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The principal barrier to an HIV cure is the presence of a latent viral reservoir (LVR) made up primarily of latently infected resting CD4+ (rCD4) T-cells. Studies in the United States have shown that the LVR decays slowly (half-life=3.8 years), but this rate in African populations has been understudied. This study examined longitudinal changes in the inducible replication competent LVR (RC-LVR) of ART-suppressed Ugandans living with HIV (n=88) from 2015-2020 using the quantitative viral outgrowth assay, which measures infectious units per million (IUPM) rCD4 T-cells. In addition, outgrowth viruses were examined with site-directed next-generation sequencing to assess for possible ongoing viral evolution. During the study period (2018-19), Uganda instituted a nationwide rollout of first-line ART consisting of Dolutegravir (DTG) with two NRTI, which replaced the previous regimen that consisted of one NNRTI and the same two NRTI. Changes in the RC-LVR were analyzed using two versions of a novel Bayesian model that estimated the decay rate over time on ART as a single, linear rate (model A) or allowing for an inflection at time of DTG initiation (model B). Model A estimated the population-level slope of RC-LVR change as a non-significant positive increase. This positive slope was due to a temporary increase in the RC-LVR that occurred 0-12 months post-DTG initiation (pAuthor SummaryHIV is a largely incurable infection despite the use of highly successful antiretroviral drugs (ARV) due to the presence of a population of long-living resting CD4+ T cells, which can harbor a complete copy of the virus integrated into the host cell’s DNA. We examined changes in the levels of these cells, referred to as the latent viral reservoir, in a group of ARV-treated Ugandans living with HIV. During this examination, Uganda authorities switched the backbone drug used in ARV regimens to a different class of drug that blocks the ability of the virus to integrate into the cell’s DNA. We found that for approximately a year after this switch to the new drug, there was a temporary spike in the size of the latent viral reservoir despite the new drug continuing to completely suppress viral replication with no apparent adverse clinical effects.

Defining clusters of epidemiologically related infections is a common problem in the surveillance of infectious disease. A popular method for generating clusters is pairwise distance clustering, which assigns pairs of sequences to the same cluster if their genetic distance falls below some threshold. The result is often represented as a network or graph of nodes. A connected component is a set of interconnected nodes in a graph that are not connected to any other node. The prevailing approach to pairwise clustering is to map clusters to the connected components of the graph on a one-to-one basis. We propose that this definition of clusters is unnecessarily rigid. For instance, the connected components can collapse into one cluster by the addition of a single sequence that bridges nodes in the respective components. Moreover, the distance thresholds typically used for viruses like HIV-1 tend to exclude a large proportion of new sequences, making it difficult to train models for predicting cluster growth. These issues may be resolved by revisiting how we define clusters from genetic distances. Community detection is a promising class of clustering methods from the field of network science. A community is a set of nodes that are more densely inter-connected relative to the number of their connections to external nodes. Thus, a connected component may be partitioned into two or more communities. Here we describe community detection methods in the context of genetic clustering for epidemiology, demonstrate how a popular method (Markov clustering) enables us to resolve variation in transmission rates within a giant connected component of HIV-1 sequences, and identify current challenges and directions for further work.

The composition of the latent human immunodeficiency virus 1 (HIV-1) reservoir is shaped by when proviruses integrated into host genomes. These integration dates can be estimated by phylogenetic methods like root-to-tip (RTT) regression. However, RTT does not accommodate variation in the number of mutations over time, uncertainty in estimating the molecular clock, or the position of the root in the tree. To address these limitations, we implemented a Bayesian extension of RTT as an R package (bayroot), which enables the user to incorporate prior information about the time of infection and start of antiretroviral therapy. Taking an unrooted maximum likelihood tree as input, we use a Metropolis–Hastings algorithm to sample from the joint posterior distribution of three parameters (the rate of sequence evolution, i.e., molecular clock; the location of the root; and the time associated with the root). Next, we apply rejection sampling to this posterior sample of model parameters to simulate integration dates for HIV proviral sequences. To validate this method, we use the R package treeswithintrees (twt) to simulate time-scaled trees relating samples of actively and latently infected T cells from a single host. We find that bayroot yields significantly more accurate estimates of integration dates than conventional RTT under a range of model settings.

MotivationGene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another, and vice versa.ResultsTo understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with any distribution of open reading frames in linear or circular genomes. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias, and the distribution of selection biases (dN/dS) in the respective reading frames.Availability and implementationOur simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License (GPL) version 3, and is available at https://github.com/PoonLab/HexSE.

Defining clusters of epidemiologically-related infections is a common problem in the surveillance of infectious disease. A popular method for generating clusters is pairwise distance clustering, which assigns pairs of sequences to the same cluster if their genetic distance falls below some threshold. The result is often represented as a network or graph of infections. A connected component is a set of interconnected nodes in a graph that are not connected to any other node. The current approach to pairwise clustering is to map clusters to the connected components of the graph. However, the distance thresholds typically used for viruses like HIV-1 tend to yield components that exclude large numbers of infections as unconnected nodes. This is problematic for public health applications of clustering, such as tracking the growth of clusters over time. We propose that this problem can be addressed with community detection, a class of clustering methods being developed in the field of network science. A community is a set of nodes that are more densely inter-connected relative to the number of connections to external nodes. Thus, a connected component may be partitioned into two or more communities. Here we describe community detection methods in the context of genetic clustering for epidemiology, demonstrate how a popular method (Markov clustering) enables us to resolve variation in transmission rates within a giant connected component of HIV-1 sequences, and identify current challenges and directions for further work.

Tracking the emergence and spread of SARS-CoV-2 lineages using phylogenetics has proven critical to inform the timing and stringency of COVID-19 public health interventions. We investigated the effectiveness of international travel restrictions at reducing SARS-CoV-2 importations and transmission in Canada in the first two waves of 2020 and early 2021. Maximum likelihood phylogenetic trees were used to infer viruses’ geographic origins, enabling identification of 2263 (95% confidence interval: 2159–2366) introductions, including 680 (658–703) Canadian sublineages, which are international introductions resulting in sampled Canadian descendants, and 1582 (1501–1663) singletons, introductions with no sampled descendants. Of the sublineages seeded during the first wave, 49% (46–52%) originated from the USA and were primarily introduced into Quebec (39%) and Ontario (36%), while in the second wave, the USA was still the predominant source (43%), alongside a larger contribution from India (16%) and the UK (7%). Following implementation of restrictions on the entry of foreign nationals on 21 March 2020, importations declined from 58.5 (50.4–66.5) sublineages per week to 10.3-fold (8.3–15.0) lower within 4 weeks. Despite the drastic reduction in viral importations following travel restrictions, newly seeded sublineages in summer and fall 2020 contributed to the persistence of COVID-19 cases in the second wave, highlighting the importance of sustained interventions to reduce transmission. Importations rebounded further in November, bringing newly emergent variants of concern (VOCs). By the end of February 2021, there had been an estimated 30 (19–41) B.1.1.7 sublineages imported into Canada, which increasingly displaced previously circulating sublineages by the end of the second wave.Although viral importations are nearly inevitable when global prevalence is high, with fewer importations there are fewer opportunities for novel variants to spark outbreaks or outcompete previously circulating lineages.

The prevailing abundance of full-length HIV type 1 (HIV-1) genome sequences provides an opportunity to revisit the standard model of HIV-1 group M (HIV-1/M) diversity that clusters genomes into largely nonrecombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for simian immunodeficiency virus (SIV) and other HIV-1 groups. Here we develop an unsupervised nonparametric clustering approach, which does not rely on predefined nonrecombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (dynamic stochastic block model [DSBM]) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial generalized linear model (GLM), P8×10−8), compared to other reference-free recombination detection programs (genetic algorithm for recombination detection [GARD], recombination detection program 4 [RDP4], and RDP5). When this method was applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 29 as the optimal number of DSBM clusters and used change-point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and undocumented recombination hotspots in the HIV-1 genome and evidence of intersubtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative framework for HIV-1 classification.

Phylogenetics has played a pivotal role in the genomic epidemiology of SARS-CoV-2, such as tracking the emergence and global spread of variants, and scientific communication. However, the rapid accumulation of genomic data from around the world — with over two million genomes currently available in the GISAID database — is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2, and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into ‘variants’, generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neigihbor-joining trees in RapidNJ and converted into a majority-rule consensus tree for the lineage. Branches with support values below 50% or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly-sampled ancestral variants. Currently, we process about million genomes in approximately nine hours on 34 cores. The resulting trees are visualized using the JavaScript framework D3.js as ‘beadplots’, in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may increase the information content of compact genomes or influence the creation of new genes. Here we report a global comparative study of overlapping reading frames (OvRFs) of 12,609 virus reference genomes in the NCBI database. We retrieved metadata associated with all annotated reading frames in each genome record to calculate the number, length, and frameshift of OvRFs. Our results show that while the number of OvRFs increases with genome length, they tend to be shorter in longer genomes. The majority of overlaps involve +2 frameshifts, predominantly found in ds-DNA viruses. However, the longest overlaps involve no shift in reading frame (+0), increasing the selective burden of the same nucleotide positions within codons, instead of exposing additional sites to purifying selection. Next, we develop a new graph-based representation of the distribution of OvRFs among the reading frames of genomes in a given virus family. In the absence of an unambiguous partition of reading frames by homology at this taxonomic level, we used an alignment-free k-mer based approach to cluster protein coding sequences by similarity. We connect these clusters with two types of directed edges to indicate (1) that constituent reading frames are adjacent in one or more genomes, and (2) that the reading frames overlap. These adjacency graphs not only provide a natural visualization scheme, but also a novel statistical framework for analyzing the effects of gene- and genome-level attributes on the frequencies of overlaps.

Tracking the emergence and spread of SARS-CoV-2 is critical to inform public health interventions. Phylodynamic analyses have quantified SARS-CoV-2 migration on global and local scales1–5, yet they have not been applied to determine transmission dynamics in Canada. We quantified SARS-CoV-2 migration into, within, and out of Canada in the context of COVID-19 travel restrictions. To minimize sampling bias, global sequences were subsampled with probabilities corrected for their countries’ monthly contribution to global new diagnoses. A time-scaled maximum likelihood tree was used to estimate most likely ancestral geographic locations (country or Canadian province), enabling identification of sublineages, defined as introduction events into Canada resulting in domestic transmission. Of 402 Canadian sublineages identified, the majority likely originated from the USA (54%), followed by Russia (7%), India (6%), Italy (6%), and the UK (5%). International introductions were mostly into Ontario (39%) and Quebec (38%). Among Pango lineages6, B.1 was imported at least 191 separate times from 11 different countries. Introduction rates peaked in late March then diminished but were not eliminated following national interventions including restrictions on non-essential travel. We further identified 1,380 singleton importations, international importations that did not result in further sampled transmission, whereby representation of lineages and location were comparable to sublineages. Although proportion of international transmission decreased over time, this coincided with exponential growth of within-province transmission – in fact, total number of sampled transmission events from international or interprovincial sources increased from winter 2020 into spring 2020 in many provinces. Ontario, Quebec, and British Columbia acted as sources of transmission more than recipients, within the caveat of higher sequence representation. We present strong evidence that international introductions and interprovincial transmission of SARS-CoV-2 contributed to the Canadian COVID-19 burden throughout 2020, despite initial reductions mediated by travel restrictions in 2020. More stringent border controls and quarantine measures may have curtailed introductions of SARS-CoV-2 into Canada and may still be warranted.Significance StatementBy analyzing SARS-CoV-2 genomes from Canada in the context of the global pandemic, we illuminate the extent to which the COVID-19 burden in Canada was perpetuated by ongoing international importations and interprovincial transmission throughout 2020. Although travel restrictions enacted in March 2020 reduced the importation rate and proportion of transmission from abroad across all Canadian provinces, SARS-CoV-2 introductions from the USA, India, Russia, and other nations were detectable through the summer and fall of 2020.

A new abundance of full-length HIV-1 genome sequences provides an opportunity to revisit the standard model of HIV-1/M diversity that clusters genomes into largely non-recombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for SIV and other HIV-1 groups. Here we develop an unsupervised non-parametric clustering approach, which does not rely on predefined non-recombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (DSBM) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial GLM, P < 8 × 10−8), compared to other reference-free recombination detection programs (GARD, RDP4 and RDP5). Applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 25 as the optimal number of DSBM clusters, and used change point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and novel recombination hotspots in the HIV-1 genome, and evidence of inter-subtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative new framework for HIV-1 classification.

HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long‐term interaction. We investigated the use of tree shape distance measures to quantify the degree of cophylogeny. We implemented a reverse‐time simulation model of pathogen phylogenies within a fixed host tree, given cospeciation probability, host switching, and pathogen speciation rates. We used this model to evaluate 18 distance measures between host and pathogen trees including two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and accommodate different size trees. Finally, we used these measures to revisit published cophylogenetic studies, where authors described the observed associations as representing a high or low degree of cophylogeny. Our simulations demonstrated that some measures are more informative than others with respect to specific coevolution parameters especially when these did not assume extreme values. For real datasets, trees’ associations projection revealed clustering of high concordance studies suggesting that investigators are describing it in a consistent way. Our results support the hypothesis that measures can be useful for quantifying cophylogeny. This motivates their usage in the field of coevolution and supports the development of simulation‐based methods, i.e., approximate Bayesian computation, to estimate the underlying coevolutionary parameters.

Hepatitis C virus (HCV) mixed genotype infections can affect treatment outcomes and may have implications for vaccine design and disease progression. Previous studies demonstrate 0–39% of high-risk, HCV-infected individuals harbor mixed genotypes however standardized, sensitive methods of detection are lacking. This study compared PCR amplicon, random primer (RP), and probe enrichment (PE)-based deep sequencing methods coupled with a custom sequence analysis pipeline to detect multiple HCV genotypes. Mixed infection cutoff values, based on HCV read depth and coverage, were identified using receiver operating characteristic curve analysis. The methodology was validated using artificially mixed genotype samples and then applied to two clinical trials of HCV treatment in high-risk individuals (ACTIVATE, 114 samples from 90 individuals; DARE-C II, 26 samples from 18 individuals) and a cohort of HIV/HCV co-infected individuals (Canadian Coinfection Cohort (CCC), 3 samples from 2 individuals with suspected mixed genotype infections). Amplification bias of genotype (G)1b, G2, G3 and G5 was observed in artificially mixed samples using the PCR method while no genotype bias was observed using RP and PE. RP and PE sequencing of 140 ACTIVATE and DARE-C II samples identified the following primary genotypes: 15% (n = 21) G1a, 76% (n = 106) G3, and 9% (n = 13) G2. Sequencing of ACTIVATE and DARE-C II demonstrated, on average, 2% and 1% of HCV reads mapping to a second genotype using RP and PE, respectively, however none passed the mixed infection cutoff criteria and phylogenetics confirmed no mixed infections. From CCC, one mixed infection was confirmed while the other was determined to be a recombinant genotype. This study underlines the risk for false identification of mixed HCV infections and stresses the need for standardized methods to improve prevalence estimates and to understand the impact of mixed infections for management and elimination of HCV.

Among people living with human immunodeficiency virus type 1 (HIV-1), the long-term persistence of a population of cells carrying transcriptionally silent integrated viral DNA (provirus) remains the primary barrier to developing an effective cure. Ongoing cell division via proliferation is generally considered to be the driving force behind the persistence of this latent HIV-1 reservoir. The contribution of this mechanism (clonal expansion) is supported by the observation that proviral sequences sampled from the reservoir are often identical. This outcome is quantified as the ‘clonality’ of the sample population, e.g. the fraction of provirus sequences observed more than once. However, clonality as a quantitative measure is inconsistently defined and its statistical properties are not well understood. In this Reflections article, we use mathematical and phylogenetic frameworks to formally examine the inherent problems of using clonality to characterize the dynamics and proviral composition of the reservoir. We describe how clonality is not adequate for this task due to the inherent complexity of how infected cells are ‘labeled’ by proviral sequences—the outcome of a sampling process from the evolutionary history of active viral replication before treatment—as well as variation in cell birth and death rates among lineages and over time. Lastly, we outline potential directions in statistical and phylogenetic research to address these issues.

In recent years, phylogenetic analysis of HIV sequence data has been used in research studies to investigate transmission patterns between individuals and groups, including analysis of data from HIV prevention clinical trials, in molecular epidemiology, and in public health surveillance programs. Phylogenetic analysis can provide valuable information to inform HIV prevention efforts, but it also has risks, including stigma and marginalization of groups, or potential identification of HIV transmission between individuals. In response to these concerns, an interdisciplinary working group was assembled to address ethical challenges in US-based HIV phylogenetic research. The working group developed recommendations regarding (1) study design; (2) data security, access, and sharing; (3) legal issues; (4) community engagement; and (5) communication and dissemination. The working group also identified areas for future research and scholarship to promote ethical conduct of HIV phylogenetic research.

Many virus-encoded proteins have intrinsically disordered regions that lack a stable, folded three-dimensional structure. These disordered proteins often play important functional roles in virus replication, such as down-regulating host defense mechanisms. With the widespread availability of next-generation sequencing, the number of new virus genomes with predicted open reading frames is rapidly outpacing our capacity for directly characterizing protein structures through crystallography. Hence, computational methods for structural prediction play an important role. A large number of predictors focus on the problem of classifying residues into ordered and disordered regions, and these methods tend to be validated on a diverse training set of proteins from eukaryotes, prokaryotes, and viruses. In this study, we investigate whether some predictors outperform others in the context of virus proteins and compared our findings with data from non-viral proteins. We evaluate the prediction accuracy of 21 methods, many of which are only available as web applications, on a curated set of 126 proteins encoded by viruses. Furthermore, we apply a random forest classifier to these predictor outputs. Based on cross-validation experiments, this ensemble approach confers a substantial improvement in accuracy, e.g., a mean 36 per cent gain in Matthews correlation coefficient. Lastly, we apply the random forest predictor to severe acute respiratory syndrome coronavirus 2 ORF6, an accessory gene that encodes a short (61 AA) and moderately disordered protein that inhibits the host innate immune response. We show that disorder prediction methods perform differently for viral and non-viral proteins, and that an ensemble approach can yield more robust and accurate predictions.

Background During combined anti-retroviral treatment, a latent HIV reservoir persists within resting memory CD4 T cells that initiates viral recrudescence upon treatment interruption. Strategies for HIV-1 cure have largely focused on latency reversing agents (LRAs) capable of reactivating and eliminating this viral reservoir. Previously investigated LRAs have largely failed to achieve a robust latency reversal sufficient for reduction of latent HIV pool or the potential of virus-free remission in the absence of treatment. Methods We utilize a polyvalent virus-like particle (VLP) formulation called Activator Vector (ACT-VEC) to ‘shock’ provirus into transcriptional activity. Ex vivo co-culture experiments were used to evaluate the efficacy of ACT-VEC in relation to other LRAs in individuals diagnosed and treated during the acute stage of infection. IFN-γ ELISpot, qRT-PCR and Illumina MiSeq were used to evaluate antigenicity, latency reversal, and diversity of induced virus respectively. Findings Using samples from HIV+ patients diagnosed and treated at acute/early infection, we demonstrate that ACT-VEC can reverse latency in HIV infected CD4 T cells to a greater extent than other major recall antigens as stimuli or even mitogens such as PMA/Iono. Furthermore, ACT-VEC activates more latent HIV-1 than clinically tested HDAC inhibitors or protein kinase C agonists. Interpretation Taken together, these results show that ACT-VEC can induce HIV reactivation from latently infected CD4 T cells collected from participants on first line combined antiretroviral therapy for at least two years after being diagnosed and treated at acute/early stage of infection. These findings could provide guidance to possible targeted cure strategies and treatments. Funding NIH and CIHR

In infectious disease epidemiology, clustering cases of infection in space and time is a standard method to identify and characterize outbreaks. Clustering cases by genetic similarity is analogous to spatial clustering, and may be more effective for pathogens transmitted at a relatively low rate by intimate contact. However, the statistical properties of genetic clustering in the context of out-break detection are not well characterized and cluster-defining criteria are generally set to arbitrary values. We describe a new method to optimize the predictive value of a clustering method by optimizing its parameters to maximize the difference in the Akaike information criterion (AIC) between individual-weighted and null models of cluster growth. This approach mirrors solutions to the modifiable areal unit problem (MAUP): the statistical association between covariates and an outcome is contingent on how their spatial distribution is partitioned into units of observation. To evaluate our method, we analyzed the distributions of pairwise Tamura-Nei (TN93) genetic distances from two published sets of anonymized HIV-1 subtype B pol sequence data stratified by collection year. We generated 46 different graphs by varying the pairwise threshold, where an edge in a graph indicates that the TN93 distance between the respective cases is below the corresponding threshold. For each graph, we generated predictions of cluster growth (numbers of new cases with edges to clusters of known cases) under two different Poisson regression models: a null model in which growth is only proportional to cluster size (i.e., no variation among individuals); and a weighted model where the variation associated with individual-level covariates are summed by cluster. Next, we calculated the AIC for each model on the distributions of observed cluster growth in two published HIV-1 pol data sets from Seattle, USA (n = 1, 653) and Alberta, Canada (n = 809). Based on the difference in AICs, we obtained different optimized TN93 thresholds for these data sets (0.014 and 0.011, respectively). We show that selection of this threshold parameter can substantially limit the utility of genetic clusters for public health, and that the optimal parameter for one population can misdirect prevention efforts in another. This statistical framework can potentially be used to optimize any clustering method, and to evaluate it against other methods including those that do not use genetic information.

Background Increasing first-line treatment failures in low- and middle-income countries (LMICs) have led to increased use of integrase strand transfer inhibitors (INSTIs) such as dolutegravir. However, HIV-1 susceptibility to INSTIs in LMICs, especially with previous raltegravir exposure, is poorly understood due to infrequent reporting of INSTI failures and testing for INSTI drug resistance mutations (DRMs). Methods A total of 51 non-subtype B HIV-1 infected patients failing third-line (raltegravir-based) therapy in Uganda were initially selected for the study. DRMs were detected using Sanger and deep sequencing. HIV integrase genes of 13 patients were cloned and replication capacities (RCs) and phenotypic susceptibilities to dolutegravir, raltegravir and elvitegravir were determined with TZM-bl cells. Spearman’s correlation coefficient was used to determine cross-resistance between INSTIs. Results INSTI DRMs were detected in 47% of patients. HIV integrase-recombinant virus carrying one primary INSTI DRM (N155H or Y143R/S) was susceptible to dolutegravir but highly resistant to raltegravir and elvitegravir (>50-fold change). Two patients, one with E138A/G140A/Q148R/G163R and one with E138K/G140A/S147G/Q148K, displayed the highest reported resistance to raltegravir, elvitegravir and even dolutegravir. The former multi-DRM virus had WT RC whereas the latter had lower RCs than WT. Conclusions In HIV-1 subtype A- and D-infected patients failing raltegravir and harbouring INSTI DRMs, there is high-level resistance to elvitegravir and raltegravir. More routine monitoring of INSTI treatment may be advised in LMICs, considering that multiple INSTI DRMs may have accumulated during prolonged exposure to raltegravir during virological failure, leading to high-level INSTI resistance, including dolutegravir resistance.

The HIV reservoir, which comprises diverse proviruses integrated into the genomes of infected, primarily CD4+ T cells, is the main barrier to developing an effective HIV cure. Our understanding of the genetics and dynamics of proviruses persisting within distinct CD4+ T cell subsets, however, remains incomplete. Using single-genome amplification, we characterized subgenomic proviral sequences (nef region) from naive, central memory, transitional memory, and effector memory CD4+ T cells from five HIV-infected individuals on long-term combination antiretroviral therapy (cART) and compared these to HIV RNA sequences isolated longitudinally from archived plasma collected prior to cART initiation, yielding HIV data sets spanning a median of 19.5 years (range, 10 to 20 years) per participant. We inferred a distribution of within-host phylogenies for each participant, from which we characterized proviral ages, phylogenetic diversity, and genetic compartmentalization between CD4+ T cell subsets. While three of five participants exhibited some degree of proviral compartmentalization between CD4+ T cell subsets, combined analyses revealed no evidence that any particular CD4+ T cell subset harbored the longest persisting, most genetically diverse, and/or most genetically distinctive HIV reservoir. In one participant, diverse proviruses archived within naive T cells were significantly younger than those in memory subsets, while for three other participants we observed no significant differences in proviral ages between subsets. In one participant, "old" proviruses were recovered from all subsets, and included one sequence, estimated to be 21.5 years old, that dominated (>93%) their effector memory subset. HIV eradication strategies will need to overcome within- and between-host genetic complexity of proviral landscapes, possibly via personalized approaches.IMPORTANCE The main barrier to HIV cure is the ability of a genetically diverse pool of proviruses, integrated into the genomes of infected CD4+ T cells, to persist despite long-term suppressive combination antiretroviral therapy (cART). CD4+ T cells, however, constitute a heterogeneous population due to their maturation across a developmental continuum, and the genetic "landscapes" of latent proviruses archived within them remains incompletely understood. We applied phylogenetic techniques, largely novel to HIV persistence research, to reconstruct within-host HIV evolutionary history and characterize proviral diversity in CD4+ T cell subsets in five individuals on long-term cART. Participants varied widely in terms of proviral burden, genetic diversity, and age distribution between CD4+ T cell subsets, revealing that proviral landscapes can differ between individuals and between infected cell types within an individual. Our findings expose each within-host latent reservoir as unique in its genetic complexity and support personalized strategies for HIV eradication.

The HIV reservoir, which comprises diverse proviruses integrated into the genomes of infected, primarily CD4(+) T cells, is the main barrier to developing an effective HIV cure. Our understanding of the genetics and dynamics of proviruses persisting within distinct CD4(+) T cell subsets, however, remains incomplete. Using single-genome amplification, we characterized subgenomic proviral sequences (nef region) from naive, central memory, transitional memory, and effector memory CD4(+) T cells from five HIV-infected individuals on long-term combination antiretroviral therapy (cART) and compared these to HIV RNA sequences isolated longitudinally from archived plasma collected prior to cART initiation, yielding HIV data sets spanning a median of 19.5 years (range, 10 to 20 years) per participant. We inferred a distribution of within-host phylogenies for each participant, from which we characterized proviral ages, phylogenetic diversity, and genetic compartmentalization between CD4(+) T cell subsets. While three of five participants exhibited some degree of proviral compartmentalization between CD4(+) T cell subsets, combined analyses revealed no evidence that any particular CD4(+) T cell subset harbored the longest persisting, most genetically diverse, and/or most genetically distinctive HIV reservoir. In one participant, diverse proviruses archived within naive T cells were significantly younger than those in memory subsets, while for three other participants we observed no significant differences in proviral ages between subsets. In one participant, “old” proviruses were recovered from all subsets, and included one sequence, estimated to be 21.5 years old, that dominated (>93%) their effector memory subset. HIV eradication strategies will need to overcome within- and between-host genetic complexity of proviral landscapes, possibly via personalized approaches. IMPORTANCE The main barrier to HIV cure is the ability of a genetically diverse pool of proviruses, integrated into the genomes of infected CD4(+) T cells, to persist despite long-term suppressive combination antiretroviral therapy (cART). CD4(+) T cells, however, constitute a heterogeneous population due to their maturation across a developmental continuum, and the genetic “landscapes” of latent proviruses archived within them remains incompletely understood. We applied phylogenetic techniques, largely novel to HIV persistence research, to reconstruct within-host HIV evolutionary history and characterize proviral diversity in CD4(+) T cell subsets in five individuals on long-term cART. Participants varied widely in terms of proviral burden, genetic diversity, and age distribution between CD4(+) T cell subsets, revealing that proviral landscapes can differ between individuals and between infected cell types within an individual. Our findings expose each within-host latent reservoir as unique in its genetic complexity and support personalized strategies for HIV eradication.

The global HIV-1 pandemic comprises many genetically divergent subtypes. Most of our understanding of drug resistance in HIV-1 derives from subtype B, which predominates in North America and western Europe. However, about 90% of the pandemic represents non-subtype B infections. Here, we use deep sequencing to analyze HIV-1 from infected individuals in Uganda who were either treatment-naïve or who experienced virologic failure on ART without the expected patterns of drug resistance. Our objective was to detect potentially novel associations between mutations in HIV-1 integrase and treatment outcomes in Uganda, where most infections are subtypes A or D. We retrieved a total of 380 archived plasma samples from patients at the Joint Clinical Research Centre (Kampala), of which 328 were integrase inhibitor-naïve and 52 were raltegravir (RAL)-based treatment failures. Next, we developed a bioinformatic pipeline for alignment and variant calling of the deep sequence data obtained from these samples from a MiSeq platform (Illumina). To detect associations between within-patient polymorphisms and treatment outcomes, we used a support vector machine (SVM) for feature selection with multiple imputation to account for partial reads and low quality base calls. Candidate point mutations of interest were experimentally introduced into the HIV-1 subtype B NL4-3 backbone to determine susceptibility to RAL in U87.CD4.CXCR4 cells. Finally, we carried out replication capacity experiments with wild-type and mutant viruses in TZM-bl cells in the presence and absence of RAL. Our analyses not only identified the known major mutation N155H and accessory mutations G163R and V151I, but also novel mutations I203M and I208L as most highly associated with RAL failure. The I203M and I208L mutations resulted in significantly decreased susceptibility to RAL (44.0-fold and 54.9-fold, respectively) compared to wild-type virus (EC50=0.32 nM), and may represent novel pathways of HIV-1 resistance to modern treatments.Author summaryThere are many different types of HIV-1 around the world. Most of the research on how HIV-1 can become resistant to drug treatment has focused on the type (B) that is the most common in high-income countries. However, about 90% of infections around the world are caused by a type other than B. We used next-generation sequencing to analyze samples of HIV-1 from patients in Uganda (mostly infected by types A and D) for whom drug treatment failed to work, and whose infections did not fit the classic pattern of adaptation based on B. Next, we used machine learning to detect mutations in these virus populations that could explain the treatment outcomes. Finally, we experimentally added two candidate mutations identified by our analysis to a laboratory strain of HIV-1 and confirmed that they conferred drug resistance to the virus. Our study reveals new pathways that other types of HIV-1 may use to evolve resistance to drugs that make up the current recommended treatment for newly diagnosed individuals.

BACKGROUND: Harm reduction has dramatically reduced HIV incidence among people who inject drugs (PWID). In Glasgow, Scotland, 90%) is indicative of sharing of injecting equipment. Monitoring the epidemic phylogenetically in real time may accelerate public health action.

To screen for drug resistance and possible treatment with Dolutegravir (DTG) in treatment-naive patients and those experiencing virologic failure during first-, second-, and third-line combined antiretroviral therapy (cART) in Uganda. Samples from 417 patients in Uganda were analyzed for predicted drug resistance upon failing a first- (N = 158), second- (N = 121), or third-line [all 51 involving Raltegravir (RAL)] treatment regimen. HIV-1 pol gene was amplified and sequenced from plasma samples. Drug susceptibility was interpreted using the Stanford HIV database algorithm and SCUEAL was used for HIV-1 subtyping. Frequency of resistance to nucleoside reverse transcriptase inhibitors (NRTIs) (95%) and non-NRTI (NNRTI, 96%) was high in first-line treatment failures. Despite lack of NNRTI-based treatment for years, NNRTI resistance remained stable in 55% of patients failing second-line or third-line treatment, and was also at 10% in treatment-naive Ugandans. DTG resistance (n = 366) was not observed in treatment-naive individuals or individuals failing first- and second-line cART, and only found in two patients failing third-line cART, while 47% of the latter had RAL- and Elvitegravir-resistant HIV-1. Secondary mutations associated with DTG resistance were found in 2%–10% of patients failing third-line cART. Of 14 drugs currently available for cART in Uganda, resistance was readily observed to all antiretroviral drugs (except for DTG) in Ugandan patients failing first-, second-, or even third-line treatment regimens. The high NNRTI resistance in first-line treatment in Uganda even among treatment-naive patients calls for the use of DTG to reach the UNAIDS 90:90:90 goals.