article · F1000Research
Drug-resistant tuberculosis remains a significant challenge in high-burden settings, where molecular diagnostic tests typically evaluate resistance markers individually. This retrospective study examined 2,430 culture-positive Mycobacterium tuberculosis samples collected in South Africa between 2021 and 2024 to determine whether cycle threshold marker profiles form connected resistance networks. Six targets were evaluated using correlation statistics, association-rule mining, and network modelling. All six markers were co-detected in 95.8 percent of specimens, with strong correlations recorded between gyrA variants and between inhA and rrs. The resulting network showed that resistance outcomes form coordinated architectures rather than isolated events, with second-line resistance displaying the highest centrality and injectable drugs forming a tightly connected module. These outcomes indicate that network-based analyses can enhance the interpretation of routinely collected tuberculosis surveillance data.
Standard tuberculosis testing often views drug resistance mutations in isolation. Demonstrating that resistance markers operate within coordinated networks allows public health programmes to gain richer insights from routine molecular diagnostics. This approach provides a clearer picture of complex resistance patterns across populations without necessarily requiring new, expensive laboratory equipment.
This methodology could inform the creation of algorithmic surveillance software and diagnostic decision-support tools for public health agencies and pathology laboratories. Because the study relies on retrospective data and explicitly notes the need for validation against sequence-confirmed and longitudinal datasets, the technology is at an early research stage and requires further verification before practical deployment.
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Background Drug-resistant tuberculosis remains a major public health challenge, particularly in high-burden settings where rapid characterization of resistance patterns is important for surveillance. Molecular diagnostics routinely detect resistance-associated targets, but these are commonly evaluated individually. This study investigated whether cycle threshold-derived molecular marker-detection profiles form reproducible resistance-associated networks among culture-positive Mycobacterium tuberculosis specimens from the Eastern Cape, South Africa. Methods A retrospective laboratory-based molecular epidemiology study analysed 2,430 unique culture-positive M. tuberculosis specimens tested between January 2021 and December 2024. Binary detection indicators were generated for inhA , katG , gyrA1 , gyrA2 , gyrA3 , and rrs. Marker co-detection and resistance relationships were examined using phi correlation, association-rule mining, resistance co-occurrence analysis, and integrated network modelling with centrality metrics. Results Simultaneous detection of all six markers occurred in 95.8% (2,327/2,430) of specimens. Strong correlations were observed between gyrA1–gyrA2 (φ = 0.84), gyrA1–gyrA3 (φ = 0.84), gyrA2–gyrA3 (φ = 0.81), and inhA–rrs (φ = 1.00). The integrated network comprised 17 nodes and 88 statistically supported edges, with a density of 0.647. Second-line resistance had the highest degree and betweenness centrality, while amikacin, capreomycin, kanamycin, and composite injectable resistance formed a tightly interconnected module. Association-rule analysis identified recurrent multidimensional marker combinations associated with injectable resistance. Conclusions Cycle threshold-derived molecular markers and resistance outcomes formed interconnected co-detection and co-occurrence architectures rather than isolated patterns. Network analysis identified coordinated isoniazid/ethionamide, fluoroquinolone, injectable, and second-line resistance modules. These findings demonstrate the potential utility of network-based approaches for interpreting routinely generated molecular tuberculosis surveillance data, though validation with sequence-confirmed and longitudinal data is required.
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DOI: 10.12688/f1000research.188203.1
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