| Literature DB >> 35313727 |
Yuxin Fan1,2,3, Jingjing Chen1,4, Meixiao Liu1,2, Xin Xu1,2, Yu Zhang1,2,3, Peng Yue1,4, Wenjing Cao1,4, Zhenhua Ji1,2, Xuan Su1,2, Shiyuan Wen1,2,3, Jing Kong1,2, Guozhong Zhou1,2, Bingxue Li1,2, Yan Dong1,2, Aihua Liu1,3,4, Fukai Bao1,2,3.
Abstract
Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (MTB) infection, which has seriously endangered human health for many years. With the emergence of multidrug-resistant and extensively drug-resistant MTB, the prevention and treatment of TB has become a pressing need. Early diagnosis, drug resistance monitoring, and control of disease transmission are critical aspects in the prevention and treatment of TB. However, the currently available diagnostic technologies and drug sensitivity tests are time consuming, and thus, it is difficult to achieve the goal of early diagnosis and detection drug sensitivity, which results in limited control of disease transmission. The development of molecular testing technology has gradually achieved the vision of rapid and accurate diagnosis of TB. Droplet digital PCR (ddPCR) is an excellent nucleic acid quantification method with high sensitivity and no need for a calibration curve. Herein, we review the application of ddPCR in TB diagnosis and drug resistance detection and transmission monitoring.Entities:
Keywords: Mycobacterium tuberculosis; diagnosis; disease transmission; droplet digital PCR; drug resistance test; tuberculosis
Year: 2022 PMID: 35313727 PMCID: PMC8934166 DOI: 10.2147/IDR.S349607
Source DB: PubMed Journal: Infect Drug Resist ISSN: 1178-6973 Impact factor: 4.003
Figure 1Principle and workflow of ddPCR.
Summary of Studies Exploring the Use of ddPCR in Mycobacterium tuberculosis and Mycobacterium leprae Infections
| Year | Author | Target | Primer and Probe | Instruments |
|---|---|---|---|---|
| 2015 | Devonshire et al | 16S rRNA | Forward 1: 5′-GGGATGCATGTCTTGTGGTG-3′ | Bio-RadQX100TM Droplet Digital PCR System |
| rpoB | Forward 1: 5′-CAAAACAGCCGCTAGTCCTAGTC-3′ | |||
| 2016 | Devonshire et al. | 16S rRNA | Forward: 5′-GTGATCTGCCCTGCACTTC-3′ | Bio-RadQX100TM droplet digital PCR System |
| rpoB | Forward: 5′-CAAAACAGCCGCTAGTCCTAGTC-3′ | |||
| 2016 | Ushio et al. | IS6110 | Forward: 5′-GGCGTACTCGACCTGAAAGA-3′ | BioRadQX200TM |
| gyrB | ||||
| 2017 | Patterson et al. | RD9 | Forward: 5′-TGAGTGGCGATGGTCAACAC-3′ | BioRadQX200TM |
| 2017 | Yang et al. | IS6110 | Forward: 5′-ACCGAAGAATCC GCTGAGAT-3′ | BioRadQX200TM |
| 2018 | Yamamoto et al | IS6110 | Forward: 5′-GGCGTACTCGACCTGAAAGA-3′ | BioRadQX200TM |
| gyrB | Forward: 5′-AAGGACCGCAAGCTACTGAA-3′ | |||
| 2018 | Song et al. | CFP10 | Forward: 5-AAGCAGCCAATAAGCAGAAGC-3′ | Bio-RadQX100TMDroplet Digital PCR System |
| Rv1768 | Forward: 5′-CGGCAACAGATTTGGCGAACA-3′ | |||
| 2019 | Cheng et al. | RLEP | Forward: 5′-GCAGCAGTATCGTGTTAGTGAA-3′ | BioRadQX200TM |
| groEL | Forward: 5′-GCCGGGTGCAGCAGTATC-3′ | |||
| 2019 | Luo et al. | IS6110 | Forward: 5′-GACCTGAAAGACGTTATCC-3′ | BioRadQX200TM |
| 2020 | Nyaruaba et al. | IS6110 | Forward: 5′-AGCGCCGCTTCGGACCACCAG-3′ | BioRadQX200TM |
| IS1081 | Forward: 5′-CAGCCCGACGCCGAATCAGTTGTT-3′ | |||
| 2020 | Cho et al. | IS6110 | Forward: 5′-GGCGTACTCGACCTGAAAGA-3′ | BioRadQX200TM |
| gyrB | Forward: 5′-AAGGACCGCAAGCTACTGAA-3′ | |||
| 2021 | Belay et al. | IS6110 | Forward: 5′-AGAAGGCGTACTCGACCTGA-3′ | BioRadQX200TM |
| rpoB | Forward: 5′-CAAAACAGCCGCTAGTCCTAGTC-3′ |