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

Circulating recombinant forms, or CRFs, arise when two different HIV-1 subtypes recombine within a coinfected host, producing a mosaic genome that can then propagate through a population. CRF67_01B is one such mosaic, and its emergence illustrates a recurring problem in HIV control: once a recombinant strain establishes a foothold, its subsequent spread can escape the attention of surveillance systems that operate within provincial boundaries. Because each region sequences and reports viral genomes largely in isolation, the true geography of transmission can remain hidden until someone stitches the fragments back together. That is precisely what the Hangzhou team set out to do, drawing on viral genetic data that carry within them a record of who infected whom, when lineages diverged, and how the virus moved across the map.

The study assembled 183 CRF67_01B pol gene sequences sampled between 2011 and 2024 across six provinces. The researchers combined sequences generated locally in Hangzhou with publicly available sequences retrieved from the Los Alamos National Laboratory HIV Sequence Database, one of the largest repositories of HIV genetic data in the world. The pol gene, which encodes the virus’s essential enzymes and is routinely sequenced for drug-resistance testing, serves as a convenient and information-rich marker for molecular epidemiology. By comparing these sequences, the team could identify viruses that were so genetically similar that their carriers were likely connected directly or through a short chain of intermediate infections.

To construct those connections, the researchers built a distance-based molecular network, a technique in which each viral sequence becomes a node and pairs of sequences separated by fewer genetic differences than a chosen threshold are joined by edges. At a genetic distance threshold of 0.6 percent, the network resolved the 183 sequences into 24 putative transmission clusters, and 99 of the 183 sequences, an entry rate of 54.1 percent, fell into one of those clusters. That is a striking figure: it means that more than half of the people carrying this strain in the dataset are genetically linked to at least one other person in the network, indicating active and recent chains of transmission rather than scattered, independent introductions. The demographic pattern within those clusters was even more pronounced. Men who have sex with men accounted for 91.9 percent of the clustered individuals, confirming that this recombinant form has become firmly embedded in that community across the sampled provinces.

When the researchers examined the links that crossed provincial boundaries, one pairing stood out above all others: connections between Jiangsu Province and Hangzhou dominated the interprovincial portion of the network. This observation immediately suggested a direction of flow, with the virus moving from an established epidemic center in Jiangsu into the Zhejiang provincial capital and beyond. But a molecular network alone can only show that two viruses are related; it cannot say which lineage came first or reconstruct the historical routes the virus took. For that, the team turned to Bayesian phylogeography, a family of statistical methods that treats the geographic locations of viral ancestors as unknown variables to be inferred from the tree of genetic relationships.

The phylogeographic analysis, combined with a technique known as Bayesian stochastic search variable selection, or BSSVS, allowed the researchers to test which migration pathways between provinces were supported by the data rather than merely plausible. BSSVS works by allowing the model to switch migration rates on and off across the phylogenetic tree and retaining only the connections that measurably improve the explanation of the observed sampling locations. From this inference, the team estimated the time to the most recent common ancestor of the sampled CRF67_01B sequences, arriving at an origin in Jiangsu Province around 2007, roughly six years before the strain was first formally identified in China. The lag between true emergence and first detection is a familiar lesson of viral genomics, and it underscores how much transmission can occur before a novel recombinant attracts scientific attention.

The reconstruction also captured the epidemic’s tempo. After its origin around 2007, CRF67_01B entered a phase of exponential growth between 2009 and 2011, the classic signature of a lineage exploiting an under-saturated network of susceptible hosts. Growth then slowed, declined between 2017 and 2020, and subsequently leveled off into a plateau. That plateau does not mean the strain has been contained; rather, it suggests that the epidemic has reached a kind of equilibrium within its host population, with ongoing transmission balanced by the natural depletion of uninfected contacts, treatment-driven reductions in infectiousness, and behavioral changes. The inferred migration pathways tell a complementary story of movement: from Jiangsu into Hangzhou, from Hangzhou onward into Anhui, and from Jiangsu, Hangzhou, Anhui, and Ningbo converging into Guangdong, a province whose economic gravity draws large numbers of internal migrants.

Technically, the study demonstrates the value of combining two complementary molecular epidemiological tools. Distance-based networks are computationally inexpensive, transparent, and well suited to identifying recent transmission pairs and clusters, but they are blind to direction and time. Bayesian phylogeography is far more computationally demanding and depends on modeling assumptions, yet it recovers the temporal depth and directional flow that networks cannot. Used together, the two approaches allowed the authors to show not only that CRF67_01B sequences from Hangzhou cluster with those from Jiangsu, but that the common ancestor of the whole lineage most likely lived in Jiangsu around 2007 and that the branching order of migrations follows a coherent historical narrative matching the growth phases visible in the skyline plot of effective population size.

The public health implications are clear. An emerging recombinant strain circulating across provincial lines within a highly connected sexual network cannot be controlled by any single jurisdiction acting alone. The authors argue that their findings call for cross-regional joint prevention and control strategies, with interventions targeted at hub cities that serve as sources and conduits of viral spread, and at the high-risk MSM populations in which the strain is concentrated. In practical terms, this means coordinated partner notification across provincial boundaries, harmonized surveillance that shares sequence data in near real time, and prevention services, including testing, pre-exposure prophylaxis, and rapid treatment initiation, concentrated in the urban nodes the phylogeography identifies as transmission hubs. Hangzhou’s epidemic, on this reading, is best understood as epidemiologically linked to surrounding and earlier epicenters, and any strategy that treats it as a local problem will miss the upstream reservoirs that continually seed new infections.

The research also highlights the necessity of open genetic data. A substantial fraction of the sequences in this analysis came from a public database rather than from the authors’ own laboratory, and the cross-provincial picture would have been impossible without that shared resource. As HIV-1 continues to generate new recombinant forms worldwide, the combination of open sequence repositories, molecular network analysis, and Bayesian phylodynamic inference offers health authorities a way to see epidemics as they truly are: continuous, connected, and indifferent to administrative borders. For CRF67_01B, the genetic record now shows a lineage born in Jiangsu around 2007, exploding within the MSM community early in the following decade, and weaving an enduring web of transmission from the Yangtze delta to the south of China. The task that remains is to translate that molecular cartography into prevention that moves as fast and as far as the virus does.

Subject of Research: Spatiotemporal dynamics and cross-regional transmission of HIV-1 CRF67_01B in China

Article Title: Spatiotemporal dynamics and cross-regional transmission network of HIV-1 CRF67_01B in six provinces in China

Article References: Ye, L., Xu, K., Luo, W., Wu, S., Zhu, M., Zhang, X., & Sun, Z. (2026). Spatiotemporal dynamics and cross-regional transmission network of HIV-1 CRF67_01B in six provinces in China. Virology Journal. https://doi.org/10.1186/s12985-026-03302-2

Image Credits: AI Generated

DOI: 10.1186/s12985-026-03302-2

Keywords: HIV-1, CRF67_01B, circulating recombinant form, molecular transmission network, Bayesian phylogeography, cross-regional transmission, MSM, China, phylodynamics, viral epidemiology, Jiangsu, Hangzhou