BAFF and BAFF Receptor Levels Correlate with B Cell Subset Activation and Redistribution in Controlled Human Malaria Infection

Anja Scholzen, Anne C. Teirlinck, Else M. Bijker, Meta Roestenberg, Cornelus C. Hermsen, Stephen L. Hoffman and Robert W. Sauerwein
J Immunol April 15, 2014, 192 (8) 3719-3729; DOI: https://doi.org/10.4049/jimmunol.1302960

Abstract

Characteristic features of Plasmodium falciparum malaria are polyclonal B cell activation and an altered composition of the blood B cell compartment, including expansion of CD21−CD27− atypical memory B cells. BAFF is a key cytokine in B cell homeostasis, but its potential contribution to the modulation of the blood B cell pool during malaria remains elusive. In the controlled human malaria model (CHMI) in malaria-naive Dutch volunteers, we therefore examined the dynamics of BAFF induction and B cell subset activation and composition, to investigate whether these changes are linked to malaria-induced immune activation and, in particular, induction of BAFF. Alterations in B cell composition after CHMI closely resembled those observed in endemic areas. We further found distinct kinetics of proliferation for individual B cell subsets across all developmental stages. Proliferation peaked either immediately after blood-stage infection or at convalescence, and for most subsets was directly associated with the peak parasitemia. Concomitantly, plasma BAFF levels during CHMI were increased and correlated with membrane-expressed BAFF on monocytes and dendritic cells, as well as blood-stage parasitemia and parasite-induced IFN-γ. Correlating with increased plasma BAFF and IFN-γ levels, IgD−CD38lowCD21−CD27− atypical B cells showed the strongest proliferative response of all memory B cell subsets. This provides unique evidence for a link between malaria-induced immune activation and temporary expansion of this B cell subset. Finally, baseline BAFF-R levels before CHMI were predictive of subsequent changes in proportions of individual B cell subsets. These findings suggest an important role of BAFF in facilitating B cell subset proliferation and redistribution as a consequence of malaria-induced immune activation.

Introduction

Humoral immune responses play a major role in conferring naturally acquired immunity to malaria (1). This immunity, however, appears to be slow to develop and ineffectively maintained (2, 3), also demonstrated by the low prevalence of Plasmodium falciparum malaria Ag-specific memory B cells (MBCs) even in high endemic areas (4–7). Although the complex nature of the parasite (2, 5) and the high degree of antigenic variation (8) certainly contribute to this apparently impaired generation of humoral immune memory, there is also increasing evidence that the malaria parasite actively alters B cell function (9). This includes not only polyclonal activation and modified responsiveness of B cells in vitro and in vivo (10–14), but also profound changes to the composition of the peripheral blood B cell compartment as recently described in naturally malaria-exposed populations (15–20). These changes observed in acutely infected or continuously exposed individuals include increased levels of transitional B cells (TBCs) (15, 17), reduced levels of IgD+CD27+ marginal zonelike nonswitched MBCs (nsMBCs) (17), and an enlarged proportion of atypical MBCs (atypMBCs), which have become a recent research focus (16–20). In malaria-endemic areas, expansion of atypMBCs appears to be linked to both cumulative duration and frequency of parasite exposure (18–20). Because of the cross-sectional nature of most of these studies, however, conclusive evidence for a causal link is missing. Also unknown are the mechanisms that govern these alterations of the blood B cell pool.

A key cytokine in mediating B cell homeostasis by regulating differentiation and survival is the constitutively expressed BAFF (21). BAFF is initially synthesized in membrane-anchored form by cytokine-activated myeloid cells such as monocytes and dendritic cells (DCs), and subsequently released after enzymatic cleavage (22). In vitro, surface BAFF production and release by P. falciparum–activated human monocytes and concomitant naive B cell activation has been demonstrated (23), whereas in a murine model of acute malaria infection, reduced surface BAFF expression by APCs corresponds with impaired MBC survival (24). Finally, in children with acute malaria, increased plasma BAFF levels have been reported, which correlate with disease severity (25).

A unique tool to gain insights into immunomodulatory effects of the P. falciparum parasite in humans is the controlled human malaria infection (CHMI) model, allowing analysis of sequential samples of previously malaria-naive volunteers during a primary P. falciparum infection in comparison with their preinfection status (26–28). We therefore took advantage of the CHMI model to study the dynamics of B cell activation and modulation during the very early stages of malaria infection. We further comprehensively investigated the kinetics and source of P. falciparum–induced BAFF during CHMI, and addressed the question whether modified BAFF secretion or B cell BAFF-R expression may provide an explanation for B cell subset activation or reshaping of the human B cell compartment during malaria.

Materials and Methods

Study subjects and CHMI

Eighteen healthy, malaria-naive Dutch adult volunteers (age 19–30 y, median 23 y) were subjected to CHMI by intradermal injection of 2500, 10,000 or 25,000 (n = 6 in each group) aseptic, purified, cryopreserved P. falciparum sporozoites (PfSPZ Challenge, strain P. falciparum NF54) in an open-label phase I clinical trial at the Radboud University Medical Centre from October 2010 to July 2011 (29). The three groups were subjected to CHMI at different time points, in 1-mo intervals. Written, informed consent was obtained from each volunteer. The trial was performed in accordance with Good Clinical Practice and an Investigational New Drug application was filed with the U.S. Food and Drug Administration. The study was approved by the Central Committee for Research Involving Human Subjects of The Netherlands (CMO CCMO NL31858.091.10). The trial was registered at Clinicaltrials.gov (identifier: NCT 01086917).

As reported previously (29), 15 volunteers (n = 5 in each group) developed patent parasitemia as determined by both thick-smear (TS; median prepatent period [range]: 12.6 [11–14.3] d) and retrospective quantitative PCR (qPCR; 10.3 [9–12] d). When TS+ (or at day 21 for volunteers remaining TS−) volunteers were treated with atovaquone/proguanil. There was no significant difference between the three groups by either time to positive qPCR or TS, parasite densities on day of TS positivity (day of treatment [DT]), or peak parasite density (measured at time of TS positivity ± 18 h).

PBMC isolation, cryopreservation, and thawing

Blood samples for PBMC isolation were collected at baseline (challenge C-1), during liver-stage infection (C+5), during developing blood stage infection (C+9), at TS positivity just before treatment (DT), 3 d after treatment (DT+3), and 35 and 140 d post challenge infection (C+35, C+140). PBMCs were isolated by density gradient centrifugation from citrate anticoagulated blood using Vacutainer cell preparation tubes (BD Diagnostics). After four washes in ice-cold PBS, cells were counted and cryopreserved at a concentration of 10 × 106 cells/ml in ice-cold FCS (Life Technologies)/10% DMSO (Merck) using Mr. Frosty freezing containers (Nalgene). Samples were stored in vapor-phase nitrogen. Immediately before use, cells were thawed, washed twice in Dutch-modified RPMI 1640 (Life Technologies/Invitrogen), and counted.

Flow cytometry analysis

Phenotypic analysis of sequential PBMC samples collected at different time points before, during, and after CHMI was conducted simultaneously for each individual donor in one experiment to avoid confounding influences of day-to-day interexperimental variation. Ki67 expression on B cell subsets was determined for all donors (n = 18; n = 15 TS+). FcR-like protein 4 (FcRL4), BAFF-R, transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI), and B cell maturation Ag (BCMA) expression on B cell subsets and surface BAFF expression on APCs was determined in those donors for which sufficient cells were available (n = 14; n = 11 TS+). Abs used for flow cytometry are listed in Table II. For immunostaining, 500,000–1,000,000 cells/stain were transferred into a 96-well V-bottom plate, washed once with 200 μl PBS, and incubated with 50 μl fixable dead cell stain dilution in PBS for 30 min on ice. Cells were then washed twice with staining buffer (PBS containing 0.5% BSA; Sigma), and stained with 50 μl Ab mixture diluted in staining buffer for 30 min at room temperature, followed by another wash step with staining buffer. This was repeated for the secondary surface staining step. Cells were then resuspended in 50 μl fixation/permeabilization buffer (eBioscience) and incubated for 30 min on ice, followed by a wash step with 150 μl permeabilization buffer (eBioscience). For intracellular staining, cells were incubated for 30 min at room temperature with 50 μl Ab mixture diluted in permeabilization buffer. Cells were washed with permeabilization buffer, resuspended in 200 μl PBS/1% paraformaldehyde, and kept on ice until analyzed. Fifty thousand to 200,000 events/sample were acquired on a Cyan ADP 9-color flow cytometer (Dako/Beckman Coulter). Flow cytometry data were analyzed using FlowJo v9.6 software.

ELISA

Plasma BAFF and IFN-γ levels were determined in cryopreserved EDTA anticoagulated plasma samples using the Human BAFF/BLyS/TNFSF13B Quantikine ELISA Kit (R&D Systems) and the Human IFN-γ ELISA Ready-SET-Go kit (eBioscience) according to the manufacturer’s recommendations.

Statistical analysis

Statistical analysis was performed using GraphPad prism software v5. We used parametric tests because the majority of data analyzed was normally distributed as determined by D’Agostino and Pearson omnibus normality test, and because nonparametric tests have limited power to detect significant differences in small data set. Data for more than two time points were analyzed by repeated-measures one-way ANOVA. Dunnett’s post hoc test was used when comparing all time points to C-1 baseline data, whereas Bonferroni posttest was applied when comparing different B cell subsets. Data for cell subsets at several time points were analyzed by repeated-measures two-way ANOVA with Bonferroni post hoc test. One-sample t tests were used to determine whether fold changes in B cell proportions at a given time point compared with C-1 were significantly different from 1 (no change). Relationships between plasma BAFF and surface BAFF or BAFF-R levels were analyzed by Pearson correlation. If parameters were not normally distributed, nonparametric Spearman correlation was used for analysis (relationships between peak parasitemia, plasma BAFF and IFN-γ levels, and B cell subset proliferation).

Results

Sequential blood samples were collected before, during, and after CHMI by intradermal administration of cryopreserved P. falciparum sporozoites (29). Fifteen volunteers became TS+ between days 11 and 14.3 postinfection and reached peak parasitemia of 3–759 parasites/μl blood (median 56, interquartile range [IQR] 15–102). Three donors remained negative by both TS and qPCR until day 21 after challenge (C+21), when they were presumptively drug treated. These donors were analyzed in parallel with TS+ donors to ensure that any changes observed after treatment were not solely related to drug treatment. Hematological parameters showed significant changes over the course of infection (Table I). Decreased total leukocyte, lymphocyte, and platelet counts in TS+ donors were most evident at 3 d after treatment (DT+3; all p < 0.001), with lymphocyte counts also significantly decreased on DT (p < 0.05). The proportion of total B cells within PBMCs mirrored this trend, showing a significant decrease at DT+3 compared with baseline (C-1; p < 0.05).

View this table:
Table I. Hematological parameters at baseline and during CHMI in TS+ volunteers

Increased plasma BAFF during CHMI is associated with parasitemia and IFN-γ secretion

During CHMI, a statistically significant increase in plasma BAFF was detected from the day of TS positivity (DT) onward (Fig. 1A, 1B). This increase was absent in the three donors who did not acquire blood-stage parasitemia (Fig. 1B). BAFF levels peaked at DT+2 or DT+3, with a median 3.3-fold increase (IQR 2.3–5.9) and an absolute increase of 1053 pg/ml (median; IQR 616–2956 pg/ml) compared with baseline. Increased plasma BAFF concentrations were preceded by elevated plasma IFN-γ (Fig. 1B), an important factor in mediating BAFF release (22). The increase in IFN-γ closely followed the increase in parasitemia, and peak IFN-γ levels correlated with peak parasitemia (Spearman r = 0.70, p = 0.004; Fig. 1C). The interval between peak IFN-γ (on DT+1) and peak BAFF levels (on DT+3) in plasma was 2 d (Fig. 1B), and peak BAFF levels positively correlated with peak IFN-γ concentrations (r = 0.78, p = 0.001; Fig. 1D). The correlation between peak BAFF and parasite load, however, was only weak and did not reach significance (r = 0.47, p = 0.08; Fig. 1E).

FIGURE 1.
FIGURE 1.

Plasma BAFF levels during CHMI. Plasma BAFF was quantified for (A, B, D, E) n = 15 TS+ and (B) n = 3 TS− donors on baseline (C-1), and from 1 d before (DT-1) until 3 d after treatment (DT+3) and after resolved infection (C+35). (A) Dots depict individual TS+ donors; error bars denote the median and IQR; asterisks show significant differences compared with C-1 by one-way ANOVA with Dunnett’s post hoc test (*p < 0.05, **p < 0.01, ***p < 0.001). (B) Kinetics of P. falciparum parasite load (open triangles), plasma IFN-γ (gray filled circles), and BAFF (black filled squares) were analyzed in TS+ donors, depicted as mean and SEM. BAFF in TS− donors is shown in open squares (light gray). The individual P. falciparum load per day was calculated per donor as the mean of all PCR samples taken on that day for this individual donor. Relationships among (C) peak P. falciparum load and peak IFN-γ, (D) peak IFN-γ and peak plasma BAFF, and (E) peak P. falciparum load and peak plasma BAFF in TS+ donors were analyzed by nonparametric Spearman correlation.

Surface BAFF expression on APC populations is increased during CHMI

Major sources of plasma BAFF are myeloid cells, which initially express the cytokine in membrane-bound form (22). Indeed, HLADR+ lineage (CD3, CD19, CD56)-negative APCs showed increased levels of surface BAFF expression at DT+3 (p < 0.001; Fig. 2A, 2B), and this increase in BAFF+ APCs correlated with the increase in plasma BAFF levels (Pearson r = 0.70, p = 0.016; Fig. 2C). We next analyzed which APC populations contributed to CHMI-induced surface-BAFF expression. Based on differential expression of the LPS receptor CD14 and the low-affinity FcγRIII CD16, HLA-DR+lin− APCs were subdivided into classical monocytes (CD14+CD16−), intermediate monocytes (CD14+CD16+), and inflammatory monocytes (CD14−CD16+). CD14−CD16− APCs were further gated on blood DC Ag 1–positive (BDCA-1+) DCs, BDCA-2+ DCs, and BDCA-3+ DCs (Fig. 2D). The proportion of BAFF+ APCs within PBMCs was already markedly elevated on DT and then further increased until DT+3 (Fig. 2E), a pattern that was again not observed in TS− individuals (Supplemental Fig. 1). CD14+CD16− classical monocytes constituted the largest proportion of BAFF+ APCs, followed by inflammatory monocytes (Fig. 2E). Classical, inflammatory, and intermediate monocytes, as well as BDCA-1 DCs, showed a significant increase in BAFF+ cells on DT+3 compared with C-1 (Fig. 2F), whereas no such increase was seen for BDCA-2 and BDCA-3 DCs. The fold increase in BAFF+ cells was highest within classical monocytes (median 2.76 [IQR 1.7–3.5]), followed by inflammatory monocytes (2.63 [2.3–4.5]) and BDCA-1 DCs (2.18 [1.4–3.7]). When analyzing absolute percentages of BAFF+ cells, there was no difference among the three monocyte subsets at baseline (C-1; median [IQR]: classical monocytes, 2.9% [2.1–5.1%]; intermediate monocytes, 3.0% [1.7–4.0%]; inflammatory monocytes, 3.2% [2.8–4.2%]). On DT+3, however, inflammatory monocytes (11.1% [8.1–14.5%]) showed significantly higher levels of BAFF expression than classical (7.1% [5.4–10.7%]; p < 0.05) and intermediate monocytes (5.2% [3.3–8.2%]; p < 0.001).

FIGURE 2.
FIGURE 2.

Surface BAFF expression on DC and monocyte populations during CHMI. Surface BAFF expression on HLADR+ CD3/CD19/CD56− APC subsets was determined by flow cytometry. Data are shown as representative flow cytometry plots for (A) one donor and (B) for all analyzed TS+ donors (n = 11), with dots depicting individual donors, and error bars the median and IQR. (C) The relationship between the increases (calculated by subtracting C-1 from DT+3 values) in plasma BAFF levels and proportion of BAFF+ APCs was determined by Pearson correlation analysis. (D) APCs were further subdivided into: (i) classical monocytes (class mono; CD14+CD16−), (ii) intermediate monocytes (interm mono; CD14+CD16+), (iii) inflammatory monocytes (inflamm mono; CD14−CD16+) and CD14−CD16−, (iv) BDCA-1+ DCs, (v) BDCA-2+ DCs, and (vi) BDCA-3+ DCs. (E) Median proportions of BAFF+ APC subsets were analyzed within viable PBMCs on C-1, DT, and DT+3. (F) Percentages of BAFF+ cells within APC subsets are shown as individual data, medians, and IQR. Asterisks show significant differences compared with C-1 by one-way ANOVA with Dunnett’s post hoc test (*p < 0.05, ***p < 0.001).

CHMI induces low, transient FcRL4 expression and proliferation of B cell subsets with distinct kinetics

Increased BAFF secretion after CHMI is likely to have an impact on B cell activation. To analyze the peripheral blood B cell compartment, we developed a 9-color B cell panel (Table II) and gating strategy to delineate 10 phenotypically distinct B cell populations (Fig. 3A) based first on IgD and CD38 expression (Fig. 3B), followed by further subdivision using CD10, CD21, and CD27 (Fig. 3C). As reported previously, CD21−CD27− MBCs in healthy, malaria-naive donors (before CHMI) constitute only a small proportion of circulating B cells (median 1.96% [IQR 1.76–2.66%]) in contrast, for instance, with classical MBCs (cMBCs; median 12.43% [IQR 9.9–16.64%]; Fig. 3D). These CD21−CD27− MBCs closely resembled the phenotype (high expression of CCR6 and CD86, low expression of CXCR5 and CD24; Fig. 3E) reported for so-called atypical CD21−CD27−MBCs expanded in malaria-exposed individuals living in highly endemic areas (16, 19). In contrast with a subset of tonsil B cells (Fig. 3F), peripheral blood B cells, including CD21−CD27− atypMBCs from healthy Dutch individuals, did not express the inhibitory FcRL4 (Fig. 3G, 3H). FcRL4 expression on B cells was not induced during or immediately after CHMI, but 2 wk posttreatment (C+35; p < 0.001) in TS+ donors (Fig. 3H), and not in those three that remained TS− (data not shown). This induction of FcRL4 expression was only temporary, occurred on a very small proportion of B cells, did not correlate with peak parasitemia, IFN-γ, or BAFF levels, and was not confined to atypMBCs (p < 0.05), but also was observed in cMBCs (p < 0.05), nsMBCs (p < 0.001), activated MBCs (actMBCs; p < 0.01), classical naive B cells (cNs; p < 0.001), and CD21−CD27− double-negative naive B cells (dnNs; p < 0.05; Fig. 3I).

View this table:
Table II. Abs used for flow cytometry
FIGURE 3.
FIGURE 3.

Proportion and phenotype of B cell subsets during CHMI. After exclusion of debris, doublets, and dead cells, lineage (CD3/CD56/CD14)-negative, CD19+ B cells were subdivided based on (A) IgD and CD38, and then on (B) CD10 expression. (C) CD38hi B cells were divided into: (i) CD10−IgD−CD38hiCD27+ PBs and (ii) CD10+ IgD+CD38hiCD27− TBCs. CD38lowCD10− B cells were subdivided first based on IgD and then CD21 and CD27 expression into four pairs of switched and nonswitched/naive B cell populations (C): (iii) CD21−CD27+ actMBCs and (iv) actNs; (v) CD21+CD27+ classical MBCs (cMBC) and (vi) nsMBCs; (vii) CD21+CD27− MBC (CD27− MBC) and (viii) cNs; and (ix) CD21−CD27− atypical MBCs (atypMBC) and (x) dnNs. (D) Proportions of individual B cell subsets within total CD19+ B cells at baseline (C-1). (E) PBMCs from healthy, malaria-naive volunteers (n = 10) were analyzed by flow cytometry to determine surface expression of CD86, CCR6, CXCR5, and CD24 on cN B cells, cMBCs, and atypMBCs. Data are depicted as whisker box plots, with boxes indicating the IQR and whiskers the min/max values. Representative flow cytometry plots are shown for FcRL4 expression on (F) total tonsil B cells as a positive staining control and (G) peripheral blood B cell subsets in healthy, malaria-naive donors (n = 11) before (C-1) or after (C+35) CHMI. FcRL4+ cells were analyzed as proportion of (H) total CD19+ B cells or (I) within individual B cell subsets in n = 11 TS+ volunteers. (H and I) Dots depict individual donors. (H) Error bars show the median and IQR. (I) Asterisks show significant differences compared with C-1 by one-way ANOVA with Dunnett’s post hoc test (*p < 0.05, ***p < 0.001).

B cell activation was assessed by Ki67 expression (30), which is found only in currently dividing cells. Increased levels of proliferation after CHMI were observed in nearly all B cell subsets (Fig. 4A) with the exception of actMBCs (Fig. 4Biii) and plasmablasts (PBs), the latter being consistently >95% Ki67+ (Fig. 4Bi). This proliferative response showed distinct kinetics for individual B cell subsets: proliferation of TBCs, cMBCs, CD27− MBCs, and atypMBCs (all p < 0.001; Fig. 4Bii, v, vii, ix), as well as dnN B cells (p < 0.05; Fig. 4Ax) peaked at DT+3. Among MBC subsets, atypMBCs showed the strongest proliferative response at DT+3 (Fig. 4C). Finally, significant proliferative responses were found 3 wk after resolved malaria infection (C+35) for cN B cells (p < 0.01; Fig. 4Bviii), activated naive B cells (actNs) and nsMBCs (both p < 0.001; Fig. 4Biv, vi), and were still ongoing for dnNs (p < 0.001; Fig. 4Bx) and cMBCs (p < 0.01; Fig. 4Bv). The three donors remaining negative for parasitemia showed no proliferation (Supplemental Fig. 2). Despite the strong proliferative responses, also in the MBC compartment, we found no evidence for hypergammaglobulinemia, with plasma IgG levels remaining stable during CHMI (median [IQR]: C-1, 8.98 mg/ml [8.2–12.3]; DT, 9.11 mg/ml [6.4–13.1]; DT+3, 7.47 mg/ml [6.5–9.3]; C+35, 8.98 mg/ml [6.9–10.4]).

FIGURE 4.
FIGURE 4.

Proliferative response of B cell subsets during CHMI. Ki67 expression by individual B cell subsets was determined by flow cytometry of PBMCs collected before CHMI (C-1) and during liver (C+5) and developing blood stage (C+9), immediately before (DT) and 3 d after treatment (DT+3) and after parasite clearance (C+35, C+140). (A) Flow cytometry plots showing Ki67 gating in TBCs, cMBCs, and atypMBCs. (B) Data are expressed as percentage of Ki67+ cells within each individual B cell subset. Dots depict individual TS+ donors (n = 15); error bars depict the median and IQR. Asterisks show significant differences compared with C-1 by one-way ANOVA with Dunnett’s post hoc test (*p < 0.05, **p < 0.01, ***p < 0.001). (C) The fold change in the percentage of Ki67+ cells within individual MBC subsets at DT+3 was compared with C-1. Dots depict individual TS+ donors (n = 15); error bars denote the median and IQR. Asterisks show significant differences between MBC subsets by one-way ANOVA with Bonferroni post hoc test (***p < 0.001). Relationships between atypMBC proliferation at DT+3 with (D) peak parasitemia, (E) DT+3 plasma BAFF levels, and (F) peak IFN-γ in TS+ donors (n = 15) were analyzed by nonparametric Spearman correlation.

The proportion of Ki67+ cells correlated with peak parasitemia within atypMBCs (Spearman r = 0.56, p = 0.03; Fig. 4D) and cMBCs (r = 0.71, p = 0.003) on DT+3, as well as within cMBCs (r = 0.63, p = 0.01), CD21−CD27− dnNs (r = 0.55, p = 0.03), and cN B cells (r = 0.62, p = 0.014) on C+35. Moreover, on DT+3, the proportion of proliferating atypMBCs (r = 0.68, p = 0.005; Fig. 4E) and their nonswitched dnN counterparts (r = 0.64, p = 0.01), but not other B cell subsets, correlated with plasma BAFF. There was no correlation between DT+3 plasma BAFF levels and B cell subset proliferation on C+35. Because plasma BAFF levels strongly correlated with plasma IFN-γ, we also assessed the relationship between peak IFN-γ levels and B cell subset proliferation. As for BAFF, peak IFN-γ concentrations correlated with the proportion of proliferating atypMBCs (r = 0.86, p < 0.0001; Fig. 4F), their nonswitched dnN counterparts (r = 0.64, p = 0.01), and in contrast with BAFF, cMBC proliferation (r = 0.57, p = 0.03).

Altered B cell subset proportions associate with BAFF-R expression, but not proliferation

Selective proliferation of individual B cell subsets at different time points during CHMI might affect the composition of the peripheral blood B cell compartment. Indeed, the percentage of PBs, TBCs, atypMBCs, and dnNs within CD19+ B cells was significantly increased at DT+3 (Fig. 5A). There was, however, no correlation between the increased proportion and proliferative response for any of these subsets at this time point. Proportions of other subsets were significantly decreased during exposure to blood-stage parasitemia (cNs and cMBCs at DT+3; nsMBCs at DT and DT+3; Fig. 5A and data not shown), despite active proliferation (cMBCs at DT+3). Finally, increases in the proportion of activated naive and MBC subsets were particularly evident after parasite clearance (C+35; Fig. 5B), but again were not associated with proliferation at this time point. These patterns of altered B cell subset proportions were again not found for the three donors who remained negative for parasitemia (Supplemental Fig. 3).

FIGURE 5.
FIGURE 5.

BAFF-R expression and its association with altered B cell subset proportions during CHMI. Flow cytometry was conducted on PBMCs collected before, during, and after CHMI. B cell subsets were analyzed as percentage of total viable CD19+ B cells, and their fold change in proportion compared with C-1 was calculated for (A) DT+3 and (B) C+35. Data are depicted as whisker box plots, indicating median, IQR, and min/max values of n = 15 TS+ donors. Asterisks show significant differences compared with 1 (no change, dashed line) as tested by one-sample t test. BAFF-R levels on (C and E) individual B cell subsets and (D) total B cells were determined by flow cytometry analysis of PBMC samples from n = 11 TS+ donors and are expressed as median fluorescence intensity (MFI). (C) The fold change in the proportion of each individual B cell subset on DT+3 (compared with C-1) plotted against baseline (C-1) BAFF-R levels, analyzed by nonparametric Spearman correlation. Small gray dots depict all individual B cell subsets from each individual donor, whereas large black dots represent the median (of n = 11 TS+ donors) fold change in subset proportion and BAFF-R expression for each of the B cell subsets: (i) PBs, (ii) TBCs, (iii) actMBCs, (iv) actNs, (v) cMBCs, (vi) nsMBCs, (vii) CD27− MBCs, (viii) cNs, (ix) atypMBC, and (x) dnNs. (D) Data for total B cells are depicted for individual donors (dots); error bars show the median and IQR. Asterisks show significant differences compared with C-1 by one-way ANOVA with Dunnett’s post hoc test. (E) Data for individual B cell subsets are shown for C-1 (white) and DT+3 (gray), depicted as whisker box plots, indicating median, IQR, and min/max values of n = 11 TS+ donors. The dashed line in (D) and (E) indicates median BAFF-R expression levels on CD19− lymphocytes. Asterisks show significant differences compared with C-1 by two-way ANOVA with Bonferroni post hoc test. (F) The relationship between the DT+3 plasma BAFF levels and median B cell BAFF-R expression levels on DT+3 was determined by Pearson correlation analysis. *p < 0.05, **p < 0.01, ***p < 0.001.

Another potential cause of altered proportions of individual circulating B cell subsets is redistribution between blood and lymphatic tissues, and this chemotactic B cell migration can be augmented by BAFF signaling through its receptor BAFF-R (31). Intriguingly, we found an inverse correlation between baseline BAFF-R expression levels of the 10 different B cell subsets and their change in proportion at DT+3 (Spearman r = −0.33, p = 0.0004): B cell subsets such as PBs and atypMBCs that were increased showed very low BAFF-R expression at C-1, whereas B cell subsets (nsMBCs and cMBCs) that were decreased the most had the highest baseline BAFF-R levels (Fig. 5C).

During acute infection (DT and DT+3), B cell BAFF-R expression was strongly reduced (Fig. 5D), particularly on B cell subsets with high baseline BAFF-R levels (Fig. 5E). DT+3 B cell BAFF-R expression levels inversely correlated with DT+3 plasma BAFF concentrations (Pearson: r = −0.63, p = 0.04; Fig. 5F), suggesting a negative impact of high plasma BAFF levels on BAFF-R expression. Expression levels of the two other BAFF receptors, TACI and BCMA, however, remained unaltered by CHMI on all B cell subsets, except cMBCs, for which we observed a slight increase at DT+3 (both p < 0.01 compared with C-1; data not shown).

Discussion

In this study, we investigated the kinetics and source of P. falciparum–induced BAFF, and its association with B cell subset activation and modulation of the composition of the human B cell compartment during the very early stages of malaria infection. Within 3 d after peak parasitemia and antimalaria treatment, we found a significant increase in both surface BAFF expression on various APC subsets and plasma BAFF concentrations, as well as strong proliferative responses and altered proportions of numerous B cell subsets. Both BAFF induction and B cell subset proliferation directly correlated with peak parasitemia. For CD21−CD27− B cell subsets (atypMBC and dnN), proliferation correlated with plasma BAFF and IFN-γ levels, and for B cell subsets with particularly high or baseline BAFF-R levels, these were inversely associated with their change in proportion after CHMI.

Increased plasma BAFF during CHMI is in line with previous findings in naturally exposed individuals, showing elevated BAFF in plasma from acutely malaria-infected children (25) and in placental tissue from malaria-infected pregnant women (32). In vitro, both malarial hemozoin and soluble parasite extract are capable of inducing BAFF release from monocytes (23). We now show for the first time, to our knowledge, that in vivo, malaria infection also results in increased expression of the membrane-bound form of BAFF on various monocyte populations, as well as on myeloid BDCA-1 DCs up to 3 wk after resolved infection. Myeloid cells expressing BAFF can act on B cells in two manners: directly by cross-linking the MBC BAFF receptor TACI via surface-expressed BAFF and indirectly by enzymatic release of soluble BAFF with high affinity for BAFF-R (33). The increase in surface BAFF+ BDCA-1 DCs observed in this study stands in contrast with findings in a murine malaria model showing a loss of surface BAFF-expressing myeloid DCs from the spleen (and as a result, reduced survival of MBCs) during acute infection (24). Because we only examined circulating, but not lymphoid tissue-resident APCs, we cannot exclude that a similar depletion of BAFF-expressing DCs might also be observed in these organs after CHMI, possibly because of transition to the circulation. Our data are reminiscent of findings in human HIV patients (34), who also show increased levels of monocyte- and DC-expressed membrane BAFF, plasma BAFF, and alterations/activation in the B cell compartment similar to what is observed in malaria (35–40). In as how far in either setting increases in APC surface-expressed BAFF via TACI, and soluble BAFF via BAFF-R, indeed contribute to activating B cells, however, remains to be established. The monocyte subset showing the highest proportion of BAFF expression 3 d after drug treatment was CD14low/−CD16+ monocytes. This subset is one of two “nonclassical” CD16+ monocyte subsets that are known to be increased upon immune activation, as also found for P. falciparum malaria (41, 42). CD14low/− monocytes have proinflammatory properties (including TNF-α secretion), whereas CD14+CD16+ intermediate monocytes secrete the anti-inflammatory cytokine IL-10 (43, 44). Significantly higher BAFF expression on the CD14low/− subset as found in our volunteers during CHMI is consistent with this functional division.

Parasite-induced BAFF secretion from monocytes might be further augmented by IFN-γ derived from innate or adaptive immune cell activation (22). Although previous experiments were performed with highly enriched monocytes/B cell cocultures, it could not be definitively concluded that this process was entirely T cell independent (23). Indeed, we also found only a weak and not significant relationship between peak parasitemia and plasma BAFF levels, indicating that in vivo, this direct effect may be of lesser importance. Instead, peak plasma BAFF levels correlated with peak plasma IFN-γ levels, an important factor in mediating BAFF release from myeloid cells (22), which preceded them by 2 d, and is in line with previous findings (25). This strongly suggests that P. falciparum–driven immune activation is an important contributing factor in parasite-mediated BAFF secretion. Peak IFN-γ levels were only reached 1 and 3 d after initiation of treatment, likely because of the fact that release of P. falciparum material upon mass parasite killing further enhances immune activation. The 2-d delay between peak plasma IFN-γ and BAFF concentrations can be explained with the time it takes for APCs to get activated by IFN-γ and initiate sufficient surface BAFF protein expression (which also peaked 2 d later) and cleavage, and is consistent with in vitro studies showing that BAFF release from IFN-γ–treated monocytes is only minimal after 24 h, peaks after 48 h, and surface expression continues to increase until 72 h (23).

Concomitantly with increasing plasma BAFF levels, we observed reduced expression of BAFF-R on B cells, corroborating findings in acutely malaria-infected children (25), HIV infection, Sjögren’s syndrome, and systemic lupus erythematosus (SLE) (34, 35, 38). At least in autoimmune diseases, this downregulation is not transcriptionally regulated (38). Instead, physiological regulation by receptor internalization or shedding, but also partial masking by BAFF binding may be the explanation. Two other members of the BAFF receptor family, TACI and BCMA, which have low or no affinity for soluble BAFF (33), did show no downregulation during acute infection.

Despite very low parasite densities during CHMI, we observed a number of profound, albeit temporary, changes in B cell composition either during or immediately after the blood stage of CHMI. These included increased proportions of TBCs, atypMBCs, and PBs, as well as the reduction in marginal zonelike nsMBCs (CD21+CD27+IgD+), and are consistent with previous observations in persistently exposed or acutely infected individuals in malaria-endemic areas (15, 17–20, 45, 46). A number of factors can influence the proportions of individual subsets within the peripheral blood B cell compartment, including redistribution between blood and tissues, cell death or proliferation. Our data demonstrate that different B cell subsets respond with different proliferation kinetics to P. falciparum exposure. Proliferative responses during acute infection are likely related to either direct interaction with blood-stage parasite products or as a bystander effect of P. falciparum–induced immune activation and release of soluble mediators, both of which would be driven by the level of parasitemia encountered during infection. Indeed, proliferative responses of several B cell subsets correlated with the degree of parasite exposure. Although Ag-specific expansion cannot be excluded, this is unlikely true for the majority of cells, seeing the large proportion of responding cells across subsets after a primary exposure. Moreover, whereas atypMBCs have been shown to contain a similar proportion of malaria Ag-specific cells as cMBCs (16), any circulating proliferating B cells seen as early as 3 d after drug treatment, and thus only 5 d after blood-stage parasites became detectable by PCR, are unlikely to stem from a memory-generating germinal center response, which would still be ongoing at this time point. Instead, any changes in cell proportion or proliferation seen already at the peak of infection are more likely related to generalized immune activation during acute infection rather than Ag-specific activation and the generation of memory responses. Whether this is mediated by cytokines such as IFN-γ or BAFF (suggested by the correlation between plasma levels of those cytokines and atypMBC and dnN proliferation) (23, 25) or by direct parasite–B cell interaction (10, 13, 14) remains to be established. One possible consequence of generalized B cell activation could be hypergammaglobulinemia (1); however, we did not detect any increase in plasma IgG during CHMI, nor does this necessarily occur in the field (25).

Next to B cell subset proliferation during acute infection, we also observed proliferative responses as late as 35 d postinfection (and thus 3 wk after parasite clearance) in cNs, activated and dnNs, as well as nsMBCs and classical MBCs. The majority of these subsets also showed reduced proportions in the B cell compartment 3 d after drug treatment. Although these reduced proportions are likely due to redistribution of B cells from the circulation, for instance, to secondary lymphoid organs such as the spleen, we cannot exclude that there might also be a loss in B cells, for example, due to apoptosis (47, 48). An explanation for these late proliferative responses might therefore be either: 1) the release of elsewhere recruited activated cells back into the circulation, or 2) a physiological counterreaction to replenish the apoptosis-diminished B cell pool.

For atypMBCs, as well as their nonswitched dnN counterparts, their particularly strong proliferative response correlated not only with peak parasitemia, but also plasma BAFF levels, a relationship not observed for other B cell subsets. This may appear counterintuitive, because CD21− B cells express lower levels of BAFF-R than other B cell subsets. A possible explanation is that BAFF may be a costimulating factor rather than a driving force of proliferation (49–51). If CD21−CD27− naive and MBCs are more receptive than other B cell subsets to BAFF-costimulated, proliferation-driving stimuli, low BAFF-R expression would not necessarily be a limiting factor. In this scenario, factors other than BAFF should also correlate with CD21−CD27− B cell proliferation. This is true for peak IFN-γ levels and may further extend to other B cell stimuli not assessed in this study. To establish whether there is indeed a causal link between the expansion of CD21−CD27− B cells and P. falciparum–induced cytokines, and to unravel potentially synergistic effects of BAFF, IFN-γ, and other cytokines or stimuli, future mechanistic in vitro studies are needed. Of note, in other diseases with elevated plasma BAFF, including HIV and SLE, atypMBCs or phenotypically similar populations are also expanded (34, 38–40).

This study provides, to our knowledge, the first direct link between malaria infection and increased proportions of atypMBCs. The temporary nature of this increase is in line with decreasing proportions of these cells in individuals from malaria-endemic areas after prolonged nonexposure (52). The fact that atypMBCs at baseline showed only little proliferation, but increased this after parasite exposure suggests that these cells may not be exhausted per se. This is also in line with a sizable proportion of atypMBCs showing proliferation in vivo in naturally exposed individuals (16). The notion of atypMBC exhaustion stems from a failed in vitro attempt to differentiate them into Ab-producing cells (19). Future studies will need to show whether activation of atypMBCs in vivo can result in the generation of Ab-producing PBs after all, as suggested previously (16), or whether atypMBCs may have alternative functional properties upon activation.

Although CHMI-activated CD21−CD27− atypMBCs closely resemble the phenotype of atypMBCs in viremic HIV patients and individuals from highly malaria-endemic areas in regard to CD21, CD27, CD86, CCR6, CXCR5, and CD24 expression (19, 40), they lack expression of FcRL4. FcRL4 is an IgA-binding inhibitory receptor (53) that impairs BCR signaling but augments TLR responses (54, 55). IgD−CD27− cells (of which IgD−CD21−CD27− MBCs are a subpopulation) in healthy individuals or in those with SLE also lack FcRL4 expression (39). FcRL4 expression was induced by CHMI 2 wk after parasite clearance, but this induction was temporary and occurred not only on atypMBCs but also numerous B cell subsets. It is thus possible that atypMBCs during CHMI may functionally differ from those found in malaria-endemic areas. However, even in frequently malaria-exposed individuals, FcRL4 expression varies (19, 52). Future studies will be necessary to determine whether sustained, high FcRL4 expression is a necessary or specific feature of atypMBCs (the function of which is still unknown), may only be induced at high levels upon chronic immune activation, and what the triggers for this induction are. Recently, the HIV envelope protein gp120 has been shown to trigger FcRL4 expression in primary human B cells by direct interaction with B cell–expressed α4β7 and subsequent induction of TGF-β (56). Whether P. falciparum similarly expresses FcRL4-inducing molecules remains to be established. Of note, FcRL4 expression by gp120 in vitro was increased within 24 h, whereas we only observed FcRL4 expression 3 wk after peak parasitemia and drug treatment–mediated parasite clearance. We cannot exclude, however, that FcRL4 expression might have already peaked much earlier and remained stable for a prolonged period, because no PBMC samples were collected between DT+3 and C+35. To our knowledge, this is the first study to investigate changes in B cell FcRL4 expression in vivo post an acute infection or immune activation, and follow-up studies are needed to further investigate the kinetics of FcRL4 expression postinfection. The temporary induction of inhibitory FcRL4 expression might be yet another facet of negative immune regulation upon activation. In T cells, this well-known process is mediated by inhibitory receptors such as PD-1 or CTLA-4 (57), pathways that have also been described and contribute to reduced T cell responsiveness in malaria infection (20, 58). Importantly, negative immune regulation of T cells not only leads to what is described as “exhaustion,” but is also an important factor in preventing immune pathology (59) and in mediating contraction of immune responses when a pathogen is cleared (60). In analogy, temporary induction of FcRL4 on activated B cells across all B cell subsets post malaria infection may be a physiological response and simply serve to bring these cells back to the steady-state.

Despite partially overlapping kinetics, we found no correlation between B cell subset proliferation and the change in their individual proportions, suggesting selective B cell subset redistribution as a more important parameter in the altered composition of the peripheral blood B cell compartment during malaria. BAFF has previously been shown to enhance B cell chemotaxis to the CCR7, CXCR4, and CXCR5 ligands CCL21, CXCL12, and CXCL13 (31), which direct B cell migration to lymphatic tissues. Among other chemokines, CXCL13 is induced during acute P. falciparum infection (61). In a high BAFF environment, B cell subsets with higher BAFF-R expression might thus be more readily induced to leave the circulation than those expressing little BAFF-R. In line with this hypothesis, we found an inverse association between baseline BAFF-R levels on individual subsets and their proportion within the B cell compartment on DT+3, that is, the time of highest plasma BAFF levels. This phenomenon might be further exacerbated by differential expression of the corresponding chemokine receptors by different B cell subsets. AtypMBCs and PBs, for instance, express low levels of CCR7, CXCR4, and CXCR5 (data not shown) (19, 40).

In summary, by analyzing longitudinal samples collected during CHMI, we were able to extract potentially causal relationships between parasite exposure and B cell activation and modulation during malaria. We show that plasma BAFF levels are increased in the context of P. falciparum–induced immune activation and may be at least partially derived from monocyte subsets and BDCA-1+ DCs, which increase membrane BAFF expression during CHMI. B cell subsets were activated and proliferated with distinct kinetics, and these responses depended on peak parasitemia levels during CHMI. Finally, our data suggest that parasite-induced BAFF elevation may contribute to orchestrating the changes in the B cell compartment by two distinct mechanisms, namely, facilitating B cell subset proliferation and redistribution. This phenomenon is likely not malaria intrinsic but may be a common pathway of B cell modulation, because malaria shares several features including polyclonal B cell activation and specific alterations in the phenotype and composition of the peripheral B cell pool with other diseases that are also characterized by excessive plasma BAFF levels, including HIV infection and SLE.

Disclosures

S.L.H. is Chief Executive and Scientific Officer at Sanaria Inc., which manufactured PfSPZ Challenge, and does thus have a potential conflict of interest.

Acknowledgments

We thank the trial volunteers and the staff from the Clinical Research Centre Nijmegen, the Radboud University Medical Center, and the Sanaria Manufacturing Team, all of whom made this study possible.

Footnotes

  • A.S. and A.C.T. conducted experiments; A.S. designed the experiments and analyzed the data; E.M.B. and M.R. performed the clinical study and collected clinical data; C.C.H. performed quantitative PCR analysis; S.L.H. contributed vital reagents; A.S., A.C.T., E.M.B., and R.W.S. interpreted the data; A.S. and R.W.S. wrote the manuscript; and A.C.T., E.M.B., and S.L.H. critically revised the manuscript.

  • This work was supported by Top Institute Pharma (Grant T4-102) and the FP7-founded European Virtual Institute of Malaria Research (Grant 242095). A.S. received a long-term postdoctoral fellowship from the European Molecular Biology Organization. A.C.T. was supported by the European Vaccine Initiative with a European Malaria Vaccine Development Association Ph.D. scholarship. The development and manufacturing of cryopreserved Plasmodium falciparum sporozoites (PfSPZ Challenge) was further supported by Small Business Innovation Research Grants R44AI058375-03, 04, 05, and 05S1 from the National Institute of Allergy and Infectious Diseases at the National Institutes of Health and through Grant 07984 from the Program for Appropriate Technology in Health (PATH) Malaria Vaccine Initiative (with funds from the Bill and Melinda Gates Foundation). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article:

    actMBC
    activated memory B cell
    actN
    activated naive B cell
    atypMBC
    atypical memory B cell
    BCMA
    B cell maturation Ag
    BDCA
    blood dendritic cell Ag
    C
    challenge
    CHMI
    controlled human malaria infection
    cMBC
    classical memory B cell
    cN
    classical naive B cell
    DC
    dendritic cell
    dnN
    double-negative naive B cell
    DT
    day of treatment
    FcRL4
    FcR-like protein 4
    IQR
    interquartile range
    MBC
    memory B cell
    nsMBC
    nonswitched MBC
    PB
    plasmablast
    PfSPZ
    Plasmodium falciparum sporozoite
    qPCR
    quantitative PCR
    SLE
    systemic lupus erythematosus
    TACI
    transmembrane activator and calcium modulator and cyclophilin ligand interactor
    TBC
    transitional B cell
    TS
    thick-smear.

  • Received November 1, 2013.
  • Accepted February 17, 2014.

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