A low resource requirement molecular diagnostic and surveillance tool for Shigella in the era of vaccination

A low resource requirement molecular diagnostic and surveillance tool for Shigella in the era of vaccination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A low resource requirement molecular diagnostic and surveillance tool for Shigella in the era of vaccination Kate Baker, Fahad Khokhar, Xiaoliang Ba, Charlotte Chong, P Malaka De Silva, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7399876/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Infection with Shigella bacteria is one of the leading causes of diarrhoeal disease globally, with significant burden in low- and middle-income countries and rising incidence in high-income settings. Effective serotyping is essential for surveillance, outbreak investigation, vaccine targeting, and can inform clinical management. Current discriminative approaches, such as seroagglutination and whole genome sequencing, are constrained by cross-reactivity, infrastructure requirements, and cost. Here, we present the development of a nine-target multiplex PCR lateral flow device assay for the rapid, accurate identification of vaccine-prioritised S. flexneri serotypes 1b, 2a, and 3a, and S. sonnei , as well as clinically relevant antimicrobial resistance genes. Validation using 138 DNA samples from clinical Shigella isolates showed 97.8% sensitivity and 100% specificity at the species level, performing comparably to in silico genomic prediction tools. Serotype classifications interpreted from the PCR assay also successfully aligned with the temporal and geographic trends observed in a large multi-country clinical dataset ( n = 1,074), supporting its utility for temporospatial surveillance. To facilitate field-deployment of the PCR assay, we integrated an oligo-chromatographic dipstick method, enabling a simple, gel-free readout without need for specialised equipment. This molecular dipstick assay provides a practical and scalable solution for Shigella serotyping and antimicrobial resistance profiling in support of vaccine implementation and surveillance efforts. Biological sciences/Microbiology/Infectious-disease diagnostics Biological sciences/Microbiology/Policy and public health in microbiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Shigellosis, caused by species of Shigella , is a major cause of bacterial dysentery and is responsible for substantial morbidity and mortality – particularly among young children in low- and middle-income countries (LMICs), where over 60,000 child deaths are estimated to be attributable to Shigella annually 1 – 3 . These infections are commonly associated with poor water quality and hygiene conditions 4 . The pathogen is classified into four species: S. boydii , S. dysenteriae , S. flexneri and S. sonnei , each demonstrating distinct epidemiological patterns 5 , 6 . Among these, S. flexneri remains the predominant cause of endemic shigellosis in LMICs whereas S. sonnei is more prevalent in industrialised nations 6 – 9 . However, increasing cases of S. sonnei outbreaks in Asia, Latin America, and the Middle East are becoming more common 10 – 14 . Since the 1970s, predominantly in higher-income countries, shigellosis has also been recognised as a sexually transmitted infection, commonly isolated among men who have sex with men 15 – 19 . Multidrug resistant (MDR) and extensively drug resistant (XDR) Shigella strains, particularly in S. flexneri and S. sonnei species, are increasingly reported worldwide, with growing resistance to both fluoroquinolones and macrolides, significantly limiting oral treatment options 20 – 22 . Since the first report of XDR S. sonnei in Vietnam in 2014 23 , international outbreaks have been linked to samples carrying bla CTX−M and mph (A) genes, conferring resistance to third-generation cephalosporins and macrolides respectively 19 , 24 – 31 . This escalating resistance trend underscores the urgent need for new treatment strategies and highlights Shigella as a global priority for vaccine development 32 , 33 . Whilst there is no approved vaccine, the existing candidates focus on specific S. flexneri serotypes and S. sonnei 34 – 39 , making serotyping and species identification essential for vaccine evaluation as well as controlling outbreaks and understanding epidemiological trends. One such vaccine candidate, a quadrivalent formulation called altSonflex1-2-3, currently in Phase-II clinical trials, is based on Generalised Modules Membrane for Antigens (GMMA) technology and combines a novel S. sonnei construct with Outer Membrane Vesicles (OMVs) derived from S. flexneri 1b, 2a, and 3a strains, selected for their epidemiological relevance 8 , 38 , 40 and their ability to induce cross-reactive antibodies, offering potential broad protection against the most prevalent Shigella serotypes 41 . Traditional serotyping methods, relying on agglutination and antisera, have long been the gold standard for Shigella classification 8 , 41 , 42 . However, these methods are relatively expensive, labour-intensive, require a high degree of technical skills and/or training, time-consuming, are limited due to antigen cross-reactivity, and require robust supply chains, making them less practical in resource-limited settings. In contrast, molecular diagnostic methods such as multiplex PCR (mPCR) offer significant advantages in terms of speed, scalability, cost-effectiveness, and offer infrastructural capabilities across a broad range of pathogens. The use of real-time quantitative PCR (qPCR) has been shown to detect Shigella at significantly higher rates than traditional culture-based techniques 43 , 44 . For example, re-analysis of the Global Enteric Multicentre Study (GEMS) 8 , 42 , 45 data using a qPCR TaqMan Array Card (TAC) suggests that the true burden of Shigella may be up to twice as high as previously estimated 7 , 46 , underscoring the limitations of conventional diagnostics. Similarly, while genomic tools such as ShigaTyper 47 and ShigaPass 48 offer in silico serotype predictions based on whole genome sequencing (WGS) data. These methods rely on high-quality sequencing and bioinformatics infrastructure. In addition, qPCR and WGS methods also remain relatively expensive, and require trained personnel and specialist equipment for use. There is currently no rapid, affordable point-of-care test available for Shigella species identification and serotyping, and diagnosis cannot reliably be made based on clinical criteria alone 49 . Here, we report the development and optimisation of a low-cost multiplex PCR assay that identifies vaccine-targeted serotypes, S. flexneri 1b, 2a, 3a, and S. sonnei , as well as antimicrobial resistance genes associated with resistance to third-generation cephalosporins and macrolides. This assay functions without the need for extensive laboratory infrastructure or whole genome sequencing, offering a rapid and scalable solution for portable, gel-free oligo-chromatographic system, enabling straightforward visual interpretation in both laboratory and remote environments. Results Development and validation of the multiplex PCR on clinical samples To evaluate primer specificity and optimise target selection for discrimination of serotypes, Samples S001-S038 were subjected to singleplex PCR, with each primer pair tested across all DNA samples. Only primers demonstrating strong, specific amplification of their respective target genes, without cross-reactivity to non-target serotypes, were selected for inclusion in the final multiplex assay (See Methods). These samples, previously serotyped only by slide agglutination and lacking genomic data, had been classified at the serotype level but not further resolved into sub-serotypes (e.g. reported as Serotype 1 rather than 1a, 1b, 1c, etc.), providing a valuable baseline for PCR-based validation. For these singleplex reactions only, additional targets for gtr IV, gtr V, wzx 6, and opt were also included (Supplementary Methods). The ipaH gene target was detected in all Shigella samples, while S. flexneri and S. sonnei markers showed no cross-reactivity with S. boydii or S. dysenteriae , confirming assay specificity. PCR results enabled refined subtyping of all S. flexneri Serotypes 1, 2, and 3 as 1b ( gtr I + oac), 2a ( gtr II), and 3a ( gtr X + oac ), respectively, providing greater resolution than the available laboratory serotyping results (Supplementary Table 1). Four samples (S008, S009, S025, and S026), originally identified as S. flexneri Serotype 4, showed discrepant PCR results, initially amplifying only gtr II or gtr X, in addition to the ipaH marker. These samples were also negative for the Serotype 4 type-specific gtr IV marker (Supplementary Fig. 1). Long-read whole genome sequencing and in silico serotype prediction classified these samples as Serotype 2a, Yv, Xv, and X, respectively (Supplementary Table 2). Serotype 5 samples were not represented in this initial set (Supplementary Fig. 1). Following initial singleplex PCR validation, we then optimised the performance of the serotype discrimination as a multiplex PCR and two key antimicrobial resistance markers were added: bla CTX−M , covering major Extended-Spectrum β-Lactamase (ESBL) variants conferring resistance to third-generation cephalosporins, and mph (A) conferring macrolide resistance, to enhance the clinical utility (See Methods). This refined mPCR panel was applied to the same Batch 1 DNA (Samples S001-S038) with results visualised by gel electrophoresis (Fig. 1). The optimised mPCR assay successfully amplified the target serotype markers ( gtr I, gtr II, SS, gtr X, and oac ) in the expected samples, consistent with the results of the singleplex testing. The ipaH genus-level Shigella /EIEC marker was reliably detected across all samples. Additionally, five samples were positive for the CTX-M marker, and Sample S035 ( S. boydii , lane 33) positive for mph (A). The faint ipaH and SS_DNAm bands observed in only Sample S035 suggest a sample-specific issue rather than a protocol-related limitation, and no spurious amplifications occurred in other samples (Fig. 1). Although targeted to the four serotypes, there is greater decoding capacity among the mPCR target combinations (shown in Table 1). Multiplex PCR on Batch 1 DNA samples using primers listed in Table 1. Lane L: 100 bp molecular ladder; Lanes 1–38: Batch 1 DNA samples; Lane 39: E. coli BL21; Lane 40: Nuclease free water (NFW). SS = S. sonnei DNAm marker. Amplicon bands are labelled. Full sample list with results is provided in Supplementary Table 1. Table 1 Decoded serotype classifications from the mPCR assay. mPCR classification ipaH SS_DNAm gtr I gtr II gtr X oac S. sonnei + + - - - - 1a/1c(7a) + - + - - - 1b + - + - - + 1d + - + - + - 2a + - - + - - 2b + - - + + - 3a/5b + - - - + + 3b/4b/5a + - - - - + X/Xv + - - - + - Shigella /EIEC a + - - - - - non- Shigella /EIEC - - - - - - Possible species/serotype classifications based on presence (+) or absence (-) of specific markers in the mPCR assay. Each classification is associated with a unique combination of markers. a Represents one of 4a/6/Y/Yv/EIEC/ S. dysenteriae / S. boydii Extended and in silico validation of mPCR To extend the external validation of the mPCR assay we then corroborated the mPCR results against a larger set of samples for which whole genome sequencing was available. Specifically, we used a second batch of DNA samples comprising S. flexneri ( n = 55), and S. sonnei ( n = 45) which had been serotyped using slide agglutination. For this, we also ran an in silico simulation of the mPCR amplicon detection using a genomic detection method (implemented with ARIBA, see Methods). Species-level identification showed 94.5% sensitivity and 100% specificity for S. flexneri , and 100% sensitivity and specificity for S. sonnei . This yielded an overall sensitivity of 97% and specificity of 100% for Batch 2 DNA samples. A summary of species-level comparisons and performance metrics is provided in Supplementary Table 3. Out of the 55 S. flexneri samples, only 35 were subtyped by the original laboratory agglutination method (Fig. 2), which identified serotypes 1a, 1b, 1c(7a), 2a, 2b, 3a, and 5b. Amongst the three discrepant mPCR results, Sample S042 had not been serotyped beyond species level by agglutination and was classified as Shigella /EIEC by mPCR, while both ShigaTyper and ShigaPass identified it as Serotype 4av, consistent with the absence of a specific target in the mPCR panel. In this context, detection of only the ipaH gene without serotype-specific amplification aligns with the expected result. Two further samples (S089 and S096) were classified as non- Shigella /EIEC by mPCR, due to the absence of all targets, yet ARIBA classified as Shigella /EIEC. Original laboratory serotyping results are shown (Lab) as well as classification from mPCR and in silico prediction using ARIBA, ShigaPass, and ShigaTyper. A maximum-likelihood phylogenetic tree provides genomic context. Coloured tiles indicate predicted serotypes, highlighting areas of concordance and discrepancy. A result of ‘No data’ in this context means a classification of S. flexneri only. Presence (teal) and absence (white) of mPCR gene targets is shown alongside for reference. Predicted utility of mPCR for surveillance in endemic settings Having shown that in silico screening was an appropriate proxy for mPCR performance, we then used a large WGS dataset from strains collected across seven LMICs to further validate assay performance as a surveillance tool, including relative to serotypic inference by WGS. Specifically, we used S. flexneri ( n = 769) and sonnei ( n = 305) isolates from GEMS with associated sequence data and agglutination results 9 . We ran three in silico serotyping methods including: ShigaPass, ShigaTyper, and ARIBA (to detect our mPCR targets as a proxy measure of performance). Although the results above suggest laboratory agglutination is fallible, we used this as a gold-standard for evaluating and comparing the results of these three tools. For S. sonnei the in silico methods showed concordance rates for species differentiation of 92.1%, 94.4%, and 97.7% for ShigaTyper, ShigaPass, and ARIBA, respectively (Table 2). Table 2 Combined species level classification results for GEMS S. flexneri ( n = 769) and S. sonnei ( n = 305) samples by in silico prediction tools. GEMS S. sonnei ( n = 305) GEMS S. flexneri ( n = 769) Genomic prediction ARIBA ShigaPass ShigaTyper ARIBA ShigaPass ShigaTyper S. sonnei 298 289 281 0 0 0 S. flexneri 0 0 0 587 755 717 EIEC 0 6 19 0 1 5 Shigella /EIEC 7 0 0 181 0 0 Non- Shigella /EIEC 0 10 0 0 6 0 Novel 0 0 0 0 0 28 No prediction 0 0 5 0 0 19 Unknown 0 0 0 0 7 0 Not tested 0 0 0 1 0 0 Total 305 305 305 769 769 769 ARIBA classified seven S. sonnei samples as Shigella /EIEC, all with the expected S. sonnei markers ( ipaH + SS_DNAm), but also unexpected serotype markers (e.g. gtr I, gtr II, oac and gtr X), suggesting in silico primer misbinding, potential sample contamination, or a previously undescribed fluidity of these mobile genetic element-borne genes in natural bacterial populations, leading to an uncertain identification. Amongst the same seven samples, ShigaTyper classified four as EIEC and two as No prediction. In contrast, ShigaPass classified all seven samples as S. sonnei , suggesting that using genome assembly sequences may provide more accurate results than FASTQ reads, which could potentially contain cross-contaminated sample reads. The agreement rates for 769 S. flexneri GEMS samples revealed 93.2%, 98.2%, and 76.3% concordance with ShigaTyper, ShigaPass, and ARIBA, respectively (Table 3, Fig. 3A). As the mPCR targets specific vaccine serotypes, results from ARIBA would classify 181 samples as Shigella /EIEC, of which 172 were non-target serotypes (Sf6 [ n = 136], 4a [ n = 34], 3b [ n = 1], Y [ n = 1]), not distinguishable by the assay. Of the remaining nine samples – originally typed as 1b, 2a, 2b, and 3a – all showed unexpected amplification in additional PCR targets leading to an uncertain serotype prediction. We recommend that Shigella /EIEC outcomes on the mPCR are most likely Serotype 6 (see Table 2), meaning if the 136 Serotype 6 samples are assigned as correctly classified, the concordance for S. flexneri serotype identification using ARIBA increases to 94%. High concordance with laboratory serotyping was observed across all methods for Serotypes 1b, 2a, and 2b, as well as Serotype 6 using ShigaTyper and ShigaPass. As Serotype 6 is not targeted by the mPCR, ARIBA classified these samples as Shigella /EIEC. All Serotype 4 samples reported by GEMS were classified as 4av and Serotype X samples were consistently identified as Xv with both in silico tools. Due to the absence of the gtr IV and opt genes in the final mPCR panel, ARIBA reported all Serotype 4 samples as Shigella /EIEC ( ipaH only) and all Serotype X as X/Xv ( ipaH and gtr X). Notably, we also observed that isolates of Serotypes 3a and 5b were intermixed on a single clade in the phylogenetic tree (Fig. 3B). Original laboratory serotyping results are shown (GEMS) as well as predictions from three in silico tools (ShigaTyper, ShigaPass and ARIBA). A phylogenetic tree is used to illustrate genomic relatedness. Coloured tiles indicate predicted serotypes, highlighting areas of concordance and discrepancy. A : All 769 S. flexneri samples. Clades corresponding to Serotypes 6 and 3a/5b are annotated. B : Phylogenetic tree and serotyping classification of 109 S. flexneri 3a and 5b samples from GEMS with additional annotations of Country and Region. Although both Serotypes 3a and 5b express gtr X and oac , only 5b samples also express the type-specific gtr V gene, making this marker essential for accurate differentiation. According to the original GEMS classification, 109 isolates were classified as Serotype 3a and only four as 5b. In contrast, in silico prediction with ShigaPass yielded markedly different results, with 67 samples classified as 3a and 36 as 5b. However, due to the absence of gtr V in the mPCR target panel, results from ARIBA classified a total of 106 samples as Serotype 3a/5b (Supplementary Table 4). As the phylogenetic admixing between Serotypes 3a and 5b may represent a meaningful means of serotype switching, we sought to explore the biological basis for this signal. We found from the short-read WGS data that only 6 of 36 Serotype 5b genome assemblies contained an intact gtr V gene on a single contiguous sequence. In the remainder, gtr V was split across two contigs. All 36 samples originated from South Asia, with 35 derived from Bangladesh. Amongst the six genomes with an intact gtr V gene, alignment revealed a 10 bp tandem repeat (TATCAAACCA) between positions 217–316 bp, with up to nine repeat units (Table 3, Fig. 4). We further verified this signal through long-read sequencing four isolates from UK travellers to South Asia (from where the signal was detected in the GEMS data, see Supplementary Data). This repetitive region likely disrupted genome assembly, potentially explaining negative gtr V results and laboratory misclassification of Serotype 5b isolates as 3a, but more importantly may also interrupt gene function as a 10 bp repeat might induce frameshift mutations, indicating the possibility that this may be a viable means of vaccine escape. Owing to this important observation, we recommend that samples positive for all of ipaH , gtr X, and oac are interpreted as being either Serotype 3a or Serotype 5b in our mPCR, particularly in South Asia. And notably, although the seroaggutinnation suggests that a vaccine generating antibodies against Serotype 3a would provide protection against these 5b isolates, close surveillance in the South Asia region during any vaccine rollout would be important. Table 3 Characterisation of tandem repeat sequences within gtr V gene amongst selected Serotype 3a and 5b samples. Sample name GEMS Sample Number % identity to gtr V reference gene Number of tandem repeat sequences Lab ShigaPass ARIBA GEMS-1 600571 95.9 4 3a 5b 3a/5b GEMS-2 604056 95.2 5 GEMS-3 600646 GEMS-4 603091 GEMS-5 601156 94.4 6 GEMS-6 601142 92.3 9 UKHSA-1 NA 95.9 4 NA 5b 3a/5b + CTX-M UKHSA-2 95.2 5 UKHSA-3 91.7 10 UKHSA-4 Alignment of short and long read assemblies to the gtr V reference gene revealed the tandem repeat sequence, likely contributing to the misclassification amongst S. flexneri 5b and 3a by laboratory serotyping in GEMS. Visualised using NCBI Multiple Sequence Alignment Viewer (v1.25.3, https://www.ncbi.nlm.nih.gov/projects/msaviewer/). Having demonstrated that serotypic prediction of our mPCR was comparable to genotypic prediction methods, we then addressed whether data generated from the mPCR diagnostic would also be valuable for surveillance. For this, we compared the proportional distribution of real (from GEMS data) and mPCR serotypes (inferred from ARIBA) across time and region (Fig. 5). This revealed that the mPCR was able to similarly detect shifts in seroprevalence in the GEMS dataset with the only major discrepancy being the refinement of classification of aforementioned Serotype 3a isolates being reclassified as Serotype 3a/5b. Notably, amongst the 188 samples classified as Shigella /EIEC by ARIBA, 136 (72.3%) were Serotype 6 by GEMS, supporting the recommendation that Shigella /EIEC samples should be interpreted as S. flexneri 6 for surveillance purposes. Therefore, our results suggest that in addition to its use as a discriminative molecular diagnostic, our mPCR could contribute to meaningful surveillance efforts in an era of vaccination. Serotype distributions presented by year of isolation (A) and geographic region (B). Each stacked bar shows the proportional assignment of Shigella serotypes based on laboratory-based serotyping (GEMS) and ARIBA in silico genomic predictions. Bars represent the percentage of isolates per method within each time period or region Transfer of mPCR to C-PAS for gel-free method of detection Having demonstrated the high utility of the mPCR in endemic regions, we then conducted mPCR with tagged primers and enabled visualisation using C-PAS lateral flow strips (dipsticks) for a gel-free method of interpreting results for serotype classification. Thirteen DNA samples representing all mPCR targets were selected to test this visualisation. The samples covered the full range of target serotypes present in the Batch 1 and 2 DNA collection available (Table 4). For cross-validation, PCR amplicons from the same samples were also visualised using standard gel electrophoresis (Fig. 6A). The C-PAS results were obtained from separate mPCR runs of the same samples. Band positions on the C-PAS differ from the gel image, with serotyping markers grouped first (from the bottom) followed by the antimicrobial resistance (AMR) markers and the internal control at the top (Fig. 6B). Markers are ordered such to facilitate clearer interpretation of the assay results. PCR products were visualised using the C-PAS and results were determined after the recommended 10-minute incubation (Fig. 6B). Gel electrophoresis confirmed that tagged primers maintained assay specificity, with distinct amplicons in all samples and no bands observed in negative controls (Lanes 14–16). Table 4 List of samples tested with tagged primers and their expected amplicons to be visualised. Lane / C-PAS in Figs. 5 and 6 Sample number Species/serotype Expected bands 1 S051 S. flexneri 1a ipaH + gtr I 2 S019 S. flexneri 1b ipaH + gtr I + oac 3 S060 S. flexneri 1c(7a) ipaH + gtr I 4 S005 S. flexneri 2a ipaH + gtr II 5 S057 S. flexneri 2b ipaH + gtr II + gtr X 6 S024 S. flexneri 3a ipaH + gtr X + oac + CTX-M 7 S068 S. flexneri 5b ipaH + gtr X + oac 8 S029 S. flexneri 6 ipaH 9 S158 S. sonnei ipaH + SS + mph (A) + CTX-M 10 S030 S. sonnei ipaH + SS 11 S033 S. boydii ipaH 12 S038 S. dysenteriae ipaH + CTX-M 13 S162 E. coli mph (A) 14 NA NFW NA 15 Blank 16 Discussion This study addresses the need for a low-cost, field-deployable molecular tool to support the diagnosis and clinical management of Shigella as well as surveillance through the era of vaccination. A key strength of this assay is its focus on the vaccine-relevant Shigella serotypes and species, S. flexneri 1b, 2a, 3a, and S. sonnei , prioritised due to their global prevalence, inclusion in candidate vaccines, and potential to induce cross-protection. The integration of internal controls and antimicrobial resistance markers within the same multiplex reaction also reflects a pragmatic approach to real-world clinical and surveillance challenges. Based on current reagent prices, the estimated per-sample cost of the assay using the tagged primers and C-PAS visualisation is around £4.70. The discrepancies identified during the initial singleplex testing of Serotype 4 samples likely reflects cross-reactivity of group antigens: Serotype 4a shares antigens 3,4 with Serotypes 2a, 5a, and Y, while 4b shares antigen 6 with Serotypes 1b, 3a, and 3b 66 . Without type-specific antigen markers such as gtr IV, accurate resolution of Serotype 4 is challenging. The gtr IV-negative singleplex PCR results confirm these were not Serotype 4, highlighting the limitations of serological methods in distinguishing closely related serotypes. The results from the Batch 2 DNA panel underscore the high analytical performance of the multiplex PCR assay. The assay demonstrated 100% specificity and an overall sensitivity of 97%, outperforming ShigaTyper, particularly in distinguishing S. sonnei and vaccine-prioritised S. flexneri serotypes. These results also confirmed the mPCR assay’s high concordance with both phenotypic serotyping and the ShigaPass in silico tool, reinforcing its reliability across diverse genetic backgrounds. Discrepancies were limited to serotypes not targeted by the current panel. These findings reinforce the value of the mPCR assay as a robust, field-deployable tool for Shigella surveillance, capable of addressing misclassification issues common with serological methods and providing more reliable data to inform vaccine strategies. Compared to traditional serotyping, the mPCR assay not only resolved more serotypes with greater accuracy but also avoided ambiguity in samples where agglutination was limited by antigen cross-reactivity or incomplete panels. Its superior performance over ShigaTyper, particularly in the accurate identification of S. sonnei , positions this assay as a more dependable option for both laboratory and field use, reducing reliance on complex genomic tools while delivering results aligned with public health priorities. When benchmarked against GEMS samples previously serotyped by agglutination, and using ARIBA as a proxy for our mPCR, the assay demonstrated strong performance. Taking the GEMS classifications as the reference, the assay achieved an overall sensitivity of 97.3% and a specificity of 100% for species-level identification. The absence of false positives and high predictive values underscore the diagnostic reliability of the assay. Moreover, the assay consistently outperformed ShigaTyper, and performed comparably to ShigaPass in silico prediction tools. The discrepancies observed in the GEMS dataset, including misclassification of 5b samples due to gtr V gene disruption, highlight the limitations of agglutination-based typing and the enhanced resolution offered by the mPCR approach. As a result, serotype 3a was overrepresented in the GEMS dataset, underscoring the limitations of phenotypic methods in distinguishing these serotypes. The C-PAS platform itself also represents a notable advancement in accessibility, especially for decentralised laboratories. Its rapid and equipment-free readout offers clear benefits in contexts where gel imaging systems or cold chain storage are not available. These properties, combined with the assay’s high specificity and minimal background signal, make it highly adaptable for use in district hospitals, outbreak investigations, or field surveillance settings. To further enhance field deployability, this assay can be performed on a compact, portable, mini-PCR platform (US $ 835) enabling amplification and detection to be performed entirely outside of conventional laboratory infrastructure. This integration aims at creating a fully portable, diagnostic workflow that is less reliant on the cold chain and capable of delivering serotype specific results at the point of need, thus supporting real-time public health responses in low-resource and remote settings. While the mPCR assay performed well with the samples included in this study, further validation on a broader range of Shigella strains is needed to ensure its effectiveness across different geographical regions and strain variants. Additional testing on a wider panel of non- Shigella bacteria is also required to robustly assess assay specificity. There is also the potential to develop this assay into a portable qPCR platform, with multiple targets detected in each of the four common detection channels, based on previous research 67 . Taken together, these findings emphasise the unique value of the mPCR assay as a field-ready diagnostic capable of supporting global Shigella control and surveillance initiatives. It overcomes several limitations of serological-based methods, and its design is intentionally modular, allowing for future adaptation as epidemiology shifts or new serotypes are prioritised for inclusion in vaccines. Conclusion We have developed and validated a rapid, cost-effective, and portable molecular assay for the serotyping of S. flexneri , capable of distinguishing vaccine-relevant serotypes. While certain discrepancies in serotype classification highlight the complexity of Shigella serotyping, the assay’s overall performance holds strong potential for advancing Shigella diagnostics, particularly in low-resource settings. The assay fills a critical gap in diagnostic capacity for Shigella surveillance and has immediate application in supporting vaccine rollout and monitoring. Methods: Bacterial DNA samples A total of 138 DNA samples were obtained for testing. Of these, 37 samples (S002 – S038, Batch 1) had only been serotyped using traditional agglutination methods, while 100 samples (S039 – S146, Batch 2) had also undergone whole genome sequencing as part of a previous study 50 . DNA of the S. sonnei reference strain (NCTC12984) was obtained from the United Kingdom Health Security Agency (UKHSA, London) and was included in Batch 1 (as Sample S001). The full list of DNA samples and their respective accession numbers (where applicable), as well as laboratory and in silico testing results, are provided in Supplementary Tables 1 and 5. In silico primer design Primers were designed using Geneious Prime (v2024.0.7 and v2025.2.1, https://www.geneious.com ) using Primer3 51 ( https://sourceforge.net/projects/primer3 ), and NCBI Primer-BLAST 52 , with distinct amplicon sizes (over 50 bp apart) for clear gel separation. The bla CTX−M primers included degenerate bases to detect multiple major variants (CTX-M-1/3/14/15/27/55). An RNaseP gene target for Human DNA was also included as an internal control for future use on clinical samples. Further details of the final primers used in the mPCR assay are listed in Table 5 . Additional primers for gtr IV, gtr V, and wzx 6 genes were designed and tested on Batch 1 DNA samples in singleplex reactions only to distinguish Serotypes 4, 5, and 6, respectively (Supplementary Methods, Supplementary Table 6). Table 5 Primer sequences for mPCR Shigella serotyping. Target Target gene Serotype/variant specificity Primer name Primer sequence (5’-3’) Amplicon size (bp) Genome accession number a Source Shigella flexneri gtr I 1a, 1b, 1d, 7a, 7d gtrI_F CTGTTAGGTGATGATGGCTTAG b 1122 AF139596 Sun et al. 53 gtrI_R ATTGAACGCCTCCTTGCTATGC b gtr II 2a, 2b gtrII_F TGCAAATCTCCTTGCCTTCA 130 AF021347.1 This study gtrII_R CCCAAGCGTGATTGTTTGATAA gtr X 1d, 2b, 3a, 5b, X, Xv gtrX_F TGGCTTAGGCGCATTGACAT 464 L05001.1 gtrX_R AATGGACCGCTCAATCCAGA oac 1b, 3a, 3b, 4b, 5a, 5b oac_F GCATAAGAGCAACTGCTTTG 627 AF547987.1 oac_R GCCATAGTGGCACCAAAA Shigella sonnei DNA methylase SS SS_F TTACCGTTCGGAATTGGGGG 398 CP000038.1 Cho et al. 54 SS_R CGTAAGGCGGATTCCCTACC Shigella /EIEC ipaH All Shigella ipaH_F TGATGCCACTGAGAGCTGTG 262 M32063.1 This study ipaH_R GGCAGTGGAGAGCTGAAGTT 3GC-resistance bla CTX−M CTX-M-1,3,14, 15,27,55 CTX-M_F GCCGCTKTATGCGCARACG 765 OQ291179.1 CTX-M_R ACATCGCGRCGGCTYTCTGC Macrolide resistance mph (A) - mphA_F TCGTCGTGGCCAGATTTCTC 199 OQ230388.1 mphA_R CCGCTTCATACGTGAGGAGG Internal control RNaseP Human DNA RNaseP_F AGATTTGGACCTGCGAGCG 65 DQ896488.2 RNaseP_R GAGCGGCTGTCTCCACAAGT a Reference genome from GenBank used for primer design. b Primer sequences were taken from a previous study 53 ; SS = S. sonnei ; 3GC = Third-generation cephalosporin Multiplex PCR protocol and visualisation Multiplex PCR was performed using the Qiagen Multiplex PCR Kit (Qiagen, USA). Reaction mixtures consisted of 2X PCR Master Mix, varying optimised concentrations of the primer pairs, and 1 µ l of template DNA in a final volume of 25 µ l. PCR reactions were performed on a T100 thermal cycler (BioRad Laboratories, USA), or on a mini16X portable thermal cycler (miniPCR bio™, USA), with the following protocol: 95°C for 10 min; 30 cycles of 94°C for 30 s, 62°C for 45 s, and 72°C for 90 s; with a final extension of 72°C for 5 min. Products were visualised on 2% (w/v) agarose gels using a ChemiDoc MP imaging system (BioRad Laboratories Inc., USA). C-PAS visualisation PCR amplicons were also visualised using a single-stranded tag hybridisation Chromatographic Printed-Array Strip (C-PAS; TBA Co., Ltd., Japan). Briefly, 10 µ l of salt-free (0 mM NaCl concentration) dilution buffer was combined with 1 µ l of latex beads for each sample, then 5 µ l of the post-PCR amplicon was added and mixed. The C-PAS was placed inside each tube and results were recorded after 10 minutes. Unlike the positive amplicon bands visualised by gel electrophoresis separated by size, the C-PAS was able to display any positive bands in any position (see Results). DNA library preparation and DNA sequencing To resolve classification discrepancies, a subset of samples from Batch 1 were whole genome sequenced using Oxford Nanopore Technologies (ONT) Rapid Barcoding Kit (RBK004) and sequenced on a MinION FLO-MIN106D R9.4.1 flow cell. Super-accuracy basecalling was performed with Guppy (v6.5.7, https://community.nanoporetech.com ) , utilising GPUs on an HPC cluster. The resulting FASTQ files were processed for adapter removal using Porechop (v0.2.4, https://github.com/rrwick/Porechop ) , length and quality filtering with Filtlong (v0.2.1, https://github.com/rrwick/Filtlong ) and NanoFilt 55 (v2.8.0), and de novo genome assembly with Flye 56 (v2.9-b1768). The resulting sequencing data and genome assemblies were used with in silico serotyping tools. An additional four S. flexneri 5b strains obtained from UKHSA were submitted for long-read Bacterial Genome Sequencing, which was performed by Plasmidsaurus ( https://plasmidsaurus.com/ ) using ONT with v14 library preparation chemistry on R10.4.1 flow cells. While the filtering and assembly tools matched those described above, genome polishing and annotation were performed using Medaka (v1.8.0, https://github.com/nanoporetech/medaka ) and Bakta 57 (v1.6.1), respectively. Sample accession numbers for the FASTQ long-read sequencing data generated in this study are provided in Supplementary Table 7. In silico serotyping In silico analysis was performed using three methods: ShigaTyper 47 (v2.0.5, https://github.com/CFSCAN-Biostatistics/shigatyper ), ShigaPass 48 (v1.5.0, https://github.com/imanyass/ShigaPass ), and ARIBA 65 (v2.14.6, https://github.com/sanger-pathogens/ariba ). For ARIBA, the mPCR amplicon sequences were used to create a custom database for screening. Results for all methods were recorded and compared to the mPCR assay and laboratory agglutination results. Analyses were performed on WGS data from two datasets: Batch 2 DNA samples ( n = 100, Supplementary Table 5), and previously published GEMS isolates ( n = 1,074, Supplementary Data). Phylogeny reconstruction For the 100 samples from batch 2, Illumina FASTQ sequence reads were retrieved using the accession numbers, trimmed and assembled with Shovill (v1.1.0, https://github.com/tseemann/shovill ) using default settings and assembly quality was assessed using QUAST 58 (v5.0.2). Prokka 59 (v1.14.0) annotated genome assemblies were analysed with Panaroo 60 (v1.2.7) under default settings to generate a core-genome alignment, from which phylogenetic trees were reconstructed using IQ-TREE 61 – 63 (v2.3.0). The optimal substitution model was selected using IQ-TREE's ModelFinder Plus 64 (-m MFP option), and branch support was assessed by performing 1000 ultrafast bootstrap replicates (-bb 1000). Whole genome phylogenies for 1,074 draft genomes generated from the Global Enteric Multicentre Study (GEMS) 9 , 42 samples analysed, which were collected across seven LMICs (Supplementary Data), were constructed as previously described 9 . Maximum-likelihood phylogenetic reconstruction was performed with a chromosomal SNP alignment (73,525 bp) and midpoint rooted. Data availability Sequencing data from this project has been deposited under the ENA BioProject PRJEB90075 (ERP173090). Author’s contribution A. M., M. A. H. and K. S. B. conceptualised and designed the study. F. K. and D. J. P. performed the laboratory experiments. P. M. D., C. J., D. M., J. J. J., M. I., B. V., and A. P. provided DNA samples used for testing. F. K., X. B., and C. E. 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18:55:32","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":273162,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/b02457a0f3e112b130970c74.png"},{"id":93169488,"identity":"e15a48a3-5a7f-42cf-bd3b-4372041cbc15","added_by":"auto","created_at":"2025-10-09 18:47:32","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178114,"visible":true,"origin":"","legend":"","description":"","filename":"NCOMMS25642140structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/474253c690cee78b783f5305.xml"},{"id":93170096,"identity":"d26a7002-edc4-40fe-bb22-9e2bff1d8157","added_by":"auto","created_at":"2025-10-09 18:55:32","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":198541,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/0ef5fff41a63cd505d26649e.html"},{"id":93170095,"identity":"d15f647e-d742-4dd5-a1ec-b4a84760f7bf","added_by":"auto","created_at":"2025-10-09 18:55:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2513078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of multiplex PCR testing on Batch 1 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eShigella\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e DNA samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/cfd3a3e8e4754474488b6012.png"},{"id":93169471,"identity":"400c52d6-d931-4b66-b5fd-115a2828942c","added_by":"auto","created_at":"2025-10-09 18:47:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":712294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of serotype classification tools for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. flexneri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e samples from Batch 2 DNA\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/98c76c0a56f367f9cc1bdae8.png"},{"id":93170357,"identity":"c87f4f79-7895-46a0-a6f6-0b31e38339a3","added_by":"auto","created_at":"2025-10-09 19:03:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1317298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of serotype classification tools for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. flexneri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e samples from GEMS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal laboratory serotyping results are shown (GEMS) as well as predictions from three \u003cem\u003ein silico\u003c/em\u003e tools (ShigaTyper, ShigaPass and ARIBA). A phylogenetic tree is used to illustrate genomic relatedness. Coloured tiles indicate predicted serotypes, highlighting areas of concordance and discrepancy. \u003cstrong\u003eA:\u003c/strong\u003e All 769 \u003cem\u003eS. flexneri\u003c/em\u003e samples. Clades corresponding to Serotypes 6 and 3a/5b are annotated. \u003cstrong\u003eB:\u003c/strong\u003e Phylogenetic tree and serotyping classification of 109 \u003cem\u003eS. flexneri\u003c/em\u003e 3a and 5b samples from GEMS with additional annotations of Country and Region.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/f4ef84ae450ee84b9c884279.png"},{"id":93169473,"identity":"d50877dc-3927-434b-9301-bb5a2d0b985a","added_by":"auto","created_at":"2025-10-09 18:47:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":721819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlignment of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. flexneri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e 5b genome assemblies to the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003egtr\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eV gene reference sequence.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/e860bfcc2aced3f2f6109139.png"},{"id":93169483,"identity":"130ef6b9-af7d-45f0-9c47-b8436a05469f","added_by":"auto","created_at":"2025-10-09 18:47:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1064994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of serotype distributions across detection methods in 1,074 GEMS samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerotype distributions presented by year of isolation (A) and geographic region (B). Each stacked bar shows the proportional assignment of \u003cem\u003eShigella\u003c/em\u003e serotypes based on laboratory-based serotyping (GEMS) and ARIBA \u003cem\u003ein silico\u003c/em\u003e genomic predictions. Bars represent the percentage of isolates per method within each time period or region\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/0169e0a944d9e48c44aaebf7.png"},{"id":93170094,"identity":"7e6511fc-037b-4d77-8adc-b0133115f2e9","added_by":"auto","created_at":"2025-10-09 18:55:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1328965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGel image and C-PAS dipsticks results of sample tested with tagged primers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA: \u003c/strong\u003eGel image with expected amplicon bands labelled.\u003cstrong\u003e B: \u003c/strong\u003eSchematic of C-PAS dipstick showing positive markers (left) and results of C-PAS testing DNA samples (right).\u003cstrong\u003e \u003c/strong\u003eThe image illustrates the detection of target amplicons and their placement on the C-PAS dipsticks. Positive results are visualised as distinct blue lines corresponding to the specific PCR targets. Red lines represent positional markers printed on the C-PAS. SS = \u003cem\u003eShigella sonnei \u003c/em\u003eIC = Internal Control.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/e408e977f3b20855bd2232f1.png"},{"id":93225136,"identity":"e2f125d2-3e4d-4698-b2d8-779037926234","added_by":"auto","created_at":"2025-10-10 12:00:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9136058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/3ca20df9-2f9d-4fd7-b3fe-1a26c070fe24.pdf"},{"id":93169472,"identity":"72773578-2e26-419b-add7-ff4d0abe7c48","added_by":"auto","created_at":"2025-10-09 18:47:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":131372,"visible":true,"origin":"","legend":"Supplementary Dataset","description":"","filename":"SupplementaryDatafinal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/5db5e3344bdc89026cf5b589.xlsx"},{"id":93169476,"identity":"4ff9939c-a5b4-4a4b-a9da-9d45fd75fd32","added_by":"auto","created_at":"2025-10-09 18:47:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":649656,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"Supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7399876/v1/4dd89501415d6d476129d05b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A low resource requirement molecular diagnostic and surveillance tool for Shigella in the era of vaccination","fulltext":[{"header":"Introduction","content":"\u003cp\u003eShigellosis, caused by species of \u003cem\u003eShigella\u003c/em\u003e, is a major cause of bacterial dysentery and is responsible for substantial morbidity and mortality \u0026ndash; particularly among young children in low- and middle-income countries (LMICs), where over 60,000 child deaths are estimated to be attributable to \u003cem\u003eShigella\u003c/em\u003e annually\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These infections are commonly associated with poor water quality and hygiene conditions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The pathogen is classified into four species: \u003cem\u003eS. boydii\u003c/em\u003e, \u003cem\u003eS. dysenteriae\u003c/em\u003e, S. \u003cem\u003eflexneri\u003c/em\u003e and \u003cem\u003eS. sonnei\u003c/em\u003e, each demonstrating distinct epidemiological patterns\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Among these, \u003cem\u003eS. flexneri\u003c/em\u003e remains the predominant cause of endemic shigellosis in LMICs whereas \u003cem\u003eS. sonnei\u003c/em\u003e is more prevalent in industrialised nations\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, increasing cases of \u003cem\u003eS. sonnei\u003c/em\u003e outbreaks in Asia, Latin America, and the Middle East are becoming more common\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Since the 1970s, predominantly in higher-income countries, shigellosis has also been recognised as a sexually transmitted infection, commonly isolated among men who have sex with men\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMultidrug resistant (MDR) and extensively drug resistant (XDR) \u003cem\u003eShigella\u003c/em\u003e strains, particularly in \u003cem\u003eS. flexneri\u003c/em\u003e and \u003cem\u003eS. sonnei\u003c/em\u003e species, are increasingly reported worldwide, with growing resistance to both fluoroquinolones and macrolides, significantly limiting oral treatment options\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Since the first report of XDR \u003cem\u003eS. sonnei\u003c/em\u003e in Vietnam in 2014\u003csup\u003e23\u003c/sup\u003e, international outbreaks have been linked to samples carrying \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e and \u003cem\u003emph\u003c/em\u003e(A) genes, conferring resistance to third-generation cephalosporins and macrolides respectively\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This escalating resistance trend underscores the urgent need for new treatment strategies and highlights \u003cem\u003eShigella\u003c/em\u003e as a global priority for vaccine development\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhilst there is no approved vaccine, the existing candidates focus on specific \u003cem\u003eS. flexneri\u003c/em\u003e serotypes and \u003cem\u003eS. sonnei\u003c/em\u003e\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, making serotyping and species identification essential for vaccine evaluation as well as controlling outbreaks and understanding epidemiological trends. One such vaccine candidate, a quadrivalent formulation called altSonflex1-2-3, currently in Phase-II clinical trials, is based on Generalised Modules Membrane for Antigens (GMMA) technology and combines a novel \u003cem\u003eS. sonnei\u003c/em\u003e construct with Outer Membrane Vesicles (OMVs) derived from \u003cem\u003eS. flexneri\u003c/em\u003e 1b, 2a, and 3a strains, selected for their epidemiological relevance\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and their ability to induce cross-reactive antibodies, offering potential broad protection against the most prevalent \u003cem\u003eShigella\u003c/em\u003e serotypes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTraditional serotyping methods, relying on agglutination and antisera, have long been the gold standard for \u003cem\u003eShigella\u003c/em\u003e classification\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, these methods are relatively expensive, labour-intensive, require a high degree of technical skills and/or training, time-consuming, are limited due to antigen cross-reactivity, and require robust supply chains, making them less practical in resource-limited settings. In contrast, molecular diagnostic methods such as multiplex PCR (mPCR) offer significant advantages in terms of speed, scalability, cost-effectiveness, and offer infrastructural capabilities across a broad range of pathogens. The use of real-time quantitative PCR (qPCR) has been shown to detect \u003cem\u003eShigella\u003c/em\u003e at significantly higher rates than traditional culture-based techniques\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. For example, re-analysis of the Global Enteric Multicentre Study (GEMS)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e data using a qPCR TaqMan Array Card (TAC) suggests that the true burden of \u003cem\u003eShigella\u003c/em\u003e may be up to twice as high as previously estimated\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, underscoring the limitations of conventional diagnostics. Similarly, while genomic tools such as ShigaTyper\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and ShigaPass\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e offer \u003cem\u003ein silico\u003c/em\u003e serotype predictions based on whole genome sequencing (WGS) data. These methods rely on high-quality sequencing and bioinformatics infrastructure. In addition, qPCR and WGS methods also remain relatively expensive, and require trained personnel and specialist equipment for use.\u003c/p\u003e\u003cp\u003eThere is currently no rapid, affordable point-of-care test available for \u003cem\u003eShigella\u003c/em\u003e species identification and serotyping, and diagnosis cannot reliably be made based on clinical criteria alone\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Here, we report the development and optimisation of a low-cost multiplex PCR assay that identifies vaccine-targeted serotypes, \u003cem\u003eS. flexneri\u003c/em\u003e 1b, 2a, 3a, and \u003cem\u003eS. sonnei\u003c/em\u003e, as well as antimicrobial resistance genes associated with resistance to third-generation cephalosporins and macrolides. This assay functions without the need for extensive laboratory infrastructure or whole genome sequencing, offering a rapid and scalable solution for portable, gel-free oligo-chromatographic system, enabling straightforward visual interpretation in both laboratory and remote environments.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eDevelopment and validation of the multiplex PCR on clinical samples\u003c/h2\u003e\n \u003cp\u003eTo evaluate primer specificity and optimise target selection for discrimination of serotypes, Samples S001-S038 were subjected to singleplex PCR, with each primer pair tested across all DNA samples. Only primers demonstrating strong, specific amplification of their respective target genes, without cross-reactivity to non-target serotypes, were selected for inclusion in the final multiplex assay (See Methods). These samples, previously serotyped only by slide agglutination and lacking genomic data, had been classified at the serotype level but not further resolved into sub-serotypes (e.g. reported as Serotype 1 rather than 1a, 1b, 1c, etc.), providing a valuable baseline for PCR-based validation. For these singleplex reactions only, additional targets for \u003cem\u003egtr\u003c/em\u003eIV, \u003cem\u003egtr\u003c/em\u003eV, \u003cem\u003ewzx\u003c/em\u003e6, and \u003cem\u003eopt\u003c/em\u003e were also included (Supplementary Methods).\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eipaH\u003c/em\u003e gene target was detected in all \u003cem\u003eShigella\u003c/em\u003e samples, while \u003cem\u003eS. flexneri\u003c/em\u003e and \u003cem\u003eS. sonnei\u003c/em\u003e markers showed no cross-reactivity with \u003cem\u003eS. boydii\u003c/em\u003e or \u003cem\u003eS. dysenteriae\u003c/em\u003e, confirming assay specificity. PCR results enabled refined subtyping of all \u003cem\u003eS. flexneri\u003c/em\u003e Serotypes 1, 2, and 3 as 1b (\u003cem\u003egtr\u003c/em\u003eI\u0026thinsp;+\u0026thinsp;oac), 2a (\u003cem\u003egtr\u003c/em\u003eII), and 3a (\u003cem\u003egtr\u003c/em\u003eX\u0026thinsp;+\u0026thinsp;\u003cem\u003eoac\u003c/em\u003e), respectively, providing greater resolution than the available laboratory serotyping results (Supplementary Table 1). Four samples (S008, S009, S025, and S026), originally identified as \u003cem\u003eS. flexneri\u003c/em\u003e Serotype 4, showed discrepant PCR results, initially amplifying only \u003cem\u003egtr\u003c/em\u003eII or \u003cem\u003egtr\u003c/em\u003eX, in addition to the \u003cem\u003eipaH\u003c/em\u003e marker. These samples were also negative for the Serotype 4 type-specific \u003cem\u003egtr\u003c/em\u003eIV marker (Supplementary Fig. 1). Long-read whole genome sequencing and \u003cem\u003ein silico\u003c/em\u003e serotype prediction classified these samples as Serotype 2a, Yv, Xv, and X, respectively (Supplementary Table 2). Serotype 5 samples were not represented in this initial set (Supplementary Fig. 1).\u003c/p\u003e\n \u003cp\u003eFollowing initial singleplex PCR validation, we then optimised the performance of the serotype discrimination as a multiplex PCR and two key antimicrobial resistance markers were added: \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e, covering major Extended-Spectrum \u0026beta;-Lactamase (ESBL) variants conferring resistance to third-generation cephalosporins, and \u003cem\u003emph\u003c/em\u003e(A) conferring macrolide resistance, to enhance the clinical utility (See Methods). This refined mPCR panel was applied to the same Batch 1 DNA (Samples S001-S038) with results visualised by gel electrophoresis (Fig.\u0026nbsp;1).\u003c/p\u003e\n \u003cp\u003eThe optimised mPCR assay successfully amplified the target serotype markers (\u003cem\u003egtr\u003c/em\u003eI, \u003cem\u003egtr\u003c/em\u003eII, SS, \u003cem\u003egtr\u003c/em\u003eX, and \u003cem\u003eoac\u003c/em\u003e) in the expected samples, consistent with the results of the singleplex testing. The \u003cem\u003eipaH\u003c/em\u003e genus-level \u003cem\u003eShigella\u003c/em\u003e/EIEC marker was reliably detected across all samples. Additionally, five samples were positive for the CTX-M marker, and Sample S035 (\u003cem\u003eS. boydii\u003c/em\u003e, lane 33) positive for \u003cem\u003emph\u003c/em\u003e(A). The faint \u003cem\u003eipaH\u003c/em\u003e and SS_DNAm bands observed in only Sample S035 suggest a sample-specific issue rather than a protocol-related limitation, and no spurious amplifications occurred in other samples (Fig. 1). Although targeted to the four serotypes, there is greater decoding capacity among the mPCR target combinations (shown in Table 1).\u003c/p\u003e\n \u003cp\u003eMultiplex PCR on Batch 1 DNA samples using primers listed in Table 1. Lane L: 100 bp molecular ladder; Lanes 1\u0026ndash;38: Batch 1 DNA samples; Lane 39: \u003cem\u003eE. coli\u003c/em\u003e BL21; Lane 40: Nuclease free water (NFW). SS\u0026thinsp;=\u0026thinsp;\u003cem\u003eS. sonnei\u003c/em\u003e DNAm marker. Amplicon bands are labelled. Full sample list with results is provided in Supplementary Table 1.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDecoded serotype classifications from the mPCR assay.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emPCR classification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS_DNAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egtr\u003c/em\u003eI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egtr\u003c/em\u003eII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egtr\u003c/em\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eoac\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. sonnei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1a/1c(7a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3a/5b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3b/4b/5a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX/Xv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e/EIEC\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-\u003cem\u003eShigella\u003c/em\u003e/EIEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ePossible species/serotype classifications based on presence (+) or absence (-) of specific markers in the mPCR assay. Each classification is associated with a unique combination of markers. \u003csup\u003ea\u003c/sup\u003eRepresents one of 4a/6/Y/Yv/EIEC/\u003cem\u003eS. dysenteriae\u003c/em\u003e/\u003cem\u003eS. boydii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExtended and\u003c/strong\u003e \u003cstrong\u003ein silico\u003c/strong\u003e \u003cstrong\u003evalidation of mPCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo extend the external validation of the mPCR assay we then corroborated the mPCR results against a larger set of samples for which whole genome sequencing was available. Specifically, we used a second batch of DNA samples comprising \u003cem\u003eS. flexneri\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55), and \u003cem\u003eS. sonnei\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45) which had been serotyped using slide agglutination. For this, we also ran an \u003cem\u003ein silico\u003c/em\u003e simulation of the mPCR amplicon detection using a genomic detection method (implemented with ARIBA, see Methods).\u003c/p\u003e\n \u003cp\u003eSpecies-level identification showed 94.5% sensitivity and 100% specificity for \u003cem\u003eS. flexneri\u003c/em\u003e, and 100% sensitivity and specificity for \u003cem\u003eS. sonnei\u003c/em\u003e. This yielded an overall sensitivity of 97% and specificity of 100% for Batch 2 DNA samples. A summary of species-level comparisons and performance metrics is provided in Supplementary Table\u0026nbsp;3.\u003c/p\u003e\n \u003cp\u003eOut of the 55 \u003cem\u003eS. flexneri\u003c/em\u003e samples, only 35 were subtyped by the original laboratory agglutination method (Fig. 2), which identified serotypes 1a, 1b, 1c(7a), 2a, 2b, 3a, and 5b. Amongst the three discrepant mPCR results, Sample S042 had not been serotyped beyond species level by agglutination and was classified as \u003cem\u003eShigella\u003c/em\u003e/EIEC by mPCR, while both ShigaTyper and ShigaPass identified it as Serotype 4av, consistent with the absence of a specific target in the mPCR panel. In this context, detection of only the \u003cem\u003eipaH\u003c/em\u003e gene without serotype-specific amplification aligns with the expected result. Two further samples (S089 and S096) were classified as non-\u003cem\u003eShigella\u003c/em\u003e/EIEC by mPCR, due to the absence of all targets, yet ARIBA classified as \u003cem\u003eShigella\u003c/em\u003e/EIEC.\u003c/p\u003e\n \u003cp\u003eOriginal laboratory serotyping results are shown (Lab) as well as classification from mPCR and \u003cem\u003ein silico\u003c/em\u003e prediction using ARIBA, ShigaPass, and ShigaTyper. A maximum-likelihood phylogenetic tree provides genomic context. Coloured tiles indicate predicted serotypes, highlighting areas of concordance and discrepancy. A result of \u0026lsquo;No data\u0026rsquo; in this context means a classification of \u003cem\u003eS. flexneri\u003c/em\u003e only. Presence (teal) and absence (white) of mPCR gene targets is shown alongside for reference.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePredicted utility of mPCR for surveillance in endemic settings\u003c/h3\u003e\n\u003cp\u003eHaving shown that \u003cem\u003ein silico\u003c/em\u003e screening was an appropriate proxy for mPCR performance, we then used a large WGS dataset from strains collected across seven LMICs to further validate assay performance as a surveillance tool, including relative to serotypic inference by WGS. Specifically, we used \u003cem\u003eS. flexneri\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;769) and \u003cem\u003esonnei\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;305) isolates from GEMS with associated sequence data and agglutination results\u003csup\u003e9\u003c/sup\u003e. We ran three \u003cem\u003ein silico\u003c/em\u003e serotyping methods including: ShigaPass, ShigaTyper, and ARIBA (to detect our mPCR targets as a proxy measure of performance).\u003c/p\u003e\n\u003cp\u003eAlthough the results above suggest laboratory agglutination is fallible, we used this as a gold-standard for evaluating and comparing the results of these three tools. For \u003cem\u003eS. sonnei\u003c/em\u003e the \u003cem\u003ein silico\u003c/em\u003e methods showed concordance rates for species differentiation of 92.1%, 94.4%, and 97.7% for ShigaTyper, ShigaPass, and ARIBA, respectively (Table 2).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCombined species level classification results for GEMS \u003cem\u003eS. flexneri\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;769) and \u003cem\u003eS. sonnei\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;305) samples by \u003cem\u003ein silico\u003c/em\u003e prediction tools.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGEMS \u003cem\u003eS. sonnei\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;305)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGEMS \u003cem\u003eS. flexneri\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;769)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic prediction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eARIBA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShigaPass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShigaTyper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eARIBA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShigaPass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShigaTyper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. sonnei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEIEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e/EIEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-\u003cem\u003eShigella\u003c/em\u003e/EIEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNovel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e305\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e305\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e305\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e769\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e769\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e769\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eARIBA classified seven \u003cem\u003eS. sonnei\u003c/em\u003e samples as \u003cem\u003eShigella\u003c/em\u003e/EIEC, all with the expected \u003cem\u003eS. sonnei\u003c/em\u003e markers (\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;SS_DNAm), but also unexpected serotype markers (e.g. \u003cem\u003egtr\u003c/em\u003eI, \u003cem\u003egtr\u003c/em\u003eII, \u003cem\u003eoac\u003c/em\u003e and \u003cem\u003egtr\u003c/em\u003eX), suggesting \u003cem\u003ein silico\u003c/em\u003e primer misbinding, potential sample contamination, or a previously undescribed fluidity of these mobile genetic element-borne genes in natural bacterial populations, leading to an uncertain identification. Amongst the same seven samples, ShigaTyper classified four as EIEC and two as No prediction. In contrast, ShigaPass classified all seven samples as \u003cem\u003eS. sonnei\u003c/em\u003e, suggesting that using genome assembly sequences may provide more accurate results than FASTQ reads, which could potentially contain cross-contaminated sample reads.\u003c/p\u003e\n\u003cp\u003eThe agreement rates for 769 \u003cem\u003eS. flexneri\u003c/em\u003e GEMS samples revealed 93.2%, 98.2%, and 76.3% concordance with ShigaTyper, ShigaPass, and ARIBA, respectively (Table 3, Fig. 3A). As the mPCR targets specific vaccine serotypes, results from ARIBA would classify 181 samples as \u003cem\u003eShigella\u003c/em\u003e/EIEC, of which 172 were non-target serotypes (Sf6 [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;136], 4a [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34], 3b [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1], Y [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1]), not distinguishable by the assay. Of the remaining nine samples \u0026ndash; originally typed as 1b, 2a, 2b, and 3a \u0026ndash; all showed unexpected amplification in additional PCR targets leading to an uncertain serotype prediction. We recommend that \u003cem\u003eShigella\u003c/em\u003e/EIEC outcomes on the mPCR are most likely Serotype 6 (see Table 2), meaning if the 136 Serotype 6 samples are assigned as correctly classified, the concordance for \u003cem\u003eS. flexneri\u003c/em\u003e serotype identification using ARIBA increases to 94%.\u003c/p\u003e\n\u003cp\u003eHigh concordance with laboratory serotyping was observed across all methods for Serotypes 1b, 2a, and 2b, as well as Serotype 6 using ShigaTyper and ShigaPass. As Serotype 6 is not targeted by the mPCR, ARIBA classified these samples as \u003cem\u003eShigella\u003c/em\u003e/EIEC. All Serotype 4 samples reported by GEMS were classified as 4av and Serotype X samples were consistently identified as Xv with both \u003cem\u003ein silico\u003c/em\u003e tools. Due to the absence of the \u003cem\u003egtr\u003c/em\u003eIV and \u003cem\u003eopt\u003c/em\u003e genes in the final mPCR panel, ARIBA reported all Serotype 4 samples as \u003cem\u003eShigella\u003c/em\u003e/EIEC (\u003cem\u003eipaH\u003c/em\u003e only) and all Serotype X as X/Xv (\u003cem\u003eipaH\u003c/em\u003e and \u003cem\u003egtr\u003c/em\u003eX). Notably, we also observed that isolates of Serotypes 3a and 5b were intermixed on a single clade in the phylogenetic tree (Fig.\u0026nbsp;3B).\u003c/p\u003e\n\u003cp\u003eOriginal laboratory serotyping results are shown (GEMS) as well as predictions from three \u003cem\u003ein silico\u003c/em\u003e tools (ShigaTyper, ShigaPass and ARIBA). A phylogenetic tree is used to illustrate genomic relatedness. Coloured tiles indicate predicted serotypes, highlighting areas of concordance and discrepancy. \u003cstrong\u003eA\u003c/strong\u003e: All 769 \u003cem\u003eS. flexneri\u003c/em\u003e samples. Clades corresponding to Serotypes 6 and 3a/5b are annotated. \u003cstrong\u003eB\u003c/strong\u003e: Phylogenetic tree and serotyping classification of 109 \u003cem\u003eS. flexneri\u003c/em\u003e 3a and 5b samples from GEMS with additional annotations of Country and Region.\u003c/p\u003e\n\u003cp\u003eAlthough both Serotypes 3a and 5b express \u003cem\u003egtr\u003c/em\u003eX and \u003cem\u003eoac\u003c/em\u003e, only 5b samples also express the type-specific \u003cem\u003egtr\u003c/em\u003eV gene, making this marker essential for accurate differentiation. According to the original GEMS classification, 109 isolates were classified as Serotype 3a and only four as 5b. In contrast, \u003cem\u003ein silico\u003c/em\u003e prediction with ShigaPass yielded markedly different results, with 67 samples classified as 3a and 36 as 5b. However, due to the absence of \u003cem\u003egtr\u003c/em\u003eV in the mPCR target panel, results from ARIBA classified a total of 106 samples as Serotype 3a/5b (Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003eAs the phylogenetic admixing between Serotypes 3a and 5b may represent a meaningful means of serotype switching, we sought to explore the biological basis for this signal. We found from the short-read WGS data that only 6 of 36 Serotype 5b genome assemblies contained an intact \u003cem\u003egtr\u003c/em\u003eV gene on a single contiguous sequence. In the remainder, \u003cem\u003egtr\u003c/em\u003eV was split across two contigs. All 36 samples originated from South Asia, with 35 derived from Bangladesh. Amongst the six genomes with an intact \u003cem\u003egtr\u003c/em\u003eV gene, alignment revealed a 10 bp tandem repeat (TATCAAACCA) between positions 217\u0026ndash;316 bp, with up to nine repeat units (Table 3, Fig. 4). We further verified this signal through long-read sequencing four isolates from UK travellers to South Asia (from where the signal was detected in the GEMS data, see Supplementary Data). This repetitive region likely disrupted genome assembly, potentially explaining negative \u003cem\u003egtr\u003c/em\u003eV results and laboratory misclassification of Serotype 5b isolates as 3a, but more importantly may also interrupt gene function as a 10 bp repeat might induce frameshift mutations, indicating the possibility that this may be a viable means of vaccine escape. Owing to this important observation, we recommend that samples positive for all of \u003cem\u003eipaH\u003c/em\u003e, \u003cem\u003egtr\u003c/em\u003eX, and \u003cem\u003eoac\u003c/em\u003e are interpreted as being either Serotype 3a or Serotype 5b in our mPCR, particularly in South Asia. And notably, although the seroaggutinnation suggests that a vaccine generating antibodies against Serotype 3a would provide protection against these 5b isolates, close surveillance in the South Asia region during any vaccine rollout would be important.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacterisation of tandem repeat sequences within \u003cem\u003egtr\u003c/em\u003eV gene amongst selected Serotype 3a and 5b samples.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGEMS Sample Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% identity to \u003cem\u003egtr\u003c/em\u003eV reference gene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of tandem repeat sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLab\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShigaPass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eARIBA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEMS-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e5b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e3a/5b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEMS-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e604056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e95.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEMS-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEMS-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e603091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEMS-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e601156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEMS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e601142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUKHSA-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e5b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e3a/5b\u0026thinsp;+\u0026thinsp;CTX-M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUKHSA-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUKHSA-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e91.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUKHSA-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAlignment of short and long read assemblies to the \u003cem\u003egtr\u003c/em\u003eV reference gene revealed the tandem repeat sequence, likely contributing to the misclassification amongst \u003cem\u003eS. flexneri\u003c/em\u003e 5b and 3a by laboratory serotyping in GEMS. Visualised using NCBI Multiple Sequence Alignment Viewer (v1.25.3, https://www.ncbi.nlm.nih.gov/projects/msaviewer/).\u003c/p\u003e\n\u003cp\u003eHaving demonstrated that serotypic prediction of our mPCR was comparable to genotypic prediction methods, we then addressed whether data generated from the mPCR diagnostic would also be valuable for surveillance. For this, we compared the proportional distribution of real (from GEMS data) and mPCR serotypes (inferred from ARIBA) across time and region (Fig. 5). This revealed that the mPCR was able to similarly detect shifts in seroprevalence in the GEMS dataset with the only major discrepancy being the refinement of classification of aforementioned Serotype 3a isolates being reclassified as Serotype 3a/5b. Notably, amongst the 188 samples classified as \u003cem\u003eShigella\u003c/em\u003e/EIEC by ARIBA, 136 (72.3%) were Serotype 6 by GEMS, supporting the recommendation that \u003cem\u003eShigella\u003c/em\u003e/EIEC samples should be interpreted as \u003cem\u003eS. flexneri\u003c/em\u003e 6 for surveillance purposes. Therefore, our results suggest that in addition to its use as a discriminative molecular diagnostic, our mPCR could contribute to meaningful surveillance efforts in an era of vaccination.\u003c/p\u003e\n\u003cp\u003eSerotype distributions presented by year of isolation (A) and geographic region (B). Each stacked bar shows the proportional assignment of \u003cem\u003eShigella\u003c/em\u003e serotypes based on laboratory-based serotyping (GEMS) and ARIBA \u003cem\u003ein silico\u003c/em\u003e genomic predictions. Bars represent the percentage of isolates per method within each time period or region\u003c/p\u003e\n\u003ch3\u003eTransfer of mPCR to C-PAS for gel-free method of detection\u003c/h3\u003e\n\u003cp\u003eHaving demonstrated the high utility of the mPCR in endemic regions, we then conducted mPCR with tagged primers and enabled visualisation using C-PAS lateral flow strips (dipsticks) for a gel-free method of interpreting results for serotype classification. Thirteen DNA samples representing all mPCR targets were selected to test this visualisation. The samples covered the full range of target serotypes present in the Batch 1 and 2 DNA collection available (Table\u0026nbsp;4). For cross-validation, PCR amplicons from the same samples were also visualised using standard gel electrophoresis (Fig.\u0026nbsp;6A). The C-PAS results were obtained from separate mPCR runs of the same samples. Band positions on the C-PAS differ from the gel image, with serotyping markers grouped first (from the bottom) followed by the antimicrobial resistance (AMR) markers and the internal control at the top (Fig.\u0026nbsp;6B). Markers are ordered such to facilitate clearer interpretation of the assay results. PCR products were visualised using the C-PAS and results were determined after the recommended 10-minute incubation (Fig.\u0026nbsp;6B). Gel electrophoresis confirmed that tagged primers maintained assay specificity, with distinct amplicons in all samples and no bands observed in negative controls (Lanes 14\u0026ndash;16).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eList of samples tested with tagged primers and their expected amplicons to be visualised.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLane / C-PAS in Figs.\u0026nbsp;5 and 6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies/serotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpected bands\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eI\u0026thinsp;+\u0026thinsp;\u003cem\u003eoac\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 1c(7a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eII\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eX\u0026thinsp;+\u0026thinsp;\u003cem\u003eoac\u003c/em\u003e\u0026thinsp;+\u0026thinsp;CTX-M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 5b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003egtr\u003c/em\u003eX\u0026thinsp;+\u0026thinsp;\u003cem\u003eoac\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. flexneri\u003c/em\u003e 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. sonnei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;SS\u0026thinsp;+\u0026thinsp;\u003cem\u003emph\u003c/em\u003e(A)\u0026thinsp;+\u0026thinsp;CTX-M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. sonnei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;SS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. boydii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. dysenteriae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;CTX-M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003emph\u003c/em\u003e(A)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBlank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study addresses the need for a low-cost, field-deployable molecular tool to support the diagnosis and clinical management of \u003cem\u003eShigella\u003c/em\u003e as well as surveillance through the era of vaccination. A key strength of this assay is its focus on the vaccine-relevant \u003cem\u003eShigella\u003c/em\u003e serotypes and species, \u003cem\u003eS. flexneri\u003c/em\u003e 1b, 2a, 3a, and \u003cem\u003eS. sonnei\u003c/em\u003e, prioritised due to their global prevalence, inclusion in candidate vaccines, and potential to induce cross-protection. The integration of internal controls and antimicrobial resistance markers within the same multiplex reaction also reflects a pragmatic approach to real-world clinical and surveillance challenges. Based on current reagent prices, the estimated per-sample cost of the assay using the tagged primers and C-PAS visualisation is around \u0026pound;4.70.\u003c/p\u003e\u003cp\u003eThe discrepancies identified during the initial singleplex testing of Serotype 4 samples likely reflects cross-reactivity of group antigens: Serotype 4a shares antigens 3,4 with Serotypes 2a, 5a, and Y, while 4b shares antigen 6 with Serotypes 1b, 3a, and 3b\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Without type-specific antigen markers such as \u003cem\u003egtr\u003c/em\u003eIV, accurate resolution of Serotype 4 is challenging. The \u003cem\u003egtr\u003c/em\u003eIV-negative singleplex PCR results confirm these were not Serotype 4, highlighting the limitations of serological methods in distinguishing closely related serotypes.\u003c/p\u003e\u003cp\u003eThe results from the Batch 2 DNA panel underscore the high analytical performance of the multiplex PCR assay. The assay demonstrated 100% specificity and an overall sensitivity of 97%, outperforming ShigaTyper, particularly in distinguishing \u003cem\u003eS. sonnei\u003c/em\u003e and vaccine-prioritised \u003cem\u003eS. flexneri\u003c/em\u003e serotypes. These results also confirmed the mPCR assay\u0026rsquo;s high concordance with both phenotypic serotyping and the ShigaPass \u003cem\u003ein silico\u003c/em\u003e tool, reinforcing its reliability across diverse genetic backgrounds. Discrepancies were limited to serotypes not targeted by the current panel. These findings reinforce the value of the mPCR assay as a robust, field-deployable tool for \u003cem\u003eShigella\u003c/em\u003e surveillance, capable of addressing misclassification issues common with serological methods and providing more reliable data to inform vaccine strategies.\u003c/p\u003e\u003cp\u003eCompared to traditional serotyping, the mPCR assay not only resolved more serotypes with greater accuracy but also avoided ambiguity in samples where agglutination was limited by antigen cross-reactivity or incomplete panels. Its superior performance over ShigaTyper, particularly in the accurate identification of \u003cem\u003eS. sonnei\u003c/em\u003e, positions this assay as a more dependable option for both laboratory and field use, reducing reliance on complex genomic tools while delivering results aligned with public health priorities.\u003c/p\u003e\u003cp\u003eWhen benchmarked against GEMS samples previously serotyped by agglutination, and using ARIBA as a proxy for our mPCR, the assay demonstrated strong performance. Taking the GEMS classifications as the reference, the assay achieved an overall sensitivity of 97.3% and a specificity of 100% for species-level identification. The absence of false positives and high predictive values underscore the diagnostic reliability of the assay. Moreover, the assay consistently outperformed ShigaTyper, and performed comparably to ShigaPass \u003cem\u003ein silico\u003c/em\u003e prediction tools. The discrepancies observed in the GEMS dataset, including misclassification of 5b samples due to \u003cem\u003egtr\u003c/em\u003eV gene disruption, highlight the limitations of agglutination-based typing and the enhanced resolution offered by the mPCR approach. As a result, serotype 3a was overrepresented in the GEMS dataset, underscoring the limitations of phenotypic methods in distinguishing these serotypes.\u003c/p\u003e\u003cp\u003eThe C-PAS platform itself also represents a notable advancement in accessibility, especially for decentralised laboratories. Its rapid and equipment-free readout offers clear benefits in contexts where gel imaging systems or cold chain storage are not available. These properties, combined with the assay\u0026rsquo;s high specificity and minimal background signal, make it highly adaptable for use in district hospitals, outbreak investigations, or field surveillance settings. To further enhance field deployability, this assay can be performed on a compact, portable, mini-PCR platform (US\u003cspan\u003e$\u003c/span\u003e835) enabling amplification and detection to be performed entirely outside of conventional laboratory infrastructure. This integration aims at creating a fully portable, diagnostic workflow that is less reliant on the cold chain and capable of delivering serotype specific results at the point of need, thus supporting real-time public health responses in low-resource and remote settings.\u003c/p\u003e\u003cp\u003eWhile the mPCR assay performed well with the samples included in this study, further validation on a broader range of \u003cem\u003eShigella\u003c/em\u003e strains is needed to ensure its effectiveness across different geographical regions and strain variants. Additional testing on a wider panel of non-\u003cem\u003eShigella\u003c/em\u003e bacteria is also required to robustly assess assay specificity. There is also the potential to develop this assay into a portable qPCR platform, with multiple targets detected in each of the four common detection channels, based on previous research\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTaken together, these findings emphasise the unique value of the mPCR assay as a field-ready diagnostic capable of supporting global \u003cem\u003eShigella\u003c/em\u003e control and surveillance initiatives. It overcomes several limitations of serological-based methods, and its design is intentionally modular, allowing for future adaptation as epidemiology shifts or new serotypes are prioritised for inclusion in vaccines.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe have developed and validated a rapid, cost-effective, and portable molecular assay for the serotyping of \u003cem\u003eS. flexneri\u003c/em\u003e, capable of distinguishing vaccine-relevant serotypes. While certain discrepancies in serotype classification highlight the complexity of \u003cem\u003eShigella\u003c/em\u003e serotyping, the assay\u0026rsquo;s overall performance holds strong potential for advancing \u003cem\u003eShigella\u003c/em\u003e diagnostics, particularly in low-resource settings. The assay fills a critical gap in diagnostic capacity for \u003cem\u003eShigella\u003c/em\u003e surveillance and has immediate application in supporting vaccine rollout and monitoring.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eBacterial DNA samples\u003c/h2\u003e\u003cp\u003eA total of 138 DNA samples were obtained for testing. Of these, 37 samples (S002 \u0026ndash; S038, Batch 1) had only been serotyped using traditional agglutination methods, while 100 samples (S039 \u0026ndash; S146, Batch 2) had also undergone whole genome sequencing as part of a previous study\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. DNA of the \u003cem\u003eS. sonnei\u003c/em\u003e reference strain (NCTC12984) was obtained from the United Kingdom Health Security Agency (UKHSA, London) and was included in Batch 1 (as Sample S001). The full list of DNA samples and their respective accession numbers (where applicable), as well as laboratory and \u003cem\u003ein silico\u003c/em\u003e testing results, are provided in Supplementary Tables\u0026nbsp;1 and 5.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eIn silico primer design\u003c/h3\u003e\n\u003cp\u003ePrimers were designed using Geneious Prime (v2024.0.7 and v2025.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneious.com\u003c/span\u003e\u003cspan address=\"https://www.geneious.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Primer3\u003csup\u003e51\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sourceforge.net/projects/primer3\u003c/span\u003e\u003cspan address=\"https://sourceforge.net/projects/primer3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and NCBI Primer-BLAST\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, with distinct amplicon sizes (over 50 bp apart) for clear gel separation. The \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e primers included degenerate bases to detect multiple major variants (CTX-M-1/3/14/15/27/55). An RNaseP gene target for Human DNA was also included as an internal control for future use on clinical samples. Further details of the final primers used in the mPCR assay are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Additional primers for \u003cem\u003egtr\u003c/em\u003eIV, \u003cem\u003egtr\u003c/em\u003eV, and \u003cem\u003ewzx\u003c/em\u003e6 genes were designed and tested on Batch 1 DNA samples in singleplex reactions only to distinguish Serotypes 4, 5, and 6, respectively (Supplementary Methods, Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequences for mPCR \u003cem\u003eShigella\u003c/em\u003e serotyping.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTarget\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTarget gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerotype/variant specificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrimer name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrimer sequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAmplicon size (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGenome accession number\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003egtr\u003c/em\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1a, 1b, 1d, 7a, 7d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egtrI_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCTGTTAGGTGATGATGGCTTAG\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAF139596\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSun \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egtrI_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eATTGAACGCCTCCTTGCTATGC\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003egtr\u003c/em\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2a, 2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egtrII_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTGCAAATCTCCTTGCCTTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAF021347.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egtrII_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCCCAAGCGTGATTGTTTGATAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003egtr\u003c/em\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1d, 2b, 3a, 5b, X, Xv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egtrX_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTGGCTTAGGCGCATTGACAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eL05001.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egtrX_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAATGGACCGCTCAATCCAGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eoac\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1b, 3a, 3b, 4b, 5a, 5b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eoac_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGCATAAGAGCAACTGCTTTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAF547987.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eoac_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGCCATAGTGGCACCAAAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eShigella sonnei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDNA methylase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSS_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTTACCGTTCGGAATTGGGGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCP000038.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCho \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSS_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCGTAAGGCGGATTCCCTACC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e/EIEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eipaH\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAll \u003cem\u003eShigella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eipaH_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTGATGCCACTGAGAGCTGTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM32063.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eipaH_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGGCAGTGGAGAGCTGAAGTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3GC-resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCTX-M-1,3,14, 15,27,55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCTX-M_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGCCGCTKTATGCGCARACG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOQ291179.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCTX-M_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eACATCGCGRCGGCTYTCTGC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMacrolide resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003emph\u003c/em\u003e(A)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emphA_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTCGTCGTGGCCAGATTTCTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOQ230388.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emphA_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCCGCTTCATACGTGAGGAGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eInternal control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRNaseP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHuman DNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRNaseP_F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAGATTTGGACCTGCGAGCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDQ896488.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRNaseP_R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGAGCGGCTGTCTCCACAAGT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003eReference genome from GenBank used for primer design. \u003csup\u003eb\u003c/sup\u003ePrimer sequences were taken from a previous study\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e; SS\u0026thinsp;=\u0026thinsp;\u003cem\u003eS. sonnei\u003c/em\u003e; 3GC\u0026thinsp;=\u0026thinsp;Third-generation cephalosporin\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMultiplex PCR protocol and visualisation\u003c/h2\u003e\u003cp\u003eMultiplex PCR was performed using the Qiagen Multiplex PCR Kit (Qiagen, USA). Reaction mixtures consisted of 2X PCR Master Mix, varying optimised concentrations of the primer pairs, and 1 \u003cem\u003e\u0026micro;\u003c/em\u003el of template DNA in a final volume of 25 \u003cem\u003e\u0026micro;\u003c/em\u003el. PCR reactions were performed on a T100 thermal cycler (BioRad Laboratories, USA), or on a mini16X portable thermal cycler (miniPCR bio\u0026trade;, USA), with the following protocol: 95\u0026deg;C for 10 min; 30 cycles of 94\u0026deg;C for 30 s, 62\u0026deg;C for 45 s, and 72\u0026deg;C for 90 s; with a final extension of 72\u0026deg;C for 5 min. Products were visualised on 2% (w/v) agarose gels using a ChemiDoc MP imaging system (BioRad Laboratories Inc., USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eC-PAS visualisation\u003c/h2\u003e\u003cp\u003ePCR amplicons were also visualised using a single-stranded tag hybridisation Chromatographic Printed-Array Strip (C-PAS; TBA Co., Ltd., Japan). Briefly, 10 \u003cem\u003e\u0026micro;\u003c/em\u003el of salt-free (0 mM NaCl concentration) dilution buffer was combined with 1 \u003cem\u003e\u0026micro;\u003c/em\u003el of latex beads for each sample, then 5 \u003cem\u003e\u0026micro;\u003c/em\u003el of the post-PCR amplicon was added and mixed. The C-PAS was placed inside each tube and results were recorded after 10 minutes. Unlike the positive amplicon bands visualised by gel electrophoresis separated by size, the C-PAS was able to display any positive bands in any position (see Results).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDNA library preparation and DNA sequencing\u003c/h2\u003e\u003cp\u003eTo resolve classification discrepancies, a subset of samples from Batch 1 were whole genome sequenced using Oxford Nanopore Technologies (ONT) Rapid Barcoding Kit (RBK004) and sequenced on a MinION FLO-MIN106D R9.4.1 flow cell. Super-accuracy basecalling was performed with Guppy (v6.5.7, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://community.nanoporetech.com\u003c/span\u003e\u003cspan address=\"https://community.nanoporetech.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, utilising GPUs on an HPC cluster. The resulting FASTQ files were processed for adapter removal using Porechop (v0.2.4, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rrwick/Porechop\u003c/span\u003e\u003cspan address=\"https://github.com/rrwick/Porechop\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, length and quality filtering with Filtlong (v0.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rrwick/Filtlong\u003c/span\u003e\u003cspan address=\"https://github.com/rrwick/Filtlong\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and NanoFilt\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e (v2.8.0), and \u003cem\u003ede novo\u003c/em\u003e genome assembly with Flye\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e (v2.9-b1768). The resulting sequencing data and genome assemblies were used with \u003cem\u003ein silico\u003c/em\u003e serotyping tools.\u003c/p\u003e\u003cp\u003eAn additional four \u003cem\u003eS. flexneri\u003c/em\u003e 5b strains obtained from UKHSA were submitted for long-read Bacterial Genome Sequencing, which was performed by Plasmidsaurus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plasmidsaurus.com/\u003c/span\u003e\u003cspan address=\"https://plasmidsaurus.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using ONT with v14 library preparation chemistry on R10.4.1 flow cells. While the filtering and assembly tools matched those described above, genome polishing and annotation were performed using Medaka (v1.8.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nanoporetech/medaka\u003c/span\u003e\u003cspan address=\"https://github.com/nanoporetech/medaka\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Bakta\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e (v1.6.1), respectively. Sample accession numbers for the FASTQ long-read sequencing data generated in this study are provided in Supplementary Table\u0026nbsp;7.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eIn silico serotyping\u003c/h2\u003e\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e analysis was performed using three methods: ShigaTyper\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (v2.0.5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/CFSCAN-Biostatistics/shigatyper\u003c/span\u003e\u003cspan address=\"https://github.com/CFSCAN-Biostatistics/shigatyper\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), ShigaPass\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e (v1.5.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/imanyass/ShigaPass\u003c/span\u003e\u003cspan address=\"https://github.com/imanyass/ShigaPass\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and ARIBA\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e (v2.14.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sanger-pathogens/ariba\u003c/span\u003e\u003cspan address=\"https://github.com/sanger-pathogens/ariba\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For ARIBA, the mPCR amplicon sequences were used to create a custom database for screening. Results for all methods were recorded and compared to the mPCR assay and laboratory agglutination results. Analyses were performed on WGS data from two datasets: Batch 2 DNA samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100, Supplementary Table\u0026nbsp;5), and previously published GEMS isolates (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,074, Supplementary Data).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePhylogeny reconstruction\u003c/h2\u003e\u003cp\u003eFor the 100 samples from batch 2, Illumina FASTQ sequence reads were retrieved using the accession numbers, trimmed and assembled with Shovill (v1.1.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/shovill\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/shovill\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using default settings and assembly quality was assessed using QUAST\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e (v5.0.2). Prokka\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e (v1.14.0) annotated genome assemblies were analysed with Panaroo\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e (v1.2.7) under default settings to generate a core-genome alignment, from which phylogenetic trees were reconstructed using IQ-TREE\u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e (v2.3.0). The optimal substitution model was selected using IQ-TREE's ModelFinder Plus\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e (-m MFP option), and branch support was assessed by performing 1000 ultrafast bootstrap replicates (-bb 1000).\u003c/p\u003e\u003cp\u003eWhole genome phylogenies for 1,074 draft genomes generated from the Global Enteric Multicentre Study (GEMS)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e samples analysed, which were collected across seven LMICs (Supplementary Data), were constructed as previously described\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Maximum-likelihood phylogenetic reconstruction was performed with a chromosomal SNP alignment (73,525 bp) and midpoint rooted.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eSequencing data from this project has been deposited under the ENA BioProject PRJEB90075 (ERP173090).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAuthor\u0026rsquo;s contribution\u003c/span\u003e\u003c/p\u003e\u003cp\u003eA. M., M. A. H. and K. S. B. conceptualised and designed the study. F. K. and D. J. P. performed the laboratory experiments. P. M. D., C. J., D. M., J. J. J., M. I., B. V., and A. P. provided DNA samples used for testing. F. K., X. B., and C. E. C. performed \u003cem\u003ein silico\u003c/em\u003e data analysis and interpreted the data. F. K. drafted the initial manuscript. All authors revised, read, and approved the final draft of the manuscript.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Bill \u0026amp; Melinda Gates Foundation (grant number INV-065695) to A. M., M. A. H., and K. S. B.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDDD (2017) Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Infect Dis 17:909\u0026ndash;948\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDDD (2018) Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016. 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Clin Vaccine Immunol 24\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRajagopal A et al (2019) Significant Expansion of Real-Time PCR Multiplexing with Traditional Chemistries using Amplitude Modulation. Sci Rep-Uk 9\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7399876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7399876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInfection with \u003cem\u003eShigella\u003c/em\u003e bacteria is one of the leading causes of diarrhoeal disease globally, with significant burden in low- and middle-income countries and rising incidence in high-income settings. Effective serotyping is essential for surveillance, outbreak investigation, vaccine targeting, and can inform clinical management. Current discriminative approaches, such as seroagglutination and whole genome sequencing, are constrained by cross-reactivity, infrastructure requirements, and cost. Here, we present the development of a nine-target multiplex PCR lateral flow device assay for the rapid, accurate identification of vaccine-prioritised \u003cem\u003eS. flexneri\u003c/em\u003e serotypes 1b, 2a, and 3a, and \u003cem\u003eS. sonnei\u003c/em\u003e, as well as clinically relevant antimicrobial resistance genes. Validation using 138 DNA samples from clinical \u003cem\u003eShigella\u003c/em\u003e isolates showed 97.8% sensitivity and 100% specificity at the species level, performing comparably to \u003cem\u003ein silico\u003c/em\u003e genomic prediction tools. Serotype classifications interpreted from the PCR assay also successfully aligned with the temporal and geographic trends observed in a large multi-country clinical dataset (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,074), supporting its utility for temporospatial surveillance. To facilitate field-deployment of the PCR assay, we integrated an oligo-chromatographic dipstick method, enabling a simple, gel-free readout without need for specialised equipment. This molecular dipstick assay provides a practical and scalable solution for \u003cem\u003eShigella\u003c/em\u003e serotyping and antimicrobial resistance profiling in support of vaccine implementation and surveillance efforts.\u003c/p\u003e","manuscriptTitle":"A low resource requirement molecular diagnostic and surveillance tool for Shigella in the era of vaccination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 18:47:27","doi":"10.21203/rs.3.rs-7399876/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e78641b-38dc-47a8-99cb-8d69e2089520","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54093841,"name":"Biological sciences/Microbiology/Infectious-disease diagnostics"},{"id":54093842,"name":"Biological sciences/Microbiology/Policy and public health in microbiology"}],"tags":[],"updatedAt":"2025-10-09T18:47:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 18:47:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7399876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7399876","identity":"rs-7399876","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}