Jialiang Yang

Using Sequence Data To Infer the Antigenicity of Influenza Virus

ABSTRACT The efficacy of current influenza vaccines requires a close antigenic match between circulating and vaccine strains. As such, timely identification of emerging influenza virus antigenic variants is central to the success of influenza vaccination programs. Empirical methods to determine influenza virus antigenic properties are time-consuming and mid-throughput and require live viruses. Here, we present a novel, experimentally validated, computational method for determining influenza virus antigenicity on the basis of hemagglutinin (HA) sequence. This method integrates a bootstrapped ridge regression with antigenic mapping to quantify antigenic distances by using influenza HA1 sequences. Our method was applied to H3N2 seasonal influenza viruses and identified the 13 previously recognized H3N2 antigenic clusters and the antigenic drift event of 2009 that led to a change of the H3N2 vaccine strain. IMPORTANCE This report supplies a novel method for quantifying antigenic distance and identifying antigenic variants using sequences alone. This method will be useful in influenza vaccine strain selection by significantly reducing the human labor efforts for serological characterization and will increase the likelihood of correct influenza vaccine candidate selection. This report supplies a novel method for quantifying antigenic distance and identifying antigenic variants using sequences alone. This method will be useful in influenza vaccine strain selection by significantly reducing the human labor efforts for serological characterization and will increase the likelihood of correct influenza vaccine candidate selection.

AntigenMap 3D

SUMMARY Antigenic cartography is a useful technique to visualize and minimize errors in immunological data by projecting antigens to 2D or 3D cartography. However, a 2D cartography may not be sufficient to capture the antigenic relationship from high-dimensional immunological data. AntigenMap 3D presents an online, interactive, and robust 3D antigenic cartography construction and visualization resource. AntigenMap 3D can be applied to identify antigenic variants and vaccine strain candidates for pathogens with rapid antigenic variations, such as influenza A virus. AVAILABILITY AND IMPLEMENTATION http://sysbio.cvm.msstate.edu/AntigenMap3D

Identifying antigenicity associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning

Since the isolation of A/goose/Guangdong/1/1996 (H5N1) in farmed geese in southern China, highly pathogenic H5N1 avian influenza viruses have posed a continuous threat to both public and animal health. The non-synonymous mutation of the H5 hemagglutinin (HA) gene has resulted in antigenic drift, leading to difficulties in both clinical diagnosis and vaccine strain selection. Characterizing H5N1s antigenic profiles would help resolve these problems. In this study, a novel sparse learning method was developed to identify antigenicity-associated sites in influenza A viruses on the basis of immunologic data sets (i.e., from hemagglutination inhibition and microneutralization assays) and HA protein sequences. Twenty-one potential antigenicity-associated sites were identified. A total of 17 H5N1 mutants were used to validate the effects of 11 of these predicted sites on H5N1s antigenicity, including 7 newly identified sites not located in reported antibody binding sites. The experimental data confirmed that mutations of these tested sites lead to changes in viral antigenicity, validating our method.

Antigenic characterization of H3N2 influenza a viruses from ohio agricultural fairs

ABSTRACT The demonstrated link between the emergence of H3N2 variant (H3N2v) influenza A viruses (IAVs) and swine exposure at agricultural fairs has raised concerns about the human health risk posed by IAV-infected swine. Understanding the antigenic profiles of IAVs circulating in pigs at agricultural fairs is critical to developing effective prevention and control strategies. Here, 68 H3N2 IAV isolates recovered from pigs at Ohio fairs (2009 to 2011) were antigenically characterized. These isolates were compared with other H3 IAVs recovered from commercial swine, wild birds, and canines, along with human seasonal and variant H3N2 IAVs. Antigenic cartography demonstrated that H3N2 IAV isolates from Ohio fairs could be divided into two antigenic groups: (i) the 2009 fair isolates and (ii) the 2010 and 2011 fair isolates. These same two antigenic clusters have also been observed in commercial swine populations in recent years. Human H3N2v isolates from 2010 and 2011 are antigenically clustered with swine-origin IAVs from the same time period. The isolates recovered from pigs at fairs did not cross-react with ferret antisera produced against the human seasonal H3N2 IAVs circulating during the past decade, raising the question of the degree of immunity that the human population has to swine-origin H3N2 IAVs. Our results demonstrate that H3N2 IAVs infecting pigs at fairs and H3N2v isolates were antigenically similar to the IAVs circulating in commercial swine, demonstrating that exhibition swine can function as a bridge between commercial swine and the human population.

Antigenic characterization of recent H5N1 highly pathogenic avian influenza viruses circulating in Egyptian poultry

The extensive circulation of Highly Pathogenic (HP) H5N1 Avian Influenza in Egypt in poultry since 2006 resulted in the emergence of distinct clades with the recent identification of a further clade: 2.2.1.1. The aim of this study was to characterize for the first time the antigenic profile of an extensive collection of genetically diverse Egyptian H5N1 HP viruses isolated between 2007 and 2010 applying antigenic cartography and principal component analysis to serological data. We identified that Egyptian H5N1 viruses have undergone significant antigenic diversification between 2007 and 2010 and two distinct antigenic clusters co-circulated in 2010. Such clusters correlated with 2.2.1 and 2.2.1.1 clades, showing for the first time that the new emerging clade 2.2.1.1 is antigenically distinct. This study highlights that the antigenic diversity of H5N1 HP Egyptian viruses may represent a potential challenge for the development of an effective vaccination programme for animal and human health in Egypt.

Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information

Rapid identification of influenza antigenic variants will be critical in selecting optimal vaccine candidates and thus a key to developing an effective vaccination program. Recent studies suggest that multiple simultaneous mutations at antigenic sites accumulatively enhance antigenic drift of influenza A viruses. However, pre-existing methods on antigenic variant identification are based on analyses from individual sites. Because the impacts of these co-evolved sites on influenza antigenicity may not be additive, it will be critical to quantify the impact of not only those single mutations but also multiple simultaneous mutations or co-evolved sites. Here, we developed and applied a computational method, AntigenCO, to identify and quantify both single and co-evolutionary sites driving the historical antigenic drifts. AntigenCO achieved an accuracy of up to 90.05% for antigenic variant prediction, significantly outperforming methods based on single sites. AntigenCO can be useful in antigenic variant identification in influenza surveillance.

Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states

BackgroundAs one of the most widely used parsimony methods for ancestral reconstruction, the Fitch method minimizes the total number of hypothetical substitutions along all branches of a tree to explain the evolution of a character. Due to the extensive usage of this method, it has become a scientific endeavor in recent years to study the reconstruction accuracies of the Fitch method. However, most studies are restricted to 2-state evolutionary models and a study for higher-state models is needed since DNA sequences take the format of 4-state series and protein sequences even have 20 states.ResultsIn this paper, the ambiguous and unambiguous reconstruction accuracy of the Fitch method are studied for N-state evolutionary models. Given an arbitrary phylogenetic tree, a recurrence system is first presented to calculate iteratively the two accuracies. As complete binary tree and comb-shaped tree are the two extremal evolutionary tree topologies according to balance, we focus on the reconstruction accuracies on these two topologies and analyze their asymptotic properties. Then, 1000 Yule trees with 1024 leaves are generated and analyzed to simulate real evolutionary scenarios. It is known that more taxa not necessarily increase the reconstruction accuracies under 2-state models. The result under N-state models is also tested.ConclusionsIn a large tree with many leaves, the reconstruction accuracies of using all taxa are sometimes less than those of using a leaf subset under N-state models. For complete binary trees, there always exists an equilibrium interval [a, b] of conservation probability, in which the limiting ambiguous reconstruction accuracy equals to the probability of randomly picking a state. The value b decreases with the increase of the number of states, and it seems to converge. When the conservation probability is greater than b, the reconstruction accuracies of the Fitch method increase rapidly. The reconstruction accuracies on 1000 simulated Yule trees also exhibit similar behaviors. For comb-shaped trees, the limiting reconstruction accuracies of using all taxa are always less than or equal to those of using the nearest root-to-leaf path when the conservation probability is not less than 1N. As a result, more taxa are suggested for ancestral reconstruction when the tree topology is balanced and the sequences are highly similar, and a few taxa close to the root are recommended otherwise.

Analyzing the Fitch Method for Reconstructing Ancestral States on Ultrametric Phylogenetic Trees

The accuracy of the Fitch method for reconstructing ancestral states on ultrametric phylogenetic trees is studied. Two recurrence relations for computing the accuracy are given here. Using these relations, we analyze the convergence of the accuracy of the Fitch method for reconstructing the root state on a complete binary tree of 2n leaves as n goes to infinity, present a closed-form formula for the accuracy on ultrametric comb trees, and provide a lower bound on the accuracy on arbitrary ultrametric phylogenetic trees.

Quartet-based methods to reconstruct phylogenetic networks

BackgroundPhylogenetic networks are employed to visualize evolutionary relationships among a group of nucleotide sequences, genes or species when reticulate events like hybridization, recombination, reassortant and horizontal gene transfer are believed to be involved. In comparison to traditional distance-based methods, quartet-based methods consider more information in the reconstruction process and thus have the potential to be more accurate.ResultsWe introduce QuartetSuite, which includes a set of new quartet-based methods, namely QuartetS, QuartetA, and QuartetM, to reconstruct phylogenetic networks from nucleotide sequences. We tested their performances and compared them with other popular methods on two simulated nucleotide sequence data sets: one generated from a tree topology and the other from a complicated evolutionary history containing three reticulate events. We further validated these methods to two real data sets: a bacterial data set consisting of seven concatenated genes of 36 bacterial species and an influenza data set related to recently emerging H7N9 low pathogenic avian influenza viruses in China.ConclusionQuartetS, QuartetA, and QuartetM have the potential to accurately reconstruct evolutionary scenarios from simple branching trees to complicated networks containing many reticulate events. These methods could provide insights into the understanding of complicated biological evolutionary processes such as bacterial taxonomy and reassortant of influenza viruses.

BinAligner: a heuristic method to align biological networks

The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposis sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and infection.