Integrating clinical, data-driven and in-vitro approaches to the study of host-pathogen interactions in bovine digital dermatitis

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Genotypic, pedigree and phenotypic clinical data are already available for a population of 3,286 Holstein dairy animals. A new population of 500 dairy heifers will be prospectively monitored. We will be inspecting feet monthly and record presence of digital dermatitis (DD) lesions. DD resistance phenotypes and a novel DD progression phenotype will be developed. Blood samples for genotyping, whole genome sequencing and serum harvesting will be collected. Individual animal data will be analysed to determine genetic variants associated with the DD-related phenotypes. We will also derive genomic breeding values for each animal and DD phenotype. Foot skin biopsies will be performed on 24 animals genetically resistant to DD that will remain healthy throughout the study and 48 genetically susceptible animals (including recovered and chronically infected). Foot skin biopsy samples will be processed for total bulk RNA-sequencing in order to define DD resistance and progression transcriptomic signatures. We will perform single cell RNA-Seq in a subset of samples to dissect which cells are the key players driving DD resistance and progression. We will isolate fibroblasts and keratinocytes from cattle foot skin of the three respective biopsied groups and will co-incubate them with both a poly-treponemal mixture and an individual treponemes species. After co-incubation we will undertake RNA sequencing to determine global differential mRNA expression. The cattle foot skin culture model will be combined with a transepithelial cell migration assay to assess migration of granulocytes through bovine foot skin keratinocytes and fibroblasts. We will prioritise candidate genes and genetic variants for DD using a combined data-driven and biology driven approach, and will validate key findings using in vitro techniques. Outcomes from the previous steps will be integrated in a series of simulation studies to determine optimal biology-driven selective breeding strategies for DD control.