Diversity of Trends of Viremia and T-Cell Markers in Experimental Acute Feline Immunodeficiency Virus Infection

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, CD8βlowCD62Lneg T-cells, CD4/CD8 ratio, and percentage of CD8βlowCD62Lneg 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 dose-response 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) CD8βlowCD62Lneg 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 CD8βlowCD62Lneg T-cells might be an early predictor of progression toward disease. The same might be expected in humans but needs confirmation.


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][2][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][5][6]. In addition, the relationships between the viral load and the changes in various immunological and virological markers are still poorly known [7,8].
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 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. 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 qRT-PCR (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].

Ethics Statement
Viremia was expressed as log 10 of viral RNA copies per mL of plasma. The detection threshold was 80 copies per mL (1.9 on the log 10 -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 CD8b low CD62L neg cell count, and the percentage of CD8b low CD62L neg among CD8 cells (herein noted percent CD8b low /CD8). First, the CD4, CD8, and CD8b low CD62L neg cell counts were expressed in thousands of cells per mL of blood then these counts, the CD4/CD8 ratio, and the percent CD8b low /CD8 were transformed into changes as follows: log 10 of the value at the end of a given week minus log 10 of the value at baseline. This allowed underlining the changes along time and the overall trends.

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][20][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. 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 CD8b low CD62L neg , 3 for viremia, CD4 and CD8, and 4 for the percent CD8b low / CD8. 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.

Descriptions of the Trajectory-groups
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 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 CD8b low CD62L neg 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 CD8b low /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.

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 CD8b low /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 CD8b low /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 CD8b low /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 CD8b low CD62L neg 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 CD8b low /CD8. There were significant positive correlations between the following changes: i) CD4 and CD8 counts, ii) CD4 and CD8b low CD62L neg counts, iii) CD8 and CD8b low CD62L neg counts, iv) CD8b low CD62L neg count and the percent CD8b low /CD8. There were significant negative

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 CD8b low /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 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 log 10 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 log 10 (X w /X 0 ) where X 0 is the value at baseline and X w the value at week w (1 to 12).     another between the change in the percent CD8b low /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 CD8b low /CD8. The p values for the trend test between inoculum titre and changes in the counts of CD4, CD8, and CD8b low CD62L neg cells, and CD4/CD8 ratio were 0.19, 0.24, 0.3 and 0.84, respectively.

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 progression [25][26][27][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 CD8b low CD62L neg 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 CD8b low CD62L neg 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 CD8b low CD62L neg 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 CD8b low /CD8 cells showed different levels of CD8-cell activation. Over the twelveweek follow-up period, viremia and changes in percent CD8b low / CD8 cells were positively correlated. Importantly, viremia was correlated with the change of percent CD8b low /CD8 but not with the changes in CD8b low CD62L neg 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 naïve 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 CD8b low CD62L neg 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 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.  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 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 CD8b low /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 CD8b low CD62L neg 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 CD8b low CD62L neg 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.