Pathways and strategies followed in the genomic epidemiology of Mycobacterium tuberculosis

Tuberculosis of the spine

Pott's spine, commonly known as spinal tuberculosis (TB), is an extrapulmonary form of TB caused by Mycobacterium TB. Pott's paraplegia occurs when the spine is involved. Spinal TB is usually caused by the hematogenous spread of infection from a central focus, which can be in the lungs or another location. Spinal TB is distinguished by intervertebral disc involvement caused by the same segmental arterial supply, which can result in severe morbidity even after years of approved therapy. Neurological impairments and spine deformities are caused by progressive damage to the anterior vertebral body. The clinical, radiographic, microbiological, and histological data are used to make the diagnosis of spinal TB. In Pott's spine, combination multidrug antitubercular therapy is the basis of treatment. The recent appearance of multidrug-resistant/extremely drug-resistant TB and the growth of human immunodeficiency virus infection have presented significant challenges in the battle against TB infection. Patients who come with significant kyphosis or neurological impairments are the only ones who require surgical care. Debridement, fusion stabilization, and correction of spinal deformity are the cornerstones of surgical treatment. Clinical results for the treatment of spinal TB are generally quite good with adequate and prompt care.

Bridging the TB data gap: in silico extraction of rifampicin-resistant tuberculosis diagnostic test results from whole genome sequence data

Background

Mycobacterium tuberculosis rapid diagnostic tests (RDTs) are widely employed in routine laboratories and national surveys for detection of rifampicin-resistant (RR)-TB. However, as next-generation sequencing technologies have become more commonplace in research and surveillance programs, RDTs are being increasingly complemented by whole genome sequencing (WGS). While comparison between RDTs is difficult, all RDT results can be derived from WGS data. This can facilitate continuous analysis of RR-TB burden regardless of the data generation technology employed. By converting WGS to RDT results, we enable comparison of data with different formats and sources particularly for low- and middle-income high TB-burden countries that employ different diagnostic algorithms for drug resistance surveys. This allows national TB control programs (NTPs) and epidemiologists to utilize all available data in the setting for improved RR-TB surveillance.

Methods

We developed the Python-based MycTB Genome to Test (MTBGT) tool that transforms WGS-derived data into laboratory-validated results of the primary RDTs-Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and GenoscholarNTM+MDRTB II. The tool was validated through RDT results of RR-TB strains with diverse resistance patterns and geographic origins and applied on routine-derived WGS data.

Results

The MTBGT tool correctly transformed the single nucleotide polymorphism (SNP) data into the RDT results and generated tabulated frequencies of the RDT probes as well as rifampicin-susceptible cases. The tool supplemented the RDT probe reactions output with the RR-conferring mutation based on identified SNPs. The MTBGT tool facilitated continuous analysis of RR-TB and Xpert probe reactions from different platforms and collection periods in Rwanda.

Conclusion

Overall, the MTBGT tool allows low- and middle-income countries to make sense of the increasingly generated WGS in light of the readily available RDT results, and assess whether currently implemented RDTs adequately detect RR-TB in their setting. With its feature to transform WGS to RDT results and facilitate continuous RR-TB data analysis, the MTBGT tool may bridge the gap between and among data from periodic surveys, continuous surveillance, research, and routine tests, and may be integrated within the national information system for use by the NTP and epidemiologists to improve setting-specific RR-TB control. The MTBGT source code and accompanying documentation are available at https://github.com/KamelaNg/MTBGT.