BMC infectious diseases · 2025
Whole genome sequencing for tuberculosis disease species identification, lineage determination, and drug resistance detection in Kashgar prefecture, China.
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Liu Dongxin, Badeerhan Gulina, Emam Mawlanjan, Jiang Mengnan, Hong Geng, Xie Mengjiao, Liu Yang, Ma Ling, Xu Lin, Wang Xijiang, Wei Qiang
Аннотация
BACKGROUND: We aimed to use whole genome sequencing (WGS) to determine species and lineage composition and drug resistance profile in a high tuberculosis (TB)-burden region of China.
METHODS: We conducted WGS to 1791 acid-fast staining positive and culture-positive isolates collected from Kashgar prefecture in 2020. Bioinformatic analysis was applied to confirm species, lineage and drug resistant-related mutations. The drug susceptibility testing was performed on confirmed Mycobacterium tuberculosis complex (MTBC) isolates. We determined the accuracy of WGS prediction by comparing with phenotypes.
RESULTS: 95.03% (1702/1791) were identified MTBC, 3.18% (57/1791) were nontuberculous mycobacteria (NTM), 0.61% (11/1791) were nocardia, 0.89% (16/1791) were gordonia and 0.056% (1/1791) were rhodococcus, the rest 4 isolations 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, while 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 resistance TB of Kashgar prefecture is at low level and the application of WGS may prevent the increase of resistance rate.
METHODS: We conducted WGS to 1791 acid-fast staining positive and culture-positive isolates collected from Kashgar prefecture in 2020. Bioinformatic analysis was applied to confirm species, lineage and drug resistant-related mutations. The drug susceptibility testing was performed on confirmed Mycobacterium tuberculosis complex (MTBC) isolates. We determined the accuracy of WGS prediction by comparing with phenotypes.
RESULTS: 95.03% (1702/1791) were identified MTBC, 3.18% (57/1791) were nontuberculous mycobacteria (NTM), 0.61% (11/1791) were nocardia, 0.89% (16/1791) were gordonia and 0.056% (1/1791) were rhodococcus, the rest 4 isolations 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, while 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 resistance TB of Kashgar prefecture is at low level and the application of WGS may prevent the increase of resistance rate.