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TOOLS FOR INVESTIGATING MECHANISMS OF ANTIGENICVARIATION: NEW EXTENSIONS TO VARDB
C. NELSON HAYES 1
nelson@kuicr.kyoto-u.ac.jp
NICOLAS JOANNIN 2,3nicolas.joannin@ki.se
MATS WAHLGREN 2,3
mats.wahlgren@ki.se

DIEGO DIEZ 1
diez@kuicr.kyoto-u.ac.jp
MINORU KANEHISA 1
kanehisa@kuicr.kyoto-u.ac.jp

CRAIG E. WHEELOCK 1,4∗
craig.wheelock@ki.se

SUSUMU GOTO 1∗
goto@kuicr.kyoto-u.ac.jp

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Ky-

oto 611-0011, Japan
2 Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Box280, SE-17177 Stockholm, Sweden
3 Swedish Institute for Infectious Disease Control (Smittskyddsinstitutet), SE-17182

Stockholm, Swedenof Physiological Chemistry II, Department of Medical Biochemistry andBiophysics, Karolinska Institutet, SE-17177 Stockholm, Sweden∗ To whom correspondence should be addressed
4 Division

The varDB project (http://www.vardb.org) aims to create and maintain a curated
database of antigenic variation sequences as well as a platform for online sequence analysis. Along with the evolution of drug resistance, antigenic variation presents a moving
target for public health endeavors and greatly complicates vaccination and eradicationeﬀorts. However, careful analysis of a large number of variant forms may reveal structuraland functional constraints that can be exploited to identify stable and cross-reactive targets. VarDB attempts to facilitate this eﬀort by providing streamlined interfaces to standard tools to help identify and prepare sequences for various forms of analysis. We have
newly implemented such tools for codon usage, selection, recombination, secondary andtertiary structure, and sequence diversity analysis. Just as the adaptive immune system
encodes a mechanism for dynamically generating diverse receptors instead of encoding a
receptor for every possible epitope, many pathogens take advantage of heritable diversity
generating mechanisms to produce progeny able to evade immune recognition. Insteadof merely cataloging the observed variation, a major goal of varDB is to characterize andpredict the potential range of antigenic variation within a pathogen by investigating themechanisms by which it attempts to expand its implicit genome. We believe that the
new sequence analysis tools will improve the usefulness and range of varDB.
Keywords: antigenic variation, hyper-variable sequence, codon usage, selection.

1. Introduction
Following infection, cells of the innate immune system rapidly detect the pathogen
intruder and present fragments of the pathogen to surveillance cells of the adaptive
immune system. This interaction triggers a cascade in which individual B and T

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cells that possess high aﬃnity for the target antigen undergo clonal selection toinitiate a highly eﬀective and speciﬁc immune response [9]. The overwhelming immune response that follows is usually suﬃcient to clear the pathogen, but the timerequired to orchestrate clonal selection and aﬃnity maturation aﬀords antigenicallydistinct subpopulations more time to replicate and disperse before an immune response against the new targets can be generated. Many of the most severe publichealth problems, including malaria and HIV/AIDS, are caused by pathogens thathave evolved mechanisms allowing them to maintain an edge in the arms race with
the immune response by stochastically varying their antigenic proﬁles. This process of antigenic variation is encountered in a vast range of organisms including
viruses, bacteria, fungi, and protists [15]. A major goal of public health is to develop preventative vaccines able to thwart this mechanism by training the immune
system to recognize conserved epitopes in one or a few common variants so thatthe pathogen can be recognized and cleared before the pathogen is able to establish
immunogenically variable subpopulations.
The success of this approach depends in part on the way in which variants
are generated, and in many cases the mechanisms are incompletely understood orvary greatly by species. Viruses such as HIV rely on a high mutation rate coupledwith a high reproductive rate [3], whereas bacteria may use mechanisms based onrecombination, gene conversion, or lateral transfer [15]. Eukaryotes may employ
these or other still more complex mechanisms involving coordinated expression ofmulti-gene families with multiple levels of control [15]. VarDB was developed bothas a repository for antigenic variation data as well as a platform for comparative
analysis of mechanisms of antigenic variation [16, 21]. It consists of a database of
sequences belonging to antigenic variation gene families as well as an integrated suiteof tools to simplify analysis of sequence data and estimate the range and natureof sequence variation. Recently, we have greatly expanded the sequence analysis
tools in varDB to include codon usage, selection, recombination, secondary andtertiary structure, and sequence diversity analyses, in addition to standard tools
such as BLAST and HMM search. Here we describe the new extensions to varDB
and discuss how they can be used to examine patterns of sequence variability within
the context of antigenic variation.

2. Tools for Investigating Mechanisms of Antigenic Variation
The current varDB release contains more than 62,000 sequences from known or suspected antigenic variation gene families in 30 organisms. Sequence data is collectedfrom genome sequencing projects and ﬁeld isolate submissions using an HMM-based
data mining pipeline described previously [16]. Because of the large number of sequence types, varDB provides a range of search and ﬁltering methods to quickly
extract datasets and prepare them for analysis. Once a working set is obtained,
interfaces to multiple external analysis tools are provided to estimate sequencevariability in a streamlined fashion. While some of these tools represent novel im-September 4, 2009

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plementations or interfaces, the major focus of varDB is not so much to develop
new tools as it is to simplify and integrate usage of standard tools already in wide
use by the research community. Many of these programs are platform-dependent,
have strict input requirements or limited output options, or are available only as
separate command line or web-based tools. Therefore, varDB attempts to bridgethese diﬀerences and help integrate diverse resources by using a ﬂexible and consistent Ajax-based interface. The application of some of these tools for estimating
sequence variability is described below.
2.1. Sequence Selection and Preparation
One of the challenging aspects of studying antigenic variation is compiling andformatting an appropriate data set for analysis. To simplify this process, sequencesin varDB can be searched using a ﬂexible query language. Matching sequences are
returned as a sortable, pageable grid view with a panel showing the number ofsequences belonging to each of a number of categories, including Pfam domain,
gene family, genome project, source (e.g., PlasmoDB, Broad Institute, GenBank
Core or dbEST), etc., which can then be used to further ﬁlter the result set.
2.2. Generating a Codon Alignment
To analyze codon usage and selection, it is helpful to ﬁrst create an alignment
from coding sequences. However, this procedure often requires multiple steps and issometimes complicated by problems in matching the nucleotide sequence with thecorrect translation, particularly in eukaryotes with complex splicing rules or whenusing sequences from unﬁnished genome sequencing projects. While nucleotide andamino acid sequences can be downloaded separately in varDB, a new feature in
the database allows pre-processed pairs of corresponding protein and nucleotide sequences to be downloaded together, along with a table of sequence annotations. In
this step introns and terminal stop codons are removed, and sequences are veriﬁed toensure that there are no internal stop codons and that sequence lengths are divisibleby three. Although nucleotide sequences can be aligned directly, protein sequencealignments are often used to generate more accurate codon-based alignments, especially among highly variable sequences in which synonymous substitutions andamino acid substitution matrix scores provide additional information to help resolvealignment ambiguities. Given a pre-computed protein alignment, codon alignments
can be generated within varDB using Pal2Nal [38] or RevTrans [41] to align coding
sequences using the protein alignment as a template.
2.3. Analyzing Codon Usage
A great deal of information can be extracted from codon alignments by examining
variation in the way alternative codons are used to code for the same amino acid.
The amino acid code is degenerate, using 64 codons to code for 20 amino acids andSeptember 4, 2009

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three stop codons. Therefore most amino acids are coded by at least two or as manyas six diﬀerent codons, usually diﬀering only in the third position. In principle,
synonymous codons are interchangeable and should be invisible to selection, but
in reality codon usage is often biased towards a subset of optimal codons [23].
This is due in part to variation in the overall GC content, which constrains thefrequency of codons that contain predominantly G/C or A/T nucleotides. Genomewide codon usage may also depend on the relative frequency of corresponding tRNAs
during translation [23], whereas variation in codon usage within a gene may reﬂect
functional constraints at the DNA level [14].
VarDB provides both standard and novel utilities to assist in codon usage
analysis. When a codon alignment is selected, the codon usage tool displays GC
content and GC3skew as well as tables of nucleotide, codon, and amino acidfrequencies (Fig. 1). Codons can be grouped by synonymous amino acid in descending order based on within-group relative codon usage or other measures[13]. For comparison with background frequencies, pathogen-speciﬁc codon usage
statistics are provided for each pathogen based on the Codon Usage Database(http://www.kazusa.or.jp/codon/).
Although codon usage can be analyzed using unaligned sequences, variation incodon usage by alignment position may be useful for detecting nucleotide patterns
involved in generating new variants. VarDB displays the number and type of codon
at each position using diﬀerent colors to indicate non-synonymous codons (Fig. 2).
This view is intended to highlight unusual codon usage patterns by alignment column.
A separate view shows codon usage for cysteine residues alone. Due to theirhigh degree of conservation and structural role in disulﬁde bond formation, certain cysteine residues may be maintained under purifying selection. Even in theface of balancing selection for cysteine conservation, the two codons coding for cysteine should occur at their respective background frequencies. Therefore, bias incodon usage that varies by position might suggest selection acting at the nucleotide
level. Conticello et al. demonstrated that snail conopeptides show exclusive usageof only one of the two codons at certain key positions immediately upstream of
hypervariable sites, leading them to propose that clusters of cysteine codons may
cause stalling of the replication fork and temporary recruitment of an error prone
DNA polymerase, thereby increasing the mutation rate in a predictable and highly
localized fashion [14]. This idea is similar in principle to the mechanism used by
the vertebrate adaptive immune response during targeted somatic hypermutation,
which also shows signiﬁcant codon usage bias by location, particularly in the case ofserine [40]. The potential importance of this type of mechanism is that it provides
a means for an organism to eﬃciently explore sequence space without disrupting
essential structural or functional motifs required by selectively varying the mutation
rate throughout the sequence [11].
Cysteines are likely to play an important role in the evolution of antigenic variation proteins because they provide a rigid super-structure that can accommodate

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Fig. 1.

5

Relative synonymous codon usage in P. falciparum var genes.

short insertions and deletions in variable regions. Selection may favor particular
combinations of codons that help to increase the probability of mutations occurring where they are likely to be favorable or decrease the probability of mutations
where they may disrupt sensitive regions [10]. To facilitate testing of these hy-September 4, 2009

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Fig. 2.

Codon usage by column in P. falciparum var genes.

potheses, varDB provides a tool to compare codon usage between conserved andvariable blocks of an alignment. The tool uses Gblocks [39] to split the alignment
into conserved and variable regions and then compares codon usage and amino acidcomposition between them. Histograms are generated to help visualize the distribution of the variable region sequence lengths. Similarly, the programs seg and dust
can be used within varDB to mask low complexity regions for amino acid and nucleotide sequences, respectively [42], making it easier to detect conserved ﬂanking
regions. Plasmodium falciparum, in particular, is unusual for the presence of long
insertions comprised of low complexity regions [32].
2.4. Nucleotide Repeats and DNA Secondary Structure
Conserved nucleotide motifs independent of codon boundaries may also play a rolein generating diversity. These may be found by examining consensus sequences or byusing motif detection tools [5]. The codon usage tool in varDB contains a view thatshows the relative frequency of each nucleotide for each column. Repetitive patterns
in a sequence are often biologically relevant, and short repeat elements have been
implicated in slippage of the replication machinery, which may introduce variation
by causing frame shift mutations, turning gene expression on or oﬀ, or changing
the rate of gene expression by altering the aﬃnity of transcription factor bindingsites [29]. Similarly, inverted repeats and pseudo-palindromes may form secondary
stem-loop structures that are thought to interfere with transcription/replication orthat may be ”repaired” to form complete palindromes [10, 11, 34]. Moreover, triplet

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repeats may lead to in-frame insertions and deletions that could substantially altera protein’s antigenic signature without disrupting translation [29]. To help identify
this kind of short direct or inverted repeat elements, VarDB provides a web service
interface for the Oligo Repeat Finder web site (http://wwwmgs.bionet.nsc.ru/
mgs/programs/oligorep/InpForm.htm).
2.5. Mutation Hotspot Motifs
Analysis of mutation spectra has identiﬁed a number of DNA motifs associatedwith an increased probability of mutation based on sequence context [36]. Notably,
RGYW/WRCY motifs have been implicated in immunoglobulin somatic hypermutation, in which the mutation rate is six orders of magnitude higher than surrounding regions as part of a mechanism to generate and select high aﬃnity variants via
C→U deamination and error prone repair using DNA polymerase η [31, 35]. Consequently, it may be helpful to examine antigenic variation sequences for the presenceof known and potential mutagenic motifs. VarDB provides ﬂexible query tools tosearch for these patterns using the IUPAC extended genetic alphabet (e.g., R for
purines and Y for pyrimidines), or sequences can be searched using Perl-like regularexpressions. Alternatively, PHI-BLAST can be used to restrict matches to thosethat also share high overall sequence homology to the query sequence to reduce therate of false positives when searching for short motifs in a large data set [4].
2.6. Recombination
One of the ways in which pathogens generate antigenic diversity involves gene duplication, in which paralogous gene families, ranging from four or ﬁve members insome bacteria to over 1000 in Trypanosoma brucei, are coordinately expressed only
one member at a time, causing an antigenic shift and triggering a new immune
challenge [8]. This strategy is eﬀective because members of these gene families areoften located in the rapidly evolving subtelomeric regions near the ends of chromosomes [6] (Fig. 3). In spite of high sequence variability, these genes often containconserved motifs, possibly in the upstream and downstream ﬂanking regions, thatprovide targets for non-homologous recombination and gene conversion [12]. This
process can help to rapidly diversify subtelomeric gene families in a parallel fashion,such that over time strains contain fewer and fewer identical genes [25]. In somecases gene conversion using only partial genes or pseudogenes can generate novel
mosaic genes [27].
VarDB provides several utilities to aid investigation of recombination patterns.
Wherever possible, sequences from multiple genome sequencing projects are included, providing a way to estimate the degree of variation within and among
strains. A simple custom genome browser is provided to visualize the location andgenomic context of genes from sequencing projects (Fig. 4). Because ﬂanking regions
may provide important information for identifying potential recombination sites, arbitrarily long regions upstream or downstream of a given feature in a genome canSeptember 4, 2009

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Fig. 3. Subtelomeric clusters of var and rif/stevor antigenic variation genes are found in all 14
chromosomes of the P. falciparum 3D7 genome.

be downloaded in bulk by uploading an accession list. BLAST and PSI-BLAST are
useful for ﬁnding stretches of homologous DNA that may provide a target for recombination [4]. Antigenic variation sequence data can also be exported for analysis
in external programs such as RDP3, a standalone tool that attempts to infer therecombination history of a set of sequences [28].
2.7. Variability and Immune Selection
Ideally vaccines targeting invariant protein structures could be developed against
some of these recalcitrant pathogens, but realistically most vaccines must protect
against a wide range of potential conformations that may be encountered during
infection [7]. Despite these sophisticated antigenic variation generating mechanisms,
however, it may be possible to ﬁnd a subset of evolutionarily constrained structural
motifs that are conserved among multiple variants [20]. Although only a few solved
crystal structures are available for antigenic variation proteins, varDB uses BLAST
to map sequences to available structures and Jmol (http://www.jmol.org/) to visualize the location of matches on the structure in order to provide a rough senseSeptember 4, 2009

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Fig. 4. Simple genome browser showing orientation of genes and a pseudogene in the subtelomericregion of P. falciparum 3D7 chromosome 1.

of the spatial orientation of residues (Fig. 5). A more powerful alternative would
be to create homology models based on existing structures using external software
such as MODELLER [17]. VarDB does not include an interface to MODELLER due
to licensing restrictions, but references to published homology models are included
where available. Given the degree of sequence variation and the limited number ofcrystal structures currently available, however, the accuracy of homology models
for antigenic variation proteins may be low, particularly within the variable loop
regions that are of most interest. Nonetheless, as additional crystal structures become available, this approach should become increasingly useful for visualizing theeﬀects of sequence variation on 3D structure and epitope conformation. VarDB also
contains links to a number of tools to predict T and B cell epitopes that may beSeptember 4, 2009

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Fig. 5. Sequence from the Trypanosoma brucei TREU927 genome project mapped onto the two
chains of PDB structure 1VSG using bl2seq and Jmol.

helpful in predicting which residues are accessible to immune receptors or whether
structural variants are likely to be antigenically distinct [18].

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Fig. 6. Wu-Kabat variability by column in a P. falciparum PfEMP1 protein multiple sequencealignment.

In the absence of a crystal structure, one approach to identifying potential drugor vaccine targets is to create a protein alignment and analyze the information
content at each position. VarDB implements many of the methods available in theProtein Variability Server [19], including Shannon Entropy, the Simpson Diversity
Index, and the Wu-Kabat Variability Coeﬃcient, each of which characterizes a different aspect of amino acid variability (Fig. 6). Positions with high amino aciddiversity may reﬂect residues exposed to the immune system and undergoing diversifying selection. Evidence for diversifying selection at the nucleotide level canbe inferred by an excess of non-synonymous changes per non-synonymous site (Ka)relative to the number of synonymous substitutions per synonymous site (Ks) [22].
VarDB can calculate the Ka/Ks ratio along a sliding window, and sequences can beformatted for more extensive analysis using external tools such as DataMonkey [33]or Selecton [37].
3. Conclusions
VarDB provides a streamlined interface and a number of utilities for compiling andanalyzing variation among sequences. Combining data from multiple such methods
should provide insight into which residues are essential for structural or functional
integrity and which tend to serve as immunodominant decoys. Vaccines which areable to target the former and avoid the latter are likely to be more eﬀective inpreempting immune evasion, thereby reducing the length of infection and the abilityof the pathogen to reproduce and disperse.
Much attention has recently been focused on heritable diversity generating mechanisms, mutator phenotypes, and the concept of an implicit genome [11]. As microorganisms rapidly evolve and freely trade genetic material able to confer resistance orevade immune recognition, the eﬀectiveness of existing treatments will continue toerode, and formerly innocuous organisms will continue to acquire virulence traits
and become emerging threats. While Lynch [26] and others [30] caution against invoking potentially adaptationist arguments in interpreting the evolution of genomicSeptember 4, 2009

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architecture, many pathogens have converged on strikingly similar patterns of antigenic variation while facing the common threat of their host immune systems [15].
Therefore, much can be learned by comparing strategies employed to great eﬀect by
distantly related pathogens. VarDB has already drawn attention from the researchcommunity [2], and it is our hope that we will be able to provide a useful resource
for investigating antigenic variation in the eﬀort to control infectious diseases that
continue to evade not only our immune defenses but also, increasingly, existing frontline drugs and vaccines.
4. Acknowledgements
This work was supported by grants from the Ministry of Education, Culture, Sports,
Science and Technology of Japan, The STINT Foundation, Marie Curie IIF Fellowship EUFP6 (02154), and the Swedish Royal Academy of Sciences. C.E.W. was
supported by The Centre for Allergy Research. Computational resources were provided by the Bioinformatics Center and the Supercomputer Laboratory, Institutefor Chemical Research, Kyoto University.
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