DOI: 10.1126/science.1097211, 371 (2004);305 Science

et al.Derek J. SmithMapping the Antigenic and Genetic Evolution of Influenza Virus

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CO2 from preindustrial levels will result in a30% decrease in carbonate ion concentrationand a 60% increase in hydrogen ion concen-tration. As the carbonate ion concentrationdecreases, the Revelle factor increases andthe ocean’s ability to absorb more CO2 fromthe atmosphere is diminished. The impact ofthis acidification can already be observedtoday and could have ramifications for thebiological feedbacks in the future (26). Ifindeed the net feedbacks are primarily posi-tive, the required socioeconomic strategies tostabilize CO2 in the future will be much morestringent than in the absence of such feed-backs. Future studies of the carbon system inthe oceans should be designed to identify andquantitatively assess these feedback mecha-nisms to provide input to models that willdetermine the ocean’s future role as a sink foranthropogenic CO2.

References and Notes1. P. J. Crutzen, E. F. Stoermer, Global Change Newslett.

41, 12 (2000).2. R. A. Houghton, J. L. Hackler, in Trends: A Compendi-

um of Data on Global Change (Carbon Dioxide Infor-mation Analysis Center, Oak Ridge National Labora-tory, TN, 2002), http://cdiac.esd.ornl.gov/trends/landuse/houghton/houghton.html.

3. C. Prentice et al., in Climate Change 2001: The Sci-entific Basis. Contribution of Working Group I to theThird Assessment Report of the IntergovernmentalPanel on Climate Change, J. T. Houghton et al., Eds.(Cambridge Univ. Press, New York, 2001), pp. 183–237.

4. D. W. R. Wallace, in Ocean Circulation and Climate, G.Siedler, J. Church, W. J. Gould, Eds. (Academic Press,San Diego, CA, 2001), pp. 489–521.

5. R. M. Key et al., in preparation.6. Bottle data and 1° gridded distributions are available

through the GLODAP Web site (http://cdiac.esd.ornl.gov/oceans/glodap/Glodap_home.htm).

7. N. Gruber, J. L. Sarmiento, T. F. Stocker, Global Bio-geochem. Cycles 10, 809 (1996).

8. C. L. Sabine et al., Global Biogeochem. Cycles 13, 179(1999).

9. C. L. Sabine et al., Global Biogeochem. Cycles 16,1083, 10.1029/2001GB001639 (2002).

10. K. Lee et al., Global Biogeochem. Cycles 17, 1116,10.1029/2003GB002067 (2003).

11. Materials and methods are available as supportingmaterial on Science Online.

12. R. Revelle, H. E. Suess Tellus 9, 18 (1957).13. T. Takahashi, J. Olafsson, J. G. Goddard, D. W. Chip-

man, S. C. Sutherland, Global Biogeochem. Cycles 7,843 (1993).

14. Five intermediate water masses and NADW weredefined using the following temperature (T) and sa-linity (S) properties: Pacific AAIW: 33.8 � S � 34.5and 2 � T � 10; NPIW: S � 34.3 and 5 � T � 12;Indian AAIW: 33.8 � S � 34.5 and 2 � T � 10; RedSea Water: S � 34.8 and 5 � T � 14; Atlantic AAIW:33.8 � S � 34.8 and 2 � T � 6; NADW: 34.8 � S �35 and 1.5 � T � 4. Water mass inventories weredetermined by summing up the gridded anthropo-genic CO2 values within a region defined by the T andS limits using the Levitus World Ocean Atlas 2001salinity and temperature fields.

15. A. Papaud, A. Poisson, J. Mar. Res. 44, 385 (1986).16. S. Mecking, M. Warner, J. Geophys. Res. 104, 11087

(1999).17. A. Poisson, C.-T. A. Chen, Deep-Sea Res. Part A 34,

1255 (1987).18. M. Stuiver, P. D. Quay, H. G. Ostlund, Science 219,

849 (1983).19. G. Marland, T. A. Boden, R. J. Andres, in Trends: A

Compendium of Data on Global Change (CarbonDioxide Information Analysis Center, Oak Ridge Na-

tional Laboratory, TN, 2003), http://cdiac.esd.ornl.gov/trends/emis/meth_reg.htm.

20. D. M. Etheridge et al., J. Geophys. Res. 101, 4115(1996).

21. C. D. Keeling, T. P. Whorf, in Trends: A Compendiumof Data on Global Change (Carbon Dioxide Informa-tion Analysis Center, Oak Ridge National Laboratory,TN, 2004), http://cdiac.esd.ornl.gov/trends/co2/sio-keel.htm.

22. R. S. de Fries, C. B. Field, I. Fung, G. J. Collatz, L.Bounoua, Global Biogeochem. Cycles 13, 803 (1999).

23. C. L. Sabine et al., in The Global Carbon Cycle:Integrating Humans, Climate, and the Natural World.SCOPE 62, C. B. Field, M. R. Raupach, Eds. (IslandPress, Washington, DC, 2004), pp. 17–44.

24. D. E. Archer, H. Kheshgi, E. Maier-Reimer, GlobalBiogeochem. Cycles 12, 259 (1998).

25. N. Gruber et al., in The Global Carbon Cycle: Inte-grating Humans, Climate, and the Natural World.SCOPE 62, C. B. Field, M. R. Raupach, Eds. (IslandPress, Washington, DC, 2004), pp. 45–76.

26. R. A. Feely et al., Science 305, 362 (2004).27. We thank all individuals who contributed to the

global data set compiled for this project, includingthose responsible for the hydrographic, nutrient,oxygen, carbon, and chlorofluorocarbon measure-ments, and the chief scientists. The amount ofwork that went into collecting, finalizing, and syn-thesizing these data in a manner that makes apublication like this possible can never be properlyacknowledged. This work was funded by grantsfrom NOAA/U.S. Department of Energy and NSF.Partial support for K.L. was also provided by theAdvanced Environmental Biotechnology ResearchCenter at Pohang University of Science and Tech-nology. This is Pacific Marine Environmental Labo-ratory contribution number 2683.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/305/5682/367/DC1Materials and MethodsFig. S1Table S1

2 March 2004; accepted 8 June 2004

Mapping the Antigenic and GeneticEvolution of Influenza Virus

Derek J. Smith,1,2*† Alan S. Lapedes,3* Jan C. de Jong,2

Theo M. Bestebroer,2 Guus F. Rimmelzwaan,2

Albert D. M. E. Osterhaus,2 Ron A. M. Fouchier2*

The antigenic evolution of influenza A (H3N2) virus was quantified and visualizedfrom its introduction into humans in 1968 to 2003. Although therewas remarkablecorrespondence between antigenic and genetic evolution, significant differenceswere observed: Antigenic evolution was more punctuated than genetic evolution,and genetic change sometimes had a disproportionately large antigenic effect. Themethod readily allows monitoring of antigenic differences among vaccine andcirculating strains and thus estimation of the effects of vaccination. Further, thisapproach offers a route to predicting the relative success of emerging strains,whichcould be achieved by quantifying the combined effects of population level immuneescape and viral fitness on strain evolution.

Much of the burden of infectious diseasetoday is caused by antigenically variablepathogens that can escape from immunityinduced by prior infection or vaccination.The degree to which immunity induced byone strain is effective against another is most-ly dependent on the antigenic difference be-tween the strains; thus, the analysis of anti-genic differences is critical for surveillanceand vaccine strain selection. These differenc-es are measured in the laboratory in variousbinding assays (1–3). Such assays give anapproximation of antigenic differences, butare generally considered unsuitable for quan-titative analyses. We present a method, based

on the fundamental ideas described by Lape-des and Farber (4), that enables a reliablequantitative interpretation of binding assaydata, increases the resolution at which anti-genic differences can be determined, and fa-cilitates visualization and interpretation ofantigenic data. We used this method to studyquantitatively the antigenic evolution of in-fluenza A (H3N2) virus, revealing both sim-ilarities to, and important differences from,its genetic evolution.

Influenza viruses are classic examples ofantigenically variable pathogens and have aseemingly endless capacity to evade the im-mune response. Influenza epidemics in hu-mans cause an estimated 500,000 deathsworldwide per year (5). Antibodies againstthe viral surface glycoprotein hemagglutinin(HA) provide protective immunity to influen-za virus infection, and this protein is thereforethe primary component of influenza vaccines.However, the antigenic structure of HA haschanged significantly over time, a processknown as antigenic drift (6 ), and in mostyears, the influenza vaccine has to be up-

1Department of Zoology, University of Cambridge,Downing Street, Cambridge CB2 3EJ, UK. 2NationalInfluenza Center and Department of Virology, Eras-mus Medical Center, Dr. Molewaterplein 50, 3015GERotterdam, Netherlands. 3Theoretical Division, T-13,MS B213, Los Alamos National Laboratory, LosAlamos, NM 87545, USA.

*These authors contributed equally to this work.†To whom correspondence should be addressed. E-mail: dsmith@zoo.cam.ac.uk

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dated to ensure sufficient efficacy againstnewly emerging variants (7, 8). The WorldHealth Organization coordinates a globalinfluenza surveillance network, currentlyconsisting of 112 national influenza centersand four collaborating centers for referenceand research. This network routinely char-acterizes the antigenic properties of influ-enza viruses using a hemagglutination in-hibition (HI) assay (1). The HI assay is abinding assay based on the ability of influ-enza viruses to agglutinate red blood cellsand the ability of animal antisera raisedagainst the same or related strains to blockthis agglutination (9). Additional surveil-lance information is provided by sequenc-ing the immunogenic HA1 domain of theHA gene for a subset of these strains. Thecombined antigenic, epidemiological, andgenetic data are used to select strains foruse in the vaccine.

Retrospective quantitative analyses of thegenetic data have revealed important insightsinto the evolution of influenza viruses (10–13). However, the antigenic data are largelyunexplored quantitatively because of difficul-ties in interpretation, even though antigenic-ity is a primary criterion for vaccine strainselection and is thought to be the main driv-ing force of influenza virus evolution. Whenantigenic data have been analyzed quantita-tively, it has usually been with the methodsof, or methods equivalent to, numerical tax-onomy (14–16). These methods have pro-vided insights (15–19); however, theysometimes give inconsistent results, do notproperly interpret data that are below thesensitivity threshold of the assay, and approx-imate antigenic distances between strains inan indirect way [discussed by (4, 16, 18)].Lapedes and Farber (4) solved these prob-lems with a geometric interpretation of bind-ing assay data, in which each antigen andantiserum is assigned a point in an “antigenicmap” [based on the theoretical concept of“shape space” (20–23)], such that the dis-tance between an antigen and antiserum inthe map directly corresponds to the HI mea-surement. Lapedes and Farber used ordinalmultidimensional scaling (MDS) (24) to po-sition the antigens and antisera in the map.

The method used in this manuscript isbased on the fundamental ideas describedby Lapedes and Farber (4 ) and, in particu-lar, takes advantage of their observationthat antigenic distance is linearly related tothe logarithm of the HI measurement. Ex-ploiting this observation allowed us to cre-ate a new method that is parametric yet stillhandles HI measurements that are beyondthe sensitivity of the HI assay (9). We usea modification of metric MDS (25 ) to po-sition the antigens and antisera in the map(9). This new approach offers computation-al advantages over the ordinal approach,

including reduced running time and fewerlocal minima, making it tractable to run ondatasets the size of the one used in thismanuscript, and on larger datasets.

Antigenic map of human influenza A(H3N2) virus. We applied this method tomapping the antigenic evolution of humaninfluenza A (H3N2) viruses, which becamewidespread in humans during the 1968 HongKong influenza pandemic and have been amajor cause of influenza epidemics eversince. Antigenic data from 35 years of influ-enza surveillance between 1968 and 2003were combined into a single dataset. We se-quenced the HA1 domain of a subset of thesevirus isolates (26, 27) and restricted the an-tigenic analysis to these sequenced isolates tofacilitate a direct comparison of antigenic andgenetic evolution. The resulting antigenicdataset consisted of a table of 79 postinfec-tion ferret antisera by 273 viral isolates, with4215 individual HI measurements as entriesin the table. Ninety-four of the isolates werefrom epidemics in the Netherlands, and 179were from elsewhere in the world.

We constructed an antigenic map fromthis dataset to determine the antigenic evolu-tion of influenza A (H3N2) virus from 1968to 2003 (Fig. 1). Because antigen-antiserumdistances in the map correspond to HI values,it was possible to predict HI values that weremissing in the original dataset and subse-quently to measure those values using the HIassay, so as to determine the resolution of themap. We predicted and then measured 481such HI values with an average absolute pre-diction error of 0.83 (SD 0.67) units (eachunit of antigenic distance corresponds to atwofold dilution of antiserum in the HI assay)and a correlation between predicted and mea-sured values of 0.80 (p �� 0.01). The accu-racy of these predictions indicates that themap has resolution higher than that previous-ly considered available from HI data andhigher than the resolution of the assay. Theresolution of the map can be greater than theresolution of the assay because the location ofa point in the map is fixed by measurementsto multiple other points, thereby averagingout errors (9).

The map reveals high-level features of theantigenic evolution of influenza A (H3N2)virus. The strains tend to group in clustersrather than to form a continuous antigeniclineage, and the order of clusters in the map ismostly chronological; from the original HongKong 1968 (HK68) cluster, to the most recentFujian 2002 (FU02) cluster. The antigenicdistance from the HK68 cluster, through con-secutive cluster centers, to the FU02 cluster is44.6 units, and the average antigenic distancebetween the centers of consecutive clusters is4.5 (SD 1.3) units. The influenza vaccine isupdated between influenza seasons whenthere is an antigenic difference of at least 2

units between the vaccine strain and thestrains expected to circulate in the next sea-son; thus, not unexpectedly, we find at leastone vaccine strain in each cluster.

The ability to define antigenic clustersallows us to identify the amino acid substitu-tions that characterize the difference betweenclusters (Table 1, fig. S1). Some of these“cluster-difference” substitutions (9) willcontribute to the antigenic difference betweenclusters, some may be compensatory muta-

Fig. 1. Antigenic map of influenza A (H3N2)virus from 1968 to 2003. The relative positionsof strains (colored shapes) and antisera (uncol-ored open shapes) were adjusted such that thedistances between strains and antisera in themap represent the corresponding HI measure-ments with the least error (9). The periphery ofeach shape denotes a 0.5-unit increase in thetotal error; thus, size and shape represent aconfidence area in the placement of the strainor antiserum. Strain color represents the anti-genic cluster to which the strain belongs. Clus-ters were identified by a k-means clusteringalgorithm (9) and named after the first vaccine-strain in the cluster—two letters refer to thelocation of isolation (Hong Kong, England, Vic-toria, Texas, Bangkok, Sichuan, Beijing, Wuhan,Sydney, and Fujian) and two digits refer to yearof isolation. The vertical and horizontal axesboth represent antigenic distance, and, becauseonly the relative positions of antigens and an-tisera can be determined, the orientation of themap within these axes is free. The spacingbetween grid lines is 1 unit of antigenic dis-tance—corresponding to a twofold dilution ofantiserum in the HI assay. Two units corre-spond to fourfold dilution, three units to eight-fold dilution, and so on.

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tions to retain function, and others may behitchhikers carried along by chance. Of the67 cluster-difference amino acid substitu-tions, 63 were in antigenic sites (28), 8 werein the receptor-binding site (29), and 21 werein codons previously identified as positivelyselected in an independent genetic datasetcovering 1985 to 1997 (10). We see twopatterns with respect to these positively se-lected codons: For the cluster transitions thathappened during the period from 1985 to1997—the period of the sample used to cal-culate the positively selected codons—most(10 of 12) of the cluster-difference substitu-tions were in positively selected codons;whereas outside of this timeframe, most [44of 55, or 16 of 20 if the underrepresentedTexas 1977 (TX77) and FU02 clusters areexcluded] were not in positively selectedcodons. A possible explanation for this differ-ence is that cluster-difference substitutions arepositively selected, but that the positively se-lected codons have changed over time, resultingin some pre-1985 positively selected codonsnot being previously identified, possibly be-cause they were underrepresented in the dataset

used by Bush et al. (10). Other possible expla-nations are that not all cluster-difference substi-tutions are positively selected or that they can-not be detected as such with methods that useonly genetic information.

Comparison of antigenic and geneticevolution. To further investigate the geneticbasis of the antigenic cluster structure, wegenerated a maximum likelihood (ML) phy-logenetic tree and a “genetic map” of theHA1 sequences of strains used in the antigen-ic analysis and color-coded both according tothe clusters identified in the antigenic map ofFig. 1 (Fig. 2). The genetic map facilitates aside-by-side comparison with the antigenicmap and is a visualization of the amino aciddistance matrix calculated from the alignmentof HA1 sequences (9). Previous comparisonsof the antigenic and genetic evolution haverevealed important insights (6, 19, 30, 31);however, a quantitative comparison has notbeen possible until now because of the pre-viously low resolution of the antigenic data.

We find a remarkable overall correspon-dence between the relative positions of clus-ters in the genetic and antigenic maps (Fig. 2,

B and C, respectively). The correlation be-tween antigenic distance and the number ofamino acid substitutions between strains was0.81, and on average, 2.9 amino acid substi-tutions resulted in one unit change in antigen-ic distance. The rate of antigenic evolutionper amino acid substitution was slower withinclusters [on average 3.1 (SD 0.06) amino acidsubstitutions for each unit of antigenicchange] than between clusters [on average2.1 (SD 0.17) amino acid substitutions foreach unit of antigenic change].

There is also a correspondence between thephylogenetic tree and antigenic map, withclosely related nucleotide sequences generallybelonging to the same antigenic cluster (Fig. 2,A and C). The correlation between antigenicdistance and ML phylogenetic tree distancebetween strains was 0.78, and on average, anML distance of 0.0085 corresponded to a 1-unitchange in antigenic distance.

Although antigenic clusters are mostly con-tiguous when shown on the phylogenetic treeand genetic map (Fig. 2, A and B), it is notpossible to reliably determine antigenic clustersfrom these genetic data alone. From the tree, it

Table 1. Cluster-difference amino acid substitutions, and distances betweenantigenic clusters. Cluster-difference amino acid substitutions defined in (9),antibody binding sites defined by (28). Substitution *at a codon in thereceptor binding site (29); †at a codon with a rapid rate of amino acidreplacement but not positively selected (11); and ‡at a positively selectedcodon (10). The TX77 and FU02 clusters are represented by fewer strains thanother clusters; thus, the number of cluster-difference substitutions into

and out of these clusters might decrease with more strains in these clusters.Cluster transitions follow the chronological order of cluster dominance, whichis occasionally different from the genetic lineage. Antigenic and geneticdistances are between cluster centroids in the antigenic map (Fig. 1) andgenetic map (Fig. 2B), respectively. The average standard error (SE) for thegenetic distances between clusters was 0.9, for the antigenic distancesbetween clusters was 0.3, and for the ratio was 0.3 (table S1).

Clustertransition

Geneticdistance

(aa changes)

Antigenicdistance(units)

Geneticantigenic

ratio

Cluster-difference substitutions

Site A Site B Site C Site D Site E Other

HK68-EN72 12.1 3.4 3.6 T122N T155Y* R207KG144D N188D

EN72-VI75 14.6 4.4 3.3 N137S*† L164Q N53D F174SS145N‡ Q189K I278S R102K‡

S193D‡ I213VI217VI230V

V175-TX77 14.8 3.4 4.4 S137Y*† G158E‡ K50R† S174F E82KQ164L D53N K201R‡ M260ID193N‡ V213I

V230ITX77-BA79 16.0 3.3 4.8 N133S‡ K156E‡ N53D D172G† 162K

P143S T160K N54S V217I K82EG146S Q197R‡ V244L

BA79-SI87 11.9 4.9 2.4 G124D‡ Y155H*K189R

SI87-BE89 6.9 4.6 1.5 N145K‡BE89-BE92 13.7 7.8 1.8 S133D‡ E156K‡ T262N‡

K145N‡ E190D*‡BE92-WU95 9.9 4.6 2.2 N145K‡WU95-SY97 16.0 4.7 3.4 K156Q‡ N276K† K62E

E158K‡V196A†

SY97-FU02 16.0 3.5 4.5 A131T H155T* R50G† H75Q L25IQ156H‡ E83K V202I

W222RG225D*

Total 131.9 44.6Average 13.2 4.5 3.2SD 2.9 1.3 1.1

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is rarely obvious if a branch or lineage belongsto the same or a different antigenic cluster as itsneighbors, and from the genetic map, it is notalways possible to determine where one anti-genic cluster ends and another begins. The moststriking example is the distance between theSichuan 1987 (SI87) and Beijing 1989 (BE89)clusters, which are genetically closely relatedbut antigenically distinct. The difficulties with anantigenic interpretation of genetic data include thevariation in the antigenic effect of amino acidsubstitutions because of the particular amino acidsubstitution, the location of the substitution, or theinteraction of multiple substitutions.

Surprisingly, a single amino acid substi-tution, N145K (32), is the only cluster-differ-ence substitution between the SI87 and BE89and between the Beijing 1992 (BE92) andWuhan 1995 (WU95) clusters. This is sur-prising because other cluster transitions arecharacterized by multiple cluster-differencesubstitutions and because, on average, a sin-gle amino acid substitution causes only 0.37units of antigenic change. Three pieces ofevidence, however, indicate that N145K has alarge antigenic effect and, thus, alone can beresponsible for a cluster transition. First,there are 12 pairs of strains in the dataset thatonly differ by N145K, and the average anti-genic distance between these pairs in theantigenic map is 4.0 units (SD 1.1). In con-trast, other amino acid substitutions at thesame position (I145S, N145S), and the samesubstitution at a different position (N92K),each resulted in less than 1 unit of antigenic

change. Second, we took a strain from theBE92 cluster and performed experimentalsite-directed mutagenesis of position 145from N to K, and this resulted in 2.6 units ofantigenic difference. Third, there were ninestrains in the genetic map for which the ge-netic cluster did not correspond with the an-tigenic cluster, and N145K was responsiblefor the difference. These nine strains wereinterdigitated between the BE92 and WU95clusters: Five strains from the BE92 antigeniccluster were genetically WU95-like butlacked the N145K substitution [seen as pinktriangles in the green WU95 genetic cluster(Fig. 3)], and vice versa, four strains from theWU95 antigenic cluster were geneticallyBE92-like but had the N145K substitution[shown as green circles in the pink BE92genetic cluster (Fig. 3)]. To exclude the pos-sibility of laboratory errors, we resequencedand regenerated the HI data for seven of theseinterdigitated strains and obtained the sameresults. These three pieces of evidence indi-cate that a single amino acid substitution, inthis case N145K, can cause sufficient anti-genic change to be responsible for a clustertransition. Thus, although there is a remark-able correspondence between the genetic andantigenic evolution, there are exceptions thathave epidemiological significance of suffi-cient magnitude that they require an update ofthe vaccine strain.

Gradual genetic evolution, but punctu-ated antigenic evolution. A season-by-sea-son analysis of the clusters in the antigenic

map shows that in some seasons strains wereisolated from more than one antigenic cluster(Fig. 4A). On average, clusters remaineddominant for 3.3 years (SD 1.9), with twoclusters being dominant for only one seasonand one for eight seasons. In this dataset, wesee strains appear in a cluster up to 2 yearsbefore, and 2 years after, the period in whichthat cluster is the dominant cluster.

The corresponding season-by-seasonanalysis of ML tree distances (Fig. 4B) showsthat the rate of genetic change is relativelycontinuous compared with the rate of anti-genic change (Fig. 4A), which is more punc-tuated. Because this relatively continuousrate of change may in part be due to silentnucleotide substitutions, we repeated theanalysis using the number of amino acidsubstitutions between strains instead of theML tree distance (Fig. 4C) and found gapsbetween some clusters, but still a gradualaccumulation of mutations, which is not re-flected in the corresponding antigenic figure.This finding suggests that some of theseamino acid substitutions have little antigeniceffect or an effect spreading the cluster side-ways in relation to the distance fromA/Bilthoven/16190/68 antigen.

The average rates of evolution are givenby the slope of the best linear fit to the data inFig. 4, A, B, and C. The average rate ofantigenic drift calculated this way was 1.2units per year, the average rate of amino acidsubstitutions was 3.6 per year, and the aver-age rate of change in ML distance was 0.0060per year. Sometimes the rate of antigenicevolution was faster than genetic evolutionand sometimes vice versa, as shown by thedeviations from the linear regression line in

Fig. 3. Detail of the genetic map (Fig. 2B)showing the BE92 and WU95 clusters and howa single amino acid substitution can determinethe antigenic cluster. Pink and green symbolsrepresent strains from the BE92 and WU95antigenic clusters, respectively. Ovals aredrawn around the BE92 (circles) and WU95(triangles) genetic clusters. Green symbols havea lysine (K) at position 145, whereas pink sym-bols have an asparagine (N) at 145. This singleN145K substitution can cause an antigeniccluster change and warrant an update of thevaccine. Two pink triangles are coincident thusonly four of the five can be seen. Grid and axesare the same as for Fig. 2B.

Fig. 2. Comparison of antigenic and genetic evolution of influenza A virus. (A) Phylogenetic tree ofthe HA1 nucleotide sequences, color-coded based on antigenic clusters of Fig. 1. Multiple treeswere built using a reversible site-dependent nucleotide ML method (37). There was good consensusamong trees, and the tree with ML is shown. (B) Genetic map of the HA1 amino acid sequences,color-coded according to the antigenic clusters of Fig. 1. The vertical and horizontal axes representgenetic distance, in this case the number of amino acid substitutions between strains; the spacingbetween grid lines is 2.5–amino acid substitutions. The orientation of the map was chosen to matchthe orientation of the antigenic map in Fig. 1. (C) The same antigenic map of influenza A virusstrains as shown in Fig. 1, except for a rigid-body rotation and translation of the pre-TX77 clusters(fig. S2) to match the genetic map and except that virus strains are represented by colored circlesand antisera by open squares. Arrows indicate the two cluster transitions for which the amino acidsubstitution N145K is the only cluster-difference substitution ( Table 1, fig. S1).

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Fig. 4D, again, this indicates a remarkablecorrespondence, with significant exceptions.

The observed pattern of clustered antigen-ic drift with similar antigenic distances be-tween consecutive cluster centroids is similarto that observed by Gog and Grenfell in atheoretical model in which strain dynamicswere governed by a combination of epidemi-ology and cross-reactive immunity based onantigenic distance (33). This similarity isphenotypic evidence that escape from immu-nity in the human population plays a majorrole in determining influenza strain dynam-ics. Furthermore, there is a selective advan-tage for clusters that move away linearlyfrom previous clusters as they most effective-ly escape existing population-level immunity,and this is a plausible explanation for thesomewhat linear antigenic evolution in re-gions of the antigenic map (Fig. 1). Theobserved deviations from a linear path, aswell as the rate of the antigenic evolution,might be determined by tradeoffs betweenintrinsic viral fitness and extrinsic fitness de-termined by population-level immunity, pos-sibly in concert with stochastic seeding pro-cesses (34), short-lived broad immunity (13),and the phylodynamics of the virus (35).

Genetic analyses of Darwinian selection oninfluenza HA have focused on the gene level,with more recent refinements to the codon level(10). The quantification of antigenic data de-

scribed here, which allows the estimation of theantigenic effect of individual amino acid sub-stitutions, provides the opportunity for analyseswhich integrate selection at the phenotypic lev-el with genetic change at the level of individualamino acid substitutions.

In summary, we have presented a quanti-fication and visualization of the antigenicevolution of influenza A (H3N2) virus from1968 to 2003 and have tested the accuracy ofthe method using blind prediction. We showthat antigenic evolution is clustered andmostly two-dimensional and reveal a higherrate of antigenic evolution between clustersthan within clusters, a remarkable correspon-dence between antigenic and genetic evolu-tion, but with important exceptions of epide-miological significance, and punctuatedantigenic evolution compared with more con-tinuous genetic evolution. The data used forthis study were collected as part of routineinfluenza surveillance, and although there aresignificant biases in such data, these biasesdo not have a significant effect on the results(9). This is the most detailed characterizationof a real antigenic shape space to date.

From a public health perspective, thesemethods increase the value of surveillancedata and facilitate vaccine strain selection byallowing a finer-grain interpretation of anti-genic data, a way to interpret complex data ina simple visual format, and a further integra-

tion of antigenic and genetic data. In addition,antigenic maps, in conjunction with strainprevalence data, could be used to quantify theextent to which emerging strains escape im-munity in the human population. This wouldallow the immune-escape component of viralfitness to be compared among multiple cocir-culating strains and, if immune-escape is adominant aspect of total fitness, to be a pre-dictor of which strains would be more likelyto seed a new epidemic. It might also bepossible to increase the efficacy of repeatedvaccination by accounting quantitatively forthe antigenic distances among vaccine andcirculating strains (23).

We have used human influenza A (H3N2)virus to develop and validate analyses ofantigenic properties from binding assay data.We have applied the same methods to thecharacterization of human H1N1, swineH3N2, and equine H3N8 influenza A viruses,as well as human influenza B virus. There areno assumptions that limit the use of thesemethods to influenza virus and the HI assay,and we have also applied the methods to therecognition of epitopes by cytotoxic T lym-phocytes. We expect these methods will ap-ply to other binding assays such as virusneutralization, complement fixation, andELISA (36) and that they will be useful for awide variety of antigenically variable patho-gens including human immunodeficiency vi-rus and hepatitis C virus. In general, thesemethods facilitate the analyses of phenotypessimilarly to the way phylogenetic algorithmsfacilitate analyses of genotypes. Such quan-titative analyses have potentially wide-ranging implications for strain surveillanceand vaccine strain selection, and for appliedand basic research involving antigenicallyvariable pathogens.

References and Notes1. G. K. Hirst, J. Exp. Med. 78, 407 (1943).2. D. D. Richman, T. Wrin, S. J. Little, C. J. Petropoulos,

Proc. Natl. Acad. Sci. U.S.A. 100, 4144 (2003).3. D. H. O’Connor et al., Nature Med. 8, 493 (2002).4. A. Lapedes, R. Farber, J. Theor. Biol. 212, 57 (2001).5. K. Stohr, Lancet Infect. Dis. 2, 517 (2002).6. A. W. Hampson, in Influenza, C. W. Potter, Ed.

(Elsevier, London, 2002), pp. 49–85.7. J. E. Salk, P. C. Suriano, Am. J. Public Health 39, 345

(1949).8. E. D. Kilbourne et al., Proc. Natl. Acad. Sci. U.S.A. 99,

10748 (2002).9. Materials and methods are available as supporting

material on Science Online. For software, see www.antigenic-cartography.org.

10. R. M. Bush, W. M. Fitch, C. A. Bender, N. J. Cox, Mol.Biol. Evol. 16, 1457 (1999).

11. R. M. Bush, C. A. Bender, K. Subbarao, N. J. Cox, W. M.Fitch, Science 286, 1921 (1999).

12. J. B. Plotkin, J. Dushoff, S. A. Levin, Proc. Natl. Acad.Sci. U.S.A. 99, 6263 (2002).

13. N. M. Ferguson, A. P. Galvani, R. M. Bush, Nature 422,428 (2003).

14. P. H. A. Sneath, R. R. Sokal, Numerical Taxonomy(Freeman, San Francisco, 1973).

15. T. F. Weijers et al., J. Virol. Methods 10, 241 (1985).16. D. J. Alexander et al., Avian Pathol. 26, 399 (1997).17. P. A. Underwood, J. Gen. Virol. 62 (Pt. 1), 153 (1982).

Fig. 4. Rates of antigenic and genetic evolu-tion of influenza A virus. (A) Season-by-sea-son analysis of antigenic evolution. Circlecenters give the average antigenic distanceto A/Bilthoven/16190/68 (BI/16190/68) an-tigen (9) for strains in each antigenic clusterand season, and they are color-coded withthe antigenic cluster colors of Fig. 1. The areaof the circles represents the proportion ofstrains per season found in each antigeniccluster. Since sampling is biased toward out-lier strains, these proportions do not reflecttheir epidemic impact. (B) Same as (A), butwith ML-tree distances calculated from thephylogenetic tree (Fig. 2A). (C) Same as (A),but with amino acid substitution distancescalculated from the genetic map (Fig. 2B).(D) Comparison of antigenic and genetic

evolution. Points are color-coded according to antigenic cluster. The solid line connects the clustercentroids, the dashed line is the best linear fit to the data with a forced zero-intercept.

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AY660991-AY661211. Previously published HA1 do-main accession numbers are in the supporting material.

27. C. A. Macken, H. Lu, J. Goodman, L. Boykin, in Optionsfor the Control of Influenza IV, A. D. M. E. Osterhaus,N. Cox, A. W. Hampson, Eds. (Elsevier Science B.V.,Amsterdam, 2001), pp. 103–106.

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31. A. J. Hay, V. Gregory, A. R. Douglas, Y. P. Lin, Philos.Trans. R. Soc. London B. Biol. Sci. 356, 1861 (2001).

32. Single-letter abbreviations for the amino acid resi-dues are as follows: I, Ile; K, Lys; N, Asn; and S, Ser.

33. J. R. Gog, B. T. Grenfell, Proc. Natl. Acad. Sci. U.S.A.99, 17209 (2002).

34. J. R. Gog, G. F. Rimmelzwaan, A. D. Osterhaus, B. T.Grenfell, Proc. Natl. Acad. Sci. U.S.A. 100, 11143(2003).

35. B. T. Grenfell et al., Science 303, 327 (2004).36. D. A. Lennette, in Diagnostic Procedures for Viral, Rick-

etssial and Chlamydial Infections, E. H. Lennette, D. A.Lennette, E. T. Lennette, Eds. (American Public HealthAssociation, Washington, DC, 1995).

37. B. Korber et al., Science 288, 1789 (2000).38. We thank R. van Beek, W. Beyer, T. Bhattacharya, R.

Bush, N. Cox, B. Grenfell, J. Gog, H. Gutowitz, A. Hay,R. Hightower, T. Jones, A. Tang, the contributors to

the WHO global influenza surveillance network, andthe maintainers of the Influenza Sequence Database(www.flu.lanl.gov). We acknowledge the support ofthe Santa Fe Institute and the Dutch National Insti-tute of Public Health and the Environment (RIVM).D.J.S. was supported by European Union grant QLRT-2001-01034. A.S.L. was supported by the U.S. De-partment of Energy, contract W-7405-ENG-36, un-der the Laboratory-Directed Research and Develop-ment program. R.A.M.F. is a fellow of the Royal DutchAcademy of Arts and Sciences.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/1097211/DC1Materials and MethodsFigs. S1 to S3Table S1References and Notes

26 February 2004; accepted 11 June 2004Published online 24 June 2004;10.1126/science.1097211Include this information when citing this paper

REPORTSPhase-Resolved Spectroscopy ofGeminga Shows Rotating Hot

Spot(s)P. A. Caraveo,1* A. De Luca,1 S. Mereghetti,1 A. Pellizzoni,1

G. F. Bignami2,3,1

Isolated neutron stars are seen in x-rays through their nonthermal and/or surfacethermal emissions. X-ray Multimirror Mission–Newton observations of the Gem-inga pulsar show a 43–electron volt spectrum from thewhole neutron star surface,as well as a power-law component above 2 kiloelectron volts. In addition, we havedetected a hot (170 electron volts) thermal emission from an �60-meter-radiusspot on the pulsar’s surface. Such a thermal emission, only visible at selected phaseintervals, may be coming from polar hot spot(s), long thought to exist as a resultof heating from magnetospheric accelerated particles. It may provide the missinglink between the x-ray and gamma-ray emission of the pulsar.

Photons emitted by pulsars carry the signature oftheir production mechanisms as well as of thegeometry of their emitting regions. Although neu-tron star physics is reflected in their photon spec-tra, geometrical constraints, such as viewing an-gles of rotational and magnetic axes, shape theirobserved light curves. Phase modulation takesplace as different emitting regions are brought intoview during the star rotation. Geometry can alsoinfluence source spectral shapes because of differ-ent emission mechanisms in different regions.

In spite of the potential interest of phase-resolved spectroscopy, the paucity of detectedx-ray photons has made it impossible to applythis method to isolated neutron stars (INS), withthe exception of the Crab pulsar (1). The Euro-pean Photon Imaging Camera (EPIC) onXMM-Newton and Chandra can now providean adequate harvest of time-tagged photons.However, phase-resolved spectroscopy is notyet commonly used. So far, it has been appliedonly to the Crab pulsar with Chandra (2) and to1E1207-5209 with EPIC (3, 4). Although inter-esting, these sources represent specific andsomewhat extreme cases among x-ray–emit-ting neutron stars. The Geminga pulsar, on theother hand, is often considered as archetypal (5)for middle-aged (350,000 years old) neutronstars, which emit x-rays mostly, but not solely,owing to their surface thermal emission. INSsurface temperatures frequently yield radiation

in the x-ray domain (6), but keV photons canalso be produced by energetic electrons in theirstrong magnetic fields. Geminga has the interest-ing characteristic of showing both thermal (7) andnonthermal processes to be at work in the sub-keV to several keV range (8, 9). With a photonnumber more than doubling all previous statistics,and with a wider (0.15 to 8 keV) spectral range,EPIC now offers the chance of a meaningfulphase-resolved spectroscopy for Geminga.

XMM-Newton performed a 100-ksec ex-posure on 4 April 2002 with its three EPICcameras. The two metal-oxide semiconductor(MOS) cameras (10) operated in “full frame”mode, while the positive-negative (pn) cam-era (11) operated in “small window” mode,ideal for accurate timing of source photons.After removing intervals with high particlebackground and correcting for dead time, weobtain a net exposure of 55.0 ksec for the pncamera and of 76.9 and 77.4 ksec for MOS1and MOS2, respectively. The EPIC observa-tion yielded a total of 76,850 photons in theenergy range 0.15 � E � 8 keV, the majorityof which (52,850 photons) are due to the pndetector. The MOS images have unveiled twotails of diffuse emission that are trailingGeminga and are well aligned with the sourceproper motion (12). Here, we present theanalysis of the pn data, which were processedwith the XMM-Newton Science AnalysisSoftware (SAS version 5.4.1).

First, we have fitted the time-averaged, totalsource data (Fig. 1), using a combination of ablack body and a power law. In view of theunsatisfactory result, we added a third compo-nent, both in the form of a black body and of apower law. The resulting �2 improved signifi-cantly, which suggests that Geminga’s spec-trum indeed requires a three-component model.

1Istituto di Astrofisica Spaziale e Fisica Cosmica, Con-siglio Nazionale delle Ricerche, Via Bassini, 15-20133Milano, Italy. 2Centre d’Etude Spatiale des Rayonne-ments, Centre National de la Recherche Scientifique,Universite Paul Sabatier, 9 Avenue du Colonel Roche,Toulouse, France. 3Universita di Pavia, DipartimentoFisica Teorica e Nucleare, Via Ugo Bassi, 6 Pavia, Italy.

*To whom correspondence should be addressed. E-mail: pat@mi.iasf.cnr.it

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Science Supplemental Online MaterialD. Smith et al., p. 1

Science Supplemental Online Material

Mapping the Antigenic and Genetic Evolution of Influenza VirusDerek J. Smith, Alan S. Lapedes, Jan C. de Jong, Theo M. Bestebroer,

Guus F. Rimmelzwaan, Albert D. M. E. Osterhaus, Ron A. M. Fouchierpublished online 24 June 2004doi: 10.1126/science.1097211

MATERIALS AND METHODS

Antigenic map constructionInfluenza antigenic data. Within the framework of the WHO global influenza surveillancenetwork, national influenza centers collect epidemic influenza virus isolates for antigeniccharacterization using the hemagglutination inhibition (HI) assay. The HI assay is basedon the ability of the influenza virus hemagglutinin protein to cause agglutination of redblood cells (RBCs) and of specific antisera to block this reaction. These antisera aregenerally raised in ferrets by infection with prototypic strains, and a fixed quantity ofeach epidemic virus is tested against a panel of such reference antisera. Two-fold serialdilutions of the antisera are used to determine the highest dilution able to block theagglutination of RBCs. This dilution is called the HI titer, or HI value. The titers canrange from <10 (lower dilutions are not used because of potential nonspecific inhibition,to limit the number of titrations, and to limit the amount of antiserum used in each test) to>10,240 (higher titrations are generally not used to limit the number of titrations). Thehigher the HI value the greater the antigenic relationship between the epidemic virus andantiserum.

Error function. We construct an antigenic map by minimizing the error function,E=�ije(Dij,dij), as a function of the antigen and antisera coordinates in the map, therebydetermining the position of the antigens and antisera in the map. Dij is the target distancebetween antigen i and antiserum j, derived from the HI measurement, Hij, using therelation determined in (S1), Dij =bj-log2(Hij), with bj set to the log2 of the maximummeasurement for antiserum j. dij is the Euclidean distance between the coordinates ofantigen i and antiserum j in the map. e(Dij,dij)=(Dij-dij)

2 when the HI measurement is anumeric value, and e(Dij,dij)=(Dij-1-dij)

2g(Dij-1-dij) when the HI measurement is athresholded value (typically <10). g(x)=1/(1+e-10x) is a squashing function which allowse(Dij,dij) to contribute to E only if dij < Dij-1. We minimize the error function usingmultiple random restarts of the conjugant gradient optimization method (S2).

Dimensionality. When constructing an antigenic map, one must not only determine thecoordinates of the antigens and antisera, but also the dimensionality of the map. Todetermine the dimensionality, one typically constructs antigenic maps in differentnumbers of dimensions and then uses a variety of techniques to determine the number ofdimensions that most suit the data. A stringent technique is the prediction of HI valueswhich were missing from the HI data used to construct the map, and then measuring thesepredicted values using the HI assay—this was the method used here. Prediction errordecreased only slightly as the number of dimensions increased from 2 to 5, and thecluster structure of the 2, 3, 4, and 5 dimensional maps were identical, indicating thatdistortion due to compression from higher dimensions to two dimensions is acceptablysmall, and that a 2-dimensional antigenic map is a good characterization of this data set.

Science Supplemental Online MaterialD. Smith et al., p. 2

Each of the dimensions in the higher dimensional maps represents antigenic distance justas in the lower dimensional maps. Mathematically, space is not limited to the usual threedimensions of which we are accustomed to thinking. The mathematical properties ofspace, such as the Euclidean distance between points, can be calculated in a unique andstandard way for any number of dimensions.

The biochemical process of binding is complex (involving among other thingscomplementarity in shape and charge, hydrogen bonding, and van der Waalsinteractions), and the dimensionality of the space necessary to describe binding has beenestimated to be in the range of five to ten dimensions (S3). Thus the evolution of apathogen such as human influenza A(H3N2), which we have shown to be mostly two-dimensional, can be thought of as a two-dimensional manifold (subspace) in a higherdimensional space. This dimension reduction is likely due to the interplay of manyfactors such as the ecological, epidemiological, and evolutionary processes which likelygovern influenza strain dynamics (S4–S7). We expect that higher dimensional antigenicmaps will be required for some pathogens, potentially corresponding to the pathogen’sphylodynamics (S7).

Comparison with previous work. Most previous approaches to analyzing antigenic datahave used the techniques of numerical taxonomy. The limitations of numerical taxonomywhen applied to binding assay data are discussed in the main text and by Lapedes andFarber (S1). In addition, numerical taxonomy methods do not produce a metric space inwhich distance directly corresponds to individual measurements in the original data—instead distance is an aggregate of multiple measurements; thus, individual HI valuescannot be directly predicted.

Lapedes and Farber introduced a fundamentally different approach, using a variant ofordinal multidimensional scaling (MDS) (S8, S9) to reconstruct a “shape-space”including both antigens and antisera (S1). This ordinal approach used an error functionthat was a function of distances in Euclidean space, dij , (which is in turn a function ofcoordinates of points in the space) and the rank of individual HI measurements. Althoughordinal MDS is a nonparametric method, it produces a metric space. In this space,antigens and antisera are both represented and thus the results can be tested by predictionof individual HI values. One advantage of the ordinal approach is that it is not necessaryto specify a priori the parametric relationship between HI value and distance, indeed thisrelationship can be determined using ordinal MDS and it turns out that distance is linearlyrelated to the logarithm of the HI value (S1).

We have used this linear relationship as the foundation of a new parametric method togenerate antigenic maps, and thus the relative positions of antigens and antisera in themap can be determined with a variant of metric (instead of ordinal) MDS (S10, S11).Metric approaches construct an error function that is a function of the distances inEuclidean space and the value of individual HI measurements (instead of the rank of theHI measurements used in ordinal approaches).

When an HI measurement is beyond the sensitivity of the assay, it is reported as athresholded value, typically "<10." To account for such relational information wemodified the metric approach to be able to deal with both values and relations, asdescribed in the section on the error function above.

Science Supplemental Online MaterialD. Smith et al., p. 3

Consensus. The value of the error function for the antigenic map of Fig. 1 is 3523.74.Consensus among the different minimizations was high; the relative positions of pre-TX77 strains was highly repeatable, as was the relative positions of post-TX77 strains;however, the relative position of the pre-TX77 region to the post-TX77 region had morethan one stable state (Fig. S2). Distances between pre- and post-TX77 strains wereexcluded when calculating the correlation between antigenic distance and the number ofaa substitutions due to this metastability.

Accuracy. Since antigen-antiserum distances in the map correspond to HI values, it waspossible to predict HI values that were missing in the original data set, and subsequentlymeasure those values using the HI assay, to determine the resolution of the map. Whenwe blindly predicted 481 such HI measurements, the average magnitude of the predictionerror for the two-dimensional map was 0.83 [SD 0.67, correlation between predicted andmeasured values 0.80 (P << 0.01)] units. In addition to blind prediction from a 2-dimensional map, we also blindly predicted from 3, 4, and 5 dimensional maps.Prediction error decreased only slightly as the number of dimensions increased,indicating (along with previous evidence that the cluster structure of the 2, 3, 4, and 5dimensional maps were the same) that distortion due to compression from higherdimensions to 2 dimensions is acceptably small for this data set.

Rigidity. The rigidity of the coordinates of points in an antigenic map depends not onlyon the number of HI measurements between the antigens and antisera, but also on howwell spread the antigens and antisera are in space. In direct analogy to geographic mapmaking, the coordinates of points can be determined most accurately if they can be welltriangulated with other points. For example, if three columns of antisera have similar HIvalues for each of the antigens they are measured against, then those antisera will likelyend up in similar locations in the antigenic map, and although they will provide repeated-measure type information to determine the antigenic distance between the antigens andthe three antisera, they will provide little information about the distances among theantigens. In contrast, three antisera that are more widely spread will be able to locate theantigens relative to each other, as well as to the antisera. Thus when selecting referenceantisera, it is best to choose antisera which are distinct from each other. Once anantigenic map has been made, the accuracy (by prediction described above), rigidity ofindividual points (described below), and sensitivity to sample by bootstrapping(described below) can be derived and confidence areas delineated; if required, extra HIvalues can be measured to increase the resolution and tighten the confidence areas. Givenantisera and antigens that are well spread in space, an antigenic map with accuracysimilar to the one shown in Fig. 1 can be made from relatively small amounts of data. Forexample, most of the HI tables used in this study contain data predominantly from asingle antigenic cluster, and contain antigens measured against typically 6 to 8 antisera,and almost all of these HI tables produce reasonable antigenic maps even when notcombined into a single data set.

To determine the rigidity confidence area for each individual antigen and antiserum, allother antigens and antisera were held fixed, and the target antigen or antiserum wasdisplaced to determine how the error function changed due to the displacement. Theperiphery of the shapes in Fig. 1 indicates an increase of 0.5 in the error function.

Science Supplemental Online MaterialD. Smith et al., p. 4

Shading illustrates the rate of error increase for each antigen, from black (no error) to thebase color of the antigenic cluster (0.5 error) at the periphery.

Sensitivity to the data set sample. We tested the sensitivity of the antigenic map to theparticular antigens, antisera, and individual HI measurements by bootstrap resampling ofeach of these components. We used the standard bootstrap technique of sampling theexisting data set with replacement and generating new data sets of the same size as theoriginal data set typically resulting in resamples with multiple entries of somecomponents and missing entries of other components. Other than the same trans-TX77metastability discussed above, the maps generated from these new data sets weregenerally robust to such resampling. We also bootstrap resampled the antigens togenerate data sets with the same number of antigens per season (separate resamples with3 and 10 strains per season). Again, apart from the trans-TX77 metastability, the mapswere generally robust to the resampling.

One of the major topographical features of the map in Fig. 1 is the change in direction ofthe antigenic evolution between the 1980s and the 1990s. The timing of this change issomewhat concomitant with the appearance of the BE89 cluster genetic dead-end. To testif this change was an artifact introduced by the BE89 cluster, we remade the mapomitting the BE89 antigens and antisera. The resulting map was almost identical to theoriginal, indicating that the change is not an artifact of the BE89 genetic dead-end.

Combining HI data. When combining HI data, either from tables made at different timesin the same laboratory, or tables made in different laboratories, the map made from thecombined data might have higher or lower resolution than maps made from the individualtables. The accuracy of the individual and combined maps, and individual antigens andantisera in the maps, can be determined by prediction (either by measuring previouslyunmeasured values, or by leaving out already measured values), calculating rigidity andbootstrap resampling confidence areas as described above. Unless there are systematicdifferences between the data sets, adding more data will increase the resolution of themap; if there are systematic differences, then they can be detected using these methods.These methods also provide a way to curate an antigenic database and maintainconsistency: as new antigenic data are produced, reference antigens and/or antisera canbe included, the positions of which are compared in the new and existing maps.

Variation in red blood cells (RBCs) and antisera. Variation in the reagents used in the HIassay, such as the animal source of red blood cells (RBCs), and animal used to raise theantisera, add potential complexity to HI data.

The data used in this study were collected in a single laboratory, all antisera were raisedin ferrets, RBCs up to and including the 1991/92 season were from chickens andthereafter from turkeys. As part of the data validation process for this study, weremeasured, using turkey RBCs, 88 titers of strains from 1968 to 1989 that hadpreviously been measured as part of routine surveillance using chicken RBCs. Theaverage titer difference was 0.01 of a 2-fold dilution, and the average of the absolutevalues of the titer differences was 0.68 of a 2-fold dilution, indicating that the switchfrom chicken to turkey RBCs is unlikely to have a significant effect in this data set.

Had the switch from chicken to turkey RBCs had a larger effect, the methods describedhere would provide techniques to examine the differences quantitatively: a map made

Science Supplemental Online MaterialD. Smith et al., p. 5

with strains only measured using chicken RBCs could be compared to a map withmeasurements using turkey RBCs, and strains in common between the maps could becompared for differences in antigenic distance to other strains.

Similarly, maps could be made for the same antigens using antisera from differentanimals to analytically compare differences among such antisera. Hyperimmune sheep(or goat) antisera for example are less specific than ferret antisera; hence, an antigenicmap made only with sheep antisera would be scaled differently from a map made onlywith ferret antisera, and the difference in scale could be determined by comparing the twomaps. Furthermore, this difference in scale could then be used to make antigenic mapsfrom HI tables containing measurements using both ferret and sheep antisera (the scalefactor would be applied in the e(Dij,dij) part of the error function, which would take athird parameter to indicate the animal source of antisera). Alternatively, the scale factorcould be a free parameter in the error function which would be then be determined as partof the error minimization process. If the difference between hyperimmune sheep or goatantisera were not linear, but varied as a function of the antigenic distance betweenantigens, then this could also be determined using the same methods, and the e(Dij,dij)term in the error function could be extended with a nonlinear function instead of a simplescale factor. In practice, it is likely that once the relation among ferret and hyperimmunesheep or goat antisera has been determined using these methods, it could then be usedtransparently in future analyses.

Genetic map constructionOur method for making genetic maps is similar to that for making antigenic maps, exceptthat the target distances are the number of aa substitutions between the antigen aasequences, and there are no antisera points. To avoid underestimating genetic distancesdue to sequential mutations at the same location, when a target distance was greater than30 aa substitutions it was allowed to contribute to the error function only when the mapdistance was <30 in the same way that a thresholded HI measurement was only allowedto contribute to the error function when the map distance was within threshold. PCOORD(S12) is a related method for making genetic maps, but does not directly handlethresholded distances. The correlation between the number of aa substitutions and thecorresponding distances between strains in the genetic map was 0.98 indicating that the2D genetic map is a reasonable representation of the target aa distance matrix.

ClusteringClusters were identified in the antigenic map by a k-means clustering algorithm (S13),using average weighting, and k=10. Two adjustments were then made. First, the threeTX77-like strains were made into a separate cluster. TX77 is a well-known antigenic andgenetic type (S14), and strains of this type were recorded in our laboratory, but apart fromthe three strains in the data set, the remaining strains, and HI data for these strains, arecurrently lost. Second, strains VI/7/87, OK/5/88, and VI/1/88 were assigned to the SI87cluster—in multiple runs of the k-means clusterer these strains appeared in either theSI87 or BE89 clusters with very small error difference between placement in eithercluster. The most parsimonious placement of these strains, based on genetic and temporalinformation is in the SI87 cluster.

Science Supplemental Online MaterialD. Smith et al., p. 6

When clusters were determined from the genetic map (instead of the antigenic map), andplotted season-by-season, the antigenic evolution was still punctuated and the geneticevolution still gradual.

“Cluster-difference” aa substitutionsAn aa substitution X to Y at location L is considered a “cluster-difference” substitutionbetween clusters A and B if all (or all but one) strains in cluster A have aa X at locationL, and all (or all but one) strains in cluster B have aa Y at location L. Fig S1 shows analignment of the aa sequences used in this study, grouped and color-coded based onantigenic cluster, and marked with the cluster-difference aa substitutions.

Evolutionary distance to A/Bilthoven/16190/68 antigenOne way to measure the distance of a strain to the root of the 1968 pandemic strainswould be to measure the straight-line distance on the map from the strain of interest to areference strain chosen to be representative of the 1968 pandemic. An obvious choice forsuch a reference strains would be the A/HongKong/1/68 (HK/1/68) vaccine strain.However, we choose a different strain, A/Bilthoven/16190/68 (BI/16190/68), because inboth the ML tree (Fig. 2A) and a protein parsimony tree (data not shown) BI/16190/68was closest to the avian outgroup, and also because with only 5 passages in tertiarymonkey kidney cells and no passages in eggs, it likely has fewer laboratory adaptationsthan the HK/1/68 vaccine strain and thus is likely to be a better representative of the earlypandemic strains. We also measure distance to BI/16190/68, not in a straight-line, but asan evolutionary distance through the centroids of clusters between the strain of interestand BI/16190/68. This path between cluster centroids is uniquely determined forinfluenza virus as the clusters are chronologically ordered (for other pathogens this routemight not be clear). Note, this implies going via the BE89 cluster when passing from theBE92 cluster back to the SI87 cluster, as this is the order in which the clusters appeared,and thus the order which will influence selection at the phenotypic level and the mostappropriate measure here of antigenic distance, rather than following the genetic lineagewhich would bypass the BE89 genetic dead-end. Similarly, the antigenic path from TX77to EN72 goes through VI75. Taking this evolutionary path, rather than the straight-linedistance, to the root antigen is a map analog of measuring genetic distance from aphylogenetic tree rather than as the number of mutations to the root strain—for shortdistances these measures are usually similar, but for longer distances the straight-linedistance will be shorter than the evolutionary distance. The evolutionary distance to theBI/16190/68 strain in the antigenic and genetic maps was thus calculated as the sum ofdistances of a strain to the centroid of the previous antigenic cluster, from this centroidthrough the intermediate centroids to the EN72 centroid, and then to BI/16190/68 antigenin the HK68 cluster.

Biases in the dataThere are three biases in the data used for this study. First, the majority of strains selectedfor sequencing as part of routine surveillance were those with unusual antigenicproperties. The remaining strains were the first and last isolate from each season, arepresentative of each antigenically distinct cluster seen in the Netherlands that season(determined each year as part of routine surveillance by classical reading of HI data), andthe vaccine strains. This sample is thus likely to contain a larger proportion of outliersthan would be obtained from a random sample; thus, the cluster structure of a map made

Science Supplemental Online MaterialD. Smith et al., p. 7

from a random sample would likely be more clustered than the map of Fig. 1, and theobservation of clustered antigenic evolution would be unlikely to change. The removal ofoutlier strains would be unlikely to alter the overall structure of the map as the map isrobust to bootstrap resampling of the antigens and to the removal of whole clusters suchas BE89. For a subset of seasons we generated an antigenic map for all strains collectedat the Dutch National Influenza Center—this is closer to a random sample than thesequenced strains; the cluster structure of Fig. 1, and the punctuated antigenic evolutionof Fig. 4A persisted. As indicated in the caption of Fig. 4, since sampling is biasedtowards outlier strains, the area of the circles in Figs. 4 A, B and C, does not reflectepidemic impact. The strains in each antigenic cluster were bootstrap resampled to testthe robustness of distances between antigenic clusters in the genetic and antigenic mapsshown in Table 1. Distances were not statistically significantly different from thedistances in original sample, and SEs were sufficiently small to indicate that distances arerobust to the surveillance sampling biases (Table S1).

Second, approximately one third of the strains in this study were isolated in a singlecountry—The Netherlands. This sampling bias is likely to affect the season in which thefirst strains in a cluster are detected, as new variants are likely to circulate locally beforetheir global emergence. A further effect is that some variants, which only circulate locallybefore dying out, might not appear in this data set. The addition or absence of such localdata, as with the removal of outlier strains, would be unlikely to change the overallstructure of the map as both the antigenic and genetic maps are robust to the presence orabsence of even major clusters.

Third, mutations can be introduced by laboratory propagation of influenza virus strains inembryonated chicken eggs. However, since 89% of strains used in this study werepropagated only in mammalian cell culture, for at most 5 passages, and usually only 1 or2 passages, this bias is unlikely to have a significant effect on this study (S15, S16).

Accession numbersIn addition to the newly sequenced HA1 domains, the following previously publishedHA1 domain sequences were used in this study: AF008665 AF008697 AF008711AF008725 AF008755 AF008769 AF008828 AF008867 AF008886 AF008888 AF008903AF008905 AF092062 AF131997 AF180570 AF180602 AF180643 AF201874 AF368444AF368446 D21173 D49961 ISDN38157 ISDNCDA001 ISDNENG72 ISDNHK71ISDNTX77 ISDNVIC75 M16739 U08858 Z46405 Z46408 Z46413 Z46414.

Science Supplemental Online MaterialD. Smith et al., p. 8

Table S1. Distances between antigenic clusters in the genetic and antigenic maps. Dataare related to the data in Table 1 of the main text except that distances and ratios are theaverage of 1,000 bootstrap resamples of the strains in each antigenic cluster, and thestandard error (SE) of the averages are shown. Each resample was of the same number ofstrains per cluster as in the original sample. Distances and ratios differ slightly, but notstatistically significantly, from those in Table 1 because these are averages of 1,000bootstrap resamples, whereas the data in Table 1 were calculated from the originalsample.

Genetic distance Antigenic distance Genetic/antigenic(aa substitutions) (units) ratioCluster

transition Av SE Av SE Av SEHK68-EN72 12.19 0.93 3.43 0.23 3.57 0.26

EN72-VI75 14.71 0.54 4.38 0.24 3.37 0.25

VI75-TX77 14.87 0.98 3.41 0.66 4.48 0.65

TX77-BA79 16.08 2.04 3.34 0.27 4.82 0.56

BA79-SI87 11.99 1.11 4.93 0.31 2.44 0.2

SI87-BE89 6.87 0.5 4.60 0.27 1.50 0.12

BE89-BE92 13.73 0.41 7.77 0.19 1.77 0.07

BE92-WU95 9.89 0.75 4.59 0.21 2.17 0.16

WU95-SY97 16.07 0.86 4.69 0.17 3.43 0.23

SY97-FU02 16.05 0.77 3.52 0.35 4.60 0.44

Average SE 0.89 0.29 0.29Total 132.43 1.79 44.66 0.97Av 13.24 0.18 4.47 0.1 3.21 0.13

Science Supplemental Online MaterialD. Smith et al., p. 9

Figures appear at the endn of the file

Fig S1. Alignment of aa sequences used in this study, grouped and color-coded accordingto the antigenic clusters, and with the cluster-difference substitutions boxed. The ninestrains interdigitated between the BE92 and WU95 clusters are underlined. Codons inwhich there were no aa substitutions are excluded. The image can be zoomed to showdetails.

Fig S2. The antigenic map of influenza A virus strains as shown in Fig. 2C, with therigid-body rotation and translation of the pre-TX77 clusters shown by the arrow. Thepositions in the original map (Fig. 1) are shown in light colors, and darker colors indicatethe positions after the rotation and translation.

Fig S3. The antigenic map of influenza A virus strains as shown in Fig. 1 except thatvirus strains are represented by colored circles and antisera by open squares. A lineconnecting a virus strain an antiserum indicates that the virus strain was used to raise theantiserum.

Science Supplemental Online MaterialD. Smith et al., p. 10

References and Notes

S1. A. Lapedes, R. Farber, J Theor Biol 212, 57-69 (Sep 7, 2001).S2. B. Flannery, S. Teukolsky, W. Vetterling, Numerical Recipes in C (Cambridge

Univ. Press, Cambridge, 1988).S3. A. S. Perelson, G. F. Oster, J Theor Biol 81, 645-70 (1979).S4. N. M. Ferguson, A. P. Galvani, R. M. Bush, Nature 422, 428-33 (2003).S5. J. R. Gog, B. T. Grenfell, Proc Natl Acad Sci U S A 99, 17209-14 (2002).S6. J. R. Gog, G. F. Rimmelzwaan, A. D. Osterhaus, B. T. Grenfell, Proc Natl Acad

Sci U S A 100, 11143-7 (2003).S7. B. T. Grenfell et al., Science 303, 327-332 (2004).S8. J. Kruskal, Psychometrika 29, 1-27 (1964).S9. J. Kruskal, Psychometrika 29, 115-129 (1964).S10. R. Shepherd, Human Factors 5, 33-48 (1963).S11. R. Shepherd, J Math Psychol 1, 54-87 (1964).S12. D. G. Higgins, Comput Appl Biosci 8, 15-22 (1992).S13. R. Duda, P. Hart, Pattern Recognition and Scene Analysis (Wiley Interscience,

1973).S14. G. W. Both, M. J. Sleigh, N. J. Cox, A. P. Kendal, J Virol 48, 52-60 (1983).S15. J. C. de Jong et al., Bull World Health Organ 66, 47-55 (1988).S16. G. C. Schild, J. S. Oxford, J. C. de Jong, R. G. Webster, Nature 303, 706-9

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DH/33/80 D L G N D N T P L I D Q I Q S S G K N N P R I L R D I H V F Q D E T V N F N D P A V S S I T G T T G T Q N G N A C K G P A S G F T K S G S T V L N T P N D N F D K V H P S N Q E T N L V Q A S G V T V S R R S I I N I S R W G Q S G R I S Y T V V V N S N K M R T K I M R D A P D T I S E N P D K K A K V K N T V E K

HK68 BI/15793/68 N I N K G I S R L S I

HK68 BI/16190/68 I N K G S R L S I

HK68 BI/16398/68 I N K G S R L S I

HK68 HK/1/68 V R S K K N K D F G K R S P R L S I

HK68 BI/808/69 I N K T G R S S R L S I V

HK68 BI/908/69 N I N K G S R L S I

HK68 BI/17938/69 N I N K G S R L S I

HK68 BI/93/70 N V N K G S R S I

HK68 BI/2668/70 N I N K I G S R L S I

HK68 BI/6449/71 E N I G N K G I S R L S I

HK68 BI/21438/71 K N I G N K S G S R L S I I I

HK68 BI/21801/71 K N I G N K G S R L S I I I G

HK68 HK/107/71 K T I N K E E D R D S T R L S I

HK68 BI/6022/72 K N I G N K G S R K P S T I I G A

EN72 BI/21793/72 F N I G N K N D Y D S R K L S I I G

EN72 BI/23290/72 F N I G N K N D Y D S R K L S I I G

EN72 BI/23337/72 F N I G N K N D Y D S R K L S I I G

EN72 EN/42/72 N I G N K N D Y S R K G L S I I G

EN72 BI/552/73 F N I G N K N D N Y D S R K L S I I G

EN72 BI/748/73 F N I G N K N T D Y D S R K L S I I G

EN72 BI/3517/73 F N I G N K N T D Y D S R K L S I I G

EN72 PC/1/73 F N I N G N K N D Y A D R K L S I I G

EN72 BI/5146/74 N F N I G N K N D I Y N S D S R K L S I I S G

EN72 BI/5930/74 N I N G N K K N N D Y D R K L S I I G

EN72 BI/5931/74 N I N G N K K N N D Y D R K L S I I G

EN72 BI/7398/74 N I N G N K K N N D I Y D R K L S I I G

EN72 BI/9459/74 N I N G N K K N N D Y K D R K M S I I R G R

EN72 BI/2600/75 F R N M G L N K N D R Y N D D S R K V P L S G I I L G

EN72 BI/2813/75 N I N G N K K N N D I Y D R K L S I I G

VI75 VI/3/75 N D I N G N K K N N S D N Y L Q S D K D K K N V V L S V I I G S

VI75 BI/1761/76 N D I N G N K K N N S D N Y Q S D K D K K V V L S V I I G S

VI75 BI/5029/76 N G I N G N K K N N S D N Y Q S D K D K K V V L S V I I G S

VI75 BI/5657/76 N D I N G N K K N N S D N Y Q A S D K D K K V V L S V I I G S

VI75 BI/6545/76 N D I N G N K K N N S D N Y Q S D K D K K V V L S V I I G S

VI75 AM/1609/77 N D I N G N K K N N S D N Y Q S D K D K K V V L S V I I G S I

VI75 BI/3895/77 N D K N G N K K N N S D N Y Q S R D K D K K V V L S V I I G S

VI75 RD/5828/77 N D I N G N K K N N S D K Y Q S D K D K K V V L S V I I L G S

VI75 RD/8179/77 N D I N G N K K N N G R D N Y Q S D K D K K V V L S V I I M G S

TX77 BI/628/76 N R I N G N K K K N N Y D N Y E D K R K V L S I I I G S

TX77 BI/2271/76 N R I N G N K K K N N Y D N Y E D K R K V L S I I I N G S

TX77 TE/1/77 N N R D S K N G N K K N N Y D N Y E G D K R K V L S I I L I G S

BK79 NL/209/80 N N R D S K N V G N K K N S N S Y S D N S Y E E K G D K R R K L S I I L I G S

BK79 RD/577/80 N N R D S K N G N K K I N N S Y S D N S Y E E K G D K R R K L S I I L I G S

BK79 BI/4791/81 N N R D S K N V G N K K N S N S Y S D N S Y E E K G D K R R K L S I I L I G S

BK79 BI/10684/82 N N R D S K N V G N K K N S N S S S D N S Y E E K G D K R R K N V L S I I L I N G S

BK79 NL/233/82 N N R D S K N G N K K N N S Y S D N S Y E E K G K D K K R R K V L S I I L T I G S R

BK79 NL/241/82 N N R D S K N G N K R N N S Y S D N S Y E E K G K D K R R K V L S I I L T I G S R

BK79 PH/2/82 N N R D S K N G N K K N N S Y T S N N S Y E E K G K I D K I R R K V L S I I L T I G S R

BK79 OS/13676/83 N N R D S K N V G N K K T N S N S Y S D N S Y E E K G D K R R K N V L S I I L I N G S

BK79 CE/1/84 K N R D S K N G N K K Y N N S Y S V N S Y E K G K D K R R K V L S I I L T I G S Y R

BK79 GF/V728/85 K N R D S K N G N K K N N S Y S V N S Y E E Y K A G K D K R R K V L S I I L T I G S R

BK79 NL/330/85 K N R D S K N G N K K N N S Y S V N S Y E E K A G K D K R R K V P L S I I L T I G S R

BK79 ST/10/85 K N R D S K N G N K I K N N S Y S V N S Y E E Y K A G K E K R R K V T L S I I L T I G S R

BK79 WE/4/85 K N R D S K N G N K K N N S Y S V N S Y E E Y K A G K D K R R K V L S I I L T I G S R

BK79 CO/2/86 K N R D S K N G N K K N D N S Y E S V N S Y E E Y K A G K E D K R R K V L S I I L T I G S R R

BK79 LE/360/86 K N R D S K N G N K I K N N S Y T S V N S Y E E Y K A G K E K R R K V L S I I L T I G S R

BK79 CC/2/88 K N R D S K N G N K I K N N Y S V N S Y E E Y K A G K I E K R R K R L S I I L T I G S R G

SI87 SH/11/87 K N R D S K N G N K K Y N D N S Y S V N S H E E Y K A G K D R R R K V L S I I L R T I G S R

SI87 SI/2/87 K N R D S K N G N K K Y N D N S Y S V N S H E Y K A G K R V D R R R K V L S I I L T I G S R

SI87 VI/7/87 K N R D S K N G N K K Y N D N S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R

SI87 EN/427/88 K N R D S K N G N K K N D N S Y S V N S H E E Y K A G K D R R R K V P L S I I L T I K G S R

SI87 NL/450/88 K N R D S K N G N K K N E N S Y S V N S H E E Y K A G R D R R R K V P L S I I L T I H G S R D

SI87 ST/12/88 K N V R D S K N G N K K Y N D N S Y S V N S H E E Y K A G K I D R R R K V L S I I L T I G S R

SI87 AT/211/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S S R R

SI87 GE/5007/89 K N N R S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R R

SI87 GU/54/89 K N H R D S K N G N K E K Y N D N A S Y S I N S H E E H K A G K I D R R R K V L S I I L T I G S R R

SI87 HK/1/89 K N R D S K N G N K E K Y N D N A S E Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 NL/620/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 NL/650/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E Y K A G K D R R R K V L S I I L T I G S R R

SI87 NL/738/89 K N R D S K N G N K E K Y N D N A S Y S V N S H P E Y K A G K D R R R K V L S I I L T I G S R R

SI87 SP/34/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 SP/35/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 SP/36/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 SP/40/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 SP/53/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S N R R

SI87 VI/1/89 K N R D S K N G N K K Y N D N S Y S V N S H E Y K A G K V D R R R K V L S I I L T I G S R

SI87 WE/5/89 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 ME/2/90 K N N R D S K N G L N K E K Y T K D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 ME/5/90 K N N R D S K N G L N K E K Y T K D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 SH/24/90 K N R D S E N G N K E K Y N D N A S Y S V N S H E Y K A G K D R D S R R K V S I I L T I N G S R R

SI87 SU/1/90 K N R D S K N G N K E K Y N D N A S Y S V N S H E E Y K A G K D R R R K V L S I I L T I G S R R

SI87 CA/1/91 K N R D S K N G N K E K Y N D N A S Y T S V N S H E E Y K A G K D R R R K V L S I I L T I G S S R R

BE89 BE/352/89 K N R D S K N G N K E K Y N D N A S E Y S V K S L H E E Y K A G K D R K R R K V L S I I L T I G S R R

BE89 BE/353/89 K N R D S K N G N K E K Y N D N A S E Y S V K S H E E Y K A G K D R R R K V L S I I L T I G S R R

BE89 VI/2/90 K N R D S K N G N K E I K Y N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 EN/260/91 K N R D S K N G N K E K Y N D N A S D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 EN/261/91 K N R D S K N G N K E K Y N D N A S E Y T S V K S H E E Y K A K E R S I R R K V L S I I L T I G S R R

BE89 GE/6447/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1149/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1182/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1189/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1276/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1337/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1373/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/1594/91 K N R D S V K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I I G S R R

BE89 LY/23672/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/24103/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 LY/24222/91 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 MA/G12/91 K N N R D S K N D N K E K Y A N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 NL/816/91 K N N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 ST/20/91 K N D S K N G N K E K Y N D N A S E Y S V K S H E D Y K A G K D R S I R R K V L S I I L T I G S R R

BE89 AM/4112/92 K N N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 ES/1285/92 K N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 FI/218/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 FI/220/92 K N D S K N G N K E K Y N D N A S E Y S V K S H E D Y K A G K D R S I R R K V L S I I L T I G S R R

BE89 GE/5113/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 HO/56798/92 K N R D S K N G N K E Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 HO/56829/92 K N R D S K N G N K E K Y E T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 HO/56941/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 MA/G58/92 K N N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A G K D R D S R R K V L S I I L T I G S R R

BE89 NI/3126/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 NI/3129/92 K N N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 NL/819/92 K N R D S K N G N K E K Y N D N A S D Y S V K S H E E Y K A K E R S I R R K V K L S I I L T I G S R R

BE89 NL/823/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 NL/935/92 K N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 OV/31/92 K N N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/320/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/325/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/407/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/417/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R G

BE89 PA/424/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/457/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/467/92 K N R D S K N G N K E K Y N D N A S E Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G N R R

BE89 PA/490/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K E G K D R S R R K V L S I I L T I G S R R

BE89 PA/512/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/548/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/564/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/583/92 K N R D S K N G N K E K Y N D N A S E Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 PA/597/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 PA/614/92 K N R D S K N G N K E K Y N D N A S D Y S V K S H E E Y K A K E R S I R R K V F K L S I I L T I G S R R

BE89 RD/100540/92 K S N T R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 SA/8/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 SA/23/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 SA/27/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 ST/7/92 K N R D S K N G N K E K Y N D N A D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 ST/8/92 K N R D S K N G N K E K Y T N D N A S E Y S V K S H E D Y K A G K D R S R R K V L S I I L T I G S R R

BE89 ST/13/92 K N D S K N D N K E K Y N D N A S D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 TI/5957/92 K N N R D S K N D N K E K Y N D N A S E Y S V K S H E E Y K A G K D R S R R K V L S I I L T I G S R R

BE89 UM/1982/92 K N D S K N D N K E K Y N D N A S D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 UM/2000/92 K N D S K N D N K E K Y N D N A S D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I T G S R R

BE89 VI/68/92 K N R D S K N G N K E K Y N D N A D Y S V K S H E E Y K A K D R S R R K V L S I I L T I G S R R

BE89 ES/5458/93 K N D S K N D N K E K Y N D N A S D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G N R R

BE89 MA/G101/93 K N R D S K N M G N K E K Y N D N A D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 MA/G116/93 K N R D S K N M G N K E K Y N D N A D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE89 NL/165/93 K N R D S K N G N K E K Y N D N A D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I V G S R R

BE89 PA/287/93 K N R D S K N D N K E K Y N D N A S D Y S V K S H E E Y K A K D R S I R R K V L S I I L T I G S R R

BE92 BE/32/92 K N R D S K N G N K E K Y N D N A D Y S V N S H E Y K A G K D R D S R R K V T S I I L T I N G S R R

BE92 FI/247/92 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N G N S R R

BE92 NL/938/92 K L N R D S K N G L N K E K Y N D N A D Y S V N S H E Y K A G K V D R D S R R K V L S I I L T I N G S R R

BE92 SE/C273/92 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T S I I L T I N G N S R R

BE92 ST/12/92 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N G N S R R

BE92 AK/4/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V P S I I L T I N G N S R R

BE92 GD/25/93 K N R D S K N G N K E E Y S N D N A D K Y S V N S H L E Y K A G K F D S D S R R K V S I I L T I N G N S R

BE92 LY/672/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V L P S I I L T I N G N S R R

BE92 LY/1803/93 K N R D S K N N G N K E K Y S T N D N A D E Y S V N S H L E Y K A G K D S D S R K K V T L S I I L T Q I N G S N R R

BE92 LY/1815/93 K N R D S K N G N K E K Y N D N A D K Y T S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R

BE92 LY/22686/93 K N R D S K N G N K E K Y N A N A D K Y T S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R

BE92 LY/23602/93 K N R D S K N G N K E K Y S N D N A D K Y S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R

BE92 MA/G102/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N V G N S R R

BE92 MA/G109/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S I R R K V L S I I L T I N G N S R R

BE92 MA/G122/93 K N R D S K N G N K E K Y N D N A D K Y T S V N S H L E Y K A G K D S D S R R K V L S I I L T I N G N S R R

BE92 MA/G130/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N V G N S R R

BE92 NL/3/93 K N R D S K N G N K E K Y V N D N A D R Y S V N S H E Y K A G K D S D S R R K V T L S I I L T I N G S R R

BE92 NL/17/93 K N R D S K N G N K E K Y V N D N A D R Y S V N S H E Y K A G K D S D S R R K V T L S I I L T I N G I S R R

BE92 NL/101/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N V G N S R R

BE92 NL/115/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K I V L S I I L T I N G N S R R

BE92 NL/126/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K I V L S I I L T I N G N S R R

BE92 NL/179/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V L S I I L T I N G N S R R

BE92 NL/241/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R

BE92 NL/357/93 K N R D S K N N G N K E K Y T N D N A D K Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N G S R R

BE92 NL/371/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R A K V F M S G I I L T I N G N S R R

BE92 NL/398/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R

BE92 NL/440/93 K N R D S K N N G N K E K Y T N D N A D E Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N G S R R

BE92 OS/2219/93 K N R D S V K N G N K E K Y P N D N A D K Y S V N S H L E Y K A G K D S D S R R K V Y F L S A I I L T I N G N S R R

BE92 OS/2352/93 K I N R D S K N N G N K E K Y T N D N A D E Y S S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N G S R R

BE92 SD/9/93 K N R G S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T S I I L T I N G N S R R

BE92 SG/6/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T S I I L T I N G N S R R

BE92 SL/142/93 K N R D S K N N G N K E K Y T N D N A D Y S V N S H L E Y K A G K D S D S R K K V T L S I I L T I N G S R R

BE92 SL/160/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R

BE92 SP/3/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T L S I I L T I N V G N S R R

BE92 ST/20/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V L S I I L T I N G N S R R

BE92 VI/104/93 K N R D S E N N G N K E K Y T N D N A D E Y S V N S H L E Y K A G K D S D S R R K V T L S I I L D T I N G S R R

BE92 WE/59/93 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R K V L S G I I L I I N G N S R R

BE92 YA/56/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T S I I L I I N G N S R R

BE92 YA/61/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T S I I L T I N G N S R R

BE92 YA/62/93 K N R D S K N G N K E K Y N D N A D Y S V N S H L E Y K A G K D S D S R R K V T S I I L T I N G N S R R

BE92 HK/1/94 K N P R D S K N G N K E K Y N N N A D K Y S V N S H L E Y K A G K D S D S R R K V D Y L S I I L T I N G N S R R

BE92 HK/2/94 K N R D S K N G N K E K Y S N D N A D K Y S V N S H L E Y K A G K D S D S R R I K V L S I I L T I G N S R R

BE92 HK/55/94 K N P R D S K N G N K E K Y N N N A D K Y S V N S H L E Y K A G K D S D S R R K V D Y L S I I L T I N G N S R R

BE92 HK/56/94 K N R D S H K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V I S I I L T I N G N N R R

BE92 JO/33/94 K N P R D S K N G N K E K Y N N N A D K Y S V N S H L E Y K A G K D S D S R R K V D Y S I I L T I N G N S R R

BE92 JO/47/94 K N N P R D S K N G L N K E K Y N N N A D K Y S V N S H L E Y K A G K D S D S R R K V D Y L S I I L T L N G N S R P R

BE92 NL/18/94 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R R K V F L S I I L T I N G N S R R I

BE92 SA/15/94 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R K V Y L S I I L T I N G N S S R R

BE92 SA/25/94 K N R D S K N G N K E K Y N D N A D K Y S V N S H L E Y K A G K D S D S R K V Y L S I I L T I N G N S S R R

BE92 HK/3/95 K N R D S K N G N K E K Y T K N A K Y S V N S H L E Y K A K D S D S R K V I S I I L T I N G N N R R

BE92 HK/32/95 K N R D S K N G N K E K Y T N N A D K Y S V N S H L E Y K A K D S D S R K V I S I I L T I N G N N R R

BE92 HK/38/95 K N R D S K N G N K E K Y T K N A D K Y S V N S H L E Y K A K D S D S R K V I S I I L T I N G N K R R

BE92 NL/1/95 K N P R D S K N G N K E K Y T N N N A D K Y S V N S H L D Y K A G K D S D S R K K V D Y L S I I L T I N G N S R R

BE92 VI/75/95 K N R D S K N G N K E K Y T N N A D K Y S V N S H L E Y K A K D S D S R K V I S I I L T I N N N R R

BE92 HK/42/96 K N P R D S N G N K E K Y N N N A D K Y S V N S H L D Y K A G K D S D I S R S R K V D Y L S I I L T L N G N S G R R

BE92 HK/434/96 K N P R D S K N G N K E K Y N N N A D K Y S V N S H L E Y K A G K D S D S R R K V D Y S G I I L T I N G N S R R

BE92 NL/91/96 K N R D S K N G N K E K Y T N N A D K Y S V N S H L E Y K A K D S D S R K V I S I I L T I N N N R R

WU95 MA/G252/93 K N R D S K N G N K E K Y N D N A D K Y S V K S H L E Y K A G K D S D S R R K K V S I I L T I N G N S R R

WU95 NL/372/93 K N R D S K N G N K E K Y N D N A D K Y S V K S H L E Y K A G K S D S R R K V F L S I I L T I N G N S R R

WU95 NL/399/93 K N R D S K N N G N K E K Y T N D N A D K Y S V K S H L E Y K A G K D S D S R R K V T L S I I L T I N G S R R

WU95 EN/7/94 K N R D S K N G N K E K Y N D N A D K Y S V K S H L E Y K A G K D S D S R R K V F L S I I L T I N I G N S R R

WU95 FI/338/95 K N R D S K N G N K E K Y T K N A D E Y S V K S H L E Y K A K D S D S R K V I S I I L T I N G N N D R R

WU95 FI/339/95 K N R D S K N G N K E K Y T N N A D K Y S V K S H L E Y K A K D S D S R K V I S I I L T I N N N R R

WU95 FI/381/95 K N R D S K N G N K E K Y T N N A D K Y S V K S H L E Y K A K D S D S R K V I S I I L T I N N N R R

WU95 GE/A9509/95 K N R D S K N G N K E K Y T K N A D K Y S V N S H L E Y K A K D S D S R K V I S I I L T I N G N N R R

WU95 HK/49/95 K N R D S K N G N K E K Y T N N A D K Y S V K S H L E Y K A K D S D S R K V I S I I L T I N G N N R R

WU95 HK/55/95 K N R D S K N G N K E K Y T N N A D T Y S S V K S H L E Y K A A K D S D S R K V V S I I L T I S G N N R R

WU95 LY/2279/95 K N R D S K N G N K E K Y T N N A D K Y S V K S H L E Y K A K D S D S R K V I S I I L T I N N N R R

WU95 NA/933/95 K N R D S K N G N K E K Y T N N A D T Y S V K S H L E Y K A K D S V S R K V I S I I L T I S G N N R R

WU95 NL/271/95 K N R D S K N G N K E K Y T N N A D K Y S V K H L E F K A K D S D S R K V I S I I L T I N N N R R

WU95 WU/359/95 K N R D S K N G N K E K Y T N N A D T Y S V K S H L E Y K A K D S D S I R K V I S I I L T I S G N N R R

WU95 BR/8/96 K N R D S K N G N K E K Y D T N N A D T Y S V K S H L E Y K A K D S D S T R K V V S G I I L T I S G N N R R

WU95 GE/3958/96 K N R D S K N G N K E K Y T N N A D T Y S V K S H L E Y K A K D S D S R K V V S I I L T I S G N N R R

WU95 HK/20/96 K N R D G I K N G N K E K Y T N S N A T Y S V K S H L E Y K A K D S D S S R K V V S I I L T I S N N R R

WU95 HK/357/96 K N R D S K N G N K E K Y S N N S N A T Y R S V K S H L E Y K A K D S D S R K V V S I I L T I S G N N R R

WU95 HK/358/96 K N D S K N G N K E K Y N N S N A T Y S V K S H L E Y K A K D S D S R K V V S I I L T I S G N N R R

WU95 LY/1781/96 K N R D S K N G N K E K Y T N N A D T Y S V K S H L E Y K A K D S D I S R K V I S I I L T I S N N R R

WU95 SP/1/96 K N I D S K N G N K E K Y T N N A D T Y S V K S H L E Y K A K D S D S R V V S I I L T I S G N N R R

WU95 HK/1/97 K N R D S K N G K N K E T Y T N N A D T Y S V K S H L E Y K A K D S D S R K V V S I I L T I S N N G R R

WU95 HK/280/97 K N R D S K N G N K E T Y T N N A D T Y R S V K S H L E Y K A K I D S D S H R K V V S I I L T I S N N R R

WU95 JO/10/97 K N R D S Q K N G K N K E T Y T N N A A D T Y S V K S H L E Y K A K D S D S R K V I S I I L S T I S I N N R R

WU95 NE/491/97 K N R D S K N G N K E K Y T N N A D T Y S V K S H L E Y K A K D S D S R K V V S I I L T I S N N R R

WU95 OS/21/97 K N R D S K N G K N K E T Y T N N A D T Y S V K S H L E Y K A T K D S D S R K V V S I I L T I S N N R R

WU95 OS/244/97 K N R D S K N G K N K E T Y T N N A A D T Y S V K S H L E Y K A K D S D S R K V V S I I L T I S N N R P R

WU95 NL/5/98 K N G R D S Q K N G N K E T Y T K N A D T Y T S V K S H L E Y K A K D S D S R K V V S I I L T I S N N R R

SY97 AU/10/97 K I N R D S E N G N K E K Y N N S N A T Y R S I K S H Q L K Y K A K D S D S A R K V V S H I I L T I S G K N R R

SY97 NL/300/97 K I N R R D S E N G N K E K Y N N S N A T Y R S I K S H Q L K Y K A K D S D S A R K V V S I I L T I S G K N R R

SY97 SY/5/97 K I N R D S E N G N K E K Y N N S N A T Y S S I K S H Q L K Y K A K D S D S I A R K V I S H I I L T I S G K N R R

SY97 NL/414/98 K N R D S E N G N K E K Y Q N N S N A T Y R S I K S H Q L K Y K A K D S D S A R K V V S I I L T I S G K N R R N

SY97 NL/427/98 K N R D S E N G N K E K Y N N S N A T S R S I K S H Q L K Y K A K D S D S A R V V S I I L T I S G K N R R

SY97 NL/462/98 K N R D S E N G N K E K Y N N S N A T S R S I K S H Q L K Y K A K D S D S A P R V V S I I I L T I S S K N R R

SY97 MW/10/99 K N R D S Q E N G N K E K Y N N S N A T S R S I K S H Q L K Y R A K D S D S T R K V I S I I L T I S G K N R R

SY97 NL/301/99 K N R D S Q E N G N K E K Y N N S N A T S R S N K S H Q L K Y K A E K D S D I S A R K V V S I I L T I S S G K N R R

SY97 PM/2007/99 K S N R D S Q E N G N K E K Y N N S N A T S R S N K S H Q L K Y K A E K L D S D I S A R K V V S I I L T I S G K N R R

SY97 NL/3/00 K V N H R D I Q E N G N K E T Y N N S N A T S R S I K S H Q L K Y K A E K D S D I S A P R K V V S I I L T I S G K N R R

SY97 NL/118/01 K N R D S Q E N G N K E K Y V N N S N A T S R S N K S H Q L K Y K A E K G D S D I S A R K G V V S G I I L T I S G K N R R

SY97 NL/124/01 K V N N R D S Q E N G N K E T Y N N S N D T S R S N K S H Q L K Y K A E K D S D I S A R K V V S I I L T I S N G K N R R

SY97 NL/126/01 K N G D S Q E N G N K E K Y N N S N A T S R S N K S H Q L K Y K A E K D S D I S A P R K V V S I I L C T I S S G K N R R

SY97 NL/1/02 K N R D S Q E N G N K E K Y V N N S N A T S R S N K S H Q L K Y K A E K G D S D I S A R K V V S I I L T I Q S G K N R R

SY97 NL/120/02 K N R D S Q E N G N K E K Y V N N S N A T S R S D K S H Q L K Y K A E K G D S D I S A R K V V S I I L T I S G K N G R R

SY97 NL/20/03 K N S R D S Q E N G N K E K Y V N N S N A T S R S D K S H Q L R Y K A E K G D S D I S A R I K V V S H I I L T I S G K N R R K

FU02 FU/411/02 K I N G D S Q E N Q G N K K K Y N N S N T S R S N K S H L K Y K A E K G D S D I S A R I K V R D V S I I L T I S G K N R R

FU02 FI/170/03 K I N G D S Q E N Q G N K K K Y N N S N T S R S N K S H L K Y K A E K G D S D I S A R I K V R D V S I I L T I S G K N R R

FU02 NL/22/03 K I N G D S Q E N Q G N K K K Y I T N S N T S R S N K S H L K Y K A E K G D S D I S A R I K V R D V S I I L T I S G K N R R K

FU02 NL/213/03 K I N G S Q E N Q G N K K K Y N N S N T S R S N K S H L K Y K A E K G D S D I S A R I K V R D V S I I L T I S G K N R R

FU02 NL/217/03 K I N G S Q E N Q G N K K K Y N N S N T S R S N K S H L K Y K A E K G D S D I S A R I K V R D V S I I L T I S G K N R R

FU02 NL/222/03 K I N G S Q E N Q G N K K K Y N N S N T S R S N K S H L K Y K A E K G D S D I S A R I K V R D V S I I L T I S G K N R R