Prevalence and Molecular Characteristics Based on Whole Genome Sequencing of Mycobacterium tuberculosis Resistant to Four Anti-Tuberculosis Drugs from Southern Xinjiang, China

Anwaierjiang, Aiketaguli; Wang, Quan; Liu, Haican; Yin, Chunjie; Xu, Mingqiang; Li, Machao; Liu, Mengwen; Liu, Yan; Zhao, Xiuqin; Liu, Jinbao; Li, Guilian; Mijiti, Xiaokaiti; Wan, Kanglin

doi:10.2147/idr.s320024

Cited by 9 publications

(10 citation statements)

References 52 publications

(82 reference statements)

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“… 28 The katG Ser140Asn and ahpC −54C>T mutations were not associated with isoniazid resistance in our study; however, they were found in isoniazid-resistant strains in Anwaierjiang et al’s study. 29 The sample size of our study was small, meaning that it could not fully confirm the function of many mutation sites. Prothionamide, an analog of ethionamide, is an orally available second-line agent frequently incorporated into regimens for MDR-TB.…”

Section: Discussionmentioning

confidence: 92%

“…In a recent study, rpoB Leu430Pro was found in rifampicin-resistant strains. 29 This mutation may be related to the activity of the strain. Studies have shown that rpoB expression is upregulated in XDR-TB strains that are exposed to rifampicin.…”

Section: Discussionmentioning

confidence: 99%

“…In recent studies, embB Gly406Ser was found in ethambutol-resistant Mtb strains. 29 , 33 The mutations embB Ala356Val and Ser380Asn are rare, and their functions need to be verified via additional investigation.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of Factors Influencing Multidrug-Resistant Tuberculosis and Validation of Whole-Genome Sequencing in Children with Drug-Resistant Tuberculosis

Zhang¹,

Zhao²,

Zhang³

et al. 2021

IDR

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Objective Pediatric tuberculosis (TB) is one of the top ten causes of death in children. Our study was to analyze influencing factors of multidrug-resistant tuberculosis (MDR-TB) and validation of whole-genome sequencing (WGS) used in children with drug-resistant TB (DR-TB). Methods All Mycobacterium tuberculosis (Mtb) strains were isolated from patients aged below 18 years old of Children’s Hospital of Chongqing Medical University, China. A total of 208 Mtb isolates were tested for eight anti-TB drugs with phenotypic drug susceptibility test (DST) and for genetic prediction of the susceptible profile with WGS. The patients corresponding to each strain were grouped according to drug resistance and genotype. Influencing factors of MDR-TB and DR-TB were analyzed. Results According to the phenotypic DST and WGS, 82.2% of Mtb strains were susceptible to all eight drugs, and 6.3% were MDR-TB. Using the phenotypic DSTs as the gold standard, the kappa value of WGS to predict isoniazid, rifampin, ethambutol, rifapentine, prothionamide, levofloxacin, moxifloxacin and amikacin was 0.84, 0.89, 0.59, 0.86, 0.89, 0.82, 0.88 and 1.00, respectively. There was significant difference in the distribution of severe TB, diagnosis, treatment and outcome between MDR and drug-susceptible group (P<0.05). The distribution of severe TB and treatment between DR and drug-susceptible group was statistically different (P<0.05). The results of binary logistic regression showed that Calmette–Guérin bacillus (BCG) vaccine is the protective factor for MDR-TB (OR=0.19), and MDR-TB is the risk factor for PTB and EPTB (OR=17.98). Conclusion The BCG vaccine is a protective factor for MDR-TB, and MDR-TB might not be confined to pulmonary infection, spreading to extrapulmonary organs in children. MDR-TB had more severe cases and a lower recovery rate than drug-susceptible TB. WGS could provide an accurate prediction of drug susceptibility test results for anti-TB drugs, which are needed for the diagnosis and precise treatment of TB in children.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Analysis of Factors Influencing Multidrug-Resistant Tuberculosis and Validation of Whole-Genome Sequencing in Children with Drug-Resistant Tuberculosis

Zhang¹,

Zhao²,

Zhang³

et al. 2021

IDR

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“…Genomic DNA of M. tuberculosis isolates was extracted using the CTAB method for sequencing as previously described. 23 , 24 DNA libraries were constructed with genomic DNA using kits provided by Illumina according to the manufacturer’s instructions. The average sequencing depth of the genome is 124.8, and the coverage is 99.3%.…”

Section: Methodsmentioning

confidence: 99%

Whole-Genome Sequencing and Epidemiological Investigation of Tuberculosis Outbreaks in High Schools in Hunan, China

Xu¹,

Liu²,

Liu³

et al. 2022

IDR

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Background Tuberculosis (TB) seriously threatens individual and public health. Recently, TB outbreaks in schools have been reported more frequently in China and have attracted widespread attention. We reported three TB outbreaks in high schools in Hunan Province, China. Methods When a tuberculosis patient was reported in a school, we carried out field epidemiological investigations, including tuberculin skin testing (TST), chest X-ray (CXR) and laboratory test for all close contacts, and whole-genome sequencing (WGS) analyses to understand the transmission patterns, the causes and the risk factors for the outbreaks, thereby providing a foundation for the control of TB epidemics in schools. Results A total of 49 students with TB patients were identified in the three schools where TB outbreaks occurred, including nine patients in School A, 14 patients in School B, and 26 patients in School C. In Schools A, B and C, the putative attack rates in the classes of the index case were 13.8% (8/58), 7.6% (5/66), and 40.4% (21/52), while the putative attack rates of expanding screening in the school were 0.3% (1/361), 0.2% (9/3955), and 0.2% (5/2080), respectively. Thirteen patients had patient delay, with a median delay interval of 69 days (IQR 30.5–113 days). Twelve patients had a healthcare diagnostic delay with a median delay interval of 32 days (IQR 24–82 days). Phylogenetic analysis of culture-positive patients revealed that most of them shared a small genetic distance (≤12 SNPs), with three separate genetic clusters (including one MDR-TB genomic cluster), indicating the recent transmission of Mycobacterium tuberculosis strains. Conclusion This combination of field investigation and WGS analysis revealed the transmission of three TB outbreaks in schools. Reinforced implementation is needed to improve timely case finding and reduce diagnosis delay in routine TB control in the school population.

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“…It may also be affected by differences in host immunity and external living environment, resulting in diversity in the distribution of M.tb lineages and sub-lineages in different geographical regions [ 10 – 12 ]. At present, M.tb lineages in the Kashgar prefecture and the Xinjiang region are primarily detected by traditional typing methods; however, there is insufficient research on M.tb lineage in terms of whole-genome SNP [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Distribution and identification of Mycobacterium tuberculosis lineage in Kashgar prefecture

Cheng

et al. 2022

BMC Infect Dis

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Objectives Kashgar prefecture is an important transportation and trade hub with a high incidence of tuberculosis. The following study analyzed the composition and differences in Mycobacterium tuberculosis (M.tb) lineage and specific tags to distinguish the lineage of the M.tb in Kashgar prefecture, thus providing a basis for the classification and diagnosis of tuberculosis in this area. Methods Whole-genome sequencing (WGS) of 161 M.tb clinical strains was performed. The phylogenetic tree was constructed using Maximum Likelihood (ML) based on single nucleotide polymorphisms (SNPs) and verified through principal component analysis (PCA). The composition structure of M.tb in different regions was analyzed by combining geographic information. Results M.tb clinical strains were composed of lineage 2 (73/161, 45.34%), lineage 3 (52/161, 32.30%) and lineage 4 (36/161, 22.36%). Moreover, the 3 lineages were subdivided into 11 sublineages, among which lineage 2 included lineage 2.2.2/Asia Ancestral 1 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 2 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 3 (18/73, 24.66%), and lineage 2.2.1-Modern Beijing (39/73, 53.42%). Lineage 3 included lineage 3.2 (14/52, 26.92%) and lineage 3.3 (38/52, 73.08%), while lineage 4 included lineage 4.1 (3/36, 8.33%), lineage 4.2 (2/36, 5.66%), lineage 4.4.2 (1/36, 2.78%), lineage 4.5 (28/36, 77.78%) and lineage 4.8 (2/36, 5.66%), all of which were consistent with the PCA results. One hundred thirty-six markers were proposed for discriminating known circulating strains. Reconstruction of a phylogenetic tree using the 136 SNPs resulted in a tree with the same number of delineated clades. Based on geographical location analysis, the composition of Lineage 2 in Kashgar prefecture (45.34%) was lower compared to other regions in China (54.35%-90.27%), while the composition of Lineage 3 (32.30%) was much higher than in other regions of China (0.92%-2.01%), but lower compared to the bordering Pakistan (70.40%). Conclusion Three lineages were identified in M.tb clinical strains from Kashgar prefecture, with 136 branch-specific SNP. Kashgar borders with countries that have a high incidence of tuberculosis, such as Pakistan and India, which results in a large difference between the M.tb lineage and sublineage distribution in this region and other provinces of China.

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Prevalence and Molecular Characteristics Based on Whole Genome Sequencing of Mycobacterium tuberculosis Resistant to Four Anti-Tuberculosis Drugs from Southern Xinjiang, China

Cited by 9 publications

References 52 publications

Analysis of Factors Influencing Multidrug-Resistant Tuberculosis and Validation of Whole-Genome Sequencing in Children with Drug-Resistant Tuberculosis

Analysis of Factors Influencing Multidrug-Resistant Tuberculosis and Validation of Whole-Genome Sequencing in Children with Drug-Resistant Tuberculosis

Whole-Genome Sequencing and Epidemiological Investigation of Tuberculosis Outbreaks in High Schools in Hunan, China

Distribution and identification of Mycobacterium tuberculosis lineage in Kashgar prefecture

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