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Abstract Wastewater surveillance of coronavirus disease 2019 (COVID-19) commonly applies reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater over time. In most applications worldwide, maximal sensitivity and specificity of RT-qPCR has been achieved, in part, by monitoring two or more genomic loci of SARS-CoV-2. In Ontario, Canada, the provincial Wastewater Surveillance Initiative reports the average copies of the CDC N1 and N2 loci normalized to the fecal biomarker pepper mild mottle virus. In November 2021, the emergence of the Omicron variant of concern, harboring a C28311T mutation within the CDC N1 probe region, challenged the accuracy of the consensus between the RT-qPCR measurements of the N1 and N2 loci of SARS-CoV-2. In this study, we developed and applied a novel real-time dual loci quality assurance and control framework based on the relative difference between the loci measurements to the City of Ottawa dataset to identify a loss of sensitivity of the N1 assay in the period from July 10, 2022 to January 31, 2023. Further analysis via sequencing and allele-specific RT-qPCR revealed a high proportion of mutations C28312T and A28330G during the study period, both in the City of Ottawa and across the province. It is hypothesized that nucleotide mutations in the probe region, especially A28330G, led to inefficient annealing, resulting in reduction in sensitivity and accuracy of the N1 assay. This study highlights the importance of implementing quality assurance and control criteria to continually evaluate, in near real-time, the accuracy of the signal produced in wastewater surveillance applications that rely on detection of pathogens whose genomes undergo high rates of mutation.

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, Washington), 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.

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.

Abstract 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.

Abstract Background Our understanding of HIV-1 and antiretroviral treatment (ART) is strongly biased towards subtype B, the predominant subtype in North America and western Europe. Efforts to characterize the response to first-line treatments in other HIV-1 subtypes have been hindered by the availability of large study cohorts in resource-limited settings. To maximize our statistical power, we combined HIV-1 sequence and clinical data from every available study population associated with the Joint Clinical Research Centre (JCRC) in Uganda. These records were combined with contemporaneous ART-naive records from Uganda in the Stanford HIVdb database. Methods Treatment failures were defined by the presence of HIV genotype records with sample collection dates after the ART start dates in the JCRC database. Drug resistances were predicted by the Stanford HIVdb algorithm, and HIV subtype classification and recombination detection was performed with SCUEAL. We used Bayesian network analysis to evaluate associations between drug exposures and subtypes, and binomial regression for associations with recombination. Results This is the largest database of first-line treatment failures ($$n=1724$$ n=1724 ) in Uganda to date, with a predicted statistical power of 80% to detect subtype associations at an odds ratio of $$\ge 1.2$$ ≥1.2 . In the subset where drug regimen data were available, we observed that use of 3TC was associated with a higher rate of first line treatment failure, whereas regimens containing AZT and TDF were associated with reduced rates of failure. In the complete database, we found limited evidence of associations between HIV-1 subtypes and treatment failure, with the exception of a significantly lower frequency of failures among A/D recombinants that comprised about 7% of the population. First-line treatment failure was significantly associated with reduced numbers of recombination breakpoints across subtypes. Conclusions Expanding access to first-line ART should confer the anticipated public health benefits in Uganda, despite known differences in the pathogenesis of HIV-1 subtypes. Furthermore, the impact of ART may actually be enhanced by frequent inter-subtype recombination in this region.

The shape of phylogenetic trees can be used to gain evolutionary insights. A tree's shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.

Abstract Background The ability of HIV-1 to integrate into the genomes of quiescent host immune cells, establishing a long-lived latent viral reservoir (LVR), is the primary obstacle to curing these infections. Quantitative viral outgrowth assays (QVOAs) are the gold standard for estimating the size of the replication-competent HIV-1 LVR, measured by the number of infectious units per million (IUPM) cells. QVOAs are time-consuming because they rely on culturing replicate wells to amplify the production of virus antigen or nucleic acid to reproducibly detectable levels. Sequence analysis can reduce the required number of culture wells because the virus genetic diversity within the LVR provides an internal replication and dilution series. Here we develop a Bayesian method to jointly estimate the IUPM and variant frequencies (a measure of clonality) from the sequence diversity of QVOAs. Results Using simulation experiments, we find our Bayesian approach confers significantly greater accuracy over current methods to estimate the IUPM, particularly for reduced numbers of QVOA replicates and/or increasing actual IUPM. Furthermore, we determine that the improvement in accuracy is greater with increasing genetic diversity in the sample population. We contrast results of these different methods applied to new HIV-1 sequence data derived from QVOAs from two individuals with suppressed viral loads from the Rakai Health Sciences Program in Uganda. Conclusions Utilizing sequence variation has the additional benefit of providing information on the contribution of clonality of the LVR, where high clonality (the predominance of a single genetic variant) suggests a role for cell division in the long-term persistence of the reservoir. In addition, our Bayesian approach can be adapted to other limiting dilution assays where positive outcomes can be partitioned by their genetic heterogeneity, such as immune cell populations and other viruses.

Abstract 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.

Despite the effectiveness of direct-acting antiviral agents in treating hepatitis C virus (HCV), cases of treatment failure have been associated with the emergence of resistance-associated substitutions. To better guide clinical decision-making, we developed and validated a near-whole-genome HCV genotype-independent next-generation sequencing strategy. HCV genotype 1–6 samples from direct-acting antiviral agent treatment-naïve and -treated HCV-infected individuals were included. Viral RNA was extracted using a NucliSens easyMAG and amplified using nested reverse transcription-polymerase chain reaction. Libraries were prepared using Nextera XT and sequenced on the Illumina MiSeq sequencing platform. Data were processed by an in-house pipeline (MiCall). Nucleotide consensus sequences were aligned to reference strain sequences for resistance-associated substitution identification and compared to NS3, NS5a, and NS5b sequence data obtained from a validated in-house assay optimized for HCV genotype 1. Sequencing success rates (defined as achieving >100-fold read coverage) approaching 90% were observed for most genotypes in samples with a viral load >5 log10 IU/mL. This genotype-independent sequencing method resulted in >99.8% nucleotide concordance with the genotype 1-optimized method, and 100% agreement in genotype assignment with paired line probe assay-based genotypes. The assay demonstrated high intra-run repeatability and inter-run reproducibility at detecting substitutions above 2% prevalence. This study highlights the performance of a freely available laboratory and bioinformatic approach for reliable HCV genotyping and resistance-associated substitution detection regardless of genotype.

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

HLA-restricted immune escape mutations that persist following HIV transmission could gradually spread through the viral population, thereby compromising host antiviral immunity as the epidemic progresses. To assess the extent and phenotypic impact of this phenomenon in an immunogenetically diverse population, we genotypically and functionally compared linked HLA and HIV (Gag/Nef) sequences from 358 historic (1979-1989) and 382 modern (2000-2011) specimens from four key cities in the North American epidemic (New York, Boston, San Francisco, Vancouver). Inferred HIV phylogenies were star-like, with approximately two-fold greater mean pairwise distances in modern versus historic sequences. The reconstructed epidemic ancestral (founder) HIV sequence was essentially identical to the North American subtype B consensus. Consistent with gradual diversification of a "consensus-like" founder virus, the median "background" frequencies of individual HLA-associated polymorphisms in HIV (in individuals lacking the restricting HLA[s]) were ∼ 2-fold higher in modern versus historic HIV sequences, though these remained notably low overall (e.g. in Gag, medians were 3.7% in the 2000s versus 2.0% in the 1980s). HIV polymorphisms exhibiting the greatest relative spread were those restricted by protective HLAs. Despite these increases, when HIV sequences were analyzed as a whole, their total average burden of polymorphisms that were "pre-adapted" to the average host HLA profile was only ∼ 2% greater in modern versus historic eras. Furthermore, HLA-associated polymorphisms identified in historic HIV sequences were consistent with those detectable today, with none identified that could explain the few HIV codons where the inferred epidemic ancestor differed from the modern consensus. Results are therefore consistent with slow HIV adaptation to HLA, but at a rate unlikely to yield imminent negative implications for cellular immunity, at least in North America. Intriguingly, temporal changes in protein activity of patient-derived Nef (though not Gag) sequences were observed, suggesting functional implications of population-level HIV evolution on certain viral proteins.

Next generation, "deep", sequencing has increasing applications both clinically and in disparate fields of research. This study investigates the accuracy and reproducibility of "deep" sequencing as applied to co-receptor prediction using the V3 loop of Human Immunodeficiency Virus-1. Despite increasing use in HIV co-receptor prediction, the accuracy and reproducibility of deep sequencing technology, and the factors which can affect it, have received only a limited level of investigation. To accomplish this, repeated deep sequencing results were generated using the Roche GS-FLX (454) from a number of sources including a non-homogeneous clinical sample (N = 47 replicates over 18 deep sequencing runs), and a large clinical cohort from the MOTIVATE and A400129 studies (N = 1521). For repeated measurements of a non-homogeneous clinical sample, increasing input copy number both decreased variance in the measured proportion of non-R5 using virus (p<

A phylogeny is a tree-based model of common ancestry that is an indispensable tool for studying biological variation. Phylogenies play a special role in the study of rapidly evolving populations such as viruses, where the proliferation of lineages is constantly being shaped by the mode of virus transmission, by adaptation to immune systems, and by patterns of human migration and contact. These processes may leave an imprint on the shapes of virus phylogenies that can be extracted for comparative study; however, tree shapes are intrinsically difficult to quantify. Here we present a comprehensive study of phylogenies reconstructed from 38 different RNA viruses from 12 taxonomic families that are associated with human pathologies. To accomplish this, we have developed a new procedure for studying phylogenetic tree shapes based on the 'kernel trick', a technique that maps complex objects into a statistically convenient space. We show that our kernel method outperforms nine different tree balance statistics at correctly classifying phylogenies that were simulated under different evolutionary scenarios. Using the kernel method, we observe patterns in the distribution of RNA virus phylogenies in this space that reflect modes of transmission and pathogenesis. For example, viruses that can establish persistent chronic infections (such as HIV and hepatitis C virus) form a distinct cluster. Although the visibly 'star-like' shape characteristic of trees from these viruses has been well-documented, we show that established methods for quantifying tree shape fail to distinguish these trees from those of other viruses. The kernel approach presented here potentially represents an important new tool for characterizing the evolution and epidemiology of RNA viruses.

At the early stage of infection, human immunodeficiency virus (HIV)-1 predominantly uses the CCR5 coreceptor for host cell entry. The subsequent emergence of HIV variants that use the CXCR4 coreceptor in roughly half of all infections is associated with an accelerated decline of CD4+ T-cells and rate of progression to AIDS. The presence of a 'fitness valley' separating CCR5- and CXCR4-using genotypes is postulated to be a biological determinant of whether the HIV coreceptor switch occurs. Using phylogenetic methods to reconstruct the evolutionary dynamics of HIV within hosts enables us to discriminate between competing models of this process. We have developed a phylogenetic pipeline for the molecular clock analysis, ancestral reconstruction, and visualization of deep sequence data. These data were generated by next-generation sequencing of HIV RNA extracted from longitudinal serum samples (median 7 time points) from 8 untreated subjects with chronic HIV infections (Amsterdam Cohort Studies on HIV-1 infection and AIDS). We used the known dates of sampling to directly estimate rates of evolution and to map ancestral mutations to a reconstructed timeline in units of days. HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. We determined that the first mutations contributing to CXCR4 use emerged about 16 (per subject range 4 to 30) months before the earliest predicted CXCR4-using ancestor, which preceded the first positive cell-based assay of CXCR4 usage by 10 (range 5 to 25) months. CXCR4 usage arose in multiple lineages within 5 of 8 subjects, and ancestral lineages following alternate mutational pathways before going extinct were common. We observed highly patient-specific distributions and time-scales of mutation accumulation, implying that the role of a fitness valley is contingent on the genotype of the transmitted variant.

The evolution of drug resistance mutations in plasma samples is relatively well-characterized. However, the viral population and diversity in other body compartments such as peripheral blood mononuclear cells (PBMC) remains poorly understood. Previous studies have mostly focused on protease and reverse transcriptase drug resistance mutations (DRMs). In this study, we used 454 "deep" sequencing technology to observe and quantify longitudinally the prevalence of resistance mutations associated with the integrase inhibitor, raltegravir, in plasma versus PBMC samples from a San Francisco-based cohort. Four heavily treatment-experienced subjects were monitored in this study over a median of 1.2 years since the initiation of raltegravir-containing regimens. We observed a consistent discordance in the prevalence of DRMs, but not resistance pathway(s), in the plasma versus PBMC viral populations. In the final paired samples that were tested while the subjects were on a raltegravir-containing regimen, DRM prevalence reached 100% in plasma but remained 1% in PBMC on day 177 post-therapy in Subject 3180 (Q148H/G140S), 100% in plasma and 36% in PBMC on day 224 in Subject 3242 (N155H), 78% in plasma and 11-12% in PBMC on day 338 in Subject 3501 (Q148H/G140S), and 100% in plasma and 0% in PBMC on day 197 in Subject 3508 (Y143R). Furthermore, absolute sequence homology comparison between the two compartments revealed that 21% - 99% of PBMC sequences had no match in plasma, whereas 14% - 100% of plasma sequences had no match in PBMC. Overall, our observations suggested that plasma and PBMC hosted drastically different HIV-1 populations even after a prolonged exposure to raltegravir selection pressure.

The difference between regional rates of HIV-associated dementia (HAD) in patients infected with different subtypes of HIV suggests that genetic determinants exist within HIV that influence the ability of the virus to replicate in the central nervous system (in Uganda, Africa, subtype D HAD rate is 89%, while subtype A HAD rate is 24%). HIV-1 nef is a multifunctional protein with known toxic effects in the brain compartment. The goal of the current study was to identify if specific three-dimensional nef structures may be linked to patients who developed HAD. HIV-1 nef structures were computationally derived for consensus brain and non-brain sequences from a panel of patients infected with subtype B who died due to varied disease pathologies and consensus subtype A and subtype D sequences from Uganda. Site directed mutation analysis identified signatures in brain structures that appear to change binding potentials and could affect folding conformations of brain-associated structures. Despite the large sequence variation between HIV subtypes, structural alignments confirmed that viral structures derived from patients with HAD were more similar to subtype D structures than to structures derived from patient sequences without HAD. Furthermore, structures derived from brain sequences of patients with HAD were more similar to subtype D structures than they were to their own non-brain structures. The potential finding of a brain-specific nef structure indicates that HAD may result from genetic alterations that alter the folding or binding potential of the protein.

Environmental metagenomics provides snippets of genomic sequences from all organisms in an environmental sample and are an unprecedented resource of information for investigating microbial population genetics. Current analytical methods, however, are poorly equipped to handle metagenomic data, particularly of short, unlinked sequences. A custom analytical pipeline was developed to calculate dN/dS ratios, a common metric to evaluate the role of selection in the evolution of a gene, from environmental metagenomes sequenced using 454 technology of flow-sorted populations of marine Synechococcus, the dominant cyanobacteria in coastal environments. The large majority of genes (98%) have evolved under purifying selection (dN/dS1), 77 out of 83 (93%) were hypothetical. Notable among annotated genes, ribosomal protein L35 appears to be under positive selection in one Synechococcus population. Other annotated genes, in particular a possible porin, a large-conductance mechanosensitive channel, an ATP binding component of an ABC transporter, and a homologue of a pilus retraction protein had regions of the gene with elevated dN/dS. With the increasing use of next-generation sequencing in metagenomic investigations of microbial diversity and ecology, analytical methods need to accommodate the peculiarities of these data streams. By developing a means to analyze population diversity data from these environmental metagenomes, we have provided the first insight into the role of selection in the evolution of Synechococcus, a globally significant primary producer.

Human immunodeficiency virus type 1 (HIV-1) genomes often carry one or more mutations associated with drug resistance upon transmission into a therapy-naïve individual. We assessed the prevalence and clinical significance of transmitted drug resistance (TDR) in chronically-infected therapy-naïve patients enrolled in a multi-center cohort in North America. Pre-therapy clinical significance was quantified by plasma viral load (pVL) and CD4+ cell count (CD4) at baseline. Naïve bulk sequences of HIV-1 protease and reverse transcriptase (RT) were screened for resistance mutations as defined by the World Health Organization surveillance list. The overall prevalence of TDR was 14.2%. We used a Bayesian network to identify co-transmission of TDR mutations in clusters associated with specific drugs or drug classes. Aggregate effects of mutations by drug class were estimated by fitting linear models of pVL and CD4 on weighted sums over TDR mutations according to the Stanford HIV Database algorithm. Transmitted resistance to both classes of reverse transcriptase inhibitors was significantly associated with lower CD4, but had opposing effects on pVL. In contrast, position-specific analyses of TDR mutations revealed substantial effects on CD4 and pVL at several residue positions that were being masked in the aggregate analyses, and significant interaction effects as well. Residue positions in RT with predominant effects on CD4 or pVL (D67 and M184) were re-evaluated in causal models using an inverse probability-weighting scheme to address the problem of confounding by other mutations and demographic or risk factors. We found that causal effect estimates of mutations M184V/I (-1.7 log₁₀pVL) and D67N/G (-2.1[³√CD4] and 0.4 log₁₀pVL) were compensated by K103N/S and K219Q/E/N/R. As TDR becomes an increasing dilemma in this modern era of highly-active antiretroviral therapy, these results have immediate significance for the clinical management of HIV-1 infections and our understanding of the ongoing adaptation of HIV-1 to human populations.

Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear.

BACKGROUND:Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. METHODOLOGY/PRINCIPAL FINDINGS:Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. CONCLUSIONS:SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.

The third variable loop (V3) of the human immunodeficiency virus type 1 (HIV-1) envelope is a principal determinant of antibody neutralization and progression to AIDS. Although it is undoubtedly an important target for vaccine research, extensive genetic variation in V3 remains an obstacle to the development of an effective vaccine. Comparative methods that exploit the abundance of sequence data can detect interactions between residues of rapidly evolving proteins such as the HIV-1 envelope, revealing biological constraints on their variability. However, previous studies have relied implicitly on two biologically unrealistic assumptions: (1) that founder effects in the evolutionary history of the sequences can be ignored, and; (2) that statistical associations between residues occur exclusively in pairs. We show that comparative methods that neglect the evolutionary history of extant sequences are susceptible to a high rate of false positives (20%-40%). Therefore, we propose a new method to detect interactions that relaxes both of these assumptions. First, we reconstruct the evolutionary history of extant sequences by maximum likelihood, shifting focus from extant sequence variation to the underlying substitution events. Second, we analyze the joint distribution of substitution events among positions in the sequence as a Bayesian graphical model, in which each branch in the phylogeny is a unit of observation. We perform extensive validation of our models using both simulations and a control case of known interactions in HIV-1 protease, and apply this method to detect interactions within V3 from a sample of 1,154 HIV-1 envelope sequences. Our method greatly reduces the number of false positives due to founder effects, while capturing several higher-order interactions among V3 residues. By mapping these interactions to a structural model of the V3 loop, we find that the loop is stratified into distinct evolutionary clusters. We extend our model to detect interactions between the V3 and C4 domains of the HIV-1 envelope, and account for the uncertainty in mapping substitutions to the tree with a parametric bootstrap.

CD8(+) cytotoxic T-lymphocytes (CTLs) perform a critical role in the immune control of viral infections, including those caused by human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV). As a result, genetic variation at CTL epitopes is strongly influenced by host-specific selection for either escape from the immune response, or reversion due to the replicative costs of escape mutations in the absence of CTL recognition. Under strong CTL-mediated selection, codon positions within epitopes may immediately "toggle" in response to each host, such that genetic variation in the circulating virus population is shaped by rapid adaptation to immune variation in the host population. However, this hypothesis neglects the substantial genetic variation that accumulates in virus populations within hosts. Here, we evaluate this quantity for a large number of HIV-1- (n > or = 3,000) and HCV-infected patients (n > or = 2,600) by screening bulk RT-PCR sequences for sequencing "mixtures" (i.e., ambiguous nucleotides), which act as site-specific markers of genetic variation within each host. We find that nonsynonymous mixtures are abundant and significantly associated with codon positions under host-specific CTL selection, which should deplete within-host variation by driving the fixation of the favored variant. Using a simple model, we demonstrate that this apparently contradictory outcome can be explained by the transmission of unfavorable variants to new hosts before they are removed by selection, which occurs more frequently when selection and transmission occur on similar time scales. Consequently, the circulating virus population is shaped by the transmission rate and the disparity in selection intensities for escape or reversion as much as it is shaped by the immune diversity of the host population, with potentially serious implications for vaccine design.