Shortly after the introduction of HAART, it was reported that some treated HIV-infected patients showing satisfactory virological responses failed to increase their absolute counts of CD4 T cells . The unexpected behavior of these patients, known as immunological nonresponders or discordant patients, has been widely studied from different viewpoints. On one hand, several groups have identified clinical predictors, such as the nadir CD4 T-cell count, for the default in CD4 T-cell recovery [2,3] and have reported how discordant responses negatively impact morbidity and mortality [4–6]. On the other hand, abundant work has aimed to understand the virological and immunological basis for this default. In general, reduced thymic function, resulting in low production of new T cells [7,8] and an increase in CD4 T-cell destruction revealed by a high sensitivity to ex-vivo cell death [7,8], has been shown to contribute to this failure (excellently reviewed in ). However, the ultimate cause of the discordant response and the relative contribution of poor T-cell production and increased destruction remain unknown. In contrast, it seems clear that the increased destruction of CD4 T cells is intimately related to immune hyperactivation, as activated cells are prone to undergo apoptosis ex vivo . Thus, the simplest scenario suggests that homeostatic CD4 T-cell proliferation is activated in response to low naive T-cell production. However, other factors modulate CD4 T-cell turnover. In particular, bacterial translocation has been shown to increase in animal models after the ablation of thymic function  and in discordant patients . Finally, the existence of low level [12,13] or latent undetectable ongoing viral replication  has also been described to influence the activation and death of CD4 T cells and, hence, may be a determinant in the discordant response.
A better knowledge of the factors influencing CD4 T-cell recovery could help to define optimal therapeutic strategies for poor immunological responders. Therefore, we sought to determine the individual contribution of bacterial translocation, CD4 T-cell production, activation and destruction to the discordant phenotype. In this study, we compared the perturbations in the CD4 and CD8 T-cell compartments in a large cohort of HIV-infected patients on stable suppressive HAART with satisfactory or unsatisfactory CD4 T-cell recovery. Different statistical approaches support the notion that immune activation and sensitivity to cell death, specifically in the CD4 T-cell compartment, are the main determinants of CD4 T-cell recovery and should be preferentially targeted in treatment strategies for discordant patients.
Patients and methods
Patients and samples
A cross-sectional, descriptive, comparative study was performed to analyze the immune status of patients with a discordant immune response. The Institutional Review Board of our center approved the study (EO code: EO-07-024), and all patients provided their written informed consent.
A total of 247 patients consecutively seen in our HIV Outpatient Unit during a period of 12 months (November 2007–November 2008) were screened for the study. Of the 247 patients, 230 met the inclusion criteria: confirmed diagnosis of HIV infection, continuous HAART with sustained undetectable HIV-1 RNA (plasma viral load <50 copies/ml) for at least the past 2 years (minimum of four determinations during this time period) and good antiretroviral treatment (ART) adherence. ART regimens included two nucleoside reverse transcriptase inhibitors (NRTIs)/nucleotide reverse transcriptase inhibitors and one boosted-ritonavir protease inhibitor or one non-NRTI (nevirapine or efavirenz). Chemotherapy or interferon/ribavirin treatments and a history of opportunistic infections or hepatic cirrhosis during the past 2 years were the exclusion criteria.
We considered patients concordant, with favorable virologic and immunologic responses, if they showed CD4 T-cell counts of more than 400 cells/μl. Conversely, discordant patients with a favorable virologic response, but an unfavorable immunologic response, persistently showed CD4 T cells less than 350 cells/μl. Clinical and demographic data were collected from medical records.
A 40 ml single blood extraction was performed on all of the participants. Blood was collected in Vacutainer tubes (Becton Dickinson, Madrid, Spain) and immediately processed either for immunophenotype or plasma and peripheral blood mononuclear cell (PBMC) isolation. Plasma was obtained by centrifugation of whole blood at 1200g for 10 min and immediately stored at −80°C until use. PBMCs were obtained from cell concentrates layered on Ficoll–Hypaque (Atom Reactiva, Barcelona, Spain) density gradients and either used immediately for ex-vivo cell death assays or stored in liquid nitrogen.
CD4 and CD8 T-cell immunophenotype
We stained fresh blood samples with the following antibody combinations: CD95–fluorescein isothiocyanate (FITC), PD-1–phycoerythrin (PE), human leukocyte antigen (HLA)-DR–peridinin chlorophyll protein complex (PerCP), CD3–allophycocyanin (APC)–Cy7, CD4–APC and CD8–PE–Cy7, which was designed to evaluate activation, and CD45RA–FITC, CD31–PE, CD38–PerCP, CD3–APC–Cy7, CD4–APC and CD8–PE–Cy7, which was designed to evaluate CD4 T-cell production (thymic output) and CD38 expression in CD45RA− cells. An unstained control and a control antibody combination containing CD3–APC–Cy7, CD4–APC and CD8–PE–Cy7 were performed for all samples. All antibodies were from Becton Dickinson. Briefly, 20 μl of blood was incubated [15 min at room temperature] with the different antibody combinations in V-bottomed 96-well plates. After incubation, blood was lysed with 200 μl of FACS Lysing solution (Becton Dickinson) for 30 min at room temperature, washed in PBS and resuspended in PBS containing 1% formaldehyde. Cells were acquired in a LSRII flow cytometer (Becton Dickinson) coupled with a high-throughput screening (HTS) loader. At least 10 000 lymphocytes were collected for each sample and analyzed with FLowJo software (Tree Star, Inc., Ashland, Oregon, USA) using the following strategy: lymphocytes were gated according to morphological parameters, and CD3+ cells were identified and gated as CD4+CD8− or CD8+CD4−. Double-positive cells were excluded from the analysis.
Evaluation of cell death
We cultured 200 000 PBMCs in Roswell Park Memorial Institute medium containing 10% fetal calf serum in 96-well plates for 24 h. At that time, cells were incubated with 40 nmol/l of the potentiometric mitochondrial probe DIOC6 (Molecular Probes; Invitrogen, Carlsbad, California, USA) and CD3–APC–Cy7, CD4–APC and CD8–PE–Cy7 antibodies for 1 h at 37°C. Samples were then acquired in a LSRII flow cytometer (Becton Dickinson) coupled with a HTS loader. At least 10 000 lymphocytes were collected for each sample and analyzed with FLowJo software (Tree Star, Inc.). After gating the CD3+CD4+ or the CD3+CD8+ subsets, dead cells were identified by their low DIOC6 fluorescence .
Determination of plasma lipopolysaccharide and soluble CD14
Lipopolysaccharide (LPS) levels were quantified in plasma samples diluted with endotoxin-free water (dilutions ranging from 1/10 to 1/100) and treated at 75°C for 10 min to inactivate plasma proteins. Samples were assayed in duplicate using a limulus amebocyte lysate assay (Charles River Laboratories International, Inc., Wilmington, Massachusetts, USA) according to the manufacturer's protocol.
Soluble CD14 (sCD14) levels, a surrogate marker of bacterial translocation , were quantified in plasma samples using an ELISA assay (Diaclone, Stamford, Connecticut, USA). Plasma samples were diluted (1/50) and tested in duplicate. Plasma from healthy individuals were used as a control and yielded lower levels of sCD14 (5.7 ± 1.6 μg/ml) than samples from HIV-infected patients (8.4 ± 2.1 μg/ml).
Continuous variables were expressed as the median (interquartile range) and compared using nonparametric tests (Mann–Whitney and Wilcoxon tests for nonpaired and paired data, respectively) because the parameters were not normally distributed. The Kruskal–Wallis test was used for multiple comparisons. Discrete variables were described as percentages (number of patients) and compared using the chi-squared or Fisher exact test. Pearson's correlation coefficient was calculated to assess the association between variables. The percentage of correct classification of the predicted response by the observed variable was used to assess the performance of the models. Univariate and multivariate logistic regressions were fitted to predict the probability of a discordant response. Statistical analyses were performed using SPSS software version 15.0 (SPSS, Inc., Chicago, Illinois, USA) with univariate two-tailed significance levels of 5%. Graphs were plotted with GraphPad Prism version 5 (GraphPad Software, Inc., San Diego, California, USA). P values are presented in scientific notation when lower than 0.0001 to precisely describe significant differences.
We included 230 patients in the study: 95 were defined as discordant and 135 were defined as concordant. Most of the demographic and clinical parameters were well balanced among both groups. However, minimal but significant differences were observed in CD8 T-cell absolute counts and in time on therapy. As expected, significantly lower levels of CD4 T-cell counts (absolute and percentage) and lower nadir CD4 T-cell counts were observed in discordant patients compared with concordant patients. These data are summarized in Table 1.
Analysis of T-cell activation
Hyperactivation of CD8 T cells is a feature of HIV infection that determines both progression to AIDS in untreated patients and response to HAART in treated patients [17,18]. Therefore, we first analyzed different parameters of CD8 T-cell activation in our patients. The expression of the CD38 marker was significantly higher in discordant patients as compared with concordant patients (P = 1.55 × 10−6, Fig. 1a). However, as the percentage of CD38+CD45RA+ cells was similar in both groups (P = 0.188), differences were restricted to the CD45RA− CD8 T-cell subset (P = 1.25 × 10−5, Fig. 1a). The expression of the death receptor Fas (CD95) and the activation marker HLA-DR as well as the frequency of activated, apoptosis-prone double-positive HLA-DR+CD95+ cells were also higher in CD8 T cells from discordant patients (P = 0.0027, P = 2.45 × 10−5 and P = 6.34 × 10−5, respectively, Fig. 1a).
Apart from CD8 T-cell activation, the level of activation of CD4 T cells has also been correlated to disease progression and treatment success . Importantly, CD4 T-cell activation is one of the major driving forces behind HIV replication. When we measured CD4 T-cell activation, the expression of CD38 was comparable in both groups. However, discordant patients showed a lower percentage of CD38+CD45RA+ CD4 T cells (P = 2.12 × 10−6, Fig. 1b) and a higher percentage of CD38+CD45RA− CD4 T cells (P = 3.51 × 10−8, Fig. 1b), probably reflecting a reduced naive compartment and a higher level of activation in CD45RA− cells. The hyperactivation in discordant patients was further confirmed by the increased frequency of CD95 or HLA-DR expressing cells and by the percentage of activated, apoptosis-prone HLA-DR+CD95+ cells (P = 7.19 × 10−10, P = 2.71 × 10−15 and P = 8.24 × 10−18, respectively, Fig. 1b). Similar results were observed when PD-1, a death receptor used as a marker of cell exhaustion , was analyzed. The percentage of PD-1+ cells and HLA-DR+PD-1+ cells was also higher in discordant patients (P = 0.0033 and P = 1.75 × 10−11, respectively, data not shown). In summary, these data confirm the existence of perturbations in T-cell homeostasis that strongly affect the CD4 T-cell compartment.
Analysis of CD4 T-cell production and destruction: relationship with activation
New CD4 T cells are produced by the thymus and expanded either by cytokine-driven proliferation or postthymic selection. The CD31 antigen can be used to identify these mechanisms of naive T-cell homeostasis. The CD45RA+CD31+ cells contain 95% of total T-cell receptor excision circles (TRECs) in CD4 T cells  and have been defined as thymic-naive CD4 T cells , although it may also contain cells that proliferate in response to cytokines . Conversely, the CD4+CD45RA+CD31− population has been used as surrogate marker of cells expanded by postthymic selection, as their TREC content is lower as compared with the CD4+CD45RA+CD31+ population . When analyzed in our cohort, the frequency of CD4+CD45RA+ cells was lower in discordant patients (P = 6.68 × 10−6, Fig. 2a). Lower levels of CD4+CD45RA+CD31+ cells (P = 0.0002, Fig. 2a) and CD4+CD45RA+CD31− cells (P = 1.19 × 10−5 data not shown) were also observed in discordant patients. However, the frequency of CD31− cells in the CD45RA+CD4+ T cells showed no significant difference between groups (Fig. 2a). Consistent with previous analyses [7,8], these data may indicate that despite similar levels of postthymic selection, limited CD4 T-cell production or cytokine-driven proliferation impedes the proper maintenance of the naive CD4 T-cell pool in discordant patients.
We next analyzed the level of spontaneous cell death occurring in CD4 and CD8 T cells in ex-vivo PBMC cultures. As described by others for reduced cohorts , discordant patients showed higher levels of CD4 T-cell death, which reached a high degree of significance (P = 2.5 × 10−12, Fig. 2a). In contrast, CD8 T cells from concordant and discordant patients showed no significant differences in sensitivity to ex-vivo cell death (Fig. 2a) despite differences observed in activation status.
To confirm the role of CD4 T-cell production and destruction in the recovery of CD4 T cells, we stratified all patients (n = 230) according to their increase in CD4 T cells, which was measured as the difference between their current and nadir CD4 T-cell counts (see Figure, Supplemental Digital Content 1, http://links.lww.com/QAD/A30). Higher increases in CD4 T cells were associated with higher thymic output and lower cell activation and death (P = 7.9 × 10−5, P = 1.5 × 10−10 and P = 8.9 × 10−12, respectively). However, none of these parameters was different in patients with the lowest CD4 T-cell recovery (increase of <100 CD4 T cells/μl) compared with patients with a CD4 T-cell gain between 100 and 200 cells/μl (see Figure, Supplemental Digital Content 1, http://links.lww.com/QAD/A30). Thus, to further explore the contribution of each factor to CD4 T-cell recovery, we performed a double analysis of correct classification rates. In this analysis, data on CD4 T-cell production from concordant and discordant patients were plotted against CD4 T-cell activation or death (Fig. 2b). In each case, four quadrants were defined according to the median values of each parameter. As expected, patients showing low CD4 T-cell production and high activation/death were mostly discordant, whereas patients showing high CD4 T-cell production and low activation/death were mostly concordant. Patients with high activation or death values were mostly discordant patients, irrespective of their level of thymic output (upper quadrants). Conversely, patients with low activation or death values were mostly Concordant in both the low and high thymic production groups (lower quadrants, Fig. 2b). This analysis indicates that the discordant phenotype is better identified by activation and death values than by CD4 T-cell production.
Having identified CD4 T-cell activation as a major determinant of discordant response, we next evaluated its relationship with bacterial translocation. Because LPS measurements in plasma yielded hardly detectable and inconsistent values, we evaluated the level of sCD14 as a surrogate marker of bacterial translocation. This marker of monocyte activation showed higher levels in discordant patients (P = 0.002, Fig. 3a) and an inverse correlation with the percentage of CD4 T cells (Fig. 3b), but appeared to be unrelated to the percentage of thymic CD45RA+CD31+ CD4 T cells (data not shown). In contrast, sCD14 positively correlated with CD4 T-cell activation (CD38+CD45RA− and HLA-DR+CD95+ CD4 T cells) and cell death (Fig. 3b). However, the correlation coefficients were low (r < 0.3) in all cases. Furthermore, similar levels of sCD14 were found in the different groups of patients defined by the increase in CD4 T cells (P = 0.652, see Figure, Supplemental Digital Content 1, http://links.lww.com/QAD/A30), suggesting a secondary role for bacterial translocation in CD4 T-cell recovery.
Analysis of the predictors of discordant response
In a final analysis, we assessed the odds ratio of being discordant for each measured parameter (Fig. 4a). First, a univariate analysis ruled out any effect of demographic parameters (age and sex) and confirmed the predominant role of the nadir CD4 T-cell count as predictor of discordance. CD8 T-cell death was unrelated to immune recovery, whereas activation markers in the CD8 T-cell subset were significant but poorly predictive of the discordant phenotype. However, the same parameters measured in CD4 T cells appeared to be more relevant. Although CD4 T-cell production (CD45RA+CD31+ cells) and CD38 expression in CD45RA− cells also showed significant but poor predictive values; the percentage of CD4+HLA-DR+CD95+ cells, CD4 T-cell death and sCD14 were the best immunological predictors of the discordant condition. The impact of the nadir CD4 T-cell count was confirmed by categorizing all patients into high (n = 84) and low (n = 146) nadir groups (cutoff 200 cells/μl). Patients with low nadir showed higher rate of discordance (58 vs. 12% in the high nadir group, P = 6.5 × 10−12), lower CD4 T-cell counts (absolute and percentage, P = 2.8 × 10−14 and P = 3.8 × 10−12, respectively), higher levels of sCD14 (P = 0.007) and activation in both CD4 (HLA-DR+CD95+ cells, P = 4.9 × 10−5) and CD8 T cells (CD38+CD45RA−, P = 0.001). Interestingly, no differences were found in the frequency of CD45RA+CD31+ CD4 T cells (P = 0.376) between both groups.
To definitively ascertain the role of activation and death in CD4 T-cell recovery, we performed a multivariate analysis in which the discordant condition was analyzed with respect to CD4 T-cell production (CD45RA+CD31+ cells), expansion (CD45RA+CD31− cells), activation (HLA-DR+CD95+ cells) and death. This analysis identified activation and death as the best predictors of a discordant response (data not shown). The analysis was further refined by adding nadir CD4 T-cell count, age, plasma sCD14 and CD8 T-cell activation to the regression model. Nadir CD4 T-cell values and, again, ex-vivo CD4 T-cell death and activation were the only predictors for a discordant response to HAART (Fig. 4b). Taken together, our data suggest that CD4 T-cell recovery during HAART seems to be a matter of increased destruction rather than reduced production of CD4 T cells.
Extensive characterization of virologically suppressed, poor immunological responders, HIV-infected individuals has provided compelling evidence for decreased thymic production and increased activation and apoptosis . However, the contribution of each one of these factors to poor CD4 T-cell recovery remains unsolved. Our experimental approach was designed to answer this question; the number of patients analyzed gave the study the necessary power to address the relative weight of CD4 T-cell production and destruction. First, we validated our patient cohort by identifying previously described criteria for the discordant response. Indeed, nadir CD4 T-cell count, CD4 T-cell production (CD45RA+CD31+CD4 T cells), CD8 and CD4 T-cell activation (HLA-DR and/or CD95 expression and CD38 expression in CD45RA− cells) and CD4 T-cell sensitivity to ex-vivo cell death showed significant differences between concordant and discordant patients.
This first analysis revealed that perturbations in T-cell homeostasis that were observed in discordant patients mainly affected CD4 T cells. Consistently, univariate analyses indicated that the differences in the CD8 T-cell compartment were much less predictive of discordance than those observed in CD4 T cells. The analysis of this latter subset confirmed that the production of new cells (CD4+CD45RA+CD31+) was not a determinant factor for a discordant response. This observation is consistent with the fact that age, which inversely correlated with CD4+CD45RA+CD31+ cells, showed no predictive value in the univariate analysis. Conversely, activation markers (CD4+ HLA-DR+CD95+ cells) and the extent of ex-vivo cell death, along with nadir CD4 T-cell counts, were the best predictors for an unsatisfactory immune reconstitution.
Bacterial translocation, coinfections, HAART composition and cryptic viral replication are some of the factors that may explain the increased activation and death of CD4 T cells in discordant patients. Bacterial translocation was analyzed as a first candidate because a low nadir CD4 T-cell count could be associated with deteriorated gut-associated lymphoid tissue, high circulating bacterial bioproducts  and, consequently, increased CD4 T-cell activation  and apoptosis. However, the level of plasma sCD14 in our patients, which was a good predictor for a discordant response in the univariate analysis, showed no relevance in the multivariate approach. Correlation analyses showed no relationship between the level of sCD14 and the recovery of CD4 T cells or the frequency of naive cells, although some degree of correlation was observed with the percentage of CD4 T cells, the expression of activation markers and ex-vivo CD4 T-cell death. Taken together, these data do not support a specific role of naive T cells in maintaining immune function in the gut  and reveal a weak impact of bacterial bioproducts on immune activation in chronically infected, virologically suppressed patients. Thus, bacterial translocation failed to fully explain the discordant immune recovery.
Increased activation and death of CD4 T cells in discordant patients may rely on coinfections  or the effects of HAART on cell viability [26,27]. However, we ruled out these possibilities in a parallel analysis in which we showed no effect of hepatitis C virus infection or drug regimens on CD4 T-cell recovery . Thus, the ultimate cause of immune discordance remains elusive. In this regard, the role of cryptic viral replication in discordant responses may be worth exploring and might explain some markers associated with immune discordance (frequency of CD4+CCR5+ T cells, cellular proviral load, monocyte turnover and function). Indeed, CD8 T-cell activation in virologically suppressed patients appears to be independent of residual viremia , whereas particularly sensitive to cryptic viral replication (Buzon et al., in preparation). Therefore, it is possible that in discordant patients, higher CD4 T-cell activation and proviral DNA  favor cryptic HIV replication in lymphoid tissue, limiting CD4 T-cell recovery and normalization of CD8 T-cell activation. The analysis of this hypothesis is strongly limited by the availability of appropriate technical tools. In the absence of data on viral replication in lymphoid tissue in our patients, ultrasensitive plasma viral load could be used as an alternate surrogate marker . However, it is still unclear whether this parameter, which also quantifies continuous reservoir draining, actually reflects ongoing viral replication [14,29–32]. Currently, clinical intensification strategies and an active search of new markers of viral replication (episomal HIV DNA, activation markers) may be required to definitively ascertain the role of viral replication in discordant responses.
Alternative approaches to identify additional immunological factors not detected by our cross-sectional analysis include retrospective analysis of patients reaching satisfactory or unsatisfactory immune recovery from a low nadir CD4 T-cell count.
We show that CD4 T-cell destruction that is driven by cellular activation is the major cause of poor immune recovery in discordant patients. The ultimate causes of CD4 T-cell activation appear to be intimately associated with the nadir CD4 T-cell count, but do not rely exclusively on homeostasis and bacterial bioproducts.
This work was supported by unrestricted grants from GILEAD and MERCK and by funds from the FIPSE project 06/3600, the Spanish AIDS network ‘Red Temática Cooperativa de Investigación en SIDA (RD06/0006)’ and the European network NEAT. We thank all the patients who participated in the study.
J.B. is a researcher from Fundació Institut de Recerca en Ciències de la Salut Germans Trias i Pujol and is supported by the ISCIII and the Health Department of the Catalan Government (Generalitat de Catalunya). M.M. is supported by a predoctoral grant from Generalitat de Catalunya and the European Social Fund. This work is part of the PhD thesis that M.M. conducts at Pompeu Fabra University.
M.M. designed and performed immunophenotype, cell death and sCD14 assays. N.P.-A. performed statistical analyses. J.P. recruited patients and collected clinical data. M.B., R.R.-H. and B.C. designed immunophenotype panels and interpreted data. E.N. and J.B. designed, coordinated the work and wrote the manuscript. All authors read and approved the final version of the manuscript.
There are no conflicts of interest.
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