Evolutionary History and Phylodynamics of Influenza A and B Neuraminidase (NA) Genes Inferred from Large- Scale Sequence Analyses

Background: Influenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking. Methodology/Principal findings: Evolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The dN/dS ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding. Conclusions/Significance: The divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance

Evolutionary History and Phylodynamics of Influenza A and B Neuraminidase (NA) Genes Inferred from Large-Scale Sequence Analyses

Background: Influenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking. Methodology/Principal findings: Evolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The dN/dS ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding. Conclusions/Significance: The divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance

Systematic Experimental Determination of Functional Constraints on Proteins and Adaptive Potential of Mutations: A Dissertation

Sequence-function relationship is a fundamental question for many branches of modern biomedical research. It connects the primary sequence of proteins to the function of proteins and fitness of organisms, holding answers for critical questions such as functional consequences of mutations identified in whole genome sequencing and adaptive potential of fast evolving pathogenic viruses and microbes. Many different approaches have been developed to delineate the genotype-phenotype map for different proteins, but are generally limited by their throughput or precision. To systematically quantify the fitness of large numbers of mutations, I modified a novel high throughput mutational scanning approach (EMPIRIC) to investigate the fitness landscape of mutations in important regions of essential proteins from the yeast or RNA viruses. Using EMPIRIC, I analyzed the interplay of the expression level and sequence of Hsp90 on the yeast growth and revealed latent effect of mutations at reduced expression levels of Hsp90. I also examined the functional constraint on the receptor binding site of the Env of Human Immunodeficiency Virus (HIV) and uncovered enhanced receptor binding capacity as a common pathway for adaptation of HIV to laboratory conditions. Moreover, I explored the adaptive potential of neuraminidase (NA) of influenza A virus to a NA inhibitor, oseltamivir, and identified novel oseltamivir resistance mutations with distinct molecular mechanisms. In summary, I applied a high throughput functional genomics approach to map the sequence-function relationship in various systems and examined the evolutionary constraints and adaptive potential of essential proteins ranging from molecular chaperones to drug-targetable viral proteins

Adapting the EMPIRIC Approach to Investigate Evolutionary Constraints in Influenza A Virus Surface Proteins

Controlling influenza A virus (IAV) infections remains a challenge largely due to the high replication and mutation rates of the virus. IAV is a negative-sense RNA virus with two main surface proteins — hemagglutinin (HA) and neuraminidase (NA). HA recognizes and binds sialic acid on host cell receptors to initiate virus entry. NA also recognizes sialic acid on host cell receptors but functions by cleaving sialic acid interactions to release progeny virus. Because both HA and NA interact with sialic acid on the host cell surface with opposing effects, their balance is essential for optimal viral infectivity. However, the evolutionary constraints that maintain HA and NA function, while conserving a functional balance, are not fully understood. I adapted the comprehensive and systematic mutational scanning technology, termed EMPIRIC (Exceedingly Meticulous and Parallel Investigation of Randomized Individual Codons), to investigate the local fitness landscape of regions of HA under standard conditions and under drug pressure. We observed that synonymous substitutions had a higher mean absolute fitness effect in the signal than a neighboring HA region used as a control. Folding ∆G calculations revealed a hairpin loop that appeared to be differentially enriched between human and swine IAV variants in sequences of circulating strains. However, the molecular mechanism resulting in the observed host species-specific constraints remains undefined. Studying the fitness landscape of the receptor binding site of HA revealed the high sensitivity of this region to mutation. However, modulating the levels of NA activity by mutation and by using the NA inhibitor oseltamivir enabled the identification of HA mutations with adaptive potential under selection pressure by oseltamivir. These results highlight the importance of the HA-NA functional balance virus replication and in the development of resistance to oseltamivir inhibitors. These studies provide improved understanding of IAV biology, and can inform the development of improved antiviral agents with reduced likelihood for resistance

MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification

Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods

PLoS One

BackgroundInfluenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking.Methodology/Principal findingsEvolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The dN/dS ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding.Conclusions/SignificanceThe divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance.R01 LM009985/LM/NLM NIH HHSUnited States/R01 LM009985-01A1/LM/NLM NIH HHSUnited States/22808012PMC33947691209

Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data

Genetic mutations play a central role in evolution. For a significantly beneficial mutation, a one-time mutation event suffices for the species to prosper and predominate through the process called "monophyletic selective sweep." However, existing methods that rely on counting the number of mutation events to detect selection are unable to find such a mutation in selective sweep. We here introduce a method to detect mutations at the single amino acid/nucleotide level that could be responsible for monophyletic selective sweep evolution. The method identifies a genetic signature associated with selective sweep using the population genetic test statistic Tajima's D We applied the algorithm to ebolavirus, influenza A virus, and severe acute respiratory syndrome coronavirus 2 to identify known biologically significant mutations and unrecognized mutations associated with potential selective sweep. The method can detect beneficial mutations, possibly leading to discovery of previously unknown biological functions and mechanisms related to those mutations.IMPORTANCE In biology, research on evolution is important to understand the significance of genetic mutation. When there is a significantly beneficial mutation, a population of species with the mutation prospers and predominates, in a process called "selective sweep." However, there are few methods that can find such a mutation causing selective sweep from genetic data. We here introduce a novel method to detect such mutations. Applying the method to the genomes of ebolavirus, influenza viruses, and the novel coronavirus, we detected known biologically significant mutations and identified mutations the importance of which is previously unrecognized. The method can deepen our understanding of molecular and evolutionary biology

Seasonality in the migration and establishment of H3N2 Influenza lineages with epidemic growth and decline

Background: Influenza A/H3N2 has been circulating in humans since 1968, causing considerable morbidity and mortality. Although H3N2 incidence is highly seasonal, how such seasonality contributes to global phylogeographic migration dynamics has not yet been established. Results: Incorporating seasonally varying migration rates improves the modeling of migration. In our global model, windows of increased immigration map to the seasonal timing of epidemic spread, while windows of increased emigration map to epidemic decline. Seasonal patterns also correlate with the probability that local lineages go extinct and fail to contribute to long term viral evolution, as measured through the trunk of the phylogeny. However, the fraction of the trunk in each community was found to be better determined by its overall human population size Conclusions: Seasonal migration and rapid turnover within regions is sustained by the invasion of 'fertile epidemic grounds' at the end of older epidemics. Thus, the current emphasis on connectivity, including air-travel, should be complemented with a better understanding of the conditions and timing required for successful establishment.Models which account for migration seasonality will improve our understanding of the seasonal drivers of influenza,enhance epidemiological predictions, and ameliorate vaccine updating by identifying strains that not only escape immunity but also have the seasonal opportunity to establish and spread. Further work is also needed on additional conditions that contribute to the persistence and long term evolution of influenza within the human population,such as spatial heterogeneity with respect to climate and seasonalityComment: in BMC Evolutionary Biology 2014, 1

Phylodynamic Patterns in Pathogen Ecology and Evolution.

The rapid evolution of viral pathogens requires us to consider epidemiological, ecological and evolutionary processes as coupled together and occurring at the same timescale. Rotavirus and influenza account for high levels of morbidity and mortality worldwide and are two important examples of such dynamics. In this work, I investigate the different evolutionary and ecological processes that shape the antigenic structure and phylogenetic characteristics of these two viruses. In the first part of my work, I use a theoretical model of influenza A/H3N2 to identify the relative importance of antigenic novelty, competition between lineages, and changes in the susceptibility of the host population to circulating strains in determining the evolutionary and epidemiological trajectory of the virus. I develop this model further to correspond with patterns of immunity and infection observed in rotavirus, and investigate how reassortment, the swapping of gene segments between viruses, influences the formation and replacement of rotavirus genotypes through immune mediated processes. In the second part of my work, I use a tool (SeasMig), which I developed, to infer alternative stochastically generated migration and mutation events along phylogenetic trees in a Bayesian manner. Using SeasMig, I first show how the seasonality of A/H3N2 influenza incidence corresponds to rates of immigration and emigration of the virus. Subsequently, I tease out the different evolutionary and ecological processes, which drive changes in the US rotavirus population following onset of routine vaccination. My work has implications for identifying likely evolutionary mechanisms, which may lead to reduced vaccine efficacy, and for vaccine strain selection.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113494/1/dzinder_1.pd