Characterization of Candida species isolated from clinical specimens: insights into virulence traits, antifungal resistance and molecular profiles.

BACKGROUND

Candida species have emerged as a significant cause of opportunistic infections. Alongside the expression of various virulence factors, the rise of antifungal resistance among Candida species presents a considerable clinical challenge.

AIM

This study aimed to identify different Candida species isolated from clinical specimens, evaluate their antifungal sensitivity patterns, identify key genes regulating virulence mechanisms using multiplex PCR and to assess any correlation between their virulence profiles and antifungal resistance patterns.

METHOD

A total of 100 Candida spp. was isolated from 630 different clinical specimens and identified to the species level. Their antifungal susceptibility was phenotypically evaluated in accordance with CLSI guidelines using the Vitek-2 Compact System. Virulence markers, including biofilm formation capacity, protease production, melanin production, coagulase production and hemolysin production, were also phenotypically detected. The genetic determinants for biofilm formation and extracellular hydrolytic enzymes were assessed using a multiplex PCR assay.

RESULTS

The prevalence of Candida spp. was 15.9%, with C. albicans (48%) and C. glabrata (16%) being the most common. C. albicans showed the highest virulence, with strong biofilm formation, and high proteinase and melanin production. Multiplex PCR revealed Hlp in 22.0%, Hwp in 80.0%, Als in 56.0%, and Sap genes in 56.0% of isolates. Virulence genes were more common in C. albicans than in non-albicans Candida (NAC). Resistance patterns significantly correlated with virulence profiles, with notable associations between flucytosine resistance and the presence of Hlp and Hwp genes.

CONCLUSION

The significant correlation between virulent markers such as germination, coagulase, hemolysin production and resistance patterns among different Candida isolates is crucial for predicting the severity and outcomes of Candida infections. This understanding aids in guiding tailored treatment strategies.