Volume 9 Supplement 3
Rules of co-occurring mutations characterize the antigenic evolution of human influenza A/H3N2, A/H1N1 and B viruses
© The Author(s). 2016
Published: 5 December 2016
The human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is driven by the simultaneous mutations at antigenic sites. Here, we analyze simultaneous or co-occurring mutations in the HA protein of human influenza A/H3N2, A/H1N1 and B viruses to predict potential mutations, characterizing the antigenic evolution.
We obtain the rules of mutation co-occurrence using association rule mining after extracting HA1 sequences and detect co-mutation sites under strong selective pressure. Then we predict the potential drifts with specific mutations of the viruses based on the rules and compare the results with the “observed” mutations in different years.
The sites under frequent mutations are in antigenic regions (epitopes) or receptor binding sites.
Our study demonstrates the co-occurring site mutations obtained by rule mining can capture the evolution of influenza viruses, and confirms that cooperative interactions among sites of HA1 protein drive the influenza antigenic evolution.
KeywordsInfluenza virus A/H3N2/H1N1 and B Antigenic evolution Co-occurring mutation Influenza vaccine
Influenza has been a major and persistent threat to public health for centuries, causing millions of deaths and huge economic loss worldwide every year. Among the three types of human influenza viruses, denoted as A, B and C, influenza A viruses are the most virulent due to their high mutation rate, frequent genetic reassortment and short generation time, which have caused several pandemics in recent history [1, 2]. The pandemics include 1918 Spanish flu (A/H1N1) , 1957 (A/H2N2) Asia flu , 1968 Hongkong flu (A/H3N2)  and 2009 swine flu (A/H1N1) . Influenza B viruses, evolving into B/Yamagata and B/Victoria lineages and frequently exchanging their segments, have been co-circulating since 2001 and cause an observably part of infections [7, 8]. Under the surveillance and monitoring by WHO (World Health Organization), influenza activity was detected to be associated with the co-circulation of influenza A/H1N1 pdm09, A/H3N2 and B viruses . Therefore, to predict and prevent potential pandemics in the future, it is important to analyze and compare the evolutionary patterns of the three types of viruses.
Haemagglutinin (HA) is a surface glycoprotein of influenza virus responsible for binding specificity and initiating the viral entry. It can be cleaved into two polypeptides: HA1 and HA2 subunits, which are covalently linked by a disulfide bond . HA1 contains the sialic acid receptor binding sites, and is considered as one of the main targets of immune system to detect influenza virus, as well as the primary protein component of vaccine [10–12]. Under rapid mutations (substitution rate estimated to be 5.7 × 10− 3per site per year ), the HA1 domain accumulates mutations causing viral antigenic drift and thus preclude effective vaccination with existing vaccines . Identifying the evolutionary trajectories and predicting future mutations would be very helpful for recommending efficient influenza vaccines before a potential variant causes an influenza outbreak. Therefore, many studies have attempted to track and predict the antigenic evolutionary dynamics of the HA protein. Phylogenetic tree analysis is a traditional technique in this field. Studies based on phylogenetic tree analysis revealed that a single predominant trunk lineage persists through time while side branches persist for 1 ∼ 5 years before going extinct [15–17], indicating a strong selection preference in the evolutionary path. Many methods have been proposed to identify single mutation sites under positive selection and thereby understand the antigenic evolution of HA [18–20]. Statistical analysis and machine learning approaches have also been applied to reveal more information about the mutational dynamics in the viral sequences. The pioneering work by Smith et al.  characterized the antigenic evolution of HA1 (A/H3N2) based on the Hemagglutination-inhibition (HI) assays, and mapped the antigenic evolution (phenotype) to the phylogenetic tree based on HA1 sequences (genotype) using a maximum-likelihood (ML) approach. Smith’s method was enhanced by Bedford et al. in  by simultaneously characterizing antigenic and genetic evolution using a diffusion model over a shared virus phylogeny. Plotkin et al.  adopted a clustering technique to investigate the spatio-temporal evolution of antigenic clusters. A Bayesian approach was applied in  to predict the antigenic relationships of H3N2 viruses, which were used to identify the antigenic clusters and infer the dynamics of antigenic evolution. The relationship between the antigenic distances based on sequences and those calculated from HI titer data was further discussed in , where an online tool named “nextflu” was provided for real-time tracking. Although those studies have obtained insightful results, most of them focus on the clusters of antigenic mutations. Currently very few studies work on the interactions among site mutations in the HA proteins and their impact on the direction of antigenic evolution.
It has been observed that simultaneous multi-site mutations (or co-occurring mutations) at antigenic sites could accumulatively enhance the antigenic drift [26, 27]. Co-occurring mutations can be categorized into stochastic co-evolution, functional co-evolution and interaction evolution . One site on a protein may compensate for another during evolution; thus mutations on these sites are under positive selection pressure and occur simultaneously (i.e. co-occurring mutations). The identification of co-occurring mutations can help uncover possible interactions among them and thereby improve our understanding of the mutational dynamics of proteins. Mutual information has been used to estimate the correlations between two site mutations [29, 30]. The correlation network (named site transition network or STN in ) based on mutual information can be used to predict the future mutations of sites in HA protein. Results in  showed that the STN can predict site mutations with 70% accuracy. However, mutual information is limited to pairwise relationships. How multiple sites interact with each other is yet to be discovered.
Here, we propose a method based on association rule mining  to identify co-occurring patterns of multiple-site mutations. Association rule mining has been shown as a promising technique in bioinformatic analysis [32, 33]. Our approach offers a flexible way to discover the interactions of multiple site mutations, not limited to pairwise interactions as in . Besides, the rules of co-occurring mutations provide interpretability, making it easy for human to understand the underlying process of antigenic evolution. Furthermore, our rules can also be used to predict potential mutations in the sites of HA1.
Rules of co-occurring mutations
Overview of extracted rules
Number of Extracted Rules
Then we classified the flu-B HA1 sequences into two lineages based on their distances from two standard HA1 sequences of Victoria and Yamagata lineages. After that, we applied our approach to the sequences of the two lineages respectively. Results of rules are visualized in Additional file 4: Figure S4 and Additional file 5: Figure S5, for Yamagata and Victoria lineages respectively. We can see that, after discriminating flu-B sequences into two lineages, the numbers of mutations (within each lineage) decrease significantly. Here we set the threshold for support of rules to 1500 (versus 5000 before classifying the lineages) and obtained 1110 and 69 rules for Yamagata and Victoria lineages respectively. The results also suggest that the Yamagata lineage may mutate more quickly than the Victoria lineage.
The analysis of co-occurring mutation patterns in A/H3N2 is given in the section “Predictions of influenza evolution” below.
Co-Mutation sites under strong selection pressure
Sites detected co-mutated frequently with other sites
Number of residues at antigenic sites/Total number of sites
50, 53, 62, 137, 144, 145, 155, 156, 158, 189, 244, 260, 275
128, 183, 186, 205, 216, 249, 272
69, 97, 143, 163, 197, 256, 260, 283
48, 56, 75, 116, 182, 183, 266
75, 88, 175, 199, 330, 235
Distribution of detected sites on epitope regions
Predictions of influenza evolution
The prediction results (for H3N2 in different years)
We also compare our results with those in , using the same dataset as  (i.e. the sequences of H3N2 from 1968 to 2002). The rules of co-mutation sites are plot as a network shown in Additional file 6: Figure S6. The following site mutations are predicted both by our method and Xia’s method (i.e. ): 50, 155 and 156. The mutation in site 144 is only predicted by our method. Since the “benchmark” for site mutations may not be unique, we introduce another set of “observed” mutations generated by BII-FluSurver . We submitted all HA1 sequences of H3N2 in 2003 to BII-FluSurver (using default parameters) and counted the frequencies of all site mutations returned by BII-FluSurver. Totally 4543 site mutations (with duplicates) were obtained. The comparison of occurrence of predicted mutations in the BII-FluSurver results is shown in Additional file 7: Table S1, which shows that the overlap between our prediction and the BII-FluSurver results is similar to that of Xia’s prediction.
Discussion and Conclusion
In this paper, we propose a method based on association rule mining to identify the co-occurring site mutations for human influenza A(H3N2), A(H1N1) and B Viruses. The rules of co-mutation sites characterize the antigenic evolution of influenza viruses. We show that the co-mutation sites in HA1 are all in the epitope regions, indicting strong selection pressure by human immune system in those sites. Furthermore, the rules obtained by our method can be used to predict potential mutations of influenza viruses in the future.
There are several directions to improve our study in this paper. First, we could increase the number of sampling process (i.e. N in Methods section) to increase the statistic power of association rule mining. Second, instead of randomly sampling two sequences from two adjacent years, we can select two sequences with closer phylogenetic distance to calculate the mutations. Of course, experimental data where phylogenetic relationships of sequences are known would bring better results. In addition, different weights could be assigned to different years during sampling, e.g. to do more samplings on the years with more sequences. Finally, aside from analyzing the co-occurring mutations in HA protein, we can also explore the co-evolving mutations patterns in other proteins, where mutations may compensate for each other. For example, HA and NA proteins of influenza viruses are responsible for the viral’s binding and cleavage from host cells. It would be very interesting to detect the co-occurring mutations in these two proteins, which are under selection pressure, to study their cooperative manner at the genetic level.
All HA protein sequences of human Influenza A/H3N2, A/H1N1 and B Viruses were retrieved from the Influenza Virus Resource at NCBI  up to October 8, 2015. The sequences were searched from the year 1918 to the year 2015. We excluded the records without the information of year and the sequences which are shorter than the full length of HA1 (327, 312 and 345 residues for H1N1, H3N2 and B viruses, respectively).
Because there is no record in some years for a particular type of virus, we used the sequences from 1976 to 2015 for H1N1, from 1968 to 2015 for H3N2, and from 1975 to 2015 for flu-B virus, to ensure the continuity. Totally 18,450 sequences were obtained after cleaning for H1N1, 18,019 sequences for H3N2, and 6538 sequences for flu-B virus. Then we aligned these sequences using MEGA6  and extracted the HA1 sequences for the three types of viruses respectively.
After obtaining the HA1 sequences, we divided the sequences into different bins according to the year information. Then a technique of sampling with replacement was applied to randomly select one sequence from each bin (i.e. year). We repeated the sampling process for N times to obtain enough statistics. After that, the sequences from every two adjacent years were aligned to obtain mutations between the two sequences. The records of site mutations were treated as “transactions” in association rule mining , which was applied to find the rules of co-occurring site mutations. Here LCM  was used to carried out association rule mining.
From the rules obtained from association rule mining, we infer which sites tend to be co-mutated during the evolution of the influenza virus. To predict potential mutations in the future, following , we first find out the sites under positive selection and obtain the sites co-evolving with the positive-selection sites, which would be predicted as the sites to be mutated. In , the positive selection site is defined as “a site that has been mutated between successive years and then remains fixed in the population for at least 1 year”. To obtain the sites under positive selection, we need to determine which sites are mutated in a particular year. Unfortunately, currently there is no standard way to obtain the yearly site mutations (i.e. the benchmark of our prediction), which make it difficult to determine the positive selection sites. Therefore, here we treat the sites occurring frequently in our rules (i.e. the sites co-mutated frequently with other sites, or with large in-degree) as the sites to be mutated.
Phylogenetic trees were constructed with 1000 randomly selected sequences from corresponding dataset mentioned above, using NCBI tools in “Influenza Virus Sequence Tree”  based on the neighbor-joining method and mPAM distance. Co-evolved sites output by our method were mapped to the virus HA protein structure retrieved from the Protein Data Bank [39–42] using Chimera (v1.11) . The epitope regions of H1N1 and H3N2 are marked according to [44–47]. The epitope information of flu-B are from [48, 49].
This article has been published as part of BMC Medical Genomics Volume 9 Supplement 3, 2016. 15th International Conference On Bioinformatics (INCOB 2016): medical genomics. The full contents of the supplement are available online https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-9-supplement-3.
Publication of this article was funded by AcRF Tier 2 grant MOE2014-T2-2-023, Ministry of Education, Singapore.
Availability of data and materials
Data, code, and Additional files are available at: https://github.com/Xinrui0523/comutation.
HC conceived and directed the project. HC and XZ performed experiments, interpreted results, and wrote the manuscript. JZ and CK revised the paper, provided overall supervision, direction and leadership to the research. All authors have read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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