Bovine digital dermatitis (DD) is a significant cause of infectious lameness and economic losses in cattle production across the world. There is a lack of a consensus across different 16S metagenomic studies on DD-associated bacteria that may be potential pathogens of the disease. The goal of this meta-analysis was to identify a consistent group of DD-associated bacteria in individual DD lesions across studies, regardless of experimental design choices including sample collection and preparation, hypervariable region sequenced, and sequencing platform. A total of 6 studies were included in this meta-analysis. Raw sequences and metadata were identified on the NCBI sequence read archive and European nucleotide archive. Bacterial community structures were investigated between normal skin and DD skin samples. Random forest models were generated to classify DD status based on microbial composition, and to identify taxa that best differentiate DD status. Among all samples, members of Treponema, Mycoplasma, Porphyromonas, and Fusobacterium were consistently identified in the majority of DD lesions, and were the best genera at differentiating DD lesions from normal skin. Individual study and 16S hypervariable region sequenced had significant influence on final DD lesion microbial composition (P < 0.05). These findings indicate that members of Treponema, Mycoplasma, Porphyromonas, and/or Fusobacterium may have significant roles in DD pathogenesis, and should be studied further in respect to elucidating DD etiopathogenic mechanisms and developing more effective treatment and mitigation strategies.
Keywords: Treponema; cattle; digital dermatitis; meta-analysis; microbiota; skin microbiome.
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