The scale of the global problem provides the backdrop. According to the World Health Organization, approximately 10.8 million people developed tuberculosis and 1.25 million died from the disease in 2023, and drug-resistant strains continue to undermine progress toward the End TB Strategy. Rifampicin-resistant tuberculosis (RR-TB), multidrug-resistant tuberculosis (MDR-TB), pre-extensively drug-resistant tuberculosis (pre-XDR-TB), and extensively drug-resistant tuberculosis (XDR-TB) all demand complex, prolonged treatment and are associated with higher costs, worse clinical outcomes, and ongoing transmission. Rapid and accurate identification of resistance is therefore central to controlling the epidemic, yet in rural settings access to molecular testing can be constrained by laboratory capacity, shortages of trained personnel, specimen transport logistics, and infrastructure limitations.

The Eastern Cape illustrates these constraints vividly. Its communities are geographically dispersed, and specialized diagnostic services may be far away. Molecular technologies such as Xpert MTB/RIF Ultra and line probe assays have improved TB detection nationally, but the researchers argue that decentralized, point-of-care approaches could complement existing laboratory-based services where centralized testing falls short. Their study, led by Ncomeka Sineke and colleagues including senior author Lindiwe Modest Faye, set out to answer a deceptively simple question: if you could build only a small, affordable diagnostic panel for this region, which resistance genes should it target first?

To answer it, the team conducted a retrospective secondary analysis of routinely collected programme data drawn from the Electronic Drug-Resistant Tuberculosis Register (EDRWeb), facility treatment registers, district reports, and laboratory records. The dataset covered patients with bacteriologically confirmed DR-TB who initiated treatment between January 2020 and December 2024. Resistance categories followed established WHO definitions: RR-TB for resistance to rifampicin with or without other drugs, MDR-TB for resistance to at least rifampicin and isoniazid, pre-XDR-TB for MDR/RR-TB with additional fluoroquinolone resistance, and XDR-TB for MDR/RR-TB that is also resistant to bedaquiline or linezolid.

The headline finding was the overwhelming dominance of first-line drug resistance. RR-TB accounted for 191 of the 402 cases (47.5 percent) and MDR-TB for 178 cases (44.3 percent), meaning these two categories together made up 91.8 percent of the DR-TB burden. Pre-XDR-TB represented just 23 cases (5.7 percent) and XDR-TB only 10 cases (2.5 percent). This distribution mattered enormously for the prioritization exercise, because it suggested that a diagnostic capable of detecting rifampicin and isoniazid resistance would address the vast majority of locally relevant resistance, while fluoroquinolone and second-line resistance detection could follow in later iterations.

The researchers formalized this reasoning in a conceptual tool they call the Diagnostic Priority Index (DPI), which weighs five domains: disease burden, frequency of resistance categories, treatment outcome vulnerability, programme impact, and potential suitability for decentralized implementation. Resistance categories accounting for at least 40 percent of the caseload were deemed high epidemiological priority, those between 5 and 39 percent medium priority, and those below 5 percent candidates for future expansion. The authors are careful to note that these thresholds were pragmatic, study-specific decision boundaries rather than statistically validated cutoffs. Applying the framework, the genes rpoB, katG, and inhA emerged as high-priority targets, because mutations in rpoB drive most rifampicin resistance while katG and the inhA promoter region are the principal determinants of isoniazid resistance. The medium-priority tier comprised gyrA, gyrB, rrs, and eis, which are associated with fluoroquinolone and injectable drug resistance, while embB, pncA, Rv0678, atpE, and pepQ were flagged for future expansion.

The surveillance data also revealed a troubling trend in treatment outcomes. Among patients with MDR-TB, treatment success declined from 80.9 percent in 2021 to 38.5 percent in 2024, while the proportion of outcomes classified as not evaluated rose from zero to 30.8 percent over the same period. Treatment success among RR-TB patients fell from 79.5 percent to 51.6 percent, with unevaluated outcomes climbing to 25.8 percent. The researchers interpret these patterns as signals of programme vulnerability, but they are emphatic that the data do not permit causal attribution to diagnostic limitations. DR-TB outcomes are shaped by many factors, including HIV status, treatment regimens, adherence, loss to follow-up, comorbidities, socioeconomic circumstances, and healthcare access, none of which could be assessed from the aggregated records.

The second half of the framework is where the study earns its molecular-biophotonic label, though the authors stress that this component is entirely conceptual. They mapped the prioritized genes onto candidate optical biosensing technologies drawn from the published literature: surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), fluorescence-based biosensing, surface-enhanced Raman spectroscopy (SERS), photonic crystal biosensors, and integrated optical waveguide biosensors. These approaches exploit optical phenomena to detect nucleic acids or biomolecules, and published studies suggest some offer advantages in analytical sensitivity, multiplexing capability, miniaturization, and potential portability. In the conceptual compatibility assessment, SPR, LSPR, and fluorescence-based platforms appeared compatible with all three high-priority targets, while SERS and photonic crystal sensors could accommodate high- and medium-priority targets, and waveguide biosensors could theoretically span the full panel.

The limitations of the study are as important as its contributions, and the authors enumerate them with unusual candor. No isolate-level mutation analysis or sequencing was performed, so the study cannot report the actual frequency of specific mutations in the population; the gene priorities reflect the distribution of resistance categories combined with established resistance mechanisms from the literature. No biosensor was fabricated or tested, and no analytical sensitivity, specificity, limit of detection, reproducibility, diagnostic accuracy, or clinical feasibility was evaluated. The DPI thresholds were exploratory rather than externally validated, and the two-facility setting may limit generalizability to regions with different resistance patterns. The authors also acknowledge that the dataset lacked bacterial load, diagnostic turnaround times, detailed phenotypic susceptibility results, and patient-level clinical characteristics.

What the study offers, then, is a hypothesis-generating translational pathway rather than a finished technology. The researchers recommend that future work begin with mutation-level characterization of the local resistance spectrum using targeted or whole-genome sequencing, followed by laboratory development and analytical validation of candidate assays starting with rpoB, katG, and inhA, and then prospective clinical validation against reference methods such as Xpert MTB/RIF Ultra, line probe assays, and phenotypic drug-susceptibility testing. Implementation studies would ultimately need to assess cost, workflow integration, quality assurance, acceptability, and compatibility with national TB programmes before any claims about faster diagnosis or better outcomes could be made. For now, the value lies in the demonstration that routine surveillance data, often collected merely for programme monitoring, can be repurposed to steer early-stage diagnostic research toward the targets that matter most in the communities where the burden is heaviest.

Subject of Research: Epidemiology-guided prioritization of molecular targets for drug-resistant tuberculosis diagnostics in rural Eastern Cape, South Africa

Article Title: Translating drug-resistant tuberculosis surveillance into molecular–biophotonic diagnostic innovation in rural Eastern Cape, South Africa

Article References: Sineke, N., Sabisa, K., Dlatu, N., Ombinda-Lemboumba, S., Apalata, T., & Faye, L. M. (2026). Translating drug-resistant tuberculosis surveillance into molecular–biophotonic diagnostic innovation in rural Eastern Cape, South Africa. Frontiers in Tuberculosis, 4, Article 1937755. https://doi.org/10.3389/ftubr.2026.1937755

Image Credits: AI Generated

DOI: 10.3389/ftubr.2026.1937755

Keywords: drug-resistant tuberculosis, molecular diagnostics, biophotonics, point-of-care testing, South Africa, Eastern Cape, surveillance, rpoB, katG, inhA, biosensors, MDR-TB

The scale of the investigation reflects the difficulty of working with organisms that are ubiquitous in soil and water. The researchers analyzed 193 clinical samples drawn from five distinct sources: 101 fecal samples, 21 nasal swabs, 13 trans-tracheal washes, 30 tissue specimens, and 28 blood samples. Each sample was processed through two parallel routes, direct polymerase chain reaction and culture-based isolation, a dual strategy designed to capture both organisms present in the sample at the time of collection and those capable of growing under laboratory conditions. Decontamination steps, essential when dealing with samples teeming with faster-growing environmental flora, were carefully applied to give the slow-growing mycobacteria a chance to emerge on culture media. The approach mirrors established diagnostic protocols for mycobacterial recovery from clinical material, where the balance between eliminating contaminants and preserving viable target organisms is notoriously delicate.

Identification proceeded in a tiered molecular fashion. Genus-level confirmation relied on PCR targeting the hsp65 gene, a workhorse marker in mycobacterial taxonomy that encodes a heat shock protein and allows discrimination of mycobacteria from other acid-fast organisms. Once the isolates were confirmed as members of the genus Mycobacterium and distinguished from the Mycobacterium tuberculosis complex, species-specific PCR assays were deployed to pin down exactly which non-tuberculous species had been recovered. This hierarchical approach, from broad genus screening to species-level resolution, is critical because the clinical and epidemiological significance of non-tuberculous mycobacteria varies enormously from one species to the next. In total, the workflow yielded eighteen confirmed non-tuberculous mycobacterial isolates, a modest but workable collection for downstream genetic analysis.

The species picture that emerged was strikingly lopsided. Mycobacterium vaccae accounted for two-thirds of the isolates, representing 66.7 percent of the confirmed collection. This organism, first described decades ago and long studied for its immunomodulatory properties, is a common inhabitant of soil, water, and dust, and its dominance in bovine samples underscores how readily environmental mycobacteria colonize or transit through livestock. The remaining isolates comprised other non-tuberculous species, though M. vaccae clearly set the tone of the collection. For veterinary diagnosticians, this predominance matters: when a bovine sample yields acid-fast organisms or tuberculosis-like lesions, the probability that the culprit is an environmental species rather than Mycobacterium bovis has direct consequences for how the finding is interpreted within national tuberculosis control frameworks.

With the isolates in hand, the team turned to the central question of the study: what resistance-associated genes do these bovine non-tuberculous mycobacteria carry? Four genes were selected for screening, each with a well-characterized role in drug resistance in Mycobacterium tuberculosis. The rpoB gene, encoding the beta subunit of RNA polymerase, is the principal target of rifampicin, and mutations within its resistance-determining region are the classic molecular signature of rifampicin resistance. The katG gene encodes catalase-peroxidase, the enzyme that activates the prodrug isoniazid, and disruptions in katG are strongly associated with high-level isoniazid resistance. The rpsL gene, encoding ribosomal protein S12, is implicated in streptomycin resistance, while gyrA, encoding the A subunit of DNA gyrase, harbors mutations in its quinolone resistance-determining region that confer fluoroquinolone resistance. Screening these four targets across the eighteen isolates provided a snapshot of the genetic resistance landscape in bovine non-tuberculous mycobacteria from the region.

The screening results revealed a clear hierarchy. The rpoB gene was the most frequently detected, present in 88.9 percent of the isolates, and its detection was mainly associated with Mycobacterium vaccae. The katG gene turned up in 16.7 percent of isolates, and the gyrA gene in just 5.6 percent, and notably both of these were found exclusively in M. vaccae. The rpsL gene, by contrast, was not amplified in any isolate, suggesting that streptomycin-associated genetic determinants, at least as targeted by the primers used, are absent from this collection. The pattern indicates that within this bovine sample set, genetic elements associated with resistance cluster heavily in one environmental species, raising questions about whether M. vaccae’s ecological lifestyle, including prolonged exposure to antimicrobial residues in agricultural environments, might shape its genetic repertoire.

Sequencing of selected amplicons added an important layer of nuance. When the researchers examined the hotspots of rpoB and katG, the regions where resistance-conferring mutations most commonly occur in Mycobacterium tuberculosis, they found no mutations at all. This is a meaningful negative result: the mere presence of a gene does not imply the presence of resistance-conferring variants within it. However, the story differed for gyrA. Sequence analysis of the quinolone resistance-determining region of gyrA revealed non-synonymous substitutions, meaning nucleotide changes that alter the encoded amino acid sequence of DNA gyrase. Whether these substitutions affect fluoroquinolone susceptibility in these isolates remains an open question, but their presence in the canonical resistance region of the enzyme is exactly the kind of finding that warrants closer phenotypic scrutiny.

The authors are careful, and rightly so, about what these results do and do not demonstrate. The detection of resistance-associated genes and even of amino acid substitutions reflects genotypic variation only. Because drug susceptibility testing was not performed on the isolates, the study cannot confirm phenotypic resistance to rifampicin, isoniazid, streptomycin, or fluoroquinolones. This distinction is fundamental in mycobacteriology. Genes such as rpoB and katG are essential housekeeping and metabolic genes present in virtually all mycobacteria; their detection by PCR says nothing about function unless the specific mutational hotspots are altered. Moreover, resistance mechanisms in non-tuberculous mycobacteria differ in important ways from those in the tuberculosis complex, involving intrinsic factors such as impermeable cell walls, efflux pumps, and species-specific enzymatic machinery, which means that extrapolating interpretive criteria from M. tuberculosis to environmental species is fraught with uncertainty.

Nevertheless, the study carries practical implications for animal health and public health alike. Non-tuberculous mycobacteria can produce tuberculosis-like lesions in bovines, and such lesions can trigger false-positive reactions in tuberculin skin testing, the backbone of bovine tuberculosis surveillance programs. Every misattributed lesion represents a potential misdiagnosis, with consequences ranging from unnecessary slaughter to missed transmission of true Mycobacterium bovis infection. By documenting which non-tuberculous species circulate in Indian cattle and buffaloes, and by beginning to map their resistance gene content, the researchers are building the species-level baseline that surveillance programs need to distinguish environmental noise from genuine tuberculosis signals. The finding that M. vaccae dominates the isolates gives diagnosticians a concrete expectation about what they are most likely to encounter when non-tuberculous organisms appear in bovine material.

The road forward, as the authors indicate, runs through phenotypic and species-specific analyses. Establishing the functional significance of the gyrA substitutions observed in M. vaccae will require correlating genotypes with measured minimum inhibitory concentrations of fluoroquinolones, and ideally with functional studies of the mutant gyrase enzymes. Broader panels of resistance genes, and ultimately whole-genome sequencing, could reveal the full complement of intrinsic and acquired resistance determinants in these organisms, an approach increasingly advocated in the literature on non-tuberculous mycobacterial susceptibility prediction. For now, the study stands as a careful first census: eighteen isolates, one dominant species, one frequently detected resistance-associated gene without hotspot mutations, and a handful of amino acid changes in a quinolone target that demand follow-up. In a world where antimicrobial resistance knows no boundaries between human clinics, farms, and soil, even the humblest environmental mycobacterium from a cow’s gut deserves that kind of attention.

Subject of Research: Molecular identification of non-tuberculous mycobacteria from bovine clinical samples and profiling of resistance-associated genes

Article Title: Molecular identification and resistance-associated gene profiling of non-tuberculous mycobacteria isolated from bovine clinical samples

Article References: Panigrahi, B., Narang, D., Chandra, M., Narang, A., Gupta, K., & Singh, S. T. (2026). Molecular identification and resistance-associated gene profiling of non-tuberculous mycobacteria isolated from bovine clinical samples. Molecular Biology Reports, 53(1), Article 1640. https://doi.org/10.1007/s11033-026-12814-7

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12814-7

Keywords: non-tuberculous mycobacteria, Mycobacterium vaccae, bovine tuberculosis, rpoB, katG, gyrA, antimicrobial resistance, PCR, cattle, diagnostics, genotypic variation, India