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

The logic behind the study begins with the pharmacology of the starting material. Isoniazid is a prodrug that must be activated by the bacterial KatG enzyme before it can inhibit enoyl-acyl carrier protein reductase, or InhA, the enzyme responsible for building the mycolic acids that form the waxy protective coat of the tuberculosis bacillus. Mutations in KatG are a principal route by which the bacterium escapes the drug. Schiff bases, compounds defined by an azomethine linkage of the form C=N, have long attracted medicinal chemists because they are easy to synthesize, interact strongly with biological targets, and display a wide range of pharmacological activities. By attaching substituted aromatic aldehydes to the hydrazide portion of isoniazid, the researchers hoped to create molecules that could engage InhA directly and retain activity even where the classical activation pathway fails.

The team designed a focused virtual library of 30 novel isoniazid-based Schiff base derivatives by condensing isoniazid with structurally diverse substituted aromatic aldehydes. The aldehydes were chosen to span both electron-withdrawing and electron-donating substituents in different positional orientations, allowing a preliminary structure-activity relationship assessment of how these electronic changes influence lipophilicity and predicted interactions with the InhA active site. Structures were drawn in ChemDraw, converted into three-dimensional conformations, and geometry-optimized with the MMFF94 force field. Before any synthesis was attempted, the entire library was subjected to a sequential computational funnel designed to eliminate weak candidates early and concentrate laboratory effort on the most promising molecules.

That funnel is the methodological heart of the study. First, drug-likeness and pharmacokinetic properties were estimated with the SwissADME and pkCSM web servers, evaluating molecular weight, lipophilicity as LogP, hydrogen bond donors and acceptors, and topological polar surface area against Lipinski’s rule of five. Of the 30 designed compounds, 13 passed this filter and advanced. Next, toxicity prediction using the ProTox web server trimmed the field to 9 compounds with acceptable safety profiles. The Prediction of Activity Spectra for Substances, or PASS, analysis then estimated the probability of antitubercular activity, and 5 compounds emerged with favorable predicted activity. This progressive reduction, from 30 to 13 to 9 to 5, illustrates how an integrated in silico workflow can spare chemists the expense of synthesizing and testing compounds that are unlikely to survive downstream development.

The five survivors, designated SLB-1, SLB-3, SLB-5, SLB-7, and SLB-10, were then docked into the active site of InhA using AutoDock Vina. The crystal structure of the enzyme from M. tuberculosis, retrieved from the Protein Data Bank under accession 6EP8, was prepared by removing water molecules and co-crystallized ligands, adding polar hydrogens, and assigning Kollman charges. A 20 by 20 by 20 angstrom grid box centered on the active site defined the search space, and identical docking parameters were applied to all ligands, the reference drug isoniazid, and the co-crystallized ligand to ensure a fair comparison. The five Schiff base derivatives achieved binding affinities ranging from minus 8.7 to minus 9.5 kcal per mole, substantially more favorable than the minus 5.8 kcal per mole score recorded for isoniazid under the same conditions.

SLB-5, subsequently identified as N’,N”’-((5-nitro-1,3-phenylene)bis(methaneylylidene))di(isonicotinohydrazide), topped the ranking with a binding affinity of minus 9.5 kcal per mole. Interaction analysis revealed hydrogen bonds, hydrophobic contacts, and pi-pi stacking with key active-site residues including SER94, LYS165, and PHE149. The docking score also compared favorably with several known InhA inhibitors reported in the literature, including luteolin and piperine at minus 9.1 kcal per mole and tiliacorinine at minus 8.3 kcal per mole. A database and literature search across PubChem, Reaxys, and related resources found no previously reported identical structure, supporting the novelty of the compound and justifying its selection for synthesis. The authors are careful to note, however, that molecular dynamics simulations and RMSD-based redocking validation were not performed, leaving formal computational validation of the predicted binding modes for future work.

Synthesis of SLB-5 followed a classic Schiff base condensation. A substituted nitrobenzene-1,3-dicarbaldehyde was dissolved in ethanol, and a warm ethanolic solution of isoniazid was added dropwise with a few drops of glacial acetic acid as catalyst. The mixture was refluxed at 70 to 80 degrees Celsius for three to four hours, with reaction progress tracked by thin-layer chromatography. Cooling the reaction mixture produced a precipitate that was filtered, washed with cold ethanol, and dried. Structural characterization then confirmed the transformation: the FT-IR spectrum displayed a prominent azomethine C=N stretching band in the 1600 to 1650 per centimeter region alongside nitro group absorptions near 1550 and 1350 per centimeter, while the disappearance of the aldehydic C=O band confirmed consumption of the starting aldehyde.

Proton nuclear magnetic resonance spectroscopy provided further confirmation. A singlet at 8.80 parts per million corresponded to the azomethine proton, the signature of successful Schiff base formation, and a downfield singlet at 12.17 parts per million was assigned to the amide NH of the hydrazide moiety. Aromatic and pyridyl protons appeared between 7.59 and 8.68 parts per million, their deshielding consistent with the electron-withdrawing nitro and azomethine groups. Crucially, the absence of aldehydic proton signals around 9.5 to 10.0 parts per million verified complete conversion. High-resolution mass spectrometry sealed the case, showing a molecular ion at m/z 417.1722 and a characteristic fragment at m/z 371.1828 corresponding to loss of the nitro group, along with a stable base peak at m/z 285.1381 arising from cleavage of one pyridine carbohydrazide unit and the azomethine linkage.

The decisive test came in the biology. Using the Alamar Blue assay against M. tuberculosis H37Rv, SLB-5 demonstrated concentration-dependent growth inhibition, reaching 17.68, 35.48, 49.47, 60.16, and 75.14 percent inhibition at concentrations of 20, 40, 60, 80, and 100 micrograms per milliliter respectively. Isoniazid tested under identical conditions produced 16.27, 25.85, 43.95, 54.94, and 65.63 percent inhibition across the same range. At every concentration, the Schiff base derivative outperformed the standard drug, and at the highest dose the margin was nearly ten percentage points. Although complete inhibition was not achieved, the consistent superiority over a first-line agent suggests that Schiff base modification can genuinely enhance the antitubercular potential of the isoniazid scaffold rather than merely decorate it computationally.

The authors are appropriately measured about what these results do and do not prove. Enzyme inhibition assays and mechanistic studies are still needed to experimentally confirm InhA as the molecular target of SLB-5, since the docking evidence, however encouraging, remains predictive. Expanded testing against drug-sensitive and drug-resistant clinical strains, additional structural characterization by carbon-13 NMR, synthesis and evaluation of other high-ranking library members, and eventually in vivo studies of pharmacokinetics, bioavailability, and safety all lie ahead. Yet the study stands as a tidy demonstration of a workflow that many laboratories are now embracing: design broadly, filter computationally through drug-likeness, ADMET, activity prediction, and docking, and synthesize only the compound that survives every gate. In an era when multidrug-resistant and extensively drug-resistant tuberculosis continue to spread, that kind of efficiency is not just elegant chemistry; it is a practical strategy for getting new antitubercular candidates to the bench, and eventually perhaps to the clinic, faster than the bacterium can adapt.

Subject of Research: Computer-aided design and biological evaluation of isoniazid-based Schiff base derivatives as antitubercular agents targeting InhA

Article Title: Design and in silico screening of novel isoniazid-based Schiff base derivatives followed by synthesis and biological evaluation of the lead compound against Mycobacterium tuberculosis H37Rv

Article References: Bhuse, S. L., Patil, P. M., & Durbule, R. R. (2026). Design and in silico screening of novel isoniazid-based Schiff base derivatives followed by synthesis and biological evaluation of the lead compound against Mycobacterium tuberculosis H37Rv. Discover Chemistry, 3(1), Article 516. https://doi.org/10.1007/s44371-026-00909-w

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00909-w

Keywords: tuberculosis, isoniazid, Schiff bases, molecular docking, InhA, drug resistance, ADMET, virtual screening, medicinal chemistry, H37Rv, Alamar Blue assay, lead compound