Diversity of Trends of Viremia and T-Cell Markers in
Experimental Acute Feline Immunodeficiency Virus
Infection
Sylvain Roche1,2,3,4*, Hanane El Garch5, Sylvie Brunet5, Hervé Poulet5, Jean Iwaz1,2,3,4,
René Ecochard1,2,3,4, Philippe Vanhems2,3,6,7
1 Service de Biostatistique, Hospices Civils de Lyon, Lyon, France, 2 Université de Lyon, Lyon, France, 3 Université Lyon 1, Villeurbanne, France, 4 Equipe BiotatistiqueSanté, Centre National de la Recherche Scientifique-Unité Mixte de Recherche 5558, Villeurbanne, France, 5 Discovery Research, Merial S.A.S., Lyon, France, 6 Service
d’Hygiène, Epidémiologie et Prévention, Hospices Civils de Lyon, Lyon, France, 7 Equipe Epidémiologie et Santé Publique, Centre National de la Recherche ScientifiqueUnité Mixte de Recherche 5558, Villeurbanne, France
Abstract
Objective: The early events of human immunodeficiency virus infection seem critical for progression toward disease and
antiretroviral therapy initiation. We wanted to clarify some still unknown prognostic relationships between inoculum size
and changes in various immunological and virological markers. Feline immunodeficiency virus infection could be a helpful
model.
Methods: Viremia and T-cell markers (number of CD4, CD8, CD8blowCD62Lneg T-cells, CD4/CD8 ratio, and percentage of
CD8blowCD62Lneg cells among CD8 T-cells) were measured over 12 weeks in 102 cats infected with different feline
immunodeficiency virus strains and doses. Viremia and T-cell markers trajectory groups were determined and the doseresponse relationships between inoculum titres and trajectory groups investigated.
Results: Cats given the same inoculum showed different patterns of changes in viremia and T-cell markers. A statistically
significant positive dose-response relationship was observed between inoculum titre and i) viremia trajectory-groups
(r = 0.80, p,0.01), ii) CD8blowCD62Lneg cell-fraction trajectory-groups (r = 0.56, p,0.01). Significant correlations were also
found between viremia and the CD4/CD8 ratio and between seven out of ten T-cell markers.
Conclusions: In cats, the infectious dose determines early kinetics of viremia and initial CD8+ T-cell activation. An expansion
of the CD8blowCD62Lneg T-cells might be an early predictor of progression toward disease. The same might be expected in
humans but needs confirmation.
Citation: Roche S, El Garch H, Brunet S, Poulet H, Iwaz J, et al. (2013) Diversity of Trends of Viremia and T-Cell Markers in Experimental Acute Feline
Immunodeficiency Virus Infection. PLoS ONE 8(2): e56135. doi:10.1371/journal.pone.0056135
Editor: Wenzhe Ho, Temple University School of Medicine, United States of America
Received October 10, 2012; Accepted January 5, 2013; Published February 7, 2013
Copyright: ß 2013 Roche et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was financially supported by Merial (fr.merial.com), the Direction Générale de la Compétitivité de l’Industrie et des Services, Le Grand Lyon,
the Conseil Général de l’Isère, and the Conseil Général du Rhône. The financial coordination was handled by Lyon Biopôle (www.lyonbiopole.org), as the FIV-Vax
project. Researchers of Merial were responsible for the study design and data collection. The other funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have the following interests: HEG, SB and HP are employed by Merial, one of the funders of this study. There are no patents,
products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials,
as detailed online in the guide for authors.
* E-mail: sylvain.roche02@chu-lyon.fr
Because data on acute HIV infection are difficult to collect,
interesting results might come from animal models such as the
simian or the feline immunodeficiency virus (SIV or FIV) infection
[9,10]. Although the routes of infection, the primary receptors,
and the genomic structures of FIV and HIV are different, the
immunopathogenesis of FIV infection is similar to that of HIV
[11]. Precisely, FIV infection results in an overall decrease of
CD4+ T cells, an activation of CD4+CD25+ regulatory T cells, a
dysregulation of cytokines, and a general immune hyperactivation
leading to AIDS [12].
In various models, the inoculum size affected the outcome of
infection [13]. In one of the few studies of the impact of the HIV
viral load in the donor on the outcome of infection in the recipient
Introduction
Past observations have repeatedly suggested that early events in
acute human immunodeficiency virus (HIV) infection may be
critical for the outcome of infection and the progression toward
disease [1–3] but the present extensive use of highly-active
antiretroviral therapy might have decreased the impact of acute
HIV infection on the outcome. Yet, the pathogenesis of the early
phase still needs to be further explored to better understand its
impact on the outcome of infection [4–6]. In addition, the
relationships between the viral load and the changes in various
immunological and virological markers are still poorly known
[7,8].
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Viremia & T-Cell Markers in FIV Infection
Viremia and T-cell markers were measured at baseline (time 0,
before virus inoculation) and at the ends of weeks 1, 3, 4, 6, 9, and
12. As carried out earlier [17], viremia was measured by qRTPCR (quantitative reverse transcription polymerase chain reaction)
after checking the absence of mismatch between the sequence of
the amplified region of the virus and the sequence used to design
the PCR primers and probe. Also, the lymphocyte populations
were characterized by flow cytometry using the same set of
monoclonal antibodies [17].
Viremia was expressed as log10 of viral RNA copies per mL of
plasma. The detection threshold was 80 copies per mL (1.9 on the
log10-scales in Figure 1). Cats with undetectable viremia (i.e., 11
animals with a titre below 1.3) were kept in the present statistical
analysis.
The T-cell markers included the CD4 and CD8 cell counts, the
CD4/CD8 ratio, the CD8blowCD62Lneg cell count, and the
percentage of CD8blowCD62Lneg among CD8 cells (herein noted
percent CD8blow/CD8). First, the CD4, CD8, and
CD8blowCD62Lneg cell counts were expressed in thousands of
cells per mL of blood then these counts, the CD4/CD8 ratio, and
the percent CD8blow/CD8 were transformed into changes as
follows: log10 of the value at the end of a given week minus log10 of
the value at baseline. This allowed underlining the changes along
time and the overall trends.
partner, a correlation was found between donor and recipient viral
loads [14]. However, that correlation reflected rather the fitness
than the amount of virus and the study did not investigate the
outcome of infection. A previous study has shown that various
pathogenesis indicators were positively correlated with the FIV
inoculum titre [15]. This issue is relevant for HIV because the
semen HIV viral load is variable.
Because the inoculum titre may play a major role on the initial
viral burst and the subsequent immune responses and changes in
lymphocyte populations [16], a longitudinal study was carried out
to monitor the diversity of changes in viremia and various T-cell
markers during experimental acute FIV infection according to the
inoculum titre. Besides, because usual statistical approaches failed
to show patterns of changes over time, the heterogeneity of trends
was dealt with using a group-based trajectory modelling. The
relevance of the FIV model to HIV infection is discussed.
Materials and Methods
Ethics Statement
All animal experiments were conducted in accordance with the
European Community regulations (Directive 2003/65/EC of the
European Parliament and of the Council of 22 July 2003
amending Council Directive 86/609/EEC on the approximation
of laws, regulations and administrative provisions of the Member
States regarding the protection of animals used for experimental
and other scientific purposes) and all procedures were supervised
and approved by Merial Ethical Committee. This Committee,
officially accredited by the French Ministry of Research and
Education, examines the ethical issues relative to experiments on
animals and checks the implementation of the European
Community regulations.
Statistical Analysis
The plots of changes in viremia and T-cell markers values
versus time in cats having had different inoculums showed a high
heterogeneity between animals. Usual statistical approaches such
as mixed models have shown the mean trends in viremia and Tcell markers [18] but were unable to depict various patterns over
time because they assume that a single set of parameters can depict
changes over time. However, when a single trajectory shape
cannot fit the sample or the distribution of the outcome is
unknown, a group-based trajectory modelling can estimate sets of
parameters that define shapes of trajectories and probabilities of
trajectory-group membership. Thus, individual differences in
trajectories can be summarized by a small number of polynomial
functions of time, each of which corresponding to a trajectorygroup; i.e., individuals that follow approximately the same
variation. Trajectory-groups can be thought of as fictional
categories approximating the unknown population distribution.
Models with various numbers of trajectory-groups were fitted
for each variable. Thus, the finally retained number was
determined statistically. The optimal number of trajectory-groups
was determined according to the highest probability of ‘‘correct
model’’ given by a formula that uses a Bayesian Information
Criterion (BIC) [19–21], except the optimal number of CD4/CD8
ratio trajectory-groups that was determined by an empirical
method (the BIC for that ratio was mathematically inadequate).
The trajectory-groups with their confidence intervals were
graphed for viremia and each T-cell marker and the percentages
of the whole cat population in each trajectory calculated. The
presence of a detection threshold for viremia led us to analyse
viremia values as left-censored.
The dose-response relationships between inoculum titre and
trajectory-groups were assessed by Mantel-Haenszel trend tests.
The relationships between inoculum titre, trajectory-groups of
viremia, and trajectory-groups of T-cell markers were assessed
using polychoric correlations.
Trajectory-group models were sought using SAS PROC TRAJ
[22]. All statistical analyses used SAS software, version 9.1.3. All
tests were two-tailed and p,0.05 was considered for statistical
significance.
Study Setting and Data Collection
The dataset originated from longitudinal experiments on cats
and pooled from various FIV vaccine calibration protocols. The
dataset was also used in another study by Ribba et al.’’ (Ribba
et al. (2012) Computational and Mathematical Methods in
Medicine. Article ID 342602, 9 pages. doi:10.1155/2012/
342602).’’ All animals were specific-pathogen free kittens purchased from Charles River laboratories (Lentilly, France). All the
cats used in the study had the same genetic background.
At baseline, 102 cats (49 males and 53 females; mean age: 22.8
weeks, SD: 7.7, range: 13–36.5) were randomized to different
inoculum groups within parentage and sex strata and infected for
the first time with a single 1 mL of viral suspension (Petaluma clade
A, Glasgow-8 clade A, or EVA clade B) via intra-muscular route
(lumbar area). The virus stocks derived from plasma of infected
cats. Inoculums were primary isolates from plasma amplified by a
single passage on Mya-1 cells. No negative controls were included.
Preliminary in vivo experiments have shown that the three
strains had comparable virulence in terms of viremia and impact
on lymphocyte sub-populations. Virus dilutions ranged from 1/
90,000 to 1/3 and the titres, calculated on Mya-1 T cells and
expressed in log10/mL of Cell Culture Infectious Dose 50%
(CCID50), ranged from 0.25 to 4.2 (Table 1). Three groups of cats
were obtained with the quartiles of the distribution of the
inoculum titre among the 102 cats: the first (1.25 log10/mL of
CCID50) and the third quartile (3.6 log10/mL of CCID50) led to
the following groups: ]0;1.25] (39 cats), ]1.25;3.6[(36 cats), and
[3.6;4.2] (27 cats). Cats with titre 3.6 were included in the third
group to ensure a sufficient number of cats in this group (Table 1).
The cats were clinically examined weekly and bled under general
anaesthesia.
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Viremia & T-Cell Markers in FIV Infection
Table 1. Number of infected cats according to the virus strain and the inoculum titre.
Titre (log10/mL of CCID50)
0.25
EVA clade B
0.7
1.25
5
1.3
1.7
5
2.7
4
6
6
3.2
3.6
3.7
4
5
5
4.2
11
Glasgow-8 clade A
Petaluma clade A
2.6
22
12
12
5
doi:10.1371/journal.pone.0056135.t001
gathered only 6.4% of the cat population. The two other
trajectories involved similar percentages of cats and decreased
moderately. The three trajectories were significantly distinct after
week 7 (Figure 1, Panel D).
Concerning the change in CD8blowCD62Lneg cell counts, the
two trajectories showed increases starting from week 3 but at very
different levels (see confidence intervals, Figure 1, Panel E).
Concerning the change in the percent CD8blow/CD8, three
of four trajectories were perfectly distinct and progressed
parallelly. Thus, there might be an association between the
variation during the first week post-infection and that seen
afterwards. Trajectory 1 gathered 14.5% of cat population and
showed a slight decrease then a steep increase (Figure 1, Panel
F).
In this approach, no predictor for trajectory-group membership was previously specified. More specifically, age and sex
were not used to construct the trajectory groups of viremia or
T-cell markers. These variables, as shown in Table 4, show little
differences related to age or sex between trajectory groups.
Results
Determination of the Optimal Number of Trajectorygroups
Table 2 shows the BIC values and the derived probabilities of
‘‘correct model’’ for each variable and various numbers of
trajectory-groups. The highest probability indicates the optimal
number of trajectory-groups to consider: 2 for CD8blowCD62Lneg,
3 for viremia, CD4 and CD8, and 4 for the percent CD8blow/
CD8.
Descriptions of the Trajectory-groups
Table 3 shows the optimal number of trajectory-groups per
variable, describes the typical changes during the first week and
the rest of follow-up, and gives the percentages of the whole cat
population in each trajectory-group.
Figure 1 shows six panels, one per variable. Trajectory-group
names and orders are independent between panels because these
trajectory-groups are not permanent categories that include the
same cats. For example, cats that follow Trajectory 3 for viremia
may be different from those that follow Trajectory 3 for the
change in CD4 cell count.
Panel A of Figure 1 shows the three trajectory-groups for
viremia. Trajectory 3 showed a steep increase after FIV
infection. It represented 45.6% of the cat population. Trajectories 1 and 2 (19.1% and 35.3% of cat population, respectively)
could not show raises in viremia before one- or three-week
delay, respectively, because these periods were concealed by the
use of value 1.9 as detection threshold. The overlapping of the
confidence intervals late during follow-up and for relatively
short periods indicates that the three trajectories were welldifferentiated. Significantly distinct between week 3 and week 8,
the trajectories for viremia ended at approximately the same
level at week 12.
The three trajectories of the change in CD4 cell counts showed
different rates of increase during the first week post-infection
(Figure 1, Panel B). Afterwards, only Trajectory 3 (11.6% of cats)
remained stable while Trajectories 1 and 2 (32.0% and 56.4% of
cats) showed transient declines. These trajectories were significantly distinct between weeks 3 and 6, but only Trajectories 2 and
3 remained significantly distinct thereafter. This suggests an
association between the changes during the first week and those
seen during the following weeks.
Concerning the change in CD8 cell counts, Trajectory 2
represented 43.7% of the cats. Trajectories 2 and 3 remained
clearly distinct throughout the follow-up. Trajectory 1 showed a
very steep decrease after week 1 followed by a sharp increase.
Trajectories 1 and 3 were not significantly distinct after week 9
(Figure 1, Panel C).
Concerning the change in the CD4/CD8 ratio, all three
trajectories decreased at different rates after week 3. Trajectory 1
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Mean Trend and Heterogeneity of Trends
The trajectory-groups show that a mean trend does not describe
well biological phenomena such as viremia and changes in T-cell
markers. For example, for CD4 (Figure 1, Panel B), all cats had an
increase during the first week after infection. Afterwards, Groups 1
and 2 cats had a temporary decrease while Group 3 cats kept
stable values.
A joint reading of Table 5 and Figure 1 illustrates the diversity
of trends regarding a given marker among a trajectory-group of
another marker. For example, in the low-viremia group (Group 1,
Panel A), the percent CD8blow/CD8 could show either a constant
slight increase (Group 2, Panel F) or an irregular progress (Group
1, Panel F). However, in the early-increase viremia groups (Group
2 or 3, Panel A), the percent CD8blow/CD8 only increased
thought at different levels (Groups 2, 3 and 4, Panel F). Cats whose
CD4 did not decrease (Group 3, Panel B) had rather low viremias
(Group 1, Panel A) and also low percent CD8blow/CD8 (Groups 1
and 2, Panel F).
Relationships between Variations of Viremia and T-cell
Markers
As shown in Table 6, there were no significant correlations
between variations in viremia and changes in CD4, CD8, and
CD8blowCD62Lneg cell counts. A significant negative correlation
was observed between viremia and the change in the CD4/CD8
ratio, as well as a significant positive correlation between viremia
and the change in the percent of CD8blow/CD8. There were
significant positive correlations between the following changes: i)
CD4 and CD8 counts, ii) CD4 and CD8blowCD62Lneg counts, iii)
CD8 and CD8blowCD62Lneg counts, iv) CD8blowCD62Lneg count
and the percent CD8blow/CD8. There were significant negative
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Viremia & T-Cell Markers in FIV Infection
Figure 1. Trajectory-groups. Trajectory-groups (with 95% confidence intervals) for viremia and changes in T-cell markers over time since
contamination (in weeks). Viremia is expressed in log10 of viral RNA copies per mL of plasma. The hatched area indicates the area below the virus
detection threshold (1.9). Each T cell marker is expressed as the predicted value of log10(Xw/X0) where X0 is the value at baseline and Xw the value at
week w (1 to 12).
doi:10.1371/journal.pone.0056135.g001
correlations between the following changes: i) CD8 count and the
percent CD8blow/CD8; ii) the CD4/CD8 ratio and
CD8blowCD62Lneg cell count; iii) the CD4/CD8 ratio and the
percent CD8blow/CD8.
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Dose-response Relationship between Inoculums and
Trajectory-groups
A statistically significant dose-response relationship was found
between inoculum titre and viremia as well as between inoculum
titre and the change in the percent CD8blow/CD8 (p,0.0001).
Table 6 shows that there was a significant positive correlation
between viremia and inoculum titre (r = 0.80, p,0.01) and
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Table 2. Criteria for the choice of the optimal number of trajectory-groups.
Parameters and number of trajectories
Bayesian Information Criterion
Probability of ‘‘correct model’’
2 trajectories
2580.24
0.00
3 trajectories
2560.06
0.99
Viremia
CD4
2 trajectories
277.41
0.14
3 trajectories
275.98
0.60
4 trajectories
276.86
0.25
2 trajectories
2118.74
0.00
3 trajectories
2110.70
0.99
2 trajectories
2155.70
0.99
3 trajectories
2162.62
0.00
CD8
CD8blowCD62Lneg
Percent CD8blow/CD8
2 trajectories
258.09
0.00
3 trajectories
254.82
0.12
4 trajectories
252.81
0.88
doi:10.1371/journal.pone.0056135.t002
Table 3. Aspect of the trajectories of viremia and changes in T-cell markers.
Parameters and trajectories
Trajectory aspect
Proportion of cats
Week 1
Weeks 2 to 12
Flat
Protracted and moderate increase
Viremia
Trajectory 1
19.1%
Trajectory 2
Flat
Moderate increase
35.3%
Trajectory 3
Steep increase
Steep increase then stability
45.6%
CD4
Trajectory 1
Slight increase
Very steep decrease then sharp increase
32.0%
Trajectory 2
Moderate increase
Slight decline
56.4%
Trajectory 3
Steep increase
Flat
11.6%
Trajectory 1
Very steep increase
Very steep decrease then sharp increase
28.1%
Trajectory 2
Slight increase
Slight decrease
43.7%
Trajectory 3
Moderate increase
Slight decrease then slight increase
28.3%
Trajectory 1
Very steep decrease
Very steep increase then steep decrease
6.4%
Trajectory 2
Slight increase
Moderate decrease
42.6%
Trajectory 3
Flat
Slight decrease
51.0%
Trajectory 1
Moderate increase
Moderate decrease then moderate increase
57.5%
Trajectory 2
Steep increase
Moderate increase
42.5%
14.5%
CD8
CD4/CD8 ratio
CD8blowCD62Lneg
Percent CD8blow/CD8
Trajectory 1
Slight decrease
Slight decrease then steep increase
Trajectory 2
Slight increase
Slight increase
43.0%
Trajectory 3
Moderate increase
Slight increase
28.1%
Trajectory 4
Very steep increase
Slight increase
14.5%
doi:10.1371/journal.pone.0056135.t003
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Viremia & T-Cell Markers in FIV Infection
Table 4. Sex and age of cats by trajectory group for viremia and changes in T-cell markers.
Number of
cats
Parameters and trajectories
Age in weeks
(mean±SD)
Sex
Viremia
Trajectory 1
20
12 females (60%)/8 males (40%)
24.267.3
Trajectory 2
36
19 females (52.78%)/17 males (47.22%)
26.266.7
Trajectory 3
46
22 females (47.83%)/24 males (52.17%)
19.667.5
CD4
Trajectory 1
26
16 females (61.54%)/10 males (38.46%)
19.066.2
Trajectory 2
67
33 females (49.25%)/34 males (50.75%)
24.168.0
Trajectory 3
9
4 females (44.44%)/5 males (55.56%)
24.166.2
CD8
Trajectory 1
24
13 females (54.17%)/11 males (45.83%)
16.863.8
Trajectory 2
57
28 females (49.12%)/29 males (50.88%)
25.667.6
Trajectory 3
21
12 females (57.14%)/9 males (42.86%)
22.467.6
CD4/CD8 ratio
Trajectory 1
5
3 females (60.00%)/2 males (40.00%)
17.369.1
Trajectory 2
32
19 females (59.38%)/13 males (40.63%)
20.367.9
65
31 females (47.69%)/34 males (52.31%)
24.567.1
Trajectory 3
CD8b
low
CD62L
neg
Trajectory 1
69
34 females (49.28%)/35 males (50.72%)
24.067.4
Trajectory 2
33
19 females (57.58%)/14 males (42.42%)
20.468.0
Percent CD8blow/CD8
Trajectory 1
12
6 females (50.00%)/6 males (50.00%)
22.365.3
Trajectory 2
57
30 females (52.63%)/27 males (47.37%)
25.666.9
Trajectory 3
21
9 females (42.86%)/12 males (57.14%)
16.366.8
Trajectory 4
12
8 females (66.67%)/4 males (33.33%)
21.868.5
doi:10.1371/journal.pone.0056135.t004
Table 5. Cross-tabulation of trajectory-groups according to viremia and changes in T-cell markers during the 12-week period after
FIV inoculation.
Parameters and
trajectories
Viremia
CD4
1
2
3
5a
8
13
CD8
1
2
3
1
2
3
CD4
1
2
10
25
32
3
5
3
1
CD8
1
5
7
12
19
5
0
2
8
21
28
7
49
1
3
7
8
6
0
13
8
Percent CD8blow/CD8
1
7
5
0
0
6
6
0
5
7
2
13
27
17
16
39
2
12
37
8
3
0
0
21
10
11
0
10
7
4
4
0
4
8
0
11
1
2
8
2
a
The content of each cell is the number of cats among the 102 cats of the study.
doi:10.1371/journal.pone.0056135.t005
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Viremia & T-Cell Markers in FIV Infection
Table 6. Polychoric correlation coefficients between groups of inoculum and trajectory-groups of viremia and changes in T-cell
markers during the 12-week period after virus inoculation.
Parameters
Inoculum
Viremia
Inoculum
1
Viremia
0.80{
1
CD4
20.19
20.23
CD8
20.16
20.18
CD4/CD8 ratio
20.02
20.32
CD8blowCD62Lneg
0.14
0.19
Percent CD8b
low
/CD8
0.56
{
0.70
{
CD4
CD8
CD4/CD8 ratio
CD8blow
CD62Lneg
Percent
CD8blow/CD8
1
*
0.88{
1
0.08
20.20
0.54{
0.51{
20.31
20.30
1
*
20.63{
1
20.34{
0.49{
1
Significance:
{
p,0.01,
*p,0.05,
Polychoric correlations are not correlations between quantitative variables but correlations between ordinal variables; here, the trajectory-groups. For example, CD4 and
CD8 are viewed as ordinal variables, each with three categories. Using the cross-tabulation of CD8 by CD4 given in Table 4, a polychoric correlation of 0.88 means that
cats that belong to group 3 for CD4 have a higher probability of belonging also to group 3 for CD8 than to the two other groups. Conversely, 0.08 means that cats that
belong to group 3 for CD4 have a low probability of belonging to group 3 for CD4/CD8 ratio and could belong to any of the three groups for the CD4/CD8 ratio.
doi:10.1371/journal.pone.0056135.t006
another between the change in the percent CD8blow/CD8 and
inoculum titre (r = 0.56, p,0.01). The higher was the inoculum
titre, the steeper was the increase of viremia and the change in the
percent CD8blow/CD8. The p values for the trend test between
inoculum titre and changes in the counts of CD4, CD8, and
CD8blowCD62Lneg cells, and CD4/CD8 ratio were 0.19, 0.24, 0.3
and 0.84, respectively.
progression [25–28]. The convergence of the trajectories toward
the same viremia set-point may be explained by the homogeneity
of our FIV model in terms of animal genetic background and FIV
strain virulence.
The inoculum viral load determined the expansion of
CD8blowCD62Lneg cells, the circulating effector T cells with
antiviral activity. This expansion is a hallmark of HIV and FIV
infections [29,30,31]. Those activated CD8 T cells have a strong
antiviral activity [17,31] but are prone to apoptosis [17,32]. The
maintenance of a high percentage of activated CD8 cells is a
consequence of a hyperactivation of the immune system associated
with the viral burden in FIV infected cats. The sustained
expansion of the CD8blowCD62Lneg cell population is specific to
FIV and is not observed in other feline retroviral infections such as
infection by the feline leukaemia virus. At the peak of viremia, the
expansion of CD8blowCD62Lneg cells was the only parameter
correlated with the infectious titre of the inoculum. Interestingly,
whereas the trajectories for viremia became indistinguishable at
week 12, the trajectories of the percent CD8blow/CD8 cells
showed different levels of CD8-cell activation. Over the twelveweek follow-up period, viremia and changes in percent CD8blow/
CD8 cells were positively correlated. Importantly, viremia was
correlated with the change of percent CD8blow/CD8 but not with
the changes in CD8blowCD62Lneg or CD8 cell counts. In this
study, we measured the rate of activation of the CD8 cell
population but did not characterize the phenotypes or differentiation statuses of all CD8 subpopulations. In early infection, the
proportion of some of those subpopulations, like naı̈ve CD8+ T
cells, may be predictive of HIV disease progression [33]. The
balance between activated CD8 cells and other CD8-cell subsets
such as central memory T cells might be more critical than the
count of activated CD8 cells.
The correlations between trajectory-groups in the present study
showed that viremia and changes in percentage of
CD8blowCD62Lneg cells were associated with a lower CD4/CD8
ratio, another feature of FIV-induced immune dysregulation. We
have also found a negative correlation (though non-significant)
between changes in viremia and changes in the CD4+ T-cell
counts. A negative correlation between viral load in plasma and
CD4+ T-cell counts has been observed in early HIV infection
[34]. In the simian HIV model, a correlation was found between
Discussion
The follow-up of cohorts of HIV infected individuals suggested
that high viral load and CD4 cell loss at seroconversion are early
markers of disease progression [23]. The impact of the initial burst
of HIV replication on the outcome of infection and the
progression toward disease remain poorly understood, mainly
because data collection on HIV patients during acute infection is
difficult. Animal models may therefore help better characterize
that early phase. Indeed, FIV and HIV infections have a similar
immunopathogenesis and comparable viral load kinetics; thus,
FIV is a natural model of HIV [6]. Here, we analyze the early
virological and immunological data from a cohort of cats after
experimental FIV infection.
To our knowledge, this is one of the first applications of the
trajectory-group approach in the field of FIV or HIV infection.
Thus, it should be stressed beforehand ‘‘that individuals do not
actually belong to a trajectory-group, that the number of
trajectory-groups in a sample is not immutable, and that
individuals do not follow the group-level trajectory in lock step’’
[24]. In other words, the trajectories that model marker changes
along time should not be considered as a classification but the
major patterns of change. This approach was of great help in
disentangling the observed heterogeneity and offering debatable
biological trends.
In the present study, the groups of viremia over three months
were positively correlated with the groups of FIV inoculum titre.
This titre affected the magnitude and the timing of viremia peak,
two comparable characteristics between FIV and HIV infections.
All viremia trajectories ended with similar values at week 12 postinfection suggesting that the viremia set-point values were
comparable whatever the inoculum. In HIV, the viremia setpoint has been already proposed as a predictive marker of disease
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Viremia & T-Cell Markers in FIV Infection
the peak viral load and the extent of CD4 T-cell depletion in acute
infection [35].
One asset of this study is the experimental design with
randomized groups of cats, which adjusts for differences between
cats. Besides, the present results are consistent with observations in
a cohort of HIV patients [34]. These indicate that the early CD8
T-cell activation (as supported by the percent CD8blow/CD8) was
correlated with the initial viremia and that the immunologic
activation set-point was established in the early phase of infection.
In HIV patients, the progress of CD8 T-cell activation and viremia
were associated with decline of CD4 cell counts. Interestingly,
CD8-cell activation was also positively correlated with the number
of symptoms observed during the acute phase of infection [34].
Although clinical signs in acute FIV infection are difficult to
observe (transient hyperthermia, lymphadenopathy, dehydration
diarrhoea, gingivitis), there is a correlation between their intensity
and early viremia (data not shown), which is consistent with
another observation [15]. A positive relationship between clinical
signs in acute infection, viral load, and T-cell activation has also
been observed in acute HIV infection [34]. In addition, clinical
observations of HIV infected patients have shown relationships
between the intensity of the acute phase of infection or the delay to
appearance of acute signs and the progression toward disease
[2,36]. Overall, those independent observations suggest that the
intensity of the initial viral burst and the extent of early T-cell
activation might have an impact on the progression toward
disease.
Our study has shown that the inoculum titre determined the
initial kinetics of viremia as well as the expansion of the
CD8blowCD62Lneg cell population. FIV-specific T-cell responses
were also related to the inoculum titre (data not shown). On week
12, most cats had similar viremias, but displayed different
lymphocyte population profiles. FIV strain virulence may play
an important role; however, here, the cats were infected with
strains of similar virulence to avoid a bias in the analysis of the
impact of the inoculum titre. Since our FIV strains have not been
cloned, it might be argued that the composition of the quasispecies
administered depends on the inoculum titre. In fact, the virus
stocks have been amplified in cats; it is therefore unlikely that the
quasispecies contain a subdominant variant with high virulence.
The infection dose is critical in models that test HIV or FIV
vaccine candidates [16,37]. Our study focused on the impact of
the inoculum titre on the kinetics of viremia and some T-cell
populations, but the infection dose may also be critical in
determining the magnitude of the immune response [13].
Conclusions
The virological and immunological group-specific trends in
more than a hundred FIV-infected cats showed that the inoculum
titre determined the early kinetics of viremia and initial activation
of CD8 cells; thus, the outcome of infection. Despite similar
viremia three months after infection, the animals displayed groupspecific trends and three different levels of CD8-cell activation.
The early expansion of the CD8blow CD62Lneg T-cell population
might be an early predictive marker of disease progression in FIV
infected cats. Because several features of FIV infection parallel
those reported in HIV infection, the latter expansion might also
happen in humans. Thus, investigations focussed on this twelveweek period might provide an appropriate confirmation and be of
real help in detecting early HIV infection and starting early
therapy.
Acknowledgments
The authors wish to thank Dr Maan Zrein for his helpful comments.
Author Contributions
Conceived and designed the experiments: HP. Performed the experiments:
HEG SB. Analyzed the data: SR RE. Wrote the paper: SR HP JI RE PV.
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