Micobiota leveduriforme oral de cães: identificação fenotípica e proteômica

The increasing interaction between humans and animals facilitates the transmission of microorganisms, making the study of the oral mycobiota of dogs increasingly relevant, especially due to the development of diseases caused by fungi such as Candida auris, Malassezia pachydermatis, and Cryptococcus spp., which require effective diagnostic methods. Although phenotypic identification is widely used, it has limitations that can be overcome by advanced technologies such as the proteomic MALDI-TOF technique. In this context, the objective of this study was to isolate yeast-like fungi from the oral cavity of dogs, perform phenotypic and proteomic identification of the isolates, and compare the effectiveness of these methodologies. For this purpose, samples were collected from 38 dogs in veterinary clinics in Seropédica and Itaguaí (Rio de Janeiro, Brazil), as well as at the Veterinary Hospital of the Federal Rural University of Rio de Janeiro, resulting in 46 yeast isolates. The samples were plated on Sabouraud agar with chloramphenicol, and the isolates were identified through macromorphological, micromorphological and biochemical tests, sugar assimilation and fermentation assays, and proteomic identification using MALDI-TOF equipment. Among the 37 isolates compared, 11 showed positive species correspondence between the phenotypic and proteomic methods. In cases such as Geotrichum spp. and Rhodotorula spp., proteomic identification proved less efficient, reinforcing the choice of the phenotypic method as the most suitable for classifying the isolates. This preference is also confirmed for predominant yeasts such as Candida spp. and Malassezia spp., demonstrating the phenotypic method as a reliable tool for characterizing the oral mycobiota of dogs, given the prevalence of these genera in the population of cultivable microorganisms. Of the 37 yeasts tested proteomically, 27 were identified as the genus Candida, with 21 confirmed by phenotypic identification, highlighting the potential of the proteomic technique as an aid in identifying these microorganisms. Although the proteomic technique is quick, it still requires improvements to identify non-Candida yeasts, making it useful as a complementary diagnostic tool. On the other hand, phenotypic identification proved to be effective and reliable for characterizing the yeasts in the oral cavity of dogs.

Authors

Institutions

Publication Details

Journal
PubVet
Published
2025-05-30
DOI
https://doi.org/10.31533/pubvet.v19n05e1780
Primary Topic
Botulinum Toxin and Related Neurological Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Micobiota leveduriforme oral de cães: identificação fenotípica e proteômica

Thainá Aparecida Pereira Moura Cerqueira, Débora Carneiro Moreira, Regina Teixeira Barbieri Ramos, Clara de Almeida Mendes et al.
PubVet
Botulinum Toxin and Related Neurological Disorders
article

Micobiota leveduriforme oral de cães: identificação fenotípica e proteômica

Thainá Aparecida Pereira Moura Cerqueira, Débora Carneiro Moreira, Regina Teixeira Barbieri Ramos, Clara de Almeida Mendes, Claudete Rodrigues Paula, Sergio Gaspar de Campos, Águida Aparecida de Oliveira, Mário Mendes Bonci, Francisco de Assis Baroni, Gabriel Silva Navarro
article en

Abstract

The increasing interaction between humans and animals facilitates the transmission of microorganisms, making the study of the oral mycobiota of dogs increasingly relevant, especially due to the development of diseases caused by fungi such as Candida auris, Malassezia pachydermatis, and Cryptococcus spp., which require effective diagnostic methods. Although phenotypic identification is widely used, it has limitations that can be overcome by advanced technologies such as the proteomic MALDI-TOF technique. In this context, the objective of this study was to isolate yeast-like fungi from the oral cavity of dogs, perform phenotypic and proteomic identification of the isolates, and compare the effectiveness of these methodologies. For this purpose, samples were collected from 38 dogs in veterinary clinics in Seropédica and Itaguaí (Rio de Janeiro, Brazil), as well as at the Veterinary Hospital of the Federal Rural University of Rio de Janeiro, resulting in 46 yeast isolates. The samples were plated on Sabouraud agar with chloramphenicol, and the isolates were identified through macromorphological, micromorphological and biochemical tests, sugar assimilation and fermentation assays, and proteomic identification using MALDI-TOF equipment. Among the 37 isolates compared, 11 showed positive species correspondence between the phenotypic and proteomic methods. In cases such as Geotrichum spp. and Rhodotorula spp., proteomic identification proved less efficient, reinforcing the choice of the phenotypic method as the most suitable for classifying the isolates. This preference is also confirmed for predominant yeasts such as Candida spp. and Malassezia spp., demonstrating the phenotypic method as a reliable tool for characterizing the oral mycobiota of dogs, given the prevalence of these genera in the population of cultivable microorganisms. Of the 37 yeasts tested proteomically, 27 were identified as the genus Candida, with 21 confirmed by phenotypic identification, highlighting the potential of the proteomic technique as an aid in identifying these microorganisms. Although the proteomic technique is quick, it still requires improvements to identify non-Candida yeasts, making it useful as a complementary diagnostic tool. On the other hand, phenotypic identification proved to be effective and reliable for characterizing the yeasts in the oral cavity of dogs.

PubVetVol. 19(05)
Universidade Federal Rural do Rio de Janeiro (BR), Universidade de São Paulo (BR), Universidade São Judas Tadeu (BR)
Openalex Percentile: Top 80%
Botulinum Toxin and Related Neurological Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.