Investigation of Genetic Variation in the Collagenous Lectins of Livestock with and without Infectious Diseases
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".