Category of paper: Brief Communications
Using
Combinatorial
Bioinformatics
Methods
to
Analyze
Annual
Perspective Changes of Influenza Viruses and to Accelerate Development of
Effective Vaccines
Conflict of Interest
Intellectual Property Center of National Chung Hsing University has ownership of the herein
described methods. The terms of this study have been reviewed and approved by the authors
and the benefiters in accordance to the policy of Intellectual Property Center of National
Chung Hsing University.
Yu-Jen Hua, Kuan-Chih Chowb, Ching-Chuan Liuc, Li-Jen Lind, Sheng-Cheng
Wange, Shulhn-Der Wangf,*
a.
Department of Mathematical Sciences, National Chung Hsing University,
Taichung, Taiwan.
b. Graduate Institute of Biomedical Sciences, National Chung Hsing
University, Taichung, Taiwan
c.
Department of Pediatrics, College of Medicine, National Cheng Kung
University and Hospital.
d. School of Chinese Medicine, College of Chinese Medicine, China Medical
University, Taichung, Taiwan
e.
National Taichung Wen Hua Senior High, Taiwan.
f.
School of Post-Baccalaureate Chinese Medicine, College of Chinese
Medicine, China Medical University, Taiwan.
Running title: Vaccine Design by Using Bioinformatics Softwares
*
Correspondence: Shulhn-Der Wang. School of Post-Baccalaureate Chinese
Medicine, College of Chinese Medicine, China Medical University, Taiwan,
R.O.C. Tel.: +886 4 22053366 x 3110; Fax: +886 4 22013703. E-mail:
wst917@gmail.com
Abstract
The standard WHO procedure for vaccine development has provided a guideline for
influenza viruses, but no systematic operational model. We recently designed a systemic
analysis method to evaluate annual perspective sequence changes of influenza virus strains.
We applied dnaml of PHYLIP 3.69, developed by Joseph Felsenstein of Washington
University, and ClustalX2 by Larkin et al., for calculating, comparing and localizing the most
plausible vaccine epitopes. This study identified the changes in biological sequences and the
associated alignment alterations, which would ultimately affect epitope structures, as well as
the plausible hidden features to search for the most conserved and effective epitopes for
vaccine development. Our new way could accelerate development of vaccines, which could
combat several viral strains at the same time.
KEYWORDS：Biological sequences; Phylogenetic tree; Vaccine development
Introduction
Influenza virus contains hemagglutinin and neuraminidase which were coding by H
stands and N stands, respectively. The different numbers stand of H and N represent different
strains of the virus.1-3 Generally, immune response loci of hemagglutinin (HA) and
neuraminidase (NA) were used for designing vaccines. The standard WHO procedure for
vaccine development4-6 has provided a guideline for influenza viruses, but no systematic
operational model.4
We recently designed a systemic analysis method to evaluate annual perspective
sequence changes of influenza virus strains, and we found a better way to design and to
accelerate vaccine development, which could be used to combat several viral strains at the
same time. By this way, this new procedure of vaccine development, which would at least
reduce 3-6 weeks from standard processing procedure of WHO.4 Our design could help
scientists shorten time of blind tests, and could be a better method for producing vaccines
against unknown strains of influenza virus based on previously known flu vaccines.
Our methods could rapidly identify similarity among strains and to localize the closest
strains. When a new strain of virus is confirmed for disease manifestation, we can
instantaneously find the closest pre-made vaccine by a whole sequence matching with
Needleman-Wunsch (N-W) Dynamic Programming to identify the common and reserved
antigenic areas. In fact, in i-ENVEX (Malaysia, March, 2013), using the same combinatorial
methods, we had predicted that H7N9 could be derived from H7N7, of which sequences of
the isolated virus were later reported by Lam et al.7
In this study, we use the same method to analyze sequences of influenza viruses that are
most devastating to humans, and we plan to develop bioinformatics methods to consolidate
the predicting consistency and reliability of our model. By such biological method, we might
be able to design a perspective way to quantitatively produce vaccine without compromising
effects of lethal viruses.
Materials and Methods
Analysis programs
In this study, we applied Unweighted Pair Group Method with Arithmetic Mean
(UPGMA)1,8-10, Maximum Likelihood Estimate (MLE)11 and N-W
12
Dynamic Programming
to evaluate evolutional changes and relationship between viruses in polymerase basic 2 (PB2)
and NA, resepectivrly. Two Evolution Analysis Softwares, dnaml of PHYLIP 3.6913and
ClustalX210 were used to show the phylogenetic pedigrees among viruses results of MLE and
UPGMA, respectively.
Sequences of influenza viruses
The genetic sequences of H1N1 (GenBank: GQ132185.1), H1N2 (GenBank: AY129157.1),
H2N2 (GenBank: DQ508843.1), H3N2 (GenBank: U71142.1), H3N8 (GenBank:
KF790582.1), H5N1 (GenBank: AF509095.1), H5N2 (GenBank: AY300929.1), H5N3
(GenBank: AY207510.1), H7N1 (GenBank: AY207538.1), H7N2 (GenBank: CY037097.2),
H7N3 (GenBank: CY076858.1), H7N7 (GenBank: CY133723.1), H7N9 (GenBank:
KF420298.1) and H10N7 (GenBank: CY157240.1) had been collected from the databases of
National Center for Biotechnology Information.
Analysis of immune loci
We applied ClustalX2 and Dynamic Programming to analysis the difference bases (immune
loci) between those 14 NA sequences.
Results
To analyze the relationships among various influenza viruses, we had collected genetic
sequences of H1N1, H1N2, H2N2, H3N2, H3N8, H5N1, H5N2, H5N3, H7N1, H7N2, H7N3,
H7N7 and H10N7 from the databases of National Center for Biotechnology Information.
Moreover, an influenza virus contains 8 nucleic acid fragments encoding for 11 proteins,
namely PB2, PB2-F1, PB1, PA, HA, NP, NA, M1, M2, NS1 and NS2.9,14 The sequence of
PB2 reveals evolutionary origin of virus and has often been used in phylogenetic
analysis.2-3,7,14 Therefore, MLE method was used to determine phylogenetic pedigrees of PB2
sequences among viruses. The results shown that H5N3 and H7N3 had the most correlation of
evolutional relationships, and there were also high similarity between H7N7、H7N9 and
H7N2 (Fig. 1A).
The NA gene of influenza virus encodes a surface protein that are essential to release
progeny virions from infected cell surface and prevent self-aggregation.15 Thus, the NA has
been targeted to design antivirals drug. In fact, NA inhibitor is one of two major classes of
antivirals licensed and effectively limits the severity of viral infections.15 Therefore, we
further investigate NA section of the 14 viruses by UPGMA. In Fig. 1B & 1C, we found that
PB2 analysis could be extrapolated to those for NA.
Production of yearly influenza virus vaccines was based on historical records to predict
the future possible prevalence.4 However, the error of prediction of influenza virus mutant is
inevitable. For example, lack of a framework for modeling nucleotide insertions/deletions
together with nucleotide substitutions also would make a mistake in phylogenetic analysis. To
avoid such conditions, multiple alignments of NA were constructed bases on the complete
nucleotide sequences using the N-W and then using UPGMA for precision calculation and
cross-examination (Fig. 1D and 1E).
To reduce an arithmetic error from calculation of phylogenetic diversity, we also
applied N-W and MLE to evaluate relationship between 14 viruses in NA sequences (Fig. 2A
and 2B). The result shown that there is not differ between UPGMA and MLE calculation (Fig.
1D and 2A; Fig.1E and 2B).
To find the bases difference between those 14 influenza viruses for determine the immune loci,
we further conducted a continuous analysis of NA sequence by N-W and MLE. We used
H7N9 and H5N3 for examples. The total length of H7N9 and H5N3 sequence were 1426 and
1422 base pairs, respectively. After calculation, H7N9 had 623 loci of immune response and
43.32% of blind tests could be cut back. The loci of immune response of H5N3 was 191 and
reduction of test was 13.55% (Fig. 2C and 2D).
Discussions
Previously available biomedical information are used for vaccine developments, the
major problems would be to predict the correct future viral strains that could cause
wide-spread infection and the possible viral mutations that might influence the presentation of
effective epitopes to provoke the protective immune responses.16 Projection of the best
epitopes is particularly important for the influenza viruses, which from time to time are
frequently lethal to the human beings. Moreover, because of the essence of the RNA viruses,
persistent gene mutations as well as possibly genome recombination and re-assortments are
inevitable, which on the other hand, could generate more deadly strains to cross species
barrier and to cause more wide-spread manifestation.
In this study, we have developed a novel method by combining UPGMA1,8-9, MLE and
N-W Dynamic Programming12 to define the annually detected mutational changes. We used
these annual results to determine the mutational ranges and the most probable mutation sites
in the gene, which could then be used to project by using computer analysis the coming
unknown virus, which would be the major target for production of the potential.12
In this way, uncertainty to produce quantitative vaccines could therefore be eliminated.
The derivation of statistical outcomes are shown in Fig. 1D to Fig. 2D. We could not only
identify the relationship as well as the subtle difference of known viruses, but also pin-point
the most plausible immune loci for biochemical experiments to raise effective antibodies (Fig.
2C and 2D). In addition, our results could be further extrapolated to the drug design, for
instance, the current design of antiviral drugs was focused primarily on NA, such as
TamifluTM and RelenzaTM which both are neuraminidase inhibitors.6 However, in our results
shown that hemagglutanins could be the next potential target such as neuraminidases.
Moreover, our previous study had been awarded by 2013 International Biochemical
Technology Invention. The patent rights for vaccine development are under application via
IPR Center of university in Taiwan (patent number:102134134 and 103111788) 9 and the US
(patent number:14/482,506). This study could effectively improve the current Reversed
Genetics procedure of vaccine development (Fig. 2C and 2D). Because the best epitopes to
provoke appropriate immunity could be identified faster, 3-6 weeks of developing time could
be saved. The cost of vaccine development could be markedly reduced as well. It also helps in
decision making of medical policy to cut down economy downtimes, and provides more
protections for the general populations.
References
1.
Krogh A. An Introduction to Hidden Markov Models for Biological Sequences. In:
Salzberg SL, Searls DB, Kasif S, editors. In Computational Methods in Molecular
Biology. E-Publishing; 1988, p. 45-63.
2.
Shors, T. Understanding Viruses. 2nd ed. Jones and Bartlett Learning; 2013.
3.
Van Regenmortel MHV, Fauquet CM, Bishop DHL, Carstens EB, Estes MK, Lemon SM,
et al. Classification and Nomenclature of Viruses. Seventh Report of the International
Committee on Taxonomy of Viruses. Virus Taxonomy, VIIth report of the ICTV.
Academic Press, 2000.
4.
Global Alert and Response (GAR), Pandemic influenza vaccine production processes
and schedules, WHO, 2009, H1N1 influenza pandemic note 7.
5.
Juang RH, Wu YJ. Proteomics and plant vaccine applications, proteomics. National
Taiwan University College of Medicine, Institute of Biochemistry and Molecular
Biology, http://juang.bst.ntu.edu.tw.
6.
Wu YJ, Chen HM, Wu TT, Wu JS, Chu RM, Juang RH. Preparation of monoclonal
antibody bank against whole water-soluble proteins from rapid-growing bamboo shoots.
Proteomics 2006; 6:5898-902.
7.
Lam TT, Wang J, Shen Y, Zhou B, Duan L, Cheung CL, et al. The genesis and source of
the H7N9 influenza viruses causing human infections in China. Nature 2013; 502:241-4.
8.
Xu ZN. Bioinformatics. Beijing Tsing Hua University Press; 2010.
9.
Chow KC, Hu YJ, Wang SD. The use of biological sequence program to accelerate the
calculation method of vaccine development process. 2013. Intellectual Property Center
of National Chung Hsing University. The ROC invention patent application. Application
number: 102134134.
10. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al.
Clustal W and Clustal X version 2.0. Bioinformatics 2007; 23:2947-8
(UPGMA)
11. Joseph F. Evolutionary Tree From DNA Sequences: A Maximum LikeLihood Approach.
J Mol Evol 1981; 17:368–376.
12. Needleman SB, Wunsch CD. A general method applicable to the search for similarities in
the amino acid sequence of two proteins. J Mol Biol 1970; 48:443–53.
13. Mahy BW, Muellen V. Topley and Wilson's Microbiology and Microbial Infections. 10th
ed. London: Hodder Arnold; 2007.
14. Decrypt H1N1. http://www.yiqiwang.com/zixun/yejiedongtai/2009/0824 /6755.html,
2009.
15. McKimm-Breschkin JL. Influenza neuraminidase inhibitors: antiviral action and
mechanisms of resistance. Influenza Other Respir Viruses 2013 ;7 Suppl 1:25-36.
16. Xie JJ, Fang JM. Design and development of flu drugs, Chemistry 2007; 66:279-92.
Figure Legends
Figure 1 Rootless tree diagram analysis of PB2 of influenza viruses (A). NA (UPGMA)
Rooted (B) or Rootless (C) tree diagram of 14 viruses before using N-W to all-sequence
comparison. NA (UPGMA) Rooted (D) or Rootless (E) tree diagram of 14 viruses after using
N-W to all-sequence comparison.
Figure 2 NA (ML) Rooted (A) or Rootless (B) tree diagram of 14 viruses after using N-W to
all-sequence comparison. NA all-sequence comparison and immune loci analysis of 14
viruses (C and D).