Estimating selection pressures on HIV-1 using phylogenetic likelihood models

Human immunodeficiency virus (HIV-1) can rapidly evolve due to selection pressures exerted by HIV-specific immune responses, antiviral agents, and to allow the virus to establish infection in different compartments in the body. Statistical models applied to HIV-1 sequence data can help to elucidate the nature of these selection pressures through comparisons of non-synonymous (or amino acid changing) and synonymous (or amino acid preserving) substitution rates. These models also need to take into account the non-independence of sequences due to their shared evolutionary history. We review how we have developed these methods and have applied them to characterize the evolution of HIV-1 in vivo.To illustrate our methods, we present an analysis of compartment-specific evolution of HIV-1 env in blood and cerebrospinal fluid and of site-to-site variation in the gag gene of subtype C HIV-1

Correcting the Bias of Empirical Frequency Parameter Estimators in Codon Models

Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a “corrected” empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes

Modeling HIV-1 Drug Resistance as Episodic Directional Selection

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance

HIV-Specific Probabilistic Models of Protein Evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses

Benchmarking multi-rate codon models

CITATION: Delport, W. et al. 2010. Benchmarking multi-rate codon models. PLoS ONE, 5(7): e11587, doi:10.1371/journal.pone.0011587.The original publication is available at http://journals.plos.org/plosoneThe single rate codon model of non-synonymous substitution is ubiquitous in phylogenetic modeling. Indeed, the use of a non-synonymous to synonymous substitution rate ratio parameter has facilitated the interpretation of selection pressure on genomes. Although the single rate model has achieved wide acceptance, we argue that the assumption of a single rate of non-synonymous substitution is biologically unreasonable, given observed differences in substitution rates evident from empirical amino acid models. Some have attempted to incorporate amino acid substitution biases into models of codon evolution and have shown improved model performance versus the single rate model. Here, we show that the single rate model of non-synonymous substitution is easily outperformed by a model with multiple non-synonymous rate classes, yet in which amino acid substitution pairs are assigned randomly to these classes. We argue that, since the single rate model is so easy to improve upon, new codon models should not be validated entirely on the basis of improved model fit over this model. Rather, we should strive to both improve on the single rate model and to approximate the general time-reversible model of codon substitution, with as few parameters as possible, so as to reduce model over-fitting. We hint at how this can be achieved with a Genetic Algorithm approach in which rate classes are assigned on the basis of sequence information content. © 2010 Delport et al.http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0011587Publisher's versio

A First Look at ARFome: Dual-Coding Genes in Mammalian Genomes

Coding of multiple proteins by overlapping reading frames is not a feature one would associate with eukaryotic genes. Indeed, codependency between codons of overlapping protein-coding regions imposes a unique set of evolutionary constraints, making it a costly arrangement. Yet in cases of tightly coexpressed interacting proteins, dual coding may be advantageous. Here we show that although dual coding is nearly impossible by chance, a number of human transcripts contain overlapping coding regions. Using newly developed statistical techniques, we identified 40 candidate genes with evolutionarily conserved overlapping coding regions. Because our approach is conservative, we expect mammals to possess more dual-coding genes. Our results emphasize that the skepticism surrounding eukaryotic dual coding is unwarranted: rather than being artifacts, overlapping reading frames are often hallmarks of fascinating biology

Assigning and visualizing germline genes in antibody repertoires.

Identifying the germline genes involved in immunoglobulin rearrangements is an essential first step in the analysis of antibody repertoires. Based on our prior work in analysing diverse recombinant viruses, we present IgSCUEAL (Immunoglobulin Subtype Classification Using Evolutionary ALgorithms), a phylogenetic approach to assign V and J regions of immunoglobulin sequences to their corresponding germline alleles, with D regions assigned using a simple pairwise alignment algorithm. We also develop an interactive web application for viewing the results, allowing the user to explore the frequency distribution of sequence assignments and CDR3 region length statistics, which is useful for summarizing repertoires, as well as a detailed viewer of rearrangements and region alignments for individual query sequences. We demonstrate the accuracy and utility of our method compared with sequence similarity-based approaches and other non-phylogenetic model-based approaches, using both simulated data and a set of evaluation datasets of human immunoglobulin heavy chain sequences. IgSCUEAL demonstrates the highest accuracy of V and J assignment amongst existing approaches, even when the reassorted sequence is highly mutated, and can successfully cluster sequences on the basis of shared V/J germline alleles.S.K.L.P. and B.M. were supported in part by the U.S. National Institutes of Health (AI110181, AI90970, AI100665, DA34978, GM93939, HL108460, GM110749, LM7092, MH97520, MH83552), the UCSD Center for AIDS Research (Developmental Grant, AI36214, Bioinformatics and Information Technologies Core), the International AIDS Vaccine Initiative (through AI90970), the UC Laboratory Fees Research Program (grant no. 12-LR-236617). G.J.S. was supported in part the U.S. National Institute of Health (AI90118, AI68063, AI40305, and NIAID HHS N272201400019C), and a grant from the Lupus Research Institute. A.S.M.M.H. was supported by an Islamic Development Bank Scholarship, and S.D.W.F. was supported in part by the UK MRC Methodology Research Programme (grant no. MR/J013862/1).This is the final published version. It first appeared at http://rstb.royalsocietypublishing.org/content/370/1676/20140240

Longitudinal characterization of HIV-1 pol-gene in treatment-naïve men-who-have-sex-with-men from acute to chronic infection stages

HIV-1 is characterized by its ability to mutate and recombine even at polymerase (pol) gene. However, pol-gene diversity is limited due to functional constraints. The aim of this study was to characterize longitudinally, by next-generation sequencing (NGS), HIV-1 variants based on pol-gene sequences, at intra- and inter-host level, from acute/early to chronic stages of infection, in the absence of antiretroviral therapy. Ten men who have sex with men (MSM) were recruited during primary infection and yearly followed for five years. Even after a maximum of a five-year follow-up period, the phylogenetic analysis of HIV-1 pol-gene sequences showed a host-defined structured pattern, with a predominance of purifying selection forces during the follow-up. MSM had been acutely infected by different HIV-1 variants mainly ascribed to pure subtype B, or BF recombinant variants and showed different genetic mosaicism patterns that last until the chronic stage, representing a major challenge for prevention strategies.Fil: Cevallos, Cintia Gisela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Culasso, Andrés Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Microbiología. Área Virología; ArgentinaFil: Modenutti, Carlos Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Gun, Ana. Fundación Huésped; ArgentinaFil: Sued, Omar Gustavo. Fundación Huésped; ArgentinaFil: Avila, Maria Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Flichman, Diego Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Delpino, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Quarleri, Jorge Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; Argentin