Genotypic predictors of human immunodeficiency virus type 1 drug resistance

Convex Modeling of Interactions with Strong Heredity

We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMILY, a very general framework for this task. Our proposal is a generalization of several existing methods, such as VANISH [Radchenko and James, 2010], hierNet [Bien et al., 2013], the all-pairs lasso, and the lasso using only main effects. It can be formulated as the solution to a convex optimization problem, which we solve using an efficient alternating directions method of multipliers (ADMM) algorithm. This algorithm has guaranteed convergence to the global optimum, can be easily specialized to any convex penalty function of interest, and allows for a straightforward extension to the setting of generalized linear models. We derive an unbiased estimator of the degrees of freedom of FAMILY, and explore its performance in a simulation study and on an HIV sequence data set.

Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning

Motivation: Human immunodeficiency virus (HIV) and cancer require personalized therapies owing to their inherent heterogeneous nature. For both diseases, large-scale pharmacogenomic screens of molecularly characterized samples have been generated with the hope of identifying genetic predictors of drug susceptibility. Thus, computational algorithms capable of inferring robust predictors of drug responses from genomic information are of great practical importance. Most of the existing computational studies that consider drug susceptibility prediction against a panel of drugs formulate a separate learning problem for each drug, which cannot make use of commonalities between subsets of drugs.

Results: In this study, we propose to solve the problem of drug susceptibility prediction against a panel of drugs in a multitask learning framework by formulating a novel Bayesian algorithm that combines kernel-based non-linear dimensionality reduction and binary classification (or regression). The main novelty of our method is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps us to eliminate off-target effects and drug-specific experimental noise. Another novelty of our method is the ability of handling missing phenotype values owing to experimental conditions and quality control reasons. We demonstrate the performance of our algorithm via cross-validation experiments on two benchmark drug susceptibility datasets of HIV and cancer. Our method obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.

Availability and implementation: Our Matlab implementations for binary classification and regression are available at https://github.com/mehmetgonen/kbmtl.

Contact:mehmet.gonen@sagebase.org

Supplementary Information:Supplementary data are available at Bioinformatics online.

Bayesian analysis of complex interacting mutations in HIV drug resistance and cross-resistance

A successful treatment of AIDS world-wide is severely hindered by the HIV virus' drug resistance capability resulting from complicated mutation patterns of viral proteins. Such a system of mutations enables the virus to survive and reproduce despite the presence of various antiretroviral drugs by disrupting their binding capability. Although these interacting mutation patterns are extremely difficult to efficiently uncover and interpret, they contribute valuable information to personalized therapeutic regimen design. The use of Bayesian statistical modeling provides an unprecedented opportunity in the field of anti-HIV therapy to understand detailed interaction structures of drug resistant mutations. Multiple Bayesian models equipped with Markov Chain Monte Carlo (MCMC) methods have been recently proposed in this field (Zhang et al. in PNAS 107:1321, 2010 [1]; Zhang et al. in J Proteome Sci Comput Biol 1:2, 2012 [2]; Svicher et al. in Antiviral Res 93(1):86-93, 2012 [3]; Svicher et al. in Antiviral Therapy 16(7):1035-1045, 2011 [4]; Svicher et al. in Antiviral Ther 16(4):A14-A14, 2011 [5]; Svicher et al. in Antiviral Ther 16(4):A85-A85, 2011 [6]; Alteri et al. in Signature mutations in V3 and bridging sheet domain of HIV-1 gp120 HIV-1 are specifically associated with dual tropism and modulate the interaction with CCR5 N-Terminus, 2011 [7]). Probabilistically modeling mutations in the HIV-1 protease or reverse transcriptase (RT) isolated from drug-treated patients provides a powerful statistical procedure that first detects mutation combinations associated with single or multiple-drug resistance, and then infers detailed dependence structures among the interacting mutations in viral proteins (Zhang et al. in PNAS 107:1321, 2010 [1]; Zhang et al. in J Proteome Sci Comput Biol 1:2, 2012 [2]). Combined with molecular dynamics simulations and free energy calculations, Bayesian analysis predictions help to uncover genetic and structural mechanisms in the HIV treatment resistance. Results obtained with such stochastic methods pave the way not only for optimization of the use for existing HIV drugs, but also for the development of the new more efficient antiretroviral medicines. In this chapter we survey current challenges in the bioinformatics of anti-HIV therapy, and outline how recently emerged Bayesian methods can help with the clinical management of HIV-1 infection. We will provide a rigorous review of the Bayesian variable partition model and the recursive model selection procedure based on probability theory and mathematical data analysis techniques while highlighting real applications in HIV and HBV studies including HIV drug resistance (Zhang et al. in PNAS 107:1321, 2010 [1]), cross-resistance (Zhang et al. in J Proteome Sci Comput Biol 1:2, 2012 [2]), HIV coreceptor usage (Svicher et al. in Antiviral Therapy 16(7):1035-1045, 2011 [4]; Svicher et al. in Antiviral Ther 16(4):A14-A14, 2011 [5]; Alteri et al. in Signature mutations in V3 and bridging sheet domain of HIV-1 gp120 HIV-1 are specifically associated with dual tropism and modulate the interaction with CCR5 N-Terminus, 2011 [7]), and occult HBV infection (Svicher et al. in Antiviral Res 93(1):86-93, 2012 [3]; Svicher et al. in Antiviral Ther 16(4):A85-A85, 2011 [6]).

Infection with the frequently transmitted HIV-1 M41L variant has no influence on selection of tenofovir resistance

OBJECTIVES

In ∼10% of newly diagnosed HIV-1 patients, drug-resistant viral variants are detected. In such transmitted HIV-1 variants, the thymidine analogue mutation (TAM) M41L is frequently observed as a single resistance mutation and these viral variants often belong to phylogenetic transmission clusters. The presence of at least three TAMs, in particular patterns with M41L/L210W, impairs the efficacy of the extensively used drug tenofovir. We investigated whether the presence of a single M41L mutation at baseline influences the selection of resistance to tenofovir and emtricitabine in vitro and in vivo.

METHODS

The impact of M41L on the development of drug resistance to tenofovir and emtricitabine was determined by extensive in vitro selection experiments and investigation of the virological outcome of patients on a first-line regimen.

RESULTS

The presence of a single M41L mutation did not influence the selected mutational profile or the genetic barrier to resistance to tenofovir and/or emtricitabine during long-term in vitro selection experiments. In vivo, virological outcome of first-line regimens containing tenofovir and emtricitabine was comparable between patients diagnosed with HIV-1 harbouring M41L (n=17, 16 were part of one transmission cluster) and WT virus (n=248).

CONCLUSIONS

Detection of a single M41L reverse transcriptase mutation at baseline did not influence the development of resistance in vitro or virological outcome on tenofovir-containing regimens in patients belonging to a large transmission cluster. Our results indicate that a high genetic barrier regimen may not be required when patients are diagnosed with HIV variants containing a single M41L mutation in reverse transcriptase.

Predicting virus mutations through statistical relational learning

BACKGROUND

Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants.

RESULTS

We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones.

CONCLUSIONS

Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.

Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure

BACKGROUND

Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy.

RESULTS

In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance.

CONCLUSION

The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.

The Cluster Elastic Net for High-Dimensional Regression With Unknown Variable Grouping

In the high-dimensional regression setting, the elastic net produces a parsimonious model by shrinking all coefficients towards the origin. However, in certain settings, this behavior might not be desirable: if some features are highly correlated with each other and associated with the response, then we might wish to perform less shrinkage on the coefficients corresponding to that subset of features. We propose the cluster elastic net, which selectively shrinks the coefficients for such variables towards each other, rather than towards the origin. Instead of assuming that the clusters are known a priori, the cluster elastic net infers clusters of features from the data, on the basis of correlation among the variables as well as association with the response. These clusters are then used in order to more accurately perform regression. We demonstrate the theoretical advantages of our proposed approach, and explore its performance in a simulation study, and in an application to HIV drug resistance data. Supplementary Materials are available online.

Sensitive deep-sequencing-based HIV-1 genotyping assay to simultaneously determine susceptibility to protease, reverse transcriptase, integrase, and maturation inhibitors, as well as HIV-1 coreceptor tropism

With 29 individual antiretroviral drugs available from six classes that are approved for the treatment of HIV-1 infection, a combination of different phenotypic and genotypic tests is currently needed to monitor HIV-infected individuals. In this study, we developed a novel HIV-1 genotypic assay based on deep sequencing (DeepGen HIV) to simultaneously assess HIV-1 susceptibilities to all drugs targeting the three viral enzymes and to predict HIV-1 coreceptor tropism. Patient-derived gag-p2/NCp7/p1/p6/pol-PR/RT/IN- and env-C2V3 PCR products were sequenced using the Ion Torrent Personal Genome Machine. Reads spanning the 3' end of the Gag, protease (PR), reverse transcriptase (RT), integrase (IN), and V3 regions were extracted, truncated, translated, and assembled for genotype and HIV-1 coreceptor tropism determination. DeepGen HIV consistently detected both minority drug-resistant viruses and non-R5 HIV-1 variants from clinical specimens with viral loads of ≥1,000 copies/ml and from B and non-B subtypes. Additional mutations associated with resistance to PR, RT, and IN inhibitors, previously undetected by standard (Sanger) population sequencing, were reliably identified at frequencies as low as 1%. DeepGen HIV results correlated with phenotypic (original Trofile, 92%; enhanced-sensitivity Trofile assay [ESTA], 80%; TROCAI, 81%; and VeriTrop, 80%) and genotypic (population sequencing/Geno2Pheno with a 10% false-positive rate [FPR], 84%) HIV-1 tropism test results. DeepGen HIV (83%) and Trofile (85%) showed similar concordances with the clinical response following an 8-day course of maraviroc monotherapy (MCT). In summary, this novel all-inclusive HIV-1 genotypic and coreceptor tropism assay, based on deep sequencing of the PR, RT, IN, and V3 regions, permits simultaneous multiplex detection of low-level drug-resistant and/or non-R5 viruses in up to 96 clinical samples. This comprehensive test, the first of its class, will be instrumental in the development of new antiretroviral drugs and, more importantly, will aid in the treatment and management of HIV-infected individuals.

Sparse Representation for Prediction of HIV-1 Protease Drug Resistance

HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×10⁴ known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.

Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance

BACKGROUND

Successful management of chronic human immunodeficiency virus type 1 (HIV-1) infection with a cocktail of antiretroviral medications can be negatively affected by the presence of drug resistant mutations in the viral targets. These targets include the HIV-1 protease (PR) and reverse transcriptase (RT) proteins, for which a number of inhibitors are available on the market and routinely prescribed. Protein mutational patterns are associated with varying degrees of resistance to their respective inhibitors, with extremes that can range from continued susceptibility to cross-resistance across all drugs.

RESULTS

Here we implement statistical learning algorithms to develop structure- and sequence-based models for systematically predicting the effects of mutations in the PR and RT proteins on resistance to each of eight and eleven inhibitors, respectively. Employing a four-body statistical potential, mutant proteins are represented as feature vectors whose components quantify relative environmental perturbations at amino acid residue positions in the respective target structures upon mutation. Two approaches are implemented in developing sequence-based models, based on use of either relative frequencies or counts of n-grams, to generate vectors for representing mutant proteins. To the best of our knowledge, this is the first reported study on structure- and sequence-based predictive models of HIV-1 PR and RT drug resistance developed by implementing a four-body statistical potential and n-grams, respectively, to generate mutant attribute vectors. Performance of the learning methods is evaluated on the basis of tenfold cross-validation, using previously assayed and publicly available in vitro data relating mutational patterns in the targets to quantified inhibitor susceptibility changes.

CONCLUSION

Overall performance results are competitive with those of a previously published study utilizing a sequence-based strategy, while our structure- and sequence-based models provide orthogonal and complementary prediction methodologies, respectively. In a novel application, we describe a technique for identifying every possible pair of RT inhibitors as either potentially effective together as part of a cocktail, or a combination that is to be avoided.

Non-nucleoside reverse transcriptase inhibitor (NNRTI) cross-resistance: implications for preclinical evaluation of novel NNRTIs and clinical genotypic resistance testing

OBJECTIVES

The introduction of two new non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the past 5 years and the identification of novel NNRTI-associated mutations have made it necessary to reassess the extent of phenotypic NNRTI cross-resistance.

METHODS

We analysed a dataset containing 1975, 1967, 519 and 187 genotype-phenotype correlations for nevirapine, efavirenz, etravirine and rilpivirine, respectively. We used linear regression to estimate the effects of RT mutations on susceptibility to each of these NNRTIs.

RESULTS

Sixteen mutations at 10 positions were significantly associated with the greatest contribution to reduced phenotypic susceptibility (≥10-fold) to one or more NNRTIs, including: 14 mutations at six positions for nevirapine (K101P, K103N/S, V106A/M, Y181C/I/V, Y188C/L and G190A/E/Q/S); 10 mutations at six positions for efavirenz (L100I, K101P, K103N, V106M, Y188C/L and G190A/E/Q/S); 5 mutations at four positions for etravirine (K101P, Y181I/V, G190E and F227C); and 6 mutations at five positions for rilpivirine (L100I, K101P, Y181I/V, G190E and F227C). G190E, a mutation that causes high-level nevirapine and efavirenz resistance, also markedly reduced susceptibility to etravirine and rilpivirine. K101H, E138G, V179F and M230L mutations, associated with reduced susceptibility to etravirine and rilpivirine, were also associated with reduced susceptibility to nevirapine and/or efavirenz.

CONCLUSIONS

The identification of novel cross-resistance patterns among approved NNRTIs illustrates the need for a systematic approach for testing novel NNRTIs against clinical virus isolates with major NNRTI-resistance mutations and for testing older NNRTIs against virus isolates with mutations identified during the evaluation of a novel NNRTI.

HIV-1 clade B pol evolution following primary infection

OBJECTIVE

Characterize intra-individual HIV-1 subtype B pol evolution in antiretroviral naive individuals.

DESIGN

Longitudinal cohort study of individuals enrolled during primary infection.

METHODS

Eligible individuals were antiretroviral naïve participants enrolled in the cohort from December 1997-December 2005 and having at least two blood samples available with the first one collected within a year of their estimated date of infection. Population-based pol sequences were generated from collected blood samples and analyzed for genetic divergence over time in respect to dual infection status, HLA, CD4 count and viral load.

RESULTS

93 participants were observed for a median of 1.8 years (Mean = 2.2 years, SD =1.9 years). All participants classified as mono-infected had less than 0.7% divergence between any two of their pol sequences using the Tamura-Nei model (TN93), while individuals with dual infection had up to 7.0% divergence. The global substitution rates (substitutions/nucleotide/year) for mono and dually infected individuals were significantly different (p<0.001); however, substitution rates were not associated with HLA haplotype, CD4 or viral load.

CONCLUSIONS

Even after a maximum of almost 9 years of follow-up, all mono-infected participants had less than 1% divergence between baseline and longitudinal sequences, while participants with dual infection had 10 times greater divergence. These data support the use of HIV-1 pol sequence data to evaluate transmission events, networks and HIV-1 dual infection.

Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction

MOTIVATION

Antiretroviral treatment regimens can sufficiently suppress viral replication in human immunodeficiency virus (HIV)-infected patients and prevent the progression of the disease. However, one of the factors contributing to the progression of the disease despite ongoing antiretroviral treatment is the emergence of drug resistance. The high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure, thus to failure of antiretroviral treatment due to the evolution of drug-resistant variants. Moreover, cross-resistance phenomena have been frequently found in HIV-1, leading to resistance not only against a drug from the current treatment, but also to other not yet applied drugs. Automatic classification and prediction of drug resistance is increasingly important in HIV research as well as in clinical settings, and to this end, machine learning techniques have been widely applied. Nevertheless, cross-resistance information was not taken explicitly into account, yet.

RESULTS

In our study, we demonstrated the use of cross-resistance information to predict drug resistance in HIV-1. We tested a set of more than 600 reverse transcriptase sequences and corresponding resistance information for six nucleoside analogues. Based on multilabel classification models and cross-resistance information, we were able to significantly improve overall prediction accuracy for all drugs, compared with single binary classifiers without any additional information. Moreover, we identified drug-specific patterns within the reverse transcriptase sequences that can be used to determine an optimal order of the classifiers within the classifier chains. These patterns are in good agreement with known resistance mutations and support the use of cross-resistance information in such prediction models.

CONTACT

dominik.heider@uni-due.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A LASSO FOR HIERARCHICAL INTERACTIONS

We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or both variables are marginally important. We give a precise characterization of the effect of this hierarchy constraint, prove that hierarchy holds with probability one and derive an unbiased estimate for the degrees of freedom of our estimator. A bound on this estimate reveals the amount of fitting "saved" by the hierarchy constraint. We distinguish between parameter sparsity-the number of nonzero coefficients-and practical sparsity-the number of raw variables one must measure to make a new prediction. Hierarchy focuses on the latter, which is more closely tied to important data collection concerns such as cost, time and effort. We develop an algorithm, available in the R package hierNet, and perform an empirical study of our method.

Transmitted HIV drug resistance in treatment-naive Romanian patients

Transmitted HIV drug resistance (TDR) remains an important concern for individuals unexposed to antiretroviral treatment. Data on the prevalence of TDR, available mainly for HIV-1 subtype B, are now also emerging for other subtypes. In Romania, a steady predominance of subtype F was reported among both long-term survivor children and newly infected adults. The pol gene of 61 drug-naïve patients infected with HIV, diagnosed between 1997 and 2011 was sequenced in order to analyze the prevalence of primary resistance mutations and to correlate these with the infecting genotype. Only 5/61 specimens were classified as infected recently using the BED-Capture Enzyme Immunoassay. Subtype F1 was prevalent (80.3%), however, other HIV-1 clades are increasingly identified, especially in the group of subjects infected recently. An HIV transmission cluster, associated to injecting drug use was identified by phylogenetic analysis. The overall prevalence of TDR was 14.75%, mainly associated with NRTI resistance (13.11%), TAMs and M184V being the most common mutations. A declining trend of TDR was recorded from 26.08% in 1997-2004 to 7.89% in 2005-2011. No primary resistance was identified among recent seroconvertors. All HIV-1 strains had minor mutations in the protease and RT genes, often detected at polymorphic positions. The declining rates of TDR might be related to the high efficacy of HAART and to the increasing number of treated patients with virological success who have a low risk of transmission. The recent increase of HIV-1 infections which involve other subtypes impose a continuous surveillance of the genetic composition of the epidemic.

Scoring methods for building genotypic scores: an application to didanosine resistance in a large derivation set

Background

Several attempts have been made to determine HIV-1 resistance from genotype resistance testing. We compare scoring methods for building weighted genotyping scores and commonly used systems to determine whether the virus of a HIV-infected patient is resistant.

Methods and Principal Findings

Three statistical methods (linear discriminant analysis, support vector machine and logistic regression) are used to determine the weight of mutations involved in HIV resistance. We compared these weighted scores with known interpretation systems (ANRS, REGA and Stanford HIV-db) to classify patients as resistant or not. Our methodology is illustrated on the Forum for Collaborative HIV Research didanosine database (N = 1453). The database was divided into four samples according to the country of enrolment (France, USA/Canada, Italy and Spain/UK/Switzerland). The total sample and the four country-based samples allow external validation (one sample is used to estimate a score and the other samples are used to validate it). We used the observed precision to compare the performance of newly derived scores with other interpretation systems. Our results show that newly derived scores performed better than or similar to existing interpretation systems, even with external validation sets. No difference was found between the three methods investigated. Our analysis identified four new mutations associated with didanosine resistance: D123S, Q207K, H208Y and K223Q.

Conclusions

We explored the potential of three statistical methods to construct weighted scores for didanosine resistance. Our proposed scores performed at least as well as already existing interpretation systems and previously unrecognized didanosine-resistance associated mutations were identified. This approach could be used for building scores of genotypic resistance to other antiretroviral drugs.

Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.

Infection with the human immunodeficiency virus (HIV) currently cannot be cured. It can however be contained through treatment with a combination of several anti-viral drugs. Yet, during treatment resistance can occur which leads to drugs becoming ineffective. Through a combination of drugs, this resistance can be deferred indefinitely. The optimal combination of drugs depends on the specific strain of HIV with which the patient is infected. Previously, methods have been developed that predict a personalized treatment regimen based on the genetic sequence (genotype) of the virus via the use of computer modeling, corner stone of the methods is drug affinity prediction. Here we have applied proteochemometric modeling which takes this genetic information into account, but also includes chemical description of the drugs that are now clinically available. We show that this combined technique performs better than models that only include genetic information. Our approach leads to personalized treatment predictions with a higher reliability compared to the current state of the art. In addition, we include a reliability measure which allows each prediction to be assessed for reliability. Finally we describe mutations of the HIV genome that were not previously described in literature and lead to resistance to treatment.

What do molecules do when we are not looking? State sequence analysis for stochastic chemical systems

Many biomolecular systems depend on orderly sequences of chemical transformations or reactions. Yet, the dynamics of single molecules or small-copy-number molecular systems are significantly stochastic. Here, we propose state sequence analysis--a new approach for predicting or visualizing the behaviour of stochastic molecular systems by computing maximum probability state sequences, based on initial conditions or boundary conditions. We demonstrate this approach by analysing the acquisition of drug-resistance mutations in the human immunodeficiency virus genome, which depends on rare events occurring on the time scale of years, and the stochastic opening and closing behaviour of a single sodium ion channel, which occurs on the time scale of milliseconds. In both cases, we find that our approach yields novel insights into the stochastic dynamical behaviour of these systems, including insights that are not correctly reproduced in standard time-discretization approaches to trajectory analysis.

Mutations in multiple domains of Gag drive the emergence of in vitro resistance to the phosphonate-containing HIV-1 protease inhibitor GS-8374

GS-8374 is a potent HIV protease inhibitor (PI) with a unique diethyl-phosphonate moiety. Due to a balanced contribution of enthalpic and entropic components to its interaction with the protease (PR) active site, the compound retains activity against HIV mutants with high-level multi-PI resistance. We report here the in vitro selection and characterization of HIV variants resistant to GS-8374. While highly resistant viruses with multiple mutations in PR were isolated in the presence of control PIs, an HIV variant displaying moderate (14-fold) resistance to GS-8374 was generated only after prolonged passaging for >300 days. The isolate showed low-level cross-resistance to darunavir, atazanavir, lopinavir, and saquinavir, but not other PIs, and contained a single R41K mutation in PR combined with multiple genotypic changes in the Gag matrix, capsid, nucleocapsid, and SP2 domains. Mutations also occurred in the transframe peptide and p6* domain of the Gag-Pol polyprotein. Analysis of recombinant HIV variants indicated that mutations in Gag, but not the R41K in PR, conferred reduced susceptibility to GS-8374. The Gag mutations acted in concert, since they did not affect susceptibility when introduced individually. Analysis of viral particles revealed that the mutations rendered Gag more susceptible to PR-mediated cleavage in the presence of GS-8374. In summary, the emergence of resistance to GS-8374 involved a combination of substrate mutations without typical resistance mutations in PR. These substrate changes were distributed throughout Gag and acted in an additive manner. Thus, they are classified as primary resistance mutations indicating a unique mechanism and pathway of resistance development for GS-8374.

Low prevalence of transmitted K65R and other tenofovir resistance mutations across different HIV-1 subtypes: implications for pre-exposure prophylaxis

Introduction

Tenofovir-containing regimens have demonstrated potential efficacy as pre-exposure prophylaxis (PrEP) in preventing HIV-1 infection. Transmitted drug resistance mutations associated with tenofovir, specifically the reverse transcriptase (RT) mutation K65R, may impact the effectiveness of PrEP. The worldwide prevalence of transmitted tenofovir resistance in different HIV-1 subtypes is unknown.

Methods

Sequences from treatment-naïve studies and databases were aggregated and analyzed by Stanford Database tools and as per the International AIDS Society (IAS-USA) resistance criteria. RT sequences were collected from GenBank, the Stanford HIV Sequence Database and the Los Alamos HIV Sequence Database. Sequences underwent rigorous quality control measures. Tenofovir-associated resistance mutations included K65R, K70E, T69-insertion and ≥3 thymidine analogue mutations (TAMs), inclusive of M41L or L210W.

Results

A total of 19,823 sequences were evaluated across diverse HIV-1 subtypes (Subtype A: 1549 sequences, B: 9783, C: 3198, D: 483, F: 372, G: 594, H: 41, J: 69, K: 239, CRF01_AE: 1797 and CRF02_AG: 1698). Overall, tenofovir resistance prevalence was 0.4% (n=77/19,823, 95% confidence interval or CI: 0.3 to 0.5). K65R was found in 20 sequences (0.1%, 95% CI: 0.06 to 0.15). Differences in the prevalence of K65R between HIV-1 subtypes were not statistically significant. K70E and ≥3 TAMs were found in 0.015% (95% CI: 0.004 to 0.04) and 0.27% (95% CI: 0.2 to 0.4) of sequences, respectively.

Conclusions

Prevalence of transmitted K65R and other tenofovir resistance mutations across diverse HIV-1 subtypes and recombinants is low, suggesting minimal effect on tenofovir-containing PrEP regimens.

Clinical, virological and biochemical evidence supporting the association of HIV-1 reverse transcriptase polymorphism R284K and thymidine analogue resistance mutations M41L, L210W and T215Y in patients failing tenofovir/emtricitabine therapy

Background

Thymidine analogue resistance mutations (TAMs) selected under treatment with nucleoside analogues generate two distinct genotypic profiles in the HIV-1 reverse transcriptase (RT): (i) TAM1: M41L, L210W and T215Y, and (ii) TAM2: D67N, K70R and K219E/Q, and sometimes T215F. Secondary mutations, including thumb subdomain polymorphisms (e.g. R284K) have been identified in association with TAMs. We have identified mutational clusters associated with virological failure during salvage therapy with tenofovir/emtricitabine-based regimens. In this context, we have studied the role of R284K as a secondary mutation associated with mutations of the TAM1 complex.

Results

The cross-sectional study carried out with >200 HIV-1 genotypes showed that virological failure to tenofovir/emtricitabine was strongly associated with the presence of M184V (P < 10^-10) and TAMs (P < 10^-3), while K65R was relatively uncommon in previously-treated patients failing antiretroviral therapy. Clusters of mutations were identified, and among them, the TAM1 complex showed the highest correlation coefficients. Covariation of TAM1 mutations and V118I, V179I, M184V and R284K was observed. Virological studies showed that the combination of R284K with TAM1 mutations confers a fitness advantage in the presence of zidovudine or tenofovir. Studies with recombinant HIV-1 RTs showed that when associated with TAM1 mutations, R284K had a minimal impact on zidovudine or tenofovir inhibition, and in their ability to excise the inhibitors from blocked DNA primers. However, the mutant RT M41L/L210W/T215Y/R284K showed an increased catalytic rate for nucleotide incorporation and a higher RNase H activity in comparison with WT and mutant M41L/L210W/T215Y RTs. These effects were consistent with its enhanced chain-terminated primer rescue on DNA/DNA template-primers, but not on RNA/DNA complexes, and can explain the higher fitness of HIV-1 having TAM1/R284K mutations.

Conclusions

Our study shows the association of R284K and TAM1 mutations in individuals failing therapy with tenofovir/emtricitabine, and unveils a novel mechanism by which secondary mutations are selected in the context of drug-resistance mutations.

Drug resistance mutations in HIV pol sequences from Argentinean patients under antiretroviral treatment: subtype, gender, and age issues

We studied drug resistance mutations (DRMs) in 2623 pol sequences. Out of 94,828 amino acid substitutions that were detected, 8749 corresponded to nucleoside reverse transcriptase inhibitor (NRTI), 3765 to nonnucleoside reverse transcriptase inhibitor (NNRTI), and 7141 to protease inhibitor (PI) resistance-associated mutations. The most common DRMs were L10I, I54V, L90M, V82A, A71V, L10V, M46I, M184V, M41L, T215Y, D67N, L210W, K70R, N348I, V118I, K103N, Y181C, G190A, K101E, V108I, L100I, V90I, K101Q, and A98G. As expected, DRMs frequencies depended on viral genotype. The amounts of NRTI and PI resistance mutations among B and BF sequences from children were higher than among sequences from adults. The frequencies of PI and NRTI resistance mutations among B and BF sequences from adult men were higher than among sequences from women. Some of these observations can be explained in light of the available epidemiological information, but some cannot, indicating that further studies are needed to understand the antiretroviral resistance epidemics in Argentina.

Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem

Drug resistance has become one of the biggest challenges in drug discovery and/or development and has attracted great research interests worldwide. During the past decade, computational strategies have been developed to predict target mutation-induced drug resistance. Meanwhile, various molecular design strategies, including targeting protein backbone, targeting highly conserved residues and dual/multiple targeting, have been used to design novel inhibitors for combating the drug resistance. In this article we review recent advances in development of computational methods for target mutation-induced drug resistance prediction and strategies for rational design of novel inhibitors that could be effective against the possible drug-resistant mutants of the target.

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.

Phenotypic analysis of HIV-1 genotypic drug-resistant isolates from China, using a single-cycle system

BACKGROUND AND OBJECTIVES

Drug resistance in HIV-1 is one of the main causes of failure of antiretroviral therapy. Phenotypic detection of drug-resistant HIV-1 can provide guidance in selecting the optimal treatment regimen. Traditional phenotype assays are labor intensive and time consuming. Thus, a rapid and convenient phenotype assay with a single cycle of replication was developed and used in this study.

METHODS

Two restriction endonuclease sites, ApaI and AgeI, were inserted into the plasmid pSG3Δenv(,) using site-directed mutagenesis. The reverse transcriptase and protease genes of HIV-1 were amplified from patients and cloned into the modified pSG3Δenv. Sixteen original recombinant pseudoviruses were generated. The phenotypic susceptibility of these 16 recombinant pseudoviruses to 12 antiretroviral drugs was determined using a luciferase reporter system, and the phenotype and genotype results were compared.

RESULTS

A modified phenotype assay with a single-cycle system was established, and its reproducibility and feasibility were validated. Approximately 89% of the phenotype results were in agreement with the genotype results; this slight disagreement may have been due to complex and multiple resistance mutations. The phenotype results showed that individual pseudoviruses with four thymidine analog mutations (TAMs).[M41L, T67N, L210W, and T215Y] in combination with various other mutations had different levels of resistance to nucleoside reverse transcriptase inhibitors (NRTIs). Mutations E44A, T69D, and V118I influenced the pattern of resistance of TAMs. The level of resistance to non-NRTIs (NNRTIs) was also variable when different NNRTI-resistance mutations were combined.

CONCLUSION

The single-cycle pseudovirus phenotypic susceptibility detection system reflects HIV-1 drug resistance, especially for complex resistance mutants, and could be used to screen new antiretroviral candidates.

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.

Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data

Background. Many statistical models have been tested to predict phenotypic or virological response from genotypic data. A statistical framework called Super Learner has been introduced either to compare different methods/learners (discrete Super Learner) or to combine them in a Super Learner prediction method. Methods. The Jaguar trial is used to apply the Super Learner framework. The Jaguar study is an "add-on" trial comparing the efficacy of adding didanosine to an on-going failing regimen. Our aim was also to investigate the impact on the use of different cross-validation strategies and different loss functions. Four different repartitions between training set and validations set were tested through two loss functions. Six statistical methods were compared. We assess performance by evaluating R(2) values and accuracy by calculating the rates of patients being correctly classified. Results. Our results indicated that the more recent Super Learner methodology of building a new predictor based on a weighted combination of different methods/learners provided good performance. A simple linear model provided similar results to those of this new predictor. Slight discrepancy arises between the two loss functions investigated, and slight difference arises also between results based on cross-validated risks and results from full dataset. The Super Learner methodology and linear model provided around 80% of patients correctly classified. The difference between the lower and higher rates is around 10 percent. The number of mutations retained in different learners also varys from one to 41. Conclusions. The more recent Super Learner methodology combining the prediction of many learners provided good performance on our small dataset.

Comparing Peripheral Blood Mononuclear Cell DNA and Circulating Plasma viral RNA pol Genotypes of Subtype C HIV-1

INTRODUCTION: Drug resistance mutations (DRM) in viral RNA are important in defining to provide effective antiretroviral therapy (ART) in HIV-1 infected patients. Detection of DRM in peripheral blood mononuclear cell (PBMC) DNA is another source of information, although the clinical significance of DRMs in proviral DNA is less clear. MATERIALS AND METHODS: From 25 patients receiving ART at a center in Zimbabwe, 32 blood samples were collected. Dideoxy-sequencing of gag-pol identified subtype and resistance mutations from plasma viral RNA and proviral DNA. Drug resistance was estimated using the calibrated population resistance tool on www.hivdb.stanford.edu database. Numerical resistance scores were calculated for all antiretroviral drugs and for the subjects' reported regimen. Phylogenetic analysis as maximum likelihood was performed to determine the evolutionary distance between sequences. RESULTS: Of the 25 patients, 4 patients (2 of which had given 2 blood samples) were not known to be on ART (NA) and had exclusively wild-type virus, 17 had received Protease inhibitors (PI), 18, non-nucleoside reverse transcriptase inhibitors (NNRTI) and 19, two or more nucleoside reverse transcriptase inhibitors (NRTI). Of the 17 with history of PI, 10 had PI mutations, 5 had minor differences between mutations in RNA and DNA. Eighteen samples had NNRTI mutations, six of which demonstrated some discordance between DNA and RNA mutations. Although NRTI resistance mutations were frequently different between analyses, mutations resulted in very similar estimated phenotypes as measured by resistance scores. The numerical resistance scores from RNA and DNA for PIs differed between 2/10, for NNRTIs between 8/18, and for NRTIs between 17/32 pairs. When calculated resistance scores were collapsed, 3 pairs showed discordance between RNA and DNA for at least one PI, 6 were discordant for at least one NNRTI and 11 for at least one NRTI. Regarding phylogenetic evolutionary analysis, all RNA and DNA sequence pairs clustered closely in a maximum likelihood tree. CONCLUSION: PBMC DNA could be useful for testing drug resistance in conjunction with plasma RNA where the results of each yielded complementary information about drug resistance. Identification of DRM, archived in proviral DNA, could be used to provide for sustainable public health surveillance among subtype C infected patients.

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.

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

BACKGROUND

Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.

RESULTS

We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.

CONCLUSIONS

Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.

Genetic Diversity and Drug Resistance Mutations in HIV-1 from Untreated Patients in Niamey, Niger

The objective of the study was to estimate the prevalence of transmitted resistance to antiretroviral of HIV-1 circulating in Niger. We collected plasmas from 96 drug-naive patients followed up in the main HIV/AIDS Care Center of Niamey, the capital city of Niger. After RNA extraction and retrotranscription to proviral DNA, nested PCR was performed to amplify PR (codons 1–99) and RT (codons 1–240) fragments for sequencing. Sequences were analysed for phylogeny, then for resistance-associated mutations according to IAS-USA and Stanford's lists of mutations. We characterized six HIV-1 genetic variants: CRF02-AG (56.3%), CRF30_0206 (15.6%), subtype G (15.6%), CRF06_cpx (9.4%), CRF11_cpx (2.1%), and CRF01_AE (1%). About 8.3% of HIV strains had at least 1 resistance mutation: 4 strains with at least 1 mutation to NRTI, 5 for NNRTI, and 1 for PI, respectiveley 4.2%, 5.2%, and 1.0%. These preliminary results gave enough information for the need of instauring HIV drug resistance national surveillance.

The impact of the nelfinavir resistance-conferring mutation D30N on the susceptibility of HIV-1 subtype B to other protease inhibitors

The human immunodeficiency virus type 1 (HIV-1) protease mutation D30N is exclusively selected by the protease inhibitor (PI) nelfinavir and confers resistance to this drug. We demonstrate that D30N increases the susceptibility to saquinavir (SQV) and amprenavir in HIV-1 subtype B isolates and that the N88D mutation in a D30N background neutralizes this effect. D30N also suppresses indinavir (IDV) resistance caused by the M46I mutation. Interestingly, in patients with viruses originally containing the D30N mutation who were treated with IDV or SQV, the virus either reversed this mutation or acquired N88D, suggesting an antagonistic effect of D30N upon exposure to these PIs. These findings can improve direct salvage drug treatment in resource limited countries where subtype B is epidemiologically important and extend the value of first and second line PIs in these populations.

[Bioinformatics studies on drug resistance against anti-HIV-1 drugs]

More than 20 drugs have been available for anti-HIV-1 treatment in Japan. Combination therapy with these drugs dramatically decreases in morbidity and mortality of AIDS. However, due to high mutation rate of HIV-1, treatment with ineffective drugs toward patients infected with HIV-1 causes accumulation of mutations in the virus, and emergence of drug resistant viruses. Thus, to achieve appropriate application of the drugs toward the respective patients living with HIV-1, methods for predicting the level of drug-resistance using viral sequence information has been developed on the basis of bioinformatics. Furthermore, ultra-deep sequencing by next-generation sequencer whose data analysis is also based on bioinformatics, or in silico structural modeling have been achieved to understand drug resistant mechanisms. In this review, I overview the bioinformatics studies about drug resistance against anti-HIV-1 drugs.

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

Background

Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.

Results

Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.

Conclusions

We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.

Dynamical basis for drug resistance of HIV-1 protease

Background

Protease inhibitors designed to bind to protease have become major anti-AIDS drugs. Unfortunately, the emergence of viral mutations severely limits the long-term efficiency of the inhibitors. The resistance mechanism of these diversely located mutations remains unclear.

Results

Here I use an elastic network model to probe the connection between the global dynamics of HIV-1 protease and the structural distribution of drug-resistance mutations. The models for study are the crystal structures of unbounded and bound (with the substrate and nine FDA approved inhibitors) forms of HIV-1 protease. Coarse-grained modeling uncovers two groups that couple either with the active site or the flap. These two groups constitute a majority of the drug-resistance residues. In addition, the significance of residues is found to be correlated with their dynamical changes in binding and the results agree well with the complete mutagenesis experiment of HIV-1 protease.

Conclusions

The dynamic study of HIV-1 protease elucidates the functional importance of common drug-resistance mutations and suggests a unifying mechanism for drug-resistance residues based on their dynamical properties. The results support the robustness of the elastic network model as a potential predictive tool for drug resistance.

Detection of minority resistance during early HIV-1 infection: natural variation and spurious detection rather than transmission and evolution of multiple viral variants

Reports of a high frequency of the transmission of minority viral populations with drug-resistant mutations (DRM) are inconsistent with evidence that HIV-1 infections usually arise from mono- or oligoclonal transmission. We performed ultradeep sequencing (UDS) of partial HIV-1 gag, pol, and env genes from 32 recently infected individuals. We then evaluated overall and per-site diversity levels, selective pressure, sequence reproducibility, and presence of DRM and accessory mutations (AM). To differentiate biologically meaningful mutations from those caused by methodological errors, we obtained multinomial confidence intervals (CI) for the proportion of DRM at each site and fitted a binomial mixture model to determine background error rates for each sample. We then examined the association between detected minority DRM and the virologic failure of first-line antiretroviral therapy (ART). Similar to other studies, we observed increased detection of DRM at low frequencies (average, 0.56%; 95% CI, 0.43 to 0.69; expected UDS error, 0.21 ± 0.08% mutations/site). For 8 duplicate runs, there was variability in the proportions of minority DRM. There was no indication of increased diversity or selection at DRM sites compared to other sites and no association between minority DRM and AM. There was no correlation between detected minority DRM and clinical failure of first-line ART. It is unlikely that minority viral variants harboring DRM are transmitted and maintained in the recipient host. The majority of low-frequency DRM detected using UDS are likely errors inherent to UDS methodology or a consequence of error-prone HIV-1 replication.

STRUCTURED, SPARSE REGRESSION WITH APPLICATION TO HIV DRUG RESISTANCE

We introduce a new version of forward stepwise regression. Our modification finds solutions to regression problems where the selected predictors appear in a structured pattern, with respect to a predefined distance measure over the candidate predictors. Our method is motivated by the problem of predicting HIV-1 drug resistance from protein sequences. We find that our method improves the interpretability of drug resistance while producing comparable predictive accuracy to standard methods. We also demonstrate our method in a simulation study and present some theoretical results and connection.

Human immunodeficiency virus type 1 resistance or cross-resistance to nonnucleoside reverse transcriptase inhibitors currently under development as microbicides

Microbicides based on nonnucleoside reverse transcriptase inhibitors (NNRTIs) are currently being developed to protect women from HIV acquisition through sexual contact. However, the large-scale introduction of these products raises two major concerns. First, when these microbicides are used by undiagnosed HIV-positive women, they could potentially select for viral resistance, which may compromise subsequent therapeutic options. Second, NNRTI-based microbicides that are inactive against NNRTI-resistant strains might promote the selective transmission of these viruses. In order to address these concerns, drug resistance was selected in vitro by the serial passage of three viral isolates from subtypes B and C and CRF02_AG (a circulating recombinant form) in activated peripheral blood mononuclear cells (PBMCs) under conditions of increasing concentrations of three NNRTIs (i.e., TMC120, UC781, and MIV-160) that are currently being developed as candidate microbicides. TMC120 and MIV-160 displayed a high genetic barrier to resistance development, whereas resistance to UC781 emerged rapidly, similarly to efavirenz and nevirapine. Phenotypically, the selected viruses appeared to be highly cross-resistant to current first-line therapeutic NNRTIs (i.e., delavirdine, nevirapine, and efavirenz), although they retained some susceptibility to the more recently developed NNRTIs lersivirine and etravirine. The ability of UC781, TMC120, and MIV-160 to inhibit the in vitro-selected NNRTI-resistant viruses was also limited, although residual activity could be observed for the candidate microbicide NNRTI MIV-170. Interestingly, only four p2/p7/p1/p6/PR/RT/INT recombinant NNRTI-resistant viruses (i.e., TMC120-resistant VI829, EFV-resistant VI829, MIV-160-resistant VI829, and EFV-resistant MP568) showed impairments in replicative fitness. Overall, these in vitro analyses demonstrate that due to potential cross-resistance, the large-scale introduction of single-NNRTI-based microbicides should be considered with caution.

Machine learning on normalized protein sequences

BACKGROUND

Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.

FINDINGS

We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.

CONCLUSIONS

We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.

A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase

The development of a quantitative understanding of viral evolution and the fitness landscape in HIV-1 drug resistance is a formidable challenge given the large number of available drugs and drug resistance mutations. We analyzed a dataset measuring the in vitro fitness of 70,081 virus samples isolated from HIV-1 subtype B infected individuals undergoing routine drug resistance testing. We assayed virus samples for in vitro replicative capacity in the absence of drugs as well as in the presence of 15 individual drugs. We employed a generalized kernel ridge regression to estimate main fitness effects and epistatic interactions of 1,859 single amino acid variants found within the HIV-1 protease and reverse transcriptase sequences. Models including epistatic interactions predict an average of 54.8% of the variance in replicative capacity across the 16 different environments and substantially outperform models based on main fitness effects only. We find that the fitness landscape of HIV-1 protease and reverse transcriptase is characterized by strong epistasis.