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Subtyping Options for Microsporum canis Using Microsatellites and MLST: A Case Study from Southern Italy

. 2021 Dec 22 ; 11 (1) : . [epub] 20211222

Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic

Document type Journal Article

Grant support
204069 Charles University Research Centre
RVO: 61388971 Czech Academy of Sciences Long-term Research Development Project

Links

PubMed 35055952
PubMed Central PMC8780581
DOI 10.3390/pathogens11010004
PII: pathogens11010004
Knihovny.cz E-resources

Microsporum canis is considered one of the most common zoophilic dermatophyte species causing infections in animals and humans worldwide. However, molecular epidemiological studies on this dermatophyte are still rare. In this study, we aimed to analyse the population structure and relationships between M. canis strains (n = 66) collected in southern Italy and those isolated from symptomatic and asymptomatic animals (cats, dogs and rabbits) and humans. For subtyping purposes, using multilocus sequence typing (MLST) and multilocus microsatellite typing (MLMT), we first used a limited set of strains to screen for variability. No intraspecies variability was detected in six out of the eight reference genes tested and only the ITS and IGS regions showed two and three sequence genotypes, respectively, resulting in five MLST genotypes. All of eight genes were, however, useful for discrimination among M. canis, M. audouinii and M. ferrugineum. In total, eighteen microsatellite genotypes (A-R) were recognized using MLMT based on six loci, allowing a subdivision of strains into two clusters based on the Bayesian iterative algorithm. Six MLMT genotypes were from multiple host species, while 12 genotypes were found only in one host. There were no statistically significant differences between clusters in terms of host spectrum and the presence or absence of lesions. Our results confirmed that the MLST approach is not useful for detailed subtyping and examining the population structure of M. canis, while microsatellite analysis is a powerful tool for conducting surveillance studies and gaining insight into the epidemiology of infections due to this pathogen.

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