Determination of Clinical Characteristics of Mycobacterium kansasii-Derived Species by Reanalysis of Isolates Formerly Reported as M. kansasii

Prevalence of Mycobacterium kansasii in clinical and environmental isolates, a systematic review and meta-analysis

Background

Mycobacterium kansasii infection is one of the most common causes of non-tuberculosis mycobacterial (NTM) disease worldwide. However, accurate information on the global prevalence of this bacterium is lacking. Therefore, this study was conducted to investigate the prevalence of M. kansasii in clinical and environmental isolates.

Methods

Databases, including PubMed, Scopus, and the Web of Science, were utilized to gather articles on the prevalence of M. kansasii in clinical and environmental isolates. The collected data were analyzed using Comprehensive Meta-Analysis software.

Results

A total of 118 and 16 studies met the inclusion criteria and were used to analyze the prevalence of M. kansasii in clinical and environmental isolates, respectively. The prevalence of M. kansasii in NTM and environmental isolates were 9.4 and 5.8%, respectively. Subsequent analysis showed an increasing prevalence of M. kansasii over the years. Additionally, the results indicated a significant difference in the prevalence of this bacteria among different regions.

Conclusion

The relatively high prevalence of M. kansasii among NTM isolates suggests the need for further implementation of infection control strategies. It is also important to establish appropriate diagnostic criteria and management guidelines for screening this microorganism in environmental samples in order to prevent its spread, given its high prevalence in environmental isolates.

Prevalence of antibiotic resistance in clinical isolates of Mycobacterium kansasii: a systematic review and meta-analysis

INTRODUCTION

The prevalence of diseases caused by non-tuberculous mycobacteria (NTM), including M. kansasii, is increasing, necessitating further information to guide prevention, control, and treatment strategies.

AREAS COVERED

A comprehensive analysis of articles published until February 2023 was conducted on PubMed, Web of Science, and Scopus databases to investigate antibiotic resistance in M. kansasii species. Stata software version 17 was employed for all analyses.

EXPERT OPINION

A total of 1647 articles were obtained through database search. After removing duplicates and unrelated studies, 17 cross-sectional studies that examined the breakpoints proposed by CLSI were included. The rates of resistance of M. kansasii to various antibiotics were as follows: clarithromycin (0%), rifampin (1%), amikacin (0%), ciprofloxacin (14%), linezolid (0%), moxifloxacin (0%), rifabutin (1%), doxycycline (96%), and SXT (49%). Our findings underscore the importance of managing and monitoring the use of these antibiotics, as well as the need for further studies to elucidate the exact mechanism of M. kansasii resistance to these antibiotics.

SnackNTM: An Open-Source Software for Sanger Sequencing-based Identification of Nontuberculous Mycobacterial Species

Background

Sequence-based identification is one of the most effective methods for species-level identification of nontuberculous mycobacteria (NTM). However, it is time-consuming because of the bioinformatics processes involved, including sequence trimming, consensus sequence generation, and public database searches. We developed a simple and fully automated software that enabled species-level identification of NTM from trace files, SnackNTM (https//github.com/Young-gonKim/SnackNTM).

Methods

JAVA programing language was used for software development. The SnackNTM diagnostic algorithm utilized 16S rRNA gene sequences, according to the Clinical & Laboratory Standards Institute guidelines, and an rpoB gene region was adjunctively utilized to narrow down the species. The software performance was validated using trace files of 234 clinical cases, comprising 217 consecutive cases and 17 additionally selected cases of unique species.

Results

SnackNTM could analyze multiple cases at once, and all the bioinformatics processes required for sequence-based NTM identification were automatically performed with a single mouse click. SnackNTM successfully identified 95.9% (208/217) of consecutive clinical cases, and the results showed 99.0% (206/208) agreement with manual classification results. SnackNTM successfully identified all 17 cases of unique species. In a processing time comparison test, the analysis and reporting of 30 cases, which took 150 minutes manually, took only 40 minutes with SnackNTM.

Conclusions

SnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.