Determination of inter- and intra-subtype/species varia-tions in polymerase acidic protein from influenza A virus using amino-acid pair predictability

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J. Biomedical Science and Engineering, 2009, 2, 273-279

doi: 10.4236/jbise.2009.24041 Published Online August 2009 (http://www.SciRP.org/journal/jbise/

JBiSE

Published Online August 2009 in SciRes. http:// www.scirp.org/journal/jbise

Determination of inter- and intra-subtype/species varia-

tions in polymerase acidic protein from influenza A virus

using amino-acid pair predictability

Shaomin Yan1, Guang Wu2*

1National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, 98 Daling Road, Nanning,

Guangxi, 530007, China; 2Computational Mutation Project, DreamSciTech Consulting, 301, Building 12, Nanyou A-zone, Jiannan

Road, Shenzhen, Guangdong Province 518054, China.

Email: hongguanglishibahao@yahoo.com

Received 23 March 2009; revised 15 April 2009; accepted 28 April 2009.

ABSTRACT

The polymerase acidic protein is an important

family of proteins from influenza A virus, which

is classified as many different subtypes or spe-

cies. Thus, an important question is if these

classifications are numerically distinguishable

with respect to the polymerase acidic protein.

The amino-acid pair predictability was used to

transfer 2432 polymerase acidic proteins into

2432 scalar data. The one-way ANOVA found

these polymerase acidic proteins distinguish-

able in terms of subtypes and species. However,

the large residuals in ANOVA suggested a pos-

sible large intra-subtype/species variation.

Therefore, the inter- and intra-subtype/species

variations were studied using the model II

ANOVA. The results showed that the in-

tra-subtype/species variations accounted most

of variation, which was 100% in total for both

inter- and intra- subtype/species variations. Our

analysis threw lights on the issue of how to de-

termine a wide variety of patterns of antigenic

variation across space and time, and within and

between subtypes as well as hosts.

Keywords: Amino-Acid Pair; Influenza A Virus; ln-

ter- and Intra-; Model II ANOVA; Polymerase Acidic

Protein; Species; Subtype; Variation

1. INTRODUCTION

The unpredictable mutations in proteins from influenza

A virus threaten the humans with possible pandemics or

epidemics, therefore it is considered important to accu-

rately, precisely and reliably predict the mutations. In

this way, the new vaccines, which would be more effec-

tive against the influenza A virus, could be manufactured

[1,2,3,4,5,6].

Currently, the manufactured vaccines are designed to

target the influenza A virus according to their subtypes,

for example, the focus in recent year would be the H5N1

subtype of influenza A virus [7,8,9,10,11,12,13,14], and

anti-flu drugs are designed to target neuraminidases and

M2 protein [15,16]. It would be understandable that

proteins should be different from one subtype to another.

Otherwise, there would be no classification of subtype.

Moreover, the proteins under the same subtype should be

different one another, otherwise a single subtype would

contain only a single protein. The same holds for pro-

teins classified according to species, where the sample

was obtained.

Here, an important question is if these classifications

are numerically distinguishable, say, if a protein is dif-

ferent from species to species and from subtype to sub-

type in number. This is the base for prediction of muta-

tion using mathematical modeling.

However this work has yet to be done, because the

difference between proteins is different in terms of let-

ters, which represent the amino acids in proteins. It is

difficult to use any statistical method to determine these

differences cross a protein family.

For this aim, it needs to transfer a protein into a datum

that should be different from protein to protein. Then it

would be possible to conduct an ANOVA statistics to

answer the question above.

Actually there are quite a few methods, which can

transfer a protein sequence into a series of numerical

codes or numerical sequence for predicting its various

attributes (see, e.g., [17,18,19,20,21,22,23,24,25]).

Since 1999, we have developed three approaches to

transfer each amino acid in a protein as well as a whole

protein (for reviews, see [26,27,28]) into either a single

datum or numerical sequence, which resulted in many

studies on proteins.

Afterward, another question would be the inter- and

intra-subtype/species variations. This issue is important

because the vaccines and anti-flu drugs manufactured

based on subtype would be more efficient and effective

if the difference within subtype/species would be smaller

274 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279

than that between subtypes/species.

Influenza viruses replicate and transcribe their seg-

mented negative-sense single-stranded RNA genome in

the nucleus of the infected host cell. All RNA synthesiz-

ing activities associated with influenza virus are per-

formed by the virally encoded RNA-dependent RNA

polymerase that consists of three subunits, polymerase

acidic protein (PA), polymerase basic proteins 1 and 2

[29]. The PA subunit is involved for the conversion of

RNA polymerase from transcriptase to replicase [30] and

contains the endonuclease active site. A recent study

strongly implicates the viral RNA polymerase complex

as a major determinant of the pathogenicity of the 1918

pandemic virus [31].

Many studies have indicated that sequence-based pre-

diction approaches, such as protein subcellular location

prediction [32,33,34], protein quaternary attribute pre-

diction [25,35], identification of membrane proteins and

their types [36,37], identification of enzymes and their

functional classes [38], identification of GPCR and their

types [24,39,40], identification of proteases and their

types [41,42], protein cleavage site prediction [43,44,45],

signal peptide prediction [46,47], and protein 3D struc-

ture prediction based on sequence alignment [48], can

timely provide very useful information and insights for

both basic research and drug design.

The present study was attempted to use the model II

ANOVA to investigate the inter- and intra- sub-

type/species variations in polymerase acidic protein from

influenza A virus in hope to shed lights on helping find

effective drugs against influenza A virus.

2. MATERIALS AND METHODS

2.1. Data

A total of 5165 full-length PA sequences of influenza A

virus sampled from 1918 to 2008 was obtained from the

influenza virus resources [49]. After excluded identical

sequences, 2432 PA proteins were used in this study.

2.2. Transferring Symbolized PA Proteins

into Scalar Data

Among these methods developed by us, the amino-acid

pair predictability is the simplest, which was thus used in

this study. According to the permutation, the adjacent

amino-acid pairs in a protein can be classified as predict-

able and unpredictable, which provided a measure to dis-

tinguish protein one another and was used in many our

previous studies (for example 2008, [28,50,51,52,53,54]).

For example, there was an avian influenza virus

(strain A/quail/Hong Kong/1721-20/99(H6N1)) and its

PA was composed of 716 amino acids (accession number

CAC84865). The first and second amino acids could be

counted as an amino-acid pair, the second and third as

another amino-acid pairs, the third and fourth, until the

715th and 716th, thus there were 715 amino-acid pairs.

Then, how many amino-acid pairs can be explained by

the permutation or random mechanism in this PA? This

can be determined using the percentage of predictable

and unpredictable amino-acid pairs.

There were 37 aspartic acids “D” and 76 glutamic ac-

ids “E” in CAC84865 PA. If the permutation could ex-

plain the appearance of amino-acid pair DE, it could

appear four times in this PA (37/716×76/715×715=3.927).

Actually there were 4 DEs in this PA. Thus, the appearance

of DE could be explained by permutation or predicted by

random mechanism. By clear contrast, there were 50 iso-

leucines “I” in the PA. If the permutation could explain the

appearance of IE, it could appear five times

(50/716×76/715×715=5.307). However, it appeared 12

times in realty, which could not be explained by permuta-

tion or randomly unpredictable. In this way, all amino-acid

pairs in this PA could be classified as predictable and un-

predictable. For this particular PA, its predictable and un-

predictable portions were 25.45% and 74.55%.

Taking another PA (accession number CAC84866) as

example, this PA had only one amino acid different from

CAC84865 PA at position 437. However, its predictable

and unpredictable portions were 25.59% and 74.41%.

Thus, the amino-acid pair predictability distinguished

the difference between different PA proteins as a very

sensitive measure.

2.3. Difference among Subtypes/Species

Influenza A viruses are classified by the serological sub-

types of the primary viral surface proteins. Currently, there

are 16 haemagglutinin subtypes from H1 to H16 and 9

neuraminidase subtypes from N1 to N9 [55]. Also, influ-

enza A viruses can be classified according to their host.

After computation of 2432 PA proteins, the predict-

able portions of PA proteins were grouped according to

their classifications of subtypes and species.

As there were more than two subtypes and two species,

and the number of PA proteins was highly different from

subtype to subtype, and from species to species, the

one-way ANOVA followed by the Holm-Sidak’s compari-

son test was used to compare the difference among and

between subtypes/species using the SigmaStat software

[56]. P < 0.05 was considered statistically significant.

2.4. Inter- and Intra-Subtype Variation

The single classification model II ANOVA with unequal

sample sizes [57] was used to determine the inter- and

intra-subtype/species variations.

3. RESULTS AND DISCUSSION

The one-way ANOVA showed that there were statisti-

cally significant difference among HA subtypes (Figure

1), NA subtypes (Figure 2) and species (Figure 3). Even

the statistical difference was found between subtypes

and between species. The detailed results were listed in

Supplementary results.

S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279 275

H1 n=375

H2 n=83

H3 n=643

H4 n=68

H5 n=605

H6 n=139

H7 n=233

H8 n=11

H9 n=175

H10 n=26

H11 n=38

H12 n=15

H13 n=13

H14 n=2

H15 n=1

H16 n=3

Predictable portion (%)

Figure 1. HA subtype comparison of PA proteins from influenza A viruses. The one-way ANOVA indicated a sta-

tistically significant difference (P < 0.001) among sixteen subtypes, and the Holm-Sidak’s comparison test indi-

cated the statistical difference between two subtypes as follows: H5 versus H9, H1 versus H9, H3 versus H9, H2

versus H9, H7 versus H9, H5 versus H6, H6 versus H9, H1 versus H6, H5 versus H7, H2 versus H6, H5 versus

H4, H3 versus H6, H1 versus H4, H1 versus H7, H2 versus H4, H4 versus H9, H5 versus H11, H2 versus H11,

H10 versus H9, H1 versus H11, H2 versus H7, H3 versus H4, H3 versus H11, H13 versus H9, H3 versus H7, H11

versus H9, H2 versus H8, H5 versus H8, H1 versus H8, H2 versus H12, H12 versus H9, H5 versus H12, H5 ver-

sus H3, H3 versus H8, and H1 versus H12.

N1 N=943

N2 N=1059

N3 N=91

N4 N=19

N5 N=36

N6 N=75

N7 N=37

N8 N=122

N9 N=48

Predictable portion (%)

Figure 2. NA subtype comparison of PA proteins from influenza A viruses. The one-way ANOVA indicated a sta-

tistically significant difference (P < 0.001) among nine subtypes, and the Holm-Sidak’s comparison test indicated

the statistical difference between two subtypes as follows: N1 versus N8, N2 versus N8, N1 versus N5, N1 versus

N2, N2 versus N5, N1 versus N6, N1 versus N9, N1 versus N3, N3 versus N8, N7 versus N8, N7 versus N5, N3

versus N5, N6 versus N8, N2 versus N6, N2 versus N9 and N6 versus N5.

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276 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279

Avian n=1310

Canine n=4

Cat n=3

Civet n=1

Environment n=53

Equine n=44

Human n=793

Mink n=1

Stone marten n=1

Swine n=183

Tiger n=3

Unknown species n=36

Predictable portion (%)

Figure 3. Species comparison of PA proteins from influenza A viruses. The one-way ANOVA indicated a

statistically significant difference (P < 0.001) among ten species, and the Holm-Sidak’s comparison test

indicated the statistical difference between two species as follows: human versus avian, human versus

equine, swine versus equine, swine versus avian, avian versus equine, human versus unknown, swine

versus unknown, environment versus equine, human versus environment, swine versus environment,

unknown versus equine, tiger versus equine, civet versus equine, cat versus equine, civet versus mink,

swine versus mink, human versus mink, and swine versus canine.

During the one-way ANOVA test, a particular phe-

nomenon got our attention, i.e. the residual was very

large in standard ANOVA table. For example, the sum of

squares (SS) was 668.97 and 6293.13 for between

groups and residual under HA subtype (Table 1).

Table 1 suggested that there were very large varia-

tions in PA proteins within each subtype or species,

which further suggested that the model II ANOVA was

in need to determine the inter- and intra-subtype/species

variations.

Table 2 listed the inter- and intra-subtype/species

variations. The model II ANOVA defined the total varia-

tion as 100%, which was further divided into inter- and

intra-subtype/species variations. As seen in Table 2, the

intra-subtype/species variation is far much larger than

the inter-subtype/species variation. For example, the

inter-subtype HA variation was 10.71% while the in-

tra-subtype HA variation was 89.28%.

Table 1. Standard ANOVA table regarding HA subtype, NA subtype and species of PA proteins from influenza A viruses.

Source of variation Degree of freedom Sum of Squares Mean Square F

HA subtype Between groups 16 668.97 41.81 16.05

Residual 2415 6293.13 2.61

Total 2431 6962.10

NA subtype Between groups 9 377.13 41.90 15.41

Residual 2422 6584.97 2.72

Total 2431 6962.10

Species Between groups 11 547.64 49.79 18.78

Residual 2420 6414.46 2.65

Total 2431 6962.10

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S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279 277

Table 2. Inter- and intra-subtype/species variations of PA pro-

teins from influenza A viruses.

Classification Inter-subtype/

species variation

Intra-subtype/

species variation

HA subtype 10.71% 89.28%

NA subtype 7.54% 92.46%

Species 11.88% 88.12%

At this point, one might wonder why the statistical

difference was found among subtypes and species while

there were so large variations within subtype and species.

These results in fact were very reasonable. In plain

words, there would be statistical difference between

males and females in performing a sport, for example,

however the difference between male sportsman and

ordinary male would be also huge, thus it could be pos-

sible this variation would be larger than that between

males and females.

In fact, the single classification model II ANOVA has

many important applications although this method is less

familiar with most researchers [58,59]. For example, it is

better to know the inter- and intra-patient variations be-

fore planning clinical experiments, say, if an experimen-

tal design should include two parallel groups (inter- pa-

tients) or a two-part crossover design (intra-patients).

In the context of this study, generally, a small intra-

subtype/species variation suggested a cost-effective way

in collecting of samples, it said, not many samples for a

particular subtype/species were in need, by clear contrast,

many samples were in need regarding a particular sub-

type/species if there was a large intra- subtype/species

variation.

Actually, the inter- and intra-subtype/species varia-

tions were the mutations occurred in the same subtype or

species, and occurred cross subtypes or species. There

was a wide variety of patterns of antigenic variation

across space and time, and within and between subtypes

as well as hosts and we did not yet understand the de-

terminants of these different patterns [60]. Our analysis

could shed lights on this issue.

The far much large intra-subtype/species variation

found in this study suggested: 1) much more PA proteins

belonging to the same subtype/species were needed in

order to better understand the mutation pattern in the

same subtype/species, and 2) the current classification

was based on the surface proteins from influenza A virus,

while under the same subtype, the PA mutations were

quite large.

On the other hand, the statistically significant differ-

ence between subtypes suggested the current classifica-

tion valid even for the PA, which was an internal protein.

The most important requirement for producing vaccines

against viruses displaying antigenic diversity is a method

of measuring antigenic distances between strains and

developing an understanding of how these distances re-

late to cross-protection [61]. The current results sup-

ported the idea to develop vaccines and anti-flu drugs

that generate effective heterosubtypic immunity based

on immune recognition of influenza A virus antigens

conserved across all viral strains [62,63].

4. ACKNOWLEDGEMENTS

This study was partly supported by International Science

& Technology Cooperation Projects (2008DFA30710),

Guangxi Science Foundation (No. 0630003A2 and

0991080), and Guangxi Academy of Sciences (project

No. 09YJ17 SW07).

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