECPM et le microbiome bovin à travers trois axes: une analyse génomique comparative des isolats ECPM afin d'identifier des marqueurs génotypiques, l'étude du microbiome du lait cru associé à E. coli en mammite clinique, et une étude de preuve de concept sur l'utilisation de CRISPR-Cas9 pour cibler des plasmides de résistance aux antimicrobiens (RAM) dans le microbiome bovin.L'analyse génomique des isolats ECPM issus de cas de mammites cliniques et des isolats environnementaux d'E. coli provenant de fermes a révélé que, bien que ces deux types d'isolats forment des clades distincts selon leurs types de séquences et antigènes H et O, ils ne se regroupent pas en fonction de la pathogénicité mammaire.Les gènes du système d'absorption du dicitrate ferrique (Fec), comme fecI, fecR, et fecA, étaient présents chez les ECPM mais seulement sous forme de gènes de coque dans les isolats environnementaux.Cela suggère que le système Fec donne un avantage compétitif à E. coli dans l'environnement appauvri en fer des glandes mammaires, ce qui pourrait en faire un marqueur diagnostique ou une cible thérapeutique pour la mammite à ECPM.ix Ensuite, nous avons analysé la composition du microbiome du lait cru de vaches Holstein en bonne santé et malades, en utilisant le séquençage du gène codant pour l'ARNr 16S et le séquençage métagénomique.L'infection par E. coli entraîne une perturbation du microbiome, réduisant sa diversité dans les quartiers malades, avec une récupération progressive après l'infection.Des genres tels que Staphylococcus et Aerococcus, ainsi que la famille des Oscillospiraceae, étaient plus abondants dans les quartiers sains.Des espèces comme S. auricularis, S. haemolyticus et A. urinaeequi possédaient des gènes capables de lutter contre E. coli, tels que ceux impliqués dans la production d'acide lactique et de bactériocines, suggérant qu'elles pourraient favoriser un microbiome protecteur sans induire d'inflammation.Enfin, nous avons testé un système CRISPR-Cas9 adapté pour cibler un plasmide de résistance aux antimicrobiens, le plasmide pDJBC01, portant des gènes de résistance au chloramphénicol et au céfotaxime (cat et blaCMY-2).Nous avons utilisé une souche d'E. coli commensale isolée de fecès de vaches Holstein saines, transformée avec un plasmide CRISPR-Cas9.Les essais in vitro ont montré que le système pouvait éliminer jusqu'à 95,82 % du plasmide cible.In vivo, chez des veaux Holstein colonisés par la souche réceptrice, les traitements avec le donneur CRISPR-Cas9 ont permis d'éliminer complètement le plasmide en 3 à 5 jours, comparativement aux groupes témoins.Cette preuve de concept démontre l'efficacité du système CRISPR-Cas9 dans l'élimination des plasmides RAM et son potentiel pour moduler le microbiome et prévenir la colonisation par E. coli.Les résultats de cette thèse offrent de nouvelles perspectives pour des stratégies diagnostiques, prophylactiques et thérapeutiques contre la mammite à E. coli, contribuant à la durabilité de l'industrie laitière canadienne.Dans une approche d'une seule santé, ce travail x enrichit notre compréhension de la manière dont la modulation du microbiome peut réduire les infections à E. coli et la résistance aux antimicrobiens.xi

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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)0.001
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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
Published2025
Admission routes1
Has abstractyes

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