A new molecular marker for species-specific identification of Microsporum canis.
Anita CiesielskaPaweł StączekPublished in: Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] (2020)
Species identification of dermatophytes by conventional mycological methods based on macro- and microscopy analysis is time-consuming and has a lot of limitations such as slow fungal growth or low specificity. Thus, there is a need for the development of molecular methods that would provide reliable and prompt identification of this group of medically important fungi. The are many reports in the literature concerning PCR identification of dermatophyte species, but still, there are not many PCR assays for the separate detection of members of the genera Microsporum, especially Microsporum canis (zoophilic species) and Microsporum audouinii (anthropophilic species). The correct distinction of these species is important to determine the source of infection to implement the appropriate action to eliminate the path of infection transmission. In this paper, we present such a PCR-based method targeting velB gene that uses a set of two primers-Mc-VelB-F (5'-CTTCCCCACCCGCAACATC-3') and Mc-VelB-R (5'-TGTGGCTGCACCTGAGAGTGG-3'). The amplified fragment is specific due to the presence of (CAGCAC)8 microsatellite sequence only in the velB gene of M. canis. DNA from 153 fungal samples was used in PCR assay followed by electrophoretic analysis. The specificity of the designed set of primers was also confirmed using the online BLAST-Primer tool. The positive results were observed only in the case of M. canis isolates, and no positive results were obtained neither for other dermatophytes and non-dermatophyte fungi nor for other Eukaryotes, including the human genome sequence, as well as the representatives of bacterial and viral taxa. The developed PCR assay using the proposed Mc-VelB-F and Mc-velB-R primers can be included in the algorithm of M. canis detection in animals and humans.
Keyphrases
- real time pcr
- genetic diversity
- high throughput
- single molecule
- systematic review
- genome wide
- machine learning
- bioinformatics analysis
- sars cov
- deep learning
- gene expression
- social media
- label free
- copy number
- drug delivery
- high resolution
- healthcare
- transcription factor
- cell free
- cancer therapy
- amino acid
- high speed
- genome wide identification