A Bioinformatics Approach to Analyzing Influenza Ray Hylock

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1 A Bioinformatics Approach to Analyzing Influenza Ray Hylock 1 Introduction During its annual emergence, influenza causes 3 to 5 million severe illness cases and 250 to 500 thousand deaths. Receiving a yearly vaccine can decrease an adult s susceptibility to severe illness by 70 to 90% and an elderly person s by as much as 60% with a reduction in death upwards of 80% (WHO, 2009). Influenza strains, however, are rapidly evolving due to antigenic drift (which is the continuous process of genetic and antigenic change among influenza strains) making it difficult to identify and identify an appropriate vaccine. Thus, it is vital to study the evolutionary behaviors of influenza. Tropical and subtropical zones as opposed to temperate have a less well defined influenza season. Therefore, larger quantities of influenza are found circulating throughout the year in these climate regions. Surveying influenza in these zones, allows for the earlier detection of new strains. Our chosen anchor paper did as such in Vietnam (D. Li et al., 2008; Simonsen, 1999; Viboud, Alonso, & Simonsen, 2006). Collecting both A/H1N1 and A/H3N2 from patients in and around Hanoi between 2001 and 2006, the authors built the first and only known source of sequences of hemagglutinin (HA), neuraminidase (NA), and matrix protein (MP) genes (see 2.1) for that locale. Their aim was to analyze the evolutionary patterns of HA, NA, and MP in order to better understand their evolution for the purpose of selecting more appropriate vaccines (D. Li et al., 2008). In this paper, we recreate the phylogenetic analysis for HA and MP from sequence to tree as described in the anchor paper. Our results coincide with those of the article. We then take two additional steps based on the following revelations. First, Li et al. (2008), claim their results show MP has a non-linear pattern of evolution not linked to that of the sequential evolution of HA. However, the mere appearance of non-associative properties does not preclude its existence. Therefore, we aim to examine the notion that it is possible to predict the phylogenetic group of MP using only the HA sequences, specifically, the columns in the multiple sequence alignment having a transition or transversion (or both). Thus, effectively showing there is a relationship between the two segments. There are, however, too few isolates provided by Li et al. (2008) to conclude one way or the other with any degree of certainty. Therefore, we collected an additional 206 isolates from the surrounding regions. The subsequent multiple sequence alignment revealed two distinctive groups of isolates, suggesting the potential emergence of another strain around the year Assumptions are made about each strain, such as the proliferation of recombination events, which, if inaccurate, can hinder the performance of a predictive algorithm. Therefore, it is of immense importance to determine if the groups are related or not before continuing with classification. 2 Influenza A 2.1 Molecular Mechanism A great deal is known about influenza A at the molecular level as it has been the topic of many studies. The genome structure of influenza A viruses consists of eight segments (11 encoded proteins) of negative-sense single-stranded RNA; here are the three we will consider in this paper. First is the hemagglutinin (HA) protein which has 16 subtypes and is segment 4. The HA protein is involved in attachment and membrane fusion in the endosome of the infected cell. Its antigenic domains are on the surface and can be subsequently altered, allowing the virus to avoid a humoral response without affecting its ability to bind to the receptor. These mutations aid in avoiding the host s immune system (Bhoumik, 2010; Compans & Orenstein, 2009; Tam & Sellwood, 2009). Second is neuraminidase (NA), segment 6, with 9 subtypes. The NA protein digests the sialic acid (neuraminic acid) bonds formed by HA and the host cell. By late in infection, the sialic acid will have been removed from the infected cell surface making it is easier for the progeny virions to diffuse away once they exit the cell. Neuraminidase is also involved in penetration of the mucus layer in the respiratory tract. Like HA, NA mutates with high frequency to avoid the host s immune system (Bhoumik, 2010; Compans & Orenstein, 2009; Tam & Sellwood, 2009). The third utilized segment is segment 7, the matrix protein (MP). There are two proteins encoded by MP: M 1 and M 2. M 1 is a coating inside the viral envelope. It is involved in virus replication and has a crucial role in viral assembly (Wakefield & Brownlee, 1989). M 2 is a transmembrane protein which allows virion acidification for efficient uncoating after fusion in endosomes (Gannagé et al., 2009). 2.2 Importance of the Matrix Protein According to the Influenza Research Database, 1 human H1N1 has the following number of isolates of HA, NA, and MP: 8,651, 6,599, and 5,486 respectively. Likewise, for H3N2, there are 9,445, 4,042, and 4,335 isolates. From the results in the anchor paper, the groupings of HA and NA were very similar. However, the MP phylogenetic groupings were quite different in that there were consistently fewer groups. As can be seen from the values above, the number of HA segments far exceed those of MP. However, MP is important in determining the overall structure of an enveloped virus and even where it buds. New approaches are being developed using information about budding to try and intercept the virus just as it 1

2 Hylock Fall :169 2 exits the cell (Durham University, 2009; Money, McPhee, Mosely, Sanderson, & Yeo, 2009). Therefore, determining whether one can effectively predict the phylogenetic group (those isolates closest evolutionarily to the isolate in question) of MP from, in this paper, transitions and transversion in HA, is of importance to the overall surveillance and ultimate forecast of upcoming strains and subsequent treatment options. 3 Data Collection We began by collecting all of the sequences deposited by Li et al. (2008) into DDBJ 2 (extracted from EMBL 3 ) for H1N1 and H3N2 pertaining to HA and MP. One caveat was we only gathered HA sequences which had an accompanying MP sequence as both were required for predictive analysis. Next, using the Influenza Research Database, 1 the reference isolates presented by Li et al. (2008) were also gathered. Upon completion of the recreated experiment, additional isolates were required to elicit a more statistically powerful prediction accuracy (see and 5.2.2). From the Influenza Research Database, an additional 206 H1N1 (HA/MP) isolates from locations surrounding Hanoi were collected. The isolate locations are as follows: Bangkok, Thailand; Cambodia; Fujian, China; Fuzhou, China; Guangdong, China; Guangzhou, China; the Philippines; and general Thailand isolates. They ranged in years from 2005 to Bioinformatics Tools, Insights, and Implementation For both the recreated and additional experiments, multiple sequence alignments and phylogenetic trees were required. A multiple sequence alignment can imply a common ancestry in terms of conserved regions and mutations affecting and not affecting structure and function. It is also a necessary first step in producing a phylogenetic tree. A molecular phylogeny is used to study the evolutionary relationships among the isolates. This provides information about the emergence or recombination of strains as well as a means to understand its evolutionary history which may lead to treatment options. Two alignment methods were implemented to determine the sensitivity of the classifier to the alignment. They were ClustalW2 3 (run online at EMBL-EBI) and ProbConsRNA 4 (downloaded and ran locally). ClustalW is a progressive alignment method which computes all pairwise alignment scores between nucleotide sequences then, beginning with the closest two sequences, progressively builds the alignment by adding subsequent sequences by score (Pevsner, 2009). The main advantage of this method is its speed compared to others. However, accuracy is traded for said decrease in time. Once two sequences are aligned, they cannot be altered. ProbConsRNA, on the other hand, trades speed for accuracy. ProbConsRNA is a consistency-based approach. It uses information about the alignment to guide the pairwise alignment and offers an iterative component to further enhance the final solution. Consistency-based alignments have repeatedly outperformed progressive alignment in benchmark studies (Do et al. 2005; Pevsner, 2009). Like the sequence alignment methods, two phylogenetic tree building algorithms were chosen. The first is neighborjoining (implemented using MEGA v5b6.1 on ClustalW alignments). Like ClustalW, it trades accuracy for speed. Starting out in a star topology, it computes all nn(nn 1) pairwise distances. It then selects the two most closely related taxa which are 2 then treated as one in the star-like structure. This process iterates until the tree has been completed. The overall tree contains the minimum of the branch lengths at each stage of clustering and produces a result under the principle of minimum evolution. However, this does not necessarily make it the minimum evolutionary tree, but experiments have shown it is quite efficient in obtaining the correct tree topologies (Pevsner, 2009; Saitou & Nei, 1987). Our second method utilized is Maximum Likelihood (implemented using MEGA v5b6.1 using ProbConsRNA alignments). Again, swapping speed for accuracy, the Maximum Likelihood approach builds a tree based on the probability that the given topology and branch lengths, along with a model for nucleotide substitutions, would give rise to the observed data set. This method is statistically well founded and more robust than others in terms of input size and large variations between sequences (Felsenstein, 1981; Pevsner, 2009). For our predictive analysis, the Bayesian Network classification model in Weka 3.7 was used. 5 A Bayesian network is a probabilistic graphical model representing a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). 6 It is basically a HMM with only one level of probabilities conditional on the classes they are associated with. This analysis provides insight into whether a set of features can be successfully utilized to predict a desired outcome. In our case, the set of features is a hemagglutinin multiple sequence alignment and the outcome is its matrix protein s phylogenetic group

3 Hylock Fall : Computational Analysis 5.1 Recreated Experiments Using ClustalW2 we aligned the H1N1 and H3N2 sequences provided in our anchor paper. The resulting alignments were then manually edited using Jalview 7 and imported into MEGA. 8 Following the specification presented in the anchor paper, we performed a bootstrap test of phylogeny using neighbor-joining with 1,000 replicates. We did have to set the model parameter ourselves and after testing several, Kimura 2-paramter produced the appropriate phylogenies. Phylogenetic groups with bootstrap values greater than 70% were identified as core groups. Upon comparing our four resulting phylogenies to those in the article, we found the following: (1) all of the groupings were the same and (2) at best the bootstrap values were identical and at worst they were no more than 5% lower. 5.2 MP Prediction from HA Sequence Data Continuing with the data provided by Li et al. (2008), we implemented a Bayesian Network classification algorithm to predict H1N1 and H3N2 MP group assignments using only the HA sequence data. To set up the training/testing set, we require two components: (1) a multiple sequence alignment and (2) a phylogenetic output of MP. Following Li et al. (2008), a ClustalW alignment was generated for each set and fed into a neighbor-joining phylogenetic algorithms ( 5.1). These results were compared to that of a ProbConsRNA alignment and a bootstrap test of phylogeny using Maximum Likelihood with 500 replicates. As was discussed in 4, both ClustalW and neighbor-joining run faster than their ProbConsRNA and Maximum Likelihood counterparts, however the latter may produce more accurate results which could impact the predictive algorithm. Thus, both are examined in this section. Beginning with the H1N1 and H3N2 HA alignments, we removed all columns not containing at least one transition or transversion. This accomplished two tasks. First, it removes noise from the data in terms of conserved nucleotides (upwards of 90% in our experiments) which serve only to confuse the probabilistic classifier. Second, it reduces the overall feature space, thus reducing the algorithmic run time. Next, we removed the additional reference isolates from both sets as they too produced noise in the model, resulting in a final set of 26 isolates in H1N1 and 32 in H3N2. Finally, we appended a class column containing the resulting MP groups from the phylogenetic output. The phylogenetic output for H1N1 and H3N2 both contain two groups (regardless of the alignment and phylogenetic methods), however the distribution in H1N1 is 24 in Group I and 2 in Group II. Therefore, in order to diversify the classes, Group I was broken into two pieces: Group Ia consists of A/Hanoi/ISMB31/2005 to A/Hanoi/BM356/2006 and Group Ib all others (see Li et al. (2008)). So what is the point of all of this? The HA sequences were labeled with their MP s phylogenetic group as that is the actual class. We evaluate the classifier using 10 iterations of a 10-fold cross-validation which does the following. For each iteration, the entire set is broken into 10 stratified folds. The first nine are used to train the classifier while the remaining one is used to test the results. That is, the final fold s class is predicted and then compared to the actual one which produces an accuracy and error. Repeating this process 10 times allows us to average out the errors (J. Han & Kamber, 2006; Tan, Steinbach, & Kumar, 2005). To classify a new isolate whose MP is unknown: (1) align the HA sequences with the new one, (2) then remove the isolate in question (cannot be used to train), (3) create an input file, (4) build a model, and (5) feed the new isolate into the produced model, resulting in a group prediction with an associated probability Bayesian Network Classification Results Original Data Sets The results from the Bayesian Network classification for H1N1 and H3N2 can be found in Table 1. Regardless of the alignment method and phylogenetic algorithm, the outcomes within each set were identical. The high accuracies of 96.15% (25/26) in H1N1 and 100% in H3N2 are encouraging, but the input sizes are far too small to make a final determination. Therefore, additional isolates from the surrounding area are needed before a conclusion can be reached. Table 1: Bayesian Network Classifier Results Original Isolates Strain Type Alignment Phylogeny HA/MP Groups Accuracy H1N1 ClustalW Neighbor-joining 96.15% (25/26) H1N1 ProbConsRNA Maximum Likelihood 96.15% (25/26) H3N2 ClustalW Neighbor-joining 100% (32/32) H3N2 ProbConsRNA Maximum Likelihood 100% (32/32)

4 Hylock Fall : Bayesian Network Classification Results Extended H1N1 Data Set Additional Sequences Due to time constraints, we elected only to expand the H1N1 set. We collected 206 additional sequences from surrounding regions ( 3). After running both alignment methods, two interesting anomalies emerged. First, two idiosyncratic sets of conserved regions were identified; 166 in Group I and 66 in Group II. Second, isolates from the first group were from prior to and including 2009 whereas the second group began in 2009 and continued into 2010 (with one exception: A/Thailand/271/2005). These raise the following questions. Why is A/Thailand/271/2005 so unlike the others? Also, could the divide indicate the emergence of a new strain? Upon visualizing the full phylogenetic output for the overall set of isolates, the nature of A/Thailand/271/2005 was discovered. As can be seen from Figure 1, this particular sequence almost perfectly (with bootstrap values of 99%) connects the two groups. After removing the isolate from the alignment, the phylogenetic tree remained in its previous order, but with little connectivity between the two groups (Figure 2). Furthermore, upon inspection of the ClustalW alignment file, this particular isolate was last, indicating it is the least like both sets. These results, therefore, may signify A/Thailand/271/2005 is some transition strain. A/Thailand/271/ Group 1 (166 isolates) Group 1 (166 isolates) 99 Group 2 (65 isolates) Figure 1: Phylogenetic output with A/Thailand/271/ Group 2 (65 isolates) Figure 2: Phylogenetic output without A/Thailand/271/2005 As mentioned in the introduction, assumptions are made about each strain, such as the proliferation of recombination events which can hinder the performance of a predictive algorithm. That is, prediction within a group follows certain rules. Trying to predict MP from HA in the same, for example, avian H1N1 strain is not the same as from a swine H1N1 MP and an avian H1N1 HA. However, a group where MP is from swine H1N1 and HA is from avian H1N1 follows the same set of rules and assumptions. Therefore, they can be seen as one cohesive and valid group. To determine whether or not two strains exist, we computed the overlapping sequences for each group, separately and then combined, using a nucleotide BLAST search. The point of this is to determine whether the groups share an evolutionary history. If so, then it may indicate some level of mutation and if not, then it may signify the emergence of a new strain. The BLAST seeds were randomly selected isolates from each group. Group I contained 5 countries (Cambodia, China, Vietnam, Thailand, and the Philippines); one randomly selected from each. Group II was made up of only two countries (Thailand and China), but the two Chinese cities were quite distant from one another so we selected one isolate at random from Thailand and one from each of the two Chinese cities. For each of the eight isolates, the top 10,000 sequences (E = ) were retrieved. Using the 50,000 sequences from Group I and 30,000 from Group II, the intersecting sequences for each group individually and both combined were determined. The intersecting group for Group I consisted of the following most frequent sequences: New York, Texas, Canterbury, Thailand, Washington, Hawaii, Managua, Niigata, Denmark, Hanoi, Philippines, Brisbane, and Johannesburg. Fascinatingly, although the dates ranged from 1997 to 2009, the strains were predominately spread out over 2000 to Group II contained the following most frequent isolates: New York, Wisconsin, Texas, California, Managua, Mexico City, Thailand, Guangdong, Nagasaki, Washington, Ontario, and Qingdao. Ranging from 1957 to 2010, these isolates were predominately from 2009 and Interestingly, the North American sequences were generally from This is consistent with the origination of the H1N1 outbreak of 2009 (Bhoumik, 2010). Subsequently, the Asian sequences began showing up in 2009 and continued into 2010, indicating the viruses spread. Furthermore, the intersection of both groups prior to and including 2005 (when A/Thailand/271/2005 emerged) consisted of only 26 isolates. All of these allude to the fact these groups are indeed two separate strains Group I pre-2009 pandemic H1N1 swine flu while Group II is the 2009 pandemic swine flu and therefore, must be considered separately Bayesian Network Classification Results for both Groups After cleaving the extended H1N1 alignment in two, we followed the preparation steps described in 5.2 to produce a ready-to-mine file. The Bayesian Network classification results for both the H1N1 groups can be found in Table 2. Contrary to the H1N1 and H3N2 findings in 5.2.1, the larger sets produce differing intra-strain results. As can be seen throughout Table 2, the ProbConsRNA and Maximum Likelihood combination significantly outperformed their competition. One reason for the improvement can be seen in the HA/MP Groups columns. ProbConsRNA and Maximum Likelihood consistently generated more groups. Remember, a group is defined as having a bootstrap value greater than 70%. This is directly attributable to the stark differences between the two Group II results. For the first one, there are only 3 MP groups whereas the second setup produced 4. The fourth group consisted of only two sequences, but those two

5 Hylock Fall :169 5 appeared to have confused the classifier. As the Bayesian network is a simple probabilistic classifier, a few poor sequences can derail its entire function. The neighbor-joining phylogeny did separate those two sequences into a single group, but their bootstrap value was only 57 which did not permit us to identify it as a core group. Table 2: Bayesian Network Classifier Results Extended Group Isolates Strain Type Alignment Phylogeny HA/MP Groups Accuracy Group I ClustalW Neighbor-joining HA 8 MP % (136/166) Group I ProbConsRNA Maximum Likelihood HA 9 MP % (161/166) Group II ClustalW Neighbor-joining MP % (31/66) Group II ProbConsRNA Maximum Likelihood HA 5 MP % (63/66) 6 Conclusions and Future Work The experiments in this article have shown strong evidence to refute Li et al.'s (2008) claim that HA and MP are unrelated. Through predictive analysis, it is possible to use the transitions and transversions within HA to forecast the phylogenetic group of its MP counterpart with a high degree of accuracy. Furthermore, our results illustrate the necessity of employing strong alignment and phylogenetic tree building algorithms as they are the foundation for classification. The logical progression from here would be to see if these results are generalizable and extensible to other segments. In order to test this hypothesis, thousands more sequences for each of the eight segments from various regions around the globe would need to be collected. Example sources of said isolates are the Influenza Research Database and the Influenza Genome Sequencing Project. 9 Also, customized computer software would be needed to perform simple and complex tasks as the time required to manually perform those steps is prohibitive for the quantities of isolates required. Straightforward tasks include the retrieval and assembly of the isolates into multiple sequence alignment files and converting inspected multiple sequence alignments into data mining files (without the class variables). Adding the class label is an example of an intricate task necessitating an algorithm from which MEGA phylogenetic output is processed, producing group assignments based on the branch bootstrap values. Furthermore, the creation and manipulation of the input and output files alone are not the only costly components. Generating a ProbConsRNA alignment and a Maximum Likelihood phylogeny are very expensive. From the isolates gathered for our experiments, we created example sets of size 25, 50, 100, and 240, each isolate of size 1,000 nucleotides. These test sets were run, recording their exact execution times, through ProbConsRNA and the Maximum Likelihood algorithm. 10 Using those times, the following equations were derived, expressing the time needed to process one isolate: ProbConsRNA: xx xx and Maximum Likelihood: xx xx Thus, if 1,000 isolates were processed, ProbConsRNA would require 6.58 days/alignment and Maximum Likelihood, 3.36 days/phylogeny. Therefore, access to a high performance computing cluster is essential to the success of this project. The Bayesian Network runtime is negligible (minutes), but there are nn(nn 1) segment to class combinations, so a parallel algorithm may be useful (e.g., Weka-Parallel 11 ). Beyond computation, several key issues must be addressed. First, strain contamination and time coverage. Published sequences may have been derived from contaminated samples. Thus, high quality sequences from sources such as the Influenza Genome Sequencing Project can be used to identify potentially tainted results. Furthermore, a region in the experiment should not contain only one isolate for the given time period as there is no means by which to verify its accuracy (Boni, de Jong, van Doorn, & Holmes, 2010). The second concern needing to be attended to is strain recombination. Strain recombination is the result of two or more strains, e.g., avian H1N1 and swine H1N1, comingling producing a new strain with, for example, three segments from the avian variant and five from the swine. From the standpoint of prediction, trying to classify a swine segment from an avian one within a cohort of avian/avian, could lead to very low accuracy rates. Therefore, those isolates must be removed from the general isolate population and placed into a group with the same segment recombinants (see ). Lastly, the concept of intra-segment homologous recombination must be dealt with. Intra-segment homologous recombination is the joining of two or more strains from the same segment into one. Some may wonder if this event might have occurred and thus the cause of any poor predictions found in the analysis. However, only three cases of this have ever been reported (Boni et al., 2010; M. J. Gibbs, Armstrong, & A. J. Gibbs, 2001; He et al., 2008, 2009; Silander et al., 2005). Of those three, two (M. J. Gibbs et al. (2001) and He et al. (2008)) have been mightily refuted and the third (He et al. (2009)) left in serious doubt (Boni et al., 2010; Strimmer et al., Run on a MacBook Pro with Mac OS X , Intel Core 2 Duo 2.26GHz processor, and 4GB 1,067MHz DDR3 RAM. 11

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