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Record W6982603783

Investigation of Genetic Variation in the Collagenous Lectins of Livestock with and without Infectious Diseases

2018· dissertation· en· W6982603783 on OpenAlexfundno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaRussian Science Foundation
KeywordsMissense mutationInfectious disease (medical specialty)AlleleInnate immune systemGeneGenetic variationIn silicoNonsense mutationImmune system
DOInot available

Abstract

fetched live from OpenAlex

Infectious diseases in livestock are a major source of economic loss, decreased welfare, and antimicrobial usage. Typical interventions rely on treatment of the host and/or environmental modifications to reduce pathogen exposure and disease occurrence and/or severity. Various host genetic factors influence the resistance of animals to infectious diseases. In particular, mutations in genes of the immune system can alter disease susceptibility. Collagenous lectins (CLs) are pattern recognition receptors of the innate immune system that contribute to disease resistance by binding surface glycans of bacteria and other potentially pathogenic organisms. Studies in humans and animals have shown that mutations in certain CL genes are associated with infectious diseases. The main objectives of this thesis were to further investigate genetic variation in CLs in cattle, horses, and pigs, and their relationship to infectious disease resistance. Pooled, targeted next-generation sequencing of the CL genes identified 43 missense mutations in cattle, 11 of which were predicted to impact protein structure. In horses, 1 nonsense and 43 missense mutations were identified, including 14 predicted to be functionally relevant. In particular, one missense mutation in the collagen-like domain of MBL1 was found that is similar to a triple-helix disrupting mutation in human MBL2 associated with susceptibility to infectious disease. Allele frequencies were compared to identify alleles (74 in cattle, 113 in horses) associated with infectious diseases. Additional in silico analysis of the equine variants associated with infectious diseases identified 2 variants predicted to impact miRNA binding, 8 variants that impacted transcription factor binding sites, and 1 missense variant. In pigs, expression quantitative trait locus (eQTL) analysis identified 298 eQTLs in innate immune genes, 74 of which were genotyped in 1013 pigs (592 healthy, 421 with infectious disease). Variants that altered expression of these genes were associated with Mycoplasma, E. coli, swine influenza virus, and porcine reproductive and respiratory syndrome virus infection. These studies identified polymorphisms in CL genes that are associated with infectious diseases of livestock. These alleles represent potential candidates for genetic selection for enhanced resistance to infectious diseases of livestock, and expand our understanding of the roles of collagenous lectins in innate immunity.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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