Phylogenetic surveillance of viral genetic diversity and the evolving molecular epidemiology of human immunodeficiency virus type 1

With ongoing generation of viral genetic diversity and increasing levels of migration, the global human immunodeficiency virus type 1 (HIV-1) epidemic is becoming increasingly heterogeneous. In this study, we investigate the epidemiological characteristics of 5,675 HIV-1 pol gene sequences sampled from distinct infections in the United Kingdom. These sequences were phylogenetically analyzed in conjunction with 976 complete-genome and 3,201 pol gene reference sequences sampled globally and representing the broad range of HIV-1 genetic diversity, allowing us to estimate the probable geographic origins of the various strains present in the United Kingdom. A statistical analysis of phylogenetic clustering in this data set identified several independent transmission chains within the United Kingdom involving recently introduced strains and indicated that strains more commonly associated with infections acquired heterosexually in East Africa are spreading among men who have sex with men. Coalescent approaches were also used and indicated that the transmission chains that we identify originated in the late 1980s to early 1990s. Similar changes in the epidemiological structuring of HIV epidemics are likely to be taking in place in other industrialized nations with large immigrant populations. The framework implemented here takes advantage of the vast amount of routinely generated HIV-1 sequence data and can provide epidemiological insights not readily obtainable through standard surveillance methods

Current assays for HIV-1 diagnostics and antiretroviral therapy monitoring: challenges and possibilities

In 2011, there were over 34 million people living with HIV infections, placing a heavy burden on public health sectors. HIV infection is a lifelong threat that cannot be prevented by vaccination or cured by antiretroviral drugs. The infected patients rely on daily antiretroviral therapy to suppress HIV viral replication. Hence, it is important to diagnose HIV infections as early as possible and to monitor the efficacy of antiretroviral therapy every 3–6 months. Different immunoassays detecting HIV antigens and antibodies have been modified to offer better sensitivity and more rapid diagnosis. Several clinical and virological parameters, including CD4+ cell counts, viral load and drug resistance mutations, are also used for treatment monitoring. Many molecular assay optimizations are now being utilized to improve patient care. This review will focus on the most updated HIV diagnostic assays, as well as discussing the upcoming possibilities of other advanced technologies.postprin

Hepatitis C virus resistance to the new direct-acting antivirals

Introduction: The treatment of hepatitis C virus (HCV) infection has dramatically improved in recent years with the widespread use of interferon-free combination regimens. Despite the high sustained virological response (SVR) rates (over 90%) obtained with direct-acting antivirals (DAAs), drug resistance has emerged as a potential challenge. The high replication rate of HCV and the low fidelity of its RNA polymerase result in a high degree of genetic variability in the HCV population, which ultimately explains the rapid selection of drug resistance associated variants (RAVs). Areas covered: Results from clinical trials and real-world experience have both provided important information on the rate and clinical significance of RAVs. They can be present in treatment-naive patients as natural polymorphisms although more frequently they are selected upon treatment failure. In patients engaged in high-risk behaviors, RAVs can be transmitted. Expert opinion: Although DAA failures generally occur in less than 10% of treated chronic hepatitis C patients, selection of drug resistance is the rule in most cases. HCV re-treatment options are available, but first-line therapeutic strategies should be optimized to efficiently prevent DAA failure due to baseline HCV resistance. Considerable progress is being made and next-generation DAAs are coming with pangenotypic activity and higher resistance barrier.Fil: Esposito, Isabella. Hospital Universitario La Paz; EspañaFil: Trinks, Julieta. Hospital Italiano. Instituto de Ciencias Básicas y Medicina Experimental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soriano, Vicente. Hospital Universitario La Paz; Españ

Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors

BACKGROUND: Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens. METHODOLOGY/PRINCIPAL FINDINGS: We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2=0.89 and Q2ext=0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant. CONCLUSIONS/SIGNIFICANCE: Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants

HIV Drug Resistant Prediction and Featured Mutants Selection using Machine Learning Approaches

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible. In order to accurately predict the HIV drug resistance, two main tasks need to be solved: how to encode the protein structure, extracting the more useful information and feeding it into the machine learning tools; and which kinds of machine learning tools to choose. In our research, we first proposed a new protein encoding algorithm, which could convert various sizes of proteins into a fixed size vector. This algorithm enables feeding the protein structure information to most state of the art machine learning algorithms. In the next step, we also proposed a new classification algorithm based on sparse representation. Following that, mean shift and quantile regression were included to help extract the feature information from the data. Our results show that encoding protein structure using our newly proposed method is very efficient, and has consistently higher accuracy regardless of type of machine learning tools. Furthermore, our new classification algorithm based on sparse representation is the first application of sparse representation performed on biological data, and the result is comparable to other state of the art classification algorithms, for example ANN, SVM and multiple regression. Following that, the mean shift and quantile regression provided us with the potentially most important drug resistant mutants, and such results might help biologists/chemists to determine which mutants are the most representative candidates for further research

Computational approaches for improving treatment and prevention of viral infections

The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptor-hiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.Die Behandlung von HIV- oder HCV-Infektionen ist herausfordernd. Daher werden neue Wirkstoffe, sowie neue computerbasierte Verfahren benötigt, welche die Therapie verbessern. In dieser Arbeit wurden Methoden zur Unterstützung der Therapieauswahl entwickelt, aber auch solche, welche neuartige Therapien vorantreiben. geno2pheno[ngs-freq] bestimmt, ob Resistenzen gegen Medikamente vorliegen, indem es Hochdurchsatzsequenzierungsdaten von HIV-1 oder HCV Proben mittels Support Vector Machines oder einem regelbasierten Ansatz interpretiert. geno2pheno[coreceptor-hiv2] bestimmt den HIV-2 Korezeptorgebrauch dadurch, dass es einen Abschnitt des viralen Oberflächenproteins mit einer Support Vector Machine analysiert. openPrimeR kann optimale Kombinationen von Primern für die Multiplex-Polymerasekettenreaktion finden, indem es ein Mengenüberdeckungsproblem löst und auf ein neues logistisches Regressionsmodell für die Vorhersage von Amplifizierungsereignissen zurückgreift. geno2pheno[ngs-freq] und geno2pheno[coreceptor-hiv2] ermöglichen die Personalisierung antiviraler Therapien und unterstützen die klinische Entscheidungsfindung. Durch den Einsatz von openPrimeR auf humanen Immunoglobulinsequenzen konnten Primersätze generiert werden, welche die Isolierung von breit neutralisierenden Antikörpern gegen HIV-1 verbessern. Die in dieser Arbeit entwickelten Methoden leisten somit einen wichtigen Beitrag zur Verbesserung der Prävention und Therapie viraler Infektionskrankheiten

Deciphering HIV genetic variability and evolution by massive parallel pyrosequencing and bioinformatics

HIV-1 is a virus with a very variable genome and therefore has the ability to adapt to new environments which include escape from immune pressure and suboptimal antiretroviral treatment. Next-generation sequencing (NGS), especially ultra-deep pyrosequencing (UDPS), has enabled in-depth sequencing studies with a previously unattainable resolution. However, the technology is more error prone than traditional sequencing which makes it challenging to interpret UDPS results. In this thesis we carried out comprehensive work to identify, characterize and reduce errors as well as investigate the UDPS performance (Papers II, III and IV). In Papers IV and V we used UDPS to study HIV-1 minority variants. Novel primer design software was developed in Paper I and a new method to tag molecules was developed and evaluated in Paper VI. The design of primers is of special importance in NGS to avoid selective amplification which may skew estimates of variant frequencies. We developed a computer program, PrimerDesign, to meet the changed requirements for primer design. PrimerDesign is tailored to design primers from a multiple alignment and is suitable for all types of NGS that is preceded by amplification. The new Primer ID methodology has the potential to provide highly accurate deep sequencing. We identified three major challenges; a skewed resampling of Primer IDs, low recovery of templates and erroneous consensus sequences. Undetected this would lead to an underestimation in diversity of the quasispecies and cause a skewed and incorrect results. As many of our other findings, the methodology is not limited to HIV or virology. The resolution of UDPS analysis is primarily determined by the number of input DNA templates, the error frequency of the method and the efficiency of data cleaning. In Papers II and IV we therefore optimized the pre-UDPS protocol and investigated the characteristics and sources of errors that occurred when UDPS was used to sequence a fragment of the HIV-1 pol gene. UDPS introduced indel errors located in homopolymeric regions that were removed by our in-house data cleaning software. The remaining errors were primarily substitution errors that were introduced in the PCR that preceded UDPS. Transitions were significantly more frequent than transversions, which will limit detection of minor variants and mutations in HIV-1 as well as other species. Further, we evaluated the quality and reproducibility of the UDPS technology in analysis of the same pol gene fragment. We concluded that the UDPS repeatability was good for both major and minor variants. In our experimental settings, in vitro recombination and sequencing directions posed a minor problem, but still needs to be considered especially for studies of minor viral variants and linkage between mutations. Minority resistance mutations have been shown to impact the clinical outcome in treated patients. We examined the presence of pre-existing drug resistance mutations in treatment-naïve HIV-1 infected individuals and found very low levels of M184I, T215A and T215I, but no presence of M184V, Y181C, Y188C or T215Y/F. This indicates that the natural occurrence of these mutations is very low. When the same individuals experienced treatment failure or interruption, almost 100 % of the wild-type virus respective drug resistance variants were replaced. Other patients were followed from primary HIV infection (PHI) until their virus switched coreceptor use from CCR5 (R5) to CXCR4 (X4). We did not find any X4-using virus present as a minority population during PHI. The results indicate that the X4-using population most probably evolved in stepwise fashion from the R5-using populations in each of the three patients. In conclusion, we have developed and used new NGS and bioinformatic methods to study HIV-1 genetic variation. We have shown that UDPS can be used to gain new insights in HIV evolution and to detect minority drug resistance mutations as well as minority variants

Only Slight Impact of Predicted Replicative Capacity for Therapy Response Prediction

BACKGROUND: Replication capacity (RC) of specific HIV isolates is occasionally blamed for unexpected treatment responses. However, the role of viral RC in response to antiretroviral therapy is not yet fully understood. MATERIALS AND METHODS: We developed a method for predicting RC from genotype using support vector machines (SVMs) trained on about 300 genotype-RC pairs. Next, we studied the impact of predicted viral RC (pRC) on the change of viral load (VL) and CD4(+) T-cell count (CD4) during the course of therapy on about 3,000 treatment change episodes (TCEs) extracted from the EuResist integrated database. Specifically, linear regression models using either treatment activity scores (TAS), the drug combination, or pRC or any combination of these covariates were trained to predict change in VL and CD4, respectively. RESULTS: The SVM models achieved a Spearman correlation (rho) of 0.54 between measured RC and pRC. The prediction of change in VL (CD4) was best at 180 (360) days, reaching a correlation of rho = 0.45 (rho = 0.27). In general, pRC was inversely correlated to drug resistance at treatment start (on average rho = -0.38). Inclusion of pRC in the linear regression models significantly improved prediction of virological response to treatment based either on the drug combination or on the TAS (t-test; p-values range from 0.0247 to 4 10(-6)) but not for the model using both TAS and drug combination. For predicting the change in CD4 the improvement derived from inclusion of pRC was not significant. CONCLUSION: Viral RC could be predicted from genotype with moderate accuracy and could slightly improve prediction of virological treatment response. However, the observed improvement could simply be a consequence of the significant correlation between pRC and drug resistance

Genotypic analysis of HIV-1 coreceptor usage

The acquired immunodeficiency syndrome (AIDS) is one of the biggest medical challenges in the world today. Its causative pathogen, the human immunodeficiency virus (HIV), is responsible for millions of deaths per year. Although about two dozen antiviral drugs are currently available, progression of the disease can only be delayed but patients cannot be cured. In recent years, the new class of coreceptor antagonists has been added to the arsenal of antiretroviral drugs. These drugs block viral cell-entry by binding to one of the receptors the virus requires for infection of a cell. However, some HIV variants can also use another coreceptor so that coreceptor usage has to be tested before administration of the drug. This thesis analyzes the use of statistical learning methods to infer HIV coreceptor usage from viral genotype. Improvements over existing methods are achieved by using sequence information of so far not used genomic regions, next generation sequencing technologies, and by combining different existing prediction systems. In addition, HIV coreceptor usage prediction is analyzed with respect to clinical outcome in patients treated with coreceptor antagonists. The results demonstrate that inferring HIV coreceptor usage from viral genotype can be reliably used in daily routine.Die Immunschwächekrankheit AIDS ist eine der größten Herausforderungen weltweit. Das verursachende Humane Immundefizienz-Virus (HIV) ist verantwortlich für Millionen Tote jährlich. Obwohl es bereits mehr als zwei Dutzend verschiedene AIDS-Medikamente gibt, können diese den Krankheitsverlauf nur verlangsamen, die Patienten jedoch nicht heilen. In den letzten Jahren wurde eine weitere Medikamentenklasse den bestehenden Therapieansätzen hinzugefügt: die Korezeptorantagonisten. Diese Wirkstoffe binden an Rezeptoren, die das Virus zum Eintritt in die Zelle benötigt und blockieren es somit. Allerdings gibt es auch Virusvarianten, die in der Lage sind Zellen mit Hilfe eines anderen Rezeptors zu infizieren. Daher sollte man vor Verschreibung eines Korezeptorantagonisten den Korezeptorgebrauch des Virus testen. Diese Arbeit befasst sich mit der Bestimmung des Korezeptorgebrauchs aus dem viralen Erbgut mit Hilfe von statistischen Lernverfahren. Verbesserungen gegenüber existierenden Methoden werden erreicht in dem bisher nicht verwendete Genomregionen analysiert werden, durch den Gebrauch von neuesten Hochdurchsatz-Sequenziertechniken, sowie durch die Kombination von zwei existierenden Vorhersagesystemen. Schließlich wird die Qualität der Korezeptorvorhersagen bezüglich klinischem Ansprechens bei Patienten untersucht, die mit Korezeptorantagonisten therapiert wurden. Die Ergebnisse zeigen, dass die Vorhersage des Korezeptorgebrauchs aus dem viralen Erbgut eine verläßliche Methode für den klinischen Alltag darstellt