Infections by human and simian immunodeficiency viruses (HIV and SIV) trigger in humans and macaques, respectively, immune suppression characterized by immunologic abnormalities, including CD4+ T-cell loss,1,2 impairment of helper cell (Th) function3,4 possibly resulting in defective immunoglobulin (Ig) class switching,5-7 reduced T-cell proliferative response to soluble antigens and mitogens,8 aberrant B-cell hyperreactivity,3,4,9-17 and an overall pathologic activation of the immune system.8,18 The HIV-induced immune deregulations of B cells translate into abnormalities in antibody responses including hypergammaglobulinemia,3,19,20 perturbation of B-cell function,21,22 or selective reduction in the number of memory B cells.23-26
In correlating virus-specific immunoglobulin levels in infected patients with hematologic parameters (including percentage of CD4+ T cells, absolute CD4+ T-cell count, ratio CD4/CD8) or virologic parameters, previous studies have focused primarily on measurements of immunoglobulin G (IgG) levels. The frequency of cells secreting gp160-specific and Gag p24-specific IgGs did not correlate with CD4 T-cell parameters in one study.27 Analysis of the subclass IgG1 p24-, Env-, and Pol-specific antibodies showed an association between the level of these virus-specific IgG1s and viral load.28,29
In the present study, we used the SIVmac251 virus/cynomolgus macaque model to analyze the virus-specific primary (IgM) and secondary (IgA and IgG) immune responses to SIV antigens Gag, Nef, Rev, and Tat. The focus of our work was to determine how these antigen-specific immunoglobulins correlate with hematologic and virologic parameters in SIV infection. Indeed, in view of the importance of T-helper functions in antibody class switching, the level of virus-specific IgMs, in addition to other isotypes, may have prognostic value. We compared the level of these antibodies with the hematologic and virologic parameters (percentage of CD4 and CD8 cells, absolute number of CD4 and CD8 cells as well as the ratio CD4/CD8, and the viral load) measured at different timepoints throughout the course of the study: preinfection, acute phase, chronic phase, antiretroviral therapy (ART) period, and after release of ART.
Animals, Virus Challenge, and ART Therapy
The 20 cynomolgus macaques (Macacca fascicularis) used in this study were housed at Bioqual, Inc. (Rockville, MD), in accordance with all federal laws and guidelines as well as recommendations provided by the National Institutes of Health (NIH) guide and in compliance with American Association for Accreditation of Laboratory Animal Care standards. Before including the macaques in the study, the animals were tested for antibodies to SIV, simian retroviruses, and simian T-cell leukemia virus type 1. No reactivities were observed.
A total of 1000 50% monkey infectious doses of SIVmac251 virus (11/88 stock) (kindly provided by Dr. Neil Almond, Medical Research Council, England) were used to infect the 20 macaques by the intravenous route. Upon challenge, all animals became infected.
Twenty-four weeks after challenge, the animals were put on ART therapy for 33 weeks (from day 169-day 404) using 20 mg/kg/d of 9-R-(2-phosphonomethoxypropyl)adenine (PMPA) and 50 mg/kg/d of β-2′,3′-dideoxy-3′-thia-5-fluorocytidine (FTC). Both drugs were given by the subcutaneous route.
The procedure used to quantitate SIV RNA has been previously described.30-32 Dulbecco phosphate-buffered saline (PBS) (1 mL) was mixed with 500 μL of plasma and the resulting sample was spun for 1 hour at 10,000 rpm. RNASTAT-60 (Tel-Test, Inc., Friendswood, TX) was then used to lyse the viral pellet. The amplification of the viral sequences in the samples was performed as previously described31 but the primers and the probe were different. SIV-P (6 FAMA-GATTTGGATTAGCAGAAAGCCTGTTGGA-TAMRA) was used as the gag probe and SIV-F (5′ AGTATGGGCAGCAAATGAAT 3′) and SIV-R (5′ TTCTCTTCTGCGTGAATGC 3′) as the gag primers. The threshold sensitivity for this assay is 200 RNA copies/mL of plasma and the average interassay variations are 0.5 log10 unit.
CD4+ and CD8+ T-Cell Counts
A FACScalibur flow cytometer (Becton Dickinson, Franklin Lakes, NJ) was used to perform the CD3+CD4+ and CD3+CD8+ T-lymphocyte counts in peripheral blood in addition to fluorescein isothiocyanate-conjugated anti-CD3 antibody (Becton Dickinson), phycoerythrin-conjugated anti-CD4 (Becton Dickinson), and peridinin chlorophyll-conjugated anti-CD8 (Becton Dickinson). A whole-blood lysis procedure was employed as directed by the manufacturer.
ELISA for Detection of Anti-Gag, -Nef-, Rev, and -Tat IgAs, IgGs, and IgMs
The antigen-specific (Gag, Nef, Rev, and Tat) IgAs, IgGs, and IgMs were detected in sera of infected monkeys using a standard enzyme-linked immunosorbent assay (ELISA) assay procedure. Samples were run in duplicates and results are representative of several experiments. Briefly, 96-well Lockwell Maxisorp plates (Nalge/Nunc, Rochester, NY) were coated with recombinant SIV Gag, Nef, Rev, and Tat in 50 mM sodium bicarbonate (pH 9.2) at 0.25 μg/well (100 μL/well) and were incubated overnight at 4°C. Plates were blocked with 3% bovine serum albumin (BSA) (Sigma, St. Louis, MO) in 1 × PBS (250 μL/well) for 6 hours at 4°C and then washed 6 times with 1 × PBS containing 0.05% polyethylene glycol sorbitan monolaurate (Tween-20) (Sigma) by using an automated plate washer (Dynex Technologies, Chantilly, VA). Serum samples were analyzed at appropriate dilutions (1:1000 for detection of serum IgAs and IgMs; 1:1000, 1:5000, and 1:20000 for detection of serum IgGs). The sera were diluted using 1 × PBS 1% BSA (Sigma), 0.05% Tween-20 (Sigma), and were added on the plates at 100 μL/well as primary antibody. The plates were incubated overnight at 4°C on a plate shaker under slight agitation. After washing the plates 6 times with 1 × PBS containing 0.05% Tween-20 (Sigma) with an automated plate washer (Dynex Technologies), horseradish peroxidase (HRP)-conjugated protein G (Bio-Rad, Hercules, CA), HRP-conjugated immunopure goat antihuman IgM (Pierce Biotechnology, Rockford, IL), and HRP-conjugated immunopure goat antihuman IgA (Pierce Biotechnology) were used as secondary antibodies (100 μL/well) at a dilution of 1:1000 in 1 × PBS 1% BSA (Sigma), 0.05% Tween-20 (Sigma), to detect, respectively, the antigen-specific IgGs, IgMs, and IgAs. After 3 hours of incubation at room temperature and under slight agitation on an ELISA plate shaker and after washing the plates 6 times in 1 × PBS containing 0.05% Tween-20 (Sigma) with an automated plate washer (Dynex Technologies), the plates were developed with 200 μL/well of Sigma Fast O-phenylenediamine dihydrochloride (OPD) solution (Sigma) according to the manufacturer's instructions. For the detection of IgMs and IgAs, the revelation times were as follows: Gag 9 minutes, Nef 16 minutes, Rev 20 minutes, and Tat 9 minutes. For the detection of IgGs, the revelation times were as follows: Gag 6 minutes, Nef 9 minutes, Rev 15 minutes, and Tat 10 minutes. The development of the reaction was stopped with the addition of 50 μL/well of 3 N HCl. Absorbance measurements were made at 490 nm with an automated plate reader (Dynex Technologies). Cutoff for detection of positive values was 3 × the mean optical density (OD) of controls.
Nonparametric statistical procedures were used for all comparisons due to the small sample size (n = 20, n = 10 for certain correlations). We used the Wilcoxon matched-pairs test for dependent data to compare the level of one immunoglobulin at different timepoints with a significance level set at α = 0.05. The correlations were evaluated using the Spearman rank method with a level of significance set at α = 0.05. Additionally, we performed linear regression analyses to confirm the results of the correlations. All the P values are 2-tailed and were calculated by exact means.
Antibody Responses Against SIVmac251 Antigens
We have analyzed the IgA, IgM, and IgG isotype antibody responses of 20 cynomolgus macaques following infection by SIVmac251. This study is part of a larger study of therapeutic vaccination for which the macaques were treated with ART therapy (PMPA and FTC for 33 weeks beginning day 169 to day 404 postinfection) and immunized 4 times during the ART treatment. No differences were found between the immunized groups and the control group for any of the hematologic and virologic parameters after release of ART. Therefore, the hematologic and virologic parameters were considered here for the whole group of 20 monkeys without restrictions. IgA, IgM, and IgG antibody responses were considered in this study only during the natural course of SIV infection and during the early phase of ART treatment, prior to immunization. All groups, including controls, ultimately gained control of viremia after release from ART (manuscript in preparation). This result was somewhat surprising, because the animals were placed on highly active anti-retroviral therapy (ART) during the chronic phase of infection. Although SIVmac251 infection in cynomolgus macaques may not be a useful model to test therapeutic vaccines, the characterization of immune responses in these animals may be important for our understanding of the pathogenesis vs. control of SIV and HIV-1 infection.
The monkey sera were tested for the presence of anti-Gag-, anti-Nef-, anti-Rev-, and anti-Tat-specific IgA, IgM, and IgG. Figure 1 shows the median values for viral load (Fig. 1A) and the timeline of development of primary (IgM) and secondary (IgA and IgG) viral antigen-specific antibodies (Fig. 1B-E) during the first 200 days following infection.
Specific anti-Gag and anti-Nef IgA and IgG could be detected following infection (Fig. 1B and C): statistical significance was first obtained at day 28 compared with the levels at day 0 and was obtained for every timepoint tested during the first 200 days of infection, after analysis by Wilcoxon matched-pairs test (data not shown). Although the anti-Gag and anti-Nef IgA responses were weak, the levels increased gradually and significantly (data not shown) during the course of the study (Fig. 1B and C). The anti-Gag and anti-Nef IgG (Fig. 1B and C) secondary responses developed throughout the acute and the chronic phases of infection and reached a peak at day 141 for Gag and at days 112 and 141 for Nef. It is worth noting that the levels of anti-Gag and anti-Nef IgGs significantly decreased (P = 0.0001 for Gag and P = 0.0002 for Nef) between days 141 and 197 (Fig. 1B and C), during the initiation of ART therapy, most likely reflecting the decrease in viral load to undetectable median level (Fig. 1A).
Similarly, we observed SIV-specific primary IgM responses to Gag, Nef, Rev, and Tat. The median levels of the immunoglobulins directed against the 4 antigens were significantly (statistical data not shown) higher at day 28 than at day 0 (Fig. 1). At day 56, the same median levels were found significantly lower than at day 28 (except the anti-Tat IgM) but were not significantly different from the median levels at day 0. Similarly, none of the median levels of these antibodies at day 141 were significantly (statistical data not shown) different from the levels at day 0. Moreover, Figure 1 shows that the peak IgM responses against the 4 SIV antigens studied correspond to the peak in viral load.
Although significant differences between the median IgM levels at day 141 and day 0 were not observed, it was interesting that certain animals exhibited elevated specific IgM levels after the peak of viremia/IgM response. Considering that each monkey served as its own control, the persistence of high postpeak viremia IgM levels for some monkeys prompted us to analyze the variation of the levels of these immunoglobulins by subtracting the values obtained at day 0 from the values obtained at day 141 (Fig. 2). The fairly high level of detection of specific IgM at day 0 (day of infection) was not surprising because these immunoglobulins, involved in the primary immune response, are known to display low affinity and specificity for their targets. Low-affinity/low-specificity IgM circulating in the blood of the healthy, uninfected macaques could thus recognize the SIV antigens by ELISA. High background of recognition of p24 Gag by IgM was reported by others in seronegative individuals.27 The analysis of the net difference for each macaque in specific IgMs shown in Figure 2 revealed that, among the 20 macaques at day 141, some maintained higher levels of specific IgMs than at day 0 (Fig. 2). Each animal showed great consistency in its IgM response regarding the 4 different antigens. For example, of the 7 monkeys that had higher anti-Nef IgM level at day 141, 6 also displayed higher anti-Gag, anti-Rev, and anti-Tat IgM at day 141 compared with day 0 (Fig. 2).
Correlations Between Lymphocyte Subsets and Viral Load
To validate our analysis methods in this model, we investigated the relationship between hematologic and virologic markers: the traditional markers of disease progression. We performed correlation studies involving the percentage of CD4+ T cells, the absolute CD4+ T-cell count, and the viral load (Fig. 3C-F). As observed in other studies, the level of viral load at the time of the peak of acute infection (day 21) had little prognostic value for the levels of viral load (Fig. 3D), the percentage of CD4+ T cells (Fig. 3E), and the absolute CD4+ T-cell count (Fig. 3F) taken at later timepoints. In contrast, the levels of viral load obtained during the chronic phase (day 168) yielded positive correlations with the viral load (Fig. 3D) and negative correlations with the percentage of CD4+ T cells (Fig. 3E) and with the absolute CD4+ T-cell count (Fig. 3F). These results are in agreement with a previous study of SIV infection in rhesus macaques (Macaca mulatta).33
The early timepoints for percentage of CD4+ T cells (including day 57, during the postacute phase of infection and day 168, during the chronic phase), also showed significant correlations with the percentage of CD4+ T cells observed at later timepoints (Fig. 3C). Interestingly, the percentage of CD4+ T cells measured 43 days before the infection (day −43) also yielded significant positive correlations with the percentage of CD4+ T cells observed at later timepoints (20 of 22 timepoints) (Fig. 3C). This result prompted us to test some other preinfection hematologic parameters for their prognostic value: the absolute CD4+ T-cell count and the ratio of CD4/CD8. Similarly, we found that the absolute CD4+ T-cell count measured the day of challenge (day 0) yielded strong positive and significant correlations with the same parameter measured during the study (for 18 of 22 timepoints; data not shown). The same was true for the ratio CD4/CD8: 20 of 22 timepoints (data not shown) measured during the study correlated strongly and positively with the same measurement at day −43 before infection.
Antigen-Specific Immunoglobulin Isotypes and Correlations With Lymphocyte Subsets and Viral Load
After characterizing the natural primary (IgM) and secondary immune responses (IgG and IgA) of the cynomolgus macaques infected with SIVmac251 and validating the traditional markers of disease progression in this model, we evaluated whether the levels of these different immunoglobulins would correlate with the hematologic and virologic parameters: percentage of CD4+ and CD8+ T cells, absolute CD4+ T-cell count, the CD4/CD8 ratio, and viral load.
The level of IgMs measured at day 141 directed toward Gag, Nef, and Rev but not Tat correlated significantly with the percentage of CD4+ T cells throughout the whole study (Fig. 4A-D). In fact, and as shown in Figure 4 by the Spearman ranks correlation analyses (left-side graphs for each panel), of the 22 timepoints of measurement of the percentage of CD4+ T cells, very few timepoints did not correlate significantly with IgM levels at day 141: just 1 for Gag, 4 for Nef, and 5 for Rev. Significant correlations were also observed with percentage of CD4+ T-cell timepoints anterior to day 141 but also following day 141 and, it is noteworthy, during ART and after the release from ART. Surprisingly, these specific IgM levels at day 141 also correlated with the percentage of CD4+ T cells before infection: at day −43 and day 0. As for anti-Tat IgM, the only timepoint of significant correlation came with the percentage of CD4+ T cells at day, 0 whereas at no other timepoint did the percentage of CD4+ T cells correlate significantly with the anti-Tat IgM level at day 141 (Fig. 4D). The same results were found when using linear regression as an analysis method: significant correlations were observed between the percentage of CD4+ T cells and the levels of IgM at day 141 for Gag, Nef, and Rev but not for Tat (Fig. 4A-D; right-side graphs for each panel).
In addition to correlations with serologic responses at day 141, we tested the levels of viral antigen-specific IgM at days 0, 28, and 56 for correlation with the same virologic and hematologic parameters. Few significant correlations were found between the percentage of CD4+ T cells and the levels of specific IgM at day 0 and day 28. Only 3 of 22 early timepoints (data not shown) showed inverse correlation between the percentage of CD4+ T cells and the anti-Rev IgM at day 0; only 2 showed correlation with the anti-Gag IgM at day 0 and none with the anti-Nef IgM at day 0 (data not shown). As shown in Figure 4 (left-side graphs), the specific IgMs at day 28 provided no correlations with the percentage of CD4+ T cells except, again, for 2 early timepoints of percentage of CD4+ T cells and the anti-Rev IgM. Antibody reactivities at day 0 presumably reflect preexisting natural or cross-reactive antibodies present prior to infection.
The significant and inverse correlations of antigen-specific IgM levels with the percentage of CD4+ T cells is clearly not observed (Fig. 4) at the peak of primary humoral response (day 28) but, on the contrary, is clearly associated with the levels of remaining specific IgM levels at the time of the peak of the secondary (IgG and IgA) specific humoral responses (day 141). As might be expected, the analysis of the correlations obtained with the specific IgM levels at day 56 and the percentage of CD4+ T cells gave an intermediate result: some significant correlations were observed for Gag and Nef but they were weaker than at day 141 (data not shown) and they were true for fewer timepoints of measurement of CD4+ T cells (13 of 22 for Gag; 12 of 22 for Nef but only 2 of 22 for Rev).
We then decided to test the influence of the viral load on the correlations obtained between the levels of antigen-specific IgMs and the percentage of CD4+ T cells. When the 20 animals were stratified into high- or low-viral-load groups, only the group of monkeys with the higher viral loads showed significant correlations between the percentage of CD4+ T cells and anti-Gag (Fig. 5A), anti-Nef (Fig. 5B), and anti-Rev (Fig. 5C) IgM at day 141 but not for the anti-Tat (Fig. 5D). We then wondered whether we could detect, for the whole group of 20 monkeys, direct correlations between these antigen-specific IgMs at day 141 and the viral load throughout the study (Fig. 5E). Interestingly, we found some positive and significant correlations with the anti-Gag, anti-Nef, and anti-Rev IgM but again not with the anti-Tat IgM (Fig. 5E). These correlations were found mainly during the period of rebound of viral load after the release of the ART therapy. These correlations were weaker than those obtained with the percentage of CD4+ T cells and were observed for fewer timepoints.
We also tested the correlations between the same specific IgM at day 141 with other hematologic parameters including absolute CD4+ T-cell count, CD4/CD8 ratio, and percentage of CD8+ T cells. We found that the level of specific IgMs at day 141 correlated significantly and inversely (data not shown) with absolute CD4+ T-cell counts and with the ratio CD4/CD8, albeit to a lesser extent and for fewer timepoints. The absolute CD4+ T-cell count parameter yielded weaker correlations than the 2 other parameters, the strongest correlations being obtained with the percentage of CD4+ T cells (data not shown). Also, the level of specific IgMs at day 141 did not correlate significantly with the percentage of CD8+ T cells (data not shown).
Finally, we extended our correlation analysis to the levels of specific anti-Gag and anti-Nef IgGs and IgAs. Correlations of IgG and IgA responses were weak and only a few were observed. Examples include negative correlation between the anti-Gag IgG measured at day 197 and the ratio CD4/CD8 obtained only for 4 nonconsecutive timepoints (out of 22); the positive correlation between the anti-Gag IgG measured at day 28 and the absolute CD4+ T-cell count obtained for only 6 nonconsecutive timepoints (out of 22); and the negative correlation between the anti-Nef IgA measured at day 141 and the percentage of CD4+ T cells obtained only for 3 nonconsecutive timepoints (out of 22). These correlations were limited in intensity and in time and there was no consistency between the antigens Gag and Nef. Therefore our study suggests that, in general, specific anti-Gag and anti-Nef IgG and IgA humoral responses do not correlate significantly with the CD4+ T-cell parameters during SIVmac251 infection of cynomolgus macaques. These results are in agreement with the work of Shirai et al.27
Our results suggest the importance of the preinfection values for CD4-related hematologic parameters including percentage of CD4+ T cells, absolute CD4+ T-cell count, and the ratio CD4/CD8 in cynomolgus macaques infected with the SIVmac251 virus. Indeed, the percentage of CD4+ T cells measured at day −43, the absolute CD4+ T-cell count measured at day 0, and the ratio CD4/CD8 measured at day −43 all yielded strong correlations with the following timepoints of the same parameters marking the evolution of infection. This was also true during the ART treatment period and after the release from ART. Our results suggest that the initial preinfection values for hematologic parameters are important prognostic indicators.
Two recent studies have used cryopreserved peripheral blood mononuclear cells to measure CD4 hematologic parameters before the known date of seroconversion in prospective cohort studies of homosexual men34 and injecting drug users.35 These studies showed that the immune status prior to HIV infection, as measured by CD4+ T-cell count, T-cell activation, and CD4+ T-cell receptor excision circle (TREC) content, has predictive value for HIV disease progression34 and for the decline of the CD4+ T cells.35 Although CD4+ T-cell counts prior to infection are rarely known in humans, studies with long-term HIV survivors have also suggested the importance of host factors, including immunogenetic36,37 and immunologic factors.38-41
In our study, only the viral load in the postacute phase of infection (and not during the acute peak) had prognostic value for subsequent CD4+ T-cell (percentage) data, a result similar to a study on SIV in rhesus macaques.33
To our knowledge, this study is the first to show that the specific anti-Gag, anti-Nef, and anti-Rev IgM postpeak levels of primary response correlate significantly, strongly, and inversely with the percentage of CD4+ T cells measured throughout the different phases of infection.
These antigen-specific IgM levels at day 141, and especially the anti-Gag and the anti-Nef antibodies, had strong prognostic value regarding the percentage of CD4+ T cells measured for all timepoints after day 141 and notably during ART therapy and after the release from ART. Indeed, the anti-Gag IgM at day 141 (Fig. 4A) had a better prognostic value for the percentage of CD4+ T cells than did the viral load at similar timepoints (Fig. 3E).
When we divided into 2 groups according to their viral load, only the group of monkeys with the higher viral load showed significant inverse correlations between the same antigen-specific IgM and the percentage of CD4+ T cells, a result that might suggest a potential threshold for viral load to influence the maintenance of antigen-specific IgM, possibly as a result of damage to the immune system. Furthermore, we could show some direct and positive correlations between the viral load and the post-primary peak anti-Gag, -Nef, and -Rev IgM levels, but the correlations were weaker than the one obtained with the percentage of CD4+ T cells and true for fewer timepoints, suggesting that the correlations observed may reflect the damage done by the virus on the host immune system (ie, Th cell defects) rather than the extent of viral replication itself.
Hypergammaglobulinemia and defective humoral immunity are hallmarks of HIV-induced deregulation of the Th and B-cell system. The hypergammaglobulinemia resulting from the pathologic hyperactivation of B cells has already been widely investigated and researchers have previously described the inverse correlation between the polyclonal B-cell activation and the CD4+ T-cell hematologic parameters (percentage of CD4+ T cells, CD4/CD8 ratio, or absolute CD4+ T-cell count).13,27,42,43 The extent of hypergammaglobulinemia has also been associated with faster disease progression.44-46 In our study of SIVmac251 infection of cynomolgus macaques, the correlations we observed between the CD4+ T-cell hematologic parameters and the post-acute-phase antigen-specific IgMs appear not to originate from polyclonal B-cell activation. Several lines of evidences support that notion. First, the correlations obtained with the antigen-specific IgMs are true only for 3 of 4 antigens tested: the anti-Tat-specific IgM response never yielded any significant correlations in any of the tests. Moreover, we could not demonstrate significant correlations between the antigen-specific IgG and IgA responses and the CD4+ T-cell hematologic parameters. In most of the studies describing the association between the CD4+ T-cell hematologic parameters (or disease progression) and the polyclonal B-cell activation, significant correlations are always obtained for those 2 immunoglobulin isotypes: IgGs and IgAs.13,42-46 Instead, in our study, we could only show that the SIV-specific IgG secondary response reflected the extent of viral replication because after the initiation of ART therapy, the plunge of viral load to an undetectable level was paralleled by the significant decrease of anti-Gag and anti-Nef IgGs (Fig. 1A-C). This result is in agreement with the studies by Ngo-Giang-Huong et al28 and Voltersvik et al.29
In HIV and SIV infection, the chronic hyperactivation of the immune system and the deregulations of the immune functions are seen simultaneously.8,18 The pathologic activation of the immune system results in functional abnormalities in B cells22,47 and T cells,3,4 including deregulation of cell surface markers on B cells22,48,49 and CD4+ and CD8+ T cells49-55 and upregulation of soluble plasma activation markers.49,56-60 In our study, the correlations between CD4 hematologic parameters and post-acute-phase anti-Gag, anti-Rev, and anti-Nef-specific IgM suggest the role of Th cell defects leading to the persistence of virus-specific IgM levels. Hyperimmune activation could play a role in this process.
In our study, the impairment in Th cells activity may translate into a partial defect in immunoglobulin class switching functions. This process may permit B-cell maturation and memory B-cell formation without prior immunoglobulin class switching, accounting for the abnormal level of antigen-specific IgMs during the secondary response. Interestingly, a study by O'Gorman et al61 demonstrated that the expression of CD40 ligand (CD154), a costimulatory molecule expressed on activated CD4+ T cells and involved in B-cell proliferation, germinal center formation, and immunoglobulin class switching, was significantly impaired in HIV-infected children compared with controls.61 Another study suggests that soluble gp 120 might be responsible for the impairment of CD4+ Th function for B cells by interfering with the contact between T and B cells.62 Finally, the impairment in immunoglobulin isotype switching functions has already been described in HIV infection in humans: in pediatric AIDS,5 in asymptomatic HIV-1-infected patients,6 in infected patients with persistent generalized lymphadenopathy,14 and even for HIV-infected patients across all disease stages.7 Similarly, impairment in immunoglobulin isotype switching was observed in the CD4C/HIV transgenic mouse.63
The abnormal level of antigen-specific IgM during the secondary humoral response might also be due to the damage inflicted on the memory B-cell pool by the SIV-induced immune dysfunction. Indeed, HIV has been shown to act on both naive (CD27−) and memory (CD27+) B cells, triggering an increased frequency of activated naive B cells64 and a selective reduction of the memory B cells.23,24 The memory B lymphocytes are rendered more susceptible to apoptosis by upregulation of Fas/FasL,23, 24 and a decreased frequency of plasma nerve growth factor (NGF) detection in HIV-1 infection was also associated with a lower number of plasma memory B cells.25 In addition, other studies demonstrated that the expression of CD70 (CD27 ligand) on T cells was higher in HIV-infected patients and that the upregulation of this marker was inversely correlated with the frequency of memory B cells.24 Indeed, CD70 expression on T cells stimulates memory B lymphocytes via CD27 interaction and promotes their differentiation into plasma cells, resulting in the elimination of circulating memory B cells in HIV-infected patients.26 Nevertheless, the abnormal level of specific IgM during the secondary response in our study does not appear to be due to the loss of memory B cells because no correlations between the CD4+ T-cell-related parameters and the virus-specific IgGs and IgAs were observed. Similarly, the IgM/IgG ratios were not significantly correlated with CD4 parameters (data not shown).
HIV/SIV infection might also damage the memory B-cell pool by another mechanism: HIV and SIV pathogenesis is associated with damage to the lymphoid germinal centers including hypertrophy and degeneration of follicular dendritic cells, follicular hyperplasia, and progressive involution of the germinal centers,65,66 a phenomenon that may alter the dynamics of memory B-cell generation in the lymph nodes.66 Consequently, the proportion of IgM memory B cells (phenotype IgD+ CD27+), cells that originate from the spleen and depend on this organ for their survival, might be increased in the total pool of memory B cells in HIV infection. Other research teams have identified HIV-specific IgM memory B cells in HIV-infected patients.67,68 In macaques infected with SIV, smallpox vaccine failed to protect animals against monkeypox virus challenge in animals with <300 CD4+ T cells/mm. The lack of protection was accompanied by a defect in IgM to IgG class switching.69 It is worth noting that the predominance of IgM memory B cells is a hallmark of the X-linked hyper-IgM syndrome, a disease characterized by immunodeficiency, increased susceptibility to infections, pronounced decreased levels of serum IgG, IgA, and IgE, and elevated levels of IgM during humoral response.70 These results support our hypothesis that an increased proportion of specific IgM memory cells might be responsible in the SIV-infected macaques for the abnormally elevated level of post-primary peak specific IgM observed in this study.
The intravenous route of virus challenge used in this study might itself influence the proportion and immunoglobulin subclass of specific memory B cells. A mucosal route (intravaginal or intrarectal) of virus exposure would certainly have yielded increased levels of mucosal IgA but also systemic serum IgA. Indeed, the site of antigen presentation to naive B cells (together with the nature of the antigen) determines the main isotype that is expressed by the induced antibody-secreting B cells.71 The proliferation of SIV antigen-activated B cells in the mucosa-associated lymphoid tissue would then enable the redistribution (homing to several other mucosal sites) through the systemic route of SIV-specific IgA antibody-secreting B cells and the creation of SIV-specific IgA memory cells. As an example, mucosal immunization against HIV antigens in mice could induce specific serum IgA and IgG as well as a long-term (memory) IgA-specific mucosal response.72 HIV-specific IgA memory B cells have been detected in HIV-infected patients.67,68 The intravenous route of viral administration may have limited or compromised viral antigen presentation in mucosal tissues and may not necessarily appropriately model the profile of IgA response compared with sexual transmission. The predictive value of the persistent antigen-specific IgM levels on the percentage of CD4+ T cells, however, most likely does not reflect the choice of route of challenge but rather the SIV-induced defect in CD4+ Th functions.
In summary, our results suggest that these specific postpeak viremia IgM levels are a reflection of defects in CD4+ Th function and are valuable prognostic indicators for disease evolution. This marker may be particularly useful because it appears prior to CD4+ T-cell depletion and may elucidate early defects in immune functions. Indeed, the loss of immune function, concomitant with HIV-induced immune activation,8,18 has been reported prior to the marked decline of CD4+ T cells.4,8,73 Given their potential value as markers of the impairment of the immune functions, the measurement of these antigen specific IgM levels post-peak viremia could provide additional useful information regarding prognosis, possibly impacting the decision regarding initiation of HAART in persons with early HIV infection. Measurements of the specific IgM would also be an advantage in low-resource settings due to the low cost of antibody measurements relative to flow cytometry for hematologic parameters (such as absolute CD4+ T-cell count or percentage of CD4+ T cells).
It is possible that cytokine-augmented therapeutic strategies that promote immunoglobulin isotype switching may be beneficial in HIV infection. Further studies are needed to confirm in humans that the specific anti-Gag, anti-Nef, and anti-Rev postpeak viremia IgM response could be used likewise as a prognostic indicator.
The authors thank Dr. Genoveffa Franchini (National Cancer Institute, NIH) for helpful discussions that stimulated this work and Dr. Neil Almond (MRC, England) for the kind gift of the SIVmac251 (11/88) viral stock; Dr. Wendy Wagner and Russ Byrum and the animal care staff at Bioqual for their expert animal care and drug dosing studies; Jack Greenhouse for virus load analysis and Jake Yalley-Ogunro and Henry Khun for the flow cytometry and other laboratory support at Bioqual; and Gilead for supplying PMPA and FTC for the drug treatment portion of the studies.
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