Mattia Prosperi

Massively parallel pyrosequencing highlights minority variants in the HIV-1 env quasispecies deriving from lymphomonocyte sub-populations

BackgroundVirus-associated cell membrane proteins acquired by HIV-1 during budding may give information on the cellular source of circulating virions. In the present study, by applying immunosorting of the virus and of the cells with antibodies targeting monocyte (CD36) and lymphocyte (CD26) markers, it was possible to directly compare HIV-1 quasispecies archived in circulating monocytes and T lymphocytes with that present in plasma virions originated from the same cell types. Five chronically HIV-1 infected patients who underwent therapy interruption after prolonged HAART were enrolled in the study. The analysis was performed by the powerful technology of ultra-deep pyrosequencing after PCR amplification of part of the env gene, coding for the viral glycoprotein (gp) 120, encompassing the tropism-related V3 loop region. V3 amino acid sequences were used to establish heterogeneity parameters, to build phylogenetic trees and to predict co-receptor usage.ResultsThe heterogeneity of proviral and viral genomes derived from monocytes was higher than that of T-lymphocyte origin. Both monocytes and T lymphocytes might contribute to virus rebounding in the circulation after therapy interruptions, but other virus sources might also be involved. In addition, both proviral and circulating viral sequences from monocytes and T lymphocytes were predictive of a predominant R5 coreceptor usage. However, minor variants, segregating from the most frequent quasispecies variants, were present. In particular, in proviral genomes harboured by monocytes, minority variant clusters with a predicted X4 phenotype were found.ConclusionThis study provided the first direct comparison between the HIV-1 quasispecies archived as provirus in circulating monocytes and T lymphocytes with that of plasma virions replicating in the same cell types. Ultra-deep pyrosequencing generated data with some order of magnitude higher than any previously obtained with conventional approaches. Next generation sequencing allowed the analysis of previously inaccessible aspects of HIV-1 quasispecies, such as co-receptor usage of minority variants present in archived proviral sequences and in actually replicating virions, which may have clinical and therapeutic relevance.

Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models

Background Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information. Methods and Findings The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii). Conclusions Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.

QuRe: software for viral quasispecies reconstruction from next-generation sequencing data

SUMMARY Next-generation sequencing (NGS) is an ideal framework for the characterization of highly variable pathogens, with a deep resolution able to capture minority variants. However, the reconstruction of all variants of a viral population infecting a host is a challenging task for genome regions larger than the average NGS read length. QuRe is a program for viral quasispecies reconstruction, specifically developed to analyze long read (>100 bp) NGS data. The software performs alignments of sequence fragments against a reference genome, finds an optimal division of the genome into sliding windows based on coverage and diversity and attempts to reconstruct all the individual sequences of the viral quasispecies--along with their prevalence--using a heuristic algorithm, which matches multinomial distributions of distinct viral variants overlapping across the genome division. QuRe comes with a built-in Poisson error correction method and a post-reconstruction probabilistic clustering, both parameterized on given error rates in homopolymeric and non-homopolymeric regions. AVAILABILITY QuRe is platform-independent, multi-threaded software implemented in Java. It is distributed under the GNU General Public License, available at https://sourceforge.net/projects/qure/. CONTACT ahnven@yahoo.it; ahnven@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing

BackgroundNext-generation sequencing (NGS) offers a unique opportunity for high-throughput genomics and has potential to replace Sanger sequencing in many fields, including de-novo sequencing, re-sequencing, meta-genomics, and characterisation of infectious pathogens, such as viral quasispecies. Although methodologies and software for whole genome assembly and genome variation analysis have been developed and refined for NGS data, reconstructing a viral quasispecies using NGS data remains a challenge. This application would be useful for analysing intra-host evolutionary pathways in relation to immune responses and antiretroviral therapy exposures. Here we introduce a set of formulae for the combinatorial analysis of a quasispecies, given a NGS re-sequencing experiment and an algorithm for quasispecies reconstruction. We require that sequenced fragments are aligned against a reference genome, and that the reference genome is partitioned into a set of sliding windows (amplicons). The reconstruction algorithm is based on combinations of multinomial distributions and is designed to minimise the reconstruction of false variants, called in-silico recombinants.ResultsThe reconstruction algorithm was applied to error-free simulated data and reconstructed a high percentage of true variants, even at a low genetic diversity, where the chance to obtain in-silico recombinants is high. Results on empirical NGS data from patients infected with hepatitis B virus, confirmed its ability to characterise different viral variants from distinct patients.ConclusionsThe combinatorial analysis provided a description of the difficulty to reconstruct a quasispecies, given a determined amplicon partition and a measure of population diversity. The reconstruction algorithm showed good performance both considering simulated data and real data, even in presence of sequencing errors.

Selecting anti-HIV therapies based on a variety of genomic and clinical factors

Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy. Results: Three different machine learning techniques were used: generative–discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome. Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org Contact: rosen@il.ibm.com

A novel methodology for large-scale phylogeny partition

Phylogenetic analysis is used to identify transmission chains, but no software is available for the automated partition of large phylogenies. Prosperiet al. apply a new search algorithm to identify transmission clusters within the phylogeny of HIV-1gene sequences linking molecular and epidemiological data. Supplementary information The online version of this article (doi:10.1038/ncomms1325) contains supplementary material, which is available to authorized users.

Evolution pathways of IgE responses to grass and mite allergens throughout childhood.

BACKGROUND Little is known about longitudinal patterns of the development of IgE to distinct allergen components. OBJECTIVE We sought to investigate the evolution of IgE responses to allergenic components of timothy grass and dust mite during childhood. METHODS In a population-based birth cohort (n = 1184) we measured IgE responses to 15 components from timothy grass and dust mite in children with available samples at 3 time points (ages 5, 8, and 11 years; n = 235). We designed a nested, 2-stage latent class analysis to identify cross-sectional sensitization patterns at each follow-up and their longitudinal trajectories. We then ascertained the association of longitudinal trajectories with asthma, rhinitis, eczema, and lung function in children with component data for at least 2 time points (n = 534). RESULTS Longitudinal latent class analysis revealed 3 grass sensitization trajectories: (1) no/low sensitization; (2) early onset; and (3) late onset. The early-onset trajectory was associated with asthma and diminished lung function, and the late-onset trajectory was associated with rhinitis. Four longitudinal trajectories emerged for mite: (1) no/low sensitization; (2) group 1 allergens; (3) group 2 allergens; and (3) complete mite sensitization. Children in the complete mite sensitization trajectory had the highest odds ratios (ORs) for asthma (OR, 7.15; 95% CI, 3.80-13.44) and were the only group significantly associated with comorbid asthma, rhinitis, and eczema (OR, 5.91; 95% CI, 2.01-17.37). Among children with wheezing, those in the complete mite sensitization trajectory (but not other longitudinal mite trajectories) had significantly higher risk of severe exacerbations (OR, 3.39; 95% CI, 1.62-6.67). CONCLUSIONS The nature of developmental longitudinal trajectories of IgE responses differed between grass and mite allergen components, with temporal differences (early vs late onset) dominant in grass and diverging patterns of IgE responses (group 1 allergens, group 2 allergens, or both) in mite. Different longitudinal patterns bear different associations with clinical outcomes, which varied by allergen.

Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy.

Background Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. Principal Findings The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. Conclusion The combined EuResist prediction engine is freely available at http://engine.euresist.org.

Predictors of first-line antiretroviral therapy discontinuation due to drug-related adverse events in HIV-infected patients: a retrospective cohort study

BackgroundDrug-related toxicity has been one of the main causes of antiretroviral treatment discontinuation. However, its determinants are not fully understood. Aim of this study was to investigate predictors of first-line antiretroviral therapy discontinuation due to adverse events and their evolution in recent years.MethodsPatients starting first-line antiretroviral therapy were retrospectively selected. Primary end-point was the time to discontinuation of therapy due to adverse events, estimating incidence, fitting Kaplan-Meier and multivariable Cox regression models upon clinical/demographic/chemical baseline patients’ markers.Results1,096 patients were included: 302 discontinuations for adverse events were observed over 1,861 person years of follow-up between 1988 and 2010, corresponding to an incidence (95% CI) of 0.16 (0.14-0.18). By Kaplan-Meier estimation, the probabilities (95% CI) of being free from an adverse event at 90 days, 180 days, one year, two years, and five years were 0.88 (0.86-0.90), 0.85 (0.83-0.87), 0.79 (0.76-0.81), 0.70 (0.67-0.74), 0.55 (0.50-0.61), respectively. The most represented adverse events were gastrointestinal symptoms (28.5%), hematological (13.2%) or metabolic (lipid and glucose metabolism, lipodystrophy) (11.3%) toxicities and hypersensitivity reactions (9.3%). Factors associated with an increased hazard of adverse events were: older age, CDC stage C, female gender, homo/bisexual risk group (vs. heterosexual), HBsAg-positivity. Among drugs, zidovudine, stavudine, zalcitabine, didanosine, full-dose ritonavir, indinavir but also efavirenz (actually recommended for first-line regimens) were associated to an increased hazard of toxicity. Moreover, patients infected by HIV genotype F1 showed a trend for a higher risk of adverse events.ConclusionsAfter starting antiretroviral therapy, the probability of remaining free from adverse events seems to decrease over time. Among drugs associated with increased toxicity, only one is currently recommended for first-line regimens but with improved drug formulation. Older age, CDC stage, MSM risk factor and gender are also associated with an increased hazard of toxicity and should be considered when designing a first-line regimen.

Prediction of Response to Antiretroviral Therapy by Human Experts and by the EuResist Data-Driven Expert System (the EVE study)

The EuResist expert system is a novel data‐driven online system for computing the probability of 8‐week success for any given pair of HIV‐1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment.