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Life sciences · Journal article
F1000research · September 7, 2026
Early or partial results. Treat as a signal, not a conclusion.
This retrospective analysis of 2,430 M. tuberculosis specimens demonstrates that resistance-associated molecular markers co-occur in reproducible networks rather than in isolation, with second-line resistance showing the highest centrality. The study identifies potential value in network-based interpretation of routine molecular surveillance data, but validation against sequence-confirmed genotypes and prospective clinical outcomes remains necessary.
Retrospective laboratory-based molecular epidemiology study. Culture-positive Mycobacterium tuberculosis specimens from rural Eastern Cape, South Africa.. Intervention: Molecular marker detection (inhA, katG, gyrA1, gyrA2, gyrA3, rrs) via cycle-threshold-derived binary indicators. n = 2,430. Rural Eastern Cape, South Africa.
Simultaneous detection of all six markers (inhA, katG, gyrA1, gyrA2, gyrA3, rrs) occurred in 95.8% (2,327/2,430) of specimens Strong phi correlations observed: gyrA1–gyrA2 φ = 0.84, gyrA1–gyrA3 φ = 0.84, gyrA2–gyrA3 φ = 0.81, inhA–rrs φ = 1.00 Integrated network comprised 17 nodes and 88 statistically supported edges with density of 0.647
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Clinicians and laboratory directors should recognize that this descriptive analysis proposes a network-based framework for interpreting resistance markers from routine molecular testing, but the approach requires validation against genotypic and phenotypic gold standards before adoption into clinical decision-making or surveillance protocols.
Retrospective, single-centre molecular epidemiology study using cycle-threshold derived markers to map resistance networks; demonstrates methodology and patterns but lacks clinical outcomes, sequence validation, and prospective confirmation.
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Clinicians and laboratory directors should recognize that this descriptive analysis proposes a network-based framework for interpreting resistance markers from routine molecular testing, but the approach requires validation against genotypic and phenotypic gold standards before adoption into clinical decision-making or surveillance protocols.
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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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