HIV-1 currently comprises four lineages: group M, for major; group N, for non-M non-O; group O, for outlier; and group P, named to continue in alphabetical order. Group M is by far the most prevalent lineage and the only pandemic strain, believed to have originated in Kinshasa, the capital of the DRC, with the emergence of HIV-1 estimated to have occurred around the 1920s in the DRC. Within group M, ten subtypes have been delineated, labeled A through D, F through H, and K through L, along with nine sub-subtypes such as A1 through A8 and F1 and F2. These subtypes are thought to have diverged from one or a few ancestral HIV-1 strains after a single chimpanzee-to-human transmission, some of them around the 1950s. The most recently recognized subtype, L, was formally established in 2020 on the basis of complete genome sequences identified in the DRC between 1983 and 2001.

The designation of a new HIV-1 subtype is governed by strict criteria. Researchers must identify at least three viral strains or complete genomes from distinct, epidemiologically unlinked individuals. Phylogenetically, each subtype should form a distinct monophyletic clade, with approximately equal genetic distances separating it from the other subtypes, producing a characteristic star-like phylogeny. Established group M subtypes differ by a mean nucleotide divergence of approximately 15 percent at the genome level, with considerable variation between genes. In addition, genomes classified within a given subtype must be non-mosaic, distinguishing them from recombinant genomes that arise when viruses of different subtypes exchange genetic material inside coinfected cells.

The new report describes an HIV-1 genome recovered from residues of peripheral blood mononuclear cells from a single blood sample collected during the summer of 2023. The team extracted viral DNA using a commercial kit and amplified the genome with conventional polymerase chain reaction primers as well as a whole-genome HIV-1 genotyping assay. Sequencing was performed using three complementary platforms: Sanger sequencing, Oxford Nanopore technology on a GridION instrument, and Illumina technology on a MiSeq instrument. The resulting near-full-length consensus genome, deposited in GenBank under accession number PZ307881, was 8,950 nucleotides long and covered 92 percent of the subtype B reference genome, with the shortfall mostly due to sequencing defects at the 5-prime and 3-prime ends.

Comparative genomic analysis revealed that the ten best matches in the GenBank database exhibited nucleotide similarities of only 83.29 to 83.60 percent to the new sequence. These closest genomes had been collected from patients in the DRC, the United States, the Netherlands, and Gabon between 1987 and 2018. When aligned against representative genomes of the ten established subtypes, the new genome showed a mean nucleotide divergence of 12.69 percent, ranging between 11.87 and 13.28 percent, while the ten established subtypes diverged from one another by a mean of 12.64 percent, ranging between 10.01 and 13.73 percent. In other words, the candidate subtype is as distant from every known subtype as those subtypes are from each other, and pairwise comparisons showed no statistically significant differences in divergence. Similar results held when the genomes were cut into eight fragments corresponding to different genomic regions, with regional divergences for the candidate genome ranging from 8.55 to 20.23 percent, closely mirroring the 8.49 to 20.25 percent range observed among established subtypes.

Phylogenetic analyses reinforced this picture. Using IQ-TREE and Nextclade software, with a simian immunodeficiency virus genome from Gabon as an outgroup, the researchers found that the candidate genome formed a distinct branch within an overall star-like tree, the hallmark topology of genuine subtypes. The closest established subtype was J, supported by a bootstrap value of 99 percent. Independent trees built from each of the eight genome fragments likewise showed star-like topologies with the candidate sequence on its own branch. Bootscan analysis, which uses a sliding window of 200 nucleotides to detect recombination, found no recombination events, satisfying another key criterion for subtype status. Nextclade classified the genome as an other clade, and a tree incorporating 1,020 genomes from the Los Alamos National Laboratory HIV Sequence Database placed it on a new node. Taken together, the researchers concluded that the genome belongs to a candidate new subtype, which they named M following subtype L in alphabetical order, while acknowledging the potential nomenclatural ambiguity of reusing the letter that already designates the pandemic group.

The clinical course of the patient offers an intriguing backdrop to the genomic findings. At the start of antiretroviral treatment, her CD4-positive T lymphocyte count was 351 cells per cubic millimeter with a CD4-to-CD8 ratio of 0.23, and plasma HIV-1 RNA stood at 5.03 log10 copies per milliliter. Subsequently, her CD4 count fluctuated between 406 cells per cubic millimeter in 1999 and 2,394 cells per cubic millimeter in 2008, and has remained above 1,000 since 2007. Plasma viral load has been undetectable during regular monitoring from 2008 onward, except for a reading of 2.2 log10 copies per milliliter in 2024. Notably, a Western blot performed in 2023 showed antibody reactivities only to envelope and capsid proteins, with no response to Pol proteins. The authors suggest this unusual profile may partly reflect reduced antibody affinity for antigenically divergent proteins, or an overall weak antibody response to HIV-1 antigens, which particularly affects Pol-encoded proteins such as reverse transcriptase, protease, and integrase, especially after more than three decades of infection and long-term antiretroviral therapy.

Genotypic resistance analysis using algorithms from the French National Agency for AIDS Research and the Stanford University HIV drug resistance database found no naturally present amino acid mutations conferring resistance to any currently available antiretroviral drug, apart from two substitutions acquired during treatment failures: Y181C in reverse transcriptase, detected transiently in 2003 during a virological failure episode on nevirapine, and I84V in protease, detected in 2007 during failure on atazanavir and ritonavir. Viral tropism was predicted to be R5, indicating use of the CCR5 co-receptor. The researchers cautioned that the absence of natural resistance mutations should be interpreted carefully given the markedly different genetic background of this genome, though the finding is congruent with observations for both rare and prevalent subtypes.

The team also turned to molecular modeling to explore how the roughly 40 amino acid differences in the candidate subtype’s reverse transcriptase, the central enzyme of HIV replication and a prime drug target, might alter its structure. Mapping the mutations onto the reference enzyme structure revealed changes predicted to reshape several surface loops, including a closure of the F130 to P150 buckle and a flattening of the K281 to P294 loop. Alterations in the N57 to D76 region, corresponding to the functionally important beta3-beta4 loop within the fingers subdomain, were associated with a greater space between the fingers and palm subdomains. The surface electrostatic potential was modified but remained globally electropositive, particularly in the ligand-interacting area. The modeled enzyme-ligand interaction energy decreased slightly, from minus 646 to minus 512 kilojoules per mole, because the number of amino acids involved in ligand interaction fell from 15 to 7. The authors speculate that such changes could modulate enzyme processivity and, by promoting dissociation and re-association of the enzyme-substrate complex, potentially increase template switching and genetic recombination, though they stress these hypotheses derive entirely from in silico modeling and require in vitro biochemical validation.

The discovery underscores that HIV-1 diversity remains far from fully charted and reinforces the status of the DRC as the epicenter of the group M pandemic and a region where rare subtypes continue to circulate. Rare established subtypes, including F, H, J, K, and L, each account for less than 1 percent of global HIV-1 infections and circulate primarily in the DRC, Angola, and Cameroon, with limited data on their transmissibility, virulence, and pathogenicity. The researchers also observed notable intra-sample diversity, with mean intra-host nucleotide divergence of 1.61 percent and a maximum of 5.52 percent, suggesting intra-patient viral evolution or, less likely given the candidate subtype’s apparent rarity, superinfection with multiple variants during the patient’s decades of stays in the DRC. Formal recognition of the new subtype will require detecting it in at least two more epidemiologically unlinked individuals, and the authors call for further studies, primarily in the DRC, to establish its prevalence and genotypic and phenotypic characteristics.

Subject of Research: Discovery of a candidate new HIV-1 group M subtype in a patient from the Democratic Republic of the Congo

Article Title: A case of infection with a candidate new subtype, tentatively termed M, of human immunodeficiency virus type 1 of group M

Article References: A case of infection with a candidate new subtype, tentatively termed M, of human immunodeficiency virus type 1 of group M. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: HIV-1, subtype M, Democratic Republic of the Congo, viral phylogenetics, whole-genome sequencing, reverse transcriptase, group M, subtype L, molecular modeling, antiretroviral resistance, HIV diversity, New Microbes and New Infections

The skills gap the project targets is real and well documented across the computing community. Quantum computers operate on fundamentally different principles than classical machines, manipulating quantum bits, or qubits, that exploit superposition and entanglement to represent and process information in ways no conventional processor can. Writing software for such devices requires an understanding of quantum mechanics, error-prone hardware behavior, and specialized algorithms, while extracting value from those algorithms in medicine additionally demands deep familiarity with molecular biology, clinical data, and drug discovery pipelines. Very few training programs ask learners to cross that entire bridge. Most quantum curricula are built by and for physicists and computer scientists, and most biomedical training barely touches quantum concepts at all. Jiang’s team is betting that the fastest route to a capable workforce is to build the bridge deliberately, in both directions, from the start.

According to the project description, the training program is domain-driven and organized around three integrated thrusts, each addressing a different layer of the expertise stack. The first thrust focuses on problem formulation: teaching participants to translate health care questions into forms that quantum algorithms can actually address. That includes quantum-compatible data representations, a subtle but crucial topic, because classical biomedical data, from imaging archives to genomic sequences, must be encoded into quantum states before any quantum speedup can even be contemplated. It also covers applications in molecular modeling and diagnostic analysis, two areas where researchers widely believe quantum hardware may eventually outperform classical methods, whether by simulating electronic structures of drug candidates more faithfully or by enhancing pattern recognition in high-dimensional clinical datasets.

The second thrust tackles a challenge that has plagued quantum software development since the field’s inception: productivity, portability, and reproducibility. Quantum programs written for one hardware platform often cannot run on another without substantial rework, because competing devices from different vendors use distinct qubit technologies, gate sets, and noise characteristics. The project’s modules therefore address cross-platform program translation, giving trainees the tools to move code between quantum systems rather than locking it to a single machine. Equally important is auditable workflow construction, a concept borrowed from the reproducibility movement in data science. In biomedical research, where results inform clinical decisions, being able to trace exactly how a computational result was produced is not optional. The curriculum also introduces uncertainty-aware evaluation, training participants to characterize and reason about noise, drift, and hardware variability, the persistent imperfections of today’s noisy intermediate-scale quantum devices that can silently corrupt results if left unexamined.

The third thrust is the most application-oriented of the three. Rather than stopping at theory or tooling, it guides participants through building complete quantum-classical workflows for concrete health care use cases. Among the named examples are protein structure prediction, a problem central to understanding disease and designing therapeutics, where quantum and classical processors would work in tandem, each handling the subtasks it performs best. The curriculum also covers quantum-ready dataset design, teaching learners how to prepare and structure biomedical data so that it can take advantage of quantum processing as hardware matures, and DNA and RNA therapeutics modeling, an area of intense pharmaceutical interest as nucleic-acid-based drugs move from laboratory curiosity to approved medicine. By working end to end, from raw problem to running workflow, trainees encounter the full friction of real-world quantum application development rather than an idealized classroom version.

What distinguishes the George Mason-led effort from many earlier workforce programs is its emphasis on sustainability and reuse. The project description states that the combined thrusts deliver a reusable and scalable training program, one that integrates rigorous instruction, hands-on practice, open-source resources, and access to both simulated and real quantum hardware. That last element matters enormously. Quantum hardware remains scarce, expensive, and geographically concentrated, and most students would never touch a genuine quantum processor without cloud-based access programs. By pairing simulators, which allow unlimited experimentation at the cost of classical computational overhead, with real devices, the program lets learners experience both the convenience of idealized models and the sobering realities of hardware noise. Open-source materials, meanwhile, mean the curriculum can outlive the grant period and be adopted by institutions far beyond the original collaborating states.

The dual-state collaboration referenced in the project title reflects the geographic spread of the investigator team, with George Mason University in Virginia anchoring a partnership that extends across state lines. Such multi-institutional structures are a deliberate feature of NSF’s CyberTraining program, which funds projects to grow and diversify the national cyberinfrastructure workforce. The logic is straightforward: workforce challenges are too large for any single campus to solve, and regional partnerships can pool complementary strengths, whether in quantum hardware expertise, biomedical research, or educational outreach. With four principal investigators sharing a $1 million budget, the project exemplifies the medium-scale implementation awards that CyberTraining uses to move promising training models from pilot scale to durable programs.

The timing of the initiative is hardly accidental. Federal and private investment in quantum information science has surged over the past decade, and national strategies in the United States and abroad explicitly identify workforce development as a bottleneck alongside hardware progress. Meanwhile, the biomedical sector is watching quantum simulation with growing interest, since molecules are themselves quantum objects and classical computers must approximate their behavior with computationally expensive methods. If quantum processors become reliable enough to simulate molecular interactions directly, the consequences for drug discovery, protein engineering, and personalized medicine could be profound. But that future depends on a generation of researchers who can stand with one foot in each world, and the supply of such people today is vanishingly small. Training programs like Jiang’s are an attempt to widen that pipeline before the hardware arrives rather than after.

The project also carries an explicitly ethical dimension. In its framing, the program positions participants to contribute to the national quantum workforce and to the responsible use of quantum computing in biomedical research. That phrase, responsible use, signals an awareness that powerful computational tools in medicine raise questions of privacy, equity, and validation that purely technical curricula often ignore. Auditable workflows and uncertainty-aware evaluation, both named components of the training, are as much about scientific integrity as about engineering practice. Teaching future practitioners to quantify and communicate the uncertainty in quantum-assisted results, and to document their methods so others can verify them, builds accountability into the workforce itself rather than bolting it on later.

For George Mason University, Virginia’s largest public research university, the award extends a growing portfolio in quantum and computing research at an institution that enrolls more than 40,000 students from 130 countries and all 50 states. For the broader community, the project’s real legacy may be its template: a domain-driven, three-thrust curriculum that others can adapt to pair quantum computing with fields beyond health care, from finance to materials science. Funding runs through late July 2030, giving the team nearly four years to refine the modules, train cohorts of participants, and demonstrate that dual expertise can be taught systematically rather than acquired by rare accident. If the model works, the quantum computers of the 2030s may find a workforce already waiting for them, fluent in qubits and clinical data alike, and ready to put one of the century’s most consequential technologies to work on some of medicine’s hardest problems.

Subject of Research: Workforce training at the intersection of quantum computing and health care through a NSF CyberTraining collaboration

Article Title: Jiang building dual-expertise workforce in quantum computing & health care through dual-state collaboration

Article References: Jiang building dual-expertise workforce in quantum computing & health care through dual-state collaboration. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum computing, health care, workforce development, CyberTraining, National Science Foundation, George Mason University, quantum algorithms, biomedical research, protein structure prediction, quantum software, molecular modeling, dual-state collaboration

The center’s mission is rooted in a deceptively straightforward observation about how modern science is organized. Infectious disease biologists at KU have long been skilled at identifying the gene products and factors that contribute to disease, mapping the molecular machinery that pathogens deploy to infect, evade and persist. Chemists and medicinal chemists, meanwhile, excel at developing chemical tools, probes and approaches that can interrogate and manipulate biological systems. Too often, Hefty noted, that expertise is not shared between the two camps. The CBID center was created precisely to bridge that divide, connecting infectious disease biologists with colleagues in chemistry, medicinal chemistry, pharmaceutical chemistry and bioengineering so that the identification of a disease-relevant target can be followed rapidly by the design of molecular tools to study and defeat it. As Hefty put it, both sides are good at what they do individually, but together is where they really excel.

What makes the Kansas effort notable is its scale and its reach across institutional boundaries. CBID recruits, mentors and supports investigators not only at the University of Kansas in Lawrence but also at KU Medical Center, Kansas State University, Wichita State University and other regional partners. The center gives participating researchers access to four core research labs organized around infectious disease high-throughput screening, computational chemical biology and molecular modeling, synthetic chemical biology and flow cytometry. These cores function as shared scientific infrastructure, allowing a researcher with a promising hypothesis but limited equipment to test it using capabilities that would be prohibitively expensive to build in an individual laboratory. Investigators can apply for pilot project funds and vouchers for low-barrier use of the core facilities, lowering the entry cost for exploratory science that might otherwise never get off the ground.

The center also invests heavily in something less tangible but arguably more important: bringing people together. Monthly programs and annual symposia create regular occasions for researchers from different departments and institutions to share results and swap ideas. You just have to get scientists in a room, sharing science, talking science, and great things come out of that, Hefty said, adding that collaborations and events often happen organically. Anyone who has watched interdisciplinary research struggle against the gravitational pull of departmental silos will recognize what the center is attempting. The most productive ideas in chemical biology frequently emerge at the boundaries between disciplines, where a biologist’s unmet need meets a chemist’s unexpected capability, and those encounters require deliberate cultivation.

The track record from the first two phases of funding offers evidence that the model works. Over its first decade, the center has recruited seven faculty members, contributed to 15 startups, and helped generate roughly $25 million in NIH funding along with $90 million from broader research projects, pilot projects and cores. Researchers affiliated with the center have authored more than 600 publications, and the scientific network now includes 80 affiliates. Those numbers matter beyond vanity metrics. Faculty recruitment supported by COBRE funding builds permanent intellectual capacity at a university, and the startup activity suggests that basic research at the center is feeding into the kind of translational pipeline that can eventually carry laboratory discoveries toward real-world applications against infectious disease.

Beyond the publications and the grant dollars, Hefty emphasized the center’s role in training and workforce development, a contribution he argued cannot be understated. Undergraduates, graduate students and postdocs have spent a decade learning techniques, tools and approaches in the center’s labs, and will continue to do so for at least another five years under the new award. Those trainees will disperse into professions across Kansas and beyond, carrying chemical biology skills into academia, industry and the biotechnology sector. In a state working to build a high-tech regional workforce, the center functions as an engine of human capital as much as a generator of papers. Hefty also credited the KU Office of Research, the College of Liberal Arts & Sciences and the Kansas Board of Regents for their support, and pointed to the School of Pharmacy and researchers at KU Medical Center in microbiology and biochemistry as key partners in the enterprise.

Scientifically, the Phase 3 era will be guided by the theme of fighting antibiotic resistance, and the strategy Hefty describes represents a meaningful departure from the traditional pharmaceutical playbook. Rather than pursuing broad-spectrum antibiotics, the center’s researchers will develop targeted treatments designed to protect the body’s beneficial microbiome while defeating resistance mechanisms. The logic is compelling. Conventional broad-spectrum antibiotics cannot distinguish between a pathogenic invader and the trillions of beneficial microbes that inhabit the human body, and the collateral damage they inflict on the microbiome has been linked to a range of secondary health problems, including opportunistic infections such as Clostridioides difficile. Hefty framed the ambition in concrete terms: if a patient has an infection with staph, strep or enterococcus, can researchers develop chemical tools, probes and approaches that target just those organisms without disrupting the rest of the flora in our bodies? And can new tools be developed to address the antibiotic resistance mechanisms that currently exist?

That question sits at the frontier of chemical biology, a field that uses small molecules as precision instruments for understanding and manipulating biological systems. Pathogen-selective compounds require an intimate knowledge of the biochemical differences between a pathogen and its human host, and between the pathogen and the harmless commensal bacteria that share the same ecological niche. Computational chemical biology and molecular modeling, one of the center’s four core capabilities, allow researchers to identify selective targets and predict how candidate molecules will interact with them. High-throughput screening can then test hundreds of thousands of compounds against those targets, while synthetic chemical biology provides the capacity to design and build new molecules from scratch. Flow cytometry, meanwhile, enables researchers to sort and analyze individual cells at high speed, a capability that is essential for studying heterogeneous bacterial populations and the emergence of resistant subpopulations within an infection.

The collaborative spirit underpinning the award will be on public display on October 16 and 17, when researchers and stakeholders gather for the fifth annual Chemical Biology Symposium at KU. The event is expected to draw more than 100 attendees, with regional participation from Kansas State and Wichita State. According to organizers, the symposium seeks to provide a forum for Graduate Training in Chemical Biology trainees and Chemical Biology of Infectious Disease researchers to present their work and to cover special topics with invited speakers. For Hefty, this cross-pollination of ideas among institutions, departments and disciplines is the whole point of the enterprise. We’re breaking down a lot of those disciplinary walls and barriers so that interdisciplinary science can occur and happen in a very productive way, he said.

Looking a decade ahead, Hefty described what success would look like: an established, formal research chemical biology center with the scientists recruited and supported over the years still participating in its activities. Such a center could continue to focus on infectious disease or expand into other areas, whether cancer, Alzheimer’s or neurological disorders, but the essential outcome would be a durable research structure that continues to support investigators at KU, KU Medical Center and other institutions. In an era when federal research funding is increasingly competitive and antibiotic resistance continues to outpace the development of new drugs, the Kansas experiment offers a test of whether deliberate, sustained investment in collaborative infrastructure can produce science that no single laboratory, department or discipline could achieve alone. With $5.8 million in new support and a decade of accumulated momentum, CBID now has the resources and the runway to find out.

Subject of Research: NIH Phase 3 COBRE funding for a chemical biology center addressing infectious disease and antibiotic resistance

Article Title: KU receives $5.8 million NIH Phase 3 COBRE funding for Chemical Biology of Infectious Disease center

Article References: KU receives $5.8 million NIH Phase 3 COBRE funding for Chemical Biology of Infectious Disease center. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: University of Kansas, NIH, COBRE, chemical biology, infectious disease, antibiotic resistance, microbiome, drug discovery, high-throughput screening, molecular modeling, workforce development, interdisciplinary research