The Veterinary Journal 184 (2010) 130–137

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The Veterinary Journal

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Review

Applying phylogenetic analysis to viral livestock diseases: Moving beyondmolecular typing

Alex Olvera a,*, Núria Busquets a, Marti Cortey a, Nilsa de Deus a, Llilianne Ganges a,b, José Ignacio Núñez a,Bibiana Peralta a, Jennifer Toskano a, Roser Dolz a,*

a Centre de Recerca en Sanitat Animal (CReSA), UAB-IRTA, Campus de Bellaterra – Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spainb Departamento de Biotecnología, INIA, Madrid, Spain

a r t i c l e i n f o

Article history:Accepted 23 February 2009

Keywords:Phylogenetic analysisMolecular diagnosisVirusesLivestock diseases

1090-0233/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.tvjl.2009.02.015

* Corresponding authors. Tel.: +34 93 581 4567; faE-mail addresses: Alex.Olvera@cresa.uab.cat (A.

uab.cat (R. Dolz).

a b s t r a c t

Changes in livestock production systems in recent years have altered the presentation of many diseasesresulting in the need for more sophisticated control measures. At the same time, new molecular assayshave been developed to support the diagnosis of animal viral disease. Nucleotide sequences generatedby these diagnostic techniques can be used in phylogenetic analysis to infer phenotypes by sequencehomology and to perform molecular epidemiology studies. In this review, some key elements of phylo-genetic analysis are highlighted, such as the selection of the appropriate neutral phylogenetic marker, theproper phylogenetic method and different techniques to test the reliability of the resulting tree. Examplesare given of current and future applications of phylogenetic reconstructions in viral livestock diseases.

� 2009 Elsevier Ltd. All rights reserved.

Introduction

The implementation of intensive production systems, morecomprehensive prevention programmes and enhanced biosecuritymeasures have contributed to the eradication of many conven-tional livestock diseases, such as classical swine fever (CSF) andfoot and mouth disease (FMD), in many regions of the world. How-ever, these factors have also modified the presentation of othercommon veterinary pathogens, such as avian infectious bronchitisand infectious bursal disease, increasing their economic impact(Lasher and Davis, 1997; Cavanagh, 2007). In addition, there isincreasing concern regarding zoonotic diseases, food safety andpublic health (Kahn, 2006). In the diagnosis of livestock disease,there is a need to establish the relationship of specific isolates toother strains of the same pathogen.

The extensive use of the polymerase chain reaction (PCR) in thedetection of infectious agents and the reduced cost of sequencinghave increasingly permitted the use of DNA sequences to confirmand more accurately diagnose disease (Zaidi et al., 2003). In com-parison with classical virological methods, molecular techniqueshave many advantages in that they are less expensive, more rapid,sensitive, have high resolution, do not require viable virus and offermore portability of the generated data (Foxman et al., 2005). Con-sequently, we can take advantage of phylogenetic analysis and use

ll rights reserved.

x: +34 93 581 4490.Olvera), Roser.Dolz@cresa.

nucleotide sequences to genotype isolates and compare them withpreviously characterised strains.

In this review we provide an overview of phylogenetic analysis,a powerful tool when applied to molecular epidemiology, but onethat can be easily misused and misinterpreted (Hall and Barlow,2006). We highlight important issues when constructing phyloge-nies, introduce the interpretation of phylogenetic trees and providesome general references for researchers who wish to pursue thesubject in more depth. Key points for performing phylogeneticanalysis are discussed and several examples of current or futureapplications of these tools in the diagnosis of livestock diseasesare included.

Phylogenetic inference

In this section, we describe some general concepts about con-struction and interpretation of phylogenetic trees. More detail isavailable in other reviews (Moreira and Philippe, 2000; Baldauf,2003; Hall and Barlow, 2006) and books (Nei and Kumar, 2000;Salemi and Vandamme, 2003; Felsenstein, 2004; Hall, 2007).

General concepts

The hierarchical genetic-evolutionary relationship amongst orwithin species is collectively referred to as a phylogeny. Phyloge-netic relationships are usually presented in the form of a tree, withthe branching pattern called a topology. The oldest point in a tree,located at its base, is the root and implies the order of branching inthe rest of the tree. The root is placed using an outgroup, usually a

1 For an extended list of available programmes with the implemented methods seehttp://evolution.genetics.washington.edu/phylip/software.html.

A. Olvera et al. / The Veterinary Journal 184 (2010) 130–137 131

homologous sequence of a distantly related taxon, and its selectionis a critical issue in the sense of evolution. Alternatively, if a properoutgroup is not obtainable or we are not interested in the order ofdescent, an unrooted tree can be built. In addition, we have to keepin mind that a gene tree is not necessarily representative of thespecies tree, which should consider many other factors, as wellas the whole genome (Nei and Kumar, 2000).

Selection of the phylogenetic marker

Determination of the whole genome sequence is the most de-tailed genetic characterisation that can be done of a given patho-gen, but systematic and routine sequencing of large genomes isstill expensive. As a consequence, accurate selection of the optimalneutral phylogenetic marker for analysis is critical. The genome re-gion to be investigated depends mostly on the final objective whenconstructing phylogenetic trees. If correlation with a particularphenotype (such as protection, antigenicity or virulence) is desired,the region to be sequenced should contain sequences responsiblefor these functions and marker positions for each phenotype (Top-liff and Kelling, 1998; Xiao et al., 1998; Jensen et al., 2003). On theother hand, in epidemiological studies, nucleotide sequences thatreconstruct a tree similar to the species tree are preferred (Halland Barlow, 2006).

Ideally, a molecular marker should show sufficient variabilitywithout losing the strength of the phylogenetic signal due tosubstitution saturation (Xia et al., 2003) conditioned by thesubstitution rate, which is highly variable amongst virus families.Generally, the highest substitution rates are observed in RNAviruses (10�2–10�3 nucleotide substitutions/site/year) whereasDNA viruses have lower mutation rates (10�4 nucleotide substitu-tions/site/year for ssDNA viruses and 10�8–10�9 nucleotide substi-tutions/site/year for dsDNA viruses) (Duffy et al., 2008).

Constructing the data set

A molecular phylogeny is highly dependent on the alignment onwhich it is based (Aagesen, 2005). The first step in tree construc-tion is to build a data set, which could include sequences obtainedin the laboratory or retrieved from a database (e.g. GenBank, EMBL,DDBJ). The sequence set must be carefully checked and edited toavoid misinterpretations of sequence changes. Several pro-grammes and software packages are available for this purpose(e.g. BioEdit; Hall, 1999). Next, the sequences are aligned usingspecialised software (e.g. ClustalX; Thompson et al., 1997). Se-quences can be analysed at the nucleotide or amino acid levelbut, for closely related sequences, there will be more informationat the DNA level and for more distant relationships amino acid se-quences hold more information (Gibbs et al., 1995; Baldauf, 2003).

Selection of substitution model

Assuming that nucleotide or amino acid sequences have gradu-ally evolved from a common ancestor, the extent of nucleotide oramino acid substitutions can be estimated with several substitu-tion models (e.g. Jukes–Cantor, Kimura-2-parameter, Tamura–Nei). Phylogenetic algorithms make assumptions about the processof substitution and therefore may be dependent on the model ofnucleotide substitution used. ModelTest is an extended tool to se-lect the best-fit model out of 56 standard nucleotide substitutionmodels using several optimisation criteria (Posada and Crandall,1998).

However, codon-based models, which consider different evolu-tionary patterns amongst first, second and third codon positions,may be a more realistic approach when protein-coding sequencesare used, although computationally time-consuming. Codon-based

models are implemented in a growing number of analytical pack-ages (e.g. Mr. Bayes, BEAST) and their use with protein-coding se-quences is recommended (Shapiro et al., 2006).

Phylogenetic tree estimations

Once the data set is constructed and the substitution model hasbeen selected, there are four main groups of statistical methodsthat can be used for reconstructing phylogenetic trees from molec-ular data, namely, parsimony methods, distance methods, likeli-hood methods and Bayesian inference. In maximum parsimonymethods, the sum of the number of substitutions over all sites re-quired to explain each topology is computed. This sum is called thetree length. The maximum parsimony tree is simply the topologythat requires the smallest tree length. Parsimony methods oftenproduce several equally parsimonious trees that can be presentedin a single consensus tree.

A nucleotide substitution model is not needed for parsimonymethods, but is required for distance, likelihood and Bayesianmethods. In distance methods, evolutionary distances are com-puted for all pairs of taxa considered and a phylogenetic tree isconstructed by considering the relationships amongst these dis-tance values. Several of the most popular inference methods fallwithin this category, including the neighbour-joining method andthe unweighted pair group method with arithmetic mean (UP-GMA). However, under a wide variety of conditions, these methodsare outperformed by maximum likelihood and Bayesian methods,based on the likelihood function (i.e. the probability of observingthe data, in this case conditional on a tree) (Huelsenbeck et al.,2001).

In maximum likelihood methods, the likelihood of observing agiven set of sequence data for a specific substitution model is max-imised for each topology and the topology that gives the highestmaximum likelihood is chosen as the final tree. Unlike maximumlikelihood, Bayesian inference can incorporate prior informationabout phylogeny. Bayesian phylogeny estimation is based on theposterior probability distribution of trees, which are calculatedusing a Markov Chain Monte Carlo approximation. Because of theirflexibility, likelihood based methods (maximum likelihood andBayesian inference) have become the standard for many phyloge-netic analyses.

However, it is important to realise that every method has somestrengths and some weaknesses and no one method is superior. Onthe contrary, routine use of several phylogenetic methods based ondifferent optimisation criteria is a recommended strategy. Withsuch an approach, knowledge is gained from several viewpoints,allowing us to detect several inherent limitations of the algorithms(i.e. short and long-branch attraction in parsimony methods).Reporting coincident topologies using several criteria enhancesthe robustness of the data and improves the strength of the resultsobtained. It is noteworthy that the routine use of several analyticalpackages is necessary to perform all of the analyses describedabove (e.g. PAUP*, MEGA 4.0, PHYLIP, Mr. Bayes, BEAST).1

Evaluating the estimation

Once a phylogenetic tree or network is constructed, it is a com-mon and recommended strategy to evaluate the reliability of thetopology obtained under each approach. The bootstrap test is oneof the most commonly used techniques and assesses whether adata set is supporting any interior branch of the phylogeny. Ithas been assumed that bootstrapping indicates which percentage

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of the subsampled trees supports the final topology, although thisassumption is not strictly correct (Efron et al., 1996). Nonetheless,bootstrapping is a simple, comprehensive and straightforwardmeasure of phylogenetic accuracy and values of 90% or higherare likely to indicate reliable groupings, although values of 70%can also yield useful information (Hillis and Bull, 1993).

In a consensus maximum parsimony tree, the reliability of thetree can be measured as the frequency of a given branching patternthat can be shown as a support percentage. Researchers areencouraged not only to use several phylogenetic methods and toevaluate branch confidences routinely, but also to test the consis-tency of the final tree obtained for each method with reliabilitytests used in other approaches (e.g. sum of branch lengths in dis-tance methods; tree length, consistency index and retention indexin parsimony; the Kishino and Hasegawa test for maximum likeli-hood) to score and evaluate which of the final trees gives the besttopology.

Phylogenetic trees versus networks

Phylogenetic analyses of livestock viral diseases are very oftenfocussed on differentiating strains of pathogen. Methods basedon classical trees, which were developed to estimate relationshipsamongst species, may not properly evaluate the exchange of genet-ic material amongst strains or individuals by recombination orreassortment. Both processes have long been recognised as a com-mon phenomena in virus evolution and as a primary cause ofincongruence amongst gene phylogenies (Worobey and Holmes,1999; Awadalla, 2003; Shackelton and Holmes, 2004). Therefore,it is highly recommended that potential recombination andreassortment events amongst sequences to be analysed aredetermined.

Numerous methods for detecting and analysing recombinationand reassortment signals are available.2 Phylogenetic networksthat examine for persistent ancestral nodes, multifurcations andreticulation could be a better representation of the viral evolution-ary process, and network algorithms for parsimony, distance, like-lihood and Bayesian approaches are also available (Posada andCrandall, 2001). Explicit models, such as hybridisation and recom-bination networks, can be used to provide an explicit description ofreticulate evolution (Huson and Bryant, 2006) that could havegreat applicability in the epidemiology of viral livestock diseases.

Applications of phylogenetic analysis in viral livestock diseases

Phylogenetic reconstructions have been used mainly to differ-entiate and classify isolates below the species level, mainly intogenotypes. Despite being designed to study evolutionary relation-ships amongst taxa, molecular phylogenetic analysis also providesa framework to show not only sequence similarity but also theclustering and relationships amongst different isolates. To link asequence to a particular phenotype, dendrograms should be usedto differentiate clusters that identify sequences with a homologousfunction that reflect a likely common ancestor. In epidemiologicalstudies, a stricter phylogenetic approach should be used to estab-lish the evolution of different sequences over time.

Species definition

Phylogeny has been demonstrated to be useful for differentiat-ing species according to taxonomy. The International Committeeon Taxonomy of Viruses (ICTV)3 lists the criteria to differentiate

2 See: http://www.bioinf.manchester.ac.uk/recombination/programs.shtml.3 See: http://www.ictvonline.org.

virus species, including demarcating new viral species; criteria canvary in different Families. Nucleotide or protein sequence phyloge-nies are usually included, since they provide sufficient power to dis-criminate intra- and inter-species relationships. Unfortunately, thelack of available genetic data often makes it difficult to define cut-off values suitable for discriminating species. Despite this, phyloge-netic concepts have been used to discover new viruses in differentGenera, such as Arenavirus (Charrel et al., 2008) and Flavivirus (Billoiret al., 2000; Gaunt et al., 2001). In the Pestivirus Genus, a threshold ofa 15% divergence in the 50 non-coding region was set to differentiatethe four species currently recognised by the ICTV: CSF virus (CSFV),bovine viral diarrhoea virus (BVDV) types 1 and 2 and border diseasevirus (BDV) (Sakoda et al., 1999). Once a species is established, spe-cies-specific PCR can be designed for routine diagnostic use (Patonet al., 2000).

Differentiation below the species level: Genotyping

The mandate of the ICTV does not include any consideration be-low the species level and the criteria for genotype definition havebeen left to the initiative of specialty groups (Fauquet and Stanley,2005). When defining genotypes for a particular virus on the basisof sequence similarities, a genetic distance threshold is usually set.Phylogenetic analysis provides a clear grouping of sequences, withstatistical support, and allows them to be classified in an intuitiveway. Clusters or genotypes can be used to extrapolate biologicalfeatures that are more difficult to determine, such as serotype (tosupport vaccine selection), zoonotic potential and virulence, or tosupport epidemiological studies at both local (reinfection, entryof a new strain or vaccine reversion) and global (relationship of aparticular isolate with others) levels.

Differences in virulence and diagnosis

Genotype differentiation has implications in the diagnosis ofviral livestock diseases, especially when strains with differentpathogenic potential co-circulate in the host population. In thisscenario, the identification of viral species has to be complementedwith the determination of the specific genotype present in theanimals.

Such is the case with infectious bursal disease virus (IBDV), amember of the Family Birnaviridae, which is distributed worldwideand causes an acute, highly contagious disease of chickens (vanden Berg et al., 1996). Traditionally, IBD control has relied on infec-tious attenuated vaccines, making it necessary to differentiate fieldand vaccine viruses when diagnosis is required (Rautenschleinet al., 2005). IBDV is classified into two serotypes, but only sero-type 1 includes pathogenic strains. This serotype can be further di-vided in subtypes based on its virulence and antigeniccharacteristics: attenuated vaccine strains, intermediate virulencevaccine strains, classical strains, highly virulent strains and anti-genic variant strains (Lukert and Saif, 1997). Genotyping basedon the VP2 gene correlates with viral subtypes, since this gene con-tains hypothetical virulence marker positions and the major epi-topes that induce viral neutralising antibodies (Brandt et al.,2001). If amino acid residues that are essential for virulence areknown, it is possible to deduce virulent phenotypes directly fromsequences (van den Berg et al., 2000).

Another example of the potential diagnostic use of the associa-tion of an increased virulence with a particular genotype is porcinecircovirus type 2 (PCV-2). Although still under discussion, it wasrecently suggested that PCV-2 genotype b might be more fre-quently associated with post-weaning multisystemic wasting syn-drome (PMWS) (Segalés et al., 2008) (Fig. 1). One of the mainproblems of the study on PMWS was that, whereas PCV-2 is aubiquitous virus, PMWS has had an epizootic pattern since it first

Genotype b

Genotype a

Genotype c

60

94

97

99

0.01

Fig. 1. Neighbour-joining tree built using Jukes–Cantor distances of 199 cap genesequences of porcine circovirus (PCV) type 2. The tree was rooted using a PCV-1 capgene sequence and evaluated using 1000 bootstraps; values below 50 are notshown. The three PCV-2 genotypes are clearly differentiated. Genotype b is morefrequently detected in recent PMWS outbreaks in Europe and North America and agenotype switch of genotype a to genotype b has been associated with an increasein PMWS incidence. However, increased pathogenic potential of genotype b has notbeen demonstrated in experimental challenge. Genotype c was detected in archivesamples from the 1980s.

4 See: http://www.oie.int/eng/maladies/en_classification.htm.

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appeared in the 1990s (Segalés et al., 2005). Unfortunately,although control-case studies support the existence of a genotypeshift to explain the emergence of PMWS, this has not been sup-ported in experimental challenges (Grau-Roma et al., 2008).

Vaccine selection and genotype

Some viruses show poor cross-immunity amongst strains, mak-ing the selection of the appropriate vaccine strain the cornerstonefor disease control. Furthermore, live attenuated vaccines are com-monly included in vaccination programmes for several species oflivestock, making it necessary to differentiate them from fieldstrains.

Such is the current situation for avian infectious bronchitis virus(IBV), which exhibits extensive antigenic variation, giving rise todozens of serotypes throughout the world, some restricted to thegeographic area of origin (Cavanagh, 2007). IBV is genotyped usingthe S1 gene that contains the main virus-neutralisation epitopes,which also determine serotype (Kant et al., 1992; Cavanagh et al.,1997). Phylogenetic clustering based on the S1 gene correlateswith the serotype independently of the geographic origin of theisolates (Wang and Huang, 2000). IBV phylogenetic clusteringbased on partial S1 gene sequences are comparable to the cluster-ing obtained using complete S1 gene sequences.

Nevertheless, when using partial S1 sequences, it should berecognised that this gene can experience recombination events,which may lead to misclassification of genotypes (Dolz et al.,2008). It is generally assumed that two IBV isolates belong to thesame serotype when nucleotide identities are >90%. Identities from80–90% may indicate antigenic differences and isolates <80% iden-tity in S1 sequences are likely to belong to different serotypes.However, rare exceptions have been reported. Once genotype is as-sessed through phylogeny, vaccination programmes can be opti-mised according to these results and the decision made whetherto introduce new vaccine serotypes (Cavanagh, 2007).

Antigenic variability is also observed in IBDV (Fig. 2). Antigenicvariant strains are not neutralised by classical vaccine strains and,therefore, the determination of the antigenic subtype of a virus in-volved in an outbreak is necessary to optimise vaccine pro-

grammes (Lukert and Saif, 1997). Determination of the subtypeby genotyping, together with longitudinal studies, helps in design-ing the vaccination programme that best fits the needs of a partic-ular farm.

Zoonotic potential and genotype

For some zoonotic viruses, genotypes with increased zoonoticpotential have been reported and phylogenetic analysis can beused to detect and identify them. This is the case with highly path-ogenic avian influenza viruses (HPAIVs). AIVs have been isolatedfrom different animal species throughout the world: poultry, pigs,horses, humans, felids, marine mammals and wild birds, which aretheir natural reservoir (Webster et al., 1992; Alexander, 2000). De-spite the ability of AIV to cause disease in humans, the occurrenceof the disease is limited due to the presence of different receptorsfor the virus in different species (Neumann and Kawaoka, 2006).However, H5N1 viruses involved in the recent HPAI outbreaks haveshown a higher capability to infect humans (WHO, 2005a).

In the 2004 and 2005 H5N1 HPAI outbreaks, phylogenetic anal-yses of the haemagglutinin (HA) genes showed two different lin-eages: clades 1 and 2 (WHO, 2005b). Clade 1 H5N1 viruses weremainly isolated from birds, humans and sporadically felids inSouthern Asia. Clade 2 viruses were isolated exclusively from birdsin Eastern Asia (Li et al., 2004; Mase et al., 2005; WHO, 2005b).Curiously, the strain responsible for the 2003–2004 outbreaks inAsia was a particular genotype of clade 1, genotype Z. This geno-type arose in chickens and ducks from a genetic reassortmentamongst AIV RNA viral segments (Li et al., 2004).

Phylogeny has also been used to study the potential zoonoticorigin of hepatitis E virus (HEV). Hepatitis E is an important humandisease that is endemic in many underdeveloped countries, wheremortality in pregnant women can be up to 25% (Panda et al., 2007).In developed countries, hepatitis E has been associated with trav-elling to endemic areas, consumption of under-cooked pork andcontact with pigs (Piper-Jenks et al., 2000; Drobeniuc et al.,2001; Meng et al., 2002; Tamada et al., 2004). HEV infection in pigsis widespread and inapparent, so PCR followed by sequencing hasbecome an important tool for HEV detection and characterisation.The best neutral phylogenetic marker for HEV is the open readingframe (ORF) 2 gene, which encodes the capsid protein (Zhai et al.,2006). Based on this gene, four genotypes have been described, butonly genotypes 3 and 4 have been detected in both humans andpigs and are potentially zoonotic (Lu et al., 2006).

Use of phylogeny in molecular epidemiology

To date, the main application of phylogenetic studies in virallivestock diseases is molecular epidemiology. This interdisciplinaryapproach uses the advantages offered by molecular biology tech-niques in epidemiology (Foxman and Riley, 2001). The analysis ofphylogenetic trees, together with epidemiological data, has im-proved epidemiological studies. They are used in local epidemiol-ogy to differentiate between reinfection (particularly treatmentfailure) or new strain introduction, mainly in endemic diseases.They can also be used to determine the relationships amongst dif-ferent isolates at different time or space points to track the originof an outbreak in epidemic diseases (Foxman et al., 2005).

The World Organisation for Animal Health (OIE)4 notifiable dis-eases (such as FMD and CSF) can cause considerable economic lossesdue to the blockage of trade of livestock and its products. This is thecase with HPAI, which is controlled by compulsory slaughter (OIE,2007). For these epidemic diseases, once the aetiological agent has

VARIANT-A

GZ29112

GLS

VARIANT-E

DEL-E

U.S. variant strains

BURSINE2

BURSINEPLUS

Spain-62-08

Intermediate virulence

classical strains

STC

BURSAVAC

Cu-1wt

52/70

Classical strains

Int/228E

Spain-34-08 Low attenuated classical strains

D78

HARBIN

CEF94

PBG98

P2

Spain-33-08HIPRAGUMBO

CU-1

Attenuated classical strains

849VB

Spain-33-026145

UK661

Spain-61-02HK46

D6948

5939

NETHERLAND

VG-248

OKYM

Very virulent strains

100

100

73

78

99

99

72

96

99

99

98

100

74

0.005

Fig. 2. Neighbour-joining unrooted tree built using gamma Tamura–Nei substitution rates (gamma parameter = 1). The tree was evaluated using 1000 bootstraps; valuesbelow 70 are not shown. A fragment of 400 bp of the IBDV VP2 gene was amplified and sequenced from different Spanish samples and aligned with reference strains from theGenBank database. Spanish isolates are highlighted in italics. Two field sequences (Spain-62-08 and Spain-33-08) were grouped with vaccine strains, which are usuallyclustered as attenuated, intermediate virulence or low attenuated strains. Two other sequences (Spain-61-02 and Spain-33-02) were clustered with very virulent strains,indicating that they are true field isolates.

134 A. Olvera et al. / The Veterinary Journal 184 (2010) 130–137

been confirmed, phylogenetic studies can be used to determine theorigin/source of the strain responsible of introducing the diseasewithin a country, as well as the evolution patterns of virus subtypesin the field (Beck and Strohmaier, 1987; Lowings et al., 1996; Moen-nig et al., 2003).

Classically, FMD virus (FMDV) has been classified into sero-types. Global phylogenetic comparison of complete sequences ofmany FMDV lineages and their geographic location has allowedfurther classification of FMDV strains from the same serotype intonew genotypes, known as ‘topotypes’ (Samuel and Knowles, 2001).For example, the PanAsia strain, responsible for a pandemic in Asiathat extended to different parts of Africa and reached Europe in2001, was clustered in the Middle East-South Asia topotype (Gib-bens et al., 2001). The use of phylogenetic studies has clarified

the origin of many FMD outbreaks caused by the introduction ofthe virus from different geographical regions. For example, theintroduction of European FMDV strains in India in the 1960s wasdetermined when C-Bombay/64 strain and European isolates circu-lating at those dates were branched together. Importation of ani-mals for cross-breeding with local cattle or pigs was the mostlikely source of infection (Hemadri et al., 2003).

In other cases, the analysis of the phylogenetic relationships be-tween field isolates and vaccine strains has identified vaccines asthe possible origin of an outbreak, as occurred during the 1980sin Europe, where FMDV isolates from 1987 and 1988 were groupedwith vaccine strain O1 Kaufbeuren Ger/66 (Sáiz et al., 1993). InArgentina in 1983 and 1984, serotype C isolates showed a closephylogenetic relationship with C3/Resende/Bra/55, a strain

North American

duck/NZL76H1N3

duck/Bavaria77H1N1

duck/MiyagiH1N1

duck/Australia80H1N1

duck/Australia80H1N1(2)

duck/HokkaidoH1N1

pintail/Shimane98H1N9

duck/Italy07H1N1

duck/Italy06H1N1

goose/Italy03H1N1

Italy

Eurasian teal/Spain07H1N1

mallard duck/Spain07H1N1

Eurasian teal/Spain/1404/07H1N1

Spain 2007

Eurasian

98

100

98

90

55

60

9999

9999

96

100

990.02

Eurasian teal/Spain07H1N1

mallard duck/Spain07H1N1

Eurasian teal/Spain/1404/07H1N1

Spain 2007

100

100

99

Fig. 3. Neighbour-joining tree built using Kimura-2-parameter distances. The tree was evaluated using 1000 bootstraps; values below 50 are not shown. The two clades ofavian influenza, North American and Eurasian, are clearly differentiated. Sequences from low pathogenicity avian influenza virus isolated in Spain in 2007 (highlighted by agrey square) were clustered with Italian sequences isolated in previous years. Italian and Spanish sequences were clustered with older isolates from Asia, indicating an Asianorigin for these strains.

5 See: http://www.wrlfmd.org/fmdv_seqs/fmdv_seq.htm.6 See: http://viro08.tiho-hannover.de/eg/eurl_virus_db.htm.7 See: http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html.8 See: http://influenza.genomics.org.cn/index.jsp.

A. Olvera et al. / The Veterinary Journal 184 (2010) 130–137 135

included in the formulation of the vaccine used locally (König et al.,2001).

With CSF virus (CSFV), the highly variable 50 region of the E2protein has been used to differentiate three genogroups, subdi-vided into 12 further groups (Lowings et al., 1996; Moenniget al., 2003). Isolates from related geographical areas and timepoints are clustered together in these groups and some of themalso include vaccine strains. The application of phylogenetic toolsto CSF epizootics allowed the origin of the 1990s outbreaks in Eur-ope to be traced to wild pigs and foreign imports (Stadejek et al.,1997; Björklund et al., 1999). The appearance of CSFV in differentregions of South America derived from several independent intro-ductions of the virus (Pereda et al., 2005).

For HPAI, phylogenetic analysis has been used to develop anepidemiological map to trace genetic origins of outbreaks fromwild birds (Li et al., 2004; Mase et al., 2005; WHO, 2005b) and todefine the two AIV lineages (Fig. 3), namely, Eurasian and NorthAmerican (Olsen et al., 2006). This map has allowed the origin ofan H5N1 AIV outbreak in a turkey farm from the UK in February2007 to be traced; the whole genome of the AIV strain responsiblefor the UK outbreak was grouped with two viruses isolated fromgeese in outbreaks in Hungary in January 2007 and showed thatthere was a direct link between the two countries. Circumstantialepidemiological evidence suggested the most likely route of entrywas the transport of turkey meat from Hungary to the UK (DEFRA,2007).

Ideally, sequences used for epidemiological analysis should in-clude a representative sample of the natural populations, includingas many sequences as possible. Therefore, some cautionary re-marks should be kept in mind when sequences available in molec-ular databases, such as GenBank, are included in a study, as they donot originate from a random sampling. Nevertheless, BLASTsearches (Altschul et al., 1990) can help to identify the most similarsequences within a database collection and should be included to-gether with reference strains in the alignment. Hence, the use of

databases, including epidemiological data, is strongly recom-mended. Unfortunately, viral sequences submitted to public dat-abases too often lack precise temporal and spatial information,sometimes even the host scientific name.

To overcome these problems, several databases are available fordiseases with high economic or public health risk, that couplegenomic and epidemiological data, such as the Food and Agricul-ture Organization (FAO) World Reference Laboratory for FMD,5

EU/OIE Community Reference Laboratory for CSF6 (Greiser-Wilkeet al., 2000), Influenza Virus Resource7 and the Influenza VirusesDatabase (IVDB)8 (Chang et al., 2007).

Conclusions

Livestock disease management can be improved by couplingroutine diagnostic testing with phylogenetic analysis using com-prehensive databases. Deducing phenotypic properties or epidemi-ological linkages from sequences will help veterinarians in the fieldto establish proper control measures. However, appropriate exper-tise is necessary and veterinary diagnostic laboratories are of keyimportance in introducing these analyses.

Conflict of interest statement

None of the authors of this paper has a financial or personalrelationship with other people or organisations that could inappro-priately influence or bias the content of the paper.

136 A. Olvera et al. / The Veterinary Journal 184 (2010) 130–137

Acknowledgements

The authors would like to thank Joaquim Segalès and VirginiaAragón for helpful comments and suggestions.

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