Leveraging Illumina iSeq100 for Whole Genome Sequencing of Salmonella Typhi: a practical approach for resource-limited setting

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Nepal, recently, has seen some increase in sequencing capabilities but faces hurdles for optimum utilisation. However, these hurdles could be alleviated using Illumina iSeq100. Therefore, this study aimed at performing whole-genome-sequencing of bacteria utilising iSeq100. Six banked isolates of S Typhi bacterial were selected, extracted for DNA, confirmed by qPCR and then sequenced in Illumina iSeq100 at 200pM. The consensus was generated by reference-based assembly, mapping onto S . Typhi CT18. These consensus genomes and coverage parameters were compared to data from HiSeq and NextSeq. The raw reads were also evaluated using pathogenwatch to observe genotype, mutations and resistance genes. The coverage parameters (coverage width and depth) of the genomes from this study were compared to same genomes sequenced using HiSeq/NextSeq. The average coverage width (96.81%) and depth (63.75x) of genomes sequenced in iSeq100 were comparable to that of HiSeq/NextSeq (width: 98.72% and depth: 69.87x). The genotypes detected, number of SNPs and genetic determinants of AMR genes were identical. The data from bacterial whole-genome-sequencing using the Illumina iSeq100 is equally informative when compared to some high-end sequencers. Thus, the primary goal of this study is to advocate for optimum utlisation of iSeq100, still ensuring for a high-quality data. Biological sciences/Biological techniques Biological sciences/Microbiology Biological sciences/Molecular biology iSeq100 Nepal Salmonella whole genome sequencing Figures Figure 1 Figure 2 Introduction Several phenotypic and molecular methods of pathogen characterization are conventionally utilised to monitor and control the spread of infections, as part of most infection and outbreak management protocols. [ 1 ] However, these conventional control approaches are slow and resource intensive while failing to distinguish between closely related strain, resistance and virulence factors, mostly due to limited genomic resolution and requiring multiple assays. [ 2 – 5 ] Therefore, for obtaining comprehensive information on phylogeny and improve outbreak investigations, full genomic information are essential. [ 2 ] Bacterial whole genome sequencing (WGS) is one of the most promising approaches of this development, which helps to improve our understanding of epidemiology and pathogenesis of bacterial infections.[ 6 ] This approach due to its comprehensiveness on pathogen biology, mutations, drug resistance, transmission, evolution, community profiling, clinical metagenomics and pathogen discovery are already transforming the research landscape in microbiology.[ 7 ] This is evident by the large number of whole genomes stored in public repositories. [ 8 , 9 ] These approaches have been known to be used in public health surveillance and control for several bacterial infections from Escherichia coli, Campylobacter jejuni, Legionella pneumophila and Mycobacterium tuberculosis , outbreaks and monitor the source of healthcare associated infections. [ 10 – 14 ] Furthermore, continuous development in high throughput sequencing have pushed current clinical microbiology field, due to its vast potential in identification of infectious agents, detection of pathogenicity, antimicrobial resistance, evolution and epidemiological surveillance. [ 10 , 15 – 17 ] For instance, identification of single nucleotide polymorphisms (SNPs) can differentiate the evolution: with low frequency of SNPs indicating bacteria are genetically similar and recently originated from the same source. [ 2 ] Further, significance of WGS has been more evident, by its application in COVID-19 pandemic. [ 18 , 19 ] Although, WGS approach can comprehend all genomic information, it is only being utlilised in niches, because clinicians and researchers have shown reluctance, due higher costs, data interpretation and burdensome process of early sequencing technologies. [ 1 , 20 , 21 ] Further, some sequencing instruments have high operational costs, require large multiplexing for effective and cost continuous sequencing. [ 22 ] Nevertheless, the encouraging prospects are recent advancements in sequencing technologies (for instance, Sequencing by Synthesis from Illumina) and investigation tools have made the platform to have high throughput, increase output, decrease analysis time and reduced cost. [ 23 , 24 ] Nepal, recently, has also seen some increase in sequencing potential, with some sites with next generation sequencing capabilities in Illumina, Nanopore and Thermo Fisher platforms. [ 25 , 26 ] However, not all the sequencers are at full potential due to technical sophistication, supply chain issue, limitation in capacity and funding and as a result, in most studies, samples are transported outside. [ 27 ] Nonetheless, these barriers of cost and technical complexity can be alleviated with optimum utilisation of Illumina iSeq 100, which is an inexpensive benchtop next-generation sequencer that minimizes the up-front instrument costs while maximizing simplicity of use and capability in obtaining bacterial whole genome, in country. [ 28 , 29 ] This study aimed at leveraging existing Illumina iSeq100 for whole genome sequencing (WGS) of bacteria isolated from Dhulikhel Hospital Kathmandu University Hospital. Methods Bacterial Isolates Selection For this study, 6 banked isolates of Salmonella enterica serovar Typhi ( S. Typhi) were selected randomly from Surveillance for Enteric Fever in Asia Project (SEAP). These isolates were previously sequenced in Illumina HiSeq and NextSeq platforms, as a part of the project. These glycerol stock isolates were subcultured on MacConkey Agar (Oxoid, Cat: CM0007) and reconfirmed biochemically and serologically. An average of 30 isolated colonies were selected for each isolate. Processing, DNA Extraction and qPCR The selected colonies were resuspended in 800ul of sterile normal saline, vortexed and centrifuged for 10 minutes at 8000 rpm, to obtain cell pellets. The supernatant was discarded and then the pellets were resuspended in 200ul sterile 1X Phosphate Buffer Saline (PBS). The pellets were subsequently revotexed and 100ul of the resuspension was taken for extraction. The extraction was done as per manufacturer manual, using Qiagen DNeasy Blood and Tissue Kit. (Qiagen, Cat: 69504), with slight modification in elution. The final elution was done in 50ul, which was then used for re-elution to increase the yield. The S . Typhi isolated were re-confirmed by qPCR targeting Ty21a gene, using forward primer: 5’-CGCGAAGTCAGAGTCGACATAG-3’, reverse primer 5’-AAGACCTCAACGCCGATCAC-3’ and probe [6-FAM] CATTTGTTCTGGAGCAGGCTGACGG [BHQ1a-Q]. The assays were run in BioRad CFX 96 Dx qPCR machine with reaction mix that included: 10ul of 2X master mix (Quantabio Perfecta, Cat: 95113-012), 0.8ul of forward primer (400nM), 0.8 ul of reverse primer (400nM), 0.4ul of probe (200nM), 4ul of nuclease free water and 4ul of DNA template. The samples with Ct values < 20.00 were selected for library preparation. Library Preparation and Whole Genome Sequencing The concentration of the samples was checked by Qubit Fluorometer 4 (Invitrogen, Cat: Q33226) using dsDNA HS Kit (Invitrogen, Cat: Q32854). All the samples were diluted to have input amount of 75ng. The libraries were subsequently prepared using NEBNext Ultra II FS DNA Library Preparation Kit (New England Biolabs, Cat: 7805L). The libraries were quality checked for library size and concentration using Agilent Tapestation 4150 using D5000 HS Assay Kit (Agilent Technologies, Cat: 5067–5593). The average library size was 222.5bp. The isolates were loaded in two batches of two and four isolates at loading concentration of 200pM. The sequencing was done using pair ended barcoding primers at 2x146bp. Assembly, Consensus and Bioinformatical Investigation The raw genomic data were trimmed for adapters (fastp), assembled (bowtie2, samtools), consensus generated, and coverage was calculated (bamCoverage, samtools depth, awk) and viewed in Integrated Genome Browser. The consensus was built using reference-based assembly and mapping on reference S . Typhi CT18. (Fig. 1 ) These consensus genomes and coverage parameters were compared to genomic data of same isolates previously sequenced using Illumina HiSeq (S3 to S6) and NextSeq (S1 and S2). The sequenced genomic data, from HiSeq, were downloaded from European Nucleotide Archive (ENA) for Samples S3 to S6 (Accession Number: ERR5311412, ERR5311447, ERR5311415 and ERR5375985), while unpublished genomic data was used for Samples S1 and S2. The raw reads were evaluated using Pathogenwatch (v22.3.8) to observe for genotype detected, number of Single Nucleotide Polymorphisms (SNPs), antimicrobial resistance determinants. Pathogenwatch is a platform which facilitates rapid identification of genomic markers of antimicrobial resistance (AMR) and includes latest analytics for typing along with epidemiological contextualization with public genomic data. [ 21 ] Ethics approval and consent to participate This study investigated banked bacterial isolates and did not contact human subjects or obtain data associated with them. The ethical approval was obtained from Institutional Review Committee at Kathmandu University School of Medical Sciences, Nepal (ref: 115/24). Results Extraction and qPCR The S . Typhi genomic DNA when evaluated for Ty21a gene, had average Ct value of 15.14, while the concentrations were variable, ranging from 1.58ng/ul to 20.6ng/ul. Whole Genome Sequencing After the reference-based assembly, the S . Typhi genomes were mapped over the S . Typhi CT18 strain in Integrated Genome Browser, with gaps observed in from ~1,033,000bp to 1,047,000bp and ~1,908,000bp to ~1,933,000bp and ~3,053,000bp to ~3,059000bp. (Figure 2) Coverage Parameters The coverage parameters (coverage width and depth) of the genomes from this study were compared to same genomes sequenced using Illumina HiSeq and NextSeq. The average coverage width (96.81%) and depth (63.75x) of genomes sequenced in iSeq100 were comparable (Table 1) to that of HiSeq and NextSeq (width: 98.72% and depth: 69.87x). Cost Evaluation The sequencing was performed in two batches (two samples in first batch and four in second batch). When the collective cost of human resource, molecular, library preparation and sequencing reagents and overhead was considered, the cost of sequencing bacterial genome in Illumina iSeq100 would be ~$158 per gb when 4 samples are pooled and ~$312 per gb when 2 samples are pooled. Comparison of Genotype, AMR genes and SNPs When the genomes sequenced from Illumina iSeq100 and HiSeq/NextSeq were compared, the genotypes detected, number of SNPs and genetic determinants of AMR genes were identical as shown in Table 2. Discussion Despite the potential, widespread adoption of WGS has been hindered by various challenges, including high costs, time-consuming protocols, and data interpretation complexities. [30] The iSeq100 represents a significant advancement in sequencing technology, offering a cost-effective and user-friendly solution that overcomes many barriers associated with traditional NGS platforms.[29] This platform has been known to be more useful when used with amplicon-based sequencing wet-lab approach. [31-33] Therefore, this study addresses these challenges by using a low-cost next generation sequencer, Illumina iSeq100 for WGS of S. Typhi . The coverage parameters of our approach were high (63.75x) and comparable to results from Illumina HiSeq and NextSeq (69.87x), which we realise could depend on the pooling. Though the threshold on the coverage depth depends upon the target pathogen, studies have estimated that 40x is required for high-quality detection of virulence genes, while 4x coverage is sufficient for target gene detection and coverage width should preferably be between 50x – 100x. [34-36] In addition, some studies have discussed that coverage depth of 20x-30x is essential for antibiotic resistance gene. [37,38] Another study that utilised iSeq100 for WGS of bacterial isolates observed coverage depth of 17x - 149x with an average of 59x, which is slightly lower than our observation. [28] Similarly, for coverage width, at least 90% is recommended, which is well below the observed average coverage width of 96.81% of this study. [39] This high coverage width is required for optimum quality data during bioinformatics analysis, as this threshold helps achieve correct gene identification. [38] The genome gaps observed in the genomic regions (~1,033,000bp to 1,047,000bp and ~1,908,000bp to ~1,933,000bp and ~3,053,000bp to ~3,059,000bp) correspond to STY_RS04890 gene (DUF2213 domain-containing protein), WP_001197078.1 (baseplate J/gp47 family protein), STY_RS09550 (phage minor head protein), STY_RS09735 (tyrosine-type recombinase/integrase), STY_RS15100 (DUF1460 domain-containing protein) and STY_RS15130 (site-specific integrase). [40] These gaps could be a result of genomic rearrangements, horizontal gene transfer as well as sequencing and alignment errors and presence of low complexity regions. [41] The pooling of isolates with varying genome size results in unequal sample coverage even with equimolar pooling, while some protocols do not mandatorily require equimolar pooling. [28,42] Further, pooling also depends on the complexity of the genome. For instance, though Escherichia coli and Salmonella enterica ( S. enterica ) have similar genome size, E. coli is more complex and has variable accessory genomes. Therefore, comparatively larger number of S. enterica genomes can be pooled. [42] However, the cost per run also depends on pooling. The number of samples pooled would be inversely proportional to cartridge/flow cell requirement, while the amount of library preparation reagents remains the same. The average cost of run per gb is $60.65 for HiSeq (using HiSeq SBS Kit V4 kit - 250 cycles PE 2x125bp, which has now been discontinued), while for iSeq would be ~$130 per gb when 4 samples are pooled and ~$87 when 6 samples are pooled. [29,43] The cost, at our site, is slightly higher (~$158 per gb when 4 samples are pooled), possibly because cost of sequencing is dependent on region and respective supply chain logistics. [44] Unfortunately, we cannot confirm the number of samples pooled for sequencing in HiSeq and NextSeq, to make more informed comparisons. While discussing low cost NGS, Oxford Nanopore provides a great argument. The flongle, which gives output upto 2Gb, costs ~$90, while iSeq100 cartridge/flowcell costs ~$630 with 1.2Gb output. [42] However, the shorter shelf life of flow cell and lower accuracy of individuals reads are counter intuitive. In this study, both genotypes (4.3.1.1 and 4.3.1.2) belonging to lineage I and II of H58 strain, were detected. Both strains had been first detected in a pediatric study conducted in Kathmandu.[45] Furthermore, the point mutations (S83F and D87N) were observed in quinolone resistance determining region of gyrA gene, which is associated with fluoroquinolone resistance. [46-48] Similarly, mutations were observed in genes conferring resistance to ampicillin (blaTEM), chloramphenicol (catA1) and resistance to co-trimoxazole (sul) and resistance to trimethoprim (dfrA7). [49-52] In this study, the genotypes, genomic determinants and number of SNPs among the S . Typhi genomes were identical, as expected, in genomes sequenced from iSeq100 and HiSeq/NextSeq. Our findings have demonstrated that iSeq platform is able to generate high-quality, accurate data for bacterial WGS, and could cost-effective addition to the genomic investigation capability. The genomes generated from iSeq have comparable coverage parameters to those obtained from more established commercial sequencing platforms like the Illumina HiSeq or NextSeq, which are not available, nor viable (due to its large number of sample requirements, reagents cost, annual maintenance cost among others) in Nepal. The findings of this study support premise that iSeq100 is well suited to small laboratories with low sample throughput, which is mostly the case in Nepal due to small molecular and genomic research ecosystem. Looking ahead, the findings of this study pave the way for broader applications of the Illumina iSeq100 platform in clinical microbiology, outbreak surveillance, and antimicrobial resistance monitoring in Nepal. Future research should focus on further optimizing sample preparation protocols, expanding sequencing capacity, and integrating bioinformatics tools to further enhance the utility and accessibility of WGS technologies in pathogen investigation. This approach will uplift the current national sequencing capabilities to full potential. Conclusion The data from bacterial whole genome sequencing using the Illumina iSeq100 is equally informative when compared to other high-end sequencers. We have shown that Illumina iSeq, though primarily designed for microbial metagenomics, is able to perform whole genome sequencing of bacteria when combined with a modified wet-lab protocol. This approach will also provide high-quality genomes for other pathogens of human health importance, with some limitations on throughput. Thus, this study also advocates for optimum utilisation of iSeq100 while still ensuring a high data quality. Declarations Additional Information The authors declare no competing interests. This study was funded and supported by Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital Kathmandu University Hospital, Nepal. Author Contribution Conceptualization: RS and NK; Methodology: NK; Investigation: NK, SRN; Resources: NK and DT; Data curation: NK, SRN, AS, SBS; Writing-original draft preparation: NK and RS; Writing-review and editing: NK, SRN, AS, SBS, DT and RS; Supervision: RS. All authors have read and agreed to the published version of the manuscript. Acknowledgement We gratefully thank Jason R. Andrews, Kesia Esther da Silva and the SEAP team for providing accessions numbers (European Nucleotide Archive) for the S. Typhi isolates. We are also thankful to all the research staffs at Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital Kathmandu University Hospital, Nepal. Data Availability The raw genomic data, from Illumina iSeq100, can be obtained from National Center for Biotechnology Information’s BioProject PRJNA1110744 (Accession: SRX24530188 to SRX24530193). References Vourli, S., Kontos, F., Pournaras, S. (2021). WGS for Bacterial Identification and Susceptibility Testing in the Clinical Lab. In: Moran-Gilad, J., Yagel, Y. (eds) Application and Integration of Omics-powered Diagnostics in Clinical and Public Health Microbiology. 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Genom. 6 (2), 1–13. https://doi.org/10.1099/mgen.0.000335 (2020) Parkhill J, Dougan G, James KD, Thomson NR, Pickard D, Wain J, Churcher C, Mungall KL, Bentley SD, Holden MT, Sebaihia M, Baker S, Basham D, Brooks K, Chillingworth T, Connerton P, Cronin A, Davis P, Davies RM, Dowd L, White N, Farrar J, Feltwell T, Hamlin N, Haque A, Hien TT, Holroyd S, Jagels K, Krogh A, Larsen TS, Leather S, Moule S, O'Gaora P, Parry C, Quail M, Rutherford K, Simmonds M, Skelton J, Stevens K, Whitehead S, Barrell BG. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18. Nature. 2001 Oct 25;413(6858):848-52. doi: 10.1038/35101607. PMID: 11677608. Nagarajan, N., Pop, M. Sequence assembly demystified. Nat Rev Genet 14 , 157–167 (2013). https://doi.org/10.1038/nrg3367 AND Kaas RS, Leekitcharoenphon P, Aarestrup FM, Lund O. Solving the problem of comparing whole bacterial genomes across different sequencing platforms. PLoS One. 2014 Aug 11;9(8):e104984. doi: 10.1371/journal.pone.0104984. PMID: 25110940; PMCID: PMC4128722. Mitchell PK, Wang L, Stanhope BJ, Cronk BD, Anderson R, Mohan S, Zhou L, Sanchez S, Bartlett P, Maddox C, DeShambo V, Mani R, Hengesbach LM, Gresch S, Wright K, Mor S, Zhang S, Shen Z, Yan L, Mackey R, Franklin-Guild R, Zhang Y, Prarat M, Shiplett K, Ramachandran A, Narayanan S, Sanders J, Hunkapiller AA, Lahmers K, Carbonello AA, Aulik N, Lim A, Cooper J, Jones A, Guag J, Nemser SM, Tyson GH, Timme R, Strain E, Reimschuessel R, Ceric O, Goodman LB. Multi-laboratory evaluation of the Illumina iSeq platform for whole genome sequencing of Salmonella, Escherichia coli and Listeria. Microb Genom. 2022 Feb;8(2):000717. doi: 10.1099/mgen.0.000717. PMID: 35113783; PMCID: PMC8942033. La Trobe University Genomics. Available at : https://www.latrobe.edu.au/__data/assets/pdf_file/0005/724748/LTU-NGS-prices-AH.pdf Accessed on: 7 September 2024 Getchell M, Wulandari S, de Alwis R, Agoramurthy S, Khoo YK, Mak TM, Moe L, Stona AC, Pang J, Momin MHFHA, Amir A, Andalucia LR, Azzam G, Chin S, Chookajorn T, Arunkumar G, Hung DT, Ikram A, Jha R, Karlsson EA, Le Thi MQ, Mahasirimongkol S, Malavige GN, Manning JE, Munira SL, Trung NV, Nisar I, Qadri F, Qamar FN, Robinson MT, Saloma CP, Setk S, Shirin T, Tan LV, Dizon TJR, Thayan R, Thu HM, Tissera H, Xangsayarath P, Zaini Z, Lim JCW, Maurer-Stroh S, Smith GJD, Wang LF, Pronyk P; Asia Pathogen Genomics Initiative (Asia PGI) consortium. Pathogen genomic surveillance status among lower resource settings in Asia. Nat Microbiol. 2024 Sep 24. doi: 10.1038/s41564-024-01809-4. Epub ahead of print. PMID: 39317773. Holt, K. E. et al. High-throughput bacterial SNP typing identifies distinct clusters of Salmonella Typhi causing typhoid in Nepalese children. BMC Infect. Dis. 2010. 10, 144. Jacoby GA. Mechanisms of resistance to quinolones. Clin Infect Dis. 2005;41:7. Brown JC, Shanahan PM, Jesudason MV, Thomson CJ, Amyes SG. Mutations responsible for reduced susceptibility to 4 quinolones in clinical isolates of multi resistant Salmonella typhi in India. J Antimicrob Chemother. 1996;37:891–900. Wain J, Hoa NT, Chinh NT, Vinh H, Everett MJ, Diep TS, et al. Quinolone resistant Salmonella typhi from Vietnam; molecular basis of resistance and clinical response to treatment. Clin Infect Dis. 1997;25:1404–10. Tamang MD, Oh JY, Seol SY, Kang HY, Lee JC, Lee YC, Cho DT, Kim J. Emergence of multidrug-resistant Salmonella enterica serovar Typhi associated with a class 1 integron carrying the dfrA7 gene cassette in Nepal. Int J Antimicrob Agents. 2007 Oct;30(4):330-5. doi: 10.1016/j.ijantimicag.2007.05.009. Epub 2007 Jul 12. PMID: 17629465. Kongsoi S, Changkwanyeun R, Yokoyama K, Nakajima C, Changkaew K, Suthienkul O, Suzuki Y. Amino acid substitutions in GyrA affect quinolone susceptibility in Salmonella typhimurium. Drug Test Anal. 2016 Oct;8(10):1065-1070. doi: 10.1002/dta.1910. Epub 2015 Oct 30. PMID: 26514939. Yusof NY, Norazzman NII, Zaidi NFM, Azlan MM, Ghazali B, Najib MA, Malik AHA, Halim MAHA, Sanusi MNSM, Zainal AA, Aziah I. Prevalence of Antimicrobial Resistance Genes in Salmonella Typhi: A Systematic Review and Meta-Analysis. Trop Med Infect Dis. 2022 Sep 27;7(10):271. doi: 10.3390/tropicalmed7100271. PMID: 36288012; PMCID: PMC9611315. da Silva KE, Tanmoy AM, Pragasam AK, Iqbal J, Sajib MSI, Mutreja A, Veeraraghavan B, Tamrakar D, Qamar FN, Dougan G, Bogoch I, Seidman JC, Shakya J, Vaidya K, Carey ME, Shrestha R, Irfan S, Baker S, Luby SP, Cao Y, Dyson ZA, Garrett DO, John J, Kang G, Hooda Y, Saha SK, Saha S, Andrews JR. The international and intercontinental spread and expansion of antimicrobial-resistant Salmonella Typhi: a genomic epidemiology study. Lancet Microbe. 2022 Aug;3(8):e567-e577. doi: 10.1016/S2666-5247(22)00093-3. Epub 2022 Jun 21. PMID: 35750070; PMCID: PMC9329132. Tables Table 1 : Comparison of coverage depth and width between Illumina HiSeq/NextSeq and Illumina iSeq100 Illumina iSeq100 Illumina HiSeq/NextSeq Sample Average Coverage Depth Coverage Width (%) at 20x Average Coverage Depth Coverage Width (%) at 20x S1 72.23x 98.91 50.43x 98.90 S2 101.91x 98.93 92.77x 99.97 S3 52.40x 98.41 65.47x 98.92 S4 34.23x 89.04 88.49x 98.94 S5 46.67x 97.00 46.81x 97.05 S6 75.02x 98.58 75.29x 98.58 Table 2 : Comparison of genetic determinants, genotype and SNPs among genomes, evaluated by Pathogenwatch scheme (v22.3.8) for S Typhi Illumina iSeq100 Illumina HiSeq/NextSeq Sample Genetic Determinants of AMR Genes Genotype SNPs Genetic Determinants of AMR Genes Genotype SNPs S1 gyrA_S83F 4.3.1.2 87 gyrA_S83F 4.3.1.2 87 S2 gyrA_S83F 4.3.1.2 87 gyrA_S83F 4.3.1.2 87 S3 blaTEM-1D, catA1, gyrA_D87N, sul1; sul2, dfrA7 4.3.1.1 87 blaTEM-1D, catA1, gyrA_D87N, sul1; sul2, dfrA7 4.3.1.1 87 S4 blaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7 4.3.1.1 87 blaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7 4.3.1.1 87 S5 blaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7 4.3.1.1 87 blaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7 4.3.1.1 87 S6 blaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7 4.3.1.1 87 blaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7 4.3.1.1 87 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5998020","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":414887830,"identity":"dd3f6572-4781-4ef1-8e88-86d410db716d","order_by":0,"name":"Nishan Katuwal","email":"","orcid":"","institution":"Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital, Kathmandu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nishan","middleName":"","lastName":"Katuwal","suffix":""},{"id":414887831,"identity":"4e802680-5d30-4c31-84b2-79db8544af20","order_by":1,"name":"Shiva Ram Naga","email":"","orcid":"","institution":"Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital, Kathmandu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shiva","middleName":"Ram","lastName":"Naga","suffix":""},{"id":414887832,"identity":"d99565b5-46cf-46f6-b9ac-6841e42d2a5f","order_by":2,"name":"Aastha Shrestha","email":"","orcid":"","institution":"Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital, Kathmandu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aastha","middleName":"","lastName":"Shrestha","suffix":""},{"id":414887833,"identity":"7a169eae-5c08-49b4-bc2a-4d94d7bbac7c","order_by":3,"name":"Sabin Bikram Shahi","email":"","orcid":"","institution":"Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital, Kathmandu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sabin","middleName":"Bikram","lastName":"Shahi","suffix":""},{"id":414887834,"identity":"73be0329-b2d0-4941-9e6a-2f2ed759c519","order_by":4,"name":"Dipesh Tamrakar","email":"","orcid":"","institution":"Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital, Kathmandu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dipesh","middleName":"","lastName":"Tamrakar","suffix":""},{"id":414887835,"identity":"ca8ba6b0-3ca3-4e5c-8136-90ecc2c0ed17","order_by":5,"name":"Rajeev Shrestha","email":"data:image/png;base64,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","orcid":"","institution":"Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital, Kathmandu University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Rajeev","middleName":"","lastName":"Shrestha","suffix":""}],"badges":[],"createdAt":"2025-02-10 10:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5998020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5998020/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-05863-8","type":"published","date":"2025-09-26T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76190521,"identity":"c73ae5e4-3ef0-4856-8aec-83f3e844888e","added_by":"auto","created_at":"2025-02-13 09:34:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15076,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatics workflow for generating and evaluating consensus genome\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5998020/v1/6a089d935916753cc29bacf1.png"},{"id":76190522,"identity":"c878c5d6-ec3c-411b-b2be-a016ceb0590b","added_by":"auto","created_at":"2025-02-13 09:34:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187398,"visible":true,"origin":"","legend":"\u003cp\u003eCoverage Visualization of \u003cem\u003eS\u003c/em\u003e. Typhi genomes (S1 to S6) against \u003cem\u003eS\u003c/em\u003e. Typhi CT18 strain reference genome, using Integrated Genome Browser\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5998020/v1/fe0cea3b82777562dcb8e13f.png"},{"id":92430436,"identity":"0dc506e9-f295-481d-aced-d55a10e0d686","added_by":"auto","created_at":"2025-09-29 16:04:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":859458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5998020/v1/f9d4c567-16b4-4734-a2ad-b204aa118bb5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging Illumina iSeq100 for Whole Genome Sequencing of Salmonella Typhi: a practical approach for resource-limited setting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSeveral phenotypic and molecular methods of pathogen characterization are conventionally utilised to monitor and control the spread of infections, as part of most infection and outbreak management protocols. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] However, these conventional control approaches are slow and resource intensive while failing to distinguish between closely related strain, resistance and virulence factors, mostly due to limited genomic resolution and requiring multiple assays. [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Therefore, for obtaining comprehensive information on phylogeny and improve outbreak investigations, full genomic information are essential. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBacterial whole genome sequencing (WGS) is one of the most promising approaches of this development, which helps to improve our understanding of epidemiology and pathogenesis of bacterial infections.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] This approach due to its comprehensiveness on pathogen biology, mutations, drug resistance, transmission, evolution, community profiling, clinical metagenomics and pathogen discovery are already transforming the research landscape in microbiology.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] This is evident by the large number of whole genomes stored in public repositories. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] These approaches have been known to be used in public health surveillance and control for several bacterial infections from \u003cem\u003eEscherichia coli, Campylobacter jejuni, Legionella pneumophila and Mycobacterium tuberculosis\u003c/em\u003e, outbreaks and monitor the source of healthcare associated infections. [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Furthermore, continuous development in high throughput sequencing have pushed current clinical microbiology field, due to its vast potential in identification of infectious agents, detection of pathogenicity, antimicrobial resistance, evolution and epidemiological surveillance. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] For instance, identification of single nucleotide polymorphisms (SNPs) can differentiate the evolution: with low frequency of SNPs indicating bacteria are genetically similar and recently originated from the same source. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Further, significance of WGS has been more evident, by its application in COVID-19 pandemic. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAlthough, WGS approach can comprehend all genomic information, it is only being utlilised in niches, because clinicians and researchers have shown reluctance, due higher costs, data interpretation and burdensome process of early sequencing technologies. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Further, some sequencing instruments have high operational costs, require large multiplexing for effective and cost continuous sequencing. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Nevertheless, the encouraging prospects are recent advancements in sequencing technologies (for instance, Sequencing by Synthesis from Illumina) and investigation tools have made the platform to have high throughput, increase output, decrease analysis time and reduced cost. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eNepal, recently, has also seen some increase in sequencing potential, with some sites with next generation sequencing capabilities in Illumina, Nanopore and Thermo Fisher platforms. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] However, not all the sequencers are at full potential due to technical sophistication, supply chain issue, limitation in capacity and funding and as a result, in most studies, samples are transported outside. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Nonetheless, these barriers of cost and technical complexity can be alleviated with optimum utilisation of Illumina iSeq 100, which is an inexpensive benchtop next-generation sequencer that minimizes the up-front instrument costs while maximizing simplicity of use and capability in obtaining bacterial whole genome, in country. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study aimed at leveraging existing Illumina iSeq100 for whole genome sequencing (WGS) of bacteria isolated from Dhulikhel Hospital Kathmandu University Hospital.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBacterial Isolates Selection\u003c/h2\u003e \u003cp\u003eFor this study, 6 banked isolates of \u003cem\u003eSalmonella enterica\u003c/em\u003e serovar Typhi (\u003cem\u003eS.\u003c/em\u003e Typhi) were selected randomly from Surveillance for Enteric Fever in Asia Project (SEAP). These isolates were previously sequenced in Illumina HiSeq and NextSeq platforms, as a part of the project. These glycerol stock isolates were subcultured on MacConkey Agar (Oxoid, Cat: CM0007) and reconfirmed biochemically and serologically. An average of 30 isolated colonies were selected for each isolate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcessing, DNA Extraction and qPCR\u003c/h3\u003e\n\u003cp\u003eThe selected colonies were resuspended in 800ul of sterile normal saline, vortexed and centrifuged for 10 minutes at 8000 rpm, to obtain cell pellets. The supernatant was discarded and then the pellets were resuspended in 200ul sterile 1X Phosphate Buffer Saline (PBS). The pellets were subsequently revotexed and 100ul of the resuspension was taken for extraction.\u003c/p\u003e \u003cp\u003eThe extraction was done as per manufacturer manual, using Qiagen DNeasy Blood and Tissue Kit. (Qiagen, Cat: 69504), with slight modification in elution. The final elution was done in 50ul, which was then used for re-elution to increase the yield.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eS\u003c/em\u003e. Typhi isolated were re-confirmed by qPCR targeting Ty21a gene, using forward primer: 5\u0026rsquo;-CGCGAAGTCAGAGTCGACATAG-3\u0026rsquo;, reverse primer 5\u0026rsquo;-AAGACCTCAACGCCGATCAC-3\u0026rsquo; and probe [6-FAM] CATTTGTTCTGGAGCAGGCTGACGG [BHQ1a-Q]. The assays were run in BioRad CFX 96 Dx qPCR machine with reaction mix that included: 10ul of 2X master mix (Quantabio Perfecta, Cat: 95113-012), 0.8ul of forward primer (400nM), 0.8 ul of reverse primer (400nM), 0.4ul of probe (200nM), 4ul of nuclease free water and 4ul of DNA template. The samples with Ct values\u0026thinsp;\u0026lt;\u0026thinsp;20.00 were selected for library preparation.\u003c/p\u003e\n\u003ch3\u003eLibrary Preparation and Whole Genome Sequencing\u003c/h3\u003e\n\u003cp\u003eThe concentration of the samples was checked by Qubit Fluorometer 4 (Invitrogen, Cat: Q33226) using dsDNA HS Kit (Invitrogen, Cat: Q32854). All the samples were diluted to have input amount of 75ng. The libraries were subsequently prepared using NEBNext Ultra II FS DNA Library Preparation Kit (New England Biolabs, Cat: 7805L). The libraries were quality checked for library size and concentration using Agilent Tapestation 4150 using D5000 HS Assay Kit (Agilent Technologies, Cat: 5067\u0026ndash;5593). The average library size was 222.5bp. The isolates were loaded in two batches of two and four isolates at loading concentration of 200pM. The sequencing was done using pair ended barcoding primers at 2x146bp.\u003c/p\u003e\n\u003ch3\u003eAssembly, Consensus and Bioinformatical Investigation\u003c/h3\u003e\n\u003cp\u003eThe raw genomic data were trimmed for adapters (fastp), assembled (bowtie2, samtools), consensus generated, and coverage was calculated (bamCoverage, samtools depth, awk) and viewed in Integrated Genome Browser. The consensus was built using reference-based assembly and mapping on reference \u003cem\u003eS\u003c/em\u003e. Typhi CT18. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese consensus genomes and coverage parameters were compared to genomic data of same isolates previously sequenced using Illumina HiSeq (S3 to S6) and NextSeq (S1 and S2). The sequenced genomic data, from HiSeq, were downloaded from European Nucleotide Archive (ENA) for Samples S3 to S6 (Accession Number: ERR5311412, ERR5311447, ERR5311415 and ERR5375985), while unpublished genomic data was used for Samples S1 and S2.\u003c/p\u003e \u003cp\u003eThe raw reads were evaluated using Pathogenwatch (v22.3.8) to observe for genotype detected, number of Single Nucleotide Polymorphisms (SNPs), antimicrobial resistance determinants. Pathogenwatch is a platform which facilitates rapid identification of genomic markers of antimicrobial resistance (AMR) and includes latest analytics for typing along with epidemiological contextualization with public genomic data. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigated banked bacterial isolates and did not contact human subjects or obtain data associated with them. The ethical approval was obtained from Institutional Review Committee at Kathmandu University School of Medical Sciences, Nepal (ref: 115/24).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eExtraction and qPCR\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eS\u003c/em\u003e. Typhi genomic DNA when evaluated for Ty21a gene, had average Ct value of 15.14, while the concentrations were variable, ranging from 1.58ng/ul to 20.6ng/ul.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhole Genome Sequencing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter the reference-based assembly, the \u003cem\u003eS\u003c/em\u003e. Typhi genomes were mapped over the \u003cem\u003eS\u003c/em\u003e. Typhi CT18 strain in Integrated Genome Browser, with gaps observed in from ~1,033,000bp to 1,047,000bp and ~1,908,000bp to ~1,933,000bp and ~3,053,000bp to ~3,059000bp. (Figure 2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCoverage Parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe coverage parameters (coverage width and depth) of the genomes from this study were compared to same genomes sequenced using Illumina HiSeq and NextSeq. The average coverage width (96.81%) and depth (63.75x) of genomes sequenced in iSeq100 were comparable (Table 1) to that of HiSeq and NextSeq (width: 98.72% and depth: 69.87x).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCost Evaluation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sequencing was performed in two batches (two samples in first batch and four in second batch). When the collective cost of human resource, molecular, library preparation and sequencing reagents and overhead was considered, the cost of sequencing bacterial genome in Illumina iSeq100 would be ~$158 per gb when 4 samples are pooled and ~$312 per gb when 2 samples are pooled.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison of Genotype, AMR genes and SNPs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen the genomes sequenced from Illumina iSeq100 and HiSeq/NextSeq were compared, the genotypes detected, number of SNPs and genetic determinants of AMR genes were identical as shown in Table 2.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the potential, widespread adoption of WGS has been hindered by various challenges, including high costs, time-consuming protocols, and data interpretation complexities. [30] The iSeq100 represents a significant advancement in sequencing technology, offering a cost-effective and user-friendly solution that overcomes many barriers associated with traditional NGS platforms.[29] This platform has been known to be more useful when used with amplicon-based sequencing wet-lab approach. [31-33] Therefore, this study addresses these challenges by using a low-cost next generation sequencer, Illumina iSeq100 for WGS of \u003cem\u003eS.\u0026nbsp;\u003c/em\u003eTyphi\u003cem\u003e.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe coverage parameters of our approach were high (63.75x) and comparable to results from Illumina HiSeq and NextSeq (69.87x), which we realise could depend on the pooling. Though the threshold on the coverage depth depends upon the target pathogen, studies have estimated that 40x is required for high-quality detection of virulence genes, while 4x coverage is sufficient for target gene detection and coverage width should preferably be between 50x \u0026ndash; 100x. [34-36] In addition, some studies have discussed that coverage depth of 20x-30x is essential for antibiotic resistance gene. [37,38] Another study that utilised iSeq100 for WGS of bacterial isolates observed coverage depth of 17x - 149x with an average of 59x, which is slightly lower than our observation. [28]\u003c/p\u003e\n\u003cp\u003eSimilarly, for coverage width, at least 90% is recommended, which is well below the observed average coverage width of 96.81% of this study. [39] This high coverage width is required for optimum quality data during bioinformatics analysis, as this threshold helps achieve correct gene identification. [38]\u003c/p\u003e\n\u003cp\u003eThe genome gaps observed in the genomic regions (~1,033,000bp to 1,047,000bp and ~1,908,000bp to ~1,933,000bp and ~3,053,000bp to ~3,059,000bp) correspond to STY_RS04890 gene (DUF2213 domain-containing protein), WP_001197078.1 (baseplate J/gp47 family protein), STY_RS09550 (phage minor head protein), STY_RS09735 (tyrosine-type recombinase/integrase), STY_RS15100 (DUF1460 domain-containing protein) and STY_RS15130 (site-specific integrase). [40] These gaps could be a result of genomic rearrangements, horizontal gene transfer as well as sequencing and alignment errors and presence of low complexity regions. [41]\u003c/p\u003e\n\u003cp\u003eThe pooling of isolates with varying genome size results in unequal sample coverage even with equimolar pooling, while some protocols do not mandatorily require equimolar pooling. [28,42] Further, pooling also depends on the complexity of the genome. For instance, though \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eSalmonella enterica\u003c/em\u003e (\u003cem\u003eS. enterica\u003c/em\u003e) have similar genome size, \u003cem\u003eE. coli\u003c/em\u003e is more complex and has variable accessory genomes. Therefore, comparatively larger number of \u003cem\u003eS. enterica\u003c/em\u003e genomes can be pooled. [42] However, the cost per run also depends on pooling. The number of samples pooled would be inversely proportional to cartridge/flow cell requirement, while the amount of library preparation reagents remains the same. The average cost of run per gb is $60.65 for HiSeq (using HiSeq SBS Kit V4 kit - 250 cycles PE 2x125bp, which has now been discontinued), while for iSeq would be ~$130 per gb when 4 samples are pooled and ~$87 when 6 samples are pooled. [29,43] The cost, at our site, is slightly higher (~$158 per gb when 4 samples are pooled), possibly because cost of sequencing is dependent on region and respective supply chain logistics. [44] Unfortunately, we cannot confirm the number of samples pooled for sequencing in HiSeq and NextSeq, to make more informed comparisons. While discussing low cost NGS, Oxford Nanopore provides a great argument. The flongle, which gives output upto 2Gb, costs ~$90, while iSeq100 cartridge/flowcell costs ~$630 with 1.2Gb output. [42] However, the shorter shelf life of flow cell and lower accuracy of individuals reads are counter intuitive.\u003c/p\u003e\n\u003cp\u003eIn this study, both genotypes (4.3.1.1 and 4.3.1.2) belonging to lineage I and II of H58 strain, were detected. Both strains had been first detected in a pediatric study conducted in Kathmandu.[45] Furthermore, the point mutations (S83F and D87N) were observed in quinolone resistance determining region of gyrA gene, which is associated with fluoroquinolone resistance. [46-48] Similarly, mutations were observed in genes conferring resistance to ampicillin (blaTEM), chloramphenicol (catA1) and resistance to co-trimoxazole (sul) and resistance to trimethoprim (dfrA7). \u0026nbsp;[49-52] In this study, the genotypes, genomic determinants and number of SNPs among the \u003cem\u003eS\u003c/em\u003e. Typhi genomes were identical, as expected, in genomes sequenced from iSeq100 and HiSeq/NextSeq.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings have demonstrated that iSeq platform is able to generate high-quality, accurate data for bacterial WGS, and could cost-effective addition to the genomic investigation capability. The genomes generated from iSeq have comparable coverage parameters to those obtained from more established commercial sequencing platforms like the Illumina HiSeq or NextSeq, which are not available, nor viable (due to its large number of sample requirements, reagents cost, annual maintenance cost among others) in Nepal. The findings of this study support premise that iSeq100 is well suited to small laboratories with low sample throughput, which is mostly the case in Nepal due to small molecular and genomic research ecosystem.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLooking ahead, the findings of this study pave the way for broader applications of the Illumina iSeq100 platform in clinical microbiology, outbreak surveillance, and antimicrobial resistance monitoring in Nepal. Future research should focus on further optimizing sample preparation protocols, expanding sequencing capacity, and integrating bioinformatics tools to further enhance the utility and accessibility of WGS technologies in pathogen investigation. This approach will uplift the current national sequencing capabilities to full potential.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe data from bacterial whole genome sequencing using the Illumina iSeq100 is equally informative when compared to other high-end sequencers. We have shown that Illumina iSeq, though primarily designed for microbial metagenomics, is able to perform whole genome sequencing of bacteria when combined with a modified wet-lab protocol. This approach will also provide high-quality genomes for other pathogens of human health importance, with some limitations on throughput. Thus, this study also advocates for optimum utilisation of iSeq100 while still ensuring a high data quality.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eAdditional Information\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003cp\u003eThis study was funded and supported by Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital Kathmandu University Hospital, Nepal.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: RS and NK; Methodology: NK; Investigation: NK, SRN; Resources: NK and DT; Data curation: NK, SRN, AS, SBS; Writing-original draft preparation: NK and RS; Writing-review and editing: NK, SRN, AS, SBS, DT and RS; Supervision: RS. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully thank Jason R. Andrews, Kesia Esther da Silva and the SEAP team for providing accessions numbers (European Nucleotide Archive) for the S. Typhi isolates. We are also thankful to all the research staffs at Center for Infectious Disease Research and Surveillance, Dhulikhel Hospital Kathmandu University Hospital, Nepal.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw genomic data, from Illumina iSeq100, can be obtained from National Center for Biotechnology Information\u0026rsquo;s BioProject PRJNA1110744 (Accession: SRX24530188 to SRX24530193).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVourli, S., Kontos, F., Pournaras, S. (2021). WGS for Bacterial Identification and Susceptibility Testing in the Clinical Lab. In: Moran-Gilad, J., Yagel, Y. (eds) Application and Integration of Omics-powered Diagnostics in Clinical and Public Health Microbiology. 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Kathmandu Univ Med J (KUMJ). 2021 Apr-Jun;19(74):137-142. PMID: 34819443.\u003c/li\u003e\n\u003cli\u003eLindsey RL, Pouseele H, Chen JC, Strockbine NA, Carleton HA. Implementation of whole genome sequencing (WGS) for identification and characterization of Shiga toxin-producing Escherichia coli (STEC) in the United States. Front Microbiol. 2016;7:1\u0026ndash;9. doi: 10.3389/fmicb.2016.00766. \u003c/li\u003e\n\u003cli\u003eLambert D, Carrillo CD, Koziol AG, Manninger P, Blais BW. GeneSippr: A rapid whole-genome approach for the identification and characterization of foodborne pathogens such as priority Shiga toxigenic Escherichia coli . PLoS One. 2015;10:1\u0026ndash;19 \u003c/li\u003e\n\u003cli\u003eSayeb Maroura. Guidance document for WGS-Benchmarking. 2021. EURL CPS. Available at : https://sitesv2.anses.fr/en/system/files/NGS%20EURL%20WG%20-%20Del5%20-%20Benchmarking.pdf. Accessed on: 7 September 2024\u003c/li\u003e\n\u003cli\u003eSanabria, A.M., Janice, J., Hjerde, E. et al. Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles. Sci Rep 11, 20848 (2021). https://doi.org/10.1038/s41598-021-00383-7 \u003c/li\u003e\n\u003cli\u003eVieira, A.A., Piccoli, B.C., y Castro, T.R. et al. Pipeline validation for the identification of antimicrobial-resistant genes in carbapenem-resistant Klebsiella pneumoniae. Sci Rep \u003cstrong\u003e13\u003c/strong\u003e, 15189 (2023). https://doi.org/10.1038/s41598-023-42154-6\u003c/li\u003e\n\u003cli\u003eDoyle, R. M. et al. Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study. Microbial. Genom. \u003cstrong\u003e6\u003c/strong\u003e(2), 1\u0026ndash;13. https://doi.org/10.1099/mgen.0.000335 (2020)\u003c/li\u003e\n\u003cli\u003eParkhill J, Dougan G, James KD, Thomson NR, Pickard D, Wain J, Churcher C, Mungall KL, Bentley SD, Holden MT, Sebaihia M, Baker S, Basham D, Brooks K, Chillingworth T, Connerton P, Cronin A, Davis P, Davies RM, Dowd L, White N, Farrar J, Feltwell T, Hamlin N, Haque A, Hien TT, Holroyd S, Jagels K, Krogh A, Larsen TS, Leather S, Moule S, O\u0026apos;Gaora P, Parry C, Quail M, Rutherford K, Simmonds M, Skelton J, Stevens K, Whitehead S, Barrell BG. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18. Nature. 2001 Oct 25;413(6858):848-52. doi: 10.1038/35101607. PMID: 11677608.\u003c/li\u003e\n\u003cli\u003eNagarajan, N., Pop, M. Sequence assembly demystified. Nat Rev Genet \u003cstrong\u003e14\u003c/strong\u003e, 157\u0026ndash;167 (2013). https://doi.org/10.1038/nrg3367 AND Kaas RS, Leekitcharoenphon P, Aarestrup FM, Lund O. Solving the problem of comparing whole bacterial genomes across different sequencing platforms. PLoS One. 2014 Aug 11;9(8):e104984. doi: 10.1371/journal.pone.0104984. PMID: 25110940; PMCID: PMC4128722.\u003c/li\u003e\n\u003cli\u003eMitchell PK, Wang L, Stanhope BJ, Cronk BD, Anderson R, Mohan S, Zhou L, Sanchez S, Bartlett P, Maddox C, DeShambo V, Mani R, Hengesbach LM, Gresch S, Wright K, Mor S, Zhang S, Shen Z, Yan L, Mackey R, Franklin-Guild R, Zhang Y, Prarat M, Shiplett K, Ramachandran A, Narayanan S, Sanders J, Hunkapiller AA, Lahmers K, Carbonello AA, Aulik N, Lim A, Cooper J, Jones A, Guag J, Nemser SM, Tyson GH, Timme R, Strain E, Reimschuessel R, Ceric O, Goodman LB. Multi-laboratory evaluation of the Illumina iSeq platform for whole genome sequencing of Salmonella, Escherichia coli and Listeria. Microb Genom. 2022 Feb;8(2):000717. doi: 10.1099/mgen.0.000717. PMID: 35113783; PMCID: PMC8942033.\u003c/li\u003e\n\u003cli\u003eLa Trobe University Genomics. Available at : https://www.latrobe.edu.au/__data/assets/pdf_file/0005/724748/LTU-NGS-prices-AH.pdf Accessed on: 7 September 2024\u003c/li\u003e\n\u003cli\u003eGetchell M, Wulandari S, de Alwis R, Agoramurthy S, Khoo YK, Mak TM, Moe L, Stona AC, Pang J, Momin MHFHA, Amir A, Andalucia LR, Azzam G, Chin S, Chookajorn T, Arunkumar G, Hung DT, Ikram A, Jha R, Karlsson EA, Le Thi MQ, Mahasirimongkol S, Malavige GN, Manning JE, Munira SL, Trung NV, Nisar I, Qadri F, Qamar FN, Robinson MT, Saloma CP, Setk S, Shirin T, Tan LV, Dizon TJR, Thayan R, Thu HM, Tissera H, Xangsayarath P, Zaini Z, Lim JCW, Maurer-Stroh S, Smith GJD, Wang LF, Pronyk P; Asia Pathogen Genomics Initiative (Asia PGI) consortium. Pathogen genomic surveillance status among lower resource settings in Asia. Nat Microbiol. 2024 Sep 24. doi: 10.1038/s41564-024-01809-4. Epub ahead of print. PMID: 39317773.\u003c/li\u003e\n\u003cli\u003eHolt, K. E. et al. High-throughput bacterial SNP typing identifies distinct clusters of Salmonella Typhi causing typhoid in Nepalese children. BMC Infect. Dis. 2010. 10, 144.\u003c/li\u003e\n\u003cli\u003eJacoby GA. Mechanisms of resistance to quinolones. Clin Infect Dis. 2005;41:7.\u003c/li\u003e\n\u003cli\u003eBrown JC, Shanahan PM, Jesudason MV, Thomson CJ, Amyes SG. Mutations responsible for reduced susceptibility to 4 quinolones in clinical isolates of multi resistant Salmonella typhi in India. J Antimicrob Chemother. 1996;37:891\u0026ndash;900. \u003c/li\u003e\n\u003cli\u003eWain J, Hoa NT, Chinh NT, Vinh H, Everett MJ, Diep TS, et al. Quinolone resistant Salmonella typhi from Vietnam; molecular basis of resistance and clinical response to treatment. Clin Infect Dis. 1997;25:1404\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eTamang MD, Oh JY, Seol SY, Kang HY, Lee JC, Lee YC, Cho DT, Kim J. Emergence of multidrug-resistant Salmonella enterica serovar Typhi associated with a class 1 integron carrying the dfrA7 gene cassette in Nepal. Int J Antimicrob Agents. 2007 Oct;30(4):330-5. doi: 10.1016/j.ijantimicag.2007.05.009. Epub 2007 Jul 12. PMID: 17629465. \u003c/li\u003e\n\u003cli\u003eKongsoi S, Changkwanyeun R, Yokoyama K, Nakajima C, Changkaew K, Suthienkul O, Suzuki Y. Amino acid substitutions in GyrA affect quinolone susceptibility in Salmonella typhimurium. Drug Test Anal. 2016 Oct;8(10):1065-1070. doi: 10.1002/dta.1910. Epub 2015 Oct 30. PMID: 26514939. \u003c/li\u003e\n\u003cli\u003eYusof NY, Norazzman NII, Zaidi NFM, Azlan MM, Ghazali B, Najib MA, Malik AHA, Halim MAHA, Sanusi MNSM, Zainal AA, Aziah I. Prevalence of Antimicrobial Resistance Genes in Salmonella Typhi: A Systematic Review and Meta-Analysis. Trop Med Infect Dis. 2022 Sep 27;7(10):271. doi: 10.3390/tropicalmed7100271. PMID: 36288012; PMCID: PMC9611315. \u003c/li\u003e\n\u003cli\u003eda Silva KE, Tanmoy AM, Pragasam AK, Iqbal J, Sajib MSI, Mutreja A, Veeraraghavan B, Tamrakar D, Qamar FN, Dougan G, Bogoch I, Seidman JC, Shakya J, Vaidya K, Carey ME, Shrestha R, Irfan S, Baker S, Luby SP, Cao Y, Dyson ZA, Garrett DO, John J, Kang G, Hooda Y, Saha SK, Saha S, Andrews JR. The international and intercontinental spread and expansion of antimicrobial-resistant Salmonella Typhi: a genomic epidemiology study. Lancet Microbe. 2022 Aug;3(8):e567-e577. doi: 10.1016/S2666-5247(22)00093-3. Epub 2022 Jun 21. PMID: 35750070; PMCID: PMC9329132.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Comparison of coverage depth and width between Illumina HiSeq/NextSeq and Illumina iSeq100\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"697\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44.7633%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllumina iSeq100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 45.7676%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllumina HiSeq/NextSeq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Coverage Depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage Width (%) at 20x\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Coverage Depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage Width (%) at 20x\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e72.23x\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e98.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e50.43x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e98.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e101.91x\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e98.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e92.77x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e99.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e52.40x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e98.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e65.47x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e98.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003eS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e34.23x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e89.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e88.49x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e98.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003eS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e46.67x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e97.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e46.81x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e97.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.46915%;\"\u003e\n \u003cp\u003eS6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.66%;\"\u003e\n \u003cp\u003e75.02x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1033%;\"\u003e\n \u003cp\u003e98.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4032%;\"\u003e\n \u003cp\u003e75.29x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3644%;\"\u003e\n \u003cp\u003e98.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Comparison of genetic determinants, genotype and SNPs among genomes, evaluated by Pathogenwatch scheme (v22.3.8) for \u003cem\u003eS\u003c/em\u003e Typhi\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 44.6779%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllumina iSeq100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 47.8992%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllumina HiSeq/NextSeq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic Determinants of AMR Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic Determinants of AMR Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8291%;\"\u003e\n \u003cp\u003egyrA_S83F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4454%;\"\u003e\n \u003cp\u003e4.3.1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003egyrA_S83F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9664%;\"\u003e\n \u003cp\u003e4.3.1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8291%;\"\u003e\n \u003cp\u003egyrA_S83F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4454%;\"\u003e\n \u003cp\u003e4.3.1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003egyrA_S83F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9664%;\"\u003e\n \u003cp\u003e4.3.1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8291%;\"\u003e\n \u003cp\u003eblaTEM-1D, catA1, gyrA_D87N, sul1; sul2, dfrA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4454%;\"\u003e\n \u003cp\u003e4.3.1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003eblaTEM-1D, catA1, gyrA_D87N, sul1; sul2, dfrA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9664%;\"\u003e\n \u003cp\u003e4.3.1.1\u003c/p\u003e\n \u003c/td\u003e\n 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\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003eS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8291%;\"\u003e\n \u003cp\u003eblaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4454%;\"\u003e\n \u003cp\u003e4.3.1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003eblaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9664%;\"\u003e\n \u003cp\u003e4.3.1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.42297%;\"\u003e\n \u003cp\u003eS6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8291%;\"\u003e\n \u003cp\u003eblaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4454%;\"\u003e\n \u003cp\u003e4.3.1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003eblaTEM-1D, catA1, gyrA_S83F, sul1; sul2, dfrA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9664%;\"\u003e\n \u003cp\u003e4.3.1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.40336%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"iSeq100, Nepal, Salmonella, whole genome sequencing","lastPublishedDoi":"10.21203/rs.3.rs-5998020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5998020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBacterial whole-genome-sequencing helps to improve our understanding of epidemiology and pathogenesis of infections and allows comprehensive investigation on virulence, evolution and resistance mechanisms. Nepal, recently, has seen some increase in sequencing capabilities but faces hurdles for optimum utilisation. However, these hurdles could be alleviated using Illumina iSeq100. Therefore, this study aimed at performing whole-genome-sequencing of bacteria utilising iSeq100.\u003c/p\u003e \u003cp\u003eSix banked isolates of \u003cem\u003eS\u003c/em\u003e Typhi bacterial were selected, extracted for DNA, confirmed by qPCR and then sequenced in Illumina iSeq100 at 200pM. The consensus was generated by reference-based assembly, mapping onto \u003cem\u003eS\u003c/em\u003e. Typhi CT18. These consensus genomes and coverage parameters were compared to data from HiSeq and NextSeq.\u0026nbsp;The raw reads were also evaluated using pathogenwatch to observe genotype, mutations and resistance genes.\u003c/p\u003e \u003cp\u003eThe coverage parameters (coverage width and depth) of the genomes from this study were compared to same genomes sequenced using HiSeq/NextSeq.\u0026nbsp;The average coverage width (96.81%) and depth (63.75x) of genomes sequenced in iSeq100 were comparable to that of HiSeq/NextSeq (width: 98.72% and depth: 69.87x). The genotypes detected, number of SNPs and genetic determinants of AMR genes were identical.\u003c/p\u003e \u003cp\u003eThe data from bacterial whole-genome-sequencing using the Illumina iSeq100 is equally informative when compared to some high-end sequencers. Thus, the primary goal of this study is to advocate for optimum utlisation of iSeq100, still ensuring for a high-quality data.\u003c/p\u003e","manuscriptTitle":"Leveraging Illumina iSeq100 for Whole Genome Sequencing of Salmonella Typhi: a practical approach for resource-limited setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 09:33:57","doi":"10.21203/rs.3.rs-5998020/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-03T11:15:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-01T22:54:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46512173263127342271572782368759646700","date":"2025-02-20T08:28:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-17T19:49:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-17T16:32:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268227958581851079658942622115040771864","date":"2025-02-17T16:09:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38149271923771861374671846153535382484","date":"2025-02-12T16:12:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-12T15:38:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-12T15:36:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-02-12T13:35:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-11T12:02:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-10T09:56:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bb3ef31-1a71-4818-a9ad-4ee1081af021","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44242098,"name":"Biological sciences/Biological techniques"},{"id":44242099,"name":"Biological sciences/Microbiology"},{"id":44242100,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-09-29T15:59:37+00:00","versionOfRecord":{"articleIdentity":"rs-5998020","link":"https://doi.org/10.1038/s41598-025-05863-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-26 15:56:52","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2025-02-13 09:33:57","video":"","vorDoi":"10.1038/s41598-025-05863-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-05863-8","workflowStages":[]},"version":"v1","identity":"rs-5998020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5998020","identity":"rs-5998020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}