Robust supervised and unsupervised statistical learning for HIV type 1 coreceptor usage analysis

Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review

Propelled by the transformative power of modern information and communication technologies, digitalization of data, and the increasing affordability of high-performance computing, Big Data science has brought forth revolutionary advancement in many areas of business, industry, health, and medicine. The HIV research and care service community is no exception to the benefits from the availability and utilization of Big Data analytics. Electronic health record (EHR) data (e.g., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) are an essential source of health and disease outcome data because of the large amount of real-world, comprehensive, and often longitudinal data, which provide a good opportunity for leveraging advanced Big Data analytics in addressing challenges in HIV prevention, treatment, and care. This review focuses on studies that apply Big Data analytics to EHR data with aims to synthesize the HIV-related issues that EHR data studies can tackle, identify challenges in the utilization of EHR data in HIV research and practice, and discuss future needs and directions that can realize the promising potential role of Big Data in ending the HIV epidemic.

A highly virulent variant of HIV-1 circulating in the Netherlands

We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log₁₀ increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV-CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences-is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.

Determination of viral tropism by genotyping and phenotyping assays in Brazilian HIV-1-infected patients

The clinical application of CCR5 antagonists involves first determining the coreceptor usage by the infecting viral strain. Bioinformatics programs that predict coreceptor usage could provide an alternative method to screen candidates for treatment with CCR5 antagonists, particularly in countries with limited financial resources. Thus, the present study aims to identify the best approach using bioinformatics tools for determining HIV-1 coreceptor usage in clinical practice. Proviral DNA sequences and Trofile results from 99 HIV-1-infected subjects under clinical monitoring were analyzed in this study. Based on the Trofile results, the viral variants present were 81.1% R5, 21.4% R5X4 and 1.8% X4. Determination of tropism using a Geno2pheno_[coreceptor] analysis with a false positive rate of 10% gave the most suitable performance in this sampling: the R5 and X4 strains were found at frequencies of 78.5% and 28.4%, respectively, and there was 78.6% concordance between the phenotypic and genotypic results. Further studies are needed to clarify how genetic diversity amongst virus strains affects bioinformatics-driven approaches for determining tropism. Although this strategy could be useful for screening patients in developing countries, some limitations remain that restrict the wider application of coreceptor usage tests in clinical practice.

Defining differential genetic signatures in CXCR4- and the CCR5-utilizing HIV-1 co-linear sequences

The adaptation of human immunodeficiency virus type-1 (HIV-1) to an array of physiologic niches is advantaged by the plasticity of the viral genome, encoded proteins, and promoter. CXCR4-utilizing (X4) viruses preferentially, but not universally, infect CD4⁺ T cells, generating high levels of virus within activated HIV-1-infected T cells that can be detected in regional lymph nodes and peripheral blood. By comparison, the CCR5-utilizing (R5) viruses have a greater preference for cells of the monocyte-macrophage lineage; however, while R5 viruses also display a propensity to enter and replicate in T cells, they infect a smaller percentage of CD4⁺ T cells in comparison to X4 viruses. Additionally, R5 viruses have been associated with viral transmission and CNS disease and are also more prevalent during HIV-1 disease. Specific adaptive changes associated with X4 and R5 viruses were identified in co-linear viral sequences beyond the Env-V3. The in silico position-specific scoring matrix (PSSM) algorithm was used to define distinct groups of X4 and R5 sequences based solely on sequences in Env-V3. Bioinformatic tools were used to identify genetic signatures involving specific protein domains or long terminal repeat (LTR) transcription factor sites within co-linear viral protein R (Vpr), trans-activator of transcription (Tat), or LTR sequences that were preferentially associated with X4 or R5 Env-V3 sequences. A number of differential amino acid and nucleotide changes were identified across the co-linear Vpr, Tat, and LTR sequences, suggesting the presence of specific genetic signatures that preferentially associate with X4 or R5 viruses. Investigation of the genetic relatedness between X4 and R5 viruses utilizing phylogenetic analyses of complete sequences could not be used to definitively and uniquely identify groups of R5 or X4 sequences; in contrast, differences in the genetic diversities between X4 and R5 were readily identified within these co-linear sequences in HIV-1-infected patients.

A predictive model for HIV type 1 coreceptor selectivity

Despite its sequence variability and structural flexibility, the V3 loop of the HIV-1 envelope glycoprotein gp120 is capable of recognizing cell-bound coreceptors CCR5 and CXCR4 and infecting cells. Viral selection of CCR5 is associated with the early stages of infection, and transition to selection of CXCR4 indicates disease progression. We have developed a predictive statistical model for coreceptor selectivity that uses the discrete property of net charge and the binary coreceptor preference markers of the N(6)X(7)[T/S](8)X(9) glycosylation motif and 11/24/25 positive amino acid rule. The model is based on analysis of 2,054 V3 loop sequences from patient data and allows us to infer the most likely state of the disease from physicochemical characteristics of the sequences. The performance of the model is comparable to established sequence-based predictive methods, and may be used in combination with other methods as a supportive diagnostic for coreceptor selection. This model may be used for personalized medical decisions in administering coreceptor-specific therapies.

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.

A case-based reasoning system for genotypic prediction of HIV-1 co-receptor tropism

Accurate co-receptor tropism (CRT) determination is critical for making treatment decisions in HIV management. We created a genotypic tropism prediction tool by utilizing the case-based reasoning (CBR) technique that attempts to solve new problems through applying the solution from similar past problems. V3 loop sequences from 732 clinical samples with diverse characteristics were used to build a case library. Additional sequence and molecular properties of the V3 loop were examined and used for similarity assessment. A similarity metric was defined based on each attribute's frequency in the CXCR4-using viruses. We implemented three other genotype-based tropism predictors, support vector machines (SVM), position specific scoring matrices (PSSM), and the 11/25 rule, and evaluated their performance as the ability to predict CRT compared to Monogram's enhanced sensitivity Trofile(®) assay (ESTA). Overall concordance of the CBR based tropism prediction algorithm was 81%, as compared to ESTA. Sensitivity to detect CXCR4 usage was 90% and specificity was at 73%. In comparison, sensitivity of the SVM, PSSM, and the 11/25 rule were 85%, 81%, and 36% respectively while achieving a specificity of 90% by SVM, 75% by PSSM, and 97% by the 11/25 rule. When we evaluated these predictors in an unseen dataset, higher sensitivity was achieved by the CBR algorithm (87%), compared to SVM (82%), PSSM (76%), and the 11/25 rule (33%), while maintaining similar level of specificity. Overall this study suggests that CBR can be utilized as a genotypic tropism prediction tool, and can achieve improved performance in independent datasets compared to model or rule based methods.

Structural dynamics of V3 loop with different electrostatics: implications on co-receptor recognition: a molecular dynamics study of HIV gp120

The HIV's envelope glycoprotein gp120 plays a major role in the entry of the virus into the host cell, through its successive interactions with the cell surface CD4 receptor and a co-receptor (CCR5 or CXCR4). The choice of a specific co-receptor by gp120 has an important consequence on HIV infection and pathogenesis. The third variable region within gp120, the V3 loop, is the principal determinant of the co-receptor usage by gp120. Here, we report the long time molecular dynamics simulations of four gp120 structures, having a V3 loop charge of +3 and +5, from both R5 and X4 specific strains of HIV. The results of the study highlight the properties of the V3 loop that can be critical for dictating the co-receptor recognition and selection in structural context. In detail, we observe that the structural orientation of the V3 loop in the 3D space is modulated by its net charge, whilst its co-receptor choice is likely dictated by a combined effect of both the electrostatics of the loop and its conformational variability at the level of its central crown region.

Selected amino acid changes in HIV-1 subtype-C gp41 are associated with specific gp120(V3) signatures in the regulation of co-receptor usage

The majority of studies have characterized the tropism of HIV-1 subtype-B isolates, but little is known about the determinants of tropism in other subtypes. So, the goal of the present study was to genetically characterize the envelope of viral proteins in terms of co-receptor usage by analyzing 356 full-length env sequences derived from HIV-1 subtype-C infected individuals. The co-receptor usage of V3 sequences was inferred by using the Geno2Pheno and PSSM algorithms, and also analyzed to the "11/25 rule". All reported env sequences were also analyzed with regard to N-linked glycosylation sites, net charge and hydrophilicity, as well as the binomial correlation phi coefficient to assess covariation among gp120(V3) and gp41 signatures and the average linkage hierarchical agglomerative clustering were also performed. Among env sequences present in Los Alamos Database, 255 and 101 sequences predicted as CCR5 and CXCR4 were selected, respectively. The classical V3 signatures at positions 11 and 25, and other specific V3 and gp41 amino acid changes were found statistically associated with different co-receptor usage. Furthermore, several statistically significant associations between V3 and gp41 signatures were also observed. The dendrogram topology showed a cluster associated with CCR5-usage composed by five gp41 mutated positions, A22V, R133M, E136G, N140L, and N166Q that clustered with T2V(V3) and G24T(V3) (bootstrap=1). Conversely, a heterogeneous cluster with CXCR4-usage, involving S11GR(V3), 13-14insIG/LG(V3), P16RQ(V3), Q18KR(V3), F20ILV(V3), D25KRQ(V3), Q32KR(V3) along with A30T(gp41), S107N(gp41), D148E(gp41), A189S(gp41) was identified (bootstrap=0.86). Our results show that as observed for HIV-1 subtype-B, also in subtype-C specific and different gp41 and gp120V3 amino acid changes are associated individually or together with CXCR4 and/or CCR5 usage. These findings strengthen previous observations that determinants of tropism may also reside in the gp41 protein.

Mutations in gp41 are correlated with coreceptor tropism but do not improve prediction methods substantially

BACKGROUND

The main determinants of HIV-1 coreceptor usage are located in the V3-loop of gp120, although mutations in V2 and gp41 are also known. Incorporation of V2 is known to improve prediction algorithms; however, this has not been confirmed for gp41 mutations.

METHODS

Samples with V3 and gp41 genotypes and Trofile assay (Monogram Biosciences, South San Francisco, CA, USA) results were taken from the HOMER cohort (n=444) and from patients screened for the MOTIVATE studies (n=1,916; 859 with maraviroc outcome data). Correlations of mutations with tropism were assessed using Fisher's exact test and prediction models trained using support vector machines. Models were validated by cross-validation, by testing models from one dataset on the other, and by analysing virological outcome.

RESULTS

Several mutations within gp41 were highly significant for CXCR4 usage; most strikingly an insertion occurring in 7.7% of HOMER-R5 and 46.3% of HOMER-X4 samples (MOTIVATE 5.7% and 25.2%, respectively). Models trained on gp41 sequence alone achieved relatively high areas under the receiver-operating characteristic curve (AUCs; HOMER 0.713 and MOTIVATE 0.736) that were almost as good as V3 models (0.773 and 0.884, respectively). However, combining the two regions improved predictions only marginally (0.813 and 0.902, respectively). Similar results were found when models were trained on HOMER and validated on MOTIVATE or vice versa. The difference in median log viral load decrease at week 24 between patients with R5 and X4 virus was 1.65 (HOMER 2.45 and MOTIVATE 0.79) for V3 models, 1.59 for gp41-models (2.42 and 0.83, respectively) and 1.58 for the combined predictor (2.44 and 0.86, respectively).

CONCLUSIONS

Several mutations within gp41 showed strong correlation with tropism in two independent datasets. However, incorporating gp41 mutations into prediction models is not mandatory because they do not improve substantially on models trained on V3 sequences alone.

Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage

Background

HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An in silico mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions.

Results

Classifier performance is evaluated based on stratified 10-fold cross-validation, stratified dataset splits (2/3 training, 1/3 validation), and leave-one-out cross-validation. Best reported values of sensitivity (85%), specificity (100%), and precision (98%) for predicting X4-capable HIV-1 virus, overall accuracy (97%), Matthew's correlation coefficient (89%), balanced error rate (0.08), and ROC area (0.97) all reach critical thresholds, suggesting that the models outperform six other state-of-the-art methods and come closer to competing with phenotype assays.

Conclusions

The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information. The classifiers are available online at http://proteins.gmu.edu/automute.

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.

Use of massive parallel pyrosequencing for near full-length characterization of a unique HIV Type 1 BF recombinant associated with a fatal primary infection

Near full length genome characterization of a BF recombinant from a patient who died from multiorgan failure during HIV-1 seroconversion is reported. Massive parallel pyrosequencing was used with the shotgun approach. Intrahost genetic variability along the whole genome was calculated and coreceptor usage of viral quasispecies was predicted. A consensus sequence was established to perform subtype assignment, phylogenetic analysis, and recombination tests. The sequence clustered with two recently described BF unique recombinant forms from Brazil, consistent with the recombination pattern, yielding breakpoints located at the same positions, with the exception of the second env breakpoint. The actual prevalence of recombinant forms is probably underestimated if partial genomic regions are considered. Here the first full length BF recombinant from Italy is described, together with an evaluation of quasispecies heterogeneity. Our data provide evidence that next generation sequencing may provide a major contribution to HIV-1 molecular epidemiology and to the comprehension of intrapatient heterogeneity.