The COMBAT-TB Workbench: Making Powerful Mycobacterium tuberculosis Bioinformatics Accessible

Heusden, Peter van; Mashologu, Ziphozakhe; Lose, Thoba; Warren, Robin M.; Christoffels, Alan

doi:10.1128/msphere.00991-21

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Reads of maximum 200 base pairs were produced. Bioinformatic sequence data analysis was performed using tools on the Galaxy web platform at https://usegalaxy.org public server [28], and the developed pipeline was based on the one proposed by the Galaxy community hub [29]. First, quality control of the obtained sequences was performed.…”

Section: Wgs and Bioinformatic Data Processingmentioning

confidence: 99%

Advantages of analysing both pairwise SNV-distance and differing SNVs between Mycobacterium tuberculosis isolates for recurrent tuberculosis cause determination

et al. 2023

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Endogenous reactivation and exogenous reinfection are two possible causes of recurrent tuberculosis (TB). However, in some cases, precise cause determination can be challenging. In this study, we used whole genome sequencing to determine pairwise SNV distances and detect differing SNVs in initial and subsequent isolates for recurrent TB cases when the first and second episodes were caused by Mycobacterium tuberculosis (Mtb) strains with an identical spoligotype pattern. In total, 104 Mtb isolates from 36 recurrent TB and 16 single TB episode patients were included in the study. Most isolate pairs belonged to the SIT1 (n=21), SIT42 (n=9), SIT53 (n=9), and SIT254 (n=7) spoligotypes, and in 27 cases, resistance to at least one anti-TB drug was found in either isolate. Drug susceptibility was more common in the recurrent TB patient cohort, and longitudinal single TB episode isolates were more prone to be drug-resistant (p=0.03), while the association between patient cohort and spoligotype was not statistically significant (p=0.07). The pairwise SNV-distance between the longitudinal single TB episode isolates was small (0-7 SNVs). Among the recurrent TB isolates, based on the high SNV-distance (38–273 SNVs), six reinfection cases (16.7%) were identified. This distance was small (<10 SNVs) in the remaining 30 isolate pairs. Further analysis of differing SNVs revealed that 22 (61.1%) cases could be classified as possible reactivation. Notably, despite the small distance of 2–7 SNVs, initial isolates of eight patients (22.2%) had several SNVs that were not found in the second isolates; therefore, these cases were classified as reinfection with a closely related Mtb strain. No statistically significant difference in the time interval between specimen collection in the reactivation and reinfection Mtb sample groups (p=0.13) or an association between recurrence cause and drug resistance status (p=0.62) or spoligotype (p=0.79) could be detected. The mycobacterial median mutation rate of longitudinal single TB episodes and possible reactivation isolate pairs (n=37) was 0.12 SNVs/genome/year (IQR 0-0.39), and in 18 cases (48.6%), it was equal to zero. No statistically significant differences in mutation rate were found between recurrent TB and longitudinal single TB episode isolates (p=0.087), drug-susceptible and resistant isolates (p=0.37) or isolates of Beijing and other genotype families (p=0.33). Furthermore, four cases of fluoroquinolone resistance development through the acquired SNVs in the gyrA gene were identified. To conclude, this study highlighted the complexity of recurrent episode cause determination and showed the usefulness of differing SNV identification in both Mtb isolates in such cases. Expected drug susceptibility was the only discriminative factor for recurrent TB episode-causing mycobacterial strains, while no differences between reactivation and reinfection sample groups could be identified.

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Section: Wgs and Bioinformatic Data Processingmentioning

confidence: 99%

Advantages of analysing both pairwise SNV-distance and differing SNVs between Mycobacterium tuberculosis isolates for recurrent tuberculosis cause determination

et al. 2023

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show abstract

“…15,[19][20][21][22] is tailored for public health use, but few funding opportunities are available globally to develop methods or algorithms for the public health setting. Global initiatives such as the PHA4GE have attempted to break this implementation barrier by providing resources to catalyze collaborations in public health laboratories.…”

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confidence: 99%