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      Antigenic Diversity, Transmission Mechanisms, and the Evolution of Pathogens

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      PLoS Computational Biology
      Public Library of Science

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          Abstract

          Pathogens have evolved diverse strategies to maximize their transmission fitness. Here we investigate these strategies for directly transmitted pathogens using mathematical models of disease pathogenesis and transmission, modeling fitness as a function of within- and between-host pathogen dynamics. The within-host model includes realistic constraints on pathogen replication via resource depletion and cross-immunity between pathogen strains. We find three distinct types of infection emerge as maxima in the fitness landscape, each characterized by particular within-host dynamics, host population contact network structure, and transmission mode. These three infection types are associated with distinct non-overlapping ranges of levels of antigenic diversity, and well-defined patterns of within-host dynamics and between-host transmissibility. Fitness, quantified by the basic reproduction number, also falls within distinct ranges for each infection type. Every type is optimal for certain contact structures over a range of contact rates. Sexually transmitted infections and childhood diseases are identified as exemplar types for low and high contact rates, respectively. This work generates a plausible mechanistic hypothesis for the observed tradeoff between pathogen transmissibility and antigenic diversity, and shows how different classes of pathogens arise evolutionarily as fitness optima for different contact network structures and host contact rates.

          Author Summary

          Infectious diseases vary widely in how they affect those who get infected and how they are transmitted. As an example, the duration of a single infection can range from days to years, while transmission can occur via the respiratory route, water or sexual contact. Measles and HIV are contrasting examples—both are caused by RNA viruses, but one is a genetically diverse, lethal sexually transmitted infection (STI) while the other is a relatively mild respiratory childhood disease with low antigenic diversity. We investigate why the most transmissible respiratory diseases such as measles and rubella are antigenically static, meaning immunity is lifelong, while other diseases—such as influenza, or the sexually transmitted diseases—seem to trade transmissibility for the ability to generate multiple diverse strains so as to evade host immunity. We use mathematical models of disease progression and evolution within the infected host coupled with models of transmission between hosts to explore how transmission modes, host contact rates and network structure determine antigenic diversity, infectiousness and duration of infection. In doing so, we classify infections into three types—measles-like (high transmissibility, but antigenically static), flu-like (lower transmissibility, but more antigenically diverse), and STI-like (very antigenically diverse, long lived infection, but low overall transmissibility).

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          Most cited references17

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          Ecological and immunological determinants of influenza evolution.

          In pandemic and epidemic forms, influenza causes substantial, sometimes catastrophic, morbidity and mortality. Intense selection from the host immune system drives antigenic change in influenza A and B, resulting in continuous replacement of circulating strains with new variants able to re-infect hosts immune to earlier types. This 'antigenic drift' often requires a new vaccine to be formulated before each annual epidemic. However, given the high transmissibility and mutation rate of influenza, the constancy of genetic diversity within lineages over time is paradoxical. Another enigma is the replacement of existing strains during a global pandemic caused by 'antigenic shift'--the introduction of a new avian influenza A subtype into the human population. Here we explore ecological and immunological factors underlying these patterns using a mathematical model capturing both realistic epidemiological dynamics and viral evolution at the sequence level. By matching model output to phylogenetic patterns seen in sequence data collected through global surveillance, we find that short-lived strain-transcending immunity is essential to restrict viral diversity in the host population and thus to explain key aspects of drift and shift dynamics.
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            Mutation rates among RNA viruses.

            The rate of spontaneous mutation is a key parameter in modeling the genetic structure and evolution of populations. The impact of the accumulated load of mutations and the consequences of increasing the mutation rate are important in assessing the genetic health of populations. Mutation frequencies are among the more directly measurable population parameters, although the information needed to convert them into mutation rates is often lacking. A previous analysis of mutation rates in RNA viruses (specifically in riboviruses rather than retroviruses) was constrained by the quality and quantity of available measurements and by the lack of a specific theoretical framework for converting mutation frequencies into mutation rates in this group of organisms. Here, we describe a simple relation between ribovirus mutation frequencies and mutation rates, apply it to the best (albeit far from satisfactory) available data, and observe a central value for the mutation rate per genome per replication of micro(g) approximately 0.76. (The rate per round of cell infection is twice this value or about 1.5.) This value is so large, and ribovirus genomes are so informationally dense, that even a modest increase extinguishes the population.
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              Theory of lethal mutagenesis for viruses.

              Mutation is the basis of adaptation. Yet, most mutations are detrimental, and elevating mutation rates will impair a population's fitness in the short term. The latter realization has led to the concept of lethal mutagenesis for curing viral infections, and work with drugs such as ribavirin has supported this perspective. As yet, there is no formal theory of lethal mutagenesis, although reference is commonly made to Eigen's error catastrophe theory. Here, we propose a theory of lethal mutagenesis. With an obvious parallel to the epidemiological threshold for eradication of a disease, a sufficient condition for lethal mutagenesis is that each viral genotype produces, on average, less than one progeny virus that goes on to infect a new cell. The extinction threshold involves an evolutionary component based on the mutation rate, but it also includes an ecological component, so the threshold cannot be calculated from the mutation rate alone. The genetic evolution of a large population undergoing mutagenesis is independent of whether the population is declining or stable, so there is no runaway accumulation of mutations or genetic signature for lethal mutagenesis that distinguishes it from a level of mutagenesis under which the population is maintained. To detect lethal mutagenesis, accurate measurements of the genome-wide mutation rate and the number of progeny per infected cell that go on to infect new cells are needed. We discuss three methods for estimating the former. Estimating the latter is more challenging, but broad limits to this estimate may be feasible.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                October 2009
                October 2009
                16 October 2009
                : 5
                : 10
                : e1000536
                Affiliations
                [1 ]MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
                [2 ]Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
                Emory University, United States of America
                Author notes

                Conceived and designed the experiments: AL NMF. Performed the experiments: AL. Analyzed the data: AL. Wrote the paper: AL NMF.

                Article
                09-PLCB-RA-0069R3
                10.1371/journal.pcbi.1000536
                2759524
                19847288
                d09fe346-3a1f-4847-93ab-210bf632bbd9
                Lange, Ferguson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 20 January 2009
                : 17 September 2009
                Page count
                Pages: 12
                Categories
                Research Article
                Computational Biology/Evolutionary Modeling
                Infectious Diseases
                Public Health and Epidemiology/Epidemiology
                Public Health and Epidemiology/Infectious Diseases

                Quantitative & Systems biology
                Quantitative & Systems biology

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