Whole genome sequencing for tuberculosis disease species identification, lineage determination, and drug resistance detection in Kashgar prefecture, China

doi:10.21203/rs.3.rs-4620683/v1

Download PDF

Research Article

Whole genome sequencing for tuberculosis disease species identification, lineage determination, and drug resistance detection in Kashgar prefecture, China

https://doi.org/10.21203/rs.3.rs-4620683/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

We aimed to use whole genome sequencing (WGS) to determine species and lineage composition and drug resistant profile in a high tuberculosis (TB)-burden region of China.

Methods

We conducted WGS to 1791 acid-fast staining positive isolates and culture-positive isolates collected from Kashgar prefecture in 2020. Bioinformatic analysis was applied to confirm species, lineage and drug resistant mutations. The drug susceptibility testing were performed on confirmed Mycobacterium tuberculosis complex(MTBC) isolates. We determined the accuracy of WGS by comparing with phenotypes.

Results

95.03% (1702/1791) were identified MTBC, 3.18% (57/1791) were nontuberculous mycobacteria (NTM), 0.61% (11/1791) and 0.89%(16/1791) were Nocardia and Gordonia respectively, 4 were identified as mixed infection. MTBC were composed of lineage 2 (45.83%, 780/1702), lineage 3 (462/1702, 27.14%), lineage 4 (455/1702, 26.73%), lineage 1(1/1702, 0.06%) and M.bovis (La1, 4/1702, 0.24%). Resistance to rifampicin, ethambutol, fluoroquinolones, aminoglycosides and ethionamide were accurately predicted with sensitivity of 96.43%, 83.33%,100%, 100% and 94.74% by WGS, and resistance to isoniazid with the sensitivity of 81.62% .

Conclusions

WGS can be an important approach in assessing TB control strategy and for determining therapeutic schemes in high TB-burden regions. The drug resistant TB of Kashgar prefecture is at low level and the application of WGS may prevent the increase of resistance rate.

Tuberculosis

WGS

drug-resistant

species identification

lineage

Tuberculosis (TB) is a leading cause of death worldwide with 10.6 million new cases and 1.3 million death cases in 2022 globally. China has the third largest TB burden, where there were an estimated 748,000 new TB cases in 2022, accounting for 7.1% of TB incident cases worldwide[1]. Kashgar prefecture in Xinjiang Autonomous Region is one of the areas with the highest tuberculosis incidence rate in China, reaching an amazing incidence of 806.75 cases per 100,000 people in 2018 [2]. Moreover, as Kashgar prefecture is located in an important hub of the Silk Road and borders Pakistan, India, and other countries, Mtb may be introduced into the region along with population migration, trade, and cultural exchange. Kashgar has therefore become the critical region in the context of TB control in China.

To formulate the targeted strategies of TB prevention and control in Kashgar, the starting point is to have scientific information associated with species identification, Mtb strain lineage distribution, drug resistance characteristics and the risk factors related with drug resistance in this region. Unfortunately, the above information remains ambiguous due to the insufficient species diagnosis accuracy of conventional Ziehl-Neelsen (ZN) smear microscopy [3], and high cost of phenotypic drug susceptibility testing [4]. Molecular platforms such as Xpert MTB/RIF Ultra have delivered the capacity to confirm the presence of Mtb and to predict most resistance to rifampin from primary clinical samples in settings that had previously relied on smear microscopy only [4, 5] Nevertheless, as such targeted molecular assays are vulnerable to off-target emerging mutations and provide limited information on susceptibility to other drugs or identification of other strains, treatment for many patients remains semiempirical, with an increased risk of treatment failure and amplification of resistance to more drugs [6].

Whole-genome sequencing (WGS) can identify species and lineage, predict drug susceptibility with high accuracy through identifying specific relevant mutations and has been heralded as a potential solution for the implementation of continuous surveillance and personalized therapy [7, 8]. In principle, WGS provides the ultimate resolution for strain classification to trace infection sources and transmission networks and also allows for the simultaneous prediction of the complete antimicrobial susceptibility profile of a given isolate.

The current study aimed to accurately identify the strains species and circulating Mtb lineages and determine their drug resistance characteristics among TB cases in one of the highest TB-burden regions in China using WGS and to provide scientific basis to foster targeted TB control strategies.

Sample source and sample collection

Kashgar prefecture lies in the southwest of Xinjiang province and consists of 11 counties and one county-level city (supplementary Fig. 1), and has consistently been amongst the regions burdened by a high prevalence of TB in China. In 2020, the TB case notification rate in Kashgar was 250.74 per 100,000 people. Kashgar CDC conducted this survey and collect sputum samples and clinical information for eligible individuals. Eligible patients were those presenting as presumptive tuberculosis cases newly registered during the survey period (January 2020 to December 2020) at selected tuberculosis clinics. A presumptive case was defined as a patient with a persistent cough for more than 2 weeks or at least two of the following symptoms: fever, drenching night sweats, unexplained weight loss (> 1·5 kg/month), general feeling of illness (malaise) and tiredness, and shortness of breath with chest pain [9]. This study was approved by the Tuberculosis Research Ethics Review Committee of the Kashgar CDC, and written informed consent was obtained from each participant. All authors vouched for the completeness and accuracy of the data presented.

Specimen processing and sequencing

Two sputum samples for culturing were obtained from each eligible patient who can provide qualified sputum before the initiation of treatment. To isolate the culture, each specimen was treated with 4% sodium hydroxide, followed by homogenization through vigorous stirring. Subsequently, an aliquot of 0.1 ml from the resulting specimen was inoculated into two tubes of acidified Löwenstein–Jensen medium, and the inoculated tubes were then incubated at 37°C. The cultures were assessed during the first week for rapidly growing bacteria and subsequently every week thereafter for slower growing bacteria. If no bacteria were detected by the eighth weeks, the result was recorded as negative [10]. All culture-positive strains were sent to Kashgar CDC for nucleic acid extraction and phenotypic drug susceptibility testing.

Drug susceptibility testing

Drug susceptibility testing was performed using the UKMYC6 96-well microdilution plate format, designed by the CRyPTIC project (Thermo Fisher Inc., UK). The UKMYC6 plate allowed for the determination of susceptibility against 13 antituberculosis drugs composed of agents used in drug-susceptible TB treatment (rifampicin, rifabutin, and isoniazid) as well as longer MDR-TB treatment corresponding to groups A (levofloxacin, moxifloxacin, bedaquiline, and linezolid), B (clofazimine), C (ethambutol, delamanid, and amikacin, ethionamide) and kanamycin[9]. The ECOFF for each drug were determined according to Philip’s research work [11].

Whole genome sequencing and bioinformatics analysis

Nucleic acid was extracted using the cetyl trimethyl ammonium bromide method. Genomic DNA was sequenced using the Illumina Hiseq-2000 with an expected depth of 200x. To identify the species, each dataset of sequencing reads was analyzed by Kraken tool (v2.1.1)[12]. If MTBC accounted for less than 70% of a sample, the sample were identified as mixed infection with other species and excluded for following analysis [13]. For those identified as MTBC, paired-end reads were aligned to the reference genome H37Rv (NC_000962·3) using the Burrows-Wheeler algorithm and sorted with SAMtools (V.1.15). Variant calling was conducted with GATK to identify single nucleotide polymorphisms, with low-quality SNPs (Phred score Q < 20 and read depth < 5) and sites with missing calls in > 10% of isolates removed from analysis. SNPs located within 12 bp of each other or that had less than 75% of supporting high quality reads were also excluded. Variations in known drug-resistant genes or intergenic regions, direct repeat regions, microsatellite like sequences, transposition insertion sequences (such as IS6110), ESX secretion system protein genes, and PE/PPE/PGRS family genes were masked for phylogenetic tree reconstruction [14]. A maximum likelihood (ML) phylogeny was inferred using RaxML-NG v1.0.3, and the substitution model was GTR + G [15]. In silico drug resistance prediction was carried out using TBProfiler [16].

Statistical analysis

Drug resistance rates are expressed as numbers (percentages). Multivariable logistic regression models were used to examine the odds ratios (ORs) and 95% confidence intervals (CIs) for the risk factors that were associated with drug resistance rate. Bar charts were used to display the percentage composition of MIC for various drugs. A pie chart was used to display the composition of bacterial types. The phenotypes were used as the gold standard to calculate the sensitivity and specificity of gene detection method. A 2-sided P value < 0.05 was used to determine statistical significance. Data cleaning and statistical analyses were performed using SAS version 9.2 (SAS Institute).

Strain data description

The Kashgar Tuberculosis Survey 2020 collected 1972 bacterial strains samples from 12 selected sites of all Kashgar prefecture. Cultures of specimens from 181 patients failed to recover or were contaminated. Among the 1791 revived strains, based on Kraken analysis, 85 acid-fast staining samples were identified to be from NTM (57 isolates), nocardia species (11 isolates), gordonia species (16 isolates) and rhodococcus species (1 isolates); 1702 isolates were identified as mycobacterium tuberculosis complex (Table 1 and supplementary Fig. 2); and 4 were identified as mixed infection. Five lineages were observed through the WGS analysis. Phylogenetic analysis showed that the majority coming from L2 (780 isolates; 45.83%), L3 (462 isolates; 27.14%) and L4 (455 isolates; 26.73%), while L1 (1 isolates; 0.06%) and M. bovis (4 isolates; 0.24%) comprised the remainder (Fig. 1)

Table 1

Composition of acid-fast staining positive strains in Kashi prefecture
Species
Nocardia cyriacigeorgica	8	0.45
Nocardia farcinica	3	0.17
Rhodococcus pyridinivorans	1	0.06
Gordonia bronchialis	2	0.11
Gordonia polyisoprenivorans	12	0.67
Gordonia sp. YC-JH1	1	0.06
Gordonia terrae	1	0.06
Mycobacterium intracellulare	1	0.06
Mycobacterium novum	3	0.17
Mycobacterium sp. DL440	5	0.28
Mycobacterium sp. DL90	2	0.11
Mycobacteroides chelonae	5	0.28
Mycolicibacillus koreensis	4	0.22
Mycolicibacter minnesotensis	2	0.11
Mycolicibacterium boenickei	9	0.50
Mycolicibacterium celeriflavumavum	2	0.11
Mycolicibacterium fortuitum	1	0.06
Mycolicibacterium mageritense	1	0.06
Mycolicibacterium phlei	1	0.06
Mycolicibacterium pulveris	18	1.01
Mycolicibacterium thermoresistibile	3	0.17
Mycobacterium tuberculosis complex	1702	95.03
Mycobacterium tuberculosis mixed infection	4	0.22
Total	1791	100%

Drug-resistant profile

Phenotypic drug susceptibility results were obtained for 1664 isolates. The drug with highest resistance rate was isoniazid (9.07%, 151/1664), and the drug rank the second was rifampin (3.25%, 54/1664). The MDR rate was 2.4% among Kashgar region. The resistant rate for the detailed resistance profile of all the drugs are showed in Table 2.

Table 2

Drug resistant prevalence of Mtb for 13 drugs among Kashgar cases
Drugs	Total number	Durg resistance number	Resistance rate(%)
Bedaquiline	1664	7	0.42
Clofazimine	1664	12	0.72
Delamanid	1664	10	0.60
Ethambutol	1664	5	0.30
Ethionamide	1664	18	1.08
Rifabutin	1664	44	2.64
Linezolid	1664	0	0
Rifampicin	1664	54	3.25
Kanamycin	1664	4	0.24
Levofloxacin	1664	18	1.08
Isoniazid	1664	151	9.07
Moxifloxacin	1664	14	0.84
Amikacin	1664	4	0.24
Multiple-drug resistant	1664	40	2.40

Different from the agar proportion method critical concentrations, MIC susceptibility testing method can provide quantitative results, the overall MIC distributions for 13 drugs are showed in Fig. 2. We discovered that although the drug resistance rate is at low level, there are many strains with MIC at ECOFF levels, such as, 49 isolates (2.94%, 49/1664) showing MIC value of isoniazid at 0.1 µg/mL, 93 isolates (5.59%,93/1664) having MIC value of rifampin at 0.5 µg/mL, and 18 isolates (1.08%) showing MIC value of levofloxacin at 1 µg/mL.

Prediction efficiency of WGS

For first-line drugs isoniazid, rifampin and ethambutol, the sensitivity of the WGS prediction was 81.62%, 95.65% and 83.33% respectively, and the specificity was 98.82%, 99.44%, and 99.1% respectively. For the second-line drugs, the sensitivities vary from 94.74–100% (Table 3). For new anti-TB drug (bedaquiline and delamanid) and repurposed drug (clofazimine and linezolid), sensitivity and specificity were not analyzed due to low drug resistance and low predictive sensitivity.

Table 3

Prediction of phenotypes of resistance to nine drugs by WGS
Drug	Resistant phenotype			Susceptible phenotype			Sensitivity(%)	Specificity(%)
Drug	R	S	total	R	S	total	Sensitivity(%)	Specificity(%)
Isoniazid	117	34	151	18	1495	1513	81.62	98.82
Rifampicin	52	2	54	9	1601	1610	96.43	99.44
Rifabutin	42	2	44	19	1601	1620	95.65	98.84
Ethambutol	4	1	5	15	1644	1659	83.33	99.1
Amikacin	4	0	4	2	1657	1659	100	100
Kanamycin	4	0	4	2	1657	1659	100	100
Ethionamide	17	1	18	19	1625	1644	94.74	98.86
Levofloxacin	18	0	18	1	1645	1646	100	100
Moxifloxacin	14	0	14	5	1645	1650	100	100
R: resistant, S: susceptible,

Kashgar has always been one of the highest TB-burden regions in China. In 2020, the reported TB incidence in Kashgar was 250.74 per 100,000 people, which was 4.3 times as that of the national average for the same time period [2]. Here, we firstly used WGS combined with high-throughput quantitative MIC measurements to determine the species composition, lineage distribution and drug-resistance profile of tuberculosis across the whole Kashgar prefecture.

In recent decades, increasing incidences and prevalences of non-tuberculous mycobacteria (NTM) and Nocard's bacillus have been reported [17], which are widely distributed in the environment and can cause clinical symptoms similar to pulmonary tuberculosis and cause positive results in acid-fast staining, but the drug regimen of them is greatly different from that of TB [18]. Our results showed that as high as 4.75% of the strains (NTM or other acid-fast staining positive species) were misidentified as Mtb in Kashgar region based on smear result. The high prevalence of NTM or nocardia or gordonia among symptomatic participants found in this population-based study demonstrates that acid-fast positive bacteria other than Mtb has also been a public health problem in this region. As smears are commonly used to diagnose TB and to initiate treatment in Kashgar region, symptomatic NTM or nocardia or gordonia cases seeking care in health facilities could be misdiagnosed and then treated as TB cases. Without adequate clinical response, these cases may subsequently be misdiagnosed as TB treatment failures. Thus, false MTB-positives may in fact be clinical NTM or nocardia or gordonia and if the treatment outcomes do not improve, the patient may mistakenly be classified as DR-TB. Such patient misidentification may negatively impact the health status of the individual and consequently pose additional cost to the health system. Therefore, precise molecular-based species identification methods were urgently needed.

Due to the special geographical location, the MTBC in Kashgar prefecture owe special lineage composition. 1702 cases of MTBC clinical strains were composed of L1-L4 and La1. Lineage 2 was still dominant in Kashgar prefecture but its proportion was relatively low compared with other region of China (45.83% VS 73.9%) [19]. Lineage 3 in Kashgar prefecture accounted for 27.14%, much higher than that of other regions of China, as Kashgar prefecture borders Pakistan (Lineage 3 was dominant; 70.40%) [20], and the border crossing (Khunjerab Port) is located there. It is speculated that the frequent movement of people between the two places caused the spread of Lineage 3 strains to this region [21]. Furthermore, the transmission probability may increase with the growth of population migration, trade, and cultural exchange between the two countries. La1 (M. bovis) account for 0.24% of all lineage and its proportion was much higher than that of other region in China [19, 22], which may be due to the pastoral livelihood in Kashgar, where TB in cattle is prevalent in virtually all major livestock-producing region, for the zoonotic M. bovis TB was the most often transmitted strain to humans by the consumption of M. bovis–contaminated dairy products that caused lymphatic TB, eventually becoming pulmonary TB [23]. Different Mtb lineage is clinically, virulency, and radiologically diversity [24, 25], lineage accurate identification can be most efficiently by using WGS.

It is notable that the prevalence rates of drug resistance in Kashgar prefecture were very low compared with other region of China [26, 27], the prevalence of MDR tuberculosis was only 2.4%. Drug-resistant TB arises for two reasons: first, the selection of de novo resistance during treatment of the index case, and second, the transmission of drug-resistant TB. Kendall et al. have demonstrated current estimates of MDR-TB prevalence among TB notifications are most consistent with the hypothesis that over 80% of incident MDR-TB in present-day epidemic settings results from transmission of MDR-TB [28]. We speculate that the main reason for this situation was that all confirmed active TB patients were given inpatient isolation treatment until completely cured in Kashgar prefecture, and thus the incidence of drug-resistant tuberculosis was reduced due to inappropriate treatment as well as the transmission of drug-resistant tuberculosis, resulting in this measure being extended throughout the whole country. In addition, we find new anti-TB drugs (bedaquinoline and delamanid) and repurposed drugs (clofazimine and linezolidine) resistant rate was very low as well, which indicates application of a short-course drug-resistant tuberculosis treatment regimen containing new drugs in Kashgar prefecture is promising.

Although Kashgar has low drug resistance rate, it remains at risk of an epidemic of drug-resistant TB. This survey not only provides the prevalence of drug-resistant TB but also presents the MIC distributions of 13 drugs, we can find a significant proportion of strains had MIC values at ECOFF and sub-ECOFF resistance level, these sub-threshold elevations in MIC may nevertheless be clinically meaningful, as the combination of significant interpatient pharmacokinetic variability and elevated MICs predisposes Mtb strains to develop resistance, risking more treatment failure and worse patient outcomes [29]. In addition, due to the limitation of laboratory conditions and the lack of skilled personnel, drug sensitivity test cannot be carried out in the whole Kashgar prefecture to detect drug-resistant tuberculosis in time. The existing drug-resistant TB is prone to epidemics easily through transmission. Using WGS combined with quantitative MIC measurements, our results showed that WGS is highly accurate at predicting phenotypic drug resistance except isoniazid in Kashgar prefecture, in consideration of high concordance rates of isoniazid prediction demonstrated by previous studies [14], we speculate that there may be a new isoniazid resistance mechanism of Mtb isolates in Kashgar area, which needs further exploration. Mtb strains that are resistant to new and repurposed anti-TB agents are rare to date, especially in low TB incidence countries. Consequently, there are insufficient phenotypic DST data globally to confidently interpret mutations associated with resistance to these agents as evidenced by the current WHO catalogue of mutations for Mtb, and the accuracy of new and repurposed anti-TB drugs will also improve as data accumulates. Thus WGS with the ability to accurately predict drug resistance could give us a more accurate picture of local resistance profile and provide the basis for the formulation of individual patient precision therapeutic regimen. Moreover, the cost of WGS is already lower than phenotypic testing and is progressively decreasing. WGS is also easy to operate and is less instrument-dependent than phenotypic testing. For these reasons, it could be particularly valuable in settings with weak laboratory capacity and a shortage of skilled people.

Our study had some limitations. First, we did not perform drug susceptibility testing on isolated NTM, Nocardia and Gordonia, and thus we cannot acquire their drug resistance characteristics and compared them with local Mtb strains. Second, different resistant mutations can confer different resistant level, but due to the small number of resistant strains, we did not analyze the resistant level of different mutations. Additionally, due to incomplete information collection and low number of drug-resistant isolates, we did not perform drug-resistant related risk factor analysis.

In conclusion, accurate strain identification, lineage determination and drug resistance detection are very important for TB treatment and control. Our findings prove that WGS can have an important role in species and lineage identification, and drug resistant tuberculosis diagnosis, especially in consideration of the limitation of laboratory equipment conditions and the lack of skilled personnel in high TB-burden regions. Additionally, from this study, we have a baseline of tuberculosis drug-resistance mutations in the whole Kashgar prefecture. According to the change in drug-resistance trends in this region, the targeted strategies for drug-resistant TB can be formulated.

TB: tuberculosis

WGS: whole-genome sequencing

MTBC: Mycobacterium tuberculosis complex

ZN: Ziehl-Neelsen

ML: maximum likelihood

ORs: odds ratios

Cis: confidence intervals

NTM: non-tuberculous mycobacteria

Ethics approval and consent to participate

Approval to conduct this study was granted by the Tuberculosis Research Ethics Review Committee of the China CDC. Written informed consent was obtained from each participant.

Consent for publication

Not applicable

Availability of data and materials

All sequence data is deposited in China National Microbiology Data Center (NMDC) with accession numbers NMDC10018971.

Competing interests

All authors report no conflicts of interest relevant to this article.

Funding

This work was supported the National Key Research and Development Project (2022YFC2602200), the Natural Science Foundation of Shenzhen (JCYJ20210324130009024). The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of this report.

Authors' contributions

QW, DL and XW contributed to the study design, DL, GB, ME, MJ, GH, MX and YL collected the data and did the laboratory testing, DL do bioinformatics and data analysis, QW and DL did the data analysis and prepared the report for publication.

Acknowledgements

We thank members of the Working Group on National Tuberculosis Drug Resistance Surveillance. We also thank the tireless contributions of staff in the Kashgar CDC and Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention.

Zumla A, George A, Sharma V, Herbert RH, Oxley A, Oliver M. The WHO 2014 global tuberculosis report--further to go. Lancet Glob Health 2015; 3:e10-12.
Tusun D, Abulimiti M, Mamuti X, Liu Z, Xu D, Li G et al. The Epidemiological Characteristics of Pulmonary Tuberculosis - Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China, 2011-2020. China CDC Wkly 2021; 3:557-561.
Gelaw B, Shiferaw Y, Alemayehu M, Bashaw AA. Comparison of loop-mediated isothermal amplification assay and smear microscopy with culture for the diagnostic accuracy of tuberculosis. BMC Infect Dis 2017; 17:79.
Cirillo DM, Miotto P, Tortoli E. Evolution of Phenotypic and Molecular Drug Susceptibility Testing. Adv Exp Med Biol 2017; 1019:221-246.
Borodulina EA, Piskun VV, Uraksina MV, Shubina AT. Molecular genetic tests GeneXpert MTB/RIF and Xpert MTB/RIF (Ultra) in the diagnosis of tuberculosis (review of literature). Klin Lab Diagn 2022; 67:544-549.
Clarke C, Kerr TJ, Warren RM, Kleynhans L, Miller MA, Goosen WJ. Identification and Characterisation of Nontuberculous Mycobacteria in African Buffaloes (Syncerus caffer), South Africa. Microorganisms 2022; 10.
Riojas MA, Frank AM, Greenfield SR, King SP, Meehan CJ, Strong M et al. Identification and Characterization of Mycobacterial Species Using Whole-Genome Sequences. Methods Mol Biol 2021; 2314:399-457.
Quan TP, Bawa Z, Foster D, Walker T, Del Ojo Elias C, Rathod P et al. Evaluation of Whole-Genome Sequencing for Mycobacterial Species Identification and Drug Susceptibility Testing in a Clinical Setting: a Large-Scale Prospective Assessment of Performance against Line Probe Assays and Phenotyping. J Clin Microbiol 2018; 56.
Ismail NA, Mvusi L, Nanoo A, Dreyer A, Omar SV, Babatunde S et al. Prevalence of drug-resistant tuberculosis and imputed burden in South Africa: a national and sub-national cross-sectional survey. Lancet Infect Dis 2018; 18:779-787.
Zhao Y, Xu S, Wang L, Chin DP, Wang S, Jiang G et al. National survey of drug-resistant tuberculosis in China. N Engl J Med 2012; 366:2161-2170.
Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis. Eur Respir J 2022; 60.
Khieu V, Ananta P, Kaewprasert O, Laohaviroj M, Namwat W, Faksri K. Whole-Genome Sequencing Analysis to Identify Infection with Multiple Species of Nontuberculous Mycobacteria. Pathogens 2021; 10.
Sobkowiak B, Glynn JR, Houben R, Mallard K, Phelan JE, Guerra-Assunção JA et al. Identifying mixed Mycobacterium tuberculosis infections from whole genome sequence data. BMC Genomics 2018; 19:613.
Liu D, Huang F, Zhang G, He W, Ou X, He P et al. Whole-genome sequencing for surveillance of tuberculosis drug resistance and determination of resistance level in China. Clin Microbiol Infect 2022; 28:731.e739-731.e715.
Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 2019; 35:4453-4455.
Phelan JE, O'Sullivan DM, Machado D, Ramos J, Oppong YEA, Campino S et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med 2019; 11:41.
Lee YM, Kim MJ, Kim YJ. Increasing Trend of Nontuberculous Mycobacteria Isolation in a Referral Clinical Laboratory in South Korea. Medicina (Kaunas) 2021; 57.
Alizadeh SA, Javadi A, Mardaneh J, Nasirian N, Alizadeh S, Mohammadbeigi M et al. Development of a SYBR Green Multiplex Real Time PCR for Simultaneous Detection of Mycobacterium Tuberculosis and Nocardia Asteroides in Respiratory Samples. Ethiop J Health Sci 2021; 31:241-246.
Chen H, He L, Cai C, Liu J, Jia J, Ma L et al. Characteristics of distribution of Mycobacterium tuberculosis lineages in China. Sci China Life Sci 2018; 61:651-659.
Jabbar A, Phelan JE, de Sessions PF, Khan TA, Rahman H, Khan SN et al. Whole genome sequencing of drug resistant Mycobacterium tuberculosis isolates from a high burden tuberculosis region of North West Pakistan. Sci Rep 2019; 9:14996.
Xu AM, He CJ, Cheng X, Abuduaini A, Tuerxun Z, Sha YZ et al. Distribution and identification of Mycobacterium tuberculosis lineage in Kashgar prefecture. BMC Infect Dis 2022; 22:312.
Chen H, He L, Huang H, Shi C, Ni X, Dai G et al. Mycobacterium tuberculosis Lineage Distribution in Xinjiang and Gansu Provinces, China. Sci Rep 2017; 7:1068.
Taye H, Alemu K, Mihret A, Wood JLN, Shkedy Z, Berg S et al. Global prevalence of Mycobacterium bovis infections among human tuberculosis cases: Systematic review and meta-analysis. Zoonoses Public Health 2021; 68:704-718.
Du DH, Geskus RB, Zhao Y, Codecasa LR, Cirillo DM, van Crevel R et al. The effect of M. tuberculosis lineage on clinical phenotype. medRxiv 2023.
Tram TTB, Nhung HN, Vijay S, Hai HT, Thu DDA, Ha VTN et al. Virulence of Mycobacterium tuberculosis Clinical Isolates Is Associated With Sputum Pre-treatment Bacterial Load, Lineage, Survival in Macrophages, and Cytokine Response. Front Cell Infect Microbiol 2018; 8:417.
Zhang H, Yang J, Zhang Z, Hu K, Wu P, Zhang H et al. Patterns and trends of primary drug-resistant tuberculosis in Chongqing, China, from 2012 to 2020. Medicine (Baltimore) 2023; 102:e33230.
Yun W, Huijuan C, Long L, Xiaolong L, Aihua Z. Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020. BMC Infect Dis 2022; 22:525.
Kendall EA, Fofana MO, Dowdy DW. Burden of transmitted multidrug resistance in epidemics of tuberculosis: a transmission modelling analysis. Lancet Respir Med 2015; 3:963-972.
McCallum AD, Sloan DJ. The importance of clinical pharmacokinetic-pharmacodynamic studies in unraveling the determinants of early and late tuberculosis outcomes. Int J Pharmacokinet 2017; 2:195-212.

No competing interests reported.

SupplementaryFigure1.jpg
Supplementary Figure 1 Geographical location and counties distribution of Kashgar
SupplementaryFigure2.jpg
Supplementary Figure 2 Composition of acid-fast positive strains in Kashgar Prefecture

Download PDF

Editorial decision: Revision requested
25 Jun, 2024
Editor assigned by journal
25 Jun, 2024
Submission checks completed at journal
25 Jun, 2024
First submitted to journal
22 Jun, 2024

You are reading this latest preprint version

Whole genome sequencing for tuberculosis disease species identification, lineage determination, and drug resistance detection in Kashgar prefecture, China

Status:

Version 1

Abstract

Figures

Introduction

Methods

Sample source and sample collection

Specimen processing and sequencing

Drug susceptibility testing

Whole genome sequencing and bioinformatics analysis

Statistical analysis

Results

Strain data description

Drug-resistant profile

Prediction efficiency of WGS

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1