Strength and tempo of selection revealed in viral gene genealogies

Abstract Background RNA viruses evolve extremely quickly, allowing them to rapidly adapt to new environmental conditions. Viral pathogens, such as influenza virus, exploit this capacity for evolutionary change to persist within the human population despite substantial immune pressure. Understanding the process of adaptation in these viral systems is essential to our efforts to combat infectious disease. Results Through analysis of simulated populations and sequence data from influenza A (H3N2) and measles virus, we show how phylogenetic and population genetic techniques can be used to assess the strength and temporal pattern of adaptive evolution. The action of natural selection affects the shape of the genealogical tree connecting members of an evolving population, causing deviations from the neutral expectation. The magnitude and distribution of these deviations lends insight into the historical pattern of evolution and adaptation in the viral population. We quantify the degree of ongoing adaptation in influenza and measles virus through comparison of census population size and effective population size inferred from genealogical patterns, finding a 60-fold greater deviation in influenza than in measles. We also examine the tempo of adaptation in influenza, finding evidence for both continuous and episodic change. Conclusions Our results have important consequences for understanding the epidemiological and evolutionary dynamics of the influenza virus. Additionally, these general techniques may prove useful to assess the strength and pattern of adaptive evolution in a variety of evolving systems. They are especially powerful when assessing selection in fast-evolving populations, where temporal patterns become highly visible.http://deepblue.lib.umich.edu/bitstream/2027.42/112626/1/12862_2011_Article_1838.pd

Strength and tempo of selection revealed in viral gene genealogies

BACKGROUND: RNA viruses evolve extremely quickly, allowing them to rapidly adapt to new environmental conditions. Viral pathogens, such as influenza virus, exploit this capacity for evolutionary change to persist within the human population despite substantial immune pressure. Understanding the process of adaptation in these viral systems is essential to our efforts to combat infectious disease. RESULTS: Through analysis of simulated populations and sequence data from influenza A (H3N2) and measles virus, we show how phylogenetic and population genetic techniques can be used to assess the strength and temporal pattern of adaptive evolution. The action of natural selection affects the shape of the genealogical tree connecting members of an evolving population, causing deviations from the neutral expectation. The magnitude and distribution of these deviations lends insight into the historical pattern of evolution and adaptation in the viral population. We quantify the degree of ongoing adaptation in influenza and measles virus through comparison of census population size and effective population size inferred from genealogical patterns, finding a 60-fold greater deviation in influenza than in measles. We also examine the tempo of adaptation in influenza, finding evidence for both continuous and episodic change. CONCLUSIONS: Our results have important consequences for understanding the epidemiological and evolutionary dynamics of the influenza virus. Additionally, these general techniques may prove useful to assess the strength and pattern of adaptive evolution in a variety of evolving systems. They are especially powerful when assessing selection in fast-evolving populations, where temporal patterns become highly visible

Ecological and Evolutionary Dynamics of Influenza Viruses.

Host-pathogen interactions, especially those involving RNA viruses and bacteria, are often characterized by a convergence of ecological and evolutionary time scales. This work explores how such convergence affects the diversity of a fast-evolving RNA virus, influenza, in different host populations. The first study evaluates molecular evidence for a theory of H3N2 dynamics in humans. There is support for episodically strong, continuous positive selection on the hemagglutinin protein, and previously described punctuated changes in antigenicity are not driven by the addition of glycosylation sites. The neuraminidase, nucleoprotein, and matrix 2 proteins also show evidence of positive selection. The second study analyzes time series of serologically confirmed cases of H3N2, H1N1, and influenza B in patients in present-day St. Petersburg, Russia, from 1969 to 1991 to determine whether there is cross-immunity between heterologous strains. Results suggest a role for cross-immunity, but further investigation is necessary. Differences in intrinsic growth rates and rates of antigenic evolution might explain age-related patterns in incidence by virus type and subtype. The third study investigates the effects of heterogeneity in hosts’ immune responses on the outcome of strain competition. When immunodominance is skewed toward a single epitope, coexistence inevitably results. When multiple epitopes can be immunodominant, coexistence, limit cycling, chaotic dynamics, and competitive exclusion can occur. Increasing the diversity and breadth of host responses increases the range of cyclic, chaotic, and exclusive dynamics. The last study considers how host ecology affects the long term evolution of influenza’s host range, assuming a tradeoff in the virus’s preference for certain forms of host sialic acid receptor. A common outcome is the coexistence of specialists, and this outcome is more sensitive to interspecific transmission rates and host population densities than the strength of the tradeoff. Finally, I map three areas of future inquiry: the ability of spatial dynamics and constant antigenic evolution alone to restrict influenza virus diversity, implications of antibody affinity versus neutralization ability for vaccine development, and long-term strategies to manage influenza virus evolution. These studies show that a phylodynamic perspective will be invaluable in developing better predictive models of influenza.Ph.D.Ecology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64670/1/cobey_1.pd

Pathogen Diversity and Hidden Regimes of Apparent Competition

Competition through cross-reacting host immune responses, a form of apparent competition, is a major driver of pathogen evolution and diversity. Most models of pathogens have focused on intraspecific interactions to explain observed patterns. Two recent experiments suggested that Haemophilus influenzae, a common nasopharyngeal colonizer of humans, might alter the immune environment in a way that favors otherwise less fit serotypes of another common pathogen, pneumococcus. Using a computational model, we demonstrate that H. influenzae, if it consistently raises the fitness of the less fit serotypes, can strongly promote pneumococcal diversity. However, the effects of H. influenzae are so sensitive to the prevalence of H. influenzae that this species is unlikely to be the main driver of serotype coexistence. Interactions that significantly affect diversity could furthermore be extremely difficult to detect through co-occurrence analysis alone. These results suggest that small differences in strains' adaptations to different immunological regimes, which are shaped by coinfections with other pathogens, can have dramatic effects on strain dynamics and patterns of phenotypic variation. Studies of microbial communities might therefore benefit from the use of varied approaches to infer the presence of indirect interactions

K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution

Viral factors in influenza pandemic risk assessment

The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk

COVID-19 and children

There has been substantial research on adult COVID-19 and how to treat it. But how do severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections afflict children? The COVID-19 pandemic has yielded many surprises, not least that children generally develop less severe disease than older adults, which is unusual for a respiratory disease. However, some children can develop serious complications from COVID-19, such as multisystem inflammatory syndrome in children (MIS-C) and Long Covid, even after mild or asymptomatic COVID-19. Why this occurs in some and not others is an important question. Moreover, when children do contract COVID-19, understanding their role in transmission, especially in schools and at home, is crucial to ensuring effective mitigation measures. Therefore, in addition to nonpharmaceutical interventions, such as improved ventilation, there is a strong case to vaccinate children so as to reduce possible long-term effects from infection and to decrease transmission. But questions remain about whether vaccination might skew immune responses to variants in the long term. As the experts discuss below, more is being learned about these important issues, but much more research is needed to understand the long-term effects of COVID-19 in children

Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method

Using weekly influenza surveillance data from the US CDC, Edward Goldstein and colleagues develop a statistical method to predict the sizes of epidemics caused by seasonal influenza strains. This method could inform decisions about the most appropriate vaccines or drugs needed early in the influenza season

Improving influenza vaccine virus selectionReport of a WHO informal consultation held at WHO headquarters, Geneva, Switzerland, 14–16 June 2010

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90035/1/j.1750-2659.2011.00277.x.pd