Abstract
Objective
Multidrug-resistant tuberculosis (MDR-TB) remains a major public health challenge in China. Hainan, China’s largest tropical island, possesses distinct socio-geographical features. However, the drug resistance patterns and molecular epidemiology of MDR-TB in this region have not been fully elucidated. This study aimed to assess the correlation between drug resistance genotypes and phenotypic resistance levels in multidrug-resistant Mycobacterium tuberculosis (MDR-MTB) strains collected from Hainan Island, using whole-genome sequencing (WGS) and phenotypic drug susceptibility testing (DST).
Methods
MDR-MTB strains isolated from patients on Hainan Island (2019–2021) were analyzed. Minimum inhibitory concentrations (MIC) for 15 anti-TB drugs were determined by broth microdilution (BMD). Whole-genome sequencing (WGS) was performed using Illumina NovaSeq 6000. Genotypic resistance was predicted via TB-Profiler, and correlations between resistance mutations and MIC levels were assessed.
Results
A total of 209 MDR-MTB strains were analyzed. Strains of lineage 2.2 exhibited significantly higher resistance to ethambutol (EMB) compared to non-lineage 2 strains (P < 0.05). The sensitivity of WGS in predicting resistance to first-line drugs isoniazid (INH), rifampicin (RIF), EMB, and pyrazinamide (PZA) was 94.7%, 99.0%, 96.5%, and 80.8%, respectively. However, specificity for EMB and PZA was lower at 60.2% and 79.4%. WGS also demonstrated high sensitivity and specificity (> 95%) for second-line injectable aminoglycosides (amikacin [AMK], capreomycin [CPM], and kanamycin [KM]), but sensitivity for other second-line drugs except for fluoroquinolone drug moxifloxacin (MOX, 94.4%) was below 80.0%. Notably, mutations in katG_S315T, rpoB_S450L, and gyrA_D94G were strongly associated with high-level resistance, while mutations in fabG1, ahpC, embA promoters, and gyrA at codon 90 were linked to low-level resistance.
Conclusions
This study quantitatively demonstrates the relationship between specific drug resistance genotypes and resistance levels. It is the first to characterize the regional resistance spectrum of MDR-MTB strains on Hainan Island. These findings offer a novel foundation for MIC-based dose adjustment and the optimization of treatment strategies in this region.
Trial registration
MR-46–23-020530. Date of registration:2023–07-03.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-025-11312-8.
Keywords: Tuberculosis(TB), Multidrug-resistant tuberculosis(MDR-TB), Whole-genome sequencing(WGS), Drug sensitivity testing (DST), Minimum inhibitory concentration(MIC)
Introduction
The World Health Organization’s (WHO) vision for the Stop TB strategy is a TB-free world by 2035 [1]. However, the global rise of drug-resistant tuberculosis (TB) poses a major challenge to this goal [2]. In 2021, among the 450,000 cases of rifampicin (RIF)-resistant TB globally, over 70% were classified as multidrug-resistant tuberculosis (MDR-TB),defined as resistance to at least isoniazid (INH) and RIF [3]. Despite the burden, only 38% of MDR-TB patients were properly diagnosed and treated, mainly due to limited access to drug susceptibility testing (DST). MDR-TB treatment remains lengthy, costly, and associated with poorer outcomes compared to drug-susceptible TB [4, 5]. Inadequate or inconsistent treatment further promotes resistance to additional drugs and sustains the transmission of multidrug-resistant Mycobacterium tuberculosis (MDR-MTB) strains [6].
To address these challenges, WHO advocates for the widespread implementation of DST to enable individualized therapy and regional surveillance—core components of TB control. Whole-genome sequencing (WGS) can accelerate resistance detection compared to traditional phenotypic DSTs, as it requires biosafety infrastructure only during initial sample processing (e.g., culture and inactivation). Post-inactivation, WGS bypasses the need for live pathogen handling and accurately predicts resistance to diverse anti-TB drugs [7, 8]. Countries such as the UK and the Netherlands have implemented WGS-guided individualized treatment for all patients with TB and WGS-based monitoring [9, 10].
Most current phenotype- and genotype-based DST yield binary results—either “resistant” or “sensitive”—and may therefore overlook strains with elevated minimum inhibitory concentration (MIC) that do not exceed resistance thresholds. However, quantifying resistance levels is clinically relevant, as it enables the use of high-dose regimens to extend the therapeutic potential of relatively safer and more accessible drugs such as RIF and INH [11]. Notably, strains with MIC below the epidemiological cutoff value (ECOFF) may still result in unfavorable clinical outcomes, including treatment failure or relapse [12].
Several studies have demonstrated that specific resistance-associated gene mutations are linked to varying levels of drug resistance [2, 13]. However, this correlation alone is insufficient to inform the design of individualized treatment regimens. Strains harboring identical resistance mutations may display different MIC values, ranging from susceptible to highly resistant. Therefore, MIC determination is essential for deciding whether to escalate the dosage of particular anti-TB agents or to revise the overall treatment strategy. WGS may further enhance understanding by revealing the relationship between specific genetic mutations and MIC distributions [13, 14].
The incidence, pattern, and extent of drug-resistant tuberculosis vary significantly across different regions of China [15]. Hainan Island, the country’s largest tropical island, is characterized by a tropical monsoon marine climate and geographic isolation due to its separation from the mainland by the Qiongzhou Strait. The island is relatively underdeveloped in terms of socioeconomic status and healthcare infrastructure. Despite its unique context, limited research has investigated the correlation between WGS-predicted resistance and phenotypic DST results in MDR-MTB strains from this region. To address this gap, we analyzed 209 MDR-MTB isolates from Hainan Island, comparing MIC values obtained from phenotypic DST with resistance profiles predicted by WGS.
The primary objective of this study was to evaluate the correlation between phenotypic DST and WGS-predicted resistance in MDR-MTB strains isolated from Hainan Island. In addition, we aimed to investigate the relationship between specific resistance-associated genotypes and corresponding MIC levels. While previous studies have reported the distribution of lineages and drug resistance patterns, this study offers a more detailed, region-specific analysis of resistance mutation spectra and MIC distributions across different lineages. The findings are intended to support the development of tailored treatment strategies for MDR-TB in this unique epidemiological setting.
Materials and methods
Clinical isolates of Mycobacterium tuberculosis
Clinical isolates were obtained from drug-resistant tuberculosis patients on Hainan Island, China, between 2019 and 2021. All strains were preserved at the Tuberculosis Laboratory of the Second Affiliated Hospital of Hainan Medical University, a 2100-bed tertiary care hospital with specialized TB wards. MDR-MTB strains were initially identified using the proportional method [16]. This study focused on assessing the correlation between resistance predicted by WGS and phenotypic MIC data.
To ensure data reliability, isolates showing inconsistent MIC results between the broth microdilution (BMD) method and the proportional method (see Supplementary Table 1) were excluded. Only strains with concordant MIC values were included in subsequent WGS analysis. Contaminated isolates were also excluded. Ultimately, 209 MDR-MTB strains were included in the study. The H37Rv reference strain of Mycobacterium tuberculosis (ATCC 27294) was used as a standard drug-susceptible control, provided by the Tuberculosis Reference Laboratory of the Chinese Center for Disease Control.
Strain transfer and MIC detection
A customized BMD plate (Baso, Zhuhai, China) containing 15 lyophilized anti-tuberculosis drugs was used for MIC testing. The tested drugs included isoniazid (INH), rifampicin (RIF), ethambutol (EMB), pyrazinamide (PZA), moxifloxacin (MOX), amikacin (AMK), capreomycin (CPM), kanamycin (KM), protionamide (PTO), p-aminosalicylic acid (PAS), cycloserine (CS), linezolid (LZD), clofazimine (CFZ), bedaquiline (BDQ), and delamanid (DLM). Gradient drug concentrations were prepared using the microdilution method, and the epidemiological cutoff values (ECOFFs) and concentration ranges are summarized in Supplementary Table 2 [17, 18].
Table 2.
Comparison of WGS and phenotypic DST in the diagnosis of MDR-MTB drug resistance
| Drug | Phenotypic drug resistance | Phenotypic Drug susceptibility | Sensitivity [%(95%CI)] | Specificity [%(95%CI)] | Positive Predictive Value [%(95%CI)] | Negative Predictive Value [%(95%CI)] | Concordance (%) | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| R | S | Total | R | S | Total | ||||||
| Isoniazid (INH) | 198 | 11 | 209 | 0 | 0 | 0 | 94.7 (90.5–97.2) | NA | 100.0 (97.6–100.0) | 0.0 (0.0–32.1) | 94.7 |
| Rifampicin(RIF) | 207 | 2 | 209 | 0 | 0 | 0 | 99.0 (96.2–99.8) | NA | 100.0 (97.7–100.0) | 0.0 (0.0–80.2) | 99.0 |
| Ethambutol (EMB) | 83 | 3 | 86 | 49 | 74 | 123 | 96.5 (89.4–99.1) | 60.2 (50.9–68.8) | 62.9 (54.0–71.0) | 96.1 (88.3–99.0) | 75.1 |
| Pyrazinamide (PZA) | 63 | 15 | 78 | 27 | 104 | 131 | 80.8 (70.0–88.5) | 79.4 (71.3–85.8) | 70.0 (59.3–79.0) | 87.4 (79.8–92.5) | 79.9 |
| Moxifloxacin (MOX) | 84 | 5 | 89 | 22 | 98 | 120 | 94.4 (86.8–97.9) | 81.7 (73.3–87.9) | 79.2 (70.1–86.3) | 95.1 (88.5–98.2) | 87.1 |
| Amikacin(AMK) | 23 | 0 | 23 | 0 | 186 | 186 | 100.0 (82.2–100.0) | 100.0 (97.5–100.0) | 100.0 (82.2–100.0) | 100.0 (97.5–100.0) | 100.0 |
| Kanamycin(KM) | 23 | 0 | 23 | 2 | 184 | 186 | 100.0 (82.2–100.0) | 98.9 (95.8–99.8) | 92.0 (72.5–98.6) | 100.0 (97.5–100.0) | 99.0 |
| Capreomycin(CPM) | 20 | 0 | 20 | 8 | 181 | 189 | 100.0 (80.0–100.0) | 95.8 (91.5–98.0) | 71.4 (51.1–86.0) | 100.0 (97.4–100.0) | 96.2 |
| Protionamide (PTO) | 13 | 4 | 17 | 23 | 169 | 192 | 76.5 (49.1–92.2) | 88.0 (82.4–92.1) | 36.1 (21.3–53.8) | 97.6 (93.8–99.3) | 87.1 |
| P-aminosalicylic acid (PAS) | 3 | 12 | 15 | 3 | 191 | 194 | 20.0 (5.3–48.6) | 98.5 (95.2–99.6) | 50.0 (13.9–86.1) | 94.1 (89.7–96.8) | 92.8 |
| Cycloserine (CS) | 1 | 4 | 5 | 0 | 204 | 204 | 20.0 (1.1–70.1) | 100.0 (97.7–100.0) | 100.0 (5.5–100.0) | 98.1 (94.8–99.4) | 98.1 |
| Linezolid (LZD) | 1 | 1 | 2 | 0 | 207 | 207 | 50.0 (2.7–97.3) | 100.0 (97.7–100.0) | 100.0 (5.5–100.0) | 99.5 (96.9–100.0) | 99.5 |
| Clofazimine(CFZ) | 0 | 0 | 0 | 1 | 208 | 209 | NA | 99.5 (97.0–100.0) | 0.0 (0.0–94.5) | 100.0 (97.8–100.0) | 99.5 |
| Bedaquiline(BDQ) | 0 | 0 | 0 | 1 | 208 | 209 | NA | 99.5 (97.0–100.0) | 0.0 (0.0–94.5) | 100.0 (97.8–100.0) | 99.5 |
| Dramani(DEL) | 0 | 1 | 1 | 0 | 208 | 208 | 0.0 (0.0–94.5) | 100.0 (97.8–100.0) | NA | 99.5 (97.0–100.0) | 99.5 |
NA Not applicable, R Resistance, S Sensitive
Isolates were classified as “susceptible” if the MIC was less than or equal to the ECOFF, and “resistant” if the MIC exceeded the ECOFF. MIC50 (the concentration required to inhibit 50% of isolates) and MIC90 (the concentration required to inhibit 90% of isolates) were also calculated. High-level resistance was defined as an MIC > 1 µg/mL for INH or > 20 µg/mL for EMB. For all other drugs, high-level resistance was defined as an MIC ≥ fourfold higher than the ECOFF. MIC values between the ECOFF and the high-level threshold were interpreted as indicating low-level resistance.
Frozen strains were stored at − 80 °C and subsequently inoculated onto neutral Löwenstein–Jensen (L-J) medium (Baso, Zhuhai, China) under a BSL-2 biosafety cabinet in the negative-pressure TB laboratory at the Second Affiliated Hospital of Hainan Medical University. Cultures were incubated at 36 ± 1 °C for 2–3 weeks to obtain fresh colonies. For MIC testing, the bacterial cultures were transferred into grinding tubes containing sterile saline and glass beads, vortexed to obtain a uniform suspension, and further diluted with sterile saline. A turbidity standard of 1 McFarland (MCF) was used to prepare a 1 µg/mL pretreated bacterial suspension.
BMD plates and Culture Solutions I and II were prepared in advance and maintained at room temperature. Culture Solution I consisted of Middlebrook 7H9 supplemented with 10% OADC at pH 6.8, while Culture Solution II contained the same medium at pH 6.0. The pretreated bacterial suspension was diluted 100-fold using the appropriate culture solution. For each BMD plate, 100 µL of the Culture Solution I–diluted suspension was added to all wells except those designated for PZA and its positive control. Wells for PZA and its control were filled with 100 µL of bacterial suspension diluted in Culture Solution II.
After inoculation, the plates were sealed with membrane film and incubated at 36 ± 1 °C, facing upward. After 48 h, plates were inspected for contamination. MIC results were interpreted on day 7. If insufficient growth was observed in the control wells, incubation continued and the plates were examined daily. The optimal window for result interpretation was between 7 and 14 days, and no plate was incubated for more than 21 days.
For each BMD assay batch, the standard drug-susceptible Mycobacterium tuberculosis strain H37Rv was used as a quality control reference.
WGS and drug susceptibility prediction
A single 10 μL inoculation loop was used to collect colonies, which were suspended in 500 μL of TE buffer and inactivated at 80 °C for 30 min. A total of 400 μL of inactivated suspension was subjected to ultrasonic dispersion. Genomic DNA was extracted using the cetyltrimethylammonium bromide–lysozyme method [19]. DNA purity, concentration, and integrity were assessed using the Agilent 5400 Fragment Analyzer (Agilent Technologies, USA).
DNA libraries were constructed using the FS DNA Library Prep Kit V6 (ABclonal, Wuhan, China), following the manufacturer’s instructions, with a targeted insert size of 300 base pairs (2 × 150 bp). WGS was performed on the Illumina NovaSeq 6000 platform (Illumina, USA).
Raw sequencing reads were quality-checked using FastQC (v0.11.9), and adapter sequences and low-quality reads (Q < 20) were removed. Clean reads were aligned to the Mycobacterium tuberculosis H37Rv reference genome (NC_000962.3) using BWA (v0.7.17) [20]. Variant calling and annotation were performed using FreeBayes (v1.3.2) and SnpEff (v4.3t), respectively [21]. The average whole-genome coverage exceeded 95%, and the mean sequencing depth was greater than 100 ×, ensuring high-confidence variant identification.
Genotypic resistance to 15 anti-TB drugs (INH, RIF, EMB, PZA, MOX, AMK, CPM, KM, PTO, PAS, CS, LZD, CFZ, BDQ, and DLM) was predicted using TB-Profiler (v2.8.12; https://github.com/jodyphelan/TBprofiler) [22]. To construct the phylogenetic tree, SNP sequences were imported into MEGA11, and the"Construct/Test Neighbor-Joining Tree"function under the"PHYLOGENY"module was applied using default parameters [23]. The resulting tree was visualized and refined using iTOL (v6.6; https://itol.embl.de/) [24].
Statistical analysis
MIC values, resistance proportions, mutation frequencies, and the predictive accuracy of WGS for drug resistance were summarized using Microsoft Excel 2019. Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). The MIC distributions of each drug and the MIC ranges associated with specific resistance-related mutations were analyzed.
Proportions were analyzed using appropriate chi-square tests or Fisher's exact test. Chi-square tests were applied for proportions with T < 5 in less than 20% of cases, and Fisher's exact test was used when at least one cell had T < 1. Statistical significance was considered at P < 0.05.
Availability of data and materials
The raw WGS data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1000054 and PRJNA1056894.
Results
Bacterial strain data description
A total of 209 MDR-MTB strains were included in this study, originating from 18 cities and counties across Hainan Island, China. Haikou contributed the highest number of isolates (Fig. 1). The lineage distribution was as follows: 6 strains (2.9%) belonged to lineage 1, 57 strains (27.3%) to lineage 2.1, 117 strains (56.0%) to lineage 2.2, and 29 strains (13.9%) to lineage 4.
Fig. 1.
Distribution map of MDR-TB on the Hainan Island, China. A The geographical location of Hainan island in China. Green means Hainan Island. B Number and lineage distribution of MDR-TB patients on the Hainan Island
Correlation between lineage and phenotypic drug sensitivity
The MIC of all 209 MDR-MTB strains against 15 anti-TB drugs were determined using BMD plates. The observed resistance rates were as follows: 100.0% (209/209) for isoniazid (INH) and rifampicin (RIF), 41.1% (86/209) for ethambutol (EMB), 37.3% (78/209) for pyrazinamide (PZA), 42.6% (89/209) for moxifloxacin (MOX), 11.0% (23/209) each for amikacin (AMK) and kanamycin (KM), 9.6% (20/209) for capreomycin (CPM), 8.1% (17/209) each for protionamide (PTO) and para-aminosalicylic acid (PAS), 2.4% (5/209) for cycloserine (CS), 1.0% (2/209) for linezolid (LZD), and 0.5% (1/209) for delamanid (DLM). Although resistance to clofazimine (CFZ) and bedaquiline (BDQ) was not detected, their MIC exceeded the epidemiological cutoff values (ECOFFs).
For new or repurposed drugs like LZD, CFZ, BDQ, and DLM, the MIC50 and MIC90 values are ≤ 0.25 μg/mL and 0.5 μg/mL, ≤ 0.06 μg/mL and 0.5 μg/mL, ≤ 0.03 μg/mL and 0.06 μg/mL, ≤ 0.007 μg/mL and 0.03 μg/mL respectively. Lineage-based analysis revealed that MDR-MTB strains from lineage 2.2 exhibited a significantly higher resistance rate to EMB compared with non-lineage 2 strains (P < 0.05). Additionally, the mutation rates associated with PZA resistance in strains from lineages 2.1 and 2.2 were significantly higher than those in non-lineage 2 strains (P < 0.05) (Table 1, Fig. 2).
Table 1.
Relationship between resistance rates of lineage phenotypes
| Drug | Range of concentration μg/mL | ECOFF μg/mL | Total(n = 209) | Non-lineage2(n = 35) | lineage2.1(n = 57) | lineage2.2(n = 117) | X2 | p | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIC50 | MIC90 | NO | % | NO | % | NO | % | NO | % | |||||
| Isoniazid (INH) | 0.05–2 | 0.1 | 2 | > 2 | 209 | 100.0% | 35 | 100.0% | 57 | 100.0% | 117 | 100.0% | NA | NA |
| Rifampicin(RIF) | 0.25–8 | 0.5 | > 8 | > 8 | 209 | 100.0% | 35 | 100.0% | 57 | 100.0% | 117 | 100.0% | NA | NA |
| Ethambutol (EMB) | 0.62–20 | 5 | 5 | 10 | 86 | 41.1% | 8 | 22.9%a | 24 | 42.1% | 54 | 46.2% | 6.068 | 0.048 |
| Pyrazinamide (PZA) | 50–600 | 100 | ≦50 | > 600 | 78 | 37.3% | 7 | 20.0%b | 24 | 42.1% | 47 | 40.2% | 5.453 | 0.065 |
| Moxifloxacin (MOX) | 0.12–4 | 0.5 | 0.5 | 4 | 89 | 42.6% | 13 | 37.1% | 31 | 54.4% | 45 | 38.5% | 4.484 | 0.106 |
| Amikacin(AMK) | 0.25–8 | 1 | 0.5 | 4 | 23 | 11.0% | 2 | 5.7% | 3 | 5.3% | 18 | 15.4% | 5.211 | 0.074 |
| Kanamycin(KM) | 0.62–20 | 5 | 1.25 | 20 | 23 | 11.0% | 2 | 5.7% | 3 | 5.3% | 18 | 15.4% | 5.211 | 0.074 |
| Capreomycin(CPM) | 0.5–16 | 2 | 1 | 2 | 20 | 9.6% | 0 | 0.0% | 7 | 12.3% | 13 | 11.1% | 4.509 | 0.105 |
| Protionamide (PTO) * | 0.62–20 | 5 | ≦0.62 | 5 | 17 | 8.1% | 1 | 2.9% | 4 | 7.0% | 12 | 10.3% | - | 0.417 |
| P-aminosalicylic acid (PAS) * | 0.5–16 | 4 | ≦0.5 | 2 | 17 | 8.1% | 2 | 5.7% | 4 | 7.0% | 11 | 9.4% | - | 0.830 |
| Cycloserine (CS) * | 6.25–200 | 25 | ≦6.25 | 25 | 5 | 2.4% | 0 | 0.0% | 1 | 1.8% | 4 | 3.4% | - | 0.836 |
| Linezolid (LZD) * | 0.25–8 | 1 | ≦0.25 | 0.5 | 2 | 1.0% | 0 | 0.0% | 0 | 0.0% | 2 | 1.7% | - | 1.000 |
| Clofazimine(CFZ) | 0.06–4 | 1 | ≦0.06 | 0.5 | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | NA | NA |
| Bedaquiline(BDQ) | 0.03–4 | 0.25 | ≦0.03 | 0.06 | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | NA | NA |
| Dramani(DEL) * | 0.0075–1 | 0.06 | ≦0.0075 | 0.03 | 1 | 0.5% | 0 | 0.0% | 0 | 0.0% | 1 | 0.9% | - | 1.000 |
NA Not applicable
*Fisher’s exact probability test (two-tailed)
#Due to the limited sample size, a statistical comparison of differences was not conducted
aThe mutation rates of Non-lineage2 and lineage2.2 in EMB were statistically different(P < 0.05)
bThe mutation rate of lineage2.1 and lineage2.2 to PZA was higher than that of Non-lineage2(P < 0.05)
Fig. 2.
MIC distribution of 209 MDR-MTB strains
Correlation between Lineage and drug resistance gene mutation
By constructing a maximum likelihood phylogenetic tree linking strain lineages with anti-tuberculosis drug resistance (Fig. 3), we observed that lineage 2.2 strains exhibited significantly higher resistance rates to EMB compared with non-lineage 2 strains (P < 0.05). Lineage 2.2 strains also showed a higher resistance rate to pyrazinamide (PZA) compared with lineage 2.1 (P < 0.05).
Fig. 3.
Maximum likelihood phylogenetic Tree of 209 MDR-MTB strains on the Hainan Island, China. Drug mutations are represented by patterns of different colors. Mutations are represented by filled (with mutations) or empty (without mutations) symbols
Regarding resistance-associated mutations, the embB_M306I mutation (for EMB) and gyrA_D94G mutation (for MOX) were more prevalent in non-lineage 2 and lineage 2.1 strains, respectively. In contrast, lineage 2.2 strains were characterized by the embB_M306V and gyrA_A94V mutations associated with resistance to EMB and MOX, respectively (Supplementary Table 3).
Comparison of consistency between BMD method and WGS drug sensitivity
For the first-line drugs (INH, RIF, EMB, and PZA) the sensitivity of WGS in predicting resistance was 94.7%, 99.0%, 96.5%, and 80.8%, respectively. However, the specificity for EMB and PZA was relatively low, at 60.2% and 79.4%, respectively. For second-line injectable aminoglycosides, including AMK, KM, and CPM, both sensitivity and specificity exceeded 95.0%. Apart from MOX, which showed a sensitivity of 94.4%, the sensitivity for other second-line drugs was below 80.0% (Table 2).
The relationship between different resistance-associated mutations and MIC
To evaluate the relationship between resistance-associated gene mutations and drug resistance levels, we analyzed 209 MDR-MTB strains from Hainan Island, China. MIC values for 15 anti-TB drugs were compared with corresponding resistance-associated mutations. The analysis revealed that specific mutations were associated with distinct MIC ranges (Supplementary Table 4).
For the first-line drug INH, non-synonymous mutations in katG were generally associated with high-level resistance (MIC ≥ 1 µg/mL). However, strains harboring katG_S315T (8 strains), Y337C (1 strain), N138D (1 strain), and P232R (1 strain) exhibited low-level resistance. Mutations in the fabG1 and ahpC promoter regions were also linked to low-level INH resistance, though co-occurrence of both mutations often correlated with increased MIC and high-level resistance.
For RIF, mutations were frequently observed at codons 435, 445, and 450 of rpoB. Double mutations in rpoB typically conferred high-level resistance (MIC ≥ 8 µg/mL), while some strains with single mutations such as D435F (2 strains), D435V (1 strain), D435Y (1 strain), H445S (1 strain), and L430P + H445Q showed low-level resistance (MIC ≤ 4 µg/mL). Two strains exhibited insertion-deletion mutations in rpoB (1295_1303_delAATTCATGG and R384W + 1299_1304_delCATGGA), and one strain with a V170F mutation located outside the RIF resistance-determining region showed high-level resistance.
EMB resistance-associated mutations were primarily located in embB and were linked to MIC near the ECOFF (5 µg/mL), indicating low-level resistance or phenotypic susceptibility. Strains with promoter mutations in embA typically had MIC ≤ ECOFF, suggesting susceptibility. However, when embA and embB mutations co-occurred, MIC tended to increase, indicating low-level resistance.
For PZA, resistance-associated mutations were distributed throughout the pncA gene and its promoter, including diverse single-nucleotide polymorphisms (SNPs) and insertions/deletions. These mutations were highly heterogeneous and lacked specific hotspots, consistent with previous reports [25, 26]. A frameshift mutation in the pncA gene led to a modest increase in mutation frequency. Additionally, different types of resistance-associated mutations were found to occur within various minimum inhibitory concentration (MIC) ranges. Interestingly, the MIC values for some wild-type strains were higher than the epidemiological cutoff values (ECOFF). Interestingly, some wild-type strains showed MIC exceeding the ECOFF, making it difficult to establish a clear correlation between pncA mutations and resistance levels.
Among second-line drugs, most fluoroquinolone-resistant strains harbored mutations in gyrA or gyrB. The most common gyrA mutations occurred at codons 90 and 94. gyrA_94 mutations were generally associated with high-level MOX resistance (MIC ≥ 2 µg/mL), while gyrA_90 mutations conferred low-level resistance or susceptibility. gyrB mutations were associated with MIC ≤ ECOFF, although combined gyrA/gyrB mutations often conferred high-level resistance.
Resistance to aminoglycosides was predominantly linked to the rrs_A1401G mutation, which typically conferred high-level resistance to AMK and KM, but only low-level resistance or even susceptibility to CPM, indicating that rrs_A1401G alone may not be predictive of CPM resistance.
The association between mutations in fabG1, the inhA promoter, and the ethA shift region with resistance to PTO was unclear. For PAS, mutations were primarily located in folC and thyA, but the link between these mutations and resistance was inconclusive, possibly due to unreliable phenotypic DST results.
Similarly, for CS, phenotypic DST was inconsistent, although one ald_433_ins_GC mutation was associated with high-level resistance. A single rplC_C154R mutation conferred high-level resistance to LZD. Additionally, an Rv0678_192_ins_G mutation led to cross-resistance to CFZ and BDQ, although phenotypic testing indicated MIC remained ≤ ECOFF for both drugs. No gene mutations associated with DLM resistance were detected.
Discussion
Drug resistance in Mycobacterium tuberculosis is primarily driven by genetic mutations [27]. WGS provides a high-resolution molecular tool for predicting resistance by identifying genomic polymorphisms, insertions, and deletions [28]. This study is the first to integrate WGS and MIC data to systematically examine the relationship between resistance genotypes and drug resistance levels in 209 MDR-TB strains from Hainan Island, China. Although the binary classification of resistance (susceptible/resistant) is practical, it oversimplifies the gradient nature of drug resistance and may lead to clinical misinterpretation [1].
For example, the katG_S315T mutation, commonly linked to high-level isoniazid (INH) resistance, showed only slightly elevated MIC in 5.7% (8/139) of strains, suggesting a gradient from low to high resistance [2, 13]. Similarly, rpoB_S450L mutations correlated with high-level rifampicin (RIF) resistance (MIC ≥ 8 µg/mL), while rpoB_L430P mutations were associated with low-level resistance (MIC ≤ 4 µg/mL). These findings indicate that single mutations may not fully explain phenotypic resistance. The genetic background and coexisting mutations should be considered [13, 28]. Quantitative MIC data are essential to explain discrepancies between molecular and phenotypic tests and to inform individualized dose adjustments [11].
WGS shows high sensitivity (> 94%) for predicting resistance to first-line drugs (e.g., INH, RIF) and aminoglycosides (e.g., amikacin [AMK]), consistent with multicenter findings [2, 29]. However, specificity for ethambutol (EMB) and pyrazinamide (PZA) remains low (60.2% and 79.4%, respectively), reflecting the complexity of resistance mechanisms. For example, 51.6% of embB_M306I-mutated strains had MIC ≤ 5 µg/mL (phenotypically susceptible), suggesting this mutation alone may be insufficient to drive high-level resistance [14, 18, 30]. Similarly, pncA mutations associated with PZA resistance are highly heterogeneous and lack defined hotspots [25, 26]. Phenotypic confirmation remains necessary. Despite reducing turnaround time for resistance testing, WGS is limited in low-resource settings by cost and analytical capacity [28]. Integrating WGS with MIC data enhances understanding of local resistance profiles, such as those on Hainan Island.
Hainan Island's geographic isolation and regional drug use may influence the evolution of drug-resistant TB strains [15]. Lineage 2.2 predominates (56.0%), followed by lineage 2.1 (27.3%), with both lineages displaying distinct resistance profiles [31–33]. EMB resistance in lineage 2.2 (46.2%) was primarily associated with embB_M306V, while lineage 2.1 was linked to embB_M306I. Similar trends in other Chinese provinces support this lineage-resistance association [5, 34]. For moxifloxacin (MOX), resistance was more frequent in lineage 2.1 (33.3% with gyrA_D94G) than lineage 2.2 (22.2% with gyrA_A90V and 17.9% with gyrA_D94G) [13].
These findings suggest that different lineages employ distinct resistance strategies: lineage 2.1 tends toward high-cost mutations (e.g., gyrA_D94G), while lineage 2.2 relies on low-cost mutations (e.g., embB_M306V) that may confer a transmission advantage. Future studies integrating epidemiologic data, such as patient movement and drug history, may help clarify these dynamics.
Some drug-resistance mutations confer MIC near the ECOFF, complicating interpretation. For example, 51.6% (16/31) of embB_M306I strains were phenotypically susceptible, highlighting the limitations of EMB phenotypic testing [35]. Similar debates exist for embB codon 306 mutations, which are found in both resistant and susceptible strains [30]. Sub-ECOFF MIC elevations may signal an increased risk of treatment failure or resistance progression [36, 37].
For PZA, 30.8% (24/78) of strains with pncA mutations had MIC ≤ 100 µg/mL, again within the susceptible range. These low-level resistant strains can be missed by routine DST, increasing relapse risk [12]. Combining MIC and WGS data enhances resistance surveillance and supports treatment optimization (e.g., dose adjustments or prolonged regimens) [14, 16]
Resistance to bedaquiline (BDQ) and delamanid (DLM) remains low (≤ 1.0%) in Hainan, likely due to limited drug use [38–40]. Historical reliance on fluoroquinolones and aminoglycosides aligns with observed resistance rates for moxifloxacin (42.6%) and amikacin/capreomycin (11.0%) [41]. However, the MIC90 of DLM (0.03 µg/mL) is close to the ECOFF (0.06 µg/mL), suggesting a narrow therapeutic margin. One Rv0678_192_ins_G mutation conferred cross-resistance to BDQ and clofazimine (CFZ), underscoring the need for ongoing monitoring [39, 42].
Interestingly, the MIC90 of linezolid (LZD) in Hainan (0.5 µg/mL) is lower than in regions like Beijing (1 µg/mL), possibly reflecting the absence of stable resistant clones [39, 43].
This study has limitations: (1) modest sample size (n = 209) and time frame (2019–2021); (2) exclusion of strains with inconsistent DST results may introduce bias; (3) Resistance rates for second-line drugs (e.g., PAS: 7.2%; CS: 2.4%) were low (≤ 11% except MOX), limiting mechanistic exploration. Additionally, phenotypic DST reliability for certain drugs (e.g., PAS) is debated [2, 43, 44]. Future studies should validate mutations via functional assays (e.g., gene complementation) to clarify resistance mechanisms; (4) Due to the lack of clinical data, we did not analyze the association between MIC and prognosis of patient treatment outcomes.
To overcome these limitations, future studies will increase the sample size and extend the monitoring period. Additionally, we will combine multi-omics data, including transcriptomics and metabolomics, with clinical follow-up information. This approach aims to uncover the factors driving the evolution of drug resistance and their effects on treatment outcomes.
Conclusion
This study provides a comprehensive analysis of the quantitative relationship between drug resistance genotypes and resistance levels by integrating WGS and MIC data. For the first time, it characterizes the regional drug resistance landscape of MDR-MTB strains on Hainan Island. The findings move beyond the limitations of traditional binary resistance classification, offering new insights into MIC-guided dose adjustment and treatment optimization. Moreover, they establish a solid foundation for developing MIC-based predictive models for drug resistance.
In future research, we plan to expand the sample size and extend the follow-up period to further investigate how varying resistance levels affect treatment outcomes. Our goal is to support the broader implementation of precision medicine in the prevention and control of TB.
Supplementary Information
Acknowledgements
We thank the Hainan Provincial Natural Science Foundation of China, Key Research and Development Project of Hainan Province and Scientific Research Project of Health Commission of Hainan Province for funding this study.
Abbreviations
- MDR-TB
Multidrug-resistant tuberculosis
- TB
Tuberculosis
- MDR-MTB
Multidrug-resistant Mycobacterium tuberculosis
- WGS
Whole-genome sequencing
- DST
Drug sensitivity testing
- MIC
Minimum inhibitory concentration
- BMD
Broth microdilution
- INH
Isoniazid
- RIF
Rifampicin
- EMB
Ethambutol
- PZA
Pyrazinamide
- MOX
Moxifloxacin
- AMK
Amikacin
- CPM
Capreomycin
- KM
Kanamycin
- PTO
Protionamide
- PAS
P-aminosalicylic acid
- CS
Cycloserine
- LZD
Linezolid
- CFZ
Clofazimine
- BQD
Bedaquiline
- DLM
Dramani
- ECOFF
Epidemiologic dividing values
- MIC50
The minimum inhibitory concentration needed to suppress 50% of bacterial growth
- MIC90
The minimum inhibitory concentration required to inhibit 90% of bacterial growth
Authors’ contributions
Conceptualization, Jieying Wang and Jinhui Dong; methodology, Jieying Wang, Yeteng Zhong and Zhuolin Chen; software, Yeteng Zhong and Jieying Wang; validation, Yuni Xu, Wenhua Qiu, Shaowen Chen and Jieying Wang; formal analysis, Yeteng Zhong,Jieying Wang and Jinhui Dong; investigation, Hua Pe and Jinhui Dongi; resources, Jieying Wang and Yeteng Zhong; data curation,Jieying Wang and Yeteng Zhong; writing—original draft preparation, Jieying Wang and Jinhui Dong; writing—review and editing, Jieying Wang and Yeteng Zhong; visualization, Jieying Wang; supervision, Hua Pe and Yeteng Zhong. All authors have read and agreed to the published version of the manuscript.
Funding
The work is funded by the Hainan Provincial Natural Science Foundation of China (No.825QN555, No. 822RC844 and No. 820MS144), Key Research and Development Project of Hainan Province (No.ZDYF2022SHFZ100), Joint Program on Health Science & Technology Innovation of Hainan Province (No.WSJK2025MS153), and Scientific Research Project of Health Commission of Hainan Province (No.22A200271, 22A200272).
Data availability
The raw data from the WGS has been submitted to the NCBI SRA database and published. The bio-project accession numbers are PRJNA1000054 and PRJNA1056894.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of the University of the Second Affiliated Hospital of Hainan Medical University, China (No.LW2020226) and waived the requirement for informed consent.This study complies with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jieying Wang and Jinhui Dong contributed equally to the work.
Contributor Information
Hua Pei, Email: phzmh61@aliyun.com.
Yeteng Zhong, Email: zhongyeteng@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The raw WGS data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1000054 and PRJNA1056894.
The raw data from the WGS has been submitted to the NCBI SRA database and published. The bio-project accession numbers are PRJNA1000054 and PRJNA1056894.