IN SILICO ANALYSIS OF THE WHOLE GENOME OF SALMONELLA ENTERICA: GENOME ASSEMBLY AND ANNOTATION

CÜNEYD YAVAŞ YUSUF ŞEFLEKÇİ RECEP ERÖZ

Northwestern Medical Journal - 2025;5(2):61-76

 

Aim: The goal is to classify Salmonella enterica using whole genome sequencing reads and explore their functional profiles. This approach simplifies resolving phylogenetic ambiguities in higher taxa compared to traditional methods. Materials and Methods: Salmonella paired-end reads (SRA: SRR27334358) were obtained from the NCBI database and analyzed for quality using FastQC v0.12.1, with low-quality reads trimmed by Trimmomatic v0.36. De novo genome assembly was performed by using Unicycler v0.4.8, with subsequent gene annotation by using RAST. TYGS was utilized for taxonomic analysis. ResFinder v.2.1 identified antimicrobial resistance genes, and PathogenFinder v.1.1 was used for pathogenicity prediction. MLST analyzed the allele profile. CRISPR regions and proteins were identified by CRISPRCasFinder, while AntiSMASH 7.0.1 determined secondary metabolites. SPIFinder detected pathogenicity islands, and the genome map was created using the CGView server. RAST performed genomic functional classification. Results: The genome, spanning 4,720,639 bp with 36 contigs, was analyzed by RAST, revealing 366 subsystems. TYGS showed a 100% dDDH with S. enteritidis ATCC 13076. The aac(6’)-Iaa gene, conferring resistance to amikacin and tobramycin, was detected. PathogenFinder predicted S. enterica as a human pathogen with a 0.942 probability. MLST revealed 100% similarity with alleles of 7 housekeeping genes of Salmonella. CRISPRFinder identified eight Type I CRISPR-Cas proteins. AntiSMASH detected two secondary metabolites: enterobactin and O-antigen. SPIFinder identified 12 SPIs across the subspecies S. Typhimurium, S. Typhi, S. Enteritidis, S. Choleraesuis, and S. Gallinarum. Conclusion: The genome showed 100% digital DNA-DNA hybridization (dDDH) with Salmonella enteritidis ATCC 13076 and was identified as a human pathogen. Recognizing pathogenic strains is crucial for timely intervention, control strategy design, and targeted vaccine development.