Genome sequencing of Mycobacterium tuberculosis clinical isolates revealed isoniazid resistance mechanisms undetected by conventional molecular methods

Transmission of drug-resistant Mycobacterium tuberculosis isolates between Finnish- and foreign-born cases, 2014-2021: A molecular epidemiological study

BACKGROUND

Data on the molecular epidemiology and transmission of drug-resistant Mycobacterium tuberculosis (MTB) in low-incidence settings with immigration from high-incidence settings is limited.

METHOD

We included 115 drug-resistant (DR) MTB isolates with whole-genome sequencing data isolated in Finland between 2014 and 2021. Potential transmission clusters were identified using a threshold of 12 single-nucleotide polymorphisms (SNPs). Highly related clusters were identified using a threshold of 5 SNPs.

RESULT

Of the 115 DR MTB isolates, 31 (27.0%) isolates were from Finnish-born cases and 84 (73.0%) were from foreign-born cases. The proportion of multidrug-resistant (MDR) MTB isolates (30/84, 35.7%) from foreign-born cases was higher than that of MDR MTB isolates from Finnish-born cases (8/31, 25.8%). Lineage 2 (40/115, 34.8%) and lineage 4 (40/115, 34.8%) were the most prevalent lineages. A total of 25 (21.7%) isolates were classified into eight potential transmission clusters (≤12 SNPs). Furthermore, five highly related clusters (≤5 SNPs) were identified, including three DR MTB isolates from Finnish-born cases and 14 DR isolates from foreign-born cases.

CONCLUSION

The risk of DR MTB transmission between Finnish- and foreign-born persons is not negligible. Further research on clustering analysis in drug-susceptible MTB is worth to inform tuberculosis management and control in low-incidence settings with increasing immigration.

Clinical Whole-Genome Sequencing Assay for Rapid Mycobacterium tuberculosis Complex First-Line Drug Susceptibility Testing and Phylogenetic Relatedness Analysis

The global rise of drug resistant tuberculosis has highlighted the need for improved diagnostic technologies that provide rapid and reliable drug resistance results. Here, we develop and validate a whole genome sequencing (WGS)-based test for identification of mycobacterium tuberculosis complex (MTB) drug resistance to rifampin, isoniazid, pyrazinamide, ethambutol, and streptomycin. Through comparative analysis of drug resistance results from WGS-based testing and phenotypic drug susceptibility testing (DST) of 38 clinical MTB isolates from patients receiving care in Los Angeles, CA, we found an overall concordance between methods of 97.4% with equivalent performance across culture media. Critically, prospective analysis of 11 isolates showed that WGS-based testing provides results an average of 36 days faster than phenotypic culture-based methods. We showcase the additional benefits of WGS data by investigating a suspected laboratory contamination event and using phylogenetic analysis to search for cryptic local transmission, finding no evidence of community spread amongst our patient population in the past six years. WGS-based testing for MTB drug resistance has the potential to greatly improve diagnosis of drug resistant MTB by accelerating turnaround time while maintaining accuracy and providing additional benefits for infection control, lab safety, and public health applications.

Nanomotion technology in combination with machine learning: a new approach for a rapid antibiotic susceptibility test for Mycobacterium tuberculosis

Nanomotion technology is a growth-independent approach that can be used to detect and record the vibrations of bacteria attached to cantilevers. We have developed a nanomotion-based antibiotic susceptibility test (AST) protocol for Mycobacterium tuberculosis (MTB). The protocol was used to predict strain phenotype towards isoniazid (INH) and rifampicin (RIF) using a leave-one-out cross-validation (LOOCV) and machine learning techniques. This MTB-nanomotion protocol takes 21 h, including cell suspension preparation, optimized bacterial attachment to functionalized cantilever, and nanomotion recording before and after antibiotic exposure. We applied this protocol to MTB isolates (n = 40) and were able to discriminate between susceptible and resistant strains for INH and RIF with a maximum sensitivity of 97.4% and 100%, respectively, and a maximum specificity of 100% for both antibiotics when considering each nanomotion recording to be a distinct experiment. Grouping recordings as triplicates based on source isolate improved sensitivity and specificity to 100% for both antibiotics. Nanomotion technology can potentially reduce time-to-result significantly compared to the days and weeks currently needed for current phenotypic ASTs for MTB. It can further be extended to other anti-TB drugs to help guide more effective TB treatment.