scholarly journals Antigenic waves of virus-immune co-evolution

2021 ◽  
Author(s):  
Jacopo Marchi ◽  
Michael Lässig ◽  
Aleksandra M. Walczak ◽  
Thierry Mora
Keyword(s):  
Mathematical Theory ◽  
Seasonal Influenza ◽  
Evolutionary Dynamics ◽  
Cross Reactivity ◽  
Host Population ◽  
Viral Population ◽  
Immune Systems ◽  
Persistence Time ◽  
Immune Pressure ◽  
Finite Dimensional

The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even further away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of co-evolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune co-evolution generates a new emergent timescale, the persistence time of the wave’s direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts on the predictability of the evolutionary dynamics. Our results provide a rigorous and tractable framework to describe pathogen-host co-evolution.

scholarly journals Antigenic waves of virus–immune coevolution

2021 ◽  
Vol 118 (27) ◽  
pp. e2103398118
Author(s):  
Jacopo Marchi ◽  
Michael Lässig ◽  
Aleksandra M. Walczak ◽  
Thierry Mora
Keyword(s):  
Mathematical Theory ◽  
Seasonal Influenza ◽  
Evolutionary Dynamics ◽  
Cross Reactivity ◽  
Host Population ◽  
Viral Population ◽  
Immune Systems ◽  
Persistence Time ◽  
Immune Pressure ◽  
Finite Dimensional

The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even farther away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral–immune coevolution generates an emergent timescale, the persistence time of the wave’s direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen–host coevolution.

scholarly journals Explaining the geographical origins of seasonal influenza A (H3N2)

2016 ◽  
Vol 283 (1838) ◽  
pp. 20161312 ◽  
Author(s):  
Frank Wen ◽  
Trevor Bedford ◽  
Sarah Cobey
Keyword(s):  
Influenza A ◽  
Seasonal Influenza ◽  
Evolutionary Dynamics ◽  
Spatial Dynamics ◽  
Basic Reproductive Number ◽  
Viral Population ◽  
Reproductive Number ◽  
Antigenic Evolution ◽  
Migratory Patterns ◽  
Seasonal Population

Most antigenically novel and evolutionarily successful strains of seasonal influenza A (H3N2) originate in East, South and Southeast Asia. To understand this pattern, we simulated the ecological and evolutionary dynamics of influenza in a host metapopulation representing the temperate north, tropics and temperate south. Although seasonality and air traffic are frequently used to explain global migratory patterns of influenza, we find that other factors may have a comparable or greater impact. Notably, a region's basic reproductive number ( R 0 ) strongly affects the antigenic evolution of its viral population and the probability that its strains will spread and fix globally: a 17–28% higher R 0 in one region can explain the observed patterns. Seasonality, in contrast, increases the probability that a tropical (less seasonal) population will export evolutionarily successful strains but alone does not predict that these strains will be antigenically advanced. The relative sizes of different host populations, their birth and death rates, and the region in which H3N2 first appears affect influenza's phylogeography in different but relatively minor ways. These results suggest general principles that dictate the spatial dynamics of antigenically evolving pathogens and offer predictions for how changes in human ecology might affect influenza evolution.

scholarly journals Evolutionary responses to environmental change: trophic interactions affect adaptation and persistence

2015 ◽  
Vol 282 (1805) ◽  
pp. 20141351 ◽  
Author(s):  
Jarad P. Mellard ◽  
Claire de Mazancourt ◽  
Michel Loreau
Keyword(s):  
Climate Change ◽  
Head Start ◽  
Trophic Interactions ◽  
Evolutionary Dynamics ◽  
Plant Interaction ◽  
Persistence Time ◽  
Plant Persistence ◽  
Plant Herbivore Interaction ◽  
Plant Herbivore ◽  
Herbivore Interaction

According to recent reviews, the question of how trophic interactions may affect evolutionary responses to climate change remains unanswered. In this modelling study, we explore the evolutionary dynamics of thermal and plant–herbivore interaction traits in a warming environment. We find the herbivore usually reduces adaptation speed and persistence time of the plant by reducing biomass. However, if the plant interaction trait and thermal trait are correlated, herbivores can create different coevolutionary attractors. One attractor has a warmer plant thermal optimum, and the other a colder one compared with the environment. A warmer plant thermal strategy is given a head start under warming, the only case where herbivores can increase plant persistence under warming. Persistence time of the plant under warming is maximal at small or large thermal niche width. This study shows that considering trophic interactions is necessary and feasible for understanding how ecosystems respond to climate change.

scholarly journals Synthesizing within-host and population-level selective pressures on viral populations: the impact of adaptive immunity on viral immune escape

2010 ◽  
Vol 7 (50) ◽  
pp. 1311-1318 ◽  
Author(s):  
Igor Volkov ◽  
Kim M. Pepin ◽  
James O. Lloyd-Smith ◽  
Jayanth R. Banavar ◽  
Bryan T. Grenfell
Keyword(s):  
Evolutionary Dynamics ◽  
Immune Escape ◽  
Adaptive Immune Response ◽  
Population Level ◽  
Analytical Framework ◽  
Host Population ◽  
Host Immunity ◽  
Host Cells ◽  
Susceptible Host ◽  
The Impact

The evolution of viruses to escape prevailing host immunity involves selection at multiple integrative scales, from within-host viral and immune kinetics to the host population level. In order to understand how viral immune escape occurs, we develop an analytical framework that links the dynamical nature of immunity and viral variation across these scales. Our epidemiological model incorporates within-host viral evolutionary dynamics for a virus that causes acute infections (e.g. influenza and norovirus) with changes in host immunity in response to genetic changes in the virus population. We use a deterministic description of the within-host replication dynamics of the virus, the pool of susceptible host cells and the host adaptive immune response. We find that viral immune escape is most effective at intermediate values of immune strength. At very low levels of immunity, selection is too weak to drive immune escape in recovered hosts, while very high levels of immunity impose such strong selection that viral subpopulations go extinct before acquiring enough genetic diversity to escape host immunity. This result echoes the predictions of simpler models, but our formulation allows us to dissect the combination of within-host and transmission-level processes that drive immune escape.

scholarly journals The Evolutionary Dynamics of the Lion Panthera leo Revealed by Host and Viral Population Genomics

PLoS Genetics ◽  
2008 ◽  
Vol 4 (11) ◽  
pp. e1000251 ◽  
Author(s):  
Agostinho Antunes ◽  
Jennifer L. Troyer ◽  
Melody E. Roelke ◽  
Jill Pecon-Slattery ◽  
Craig Packer ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Population Genomics ◽  
Viral Population ◽  
Panthera Leo

scholarly journals Immunogenicity and Cross-Reactivity of 2009–2010 Inactivated Seasonal Influenza Vaccine in US Adults and Elderly

PLoS ONE ◽  
2011 ◽  
Vol 6 (1) ◽  
pp. e16650 ◽  
Author(s):  
Hang Xie ◽  
Xianghong Jing ◽  
Xing Li ◽  
Zhengshi Lin ◽  
Ewan Plant ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Seasonal Influenza ◽  
Cross Reactivity ◽  
Seasonal Influenza Vaccine

scholarly journals Extensive T cell cross-reactivity between diverse seasonal influenza strains in the ferret model

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Adrian J. Reber ◽  
Nedzad Music ◽  
Jin Hyang Kim ◽  
Shane Gansebom ◽  
Jufu Chen ◽  
...  
Keyword(s):  
T Cell ◽  
Seasonal Influenza ◽  
Cross Reactivity

scholarly journals Molecular Phylogenesis and Spatiotemporal Spread of SARS-CoV-2 in Southeast Asia

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingjian Zhu ◽  
Jian Shen ◽  
Qianli Zeng ◽  
Joanna Weihui Tan ◽  
Jirapat Kleepbua ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Evolutionary Dynamics ◽  
Phylogenetic Analyses ◽  
Population Expansion ◽  
Principal Component ◽  
Viral Population ◽  
Recent Common Ancestor ◽  
Tropical Region ◽  
Effective Population ◽  
Most Recent Common Ancestor

Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has posed an unprecedented challenge to public health in Southeast Asia, a tropical region with limited resources. This study aimed to investigate the evolutionary dynamics and spatiotemporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the region.Materials and Methods: A total of 1491 complete SARS-CoV-2 genome sequences from 10 Southeast Asian countries were downloaded from the Global Initiative on Sharing Avian Influenza Data (GISAID) database on November 17, 2020. The evolutionary relationships were assessed using maximum likelihood (ML) and time-scaled Bayesian phylogenetic analyses, and the phylogenetic clustering was tested using principal component analysis (PCA). The spatial patterns of SARS-CoV-2 spread within Southeast Asia were inferred using the Bayesian stochastic search variable selection (BSSVS) model. The effective population size (Ne) trajectory was inferred using the Bayesian Skygrid model.Results: Four major clades (including one potentially endemic) were identified based on the maximum clade credibility (MCC) tree. Similar clustering was yielded by PCA; the first three PCs explained 46.9% of the total genomic variations among the samples. The time to the most recent common ancestor (tMRCA) and the evolutionary rate of SARS-CoV-2 circulating in Southeast Asia were estimated to be November 28, 2019 (September 7, 2019 to January 4, 2020) and 1.446 × 10−3 (1.292 × 10−3 to 1.613 × 10−3) substitutions per site per year, respectively. Singapore and Thailand were the two most probable root positions, with posterior probabilities of 0.549 and 0.413, respectively. There were high-support transmission links (Bayes factors exceeding 1,000) in Singapore, Malaysia, and Indonesia; Malaysia involved the highest number (7) of inferred transmission links within the region. A twice-accelerated viral population expansion, followed by a temporary setback, was inferred during the early stages of the pandemic in Southeast Asia.Conclusions: With available genomic data, we illustrate the phylogeography and phylodynamics of SARS-CoV-2 circulating in Southeast Asia. Continuous genomic surveillance and enhanced strategic collaboration should be listed as priorities to curb the pandemic, especially for regional communities dominated by developing countries.

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