Rapid identification of clinical common invasive fungi via a multi-channel real-time fluorescent polymerase chain reaction melting curve analysis

The incidence of invasive fungal infections (IFIs) has recently increased, and early and accurate diagnosis of IFIs is important for the rational selection of antifungal drugs with high efficacy. We developed a method for rapid and accurate clinical diagnosis of IFIs and provide a reference for personalized drug treatment. We designed and screened fungal internal transcribed spacer regions with universal primers and designed 8 TaqMan detection probes to establish a multi-channel real-time fluorescent polymerase chain reaction (PCR) melting curve analysis (MCA) method. The sensitivity, specificity, and reproducibility of this method were investigated using standard fungal strains and clinical isolates. Candidemia was detected using the MCA method. The limit of detection and assay cut-off (melting temperature [Tm]) for Candida albicans were 0.05 pg/μL and 66.50 °C; Candida glabrata were 0.1 pg/μL and 66.25 °C; Candida tropicalis were 0.1 pg/μL and 60.15 °C; Candida krusei were 0.1 pg/μL and 72.15 °C; Candida parapsilosis were 0.2 pg/μL and 63.10 °C; Candida guilliermondii were 0.1 pg/μL and 61.84 °C; Cryptococcus neoformans were 0.1 pg/μL and 65.50 °C; Aspergillus flavus were 0.05 pg/μL and 71.50 °C; Aspergillus terreus, Aspergillus fumigatus, and Aspergillus niger were 0.05 pg/μL and 76.80 °C. Analytical specificity was evaluated using 13 clinical pathogens including Streptococcus pneumoniae, Staphylococcus aureus, and Haemophilus influenzae, etc. No false-positive results were obtained for any of these samples. The MCA method can detect and identify different candidemia simulations. The limit detection concentration of C albicans was 44 cfu/mL, C glabrata was 73 cfu/mL, C tropicalis was 29 cfu/mL, C parapsilosis was 21 cfu/mL, C krusei was 71 cfu/mL, and C guilliermondii was 53 cfu/mL. The multi-channel real-time fluorescence PCR melting curve analysis displayed high sensitivity and specificity in detecting various clinically invasive fungi. Furthermore, it simultaneously detected the genera Candida, Cryptococcus, and Aspergillus and identified Candida at the species level. Our method can facilitate early and accurate clinical diagnosis and personalized medication regimens.