Xiaoying Wang1, Haican Liu2, Jianhao Wei3, Xiaocui Wu4, Qin Yu5, Xiuqin Zhao2, Jianxin Lyu6, Yongliang Lou6, Kanglin Wan7. 1. Department of Pathophysiology, West China School of Preclinical and Forrensic Medicine, Sichuan University, China; State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China. 2. State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China. 3. State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China; School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou 325035, China; Department of Clinical Laboratory, Shanghai Public Health Clinical Center, Shanghai 201508, China. 4. State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China; School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou 325035, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Medical School, Tongji University, Shanghai, China. 5. State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China; Tuberculosis Clinic, Chaoyang Distric Center for Disease Control and Prevention, Chaoyang Distric, Beijing, China. 6. School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou 325035, China. 7. State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China; School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou 325035, China. Electronic address: wankanglin@icdc.cn.
Abstract
OBJECTIVES: Mixed infections of Mycobacterium tuberculosis strains have attracted more attention due to their increasing frequencies worldwide, especially in the areas of high tuberculosis (TB) prevalence. In this study, we accessed the rates of mixed infections in a setting with high TB prevalence in Inner Mongolia Autonomous Region of China. METHODS: A total of 384 M. tuberculosis isolates from the local TB hospital were subjected to mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing method. The single clones of the strains with mixed infections were separated by subculturing them on the Löwenstein-Jensen medium. RESULTS: Of these 384 isolates, twelve strains (3.13%) were identified as mixed infections by MIRU-VNTR. Statistical analysis indicated that demographic characteristics and drug susceptibility profiles showed no statistically significant association with the mixed infections. We further subcultured the mixed infection strains and selected 30 clones from the subculture for each mixed infection. Genotyping data revealed that eight (8/12, 66.7%) strains with mixed infections had converted into single infection through subculture. The higher growth rate was associated with the increasing proportion of variant subpopulation through subculture. CONCLUSIONS: In conclusion, by using the MIRU-VNTR method, we demonstrate that the prevalence of mixed infections in Inner Mongolia is low. Additionally, our findings reveal that the subculture changes the population structures of mixed infections, and the subpopulation with higher growth rate show better fitness, which is associated with high proportion among the population structure after subculture. This study highlights that the use of clinical specimens, rather than subcultured isolates, is preferred to estimate the prevalence of mixed infections in the specific regions.
OBJECTIVES: Mixed infections of Mycobacterium tuberculosis strains have attracted more attention due to their increasing frequencies worldwide, especially in the areas of high tuberculosis (TB) prevalence. In this study, we accessed the rates of mixed infections in a setting with high TB prevalence in Inner Mongolia Autonomous Region of China. METHODS: A total of 384 M. tuberculosis isolates from the local TB hospital were subjected to mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing method. The single clones of the strains with mixed infections were separated by subculturing them on the Löwenstein-Jensen medium. RESULTS: Of these 384 isolates, twelve strains (3.13%) were identified as mixed infections by MIRU-VNTR. Statistical analysis indicated that demographic characteristics and drug susceptibility profiles showed no statistically significant association with the mixed infections. We further subcultured the mixed infection strains and selected 30 clones from the subculture for each mixed infection. Genotyping data revealed that eight (8/12, 66.7%) strains with mixed infections had converted into single infection through subculture. The higher growth rate was associated with the increasing proportion of variant subpopulation through subculture. CONCLUSIONS: In conclusion, by using the MIRU-VNTR method, we demonstrate that the prevalence of mixed infections in Inner Mongolia is low. Additionally, our findings reveal that the subculture changes the population structures of mixed infections, and the subpopulation with higher growth rate show better fitness, which is associated with high proportion among the population structure after subculture. This study highlights that the use of clinical specimens, rather than subcultured isolates, is preferred to estimate the prevalence of mixed infections in the specific regions.
Authors: David H Wyllie; Esther Robinson; Tim Peto; Derrick W Crook; Adebisi Ajileye; Priti Rathod; Rosemarie Allen; Lisa Jarrett; E Grace Smith; A Sarah Walker Journal: J Clin Microbiol Date: 2018-10-25 Impact factor: 5.948