Antimicrobial resistance profiling and phylogenetic analysis of Neisseria gonorrhoeae clinical isolates from Kenya in a resource-limited setting

Abstract

Background Africa has one of the highest incidences of gonorrhoea, but not much information is available on the relatedness with strains from other geographical locations. Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is a major public health threat, with the bacteria gaining resistance to most of the available antibiotics, compromising treatment across the world. Whole-genome sequencing is an efficient way of predicting AMR determinants and their spread in the human population. Previous studies on Kenyan gonococcal samples have focused on plasmid-mediated drug resistance and fluoroquinolone resistance using Illumina sequencing. Recent advances in next-generation sequencing technologies like Oxford Nanopore Technology (ONT) have helped in the generation of longer reads of DNA in a shorter duration with lower cost. However, long-reads are error-prone. The increasing accuracy of base-calling algorithms, high throughput, error-correction strategies, and ease of using the mobile sequencer in remote areas is leading to the adoption of the MinION sequencer (ONT), for routine microbial genome sequencing. Methods To investigate whether MinION-only sequencing is sufficient for diagnosis, genome sequencing and downstream analysis like inferring phylogenetic relationships and detection of AMR in resource-limited settings, we sequenced the genomes of fourteen clinical isolates suspected to be N. gonorrhoeae from Nairobi, Kenya. The isolates were tested using standard bacteriological methods for identification, interpretted using analytical profile index and antibiotic susceptibility tests had indicated ciprofloxacin and gentamycin resistance. Using whole genome sequencing, the isolates were confirmed to be cases of N. gonorrhoeae (n=12), Additionally, we identified reads from N. meningitidis (n=2) and both of N. gonorrhoeae and Moraxella osloensis (n=3) in the sample (co-infections) respectively, which have been implicated in sexually transmitted infections in the recent years. The near-complete N. gonorrhoeae genomes (n=10) were anaysed further for mutations/factors causing AMR using an in-house database of mutations curated from the literature. We attempted to understand the basis of drug resistance using homology modelling of AMR proteins, using known structures from other bacteria. Results We observe that Ciprofloxacin resistance is associated with multiple mutations in both gyrA and parC. We identified mutations conferring tetracycline (rpsJ) and Sulfonamide (folA) resistance in all the isolates and plasmids encoding beta-lactamase and tet(M) were identified in almost all of the strains. Phylogenetic analysis clustered the nine isolates into clades containing previously sequenced genomes from Kenya and countries across the world. Conclusion Here, we demonstrate the utility of mobile DNA sequencing technology supplemented with reference-based assembly in sequence typing and elucidating the basis of AMR. Bioinformatics profiling to predict AMR can be used along with routine AMR susceptibily tests in clinics. The workflow followed in the study, including AMR mutation dataset creation and the genome identification, assembly and analysis, can be used for the genome assembly and analysis of any clinical isolate. Further studies are required to determine the utility of real-time sequencing in the outbreak investigations, diagnosis and management of infections, especially in resource-limited settings.