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      Divergent evolutionary trajectories of influenza B viruses underlie their contemporaneous epidemic activity

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          Significance

          Two influenza B viruses (Victoria and Yamagata) cocirculate in humans and contribute to the estimated 290,000–650,000 annual influenza-attributed deaths. Here, we analysed influenza B genomic data to understand the causes of a recent surge in human influenza B infections. We found that evolution is acting differently on Yamagata and Victoria viruses and that this has led to the cocirculation of a diverse group of influenza B viruses. If this phenomenon continues, we could potentially witness the emergence of 3 or more distinct influenza B viruses that could require their own vaccine component, thereby complicating influenza vaccine formulation and highlighting the urgency of developing universal influenza vaccines.

          Abstract

          Influenza B viruses have circulated in humans for over 80 y, causing a significant disease burden. Two antigenically distinct lineages (“B/Victoria/2/87-like” and “B/Yamagata/16/88-like,” termed Victoria and Yamagata) emerged in the 1970s and have cocirculated since 2001. Since 2015 both lineages have shown unusually high levels of epidemic activity, the reasons for which are unclear. By analyzing over 12,000 influenza B virus genomes, we describe the processes enabling the long-term success and recent resurgence of epidemics due to influenza B virus. We show that following prolonged diversification, both lineages underwent selective sweeps across the genome and have subsequently taken alternate evolutionary trajectories to exhibit epidemic dominance, with no reassortment between lineages. Hemagglutinin deletion variants emerged concomitantly in multiple Victoria virus clades and persisted through epistatic mutations and interclade reassortment—a phenomenon previously only observed in the 1970s when Victoria and Yamagata lineages emerged. For Yamagata viruses, antigenic drift of neuraminidase was a major driver of epidemic activity, indicating that neuraminidase-based vaccines and cross-reactivity assays should be employed to monitor and develop robust protection against influenza B morbidity and mortality. Overall, we show that long-term diversification and infrequent selective sweeps, coupled with the reemergence of hemagglutinin deletion variants and antigenic drift of neuraminidase, are factors that contributed to successful circulation of diverse influenza B clades. Further divergence of hemagglutinin variants with poor cross-reactivity could potentially lead to circulation of 3 or more distinct influenza B viruses, further complicating influenza vaccine formulation and highlighting the urgent need for universal influenza vaccines.

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          Most cited references31

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          Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology.

          Datamonkey is a popular web-based suite of phylogenetic analysis tools for use in evolutionary biology. Since the original release in 2005, we have expanded the analysis options to include recently developed algorithmic methods for recombination detection, evolutionary fingerprinting of genes, codon model selection, co-evolution between sites, identification of sites, which rapidly escape host-immune pressure and HIV-1 subtype assignment. The traditional selection tools have also been augmented to include recent developments in the field. Here, we summarize the analyses options currently available on Datamonkey, and provide guidelines for their use in evolutionary biology. Availability and documentation: http://www.datamonkey.org.
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            Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

            Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.
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              Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences.

              Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                7 January 2020
                16 December 2019
                16 December 2019
                : 117
                : 1
                : 619-628
                Affiliations
                [1] aProgramme in Emerging Infectious Diseases, Duke-National University of Singapore (NUS) Medical School , Singapore 169857;
                [2] bDepartment of Clinical Virology, Christian Medical College , Vellore, India 632004;
                [3] cNational Public Health Laboratory, Ministry of Health , Singapore 308442;
                [4] dDepartment of Molecular Pathology, Singapore General Hospital , Singapore 169608;
                [5] eDepartment of Laboratory Medicine, National University Hospital , Singapore 117597;
                [6] fDepartment of Microbiology, Biomedicine Discovery Institute, Monash University , VIC 3800, Australia;
                [7] gWorld Health Organization Collaborating Centre in Influenza Research and Surveillance, Peter Doherty Institute , VIC 3000, Australia;
                [8] hSingHealth Duke-NUS Global Health Institute, SingHealth Duke-NUS Academic Medical Centre , Singapore 169857;
                [9] iDuke Global Health Institute, Duke University , Durham, NC 27710
                Author notes
                1To whom correspondence may be addressed. Email: gavin.smith@ 123456duke-nus.edu.sg or yvonne.su@ 123456duke-nus.edu.sg .

                Edited by Peter Palese, Icahn School of Medicine at Mount Sinai, New York, NY, and approved November 18, 2019 (received for review September 25, 2019)

                Author contributions: G.J.D.S. and Y.C.F.S. designed research; R.K.V., J.J., P.L., M.L., and J.L. performed research; C.L., L.L.E.O., H.K.L., and E.S.C.K. contributed new reagents; R.K.V., J.J., I.H.M., M.M., and Y.C.F.S. analyzed data; R.K.V., M.M., D.V., G.J.D.S., and Y.C.F.S. wrote the paper; and J.J. wrote in-house scripts.

                Author information
                http://orcid.org/0000-0003-4250-6459
                http://orcid.org/0000-0001-5031-468X
                Article
                201916585
                10.1073/pnas.1916585116
                6955377
                31843889
                b9d99938-99c4-4e7a-9e9e-817f8d36884f
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 10
                Funding
                Funded by: MOH | National Medical Research Council (NMRC) 501100001349
                Award ID: NMRC/OFIRG/0008/2016
                Award Recipient : Ramandeep Virk Award Recipient : Jayanthi Jayakumar Award Recipient : Ian H Mendenhall Award Recipient : Mahesh Moorthy Award Recipient : Pauline Lam Award Recipient : Martin Linster Award Recipient : Julia Lim Award Recipient : Cui Lin Award Recipient : Lynette Oon Award Recipient : Hong Kai Lee Award Recipient : Evelyn Siew-Chuan Koay Award Recipient : Dhanasekaran Vijaykrishna Award Recipient : Gavin JD Smith Award Recipient : Yvonne Su
                Funded by: HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 100000060
                Award ID: HHSN272201400006C
                Award Recipient : Ramandeep Virk Award Recipient : Jayanthi Jayakumar Award Recipient : Ian H Mendenhall Award Recipient : Mahesh Moorthy Award Recipient : Pauline Lam Award Recipient : Martin Linster Award Recipient : Julia Lim Award Recipient : Cui Lin Award Recipient : Lynette Oon Award Recipient : Hong Kai Lee Award Recipient : Evelyn Siew-Chuan Koay Award Recipient : Dhanasekaran Vijaykrishna Award Recipient : Gavin JD Smith Award Recipient : Yvonne Su
                Funded by: Ministry of Health -Singapore (MOH) 501100001350
                Award ID: MOH/CDPHRG/0012/2014
                Award Recipient : Ramandeep Virk Award Recipient : Jayanthi Jayakumar Award Recipient : Ian H Mendenhall Award Recipient : Mahesh Moorthy Award Recipient : Pauline Lam Award Recipient : Martin Linster Award Recipient : Julia Lim Award Recipient : Cui Lin Award Recipient : Lynette Oon Award Recipient : Hong Kai Lee Award Recipient : Evelyn Siew-Chuan Koay Award Recipient : Dhanasekaran Vijaykrishna Award Recipient : Gavin JD Smith Award Recipient : Yvonne Su
                Funded by: Ministry of Health -Singapore (MOH) 501100001350
                Award ID: Duke-NUS Signature Research Programme
                Award Recipient : Ramandeep Virk Award Recipient : Jayanthi Jayakumar Award Recipient : Ian H Mendenhall Award Recipient : Mahesh Moorthy Award Recipient : Pauline Lam Award Recipient : Martin Linster Award Recipient : Julia Lim Award Recipient : Cui Lin Award Recipient : Lynette Oon Award Recipient : Hong Kai Lee Award Recipient : Evelyn Siew-Chuan Koay Award Recipient : Dhanasekaran Vijaykrishna Award Recipient : Gavin JD Smith Award Recipient : Yvonne Su
                Categories
                Biological Sciences
                Microbiology

                phylogeny,genetic diversity,natural selection,antigenic,vaccine

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