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Record W3109849117 · doi:10.3389/fimmu.2020.586659

A Bovine Enteric Mycobacterium Infection Model to Analyze Parenteral Vaccine-Induced Mucosal Immunity and Accelerate Vaccine Discovery

2020· article· en· W3109849117 on OpenAlexafffund
Antonio Facciuolo, Amy Huei‐Yi Lee, Michael J. Trimble, Neil Rawlyk, Hugh G.G. Townsend, Manjeet Bains, Natasa Arsic, Lucy M. Mutharia, Andrew Potter, Volker Gerdts, Scott Napper, Robert E. W. Hancock, Philip Griebel

Bibliographic record

VenueFrontiers in Immunology · 2020
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of British ColumbiaUniversity of GuelphSimon Fraser UniversityUniversity of Saskatchewan
FundersSimon Fraser UniversityKillam TrustsUniversity of SaskatchewanGenome British ColumbiaMinistry of Agriculture - SaskatchewanCanadian Institutes of Health ResearchGenome Canada
KeywordsImmune systemVaccinationImmunologyBiologyVaccine efficacyParatuberculosisMicrobiologyImmunityIntestinal mucosaPathogenMucosal immunityMycobacteriumMedicineBacteria

Abstract

fetched live from OpenAlex

Mycobacterial diseases of cattle are responsible for considerable production losses worldwide. In addition to their importance in animals, these infections offer a nuanced approach to understanding persistent mycobacterial infection in native host species. Mycobacteriumavium ssp. paratuberculosis (MAP) is an enteric pathogen that establishes a persistent, asymptomatic infection in the small intestine. Difficulty in reproducing infection in surrogate animal models and limited understanding of mucosal immune responses that control enteric infection in the natural host have been major barriers to MAP vaccine development. We previously developed a reproducible challenge model to establish a consistent MAP infection using surgically isolated intestinal segments prepared in neonatal calves. In the current study, we evaluated whether intestinal segments could be used to screen parenteral vaccines that alter mucosal immune responses to MAP infection. Using Silirum® – a commercial MAP bacterin – we demonstrate that intestinal segments provide a platform for assessing vaccine efficacy within a relatively rapid period of 28 days post-infection. Significant differences between vaccinates and non-vaccinates could be detected using quantitative metrics including bacterial burden in intestinal tissue, MAP shedding into the intestinal lumen, and vaccine-induced mucosal immune responses. Comparing vaccine-induced responses in mucosal leukocytes isolated from the site of enteric infection versus blood leukocytes revealed substantial inconsistences between these immune compartments. Moreover, parenteral vaccination with Silirum did not induce equal levels of protection throughout the small intestine. Significant control of MAP infection was observed in the continuous but not the discrete Peyer’s patches. Analysis of these regional mucosal immune responses revealed novel correlates of immune protection associated with reduced infection that included an increased frequency of CD335+ innate lymphoid cells, and increased expression of IL21 and IL27. Thus, intestinal segments provide a novel model to accelerate vaccine screening and discovery by testing vaccines directly in the natural host and provides a unique opportunity to interrogate mucosal immune responses to mycobacterial infections.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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