Genomic Insights into Antimicrobial Resistance in Salmonella typhi: A Bioinformatics-Based Surveillance Model from Public Datasets with Implications for Resource-Limited Settings

Antimicrobial resistance (AMR) in Salmonella typhi represents an escalating global health challenge, particularly in regions with limited capacity for surveillance and treatment. This study investigates the genetic diversity and AMR mechanisms of S. typhi strains using publicly available genomic data. Twenty genomes were retrieved from GenBank and analyzed to identify resistance genes and genetic variations. The analysis focused on key AMR determinants, including blaTEM (beta-lactam resistance), qnrS (quinolone resistance), and aac(3)-I (aminoglycoside resistance), assessing their distribution across isolates. Phylogenetic analysis revealed substantial genetic diversity and indicated clonal dissemination of strains with similar resistance profiles. Mutation screening of gyrA and parC genes associated with fluoroquinolone resistance identified recurrent mutations, underscoring their role in resistance development. Bioinformatics tools such as BLAST+, Prokka, ResFinder, and iTOL were employed for sequence alignment, gene annotation, AMR gene detection, and phylogenetic reconstruction. The findings demonstrate the effectiveness of bioinformatics approaches in AMR surveillance, especially in resource-constrained settings where direct sample collection is often impractical. This study highlights the pervasive presence of AMR genes in S. typhi and reinforces the value of genomic surveillance in tracking resistance trends and informing targeted public health interventions. The research offers a novel and efficient model for AMR monitoring and provides foundational insights into resistance mechanisms in S. typhi, with implications for regions affected by AMR, such as Northeast Nigeria.