Prediction of R5, X4, and R5X4 HIV-1 coreceptor usage with evolved neural networks

HIV-1 subtypes maintain distinctive physicochemical signatures in Nef domains associated with immunoregulation

BACKGROUND

HIV subtype is associated with varied rates of disease progression. The HIV accessory protein, Nef, continues to be present during antiretroviral therapy (ART) where it has numerous immunoregulatory effects. In this study, we analyzed Nef sequences from HIV subtypes A1, B, C, and D using a machine learning approach that integrates functional amino acid information to identify if unique physicochemical features are associated with Nef functional/structural domains in a subtype-specific manner.

METHODS

2253 sequences representing subtypes A1, B, C, and D were aligned and domains with known functional properties were scored based on amino acid physicochemical properties. Following feature generation, we used statistical pruning and evolved neural networks (ENNs) to determine if we could successfully classify subtypes. Next, we used ENNs to identify the top five key Nef physicochemical features applied to specific immunoregulatory domains that differentiated subtypes. A signature pattern analysis was performed to the assess amino acid diversity in sub-domains that differentiated each subtype.

RESULTS

In validation studies, ENNs successfully differentiated each subtype at A1 (87.2%), subtype B (89.5%), subtype C (91.7%), and subtype D (85.1%). Our feature-based domain scoring, followed by t-tests, and a similar ENN identified subtype-specific domain-associated features. Subtype A1 was associated with alterations in Nef CD4 binding domain; subtype B was associated with alterations with the AP-2 Binding domain; subtype C was associated with alterations in a structural Alpha Helix domain; and, subtype D was associated with alterations in a Beta-Sheet domain.

CONCLUSIONS

Recent studies have focused on HIV Nef as a driver of immunoregulatory disease in those HIV infected and on ART. Nef acts through a complex mixture of interactions that are directly linked to the key features of the subtype-specific domains we identified with the ENN. The study supports the hypothesis that varied Nef subtypes contribute to subtype-specific disease progression.

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable “recent PWID” is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group.

HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage

Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.

Eradication of HIV from Tissue Reservoirs: Challenges for the Cure

The persistence of HIV infection, even after lengthy and successful combined antiretroviral therapy (cART), has precluded an effective cure. The anatomical locations and biological mechanisms through which the viral population is maintained remain unknown. Much research has focused nearly exclusively on circulating resting T cells as the predominant source of persistent HIV, a strategy with limited success in developing an effective cure strategy. In this study, we review research supporting the importance of anatomical tissues and other immune cells for HIV maintenance and expansion, including the central nervous system, lymph nodes, and macrophages. We present accumulated research that clearly demonstrates the limitations of using blood-derived cells as a proxy for tissue reservoirs and sanctuaries throughout the body. We cite recent studies that have successfully used deep-sequencing strategies to uncover the complexity of HIV infection and the ability of the virus to evolve despite undetectable plasma viral loads. Finally, we suggest new strategies and highlight the importance of tissue banks for future research.

Brain-specific HIV Nef identified in multiple patients with neurological disease

HIV-1 Nef is a flexible, multifunctional protein with several cellular targets that is required for pathogenicity of the virus. This protein maintains a high degree of genetic variation among intra- and inter-host isolates. HIV Nef is relevant to HIV-associated neurological diseases (HAND) in patients treated with combined antiretroviral therapy because of the protein's role in promoting survival and migration of infected brain macrophages. In this study, we analyzed 2020 HIV Nef sequences derived from 22 different tissues and 31 subjects using a novel computational approach. This approach combines statistical regression and evolved neural networks (ENNs) to classify brain sequences based on the physical and chemical characteristics of functional Nef domains. Based on training, testing, and validation data, the method successfully classified brain Nef sequences at 84.5% and provided informative features for further examination. These included physicochemical features associated with the Src-homology-3 binding domain, the Nef loop (including the AP-2 Binding region), and a cytokine-binding domain. Non-brain sequences from patients with HIV-associated neurological disease were frequently classified as brain, suggesting that the approach could indicate neurological risk using blood-derived virus or for the development of biomarkers for use in assay systems aimed at drug efficacy studies for the treatment of HIV-associated neurological diseases.

On the Physicochemical and Structural Modifications Associated with HIV-1 Subtype B Tropism Transition

HIV-1 enters immune cells via binding the viral envelope to a host cell CD4 receptor, and then a secondary co-receptor, usually CCR5 (R5) or CXCR4 (X4), and some HIV can utilize both co-receptors (R5X4). Although a small set of amino-acid properties such as charge and sequence length applied to HIV-1 envelope V3 loop sequence data can be used to predict co-receptor usage, we sought to expand the fundamental understanding of the physiochemical basis of tropism by analyzing many, perhaps less obvious, amino-acid properties over a diverse array of HIV sequences. We examined 74 amino-acid physicochemical scales over 1,559 V3 loop sequences with biologically tested tropisms downloaded from the Los Alamos HIV sequence database. Linear regressions were then calculated for each feature relative to three tropism transitions (R5→X4; R5→R5X4; R5X4→X4). Independent correlations were rank ordered to determine informative features. A structural analysis of the V3 loop was performed to better interpret these findings relative to HIV tropism states. Similar structural changes are required for R5 and R5X4 to transition to X4, thus suggesting that R5 and R5X4 types are more similar than either phenotype is to X4. Overall, the analysis suggests a continuum of viral tropism that is only partially related to charge; in fact, the analysis suggests that charge modification may be primarily attributed to decreased R5 usage, and further structural changes, particularly those associated with β-sheet structure, are likely required for full X4 usage.

Identification of dual-tropic HIV-1 using evolved neural networks

Blocking the binding of the envelope HIV-1 protein to immune cells is a popular concept for development of anti-HIV therapeutics. R5 HIV-1 binds CCR5, X4 HIV-1 binds CXCR4, and dual-tropic HIV-1 can bind either coreceptor for cellular entry. R5 viruses are associated with early infection and over time can evolve to X4 viruses that are associated with immune failure. Dual-tropic HIV-1 is less studied; however, it represents functional antigenic intermediates during the transition of R5 to X4 viruses. Viral tropism is linked partly to the HIV-1 envelope V3 domain, where the amino acid sequence helps dictate the receptor a particular virus will target; however, using V3 sequence information to identify dual-tropic HIV-1 isolates has remained difficult. Our goal in this study was to elucidate features of dual-tropic HIV-1 isolates that assist in the biological understanding of dual-tropism and develop an approach for their detection. Over 1559 HIV-1 subtype B sequences with known tropisms were analyzed. Each sequence was represented by 73 structural, biochemical and regional features. These features were provided to an evolved neural network classifier and evaluated using balanced and unbalanced data sets. The study resolved R5X4 viruses from R5 with an accuracy of 81.8% and from X4 with an accuracy of 78.8%. The approach also identified a set of V3 features (hydrophobicity, structural and polarity) that are associated with tropism transitions. The ability to distinguish R5X4 isolates will improve computational tropism decisions for R5 vs. X4 and assist in HIV-1 research and drug development efforts.

HIV-associated neuropathogenesis: a systems biology perspective for modeling and therapy

Despite the development of powerful antiretroviral drugs, HIV-1 associated neurological disorders (HAND) will affect approximately half of those infected with HIV-1. Combined anti-retroviral therapy (cART) targets viral replication and increases T-cell counts, but it does not always control macrophage polarization, brain infection or inflammation. Moreover, it remains difficult to identify those at risk for HAND. New therapies that focus on modulating host immune response by making use of biological pathways could prove to be more effective than cART for the treatment of neuroAIDS. Additionally, while numerous HAND biomarkers have been suggested, they are of little use without methods for appropriate data integration and a systems-level interpretation. Machine learning, could be used to develop multifactorial computational models that provide clinicians and researchers with the ability to identify which factors (in what combination and relative importance) are considered important to outcome.

Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence

Background

HIV-1 infects the host cell by interacting with the primary receptor CD4 and a coreceptor CCR5 or CXCR4. Maraviroc, a CCR5 antagonist binds to CCR5 receptor. Thus, it is important to identify the coreceptor used by the HIV strains dominating in the patient. In past, a number of experimental assays and in-silico techniques have been developed for predicting the coreceptor tropism. The prediction accuracy of these methods is excellent when predicting CCR5(R5) tropic sequences but is relatively poor for CXCR4(X4) tropic sequences. Therefore, any new method for accurate determination of coreceptor usage would be of paramount importance to the successful management of HIV-infected individuals.

Results

The dataset used in this study comprised 1799 R5-tropic and 598 X4-tropic third variable (V3) sequences of HIV-1. We compared the amino acid composition of both types of V3 sequences and observed that certain types of residues, e.g., Asparagine and Isoleucine, were preferred in R5-tropic sequences whereas residues like Lysine, Arginine, and Tryptophan were preferred in X4-tropic sequences. Initially, Support Vector Machine-based models were developed using amino acid composition, dipeptide composition, and split amino acid composition, which achieved accuracy up to 90%. We used BLAST to discriminate R5- and X4-tropic sequences and correctly predicted 93.16% of R5- and 75.75% of X4-tropic sequences. In order to improve the prediction accuracy, a Hybrid model was developed that achieved 91.66% sensitivity, 81.77% specificity, 89.19% accuracy and 0.72 Matthews Correlation Coefficient. The performance of our models was also evaluated on an independent dataset (256 R5- and 81 X4-tropic sequences) and achieved maximum accuracy of 84.87% with Matthews Correlation Coefficient 0.63.

Conclusion

This study describes a highly efficient method for predicting HIV-1 coreceptor usage from V3 sequences. In order to provide a service to the scientific community, a webserver HIVcoPred was developed (http://www.imtech.res.in/raghava/hivcopred/) for predicting the coreceptor usage.

Clinical significance of HIV-1 coreceptor usage

The identification of phenotypically distinct HIV-1 variants with different prevalence during the progression of the disease has been one of the earliest discoveries in HIV-1 biology, but its relevance to AIDS pathogenesis remains only partially understood. The physiological basis for the phenotypic variability of HIV-1 was elucidated with the discovery of distinct coreceptors employed by the virus to infect susceptible cells. The role of the viral phenotype in the variable clinical course and treatment outcome of HIV-1 infection has been extensively investigated over the past two decades. In this review, we summarize the major findings on the clinical significance of the HIV-1 coreceptor usage.

Human immunodeficiency virus-1 evolutionary patterns associated with pathogenic processes in the brain

The interplay between pathology and human immunodeficiency virus (HIV) expansion in brain tissues has not been thoroughly assessed in the highly active antiretroviral therapy (HAART) era. HIV-associated dementia (HAD) is marked by progressive brain infection due to recruitment and migration of macrophages in brain tissues; however, the cellular and viral events occurring prior to HAD development and death are under debate. In this study, 66 brain tissues from 11 autopsies were analyzed to assess HIV-1 DNA concentration in brain tissues. In most patients without HAD, it was impossible to amplify HIV-1 from brain tissues. Amplifiable DNA was obtained from three cases of patients on HAART who died due to primary pathology other than HAD: (1) cardiovascular disease, a disease associated with HAART therapy; (2) bacterial infections, including Mycobacterium avium complex, rapid occurrence of extreme dementia; and (3) acquired immunodeficiency syndrome (AIDS)-related lymphoma with meningeal involvement. HIV-1 DNA was also amplified from multiple tissues of two HAD patients. Analysis of HIV-1 nef, gp120, and gp41 sequences showed reduced viral evolution within brain tissues for the non-HAD cases relative to patients with clinical and histological HAD. The present study is the first to show a potential correlation between HIV-1 evolutionary patterns in the brain and different neuropathologies.

Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping

Background

Trofile^® is the prospectively validated HIV-1 tropism assay. Its use is limited by high costs, long turn-around time, and inability to test patients with very low or undetectable viremia. We aimed at assessing the efficiency of population genotypic assays based on gp120 V3-loop sequencing for the determination of tropism in plasma viral RNA and in whole-blood viral DNA. Contemporary and follow-up plasma and whole-blood samples from patients undergoing tropism testing via the enhanced sensitivity Trofile^® (ESTA) were collected. Clinical and clonal geno2pheno_[coreceptor] (G2P) models at 10% and at optimised 5.7% false positive rate cutoff were evaluated using viral DNA and RNA samples, compared against each other and ESTA, using Cohen's kappa, phylogenetic analysis, and area under the receiver operating characteristic (AUROC).

Results

Both clinical and clonal G2P (with different false positive rates) showed good performances in predicting the ESTA outcome (for V3 RNA-based clinical G2P at 10% false positive rate AUROC = 0.83, sensitivity = 90%, specificity = 75%). The rate of agreement between DNA- and RNA-based clinical G2P was fair (kappa = 0.74, p < 0.0001), and DNA-based clinical G2P accurately predicted the plasma ESTA (AUROC = 0.86). Significant differences in the viral populations were detected when comparing inter/intra patient diversity of viral DNA with RNA sequences.

Conclusions

Plasma HIV RNA or whole-blood HIV DNA V3-loop sequencing interpreted with clinical G2P is cheap and can be a good surrogate for ESTA. Although there may be differences among viral RNA and DNA populations in the same host, DNA-based G2P may be used as an indication of viral tropism in patients with undetectable plasma viremia.

Prediction of co-receptor usage of HIV-1 from genotype

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937±0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine.

Human Immunodeficiency Virus is the pathogen causing the disease AIDS. A precondition for virus entry into human cells is the contact of its glycoprotein gp120 with two cellular proteins, a receptor and a co-receptor. Depending on the viral strain, one specific co-receptor is used. The type of co-receptor used is crucial for the aggressiveness of the viral strain and the available treatment options. Hence, it is important to identify which co-receptor is used by the virus in an individual patient. Since the genome of the virus in the patient can be readily sequenced, and thus the composition of the viral proteins be determined, it could be possible to predict co-receptor usage from the viral genome sequences. To this end, we developed a method that is motivated by the insight that physical properties of gp120 will determine its specificity for a co-receptor. The method learns a computational model from structures and sequences of a crucial part of gp120, and the corresponding experimentally measured co-receptor usage. It then employs the model to predict co-receptor usage for new sequences. The high accuracy of the method could make it helpful for diagnosis and suggests that the model captures the determinants of co-receptor usage.

Current tests to evaluate HIV-1 coreceptor tropism

PURPOSE OF REVIEW

HIV-1 entry into target cells is a complex multistage process involving the envelope glycoprotein, primary cellular receptor CD4, and at least two main cellular coreceptors, CCR5 and CXCR4. The identification of the HIV-1 coreceptors led to the rapid development of several drug candidates that selectively block this interaction, that is, CCR5 or CXCR4 antagonists. Here, we review different methodologies used to determine the ability of the virus to use one or both coreceptors and their potential role in managing HIV-infected individuals treated with these novel drugs.

RECENT FINDINGS

Most commercially available HIV-1 tropism assays are cell-based (phenotypic) tests, which use different methodologies to generate env-recombinant viruses and distinct detection systems. On the other hand, a large effort is being devoted to develop more robust bioinformatic (genotypic) tools that may expedite HIV-1 tropism assays without compromising their accuracy. The main goal, however, continues to be to improve the sensitivity to detect minor CXCR4-tropic variants within the in-vivo HIV-1 quasispecies.

SUMMARY

An accurate determination, and perhaps quantification, of HIV-1 coreceptor usage is necessary for the successful management of HIV-infected individuals in the new era of entry inhibitors. Further studies, aimed to the development of novel methodologies, are essential for the success of this new class of drugs.

How HIV changes its tropism: evolution and adaptation?

PURPOSE OF REVIEW

To present recent information on the evolution of coreceptor use from CCR5 alone to CCR5 and CXCR4, the impact CCR5 inhibitors have on this process, and new insights into HIV-1 binding to CD4 and CCR5.

RECENT FINDINGS

The findings that are summarized include resistance to CCR5 inhibitors, genotypic predictors of coreceptor use, the link between coreceptor use and cell tropism, and new data on CCR5 structure and function.

SUMMARY

Resistance to CCR5 inhibitors is uncommon, and frequently involves selection of minor populations of R5X4 virus. Genotypic predictors of coreceptor use need to take into account the entire envelope sequence, not just V3. Genetic polymorphisms in humans that affect CCR5 or chemokines that bind CCR5 affect not only virus entry but also immune reconstitution.

HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels

BACKGROUND

Human immunodeficiency virus type 1 (HIV-1) infects cells by means of ligand-receptor interactions. This lentivirus uses the CD4 receptor in conjunction with a chemokine coreceptor, either CXCR4 or CCR5, to enter a target cell. HIV-1 is characterized by high sequence variability. Nonetheless, within this extensive variability, certain features must be conserved to define functions and phenotypes. The determination of coreceptor usage of HIV-1, from its protein envelope sequence, falls into a well-studied machine learning problem known as classification. The support vector machine (SVM), with string kernels, has proven to be very efficient for dealing with a wide class of classification problems ranging from text categorization to protein homology detection. In this paper, we investigate how the SVM can predict HIV-1 coreceptor usage when it is equipped with an appropriate string kernel.

RESULTS

Three string kernels were compared. Accuracies of 96.35% (CCR5) 94.80% (CXCR4) and 95.15% (CCR5 and CXCR4) were achieved with the SVM equipped with the distant segments kernel on a test set of 1425 examples with a classifier built on a training set of 1425 examples. Our datasets are built with Los Alamos National Laboratory HIV Databases sequences. A web server is available at http://genome.ulaval.ca/hiv-dskernel.

CONCLUSION

We examined string kernels that have been used successfully for protein homology detection and propose a new one that we call the distant segments kernel. We also show how to extract the most relevant features for HIV-1 coreceptor usage. The SVM with the distant segments kernel is currently the best method described.