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Introduction

Dengue viruses (DENVs) are mosquito-borne pathogens of the family Flaviviridae, genus Flavivirus, which are phylogenetically related to other important human pathogens, such as Yellow fever (YFV), Japanese encephalitis (JEV), and West Nile (WNV) viruses, among others. DENVs are enveloped, single-stranded RNA (+) viruses coding for a polyprotein precursor of approximately 3,400 amino acids, which is cleaved into three structural (capsid, C; precursor membrane and membrane, prM/M; envelope, E) and seven nonstructural proteins (NS1, 2a, 2b, 3, 4a, 4b and 5). Viral replication occurs in the cytoplasm in association with virus-induced membrane structures and involves the NS proteins. There are 4 genetically distinct DENV types, referred to as DENV-1 to -4, with multiple genotypic variants [1],[2]. DENVs are transmitted to humans primarily by Aedes aegypti mosquitoes and cause a wide range of symptoms from an unapparent or mild dengue fever (DF) to severe dengue hemorrhagic fever (DHF)/dengue shock syndrome (DSS) that may be fatal. It is estimated that more than 100 million people are infected each year, with up to several hundred thousand DHF/DSS cases [3]. To date, there is no licensed prophylactic vaccine and no specific therapeutic formulation available.

Adaptive immune responses include cellular responses to short peptides derived from self and foreign proteins by proteolysis. The peptides are presented to T-cell receptors (TCRs) by major histocompatibility complex (MHC) molecules, referred to as human leukocyte antigen (HLA) molecules in humans. HLA class I and class II molecules bind and present peptides to CD8 and CD4 T-cells, respectively, that play a critical role in antigen (Ag)-specific cytotoxic responses and the induction and maintenance of Ag-specific memory responses [4]–[6]. Peptides that are recognized by the T cells and trigger an immune response are referred to as T-cell determinants. One problem in developing a tetravalent DENV vaccine is the viral diversity [7], with rather low intra-type, but high inter-type variability, resulting in type-specific and type cross-reactive T-cell determinants [8]. This variability of related structures gives rise to a large number of variant peptide sequences with one or more amino acid differences that may function as alternative determinants, or altered peptide ligands [9], and affect anti-DENV host immunity [10],[11]. There is abundant evidence that interactions of memory T cells with peptide ligands bearing amino acid substitutions at TCR contact residues may alter T-cell activation and effector function [9], [12]–[15]. Even a single amino acid substitution can impair the function of T cells in a variety of ways, producing profoundly different phenotypes that range from modified stimulatory function to complete inhibition [14]. These findings suggest that infection or immunization with multiple DENV types, as is the case with some tetravalent vaccines, may lead to T-cell responses to variant peptides that might be deleterious. There is also the possibility that the altered-ligand phenomenon and cross-reactive T-cell responses, referred to as original antigenic sin, may play a role in DHF/DSS [7],[11],[16],[17]. Although the etiology of DHF and DSS is only partially understood, this consideration may have profound implications for the safety and efficiency of candidate vaccines.

The objective of this study was to search for sequence regions conserved across the majority of DENVs and representing potential immune targets [18]. Bioinformatics-based approaches were used to (a) extract all DENV sequences available in public databases, (b) identify and examine the structure-function relationship and distribution in nature of sequences that are highly conserved in the majority of DENVs (referred to as pan-DENV sequences), (c) analyze the variability of DENV sequences, and (d) examine the immune relevance of the conserved sequences as potential T-cell determinants that would be applicable to the majority of the human population worldwide [19]. We have also correlated the conserved DENV sequences to previously reported T-cell determinants and further identified novel candidate T-cell determinants by analyzing HLA-restricted immune responses in HLA transgenic mice.

Methods

Methodology overview

The bioinformatics approaches and rationale for the methodology adopted in this study have been previously described [20] and are summarized in Figure 1.

Figure 1. Overview of the bioinformatics and experimental approaches employed for the identification and analysis of the pan-DENV sequences.

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Data collection and sequence organization

DENV protein sequences were retrieved from the NCBI Entrez protein database in December 2005, and again in December 2007 for validation purposes, by use of a taxonomy ID search via the NCBI taxonomy browser [21]. The taxonomy IDs for DENV-1 to -4 were 11053, 11060, 11069 and 11070, respectively. The data for 2007 were processed separately from the 2005 dataset, but using identical procedures.

The sequences of the DENV proteins C, prM, E, NS1, NS2a, NS2b, NS3, NS4a, NS4b and NS5 were extracted from the database records (Dataset S1) by multiple sequence alignments, and application of the known cleavage sites obtained from the annotation of the GenPept [21] reference polyprotein sequences of DENV-1 to -4 (AAF59976, P14340, AAM51537, AAG45437, respectively), and from the literature [22]. Grouping of the sequences of each DENV type was performed by BLAST [23] followed by CLUSTALX 1.83 [24] multiple sequence alignments. Both full-length and partial sequences of each DENV protein were used for analysis, and identical sequences were not removed from datasets, unless otherwise indicated. All multiple sequence alignments were manually inspected and corrected for misalignments.

Identification of pan-DENV sequences

The DENV protein sequences were examined by a consensus-sequence based approach [25] to identify sequence fragments that were common across the 4 types. The consensus sequences for the proteins of each type (intra-type consensus) were first derived by multiple sequence alignments to select the predominant residue at each amino acid position. The 4 intra-type consensus sequences for a given protein (one from each type) were then aligned to reveal sequence fragments identical across each of the types that were at least 9 amino acids long. This minimum length was chosen because it represents the binding core length of a majority of HLA-restricted T-cell determinants [26]. Only sequence fragments that were identical in at least 80% of the sequences of each of the 4 types were retained for further analyses. Peptides with residue X in the alignment were ignored from the percentage representation (i.e. frequency) computation. The 80% intra-type representation cut-off was chosen because 44 of the 46 sequence fragments that were common across the 4 DENV types exhibited intra-type representation of ≥81%, and those two that did not had significantly lower representation (~56–67%) in one of the 4 types.

Information entropy analysis of pan-DENV sequences

Shannon information entropy [20],[27] was used to study the diversity of DENV protein sequences within each type (intra-type diversity) and across all DENVs (pan-DENV diversity) and to assess the predicted evolutionary stability of the identified pan-DENV sequences. All entropy analyses were carried out by using the in-house developed Antigenic Variability Analyser tool (AVANA) [28]. For immunological applications, the entropy measure for antigenic sequences was based on nonamer peptides [26], centered at any given position in the alignment. Applying Shannon's formula, the nonamer peptide entropy H(x) at any given position x in the alignment was computed by

where p(i, x) is the probability of a particular nonamer peptide i being centered at position x. The entropy value increases with n(x), the total number of peptides observed at position x; it is also sensitive to the relative frequency of the peptides; such that it decreases when one peptide is clearly dominant (i.e. the position is conserved). Only sequences that contain a valid amino acid at position x were used for the entropy computation, and the alignment gaps were ignored. Although gaps tend to occur in high-diversity regions, proteins that have a high fraction of gaps have reduced statistical support, yielding an artificially low entropy value; for this reason, positions where more than 50% of sequences contained a gap were discarded. Because of the statistical nature of the entropy measure, both complete protein and shorter fragment sequences were used in this computation. The first and last 4 positions in the alignment of each protein were not assigned any peptide entropy value as they cannot be the center of a nonamer.

In theory, nonamer entropy values can range from 0, for a completely conserved nonamer peptide in all sequences analyzed, to 39 (log₂ 20⁹); in practice, however, the upper bound is very much lower for alignments of closely related sequences. For finite-size sets of sequences, entropy computations are affected by the sequence count in the alignment. For an alignment of N sequences, alignment size bias is proportional to 1/N [29]. This relationship allows a correction for size bias by applying to each alignment a statistical adjustment that estimates entropy values for an infinitely-sized alignment with analogous peptide distribution. To obtain such an estimate, the alignment was repeatedly randomly sampled to create smaller alignments of varying size, whose entropy was measured. At each alignment position, the entropy of these subset alignments of size N was plotted against 1/N, using a linear regression to extrapolate the entropy estimate for N→∞. The regression's coefficient of determination (r²) was used as a goodness-of-fit of the resulting estimate. In this study, size bias correction was applied to all entropy calculations, so that alignment sequence counts could be ignored in comparisons. All entropy values reported are therefore infinite-size set estimates, rather than the values directly computed from the alignments.

Nonamer variant analysis of pan-DENV sequences

Data from information entropy analysis were used to study the distribution of the representation of nonamer variant peptides in DENV sequences, within and across the types. For any given position x in the alignment, the combined representation of all nonamers, excluding the predominant peptide, was computed. The predominant nonamer was the peptide that was contained in the majority of the sequences at the position in the alignment. All the other peptides that differed by at least one amino acid from the predominant nonamer were defined as variants.

Functional and structural analyses of pan-DENV sequences

The known and putative structural and functional properties of pan-DENV sequences were searched in the literature and by use of the Prosite [30], via ScanProsite [31], and Pfam [32] databases. When possible, the sequences were mapped on the three-dimensional (3-D) structures of available DENV Ag in the PDB database [33] by use of ICM-Browser version 3.3 (www.molsoft.com). X-ray diffraction 3-D structures were visualized by use of the Corey, Pauling and Koltun (cpk) representation in the ICM-Browser.

Identification of pan-DENV sequences common to other viruses and organisms

Pan-DENV sequences that overlapped at least 9 consecutive amino acid sequences of other viruses and organisms were identified by performing BLAST search against viral protein sequences reported at NCBI (as of July 2007), excluding DENV sequences (parameters set: limit by Entrez query “txid10239[Organism:exp] NOT txid12637[Organism:exp]”; automatically adjust parameters for short sequences option enabled; low-complexity filter disabled; alignments: 20,000), and against protein sequences of all organisms excluding viruses (parameters set: limit by Entrez query “Root[ORGN] NOT Viruses[ORGN] NOT txid81077[ORGN]”; automatically adjust parameters for short sequences option enabled; low-complexity filter disabled; alignments: 20,000). The keyword “NOT txid81077 [ORGN]” was used to remove artificial sequence hits.

Identification of known and predicted pan-DENV HLA supertype binding sequences

Both literature search and query against the Immune Epitope Database [34] (www.immuneepitope.org) were performed to detect reported immunogenic, human T-cell determinants (both class I and II) of DENV that either fully or partially overlapped with the pan-DENV sequences. In addition, dedicated algorithms based on several prediction models were used to identify candidate putative HLA-binding sequences to multiple HLA class I and II supertype alleles within the pan-DENV sequences. Putative HLA supertypes class I-restricted peptides were identified by use of NetCTL [35], Multipred [36], ARB [37], and class II-restricted peptides by Multipred and TEPITOPE [38]. Further, the intra-type representation of the putative T-cell determinants was analyzed.

The NetCTL 1.2 algorithm (www.cbs.dtu.dk/services/NetCTL/) predicts peptides restricted by 12 HLA class I supertypes (A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58 and B62). The algorithm integrates the predictions of HLA binding, proteasomal C-terminal cleavage and transport efficiency by the transporter associated with antigen processing (TAP) molecules. HLA binding and proteasomal cleavage predictions are performed by an artificial neural networks (ANN) method, while TAP transport efficiency is predicted using a weight matrix method. The parameters used for NetCTL prediction were: 0.15 weight on C terminal cleavage (default), 0.05 weight on TAP transport efficiency (default), and 0.5 threshold for HLA supertype binding, which was reported to be optimal (sensitivity (SN), 0.89 and specificity (SP), 0.94) in a large benchmark study containing more than 800 known class I T-cell determinants [35].

The TEPITOPE software (2000 beta version; courtesy of J. Hammer) utilizes quantitative matrix-based motifs, obtained from experimental scanning of the binding of P1-anchored designer peptides to soluble HLA-DR molecules in in-vitro competition assays, to predict peptides binding to 25 common HLA-DR alleles (DRB1*0101, *0102, *0301, *0401, *0402, *0404, *0405, *0410, *0421, *0701, *0801, *0802, *0804, *0806, *1101, *1104, *1106, *1107, *1305, *1307, *1311, *1321, *1501, *1502, and DRB5*0101) [38],[39]. The parameters for TEPITOPE predictions were: 5% quantitative threshold and putative determinants with a 10-fold inhibitory residue included. Nonamer peptides predicted to bind at least 10 out of the 25 HLA-DR alleles were selected as putative supertype-restricted determinants.

Multipred (research.i2r.a-star.edu.sg/multipred/) is a computational system for the prediction of peptides that bind to HLA class I supertypes A2 and A3 and class II HLA-DR supertype [36]. The HLA alleles selected to represent these supertypes by Multipred were as follows: A2 supertype, A*0201, *0202, *0203, *0204, *0205, *0206, *0207 and *0209; A3 supertype, A*0301, *0302, *1101, *1102, *3101, *3301 and *6801; DR supertype, DRB1*0101, *0301, *0401, *0701, *0801, *1101, *1301, and *1501. Hidden Markov model (HMM) and ANN methods are the predictive models of Multipred; both have been optimized and show similar performances [36]. The sum thresholds used for prediction of peptides restricted to the three HLA supertypes by ANN and HMM methods were: A2, 31.33 (ANN; SN = 0.80 and SP = 0.83) and 47.08 (HMM; SN = 0.80 and SP = 0.78); A3, 24.53 (ANN; SN = 0.90 and SP = 0.95) and 37.58 (HMM; SN = 0.80 and SP = 0.87); and DR, 23.42 (ANN; SN = 0.90 and SP = 0.92) and 51.08 (HMM; SN = 0.90 and SP = 1.00). Consensus predictions of the two methods were taken as final predictions for each HLA supertype.

The ARB matrix method (epitope.liai.org:8080/matrix/matrix_prediction.jsp) is based on a matrix of coefficients to predict IC50 values [37]. The HLA class I alleles predicted by ARB were grouped according to the current supertype classification [19],[40] and supertypes containing more than two alleles were selected, namely A2 (A*0201, *0202, *0203, *0206, and *6802), A3 (A*0301, *1101, *3101, *3301 and *6801), B7 (B*0702, A*3501, *5101, *5301, and *5401), and B44 supertypes (B*4001, *4002, *4402, *4403, and *4501). The prediction threshold value chosen for optimum sensitivity and specificity was IC50≤1000 nM and nonamer peptides predicted to bind 3 or more alleles of the supertype were considered as putative promiscuous HLA supertype-restricted determinants.

ELISpot analysis of HLA-DR restricted determinants in pan-DENV sequences

All experiments were approved by the Johns Hopkins University Institutional Animal Care and Use Committee. Murine H-2 class II-deficient, HLA-DR2 [41], HLA-DR3 [42],[43], HLA-DR4 (referred to as DR4/IE) [44] and HLA-DR4/human CD4 (huCD4) [45],[46] Tg mice were used, bred and maintained in the Johns Hopkins University School of Medicine Animal Facility. Specific pathogen-free (SFP) colonies were maintained in a helicobacter-negative mice facility. The HLA-DR expression of the experimental transgenic mice was evaluated by flow cytometry.

Mice were immunized subcutaneously at the base of the tail, twice at two weeks interval, with pools of overlapping peptides covering the DENV-3 protein (15–17 aa, overlapping by 10–11 aa) (Schafer-N Inc., Copenhagen, Denmark; BEI Resources, Manassas, VA). Peptide pools (73–155 peptides per pool) contained 1 µg of each peptide and were emulsified (1:1) in TiterMax adjuvant (TiterMax USA, Inc.). An aqueous preparation of TiterMax (1:1) was used as a negative control. Two weeks after the second immunization, the mice were sacrificed and HLA-DR-restricted CD4 T cell reponses were assessed by ex vivo IFN-γ ELISpot assay using CD8-depleted splenocytes. Each target peptide was tested in duplicate. Spot-forming cell (SFC) counts were normalized to 10⁶ cells. The results were considered significant when the average SFC minus two standard deviations (SD) was greater than the average of the background plus two SD; and the average values were greater than 10 SFC per 10⁶ splenocytes. The initial screening assays were performed with peptide matrices [47], followed by assays with the relevant individual peptides (Nascimento et al., manuscript in preparation).

Results

Dengue virus type protein datasets

A total of 9,512 and 12,404 complete and partial DENV protein sequences were collected from the NCBI Entrez protein database of December 2005 and 2007, respectively, representing an increase of approximately 30% (2892 sequences) in the 24-months interval (Table 1). The total number of sequences (2007) varied from 4,011 for DENV-2 to 1,415 for DENV-4 and from 3,845 for E to 523 for NS4a proteins. Most of the individual protein sequences originated from DENV strains that were unique variants with respect to the entire polyprotein, but were identical to other strains with respect to individual proteins [48].

Table 1. Number and distribution of reported DENV protein sequences.

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Conserved pan-DENV sequences

The consensus-sequence approach [20],[25] identified a total of 44 pan-DENV sequences of at least 9 amino acids that were present in ≥80% of all sequences of each DENV type for both 2005 and 2007 datasets (Figure 2; Table S1). Strikingly, 34 of the 44 (~77%) were conserved in ≥95% of all reported DENV sequences. The size of the pan-DENV sequences ranged from 9 to 22 amino acids, with a combined size of 514 residues, corresponding approximately to 15% of the complete DENV polyprotein (~3390 amino acids) (Table 2). The vast majority (42/44) of the pan-DENV sequences were localized in the NS proteins, with 17, 12, 7 and 5 sequences found in NS5, NS3, NS1 and NS4b, respectively, and 1 in the NS4a protein. Notably, the remaining two pan-DENV sequences were localized in the E protein. No region of at least 9 amino acids and conserved in ≥80% of the sequences of each DENV type was found in the C, prM, NS2a and NS2b proteins. The largest size of the combined pan-DENV sequences was in the NS5 protein, representing a total of 215 amino acid positions covering ~24% of the protein, followed by NS3, NS1 and NS4b with 122, 74 and 69 amino acid positions covering ~20, ~21 and ~28% of the corresponding proteins, respectively. The two pan-DENV sequences in the E protein had a combined size of only 25 amino acids, corresponding to ~5% of the protein.

Figure 2. Pan-DENV sequences and their representations in the 4 DENV types.

The 44 pan-DENV sequences of at least 9 amino acids that were found present in ≥80% of the recorded sequences of each DENV type are shown. The representation values are shown for the 2005 dataset; see Table S1 for values of both 2005 and 2007 datasets. Amino acid positions were numbered according to the sequence alignments of the 4 DENV types. The corresponding proteins are indicated on the left.

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Table 2. Distribution and size of the pan-DENV sequences.

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In large-scale genomic analyses such as this study, biases may result from the collection of completely or partially overlapping redundant sequences, corresponding to identical or highly similar circulating DENV isolates sequenced by various dengue surveillance programs in different countries. Although to some extent this redundancy may be accepted as a reflection of the incidence of the corresponding DENV isolates in nature, we assessed its potential bias effect by repeating the analysis of conservation after discarding duplicate sequences from the datasets. The analysis of unique sequences identified all the pan-DENV sequences that were identified when including duplicates (Figure 2), except for NS1_12–20, NS1_25–35 and NS5_597–616. Therefore, the presence of duplicates in the DENV datasets did not significantly affect the results. Although the removal of duplicates does not fully compensate for biases in the datasets, the removal of highly similar sequences, which may have been generated from relatively large sequencing efforts in single outbreaks, was deemed undesirable, since such arbitrary selection would introduce additional biases.

Evolutionary diversity of DENV protein nonamer peptide sequences

The evolutionary diversity of each DENV type, and the 4 types combined, was studied by use of Shannon information entropy [27], modified to examine the variability of nonamer peptide sequences, as described in the Methods. The entropy of the proteome of the recorded viruses of each type showed numerous long regions of low entropy (≤1), reflecting the relatively high degree of intra-type sequence conservation, in particular in the NS3, NS4b and NS5 proteins (Figure 3A–D). Overall, the average intra-type nonamer entropy values of the individual protein sequences of DENV-1, -2, -3 and -4 ranged from 0.2 for the DENV-4 NS4b to 1.0 for DENV-2 prM (Figure S1). Of note, however, were the marked differences in the relative degree of entropy of each protein between the 4 DENV types. For example, NS4b had the least diversity of the proteins of 3 types, but was replaced in DENV-2 by NS2b, which was the second most variable in DENV-3. The consequence of the differences in the sequences of each protein between the 4 types was a marked increase in the peptide entropy across the DENV 1-4 proteomes (Figure 3E), with average peptide entropy ranging from 1.6 for NS3 to 2.6 for NS2a (Figure S1), except for 44 sharply defined regions of low nonamer entropy (≤0.5) where the sequences were highly conserved in all DENVs (Figure 3E), with no significant difference between the 2005 and 2007 datasets (Table S2). Majority of the pan-DENV sequences had entropy values of ≤0.3, corresponding to the intra-type representation of ≥90%. Thus, the congruent consensus- and entropy-based analyses of the DENV nonamer peptides revealed highly conserved and evolutionarily stable pan-DENV sequences distributed in several viral proteins, despite the marked viral diversity defining multiple DENV types, genotypes and variants [49].

Figure 3. Shannon entropy of nonamer peptides within and across DENV types sequences.

The entropy values were computed from the alignments of DENV sequences using the Antigenic Variability Analyzer software, as described in the Methods. Values were plotted for DENV-1 (A), DENV-2 (B), DENV-3 (C), DENV-4 (D), and all 4 DENV types (E) sequences (2005 dataset). Entropy values around protein cleavage sites are non significant, since the corresponding positions cannot be the center of a nonamer (see Methods). The triangles below indicate the locations of the pan-DENV sequences in the corresponding proteins.

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Representation of DENV variant nonamer peptide sequences

The combined representation of variant peptides that differed by at least one amino acid from the predominant peptide was also analyzed at each nonamer position. Examples of this analysis for DENV-3 proteins are shown in Table 3. Nonamers that lack entropy (zero entropy) have one sequence in all of the recorded virus isolates, and therefore have no variants. Positions with high entropy can contain many different variant peptides, each at lesser (or equal) frequency than the predominant peptide. The combined representation of variant peptides at each nonamer position across the proteome of each individual DENV type was generally low, representing less than 10% of the corresponding sequences, except for some positions where it was more than 50% (Figure 4A–D). Notably, the nonamer position with the highest combined variant representation for each DENV type was found in the nonstructural proteins and not the structural ones, with representation values ranging from ~61 to ~78% (DENV-1 NS5, DENV-2 NS5, DENV-3 NS2a, and DENV-4 NS1 and NS3 proteins). When representations of variants across all DENVs were calculated, the majority of all nonamer sites contained variants that together represented ~60–85% of the total DENV sequences at that site (the highest representation of ~85% was in the NS1 protein) (Figure 4E). This was in striking contrast to the 0 to ~5% combined representation of variants at each nonamer position in the pan-DENV sequences, with no significant difference between the 2005 and 2007 datasets (Table S2). The majority of all nonamer sites in the pan-DENV sequences lacked variant or contained variants that together represented <1% of all recorded DENVs. These data further illustrate the extremely high genetic stability of the 44 pan-DENV sequences, among all recorded DENV sequences and demonstrate that irrespective of the high variability between the sequences of the 4 DENV types, the representation of variants in the pan-DENV sequences was almost negligible.

Figure 4. Percentage representations of variant nonamer peptides within and across DENV types sequences.

The percentage of sequences that contained variant peptides at each nonamer position are shown for DENV-1 (A), DENV-2 (B), DENV-3 (C), DENV-4 (D), and all 4 DENV types (E) (2005 dataset). Values around protein cleavage sites are non significant (see Figure 3). The triangles below indicate the locations of the pan-DENV sequences in the corresponding proteins.

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Table 3. Examples of the distribution of variant nonamer peptides in DENV-3.

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Functional and structural correlates of the pan-DENV sequences

Highly conserved protein sequences are likely to represent critical sites and domains [50]. A search of the literature and the Prosite and Pfam databases [30],[32] revealed that 27 of the 44 pan-DENV sequences were associated with biological activities (Table S3); the functional significance of the remaining 17 pan-DENV sequences was not known. The two pan-DENV sequences in the E protein corresponded to the fusion peptide (positions 98 to 110) and dimerisation domain [51],[52]. In NS3, one pan-DENV sequence corresponded to the peptidase family S7 (Flavivirus serine protease) domain and comprised the His-51 catalytic residue [53], 3 sequences corresponded to known/putative Flavivirus Asp-Glu-Ala-Asp/His (DEAD/H) domain associated with ATP-dependent helicase activity [54], and two sequences were predicted to be required for cell attachment and targeting signal for microbodies. In NS5, one pan-DENV sequence corresponded to the conserved methyltransferase (MTase) S-adenosyl-L-methionine binding motif I (positons 77–86) involved in viral RNA capping [55], and two sequences corresponded to RNA dependent RNA polymerase (RdRp) domain [56]. Furthermore, 6 of the 27 pan-DENV sequences were predicted to exhibit post-translational modification(s), including N-glycosylation, protein kinase C and casein kinase II phosphorylation, N-myristoylation and/or amidation (Table S3).

It is generally recognized that amino acids buried inside proteins are subject to greater interactions and packing constraints [57] than those exposed on the outer surface. Although none of the DENV protein structures in the protein data bank (PDB) [33] was full-length, 19 of the 44 pan-DENV sequences could be mapped on the available crystallographic models of the E ectodomain (Accession No. 1OAN; 394 out of 493–495 residues), NS3 (1BEF and 2BMF, 181 and 451 out of 618–619 residues, respectively) and NS5 fragments (1R6A, 295 out of 900–904 residues). Eleven of the 19 pan-DENV sequences were buried, 2 partially exposed and 6 exposed at the surface of the corresponding structures (Figure S2). However, these results should be considered preliminary until full-length 3-D structures are available.

Distribution of pan-DENV sequences in nature

Twenty-seven (27) of the 44 pan-DENV sequences overlapped at least 9 amino acid sequences of as many as 64 other viruses of the family Flaviviridae, genus Flavivirus (Figure 5). Zika virus shared 22 of the 27 sequences; Ilheus and Kedougou viruses, 18; and representatives of some of the significant human pathogens, West Nile, St. Louis encephalitis, Japanese encephalitis, Yellow fever and Tick-borne encephalitis viruses, shared from 16 to 9 pan-DENV sequences. Thirteen (13) of the 27 sequences represented NS5, of which 9 were present in at least 27 Flavivirus species; 9 represented NS3, of which two were found in 35 and 23 species; one E sequence was found in 19 species; and the remaining were in NS1 and NS4b (Figure 6; Table S4). Five (5) of the 27 were associated with known biological activities (NS5_79–90 MTase, NS5_658–670 RdRp, NS3_46–55 peptidase S7, NS3_284–292 DEAD/H and E_97–111 dimerisation/fusion domains). Interestingly, two sequences, NS3_406–418 and NS5_597–616, overlapped 9 amino acid sequences of the cell fusing agent virus polyprotein-like protein from the mosquito Aedes albopictus [58], and the phage-related tail fibre protein-like protein from the bacteria Chromohalobacter salexigens DSM 3043, respectively.

Figure 5. Number of pan-DENV sequences conserved in the different Flaviviruses.

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Figure 6. Number of Flaviviruses shared by the pan-DENV sequences.

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The representation of many of the pan-DENV sequences was high among known sequences of several of the highly studied Flaviviruses (Table S4): St. Louis encephalitis, West Nile, Japanese encephalitis, Murray Valley encephalitis, Usutu, Kokobera, Ilheus, Tick-borne encephalitis, Langat, Omsk hemorrhagic fever, Louping ill, Powassan, Kyasanur forest disease and Yellow fever viruses. Protein sequence data for the rest of the Flaviviruses that shared pan-DENV sequences was limited (<10 sequences) in the public database. Seven of the 27 pan-DENV sequences, NS1_12–20, NS3_256–267, NS3_383–392, NS3_491–499, NS4b_223–236, NS5_6–14 and NS5_302–310, were present in a few species with less than 10 reported total sequences (Table S4).

Known and predicted HLA supertype-restricted, pan-DENV T-cell determinants

Literature survey and database search revealed that 10 of the pan-DENV sequences (9 in NS3, one in E) overlapped at least 9 amino acids of 15 previously reported DENV T-cell determinants immunogenic in human, with their HLA restriction, when known, showed both class II (DR*15, DPw2) and class I (A*11) specificities (Table 4). Further evaluation of the immune-relevance of the pan-DENV sequences included a search for candidate putative promiscuous HLA supertype-restricted T-cell determinants within these regions by use of several computational algorithms: NetCTL [35], Multipred [36], ARB [37] and TEPITOPE [38]. Overall, 34 of 44 (~77%) pan-DENV sequences (Figure 7), identified in the NS5, NS3, NS1, E and NS4a proteins were predicted to contain 100 supertype-restricted binding nonamers (Table S5). The majority (88/100) of the predicted promiscuous HLA-binding nonamers were present in ≥95% of the sequences of each DENV type (Table S6). Thirty-one (~91%) of the 34 putative supertype pan-DENV sequences contained HLA-binding nonamers for multiple HLA supertypes. Clusters (hotspots) of two or more overlapping HLA-binder nonamer core peptides were present in 27 (~79%) of the 34 putative supertype pan-DENV sequences. About half (14/27) of these clusters contained three or more nonamer binders overlapping by 8 amino acids, covering most or the entire corresponding conserved region.

Figure 7. Candidate putative HLA supertype-restricted, pan-DENV T-cell determinants predicted by computational algorithms.

Amino acid positions of the pan-DENV sequences are numbered according to the sequence alignments of the 4 DENV types; the corresponding DENV proteins are indicated on the left. Predicted HLA-restricted T-cell determinants were identified using NetCTL, Multipred, ARB and TEPITOPE algorithms (see Methods).

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Table 4. Reported human T-cell determinants in the pan-DENV sequences.

doi:10.1371/journal.pntd.0000272.t004

Immunogenicity of HLA-DR-restricted pan-DENV sequences in HLA Tg mice

The immunogenicity of the pan-DENV sequences was also analyzed by assay of peptide-specific HLA-restricted T-cell responses in murine H-2 class II-deficient, HLA-DR Tg mice expressing 3 prototypic HLA-DR alleles, corresponding to the divergent subgroups HLA-DR2 (DRB1*1501), HLA-DR3 (DRB1*0301), and HLA-DR4 (DRB1*0401). Mice were immunized with pools of overlapping peptides covering the sequences of the E, NS1, NS3, and NS5 proteins of DENV-3, and HLA-DR-restricted CD4 T-cell responses were assessed by IFN-γ ELISpot assays using CD8-depleted splenocytes. Thirty peptides eliciting positive T-cell responses in the HLA Tg mice contained 9 or more consecutive amino acids of 22 pan-DENV sequences, that were localized in the NS5 (11), NS3 (6), NS1 (4), and E proteins (one) (Table 5). Overall, 9, 10 and 18 peptides elicited positive responses in HLA-DR2, -DR3, and/or -DR4 Tg mice, respectively; 20 corresponded to sequences of NS5, 10 of NS3, 6 of NS1, and one of E. Furthermore, at least 7 of the pan-DENV sequences, all localized in the NS5 and NS1 proteins, contained promiscuous T-cell determinants for multiple HLA-DR alleles (Table 5). These data, together with those previously reported (Table 4), showed that a minimum of 26 of the 44 pan-DENV sequences, distributed predominantly in the NS5 and NS3 proteins, and to a lesser extent in NS1 and E, contained numerous HLA-restricted class II and/or class I determinants demonstrated by assays of T-cell responses in vivo.

Table 5. Immunogenicity of the pan-DENV sequences in HLA-DR transgenic mice.

doi:10.1371/journal.pntd.0000272.t005

Discussion

In this study, we identifi