Patricia Buendia

A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance

MOTIVATION Modern HIV-1, hepatitis B virus and hepatitis C virus antiviral therapies have been successful at keeping viruses suppressed for prolonged periods of time, but therapy failures attributable to the emergence of drug resistant mutations continue to be a distressing reminder that no therapy can fully eradicate these viruses from their host organisms. To better understand the emergence of drug resistance, we combined phylogenetic and statistical models of viral evolution in a 2-phase computational approach that reconstructs mutational pathways of drug resistance. RESULTS The first phase of the algorithm involved the modeling of the evolution of the virus within the human host environment. The inclusion of longitudinal clonal sequence data was a key aspect of the model due to the progressive fashion in which multiple mutations become linked in the same genome creating drug resistant genotypes. The second phase involved the development of a Markov model to calculate the transition probabilities between the different genotypes. The proposed method was applied to data from an HIV-1 Efavirenz clinical trial study. The obtained model revealed the direction of evolution over time with greater detail than previous models. Our results show that the mutational pathways facilitate the identification of fast versus slow evolutionary pathways to drug resistance. AVAILABILITY Source code for the algorithm is publicly available at http://biorg.cis.fiu.edu/vPhyloMM/

Sliding MinPD

MOTIVATION Traditional phylogenetic methods assume tree-like evolutionary models and are likely to perform poorly when provided with sequence data from fast-evolving, recombining viruses. Furthermore, these methods assume that all the sequence data are from contemporaneous taxa, which is not valid for serially-sampled data. A more general approach is proposed here, referred to as the Sliding MinPD method, that reconstructs evolutionary networks for serially-sampled sequences in the presence of recombination. RESULTS Sliding MinPD combines distance-based phylogenetic methods with automated recombination detection based on the best-known sliding window approaches to reconstruct serial evolutionary networks. Its performance was evaluated through comprehensive simulation studies and was also applied to a set of serially-sampled HIV sequences from a single patient. The resulting network organizations reveal unique patterns of viral evolution and may help explain the emergence of disease-associated mutants and drug-resistant strains with implications for patient prognosis and treatment strategies.

Serial NetEvolve: a flexible utility for generating serially-sampled sequences along a tree or recombinant network

UNLABELLED Serial NetEvolve is a flexible simulation program that generates DNA sequences evolved along a tree or recombinant network. It offers a user-friendly Windows graphical interface and a Windows or Linux simulator with a diverse selection of parameters to control the evolutionary model. Serial NetEvolve is a modification of the Treevolve program with the following additional features: simulation of serially-sampled data, the choice of either a clock-like or a variable rate model of sequence evolution, sampling from the internal nodes and the output of the randomly generated tree or network in our newly proposed NeTwick format. AVAILABILITY From website http://biorg.cis.fiu.edu/SNE Contacts: giri@cis.fiu.edu SUPPLEMENTARY INFORMATION Manual and examples available from http://biorg.cis.fiu.edu/SNE.

Reconstructing ancestor-descendant lineages from serially-sampled data: a comparison study

The recent accumulation of serially-sampled viral sequences in public databases attests to the need for development of algorithms that infer phylogenetic relationships among such data with the goal of elucidating patterns and processes of viral evolution. Phylogenetic methods are typically applied to contemporaneous taxa, and result in the taxa being placed at the tips or leaves of the tree. In a serial sampling scenario an evolutionary framework may offer a more meaningful alternative in which the rise, persistence, and extinction of different viral lineages is readily observable. Recently, algorithms have been developed to study such data. We evaluate the performance of 5 different methods in correctly inferring ancestor-descendant relationships by using empirical and simulated sequence data. Our results suggest that for inferring ancestor-descendant relationships among serially-sampled taxa, the MinPD program is an accurate and efficient method, and that traditional ML methods, while marginally more accurate, are far less efficient.

Serial evolutionary networks of within-patient HIV-1 sequences reveal patterns of evolution of X4 strains.

BackgroundThe HIV virus is known for its ability to exploit numerous genetic and evolutionary mechanisms to ensure its proliferation, among them, high replication, mutation and recombination rates. Sliding MinPD, a recently introduced computational method [1], was used to investigate the patterns of evolution of serially-sampled HIV-1 sequence data from eight patients with a special focus on the emergence of X4 strains. Unlike other phylogenetic methods, Sliding MinPD combines distance-based inference with a nonparametric bootstrap procedure and automated recombination detection to reconstruct the evolutionary history of longitudinal sequence data. We present serial evolutionary networks as a longitudinal representation of the mutational pathways of a viral population in a within-host environment. The longitudinal representation of the evolutionary networks was complemented with charts of clinical markers to facilitate correlation analysis between pertinent clinical information and the evolutionary relationships.ResultsAnalysis based on the predicted networks suggests the following:: significantly stronger recombination signals (p = 0.003) for the inferred ancestors of the X4 strains, recombination events between different lineages and recombination events between putative reservoir virus and those from a later population, an early star-like topology observed for four of the patients who died of AIDS. A significantly higher number of recombinants were predicted at sampling points that corresponded to peaks in the viral load levels (p = 0.0042).ConclusionOur results indicate that serial evolutionary networks of HIV sequences enable systematic statistical analysis of the implicit relations embedded in the topology of the structure and can greatly facilitate identification of patterns of evolution that can lead to specific hypotheses and new insights. The conclusions of applying our method to empirical HIV data support the conventional wisdom of the new generation HIV treatments, that in order to keep the virus in check, viral loads need to be suppressed to almost undetectable levels.

The role of internal node sequences and the molecular clock in the analysis of serially-sampled data

Algorithms that infer phylogenetic relationships between serially-sampled sequences have been developed in recent years to assist in the analysis of rapidly-evolving human pathogens. Our study consisted of evaluating seven relevant methods using empirical as well as simulated data sets. In particular, we investigated how the molecular clock hypothesis affected their relative performance, as three of the algorithms that accept serially-sampled data as input assume a molecular clock. Our results show that the standard phylogenetic methods and MinPD had a better overall performance. Surprisingly, when all internal node sequences were included in the data, the topological performance measure of all the methods, with the exception of MinPD, dropped significantly.

Searching for recombinant donors in a phylogenetic network of serial samples

Determining the evolutionary history of a sampled sequence can become quite complex when multiple recombination events are part of its past. With at least five new recombination detection methods published in the last year, the growing list of over 40 methods suggests that this field is generating a lot of interest. In previous studies comparing recombination detection methods, the evaluation procedures did not measure how many recombinant sequences, breakpoints and donors were correctly identified. In this paper we will present the algorithm RecIdentify that scans a phylogenetic network and uses its edge lengths and topology to identify the parental/donor sequences and breakpoint positions for each query sequence. RecIdentify findings can be used to evaluate the output of recombination detection programs. RecIdentify may also assist in understanding how network size and complexity may shape recombination signals in a set of DNA sequences. The results may prove useful in the phylogenetic study of serially-sampled viral data with recombination events.

Reconstructing Mutational Pathways from Serial Evolutionary Trees

RNA viruses like HIV and HCV have an extraordinary evolutionary potential to escape from both immune pressures and targeted drug therapies. In HIV infections, the emergence of drug resistant strains is of particular interest, as it complicates the choice of an optimal follow-up regimen. A series of bioinformatics tools for predicting drug resistance were previously developed to support physicians in this task. A new method is proposed that captures the order of occurrence of drug-resistant mutations and can be applied to serially-sampled viral sequence data from patients taking antiretroviral drugs. The new phylogenetic approach reduces a serial evolutionary tree inferred by the Sliding MinPD program [12] to a set of mutational pathways of drug resistance. The method is applied to data from an HIV-1 clinical study of the reverse transcriptase inhibitor, Efavirenz. This approach can effectively identify mutational pathways by considering all available information and the statistical support for each prediction.