Genomic analysis of Mycobacterium tuberculosis in Thailand reveals circulating lineage diversity, transmission, and drug resistance mutations

Genomic analysis of Mycobacterium tuberculosis in Thailand reveals circulating lineage diversity, transmission, and drug resistance mutations | 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 Genomic analysis of Mycobacterium tuberculosis in Thailand reveals circulating lineage diversity, transmission, and drug resistance mutations Naphatcha Thawong, Prapaporn Srilohasin, Jody E. Phelan, Worawich Phornsiricharoenphant, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6333922/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Thailand has a high burden of tuberculosis, with control efforts hindered by drug-resistant Mycobacterium tuberculosis (Mtb). The increasing use of whole-genome sequencing (WGS) of Mtb offers valuable insights for clinical management and public health surveillance. WGS can be used to profile drug resistance, identify circulating sub-lineages, and trace transmission pathways or outbreaks. We analysed WGS data from 2,005 Mtb isolates collected from 1994–2020, across four regions of Thailand, including 1,189 newly sequenced samples. Most isolates are lineage two strains (78·3%), primarily the Beijing sub-lineage (L2.2.1). Drug resistance profiling revealed substantial isoniazid and rifampicin resistance, and 67·3% classified as multidrug-resistant (MDR-TB). Phenotypic and genotypic drug susceptibility testing showed high concordance (91·1%). Clustering analysis identified 206 transmission clades (maximum size 288), predominantly with MDR-TB, especially in Central and Northeastern regions. One cluster (n = 22) contains the ddn Gly81Ser mutation, linked to delamanid resistance, with some members pre-dating drug roll-out. In the largest cluster (n = 288), containing isolates spanning two decades, we applied transmission reconstruction methods to estimate a mutation rate of 1·1×10 − 7 substitutions per site per year. Overall, this study demonstrates the value of WGS in uncovering TB transmission and drug resistance, offering key data to inform better control strategies in Thailand and elsewhere. 200/200 Biological sciences/Genetics Biological sciences/Genetics/Microbial genetics Biological sciences/Genetics/Microbial genetics/Bacterial genetics Health sciences/Diseases Health sciences/Diseases/Infectious diseases Health sciences/Diseases/Infectious diseases/Tuberculosis Mycobacterium tuberculosis Tuberculosis transmission drug resistance genomics Figures Figure 1 Figure 2 Figure 3 BACKGROUND Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), remains a persistent and formidable global health challenge, causing 10·8M cases and 1·1M deaths in 2023 alone. Despite significant advancements in medical science and public health initiatives, 1 the WHO South-East Asia region, which is home to around one-fourth of the world's population, has more than 45% burden of annual TB incidence. Thailand had amongst the highest rates of TB incidence (157/100K population, ~ 113K cases) in 2023, with an increase by 4·4% from 2022, and combined with prevalent drug resistance, will make World Health Organization (WHO) “End TB” targets difficult to achieve. Resistance levels to frontline rifampicin and isoniazid drugs, together called multi-drug resistance (MDR-TB), are high across SEA (172K cases; 8·4/100K population). Worryingly, extensively drug-resistant TB strains (XDR; MDR + fluoroquinolones + group A resistance) and pre-XDR forms exist, providing a progression of resistance and limiting treatment options. In response, recent WHO guidelines suggest a 6-month regimen comprising bedaquiline, pretomanid, linezolid and moxifloxacin (BPaLM) to reduce both treatment duration and non-compliance levels due to drug toxicity. However, bedaquiline resistance is increasing, and with anti-TB drugs being costly with long durations (> 6-months), personalised treatment based on Mtb resistance knowledge is crucial. 2 Successful worldwide efforts to decrease TB burden have focused on advanced algorithms for early diagnosis, appropriate therapy choice and active case finding. However, these approaches have not been applied systematically across SEA. 3 Whilst diagnostics endorsed for TB and drug resistance detection (e.g., Xpert MTB/RIF, XDR) as part of the WHO’s “End TB” strategy are rapid compared to laboratory phenotypic drug susceptibility tests (pDSTs), they are costly and do not capture all genetic mutations required for precise management of advanced drug resistance forms. For example, the new Xpert XDR cartridge will not detect bedaquiline, linezolid, clofazimine and delamanid resistance, and does not cover some useful first-line drugs (e.g., rifabutin, ethambutol). Recent successes in TB treatment decision-making in developed countries have been led by advances in next generation sequencing technologies (NGS; e.g., Illumina, Oxford Nanopore Technology (ONT)), 4 with increasing opportunities to use these directly from sputum or DNA from limited Mtb culture (MGIT), in near real time, at decreasing costs. 5 Whole genome (WGS) and targeted gene amplicon (AMP-SEQ) sequencing data generated using NGS can be used to profile Mtb for drug resistance and strain-types (sub-lineages), as well as infer transmission events or outbreaks through sequence similarity, 5 – 7 facilitated through advances in health informatics (e.g., TB-Profiler software). 6 In UK, the UKHSA and some hospitals now use NGS-based characterisation as a clinical standard of care for TB management. 8 Thailand is seeking to adopt NGS as part of clinical care and surveillance, with government investment in genomics capacity. Recently, the WHO released the “genotype to phenotype” interpretation of NGS data, 7 , 9 but this approach needs to be adopted by National Tuberculosis Control Programmes (NTPs). NTP Guidelines developed in Thailand recommend using Mtb WGS to investigate cases involving clusters of TB patients or suspected outbreaks, 10 but to date the sample sizes have been small. 11 – 13 Genomics studies of Mtb from Thailand have revealed a dominance of lineage 2 strains, 11 and interactions with host genetics. 14 Our study analysed a large Mtb sample set from Thailand (n = 2,005; spanning 1994 to 2020), including 1,189 newly sequenced isolates, aims to identify mutations associated with drug resistance and uncover evidence of transmission. By establishing a baseline analysis of circulating strains, we seek to assist infection control teams embarking on using WGS to assist clinical and surveillance activities. METHODS Sequence data A total of 2,005 Mtb WGS isolates from Thailand were collected through convenience sampling, predominantly from drug resistance cases (e.g., MDR-TB and XDR-TB). Of these, 1,189 (59.3%) were recently gathered from Siriraj Hospital (years 2017–2020), a specialist TB hub for nationwide care, and 816 were retrieved from the ENA database (1994–2016). 11 , 15 All sequence data were generated using the Illumina sequencing platform (see Table S1 for ENA accession numbers). Metadata, when available, included the year of collection, location, HIV status, patient type, and phenotypic drug susceptibility tests (pDSTs) results. The pDSTs were conducted as part of routine TB laboratory processes using the standard agar proportion method on Lowenstein-Jensen medium. The number of isolates tested varied depending on the study, and the drugs included in this analysis were isoniazid (INH), rifampicin (RIF), ethambutol (EMB), streptomycin (STR), levofloxacin (LFX), moxifloxacin (MFX), amikacin (AMK), kanamycin (KAN), and linezolid (LZD). Pre-XDR and XDR have been defined under new WHO definitions. 16 For some isolates where XDR-TB could not be confirmed due to missing linezolid (LZD) and bedaquiline (BDQ) data, we classified them as pre-extensively drug-resistant plus tuberculosis (Pre-XDR + TB). The new study was approved by the Mahidol University ethics committee (SIRB 510/2561, MOPH EC 16/2562, Makarak EC30/2561). Informed consent was obtained from all subjects. Bioinformatic and phylogenetic analysis All raw sequence files were aligned to the H37Rv reference genome using bwa-mem software. High-quality SNP and insertion/deletion (indel) variants were called using samtools and GATK software (minimum depth 30-fold), and merged into a variant FASTA file using fastq2matrix. TB-Profiler software (v6.2.2) was used to determine isolate sub-lineages and genotypic drug resistance across 18 antibiotics. 6 Phylogenetic trees were constructed from the multi-sample FASTA file, using the maximum likelihood method implemented in IQ-TREE software (v2.2.6) with a set of 75,060 genome-wide SNPs. The trees were subsequently visualised using iTOL (v6). The geographic distribution of isolates was mapped using QGIS software (v3.36.0-Maidenhead). For the transmission analyses, pairwise SNP distances were calculated using the snp-dists tool within the fastq2matrix pipeline. A Gaussian Mixture Model (GMM) was applied to cluster the SNP distances, enabling the determination of an appropriate cut-off for clustering in the primary analysis. Timed phylogenetic trees were constructed for the largest clusters using BEAST2 (v2.7.6), 17 with a General Time Reversible (GTR) model, and settings applied elsewhere. 18 A resulting maximum clade credibility (MCC) tree was annotated using Tree annotate software (v2.7.6). The Transphylo package (v1.4.10) was used to determine the direction of any transmission events. The transmission graph was inferred and formatted using the tgv web tool. 19 The frequency of SNP and indel variants is compared to a global dataset (n = 50,723) curated in the TB-Profiler database. Statistical analysis Logistic regression models were employed to determine associations between transmissibility outcomes (whether an isolate was part of a transmission cluster), lineage and genotypic drug resistance profiles. Additionally, SNP loci associated with transmissibility were assessed using linear mixed models implemented in the Genome-wide Efficient Mixed Model Association (GEMMA) software (v0.98.5). This approach, related to genome-wide association study (GWAS) methods, accounted for the kinship matrix, incorporating SNPs, lineage, and drug resistance to control for relatedness among isolates. 20 Associations between geographical and SNPs distances of isolates were assessed using non-parametric Kruskal-Wallis tests and Spearman’s correlations. Statistical analyses were performed in R software (4.4.1). RESULTS Study population A total of 2,005 Mtb samples from Thailand, collected between 1994 and 2020, were included in this study (Table 1 ). A high number of Mtb isolates (838/2005, 41·8%) were collected between 2013 and 2017, spanning 4 regions of Thailand (Central, Northeastern, Northern, Southern). Most samples originated from the Central region (1299/2005; 64·8%), with Kanchanaburi province contributing the largest number, 558 isolates (27·8%). The majority of Mtb strain-types were lineage L2 (East-Asian; 1569/2005, 78·3%), followed by L1 (Indo-Oceanic, 324/2005, 16·2%), L4 (Euro-American, 110/2005, 5·5%) and L3 (East-African-Indian, 2/2005, 0·1%). The most prevalent sub-lineage, L2.2.1 (Beijing strain-type) accounted for most isolates (1317/2005, 65.7%). Of those with complete HIV-status, 27·7% (163/590) were HIV-positive. Of those with patient status (768/2005; 38·3%), the majority were new patients (411/768; 53·5%), followed by relapse cases (356/768; 46·4%), and treatment failures (1/768; 0·1%). Across the 2,005 isolates, 75,060 high-confidence SNPs were identified, and the resulting phylogenetic tree revealed the expected grouping of isolates by (sub-)lineage (Fig. 1 ). A principal component analysis (PCA) using the SNPs supported the lineage-based clustering patterns in the phylogenetic tree ( Figure S1 ). Table 1 Demographic of characteristics of 2,005 TB cases, Thailand, 1994–2020 Characteristics N % Year 1994–2002 45 2·2 2003–2007 378 18·9 2008–2012 592 29·5 2013–2017 838 41·8 2018+ 152 7·6 Region Central 1299 64·8 Northeastern 379 18·9 Northern 145 7·2 Southern 182 9·1 Province Kanchanaburi 558 27·8 Bangkok 297 14·8 Nakhon Ratchasima 108 5·4 Buri Ram 104 5·2 Other 938 46·8 Drug resistance Sensitive 233 11·6 RR-TB 39 1·9 HR-TB 53 2·6 MDR-TB 1349 67·3 Pre-XDR TB 293 14·6 XDR-TB 5 0·3 Other 33 1·7 Anti-TB drug INH 1686 84·1 Resistance RIF 1693 84·4 EMB 1095 54·6 STR 1212 60·5 PZA 812 40·5 LFX 305 15·2 MFX 305 15·2 AMK 108 5·4 KAN 128 6·4 ETO 875 43·6 BDQ 14 0·7 LZD 0 0 Lineage L2 1569 78·2 L1 324 16·2 L4 110 5·5 L3 2 0·1 RR-TB Rifampicin-Resistant tuberculosis; HR-TB isoniazid-monoresistant; MDR-TB, Multidrug-Resistant; Pre-XDR TB Pre-Extensively Drug-Resistant; XDR-TB Extensively Drug-Resistant; INH Isoniazid; RIF Rifampicin; EMB Ethambutol; STM Streptomycin; PZA Pyrazinamide; LFX, Levofloxacin; MFX, Moxifloxacin; AMK Amikacin, KAN Kanamycin; ETO Ethionamide; PAS Para-aminosalicylic acid; CAP Capreomycin Genotypic drug resistance Genotypic drug resistance profiling (Table 1 ) revealed that 88·4% of isolates (1772/2005) were resistant to at least one anti-TB drug, with the highest resistance observed for isoniazid (1686/2005 isolates, 84·1%), rifampicin (1693/2005 isolates, 84·4%), and streptomycin (1212/2005 isolates, 60·5%). WGS-based profiling indicated that 67·3% (1349/2005) of isolates were classified as MDR-TB, 14·6% (293/2005) as pre-XDR-TB, 11·6% (233/2005) as sensitive, and 0·2% (5/2005) as XDR-TB (Table 1 ). The geographic distribution of lineages and genotypic drug resistance categories across Thailand, suggested that MDR-TB was present across most regions (Fig. 2 ). The most common mutations linked to drug resistance were katG Ser315Thr (71·6%, isoniazid), rpsL Lys43Arg (46·4%, streptomycin), rpoB Ser450Leu (46·0%, rifampicin), ethA c.639_640delGT (26·6%, ethionamide), embB (Gly406Asp) (16·0%, ethambutol), pncA Ile31Thr (16·0%, pyrazinamide), and embB Met306Val (14·4%, ethambutol). All mutations have been observed in the TB-Profiler database, which includes global isolates (N = 50,723). However, there were differences in allele frequencies. For example, the pncA Ile31Thr mutation, linked to pyrazinamide resistance, was over 288 times more frequent than in the global dataset. Similarly, the ethA (c.639_640delGT) mutation, associated with ethionamide resistance, occurred at a frequency more than 140 times higher than in the global collection (Table 2 ). A detailed catalogue of identified mutations associated with drug resistance is presented ( Table S2 ). The degree of resistance to drugs may be underestimated, especially as functional genotype-phenotype mutations are being found and may need to be included in updates to databases. For example, the ddn Gly81Ser mutation has recently been associated with delamanid resistance. 21 This mutation was identified in 22 isolates (1.1%), including 6 Mtb isolates from Kanchanaburi (2005–2017) in the ENA database, 11 , 15 and 16 isolates collected between 2007 and 2014 from Kanchanaburi, Bangkok, Ratchaburi, Suphan Buri, and Chachoengsao, all located in or around the central region of Thailand. Table 2 Summary of Anti-TB Drug Resistance Mutations in 2,005 Isolates. Anti-TB drugs Gene name Changes Our study N = 2,005 Our study (%) Global (%) N = 50,723 1 INH katG Ser315Thr 1436 71·62 26·55 inhA c.-777C > T 182 9·08 4·06 inhA Ser94Ala 29 1·45 0·62 katG Ser315Asn 25 1·25 0·53 RIF rpoB Ser450Leu 923 46·03 18·80 rpoB His445Tyr 209 10·42 1·21 rpoB Asp435Val 114 5·69 2·30 rpoB His445Asp 107 5·34 1·04 EMB embB Gly406Asp 320 15·96 0·78 embB Met306Val 261 13·02 7·73 embB Met306Ile 236 11·77 5·94 STR rpsL Lys43Arg 931 46·43 13·49 rpsL Lys88Arg 74 3·69 2·55 rrs n.514A > C 34 1·70 2·53 gid c.102delG 30 1·50 1·25 PZA pncA Ile31Thr 289 14·41 0·05 pncA Ile90Ser 31 1·55 0·05 pncA c.-11A > G 24 1·20 0·90 LFX gyrA Asp94Gly 119 5·94 4·30 gyrA Ala90Val 88 4·39 3·15 gyrA Asp94Ala 39 1·95 1·18 MFX gyrA Asp94Gly 119 5·94 4·30 gyrA Ala90Val 88 4·39 3·15 gyrA Asp94Ala 39 1·95 1·18 AMK rrs n.1401A > G 98 4·89 4·71 eis c.-14C > T 8 0·40 0·48 KAN rrs n.1401A > G 98 4·89 4·71 eis c.-10G > A 17 0·85 0·72 eis c.-14C > T 8 0·40 0·48 ETO ethA c.639_640delGT 534 26·63 0·18 inhA c.-777C > T 182 9·08 4·06 inhA Ser94Ala 29 1·45 0·62 PAS folC Ser150Gly 91 4·54 0·31 thyX c.-16C > T 76 3·79 0·75 folC Glu40Gly 29 1·45 0·17 CAP rrs n.1401A > G 98 4·89 4·71 tlyA c.52_53dupCG 3 0·15 1 mutation associated with resistance to a single drug; 1 The global percentage of anti-TB drug resistance mutations identified by TB-Profiler database; INH, Isoniazid; RIF, Rifampicin; EMB, Ethambutol; STR, Streptomycin; PZA, Pyrazinamide; LFX, Levofloxacin; MFX, Moxifloxacin; AMK, Amikacin, KAN, Kanamycin; ETO, Ethionamide; PAS, Para-aminosalicylic acid; CAP, Capreomycin Concordance with phenotypic DST Phenotypic DST assays were performed on a subset of the samples (n = 1,826) ( Table S3 ). Isoniazid exhibited the highest resistance rate at 95·6% (1745/1826), followed by rifampicin at 94·7% (1730/1826), streptomycin at 61·8% (1113/1801), ethambutol at 44·0% (727/1653), and levofloxacin at 12·2% (199/1602). All other resistance rates were below 10%, with moxifloxacin at 8·6% (95/1101), kanamycin at 7·5% (127/1687), amikacin at 6·9% (81/1,172), and linezolid at 0·4% (4/1,097). Overall, 1,522 isolates (75·9%) were classified as MDR-TB, followed by 201 isolates (10·0%) with Pre-XDR TB+, and 59 isolates (2·9%) being pan-sensitive. XDR-TB was identified in 3 isolates (0·2%) ( Table S3 ). The overall concordance rate between phenotypic DSTs and genotypic drug resistance was 91·1% (11,522/12,640 tests). Concordance exceeded 88% for all drugs except ethambutol (71·7%), with rates of 96·1% for isoniazid, 96·9% for rifampicin, 97·5% for amikacin, 91·9% for levofloxacin, 89·7% for streptomycin, 96·8% for kanamycin, and 88·6% for moxifloxacin ( Table S4 ). Assuming phenotypic DST results as the gold standard, the sensitivity and specificity for isoniazid and rifampicin, which are critical for defining MDR-TB, were approximately 96%. The remaining drugs demonstrated high levels of sensitivity and specificity, ranging from 70–99%, with ethambutol showing a notably lower specificity of 60·4%. Despite the potential for DST errors, we identified 88 mutations that may account for cases of phenotypic resistance with genotypic susceptibility. These include 24 mutations linked to isoniazid ( katG 22, aphC 2), 7 to rifampicin ( rpoB 5, rpoC 2), 16 to ethambutol ( embA 7, embB 8, embC 1), and 19 to streptomycin ( gid 11, rpsL 2, rrs 6) ( Table S5 ). Some isolates with known drug resistance mutations also harboured additional mutations that might act as compensatory mechanisms (e.g., rpoB Lys37Arg, rpoB Met655Thr; rpoC Gly1198Ser). At the composite resistance level (e.g., MDR-TB), the concordance between phenotypic DSTs and genotypic profiles was high at 87·4% (1589/1819) ( Table S6 ). Most discordances (181/230; 78·7%) were linked to variations in MDR-TB classification, particularly cases where genotypic profiling identified mutations meeting the new definition of (pre-)XDR-TB (114/181). Instances of discordance where DST indicated susceptibility, but genotypic profiles suggested resistance (8/59) likely reflected phenotypic errors. These cases included several well-established resistance mutations, such as embB Gly406Asp (ethambutol), gyrA Asp94Ala (moxifloxacin), and eis c.-10G > A (kanamycin) ( Table S7 ). Transmission cluster analysis The phylogenetic tree revealed clusters of Mtb isolates within the same lineage that were highly similar, and we sought to investigate whether these are indicative of transmission events (Fig. 1 ). An analysis of the pairwise SNP distance distribution across 2,005 isolates (median 452 SNPs; range: 0 to 2,369 SNPs) ( Figure S2 ) suggested a cut-off of 13 SNPs to define potential transmission links. At this cut-off, we identified 206 transmission clusters (total n = 1,265 (63·1%); median size: 2, range: 2–288), including 190 clusters with 2–9 isolates, 15 clusters with 10–100 isolates, and one large cluster containing 288 isolates ( Figure S3; Table S8 ). As expected, each cluster was homogenous for sub-lineage. The most prevalent sub-lineage was L2.2.1 (131/206 clusters; total n = 1,317), followed by L1.1.1 (10/206 clusters; total n = 159). The 1,265 Mtb isolates in clusters were identified in every region of Thailand, with the majority classified as MDR-TB and pre-XDR TB (93·1%; 1,178/1,265). The clusters span several years (median 2: range 1–18 years), with a notable presence from 2005 to 2017. Within the set of 16 clusters comprising 10 or more isolates ( Table S8 ), distinct geographical distributions were observed. For example, one cluster includes 22 isolates, all classified as L2.2.1 and pre-XDR-TB, originating from the central region, with samples collected between 2005 and 2017, and having the ddn Gly81Ser mutation described earlier. Another cluster consists of 48 isolates classified as L2.1.1, predominantly from southern Thailand, with 43 (93·8%) identified as MDR-TB; these samples were collected from 2001 to 2020. Additionally, a cluster of L4.2.2 contains 12 isolates, mainly from the central region, with 9 (75%) classified as MDR-TB and 3 (25%) as pre-XDR-TB, with samples collected from 2003 to 2017. Overall, most clusters included isolates from both the newly sequenced and older public datasets ( Figure S4 ), highlighting the persistence of lineages L1, L2, and L4 over time. To further explore the geographical spread of these clusters, we assessed the correlation between geographic and SNP distances ( Figure S5 ). The analysis showed a small but significant positive trend (Kruskal-Wallis P < 0·001) with a weak positive correlation (Spearman’s rho = 0·046). A logistic regression analytical approach was used to assess the risk factors for being in a transmission cluster. Compared to lineage L1, being an isolate in L2 (odds ratio [OR] 7·41, p-value < 0·001) or L4 (OR 2·03, p-value < 0·05) had higher risk. Whilst, compared to sensitive strains, those with MDR-TB (OR 5·82, p-value < 0·001) or pre-XDR-TB (OR 10·98, p-value 2·5, P 0·01), we identified 4 loci potentially associated with being in transmission clusters, located in the genes katG (Rv1908c), folC (Rv2447c), ppe8 (Rv0355c), and folK (Rv3606c) (P < 0·001) ( Table S10 ). Among loci with odds ratios greater than one, we detected the katG Ser315Thr mutation—the most common INH resistance mutation 22 as most clusters are linked to at least MDR-TB (OR 7·93, P < 0·00001). The folC ( Rv2447c ) S150G mutation, involved in the production of para-aminobenzoic acid (pABA) in the folate biosynthetic pathway, 23 was found in lineages L2 and L4 (OR 6·5, P < 0.00001). Additionally, the ppe8 ( Rv0355c ) V2309I mutation was linked to adaptations in response to host defence mechanisms (OR 6·5, P < 0·05). 24 These mutations were observed across multiple clusters, including katG Ser315Thr (147 clusters), folC S150G (6 clusters), and ppe8 V2309I (5 clusters) ( Figure S4 ). Like folC , the folK ( Rv3606c ) locus is associated with folate metabolism. While the folK Gln47His mutation has an odds ratio below one, there is no strong evidence of compensatory effects ( Figure S4 ). Investigation of the largest cluster The time-calibrated phylogenetic tree of the largest cluster (n = 288 isolates, L2.2.1, 96·9% central region, 93·8% MDR-TB; Table S8 ; Figure S6 ) was analysed using BEAST2 software. This analysis estimated the mutation rate to be 1·10x10 − 7 substitutions per site per year (95% highest posterior density (HPD) interval: 9·89x10 − 8 to 1·22x10 − 7 ), which corresponds to ~ 0·48 substitutions per genome per year (HPD: 0·44 to 0·53), and in keeping with estimates for L2 from other settings. 25 , 26 The estimated time of the most recent common ancestor is 1989 (95% HPD: 1984–1994), which is plausible given the known roll-out of the anti-TB drugs. All parameters had an effective sample size score greater than 200. The TransPhylo package was used to reconstruct the transmission history among cases, offering detailed insights into the source of infections within the studied population. Using maximum a posteriori estimates, we identified the most probable transmission pathways (Fig. 3 ), which revealed the evolution of Mtb, including from MDR-TB to pre-XDR, through time. Furthermore, this analysis estimated there were potentially 132 unsampled Mtb cases, in part due to gaps caused by the passive and convenient nature of the sampling ( Figure S7 ). By focusing on strongly supported transmission links (probabilities > 0·2), the large cluster was split into 83 sub-clusters. This approach enabled detailed investigation of smaller clusters, visualization of potential transmission pathways (i.e., who-infected-whom), integration of drug resistance profiles and collection years, and provided valuable insights to guide more targeted contact investigations by infection control agencies ( Figure S8 ). DISCUSSION The WHO classifies Thailand as a high-burden TB country. This study presents a WGS analysis of 2,005 Mtb isolates from Thailand, spanning three decades (1994–2020), to examine circulating strains, drug resistance profiles, and transmission dynamics. The dataset includes isolates from previous studies 11 , 15 and new data from Siriraj Hospital, with most samples originating from cases involving drug resistant TB, of which more than sixty percent were MDR-TB. The frequency distribution of randomly recruited Mtb isolates in Thailand predominantly comprises L1 and L2. 13 Our findings reveal a high prevalence of L2, particularly the Beijing sub-lineage (L2.2.1). This aligns with previous studies indicating that L2 is the main lineage related to clusters of outbreaks and drug-resistant isolates in Thailand, while L1 is more prevalent in general Mtb cases. 13 The geographic patterns observed suggest that specific lineages may be driving local transmission, especially in densely populated regions such as the Central area. The genotypic drug resistance profiles generated by TB-Profiler software had high concordance with phenotypic DST, confirming its reliability. Since most isolates were MDR-TB, resistance rates detected through both phenotypic DST and genotypic resistance profiling were highest for isoniazid and rifampicin. Discrepancies between phenotypic and genotypic approaches may arise due to DST laboratory errors, limitations in WGS’s ability to capture certain mutations, or gaps in the drug resistance mutation database used for annotation. We identified 88 low-frequency mutations that explained phenotypic resistance and genotypic sensitivity inconsistencies, including 66 linked to four drugs: isoniazid (24), rifampicin (7), ethambutol (16), and streptomycin (19). These mutations, confirmed using phenotypic DST, were not lineage-specific and occurred at low frequencies in a dataset of 50,723 global Mtb samples. As such, they should be considered for inclusion in the TB-Profiler database. At the DR profile level, samples initially classified as MDR-TB by phenotypic DST were re-categorised genotypically in over 4·9% of cases to a lower resistance level (sensitive, RR-TB, or HR-TB) and in 7·5% of cases to a higher resistance level (pre-XDR-TB or XDR-TB). This reclassification is a crucial consideration, as treatment for each type of TB is specific, with distinct drug regimens and durations. 27 Inappropriate treatment could lead to further complications. A particularly concerning finding is the presence of the ddn Gly81Ser mutation, which is associated with delamanid resistance, 21 even in isolates predating the drug's rollout. Intrinsic resistance to delamanid and pretomanid—both sharing a similar mechanism of action—could undermine the effectiveness of the latest BPaLM regimen, especially as bedaquiline resistance continues to rise. These findings emphasise the importance of using WGS data to identify mutations potentially associated with drug resistance, even in cases where phenotypic DST results indicate sensitivity. The phylogenetic analysis of Mtb isolates revealed distinct clusters within the same lineage, suggesting potential transmission chains. Using a pairwise SNP distance cut-off of 13, we identified 206 transmission clusters comprising 1,265 isolates. These clusters ranged from small groups (190 clusters with 2–9 isolates) to larger networks (16 clusters with 10–100 isolates), including one exceptionally large cluster of 288 isolates, all highly homogenous for sub-lineage. The presence of transmission clusters in every region of Thailand, coupled with a substantial proportion of MDR-TB and pre-XDR TB cases, underscores the widespread nature of drug-resistant TB across the country. Clusters spanned several years, with the majority arising between 2005 and 2017, reflecting ongoing transmission. Due to the convenient sampling method, it is possible that other isolates existed outside the given collection years. Clusters with higher percentages in more recent years (e.g., 2012–2017) may indicate ongoing transmission. It suggests that certain strains have persisted over time, reflecting continuous transmission of similar strains across years. The study found that lineage, drug resistance, and geographic factors were significantly associated with the transmission dynamics of Mtb. Lineage L2 exhibited the highest transmissibility and adaptability, showing the strongest odds of clustering. Although less frequently involved in outbreaks than L2, L4 still presented an increased risk of clustering, 28 with both being modern strains. Drug resistance, particularly MDR-TB and pre-XDR-TB, further heightened the risk of clustering. Geographic factors also played a key role, with isolates from the Central and Northeastern regions having higher odds of clustering. These findings can inform the design of tailored interventions aimed at high-risk areas and populations. Using a GWAS approach, three loci in katG , folC , and ppe8 were identified as associated with clustering or transmissibility, suggesting a potential increase in Mtb fitness and transmission. These loci have not been reported in similar analyses in Asia with comparable lineages. 29 , 30 Both the katG and folC genes are associated with drug resistance. 23 , 29 However, studies suggest that the increased transmission risk of drug-resistant tuberculosis is driven more by delays in diagnosis and treatment than by the inherent transmissibility of resistant strains. 31 The ppe8 locus exhibits complexity, with deletions at the gene's start in L1 strains and within the encompassed RD304 region in L2 strains. 4 Although Mtb GWAS can be used to identify associations between SNPs and transmissibility, some suggest incorporating host genomics into the analysis, such as through genome-to-genome approaches, for more comprehensive results. 14 We identified a large cluster of 288 isolates and employed time-calibrated phylogenetic analysis using BEAST2 to reconstruct the outbreak’s transmission history from WGS data. We subsequently used TransPhylo to infer transmission patterns within the studied population. It enabled us to estimate the number of unsampled cases, revealing a hidden burden of TB transmission and suggesting that undetected infections may sustain ongoing transmission chains. To improve confidence in our inferences, we filtered out transmission probabilities below 0·2, reflecting the varied, convenience-based nature of sampling. Consequently, we could more reliably focus on smaller transmission clusters. Occasionally, we observed transmission patterns that appeared counterintuitive, such as transmission from Pre-XDR-TB to MDR-TB cases or from more recent years to earlier years (e.g., 2008 to 2006). These anomalies may reflect unsampled transmission links or uncertainties in determining the actual timing of infection. Specifically, our analysis relied on the year of sample collection rather than the precise year of infection, which can introduce discrepancies. By continually integrating updated WGS data, we can enhance the monitoring of patients and their transmission networks, thereby strengthening efforts to contain and prevent the spread of transmission clusters. In conclusion, this study provides a comprehensive analysis of WGS data from Mtb isolates in Thailand, offering valuable insights into circulating strains, drug resistance profiles, and transmission dynamics, particularly among drug-resistant TB cases. The findings highlight the critical role of integrating genomic and epidemiological data to deepen our understanding of Mtb and to guide targeted interventions for TB control. However, WGS alone cannot address all TB-related challenges. Sustained efforts combining WGS with strengthened public health strategies, enhanced surveillance systems, and multifaceted interventions are essential to effectively combat TB in Thailand and beyond. Declarations DECLARATION OF INTERESTS Authors declare no competing interests. DATA SHARING Raw sequencing data are available in the European Nucleotide Archive (ENA). A complete list of accession numbers is provided in Table S1 . Author Contribution SM, AC, PSu, KF, and TGC conceived and supervised the project. PS, TP, KF, PSu, and AC conducted sample processing and DNA extraction. PS performed bacterial culture. SC and PS conducted sequencing. NT and WP performed bioinformatics and statistical analyses under the supervision of PS, JEP, SC, SW, SM, AC, and TGC. NT, PS, JEP, WS, MLH, SC, SW, SM, AC, and TGC contributed to data interpretation. NT drafted the manuscript, with input from all authors. All authors reviewed, edited, and approved the final manuscript. NT, PS, SM, AC, and TGC compiled the final submission. ACKNOWLEDGEMENTS The study was funded by MRC – NSTDA – Newton (ref. MR/R020973/1; P-18-50228). NT is funded by a Thailand government Ministry of Public Health PhD scholarship. LW is funded by a BBSRC LIDO PhD studentship. WH is a recipient of a Chulabhorn Royal Academy fellowship. TGC and SC are funded by the UKRI (BBSRC BB/X018156/1; MRC MR/X005895/1; EPSRC EP/Y018842/1)). NT, WS, SW and SM were funded by the Department of Medical Sciences (Thailand Ministry of Public Health). The Thailand Health Systems Research Institute provided funding through HSRI 64–179 (NT, WS, SW and SM) and HSRI 65–113 (KF, ST, AC, TP, PS, WP). KF, ST and AC is funded by the National Research Council of Thailand (NRCT) (NRC MHESI 483/2563). The funders had no role in study design, data collection and analysis, decision to publish, or the preparation of the manuscript. References Salina, E. G. Mycobacterium tuberculosis Infection: Control and Treatment. Microorganisms 11 , 1057 (2023). WHO. Global tuberculosis report 2023 (World Health Organization, 2023). https://iris.who.int/handle/10665/373828 Maung, H. M. W. et al. Geno-Spatial Distribution of Mycobacterium Tuberculosis and Drug Resistance Profiles in Myanmar–Thai Border Area. Trop. Med. Infect. Dis. 5 , 153 (2020). Thorpe, J. et al. Multi-platform whole genome sequencing for tuberculosis clinical and surveillance applications. Sci. Rep. 14 , 5201 (2024). Dippenaar, A. et al. Nanopore Sequencing for Mycobacterium tuberculosis: a Critical Review of the Literature, New Developments, and Future Opportunities. J. Clin. Microbiol. 60 , e0064621 (2022). Phelan, J. E. et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med. 11 , 41 (2019). WHO. The use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in Mycobacterium tuberculosis complex: technical guide (World Health Organization, 2018). https://iris.who.int/handle/10665/274443 Satta, G. et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? Clin. Microbiol. Infect. 24 , 604–609 (2018). WHO. Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance 2nd edn (World Health Organization, 2023). https://iris.who.int/handle/10665/374061 Division of Tuberculosis. National Tuberculosis Control Programme Guideline, Thailand 2021 (Bangkok, 2021). Nonghanphithak, D. et al. Clusters of Drug-Resistant Mycobacterium tuberculosis Detected by Whole-Genome Sequence Analysis of Nationwide Sample, Thailand, 2014–2017. Emerg. Infect. Dis. 27 , 813–822 (2021). Faksri, K. et al. Comparisons of whole-genome sequencing and phenotypic drug susceptibility testing for Mycobacterium tuberculosis causing MDR-TB and XDR-TB in Thailand. Int. J. Antimicrob. Agents . 54 , 109–116 (2019). Miyahara, R. et al. Risk for Prison-to-Community Tuberculosis Transmission, Thailand, 2017–2020. Emerg. Infect. Dis. 29 , 477–483 (2023). Phelan, J. et al. Genome-wide host-pathogen analyses reveal genetic interaction points in tuberculosis disease. Nat. Commun. 14 , 549 (2023). Srilohasin, P. et al. Genomic evidence supporting the clonal expansion of extensively drug-resistant tuberculosis bacteria belonging to a rare proto-Beijing genotype. Emerg. Microbes Infect. 9 , 2632–2641 (2020). Organization, W. H. Meeting report of the WHO expert consultation on the definition of extensively drug-resistant tuberculosis, 27–29 October 2020 (World Health Organization, 2021). https://iris.who.int/handle/10665/338776 Bouckaert, R. et al. BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. PLoS Comput. Biol. 10 , e1003537 (2014). Sobkowiak, B. et al. Bayesian reconstruction of Mycobacterium tuberculosis transmission networks in a high incidence area over two decades in Malawi reveals associated risk factors and genomic variants. Microb. Genom . 6. 10.1099/mgen.0.000361 (2020). Phelan, J. E. et al. TGV: suite of tools to visualize transmission graphs. NAR Genom Bioinform . 6 10.1093/nargab/lqae158 (2024). Coll, F. et al. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat. Genet. 50 , 307–316 (2018). Kim, S. et al. Functional analysis of genetic mutations in ddn and fbiA linked to delamanid resistance in rifampicin-resistant Mycobacterium tuberculosis, Tuberculosis. https://doi.org/10.1016/j.tube.2025.102630 Ando, H. et al. Identification of katG mutations associated with high-level isoniazid resistance in Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 54 , 1793–1799 (2010). Wang, R., Li, K., Yu, J., Deng, J. & Chen, Y. Mutations of folC cause increased susceptibility to sulfamethoxazole in Mycobacterium tuberculosis. Sci. Rep. 11 10.1038/s41598-020-80213-4 (2021). Chitwood, M. H. et al. The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova. Nat. Commun. 15 10.1038/s41467-024-47282-9 (2024). Torres Ortiz, A. et al. Genomic signatures of pre-resistance in Mycobacterium tuberculosis. Nat. Commun. 12 10.1038/s41467-021-27616-7 (2021). Rutaihwa, L. K. et al. Multiple introductions of Mycobacterium tuberculosis Lineage 2-Beijing into Africa over centuries. Front. Ecol. Evol. 7 10.3389/fevo.2019.00112 (2019). Alsayed, S. S. R., Gunosewoyo, H. & Tuberculosis Pathogenesis, Current Treatment Regimens and New Drug Targets. Int. J. Mol. Sci. 24 10.3390/ijms24065202 (2023). Holt, K. E. et al. Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam. Nat. Genet. 50 , 849–856 (2018). Wang, L. et al. Whole genome sequencing analysis of Mycobacterium tuberculosis reveals circulating strain types and drug-resistance mutations in the Philippines. Sci. Rep. 14. 10.1038/s41598-024-70471-x (2024). Napier, G. et al. Characterisation of drug-resistant Mycobacterium tuberculosis mutations and transmission in Pakistan. Sci. Rep. 12 10.1038/s41598-022-11795-4 (2022). Atre, S. R. et al. Tuberculosis Pathways to Care and Transmission of Multidrug Resistance in India. Am. J. Respir Crit. Care Med. 205 , 233–241 (2022). Additional Declarations No competing interests reported. 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Clark","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBADGQYGNsYHDxgOwAQScCrlQdBszAYJDAckSNLCJkGUFnv2w88efGCw4+GffSytIuHPnTrdBuaHHxjb0nDbwpNmbjiDIZlH4lzasRuJbc8kzA6wGUswtuXgcViCmTQPwwEehjPsbTcSGw4DtTCYMTC2VeDWwv/8m/QfoBZ5oJaChD8gLezf8GuRyDGTZgBqMTjDdowhgQ2khQdkCx6H3XhTJtljkMxjeIYtWSKx7bDktsM8xRIJ53B7n70/fZvEjwo7ObkzbIYfPvw5zG92vH3jhw9lyTi1QIABMoeZAV+sjIJRMApGwSggBgAAwsNOAgwAEAgAAAAASUVORK5CYII=","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Taane","middleName":"G.","lastName":"Clark","suffix":""}],"badges":[],"createdAt":"2025-03-29 11:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6333922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6333922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-15093-7","type":"published","date":"2025-08-13T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81532486,"identity":"cead5be4-d4d8-44e6-a4cc-7dfc0980ce98","added_by":"auto","created_at":"2025-04-28 09:41:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1091235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic Tree of 2,005 Mtb isolates from Thailand, 1994–2020. \u003c/strong\u003eThe scale bar indicates 0.01 substitutions per site SNP. The outer ring highlights isolates within the transmission cluster, defined by an SNP distance cut-off of 13. Genotypic DSTs refer to WGS-based drug resistance profiles. Blue arrow indicates a cluster of highly similar Mtb isolates within lineage L2 (n=531).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6333922/v1/fdbc26ba45713011923f7ce1.png"},{"id":81533006,"identity":"51e8198f-46f1-4647-86d8-1b61f4d44114","added_by":"auto","created_at":"2025-04-28 09:49:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic Distribution of Mtb Lineages and Genotypic Drug resistance profiles in Thailand (N=2,005 isolates). \u003c/strong\u003eThe size of the diagrams corresponds to the number of isolates; however, the size of smaller diagrams has been adjusted to enhance visibility, as some provinces reported very few TB cases.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6333922/v1/bd17b50c419f75f5f0b6e6a7.png"},{"id":81532491,"identity":"2a4de317-b556-43a2-93db-4b2fd0229bc4","added_by":"auto","created_at":"2025-04-28 09:41:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe maximum \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ea posteriori\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(MAP) transmission tree generated from TransPhylo results, showing the transmission dynamics of TB cases. \u003c/strong\u003eNodes represent hosts, coloured according to the estimated infection year intervals: 1988 (1988-1992), 1993 (1993-1997), 1998 (1998-2002), 2003 (2003-2007), 2008 (2008-2012), and 2013 (2013-2016). Squares denote unsampled cases, while grey nodes represent the initial infection source. Nodes labelled with “P” represent Pre-XDR-TB cases, while unlabelled circles indicate MDR-TB cases.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6333922/v1/e2e306301597fd577acb9790.png"},{"id":89310520,"identity":"b0bab2b1-6195-4715-99cb-60d332c7b76b","added_by":"auto","created_at":"2025-08-18 16:05:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2788338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6333922/v1/78c03927-08eb-4c7c-b769-c1c9e3d97f8d.pdf"},{"id":81532490,"identity":"80f70bb0-7c38-411a-900f-350a67fc3239","added_by":"auto","created_at":"2025-04-28 09:41:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3462104,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6333922/v1/aa84b55c3851e11d82b98bad.pdf"},{"id":81532493,"identity":"55065902-ec68-42c3-ad8f-280434c95840","added_by":"auto","created_at":"2025-04-28 09:41:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278363,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6333922/v1/b019109206c60e3cd579f328.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic analysis of Mycobacterium tuberculosis in Thailand reveals circulating lineage diversity, transmission, and drug resistance mutations","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eTuberculosis (TB), caused by the bacterium \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), remains a persistent and formidable global health challenge, causing 10\u0026middot;8M cases and 1\u0026middot;1M deaths in 2023 alone. Despite significant advancements in medical science and public health initiatives,\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e the WHO South-East Asia region, which is home to around one-fourth of the world's population, has more than 45% burden of annual TB incidence. Thailand had amongst the highest rates of TB incidence (157/100K population, ~\u0026thinsp;113K cases) in 2023, with an increase by 4\u0026middot;4% from 2022, and combined with prevalent drug resistance, will make World Health Organization (WHO) \u0026ldquo;End TB\u0026rdquo; targets difficult to achieve. Resistance levels to frontline rifampicin and isoniazid drugs, together called multi-drug resistance (MDR-TB), are high across SEA (172K cases; 8\u0026middot;4/100K population). Worryingly, extensively drug-resistant TB strains (XDR; MDR\u0026thinsp;+\u0026thinsp;fluoroquinolones\u0026thinsp;+\u0026thinsp;group A resistance) and pre-XDR forms exist, providing a progression of resistance and limiting treatment options. In response, recent WHO guidelines suggest a 6-month regimen comprising bedaquiline, pretomanid, linezolid and moxifloxacin (BPaLM) to reduce both treatment duration and non-compliance levels due to drug toxicity. However, bedaquiline resistance is increasing, and with anti-TB drugs being costly with long durations (\u0026gt;\u0026thinsp;6-months), personalised treatment based on Mtb resistance knowledge is crucial.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSuccessful worldwide efforts to decrease TB burden have focused on advanced algorithms for early diagnosis, appropriate therapy choice and active case finding. However, these approaches have not been applied systematically across SEA.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Whilst diagnostics endorsed for TB and drug resistance detection (e.g., Xpert MTB/RIF, XDR) as part of the WHO\u0026rsquo;s \u0026ldquo;End TB\u0026rdquo; strategy are rapid compared to laboratory phenotypic drug susceptibility tests (pDSTs), they are costly and do not capture all genetic mutations required for precise management of advanced drug resistance forms. For example, the new Xpert XDR cartridge will not detect bedaquiline, linezolid, clofazimine and delamanid resistance, and does not cover some useful first-line drugs (e.g., rifabutin, ethambutol). Recent successes in TB treatment decision-making in developed countries have been led by advances in next generation sequencing technologies (NGS; e.g., Illumina, Oxford Nanopore Technology (ONT)),\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e with increasing opportunities to use these directly from sputum or DNA from limited Mtb culture (MGIT), in near real time, at decreasing costs.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Whole genome (WGS) and targeted gene amplicon (AMP-SEQ) sequencing data generated using NGS can be used to profile Mtb for drug resistance and strain-types (sub-lineages), as well as infer transmission events or outbreaks through sequence similarity,\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e facilitated through advances in health informatics (e.g., TB-Profiler software).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn UK, the UKHSA and some hospitals now use NGS-based characterisation as a clinical standard of care for TB management.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Thailand is seeking to adopt NGS as part of clinical care and surveillance, with government investment in genomics capacity. Recently, the WHO released the \u0026ldquo;genotype to phenotype\u0026rdquo; interpretation of NGS data,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e but this approach needs to be adopted by National Tuberculosis Control Programmes (NTPs). NTP Guidelines developed in Thailand recommend using Mtb WGS to investigate cases involving clusters of TB patients or suspected outbreaks,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e but to date the sample sizes have been small.\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Genomics studies of Mtb from Thailand have revealed a dominance of lineage 2 strains,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and interactions with host genetics.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Our study analysed a large Mtb sample set from Thailand (n\u0026thinsp;=\u0026thinsp;2,005; spanning 1994 to 2020), including 1,189 newly sequenced isolates, aims to identify mutations associated with drug resistance and uncover evidence of transmission. By establishing a baseline analysis of circulating strains, we seek to assist infection control teams embarking on using WGS to assist clinical and surveillance activities.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSequence data\u003c/h2\u003e \u003cp\u003eA total of 2,005 Mtb WGS isolates from Thailand were collected through convenience sampling, predominantly from drug resistance cases (e.g., MDR-TB and XDR-TB). Of these, 1,189 (59.3%) were recently gathered from Siriraj Hospital (years 2017\u0026ndash;2020), a specialist TB hub for nationwide care, and 816 were retrieved from the ENA database (1994\u0026ndash;2016).\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e All sequence data were generated using the Illumina sequencing platform (see \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e for ENA accession numbers). Metadata, when available, included the year of collection, location, HIV status, patient type, and phenotypic drug susceptibility tests (pDSTs) results. The pDSTs were conducted as part of routine TB laboratory processes using the standard agar proportion method on Lowenstein-Jensen medium. The number of isolates tested varied depending on the study, and the drugs included in this analysis were isoniazid (INH), rifampicin (RIF), ethambutol (EMB), streptomycin (STR), levofloxacin (LFX), moxifloxacin (MFX), amikacin (AMK), kanamycin (KAN), and linezolid (LZD). Pre-XDR and XDR have been defined under new WHO definitions.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e For some isolates where XDR-TB could not be confirmed due to missing linezolid (LZD) and bedaquiline (BDQ) data, we classified them as pre-extensively drug-resistant plus tuberculosis (Pre-XDR\u0026thinsp;+\u0026thinsp;TB). The new study was approved by the Mahidol University ethics committee (SIRB 510/2561, MOPH EC 16/2562, Makarak EC30/2561). Informed consent was obtained from all subjects.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioinformatic and phylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eAll raw sequence files were aligned to the H37Rv reference genome using bwa-mem software. High-quality SNP and insertion/deletion (indel) variants were called using samtools and GATK software (minimum depth 30-fold), and merged into a variant FASTA file using fastq2matrix. TB-Profiler software (v6.2.2) was used to determine isolate sub-lineages and genotypic drug resistance across 18 antibiotics.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Phylogenetic trees were constructed from the multi-sample FASTA file, using the maximum likelihood method implemented in IQ-TREE software (v2.2.6) with a set of 75,060 genome-wide SNPs. The trees were subsequently visualised using iTOL (v6). The geographic distribution of isolates was mapped using QGIS software (v3.36.0-Maidenhead). For the transmission analyses, pairwise SNP distances were calculated using the snp-dists tool within the fastq2matrix pipeline. A Gaussian Mixture Model (GMM) was applied to cluster the SNP distances, enabling the determination of an appropriate cut-off for clustering in the primary analysis. Timed phylogenetic trees were constructed for the largest clusters using BEAST2 (v2.7.6),\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e with a General Time Reversible (GTR) model, and settings applied elsewhere.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e A resulting maximum clade credibility (MCC) tree was annotated using Tree annotate software (v2.7.6). The Transphylo package (v1.4.10) was used to determine the direction of any transmission events. The transmission graph was inferred and formatted using the tgv web tool.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The frequency of SNP and indel variants is compared to a global dataset (n\u0026thinsp;=\u0026thinsp;50,723) curated in the TB-Profiler database.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eLogistic regression models were employed to determine associations between transmissibility outcomes (whether an isolate was part of a transmission cluster), lineage and genotypic drug resistance profiles. Additionally, SNP loci associated with transmissibility were assessed using linear mixed models implemented in the Genome-wide Efficient Mixed Model Association (GEMMA) software (v0.98.5). This approach, related to genome-wide association study (GWAS) methods, accounted for the kinship matrix, incorporating SNPs, lineage, and drug resistance to control for relatedness among isolates.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Associations between geographical and SNPs distances of isolates were assessed using non-parametric Kruskal-Wallis tests and Spearman\u0026rsquo;s correlations. Statistical analyses were performed in R software (4.4.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eA total of 2,005 Mtb samples from Thailand, collected between 1994 and 2020, were included in this study (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A high number of Mtb isolates (838/2005, 41\u0026middot;8%) were collected between 2013 and 2017, spanning 4 regions of Thailand (Central, Northeastern, Northern, Southern). Most samples originated from the Central region (1299/2005; 64\u0026middot;8%), with Kanchanaburi province contributing the largest number, 558 isolates (27\u0026middot;8%). The majority of Mtb strain-types were lineage L2 (East-Asian; 1569/2005, 78\u0026middot;3%), followed by L1 (Indo-Oceanic, 324/2005, 16\u0026middot;2%), L4 (Euro-American, 110/2005, 5\u0026middot;5%) and L3 (East-African-Indian, 2/2005, 0\u0026middot;1%). The most prevalent sub-lineage, L2.2.1 (Beijing strain-type) accounted for most isolates (1317/2005, 65.7%). Of those with complete HIV-status, 27\u0026middot;7% (163/590) were HIV-positive. Of those with patient status (768/2005; 38\u0026middot;3%), the majority were new patients (411/768; 53\u0026middot;5%), followed by relapse cases (356/768; 46\u0026middot;4%), and treatment failures (1/768; 0\u0026middot;1%). Across the 2,005 isolates, 75,060 high-confidence SNPs were identified, and the resulting phylogenetic tree revealed the expected grouping of isolates by (sub-)lineage (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A principal component analysis (PCA) using the SNPs supported the lineage-based clustering patterns in the phylogenetic tree (\u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic of characteristics of 2,005 TB cases, Thailand, 1994\u0026ndash;2020\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 69.0365%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\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\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003e1994\u0026ndash;2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e2\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003e2003\u0026ndash;2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e18\u0026middot;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e29\u0026middot;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e41\u0026middot;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003e2018+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e7\u0026middot;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e64\u0026middot;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eNortheastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e18\u0026middot;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e7\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eSouthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e9\u0026middot;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eProvince\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eKanchanaburi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e27\u0026middot;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eBangkok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e14\u0026middot;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eNakhon Ratchasima\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e5\u0026middot;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eBuri Ram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e5\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e46\u0026middot;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eDrug resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e11\u0026middot;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eRR-TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e1\u0026middot;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eHR-TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e2\u0026middot;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eMDR-TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e67\u0026middot;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003ePre-XDR TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e14\u0026middot;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eXDR-TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e0\u0026middot;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e1\u0026middot;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eAnti-TB drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eINH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e84\u0026middot;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eResistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e84\u0026middot;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eEMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e54\u0026middot;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e60\u0026middot;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003ePZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e40\u0026middot;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eLFX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e15\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eMFX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e15\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eAMK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e5\u0026middot;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eKAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e6\u0026middot;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eETO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e43\u0026middot;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eBDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e0\u0026middot;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eLZD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.3993%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\n \u003cp\u003eLineage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e1569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.0305%;\"\u003e\n \u003cp\u003e78\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.0305%;\"\u003e\n \u003cp\u003e16\u0026middot;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.0305%;\"\u003e\n \u003cp\u003e5\u0026middot;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 30.1957%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 38.8409%;\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2764%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.0305%;\"\u003e\n \u003cp\u003e0\u0026middot;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 93.3434%;\"\u003eRR-TB Rifampicin-Resistant tuberculosis; HR-TB isoniazid-monoresistant; MDR-TB, Multidrug-Resistant; Pre-XDR TB Pre-Extensively Drug-Resistant; XDR-TB Extensively Drug-Resistant; INH Isoniazid; RIF Rifampicin; EMB Ethambutol; STM Streptomycin; PZA Pyrazinamide; LFX, Levofloxacin; MFX, Moxifloxacin; AMK Amikacin, KAN Kanamycin; ETO Ethionamide; PAS Para-aminosalicylic acid; CAP Capreomycin\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eGenotypic drug resistance\u003c/h2\u003e\n \u003cp\u003eGenotypic drug resistance profiling (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed that 88\u0026middot;4% of isolates (1772/2005) were resistant to at least one anti-TB drug, with the highest resistance observed for isoniazid (1686/2005 isolates, 84\u0026middot;1%), rifampicin (1693/2005 isolates, 84\u0026middot;4%), and streptomycin (1212/2005 isolates, 60\u0026middot;5%). WGS-based profiling indicated that 67\u0026middot;3% (1349/2005) of isolates were classified as MDR-TB, 14\u0026middot;6% (293/2005) as pre-XDR-TB, 11\u0026middot;6% (233/2005) as sensitive, and 0\u0026middot;2% (5/2005) as XDR-TB (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The geographic distribution of lineages and genotypic drug resistance categories across Thailand, suggested that MDR-TB was present across most regions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe most common mutations linked to drug resistance were \u003cem\u003ekatG\u003c/em\u003e Ser315Thr (71\u0026middot;6%, isoniazid), \u003cem\u003erpsL\u003c/em\u003e Lys43Arg (46\u0026middot;4%, streptomycin), \u003cem\u003erpoB\u003c/em\u003e Ser450Leu (46\u0026middot;0%, rifampicin), \u003cem\u003eethA\u003c/em\u003e c.639_640delGT (26\u0026middot;6%, ethionamide), \u003cem\u003eembB\u003c/em\u003e (Gly406Asp) (16\u0026middot;0%, ethambutol), \u003cem\u003epncA\u003c/em\u003e Ile31Thr (16\u0026middot;0%, pyrazinamide), and \u003cem\u003eembB\u003c/em\u003e Met306Val (14\u0026middot;4%, ethambutol). All mutations have been observed in the TB-Profiler database, which includes global isolates (N\u0026thinsp;=\u0026thinsp;50,723). However, there were differences in allele frequencies. For example, the \u003cem\u003epncA\u003c/em\u003e Ile31Thr mutation, linked to pyrazinamide resistance, was over 288 times more frequent than in the global dataset. Similarly, the \u003cem\u003eethA\u003c/em\u003e (c.639_640delGT) mutation, associated with ethionamide resistance, occurred at a frequency more than 140 times higher than in the global collection (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). A detailed catalogue of identified mutations associated with drug resistance is presented (\u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). The degree of resistance to drugs may be underestimated, especially as functional genotype-phenotype mutations are being found and may need to be included in updates to databases. For example, the \u003cem\u003eddn\u003c/em\u003e Gly81Ser mutation has recently been associated with delamanid resistance.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This mutation was identified in 22 isolates (1.1%), including 6 Mtb isolates from Kanchanaburi (2005\u0026ndash;2017) in the ENA database, \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and 16 isolates collected between 2007 and 2014 from Kanchanaburi, Bangkok, Ratchaburi, Suphan Buri, and Chachoengsao, all located in or around the central region of Thailand.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Anti-TB Drug Resistance Mutations in 2,005 Isolates.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnti-TB drugs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChanges\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOur study\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,005\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOur study\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlobal (%)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;50,723\u003csup\u003e1\u003c/sup\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\u003eINH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ekatG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer315Thr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u0026middot;62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026middot;55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003einhA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-777C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026middot;08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003einhA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer94Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ekatG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer315Asn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erpoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer450Leu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026middot;03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026middot;80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erpoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHis445Tyr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026middot;42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erpoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsp435Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026middot;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026middot;30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erpoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHis445Asp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026middot;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eembB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGly406Asp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026middot;96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eembB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMet306Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026middot;02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026middot;73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eembB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMet306Ile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026middot;77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026middot;94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erpsL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLys43Arg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026middot;43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026middot;49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erpsL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLys88Arg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026middot;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026middot;55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003errs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.514A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026middot;53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egid\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.102delG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003epncA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle31Thr\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\u003e14\u0026middot;41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003epncA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle90Ser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003epncA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-11A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLFX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsp94Gly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026middot;94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla90Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026middot;15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsp94Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsp94Gly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026middot;94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla90Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026middot;15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsp94Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003errs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.1401A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eeis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-14C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003errs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.1401A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eeis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-10G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eeis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-14C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eethA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.639_640delGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026middot;63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003einhA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-777C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026middot;08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003einhA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer94Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003efolC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer150Gly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ethyX\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-16C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026middot;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003efolC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlu40Gly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026middot;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003errs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.1401A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026middot;71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003etlyA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.52_53dupCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026middot;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eThere could be \u0026gt;\u0026thinsp;1 mutation associated with resistance to a single drug; \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The global percentage of anti-TB drug resistance mutations identified by TB-Profiler database; INH, Isoniazid; RIF, Rifampicin; EMB, Ethambutol; STR, Streptomycin; PZA, Pyrazinamide; LFX, Levofloxacin; MFX, Moxifloxacin; AMK, Amikacin, KAN, Kanamycin; ETO, Ethionamide; PAS, Para-aminosalicylic acid; CAP, Capreomycin\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eConcordance with phenotypic DST\u003c/h3\u003e\n\u003cp\u003ePhenotypic DST assays were performed on a subset of the samples (n\u0026thinsp;=\u0026thinsp;1,826) (\u003cstrong\u003eTable S3\u003c/strong\u003e). Isoniazid exhibited the highest resistance rate at 95\u0026middot;6% (1745/1826), followed by rifampicin at 94\u0026middot;7% (1730/1826), streptomycin at 61\u0026middot;8% (1113/1801), ethambutol at 44\u0026middot;0% (727/1653), and levofloxacin at 12\u0026middot;2% (199/1602). All other resistance rates were below 10%, with moxifloxacin at 8\u0026middot;6% (95/1101), kanamycin at 7\u0026middot;5% (127/1687), amikacin at 6\u0026middot;9% (81/1,172), and linezolid at 0\u0026middot;4% (4/1,097). Overall, 1,522 isolates (75\u0026middot;9%) were classified as MDR-TB, followed by 201 isolates (10\u0026middot;0%) with Pre-XDR TB+, and 59 isolates (2\u0026middot;9%) being pan-sensitive. XDR-TB was identified in 3 isolates (0\u0026middot;2%) (\u003cstrong\u003eTable S3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe overall concordance rate between phenotypic DSTs and genotypic drug resistance was 91\u0026middot;1% (11,522/12,640 tests). Concordance exceeded 88% for all drugs except ethambutol (71\u0026middot;7%), with rates of 96\u0026middot;1% for isoniazid, 96\u0026middot;9% for rifampicin, 97\u0026middot;5% for amikacin, 91\u0026middot;9% for levofloxacin, 89\u0026middot;7% for streptomycin, 96\u0026middot;8% for kanamycin, and 88\u0026middot;6% for moxifloxacin (\u003cstrong\u003eTable S4\u003c/strong\u003e). Assuming phenotypic DST results as the gold standard, the sensitivity and specificity for isoniazid and rifampicin, which are critical for defining MDR-TB, were approximately 96%. The remaining drugs demonstrated high levels of sensitivity and specificity, ranging from 70\u0026ndash;99%, with ethambutol showing a notably lower specificity of 60\u0026middot;4%.\u003c/p\u003e\n\u003cp\u003eDespite the potential for DST errors, we identified 88 mutations that may account for cases of phenotypic resistance with genotypic susceptibility. These include 24 mutations linked to isoniazid (\u003cem\u003ekatG\u003c/em\u003e 22, \u003cem\u003eaphC\u003c/em\u003e 2), 7 to rifampicin (\u003cem\u003erpoB\u003c/em\u003e 5, \u003cem\u003erpoC\u003c/em\u003e 2), 16 to ethambutol (\u003cem\u003eembA\u003c/em\u003e 7, \u003cem\u003eembB\u003c/em\u003e 8, \u003cem\u003eembC\u003c/em\u003e 1), and 19 to streptomycin (\u003cem\u003egid\u003c/em\u003e 11, \u003cem\u003erpsL\u003c/em\u003e 2, \u003cem\u003errs\u003c/em\u003e 6) (\u003cstrong\u003eTable S5\u003c/strong\u003e). Some isolates with known drug resistance mutations also harboured additional mutations that might act as compensatory mechanisms (e.g., \u003cem\u003erpoB\u003c/em\u003e Lys37Arg, \u003cem\u003erpoB\u003c/em\u003e Met655Thr; \u003cem\u003erpoC\u003c/em\u003e Gly1198Ser). At the composite resistance level (e.g., MDR-TB), the concordance between phenotypic DSTs and genotypic profiles was high at 87\u0026middot;4% (1589/1819) (\u003cstrong\u003eTable S6\u003c/strong\u003e). Most discordances (181/230; 78\u0026middot;7%) were linked to variations in MDR-TB classification, particularly cases where genotypic profiling identified mutations meeting the new definition of (pre-)XDR-TB (114/181). Instances of discordance where DST indicated susceptibility, but genotypic profiles suggested resistance (8/59) likely reflected phenotypic errors. These cases included several well-established resistance mutations, such as \u003cem\u003eembB\u003c/em\u003e Gly406Asp (ethambutol), \u003cem\u003egyrA\u003c/em\u003e Asp94Ala (moxifloxacin), and \u003cem\u003eeis\u003c/em\u003e c.-10G\u0026thinsp;\u0026gt;\u0026thinsp;A (kanamycin) (\u003cstrong\u003eTable S7\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003eTransmission cluster analysis\u003c/h3\u003e\n\u003cp\u003eThe phylogenetic tree revealed clusters of Mtb isolates within the same lineage that were highly similar, and we sought to investigate whether these are indicative of transmission events (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). An analysis of the pairwise SNP distance distribution across 2,005 isolates (median 452 SNPs; range: 0 to 2,369 SNPs) (\u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e) suggested a cut-off of 13 SNPs to define potential transmission links. At this cut-off, we identified 206 transmission clusters (total n\u0026thinsp;=\u0026thinsp;1,265 (63\u0026middot;1%); median size: 2, range: 2\u0026ndash;288), including 190 clusters with 2\u0026ndash;9 isolates, 15 clusters with 10\u0026ndash;100 isolates, and one large cluster containing 288 isolates (\u003cstrong\u003eFigure S3; Table S8\u003c/strong\u003e). As expected, each cluster was homogenous for sub-lineage. The most prevalent sub-lineage was L2.2.1 (131/206 clusters; total n\u0026thinsp;=\u0026thinsp;1,317), followed by L1.1.1 (10/206 clusters; total n\u0026thinsp;=\u0026thinsp;159). The 1,265 Mtb isolates in clusters were identified in every region of Thailand, with the majority classified as MDR-TB and pre-XDR TB (93\u0026middot;1%; 1,178/1,265). The clusters span several years (median 2: range 1\u0026ndash;18 years), with a notable presence from 2005 to 2017. Within the set of 16 clusters comprising 10 or more isolates (\u003cstrong\u003eTable S8\u003c/strong\u003e), distinct geographical distributions were observed. For example, one cluster includes 22 isolates, all classified as L2.2.1 and pre-XDR-TB, originating from the central region, with samples collected between 2005 and 2017, and having the \u003cem\u003eddn\u003c/em\u003e Gly81Ser mutation described earlier. Another cluster consists of 48 isolates classified as L2.1.1, predominantly from southern Thailand, with 43 (93\u0026middot;8%) identified as MDR-TB; these samples were collected from 2001 to 2020. Additionally, a cluster of L4.2.2 contains 12 isolates, mainly from the central region, with 9 (75%) classified as MDR-TB and 3 (25%) as pre-XDR-TB, with samples collected from 2003 to 2017. Overall, most clusters included isolates from both the newly sequenced and older public datasets (\u003cstrong\u003eFigure S4\u003c/strong\u003e), highlighting the persistence of lineages L1, L2, and L4 over time. To further explore the geographical spread of these clusters, we assessed the correlation between geographic and SNP distances (\u003cstrong\u003eFigure S5\u003c/strong\u003e). The analysis showed a small but significant positive trend (Kruskal-Wallis P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) with a weak positive correlation (Spearman\u0026rsquo;s rho\u0026thinsp;=\u0026thinsp;0\u0026middot;046).\u003c/p\u003e\n\u003cp\u003eA logistic regression analytical approach was used to assess the risk factors for being in a transmission cluster. Compared to lineage L1, being an isolate in L2 (odds ratio [OR] 7\u0026middot;41, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) or L4 (OR 2\u0026middot;03, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05) had higher risk. Whilst, compared to sensitive strains, those with MDR-TB (OR 5\u0026middot;82, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) or pre-XDR-TB (OR 10\u0026middot;98, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with a higher risk of clustering. Further, those isolates from Central and Northeastern regions were more likely to cluster compared to the Northern region (OR\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026middot;5, P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) (\u003cstrong\u003eTable S9\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThrough a genome-wide analysis using common SNPs (minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;01), we identified 4 loci potentially associated with being in transmission clusters, located in the genes \u003cem\u003ekatG\u003c/em\u003e (Rv1908c), \u003cem\u003efolC\u003c/em\u003e (Rv2447c), \u003cem\u003eppe8\u003c/em\u003e (Rv0355c), and \u003cem\u003efolK\u003c/em\u003e (Rv3606c) (P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) (\u003cstrong\u003eTable S10\u003c/strong\u003e). Among loci with odds ratios greater than one, we detected the \u003cem\u003ekatG\u003c/em\u003e Ser315Thr mutation\u0026mdash;the most common INH resistance mutation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e as most clusters are linked to at least MDR-TB (OR 7\u0026middot;93, P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;00001). The \u003cem\u003efolC\u003c/em\u003e (\u003cem\u003eRv2447c\u003c/em\u003e) S150G mutation, involved in the production of para-aminobenzoic acid (pABA) in the folate biosynthetic pathway,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e was found in lineages L2 and L4 (OR 6\u0026middot;5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001). Additionally, the \u003cem\u003eppe8\u003c/em\u003e (\u003cem\u003eRv0355c\u003c/em\u003e) V2309I mutation was linked to adaptations in response to host defence mechanisms (OR 6\u0026middot;5, P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05).\u003csup\u003e24\u003c/sup\u003e These mutations were observed across multiple clusters, including \u003cem\u003ekatG\u003c/em\u003e Ser315Thr (147 clusters), \u003cem\u003efolC\u003c/em\u003e S150G (6 clusters), and \u003cem\u003eppe8\u003c/em\u003e V2309I (5 clusters) (\u003cstrong\u003eFigure S4\u003c/strong\u003e). Like \u003cem\u003efolC\u003c/em\u003e, the \u003cem\u003efolK\u003c/em\u003e (\u003cem\u003eRv3606c\u003c/em\u003e) locus is associated with folate metabolism. While the \u003cem\u003efolK\u003c/em\u003e Gln47His mutation has an odds ratio below one, there is no strong evidence of compensatory effects (\u003cstrong\u003eFigure S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eInvestigation of the largest cluster\u003c/h2\u003e\n \u003cp\u003eThe time-calibrated phylogenetic tree of the largest cluster (n\u0026thinsp;=\u0026thinsp;288 isolates, L2.2.1, 96\u0026middot;9% central region, 93\u0026middot;8% MDR-TB; \u003cstrong\u003eTable S8\u003c/strong\u003e; \u003cstrong\u003eFigure S6\u003c/strong\u003e) was analysed using BEAST2 software. This analysis estimated the mutation rate to be 1\u0026middot;10x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e substitutions per site per year (95% highest posterior density (HPD) interval: 9\u0026middot;89x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e to 1\u0026middot;22x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), which corresponds to ~\u0026thinsp;0\u0026middot;48 substitutions per genome per year (HPD: 0\u0026middot;44 to 0\u0026middot;53), and in keeping with estimates for L2 from other settings.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e The estimated time of the most recent common ancestor is 1989 (95% HPD: 1984\u0026ndash;1994), which is plausible given the known roll-out of the anti-TB drugs. All parameters had an effective sample size score greater than 200. The TransPhylo package was used to reconstruct the transmission history among cases, offering detailed insights into the source of infections within the studied population. Using maximum \u003cem\u003ea posteriori\u003c/em\u003e estimates, we identified the most probable transmission pathways (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which revealed the evolution of Mtb, including from MDR-TB to pre-XDR, through time. Furthermore, this analysis estimated there were potentially 132 unsampled Mtb cases, in part due to gaps caused by the passive and convenient nature of the sampling (\u003cstrong\u003eFigure S7\u003c/strong\u003e). By focusing on strongly supported transmission links (probabilities\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;2), the large cluster was split into 83 sub-clusters. This approach enabled detailed investigation of smaller clusters, visualization of potential transmission pathways (i.e., who-infected-whom), integration of drug resistance profiles and collection years, and provided valuable insights to guide more targeted contact investigations by infection control agencies (\u003cstrong\u003eFigure S8\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe WHO classifies Thailand as a high-burden TB country. This study presents a WGS analysis of 2,005 Mtb isolates from Thailand, spanning three decades (1994\u0026ndash;2020), to examine circulating strains, drug resistance profiles, and transmission dynamics. The dataset includes isolates from previous studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and new data from Siriraj Hospital, with most samples originating from cases involving drug resistant TB, of which more than sixty percent were MDR-TB. The frequency distribution of randomly recruited Mtb isolates in Thailand predominantly comprises L1 and L2.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Our findings reveal a high prevalence of L2, particularly the Beijing sub-lineage (L2.2.1). This aligns with previous studies indicating that L2 is the main lineage related to clusters of outbreaks and drug-resistant isolates in Thailand, while L1 is more prevalent in general Mtb cases.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The geographic patterns observed suggest that specific lineages may be driving local transmission, especially in densely populated regions such as the Central area.\u003c/p\u003e \u003cp\u003eThe genotypic drug resistance profiles generated by TB-Profiler software had high concordance with phenotypic DST, confirming its reliability. Since most isolates were MDR-TB, resistance rates detected through both phenotypic DST and genotypic resistance profiling were highest for isoniazid and rifampicin. Discrepancies between phenotypic and genotypic approaches may arise due to DST laboratory errors, limitations in WGS\u0026rsquo;s ability to capture certain mutations, or gaps in the drug resistance mutation database used for annotation. We identified 88 low-frequency mutations that explained phenotypic resistance and genotypic sensitivity inconsistencies, including 66 linked to four drugs: isoniazid (24), rifampicin (7), ethambutol (16), and streptomycin (19). These mutations, confirmed using phenotypic DST, were not lineage-specific and occurred at low frequencies in a dataset of 50,723 global Mtb samples. As such, they should be considered for inclusion in the TB-Profiler database. At the DR profile level, samples initially classified as MDR-TB by phenotypic DST were re-categorised genotypically in over 4\u0026middot;9% of cases to a lower resistance level (sensitive, RR-TB, or HR-TB) and in 7\u0026middot;5% of cases to a higher resistance level (pre-XDR-TB or XDR-TB). This reclassification is a crucial consideration, as treatment for each type of TB is specific, with distinct drug regimens and durations.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Inappropriate treatment could lead to further complications. A particularly concerning finding is the presence of the \u003cem\u003eddn\u003c/em\u003e Gly81Ser mutation, which is associated with delamanid resistance,\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e even in isolates predating the drug's rollout. Intrinsic resistance to delamanid and pretomanid\u0026mdash;both sharing a similar mechanism of action\u0026mdash;could undermine the effectiveness of the latest BPaLM regimen, especially as bedaquiline resistance continues to rise. These findings emphasise the importance of using WGS data to identify mutations potentially associated with drug resistance, even in cases where phenotypic DST results indicate sensitivity.\u003c/p\u003e \u003cp\u003eThe phylogenetic analysis of Mtb isolates revealed distinct clusters within the same lineage, suggesting potential transmission chains. Using a pairwise SNP distance cut-off of 13, we identified 206 transmission clusters comprising 1,265 isolates. These clusters ranged from small groups (190 clusters with 2\u0026ndash;9 isolates) to larger networks (16 clusters with 10\u0026ndash;100 isolates), including one exceptionally large cluster of 288 isolates, all highly homogenous for sub-lineage. The presence of transmission clusters in every region of Thailand, coupled with a substantial proportion of MDR-TB and pre-XDR TB cases, underscores the widespread nature of drug-resistant TB across the country. Clusters spanned several years, with the majority arising between 2005 and 2017, reflecting ongoing transmission. Due to the convenient sampling method, it is possible that other isolates existed outside the given collection years. Clusters with higher percentages in more recent years (e.g., 2012\u0026ndash;2017) may indicate ongoing transmission. It suggests that certain strains have persisted over time, reflecting continuous transmission of similar strains across years.\u003c/p\u003e \u003cp\u003eThe study found that lineage, drug resistance, and geographic factors were significantly associated with the transmission dynamics of Mtb. Lineage L2 exhibited the highest transmissibility and adaptability, showing the strongest odds of clustering. Although less frequently involved in outbreaks than L2, L4 still presented an increased risk of clustering,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e with both being modern strains. Drug resistance, particularly MDR-TB and pre-XDR-TB, further heightened the risk of clustering. Geographic factors also played a key role, with isolates from the Central and Northeastern regions having higher odds of clustering. These findings can inform the design of tailored interventions aimed at high-risk areas and populations.\u003c/p\u003e \u003cp\u003eUsing a GWAS approach, three loci in \u003cem\u003ekatG\u003c/em\u003e, \u003cem\u003efolC\u003c/em\u003e, and \u003cem\u003eppe8\u003c/em\u003e were identified as associated with clustering or transmissibility, suggesting a potential increase in Mtb fitness and transmission. These loci have not been reported in similar analyses in Asia with comparable lineages.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Both the \u003cem\u003ekatG\u003c/em\u003e and \u003cem\u003efolC\u003c/em\u003e genes are associated with drug resistance.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e However, studies suggest that the increased transmission risk of drug-resistant tuberculosis is driven more by delays in diagnosis and treatment than by the inherent transmissibility of resistant strains.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The \u003cem\u003eppe8\u003c/em\u003e locus exhibits complexity, with deletions at the gene's start in L1 strains and within the encompassed RD304 region in L2 strains.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Although Mtb GWAS can be used to identify associations between SNPs and transmissibility, some suggest incorporating host genomics into the analysis, such as through genome-to-genome approaches, for more comprehensive results.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe identified a large cluster of 288 isolates and employed time-calibrated phylogenetic analysis using BEAST2 to reconstruct the outbreak\u0026rsquo;s transmission history from WGS data. We subsequently used TransPhylo to infer transmission patterns within the studied population. It enabled us to estimate the number of unsampled cases, revealing a hidden burden of TB transmission and suggesting that undetected infections may sustain ongoing transmission chains. To improve confidence in our inferences, we filtered out transmission probabilities below 0\u0026middot;2, reflecting the varied, convenience-based nature of sampling. Consequently, we could more reliably focus on smaller transmission clusters. Occasionally, we observed transmission patterns that appeared counterintuitive, such as transmission from Pre-XDR-TB to MDR-TB cases or from more recent years to earlier years (e.g., 2008 to 2006). These anomalies may reflect unsampled transmission links or uncertainties in determining the actual timing of infection. Specifically, our analysis relied on the year of sample collection rather than the precise year of infection, which can introduce discrepancies. By continually integrating updated WGS data, we can enhance the monitoring of patients and their transmission networks, thereby strengthening efforts to contain and prevent the spread of transmission clusters.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides a comprehensive analysis of WGS data from Mtb isolates in Thailand, offering valuable insights into circulating strains, drug resistance profiles, and transmission dynamics, particularly among drug-resistant TB cases. The findings highlight the critical role of integrating genomic and epidemiological data to deepen our understanding of Mtb and to guide targeted interventions for TB control. However, WGS alone cannot address all TB-related challenges. Sustained efforts combining WGS with strengthened public health strategies, enhanced surveillance systems, and multifaceted interventions are essential to effectively combat TB in Thailand and beyond.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDECLARATION OF INTERESTS\u003c/h2\u003e \u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDATA SHARING\u003c/h2\u003e \u003cp\u003eRaw sequencing data are available in the European Nucleotide Archive (ENA). A complete list of accession numbers is provided in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSM, AC, PSu, KF, and TGC conceived and supervised the project. PS, TP, KF, PSu, and AC conducted sample processing and DNA extraction. PS performed bacterial culture. SC and PS conducted sequencing. NT and WP performed bioinformatics and statistical analyses under the supervision of PS, JEP, SC, SW, SM, AC, and TGC. NT, PS, JEP, WS, MLH, SC, SW, SM, AC, and TGC contributed to data interpretation. NT drafted the manuscript, with input from all authors. All authors reviewed, edited, and approved the final manuscript. NT, PS, SM, AC, and TGC compiled the final submission.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThe study was funded by MRC \u0026ndash; NSTDA \u0026ndash; Newton (ref. MR/R020973/1; P-18-50228). NT is funded by a Thailand government Ministry of Public Health PhD scholarship. LW is funded by a BBSRC LIDO PhD studentship. WH is a recipient of a Chulabhorn Royal Academy fellowship. TGC and SC are funded by the UKRI (BBSRC BB/X018156/1; MRC MR/X005895/1; EPSRC EP/Y018842/1)). NT, WS, SW and SM were funded by the Department of Medical Sciences (Thailand Ministry of Public Health). The Thailand Health Systems Research Institute provided funding through HSRI 64\u0026ndash;179 (NT, WS, SW and SM) and HSRI 65\u0026ndash;113 (KF, ST, AC, TP, PS, WP). KF, ST and AC is funded by the National Research Council of Thailand (NRCT) (NRC MHESI 483/2563). The funders had no role in study design, data collection and analysis, decision to publish, or the preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSalina, E. G. Mycobacterium tuberculosis Infection: Control and Treatment. \u003cem\u003eMicroorganisms\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1057 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. \u003cem\u003eGlobal tuberculosis report 2023\u003c/em\u003e (World Health Organization, 2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/373828\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/373828\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaung, H. M. W. et al. Geno-Spatial Distribution of Mycobacterium Tuberculosis and Drug Resistance Profiles in Myanmar\u0026ndash;Thai Border Area. \u003cem\u003eTrop. Med. Infect. Dis.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 153 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThorpe, J. et al. Multi-platform whole genome sequencing for tuberculosis clinical and surveillance applications. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 5201 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDippenaar, A. et al. Nanopore Sequencing for Mycobacterium tuberculosis: a Critical Review of the Literature, New Developments, and Future Opportunities. \u003cem\u003eJ. Clin. Microbiol.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, e0064621 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhelan, J. E. et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. \u003cem\u003eGenome Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 41 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. \u003cem\u003eThe use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in Mycobacterium tuberculosis complex: technical guide\u003c/em\u003e (World Health Organization, 2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/274443\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/274443\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatta, G. et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? \u003cem\u003eClin. Microbiol. Infect.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 604\u0026ndash;609 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. \u003cem\u003eCatalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance\u003c/em\u003e 2nd edn (World Health Organization, 2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/374061\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/374061\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDivision of Tuberculosis. \u003cem\u003eNational Tuberculosis Control Programme Guideline, Thailand 2021\u003c/em\u003e (Bangkok, 2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNonghanphithak, D. et al. Clusters of Drug-Resistant Mycobacterium tuberculosis Detected by Whole-Genome Sequence Analysis of Nationwide Sample, Thailand, 2014\u0026ndash;2017. \u003cem\u003eEmerg. Infect. Dis.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 813\u0026ndash;822 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaksri, K. et al. Comparisons of whole-genome sequencing and phenotypic drug susceptibility testing for Mycobacterium tuberculosis causing MDR-TB and XDR-TB in Thailand. \u003cem\u003eInt. J. Antimicrob. Agents\u003c/em\u003e. \u003cb\u003e54\u003c/b\u003e, 109\u0026ndash;116 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyahara, R. et al. Risk for Prison-to-Community Tuberculosis Transmission, Thailand, 2017\u0026ndash;2020. \u003cem\u003eEmerg. Infect. Dis.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 477\u0026ndash;483 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhelan, J. et al. Genome-wide host-pathogen analyses reveal genetic interaction points in tuberculosis disease. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 549 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrilohasin, P. et al. Genomic evidence supporting the clonal expansion of extensively drug-resistant tuberculosis bacteria belonging to a rare proto-Beijing genotype. \u003cem\u003eEmerg. Microbes Infect.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 2632\u0026ndash;2641 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization, W. H. \u003cem\u003eMeeting report of the WHO expert consultation on the definition of extensively drug-resistant tuberculosis, 27\u0026ndash;29 October 2020\u003c/em\u003e (World Health Organization, 2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/338776\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/338776\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouckaert, R. et al. BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. \u003cem\u003ePLoS Comput. Biol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, e1003537 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobkowiak, B. et al. Bayesian reconstruction of Mycobacterium tuberculosis transmission networks in a high incidence area over two decades in Malawi reveals associated risk factors and genomic variants. \u003cem\u003eMicrob. Genom\u003c/em\u003e. 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1099/mgen.0.000361\u003c/span\u003e\u003cspan address=\"10.1099/mgen.0.000361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhelan, J. E. et al. TGV: suite of tools to visualize transmission graphs. \u003cem\u003eNAR Genom Bioinform\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nargab/lqae158\u003c/span\u003e\u003cspan address=\"10.1093/nargab/lqae158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColl, F. et al. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 307\u0026ndash;316 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, S. et al. Functional analysis of genetic mutations in ddn and fbiA linked to delamanid resistance in rifampicin-resistant Mycobacterium tuberculosis, Tuberculosis. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tube.2025.102630\u003c/span\u003e\u003cspan address=\"10.1016/j.tube.2025.102630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndo, H. et al. Identification of katG mutations associated with high-level isoniazid resistance in Mycobacterium tuberculosis. \u003cem\u003eAntimicrob. Agents Chemother.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 1793\u0026ndash;1799 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, R., Li, K., Yu, J., Deng, J. \u0026amp; Chen, Y. Mutations of folC cause increased susceptibility to sulfamethoxazole in Mycobacterium tuberculosis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-80213-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-80213-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChitwood, M. H. et al. The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-024-47282-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-47282-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres Ortiz, A. et al. Genomic signatures of pre-resistance in Mycobacterium tuberculosis. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-021-27616-7\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-27616-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutaihwa, L. K. et al. Multiple introductions of Mycobacterium tuberculosis Lineage 2-Beijing into Africa over centuries. \u003cem\u003eFront. Ecol. Evol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fevo.2019.00112\u003c/span\u003e\u003cspan address=\"10.3389/fevo.2019.00112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsayed, S. S. R., Gunosewoyo, H. \u0026amp; Tuberculosis Pathogenesis, Current Treatment Regimens and New Drug Targets. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24065202\u003c/span\u003e\u003cspan address=\"10.3390/ijms24065202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolt, K. E. et al. Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 849\u0026ndash;856 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L. et al. Whole genome sequencing analysis of Mycobacterium tuberculosis reveals circulating strain types and drug-resistance mutations in the Philippines. \u003cem\u003eSci. Rep.\u003c/em\u003e 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-70471-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-70471-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNapier, G. et al. Characterisation of drug-resistant Mycobacterium tuberculosis mutations and transmission in Pakistan. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-11795-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-11795-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtre, S. R. et al. Tuberculosis Pathways to Care and Transmission of Multidrug Resistance in India. \u003cem\u003eAm. J. Respir Crit. Care Med.\u003c/em\u003e \u003cb\u003e205\u003c/b\u003e, 233\u0026ndash;241 (2022).\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mycobacterium tuberculosis, Tuberculosis, transmission, drug resistance, genomics","lastPublishedDoi":"10.21203/rs.3.rs-6333922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6333922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThailand has a high burden of tuberculosis, with control efforts hindered by drug-resistant \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb). The increasing use of whole-genome sequencing (WGS) of Mtb offers valuable insights for clinical management and public health surveillance. WGS can be used to profile drug resistance, identify circulating sub-lineages, and trace transmission pathways or outbreaks. We analysed WGS data from 2,005 Mtb isolates collected from 1994\u0026ndash;2020, across four regions of Thailand, including 1,189 newly sequenced samples. Most isolates are lineage two strains (78\u0026middot;3%), primarily the Beijing sub-lineage (L2.2.1). Drug resistance profiling revealed substantial isoniazid and rifampicin resistance, and 67\u0026middot;3% classified as multidrug-resistant (MDR-TB). Phenotypic and genotypic drug susceptibility testing showed high concordance (91\u0026middot;1%). Clustering analysis identified 206 transmission clades (maximum size 288), predominantly with MDR-TB, especially in Central and Northeastern regions. One cluster (n\u0026thinsp;=\u0026thinsp;22) contains the \u003cem\u003eddn\u003c/em\u003e Gly81Ser mutation, linked to delamanid resistance, with some members pre-dating drug roll-out. In the largest cluster (n\u0026thinsp;=\u0026thinsp;288), containing isolates spanning two decades, we applied transmission reconstruction methods to estimate a mutation rate of 1\u0026middot;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e substitutions per site per year. Overall, this study demonstrates the value of WGS in uncovering TB transmission and drug resistance, offering key data to inform better control strategies in Thailand and elsewhere.\u003c/p\u003e \u003cp\u003e200/200\u003c/p\u003e","manuscriptTitle":"Genomic analysis of Mycobacterium tuberculosis in Thailand reveals circulating lineage diversity, transmission, and drug resistance mutations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 09:41:41","doi":"10.21203/rs.3.rs-6333922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T05:44:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T05:11:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-04T09:56:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-30T12:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231133038044047033793371748285647749303","date":"2025-04-28T00:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93025984449602116784171994384110337831","date":"2025-04-25T05:48:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193935761604945534715554626988831164811","date":"2025-04-23T10:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57989390075426004906778084642095765919","date":"2025-04-23T09:32:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T05:09:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-18T04:25:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-07T12:00:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T07:05:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-29T11:48:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6274c8c6-add2-4943-bee9-ecf0b4745611","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47663926,"name":"Biological sciences/Genetics"},{"id":47663927,"name":"Biological sciences/Genetics/Microbial genetics"},{"id":47663928,"name":"Biological sciences/Genetics/Microbial genetics/Bacterial genetics"},{"id":47663929,"name":"Health sciences/Diseases"},{"id":47663930,"name":"Health sciences/Diseases/Infectious diseases"},{"id":47663931,"name":"Health sciences/Diseases/Infectious diseases/Tuberculosis"}],"tags":[],"updatedAt":"2025-08-18T15:59:10+00:00","versionOfRecord":{"articleIdentity":"rs-6333922","link":"https://doi.org/10.1038/s41598-025-15093-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-13 15:57:04","publishedOnDateReadable":"August 13th, 2025"},"versionCreatedAt":"2025-04-28 09:41:41","video":"","vorDoi":"10.1038/s41598-025-15093-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-15093-7","workflowStages":[]},"version":"v1","identity":"rs-6333922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6333922","identity":"rs-6333922","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}