Decreased T_FR/T_FH ratio in SIV-infected rhesus macaques may contribute to accumulation of T_FH cells in chronic infection

Abstract

T follicular helper cells (T_FH) are critical for the development and maintenance of germinal centers (GC) and humoral immune responses. During chronic HIV/SIV infection T_FH accumulate, possibly as a result of antigen persistence. The HIV/SIV-associated T_FH expansion may also reflect lack of regulation by suppressive follicular regulatory CD4⁺ T-cells (T_FR). T_FR are natural regulatory T-cells (T_REG) that migrate into the follicle and, similarly to T_FH, up-regulate CXCR5, Bcl-6, and PD1. Here we identified T_FR as CD4⁺CD25⁺FoxP3⁺CXCR5⁺PD1^hiBcl-6⁺ within lymph nodes of rhesus macaques (RM) and confirmed their localization within the GC by immunohistochemistry. RNA sequencing showed that T_FR exhibit a distinct transcriptional profile with shared features of both T_FH and T_REG, including intermediate expression of FoxP3, Bcl-6, PRDM1, IL-10, and IL-21. In healthy, SIV-uninfected RM, we observed a negative correlation between frequencies of T_FR and both T_FH and GC B-cells as well as levels of CD4⁺ T-cell proliferation. Following SIV infection, the T_FR/T_FH ratio was reduced with no change in the frequency of T_REG or T_FR within the total CD4⁺ T-cell pool. Finally, we examined whether higher levels of direct virus infection of T_FR were responsible for their relative depletion post-SIV infection. We found that T_FH, T_FR and T_REG sorted from SIV- infected RM harbor comparable levels of cell-associated viral DNA. Our data suggests that T_FR may contribute to the regulation and proliferation of T_FH and GC B-cells in vivo and that a decreased T_FR/T_FH ratio in chronic SIV infection may lead to unchecked expansion of both T_FH and GC B-cells.

Introduction

Several key findings over the past few years have energized efforts towards the development of an effective HIV vaccine, including the discovery and characterization of a number of broadly neutralizing antibodies (bnAbs) that develop in a subset of HIV-infected individuals. However, the mechanisms involved in shaping antibody responses to immunization with HIV antigens or natural HIV infection, including the generation of bnAbs remain incompletely understood (1). Importantly, HIV-Env-specific bnAbs develop at relatively late stages of HIV infection, and show peculiar genetic and molecular features, including a high level of divergence from germ line predecessors, which indicates that they are the products of extensive somatic hyper-mutation within germinal centers (GCs), as well as the presence of unusually long CDR3 regions (2). Perplexingly, there appear to be no direct or predictable routes to the development of these bnAbs from the germ line predecessors, and it remains unclear whether this process is driven by antigenic mutations and/or escape as opposed to specific intrinsic aspects of the B-cell or T-helper cell response (3). A better understanding of the mechanisms responsible for the development of bnAbs is crucial to harness this type of immunity for HIV prevention and therapy in humans.

T follicular helper cells (T_FH) are critical for the development and maintenance of GCs and competition for survival signals from T_FH via molecules such as CD40L and IL-21 is thought to be a key mechanism of selection of high affinity B-cells (4). The regulation of T_FH frequency, and by extension the regulation of their impact on GC B-cell development and function, is vital to the quality of the humoral immune response (5). While the presence of too few T_FH may lead to abortive GC formation and defective B-cell responses, an over-expansion was found to be associated with the prevalence of autoantibodies (6, 7). It is possible that an expansion of T_FH also lowers the selection pressure on GC B-cells and leads to the emergence of low-affinity B-cells (8).

Several studies have shown that T_FH accumulate during the chronic stages of HIV/SIV infection. This accumulation occurs even though these cells support high levels of viral replication and represent an important component of the persistent virus reservoir under anti-retroviral therapy (9). The chronic expansion of T_FH in the case of HIV/SIV infection with persistent virus replication may be a direct result of antigenic persistence. As expected, HIV/SIV-associated expansion of T_FH is associated with dysregulation of B-cell responses with ineffective memory cell formation and hyper-gammaglobulinemia (10),(11). Whether and to what extent this T_FH expansion is also related to a deficit in the physiologic regulation of specific T_FH immune response within the lymph nodes remains unknown. However, this possibility would be consistent with the well-known observation that the chronic phase of pathogenic HIV/SIV infections is associated with a state of generalized immune activation that is resistant to the normal mechanisms of immune regulation.

Under normal circumstances, regulation of T_FH function is mediated at least in part by a recently described subset of CD4⁺ T-cells termed T follicular regulatory (T_FR) cells. T_FR are thought to develop from natural regulatory T-cells (T_REG) that express lineage-associated markers such as FoxP3, CD25, as well as low levels of CD127. These T_FR migrate into the follicles and GCs of lymph nodes by virtue of their expression of CXCR5 (and down-modulation of CCR7) and, similarly to T_FH, express high levels of Bcl-6 and PD1 (12) (13). Of note, the role of T_FR in the immuno-pathogenesis of HIV/SIV infections is currently unknown, both in terms of ability to negatively regulate HIV-specific B-cell responses (including, potentially, the production of bnAbs) and to suppress local virus-induced immune activation. Indeed, none of the previous reports on T_FH dynamics in the context of SIV or HIV infection have distinguished between cells that either do or do not co-express T_REG-associated markers. Thus the T_FR subset within the broader CXCR5⁺Bcl-6⁺PD1⁺ is not fully characterized in the setting of HIV/SIV infection.

In this study, we described and characterized phenotypically, histologically, and genomically a T_FR population that is found within the GCs of rhesus macaques (RM) and express markers associated with both T_FH and T_REG cells. The hypothesis that T_FR play a suppressive role in vivo is supported by the observation that their frequency is inversely correlated with both the levels of T_FH and GC B-cells and the percentage of proliferating CD4⁺ T-cells. In the setting of SIV infection, we found that T_FR show a slow in vivo proliferative response after the initial infection and exhibit only a small increase in their frequency within the total CD4⁺ T-cell pool during the chronic phase. In conjunction with the large expansion of T_FH observed following SIV infection, this phenomenon leads to a significantly decreased T_FR/T_FH ratio in the lymph nodes of chronically SIV-infected RM. These data may suggest that, during SIV infection, a lack of T_FR expansion may allow for a progressive accumulation of T_FH cells in the lymph nodes of chronically infected RM, thus indirectly contributing to the aberrant immune activation that characterizes this pathogenic infection.

Materials and Methods

Animals

The study involved a total of 40 Indian origin female rhesus macaques (RM) divided as follows: (i) Ten healthy, unvaccinated and SIV-uninfected RM; (ii) Ten healthy, SIV-immunized but SIV-uninfected RM; (iii) Eleven unvaccinated SIV-infected animals; and (iv) Nine vaccinated and SIV-infected RM. Animals were vaccinated with a SIVmac239 Gag-, Pol-, and Env-expressing DNA vaccine with inactivating mutations in proteases, half of which also co-expressed GM-CSF. These were followed by two boosts of a SIVmac239 Gag-, Pol-, and Env-expressing MVA vaccine as described previously (14). All infections were a result of SIVsmE660 intra-vaginal challenge at 2.06×10⁴ TCID₅₀ grown in RM peripheral blood mononuclear cells (PBMC). Lymph node biopsies were collected for measurement of a number of immunological parameters at day -35 prior to infection and days 14 and 168 after infection (i.e., acute and chronic phase, respectively). Spleen and lymph nodes were collected at necropsies performed at six months post infection. All animals were housed at Yerkes National Primate Center at Emory University and were cared for in accordance with National Institute of Health guidelines and following protocols approved by the Institutional Animal Care and Use Committee.

Tissue processing

Lymphocytes were isolated from freshly obtained lymph node and spleens by passing homogenized tissue through a 70-μm cell strainer and lysing blood cells with ACK Lysis buffer. Tissue collection was performed as previously described (15). Cells to be later used for sorting were cryopreserved at −80 degrees C in FBS media containing 10% DMSO.

Immunophenotyping and flow cytometry

Multi-color flow cytometric analysis was performed on mononuclear cells isolated from blood and lymph nodes according to standard procedures using monoclonal antibodies directed against RM markers and human markers that also cross-react with the same markers in RM. For optimum staining of intra-cellular markers, permeabilization of cells using the eBioscience FoxP3-permeabalization buffer was performed as recommended by the manufacturers. Pre-determined optimal concentrations of the following antibodies and reagents were used: CD3-Alexa700 (clone SP34-2), CD4-Allophycocyanin-Cy7 (clone OKT-4), Bcl-6-PeTexasRed (clone K112-91), Ki67- FITC (clone B56), CCR5-PE (clone 3A9), CTLA4- BV421 (clone BNI3) from BD, CXCR5-PerCP eFlour 710 (clone MU5BEE), PD1-PeCy7 (clone J105), CD127-PeCy5 (clone eBio-RDR5) from eBioscience and CD20-BV650 or PE-CF594 (clone 2H7), CD25-BV711 (clone BC96), Helios-FITC (clone 22F6) and FoxP3-Allophycocyanin (clone 150D) from Biolegend, and Live/Dead Fixable Aqua from Invitrogen. Flow cytometric data were acquired using LSRII flow cytometer using BD’s FACS DiVA software. Acquired data were analyzed using Flow Jo version9.3.2 following the gating strategy described in Figure1. Further analyses were performed using PRISM (GraphPad) and Excel (Microsoft Office 2011) software.

Figure 1. T_FR can be defined by flow cytometry and identified by confocal microscopy within the germinal centers of RM.

(A) Representative flow cytometry plot of lymphocytes from lymph nodes of untreated uninfected RM showing the gating strategy used to define T_FR, T_FH and T_REG cell populations. (B) Representative confocal microscope image showing a single T_FR within the lymph node of an uninfected RM. The first image shows staining for DAPI (green) and FoxP3 (red). The second image shows the same section with CD4 (green) and FoxP3 (red), the third PD1 (blue) and FoxP3 (red) and the last image CD4 (green), PD1 (blue) and FoxP3 (red). (C) Representative images of lymph node biopsies from SIV uninfected and acutely infected RM showing cells stained with CD4 (green), FoxP3 (red) and PD1 (blue) within the GC regions.

Cell Sorting

Cryopreserved cells were thawed in a 37 degree C water bath and rested overnight for 8-10 hours and then stained for sorting. Splenocytes from 5 SIV-uninfected and vaccinated RM as well as 9 unvaccinated SIV-infected animals were used for sorting of T_FH, T_FR, and T_REG. Cell populations were sorted using FACS Aria II flow cytometer. Cells were first gated based on light scatter followed by positive gating on cells negative for Live/Dead Fixable Aqua and positive for CD3 and CD4. After collecting bulk CD4⁺ cells the following three populations were collected: T_FR (CXCR5⁺PD1^hiCD127⁻CD25⁺), T_FH (CXCR5⁺PD1^hiCD127^+/−CD25⁻) and T_REG (CXCR5^+/−PD1^lo/intCD127⁻CD25⁺).

Immunohistochemistry and Confocal Microscopy

Immunohistochemstry was performed on 5-μm tissue sections mounted on glass-slides, which were deparaffinized and rehydrated with double-distilled H₂O. Antigen retrieval was performed in 1xDako Target Retrieval Solution (pH 6.0) in a pressure cooker heating slides to 122 degrees C for 30s. Slides were then rinsed in ddH₂O and incubated for 10 minutes using Dako Protein block. Slides were then incubated with rabbit anti-(1:200), mouse anti-FoxP3 (1:100) and goat anti-PD1 (1:500) for 1 hour at room temperature. Next, slides were washed in TBS with 0.05% Tween-20. Slides were then incubated for an hour in the dark with secondary antibody cocktail containing donkey anti-rabbit Alexa 488 (1:500), donkey anti-mouse Alexa-594 (1:500) and donkey anti-goat Alexa 647 (1:500). After washing in TBS with 0.05% Tween-20, Prolong Gold with DAPI was applied to all the slides. Confocal microscope images were obtained using Olympus FV10i® Confocal Microscope with CellSens® 1.9 Digital Imaging software.

Quantitative PCR for SIV gag DNA

Cell-associated viral DNA was measured in sorted cell populations from RM lymph nodes by RT-PCR as previously described(16-18).

RNA-Seq Library Preparation

Total RNA was prepared using the QIAGEN RNEasy Micro Kit. Libraries were generated using the CLONTECH SMARTer HV kit, barcoding and sequencing primers were added using NexteraXT DNA kit. Libraries were validated by microelectrophoresis, quantified, pooled and clustered on Illumina TruSeq v3 flowcell. Clustered flowcell was sequenced on an Illumina HiSeq 1000 in 100-base single-read reactions.

RNA-Seq Data Analysis

RNA-Seq data were submitted to the GEO repository at the National Center for Biotechnology Information (NCBI). RNA-Seq data were aligned to a provisional assembly of Indian Macaca mulatta (MaSuRCA rhesus assembly v.7_20130927) using STAR version 2.3.0e (19) (20). Transcripts were annotated using the provisional UNMC annotation v7.6. Transcript assembly, abundance estimates, and differential expression analysis was performed using Cufflinks v2.1.1 and Cuffdiff (21). All samples had read counts > 12000000 and unique mapping percentages in the range of 63 – 76 %; no samples were excluded from the analysis for technical issues. Differentially expressed genes were defined by pair-wise comparison of each phenotype. Differential gene lists were uploaded to Ingenuity Pathway Analysis software (v1.0 Ingenuity Systems, http://www.ingenuity.com/) and pathways with significant enrichment by Fisher’s Exact test and the Benjamini-Hochberg multiple testing correction. Heat maps and other visualization were generated using Partek Genomics Suite v6.6. RNA-seq data is publically available at the GEO repositories (accession number: GSE69756, URL: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69756).

RT-PCR validation of RNA sequencing data

Total RNA was prepared using the QIAGEN RNEasy Micro Kit from sorted T_FR, T_FH and T_REG cells. RNA quantity was measured using Nanodrop analysis and reverse transcribed as previously described for RNA sequencing. Finally, 0.1 μl of cDNA was used for real time SYBR green PCR analysis using an ABI 7900 HT Real-time PCR instrument (Applied Biosystems). Primer sequences for PCR were GAPDH: Fwd5′-GCACCACCAACTGCTTAGCAC-3′, Rev 5′- TCTTCTGGGTGGCAGTGATG-3′. IL2RA: Fwd5′- GGCTTCATTTTCCCACGGT-3′, Rev 5′- GCAGCTGGCGGACCAA-3′. IL6R: Fwd5′- TTCGGCCGGACTGTTCTG-3′, Rev 5′- GCACCCCATCTCCGACG-3′. SLAMF6: Fwd5′- TGG AAC ATC TCT TGC CTT CAT AG-3′, Rev 5′- GTT GCT GAG TTT CAG GGA GTA G-3′. SAP/SH2D1A: Fwd5′- CTC TGC AGT ATC CAG TTG AGA AG-3′, Rev 5′- GGC TTT CAG GCA GAC ATC A-3′. XIAP: Fwd 5′- GAG GAA CCC TGC CAT GTA TAG-3′, Rev 5′- GTG TAG TAG AGT CCA GCA CTT G-3′; PRDM1: Fwd 5′- TGT GGT ATT GTC GGG ACT TTG-3′, Rev 5′- GCT TGA GAT TGC TCT GTG TTT G-3′; CCL20: Fwd 5′- GCA ACT TTG ACT GCT GTC TTC-3′, Rev 5′- CAG CAT TGA TGT CAC AGG TTT C-3′; PD1: Fwd 5′- TCCTTGGCCACTGGTGTTC-3′, Rev 5′- CTTCTCCTGAGGGAAGGAGC-3′; IL10: Fwd 5′- AAGACCCTCAGGCTGAGGCT-3′, Rev 5′- TCCACGGCCTTGCTCTTG-3′; IL21: Fwd 5′- TGTGAATGACTTGGACCCTGAA-3′, Rev 5′AAACAGGAAATAGCTGACCACTCA-3′. Relative RNA transcript levels were calculated normalized to primer efficiency and housekeeping gene RNA (GAPDH).

Statistical Analyses

Except for RNA sequencing data, all statistical analyses were conducted using GraphPad Prism 5.0. Comparisons of mean fluorescence intensity between cell populations in uninfected RM were made using Wilcoxon signed rank tests (Fig 2). Man-Whitney U tests were used to compare frequencies of populations in uninfected, acutely infected and chronically infected RM (Figure 4, 5). Spearman rank correlation tests were used to analyze all correlations (Figure 6). All p values less than 0.05 were defined as significant.

Figure 2. T_FR share immunophenotypical features of both T_FH and T_REG populations.

Mean fluorescence intensity, percent positive, representative flow cytometry plots and histograms (Panels A, B, C, D, E, F) for expression of various immunophenotypical markers (i.e., FoxP3, CD25, CXCR5, PD-1, CD127, CTLA4, Bcl-6 and Helios) among T_REG, non- T_REG, T_FR and T_FH populations from LN of healthy, unvaccinated and uninfected RM. Non-T_REG here are defined as all CD4⁺CD25⁻FoxP3⁻ T-cells. Significance was determined by Wilcoxon signed rank tests.

Figure 4. Kinetics of T_FR, T_FH and T_REG after SIV infection.

(A) Frequency of T_FH, T_FR and T_REG as percentage of the total CD4⁺ T-cell population within lymph nodes of uninfected, acutely (week 2) SIV-infected and chronically (week 24) SIV-infected RM. (B) Frequency of T_FR as a percent of T_FH within the lymph nodes of the same animals. (C) Ratio of frequencies of T_FR to the frequency of T_FH (both calculated as a percent of the total CD4⁺ T-cell population). (D) Percent of proliferating, Ki67⁺, T_FH, T_FR and T_REG within the lymph nodes of uninfected, acutely SIV-infected and chronically SIV-infected RM. Statistical analyses were performed using Mann-Whitney U tests.

Figure 5. Comparable levels of SIV infection in T_FH, T_FR and T_REG isolated from the spleen of chronically SIV-infected RM.

(A) Viral DNA copies per million sorted T_FH, T_FR and T_REG from spleens of unvaccinated chronically SIV-infected RM. (B) Percent of CCR5-expressing cells among T_FH, T_FR and T_REG in unvaccinated SIV-infected RM. Statistical analyses were performed using Mann-Whitney U tests.

Figure 6. T_FR frequencies negatively correlate with T_FH and GC B-cell frequencies in the lymph nodes of RM.

Correlations between the frequencies of T_FR (calculated as frequency of T_FH) and the frequencies of T_FH calculated as percent of total CD4⁺ T-cells (left) and GC B-cells calculated as percent of total B-cells (center) and proliferating (i.e., Ki67⁺) CD4⁺ T-cells (right) within the lymph nodes of SIV-uninfected (A) and chronically SIV-infected (B) RM. Statistical analyses were performed using Spearman rank correlation tests.

Results

T_FR are distinct from T_FH and T_REG and can be found within lymph node GCs in RM

Recent studies of GC T_FH have defined these cells based on their surface expression of the chemokine receptor CXCR5 and very high levels of the co-inhibitory receptor PD1 (10). However, a fraction of these canonically defined T_FH also express the lineage-specific T_REG marker FoxP3 and have been therefore defined as T_FR as proposed in (22-24). Here we identified CD4⁺ T_FR by flow cytometry by their co-expression of CXCR5, PD1, FoxP3 and CD25 within lymph nodes of uninfected RM. The gating strategy used to define T_FH, T_FR, and T_REG throughout this study is shown in Figure 1A. Of note, the gating strategy for T_REG cells includes both CXCR5⁺ and CXCR5⁻ cells. To confirm the presence of T_FR within GCs we conducted an immuno-histochemistry (IHC) analysis. As shown in Figure 1B, single cells with nuclear expression for FoxP3 and surface expression of and PD1 were identified with GCs of uninfected RM. These T_FR can also be readily identified within GCs of SIV-infected RM (Fig 1C). Interestingly, several bonafide T_REG, identified by their expression of FoxP3 and but not PD1, are visible in the T-cell zone just outside the GC (Fig 1C). Presumably, some of these T_REG migrate into the GC and up-regulate T_FH-like markers along their differentiation pathway to T_FR. Figure 1C also shows that, as expected, non-FoxP3 expressing “true” T_FH are also seen within GCs of the same animals.

T_FR express markers of both T_FH and T_REG differentiation

We next performed a comprehensive examination of the T_FR phenotype in healthy, SIV-uninfected RM. As shown in Figure 2, our analysis of relative mean fluorescence intensities (MFI) for T_FR markers confirmed that T_FR express FoxP3 and CD25 at comparable levels to T_REG (Figure 2A) and both CXCR5 and PD1 at comparable levels to T_FH (Figure 2B). We next examined in T_FR the expression patterns of a series of markers (i.e., CD127, CTLA4, Bcl-6, and Helios) that have been linked to either T_FH or T_REG phenotype and function (1, 25). CD127, the IL-7 receptor α-chain, is expressed at low levels on T_REG in humans (26), (27) (28). As expected, we find that T_FR express CD127 at lower levels than the bulk of CD4⁺ T-cells, and similar or even lower levels than those observed in T_REG and T_FH (Figure 2C). CTLA4 is a key negative T-cell regulator that is constitutively expressed on T_REG and, upon ligation, induces down-modulation of cytokine production and inhibition of cell-cycle progression (25). Consistent with previous reports in murine models (12), we observed that T_FR express CTLA4 at a higher frequency and MFI than both T_REG and T_FH cell populations (Figure 2D), consistent with a putative role of T_FR as negative regulators of GC responses. Helios is a transcription factor expressed in thymus-derived natural T_REG cells (29). As shown in Figure 2F, T_FR express Helios at levels that are even higher than those observed in T_REG in terms of both frequency of positive cells and MFI, thus suggesting that T_FR originate from natural T_REG in RM as well as in mice.

Transcriptome analysis of T_FR reveals a distinct but overlapping transcriptional profile compared to T_FH and T_REG

To further define the functional features of T_FR in RM, we next examined the transcriptional profiles of T_FH, T_FR and T_REG using RNA-Seq by Illumina technology. Splenocytes from five healthy, SIV-uninfected and unvaccinated RM were sorted into “bulk” CD3⁺CD4⁺ T-cells, T_REG, T_FH and T_FR based on the following phenotypic markers: T_FR (CXCR5⁺PD1^hiCD127⁻CD25⁺), T_FH (CXCR5⁺PD1^hiCD127^+/−CD25⁻) and T_REG (CXCR5^+/−PD1^lo/intCD127⁻CD25⁺). In mice, T_FR are derived from thymic T_REG precursors and acquire homing markers that allow them to traffic to GCs, while maintaining a transcriptome and suppressive function that most closely resembles T_REG (12, 13). To examine the transcriptional profile of T_FR relative to T_REG and T_FH in healthy, SIV-uninfected RM, we first performed principal component analysis (PCA) on a subset of the most highly expressed transcripts detected in T_FH, T_REG and T_FR (Figure 3A). The transcriptomes of each subset were clearly distinct and grouped by subset, with T_REG displaying the highest degree of intra-subset variability, and T_FH and T_FR subsets being more tightly distributed. We next compared the expression of several canonical T_FH and T_REG transcripts between the three subsets. As shown in Figure 3B-D, T_FH- and T_REG-related genes showed expression patterns that behaved as predicted with genes such as IL-10 expressed in T_FR and T_REG but absent in T_FH. Importantly, RNA sequencing data confirmed that T_FR share expression of T_REG signature transcripts such as FoxP3, GZMB, PRDM1 and IL2RA (Figure 3A). However, we found that several other T_REG-specific transcripts were expressed at lower levels in T_FR than in T_REG, including TRAF6, CD74, CCL20 and IL1R1. Similar to previous studies in mice, T_FR also showed elevated expression of the prototypical T_FH genes CXCR5, PD1/PDCD1, BCL-6, CXCL13, and ICOS. In fact, T_FR demonstrated a peculiarly high expression of the T_FH and T_REG-specific genes Bcl-6 and FoxP3, respectively. Interestingly, for several genes (SH2D1A/SAP, IL-21, CXCR5, IL-10) gene expression was higher in T_FR than either T_REG or T_FH, thus suggesting that the CD25⁺CXR5⁺PD1^hi phenotype may represent a more transcriptionally active population than classical T_REG or T_FH. This latter set of RNA-sequencing data provides strong evidence that T_FR are indeed a distinct cell subset and that the somewhat hybrid transcriptional profile of T_FR is not simply due to the sample being a mixture of T_REG and T_FH. Of note, elevated expression of IL-10 in T_FR compared to T_REG has been previously reported in murine studies (13).

Figure 3. RNA expression patterns confirm that T_FR share T_FH and T_REG like phenotype.

(A) Principal components analysis of RNA transcripts from five healthy, SIV-uninfected RM. Each circle represents the transcriptome of a sorted population of T_FH (blue), T_REG (green), or T_FR (red) from a single animal. (B) Expression in FPKM of select T_FH and T_REG genes in sorted populations from uninfected RM. (C) Heat map of log 2 transformed gene expression of transcripts in FPKM. Transcripts represent T_FH and T_REG signature genes. The T_FH gene signature was defined as transcripts that were significantly differentially expressed in sorted T_FH compared to bulk CD4⁺ T-cells. T_REG gene signature was defined as genes that were significantly differentially expressed in sorted T_REG compared to bulk CD4⁺ T-cells. (D) Expression pattern of key T_FH and T_REG genes in sorted bulk CD4⁺ T-cell, T_FH, T_FR and T_REG populations.

To then compare the profile of gene expression between T_FR with T_REG and T_FH subsets without using any a priori information, we defined T_FH and T_REG expression signatures by statistically contrasting RNA-sequencing data from T_FH and T_REG with bulk CD4⁺ T-cells. After exclusion of transcripts that had zero expression in any of the populations, a total of 88 genes made up the combined T_FH and T_REG signature of which 12 genes were T_REG related. Many, but not all, canonical T_REG and T_FH genes were also identified as significantly upregulated compared to bulk CD4⁺ T-cells. The lack of statistical significance for some prototypical transcripts is likely due to the presence T_REG and T_FH subsets within the bulk CD4⁺ population used as a comparator sample. Nevertheless, we found that T_FR cells show similar levels of expression of T_FH signature genes such as Bcl-6, TIGIT, CD200, LAT and BATF (Figure 3C). T_FR cells also express mRNA for key T_FH-related genes that are important for B-cell help, including IL-21, SH2D1A, CD40L and CD84. One notable difference between our data in RM and previously published mouse studies is that we observed high expression of IL-21 in T_FR, suggesting that these cells have acquired some specific genomic and functional features in primates. As shown in Supplementary Figure 2A-C, the expression patterns of IL-21, SH2D1A, SLAMF6, PD1, IL6R, CCL20, IL2RA, IL10, PRDM1, and XIAP were confirmed by RT-PCR quantification. In addiction, levels of protein expression of IL-21 by T_FR , T_FH, and T_REG were also measured by flow cytometry and further confirmed the pattern observed by RNA sequencing and RT-PCR (Supplementary Figure 2D).

SIV infection is associated with a decrease in the T_FR/T_FH ratio

The dynamics of T_FR in the setting of HIV or SIV infection have not been previously investigated, and in fact all published studies of T_FH dynamics during HIV/SIV infection used a definition of these cells that included T_FR as well. To study the kinetics of T_FR, T_FH, and T_REG following SIV infection of RM we measured the frequency of these cells within the lymph nodes prior to infection, 2 weeks post infection and 6 months post infection with SIVsmE660. The RM included in these kinetics analyses included both unvaccinated as well as animals that were challenged following immunization with a SIVmac239 Gag-, Pol-, and Env-expressing DNA vaccine (with or without GM-CSF) followed by two boosts of a SIV239 Gag-, Pol-, and Env-expressing MVA vaccine. As previously reported, we found a significant increase (p<0.0001) in frequency of T_FH at 24 weeks post infection (Figure 4A). Interestingly, the frequency of T_FR measured as percent of total CD4⁺ T-cells also showed a significant (p=0.0001) increase during chronic SIV infection (Figure 4A). However, when the frequency of T_FR is measured as percentage of total T_FH, we found that the T_FR decrease significantly at both the acute (p=0.0385) and chronic (p=0.0016) stages of SIV infection (Figure 4B). Accordingly, the overall ratio of T_FR to T_FH cells also decreased significantly (p=0.0018) at the week 24 post-infection time point as compared to baseline (Figure 4C). The increase of both T_FH and T_FR as a percent of CD4⁺ T-cells after infection is likely the result of proliferation driven by antigen-persistence as well as virus-mediated depletion of other CD4⁺ T-cell subsets. However, the relative decrease in the frequency of T_FR when measured as percentage of T_FH suggests that the low frequency of these regulatory cells might contribute to the expansion and accumulation of T_FH in chronically SIV-infected RM. Of note, we found no significant changes in T_REG frequencies after SIV infection within the lymph nodes. To better define the kinetics of T_FH and T_FR during SIV infection we next measured the level of cell proliferation using the well-established marker Ki67. We observed that T_FH show a significant increase in proliferating cells during the acute (p<0.0001) phase and chronic (p=0.0001) phase of infection (Figure 4D). T_FR have a similar pattern of proliferation, with a significant increase in proliferating cells during the acute (p<0.0001) phase and chronic (p=0.0376) phase of infection (Figure 4D). In contrast, the level of Ki67 expression in T_REG remains relatively low throughout our analysis with a small significant increase (p=0.0141) during the chronic phase of infection (Figure 4D).

Similar levels of SIV infection of T_FR as compared to T_FH and T_REG despite higher CCR5 expression

Several studies have shown that, during HIV and SIV infection, T_FH are highly infected with the virus despite their relative increase within the total CD4⁺ T-cell pool (9). While the actual in vivo lifespan of T_FH, either infected or uninfected, remains unknown in the setting of HIV/SIV infection, the presence of a notable fraction of these cells expressing the proliferation marker Ki67 suggests that their number could be maintained through continual replenishing from precursors located outside the GC. To measure the level of direct SIV infection of T_FR, T_FH, and T_REG we sorted these subpopulations from the lymph nodes of a subset of our studied animals and quantified the levels of total cell-associated SIV-DNA by RT-PCR. This analysis revealed that T_FH, T_FR and T_REG derived from chronically SIV-infected RM all harbor comparably high levels of cell-associated viral DNA (Figure 5A). Interestingly, the levels of SIV infection were similarly high between T_FR and T_FH even though the surface expression levels of the main SIV co-receptor CCR5 were significantly higher in T_FR as compared to T_FH (Figure 5B).

The frequency of T_FR is negatively correlated with the number and proliferation of both T_FH and GC B-cells

To further examine the relationship between T_FR and T_FH and GC B-cells we next performed a set of correlation analyses in the RM included in this study, both SIV-infected and uninfected. We first observed that, in healthy uninfected RM, the frequency of T_FR (as fraction of the total T_FH pool) is negatively correlated with the percentages of T_FH (as fraction of total CD4⁺ T-cells) and GC B-cells (as fraction of total B-cells) (Figure 6A). In addition, we found that, in the same animals, the frequency of T_FR (as fraction of T_FH) is negatively correlated with the level of CD4⁺ T-cell proliferation as measured by Ki67 expression (Figure 6A).

We next performed the same correlation analyses in our cohort of SIV-infected RM. The SIV-infected RM included in these regression analyses included both unvaccinated as well as animals that were challenged following immunization with a SIVmac239 Gag-, Pol-, and Env-expressing DNA vaccine (with or without GM-CSF) followed by two boosts of a SIV239 Gag-, Pol-, and Env-expressing MVA vaccine. In the SIV- infected RM, similar to what was observed in uninfected animals, the frequency of T_FR (as fraction of T_FH) is negatively correlated with the percentages of both T_FH and GC B-cells (Figure 6B). However, the negative correlation between frequency of T_FR (as fraction of T_FH) and the level of CD4⁺ T-cell proliferation as measured by Ki67 expression is not seen in SIV-infected RM (Figure 6B). The negative correlation between T_FR cells (as a frequency of T_FH cells) and both T_FH and GC B-cell frequencies is consistent with the hypothesis that T_FR cells play a role in regulating T_FH and GC responses under normal circumstances and in the setting of chronic SIV infection.

Comparative analysis of the T_FR transcriptome in SIV-infected and uninfected RM

To further define the effect of SIV infection on T_FR, we next compared the transcription profiles of T_FR that were isolated from unvaccinated chronically SIV-infected and uninfected RM (Fig. 7). We performed RNA-Seq analysis and transcripts that were significantly differentially expressed in T_FR sorted from SIV infected vs. uninfected RM were analyzed by Ingenuity Pathway Analysis. Unsurprisingly, a large proportion of genes induced during SIV infection in T_FR (CD3G, FOS, CD4, ZAP70, PIK3CD, STAT3) were components of T-cell proliferation, activation of T-cell effector function, and co-stimulatory activation (data not shown). The enhanced T-cell activation was consistent with our observation that T_FR cells express higher levels of the proliferation marker Ki67 compared to T_REG (Figure 4D). We also observed that several genes implicated in pathways regulating apoptosis or cell cycle control were perturbed in SIV-infected RMs. Of particular interest was the observation that the pro-apoptotic gene FASLG was >100-fold induced, while the anti-apoptotic regulator XIAP was significantly down-regulated. This latter finding was again validated by RT-PCR (Supplementary Figure 2B). Thus, while we observed a significant increase of the proliferation marker Ki67 in T_FR after SIV infection (Figure 4D), a pro-apoptotic shift of gene expression may explain why only a modest increase in T_FR frequency was observed (Figure 4A).

Figure 7. RNA expression within T_FR cells in uninfected and infected RM.

(A) Enrichment of gene pathways in T_FR derived from the lymph nodes of unvaccinated SIV infected RM as compared to T_FR from SIV-uninfected animals as determined by Ingenuity Pathway Analysis. (B) Log 2 fold change of expression of T_FH and T_REG related gene transcripts in T_FR sorted from unvaccinated SIV-infected RM and SIV-uninfected RM. Significantly upregulated genes are in red and significantly down-regulated genes are in blue.

T_FH require IL-6 signaling and STAT3 expression for differentiation and, once mature, produce several factors that support B-cell activation. Conversely, IL-2 receptor signaling drives STAT5 to activate Blimp1/PRDM1, which ultimately blocks T_FH differentiation (30). However, T_FR express both Blimp/PRDM1 and Bcl-6. Here we find that both IL-6 and IL-2 signaling genes are enriched in T_FR after SIV infection. However, several of these genes, such as MAPK1, are common to different cytokine signaling pathways, thus making it difficult to establish if SIV infection causes a shift in the T_FR/T_FH differentiation pressure. Interestingly, downstream signaling for IL-10, a regulatory cytokine produced by both T_FR and T_REG, is also enriched in T_FR post infection. Additionally, ICOS-ICOSL signaling was also enriched in T_FR post infection. These data suggest that T_FR may be engaged in similar T_FH-like cell-surface receptor-ligand interactions with B-cells. In addition to genes that were identified with differential expression without any a priori knowledge, we also examined genes with known function in T_FR and T_REG. After infection, T_FR cells show a significant increase in the expression of PD1, IL6R, SLAMF6 and CD84, i.e., all markers associated with T_FH differentiation and function (Figure 7B). We also found a significant decrease in STAT3 and IL2RA in T_FR after SIV infection and a non-significant decrease in Bcl-6 expression. Finally, as expected, we also observed several other changes in expression patterns of the T_FH and T_FR signature gene sets as we had previously determined (Supplementary Figure 1). Overall, these data indicate a complex remodeling of gene expression in T_FR following SIV infection of RM.

Discussion

T_FH cells are critical to the development of the humoral response to infections, and their role in the setting of HIV and SIV infection (and vaccination) is the subject of intense investigation. However, some aspects of the complex T_FH response to HIV/SIV infection remain poorly understood, including (i) their role in promoting the development of broadly neutralizing HIV/SIV-specific antibodies, and (ii) their role in the immunopathogenesis of the infection. In particular, the mechanisms by which T_FH accumulate during the chronic stage of infection despite high levels of direct virus infection are unclear. Importantly, a series of recent studies have shown that T_FH include a subset of cells that are derived from thymic T_REG precursors, express the classical T_REG markers (i.e., FoxP3 and CD25 as well as low levels of CD127), and acquire T_FH markers (i.e., PD1, CXCR5, and Bcl-6) while migrating into the GC of lymph nodes, where they are thought to act as regulators of the host humoral immune response. To the best of our knowledge this study-- together with the independently generated set of data that are included in the accompanying manuscript by the group of Dr. Franchini and Dr. Vaccari -- represents the first description of the main features of T_FR in a non-human primate species. In this work we also investigated the dynamics of this cell subset during SIV infection of rhesus macaque (RM).

The main findings of the current study are the following: (i) T_FR show distinct yet overlapping phenotype as compared to T_FH and T_REG based on a combination of flow cytometric, histological, and transcriptional analyses; (ii) in healthy, SIV-uninfected RM, the frequencies of T_FR are negatively correlated with the levels of both T_FH and GC B-cells; (iii) following SIV infection, the T_FR/T_FH ratio is reduced; and (iv) T_FR sorted from SIV-infected RM harbor comparable levels of cell-associated viral DNA as compared to T_FH and T_REG. Collectively, these data indicate that while T_FR closely resemble T_FH in several biological aspects, they are also clearly distinguishable from this cell subset in terms of both their immunophenotype and transcriptional profile. It is therefore important that, in future studies of T_FH, a distinction be made between T_FR and true, “non-T_FR” T_FH to fully take into account the complexity of the different CD4⁺ T-cell subsets that are present in the GC of lymph nodes.

The observation that T_FR express proteins typically expressed by T_FH, such as CD40L, as well as proteins typically expressed by T_REG, like IL-10 and CTLA4, is consistent with studies in mice showing that T_FR are thymic-derived T regulatory (nT_REG) cells that migrate into the follicle and, in a manner similar to T_FH, up-regulate CXCR5, Bcl-6 and PD1 in a B-cell dependent manner (24). The production of IL-21 by T_FR cells is an intriguing new finding and suggests that T_FR play a more complex role in RMs than has been described in mice. In addition, PCA suggests that the transcriptional profile of T_FR tend to be more similar to T_FH than T_REG. Further studies are required to fully investigate the functional role played by T_FR in RMs and, specifically, in the context of SIV infection. The finding that over 90% of T_FR express Helios as measured by flow cytometry is also consistent with the nT_REG origin of these cells_. Importantly, these immunophenotypic and RNA sequencing data were complemented by histological analyses showing that T_FR are found within GCs of both uninfected and infected RM. While CD4⁺FoxP3⁺ T_REG were found in abundance outside the GC, CD4⁺PD1⁺T_FH and CD4⁺PD1⁺FoxP3⁺T_FR were both only seen within the GC. The hypothesis that T_FR play an immune regulatory role in vivo is supported by the observation that their frequency is inversely correlated with both the levels of T_FH and GC B-cells. These data are consistent with mouse studies indicating that (i) T_FR suppress T_FH in vitro and prevent the outgrowth of non-antigen specific B-cells (13), and that (ii) T_FR may inhibit antibody production without an effect on T-cell activation, thus indicating an ability to directly regulate B-cells(31).

In the setting of in vivo SIV infection, we found that T_FR exhibited only a small increase in their frequency within the total CD4⁺ T-cell pool. In conjunction with the large expansion of T_FH that is consistently observed following SIV infection, the minor expansion of T_FR leads to a significantly decreased T_FR/T_FH ratio in the lymph nodes of chronically SIV-infected RM. We confirmed this trend in the ratio of T_FR/T_FH cells after SIV infection by quantifying the number of T_FH and T_FR cells by immunohistochemistry (Supplementary Table 1). While the current set of results does not allow us to determine whether and to what extent the kinetics of T_FH and T_FR during SIV infection are mechanistically linked, it is conceivable that the limited T_FR expansion facilitates progressive accumulation of T_FH in chronically SIV-infected RM, thus indirectly contributing to the aberrant immune activation that characterizes this pathogenic infection. On the other hand, it is also possible that the strong proliferation of T_FH and associated increase in PD1 expression following SIV infection hampers the development or differentiation of T_FR as suggested (31).

Comparison of the transcriptional profiles of T_FR cells pre and post-SIV infection showed a significant up-regulation of transcripts typically expressed by activated T_REG including FOSB, FABP5, USP2 and USP13 (data not shown) (32), thus suggesting that T_FR may be involved in the generalized immune activation associated with pathogenic SIV infection. Interestingly, we also observed a down regulation of several T_FH and T_REG signature genes as established by our own algorithm. This somewhat unexpected observation indicates that SIV infection has a complex effect on the in vivo phenotype and function of T_FR. A better understanding of the overall contribution of T_FR to the immunopathogenesis of AIDS, in terms of causing either the virus-associated B-cell dysfunction or the changes in the lymph node architecture and function, will require further in vitro and in vivo investigation of the suppressive effect by these T_FR on the function of either T_FH or GC B-cells.

While CD4⁺ T-cells are the main target for HIV and SIV infection, substantial difference exist between various CD4⁺ T-cell subsets in terms of their relative levels of direct virus infection in vivo (33-35). In this study we tested the possibility that the decrease in the T_FR/T_FH ratio observed during SIV infection of RM was associated with higher level of virus infection in T_FR as compared to T_FH. However, our comparative analysis of the cell-associated viral burdens in sorted T_FR, T_FH, and T_REG revealed similar levels of SIV-DNA in the three CD4⁺ T-cell subsets, even though T_FR exhibited higher levels of the SIV co-receptor CCR5 as compared to the other two subsets.

In summary, the presented data provide the first comprehensive description of T_FR in healthy, uninfected RM, as well as the first examination of the kinetics of these cells in the setting of pathogenic SIV infection. These results support the hypothesis that these cells play an important immune regulatory role in vivo, and that a relative decline of the T_FR/T_FH ratio may be involved in establishing a state of chronic immune activation in the B-cell areas of lymph nodes during pathogenic HIV and SIV infection.