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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.

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14,221 sequences) from four sites in USA (Tennessee, Seattle), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007–0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 – 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

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.

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.

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.

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.

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.

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.

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 open reading frames (OvRFs) of 12,609 virus reference genomes in the NCBI database. We retrieved metadata associated with all annotated open reading frames (ORFs) 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 dsDNA viruses. Antisense overlaps in which one of the ORFs was encoded in the same frame on the opposite strand (−0) tend to be longer. Next, we develop a new graph-based representation of the distribution of overlaps among the ORFs of genomes in a given virus family. In the absence of an unambiguous partition of ORFs 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 ORFs are adjacent in one or more genomes, and (2) that these ORFs 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.

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.

Objective: Long-acting rilpivirine is a candidate for preexposure prophylaxis (PrEP) for prevention of HIV-1 infection. However, rilpivirine resistance mutations at reverse transcriptase codon 138 (E138X) occur naturally in a minority of HIV-1-infected persons; in particular those expressing human leukocyte antigen (HLA)-B*18 where reverse transcriptase-E138X arises as an immune escape mutation. We investigate the global prevalence, B*18-linkage and replicative cost of reverse transcriptase-E138X and its regional implications for rilpivirine PrEP. Methods: We analyzed linked reverse transcriptase-E138X/HLA data from 7772 antiretroviral-naive patients from 16 cohorts spanning five continents and five HIV-1 subtypes, alongside unlinked global reverse transcriptase-E138X and HLA frequencies from public databases. E138X-containing HIV-1 variants were assessed for in-vitro replication as a surrogate of mutation stability following transmission. Results: Reverse transcriptase-E138X variants, where the most common were rilpivirine resistance-associated mutations E138A/G/K, were significantly enriched in HLA-B*18-positive individuals globally (P = 3.5 × 10−20) and in all HIV-1 subtypes except A. Reverse transcriptase-E138X and B*18 frequencies correlated positively in 16 cohorts with linked HIV/HLA genotypes (Spearman's R = 0.75; P = 7.6 × 10−4) and in unlinked HIV/HLA data from 43 countries (Spearman's R = 0.34, P = 0.02). Notably, reverse transcriptase-E138X frequencies approached (or exceeded) 10% in key epidemic regions (e.g. sub-Saharan Africa, Southeastern Europe) where B*18 is more common. This, along with the observation that reverse transcriptase-E138X variants do not confer in-vitro replicative costs, supports their persistence, and ongoing accumulation in circulation over time. Conclusions: Results illustrate the potential for a natural immune-driven HIV-1 polymorphism to compromise antiretroviral-based prevention, particularly in key epidemic regions. Regional reverse transcriptase-E138X surveillance should be undertaken before use of rilpivirine PrEP.

Genetic sequencing ("genotyping") plays a critical role in the modern clinical management of HIV infection. This virus evolves rapidly within patients because of its error-prone reverse transcriptase and short generation time. Consequently, HIV variants with mutations that confer resistance to one or more antiretroviral drugs can emerge during sub-optimal treatment. There are now multiple HIV drug resistance interpretation algorithms that take the region of the HIV genome encoding the major drug targets as inputs; expert use of these algorithms can significantly improve to clinical outcomes in HIV treatment. Next-generation sequencing has the potential to revolutionize HIV resistance genotyping by lowering the threshold that rare but clinically significant HIV variants can be detected reproducibly, and by conferring improved cost-effectiveness in high-throughput scenarios. In this review, we discuss the relative merits and challenges of deploying the Illumina MiSeq instrument for clinical HIV genotyping.

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.

PURPOSE OF REVIEW The purpose of this study is to summarize recent advances in public health applications of comparative methods for HIV-1 sequence analysis in real time, including genetic clustering methods. RECENT FINDINGS Over the past 2 years, several groups have reported the deployment of established genetic clustering methods to guide public health decisions for HIV prevention in 'near real time'. However, it remains unresolved how well the readouts of comparative methods like clusters translate to events that are actionable for public health. A small number of recent studies have begun to elucidate the linkage between clusters and HIV-1 incidence, whereas others continue to refine and develop new comparative methods for such applications. SUMMARY Although the use of established methods to cluster HIV-1 sequence databases has become a widespread activity, there remains a critical gap between clusters and public health value.

Reconstructing the early dynamics of the HIV-1 pandemic can provide crucial insights into the socioeconomic drivers of emerging infectious diseases in human populations, including the roles of urbanization and transportation networks. Current evidence indicates that the global pandemic comprising almost entirely of HIV-1/M originated around the 1920s in central Africa. However, these estimates are based on molecular clock estimates that are assumed to apply uniformly across the virus genome. There is growing evidence that recombination has played a significant role in the early history of the HIV-1 pandemic, such that different regions of the HIV-1 genome have different evolutionary histories. In this study, we have conducted a dated-tip analysis of all near full-length HIV-1/M genome sequences that were published in the GenBank database. We used a sliding window approach similar to the ‘bootscanning’ method for detecting breakpoints in intersubtype recombinant sequences. We found evidence of substantial variation in estimated root dates among windows, with an estimated mean time to the most recent common ancestor (tMRCA) of 1922. Estimates were significantly autocorrelated, which was more consistent with an early recombination event than with stochastic error variation in phylogenetic reconstruction and dating analyses. A piecewise regression analysis supported the existence of at least one recombination breakpoint in the HIV-1/M genome with interval-specific means around 1929 and 1913, respectively. This analysis demonstrates that a sliding window approach can accommodate early recombination events outside the established nomenclature of HIV-1/M subtypes, although it is difficult to incorporate the earliest available samples due to their limited genome coverage.

Summary Background HIV evolves rapidly and therefore infections with similar genetic sequences are likely linked by recent transmission events. Clusters of related infections can represent subpopulations with high rates of transmission. We describe the implementation of an automated near real-time system to monitor and characterise HIV transmission hotspots in British Columbia, Canada. Methods In this implementation case study, we applied a monitoring system to the British Columbia drug treatment database, which holds more than 32 000 anonymised HIV genotypes for nearly 9000 residents of British Columbia living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated reanalysis of the entire database. We extracted clusters of five or more individuals with short phylogenetic distances between their respective HIV sequences. The system generated monthly reports of the growth and characteristics of clusters that were distributed to public health officers. Findings In June, 2014, the monitoring system detected the expansion of a cluster by 11 new cases during 3 months, including eight cases with transmitted drug resistance. This cluster generally comprised young men who have sex with men. The subsequent report precipitated an enhanced public health follow-up to ensure linkage to care and treatment initiation in the affected subpopulation. Of the nine cases associated with this follow-up, all had already been linked to care and five cases had started treatment. Subsequent to the follow-up, three additional cases started treatment and most cases achieved suppressed viral loads. During the next 12 months, we detected 12 new cases in this cluster with reduction in the onward transmission of drug resistance. Interpretation Our findings show the first application of an automated phylogenetic system monitoring a clinical database to detect a recent HIV outbreak and support the ensuing public health response. By making secondary use of routinely collected HIV genotypes, this approach is cost-effective, attains near real-time monitoring of new cases, and can be implemented in all settings in which HIV genotyping is the standard of care. Funding BC Centre for Excellence in HIV/AIDS, the Canadian Institutes for Health Research, the Genome Canada-CIHR Partnership in Genomics and Personalized Health, and the US National Institute on Drug Abuse.

The comparative study of homologous proteins can provide abundant information about the functional and structural constraints on protein evolution. For example, an amino acid substitution that is deleterious may become permissive in the presence of another substitution at a second site of the protein. A popular approach for detecting coevolving residues is by looking for correlated substitution events on branches of the molecular phylogeny relating the protein-coding sequences. Here we describe a machine learning method (Bayesian graphical models) implemented in the open-source phylogenetic software package HyPhy, http://hyphy.org , for extracting a network of coevolving residues from a sequence alignment.

More persons living with HIV reside in the Southern United States than in any other region, yet little is known about HIV molecular epidemiology in the South. We used cluster and phylodynamic analyses to evaluate HIV transmission patterns in middle Tennessee. We performed cross-sectional analyses of HIV-1 pol sequences and clinical data collected from 2001 to 2015 among persons attending the Vanderbilt Comprehensive Care Clinic. Transmission clusters were identified using maximum likelihood phylogenetics and patristic distance differences. Demographic, risk behavior, and clinical factors were assessed evaluating “active” clusters (clusters including sequences sampled 2011–2015) and associations estimated with logistic regression. Transmission risk ratios for men who have sex with men (MSM) were estimated with phylodynamic models. Among 2915 persons (96% subtype-B sequences), 963 (33%) were members of 292 clusters (distance ≤1.5%, size range 2–39). Most clusters (62%, n = 690 persons) were active, either being newly identified (n = 80) or showing expansion on existing clusters (n = 101). Correlates of active clustering among persons with sequences collected during 2011–2015 included MSM risk and ≤30 years of age. Active clusters were significantly more concentrated in MSM and younger persons than historical clusters. Young MSM (YMSM) (≤26.4 years) had high estimated transmission risk [risk ratio = 4.04 (2.85–5.65) relative to older MSM] and were much more likely to transmit to YMSM. In this Tennessee cohort, transmission clusters over time were more concentrated by MSM and younger age, with high transmission risk among and between YMSM, highlighting the importance of interventions among this group. Detecting active clusters could help direct interventions to disrupt ongoing transmission chains.

The comparative study of homologous proteins can provide abundant information about the functional and structural constraints on protein evolution. For example, an amino acid substitution that is deleterious may become permissive in the presence of another substitution at a second site of the protein. A popular approach for detecting coevolving residues is by looking for correlated substitution events on branches of the molecular phylogeny relating the protein-coding sequences. Here we describe a machine learning method (Bayesian graphical models) implemented in the open-source phylogenetic software package HyPhy, http://hyphy.org , for extracting a network of coevolving residues from a sequence alignment.

Given that HIV evolution and latent reservoir establishment occur continually within-host, and that latently infected cells can persist long-term, the HIV reservoir should comprise a genetically heterogeneous archive recapitulating within-host HIV evolution. However, this has yet to be conclusively demonstrated, in part due to the challenges of reconstructing within-host reservoir establishment dynamics over long timescales. We developed a phylogenetic framework to reconstruct the integration dates of individual latent HIV lineages. The framework first involves inference and rooting of a maximum-likelihood phylogeny relating plasma HIV RNA sequences serially sampled before the initiation of suppressive antiretroviral therapy, along with putative latent sequences sampled thereafter. A linear model relating root-to-tip distances of plasma HIV RNA sequences to their sampling dates is used to convert root-to-tip distances of putative latent lineages to their establishment (integration) dates. Reconstruction of the ages of putative latent sequences sampled from chronically HIV-infected individuals up to 10 y following initiation of suppressive therapy revealed a genetically heterogeneous reservoir that recapitulated HIV’s within-host evolutionary history. Reservoir sequences were interspersed throughout multiple within-host lineages, with the oldest dating to >20 y before sampling; historic genetic bottleneck events were also recorded therein. Notably, plasma HIV RNA sequences isolated from a viremia blip in an individual receiving otherwise suppressive therapy were highly genetically diverse and spanned a 20-y age range, suggestive of spontaneous in vivo HIV reactivation from a large latently infected cell pool. Our framework for reservoir dating provides a potentially powerful addition to the HIV persistence research toolkit.

For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source supervised and alignment-free subtyping method (KAMERIS) that operates onk-mer frequencies in HIV-1 sequences. We performed a detailed study of the accuracy and performance of subtype classification in comparison to four state-of-the-art programs. Based on our testing data set of manually curated real-world HIV-1 sequences (n= 2, 784), Kameris obtained an overall accuracy of 97%, which matches or exceeds all other tested software, with a processing rate of over 1,500 sequences per second. Furthermore, our fully standalone general-purpose software provides key advantages in terms of data security and privacy, transparency and reproducibility. Finally, we show that our method is readily adaptable to subtype classification of other viruses including dengue, influenza A, and hepatitis B and C virus.

Persons with hepatitis C virus (HCV) genotype 1a (GT1a) infections harboring a baseline Q80K polymorphism in nonstructural protein 3 (NS3) have a reduced virologic response to simeprevir in combination with pegylated interferon-alfa and ribavirin. We aimed to develop, validate, and freely disseminate an NS3 clinical sequencing assay to detect the Q80K polymorphism and potentially other HCV NS3 drug resistance mutations. HCV RNA was extracted from frozen plasma using a NucliSENS easyMAG automated nucleic acid extractor, amplified by nested reverse transcription-PCR, and sequenced using Sanger and/or next-generation (MiSeq) methods. Sanger chromatograms were analyzed using in-house software (RECall), and nucleotide mixtures were called automatically. MiSeq reads were iteratively mapped to the H77 reference genome, and consensus NS3 sequences were generated with nucleotides present at >20% called as mixtures. The accuracy, precision, and sensitivity for detecting the Q80K polymorphism were assessed in 70 samples previously sequenced by an external laboratory. A comparison of the sequences generated by the Sanger and MiSeq methods with those determined by an external lab revealed >98.5% nucleotide sequence concordance and zero discordant calls of the Q80K polymorphism. The results were both highly repeatable and reproducible (>99.7% nucleotide concordance and 100% Q80K concordance). The limits of detection (>2 and ∼5 log 10 IU/ml for the Sanger and MiSeq assays, respectively) are sufficiently low to allow genotyping in nearly all chronically infected treatment-naive persons. No systematic bias in the under- or overamplification of minority variants was observed. Coinfection with other viruses (e.g., HIV and hepatitis B virus [HBV]) did not affect the assay results. The two independent HCV NS3 sequencing assays with the automated analysis procedures described here are useful tools to screen for the Q80K polymorphism and other HCV protease inhibitor drug resistance mutations.

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods.

Table of contents A1 Introduction to the 2nd synchronicity forum of GHRI/CHVI-funded Canadian and African HIV prevention and vaccine teams O1 Voluntary medical male circumcision for prevention of heterosexual transmission of HIV in adult males in Soweto: What do indicators and incidence rate show? Hillary Mukudu, Neil Martinson, Benn Sartorius O2 Developing a peer-led community mobilization program for sex workers in Soweto: HIV risk and demographics Jenny Coetzee, Janan Dietrich, Kgaugelo Mokgatswana, Rachel Jewkes, Glenda E. Gray O3 Salient beliefs about adherence: A qualitative survey conducted as part of the demonstration study on "treatment as prevention" (TasP) and "pre-exposure prophylaxis" (PrEP) among female sex workers (FSWS) in Cotonou, Benin Marylène Dugas, Luc Béhanzin, Fernand A. Guédou, Marie-Pierre Gagnon, Michel Alary O4 Relative perception of risk as a driver of unsafe sexual practices among key populations: Cases of fisherfolk and women and their partners involved in multiple sexual partnerships in Uganda Rwamahe Rutakumwa, Martin Mbonye, Thadeus Kiwanuka, Sarah Nakamanya, Richard Muhumuza, Winfred Nalukenge, Janet Seeley O5 Exploring the acceptability of new biomedical HIV prevention technologies among MSM, adolescents and heterosexual adults in South Africa Millicent Atujuna, Melissa Wallace, Ben Brown, Linda Gail Bekker, Peter A. Newman O6 HIV-susceptible target cells in foreskins after voluntary medical male circumcision in South Africa Rushil Harryparsad, Abraham J. Olivier, Heather B. Jaspan, Douglas Wilson, Janan Dietrich, Neil Martinson, Hillary Mukudu, Nonhlanhla Mkhize, Lynn Morris, Gianguido Cianci, Minh Dinh, Thomas Hope, Jo-Ann S. Passmore, Clive M. Gray O7 HIV-1 proteins activate innate immune responses via TLR2 heterodimers Bethany M. Henrick, Xiao-Dan Yao, Kenneth L. Rosenthal, the INFANT Study Team O8 Characterization of an innate factor in human milk and mechanisms of action against HIV-1 Bethany M. Henrick, Xiao-Dan Yao, Anna G. Drannik, Alash’le Abimiku, Kenneth L. Rosenthal, the INFANT Study Team O9 Secretor status and susceptibility to HIV infections among female sex workers in Nairobi, Kenya Nadia Chanzu, Walter Mwanda, Julius Oyugi, Omu Anzala O10 Natural Killer cell recall responsiveness to Gag-HIV-1 peptides of HIV-1 exposed but uninfected subjects are associated with peripheral CXCR6+ NK cell subsets Moustapha Mbow, Sabelle Jallow, Moussa Thiam, Alberta Davis, Assane Diouf, Cheikh T. Ndour, Moussa Seydi, Tandakha N. Dieye, Souleymane Mboup, Martin Goodier, Eleanor Rilley, Assan Jaye O11 Profiles of resistance: Local innate mucosal immunity to HIV-1 in commercial sex workers Xiao-Dan Yao, RW. Omange, Bethany M. Henrick, Richard T. Lester, Joshua Kimani, T. Blake Ball, Francis A. Plummer, Kenneth L. Rosenthal O12 Early antiretroviral therapy and pre-exposure prophylaxis for HIV prevention among female sex workers in Cotonou, Benin: A demonstration project Luc Béhanzin, Fernand A. Guédou, Nassirou Geraldo, Ella Goma Mastétsé, Jerôme Charles Sossa, Marcel Djimon Zannou, Michel Alary O13 Building capacity for HIV prevention trials: Preliminary data from a Nigerian cohort of HIV exposed sero-negatives (HESN) Sophia Osawe, Evaezi Okpokoro, Felicia Okolo, Stephen Umaru, Rebecca Abimiku, Sam Audu, Pam Datong, Alash’le Abimiku O14 Equipping healthcare professionals with skills required for the conduct of clinical trials in an effort to build capacity. Lessons learned Jacquelyn Nyange, Joyce Olenja, Gaudensia Mutua, Walter Jaoko, Gloria Omosa-Manyonyi, Bashir Farah, Maureen Khaniri, Omu Anzala O15 Educational technology to support active learning for HIV researchers and planners Anne Cockcroft, Kendra Tonkin, Indu Girish, Puna Mhati, Ashley Cunningham, Neil Andersson O16 From Lake Kivu (Rwanda) and Lake Malawi (Tanzania) to the shores of Lake Victoria (Uganda): Strengthening laboratory capacity through Good Clinical Laboratory Practice training Bashir Farah, Jackton Indangasi, Walter Jaoko, Gaudensia Mutua, Maureen Khaniri, Jacquelyn Nyange, Omu Anzala O17 Rilpivirine and etravirine resistance mutations in HIV-1 subtype C infected patients on a non-nucleoside reverse transcriptase inhibitor-based combination antiretroviral therapy in Botswana Thabo Diphoko, Simani Gaseitsiwe, Victoria Maiswe, Thato Iketleng, Dorcas Maruapula, Keabetswe Bedi, Sikhulile Moyo, Rosemary Musonda, Mark Wainberg, Joseph Makhema, Vladimir Novitsky, Richard Marlink, Max Essex O18 From home-based HIV testing to initiation of treatment: The AIDS Support Organization (TASO) Experience with Home-based HIV Counselling and Testing (HBHCT) among Adolescents in Uganda, 2005-2011 Stephen Okoboi, Livingstone Ssali, Sam Kalibala, Josephine Birungi, Aggrey Egessa, Jonathan Wangisi, Lyavala Joanne Okullu, Celestin Bakanda, Francis Obare41 O19 Feasibility study on using real time medication monitoring among HIV infected and Tuberculosis patients in Kilimanjaro, Tanzania I. Marion Sumari-de Boer, Hadija H. Semvua, Jossy van den Boogaard, Krisanta W. Kiwango, Kennedy M. Ngowi, Pythia T. Nieuwkerk, Rob E. Aarnoutse, Ireen Kiwelu, Eva Muro, Gibson S. Kibiki O20 Deaths still among sero-discordant cohort in Nigeria despite Access to treatment Ruth Datiri, Grace Choji, Sophia Osawe, Evaezi Okpokoro, Felicia Okolo, Stephen Umaru, Rebecca Abimiku, Samuel Audu, Pam Datong, Alash’le Abimiku O21 Therapeutic HIV-1 vaccine trials in Denmark and Guinea-Bissau Fomsgaard A, Karlsson I, Jensen KJ, Jensen SS, Leo-Hansen C, Jespersen S, Da Silva Té D, Rodrigues CM, da Silva ZJ, Janitzek CM, Gerstoft J, Kronborg G, the WAPHIR Group O22 Willingness to participate in a HIV vaccine Trial among HIV exposed sero-negative (HESN) persons in Jos, Nigeria Evaezi Okpokoro, Sophia Osawe, Ruth Daitiri, Grace Choji, Stephen Umaru, Felicia Okolo, Pam Datong, Alash'le Abimiku O23 Clinical research volunteers’ perceptions and experiences of screening for enrolment at KAVI-Institute of Clinical Research, Kenya Nyariki Emily, Olenja Joyce, Lorway R. Robert, Anzala Anzala O24 Gut microbiome, HIV-exposure, and vaccine responses in South African infants Katie Viljoen, Jerome Wendoh, Elvis Kidzeru, Ulas Karaoz, Eoin Brodie, Gerrit Botha, Nicola Mulder, Clive Gray, William Cameron, Alain Stintzi, Heather Jaspan, for the INFANT study team O25 Analysis of HIV pol diversity in the concentrated HIV epidemic in Saskatchewan Paul N. Levett, David Alexander, Naveed Gulzar, Prabvir S. Grewal, Art F. Y. Poon, Zabrina Brumme, P. Richard Harrigan, James I. Brooks, Paul A. Sandstrom, Stryker Calvez, Stephen E. Sanche, Jamie K. Scott P1 Evaluating a HIV vaccine research community engagement programme at two HIV prevention research centres in the Western Cape Leslie Swartz, Ashraf Kagee, Anthea Lesch, Zuhayr Kafaar, Anneliese De Wet P2 Validating HIV acquisition risk score using a cohort HIV exposed sero-negative persons in a discordant relationship in Jos, Nigeria, West Africa Evaezi Okpokoro, Sophia Osawe, Ruth Daitiri, Grace Choji, Stephen Umaru, Felicia Okolo, Pam Datong, Alash'le Abimiku P3 Bridging the gap between adults and adolescents and youth adults (AYA) – Employing a youth-centred approach to investigate HIV risk among AYA in Soweto and Durban, South Africa Janan Dietrich, Tricia Smith, Laura Cotton, Stefanie Hornschuh, Martin van der Watt, Cari L. Miller, Glenda Gray, Jenni Smit, Manjeetha Jaggernath, Thumbi Ndung’u, Mark Brockman, Angela Kaida, on behalf of the AYAZAZI study teams P4 Neighbours to sex workers: A key population that has been ignored Maureen Akolo, Joshua Kimani, Prof Larry Gelmon, Michael Chitwa, Justus Osero P5 Young women’s access to structural support programmes in a district of Botswana Anne Cockcroft, Nobantu Marokoane, Leagajang Kgakole, Boikhutso Maswabi, Neo Mpofu, Umaira Ansari, Neil Andersson P6 Voices for action from peri-urban Ugandan students, teachers and parents on HIV/STI prevention: Qualitative research results Nakinobe Elizabeth, Miiro George Mukalazi, Zalwango Flavia, Nakiyingi-Miiro Jessica, Kaleebu Potiano P7 Engaging Social Media as an education tool on the fly: The use of Facebook for HIV and Ebola prevention and awareness amongst adolescents in Uganda John Ross Semwanga, Emily Nyanzi, Saidat Namuli Musoke, Elizabeth Nakinobe, George Miiro, Edward Katongole Mbidde, Tom Lutalo, Pontiano Kaleebu P8 Circulating HIV-1 subtypes among sexual minority populations in Zambia Ray Handema, Graham P. Chianzu P9 The Development of HIV Bio-bank resource management to support clinical trial and Intervention research: WAPHIR experience Moussa Thiam, Diabou Diagne-Gueye, Mame K. Ndiaye, Moustapha Mbow, Birahim P. Ndiaye, Ibrahima Traore, Mamadou C. Dia, Gilleh Thomas, Coumba Tour-Kane, Souleymane Mboup, Assan Jaye P10 Capacity building for clinical trials as a novel approach for scaling up HIV prevention research initiatives in East Africa: achievements and challenges Emily Nyanzi, Edward Katongole Mbidde, Pontiano Kaleebu, Juliet Mpendo, Joshua Kimani, Josephine Birungi, Winnie Muyindike, Andrew Kambugu P11 Community and media perspective of research; an advocacy workshop on HIV prevention research Hachizovu Sebastian, Handema Ray, Chaponda Mike, Kabuya Jean Bertin, Mulenga Modest P12 Development of a quantitative HIV-1 and HIV-2 real time PCR (qRT-PCR) viral load assay Moussa Thiam, Omar Janha, Alberta Davis, Alfred Amambua-Ngwa, Davis C. Nwakanma, Souleymane Mboup, Assan Jaye P13 Differential effects of sex in a West African Cohort of HIV-1, HIV-2 and HIV-1/2 dual infected patients: Men are worse off Sanne Jespersen, Bo Langhoff Hønge, Joakim Esbjörnsson, Candida Medina, David Da Silva TÉ, Faustino Gomes Correira, Alex Lund Laursen, Lars Østergaard, Andreas Andersen, Peter Aaby, Christian Erikstrup, Christian Wejse, for the Bissau HIV Cohort study group P14 HIV-infected adolescents in transition from pediatric to adult HIV care in Dakar, Senegal: sample characteristics and immunological and virological profiles Siry Dieye, Moussa Sarr, Haby Sy, Helene D Mbodj, Marianne Ndiaye, Amy Ndiaye, Seydi Moussa, Assan Jaye, Souleymane Mboup100 P15 Molecular characterization of vertically transmitted HIV-1 among children born to HIV-1 seropositive mothers in Northern Tanzania Balthazar M. Nyombi, Elichilia R. Shao, Innocent B. Chilumba, Sikhulile Moyo, Simani Gaseitsiwe, Rosemary Musonda P16 Breast-fed HIV-1 exposed infants play catch up. A preliminary report Pam Datong, Bucky Inyang, Sophia Osawe, Abel Izang, Chundung Cole, Felicia Okolo, Bill Cameron, Kenneth Rosenthal, Clive Gray, Heather Jaspan, Alash’le Abimiku, the INFANT study team P17 The frequency of N348I mutation in patient failing combination antiretroviral treatment In Botswana Boitumelo Seraise, Kerstin Andrea-Marobela, Sikhulile Moyo, Rosemary Musonda, Joseph Makhema, Max Essex, Simani Gaseitsiwe

Background Thymidine analogs, namely AZT (Zidovudine or Retrovir™) and d4T (Stavudine or Zerit™) are antiretroviral drugs still employed in over 75% of first line combination antiretroviral therapy (cART) in Kampala, Uganda despite aversion to prescribing these drugs for cART in high income countries due in part to adverse events. For this study, we explored how the continued use of these thymidine analogs in cART could impact emergence of drug resistance and impact on future treatment success in Uganda, a low-income country. Methods We examined the drug resistance genotypes by Sanger sequencing of 262 HIV-infected patients failing a first line combined antiretroviral treatment containing either AZT or d4T, which represents approximately 5% of the patients at the Joint Clinical Research Center receiving a AZT or d4T containing treatment. Next generation sequencing (DEEPGEN™HIV) and multiplex oligonucleotide ligation assays (AfriPOLA) were then performed on a subset of patient samples to detect low frequency drug resistant mutations. CD4 cell counts, viral RNA loads, and treatment changes were analyzed in a cohort of treatment success and failures. Results Over 80% of patients failing first line AZT/d4T-containing cART had predicted drug resistance to 3TC (Lamivudine) and non-nucleoside RT inhibitors (NNRTIs) in the treatment regimen but only 45% had resistance AZT/d4T associated resistance mutations (TAMs). TAMs were however detected at low frequency within the patients HIV quasispecies (1–20%) in 21 of 34 individuals who were failing first-line AZT-containing cART and lacked TAMs by Sanger. Due to lack of TAMs by Sanger, AZT was typically maintained in second-line therapies and these patients had a low frequency of subsequent virologic success. Conclusions Our findings suggest that continued use of AZT and d4T in first-line treatment in low-to-middle income countries may lead to misdiagnosis of HIV-1 drug resistance and possibly enhance a succession of second- and third-line treatment failures. Electronic supplementary material The online version of this article (doi: 10.1186/s40249-017-0377-0) contains supplementary material, which is available to authorized users.

Hepatitis C virus (HCV) has a naturally occurring polymorphism, Q80K, in the nonstructural protein 3 (NS3) gene encoding the viral protease, which has been associated with reduced susceptibility to the direct-acting antiviral inhibitor simeprevir. Q80K is observed predominantly in HCV genotype 1a and seldom in other HCV genotypes; moreover, it has a markedly high prevalence in the United States. Here, we reconstruct the evolutionary history of this polymorphism to investigate why it is so highly localized in prevalence and whether it is stably transmitted between hosts. We found that the majority (96%) of HCV infections carrying Q80K were descended from a single lineage in which a Q80K substitution occurred around the 1940s in the United States, which implies that this polymorphism is likely highly transmissible. Furthermore, we identified 2 other substitutions in NS3 that may interact with Q80K and contribute to its stability. Our results imply that the current distribution and prevalence of Q80K are unlikely to change significantly in the short term.

Background. The diversification of human immunodeficiency virus (HIV) is shaped by its transmission history. We therefore used a population based province wide HIV drug resistance database in British Columbia (BC), Canada, to evaluate the impact of clinical, demographic, and behavioral factors on rates of HIV transmission. Methods. We reconstructed molecular phylogenies from 27 296 anonymized bulk HIV pol sequences representing 7747 individuals in BC—about half the estimated HIV prevalence in BC. Infections were grouped into clusters based on phylogenetic distances, as a proxy for variation in transmission rates. Rates of cluster expansion were reconstructed from estimated dates of HIV seroconversion. Results. Our criteria grouped 4431 individuals into 744 clusters largely separated with respect to risk factors, including large established clusters predominated by injection drug users and more-recently emerging clusters comprising men who have sex with men. The mean log10 viral load of an individual’s phylogenetic neighborhood (composed of 5 other individuals with shortest phylogenetic distances) increased their odds of appearing in a cluster by >2-fold per log10 viruses per milliliter. Conclusions. Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention.

Hepatitis C virus (HCV) transmission continues to occur among people who inject drugs (PWID), with HCV incidence ranging from 10-40 cases per 100 person-years (1-7). Although needle and syringe programs, opioid substitution treatment (OST) and other harm reduction strategies have been successful at reducing HIV incidence in PWID (8), these programs have been less effective for preventing HCV (9-11). However, in the near future, HCV treatment will be highly curative (>90%), simple (once-daily), short duration (8-12 weeks) and well-tolerated: features that hold great promise for the potential effectiveness of HCV “treatment as prevention” among PWID (12, 13). Given the lack of protective immunity following spontaneous and treatment-induced HCV clearance (14), better preventive strategies are necessary to maximize the impact of HCV “treatment as prevention” initiatives. Mathematical modeling studies have suggested that modest increases in HCV treatment uptake could lead to substantial reductions in HCV prevalence (15), particularly if combined with improved coverage of needle and syringe and OST programs (16). Identifying characteristics of people at high risk of HCV transmission may provide important information for the design and implementation of targeted and more effective public health and treatment strategies for the elimination of HCV among PWID. Phylogenetic studies provide an opportunity to model underlying transmission patterns that cannot be determined through epidemiological studies, as demonstrated in HIV (17, 18). Phylogenetic analyses have identified pan-European HCV clustering among PWID infected with HIV (19), and have been used to document HCV networks within and between cities (20, 21). Social networks (22), younger age (23) and acute HCV (24) are associated with phylogenetic clustering. However, phylogenetic studies among PWID with HCV have been limited by small sample sizes, cross-sectional designs, diverse geographic sampling, and insufficient epidemiological data available to link with sequence data. The aim of this study was to investigate phylogenetic clustering of HCV and associated factors among participants enrolled in a longstanding prospective cohort of PWID in Vancouver, Canada.

A population of human immunodeficiency virus (HIV) within a host often descends from a single transmitted/founder virus. The high mutation rate of HIV, coupled with long delays between infection and diagnosis, make isolating and characterizing this strain a challenge. In theory, ancestral reconstruction could be used to recover this strain from sequences sampled in chronic infection; however, the accuracy of phylogenetic techniques in this context is unknown. To evaluate the accuracy of these methods, we applied ancestral reconstruction to a large panel of published longitudinal clonal and/or single-genome-amplification HIV sequence data sets with at least one intrapatient sequence set sampled within 6 months of infection or seroconversion ( n = 19,486 sequences, median [interquartile range] = 49 [20 to 86] sequences/set). The consensus of the earliest sequences was used as the best possible estimate of the transmitted/founder. These sequences were compared to ancestral reconstructions from sequences sampled at later time points using both phylogenetic and phylogeny-naive methods. Overall, phylogenetic methods conferred a 16% improvement in reproducing the consensus of early sequences, compared to phylogeny-naive methods. This relative advantage increased with intrapatient sequence diversity ( P < 10 −5 ) and the time elapsed between the earliest and subsequent samples ( P < 10 −5 ). However, neither approach performed well for reconstructing ancestral indel variation, especially within indel-rich regions of the HIV genome. Although further improvements are needed, our results indicate that phylogenetic methods for ancestral reconstruction significantly outperform phylogeny-naive alternatives, and we identify experimental conditions and study designs that can enhance accuracy of transmitted/founder virus reconstruction. IMPORTANCE When HIV is transmitted into a new host, most of the viruses fail to infect host cells. Consequently, an HIV infection tends to be descended from a single “founder” virus. A priority target for the vaccine research, these transmitted/founder viruses are difficult to isolate since newly infected individuals are often unaware of their status for months or years, by which time the virus population has evolved substantially. Here, we report on the potential use of evolutionary methods to reconstruct the genetic sequence of the transmitted/founder virus from its descendants at later stages of an infection. These methods can recover this ancestral sequence with an overall error rate of about 2.3%—about 15% more information than if we had ignored the evolutionary relationships among viruses. Although there is no substitute for sampling infections at earlier points in time, these methods can provide useful information about the genetic makeup of transmitted/founder HIV.

In the United States (US), foreign-born persons are disproportionately affected by HIV and differ epidemiologically from US-born persons with diagnosed HIV infection. Understanding HIV transmission dynamics among foreign-born persons is important to guide HIV prevention efforts for these populations. We conducted molecular transmission network analysis to describe HIV transmission dynamics among foreign-born persons with diagnosed HIV.Using HIV-1 polymerase nucleotide sequences reported to the US National HIV Surveillance System for persons with diagnosed HIV infection during 2001-2013, we constructed a genetic distance-based transmission network using HIV-TRACE and examined the birth region of potential transmission partners in this network.Of 77,686 people, 12,064 (16%) were foreign born. Overall, 28% of foreign-born persons linked to at least one other person in the transmission network. Of potential transmission partners, 62% were born in the United States, 31% were born in the same region as the foreign-born person, and 7% were born in another region of the world. Most transmission partners of male foreign-born persons (63%) were born in the United States, whereas most transmission partners of female foreign-borns (57%) were born in their same world region.These finding suggests that a majority of HIV infections among foreign-born persons in our network occurred after immigrating to the United States. Efforts to prevent HIV infection among foreign-born persons in the United States should include information of the transmission networks in which these individuals acquire or transmit HIV to develop more targeted HIV prevention interventions.

Objectives Protein biomarker, S100A7, in oral dysplasia and squamous cell carcinoma has shown some predictive value for the transformation of dysplasia to cancer. The objectives of this study are: (1) to determine a correlation between the expression of S100A7 and histologic grade of oral dysplastic lesions using immunohistochemistry and an algorithm based on image analysis; and (2) to evaluate whether S100A7 can be utilized as a reliable predictor for progression of low grade oral dysplastic lesions or transformation to carcinoma. Findings 8 low grade lesions evolved into high grade lesions, and 7 high grade lesions evolved into higher grade lesions, over time. For the low grade lesions, the average S100A7 immunostaining score was 5.6; three were graded low risk and 5 were graded medium risk by algorithm. One low grade and 3 high grade lesions did not progress and remained stable. For these, the average S100A7 immunostaining score was 5.8; one was graded low risk and 3 were graded medium risk by algorithm. Preliminary analysis suggests S100A7 has increased expression in higher risk lesions. Conclusion The identification of a reliable, quantitative measure in the diagnosis of dysplasia and the ability to predict the likelihood of transformation to malignancy will potentially lead to more individualized treatment and better patient outcomes.

The APOBEC3 enzymes can induce mutagenesis of HIV-1 proviral DNA through the deamination of cytosine. HIV-1 overcomes this restriction through the viral protein Vif that induces APOBEC3 proteasomal degradation. Within this dynamic host-pathogen relationship, the APOBEC3 enzymes have been found to be beneficial, neutral, or detrimental to HIV-1 biology. Here, we assessed the ability of co-expressed APOBEC3F and APOBEC3G to induce HIV-1 resistance to antiviral drugs. We found that co-expression of APOBEC3F and APOBEC3G enabled partial resistance of APOBEC3F to Vif-mediated degradation with a corresponding increase in APOBEC3F-induced deaminations in the presence of Vif, in addition to APOBEC3G-induced deaminations. We recovered HIV-1 drug resistant variants resulting from APOBEC3-induced mutagenesis, but these variants were less able to replicate than drug resistant viruses derived from RT-induced mutations alone. The data support a model in which APOBEC3 enzymes cooperate to restrict HIV-1, promoting viral inactivation over evolution to drug resistance.

HIV-1 tropism can be predicted using V3 genotypic algorithms. The performance of these prediction algorithms for non-B subtypes is poorly characterized. Here, we use these genotypic algorithms to predict viral tropism of HIV-1 subtype A, B, C, and D to find apparent sensitivity, specificity, and concordance against a recombinant phenotypic assay, the original Trofile assay. This is a substudy of an epidemiological study (Pfizer A4001064). Plasma samples were selected to represent a large number of DM/X4 and R5 viruses. The HIV-1 env gene V3 loop was genotyped by Sanger sequencing (N=260) or 454 "deep" sequencing (N=280). Sequences were scored with g2p[coreceptor], PSSM X4/R5, PSSM SI/NSI, and PSSM subtype C matrices. Overall, non-B subtypes tropism prediction had similar concordance and apparent sensitivity and specificity as subtype B in predicting Trofile's results in both population sequencing (81.3%, 65.6%, and 90.5% versus 84.2%, 78.5%, and 88.2%) and 454 "deep" sequencing (82.3%, 80.0%, and 83.6% versus 86.8%, 92.0%, and 82.6%) using g2p[coreceptor]. By population sequencing, subtype A had lower sensitivity, whereas subtype D had lower specificity for non-R5 predictions, both in comparison to subtype B. 454 "deep" sequencing improved subtype A sensitivity but not subtype D. Subtype C had greater concordance than subtype B regardless of sequencing methods. In conclusion, genotypic tropism prediction algorithms may be applied to non-B HIV-1 subtypes with caution. Collective analysis of non-B subtypes revealed a performance similar to subtype B, whereas a subtype-specific analysis revealed overestimation (subtype D) or underestimation (subtype A).

Human immunodeficiency virus type 1 (HIV-1) V3 loop sequence can be used to infer viral coreceptor use. The effect of input copy number on population-based sequencing of the V3 loop of HIV-1 was examined through replicate deep and population-based sequencing of samples with known tropism, a heterogeneous clinical sample (624 population-based sequences and 47 deep-sequencing replicates), and a large cohort of clinical samples from phase III clinical trials of maraviroc including the MOTIVATE/A4001029 studies ( n = 1,521). Proviral DNA from two independent samples from each of 101 patients from the MOTIVATE/A4001029 studies was also analyzed. Cumulative technical error occurred at a rate of 3 × 10 −4 mismatches/bp, without observed effect on inferred tropism. Increasing PCR replication increased minority species detection with an ∼10% minority population detected in 18% of cases using a single replicate at a viral load of 1,072 copies/ml and in 44% of cases using three replicates. The nucleotide prevalence detected by population-based and deep sequencing were highly correlated (Spearman's ρ, 0.73), and the accuracy increased with increasing input copy number ( P < 0.001). Triplicate sequencing was able to predict tropism changes in the MOTIVATE/A4001029 studies for both low ( P = 0.05) and high ( P = 0.02) viral loads. Sequences derived from independently extracted and processed samples of proviral DNA for the same patient were equivalent to replicates from the same extraction ( P = 0.45) and had correlated position-specific scoring matrix scores (Spearman's ρ, 0.75; P ≪ 0.001); however, concordance in tropism inference was only 83%. Input copy number and PCR replication are important factors in minority species detection in samples with significant heterogeneity.

Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods’ development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention., Molecular Biology and Evolution, 34 (1), ISSN:0737-4038, ISSN:1537-1719