Selective Forces That Shape the VLS Antigenic Variation System in Borrelia Burgdorferi

Evolutionary success of microbial pathogens requires survival within hosts, despite the rapidly changing and lethal immune response. Pathogens such as the Lyme disease bacteria Borrelia burgdorferi have evolved antigenic variation systems that are necessary for survival within the adverse immune environment. Although antigenic variation systems are essential to both microbial pathogenesis and microbial evolution, it is largely unclear what selective forces have influenced the evolution of antigenic variation systems. In this thesis, we investigate evolution of the vls antigenic variation system in B. burgdorferi by asking two major questions: First, what traits relevant to the vls antigenic variation system have natural selection acted on? Second, how did the selective forces shape the genetic sequences of the vls antigenic variation systems? We characterize sources of natural selection using mathematical modeling, computational simulation and mutagenesis experiments. Our findings show that natural selection has promoted diversity among VlsE variants on both sequence and structure by organizing the variable sites in the vls unexpressed cassettes. We also show that the level of diversity among the VlsE variants may strongly influence the within-host dynamics of Bb, an important fitness component of B. burgdorferi. Finally, our results indicate that diversity among VlsE variants might be constrained by purifying or stabilizing selections on translational efficiency and structural stability of the VlsE variants

The Impact of Receptor Binding Avidity and Immune History on the Antigenic Determination of Influenza A Viruses

Most humans are repeatedly infected with new strains of influenza throughout their lifetime even though protective neutralizing antibodies against the viral hemagglutinin (HA) are generated after both natural infection and vaccination. This observed lack of protection against variant strains is largely attributed to a process termed `antigenic drift\u27, where accumulating mutations in HA quickly abrogate recognition by antibodies elicited by earlier strains. Consequently, current influenza vaccines must be updated frequently in an attempt to match the antigenic profiles of vaccine strains to those of circulating strains. However, the existing process of antigenic determination is imperfect: it fails to consider the effects of receptor binding avidity in the interpretation of hemagglutination inhibition (HAI) assays or the effects of pre-exposure history on how a novel virus is viewed antigenically by an altered immune system. Here, we designed a series of experiments to address these issues. First, we computationally modeled how variation in receptor binding avidity could affect the antigenic characterization of historic H3N2 strains and experimentally demonstrated that single point mutations in HA can skew HAI titers without actually affecting antibody binding. Additionally, using the same H3N2 system, we showed that a single amino acid mutation can significantly alter the immunodominance of the anti-HA antibody response. We then completed a series of studies to determine how immune history influences the specificity of antibody repertoires. In examining patient serology, we found that the specificity of the human antibody response against the 2009 pandemic H1N1 virus (pH1N1) was highly correlated with pre-exposure history to different seasonal H1N1 (sH1N1) strains during childhood. Using a ferret model, we demonstrate that the anti-pH1N1 antibody response can be shifted to highly conserved epitopes on HA when the animals were primed with sH1N1s that are otherwise antigenically distinct. Collectively, our studies demonstrate that accounting for receptor binding avidity and factors that alter antibody repertoires will improve influenza vaccination strategies in the future

Quantitative Immunology for Physicists

The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field.Comment: 78 page revie

Selected Topics on Mathematical Models in Immunology and Medicine

In 1988 the new IIASA project on System Immunology was inaugurated. The new activity focuses theoretical and experimental research in immunology and system mathematics to experimental planning and prediction for relevant disease applications and systematic understanding of immunology. IIASA analysis and simulation should lead to an effective plan of successive experiments to identify and to quantify particularly sensitive parameters in this most complex system of information processing, decision and control. The integration of such diverse disciplines is extremely difficult but some basis has already been established. For several years IIASA has sponsored international workshops dealing with dynamical systems and their applications to biology. These include: (1) The conference on "Dynamics of Macrosystems"; (2) The Working Conference on "Theoretical Immunology"; (3) The Workshop on "Selected Topics in Biomathematics"; The present volume contains the proceedings of the latest Workshop "Mathematical Modelling in Immunology and Medicine", Part 1 deals with the mathematical models of autoimmune, infectious diseases and AIDS. The models are studied with the intent to establish a basis for more effective treatment. In Part 2, questions of computer simulation and data analysis in cancer research are analyzed. Part 3 is devoted to the models for antibody binding, immunoassay dynamics and immunogenetic systems. The problems of system analysis and medical decision making are discussed in Part 4

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun