Chapter 4 A Bayesian Approach for Quantifying the Antigenic Distance of Influenza A (H3N2)
4.3. Materials and methods
4.4.7. Antigenic drift and vaccine strain selection
We considered the ADLR together with the data on antigenic drift. The WHO has updated the component for A (H3N2) influenza vaccine 23 times from the 1968 to 2008 influenza season [14, 118]. We detected the antigenic drift as well as to detect the emerging of antigenic variants, which measures the match of WER strain and circulating strains, and we judged the variants are emerging when the variant ratio ≥ 0.5. The WER strains are the dominant antigenic type in each influenza season and the summary are listed in Table 4.2. The average change of ADLR from 1982-1983 to 2008 is shown in Fig. 4.9B, in which the ADLR shows that the antigenic variant usually emerges before the season that WER strain replacement. For example, the ADLR between circulating strains and WER strain (A/Sydney/5/97) increased from 1997-1998 to 1999, in which season the mean of ADLR is 2.10 and VR is 0.57, we judged the variants emerged in 1999. Indeed the next WER strain (A/Moscow/10/99) dominated at next influenza season.
Figure 4.9B shows more details of the evolution of 5 segments than Fig. 4.8B. For example,
the segment V started to mutate in 1999-2000 season in 2,789 HA sequences, while the segment started to mutate in 2002 in Smith's 253 sequences. There are 7 remarkable peaks for ADLR
before the season of 2004 (1987-87, 1989-90, 1991-92, 1995-1996, 1999, 2002-2003 and 2004) and 5 of them followed by the dominant of new WER strains that in different antigenic clusters.
This result implies that antigenic cluster change have higher ADLR than the update of other WER strain. Furthermore, the variant ratio for 14 seasons are larger or equal to 0.5 and 12 of them followed by a WER strain replacement in next season, which suggested that ADLR can detected the emerging of antigenic variants.
0
1982-1983 1983-1984 1984-1985 1985-1986 1986-1987 1987-1988 1988-1989 1989-1990 1990-1991 1991-1992 1992-1993 1993-1994 1994-1995 1995-1996 1996-1997 1997-1998 1998-1999 1999 1999-2000 2000 2000-2001 2001 2001-2002 2002 2002-2003 2003 2003-2004 2004 2004-2005 2005 2005-2006 2006 2006-2007 2007 2007-2008 2008
ADLR
0000 1982-1983 0000 1983-1984 0000 1984-1985 0000 1985-1986 0000 1986-1987 0000 1987-1988 0000 1988-1989 0000 1989-1990 0000 1990-1991 0000 1991-1992 0000 1992-1993 0000 1993-1994 0000 1994-1995 0000 1995-1996 0000 1996-1997 0000 1997-1998 0000 1998-1999 0000 1999 0000 1999-2000 0000 2000 0000 2000-2001 0000 2001 0000 2001-2002 0000 2002 0000 2002-2003 0000 2003 0000 2003-2004 0000 2004 0000 2004-2005 0000 2005 0000 2005-2006 0000 2006 0000 2006-2007 0000 2007 0000 2007-2008 0000 2008
V ar ia nt R atio
Figure 4.9 The distribution of ADLR and the antigenic drift from 1982-1983 to 2008 influenza season. (A) The distributions of variant ratios of WER strains from 1982-1983 to 2008 season.
The match between Model four and WER are labelled (Match in red arrow; Not match in blue arrows). (B) The average ADLR from 1982-1983 to 2008.
4.5. Discussion
The LR quantify the antigenic distance of amino acid position from HI assays and identified 69 positions that with LR larger than 1. Among these positions, there were 6 positions that are almost conserved from 1968 to 2000 and underwent frequency switch [41] after year 2000 (position 25-Other, 75-E, 140-A, 202-Other, 222-Other and 225-Other). The new emerging positions suggest that the previously conserved positions may become new antibody binding sites and LR can identify these positions from HI assay. Due to the emerging of new mutations, the 131 positions that were previously identified as epitope in year before year 1999 [9, 31] are suggested to be updated to incorporate new positions. These new mutations also indicated that the Bayesian method similar to our method must incorporate new HI assay data to capture the emerging mutations. Recently, Lees et al. proposed 109 additional positions to extend the original 131 epitope positions [119], which also suggested the need of update the definition of epitope.
We used the five identified segments, which are located within 20Å to the sialic acid, to model the antigenic evolution and the results showed some interesting patterns. The first pattern is the interactions among ≥ 3 positions in segment III and IV that both located on epitope B, which are observed in the seasons 1985-1986, 1986-87 and 1987-1988 and the 10 vaccine-pairs.
In the training set, there are 129 virus-pairs match this pattern and 118 of them are antigenic variant (91%). For example, there are three mutations in the vaccine-pair A/Mississippi/1/85 and A/Leningrad/360/86 (156-B, 159-B and 188-B). The other pattern is the interactions among ≥ 3 positions in three segments, which are observed in 12 vaccine-pairs. In the training set, there are 158 virus-pairs matching this pattern and 138 of them are antigenic variants (87%). For example, there are three mutations in the vaccine-pair A/Wisconsin/67/2005 and A/Brisbane/10/2007 (140-A, 156-B, 186-B).
From the view of occlusion of BRS by antibodies, LR identified positions within 20Å to sialic acid have higher antigenic distance and ADLR correlated to antigenic distance well based on these positions. Most of the positions with LR > 1 within this distance are located on epitopes A, B and D (30/35), and previous studies suggest epitopes A and B are more antigenic important than other epitopes. Although the epitopes C and E that located more than 20Å to sialic acid were observed to be recognized by antibodies (PDB code 1EO8 [120] and 1QFU [60]), the neutralizing efficiency of them are lower than epitopes A and B [11]. Recently, Ndifon et al.
studied the neutralization efficiency of epitopes and proposed that mutations on epitopes C and E
possibly increased the neutralization efficiency [61]. In other words, mutations on these two epitopes possibly increased the efficiency of antibodies neutralization.
Recently, Ekiert et al. identified an antibody recognizing a highly conserved epitope among several subtypes of influenza viruses (Subtypes H1, H2, H5, H6, H8 and H9) [63]. This identified antibody presents a new prospective to design the influenza vaccines against diverse subtypes of influenza viruses [121]. The conserved epitope are composed of residues from HA1 and HA2 chains. However, most studies focused on the evolution of HA1 domain and the HA sequences in database often lack the HA2 domain. The proposed index, LR, may provide new insights for the development of influenza vaccine when we consider both HA1 and HA2 sequences.
For the modeling of vaccine update, we proposed the variant ratio, which is an index to detect the emerging of antigenic variants. However, the variant ratio can only measure the degree of match between vaccine strains and circulating strains, but can not determine whether the variants will be dominant in future seasons or not.
4.6. Summary
This study demonstrates our model is robust and feasible for quantifying the antigenic distance of amino acid positions by LR. Based on naïve Bayesian network and LR, we developed an index, ADLR, to quantify the antigenic distance of a given pair of HA sequences. According to the LR values and entropies of positions, we found that the positions locating on the epitopes and near the receptor-binding site are crucial to the antigenic variants. The accumulated critical mutations, which are near (≤ 20 Å) to the receptor-binding site, often drive the antigenic drift due to the conformation change to escape from neutralizing antibodies. The ADLR values are highly correlated to the HI assays and can explain the selection of WHO vaccine strains.
Chapter 5 Conclusion
5.1. Summary
In this thesis, we study the relationships between genetic evolution and antigenic evolution focusing on three dimensions. In short, the major contributions of this thesis can be summarized as follows:
1. We identified critical amino acid positions, rules, and co-mutated positions for antigenic variants. The information gain (IG) and the entropy are used to select critical positions. The co-mutated positions can infer the co-evolution between amino acid positions on HA. The rules, which are derived from the decision tree, describe when one (e.g. circulating) strain will not be recognized by antibodies against another (e.g. vaccine) strain based on a given pair of HA sequences.
2. We developed an epitope-based method for identifying the antigenic drift of influenza A utilizing the conformation changes on antigenic sites (epitopes). Our experimental results show that two critical mutations can induce the conformation change of an epitope. The epitopes (A and B), which are near the receptor-binding site of HA, play a key role for neutralizing antibodies. Two changed epitopes often drive the antigenic drift and can explain the WHO vaccine strain selection.
3 We developed a Bayesian method for identifying the antigenic drift of influenza A by quantifying the antigenic effect of each amino acid position on HA. We utilized the likelihood ratio (LR) and a developed index, ADLR, to quantify the antigenic distance of an amino acid position and a given pair of HA sequences, respectively. Our experimental results show that accumulated critical mutations, which are near (≤ 20 Å) the receptor-binding site, often drive the antigenic drift due to the conformation change to evade the recognition by immune system. The ADLR can predict antigenic variants; detect vaccine-vaccine transitions and explain WHO vaccine strain selection.
5.2. Future work
There are several directions for the future work
1. For the antigenic drift, which is related to the recognition between antigen (HA) and antibodies, the structural information should be incorporated to improve the current understanding of the structural change on HA for antigenic drift.
2. Due to the high degree of structural similarity between HA of different subtypes of influenza viruses, our findings on the H3N2 virus can be mapped to other subtypes of influenza viruses (e.g. H1N1 and H5N1 viruses) for comparison. We expect the findings from H3N2 virus to provide new insights for the studies of other subtypes of influenza virus.
3. For the vaccine strain selection, our models for predicting antigenic variants may provide new insights to select which of today's strain is likely to be dominant in the coming year's epidemic
4. Based on our study of interactions between influenza viruses and antibodies, we are interested in the interactions between antigens and antibodies. We may extend our research to general antigen-antibody interactions in the future.
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