Breaking the culture habit: metagenomic diagnosis of companion animal skin infections
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Background
Skin infections have been described as the primary cause for presentation in veterinary small animal practices and they frequently result in prescription of both topical and systemic antibiotics. Because such infections are often secondary complications of other underlying pathologies, recurrent infections are common and can lead to multiple antibiotic exposures. This scenario creates steady selection pressure toward antibiotic resistance at the confluence of the skin (the largest mammalian organ), the bloodstream, and shared human and animal environments. This case study compares metagenomic (MGX) data with aerobic culture to evaluate diagnostic utility for simultaneous identification and characterization of pathogens, microbiomes, and resistomes of companion animal skin infections.
Results
One feline and eight canine skin swabs were analyzed with aerobic culture and traditional antimicrobial susceptibility testing (AST) and compared with MGX profiling. Veterinary laboratory diagnostic (VDL) culture and AST identified Staphylococcus aureus, S. pseudintermedius , S. schleiferi, methicillin resistant (MR) S. schleiferi (MRSS), MR S. pseudintermedius (MRSP) and Pseudomonas aeruginosa from skin swabs. MGX data described the identical bacterial pathogens recovered by aerobic culture and methicillin resistance genes mecA, mecI, mecR1 in samples for which AST confirmed MRSP and MRSS. MGX data also identified mec genes in samples without culture-based confirmation of MR phenotypes. MGX data also described multi-domain composition of microbiomes of infected skin including bacteria, fungi, viruses, phages, AMR, plasmids, and metabolic features associated with skin infections.
Conclusions
MGX data identified the identical pathogens and inferred AMR phenotypes as culture-based diagnostic testing, and additionally characterizedo multi-domain microbiota, mobile AMR elements, and metabolic features. Efforts to accelerate cures by precision medical responses depend on accelerated precision diagnostics. Challenges remain for the implementation of MGX data into veterinary diagnostic laboratory investigation and response. We demonstrate with a small case study, that MGX data can be used to complement current state of the art VDL results and potentially advance a judicious veterinary medical response regarding antibiotic administration for companion animal skin infections. In the future, simultaneous description of the polymicrobial ecology of skin infections (bacterial, viruses, phages, fungi, and even functional metabolomic features) provided by MGX data can advance epidemiology, develop new treatment strategies, accelerate diagnostics and provide data for artificial intelligence (AI) models focused on advancing veterinary diagnostics and medical treatments.
