Phenotypic, Genotypic and DNA sequencing detection of pathogenic molds isolated from different clinical cases of immunocompromised patients

Background: Pathogenic fungi are infectious agents of great concern for immunocompromised persons. Timely and accurate identification of pathogenic molds is critical for their effective detection and treatment. This study aimed to characterize the species of mold isolated from clinical samples using phenotypic, genotypic, and molecular methods and compare them with reference sequences from international repositories. Objectives: To create a local repository of mold species infecting immunocompromised patients, undertake genetic characterization, and determine relatedness to standard strains as documented in NCBI. Methods: From February to April 2022, a total of 256 clinical samples (blood, urine, oral swabs, and sputum) were collected from Al-Sadder Medical City in Al-Najaf province. Phenotypic identification was performed using standard morphological techniques on SDA medium. Molecular identification included PCR amplification of the aspergillopepsin gene with PEPO primers and sequencing of some isolates. Results: Mold growth was positive in 88 out of 256 samples with a frequency of 34.4 percent in blood (49), urine (25), sputum (10), and oral swabs (4). A total of five genera of fungi were identified and most of them were Aspergillus spp. at 70.4 percent, followed by Penicillium (20.4%), Cladosporium (5.6%), Alternaria (2.2%), and Curvularia (1.1%). Within Aspergillus isolates, A. flavus was the most prevalent, at 69.3%. Of the 20 PCR-tested Aspergillus isolates, all were identified as A. flavus genetically. A high identity (98–99%) of the DNA sequence with A. flavus strain AFLA-EME-EG-1 obtained from NCBI was confirmed. Conclusion: The authors consider this finding, along with A. flavus being dominant in mold infections in clinical settings of immunosuppressed patients as well as the precise identification using molecular tools, underscores the need of further study. The developed local databases are relevant for clinical diagnosis.