Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease

Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure

Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interaction between a DRM and other residues in HIV-1 protein sequences, is key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV-1 protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naive patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, RT, and integrase, the primary targets of antiretroviral therapy. The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV-1 and other infectious agents.

Parallel evolution and enhanced virulence upon in vivo passage of an RNA virus in Drosophila melanogaster

Virus evolution is strongly affected by antagonistic co-evolution of virus and host. Host immunity positively selects for viruses that evade the immune response, which in turn may drive counter-adaptations in host immune genes. We investigated how host immune pressure shapes virus populations, using the fruit fly Drosophila melanogaster and its natural pathogen Drosophila C virus (DCV), as a model. We performed an experimental evolution study in which DCV was serially passaged for ten generations in three fly genotypes differing in their antiviral RNAi response: wild-type flies and flies in which the endonuclease gene Dicer-2 was either overexpressed or inactivated. All evolved virus populations replicated more efficiently in vivo and were more virulent than the parental stock. The number of polymorphisms increased in all three host genotypes with passage number, which was most pronounced in Dicer-2 knockout flies. Mutational analysis showed strong parallel evolution, as mutations accumulated in a specific region of the VP3 capsid protein in every lineage in a host genotype-independent manner. The parental tyrosine at position ninety-five of VP3 was substituted with either one of five different amino acids in fourteen out of fifteen lineages. However, no consistent amino acid changes were observed in the viral RNAi suppressor gene 1A, nor elsewhere in the genome in any of the host backgrounds. Our study indicates that the RNAi response restricts the sequence space that can be explored by viral populations. Moreover, our study illustrates how evolution towards higher virulence can be a highly reproducible, yet unpredictable process.

ViReMa: a virus recombination mapper of next-generation sequencing data characterizes diverse recombinant viral nucleic acids

BACKGROUND

Genetic recombination is a tremendous source of intrahost diversity in viruses and is critical for their ability to rapidly adapt to new environments or fitness challenges. While viruses are routinely characterized using high-throughput sequencing techniques, characterizing the genetic products of recombination in next-generation sequencing data remains a challenge. Viral recombination events can be highly diverse and variable in nature, including simple duplications and deletions, or more complex events such as copy/snap-back recombination, intervirus or intersegment recombination, and insertions of host nucleic acids. Due to the variable mechanisms driving virus recombination and the different selection pressures acting on the progeny, recombination junctions rarely adhere to simple canonical sites or sequences. Furthermore, numerous different events may be present simultaneously in a viral population, yielding a complex mutational landscape.

FINDINGS

We have previously developed an algorithm called ViReMa (Virus Recombination Mapper) that bootstraps the bowtie short-read aligner to capture and annotate a wide range of recombinant species found within virus populations. Here, we have updated ViReMa to provide an "error density" function designed to accurately detect recombination events in the longer reads now routinely generated by the Illumina platforms and provide output reports for multiple types of recombinant species using standardized formats. We demonstrate the utility and flexibility of ViReMa in different settings to report deletion events in simulated data from Flock House virus, copy-back RNA species in Sendai viruses, short duplication events in HIV, and virus-to-host recombination in an archaeal DNA virus.

Contingency and Entrenchment of Drug-Resistance Mutations in HIV Viral Proteins

The ability of HIV-1 to rapidly mutate leads to antiretroviral therapy (ART) failure among infected patients. Drug-resistance mutations (DRMs), which cause a fitness penalty to intrinsic viral fitness, are compensated by accessory mutations with favorable epistatic interactions which cause an evolutionary trapping effect, but the kinetics of this overall process has not been well characterized. Here, using a Potts Hamiltonian model describing epistasis combined with kinetic Monte Carlo simulations of evolutionary trajectories, we explore how epistasis modulates the evolutionary dynamics of HIV DRMs. We show how the occurrence of a drug-resistance mutation is contingent on favorable epistatic interactions with many other residues of the sequence background and that subsequent mutations entrench DRMs. We measure the time-autocorrelation of fluctuations in the likelihood of DRMs due to epistatic coupling with the sequence background, which reveals the presence of two evolutionary processes controlling DRM kinetics with two distinct time scales. Further analysis of waiting times for the evolutionary trapping effect to reverse reveals that the sequences which entrench (trap) a DRM are responsible for the slower time scale. We also quantify the overall strength of epistatic effects on the evolutionary kinetics for different mutations and show these are much larger for DRM positions than polymorphic positions, and we also show that trapping of a DRM is often caused by the collective effect of many accessory mutations, rather than a few strongly coupled ones, suggesting the importance of multiresidue sequence variations in HIV evolution. The analysis presented here provides a framework to explore the kinetic pathways through which viral proteins like HIV evolve under drug-selection pressure.

Protease and gag diversity and drug resistance mutations among treatment-naive Mexican people living with HIV

Introduction

In Mexico, HIV genotyping is performed in people living with HIV (PLWH) failing their first-line antiretroviral (ARV) regimen; it is not routinely done for all treatment-naive PLWH before ARV initiation. The first nationally representative survey published in 2016 reported that the prevalence of pretreatment drug mutations in treatment-naive Mexican PLWH was 15.5% to any antiretroviral drug and 10.6% to non-nucleoside reverse transcriptase inhibitors (NNRTIs) using conventional Sanger sequencing. Most reports in Mexico focus on HIV pol gene and nucleoside and non-nucleoside reverse transcriptase inhibitor (NRTI and NNRTI) drug resistance mutations (DRMs) prevalence, using Sanger sequencing, next-generation sequencing (NGS) or both. To our knowledge, NGS has not be used to detect pretreatment drug resistance mutations (DRMs) in the HIV protease (PR) gene and its substrate the Gag polyprotein.

Methods

Treatment-naive adult Mexican PLWH were recruited between 2016 and 2019. HIV Gag and protease sequences were obtained by NGS and DRMs were identified using the WHO surveillance drug resistance mutation (SDRM) list.

Results

One hundred PLWH attending a public national reference hospital were included. The median age was 28 years-old, and most were male. The median HIV viral load was 4.99 [4.39–5.40] log copies/mL and median CD4 cell count was 150 [68.0–355.78] cells/mm³. As expected, most sequences clustered with HIV-1 subtype B (97.9%). Major PI resistance mutations were detected: 8 (8.3%) of 96 patients at a detection threshold of 1% and 3 (3.1%) at a detection threshold of 20%. A total of 1184 mutations in Gag were detected, of which 51 have been associated with resistance to PI, most of them were detected at a threshold of 20%. Follow-up clinical data was available for 79 PLWH at 6 months post-ART initiation, seven PLWH failed their first ART regimen; however no major PI mutations were identified in these individuals at baseline.

Conclusions

The frequency of DRM in the HIV protease was 7.3% at a detection threshold of 1% and 3.1% at a detection threshold of 20%. NGS-based HIV drug resistance genotyping provide improved detection of DRMs. Viral load was used to monitor ARV response and treatment failure was 8.9%.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07446-8.

Covariation of viral recombination with single nucleotide variants during virus evolution revealed by CoVaMa

Adaptation of viruses to their environments occurs through the acquisition of both novel single-nucleotide variants (SNV) and recombination events including insertions, deletions, and duplications. The co-occurrence of SNVs in individual viral genomes during their evolution has been well-described. However, unlike covariation of SNVs, studying the correlation between recombination events with each other or with SNVs has been hampered by their inherent genetic complexity and a lack of bioinformatic tools. Here, we expanded our previously reported CoVaMa pipeline (v0.1) to measure linkage disequilibrium between recombination events and SNVs within both short-read and long-read sequencing datasets. We demonstrate this approach using long-read nanopore sequencing data acquired from Flock House virus (FHV) serially passaged in vitro. We found SNVs that were either correlated or anti-correlated with large genomic deletions generated by nonhomologous recombination that give rise to Defective-RNAs. We also analyzed NGS data from longitudinal HIV samples derived from a patient undergoing antiretroviral therapy who proceeded to virological failure. We found correlations between insertions in the p6Gag and mutations in Gag cleavage sites. This report confirms previous findings and provides insights on novel associations between SNVs and specific recombination events within the viral genome and their role in viral evolution.

Limits to detecting epistasis in the fitness landscape of HIV

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence “prevalences” expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.

Detection of Gag C-terminal mutations among HIV-1 non-B subtypes in a subset of Cameroonian patients

Response to ritonavir-boosted-protease inhibitors (PI/r)-based regimen is associated with some Gag mutations among HIV-1 B-clade. There is limited data on Gag mutations and their covariation with mutations in protease among HIV-1 non-B-clades at PI/r-based treatment failure. Thus, we characterized Gag mutations present in isolates from HIV-1 infected individuals treated with a PI/r-regimen (n = 143) and compared them with those obtained from individuals not treated with PI/r (ART-naïve [n = 101] or reverse transcriptase inhibitors (RTI) treated [n = 118]). The most frequent HIV-1 subtypes were CRF02_AG (54.69%), A (13.53%), D (6.35%) and G (4.69%). Eighteen Gag mutations showed a significantly higher prevalence in PI/r-treated isolates compared to ART-naïve (p < 0.05): Group 1 (prevalence < 1% in drug-naïve): L449F, D480N, L483Q, Y484P, T487V; group 2 (prevalence 1–5% in drug-naïve): S462L, I479G, I479K, D480E; group 3 (prevalence ≥ 5% in drug-naïve): P453L, E460A, R464G, S465F, V467E, Q474P, I479R, E482G, T487A. Five Gag mutations (L449F, P453L, D480E, S465F, Y484P) positively correlated (Phi ≥ 0.2, p < 0.05) with protease-resistance mutations. At PI/r-failure, no significant difference was observed between patients with and without these associated Gag mutations in term of viremia or CD4 count. This analysis suggests that some Gag mutations show an increased frequency in patients failing PIs among HIV-1 non-B clades.

MrHAMER yields highly accurate single molecule viral sequences enabling analysis of intra-host evolution

Technical challenges remain in the sequencing of RNA viruses due to their high intra-host diversity. This bottleneck is particularly pronounced when interrogating long-range co-evolved genetic interactions given the read-length limitations of next-generation sequencing platforms. This has hampered the direct observation of these genetic interactions that code for protein-protein interfaces with relevance in both drug and vaccine development. Here we overcome these technical limitations by developing a nanopore-based long-range viral sequencing pipeline that yields accurate single molecule sequences of circulating virions from clinical samples. We demonstrate its utility in observing the evolution of individual HIV Gag-Pol genomes in response to antiviral pressure. Our pipeline, called Multi-read Hairpin Mediated Error-correction Reaction (MrHAMER), yields >1000s of viral genomes per sample at 99.9% accuracy, maintains the original proportion of sequenced virions present in a complex mixture, and allows the detection of rare viral genomes with their associated mutations present at <1% frequency. This method facilitates scalable investigation of genetic correlates of resistance to both antiviral therapy and immune pressure and enables the identification of novel host-viral and viral-viral interfaces that can be modulated for therapeutic benefit.

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

HIV-1 Gag mutations alone are sufficient to reduce darunavir susceptibility during virological failure to boosted PI therapy

BACKGROUND

Virological failure (VF) to boosted PIs with a high genetic barrier is not usually linked to the development of resistance-associated mutations in the protease gene.

METHODS

From a cohort of 520 HIV-infected subjects treated with lopinavir/ritonavir or darunavir/ritonavir monotherapy, we retrospectively identified nine patients with VF. We sequenced the HIV-1 Gag-protease region and generated clonal virus from plasma samples. We characterized phenotypically clonal variants in terms of replicative capacity and susceptibility to PIs. Also, we used VESPA to identify signature mutations and 3D molecular modelling information to detect conformational changes in the Gag region.

RESULTS

All subjects analysed harboured Gag-associated polymorphisms in the absence of resistance mutations in the protease gene. Most Gag changes occurred outside Gag cleavage sites. VESPA analyses identified K95R and R286K (P < 0.01) as signature mutations in Gag present at VF. In one out of four patients with clonal analysis available, we identified clonal variants with high replicative capacity and 8- to 13-fold reduction in darunavir susceptibility. These clonal variants harboured K95R, R286K and additional mutations in Gag. Low susceptibility to darunavir was dependent on the Gag sequence context. All other clonal variants analysed preserved drug susceptibility and virus replicative capacity.

CONCLUSIONS

Gag mutations may reduce darunavir susceptibility in the absence of protease mutations while preserving viral fitness. This effect is Gag-sequence context dependent and may occur during boosted PI failure.

Quasispecies dynamics in disease prevention and control

Medical interventions to prevent and treat viral disease constitute evolutionary forces that may modify the genetic composition of viral populations that replicate in an infected host and influence the genomic composition of those viruses that are transmitted and progress at the epidemiological level. Given the adaptive potential of viruses in general and the RNA viruses in particular, the selection of viral mutants that display some degree of resistance to inhibitors or vaccines is a tangible challenge. Mutant selection may jeopardize control of the viral disease. Strategies intended to minimize vaccination and treatment failures are proposed and justified based on fundamental features of viral dynamics explained in the preceding chapters. The recommended use of complex, multiepitopic vaccines, and combination therapies as early as possible after initiation of infection falls under the general concept that complexity cannot be combated with simplicity. It also follows that sociopolitical action to interrupt virus replication and spread as soon as possible is as important as scientifically sound treatment designs to control viral disease on a global scale.

Epistasis and entrenchment of drug resistance in HIV-1 subtype B

The development of drug resistance in HIV is the result of primary mutations whose effects on viral fitness depend on the entire genetic background, a phenomenon called 'epistasis'. Based on protein sequences derived from drug-experienced patients in the Stanford HIV database, we use a co-evolutionary (Potts) Hamiltonian model to provide direct confirmation of epistasis involving many simultaneous mutations. Building on earlier work, we show that primary mutations leading to drug resistance can become highly favored (or entrenched) by the complex mutation patterns arising in response to drug therapy despite being disfavored in the wild-type background, and provide the first confirmation of entrenchment for all three drug-target proteins: protease, reverse transcriptase, and integrase; a comparative analysis reveals that NNRTI-induced mutations behave differently from the others. We further show that the likelihood of resistance mutations can vary widely in patient populations, and from the population average compared to specific molecular clones.

Reviewing HIV-1 Gag Mutations in Protease Inhibitors Resistance: Insights for Possible Novel Gag Inhibitor Designs

HIV protease inhibitors against the viral protease are often hampered by drug resistance mutations in protease and in the viral substrate Gag. To overcome this drug resistance and inhibit viral maturation, targeting Gag alongside protease rather than targeting protease alone may be more efficient. In order to successfully inhibit Gag, understanding of its drug resistance mutations and the elicited structural changes on protease binding needs to be investigated. While mutations on Gag have already been mapped to protease inhibitor resistance, there remain many mutations, particularly the non-cleavage mutations, that are not characterized. Through structural studies to unravel how Gag mutations contributes to protease drug resistance synergistically, it is thus possible to glean insights to design novel Gag inhibitors. In this review, we discuss the structural role of both novel and previously reported Gag mutations in PI resistance, and how new Gag inhibitors can be designed.

Molecular Determinants of Epistasis in HIV-1 Protease: Elucidating the Interdependence of L89V and L90M Mutations in Resistance

Protease inhibitors have the highest potency among antiviral therapies against HIV-1 infections, yet the virus can evolve resistance. Darunavir (DRV), currently the most potent Food and Drug Administration-approved protease inhibitor, retains potency against single-site mutations. However, complex combinations of mutations can confer resistance to DRV. While the interdependence between mutations within HIV-1 protease is key for inhibitor potency, the molecular mechanisms that underlie this control remain largely unknown. In this study, we investigated the interdependence between the L89V and L90M mutations and their effects on DRV binding. These two mutations have been reported to be positively correlated with one another in HIV-1 patient-derived protease isolates, with the presence of one mutation making the probability of the occurrence of the second mutation more likely. The focus of our investigation is a patient-derived isolate, with 24 mutations that we call "KY"; this variant includes the L89V and L90M mutations. Three additional KY variants with back-mutations, KY(V89L), KY(M90L), and the KY(V89L/M90L) double mutation, were used to experimentally assess the individual and combined effects of these mutations on DRV inhibition and substrate processing. The enzymatic assays revealed that the KY(V89L) variant, with methionine at residue 90, is highly resistant, but its catalytic function is compromised. When a leucine to valine mutation at residue 89 is present simultaneously with the L90M mutation, a rescue of catalytic efficiency is observed. Molecular dynamics simulations of these DRV-bound protease variants reveal how the L90M mutation induces structural changes throughout the enzyme that undermine the binding interactions.

Neutral Theory and Rapidly Evolving Viral Pathogens

The evolution of viral pathogens is shaped by strong selective forces that are exerted during jumps to new hosts, confrontations with host immune responses and antiviral drugs, and numerous other processes. However, while undeniably strong and frequent, adaptive evolution is largely confined to small parts of information-packed viral genomes, and the majority of observed variation is effectively neutral. The predictions and implications of the neutral theory have proven immensely useful in this context, with applications spanning understanding within-host population structure, tracing the origins and spread of viral pathogens, predicting evolutionary dynamics, and modeling the emergence of drug resistance. We highlight the multiple ways in which the neutral theory has had an impact, which has been accelerated in the age of high-throughput, high-resolution genomics.

Specific reverse transcriptase slippage at the HIV ribosomal frameshift sequence: potential implications for modulation of GagPol synthesis

Synthesis of HIV GagPol involves a proportion of ribosomes translating a U6A shift site at the distal end of the gag gene performing a programmed -1 ribosomal frameshift event to enter the overlapping pol gene. In vitro studies here show that at the same shift motif HIV reverse transcriptase generates -1 and +1 indels with their ratio being sensitive to the relative concentration ratio of dNTPs specified by the RNA template slippage-prone sequence and its 5' adjacent base. The GGG sequence 3' adjacent to the U6A shift/slippage site, which is important for ribosomal frameshifting, is shown here to limit reverse transcriptase base substitution and indel 'errors' in the run of A's in the product. The indels characterized here have either 1 more or less A, than the corresponding number of template U's. cDNA with 5 A's may yield novel Gag product(s), while cDNA with an extra base, 7 A's, may only be a minor contributor to GagPol polyprotein. Synthesis of a proportion of non-ribosomal frameshift derived GagPol would be relevant in efforts to identify therapeutically useful compounds that perturb the ratio of GagPol to Gag, and pertinent to the extent in which specific polymerase slippage is utilized in gene expression.

Gag-protease coevolution analyses define novel structural surfaces in the HIV-1 matrix and capsid involved in resistance to Protease Inhibitors

Despite the major role of Gag in establishing resistance of HIV-1 to protease inhibitors (PIs), very limited data are available on the total contribution of Gag residues to resistance to PIs. To identify in detail Gag residues and structural interfaces associated with the development of HIV-1 resistance to PIs, we traced viral evolution under the pressure of PIs using Gag-protease single genome sequencing and coevolution analysis of protein sequences in 4 patients treated with PIs over a 9-year period. We identified a total of 38 Gag residues correlated with the protease, 32 of which were outside Gag cleavage sites. These residues were distributed in 23 Gag-protease groups of coevolution, with the viral matrix and the capsid represented in 87% and 52% of the groups. In addition, we uncovered the distribution of Gag correlated residues in specific protein surfaces of the inner face of the viral matrix and at the Cyclophilin A binding loop of the capsid. In summary, our findings suggest a tight interdependency between Gag structural proteins and the protease during the development of resistance of HIV-1 to PIs.

Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease

Understanding the complex mutation patterns that give rise to drug resistant viral strains provides a foundation for developing more effective treatment strategies for HIV/AIDS. Multiple sequence alignments of drug-experienced HIV-1 protease sequences contain networks of many pair correlations which can be used to build a (Potts) Hamiltonian model of these mutation patterns. Using this Hamiltonian model, we translate HIV-1 protease sequence covariation data into quantitative predictions for the probability of observing specific mutation patterns which are in agreement with the observed sequence statistics. We find that the statistical energies of the Potts model are correlated with the fitness of individual proteins containing therapy-associated mutations as estimated by in vitro measurements of protein stability and viral infectivity. We show that the penalty for acquiring primary resistance mutations depends on the epistatic interactions with the sequence background. Primary mutations which lead to drug resistance can become highly advantageous (or entrenched) by the complex mutation patterns which arise in response to drug therapy despite being destabilizing in the wildtype background. Anticipating epistatic effects is important for the design of future protease inhibitor therapies.

Rational design of vaccine targets and strategies for HIV: a crossroad of statistical physics, biology, and medicine

Vaccination has saved more lives than any other medical procedure. Pathogens have now evolved that have not succumbed to vaccination using the empirical paradigms pioneered by Pasteur and Jenner. Vaccine design strategies that are based on a mechanistic understanding of the pertinent immunology and virology are required to confront and eliminate these scourges. In this perspective, we describe just a few examples of work aimed to achieve this goal by bringing together approaches from statistical physics with biology and clinical research.

Wide variation in susceptibility of transmitted/founder HIV-1 subtype C Isolates to protease inhibitors and association with in vitro replication efficiency

The gag gene is highly polymorphic across HIV-1 subtypes and contributes to susceptibility to protease inhibitors (PI), a critical class of antiretrovirals that will be used in up to 2 million individuals as second-line therapy in sub Saharan Africa by 2020. Given subtype C represents around half of all HIV-1 infections globally, we examined PI susceptibility in subtype C viruses from treatment-naïve individuals. PI susceptibility was measured in a single round infection assay of full-length, replication competent MJ4/gag chimeric viruses, encoding the gag gene and 142 nucleotides of pro derived from viruses in 20 patients in the Zambia-Emory HIV Research Project acute infection cohort. Ten-fold variation in susceptibility to PIs atazanavir and lopinavir was observed across 20 viruses, with EC50s ranging 0.71-6.95 nM for atazanvir and 0.64-8.54 nM for lopinavir. Ten amino acid residues in Gag correlated with lopinavir EC50 (p < 0.01), of which 380 K and 389I showed modest impacts on in vitro drug susceptibility. Finally a significant relationship between drug susceptibility and replication capacity was observed for atazanavir and lopinavir but not darunavir. Our findings demonstrate large variation in susceptibility of PI-naïve subtype C viruses that appears to correlate with replication efficiency and could impact clinical outcomes.

Understanding the complex evolution of rapidly mutating viruses with deep sequencing: Beyond the analysis of viral diversity

With the advent of affordable deep sequencing technologies, detection of low frequency variants within genetically diverse viral populations can now be achieved with unprecedented depth and efficiency. The high-resolution data provided by next generation sequencing technologies is currently recognised as the gold standard in estimation of viral diversity. In the analysis of rapidly mutating viruses, longitudinal deep sequencing datasets from viral genomes during individual infection episodes, as well as at the epidemiological level during outbreaks, now allow for more sophisticated analyses such as statistical estimates of the impact of complex mutation patterns on the evolution of the viral populations both within and between hosts. These analyses are revealing more accurate descriptions of the evolutionary dynamics that underpin the rapid adaptation of these viruses to the host response, and to drug therapies. This review assesses recent developments in methods and provide informative research examples using deep sequencing data generated from rapidly mutating viruses infecting humans, particularly hepatitis C virus (HCV), human immunodeficiency virus (HIV), Ebola virus and influenza virus, to understand the evolution of viral genomes and to explore the relationship between viral mutations and the host adaptive immune response. Finally, we discuss limitations in current technologies, and future directions that take advantage of publically available large deep sequencing datasets.

Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness

Potts Hamiltonian models of protein sequence co-variation are statistical models constructed from the pair correlations observed in a multiple sequence alignment (MSA) of a protein family. These models are powerful because they capture higher order correlations induced by mutations evolving under constraints and help quantify the connections between protein sequence, structure, and function maintained through evolution. We review recent work with Potts models to predict protein structure and sequence-dependent conformational free energy landscapes, to survey protein fitness landscapes and to explore the effects of epistasis on fitness. We also comment on the numerical methods used to infer these models for each application.

HIV Genome-Wide Protein Associations: a Review of 30 Years of Research

The HIV genome encodes a small number of viral proteins (i.e., 16), invariably establishing cooperative associations among HIV proteins and between HIV and host proteins, to invade host cells and hijack their internal machineries. As a known example, the HIV envelope glycoprotein GP120 is closely associated with GP41 for viral entry. From a genome-wide perspective, a hypothesis can be worked out to determine whether 16 HIV proteins could develop 120 possible pairwise associations either by physical interactions or by functional associations mediated via HIV or host molecules. Here, we present the first systematic review of experimental evidence on HIV genome-wide protein associations using a large body of publications accumulated over the past 3 decades. Of 120 possible pairwise associations between 16 HIV proteins, at least 34 physical interactions and 17 functional associations have been identified. To achieve efficient viral replication and infection, HIV protein associations play essential roles (e.g., cleavage, inhibition, and activation) during the HIV life cycle. In either a dispensable or an indispensable manner, each HIV protein collaborates with another viral protein to accomplish specific activities that precisely take place at the proper stages of the HIV life cycle. In addition, HIV genome-wide protein associations have an impact on anti-HIV inhibitors due to the extensive cross talk between drug-inhibited proteins and other HIV proteins. Overall, this study presents for the first time a comprehensive overview of HIV genome-wide protein associations, highlighting meticulous collaborations between all viral proteins during the HIV life cycle.

HIV Genome-Wide Protein Associations: a Review of 30 Years of Research

The HIV genome encodes a small number of viral proteins (i.e., 16), invariably establishing cooperative associations among HIV proteins and between HIV and host proteins, to invade host cells and hijack their internal machineries. As a known example, the HIV envelope glycoprotein GP120 is closely associated with GP41 for viral entry. From a genome-wide perspective, a hypothesis can be worked out to determine whether 16 HIV proteins could develop 120 possible pairwise associations either by physical interactions or by functional associations mediated via HIV or host molecules. Here, we present the first systematic review of experimental evidence on HIV genome-wide protein associations using a large body of publications accumulated over the past 3 decades. Of 120 possible pairwise associations between 16 HIV proteins, at least 34 physical interactions and 17 functional associations have been identified. To achieve efficient viral replication and infection, HIV protein associations play essential roles (e.g., cleavage, inhibition, and activation) during the HIV life cycle. In either a dispensable or an indispensable manner, each HIV protein collaborates with another viral protein to accomplish specific activities that precisely take place at the proper stages of the HIV life cycle. In addition, HIV genome-wide protein associations have an impact on anti-HIV inhibitors due to the extensive cross talk between drug-inhibited proteins and other HIV proteins. Overall, this study presents for the first time a comprehensive overview of HIV genome-wide protein associations, highlighting meticulous collaborations between all viral proteins during the HIV life cycle.

Structure of FIV capsid C-terminal domain demonstrates lentiviral evasion of genetic fragility by coevolved substitutions

Viruses use a strategy of high mutational rates to adapt to environmental and therapeutic pressures, circumventing the deleterious effects of random single-point mutations by coevolved compensatory mutations, which restore protein fold, function or interactions damaged by initial ones. This mechanism has been identified as contributing to drug resistance in the HIV-1 Gag polyprotein and especially its capsid proteolytic product, which forms the viral capsid core and plays multifaceted roles in the viral life cycle. Here, we determined the X-ray crystal structure of C-terminal domain of the feline immunodeficiency virus (FIV) capsid and through interspecies analysis elucidate the structural basis of co-evolutionarily and spatially correlated substitutions in capsid sequences, which when otherwise uncoupled and individually substituted into HIV-1 capsid impair virion assembly and infectivity. The ability to circumvent the deleterious effects of single amino acid substitutions by cooperative secondary substitutions allows mutational flexibility that may afford viruses an important survival advantage. The potential of such interspecies structural analysis for preempting viral resistance by identifying such alternative but functionally equivalent patterns is discussed.

Quantifying Next Generation Sequencing Sample Pre-Processing Bias in HIV-1 Complete Genome Sequencing

Genetic analyses play a central role in infectious disease research. Massively parallelized “mechanical cloning” and sequencing technologies were quickly adopted by HIV researchers in order to broaden the understanding of the clinical importance of minor drug-resistant variants. These efforts have, however, remained largely limited to small genomic regions. The growing need to monitor multiple genome regions for drug resistance testing, as well as the obvious benefit for studying evolutionary and epidemic processes makes complete genome sequencing an important goal in viral research. In addition, a major drawback for NGS applications to RNA viruses is the need for large quantities of input DNA. Here, we use a generic overlapping amplicon-based near full-genome amplification protocol to compare low-input enzymatic fragmentation (Nextera™) with conventional mechanical shearing for Roche 454 sequencing. We find that the fragmentation method has only a modest impact on the characterization of the population composition and that for reliable results, the variation introduced at all steps of the procedure—from nucleic acid extraction to sequencing—should be taken into account, a finding that is also relevant for NGS technologies that are now more commonly used. Furthermore, by applying our protocol to deep sequence a number of pre-therapy plasma and PBMC samples, we illustrate the potential benefits of a near complete genome sequencing approach in routine genotyping.

CoVaMa: Co-Variation Mapper for disequilibrium analysis of mutant loci in viral populations using next-generation sequence data

Next-Generation Sequencing (NGS) has transformed our understanding of the dynamics and diversity of virus populations for human pathogens and model systems alike. Due to the sensitivity and depth of coverage in NGS, it is possible to measure the frequency of mutations that may be present even at vanishingly low frequencies within the viral population. Here, we describe a simple bioinformatic pipeline called CoVaMa (Co-Variation Mapper) scripted in Python that detects correlated patterns of mutations in a viral sample. Our algorithm takes NGS alignment data and populates large matrices of contingency tables that correspond to every possible pairwise interaction of nucleotides in the viral genome or amino acids in the chosen open reading frame. These tables are then analysed using classical linkage disequilibrium to detect and report evidence of epistasis. We test our analysis with simulated data and then apply the approach to find epistatically linked loci in Flock House Virus genomic RNA grown under controlled cell culture conditions. We also reanalyze NGS data from a large cohort of HIV infected patients and find correlated amino acid substitution events in the protease gene that have arisen in response to anti-viral therapy. This both confirms previous findings and suggests new pairs of interactions within HIV protease. The script is publically available at http://sourceforge.net/projects/covama.