Molecular Epidemiology of Drug-Resistant Tuberculosis in Shantou, China: Associations between Drug Resistance and Genotypic Variation

Molecular Epidemiology of Drug-Resistant Tuberculosis in Shantou, China: Associations between Drug Resistance and Genotypic Variation | 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 Research Article Molecular Epidemiology of Drug-Resistant Tuberculosis in Shantou, China: Associations between Drug Resistance and Genotypic Variation Yannan Xu, Wenli Zhao, Liwei Gi, Shumiao Chen, Chenglong Chen, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7713641/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Background Drug-resistant tuberculosis (DR-TB) is a major global health threat, particularly in resource-limited regions. Understanding lineage-specific mutations is critical for improving diagnostics and guiding precision treatment. Methods Drug susceptibility testing was performed using Löwenstein-Jensen medium. Resistance-associated gene fragments were amplified via PCR, and mutation sites were analyzed using SnapGene. Genotyping was conducted using IS6110 and 8-locus MIRU-VNTR, followed by phylogenetic analysis (MEGA11, Neighbor-Joining method). Fisher’s exact test assessed genotype-mutation and phenotype-resistance correlations. Results We analyzed 148 MTB isolates from Shantou, China (2024). Resistance prevalence was 31.8% (47/148), with 98.6% (146/148) harboring mutations. Highest resistance was to isoniazid (20.9%), followed by streptomycin (10.1%), rifampicin (6.2%), and ethambutol (6.2%). Retreated cases (20.4%) showed significantly higher resistance than new cases ( p < 0.05). Key mutations included katG Arg463Leu (66.2%) and gyrA Ser95Thr (98.0%). Lineage 2 strains were the dominant transmitted strains in Shantou, carrying only inhA , rpsL , rrs , and pncA resistance-associated mutations. MIRU-VNTR identified 134 genotypes (clustering rate: 9.46%), of which 8 clusters shared the same resistance mutation profile. Conclusions Retreatment cases pose a higher DR-TB risk in this region, with notable isoniazid resistance. Resistance mutation patterns are regional and consistent with Erdman and CDC1551 standard strains. Integration of genotyping and resistance data may improve the efficiency of precision therapy and transmission monitoring. Enhanced molecular surveillance, standardised treatment and optimised retreatment regimens are recommended to control the spread of DR-TB. Drug resistance Gene mutation Genotyping Mycobacterium tuberculosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction According to the Global Tuberculosis(TB) Report 2024, it is estimated that 10.8 million new TB cases and 1.25 million deaths will occur in 2023, and TB will once again be the deadliest single-pathogen disease worldwide [ 1 , 2 ]. Drug-resistant tuberculosis (DR-TB) prevention and control is a key entry point for implementing the One Health strategy, and its emergence, especially in resource-limited areas, complicates global TB control efforts [ 3 ]. China has a heavy burden of DR-TB, with the fourth highest number of new multidrug/rifampicin-resistant tuberculosis (MDR/RR-TB) cases globally, accounting for 7.3% of all global cases in 2023 [ 2 ]. There is an urgent need to understand better and promote a new public health prevention and control system that integrates genomic epidemiology, drug resistance patterns, and precision treatment strategies. Resistance in Mycobacterium tuberculosis (MTB) is primarily driven by mutations in drug-targeted genes[ 4 ]. The World Health Organization (WHO) has classified resistance-associated mutations into tiers based on their clinical relevance[ 5 ]. Rifampicin (RIF) resistance commonly arises from rpoB mutations, while isoniazid (INH) resistance is frequently linked to katG Ser315Thr mutations [ 6 , 7 ]. Fluoroquinolones (FQs) resistance often involves gyrA and gyrB mutations[ 8 ]. However, mutation prevalence varies geographically and across MTB lineages, reflecting diverse selective pressures and transmission dynamics. Lineage-specific resistance patterns further complicate the management of DR-TB. The Beijing genotype (Lineage 2), a globally disseminated strain (particularly in China), exhibits a high prevalence of MDR-TB in regions such as Eastern Europe and Central Asia [ 9 , 10 ]. In contrast, other lineages (e.g., Lineage 4 in Africa or Lineage 1 in South Asia) display distinct resistance profiles, suggesting lineage-specific evolutionary adaptations [ 11 ]. IS6110-RFLP remains the gold standard for MTB genotyping [ 12 ], but strains with low IS6110 copy numbers require supplementary methods such as MIRU-VNTR (8–24 loci) for accurate clustering and lineage identification [ 13 ]. Despite advances in understanding genotype-resistance associations, the mechanistic basis remains incompletely elucidated. This study integrates phenotypic DST with sequencing of resistance-associated genes and multi-locus genotyping to (1) characterize mutation patterns in DR-TB and (2) explore genotype-resistance correlations. The findings provide both localized strategies for TB control in high-density tropical cities and foundational data for global One Health initiatives aimed at combating antimicrobial resistance. 2. Methods 2.1 Strains We retrospectively analysed 301 sputum samples from the Shantou Institute of Tuberculosis Control and Prevention, Shantou, China, from January to December 2024. The H37Rv MTB standard strain was provided by Shanghai Jingnuo Biotechnology Co. Ltd. as a control. 301 strains were pathogenetically detected, and the results of IS6110 and MIRN-VNTR 8-locus amplification were finally included in the gene sequences of 148 strains for comparative analysis. The number of strains isolated from each jurisdiction was as follows: Chaonan District, 16; Chaoyang District, 16; Chenghai District, 27; Jinping District, 32; Longhu District, 29; Nan'ao County, 2; Chao'an District, 7; Raoping County, 11; Xiangqiao District, 2; Jiedong District, 1; Puning City, 1; and Rongcheng District, 2. 2.2 Strain Culture and Drug Susceptibility Testing (DST) Clinical sputum specimens were processed according to the Technical Standard for Bacteriological Examination in Tuberculosis Diagnosis [ 14 ]. Briefly, sputum specimens were digested with 1–2 volumes of 4% NaOH (15–30 mL depending on viscosity), neutralized with phosphate buffer (1:9), centrifuged at 4800 × g for 15 min, and inoculated with 0.5 mL of the sediment into Mycobacteria Growth Indicator Tubes (BACTEC™ MGIT™ 960 system; Becton Dickinson, USA). Cultures were incubated at 37°C and monitored for fluorescence-based growth detection. Tubes reporting ≥ 100 growth units (GU) within 42 days were considered positive; otherwise, results were recorded as negative. For DST, positive cultures were subcultured onto Löwenstein-Jensen (L-J) slants containing critical antituberculosis drugs: Isoniazid (INH), Rifampicin (RFP), Streptomycin (SM), Ethambutol (EMB), Pyrazinamide (PZA), and Quinolones (QS). Resistance was defined as ≥ 1% bacterial growth on a drug-containing medium compared to drug-free controls. 2.3 PCR Amplification Amplification of drug resistance-associated genetic loci ( rpoB , katG , inhA , embB , rpsL , rrs , gyrA , gyrB , and pncA ) was performed employing primer sets specified in Table 1 [ 14 – 17 ]. Molecular identification of MTBC was achieved through the amplification of IS6110-specific genomic fragments. Subsequent strain genotyping was conducted via MIRU-VNTR analysis targeting eight loci (QUB-11b, QUB-18, QUB-26, MIRU26, MIRU31, MIRU40, Mtub04, and Mtub21)[ 18 – 24 ]. The detailed PCR reaction system compositions and thermal cycling conditions are provided in S1 Tables 1 and 2 . All PCR amplicons were electrophoresed on 1% agarose gels at 12.5 V/cm for 45 min and visualized using a gel documentation system. The primers used in this study and PCR products (including drug resistance genes and IS6110 amplicons) were synthesized and sequenced by Tsingke Biotechnology Co., Ltd. (Beijing, China). Table 1 Oligonucleotide primer sequences and corresponding PCR product lengths. Gene (Drug) Primer sequences Product lengths(bp) ropB (RIF) L: 5’-CTTGCACGAGGGTCAGACCA−3’ R: 5’-ATCTCGTCGCTAACCACGCC−3’ 543 katG (INH) L: 5’-CCGCCTTTGCTGCTTTCTC−3’ R: 5’-GGGGCTGATCTACGTGAAC−3’ 1000 inhA (INH) L: 5’-GCAGCATGCAGCGCAACAAATTC−3’ R: 5’-GTAGGGCCGCAGTTTTACCAGTTC−3’ 1624 embB (EMB) L: 5’-TGATTGGCTTTGTGTTGTCG−3’ R: 5’-GAACACCCCGACAATCTGCG−3’ 445 rpsl (SM) L: 5’-ATGAGACGAATCGAGTTTGAGG−3’ R: 5’-GATCGGTGCCGGTCTTGTCG−3’ 640 rrs (SM) L: 5’-AAACCTCTTTCACCATCGAC−3’ R: 5’-GTATCCATTGATGCTCGCAA−3’ 1328 gryA (QS) L: 5’-TCGACTATGCGATGAGCGTG−3’ R: 5’-GGTAGCACCGTCGGCTCTTG−3’ 415 gryB (QS) L: 5’-CCACCGACGCGAAAGTC−3’ R: 5’-CTGCCACTTGAGTTTGTACA−3’ 524 pncA (PZA) L: 5’-AACAGTTCATCCCGGTTC−3’ R: 5’-GCGTCATG GACCCTATATC−3’ 668 IS6110 L: 5’-ATCTGAACCGCCCCGGCATGTCCGG-3’ R: 5’-ATCTGAACCGCCCCGGTGAGTCCGG-3’ 850、361 QUB-11b L: 5’-CGTAAGGGGGATGCGGGAAATAGG-3’ R: 5’-CGAAGTGAATGGTGGCAT-3’ — QUB-18 L: 5’-ATCGTCAGCTGCGGAATAGT-3’ R: 5’-AATACCGGGGATATCGGTTC-3’ — QUB-26 L: 5’-AACGCTCAGCTGTCGGAT-3’ R: 5’-GGCCAGGTCCTTCCCGAT-3’ — MIRU26 L: 5’-GCGGATAGGTCTACCGTCGAAATC-3’ R: 5’-TCCGGGTCATACAGCATGATCA-3’ — MIRU31 L: 5’-GATTCCAACAAGACGCAGATCAAGA-3’ R: 5’-TCAGGTCTTTCTCTCACGCTCTCG-3’ — MIRU40 L: 5’-CGTCGAAGAGAGCCTCATCAATCAT-3’ R: 5’-AACCTGCTGACCGATGGCAATATC-3’ — Mtub04 L: 5’-GTCCAGGTTGCAAGAGATGG-3’ R: 5’-GGCATCCTCAACAACGGTAG-3’ — Mtub21 L: 5’-AGATCCCAGTTGTCGTCGTC-3’ R: 5’-CAACATCGCCTGGTTCTGTA-3’ — "—" indicates undetermined fragment size. During 8-locus VNTR amplification, multiple amplicon sizes were observed for each locus, precluding definitive assignment of a single fragment length. 2.4 Data interpretation, Analysis and Statistics IS6110-specific fragments: PCR products of 850 bp and 361 bp indicated Mycobacterium tuberculosis , while a single 850 bp product suggested MTBC. MIRU-VNTR 8-loci: The repeat unit numbers at each locus were calculated according to the 8-loci interpretation criteria ( S2 Tables 1 and 2 ), with the results utilized for strain typing and cluster analysis. Sequence mutations in drug resistance-associated genes were analyzed using SnapGene 6.0.2. An unweighted pair-group method with an arithmetic mean (UPGMA) tree was constructed using MIRU-VNTRplus ( http://www.miru-vntrplus.org ). Phylogenetic analysis was performed using MEGA 11, with reference strains ( S3 Table 1 ) selected for comparative evolutionary inference. Statistical analyses were conducted in SPSS 26.0, with Fisher's exact test applied for rate comparisons between groups. A P-value < 0.05 was considered statistically significant. 3. Results 3.1 Characteristics of study subjects 148 MTB strains were successfully isolated through culture and subsequently identified by IS6110-based genotyping and MIRU-VNTR analysis. Demographic characteristics of the 147 TB patients: males predominated (109 cases, 74.2%), and females comprised 38 cases (25.8%). In terms of age distribution, patients aged 60 years or older were the most prevalent (63 cases, 42.9%), followed by those aged 40–59 years (47 cases, 32.0%). In terms of occupation, homemakers/unemployed people accounted for the majority (87 cases, 59.2%), and retirees made up the second largest group (30 cases, 20.4%). Sociodemographic analysis of the 109 Shantou residents revealed that they were distributed across 32 townships and districts, of which 27 administrative districts (84.4% of the total) had only a single case. The basic information is shown in Table 2 . Table 2 Demographic characteristics of 147 TB patients. Variable Total, N (%) Sex Male 109(74.2) Female 38(25.9) Age (years) 0–20 6(4.1) 20–40 31(21.1) 40–60 47(32.0) > 60 63(42.9) Population category Household and unemployed 87(59.18) Retirees 30(20.41) Farmers 15(10.21) Students 6(4.08) Commercial services 4(2.72) Workers 3(2.04) Others 2(1.36) Treatment history New cases 117(79.6) Retreatment cases 30(20.4) City of residence Shantou 122(83.0) Chaozhou 20(13.6) Jieyang 5(3.4) 3.2 Characteristics of phenotypic drug resistance and drug-resistance gene mutations Among the 148 isolated strains, 101 (68.2%) were susceptible to all anti-tuberculosis drugs, while 47 (31.8%) exhibited drug resistance. The monoresistance rate was 24.3% (36/148), and the non-monoresistance rate was 7.4% (11/148). The resistance rates for individual drugs were as follows: INH, 20.9% (31/148); RIF, 6.2% (9/148); EMB, 6.2% (9/148); and QS, 10.1% (15/148). Patients were stratified into new cases (23.9% resistance) and retreatment (53.3% resistance) cases based on their treatment history. A statistically significant difference in resistance rates was observed between the two groups (χ² ≈ 11.07, p < 0.05). Table 3 presents the phenotypic drug susceptibility testing results for the 148 isolated strains. Table 3 Phenotypic drug susceptibility profiles of the 148 isolates. Drug No of isolates Resistance rate (%) INH 21 14.2 QS 12 8.1 RIF 1 0.7 EMB 2 1.4 INH + EMB 3 2.0 INH + RIF(MDR) 2 1.4 RIF + QS 1 0.7 INH + RIF + EMB 3 2.0 INH + RIF + QS(pre-XDR) 1 0.7 Extensive drug resistance 1 0.7 Pansusceptible 101 68.2 Total 148 100 INH = isoniazid; RIF = rifampicin; EMB = ethambutol; QS = quinolone; MDR = multidrug-resistant; pre-XDR = pre-extensively drug resistant Genetic analysis identified resistance-associated mutations in 146 of 148 isolates (98.6%), with two predominant mutation patterns: (1) katG Arg463Leu (66.2%, 98/148) showing consistent CGG→CTG codon changes and (2) gyrA Ser95Thr (98.0%, 145/148) exhibiting uniform AGC→ACC substitutions (Fig. 1 ). Further characterization revealed: (1) 11 carried katG 315 (AGC→ACC) mutations and 1 showed combined inhA promoter mutations (-56 T→C and − 22 GCG→ACG); (2) 3 had rpoB 531 (TCG→TTG) mutations including 1 with concurrent rpoB 531 + 590 mutations. The complete distribution of resistance-associated mutations is presented in Table 4 . Table 4 Mutation profiles of drug resistance genes in 148 Mycobacterium tuberculosis isolates. Drug No of Phenotypic Resistant Isolates Gene Codon change(Amino acid change) Frequency INH 31 katG CGG463CTG(Arg-Leu) 98 AGC315ACC(Ser-Thr) 11 AGC315ACC(Ser-Asn) 1 Others a 5 inhA GGA3GGC(Gly-Gly) 6 G-48A 2 Others a 4 RIF 9 rpoB TCG531TTG(Ser-Leu) 3 CAC526GAC(His-Asp) 1 CAC526TAC(His-Tyr) 1 Others a 2 QS 15 gyrA AGC95ACC(Ser-Thr) 145 GAC94AAC(Asp-Asn) 1 GAC94GGC(Asp-Gly) 1 Others a 6 gryB AGC431AGT(Ser-Ser) 2 G1365A 2 Others a 3 a. Mutations occurring at other loci of this gene. 3.3 Association between IS6110 fragment identification and drug resistance Amplification was successful in 139 of 148 isolates, with 124 strains confirmed as MTB human-type strains and 15 as non-human-type MTB strains. The remaining nine isolates showed no amplification, potentially representing non-tuberculous mycobacteria(NTM) or PCR failures. Among the 124 human-type strains, 44 (35.5%) demonstrated phenotypic resistance, with 43 (34.7%) harboring resistance-associated mutations. Of the 15 non-human-type strains, 1 (6.7%) exhibited phenotypic resistance while 3 (20.0%) carried resistance-associated mutations. Statistical analysis revealed significantly higher drug resistance rates in human-type versus non-human-type strains (35.5% vs 6.7%, p 0.05) ( Table 5 ) Table 5. Mutation and resistance patterns in human-type vs. non-human-type MTBC strains . Sequence assembly using CLC Main Workbench generated 10 contigs, from which representative strains were selected for phylogenetic tree construction with 16 reference sequences in MEGA11[ 17 – 24 ]. The Neighbor-Joining tree (Fig. 2 ) demonstrated that strains 63, 53, 8, 65, 50, 27, and 21 clustered with L2 lineage reference strains H2255 and SAWC0540, confirming their L2 lineage classification. In contrast, strains 1, 73, and 77 formed a separate clade from all reference strains, indicating non-L2 lineage status. Among the 121 sequenced isolates, 114 (94.2%) belonged to the L2 lineage, including 35 phenotypically resistant and 26 genotypically mutated strains. The remaining seven isolates, not part of the L2 lineage, consisted of 5 resistant and three mutated strains. Notably, mutations in rpoB , inhA , rpsL , rrs , and pncA were exclusively observed in L2 lineage strains, whereas non-L2 lineage strains exhibited only single occurrences of katG Ala312Ala (synonymous), katG Pro443Pro (synonymous), and gyrA Gly512Arg mutations. 3.4 Association between 8-locus MIRU-VNTR genotyping and drug resistance profiles The distribution of repeat numbers across all 8-locus MIRU-VNTR in clinical isolates generally followed a typical distribution pattern (centrally peaked with tapered ends), with the majority (>90%) exhibiting ≤ 10 repeats (Fig. 3 ). Notably, QUB-11b displayed the widest variability, spanning 15 distinct repeat numbers (1–15 repeats), where seven repeats were most prevalent (34 strains, 22.97%). Conversely, MIRU40 showed the narrowest range with only five repeat variants (1–5 repeats), dominated by three repeats (83 strains, 56.08%). 27 MTB were grouped into 13 genotypic clusters, resulting in a clustering rate of 9.46%. The remaining 121 strains displayed unique genotypes. Phylogenetic analysis based on the sequencing data of all 148 isolates (Fig. 4 ) showed that 12 of the 13 clusters consisted of only two strains each, while one cluster comprised three strains (141, 143, and 144). Further comparison of these clusters with their respective drug resistance gene mutation profiles indicated that strains within eight clusters (clusters 9/99, 6/30, 75/76, 86/87, 114/115, 140/119, 129/130, and 135/146) shared identical drug resistance-associated mutations. 3.5 Two strains originating from the same patient Strains 101 and 145 were isolated from the same patient. Both exhibited phenotypically drug-sensitive profiles with no associated resistance gene mutations and showed identical drug susceptibility results. However, genotypic analysis revealed that strains 101 and 145 were classified into Contig3 and Contig2, respectively, based on the alignment of IS6110. MIRU-VNTR genotyping further demonstrated distinct profiles: Strain 101 displayed copy numbers of QUB-11b:5, QUB-18:5, QUB-26:9, MIRU26:5, MIRU31:3, MIRU40:3, Mtub04:3, and Mtub21:4, whereas Strain 145 exhibited QUB-11b:5, QUB-18:5, QUB-26:8, MIRU26:7, MIRU31:6, MIRU40:3, Mtub04:2, and Mtub21:5. The discordance results indicates genomic divergence between the two strains despite their shared phenotypic susceptibility. 4. Discussion In this study, a comprehensive molecular epidemiological analysis of drug-resistant MTB in Chaoshan district was carried out by combining phenotypic drug susceptibility testing, mutation analysis of drug-resistant genes and genotyping. The results of the study provide an important basis for understanding the specific distribution of drug resistance and genetic diversity in MTB populations in this climate. Ultimately, 101 strains (68.2%) were fully sensitive, and 47 strains (31.8%) were resistant to anti-tuberculosis drugs. The study revealed an elevated overall drug resistance rate, surpassing that reported among retreatment patients in the 2012 national survey (25.6%)[ 25 ]. However, it remained below the rates observed in high-burden settings, such as India (58.4%) and Russia (32.5%) [ 26 , 27 ]. When analyzing resistance by treatment history, re-treated patients showed significantly higher resistance rates (53.3%) than new cases (23.9%), with 20 confirmed cases of secondary TB observed. The observed disparity in resistance rates may reflect suboptimal implementation of first-line treatment protocols, potentially contributing to the development of acquired resistance in patients who are retreated[ 28 ]. Notably, resistance to second-line antituberculosis agents, particularly QS, remained significantly lower than both first-line drug resistance rates and the global average (19%), consistent with findings reported by Wang et al. [ 29 ] and Jin et al. [ 30 ]. To mitigate further resistance development, implementation of enhanced therapeutic drug monitoring for first-line agents, especially INH and RFP, appears imperative. In this study, the target gene sequence was analyzed and compared with that of the standard strain (H37Rv), and it was found that 146 strains (98.6%) had gene mutations. An important finding was the high mutation rates observed in katG Arg463Leu (CGG→CTG; 66.2%, 98/148) and gyrA Ser95Thr (AGC→ACC; 98.0%, 145/148), neither of which was associated with drug resistance (p > 0.05). Consistent with the findings of van Doorn et al.[ 31 ], The katG Arg463Leu mutation does not reliably predict INH resistance in MTB. However, this mutation was potentially valuable in a rapid diagnostic assay for MDR/XDR M. tuberculosis clinical isolates in Belarus[ 32 ]. When comparing the gyrA Ser95Thr sequence with that of the H37Rv, the exceptionally high mutation rate prompted us to reconsider the reference standard. As expected, sequence alignment with Erdman (GenBank accession no. NC_020559.1 ) and CDC1551 (GenBank accession no. GCF_000669715.1 ) revealed much higher concordance. Unlike H37Rv, the Erdman and CDC1551 strains are clinically derived and have genomic profiles that better represent naturally circulating isolates, such as the Beijing genotype—the predominant lineage in China. Relying solely on H37Rv as a reference may lead to erroneous estimation of resistance mutation frequencies in clinical strains, potentially resulting in false-negative findings. Therefore, for region-specific epidemiological studies, we recommend using standard strains with matched genomic backgrounds to minimize sequence alignment bias. In addition, corresponding drug resistance gene mutations were observed in INH-resistant strains ( katG 315/ inhA ), RFP-resistant strains ( rpoB 531), and QS-resistant strains ( gyrA / gyrB ). In contrast, classic embB gene mutations were not detected in ethambutol-resistant strains. This differs from the result that the embB mutation rate is approximately 70% in the US CDC database [ 33 ]. It is speculated that the drug resistance in this area may be caused by mutations in other genes (such as embA and embC ) or regulatory regions[ 34 , 35 ]. Notably, although no phenotypic resistance to SM or PZA was observed, the genomic analysis identified mutations in established resistance-associated loci, including rrs Leu8Pro (2 isolates), rpsL Lys43Arg (10 isolates), and pncA Val128Gly (1 isolate). These findings may indicate heteroresistance or context-dependent resistance, where pncA mutations require specific genomic backgrounds to confer resistance [ 36 , 37 ], emphasizing the critical value of supplementing phenotypic DST with molecular surveillance to detect emerging resistance threats, even in ostensibly drug-susceptible isolates. 15 strains could not be successfully identified in this study. Consistent with the drug resistance advantage of the MTB strain in the South African population [ 38 ], it suggests that host species specificity may affect the expression of the pathogen's drug resistance phenotype. Perhaps due to the existence of some variant strains with a relatively small copy number of IS6110, it is impossible to accurately identify MTB strains with a copy number of ≤ 5 based on the typing of IS6110-specific fragments. This is consistent with the research results of LIEBANA et al. [ 39 ]. Among the 124 strains of MTB, 44 strains exhibited phenotypic drug resistance, and the phenotypic drug resistance rate was significantly higher than that of non-human type tuberculosis (p < 0.05). Through the analysis of the sequencing results of the IS6110 amplification products, 114 strains (94.2%) belonged to the L2 lineage, and within each lineage, the subtype distribution of the strains was relatively concentrated. By Fisher's test, the drug resistance rate of non-L2 lineage strains was higher than that of L2 lineage strains (p < 0.05); however, due to the limitation of a small sample size, this result still needs to be interpreted with caution. The mutation rate of L2 lineage genes in this study was 22.4%, which was significantly lower than that in Shanghai [ 40 ] (66.3%), where the main prevalent L2 type strains were present. Mutations in rpoB , inhA , rpsL , rrs , and pncA were restricted to L2 lineage strains, with some sites being genotype-specific. This differs from the rpoB mutations reported in the German L4 lineage[ 41 ]. It reflects the regional specificity of the genotype-drug resistance association: The environmental pressure composed of the hot and humid climate and high-density population in the Chaoshan region may have promoted the spread of L2 lineage strains among hosts, and the household and unemployed people (59.2%) who stay at home for a long time are the primary infection targets. Their continuous human-to-human transmission provides cumulative opportunities for drug-resistance mutations of specific genotypes. Although related drug resistance gene mutations occurred in non-L2 lineage strains, most of them were synonymous mutations, such as katG Ala312Ala and katG Pro443Pro. The strong linkage between L2 lineage strains and canonical resistance mutations in rpoB , inhA , and katG reinforces the well-documented association between the Beijing genotype and drug resistance. The MIRU-VNTR genotyping method, compared to the former MTB genotyping gold standard, IS6110-RFLP, has a stronger resolution ability [ 42 ] and can better supplement the IS6110 genotyping results. The results showed that the sizes of amplified fragments and the number of repeat units of the strains at different VNTR loci were diverse. Ultimately, 148 clinical isolates were divided into 134 genotypes. Twenty-seven strains formed 13 gene clusters, and the strains within eight gene clusters had entirely consistent drug-resistance gene profiles. The clustering rate was 9.46%. In high-burden countries, clustering rates typically range from 20% to 50%, reflecting ongoing transmission. This value was not only lower than the national average of 31% [ 43 ] but also significantly lower than the average level of New York, USA [ 44 ]. Our low clustering rate implies that most drug-resistant TB cases in this region arise from reactivation of latent infections or independent mutation events rather than recent transmission. We also compared the resistance and typing results of the two strains from the same patient. We found that the resistance phenotype and associated genes were highly stable, yielding identical results. However, the MIRU-VNTR genotyping results of the two patients differed, and the typing based on the IS6110 sequence polymorphism was also classified into distinct contigs, suggesting that the genetic backgrounds of the two clinical isolates from the patient were different. Therefore, the patient's second visit may have been due to exogenous reinfection. This case underscores the clinical utility of integrating genotypic typing with resistance profiling to discriminate between relapse events and novel infections, even when resistance patterns remain unchanged. In this study, the correlation between MTB resistance characteristics and genotyping in Chaoshan was studied in depth using molecular biology methods. The results showed that: (1) Regarding drug resistance characteristics, high incidence and elevated drug resistance rates prevailed, with the resistance rates among retreatment patients being significantly higher than those of new-onset patients. However, the resistance rate to second-line antituberculosis drugs remained within a controllable range. (2) The overall mutation rate of drug resistance genes was relatively high, mainly due to frequent mutations in the katG 463 and gyrA 95 genes, which were not related to drug resistance. Notably, the mutation rate decreased significantly when a different reference strain was used. (3) Regarding the correlation between drug resistance and genotypes, the L2 strain was dominant. Different genotypes exhibited varying resistance profiles, with human-type MTB and non-L2 strains demonstrating a higher propensity for phenotypic resistance than non-human MTB and L2 strains. (4) MIRU-VNTR genotyping showed considerable genetic diversity among MTB strains in the region, with a clustering rate of 9.46%, suggesting limited recent transmission. This study has certain limitations. Firstly, the phenomenon that the classic gene embB mutation has not been found in any strains resistant to EMB has not been explored or verified. Other related genes (such as embA and embC ) should be selected for further research. Secondly, although the phenotypic drug resistance rate of non-L2 lineage strains is higher than that of L2 lineage strains, the results may be biased due to the limitations of the samples. The sample content should be further increased for verification. Finally, although this study deeply explored the correlation between drug resistance of MTB and genotypes, the discriminatory ability of each typing method has not been strictly evaluated. Subsequent studies should adopt more authoritative Whole Genome Sequencing (WGS) technology for comparative analysis to improve the reliability of the results. In conclusion, this study effectively utilized molecular biology methods, yielding reliable results. These findings highlight the necessity to standardize retreatment regimens for patients in Shantou through stringent institutional protocols and to enhance monitoring of isoniazid- and rifampicin-resistant tuberculosis caused by Lineage 2 MDR strains using molecular detection methods. Furthermore, the observed variations in drug resistance-associated mutations across different genotypes suggest that establishing a region-specific reference sequence has potential value for improving the accuracy of estimating tuberculosis transmission dynamics. Finally, it is recommended that Shantou consistently integrate drug resistance profiles and genotyping data into a comprehensive analytical framework, which could provide a robust foundation for formulating precise tuberculosis control strategies and reconstructing transmission chains. Abbreviations Tuberculosis TB Drug-resistant tuberculosis DR-TB multidrug/rifampicin-resistant tuberculosis MDR/RR-TB Mycobacterium tuberculosis MTB World Health Organization WHO Rifampicin RIF Isoniazid INH Streptomycin SM Ethambutol EMB Pyrazinamide PZA Fluoroquinolones FQs Drug Susceptibility Testing DST Löwenstein-Jensen L-J Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Shantou University (approval number: STU202505006). Written informed consent was obtained from all participants. Our research strictly adheres to the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work is supported by grants received from the Research Foundation for Advanced Talents of Wenzhou Medical University (Grant No. QTJ24005), the National Natural Science Foundation of China (Grant No. 32072885), and the Medical Scientific Research Foundation of Guangdong Province of China (Grant No. A2024104). Author Contribution Conceptualization and Data Curation, Y.X., W.Z.; Formal Analysis and Acquisition of Data, L.G., S.C., C.C., L.Y, X.Y.; Methodology, Z.C., Z.F., and X.H.; Writing—original draft, Y.X., W.Z., S.L.; Project Administration and Resources, Q.C., J.L.(Jianxiong Lin) and J.L.(Jiahai Lu). All authors have read and agreed to the published version of the manuscript. Acknowledgement We sincerely thank the laboratory staff for their invaluable assistance in isolating and culturing Mycobacterium tuberculosis strains. 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Tsolaki AG, Hirsh AE, DeRiemer K, et al. Functional and evolutionary genomics of Mycobacterium tuberculosis: insights from genomic deletions in 100 strains. Proc Natl Acad Sci U S A. 2004;101(14):4865–70. 10.1073/pnas.0305634101 . Yang C, Shen X, Peng Y, et al. Transmission of Mycobacterium tuberculosis in China: a population-based molecular epidemiologic study. Clin Infect Dis. 2015;61(2):219–27. 10.1093/cid/civ255 . Schildknecht K, Cowen L, Posey J, Talarico et al. Use of different genotyping methods to estimate TB transmission in the United States, 2020–2021. The international journal of tuberculosis and lung disease: the official journal of the International Union against Tuberculosis and Lung Disease. DOI:29. 193–5. 10.5588/ijtld.24.0464 Additional Declarations No competing interests reported. Supplementary Files S1Tables.docx S1 Table 1. PCR reaction mixtures for drug resistance genes, IS6110, and MIRU-VNTR amplification. S1 Table 2. PCR reaction conditions for drug resistance genes, IS6110, and MIRU-VNTR amplification. S2Tables.docx S2 Table 1. Repeat unit scoring rules for the 8-locus MIRU-VNTR typing system. S2 Table 2. Correspondence between amplicon sizes and repeat unit numbers at 8 MIRU-VNTR loci. S3Table.docx S3 Table1. Reference sequence of IS6110 in Mycobacterium tuberculosis. 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02:18:53","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139998,"visible":true,"origin":"","legend":"","description":"","filename":"bf015878518a49198970796173bdd2141structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/1c09908ee6a341a778c0b35b.xml"},{"id":93730092,"identity":"377cb86f-2916-4466-86ab-aa9006c497d6","added_by":"auto","created_at":"2025-10-17 02:18:53","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154264,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/f0fbd2ee37cc4b5299910a73.html"},{"id":93732075,"identity":"669c6ab1-0c06-4c46-9818-a4b52999f461","added_by":"auto","created_at":"2025-10-17 02:26:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":307409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequence alignment of major mutation sites in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ekatG\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(CGG463CTG) and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003egyrA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (AGC95ACC) genes. \u003c/strong\u003eThe upper sequence represents the H37Rv reference strain (wild-type), while the lower sequence shows the clinical isolate with mutations at \u003cem\u003ekatG\u003c/em\u003e 463 (CGG→CTG) and \u003cem\u003egyrA\u003c/em\u003e 95 (AGC→ACC).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/8ab628750513172429006a05.jpeg"},{"id":93732074,"identity":"6fa7c9b1-9942-4e34-8085-711e174ea97e","added_by":"auto","created_at":"2025-10-17 02:26:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":365089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic tree of representative clinical isolates and reference sequences.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/ace143339ff72a58ca980ecb.jpeg"},{"id":93730064,"identity":"28a9ea73-225a-4e28-b80f-1ee31ffa9a08","added_by":"auto","created_at":"2025-10-17 02:18:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of repeat unit numbers at 8-locus MIRU-VNTR.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/ab7244219a89c7c16be02602.jpeg"},{"id":93732076,"identity":"2b7f36e0-2b53-4659-9aec-de36077da8e6","added_by":"auto","created_at":"2025-10-17 02:26:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":943491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic tree of 148 clinical isolates based on MIRU-VNTR genotyping profiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/80e0fbcf6d231836f2feb46c.jpeg"},{"id":93732554,"identity":"0bb2ef75-ac37-40bf-a6d4-43877e92db32","added_by":"auto","created_at":"2025-10-17 02:34:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3192480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/564806c7-98a3-4ac2-be1f-59bdc0cf4459.pdf"},{"id":93730059,"identity":"6b5a7309-36b3-4131-940f-22fe891af6ef","added_by":"auto","created_at":"2025-10-17 02:18:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS1 Table 1. \u003c/strong\u003ePCR reaction mixtures for drug resistance genes, IS6110, and MIRU-VNTR amplification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS1 Table 2. \u003c/strong\u003ePCR reaction conditions for drug resistance genes, IS6110, and MIRU-VNTR amplification.\u003c/p\u003e","description":"","filename":"S1Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/ec45494e687397db8844ab4e.docx"},{"id":93730063,"identity":"b54fc765-cf4e-4364-b5f9-a42b07de3562","added_by":"auto","created_at":"2025-10-17 02:18:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS2 Table 1. \u003c/strong\u003eRepeat unit scoring rules for the 8-locus MIRU-VNTR typing system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS2 Table 2. \u003c/strong\u003eCorrespondence between amplicon sizes and repeat unit numbers at 8 MIRU-VNTR loci.\u003c/p\u003e","description":"","filename":"S2Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/303b4dd9857cb3d9dc9f1574.docx"},{"id":93730080,"identity":"e3f75d93-dde9-48b5-bcb2-dcf9a07acec0","added_by":"auto","created_at":"2025-10-17 02:18:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS3 Table1. \u003c/strong\u003eReference sequence of IS6110 in Mycobacterium tuberculosis.\u003c/p\u003e","description":"","filename":"S3Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7713641/v1/307e5c7f0169276327e4b888.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Epidemiology of Drug-Resistant Tuberculosis in Shantou, China: Associations between Drug Resistance and Genotypic Variation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the Global Tuberculosis(TB) Report 2024, it is estimated that 10.8\u0026nbsp;million new TB cases and 1.25\u0026nbsp;million deaths will occur in 2023, and TB will once again be the deadliest single-pathogen disease worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Drug-resistant tuberculosis (DR-TB) prevention and control is a key entry point for implementing the One Health strategy, and its emergence, especially in resource-limited areas, complicates global TB control efforts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. China has a heavy burden of DR-TB, with the fourth highest number of new multidrug/rifampicin-resistant tuberculosis (MDR/RR-TB) cases globally, accounting for 7.3% of all global cases in 2023 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There is an urgent need to understand better and promote a new public health prevention and control system that integrates genomic epidemiology, drug resistance patterns, and precision treatment strategies.\u003c/p\u003e\u003cp\u003eResistance in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) is primarily driven by mutations in drug-targeted genes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The World Health Organization (WHO) has classified resistance-associated mutations into tiers based on their clinical relevance[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Rifampicin (RIF) resistance commonly arises from \u003cem\u003erpoB\u003c/em\u003e mutations, while isoniazid (INH) resistance is frequently linked to \u003cem\u003ekatG\u003c/em\u003e Ser315Thr mutations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Fluoroquinolones (FQs) resistance often involves \u003cem\u003egyrA\u003c/em\u003e and \u003cem\u003egyrB\u003c/em\u003e mutations[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, mutation prevalence varies geographically and across MTB lineages, reflecting diverse selective pressures and transmission dynamics.\u003c/p\u003e\u003cp\u003eLineage-specific resistance patterns further complicate the management of DR-TB. The Beijing genotype (Lineage 2), a globally disseminated strain (particularly in China), exhibits a high prevalence of MDR-TB in regions such as Eastern Europe and Central Asia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, other lineages (e.g., Lineage 4 in Africa or Lineage 1 in South Asia) display distinct resistance profiles, suggesting lineage-specific evolutionary adaptations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. IS6110-RFLP remains the gold standard for MTB genotyping [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but strains with low IS6110 copy numbers require supplementary methods such as MIRU-VNTR (8\u0026ndash;24 loci) for accurate clustering and lineage identification [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite advances in understanding genotype-resistance associations, the mechanistic basis remains incompletely elucidated. This study integrates phenotypic DST with sequencing of resistance-associated genes and multi-locus genotyping to (1) characterize mutation patterns in DR-TB and (2) explore genotype-resistance correlations. The findings provide both localized strategies for TB control in high-density tropical cities and foundational data for global One Health initiatives aimed at combating antimicrobial resistance.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Strains\u003c/h2\u003e\u003cp\u003eWe retrospectively analysed 301 sputum samples from the Shantou Institute of Tuberculosis Control and Prevention, Shantou, China, from January to December 2024. The H37Rv MTB standard strain was provided by Shanghai Jingnuo Biotechnology Co. Ltd. as a control. 301 strains were pathogenetically detected, and the results of IS6110 and MIRN-VNTR 8-locus amplification were finally included in the gene sequences of 148 strains for comparative analysis. The number of strains isolated from each jurisdiction was as follows: Chaonan District, 16; Chaoyang District, 16; Chenghai District, 27; Jinping District, 32; Longhu District, 29; Nan'ao County, 2; Chao'an District, 7; Raoping County, 11; Xiangqiao District, 2; Jiedong District, 1; Puning City, 1; and Rongcheng District, 2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Strain Culture and Drug Susceptibility Testing (DST)\u003c/h2\u003e\u003cp\u003eClinical sputum specimens were processed according to the Technical Standard for Bacteriological Examination in Tuberculosis Diagnosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Briefly, sputum specimens were digested with 1\u0026ndash;2 volumes of 4% NaOH (15\u0026ndash;30 mL depending on viscosity), neutralized with phosphate buffer (1:9), centrifuged at 4800 \u0026times; g for 15 min, and inoculated with 0.5 mL of the sediment into Mycobacteria Growth Indicator Tubes (BACTEC\u0026trade; MGIT\u0026trade; 960 system; Becton Dickinson, USA). Cultures were incubated at 37\u0026deg;C and monitored for fluorescence-based growth detection. Tubes reporting\u0026thinsp;\u0026ge;\u0026thinsp;100 growth units (GU) within 42 days were considered positive; otherwise, results were recorded as negative.\u003c/p\u003e\u003cp\u003eFor DST, positive cultures were subcultured onto \u003cem\u003eL\u0026ouml;wenstein-Jensen\u003c/em\u003e (L-J) slants containing critical antituberculosis drugs: Isoniazid (INH), Rifampicin (RFP), Streptomycin (SM), Ethambutol (EMB), Pyrazinamide (PZA), and Quinolones (QS). Resistance was defined as \u0026ge;\u0026thinsp;1% bacterial growth on a drug-containing medium compared to drug-free controls.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 PCR Amplification\u003c/h2\u003e\u003cp\u003eAmplification of drug resistance-associated genetic loci (\u003cem\u003erpoB\u003c/em\u003e, \u003cem\u003ekatG\u003c/em\u003e, \u003cem\u003einhA\u003c/em\u003e, \u003cem\u003eembB\u003c/em\u003e, \u003cem\u003erpsL\u003c/em\u003e, \u003cem\u003errs\u003c/em\u003e, \u003cem\u003egyrA\u003c/em\u003e, \u003cem\u003egyrB\u003c/em\u003e, and \u003cem\u003epncA\u003c/em\u003e) was performed employing primer sets specified in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Molecular identification of MTBC was achieved through the amplification of IS6110-specific genomic fragments. Subsequent strain genotyping was conducted via MIRU-VNTR analysis targeting eight loci (QUB-11b, QUB-18, QUB-26, MIRU26, MIRU31, MIRU40, Mtub04, and Mtub21)[\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The detailed PCR reaction system compositions and thermal cycling conditions are provided in \u003cb\u003eS1\u003c/b\u003e Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All PCR amplicons were electrophoresed on 1% agarose gels at 12.5 V/cm for 45 min and visualized using a gel documentation system. The primers used in this study and PCR products (including drug resistance genes and IS6110 amplicons) were synthesized and sequenced by Tsingke Biotechnology Co., Ltd. (Beijing, China).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eOligonucleotide primer sequences and corresponding PCR product lengths.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene (Drug)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer sequences\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProduct lengths(bp)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eropB\u003c/em\u003e(RIF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-CTTGCACGAGGGTCAGACCA\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-ATCTCGTCGCTAACCACGCC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ekatG\u003c/em\u003e(INH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-CCGCCTTTGCTGCTTTCTC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GGGGCTGATCTACGTGAAC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003einhA\u003c/em\u003e(INH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-GCAGCATGCAGCGCAACAAATTC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GTAGGGCCGCAGTTTTACCAGTTC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eembB\u003c/em\u003e(EMB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-TGATTGGCTTTGTGTTGTCG\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GAACACCCCGACAATCTGCG\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003erpsl\u003c/em\u003e(SM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-ATGAGACGAATCGAGTTTGAGG\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GATCGGTGCCGGTCTTGTCG\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003errs\u003c/em\u003e(SM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-AAACCTCTTTCACCATCGAC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GTATCCATTGATGCTCGCAA\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1328\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egryA\u003c/em\u003e(QS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-TCGACTATGCGATGAGCGTG\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GGTAGCACCGTCGGCTCTTG\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egryB\u003c/em\u003e(QS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-CCACCGACGCGAAAGTC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-CTGCCACTTGAGTTTGTACA\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003epncA\u003c/em\u003e(PZA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-AACAGTTCATCCCGGTTC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GCGTCATG GACCCTATATC\u0026minus;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS6110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-ATCTGAACCGCCCCGGCATGTCCGG-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-ATCTGAACCGCCCCGGTGAGTCCGG-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e850、361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQUB-11b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-CGTAAGGGGGATGCGGGAAATAGG-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-CGAAGTGAATGGTGGCAT-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQUB-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-ATCGTCAGCTGCGGAATAGT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-AATACCGGGGATATCGGTTC-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQUB-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-AACGCTCAGCTGTCGGAT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GGCCAGGTCCTTCCCGAT-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIRU26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-GCGGATAGGTCTACCGTCGAAATC-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-TCCGGGTCATACAGCATGATCA-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIRU31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-GATTCCAACAAGACGCAGATCAAGA-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-TCAGGTCTTTCTCTCACGCTCTCG-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIRU40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-CGTCGAAGAGAGCCTCATCAATCAT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-AACCTGCTGACCGATGGCAATATC-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMtub04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-GTCCAGGTTGCAAGAGATGG-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-GGCATCCTCAACAACGGTAG-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMtub21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL: 5\u0026rsquo;-AGATCCCAGTTGTCGTCGTC-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eR: 5\u0026rsquo;-CAACATCGCCTGGTTCTGTA-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\"\u0026mdash;\" indicates undetermined fragment size. During 8-locus VNTR amplification, multiple amplicon sizes were observed for each locus, precluding definitive assignment of a single fragment length.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data interpretation, Analysis and Statistics\u003c/h2\u003e\u003cp\u003eIS6110-specific fragments: PCR products of 850 bp and 361 bp indicated \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, while a single 850 bp product suggested MTBC.\u003c/p\u003e\u003cp\u003eMIRU-VNTR 8-loci: The repeat unit numbers at each locus were calculated according to the 8-loci interpretation criteria (\u003cb\u003eS2\u003c/b\u003e Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with the results utilized for strain typing and cluster analysis.\u003c/p\u003e\u003cp\u003eSequence mutations in drug resistance-associated genes were analyzed using SnapGene 6.0.2. An unweighted pair-group method with an arithmetic mean (UPGMA) tree was constructed using MIRU-VNTRplus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.miru-vntrplus.org\u003c/span\u003e\u003cspan address=\"http://www.miru-vntrplus.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Phylogenetic analysis was performed using MEGA 11, with reference strains (\u003cb\u003eS3\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) selected for comparative evolutionary inference. Statistical analyses were conducted in SPSS 26.0, with Fisher's exact test applied for rate comparisons between groups. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Characteristics of study subjects\u003c/h2\u003e\u003cp\u003e148 MTB strains were successfully isolated through culture and subsequently identified by IS6110-based genotyping and MIRU-VNTR analysis. Demographic characteristics of the 147 TB patients: males predominated (109 cases, 74.2%), and females comprised 38 cases (25.8%). In terms of age distribution, patients aged 60 years or older were the most prevalent (63 cases, 42.9%), followed by those aged 40\u0026ndash;59 years (47 cases, 32.0%). In terms of occupation, homemakers/unemployed people accounted for the majority (87 cases, 59.2%), and retirees made up the second largest group (30 cases, 20.4%). Sociodemographic analysis of the 109 Shantou residents revealed that they were distributed across 32 townships and districts, of which 27 administrative districts (84.4% of the total) had only a single case. The basic information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of 147 TB patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal, N (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109(74.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38(25.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6(4.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(21.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47(32.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63(42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold and unemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87(59.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetirees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30(20.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15(10.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6(4.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4(2.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorkers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3(2.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2(1.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117(79.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetreatment cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30(20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShantou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122(83.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChaozhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20(13.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJieyang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5(3.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Characteristics of phenotypic drug resistance and drug-resistance gene mutations\u003c/h2\u003e\u003cp\u003eAmong the 148 isolated strains, 101 (68.2%) were susceptible to all anti-tuberculosis drugs, while 47 (31.8%) exhibited drug resistance. The monoresistance rate was 24.3% (36/148), and the non-monoresistance rate was 7.4% (11/148). The resistance rates for individual drugs were as follows: INH, 20.9% (31/148); RIF, 6.2% (9/148); EMB, 6.2% (9/148); and QS, 10.1% (15/148).\u003c/p\u003e\u003cp\u003ePatients were stratified into new cases (23.9% resistance) and retreatment (53.3% resistance) cases based on their treatment history. A statistically significant difference in resistance rates was observed between the two groups (χ\u0026sup2; \u0026asymp; 11.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the phenotypic drug susceptibility testing results for the 148 isolated strains.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhenotypic drug susceptibility profiles of the 148 isolates.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo of isolates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eResistance rate (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINH\u0026thinsp;+\u0026thinsp;EMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINH\u0026thinsp;+\u0026thinsp;RIF(MDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIF\u0026thinsp;+\u0026thinsp;QS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINH\u0026thinsp;+\u0026thinsp;RIF\u0026thinsp;+\u0026thinsp;EMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINH\u0026thinsp;+\u0026thinsp;RIF\u0026thinsp;+\u0026thinsp;QS(pre-XDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtensive drug resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePansusceptible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eINH\u0026thinsp;=\u0026thinsp;isoniazid; RIF\u0026thinsp;=\u0026thinsp;rifampicin; EMB\u0026thinsp;=\u0026thinsp;ethambutol; QS\u0026thinsp;=\u0026thinsp;quinolone; MDR\u0026thinsp;=\u0026thinsp;multidrug-resistant; pre-XDR\u0026thinsp;=\u0026thinsp;pre-extensively drug resistant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGenetic analysis identified resistance-associated mutations in 146 of 148 isolates (98.6%), with two predominant mutation patterns: (1) \u003cem\u003ekatG\u003c/em\u003e Arg463Leu (66.2%, 98/148) showing consistent CGG\u0026rarr;CTG codon changes and (2) \u003cem\u003egyrA\u003c/em\u003e Ser95Thr (98.0%, 145/148) exhibiting uniform AGC\u0026rarr;ACC substitutions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Further characterization revealed: (1) 11 carried \u003cem\u003ekatG\u003c/em\u003e 315 (AGC\u0026rarr;ACC) mutations and 1 showed combined \u003cem\u003einhA\u003c/em\u003e promoter mutations (-56 T\u0026rarr;C and \u0026minus;\u0026thinsp;22 GCG\u0026rarr;ACG); (2) 3 had \u003cem\u003erpoB\u003c/em\u003e 531 (TCG\u0026rarr;TTG) mutations including 1 with concurrent \u003cem\u003erpoB\u003c/em\u003e 531\u0026thinsp;+\u0026thinsp;590 mutations. The complete distribution of resistance-associated mutations is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMutation profiles of drug resistance genes in 148 Mycobacterium tuberculosis isolates.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo of Phenotypic Resistant Isolates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCodon change(Amino acid change)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ekatG\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCGG463CTG(Arg-Leu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGC315ACC(Ser-Thr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGC315ACC(Ser-Asn)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003einhA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGGA3GGC(Gly-Gly)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG-48A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003erpoB\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTCG531TTG(Ser-Leu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAC526GAC(His-Asp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAC526TAC(His-Tyr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGC95ACC(Ser-Thr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAC94AAC(Asp-Asn)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAC94GGC(Asp-Gly)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003egryB\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGC431AGT(Ser-Ser)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG1365A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ea. Mutations occurring at other loci of this gene.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Association between IS6110 fragment identification and drug resistance\u003c/h2\u003e\u003cp\u003eAmplification was successful in 139 of 148 isolates, with 124 strains confirmed as MTB human-type strains and 15 as non-human-type MTB strains. The remaining nine isolates showed no amplification, potentially representing non-tuberculous mycobacteria(NTM) or PCR failures. Among the 124 human-type strains, 44 (35.5%) demonstrated phenotypic resistance, with 43 (34.7%) harboring resistance-associated mutations. Of the 15 non-human-type strains, 1 (6.7%) exhibited phenotypic resistance while 3 (20.0%) carried resistance-associated mutations. Statistical analysis revealed significantly higher drug resistance rates in human-type versus non-human-type strains (35.5% vs 6.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though their mutation frequencies showed no significant difference (34.7% vs 20.0%, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (\u003cb\u003eTable\u0026nbsp;5\u003c/b\u003e)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMutation and resistance patterns in human-type vs. non-human-type\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;MTBC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;strains\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1760629789.png\" style=\"width: 667px;\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eSequence assembly using CLC Main Workbench generated 10 contigs, from which representative strains were selected for phylogenetic tree construction with 16 reference sequences in MEGA11[\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The Neighbor-Joining tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated that strains 63, 53, 8, 65, 50, 27, and 21 clustered with L2 lineage reference strains H2255 and SAWC0540, confirming their L2 lineage classification. In contrast, strains 1, 73, and 77 formed a separate clade from all reference strains, indicating non-L2 lineage status. Among the 121 sequenced isolates, 114 (94.2%) belonged to the L2 lineage, including 35 phenotypically resistant and 26 genotypically mutated strains. The remaining seven isolates, not part of the L2 lineage, consisted of 5 resistant and three mutated strains. Notably, mutations in \u003cem\u003erpoB\u003c/em\u003e, \u003cem\u003einhA\u003c/em\u003e, \u003cem\u003erpsL\u003c/em\u003e, \u003cem\u003errs\u003c/em\u003e, and \u003cem\u003epncA\u003c/em\u003e were exclusively observed in L2 lineage strains, whereas non-L2 lineage strains exhibited only single occurrences of \u003cem\u003ekatG\u003c/em\u003e Ala312Ala (synonymous), \u003cem\u003ekatG\u003c/em\u003e Pro443Pro (synonymous), and \u003cem\u003egyrA\u003c/em\u003e Gly512Arg mutations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Association between 8-locus MIRU-VNTR genotyping and drug resistance profiles\u003c/h2\u003e\u003cp\u003eThe distribution of repeat numbers across all 8-locus MIRU-VNTR in clinical isolates generally followed a typical distribution pattern (centrally peaked with tapered ends), with the majority (\u0026gt;90%) exhibiting\u0026thinsp;\u0026le;\u0026thinsp;10 repeats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, QUB-11b displayed the widest variability, spanning 15 distinct repeat numbers (1\u0026ndash;15 repeats), where seven repeats were most prevalent (34 strains, 22.97%). Conversely, MIRU40 showed the narrowest range with only five repeat variants (1\u0026ndash;5 repeats), dominated by three repeats (83 strains, 56.08%).\u003c/p\u003e\u003cp\u003e27 MTB were grouped into 13 genotypic clusters, resulting in a clustering rate of 9.46%. The remaining 121 strains displayed unique genotypes. Phylogenetic analysis based on the sequencing data of all 148 isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that 12 of the 13 clusters consisted of only two strains each, while one cluster comprised three strains (141, 143, and 144). Further comparison of these clusters with their respective drug resistance gene mutation profiles indicated that strains within eight clusters (clusters 9/99, 6/30, 75/76, 86/87, 114/115, 140/119, 129/130, and 135/146) shared identical drug resistance-associated mutations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Two strains originating from the same patient\u003c/h2\u003e\u003cp\u003eStrains 101 and 145 were isolated from the same patient. Both exhibited phenotypically drug-sensitive profiles with no associated resistance gene mutations and showed identical drug susceptibility results. However, genotypic analysis revealed that strains 101 and 145 were classified into Contig3 and Contig2, respectively, based on the alignment of IS6110. MIRU-VNTR genotyping further demonstrated distinct profiles: Strain 101 displayed copy numbers of QUB-11b:5, QUB-18:5, QUB-26:9, MIRU26:5, MIRU31:3, MIRU40:3, Mtub04:3, and Mtub21:4, whereas Strain 145 exhibited QUB-11b:5, QUB-18:5, QUB-26:8, MIRU26:7, MIRU31:6, MIRU40:3, Mtub04:2, and Mtub21:5. The discordance results indicates genomic divergence between the two strains despite their shared phenotypic susceptibility.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, a comprehensive molecular epidemiological analysis of drug-resistant MTB in Chaoshan district was carried out by combining phenotypic drug susceptibility testing, mutation analysis of drug-resistant genes and genotyping. The results of the study provide an important basis for understanding the specific distribution of drug resistance and genetic diversity in MTB populations in this climate.\u003c/p\u003e\u003cp\u003eUltimately, 101 strains (68.2%) were fully sensitive, and 47 strains (31.8%) were resistant to anti-tuberculosis drugs. The study revealed an elevated overall drug resistance rate, surpassing that reported among retreatment patients in the 2012 national survey (25.6%)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, it remained below the rates observed in high-burden settings, such as India (58.4%) and Russia (32.5%) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. When analyzing resistance by treatment history, re-treated patients showed significantly higher resistance rates (53.3%) than new cases (23.9%), with 20 confirmed cases of secondary TB observed. The observed disparity in resistance rates may reflect suboptimal implementation of first-line treatment protocols, potentially contributing to the development of acquired resistance in patients who are retreated[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notably, resistance to second-line antituberculosis agents, particularly QS, remained significantly lower than both first-line drug resistance rates and the global average (19%), consistent with findings reported by Wang et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and Jin et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To mitigate further resistance development, implementation of enhanced therapeutic drug monitoring for first-line agents, especially INH and RFP, appears imperative.\u003c/p\u003e\u003cp\u003eIn this study, the target gene sequence was analyzed and compared with that of the standard strain (H37Rv), and it was found that 146 strains (98.6%) had gene mutations. An important finding was the high mutation rates observed in \u003cem\u003ekatG\u003c/em\u003e Arg463Leu (CGG\u0026rarr;CTG; 66.2%, 98/148) and \u003cem\u003egyrA\u003c/em\u003e Ser95Thr (AGC\u0026rarr;ACC; 98.0%, 145/148), neither of which was associated with drug resistance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Consistent with the findings of van Doorn et al.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], The \u003cem\u003ekatG\u003c/em\u003e Arg463Leu mutation does not reliably predict INH resistance in MTB. However, this mutation was potentially valuable in a rapid diagnostic assay for MDR/XDR M. tuberculosis clinical isolates in Belarus[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. When comparing the \u003cem\u003egyrA\u003c/em\u003e Ser95Thr sequence with that of the H37Rv, the exceptionally high mutation rate prompted us to reconsider the reference standard. As expected, sequence alignment with Erdman (GenBank accession no.\u003cb\u003eNC_020559.1\u003c/b\u003e) and CDC1551 (GenBank accession no.\u003cb\u003eGCF_000669715.1\u003c/b\u003e) revealed much higher concordance. Unlike H37Rv, the Erdman and CDC1551 strains are clinically derived and have genomic profiles that better represent naturally circulating isolates, such as the Beijing genotype\u0026mdash;the predominant lineage in China. Relying solely on H37Rv as a reference may lead to erroneous estimation of resistance mutation frequencies in clinical strains, potentially resulting in false-negative findings. Therefore, for region-specific epidemiological studies, we recommend using standard strains with matched genomic backgrounds to minimize sequence alignment bias. In addition, corresponding drug resistance gene mutations were observed in INH-resistant strains (\u003cem\u003ekatG\u003c/em\u003e 315/\u003cem\u003einhA\u003c/em\u003e), RFP-resistant strains (\u003cem\u003erpoB\u003c/em\u003e 531), and QS-resistant strains (\u003cem\u003egyrA\u003c/em\u003e/\u003cem\u003egyrB\u003c/em\u003e). In contrast, classic \u003cem\u003eembB\u003c/em\u003e gene mutations were not detected in ethambutol-resistant strains. This differs from the result that the \u003cem\u003eembB\u003c/em\u003e mutation rate is approximately 70% in the US CDC database [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is speculated that the drug resistance in this area may be caused by mutations in other genes (such as \u003cem\u003eembA\u003c/em\u003e and \u003cem\u003eembC\u003c/em\u003e) or regulatory regions[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Notably, although no phenotypic resistance to SM or PZA was observed, the genomic analysis identified mutations in established resistance-associated loci, including \u003cem\u003errs\u003c/em\u003e Leu8Pro (2 isolates), \u003cem\u003erpsL\u003c/em\u003e Lys43Arg (10 isolates), and \u003cem\u003epncA\u003c/em\u003e Val128Gly (1 isolate). These findings may indicate heteroresistance or context-dependent resistance, where \u003cem\u003epncA\u003c/em\u003e mutations require specific genomic backgrounds to confer resistance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], emphasizing the critical value of supplementing phenotypic DST with molecular surveillance to detect emerging resistance threats, even in ostensibly drug-susceptible isolates.\u003c/p\u003e\u003cp\u003e15 strains could not be successfully identified in this study. Consistent with the drug resistance advantage of the MTB strain in the South African population [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], it suggests that host species specificity may affect the expression of the pathogen's drug resistance phenotype. Perhaps due to the existence of some variant strains with a relatively small copy number of IS6110, it is impossible to accurately identify MTB strains with a copy number of \u0026le;\u0026thinsp;5 based on the typing of IS6110-specific fragments. This is consistent with the research results of LIEBANA et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Among the 124 strains of MTB, 44 strains exhibited phenotypic drug resistance, and the phenotypic drug resistance rate was significantly higher than that of non-human type tuberculosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Through the analysis of the sequencing results of the IS6110 amplification products, 114 strains (94.2%) belonged to the L2 lineage, and within each lineage, the subtype distribution of the strains was relatively concentrated. By Fisher's test, the drug resistance rate of non-L2 lineage strains was higher than that of L2 lineage strains (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); however, due to the limitation of a small sample size, this result still needs to be interpreted with caution. The mutation rate of L2 lineage genes in this study was 22.4%, which was significantly lower than that in Shanghai [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] (66.3%), where the main prevalent L2 type strains were present. Mutations in \u003cem\u003erpoB\u003c/em\u003e, \u003cem\u003einhA\u003c/em\u003e, \u003cem\u003erpsL\u003c/em\u003e, \u003cem\u003errs\u003c/em\u003e, and \u003cem\u003epncA\u003c/em\u003e were restricted to L2 lineage strains, with some sites being genotype-specific. This differs from the \u003cem\u003erpoB\u003c/em\u003e mutations reported in the German L4 lineage[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It reflects the regional specificity of the genotype-drug resistance association: The environmental pressure composed of the hot and humid climate and high-density population in the Chaoshan region may have promoted the spread of L2 lineage strains among hosts, and the household and unemployed people (59.2%) who stay at home for a long time are the primary infection targets. Their continuous human-to-human transmission provides cumulative opportunities for drug-resistance mutations of specific genotypes. Although related drug resistance gene mutations occurred in non-L2 lineage strains, most of them were synonymous mutations, such as \u003cem\u003ekatG\u003c/em\u003e Ala312Ala and \u003cem\u003ekatG\u003c/em\u003e Pro443Pro. The strong linkage between L2 lineage strains and canonical resistance mutations in \u003cem\u003erpoB\u003c/em\u003e, \u003cem\u003einhA\u003c/em\u003e, and \u003cem\u003ekatG\u003c/em\u003e reinforces the well-documented association between the Beijing genotype and drug resistance.\u003c/p\u003e\u003cp\u003eThe MIRU-VNTR genotyping method, compared to the former MTB genotyping gold standard, IS6110-RFLP, has a stronger resolution ability [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and can better supplement the IS6110 genotyping results. The results showed that the sizes of amplified fragments and the number of repeat units of the strains at different VNTR loci were diverse. Ultimately, 148 clinical isolates were divided into 134 genotypes. Twenty-seven strains formed 13 gene clusters, and the strains within eight gene clusters had entirely consistent drug-resistance gene profiles. The clustering rate was 9.46%. In high-burden countries, clustering rates typically range from 20% to 50%, reflecting ongoing transmission. This value was not only lower than the national average of 31% [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] but also significantly lower than the average level of New York, USA [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our low clustering rate implies that most drug-resistant TB cases in this region arise from reactivation of latent infections or independent mutation events rather than recent transmission.\u003c/p\u003e\u003cp\u003eWe also compared the resistance and typing results of the two strains from the same patient. We found that the resistance phenotype and associated genes were highly stable, yielding identical results. However, the MIRU-VNTR genotyping results of the two patients differed, and the typing based on the IS6110 sequence polymorphism was also classified into distinct contigs, suggesting that the genetic backgrounds of the two clinical isolates from the patient were different. Therefore, the patient's second visit may have been due to exogenous reinfection. This case underscores the clinical utility of integrating genotypic typing with resistance profiling to discriminate between relapse events and novel infections, even when resistance patterns remain unchanged.\u003c/p\u003e\u003cp\u003eIn this study, the correlation between MTB resistance characteristics and genotyping in Chaoshan was studied in depth using molecular biology methods. The results showed that: (1) Regarding drug resistance characteristics, high incidence and elevated drug resistance rates prevailed, with the resistance rates among retreatment patients being significantly higher than those of new-onset patients. However, the resistance rate to second-line antituberculosis drugs remained within a controllable range. (2) The overall mutation rate of drug resistance genes was relatively high, mainly due to frequent mutations in the \u003cem\u003ekatG\u003c/em\u003e463 and \u003cem\u003egyrA\u003c/em\u003e95 genes, which were not related to drug resistance. Notably, the mutation rate decreased significantly when a different reference strain was used. (3) Regarding the correlation between drug resistance and genotypes, the L2 strain was dominant. Different genotypes exhibited varying resistance profiles, with human-type MTB and non-L2 strains demonstrating a higher propensity for phenotypic resistance than non-human MTB and L2 strains. (4) MIRU-VNTR genotyping showed considerable genetic diversity among MTB strains in the region, with a clustering rate of 9.46%, suggesting limited recent transmission.\u003c/p\u003e\u003cp\u003eThis study has certain limitations. Firstly, the phenomenon that the classic gene \u003cem\u003eembB\u003c/em\u003e mutation has not been found in any strains resistant to EMB has not been explored or verified. Other related genes (such as \u003cem\u003eembA\u003c/em\u003e and \u003cem\u003eembC\u003c/em\u003e) should be selected for further research. Secondly, although the phenotypic drug resistance rate of non-L2 lineage strains is higher than that of L2 lineage strains, the results may be biased due to the limitations of the samples. The sample content should be further increased for verification. Finally, although this study deeply explored the correlation between drug resistance of MTB and genotypes, the discriminatory ability of each typing method has not been strictly evaluated. Subsequent studies should adopt more authoritative Whole Genome Sequencing (WGS) technology for comparative analysis to improve the reliability of the results.\u003c/p\u003e\u003cp\u003eIn conclusion, this study effectively utilized molecular biology methods, yielding reliable results. These findings highlight the necessity to standardize retreatment regimens for patients in Shantou through stringent institutional protocols and to enhance monitoring of isoniazid- and rifampicin-resistant tuberculosis caused by Lineage 2 MDR strains using molecular detection methods. Furthermore, the observed variations in drug resistance-associated mutations across different genotypes suggest that establishing a region-specific reference sequence has potential value for improving the accuracy of estimating tuberculosis transmission dynamics. Finally, it is recommended that Shantou consistently integrate drug resistance profiles and genotyping data into a comprehensive analytical framework, which could provide a robust foundation for formulating precise tuberculosis control strategies and reconstructing transmission chains.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTuberculosis\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTB\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDrug-resistant tuberculosis\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDR-TB\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emultidrug/rifampicin-resistant tuberculosis\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMDR/RR-TB\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMTB\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWorld Health Organization\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWHO\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRifampicin\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRIF\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIsoniazid\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eINH\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eStreptomycin\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSM\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEthambutol\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEMB\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePyrazinamide\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePZA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFluoroquinolones\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFQs\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDrug Susceptibility Testing\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDST\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eL\u0026ouml;wenstein-Jensen\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eL-J\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e This study was approved by the Ethics Committee of Shantou University (approval number: STU202505006). Written informed consent was obtained from all participants. Our research strictly adheres to the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work is supported by grants received from the Research Foundation for Advanced Talents of Wenzhou Medical University (Grant No. QTJ24005), the National Natural Science Foundation of China (Grant No. 32072885), and the Medical Scientific Research Foundation of Guangdong Province of China (Grant No. A2024104).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and Data Curation, Y.X., W.Z.; Formal Analysis and Acquisition of Data, L.G., S.C., C.C., L.Y, X.Y.; Methodology, Z.C., Z.F., and X.H.; Writing\u0026mdash;original draft, Y.X., W.Z., S.L.; Project Administration and Resources, Q.C., J.L.(Jianxiong Lin) and J.L.(Jiahai Lu). All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank the laboratory staff for their invaluable assistance in isolating and culturing Mycobacterium tuberculosis strains.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll sequences analyzed during this study are available from GenBank (accession no. PV878406-15). Other datasets used and/or analysed in this study are available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKanabalan RD, Lee LJ, Lee TY, et al. Human tuberculosis and Mycobacterium tuberculosiscomplex: A review on genetic diversity, pathogenesis and omics approaches in host biomarkers discovery. 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The international journal of tuberculosis and lung disease: the official journal of the International Union against Tuberculosis and Lung Disease. DOI:29. 193\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5588/ijtld.24.0464\u003c/span\u003e\u003cspan address=\"10.5588/ijtld.24.0464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug resistance, Gene mutation, Genotyping, Mycobacterium tuberculosis","lastPublishedDoi":"10.21203/rs.3.rs-7713641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7713641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDrug-resistant tuberculosis (DR-TB) is a major global health threat, particularly in resource-limited regions. Understanding lineage-specific mutations is critical for improving diagnostics and guiding precision treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eDrug susceptibility testing was performed using \u003cem\u003eL\u0026ouml;wenstein-Jensen\u003c/em\u003e medium. Resistance-associated gene fragments were amplified via PCR, and mutation sites were analyzed using SnapGene. Genotyping was conducted using IS6110 and 8-locus MIRU-VNTR, followed by phylogenetic analysis (MEGA11, Neighbor-Joining method). Fisher\u0026rsquo;s exact test assessed genotype-mutation and phenotype-resistance correlations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe analyzed 148 MTB isolates from Shantou, China (2024). Resistance prevalence was 31.8% (47/148), with 98.6% (146/148) harboring mutations. Highest resistance was to isoniazid (20.9%), followed by streptomycin (10.1%), rifampicin (6.2%), and ethambutol (6.2%). Retreated cases (20.4%) showed significantly higher resistance than new cases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Key mutations included \u003cem\u003ekatG\u003c/em\u003e Arg463Leu (66.2%) and \u003cem\u003egyrA\u003c/em\u003e Ser95Thr (98.0%). Lineage 2 strains were the dominant transmitted strains in Shantou, carrying only \u003cem\u003einhA\u003c/em\u003e, \u003cem\u003erpsL\u003c/em\u003e, \u003cem\u003errs\u003c/em\u003e, and \u003cem\u003epncA\u003c/em\u003e resistance-associated mutations. MIRU-VNTR identified 134 genotypes (clustering rate: 9.46%), of which 8 clusters shared the same resistance mutation profile.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eRetreatment cases pose a higher DR-TB risk in this region, with notable isoniazid resistance. Resistance mutation patterns are regional and consistent with Erdman and CDC1551 standard strains. Integration of genotyping and resistance data may improve the efficiency of precision therapy and transmission monitoring. Enhanced molecular surveillance, standardised treatment and optimised retreatment regimens are recommended to control the spread of DR-TB.\u003c/p\u003e","manuscriptTitle":"Molecular Epidemiology of Drug-Resistant Tuberculosis in Shantou, China: Associations between Drug Resistance and Genotypic Variation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:18:47","doi":"10.21203/rs.3.rs-7713641/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T06:14:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T11:59:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T11:45:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T09:04:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T06:03:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90348148827362058361731893071117101685","date":"2025-10-07T12:05:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T11:56:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29040367723686387934297151804682489423","date":"2025-10-06T16:01:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6055193917619303864979344895520431899","date":"2025-10-06T05:45:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193935761604945534715554626988831164811","date":"2025-10-06T01:03:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3464365045675327266551560358990164199","date":"2025-10-05T20:09:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266957697458424302422026424194851010329","date":"2025-10-05T10:48:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-05T10:22:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-05T08:39:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-03T06:56:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-01T18:40:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-10-01T15:17:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da1f4276-031f-4640-8433-9bdc8b5055fb","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T02:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 02:18:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7713641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7713641","identity":"rs-7713641","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}