Predicting the Evolution of Human Influenza A

Bush, R. Mitchell; Bender, Catherine A.; Subbarao, Kanta; Cox, Nancy J.; Fitch, Walter M.

doi:10.1126/science.286.5446.1921

Cited by 451 publications

(456 citation statements)

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“…For example, research into the influenza virus has been able to identify codons in the hemagglutinin HA1 gene that predict the evolutionary success of a strain by using genetic data without detailed epidemiological or immunological information (14,15). Broadly, the question addressed in this article of which strains will emerge and the information used (genetic data alone) are similar to those of the influenza studies.…”

Section: Discussionmentioning

confidence: 82%

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Detecting emerging strains of tuberculosis by using spoligotypes

Tanaka

Francis

2006

Proc. Natl. Acad. Sci. U.S.A.

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The W-Beijing strain of tuberculosis has been identified in many molecular epidemiological studies as being particularly prevalent. This identification has been made possible through the development of a number of genotyping technologies including spoligotyping. Highly prevalent genotypes associated with outbreaks, such as the W-Beijing strain, are implicitly regarded as fast spreading. Here we present a quantitative method to identify ''emerging'' strains, those that are spreading faster than the background rate inferred from spoligotype data. The approach uses information about the mutation process specific to spoligotypes, combined with a model of both transmission and mutation. The core principle is that if two comparable strains have the same number of isolates, then the strain with fewer inferred mutation events must have spread faster if the mutation process is common. Applying this method to four different data sets, we find not only the W-Beijing strain, but also a number of other strains, to be emerging in this sense. Importantly, the strains that are identified as emerging are not simply those with the largest number of cases. The use of this method should facilitate the targeting of individual genotypes in intervention programs. mutation ͉ transmission rate ͉ Beijing strain ͉ infectious disease ͉ molecular marker A broad goal of the development of effective tools for genotyping the bacteria or viruses causing infectious diseases has been the classification of isolates into distinct types. In the case of Mycobacterium tuberculosis, one of the outcomes of this development has been the identification of a particularly aggressive strain known as the Beijing or W-strain (1, 2). These genotypic data, however, can also be used to verify chains of transmission and to make inferences about population level transmission patterns. For example, the occurrence of large clusters of identical genotypes in a sample is thought to be indicative of recent tuberculosis transmission (3, 4), and in this context, the size of a genotype cluster carries some information about the rate of transmission associated with the genotype. One use of such information is to study possible risk factors for infection, such as HIV status, by correlating them with the extent to which these data form clusters (3, 4).An unusually large cluster in a sample may indicate a rapidly spreading strain; however, it may simply indicate the age of the genotype (5). For instance, strains that have been present in a population for a long time may have accumulated a large number of cases despite having a slow transmission rate. One way to access information about the age of a strain is to consider the number of mutation events identified in the history of that strain. That is, an old genotype has had ample time to generate many mutants, which should be manifested in the sample. We observe that on average, if two strains have the same cluster size and the same mutation rate, then the one with more observed mutation events is older, and correspondingly, be...

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Section: Discussionmentioning

confidence: 82%

“…They also differ in the process and rate of mutation. Instead of predicting the evolution of the pathogen, as in studies of influenza (14,15), our method identifies tuberculosis strains that are currently spreading significantly faster than the others.…”

Section: Discussionmentioning

confidence: 99%

Detecting emerging strains of tuberculosis by using spoligotypes

Tanaka

Francis

2006

Proc. Natl. Acad. Sci. U.S.A.

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“…In particular, a precise determination of which epitopes are dominant in the proposed vaccine strain and an experimental measure of p epitope and of cross activity between the proposed vaccine strain and the circulating strains would be highly productive. In absence of this determination, we suggest to estimate the dominant epitope as that which shows the most antigenic drift [22][23][24][25], as we do in the present work. From either epitope sequence drift or cross activity, Figure 1 can be used to estimate the degree of the immune response, which is a non-linear and non-monotonic function of the measured data.…”

Section: Discussionmentioning

confidence: 99%

“…The hemagglutinin H3 protein has five epitope regions (A, B, C, D, E) that have been identified and sequenced [19], among which A and B are usually dominant [20,21]. There exists experimental and clinical evidence that epitope regions mutate much faster than other regions in the viral proteins, presumably due to antibody selective pressure [22][23][24], with the dominant epitopes mutating most rapidly [25]. Therefore, in the absence of more detailed information, we take an observed high mutation rate (i.e., a high p epitope value) in a given epitope to correlate with dominance.…”

Section: Methodsmentioning

confidence: 99%

Epitope analysis for influenza vaccine design

Muñoz

Deem

2005

Vaccine

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Until now, design of the annual influenza vaccine has relied on phylogenetic or whole-sequence comparisons of the viral coat proteins hemagglutinin and neuraminidase, with vaccine effectiveness assumed to correlate monotonically to the vaccine-influenza sequence difference. We use a theory from statistical mechanics to quantify the non-monotonic immune response that results from antigenic drift in the epitopes of the hemagglutinin and neuraminidase proteins. The results explain the ineffectiveness of the 2003-2004 influenza vaccine in the United States and provide an accurate measure by which to optimize the effectiveness of future annual influenza vaccines.

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“…How can we best predict which variants of influenza virus will be most prevalent during the next flu season, given that stocks of the flu vaccine must be prepared and distributed to medical workers long before the flu season actually strikes? Mathematical and genomic approaches developed for evolutionary phylogenetics have proven to be highly useful for this vital medical task (Bush et al 1999;Koelle et al 2006;Coburn et al 2009). Essentially, one can look retrospectively to determine which virus variants have been recent evolutionary successes and then apply an algorithm to deduce which of these viruses will be prevalent in the near future to warrant choosing them as vaccine targets.…”

Section: Evolvability Studied Using Mathematical Theory and Biologicamentioning

confidence: 99%

Predicting Virus Evolution: The Relationship between Genetic Robustness and Evolvability of Thermotolerance

Ogbunugafor

McBride²,

Turner³

2009

Cold Spring Harbor Symposia on Quantitative Biology

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As a naturalist, Charles Darwin was understandably fascinated by the species diversity visible in the natural world. However, he understood little of the invisible realm of genes-the units of inheritance that are passed across generations and contribute to the phenotypic variation evident in biological populations. Yet Darwin showed remarkable insight when formulating his ideas on evolution via natural selection, as a process whereby the environment determines which variants in a population are favored to contribute their traits to succeeding generations, leading to microevolutionary changes over short timescales. Furthermore, Darwin perceptively understood that natural selection is a process that can also primarily account for nature's vast biodiversity-resulting from macroevolution occurring over long timescales-which Darwin described as "endless forms most wonderful" (Darwin 1859).Many modern-day evolutionary biologists are concerned with elucidating patterns of extant diversity, seeking to understand how descent with modification from common ancestry accounts for Earth's myriad forms that have evolved since life arose billions of years ago. In contrast, other evolutionary biologists are largely concerned with the processes of evolutionary change that underlie these patterns, examining how mechanisms such as selection and drift can lead to altered phenotypes and genotypes across relatively few generations. Both camps of evolutionary biologists have increasingly powerful and sophisticated approaches for studying patterns and processes of evolution, allowing more accurate glimpses into the mysterious "black boxes" of past and ongoing evolutionary events (see, e.g., Lenski et al. 2003;Thornton et al. 2003;Vrba and DeGusta 2004;Weinreich et al. 2006).Arguably, evolutionary biology mostly concerns scrutiny of past and present events, rather than forthcoming events, i.e., evolutionary biologists tend to resist the temptation to study and predict how evolution will play out in the future. Such predictive efforts are necessarily more difficult than retrospective analyses, because foretelling the future is inherently less precise than resolving the past. For this reason, relatively less energy has been placed into developing evolutionary biology into a truly predictive science. Of course, like other scientists, evolutionary biologists often make explicit predictions, referred to as hypothesis testing. But there is a distinct difference between formulating a hypothesis that concerns events that have already occurred and formulating a hypothesis that predicts events yet to occur. EVOLVABILITY STUDIED USING MATHEMATICAL THEORY AND BIOLOGICAL EXPERIMENTSMathematical studies in evolutionary biology are sometimes future-minded. For example, John Maynard Smith (1982) famously popularized evolutionary game theory, a discipline that often seeks to mathematically determine whether a population can evolve an unbeatable strategy when dealing with competition for finite resources, such as food, mates, or nesting sites. If this evo...

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Predicting the Evolution of Human Influenza A

Cited by 451 publications

References 8 publications

Detecting emerging strains of tuberculosis by using spoligotypes

Detecting emerging strains of tuberculosis by using spoligotypes

Epitope analysis for influenza vaccine design

Predicting Virus Evolution: The Relationship between Genetic Robustness and Evolvability of Thermotolerance

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