The study addressed two distinct but connected forms of drug resistance. Pretreatment drug resistance, abbreviated PDR, refers to resistance mutations detected in people who have never yet received antiretroviral therapy, whether acquired at the moment of infection from a partner carrying resistant virus or developed during earlier, sometimes unrecorded, exposure to antiretroviral drugs. Acquired drug resistance, or ADR, by contrast, arises in people receiving treatment, typically when the virus replicates in the presence of suboptimal drug pressure, allowing mutations that compromise drug efficacy to accumulate. Both forms threaten the long-term effectiveness of first-line therapy regimens, and their relative prevalence in a given region shapes treatment policy.

To quantify these phenomena, the team collected blood samples from two groups: individuals experiencing antiretroviral therapy failure between 2023 and 2024, and treatment-naive individuals diagnosed in 2024. From these samples, the researchers used nested reverse transcription polymerase chain reaction to amplify two critical regions of the HIV-1 pol gene, which encodes the viral protease and reverse transcriptase enzymes. These enzymes are the principal targets of the most widely used antiretroviral drug classes, so mutations in the corresponding gene segments translate directly into clinical resistance. The purified amplicons were then sequenced by the Sanger method, and the resulting mutation profiles were interpreted against the Stanford HIV Drug Resistance Database, a widely used reference tool that scores which mutations confer resistance to which drugs.

The results revealed a striking asymmetry between the two populations. Among 341 treatment-naive individuals, the prevalence of pretreatment drug resistance was 10.85 percent, meaning 37 people carried at least one resistance-associated mutation before ever starting therapy. Resistance to non-nucleoside reverse transcriptase inhibitors, or NNRTIs, dominated this picture, accounting for 7.62 percent of the cohort. The specific mutations most frequently observed were at positions E138, found in 7.33 percent of samples, V179 at 4.99 percent, and K103 at 4.40 percent. Resistance to protease inhibitors stood at 2.93 percent, while resistance to nucleoside reverse transcriptase inhibitors, or NRTIs, was comparatively rare at 0.88 percent. This pattern matters clinically because NNRTIs remain components of standard first-line regimens in many settings, and elevated pretreatment resistance to this class can undermine treatment from the outset.

Among the 743 individuals who had experienced treatment failure, the picture was considerably more severe. Acquired drug resistance was detected in 41.45 percent of these samples, with NNRTI resistance again the most prevalent category at 37.55 percent. The mutation landscape mirrored that seen in the naive group: K103 mutations appeared in 24.76 percent of samples, E138 in 11.04 percent, and V179 in 10.77 percent. NRTI resistance affected 17.23 percent of the treatment-failure cohort, with the M184 mutation, which compromises the widely used drugs lamivudine and emtricitabine, predominant at 15.61 percent. Protease inhibitor resistance remained relatively uncommon at 2.56 percent. The overlap in dominant mutations between the two groups suggested to the researchers that resistant viruses generated during failed therapy may be passing into new infections rather than arising independently in each newly diagnosed person.

To test that hypothesis, the team turned to molecular transmission network analysis, a technique that compares viral genetic sequences across individuals and links those whose viruses are sufficiently similar, typically within a small genetic distance threshold, into clusters that represent recent or ongoing chains of transmission. The analysis identified CRF08_BC, a circulating recombinant form that combines subtype C and subtype B ancestry and is well established in parts of western China, as the predominant HIV-1 subtype in Qujing and as the major genetic background underlying local transmission clusters. Within these clusters, the E138 and K103 resistance mutations appeared frequently, indicating that NNRTI-resistant viruses were embedded in the very networks driving new infections.

The network analysis also yielded insights into who is most likely to be drawn into transmission clusters. Among treatment-naive individuals, those aged 50 years or older had markedly higher odds of being included in a network, with an adjusted odds ratio of 7.499. HIV-1 subtype and drug resistance status were also independently associated with network membership. This finding points to older adults as a population in which clustered, and potentially rapid, transmission is occurring, a demographic that public health programs have not always prioritized for intensive prevention efforts. The association between resistance status and network inclusion further suggests that resistant viruses are not isolated curiosities but active participants in the local epidemic.

Perhaps the most consequential finding emerged when the researchers constructed a combined network including both treatment-naive and treatment-failure individuals. Of the 16 pretreatment drug resistance cases that fell within the network, 13, or 81.25 percent, were located in mixed clusters that also contained individuals with acquired drug resistance. This co-clustering provides strong molecular evidence that resistance generated under the selective pressure of failing therapy is being transmitted onward to newly infected people, rather than each resistant case in a newly diagnosed individual arising de novo. In other words, the treatment clinic and the transmission network are connected: unmanaged virological failure today becomes pretreatment resistance in tomorrow’s newly diagnosed patients.

Bayesian phylogenetic analysis reinforced this conclusion. By modeling the evolutionary relationships and estimated timing of viral lineages, the analysis supported sustained local circulation of CRF08_BC drug-resistant strains within Qujing itself, rather than repeated introductions of resistant viruses from elsewhere. This distinction is epidemiologically important. Sustained local circulation means that resistant lineages have taken root in the regional epidemic and will continue to propagate unless the chains of transmission are interrupted and virological failure is detected and managed promptly. Imported resistance, by contrast, might be addressed through screening at entry points into the care system; locally entrenched resistance demands sustained surveillance within the community.

The authors conclude that the dominance of NNRTI resistance in both pretreatment and acquired settings, the central role of CRF08_BC in local transmission clusters, the co-clustering of resistant newly diagnosed and treatment-failing individuals, and the sustained local circulation of drug-resistant strains together argue for strengthened drug resistance surveillance, timely detection and management of virological failure, and targeted interventions aimed at highly clustered populations, particularly older adults. The study was approved by the Ethics Review Committee of Yunnan Infectious Disease Hospital and was conducted as a secondary analysis of anonymized surveillance and laboratory data, with the informed consent requirement formally waived. Funded by Yunnan provincial science and technology programs and a national special project on major infectious disease prevention and control, the work illustrates how integrating genotypic resistance testing with molecular epidemiology can transform routine surveillance data into actionable maps of where resistance is born, where it travels, and whom public health programs must reach to stop it.

Subject of Research: HIV-1 drug resistance and molecular transmission networks in Qujing, Yunnan, China

Article Title: HIV-1 genotypic drug resistance and molecular transmission network characteristics in Qujing, Northeastern Yunnan, China

Article References: Wei, F., Li, J., Fan, H., Li, Y., Li, X., Lao, Y., Lou, J., Dong, X., & Liu, J. (2026). HIV-1 genotypic drug resistance and molecular transmission network characteristics in Qujing, Northeastern Yunnan, China. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14544-4

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14544-4

Keywords: HIV-1, drug resistance, pretreatment drug resistance, acquired drug resistance, NNRTI, molecular transmission network, CRF08_BC, Bayesian phylogenetics, antiretroviral therapy, Yunnan, China, genotypic resistance testing

A new study published in Nature introduces Delphy, a scalable Bayesian phylogenetics platform designed from the ground up for near-real-time analysis of outbreak-scale datasets. The work addresses a bottleneck that has frustrated the genomics community since 2020: how to preserve the statistical rigor of Bayesian inference while making it fast enough to run continuously on datasets containing hundreds of thousands of genomes. The authors demonstrate that Delphy can produce dated, uncertainty-aware phylogenies from massive SARS-CoV-2 datasets on timescales of hours rather than days or weeks, opening the door to a style of genomic epidemiology in which phylogenetic estimates are refreshed as routinely as case counts.

The core challenge Delphy tackles is one of combinatorial explosion. Bayesian phylogenetic inference requires exploring an astronomically large space of possible trees, branch lengths, and substitution model parameters, typically via Markov chain Monte Carlo (MCMC) sampling. Each step of an MCMC chain proposes a small change to the current state of the tree and evaluates how well the resulting configuration explains the sequence data, a calculation whose cost scales with both the number of taxa and the length of the genome alignment. With a few hundred genomes, a desktop workstation can handle the workload. With hundreds of thousands, the likelihood evaluations become prohibitively expensive, and the mixing of the chain—the efficiency with which it explores the space of trees—deteriorates because the vast majority of proposed moves are rejected. Traditional implementations such as BEAST, which has served the field admirably for years, were architected for datasets orders of magnitude smaller than those generated during the pandemic.

Delphy’s designers approached the problem by rethinking the inference engine rather than simply optimizing existing code. The platform restructures the likelihood computation to take advantage of modern hardware accelerators, particularly graphics processing units (GPUs), which excel at the kind of massively parallel arithmetic that underlies phylogenetic likelihood calculations. In a phylogenetic likelihood evaluation, the probability of observing the sequence data at each site of the genome must be computed for every node of the tree, and these computations are largely independent of one another. By mapping them onto thousands of parallel processing cores, Delphy amortizes the cost of each likelihood evaluation across hardware that consumer laptops and cloud instances now routinely carry. The result is a dramatic speedup in the inner loop of Bayesian inference, the very component that dominated runtime in earlier tools.

Hardware alone, however, would not have been sufficient. The study also describes algorithmic innovations in how the chain explores tree space and how the analysis is partitioned. Rather than requiring the entire dataset to be re-analyzed from scratch each time new genomes arrive, Delphy is built to support incremental updates, in which an existing posterior sample of trees is extended and refined as additional sequences become available. This design mirrors the operational rhythm of outbreak response, where sequencing data arrive in a continuous stream and analysts need updated estimates on a daily or even hourly cadence. The ability to warm-start an analysis from a previous posterior, rather than beginning each run cold, converts what was historically a batch process into something closer to a living model of an epidemic.

Another distinguishing feature of the platform is its treatment of time. During the COVID-19 pandemic, the field converged on the value of time-calibrated phylogenies, in which branch lengths are expressed in units of calendar time using sampling dates as calibration points. These dated trees allow epidemiologists to estimate when lineages diverged, when a variant likely entered a region, and how quickly lineages were growing or declining in frequency. Delphy incorporates temporal structure directly into its Bayesian model, jointly estimating the evolutionary rate and the timing of divergence events alongside the tree topology. Crucially, it retains full posterior distributions over these quantities, so that an analyst can report not just a point estimate of when a lineage emerged but a credible interval that honestly reflects the limits of the data. In fast-moving outbreak situations, where decisions about border measures, vaccine updates, and public communications hinge on timing estimates, that quantified uncertainty is not a luxury; it is the difference between a defensible inference and an overconfident one.

The authors validate the platform on datasets drawn from the SARS-CoV-2 pandemic, the largest real-world stress test genomic epidemiology has ever faced. Global repositories accumulated many millions of viral genomes during that outbreak, and even curated subsets routinely exceeded the practical limits of legacy Bayesian tools. Delphy’s benchmarks show that analyses on datasets of this scale, which would have been effectively impossible with prior software, complete in hours on appropriate hardware, with posterior estimates that are consistent with those obtained by slower reference methods on smaller subsets. This kind of cross-validation matters: speed is worthless if the fast answer is wrong. By demonstrating agreement between its accelerated inference and established approaches on tractable data, the study builds the case that Delphy’s approximations and engineering choices preserve the statistical integrity that Bayesian methods are meant to guarantee.

The implications for public health practice extend well beyond coronaviruses. The same architecture applies to any rapidly evolving pathogen for which dense genomic sampling is available, including influenza, respiratory syncytial virus, dengue, mpox, and foodborne bacterial outbreaks. In each of these settings, the operational question is similar: can a public health laboratory turn a week’s worth of new genomes into an updated picture of transmission before the picture changes again? A tool that delivers dated phylogenies with quantified uncertainty on a near-real-time cadence shifts genomic epidemiology from retrospective analysis toward genuine situational awareness. It also lowers the barrier for smaller laboratories and public health agencies in low-resource settings, since the computational demands, while substantial, are within reach of cloud computing budgets that are far smaller than the supercomputing clusters previously imagined necessary for pandemic-scale Bayesian work.

The study also speaks to a broader lesson about scientific software. The pandemic exposed a gap between the methods developed in academic statistics and systematics communities and the operational needs of public health. Tools that were exemplary for studies of dozens or hundreds of taxa could not simply be scaled by throwing more processors at them; they required rethinking data structures, sampling strategies, and the software engineering practices needed for continuous, reliable operation. Delphy represents the kind of ground-up redesign that gap demands, combining modern statistical machinery with the performance engineering of contemporary high-performance computing. Its open availability means that the wider community can scrutinize, extend, and build upon the platform, which is essential if it is to become trusted infrastructure rather than a one-off demonstration.

Challenges remain, of course. Bayesian inference on enormous datasets still requires careful attention to model adequacy, because a fast and precise answer to a poorly specified model is of limited value. Recombination, which is substantial in coronaviruses, complicates the strictly tree-based assumptions underlying most phylogenetic software, and integrating recombination-aware inference at pandemic scale remains an open problem. Sampling bias—where genomes are sequenced unevenly across geography, time, and severity of illness—continues to shape what any phylogenetic method can honestly conclude. None of these caveats diminishes the significance of the advance, but they frame the work realistically: Delphy removes a computational barrier that has constrained the field for years, leaving the scientific community freer to focus on the biological and epidemiological subtleties that no amount of computing power can resolve on its own.

If the trajectory of genomic surveillance continues, the coming years will see sequencing embedded ever more deeply in routine public health, from wastewater monitoring to hospital diagnostics. In that world, the demand for phylogenetic analyses that are simultaneously rigorous, current, and scalable will only grow. Delphy’s demonstration that Bayesian phylogenetics can operate at near-real-time speed on outbreak-scale data marks a turning point in that trajectory, promising that the statistical rigor that made phylogenetics indispensable to evolutionary biology can finally be delivered at the tempo at which epidemics actually unfold.

Subject of Research: Scalable near-real-time Bayesian phylogenetic inference for infectious disease outbreak genomics

Article Title: Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy

Article References: Varilly, P., Schifferli, M., Yang, K., Cronan, P., Specht, I., Burcham, T., Glennon, O., Jacks, O., Laning, E., Marrs, L., Oba, K., Yeung, S., Zhao, K. W., Parker, E., Omah, I., Pekar, J. E., Luebbert, L., Andersen, K. G., Park, D. J., … Sabeti, P. C. (2026). Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy. Nature. https://doi.org/10.1038/s41586-026-11012-6

Image Credits: AI Generated

DOI: 10.1038/s41586-026-11012-6

Keywords: Bayesian phylogenetics, Delphy, outbreak genomics, viral evolution, SARS-CoV-2, genomic epidemiology, GPU computing, Markov chain Monte Carlo, phylodynamics, public health surveillance, time-calibrated phylogenies, real-time inference