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J Virol, January 1998, p. 677-683, Vol. 72, No. 1
0022-538X/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Extent of Antigenic Diversity in the V3 Region of
the Surface Glycoprotein, gp120, of Human Immunodeficiency Virus
Type 1 Group M and Consequences for Serotyping
Jean-Christophe
Plantier,1
Sophie
Le
Pogam,1
Francis
Poisson,1
Laurence
Buzelay,1
Bernard
Lejeune,2 and
Francis
Barin1,*
Laboratoire de Virologie, EP CNRS
117,1 and
Laboratoire de Biophysique
Pharmaceutique et Biomathématiques,2
Université François Rabelais, Tours, France
Received 20 February 1997/Accepted 24 September 1997
 |
ABSTRACT |
Human immunodeficiency virus type 1 (HIV-1) may be studied by
molecular or immunological approaches. Most analyses have been performed by genetic comparison of isolates and have led to the definition of clades or subtypes within the major (M) group of HIV-1.
Five subtypes (A to E) were initially identified by comparison of
genomic sequences. Four new subtypes (F to I) were identified more
recently. Amino acid differences in the immunogenic V3 loop between
isolates have also been studied, leading to a phenetic classification
of at least 14 clusters (1 to 14) of sequences (B. T. M. Korber, K. McInnes, R. F. Smith, and G. Myers, J. Virol. 68:6730-6744, 1994). In this study, we compared the
antigenicity of the V3 consensus sequences defined by phylogenetic
analysis to the antigenicity of those defined by phenetic analysis. We used a recently developed subtype-specific enzyme immunoassay (SSEIA)
that uses the principle of blocking with an excess of peptide in the
liquid phase. Two SSEIAs were performed, the first with five V3
sequences defined by phylogenetic analysis and the second with 14 V3
sequences defined by phenetic analysis. A total of 168 HIV-1 sera taken
from seropositive individuals from seven different countries or regions
were studied. Experimental and statistical data, including correlation
matrix and cluster analyses, demonstrated associations between the
genetic subtypes and phenetically associated groups. Most of these were
predicted by Korber et al. (J. Virol. 68:6730-6744, 1994) by
theoretical analysis. We also found that V3 sequences can be grouped
into between three and five antigenically unrelated categories.
Residues that may be responsible for major antigenic differences were
identified at the apex of the V3 loop, within the octapeptide xIGPGxxx,
where x represents the critical positions. Our study provides evidence that there is a limited number of V3 serotypes which could be easily
monitored by serological assays to study the diversity and dynamics of
HIV-1 strains.
| |
INTRODUCTION |
The diversity of human
immunodeficiency virus type 1 (HIV-1) is a major problem in the
development of an effective vaccine against AIDS. Many HIV-1 sequences
are now available, and phylogenetic analysis resulting in a
continuously developing classification into subtypes or clades is
possible (45). HIV-1 isolates are classified into the M
group (for major) or O group (for outlier). The O group contains only a
few variants, all from a limited area of Africa (19, 27,
50). The M group includes variants responsible for the present
AIDS pandemic. It contains at least five subtypes (A to E), to which
have been added more recently four other subtypes (F to I) (23,
28, 34, 36, 37). Subtypes A, C, D, G, and H are common in Africa
(21, 35, 37, 38). Subtype B is the most common in America
and Europe (24, 26, 51). Subtype E occurs mainly in Asia
(25, 30, 41), and subtype F has been detected in Brazil and
Romania (3, 28, 34). These distributions are not
restrictive. Subtype C is also present in Asia (India and China), and
subtype G is also present in Russia (7, 12, 29). The African
subtypes (A, C, and D) and the Asian subtype (E) have also been
identified in North America and in European countries (9, 13, 14,
32, 48). All the subtypes are present in Africa, including B
(detected in West Africa), E (Central African Republic), and F
(Cameroon) (1, 35, 38). Analysis of the genetic diversity of
HIV-1 is becoming more difficult due to the increasing frequency of
coinfections and recombinations (15, 20, 44).
Phylogenetic trees have been generated with gag,
env, or tat nucleotide sequences. Shorter DNA
sequences encoding the functionally important V3 region of the envelope
protein are most frequently used to provide reliable subtype
designations (37). The diversity of the immunogenic V3 loop
has also been studied by comparing the amino acids of different
isolates, leading to a phenetic classification of at least 14 clusters
of sequences, each one characterized by a consensus sequence based on
the most common amino acid in a given position (22).
The heterogeneity of HIV-1 strains is studied mostly by molecular
characterization of genomic sequences. This involves sequencing fragments amplified by the PCR or the use of the heteroduplex mobility
assay (10, 11). However, although these methods allow direct
subtype classification, they are time-consuming and expensive and
require highly trained workers. Serotyping of HIV-1 by antibody (Ab)
binding to the V3 region has been suggested as an alternative approach
(8, 40, 49, 51). Such an approach may make it possible to
identify subtypes based on antigenic rather than genetic properties.
This immunological information about antigenic diversity might be of
value in vaccine development. We recently developed a subtype-specific
enzyme immunoassay (SSEIA) which gave results consistent with those of
genotyping (4, 48). This assay used V3 consensus sequences
defined by genetic classification, so we wanted to compare the
antigenicity of these V3 consensus sequences to the antigenicity of
those defined by phenetic analysis. The phenetic clustering of V3 loop
amino acid sequences is not always consistent with phylogenetic
analysis. Our results suggested that a limited number of serotypes may
exist and identified amino acids at the tip of the V3 loop that may be
responsible for serological discrimination.
| |
MATERIALS AND METHODS |
Peptides.
Nineteen peptides whose sequences corresponded to
sequences in the V3 region of HIV-1 were synthesized (Table
1). These peptides corresponded to the
consensus sequences of the five major subtypes of HIV-1 group M (A to
E) (36) or to the consensus sequences of the 14 phenetic
clusters described by Korber et al. in 1994 (22). The
peptides were synthesized by Merrifield's solid-phase procedure with
an automated peptide synthesizer (Applied Biosystems 431A),
9-fluorenylmethoxycarbonyl-protected amino acids, and
hydroxymethylphenoacetic-polystyrene resin (31). The resin
support and side-chain-protecting groups were removed with
trifluoroacetic acid after synthesis, and distilled water, phenol,
ethanedithiol, and thioanisole were used as scavengers. The peptides
were cleaved and purified by reverse-phase chromatography on
C8 columns (Aquapore octyl, 20 µm, 100 by 10 mm; Applied
Biosystems). The purity of the preparations was checked both by
observation of a single sharp peak in high-pressure liquid
chromatography analysis with C8 columns (Aquapore octyl
RP-300, 7 µm, 220 by 4.6 mm; Applied Biosystems) and by amino acid
analysis. Peptide compositions were as planned.
Sera.
A total of 168 HIV-1 Ab-positive sera collected in
seven different countries or regions were used (24 sera per country).
The regions were selected so that the widest antigenic diversity was obtained. They were France and West Indies (subtype B), Thailand (subtype E), and four African countries (Burundi, Burkina-Faso, Congo,
and Côte d'Ivoire) (subtypes A, C, and D).
Immunoassays.
We have shown that cross-reactivity between V3
sequences is both very strong and very frequent when analyzed by
indirect enzyme immunoassays (EIA) (2). We developed an
SSEIA that uses the principle of blocking with an excess of peptide in
the liquid phase (4). The SSEIA is more discriminating than
normal indirect EIA, probably because it is more dependent than
indirect EIA on Ab affinity. Two SSEIAs were performed, the first with
5 V3 sequences defined by phylogenetic analysis (SSEIAgen) and the
second with 14 V3 sequences defined by phenetic analysis (SSEIAphen).
For the SSEIAgen, wells of polyvinyl microtiter plates (Falcon) were
coated with an equimolar mixture of the five V3 peptides (0.5 µg/ml
each in 0.05 M bicarbonate buffer [pH 9.6]; 100 µl per well) by
incubation for 20 h at 37°C. The wells were washed twice with
phosphate-buffered saline containing 0.5% Tween 20 (PBS-TW), and the
unoccupied sites of the wells were saturated with phosphate-buffered
saline containing 2% newborn calf serum by incubation for 45 min at
37°C. Serum samples were diluted 1:100 in 0.01 M sodium phosphate
buffer (pH 7.4) containing 0.75 M NaCl, 10% newborn calf serum, and
0.05% Tween 20 (PBS-TW-NBCS). Each sample was tested in seven wells in
the presence of various blocking solutions. Preliminary assays were
used to select the optimal concentrations of peptides to be used for
both coating solutions and blocking solutions (4). Ten
microliters of a 100-µg/ml solution (in PBS-TW-NBCS) of V3 peptide of
the A, B, C, D, or E subtype was added to the wells (A to well 1, B to
well 2, and so forth). Ten microliters of a 100-µg/ml solution of an
equimolar mixture of the five peptides (theoretically 100% blocking)
was added to well 6. Ten microliters of PBS-TW-NBCS (theoretically 0%
blocking) was added to well 7. One hundred microliters of diluted serum
was added to each well and incubated for 30 min at room temperature,
and the wells were washed four times with PBS-TW. Peroxidase-conjugated
goat F(ab')2 anti-human immunoglobulin (TAGO, Burlingame,
Calif.; 100 µl of a 1:5,000 dilution in PBS-TW-NBCS) was added, and
the mixture was incubated for 30 min at room temperature. The wells
were washed four times with PBS-TW and incubated with hydrogen
peroxide-o-phenylenediamine for 15 min at room temperature. Color development was stopped with 2 N H2SO4,
and the absorbance value (optical density [OD]) was read at 492 nm.
The percent inhibition of binding induced by each of the five peptides
for each serum sample was calculated with the following
formula: {(OD
without blocking [well 7]
OD in the presence of
peptide)/(OD
without blocking [well 7]
OD in the presence of
the five peptides
(well 6)]} ×100.
The SSEIAgen indicates, for each serum sample, the immunodominant
subtype (the peptide with the strongest blocking) as well
as a
serological profile defined by the five values of inhibition
(percent
inhibition by peptide A, peptide B, peptide C, peptide
D, and peptide
E).
The same experimental conditions were used for the SSEIAphen, except
that wells were coated with an equimolar mixture of the
14 V3 peptides
(0.2 µg/ml each in 0.05 M bicarbonate buffer [pH
9.6]; 100 µl per
well). Each serum sample diluted 1:100 was tested
in 16 wells in the
presence of various blocking solutions. Ten
microliters of a
100-µg/ml solution (in PBS-TW-NBCS) each of peptides
1 to 14 was
added to wells 1 to 14 (peptide 1 to well 1, peptide
2 to well 2, and
so forth). Ten microliters of a 100-µg/ml solution
of an equimolar
mixture of the 14 peptides (theoretically 100%
blocking) was added to
well 15. Ten microliters of PBS-TW-NBCS
(theoretically 0% blocking)
was added to well 16. The remaining
steps were identical to those for
SSEIAgen. The percent inhibition
of binding induced by each of the 14 peptides for each serum sample
was calculated with the following
formula: [OD without blocking
(well 16)
OD in the presence of
peptide]/[OD without blocking
(well 16)
OD in the presence of the
14 peptides (well 15)] ×
100.
The SSEIAphen indicates, for each serum sample, the immunodominant
subtype (the peptide with the strongest blocking) as well
as a
serological profile defined by the 14 values of inhibition.
Statistical analysis.
The antigenic relationship between the
various peptides was studied by calculating the Pearson correlation
matrix with Systat statistical software (Deltasoft, Meylan, France).
For each SSEIAgen and SSEIAphen, specimens were grouped according to a
similar serological profile as defined above. We used the cluster
analysis of PCSM statistical software (Deltasoft). The serological
profiles of all the reactive samples for each assay were entered into a
computer. We used agglomerative hierarchical clustering of
observations, taking into account the euclidean distance and the
average linkage. The results were displayed as dendrograms. The two
dendrograms obtained were drawn on x (SSEIAphen) and
y (SSEIAgen) axes (see Fig. 2). Each sample was plotted at
the intersection of its x and y positions (at the
intersection of its positions within each dendrogram).
| |
RESULTS |
Antigenicity of the V3 peptides.
Among the 168 sera positive
for the HIV-1 antibody, 154 were reactive with V3 peptides (Table
2). All the sera that reacted in the
SSEIAgen were also reactive in the SSEIAphen. The nonreactive samples
were negative in both assays. The serum samples collected in France or
the West Indies were reactive to peptide B, most of those collected in
Thailand were reactive to peptide E, and various reactivities were
observed with African samples. Of the 86 reactive African samples, 22 (25.6%) were most reactive with peptide A, 1 (1.2%) was most reactive
with peptide B, 53 (61.6%) were most reactive with peptide C, and 10 (11.6%) were most reactive with peptide D. The most striking
observation with the SSEIAphen was the limited number of dominant
reactivities. Indeed, although 14 peptides were included, only 6 were
bound preferentially by the tested sera. The dominant reactivities
involved peptides 1 (1 case), peptides 2 and 5 (59 cases), peptide 4 (76 cases), peptide 7 (7 cases), and peptide 9 (11 cases) (Table
3). All the B-reactive serum samples were
reactive predominantly to peptides 2 and 5, the same intensity of
binding being observed with these two very similar sequences. Of the 47 B- or peptide 2- or 5-reactive samples, 23 cross-reacted with peptides
1, 3, and 6 but not with other peptides. An example is shown in Fig.
1 (sample F7). All the C-reactive samples
reacted with peptide 4, with no cross-reactivity. All the D-reactive
samples reacted with peptide 9, with no cross-reactivity. The
E-reactive samples reacted with either peptide 4 (10 cases) or peptide
7 (7 cases). The most diverse reactivities were observed with
A-reactive samples, which bound preferentially to peptide 1 (1 case), 2 or 5 (12 cases), or 4 (10 cases), with various degrees of
cross-reactivity (Table 3). Representative serological profiles of
serum samples are shown in Fig. 1.
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TABLE 2.
Reactivity of serum samples to subtype-specific V3
consensus sequences in SSEIAgen according to geographical origin
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FIG. 1.
Serological profiles in the two SSEIAs of typical
HIV-1-positive sera from various regions. Shown are two A-reactive
sera, BF2 reacting predominantly to peptides 2 and 5 and BF 228 reacting predominantly to peptide 4; a B-reactive sample (F7); a
C-reactive sample (Bu 192); a D-reactive sample (Co 119); and an
E-reactive sample (Th 4).
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|
The antigenic relationships among these peptides were further analyzed
by a statistical approach. The serological profiles
of every serum
sample, defined as the percentages of binding inhibition
by each of the
19 peptides, were entered into a computer, and
a Pearson correlation
matrix was established (Table
4). This
analysis showed positive correlations between sequence B and sequences
1, 2, 3, 5, and 6 (
r, 0.69 to 0.85), between sequences C and
4
(
r, 0.66), between sequences D and 9 (
r, 0.67),
and between sequences
E and 7 (
r, 0.67). No significant
correlation was observed for
A-reactive serum samples, consistent with
the heterogeneity of
responses for these samples. The matrix also
yielded negative
correlation coefficients, showing that reactivity to
some sequences
systematically excluded reactivity to others. These
results show
the value of SSEIA for serological discrimination. For
example,
reactivity to peptide 1 strongly correlated with reactivity to
peptides 2 (
r, 0.87), 3 (
r, 0.75), 5 (
r, 0.89), 6 (
r, 0.89), and
B (
r,
0.85) and with no or low reactivity to peptides 4 (
r,
0.45),
7 (
r,
0.55), 9 (
r,
0.37), C
(
r,
0.35), and E (
r,
0.33).
Cluster analysis.
Groupings among the 154 reactive samples
were identified by cluster analysis. Results are displayed as two
dendrograms corresponding to results obtained with SSEIAgen and
SSEIAphen (Fig. 2). This analysis
demonstrated a correlation between the two classifications and
identified five serological clusters, two of which overlap. Cluster 1 contains samples from France and the West Indies reactive with peptides
B, 2, and 5. Cluster 2 contains African samples reactive with peptides
D and 9. Cluster 3 contains African samples reactive mostly with
peptides A, 2, and 5. Clusters 4 and 5, which overlap, contain samples
mainly from Africa reactive mostly with peptides C and 4 and samples
mainly from Thailand reactive with peptides E and 7, respectively. The
overlapping area contains samples with high cross-reactivity with
peptides C and E and/or peptides 4 and 7. Cluster 4 also contains
A-reactive samples reactive mostly with peptide 4.
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FIG. 2.
Result of the cluster analysis. Each sample, represented
by a symbol, is located at the intersection between the two
dendrograms. The symbols correspond to the dominant reactivities in
both assays. Clusters correspond to samples with similar dominant
reactivities in both classifications, with only a few exceptions.
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|
Amino acids responsible for subtype-specific serological
identification.
We compared the amino acid sequences of the 19 peptides used in the study. The comparison was based on both
antigenicity in SSEIA, i.e., positive or negative correlation as
described above, and biochemical properties, i.e., conservative or
nonconservative substitutions. We identified three major groups of
independent (antigenically unrelated) reactive sequences from the SSEIA
data (Table 5). The first comprised peptides B, 2, and 5. The second comprised peptides A, C, E, 4, and 7. The third comprised peptides D
and 9. The major difference between the first group and the others is
the presence at the tip of the loop of an arginine residue at position
322 in the first group. This arginine is replaced by glutamine in the
others. The major difference between the second and the third groups
occurs at position 324, where there is a leucine residue in peptides D
and 9 but a more hydrophobic residue, phenylalanine, in peptides A, C,
E, 4, and 7 (5). There is another, smaller difference
between the second group and the two others at position 315. A
histidine residue in the first and third groups is replaced by an
arginine or a threonine residue in the second group, except in the
consensus sequence of subtype A, which conserves this histidine.
| |
DISCUSSION |
Studies on HIV-1 diversity led to the identification of five major
genetic subtypes within group M and at least 14 phenetically associated
groups (22, 36). The analysis of genetic relationships between isolates is complicated, as many isolates are mosaic viruses (15, 19, 44). Serotyping of HIV-1 infections based on V3 seroreactivity would be of great value in the study of HIV-1 diversity and would provide important information for both epidemiology and
future vaccine composition. This work studied the antigenic relationships between consensus sequences defined by two different approaches. The aim was to investigate the extent of the V3 serotypes and to identify, at least in part, the molecular determinants of the
serotype-associated antigenic specificity. Our experimental data
confirmed most of the associations between the genetic subtypes and
phenetic groups that were predicted by theoretical analysis (22). However, our data showed that only a limited number of V3 serotypes can be identified due to the immunodominance of a few
sequences. Korber et al. suggested that genetic subtype B was
associated mainly with phenetic groups 1, 2, 3, 5, and 6 (22). Our results are consistent with these results but
showed that the consensus sequences of groups 2 and 5 are
immunodominant. Serological discrimination of these two sequences was
almost impossible due to a single synonymous substitution (glutamic
acid
aspartic acid at position 329; Table 1). We also found
associations between genetic subtype C and phenetic group 4, genetic
subtype D and phenetic group 9, and genetic subtype E and phenetic
group 7. However, our data also suggested that consensus sequence 7 is immunodominant for subtype E and that consensus sequence 9 is immunodominant for subtype D. The phenogram of HIV-1 V3 loop protein similarities indicated that for subtype A, the association between genetic subtype and phenetic group was weaker (22). Our
results are also consistent with this observation. Indeed, some subtype A-reactive samples bound to peptides containing the consensus sequences
of phenetic group 2 or 5, whereas others bound to peptides containing
the consensus sequences of phenetic group 4. This may be because most
isolates of subtype A have the sequence GPGQ at the tip of the V3 loop
but others possess the sequence GPGR. Genetic subtypes C and E appear
distantly related, but cross-reactivity between peptides containing
their V3 consensus sequences has been reported (4, 8, 40).
We also found cross-reactivity between subtypes C and E based on data
obtained with consensus sequences of phenetic groups 4 and 7.
V3 consensus sequences can be grouped into three to five major
antigenically unrelated categories on the basis of our statistical data, including correlation matrix and cluster analyses. A comparison of protein sequences in these categories identified residues at positions 322 to 324 that may be responsible for major antigenic differences (Table 5). This result is
consistent with previous data showing that human antibodies to the V3
region are mostly directed to the central area of V3 (51).
The crystal structure of a complex between a V3 peptide and the Fab
fragment of a neutralizing antibody, 59.1, showed that amino acids at
the these three positions are involved in Fab binding (16).
Table 5 shows the sequences responsible for serotype specificity and
the contribution of individual residues to antibody interactions
according to Ghiara et al. (16). Two-thirds of the hydrogen
bonds, salt bridges, and Van der Waals contacts between V3 and Fab
involve residues 322 to 324. Sherefa et al. used substitution peptide
analogs to show that the lack of cross-reactivity between subtype B and
subtype C peptides was due to an arginine-322glutamine-322
substitution (46). They also found that the phenylalanine at
position 324 was also essential for subtype specificity and showed that
an alanine-323threonine-323 substitution, which would not cause any
major structural changes in the peptide backbone, might explain the
cross-reactivities among peptides A, C, and 4. In another study,
Sherefa et al. compared V3 serotyping and genotyping by V3 sequencing
(47). They obtained results similar to ours. Amino acids
present upstream at positions 315 and 316, which are part of the core
of another V3 epitope recognized by human sera, might play a
complementary role (6, 17, 18, 43). This fact may explain
why some subtype A-reactive samples bound preferentially to peptide 1, 2 or 5, or 4 with various degrees of cross-reactivity. Peptides A, 1, 2, and 5 have a histidine residue at position 315, whereas peptide 4 has an arginine. Thus, the subtype-specific signature sequences are
located at the tip of the V3 loop and involve the octapeptide xIGPGxxx.
The immunodominance of some peptides within a "serogroup" may be
due to slight modifications within the flanking residues that affect
the conformation (Table 1).
Our study did not include samples from patients in whom the genotype of
the infecting strain had been identified. We believed that this
information was not relevant to the present analysis, as previous
studies have shown a strong correlation between SSEIAgen and
genotyping. In three independent studies involving 285 samples, the
specificities of SSEIAgen were between 0.96 and 0.98 for subtype A,
0.84 and 0.98 for subtype B, 0.74 and 0.81 for subtype C, and 0.99 and
1 for subtype D; the specificity was 1 for subtype E (6a,
48).
Multivariate analysis of HIV-1 neutralization data shows the existence
of a small number of neutralization clusters not correlated with the
genetic clades (39). The neutralization epitopes
responsible for this clustering have not been identified and may be
independent of the V3 region, but this previous study and our study,
addressing different questions and using different approaches, have
both suggested the existence of a limited number of serotypes despite the genetic diversity of HIV-1.
Surveys of the diversity and dynamics of HIV-1 strains are an important
challenge in the current AIDS pandemic and for the future. There are at
least two reasons for typing: (i) surveillance of diversity,
particularly its dynamics for epidemiological studies, and (ii)
understanding the biological significance of this diversity. Both are
essential for public health. Genotyping is probably the reference
method for epidemiological surveillance of HIV-1 diversity, but current
molecular methods cannot be used for large populations. Alternative
methods, such as serotyping with simple immunoassays, would be of great
value. Our study provides evidence for the existence of a small number
of V3 serotypes. The statistical analysis indicated strong positive and
negative correlations between the different sequences. This result
indicates the discriminative power and reliability of SSEIA. The
biological significance of HIV-1 diversity is unknown, but immunity in
vivo is probably the most important property. For many viruses, such as
poliovirus or influenza viruses, protection in vivo is associated with
seroneutralization in vitro. Immunity in vitro does not correlate with
protection in vivo for HIV-1, but a few authors have studied the
association between neutralization serotypes and genotypes. Moore et
al. clearly showed that genetic clades do not correlate with
neutralization serotypes, indicating that genotyping may not be the
ideal method for studying HIV-1 diversity for vaccine development
purposes (33). It is therefore still unclear what assays or
tools might be most adaptable for predicting the antigenic composition
of future vaccines. The biological relevance of the V3 serotypes is
unknown. However, it is unclear which methods are best suited, and all
approaches to studying diversity require further evaluation.
| |
ACKNOWLEDGMENTS |
This work was supported by funds from the Agence Nationale de
Recherche sur le SIDA (Paris, France) and the Institut Universitaire de
France.
We thank J. Hoebeke for helpful discussion of the manuscript and R. Chout, F. Denis, and V. Vithayasai for providing serum samples.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Laboratoire de
Virologie, EP CNRS 117, CHRU Bretonneau, 37044 Tours Cedex, France. Phone: (33) 2 47 47 80 58. Fax: (33) 2 47 47 36 10.
| |
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Copyright © 1998, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
