Sweep Dynamics (SD) plots: Computational identification of selective sweeps to monitor the adaptation of influenza A viruses

AbstractMonitoring changes in influenza A virus genomes is crucial to understand its rapid evolution and adaptation to changing conditions e.g. establishment within novel host species. Selective sweeps represent a rapid mode of adaptation and are typically observed in human influenza A viruses. We describe Sweep Dynamics (SD) plots, a computational method combining phylogenetic algorithms with statistical techniques to characterize the molecular adaptation of rapidly evolving viruses from longitudinal sequence data. To our knowledge, it is the first method that identifies selective sweeps, the time periods in which these occurred and associated changes providing a selective advantage to the virus. We studied the past genome-wide adaptation of the 2009 pandemic H1N1 influenza A (pH1N1) and seasonal H3N2 influenza A (sH3N2) viruses. The pH1N1 influenza virus showed simultaneous amino acid changes in various proteins, particularly in seasons of high pH1N1 activity. Partially, these changes resulted in functional alterations facilitating sustained human-to-human transmission. In the evolution of sH3N2 influenza viruses, we detected changes characterizing vaccine strains, which were occasionally revealed in selective sweeps one season prior to the WHO recommendation. Taken together, SD plots allow monitoring and characterizing the adaptive evolution of influenza A viruses by identifying selective sweeps and their associated signatures.

Download Full-text

Improving pandemic influenza risk assessment

eLife ◽

10.7554/elife.03883 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 43

Author(s):

Colin A Russell ◽

Peter M Kasson ◽

Ruben O Donis ◽

Steven Riley ◽

John Dunbar ◽

...

Keyword(s):

Risk Assessment ◽

Pandemic Influenza ◽

Influenza A ◽

Sequence Data ◽

Virus Genome ◽

Influenza Viruses ◽

Influenza Surveillance ◽

Human Influenza ◽

Influenza A Viruses ◽

Virus Genotype

Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.

Download Full-text

Rapid evolution and gene communication of H3N2 and H1N1 influenza a viruses

Journal of Infection ◽

10.1016/j.jinf.2019.03.009 ◽

2019 ◽

Vol 78 (6) ◽

pp. 491-503 ◽

Cited By ~ 1

Author(s):

Xiao Li ◽

jianglin Chen ◽

Jingkai Hu ◽

Xuanjiang Jin ◽

Shumin Xie ◽

...

Keyword(s):

Influenza A ◽

Rapid Evolution ◽

H1n1 Influenza ◽

Influenza A Viruses

Download Full-text

Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

10.1101/666982 ◽

2019 ◽

Author(s):

Susanne Reimering ◽

Sebastian Muñoz ◽

Alice C. McHardy

Keyword(s):

Influenza A ◽

State Of The Art ◽

Sequence Data ◽

Air Transportation ◽

H1n1 Influenza ◽

Real Sequence ◽

Influenza A Viruses ◽

Geographic Spread ◽

2009 H1n1 ◽

Genetic Sequences

AbstractInfluenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST, the current state-of-the-art. Our method extends the state-of-the-art by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.Author summaryInfluenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.

Download Full-text

Molecular basis of mammalian transmissibility of avian H1N1 influenza viruses and their pandemic potential

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1713974114 ◽

2017 ◽

Vol 114 (42) ◽

pp. 11217-11222 ◽

Cited By ~ 12

Author(s):

Mark Zanin ◽

Sook-San Wong ◽

Subrata Barman ◽

Challika Kaewborisuth ◽

Peter Vogel ◽

...

Keyword(s):

Genetic Markers ◽

Nuclear Export ◽

Influenza A ◽

Nonstructural Protein ◽

H1n1 Influenza ◽

Influenza Viruses ◽

Upper Respiratory Tract ◽

Influenza A Viruses ◽

Airborne Transmission ◽

Nonstructural Protein 1

North American wild birds are an important reservoir of influenza A viruses, yet the potential of viruses in this reservoir to transmit and cause disease in mammals is not well understood. Our surveillance of avian influenza viruses (AIVs) at Delaware Bay, USA, revealed a group of similar H1N1 AIVs isolated in 2009, some of which were airborne-transmissible in the ferret model without prior adaptation. Comparison of the genomes of these viruses revealed genetic markers of airborne transmissibility in the Polymerase Basic 2 (PB2), PB1, PB1-F2, Polymerase Acidic-X (PA-X), Nonstructural Protein 1 (NS1), and Nuclear Export Protein (NEP) genes. We studied the role of NS1 in airborne transmission and found that NS1 mutants that were not airborne-transmissible caused limited tissue pathology in the upper respiratory tract (URT). Viral maturation was also delayed, evident as strong intranuclear staining and little virus at the mucosa. Our study of this naturally occurring constellation of genetic markers has provided insights into the poorly understood phenomenon of AIV airborne transmissibility by revealing a role for NS1 and characteristics of viral replication in the URT that were associated with airborne transmission. The transmissibility of these viruses further highlights the pandemic potential of AIVs in the wild bird reservoir and the need to maintain surveillance.

Download Full-text

Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks

Bioinformatics ◽

10.1093/bioinformatics/btaa050 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2697-2704 ◽

Cited By ~ 12

Author(s):

Rui Yin ◽

Emil Luusua ◽

Jan Dabrowski ◽

Yu Zhang ◽

Chee Keong Kwoh

Keyword(s):

Neural Networks ◽

Time Series ◽

Recurrent Neural Networks ◽

Influenza A ◽

Sequence Data ◽

Predictive Performance ◽

Influenza Viruses ◽

Supplementary Information ◽

Influenza A Viruses ◽

Mutation Prediction

Abstract Motivation Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. The goal of this work is to predict whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data. One of the major challenges is to model the temporality and dimensionality of sequential influenza strains and to interpret the prediction results. Results In this article, we propose an efficient and robust time-series mutation prediction model (Tempel) for the mutation prediction of influenza A viruses. We first construct the sequential training samples with splittings and embeddings. By employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are being increasingly used to improve the performance of mutation prediction by selectively focusing on the parts of the residues. A framework is established based on Tempel that enables us to predict the mutations at any specific residue site. Experimental results on three influenza datasets show that Tempel can significantly enhance the predictive performance compared with widely used approaches and provide novel insights into the dynamics of viral mutation and evolution. Availability and implementation The datasets, source code and supplementary documents are available at: https://drive.google.com/drive/folders/15WULR5__6k47iRotRPl3H7ghi3RpeNXH. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Minor variants of swine H1N1 influenza viruses with enhanced polymerase activity and HA stability promote airborne transmission in ferrets

10.1101/2021.09.22.461336 ◽

2021 ◽

Author(s):

Meng Hu ◽

Jeremy Jones ◽

Balaji Banoth ◽

Chet R Ojha ◽

Jeri Carol Crumpton ◽

...

Keyword(s):

Risk Assessment ◽

Deep Sequencing ◽

Influenza A ◽

H1n1 Influenza ◽

Polymerase Activity ◽

Protein Stabilization ◽

Influenza Viruses ◽

Influenza A Viruses ◽

Airborne Transmission ◽

Ha Protein

Understanding how animal influenza A viruses (IAVs) acquire airborne transmissibility in humans and ferrets is needed to prepare for and respond to pandemics. Previously, we showed that hemagglutinin (HA) protein stabilization promotes replication and airborne transmission in ferrets using swine H1N1 gamma strains P4 and G15 (Hu et al. 2020). Here, we show that a combination of enhanced polymerase activity and HA stability is necessary for efficient airborne transmission in ferrets and that minor variants containing both properties are quickly selected. P4 and G15 were found to have decreased polymerase activity and relatively poor HA stability, respectively. Polymerase-enhancing variant PA-S321 was selected in half of P4 isolates that airborne-transmitted in ferrets. HA-stabilizing variant HA1-S210 was selected in all G15-inoculated ferrets and was transmitted. With an efficient polymerase and a stable HA, purified G15-HA1-S210 had earlier and higher peak titers in inoculated ferrets and was recovered at a higher frequency after airborne transmission than P4 and G15. Pandemic risk-assessment studies may benefit from deep sequencing capable of identifying minor variants with human-adapted traits.

Download Full-text

Comparison of Avian and Human Influenza A Viruses Reveals a Mutational Bias on the Viral Genomes

Journal of Virology ◽

10.1128/jvi.01414-06 ◽

2006 ◽

Vol 80 (23) ◽

pp. 11887-11891 ◽

Cited By ~ 86

Author(s):

Raul Rabadan ◽

Arnold J. Levine ◽

Harlan Robins

Keyword(s):

Influenza Virus ◽

Influenza A ◽

Genomic Sequence ◽

Sequence Data ◽

H1n1 Influenza ◽

Mutational Bias ◽

Influenza A Viruses ◽

Viral Genomes ◽

Avian Influenza Virus H5n1 ◽

History Of

ABSTRACT In the last few years, the genomic sequence data for thousands of influenza A virus strains, including the 1918 pandemic strain, and hundreds of isolates of the avian influenza virus H5N1, which is causing an increasing number of human fatalities, have become publicly available. This large quantity of sequence data allows us to do comparative genomics with the human and avian versions of the virus. We find that the nucleotide compositions of influenza A viruses infecting the two hosts are sufficiently different that we can determine the host at almost 100% accuracy. This assignment works at the segment level, which allows us to construct the reassortment history of individual segments within each strain. We suggest that the different nucleotide compositions can be explained by a host-dependent mutation bias. To support this idea, we estimate the fixation rates for the different polymerase segments and the ratios of synonymous to nonsynonymous changes. Additionally, we provide evidence supporting the hypothesis that the H1N1 influenza virus entered the human population just prior to the 1918 outbreak, with an earliest bound of 1910.

Download Full-text

Antigenic drift in the evolution of H1N1 influenza A viruses resulting from deletion of a single amino acid in the haemagglutinin gene

Journal of General Virology ◽

10.1099/vir.0.83184-0 ◽

2007 ◽

Vol 88 (12) ◽

pp. 3209-3213 ◽

Cited By ~ 29

Author(s):

Natalie J. McDonald ◽

Catherine B. Smith ◽

Nancy J. Cox

Keyword(s):

Amino Acid ◽

Influenza A ◽

Reverse Genetics ◽

Selective Advantage ◽

H1n1 Influenza ◽

Antigenic Drift ◽

Surface Glycoprotein ◽

Single Amino Acid ◽

Influenza A Viruses ◽

Sequence Comparisons

Two genetically distinct lineages of H1N1 influenza A viruses, circulated worldwide before 1994, were antigenically indistinguishable. In 1994, viruses emerged in China, including A/Beijing/262/95, with profound antigenic differences from the contemporary circulating H1N1 strains. Haemagglutinin sequence comparisons of either a predecessor virus, A/Hebei/52/94, or one representative of the cocirculating A/Bayern/7/95-like clade, A/Shenzhen/227/95, revealed a deletion of K at position 134 (H3 numbering) in the antigenic variants. The K134 deletion conferred a selective advantage to the Chinese deletion lineage, such that it eventually gave rise to currently circulating H1 viruses. Using reverse genetics to generate viruses with either an insertion or deletion of aa 134, we have confirmed that the K134 deletion, rather than a constellation of sublineage specific amino acid changes, was sufficient for the antigenic difference observed in the Chinese deletion lineage, and reinsertion of K134 revealed the requirement of a compatible neuraminidase surface glycoprotein for viral growth.

Download Full-text

The effective population size and mutation rate of influenza A virus in acutely infected individuals

10.1101/2020.10.24.353748 ◽

2020 ◽

Author(s):

John T. McCrone ◽

Robert J. Woods ◽

Arnold S. Monto ◽

Emily T. Martin ◽

Adam S. Lauring

Keyword(s):

Population Size ◽

Effective Population Size ◽

Mutation Rate ◽

Influenza A ◽

Evolutionary Dynamics ◽

Sequence Data ◽

Influenza Viruses ◽

Influenza A Viruses ◽

Effective Population

AbstractThe global evolutionary dynamics of influenza viruses ultimately derive from processes that take place within and between infected individuals. Recent work suggests that within-host populations are dynamic, but an in vivo estimate of mutation rate and population size in naturally infected individuals remains elusive. Here we model the within-host dynamics of influenza A viruses using high depth of coverage sequence data from 200 acute infections in an outpatient, community setting. Using a Wright-Fisher model, we estimate a within-host effective population size of 32-72 and an in vivo mutation rate of 3.4×10−6 per nucleotide per generation.

Download Full-text

Adaptation of Pandemic H1N1 Influenza Viruses in Mice

Journal of Virology ◽

10.1128/jvi.00159-10 ◽

2010 ◽

Vol 84 (17) ◽

pp. 8607-8616 ◽

Cited By ~ 158

Author(s):

Natalia A. Ilyushina ◽

Alexey M. Khalenkov ◽

Jon P. Seiler ◽

Heather L. Forrest ◽

Nicolai V. Bovin ◽

...

Keyword(s):

Amino Acid ◽

Pandemic Influenza ◽

Influenza A ◽

H1n1 Influenza ◽

Influenza Viruses ◽

Mouse Lung ◽

Mammalian Host ◽

Influenza A Viruses ◽

Receptor Specificity ◽

Viral Receptor

ABSTRACT The molecular mechanism by which pandemic 2009 influenza A viruses were able to sufficiently adapt to humans is largely unknown. Subsequent human infections with novel H1N1 influenza viruses prompted an investigation of the molecular determinants of the host range and pathogenicity of pandemic influenza viruses in mammals. To address this problem, we assessed the genetic basis for increased virulence of A/CA/04/09 (H1N1) and A/TN/1-560/09 (H1N1) isolates, which are not lethal for mice, in a new mammalian host by promoting their mouse adaptation. The resulting mouse lung-adapted variants showed significantly enhanced growth characteristics in eggs, extended extrapulmonary tissue tropism, and pathogenicity in mice. All mouse-adapted viruses except A/TN/1-560/09-MA2 grew faster and to higher titers in cells than the original strains. We found that 10 amino acid changes in the ribonucleoprotein (RNP) complex (PB2 E158G/A, PA L295P, NP D101G, and NP H289Y) and hemagglutinin (HA) glycoprotein (K119N, G155E, S183P, R221K, and D222G) controlled enhanced mouse virulence of pandemic isolates. HA mutations acquired during adaptation affected viral receptor specificity by enhancing binding to α2,3 together with decreasing binding to α2,6 sialyl receptors. PB2 E158G/A and PA L295P amino acid substitutions were responsible for the significant enhancement of transcription and replication activity of the mouse-adapted H1N1 variants. Taken together, our findings suggest that changes optimizing receptor specificity and interaction of viral polymerase components with host cellular factors are the major mechanisms that contribute to the optimal competitive advantage of pandemic influenza viruses in mice. These modulators of virulence, therefore, may have been the driving components of early evolution, which paved the way for novel 2009 viruses in mammals.

Download Full-text