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

Finding Direction in the Search for Selection.

Tests for positive selection have mostly been developed to look for diversifying selection where change away from the current amino acid is often favorable. However, in many cases we are interested in directional selection where there is a shift toward specific amino acids, resulting in increased fitness in the species. Recently, a few methods have been developed to detect and characterize directional selection on a molecular level. Using the results of evolutionary simulations as well as HIV drug resistance data as models of directional selection, we compare two such methods with each other, as well as against a standard method for detecting diversifying selection. We find that the method to detect diversifying selection also detects directional selection under certain conditions. One method developed for detecting directional selection is powerful and accurate for a wide range of conditions, while the other can generate an excessive number of false positives

The Influence of HIV on the Evolution of Mycobacterium tuberculosis.

HIV significantly affects the immunological environment during tuberculosis coinfection, and therefore may influence the selective landscape upon which M. tuberculosis evolves. To test this hypothesis whole genome sequences were determined for 169 South African M. tuberculosis strains from HIV-1 coinfected and uninfected individuals and analyzed using two Bayesian codon-model based selection analysis approaches: FUBAR which was used to detect persistent positive and negative selection (selection respectively favoring and disfavoring nonsynonymous substitutions); and MEDS which was used to detect episodic directional selection specifically favoring nonsynonymous substitutions within HIV-1 infected individuals. Among the 25,251 polymorphic codon sites analyzed, FUBAR revealed that 189-fold more were detectably evolving under persistent negative selection than were evolving under persistent positive selection. Three specific codon sites within the genes celA2b, katG, and cyp138 were identified by MEDS as displaying significant evidence of evolving under directional selection influenced by HIV-1 coinfection. All three genes encode proteins that may indirectly interact with human proteins that, in turn, interact functionally with HIV proteins. Unexpectedly, epitope encoding regions were enriched for sites displaying weak evidence of directional selection influenced by HIV-1. Although the low degree of genetic diversity observed in our M. tuberculosis data set means that these results should be interpreted carefully, the effects of HIV-1 on epitope evolution in M. tuberculosis may have implications for the design of M. tuberculosis vaccines that are intended for use in populations with high HIV-1 infection rates

Improved models of biological sequence evolution

Thesis (PhD)--Stellenbosch University, 2012.ENGLISH ABSTRACT: Computational molecular evolution is a field that attempts to characterize how genetic sequences evolve over phylogenetic trees – the branching processes that describe the patterns of genetic inheritance in living organisms. It has a long history of developing progressively more sophisticated stochastic models of evolution. Through a probabilist’s lens, this can be seen as a search for more appropriate ways to parameterize discrete state continuous time Markov chains to better encode biological reality, matching the historical processes that created empirical data sets, and creating useful tools that allow biologists to test specific hypotheses about the evolution of the organisms or the genes that interest them. This dissertation is an attempt to fill some of the gaps that persist in the literature, solving what we see as existing open problems. The overarching theme of this work is how to better model variation in the action of natural selection at multiple levels: across genes, between sites, and over time. Through four published journal articles and a fifth in preparation, we present amino acid and codon models that improve upon existing approaches, providing better descriptions of the process of natural selection and better tools to detect adaptive evolution.AFRIKAANSE OPSOMMING: Komputasionele molekulêre evolusie is ’n navorsingsarea wat poog om die evolusie van genetiese sekwensies oor filogenetiese bome – die vertakkende prosesse wat die patrone van genetiese oorerwing in lewende organismes beskryf – te karakteriseer. Dit het ’n lang geskiedenis waartydens al hoe meer gesofistikeerde waarskynlikheidsmodelle van evolusie ontwikkel is. Deur die lens van waarskynlikheidsleer kan hierdie proses gesien word as ’n soektog na meer gepasde metodes om diskrete-toestand kontinuë-tyd Markov kettings te parametriseer ten einde biologiese realiteit beter te enkodeer – op so ’n manier dat die historiese prosesse wat tot die vorming van biologiese sekwensies gelei het nageboots word, en dat nuttige metodes geskep word wat bioloë toelaat om spesifieke hipotesisse met betrekking tot die evolusie van belanghebbende organismes of gene te toets. Hierdie proefskrif is ’n poging om sommige van die gapings wat in die literatuur bestaan in te vul en bestaande oop probleme op te los. Die oorkoepelende tema is verbeterde modellering van variasie in die werking van natuurlike seleksie op verskeie vlakke: variasie van geen tot geen, variasie tussen posisies in gene en variasie oor tyd. Deur middel van vier gepubliseerde joernaalartikels en ’n vyfde artikel in voorbereiding, bied ons aminosuur- en kodon-modelle aan wat verbeter op bestaande benaderings – hierdie modelle verskaf beter beskrywings van die proses van natuurlike seleksie sowel as beter metodes om gevalle van aanpassing in evolusie te vind

Natural selection favoring more transmissible HIV detected in United States molecular transmission network.

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters

Darwin and Fisher meet at biotech : on the potential of computational molecular evolution in industry

Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data - ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin's theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics

Porcine circovirus type 2 (PCV2) evolution before and after the vaccination introduction: A large scale epidemiological study

open4noSince their commercialization, vaccines against Porcine circovirus type 2 (PCV2) have been the cornerstone control strategy. Nevertheless, the periodic emergence of new genotype waves and the recent reports of vaccine failure outbreaks have raised the question if widespread vaccination strategies could have driven viral evolution and affected different genotype fitness. To investigate this issue an in-deep analysis, based on a bioinformatics and biostatistics approach, has been implemented. ORF2 sequences from vaccinated and non-vaccinated populations (i.e. domestic pigs before and after vaccine introduction and wild boars) were considered. The action of selective forces on PCV2 strains has been analyzed and compared among groups. Remarkable differences were found in the selective forces acting on viral populations circulating in different “immune environments”. Particularly for PCV2a, a directional selection promoting a change in the viral capsid away from the vaccine specific antigenic determinants has been detected after vaccine introduction. Involved amino acids were previously reported to be part of viral epitopes whose variability is responsible of immune escape. Our findings support a change in PCV2 evolutionary pattern after widespread vaccination introduction and stress once more the compulsoriness of a continuous monitoring of PCV2 epidemiology to promptly act in response to the emergence of possible vaccine-escaping mutants.openFranzo, Giovanni; Tucciarone, Claudia Maria; Cecchinato, Mattia; Drigo, MicheleFranzo, Giovanni; Tucciarone, CLAUDIA MARIA; Cecchinato, Mattia; Drigo, Michel

Bayesian codon models for detecting convergent molecular adaptation

Modéliser le jeu combiné de la mutation et de la sélection au niveau moléculaire représente un des objectifs majeurs des sciences de l’évolution. L’acquisition massive de séquences génétiques au cours des dernières années a fourni un matériel abondant pour de telles analyses empiriques. Les modèles à codons sont de plus en plus utilisés en vue de fournir une description réaliste des processus de substitution des séquences codant pour les protéines. Parmi eux, les modèles mécanistes paramétrisent de façon séparée les effets mutationnels et sélectifs qui se combinent au sein du processus substitutionnel. Ces approches mécanistes caractérisent les effets sélectifs en s’appuyant sur un modèle explicite du paysage de fitness auquel la séquence protéique est soumise. Toutefois, jusqu’à présent, le paysage de fitness a toujours été considéré comme constant, alors qu’il existe des situations empiriques pour lesquelles le paysage de fitness subit en réalité des fluctuations écologiques au cours du temps. Lorsqu’une information empirique est par ailleurs disponible, concernant des différences systématiques de pression de sélection en fonction des fluctuations environnementales, il est alors possible de modéliser explicitement ces modulations du paysage de fitness. Nous avons développé un modèle à codons mécaniste, dont le but est de détecter ces effets sélectifs différentiels dépendant des conditions environnementales. Ce modèle a été implémenté dans un cadre d’inférence bayésienne, et a tout d’abord été appliqué au cas de l’évolution du VIH. Le VIH évolue sous la pression du système immunitaire de son hôte humain. Notre modèle de sélection différentielle (DS) décrit les mécanismes détaillés de l’évolution du VIH sous les contraintes induites par le fond génétique de l’hôte (par exemple, le HLA). De ce fait, il permet de trouver des associations entre adaptations du virus et profil HLA des hôtes. À long terme, notre approche permettra une meilleure compréhension du phénomène d’échappement du virus à la surveillance immunitaire de l’hôte, ce qui fournira alors des informations utiles en vue de l’élaboration d’un vaccin efficace contre le SIDA. Nous avons également appliqué notre modèle au gène de la Rubisco, une enzyme responsable d’une étape majeure de la photosynthèse. L’évolution de la Rubisco semble montrer des différences systématiques entre plantes dites C3 et C4, différences liées à des changements environnementaux. En utilisant le modèle DS, nous avons mis en évidence des effets systématiques d’adaptation convergente au niveau moléculaire, chez les espèces C4, par rapport aux espèces C3. Finalement, nous avons contrasté les résultats obtenus avec le modèle DS sur cet exemple avec ceux fournis par les modèles à codons classiques, basés sur l’estimation du dN/dS. Cette analyse comparée nous permet d’illustrer une différence conceptuelle fondamentale entre ces deux types de modèles à codons, concernant le type de régime sélectif que chaque type de modèle cherche à caractériser: à savoir, sélection directionnelle, contre adaptation continuelle.Modeling the interplay between mutation and selection at the molecular level is one of the primary goals in molecular evolution. Massive acquisition of genetic sequence data in recent years has provided a wealth of information for such empirically-driven studies. Codon-based models are increasingly used to give a realistic description of the substitution process in protein-coding genes. Among them, the mechanistic codon-based modeling approach distinctly parameterizes mutational and selective effects bearing on the overall substitution process. These mechanistic approaches characterize the selective pressure by relying on an explicit model of the amino acid fitness landscape over the sequence. Thus far, a constant fitness landscape has generally been assumed. Yet, there are some situations in which the fitness landscape experiences some environmental fluctuations through time. When the empirical knowledge about the systematic difference in selective pressures is available, regarding the fluctuating environment, it is possible to explicitly model condition-specific amino acid fitness modulations. In this thesis, we developed a codon-based model to capture these differential condition-specific selective effects on coding sequences. This model was implemented in a Bayesian framework and was first applied to HIV, which evolves under the selection pressure of the host immune system. Our Differential Selection (DS) model describes the detailed mechanisms of evolution of HIV under the constraints defined by host genetic backgrounds (e.g., Human Leukocyte Antigen). Therefore, it is possible to find associations between specific viral adaptations and specific HLA alleles of the hosts. Ultimately, our approach will enable us to understand better how the virus escapes from the host immune response, which will, in turn, provide a useful guideline for designing an efficient vaccine against AIDS. We also applied the DS model on Rubisco, an enzyme responsible for a major step in photosynthesis. The evolution of Rubisco has been shown to be different in C3 and C4 plants, as a consequence of differing environmental conditions. We used the DS model to reveal the consistent patterns of convergent adaptation in Rubisco in C4 plants, compared to C3 plants. Finally, we contrasted our results from DS model with those obtained under classical codon models based on the estimation of dN/dS. This comparative analysis allows us to illustrate a fundamental conceptual difference between these two types of codon models, which are meant to detect different selective regimes: directional selection versus ongoing adaptation

Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models

Background: Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine. Methods: We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope. Results: We applied our method to ID50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF) > 6), a subset of which were experimentally confirmed using site-directed mutagenesis. Conclusions: Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth

Patterns and rates of viral evolution in HIV-1 subtype B infected females and males.

Biological sex differences affect the course of HIV infection, with untreated women having lower viral loads compared to their male counterparts but, for a given viral load, women have a higher rate of progression to AIDS. However, the vast majority of data on viral evolution, a process that is clearly impacted by host immunity and could be impacted by sex differences, has been derived from men. We conducted an intensive analysis of HIV-1 gag and env-gp120 evolution taken over the first 6-11 years of infection from 8 Women's Interagency HIV Study (WIHS) participants who had not received combination antiretroviral therapy (ART). This was compared to similar data previously collected from men, with both groups infected with HIV-1 subtype B. Early virus populations in men and women were generally homogenous with no differences in diversity between sexes. No differences in ensuing nucleotide substitution rates were found between the female and male cohorts studied herein. As previously reported for men, time to peak diversity in env-gp120 in women was positively associated with time to CD4+ cell count below 200 (P = 0.017), and the number of predicted N-linked glycosylation sites generally increased over time, followed by a plateau or decline, with the majority of changes localized to the V1-V2 region. These findings strongly suggest that the sex differences in HIV-1 disease progression attributed to immune system composition and sensitivities are not revealed by, nor do they impact, global patterns of viral evolution, the latter of which proceeds similarly in women and men