HIV-1 subtype B protease and reverse transcriptase amino acid covariation

Antigenic cooperation in viral populations: Transformation of functions of intra-host viral variants

In this paper, we study intra-host viral adaptation by antigenic cooperation - a mechanism of immune escape that serves as an alternative to the standard mechanism of escape by continuous genomic diversification and allows to explain a number of experimental observations associated with the establishment of chronic infections by highly mutable viruses. Within this mechanism, the topology of a cross-immunoreactivity network forces intra-host viral variants to specialize for complementary roles and adapt to the host's immune response as a quasi-social ecosystem. Here we study dynamical changes in immune adaptation caused by evolutionary and epidemiological events. First, we show that the emergence of a viral variant with altered antigenic features may result in a rapid re-arrangement of the viral ecosystem and a change in the roles played by existing viral variants. In particular, it may push the population under immune escape by genomic diversification towards the stable state of adaptation by antigenic cooperation. Next, we study the effect of a viral transmission between two chronically infected hosts, which results in the merging of two intra-host viral populations in the state of stable immune-adapted equilibrium. In this case, we also describe how the newly formed viral population adapts to the host's environment by changing the functions of its members. The results are obtained analytically for minimal cross-immunoreactivity networks and numerically for larger populations.

Reconstruction of Viral Variants via Monte Carlo Clustering

Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate these methods on four benchmarks: two SARS-CoV-2 interhost data sets and two HIV intrahost data sets. A parallelized version of our tool is scalable to very large data sets. We show that both entropy and Hamming distance-based MC clusterings discern the meaningful information from sequencing data. The proposed clustering methods consistently converge to similar clusterings across different runs. Finally, we show that MC clustering improves reconstruction of intrahost viral population from sequencing data.

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.

Spectrum of Atazanavir-Selected Protease Inhibitor-Resistance Mutations

Ritonavir-boosted atazanavir is an option for second-line therapy in low- and middle-income countries (LMICs). We analyzed publicly available HIV-1 protease sequences from previously PI-naïve patients with virological failure (VF) following treatment with atazanavir. Overall, 1497 patient sequences were identified, including 740 reported in 27 published studies and 757 from datasets assembled for this analysis. A total of 63% of patients received boosted atazanavir. A total of 38% had non-subtype B viruses. A total of 264 (18%) sequences had a PI drug-resistance mutation (DRM) defined as having a Stanford HIV Drug Resistance Database mutation penalty score. Among sequences with a DRM, nine major DRMs had a prevalence >5%: I50L (34%), M46I (33%), V82A (22%), L90M (19%), I54V (16%), N88S (10%), M46L (8%), V32I (6%), and I84V (6%). Common accessory DRMs were L33F (21%), Q58E (16%), K20T (14%), G73S (12%), L10F (10%), F53L (10%), K43T (9%), and L24I (6%). A novel nonpolymorphic mutation, L89T occurred in 8.4% of non-subtype B, but in only 0.4% of subtype B sequences. The 264 sequences included 3 (1.1%) interpreted as causing high-level, 14 (5.3%) as causing intermediate, and 27 (10.2%) as causing low-level darunavir resistance. Atazanavir selects for nine major and eight accessory DRMs, and one novel nonpolymorphic mutation occurring primarily in non-B sequences. Atazanavir-selected mutations confer low-levels of darunavir cross resistance. Clinical studies, however, are required to determine the optimal boosted PI to use for second-line and potentially later line therapy in LMICs.

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.

Electronic Health Diary Campaigns to Complement Longitudinal Assessments in Persons With Multiple Sclerosis: Nested Observational Study (Preprint)

Background

Electronic health diaries hold promise in complementing standardized surveys in prospective health studies but are fraught with numerous methodological challenges.

Objective

The study aimed to investigate participant characteristics and other factors associated with response to an electronic health diary campaign in persons with multiple sclerosis, identify recurrent topics in free-text diary entries, and assess the added value of structured diary entries with regard to current symptoms and medication intake when compared with survey-collected information.

Methods

Data were collected by the Swiss Multiple Sclerosis Registry during a nested electronic health diary campaign and during a regular semiannual Swiss Multiple Sclerosis Registry follow-up survey serving as comparator. The characteristics of campaign participants were descriptively compared with those of nonparticipants. Diary content was analyzed using the Linguistic Inquiry and Word Count 2015 software (Pennebaker Conglomerates, Inc) and descriptive keyword analyses. The similarities between structured diary data and follow-up survey data on health-related quality of life, symptoms, and medication intake were examined using the Jaccard index.

Results

Campaign participants (n=134; diary entries: n=815) were more often women, were not working full time, did not have a higher education degree, had a more advanced gait impairment, and were on average 5 years older (median age 52.5, IQR 43.25-59.75 years) than eligible nonparticipants (median age 47, IQR 38-55 years; n=524). Diary free-text entries (n=632; participants: n=100) most often contained references to the following standard Linguistic Inquiry and Word Count word categories: negative emotion (193/632, 30.5%), body parts or body functioning (191/632, 30.2%), health (94/632, 14.9%), or work (67/632, 10.6%). Analogously, the most frequently mentioned keywords (diary entries: n=526; participants: n=93) were “good,” “day,” and “work.” Similarities between diary data and follow-up survey data, collected 14 months apart (median), were high for health-related quality of life and stable for slow-changing symptoms such as fatigue or gait disorder. Similarities were also comparatively high for drugs requiring a regular application, including interferon beta-1a (Avonex) and glatiramer acetate (Copaxone), and for modern oral therapies such as fingolimod (Gilenya) and teriflunomide (Aubagio).

Conclusions

Diary campaign participation seemed dependent on time availability and symptom burden and was enhanced by reminder emails. Electronic health diaries are a meaningful complement to regular structured surveys and can provide more detailed information regarding medication use and symptoms. However, they should ideally be embedded into promotional activities or tied to concrete research study tasks to enhance regular and long-term participation.

TNet: Transmission Network Inference Using Within-Host Strain Diversity and its Application to Geographical Tracking of COVID-19 Spread

The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.

Using machine learning and big data to explore the drug resistance landscape in HIV

Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs.

We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs.

When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance.

Almost all drugs to treat HIV target the Reverse Transcriptase (RT) and Drug resistance mutations (DRMs) appear in HIV under treatment pressure. Resistant strains can be transmitted and limit treatment options at the population level. Classically, multiple statistical testing is used to find DRMs, by comparing virus sequences of treated and naive populations. However, with this method, each mutation is considered individually and we cannot hope to reveal any interaction (epistasis) between them. Here, we used machine learning to discover new DRMs and study potential epistasis effects. We applied this approach to a very large UK dataset comprising ≈ 55, 000 RT sequences. Results robustness was checked on different UK and African datasets.

Six new mutations associated to resistance were found. All six have a low genetic barrier and show high correlations with known DRMs. Moreover, all these mutations are close to either the active site or the regulatory binding pocket of RT. Thus, they are good candidates for further wet experiments to establish their role in drug resistance. Importantly, our results indicate that epistasis seems to be limited to the classical scheme where primary DRMs confer resistance and associated mutations modulate the strength of the resistance and/or compensate for the fitness cost induced by DRMs.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

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.

Analysis of heterogeneous genomic samples using image normalization and machine learning

BACKGROUND

Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.

RESULTS

We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.

CONCLUSIONS

Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

COVTree: Coevolution in OVerlapped sequences by Tree analysis server

Overlapping genes are commonplace in viruses and play an important role in their function and evolution. For these genes, molecular coevolution may be seen as a mechanism to decrease the evolutionary constraints of amino acid positions in the overlapping regions and to tolerate or compensate unfavorable mutations. Tracing these mutational sites, could help to gain insight on the direct or indirect effect of the mutations in the corresponding overlapping proteins. In the past, coevolution analysis has been used to identify residue pairs and coevolutionary signatures within or between proteins that served as markers of physical interactions and/or functional relationships. Coevolution in OVerlapped sequences by Tree analysis (COVTree) is a web server providing the online analysis of coevolving amino-acid pairs in overlapping genes, where residues might be located inside or outside the overlapping region. COVTree is designed to handle protein families with various characteristics, among which those that typically display a small number of highly conserved sequences. It is based on BIS2, a fast version of the coevolution analysis tool Blocks in Sequences (BIS). COVTree provides a rich and interactive graphical interface to ease biological interpretation of the results and it is openly accessible at http://www.lcqb.upmc.fr/COVTree/.

Retrospective analysis of HIV-1 drug resistance mutations in Suzhou, China from 2009 to 2014

In this study, we investigated drug resistance levels in human immunodeficiency virus (HIV)-1-infected patients in Suzhou by retrospectively analyzing this property and the characteristics of circulating HIV-1 strains collected from 2009 to 2014. A total of 261 HIV-1-positive plasma samples, confirmed by the Suzhou CDC, were collected and evaluated to detect HIV-1 drug resistance genotypes using an in-house method. The pol gene fragment was amplified, and its nucleic acid sequence was determined by Sanger sequencing. Drug resistance mutations were then analyzed using the Stanford University HIV resistance database ( https://hivdb.stanford.edu ). A total of 216 pol gene fragments were amplified and sequenced with 16.7% (36/216) of sequences revealing these mutations. The drug resistance rates of protease, nucleoside reverse transcriptase, and non-nucleoside reverse transcriptase inhibitors (NNRTIs) were 4/36 (11.1%), 2/36 (5.6%), and 30/36 (83.3%), respectively. Five surveillance drug resistance mutations were found in 36 sequences, of which, three were found among specimens of men who have sex with men. Potential low-level resistance accounted for 33% of amino acid mutations associated with NNRTIs. Two of the mutations, M230L and L100I, which confer a high level of resistance efavirenz (EFV) and nevirapine (NVP) used as NNRTIs for first-line antiretroviral therapy (ART), were detected in this study. Therefore, when HIV-1 patients in Suzhou are administered fist-line ART, much attention should be paid to the status of these mutations that cause resistance to EVP, EFV, and NVP.

Accessory mutations balance the marginal stability of the HIV-1 protease in drug resistance

The HIV-1 protease is a major target of inhibitor drugs in AIDS therapies. The therapies are impaired by mutations of the HIV-1 protease that can lead to resistance to protease inhibitors. These mutations are classified into major mutations, which usually occur first and clearly reduce the susceptibility to protease inhibitors, and minor, accessory mutations that occur later and individually do not substantially affect the susceptibility to inhibitors. Major mutations are predominantly located in the active site of the HIV-1 protease and can directly interfere with inhibitor binding. Minor mutations, in contrast, are typically located distal to the active site. A central question is how these distal mutations contribute to resistance development. In this article, we present a systematic computational investigation of stability changes caused by major and minor mutations of the HIV-1 protease. As most small single-domain proteins, the HIV-1 protease is only marginally stable. Mutations that destabilize the folded, active state of the protease therefore can shift the conformational equilibrium towards the unfolded, inactive state. We find that the most frequent major mutations destabilize the HIV-1 protease, whereas roughly half of the frequent minor mutations are stabilizing. An analysis of protease sequences from patients in treatment indicates that the stabilizing minor mutations are frequently correlated with destabilizing major mutations, and that highly resistant HIV-1 proteases exhibit significant fractions of stabilizing mutations. Our results thus indicate a central role of minor mutations in balancing the marginal stability of the protease against the destabilization induced by the most frequent major mutations.

Better ILP models for haplotype assembly

Background

The haplotype assembly problem for diploid is to find a pair of haplotypes from a given set of aligned Single Nucleotide Polymorphism (SNP) fragments (reads). It has many applications in association studies, drug design, and genetic research. Since this problem is computationally hard, both heuristic and exact algorithms have been designed for it. Although exact algorithms are much slower, they are still of great interest because they usually output significantly better solutions than heuristic algorithms in terms of popular measures such as the Minimum Error Correction (MEC) score, the number of switch errors, and the QAN50 score. Exact algorithms are also valuable because they can be used to witness how good a heuristic algorithm is. The best known exact algorithm is based on integer linear programming (ILP) and it is known that ILP can also be used to improve the output quality of every heuristic algorithm with a little decline in speed. Therefore, faster ILP models for the problem are highly demanded.

Results

As in previous studies, we consider not only the general case of the problem but also its all-heterozygous case where we assume that if a column of the input read matrix contains at least one 0 and one 1, then it corresponds to a heterozygous SNP site. For both cases, we design new ILP models for the haplotype assembly problem which aim at minimizing the MEC score. The new models are theoretically better because they contain significantly fewer constraints. More importantly, our experimental results show that for both simulated and real datasets, the new model for the all-heterozygous (respectively, general) case can usually be solved via CPLEX (an ILP solver) at least 5 times (respectively, twice) faster than the previous bests. Indeed, the running time can sometimes be 41 times better.

Conclusions

This paper proposes a new ILP model for the haplotype assembly problem and its all-heterozygous case, respectively. Experiments with both real and simulated datasets show that the new models can be solved within much shorter time by CPLEX than the previous bests. We believe that the models can be used to improve heuristic algorithms as well.

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.

Polymorphisms and Mutational Covariation Associated with Death in a Prospective Cohort of HIV/AIDS Patients Receiving Long-Term ART in China

Background

HIV drug resistance is associated with faster clinical progression of AIDS. However, the effect of significant polymorphisms and mutational covariation on mortality among HIV/AIDS patients receiving long-term antiretroviral therapy (ART), have rarely been studied.

Methods

In this prospective cohort study from December 2003 to December 2014, we present a new computational modelling approach based on bioinformatics-based models and several statistical methods to elucidate the molecular mechanisms involved in the acquisition of polymorphisms and mutations on death in HIV/AIDS patients receiving long-term ART in China.

Results

This study involved 654 ART-treated patients, who had been followed for 5473.4 person-years, a median of 9.8 years, and 178 died (25.2%, 3.3/100 person-years). The first regimens included AZT/d4T + NVP+ ddI (78.9%) or AZT/d4T + NVP+ 3TC (20.0%). We calculated an individual Ka/Ks value for each specific amino acid mutation. Result showed that 20 polymorphisms (E6D, Q18H, E35D, S37N, T39A, K43E, S68N, L74I, I93L, K103N, V106A, E169D, Y181C, G190A, Q197K, T200V, T200E, T215I, E224D and P225H) were strongly associated with AIDS related deaths. Among them, 7 polymorphisms (L74I, K103N, V106A, Y181C, G190A, T215I and P225H) were known to be drug resistance mutations, 7 polymorphisms (E6D, E35D, S37N, I93L, E169D, T200V and T200E were considered to be potential drug resistance mutations, and 6 polymorphisms (T39A, K43E, S68N, Q197K, T200V and E224D) were newly found to have an association with drug resistance mutations, which formed a complex network of relationships.

Conclusions

Some polymorphisms and mutational covariation may be the important influencing factors in the failure of treatment. Understanding these mechanisms is essential for the development of new therapies, designing optimal drug combinations, and determining effective clinical management of individual patients.

HIV-1 drug resistance and resistance testing

The global scale-up of antiretroviral (ARV) therapy (ART) has led to dramatic reductions in HIV-1 mortality and incidence. However, HIV drug resistance (HIVDR) poses a potential threat to the long-term success of ART and is emerging as a threat to the elimination of AIDS as a public health problem by 2030. In this review we describe the genetic mechanisms, epidemiology, and management of HIVDR at both individual and population levels across diverse economic and geographic settings. To describe the genetic mechanisms of HIVDR, we review the genetic barriers to resistance for the most commonly used ARVs and describe the extent of cross-resistance between them. To describe the epidemiology of HIVDR, we summarize the prevalence and patterns of transmitted drug resistance (TDR) and acquired drug resistance (ADR) in both high-income and low- and middle-income countries (LMICs). We also review to two categories of HIVDR with important public health relevance: (i) pre-treatment drug resistance (PDR), a World Health Organization-recommended HIVDR surveillance metric and (ii) and pre-exposure prophylaxis (PrEP)-related drug resistance, a type of ADR that can impact clinical outcomes if present at the time of treatment initiation. To summarize the implications of HIVDR for patient management, we review the role of genotypic resistance testing and treatment practices in both high-income and LMIC settings. In high-income countries where drug resistance testing is part of routine care, such an understanding can help clinicians prevent virological failure and accumulation of further HIVDR on an individual level by selecting the most efficacious regimens for their patients. Although there is reduced access to diagnostic testing and to many ARVs in LMIC, understanding the scientific basis and clinical implications of HIVDR is useful in all regions in order to shape appropriate surveillance, inform treatment algorithms, and manage difficult cases.

Analysis of coevolution in nonstructural proteins of chikungunya virus

Background

RNA viruses are characterized by high rate of mutations mainly due to the lack of proofreading repair activities associated with its RNA-dependent RNA-polymerase (RdRp). In case of arboviruses, this phenomenon has lead to the existence of mixed population of genomic variants within the host called quasi-species. The stability of strains within the quasi-species lies on mutations that are positively selected which in turn depend on whether these mutations are beneficial in either or both hosts. Coevolution of amino acids (aa) is one phenomenon that leads to establishment of favorable traits in viruses and leading to their fitness.

Results

Fourteen CHIKV clinical samples collected over three years were subjected to RT-PCR, the four non-structural genes amplified and subjected to various genetic analyses. Coevolution analysis showed 30 aa pairs coevolving in nsP1, 23 aa pairs coevolving in nsP2, 239 in nsP3 and 46 aa coevolving pairs in nsP4 when each non-structural protein was considered independently. Further analysis showed that 705 amino acids pairs of the non-structural polyproteins coevolved together with a correlation coefficient of ≥0.5. Functional relevance of these coevolving amino acids in all the nonstructural proteins of CHIKV were predicted using Eukaryotic Linear Motifs (ELMs) of human.

Conclusions

The present study was undertaken to study co-evolving amino acids in the non-structural proteins of chikungunya virus (CHIKV), an important arbovirus. It was observed that several amino acids residues were coevolving and shared common functions.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

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.

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

While the role of drug resistance mutations in HIV protease has been studied comprehensively, mutations in its substrate, Gag, have not been extensively cataloged. Using deep sequencing, we analyzed a unique collection of longitudinal viral samples from 93 patients who have been treated with therapies containing protease inhibitors (PIs). Due to the high sequence coverage within each sample, the frequencies of mutations at individual positions were calculated with high precision. We used this information to characterize the variability in the Gag polyprotein and its effects on PI-therapy outcomes. To examine covariation of mutations between two different sites using deep sequencing data, we developed an approach to estimate the tight bounds on the two-site bivariate probabilities in each viral sample, and the mutual information between pairs of positions based on all the bounds. Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease. Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity. This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity. Moreover, the strong covariation between residues in matrix and capsid at the N-terminus with p1 and p6 at the C-terminus is consistent with residue-residue contacts between these proteins at some point in the viral life cycle.

Understanding the structure of HIV proteins and the function of drug-resistant mutations of these proteins is critical for the development of effective HIV treatments. Selected gag mutations have been shown to provide compensatory functions for protease resistance mutations and may directly contribute to the development of drug resistance. To determine associations between protease inhibitor mutations and gag, we utilized deep sequencing of HIV gag and protease from a collection of viral isolates from patients treated with highly active retroviral protease inhibitors. Deep sequencing allows for accurate measurement of mutation frequencies at each position, allowing estimation, using a novel method we developed, of the covariation between any two residues on gag. Using this information, we characterize the variation within gag and protease and identify the most strongly correlated pairs of inter- and intra-protein residues. Our results suggest that matrix and p1/p6 mutations form the core of a network of strongly correlated gag mutations and contribute to recurrent treatment failure. Extracting gag residue covariation information from the deep sequencing of patient viral samples may provide insight into structural aspects of the Gag polyprotein as well new areas for small molecule targeting to disrupt Gag function.

A Functional Interplay between Human Immunodeficiency Virus Type 1 Protease Residues 77 and 93 Involved in Differential Regulation of Precursor Autoprocessing and Mature Protease Activity

HIV-1 protease (PR) is a viral enzyme vital to the production of infectious virions. It is initially synthesized as part of the Gag-Pol polyprotein precursor in the infected cell. The free mature PR is liberated as a result of precursor autoprocessing upon virion release. We previously described a model system to examine autoprocessing in transfected mammalian cells. Here, we report that a covariance analysis of miniprecursor (p6*-PR) sequences derived from drug naïve patients identified a series of amino acid pairs that vary together across independent viral isolates. These covariance pairs were used to build the first topology map of the miniprecursor that suggests high levels of interaction between the p6* peptide and the mature PR. Additionally, several PR-PR covariance pairs are located far from each other (>12 Å Cα to Cα) relative to their positions in the mature PR structure. Biochemical characterization of one such covariance pair (77-93) revealed that each residue shows distinct preference for one of three alkyl amino acids (V, I, and L) and that a polar or charged amino acid at either of these two positions abolishes precursor autoprocessing. The most commonly observed 77V is preferred by the most commonly observed 93I, but the 77I variant is preferred by other 93 variances (L, V, or M) in supporting precursor autoprocessing. Furthermore, the 77I93V covariant enhanced precursor autoprocessing and Gag polyprotein processing but decreased the mature PR activity. Therefore, both covariance and biochemical analyses support a functional association between residues 77 and 93, which are spatially distant from each other in the mature PR structure. Our data also suggests that these covariance pairs differentially regulate precursor autoprocessing and the mature protease activity.

Synthetic lethals in HIV: ways to avoid drug resistance : Running title: Preventing HIV resistance

Background

RNA viruses rapidly accumulate genetic variation, which can give rise to synthetic lethal (SL) and deleterious (SD) mutations. Synthetic lethal mutations (non-lethal when alone but lethal when combined in one genome) have been studied to develop cancer therapies. This principle can also be used against fast-evolving RNA-viruses. Indeed, targeting protein sites involved in SD + SL interactions with a drug would render any mutation of such sites, lethal.

Results

Here, we set up a strategy to detect intragenic pairs of SL and SD at the surface of the protein to predict less escapable drug target sites. For this, we detected SD + SL, studying HIV protease (PR) and reverse transcriptase (RT) sequence alignments from two groups of VIH⁺ individuals: treated with drugs (T) or not (NT). Using a series of statistical approaches, we were able to propose bona fide SD + SL couples. When focusing on spatially close co-variant SD + SL couples at the surface of the protein, we found 5 SD + SL groups (2 in the protease and 3 in the reverse transcriptase), which could be good candidates to form pockets to accommodate potential drugs.

Conclusions

Thus, designing drugs targeting these specific SD + SL groups would not allow the virus to mutate any residue involved in such groups without losing an essential function. Moreover, we also show that the selection pressure induced by the treatment leads to the appearance of new mutations, which change the mutational landscape of the protein. This drives the existence of differential SD + SL couples between the drug-treated and non-treated groups. Thus, new anti-viral drugs should be designed differently to target such groups.

Reviewers

This article was reviewed by Neil Greenspan Csaba Pal and István Simon.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0044-y) contains supplementary material, which is available to authorized users.

Integrated analysis of residue coevolution and protein structures capture key protein sectors in HIV-1 proteins

HIV type 1 (HIV-1) is characterized by its rapid genetic evolution, leading to challenges in anti-HIV therapy. However, the sequence variations in HIV-1 proteins are not randomly distributed due to a combination of functional constraints and genetic drift. In this study, we examined patterns of sequence variability for evidence of linked sequence changes (termed as coevolution or covariation) in 15 HIV-1 proteins. It shows that the percentage of charged residues in the coevolving residues is significantly higher than that in all the HIV-1 proteins. Most of the coevolving residues are spatially proximal in the protein structures and tend to form relatively compact and independent units in the tertiary structures, termed as "protein sectors". These protein sectors are closely associated with anti-HIV drug resistance, T cell epitopes, and antibody binding sites. Finally, we explored candidate peptide inhibitors based on the protein sectors. Our results can establish an association between the coevolving residues and molecular functions of HIV-1 proteins, and then provide us with valuable knowledge of pathology of HIV-1 and therapeutics development.

A new ensemble coevolution system for detecting HIV-1 protein coevolution

BACKGROUND

A key challenge in the field of HIV-1 protein evolution is the identification of coevolving amino acids at the molecular level. In the past decades, many sequence-based methods have been designed to detect position-specific coevolution within and between different proteins. However, an ensemble coevolution system that integrates different methods to improve the detection of HIV-1 protein coevolution has not been developed.

RESULTS

We integrated 27 sequence-based prediction methods published between 2004 and 2013 into an ensemble coevolution system. This system allowed combinations of different sequence-based methods for coevolution predictions. Using HIV-1 protein structures and experimental data, we evaluated the performance of individual and combined sequence-based methods in the prediction of HIV-1 intra- and inter-protein coevolution. We showed that sequence-based methods clustered according to their methodology, and a combination of four methods outperformed any of the 27 individual methods. This four-method combination estimated that HIV-1 intra-protein coevolving positions were mainly located in functional domains and physically contacted with each other in the protein tertiary structures. In the analysis of HIV-1 inter-protein coevolving positions between Gag and protease, protease drug resistance positions near the active site mostly coevolved with Gag cleavage positions (V128, S373-T375, A431, F448-P453) and Gag C-terminal positions (S489-Q500) under selective pressure of protease inhibitors.

CONCLUSIONS

This study presents a new ensemble coevolution system which detects position-specific coevolution using combinations of 27 different sequence-based methods. Our findings highlight key coevolving residues within HIV-1 structural proteins and between Gag and protease, shedding light on HIV-1 intra- and inter-protein coevolution.

Defective hydrophobic sliding mechanism and active site expansion in HIV-1 protease drug resistant variant Gly48Thr/Leu89Met: mechanisms for the loss of saquinavir binding potency

HIV drug resistance continues to emerge; consequently, there is an urgent need to develop next generation antiretroviral therapeutics.1 Here we report on the structural and kinetic effects of an HIV protease drug resistant variant with the double mutations Gly48Thr and Leu89Met (PR_G48T/L89M), without the stabilizing mutations Gln7Lys, Leu33Ile, and Leu63Ile. Kinetic analyses reveal that PR_G48T/L89M and PR_WT share nearly identical Michaelis–Menten parameters; however, PR_G48T/L89M exhibits weaker binding for IDV (41-fold), SQV (18-fold), APV (15-fold), and NFV (9-fold) relative to PR_WT. A 1.9 Å resolution crystal structure was solved for PR_G48T/L89M bound with saquinavir (PR_{G48T/L89M-SQV}) and compared to the crystal structure of PR_WT bound with saquinavir (PR_WT-SQV). PR_{G48T/L89M-SQV} has an enlarged active site resulting in the loss of a hydrogen bond in the S3 subsite from Gly48 to P3 of SQV, as well as less favorable hydrophobic packing interactions between P1 Phe of SQV and the S1 subsite. PR_{G48T/L89M-SQV} assumes a more open conformation relative to PR_WT-SQV, as illustrated by the downward displacement of the fulcrum and elbows and weaker interatomic flap interactions. We also show that the Leu89Met mutation disrupts the hydrophobic sliding mechanism by causing a redistribution of van der Waals interactions in the hydrophobic core in PR_{G48T/L89M-SQV}. Our mechanism for PR_{G48T/L89M-SQV} drug resistance proposes that a defective hydrophobic sliding mechanism results in modified conformational dynamics of the protease. As a consequence, the protease is unable to achieve a fully closed conformation that results in an expanded active site and weaker inhibitor binding.

HIV-1 transmitted drug resistance-associated mutations and mutation co-variation in HIV-1 treatment-naïve MSM from 2011 to 2013 in Beijing, China

Background

Transmitted drug resistance (TDR) is an important public health issue, because TDR-associated mutation may affect the outcome of antiretroviral treatment potentially or directly. Men who have sex with men (MSM) constitute a major risk group for HIV transmission. However, current reports are scarce on HIV TDR-associated mutations and their co-variation among MSM.

Methods

Blood samples from 262 newly diagnosed HIV-positive, antiretroviral therapy (ART)-naïve MSM, were collected from January 2011 and December 2013 in Beijing. The polymerase viral genes were sequenced to explore TDR-associated mutations and mutation co-variation.

Results

A total of 223 samples were sequenced and analyzed. Among them, HIV-1 CRF01_AE are accounted for 60.5%, followed by CRF07_BC (27.8%), subtype B (9.9%), and others. Fifty-seven samples had at least one TDR-associated mutation, mainly including L10I/V (6.3%), A71L/T/V (6.3%), V179D/E (5.4%), and V106I (2.7%), with different distributions of TDR-associated mutations by different HIV-1 subtypes and by each year. Moreover, eight significant co-variation pairs were found between TDR-associated mutations (V179D/E) and seven overlapping polymorphisms in subtype CRF01_AE.

Conclusions

To date, this work consists the most comprehensive genetic characterization of HIV-1 TDR-associated mutations prevalent among MSM. It provides important information for understanding TDR and viral evolution among Chinese MSM, a population currently at particularly high risk of HIV transmission.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-014-0689-7) contains supplementary material, which is available to authorized users.

A bioinformatics pipeline for the analyses of viral escape dynamics and host immune responses during an infection

Rapidly mutating viruses, such as hepatitis C virus (HCV) and HIV, have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immuno-virology of viral infections, as these allow fast and cheap generation of genomic data. However, due to the large volumes of data generated, a thorough understanding of the biological and immunological significance of such information is often difficult. This paper proposes a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Taking next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, we demonstrate the applicability of these tools in the context of primary HCV infection. We provide a statistical and visual explanation of the relationship between cooccurring mutations on the viral genome and the parallel adaptive immune response against HCV.

CorMut: an R/Bioconductor package for computing correlated mutations based on selection pressure

Correlated mutations constitute a fundamental idea in evolutionary biology, and understanding correlated mutations will, in turn, facilitate an understanding of the genetic mechanisms governing evolution. CorMut is an R package designed to compute correlated mutations in the unit of codon or amino acid mutation. Three classical methods were incorporated, and the computation results can be represented as correlation mutation networks. CorMut also enables the comparison of correlated mutations between two different evolutionary conditions.

AVAILABILITY AND IMPLEMENTATION

CorMut is released under the GNU General Public License within bioconductor project, and freely available at http://bioconductor.org/packages/release/bioc/html/CorMut.html.

Natural polymorphisms and unusual mutations in HIV-1 protease with potential antiretroviral resistance: a bioinformatic analysis

Background

The correlations of genotypic and phenotypic tests with treatment, clinical history and the significance of mutations in viruses of HIV-infected patients are used to establish resistance mutations to protease inhibitors (PIs). Emerging mutations in human immunodeficiency virus type 1 (HIV-1) protease confer resistance to PIs by inducing structural changes at the ligand interaction site. The aim of this study was to establish an in silico structural relationship between natural HIV-1 polymorphisms and unusual HIV-1 mutations that confer resistance to PIs.

Results

Protease sequences isolated from 151 Mexican HIV-1 patients that were naïve to, or subjected to antiretroviral therapy, were examined. We identified 41 unrelated resistance mutations with a prevalence greater than 1%. Among these mutations, nine exhibited positive selection, three were natural polymorphisms (L63S/V/H) in a codon associated with drug resistance, and six were unusual mutations (L5F, D29V, L63R/G, P79L and T91V). The D29V mutation, with a prevalence of 1.32% in the studied population, was only found in patients treated with antiretroviral drugs. Using in silico modelling, we observed that D29V formed unstable protease complexes when were docked with lopinavir, saquinavir, darunavir, tipranavir, indinavir and atazanavir.

Conclusions

The structural correlation of natural polymorphisms and unusual mutations with drug resistance is useful for the identification of HIV-1 variants with potential resistance to PIs. The D29V mutation likely confers a selection advantage in viruses; however, in silico, presence of this mutation results in unstable enzyme/PI complexes, that possibly induce resistance to PIs.

Directed evolution of the substrate specificity of biotin ligase

We have developed selection scheme for directing the evolution of Escherichia coli biotin protein ligase (BPL) via in vitro compartmentalization, and have used this scheme to alter the substrate specificity of the ligase towards the utilization of the biotin analogue desthiobiotin. In this scheme, a peptide substrate (BAP) was conjugated to a DNA library encoding BirA, emulsified such that there was a single template per compartment, and protein variants were transcribed and translated in vitro. Those variants that could efficiently desthiobiotinylate their corresponding peptide:DNA conjugate were subsequently captured and amplified. Following just six rounds of selection and amplification several variants that demonstrated higher activity with desthiobiotin were identified. The best variants from Round 6, BirA6-40 and BirA6-47 , showed 17-fold and 10-fold higher activity, respectively, their abilities to use desthiobiotin as a substrate. While selected enzymes contained a number of substitutions, a single mutation, M157T, proved sufficient to provide much greater activity with desthiobiotin. Further characterization of BirA6-40 and the single substitution variant BirAM157T revealed that they had twoto threefold higher kcat values for desthiobiotin. These variants had also lost much of their ability to utilize biotin, resulting in orthogonal enzymes that in conjunction with streptavidin variants that can utilize desthiobiotin may prove to be of great use in developing additional, robust conjugation handles for a variety of biological and biotechnological applications.

Nucleoside reverse transcriptase inhibitor resistance mutations associated with first-line stavudine-containing antiretroviral therapy: programmatic implications for countries phasing out stavudine

BACKGROUND

The World Health Organization Antiretroviral Treatment Guidelines recommend phasing-out stavudine because of its risk of long-term toxicity. There are two mutational pathways of stavudine resistance with different implications for zidovudine and tenofovir cross-resistance, the primary candidates for replacing stavudine. However, because resistance testing is rarely available in resource-limited settings, it is critical to identify the cross-resistance patterns associated with first-line stavudine failure.

METHODS

We analyzed HIV-1 resistance mutations following first-line stavudine failure from 35 publications comprising 1,825 individuals. We also assessed the influence of concomitant nevirapine vs. efavirenz, therapy duration, and HIV-1 subtype on the proportions of mutations associated with zidovudine vs. tenofovir cross-resistance.

RESULTS

Mutations with preferential zidovudine activity, K65R or K70E, occurred in 5.3% of individuals. Mutations with preferential tenofovir activity, ≥ two thymidine analog mutations (TAMs) or Q151M, occurred in 22% of individuals. Nevirapine increased the risk of TAMs, K65R, and Q151M. Longer therapy increased the risk of TAMs and Q151M but not K65R. Subtype C and CRF01_AE increased the risk of K65R, but only CRF01_AE increased the risk of K65R without Q151M.

CONCLUSIONS

Regardless of concomitant nevirapine vs. efavirenz, therapy duration, or subtype, tenofovir was more likely than zidovudine to retain antiviral activity following first-line d4T therapy.

Subtype-specific differences in the development of accessory mutations associated with high-level resistance to HIV-1 nucleoside reverse transcriptase inhibitors

OBJECTIVES

To identify accessory mutations associated with high-level resistance to reverse transcriptase (RT) inhibitors in HIV-1 subtypes B and C.

METHODS

Changes relative to the wild-type for codons 1-400 of RT were analysed from treatment-experienced patients infected with subtypes B (5464 patients) and C (1920 patients). Positions associated with the accumulation of mutations conferring resistance to thymidine analogues and to non-nucleoside RT inhibitors (NNRTIs) were identified. A subtype-specific single-replication cycle drug susceptibility assay was used to determine whether some of the mutations affected drug susceptibility or viral infectivity.

RESULTS

In subtype B, mutations at 31 and 26 positions were associated with the accumulation of thymidine analogue mutations (TAMs) and NNRTI mutations, respectively; in subtype C, 18 and 13 positions were identified, respectively. Amino acid changes at the following positions were differentially associated with (i) the accumulation of 0-4+ TAMs in subtypes B and C (away from consensus): 43 (27.0% B versus 2.5% C); 118 (36.4% B versus 16.2% C); 135 (12.5% B versus 28.0% C); and 326 (2.6% towards consensus in B versus 7.6% away in C) and (ii) the accumulation of 0-3+ NNRTI mutations (away from consensus): 43 (10.2% B versus 0.5% C); and 68 (5.2% B versus 10.3% C). Codon changes K43E, E44D and V118I were found to have no effect on susceptibility to three NRTIs with or without TAMs in either subtype; however, some accessory mutations had subtype-specific effects on viral infectivity.

CONCLUSIONS

Differences between subtypes B and C were observed in the development and effect of accessory mutations associated with high-level resistance to RT inhibitors.

Low-level persistence of drug resistance mutations in hepatitis B virus-infected subjects with a past history of Lamivudine treatment

We sought to determine the prevalence of hepatitis B virus (HBV) lamivudine (LAM)-resistant minority variants in subjects who once received LAM but had discontinued it prior to virus sampling. We performed direct PCR Sanger sequencing and ultradeep pyrosequencing (UDPS) of HBV reverse transcriptase (RT) of plasma viruses from 45 LAM-naive subjects and 46 LAM-experienced subjects who had discontinued LAM a median of 24 months earlier. UDPS was performed to a depth of ∼3,000 reads per nucleotide. Minority variants were defined as differences from the Sanger sequence present in ≥0.5% of UDPS reads in a sample. Sanger sequencing identified ≥1 LAM resistance mutations (rtL80I/V, rtM204I, and rtA181T) in samples from 5 (11%) of 46 LAM-experienced and none of 45 LAM-naive subjects (0%; P = 0.06). UDPS detected ≥1 LAM resistance mutations (rtL80I/V, rtV173L, rtL180M, rtA181T, and rtM204I/V) in 10 (22%) of the 46 LAM-experienced subjects, including 5 in whom LAM resistance mutations were not identified by Sanger sequencing. Overall, LAM resistance mutations were more likely to be present in LAM-experienced (10/46, 22%) than LAM-naive subjects (0/45, 0%; P = 0.001). The median time since LAM discontinuation was 12.8 months in the 10 subjects with a LAM resistance mutation compared to 30.5 months in the 36 LAM-experienced subjects without a LAM resistance mutation (P < 0.001). The likelihood of detecting a LAM resistance mutation was significantly increased using UDPS compared to Sanger sequencing and was inversely associated with the time since LAM discontinuation.

HIV-1 antiretroviral resistance: scientific principles and clinical applications

The efficacy of an antiretroviral (ARV) treatment regimen depends on the activity of the regimen's individual ARV drugs and the number of HIV-1 mutations required for the development of resistance to each ARV - the genetic barrier to resistance. ARV resistance impairs the response to therapy in patients with transmitted resistance, unsuccessful initial ARV therapy and multiple virological failures. Genotypic resistance testing is used to identify transmitted drug resistance, provide insight into the reasons for virological failure in treated patients, and help guide second-line and salvage therapies. In patients with transmitted drug resistance, the virological response to a regimen selected on the basis of standard genotypic testing approaches the responses observed in patients with wild-type viruses. However, because such patients are at a higher risk of harbouring minority drug-resistant variants, initial ARV therapy in this population should contain a boosted protease inhibitor (PI) - the drug class with the highest genetic barrier to resistance. In patients receiving an initial ARV regimen with a high genetic barrier to resistance, the most common reasons for virological failure are nonadherence and, potentially, pharmacokinetic factors or minority transmitted drug-resistant variants. Among patients in whom first-line ARVs have failed, the patterns of drug-resistance mutations and cross-resistance are often predictable. However, the extent of drug resistance correlates with the duration of uncontrolled virological replication. Second-line therapy should include the continued use of a dual nucleoside/nucleotide reverse transcriptase inhibitor (NRTI)-containing backbone, together with a change in the non-NRTI component, most often to an ARV belonging to a new drug class. The number of available fully active ARVs is often diminished with each successive treatment failure. Therefore, a salvage regimen is likely to be more complicated in that it may require multiple ARVs with partial residual activity and compromised genetic barriers of resistance to attain complete virological suppression. A thorough examination of the patient's ARV history and prior resistance tests should be performed because genotypic and/or phenotypic susceptibility testing is often not sufficient to identify drug-resistant variants that emerged during past therapies and may still pose a threat to a new regimen. Phenotypic testing is also often helpful in this subset of patients. ARVs used for salvage therapy can be placed into the following hierarchy: (i) ARVs belonging to a previously unused drug class; (ii) ARVs belonging to a previously used drug class that maintain significant residual antiviral activity; (iii) NRTI combinations, as these often appear to retain in vivo virological activity, even in the presence of reduced in vitro NRTI susceptibility; and rarely (iv) ARVs associated with previous virological failure and drug resistance that appear to have possibly regained their activity as a result of viral reversion to wild type. Understanding the basic principles of HIV drug resistance is helpful in guiding individual clinical decisions and the development of ARV treatment guidelines.

Correlated electrostatic mutations provide a reservoir of stability in HIV protease

HIV protease, an aspartyl protease crucial to the life cycle of HIV, is the target of many drug development programs. Though many protease inhibitors are on the market, protease eventually evades these drugs by mutating at a rapid pace and building drug resistance. The drug resistance mutations, called primary mutations, are often destabilizing to the enzyme and this loss of stability has to be compensated for. Using a coarse-grained biophysical energy model together with statistical inference methods, we observe that accessory mutations of charged residues increase protein stability, playing a key role in compensating for destabilizing primary drug resistance mutations. Increased stability is intimately related to correlations between electrostatic mutations – uncorrelated mutations would strongly destabilize the enzyme. Additionally, statistical modeling indicates that the network of correlated electrostatic mutations has a simple topology and has evolved to minimize frustrated interactions. The model's statistical coupling parameters reflect this lack of frustration and strongly distinguish like-charge electrostatic interactions from unlike-charge interactions for of the most significantly correlated double mutants. Finally, we demonstrate that our model has considerable predictive power and can be used to predict complex mutation patterns, that have not yet been observed due to finite sample size effects, and which are likely to exist within the larger patient population whose virus has not yet been sequenced.

HIV is incurable because its enzymes evolve rapidly by developing resistance mutations to retroviral inhibitors. Most of these mutations work synergistically, but the biophysical basis behind their cooperation is not well understood. Our work addresses these important issues by bridging the gap between the statistical modeling of HIV protease subtype B sequences with the energetics of mutations involving charged amino acids by showing that electrostatic stability is intimately related to correlations. Moreover, we demonstrate that our statistical model has considerable predictive power and can be used to predict complex mutation patterns that have not yet been observed due to the finite sizes of the current sequence databases. In other words, as the database size increases, our model has the ability to predict the identities of the high probability mutations patterns, which are more likely to be observed. Knowing which currently unobserved mutations are more likely to be observed can be very advantageous in combating the disease.

Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design

Predicting which mutations proteins tolerate while maintaining their structure and function has important applications for modeling fundamental properties of proteins and their evolution; it also drives progress in protein design. Here we develop a computational model to predict the tolerated sequence space of HIV-1 protease reachable by single mutations. We assess the model by comparison to the observed variability in more than 50,000 HIV-1 protease sequences, one of the most comprehensive datasets on tolerated sequence space. We then extend the model to a second protein, reverse transcriptase. The model integrates multiple structural and functional constraints acting on a protein and uses ensembles of protein conformations. We find the model correctly captures a considerable fraction of protease and reverse-transcriptase mutational tolerance and shows comparable accuracy using either experimentally determined or computationally generated structural ensembles. Predictions of tolerated sequence space afforded by the model provide insights into stability-function tradeoffs in the emergence of resistance mutations and into strengths and limitations of the computational model.

Many related protein sequences can be consistent with the structure and function of a given protein, suggesting that proteins may be quite robust to mutations. This tolerance to mutations is frequently exploited by pathogens. In particular, pathogens can rapidly evolve mutated proteins that have a new function - resistance against a therapeutic inhibitor - without abandoning other functions essential for the pathogen. This principle may also hold more generally: Proteins tolerant to mutational changes can more easily acquire new functions while maintaining their existing properties. The ability to predict the tolerance of proteins to mutation could thus help both to analyze the emergence of resistance mutations in pathogens and to engineer proteins with new functions. Here we develop a computational model to predict protein mutational tolerance towards point mutations accessible by single nucleotide changes, and validate it using two important pathogenic proteins and therapeutic targets: the protease and reverse transcriptase from HIV-1. The model provides insights into how resistance emerges and makes testable predictions on mutations that have not been seen yet. Similar models of mutational tolerance should be useful for characterizing and reengineering the functions of other proteins for which a three-dimensional structure is available.

Panel of prototypical recombinant infectious molecular clones resistant to nevirapine, efavirenz, etravirine, and rilpivirine

We created a panel of 10 representative multi-nonnucleoside reverse transcriptase inhibitor (NNRTI)-resistant recombinant infectious molecular HIV-1 clones to assist researchers studying NNRTI resistance or developing novel NNRTIs. The cloned viruses contain most of the major NNRTI resistance mutations and most of the significantly associated mutation pairs that we identified in two network analyses. Each virus in the panel has intermediate- or high-level resistance to all or three of the four most commonly used NNRTIs.

Standardized comparison of the relative impacts of HIV-1 reverse transcriptase (RT) mutations on nucleoside RT inhibitor susceptibility

Determining the phenotypic impacts of reverse transcriptase (RT) mutations on individual nucleoside RT inhibitors (NRTIs) has remained a statistical challenge because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns, complicating the task of determining the relative contribution of each mutation to HIV drug resistance. Furthermore, the NRTIs have highly variable dynamic susceptibility ranges, making it difficult to determine the relative effect of an RT mutation on susceptibility to different NRTIs. In this study, we analyzed 1,273 genotyped HIV-1 isolates for which phenotypic results were obtained using the PhenoSense assay (Monogram, South San Francisco, CA). We used a parsimonious feature selection algorithm, LASSO, to assess the possible contributions of 177 mutations that occurred in 10 or more isolates in our data set. We then used least-squares regression to quantify the impact of each LASSO-selected mutation on each NRTI. Our study provides a comprehensive view of the most common NRTI resistance mutations. Because our results were standardized, the study provides the first analysis that quantifies the relative phenotypic effects of NRTI resistance mutations on each of the NRTIs. In addition, the study contains new findings on the relative impacts of thymidine analog mutations (TAMs) on susceptibility to abacavir and tenofovir; the impacts of several known but incompletely characterized mutations, including E40F, V75T, Y115F, and K219R; and a tentative role in reduced NRTI susceptibility for K64H, a novel NRTI resistance mutation.

Polymorphic mutations associated with the emergence of the multinucleoside/tide resistance mutations 69 insertion and Q151M

BACKGROUND

We hypothesized that polymorphic mutations exist that are associated with the emergence of the multinucleoside resistance mutations (MNR), 69 insertion and Q151M.

METHODS

The Swiss HIV Cohort Study was screened, and the frequencies of polymorphic mutations in HIV-1 (subtype B) were compared between patients detected with the 69 insertion (n = 17), Q151M (n = 29), ≥2 thymidine analogue mutations (TAM) 1 (n = 400) or ≥2 TAM 2 (n = 249). Logistic regressions adjusted for the antiretroviral treatment history were performed to analyze the association of the polymorphic mutations with MNR.

RESULTS

The 69 insertion and TAM 1 were strongly associated and occurred in 94.1% (16 of 17) together. The 69 insertion seemed to emerge as a consequence of the TAM 1 pathway (median years until detection: 6.8 compared with 4.4 for ≥2 TAM 1, P Wilcoxon = 0.009). Frequencies of 8 polymorphic mutations (K43E, V60I, S68G, S162C, T165I, I202V, R211K, F214L) were significantly different between groups. Logistic regression showed that F214L and V60I were associated with the emergence of Q151M/TAM 2 opposed to 69 insertion/TAM 1. S68G, T165I, and I202V were associated with Q151M instead of TAM 2.

CONCLUSIONS

Besides antiretroviral therapy, polymorphic mutations may contribute to the emergence of specific MNR mutations.

A multifaceted analysis of HIV-1 protease multidrug resistance phenotypes

Background

Great strides have been made in the effective treatment of HIV-1 with the development of second-generation protease inhibitors (PIs) that are effective against historically multi-PI-resistant HIV-1 variants. Nevertheless, mutation patterns that confer decreasing susceptibility to available PIs continue to arise within the population. Understanding the phenotypic and genotypic patterns responsible for multi-PI resistance is necessary for developing PIs that are active against clinically-relevant PI-resistant HIV-1 variants.

Results

In this work, we use globally optimal integer programming-based clustering techniques to elucidate multi-PI phenotypic resistance patterns using a data set of 398 HIV-1 protease sequences that have each been phenotyped for susceptibility toward the nine clinically-approved HIV-1 PIs. We validate the information content of the clusters by evaluating their ability to predict the level of decreased susceptibility to each of the available PIs using a cross validation procedure. We demonstrate the finding that as a result of phenotypic cross resistance, the considered clinical HIV-1 protease isolates are confined to ~6% or less of the clinically-relevant phenotypic space. Clustering and feature selection methods are used to find representative sequences and mutations for major resistance phenotypes to elucidate their genotypic signatures. We show that phenotypic similarity does not imply genotypic similarity, that different PI-resistance mutation patterns can give rise to HIV-1 isolates with similar phenotypic profiles.

Conclusion

Rather than characterizing HIV-1 susceptibility toward each PI individually, our study offers a unique perspective on the phenomenon of PI class resistance by uncovering major multidrug-resistant phenotypic patterns and their often diverse genotypic determinants, providing a methodology that can be applied to understand clinically-relevant phenotypic patterns to aid in the design of novel inhibitors that target other rapidly evolving molecular targets as well.

Accessory mutations maintain stability in drug-resistant HIV-1 protease

The underlying mechanisms driving the evolution of drug resistance in human immunodeficiency virus (HIV) are only partially understood. We investigated the evolutionary cost of the major resistance mutations in HIV-1 protease in terms of protein stability. The accumulation of resistance mutations destabilizes the protease, limiting further adaptation. From an analysis of clinical isolates, we identified specific accessory mutations that were able to restore the stability of the protease or even increase it beyond the wild-type baseline. Resistance mutations were also found to decrease the activity of HIV protease near neutral pH values, while incorporating stabilizing mutations improved the enzyme's pH tolerance. These findings help us to explain the prevalence of mutations located far from the active site and underscore the importance of protein stability during the evolution of drug resistance in HIV.

Inferring viral quasispecies spectra from 454 pyrosequencing reads

BACKGROUND

RNA viruses infecting a host usually exist as a set of closely related sequences, referred to as quasispecies. The genomic diversity of viral quasispecies is a subject of great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software was originally designed for single genome assembly and cannot be used to simultaneously assemble and estimate the abundance of multiple closely related quasispecies sequences.

RESULTS

In this paper, we introduce a new Viral Spectrum Assembler (ViSpA) method for quasispecies spectrum reconstruction and compare it with the state-of-the-art ShoRAH tool on both simulated and real 454 pyrosequencing shotgun reads from HCV and HIV quasispecies. Experimental results show that ViSpA outperforms ShoRAH on simulated error-free reads, correctly assembling 10 out of 10 quasispecies and 29 sequences out of 40 quasispecies. While ShoRAH has a significant advantage over ViSpA on reads simulated with sequencing errors due to its advanced error correction algorithm, ViSpA is better at assembling the simulated reads after they have been corrected by ShoRAH. ViSpA also outperforms ShoRAH on real 454 reads. Indeed, 7 most frequent sequences reconstructed by ViSpA from a real HCV dataset are viable (do not contain internal stop codons), and the most frequent sequence was within 1% of the actual open reading frame obtained by cloning and Sanger sequencing. In contrast, only one of the sequences reconstructed by ShoRAH is viable. On a real HIV dataset, ShoRAH correctly inferred only 2 quasispecies sequences with at most 4 mismatches whereas ViSpA correctly reconstructed 5 quasispecies with at most 2 mismatches, and 2 out of 5 sequences were inferred without any mismatches. ViSpA source code is available at http://alla.cs.gsu.edu/~software/VISPA/vispa.html.

CONCLUSIONS

ViSpA enables accurate viral quasispecies spectrum reconstruction from 454 pyrosequencing reads. We are currently exploring extensions applicable to the analysis of high-throughput sequencing data from bacterial metagenomic samples and ecological samples of eukaryote populations.

Impaired infectivity of ritonavir-resistant HIV is rescued by heat shock protein 90AB1

Certain ritonavir resistance mutations impair HIV infectivity through incomplete Gag processing by the mutant viral protease. Analysis of the mutant virus phenotype indicates that accumulation of capsid-spacer peptide 1 precursor protein in virus particles impairs HIV infectivity and that the protease mutant virus is arrested during the early postentry stage of HIV infection before proviral DNA synthesis. However, activation of the target cell can rescue this defect, implying that specific host factors expressed in activated cells can compensate for the defect in ritonavir-resistant HIV. This ability to rescue impaired HIV replication presented a unique opportunity to identify host factors involved in postentry HIV replication, and we designed a functional genetic screen so that expression of a given host factor extracted from activated T cells would lead directly to its discovery by rescuing mutant virus replication in nonactivated T cells. We identified the cellular heat shock protein 90 kDa α (cytosolic), class B member 1 (HSP90AB1) as a host factor that can rescue impaired replication of ritonavir-resistant HIV. Moreover, we show that pharmacologic inhibition of HSP90AB1 with 17-(allylamino)-17-demethoxygeldanamycin (tanespimycin) has potent in vitro anti-HIV activity and that ritonavir-resistant HIV is hypersensitive to the drug. These results suggest a possible role for HSP90AB1 in postentry HIV replication and may provide an attractive target for therapeutic intervention.

Within-host co-evolution of Gag P453L and protease D30N/N88D demonstrates virological advantage in a highly protease inhibitor-exposed HIV-1 case

To better understand the mechanism of HIV group-specific antigen (Gag) and protease (PR) co-evolution in drug-resistance acquisition, we analyzed a drug-resistance case by both bioinformatics and virological methods. We especially considered the quality of sequence data and analytical accuracy by introducing single-genome sequencing (SGS) and Spidermonkey/Bayesian graphical models (BGM) analysis, respectively. We analyzed 129 HIV-1 Gag-PR linkage sequences obtained from 8 time points, and the resulting sequences were applied to the Spidermonkey co-evolution analysis program, which identified ten mutation pairs as significantly co-evolving. Among these, we focused on associations between Gag-P453L, the P5' position of the p1/p6 cleavage-site mutation, and PR-D30N/N88D nelfinavir-resistant mutations, and attempted to clarify their virological significance in vitro by constructing recombinant clones. The results showed that P453L(Gag) has the potential to improve replication capacity and the Gag processing efficiency of viruses with D30N(PR)/N88D(PR) but has little effect on nelfinavir susceptibility. Homology modeling analysis suggested that hydrogen bonds between the 30th PR residue and the R452Gag are disturbed by the D30N(PR) mutation, but the impaired interaction is compensated by P453L(Gag) generating new hydrophobic interactions. Furthermore, database analysis indicated that the P453L(Gag)/D30N(PR)/N88D(PR) association was not specific only to our clinical case, but was common among AIDS patients.