Abstract
Malassezia pachydermatis is a zoophilic yeast found on the skin and in the outer ear canal of many mammals. It normally maintains a commensal lifestyle but can cause dermatitis and otitis in predisposed hosts, particularly in atopic dogs. M. pachydermatis is genetically diverse, with strains clustering into at least three phylogroups based on molecular typing, a pattern we now confirm through whole-genome sequencing (WGS). Accurate species and strain-level identification is essential for understanding its epidemiology, pathogenic potential, and response to treatment. In this study, we established Fourier Transform Infrared (FTIR) spectroscopy as a rapid, cost-effective method for distinguishing M. pachydermatis from other Malassezia species, including M. globosa, M. furfur, M. restricta, and M. sympodialis. Within M. pachydermatis, FTIR spectroscopy resolved even closely related strains with high accuracy producing clusters congruent with WGS-based phylogeny. The incorporation of an Artificial Neural Network classifier further enhanced the discriminatory power, enabling robust and automated strain assignment. These findings demonstrate the potential of FTIR spectroscopy as a practical tool for large-scale epidemiological surveillance of M. pachydermatis and for clinical and veterinary applications where strain-level identification could inform treatment and management of Malassezia-associated diseases.