Analysis of Ag-specific CD4+ T cells in mycobacterial infections at the transcriptome level is informative but technically challenging. Although several methods exist for identifying Ag-specific T cells, including intracellular cytokine staining, cell surface cytokine-capture assays, and staining with peptide:MHC class II multimers, all of these have significant technical constraints that limit their usefulness. Measurement of activation-induced expression of CD154 has been reported to detect live Ag-specific CD4+ T cells, but this approach remains underexplored and, to our knowledge, has not previously been applied in mycobacteria-infected animals. In this article, we show that CD154 expression identifies adoptively transferred or endogenous Ag-specific CD4+ T cells induced by Mycobacterium bovis bacillus Calmette-Guérin vaccination. We confirmed that Ag-specific cytokine production was positively correlated with CD154 expression by CD4+ T cells from bacillus Calmette-Guérin–vaccinated mice and show that high-quality microarrays can be performed from RNA isolated from CD154+ cells purified by cell sorting. Analysis of microarray data demonstrated that the transcriptome of CD4+ CD154+ cells was distinct from that of CD154− cells and showed major enrichment of transcripts encoding multiple cytokines and pathways of cellular activation. One notable finding was the identification of a previously unrecognized subset of mycobacteria-specific CD4+ T cells that is characterized by the production of IL-3. Our results support the use of CD154 expression as a practical and reliable method to isolate live Ag-specific CD4+ T cells for transcriptomic analysis and potentially for a range of other studies in infected or previously immunized hosts.
This article is featured in In This Issue, p.2189
Commonly used methods to analyze Ag-specific CD4+ T cells have limited compatibility with downstream applications, such as isolation of live cells for further manipulation or isolation of high-quality RNA from cells for transcriptome analysis. Typically, Ag-specific responses are assessed ex vivo by measuring cytokines secreted into the culture supernatant using ELISA or by enumerating cytokine-producing cells using ELISPOT (1). Neither of these methods permits phenotypic analysis of the cytokine-producing cells of interest. Individual cytokine-producing cells can be identified by intracellular cytokine staining (ICS) and subsequent analysis by flow cytometry; however, this technique requires fixation and permeabilization of cells, leading to cell death and RNA degradation (2). Alternate buffer conditions for preservation of RNA integrity during intracellular staining have been suggested but have yet to be validated (3, 4). Cell surface cytokine capture is a promising method that potentially allows identification of live cytokine-producing cells, but this technique requires bifunctional Abs and is laborious, while suffering from limited sensitivity (5). In addition, all of the above methods require that a particular cytokine of interest be identified a priori, which may not be appropriate given the plasticity and heterogeneity of Th cells (6). Although identification of Ag-specific CD4+ T cells using multimers of peptide-loaded MHC class II (MHCII) molecules does not require prior knowledge of cytokine production, this technique is limited by the need for epitope discovery and synthesis of peptide–MHC multimers, as well as by the heterogeneity of MHCII (2).
Alternatively, Ag-specific CD4+ T cells can be identified by surface molecules that are expressed specifically upon Ag stimulation, but this approach remains underexplored (7–10). CD154, also known as CD40L, is an activation-induced marker on CD4+ T cells, and its binding partner CD40 is expressed on a variety of hematopoietic and nonhematopoietic cells (11–15). Because expression of CD154 on CD4+ T cells after activation is transient, peaking at 6 h postactivation and then declining as a result of endocytosis and degradation (16), it is technically challenging to identify populations that upregulate this marker as an indicator of Ag-specific activation. To overcome this issue, two modifications of the staining procedure have been previously proposed. In the first approach, inclusion of monensin and fluorochrome-conjugated Ab against CD154 during the stimulation phase resulted in enhanced detection of CD154 by retaining the fluorescent conjugate within the cells after endocytosis (8). In the second approach, by including an Ab against CD40 that functionally blocks interaction with CD154, greater levels of CD154 were retained on the surface of CD4+ T cells (9). Both methods were used to identify and isolate Ag-specific CD4+ T cells, with the former method showing potentially greater sensitivity.
In this article, we report the use of activation-induced CD154 expression as a method to identify Ag-specific CD4+ T cells in two experimental models in mice, and we show that this method can be used to isolate high-quality samples for gene expression analysis by microarray. In one case, we assessed CD154 expression on adoptively transferred transgenic (Tg) CD4+ T cells from OT-II mice that are specific to a peptide of OVA. In the second model, we assessed CD154 expression on endogenous CD4+ T cells specific to Mycobacterium tuberculosis Ags in mice vaccinated with Mycobacterium bovis bacillus Calmette-Guérin (BCG). Analysis of both models showed that CD154 expression upon restimulation is a valid approach to identify Ag-specific CD4+ T cells, and it can be used to facilitate their transcriptome analysis. In addition, an in-depth analysis of the microarray data from cells obtained by this method resulted in the identification of a previously unrecognized subset of mycobacteria-specific CD4+ T cells that secrete the cytokine IL-3.
Materials and Methods
Six- to eight-week-old female wild-type C57BL/6 mice were obtained from The Jackson Laboratory. C57BL/6–OT-II TCR-Tg mice expressing GFP were bred in our facility from founders provided by G. Lauvau (Albert Einstein College of Medicine). All mice were maintained in specific pathogen–free conditions. All procedures involving the use of animals were in compliance with protocols approved by the Einstein Institutional Animal Use and Biosafety Committees.
M. bovis BCG-Danish was obtained from the Statens Serum Institute (Copenhagen, Denmark) and grown in Middlebrook 7H9 medium (Difco Laboratories, BD Diagnostic Systems, Sparks, MD) with oleic acid–albumin–dextrose–catalase (OADC Enrichment; Difco Laboratories, BD Diagnostic Systems) and 0.05% tyloxapol (Sigma-Aldrich, St. Louis, MO). Mycobacterium smegmatis (strain mc2155) were grown in liquid cultures in Sauton medium (17). Bacteria were grown from low passage number frozen stocks, cultured to midlog phase, and then frozen in medium with 5% glycerol at −80°C. Bacteria were thawed, washed, resuspended in PBS containing 0.05% Tween-80, and sonicated to obtain a single-cell suspension prior to infection. Mice were vaccinated with 2 × 106 CFU bacteria at the base of the tail, unless otherwise indicated. Mice were euthanized 4–6 wk after vaccination, spleens were removed, and splenocyte suspensions were prepared by gently forcing the tissue through a 70-μm cell strainer. RBC lysis was performed using Hybri-Max RBC lysing buffer (Sigma-Aldrich).
Aerosol infection with M. tuberculosis
Low-density freezer stocks were made from M. tuberculosis H37Rv cultured in Middlebrook 7H9 medium containing oleic acid–albumin–dextrose–catalase, 0.5% glycerol, and 0.05% tyloxapol. Prior to infection, bacteria were thawed, sonicated, and resuspended in PBS-Tween containing 0.05% antifoam Y-30 (Sigma-Aldrich). The suspension was loaded into a nebulizer attached to an airborne infection system (University of Wisconsin Mechanical Engineering Workshop). Mice were exposed to aerosolized bacteria for 20 min, and ∼100 bacteria were deposited into the lungs of each animal. The inoculum dose was confirmed by plating of whole-lung homogenates at 24 h postexposure, with quantification of CFU 4 wk later. Four weeks postinfection, lungs from infected mice were removed and perfused with PBS via the pulmonary artery. Lungs were then treated with Liberase TL (0.3 Wünsch unit/ml; Roche) and DNase I (10 μg/ml; Sigma-Aldrich) in serum-free RPMI 1640. The lungs were then passed through a 70-μm cell strainer. Mediastinal lymph nodes were passed through a 70-μm cell strainer to make a single-cell suspension.
Adoptive transfer of OT-II Tg CD4+ T cells
CD4+ T cells from OT-II TCR-Tg/GFP mice were purified by negative selection using a commercially available kit, according to the manufacturer’s instructions (Miltenyi Biotec), and 20,000 cells in 100 μl of PBS were injected i.v. into wild-type C57BL/6 mice. Twenty four hours later, OVA and polyinosinic-polycytidylic acid [poly (I:C)] were administered cutaneously at the base of the tail. On day 7 after vaccination, mice were euthanized to isolate splenocytes.
Restimulation and flow cytometry analysis of CD4+ T cells
For CD154 staining, splenocytes were incubated for 6 h with appropriate Ag, monensin (2 μM; Sigma-Aldrich), and anti-CD154 Ab (0.25 μg/ml) conjugated with PE in 96-well round-bottom plates. For staining intracellular IL-3, splenocytes were stimulated with Ag for 6 h, with the last 4 h in the presence of monensin (5 μM) and brefeldin A (5 μg/ml; Sigma-Aldrich). Restimulation was performed in RPMI 1640 medium supplemented with HEPES, penicillin-streptomycin (all from Life Technologies), FBS (10%; Atlanta Biologicals), 2-ME, essential amino acids, and nonessential amino acids (all from Life Technologies). The final concentration of OT-II peptide (ISQAVHAAHAEINEAGR), ESAT-6 peptide (MTEQQWNFAGIEAAASAIQG) and P25 peptide (FQDAYNAAGGHNAVF; all from Mimotopes) was 5 μg/ml. M. tuberculosis or M. smegmatis lysate was used at a final protein concentration of 50 μg/ml and was prepared as previously described (18). After restimulation, cells were suspended in PBS and stained with viability dye (LIVE/DEAD Fixable Blue Dead Cell Stain; Molecular Probes) and subsequently with fluorochrome-conjugated Abs against surface markers in PBS containing FBS (2%) and sodium azide (0.05%; Sigma-Aldrich). In some experiments, cells were fixed using paraformaldehyde (2% in PBS; Electron Microscopy Sciences), permeabilized (Fixation & Permeabilization Buffer; eBioscience), and stained for intracellular cytokines. ICS buffer used was Dulbecco’s PBS with calcium and magnesium (Life Technologies) containing FBS (2%), sodium azide (0.05%), HEPES (1%), and saponin (0.1%; Sigma-Aldrich). The following Abs were used for staining: CD154-PE (clone MR1; BD Biosciences), CD4–allophycocyanin–Cy7 (clone RM4-5; Tonbo Biosciences), CD44–eFluor 450 (clone IM7; eBioscience), CD8α–PE–Cy5 (clone 53-6.7; Tonbo Biosciences), B220–PE–Cy5 (clone RA3-6B2; BD Biosciences), MHCII–PE–Cy5 (clone M5/114.15.2; eBioscience), IFN-γ–Alexa Fluor 700 (clone XMG1.2; BD Biosciences), TNF–Alexa Fluor 488 (clone MP6-XT22; BD Biosciences), IL-2–PE–Cy7 (clone JES6-5H4; eBioscience), IL-4–allophycocyanin (clone 11B11; BD Biosciences), IL-17A–PerCP–Cy5.5 (clone eBio17B7; eBioscience), IL-21 (FFA21; eBioscience), T-bet (clone eBio4B10; eBioscience), and IL-3–PE (clone MP2-8F8; BD Biosciences).
Purified CD4+ T cells (positive selection, L3T4 MicroBeads; Miltenyi Biotec) isolated from immunized spleens were incubated in nitrocellulose ELISPOT plates (Millipore) coated with anti–IFN-γ (clone R4-6A2; BD Biosciences), anti–IL-4 (clone 11B11; BD Biosciences), anti–IL-17A (clone TC11-18H10; BD Biosciences), or anti–IL-3 (MP2-8F8, purified in-house) Abs with naive T cell–depleted splenocytes and TB9.8 peptide (ESSAAFQAAHARFVAA; 10 μg/ml; Mimotopes) (19). Mice used for these experiments received 1 × 107 CFU BCG s.c. at the scruff of the neck. After 20 h of incubation at 37°C, plates were washed with PBS containing 0.05% Tween 20 and incubated with biotinylated anti–IFN-γ (clone XMG1.2), anti–IL-4 (clone BVD6-24G2), anti–IL-17A (clone TC11-8H4), or anti–IL-3 (clone MP2-43D11) Abs. Bound Abs were detected using streptavidin-alkaline phosphatase (Life Technologies), 5-bromo-4-chloro-3-indolyl-phosphate, and NBT chloride (Sigma-Aldrich). Plates were counted with the aid of computer-assisted image analysis using an AID ELISPOT reader (Autoimmun Diagnostika). IL-21 assay was performed using an IL-21 mouse ELISPOT kit, as per the instructions provided by the manufacturer (eBioscience).
Sorting and microarray experiments
CD154 staining was performed with splenocytes isolated from mice vaccinated with BCG and restimulated with M. tuberculosis lysate, as described above. For each mouse, Ag-experienced CD4+ T cells were identified as live cells in the lymphocyte gate that were negative for CD8α, B220, and MHCII, and positive for CD4 and CD44. From this population, CD154+ and CD154− cells were sorted separately into tubes containing RNA Protect Cell Reagent (QIAGEN). The cells were pelleted, and RNA isolation was performed using an RNeasy Micro Kit (QIAGEN). Isolated RNA was quantified using the RiboGreen method (Quant-iT RiboGreen RNA Assay Kit; Molecular Probes) with a NanoDrop 3300 instrument (Thermo Fisher Scientific). RNA quality was assessed using a Bioanalyzer (Agilent Technologies). Twenty-five nanograms of total RNA were amplified using an Ovation Pico WTA System (http://www.nugen.com/products/ovation-pico-wta-system-v2; NuGEN) and hybridized onto GeneChip Mouse Gene 1.0 ST arrays (20) (https://www.thermofisher.com/order/catalog/product/901168; Affymetrix). The arrays were scanned using a GeneChip Scanner 3000, and fluorescent intensity for each feature on the array was quantified using GeneChip Operating Software (both from Affymetrix). Probe summarization and normalization using the Robust Multiarray Average method was performed using Expression Console software (Affymetrix). The microarray files have been submitted to the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) under accession number GSE98182.
Analysis of microarray data
Principal component analysis (PCA) was performed using the top 15% most varying transcripts identified based on SD with R software using the library “ggbiplot” (https://www.rdocumentation.org/packages/ggbiplot). Differentially expressed transcripts were identified by rank product analysis performed with R software using the library “RankProd” (21). Rank product is a nonparametric statistic derived from biological reasoning that detects items that are consistently highly ranked in a number of lists (22). Heat maps were generated using TIGR Multiple Experiment Viewer (http://mev.tm4.org/). Pathway analysis was performed using Gene Set Enrichment Analysis (GSEA) software obtained from the Molecular Signatures Database (23). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (http://www.genome.jp/kegg/pathway.html) and the TRANSFAC database (http://gene-regulation.com/pub/databases.html) were accessed through GSEA software.
The SYBR Green method was used to quantify relative levels of selected transcripts as per the protocols provided by the supplier (Molecular Probes). ssDNA obtained after amplification of RNA was used as template. Transcripts were selected to cover the entire range of microarray intensity and included genes that were differentially and nondifferentially expressed between CD154+ and CD154− populations. Primers were designed using the National Center for Biotechnology Information Primer-BLAST software, which uses Primer3 to design primers and Basic Local Alignment Search Tool and global alignment algorithm to screen primers against nonspecific amplifications (24). Primer sequences are provided in Supplemental Table II.
Detection of adoptively transferred Ag-specific CD4+ T cells based on activation-induced expression of CD154
Inclusion of fluorochrome-conjugated anti-CD154 Ab and monensin in the culture medium during stimulation has been shown to enhance the detection of CD154+ Ag-specific CD4+ T cells (8). To evaluate the sensitivity and specificity of this assay, we used a defined monoclonal population of GFP-expressing TCR-Tg CD4+ T cells specific for OVA323–339 peptide (OT-II cells) adoptively transferred into naive recipient hosts. Following i.v. transfer of OT-II cells, recipient mice were vaccinated with OVA in the presence of poly (I:C). Seven days after vaccination, we isolated splenocytes and restimulated them with OVA peptide in the presence of PE-conjugated anti-CD154 and 2 μM monensin. Flow cytometry analysis showed that restimulation with OVA323–339 resulted in specific upregulation of CD154 on GFP+ CD4+ T cells, whereas the levels of CD154 on endogenous cells remained at background levels in stimulated and unstimulated conditions (Fig. 1A). Over a range of anti-CD154 Ab concentrations, >90% of GFP+ OT-II cells were positive for CD154 based on detection of PE by flow cytometry (Fig. 1B).
Staining of intracellular cytokines following Ag stimulation is a standard method used to characterize Ag-specific CD4+ T cells. Because staining for CD154 in the presence of monensin is compatible with subsequent staining for intracellular cytokines (8), we combined staining for activation-induced CD154, as described above, with staining for intracellular IFN-γ, TNF, and IL-2. These cytokines were chosen for initial studies because poly (I:C) is a known Th1-polarizing adjuvant (25). As expected, most cytokine-producing OT-II cells were costained for CD154 (Fig. 1C). Similar results were also obtained when the cytokine-producing cells were grouped into single-, double-, or triple-cytokine producers (Fig. 1D). We also observed that ∼25% of CD154+ cells did not secrete any of these cytokines. It is possible that these cells secrete other cytokines that were not assessed or may represent Ag-specific CD4+ T cells that do not produce detectable cytokines under the conditions used for activation.
Detection of endogenous mycobacteria-specific CD4+ T cells based on activation-induced expression of CD154
We next evaluated the ability of the assay to detect endogenous polyclonal populations of pathogen-specific CD4+ T cells. For this, we tested whether CD154 expression allows identification of mycobacteria-specific CD4+ T cells in a murine model of BCG vaccination. We isolated splenocytes from mice that were previously vaccinated with BCG and stimulated them with P25 peptide of the immunodominant Ag 85B or lysate of M. tuberculosis in the presence of PE-conjugated anti-CD154 Ab and monensin. Splenocytes from unvaccinated mice were used as negative controls. We observed that CD4+ T cells from unvaccinated mice showed minimal background levels of CD154, which did not change across various stimulation conditions (Fig. 2A). The background level was slightly higher in the CD44+ Ag-experienced population compared with the CD44− population, as shown previously (26). Similar background levels of CD154 were also noted on the unstimulated cells from mice vaccinated with BCG; however, stimulation with P25 peptide or lysate resulted in a significant increase in CD154 levels on cells from vaccinated mice (Fig. 2A, 2B). This increase was seen only in CD44+ cells, consistent with stimulation-induced CD154 expression occurring only in Ag-experienced cells. We observed that, compared with unvaccinated mice, peptide stimulation in vaccinated mice resulted in a 9-fold increase in the percentage of CD154+ CD4+ T cells, whereas the increase was 15-fold with lysate stimulation. The higher number of Ag-specific cells with lysate stimulation was consistent with the presence of multiple Ags in the lysate. Based on these fold calculations, we estimated that the percentage of Ag-specific cells within the CD154+ population was ∼90% (peptide stimulation) or 94% (lysate stimulation).
We also combined CD154 staining with ICS and found that most cells expressing IFN-γ, TNF, and IL-2 were positive for CD154, as we observed with adoptively transferred cells (Fig. 2C). Similar results were also obtained when the cytokine-producing cells were grouped into single-, double-, or triple-cytokine producers (Fig. 2D). Cells that produced only IL-2 showed the least coexpression with CD154, consistent with a previous study of human PBMCs (8). We also noted that ∼75% of CD154+ endogenous CD4+ T cells did not secrete any of the cytokines that we assayed by ICS (Fig. 2C). This percentage of cytokine-negative CD154+ cells was higher than the ∼25% that we observed in our adoptive transfer model using OT-II peptide for restimulation (Fig. 1C). This may be explained, in part, by secretion of cytokines other than those associated with Th1 cells by a substantial portion of endogenous T cells. In addition, it likely reflects the stronger signal generated by stimulation with optimal concentrations of synthetic peptide epitopes, as in the case of the OT-II experiments. Thus, in the case of M. tuberculosis lysate stimulation of endogenous BCG-primed T cells, stimulation was sufficient to induce CD154 expression but not cytokine production. This would be consistent with the known differences that can exist in the thresholds of TCR stimulation for different outcomes of activation (27–29) and is also mirrored by previous studies of human T cells showing a similar higher percentage of cytokine expression by CD154+ cells with peptide stimulation compared with a more polyclonal stimulation using Staphylococcus aureus enterotoxin B (8, 9).
Transcriptome analysis of mycobacteria-specific CD4+ T cells induced by M. bovis BCG vaccination
To characterize CD154+ CD4+ T cells in a broader, unbiased fashion, we performed transcriptome analysis of these cells. We isolated splenocytes from mice that were previously vaccinated with BCG and stimulated them with lysate of M. tuberculosis in the presence of PE-conjugated anti-CD154 Ab and monensin. We then sorted CD154+ and CD154− Ag-experienced (CD44+) CD4+ T cells using high-speed cell sorting (FACS). Although we sorted higher numbers of CD154− cells compared with CD154+ cells and subsequently obtained greater amounts of RNA from these cells, CD154+ and CD154− cells yielded high-quality RNA, as indicated by their high RNA integrity number values. (Supplemental Fig. 1A, 1B). After amplifying the RNA using standard protocols, microarray experiments were carried out with Affymetrix Gene Chips (30). The quality of the microarrays was assessed using guidelines adopted by the Immunological Genome Project, which is a compendium that characterizes microarray gene-expression profiles of cells in the murine immune system (https://www.immgen.org/). Comparison of the quality metrics showed that the microarrays in the current study outperformed the arrays from the Immunological Genome Project, based on higher dynamic range and area under the curve and lower coefficient of variation (Supplemental Fig. 1C). In addition, no significant differences were observed for these metrics between microarrays performed for the CD154+ and CD154− populations (Supplemental Fig. 1C). These results showed that high-quality microarray data can be obtained from FACS-sorted CD154+ cells.
CD154+ cells were expected to have a different transcriptome signature than CD154− cells for at least two reasons. First, because they were activated by M. tuberculosis lysate, they should preferentially express transcripts associated with cellular activation. Second, CD154+ cells should be enriched for helper populations generated by the original BCG vaccination. We performed PCA to test whether CD154+ cells had a unique transcriptome signature. The analysis revealed that the CD154+ cells grouped separately from CD154− cells, indicating differences in the transcriptome of these populations (Fig. 3A). We next identified genes that were differentially expressed between the CD154+ and CD154− populations using Rank product, a nonparametric method of identifying differentially transcribed genes in microarrays (22). Compared with CD154− cells, 271 genes were upregulated in CD154+ cells, whereas 195 genes were downregulated (Fig. 3B, Supplemental Table I). All of these genes were included in the gene set used for PCA. Quantitative PCR analysis of selected transcripts showed that relative transcript levels were highly correlated with microarray intensity (Fig. 3C). Many of these differentially expressed transcripts were from genes known to be associated with CD4+ T cell responses. For example, the top 10 transcripts that were more highly expressed in CD154+ cells compared with CD154− cells were predominantly from genes encoding cytokines and chemokines known to be secreted by CD4+ T cells (Fig. 3D). Similarly, compared with CD154+ cells, CD154− cells expressed higher levels of transcripts encoding LRRC32/GARP, a cell surface protein known to be associated with regulatory T cells, and FOXP3, the master transcription factor for regulatory T cells. However, there were notable exceptions, indicating previously unrecognized features of the transcriptional profiles of CD4+ T cells undergoing activation by cognate Ags. For example, the most highly expressed transcript in CD154+ cells versus CD154− cells encodes a cytoskeletal protein called Ermin (ERMN), which is believed to be restricted to oligodendrocytes (31). Similarly, the most highly expressed gene for a transcription factor in CD154+ versus CD154− cells encodes zinc finger E-box binding homeobox 2 (ZEB2), which has been implicated in CD8+ T cell and myeloid cell function but, to our knowledge, has not been investigated for its potential functions in CD4+ T cells (32, 33).
Pathway analysis of transcriptome data and cytokine expression
We hypothesized that those transcriptional changes seen in CD154+ cells should mirror changes associated with cellular activation because these cells are activated by cognate Ag. To explore this possibility, we performed pathway analysis of the microarray data using GSEA (23). By using genes sets derived from the KEGG pathway database, we found that the pathways that were significantly enriched in the CD154+ transcriptome represented metabolic changes associated with activation of T cells (Fig. 4A). We also investigated whether targets of certain transcription factors were selectively enriched in the CD154+ population using sets of genes sharing a transcription factor binding site, as defined in the TRANSFAC database. This analysis showed that the targets of transcription factors known to be associated with cellular activation, growth, and proliferation were enriched in the CD154+ population (Fig. 4B).
We also carried out a focused analysis of the expression of cytokines, chemokines, and their receptors, based on the microarray data. This identified 31 transcripts that were differentially expressed between CD154+ and CD154− populations. Among these, 27 were more highly expressed in the CD154+ population, whereas only 4 were more highly expressed in the CD154− population (Fig. 4C). Transcripts more highly expressed in the CD154+ population included those encoding cytokines known to be associated with various Th populations, such as the signature cytokines of Th1 (IFN-γ), Th2 (IL-4, IL-5, and IL-13), Th17 (IL-17A), and T follicular helper (IL-21) populations (Fig. 4C).
Although mycobacteria are well known to induce Th1 cells, induction of transcripts associated with other helper cell subsets was unexpected. We selected signature cytokines for Th2 (IL-4), Th17 (IL-17A), and T follicular helper (IL-21) cells for protein level validation using ICS. Although we were able to detect IL-4– and IL-17A–secreting cells in BCG-vaccinated mice in some cases (Supplemental Fig. 2A, 2B), we were unable to detect them consistently in all experiments. ELISPOT assays were also performed to analyze CD4+ T cells secreting these cytokines, which showed that significantly higher frequencies of cells producing IL-17A, but not IL-4, were observed in BCG-vaccinated mice compared with naive mice (Supplemental Fig. 2C). Neither technique detected IL-21–producing CD4+ T cells, possibly reflecting the known difficulties in detecting IL-21–producing germinal center T follicular helper cells (34, 35). Based on our results, published reports (36–40), and known caveats in directly comparing RNA and protein levels (41, 42), we surmise that helper cells other than Th1 cells are generated during BCG vaccination but at low frequencies.
Transcripts that were more highly expressed in the CD154− population included that encoding IL-10, a cytokine associated with regulatory T cells. The expression of IL-10 transcript, together with the relative increase in Lrrc32 and Foxp3 transcripts in the CD154− population compared with the CD154+ population (Figs. 3D, 4C), was consistent with previous reports showing that regulatory T cells lack preformed CD154 and that induced regulatory T cells are unlikely to be included in the CD154+ population (43–45).
Identification of an IL-3–secreting subset of mycobacteria-specific CD4+ T cells
An in-depth analysis of our transcriptome data revealed that, among the cytokines not classically associated with known Th subsets, transcripts encoding IL-3 showed the highest differential expression between CD154+ and CD154− populations of CD4+ T cells in BCG-vaccinated mice. This was an unexpected finding because IL-3, a cytokine primarily associated with antiparasite immunity and hematopoiesis, has seldom been considered or studied in the context of mycobacterial immunity (46). We validated the microarray results by quantitative PCR, which showed that transcripts encoding IL-3 were nine times more abundant in CD154+ cells compared with CD154− cells (Fig. 5A). We then examined whether IL-3 secretion by CD4+ T cells could be detected at the protein level. Mice were vaccinated with BCG, and 4 wk after vaccination, CD4+ T cells were restimulated in vitro with M. tuberculosis lysate and stained for intracellular IL-3. We observed that ∼0.2% of CD4+ T cells secreted IL-3 after restimulation, and the secretion was restricted to Ag-experienced (i.e., CD44+) CD4+ T cells (Fig. 5B). This was ∼18-fold less than the frequency of IFN-γ–producing CD4+ T cells detected by ICS (Fig. 5C) or by ELISPOT assays (Fig. 5D). IL-3–secreting CD4+ T cells coexpressed IFN-γ (Fig. 5E), indicating that IL-3–producing cells were also CD154+, because IFN-γ–producing cells were almost uniformly CD154+. The coexpression of IFN-γ, but not IL-4 or IL-17A, suggested that these cells were related to Th1 cells (Fig. 5E), and this was further reinforced by the finding that they also expressed T-bet (Fig. 5F).
Because BCG is derived from a pathogenic species of Mycobacterium, it is possible that it has retained properties that interfere with its ability to induce optimal protective immunity (47). Generation of IL-3 may be one of the mechanisms, because mast cells and basophils that express IL-3R and mediate type II immunity may have adverse effects on the generation of an effective type I immune response (48). To determine whether IL-3 production by CD4+ T cells is restricted to BCG or is a more general feature of immune responses to mycobacteria, we injected mice with the nonpathogenic M. smegmatis and examined IL-3 production by CD4+ T cells 4 wk later. We found that M. smegmatis also induced IL-3 production from CD4+ T cells, albeit at a lower level than BCG, suggesting that IL-3 induction is likely to be a conserved feature of host responses to various mycobacterial species (Fig. 5G).
We also assessed whether IL-3–producing CD4 T cells are induced postinfection with M. tuberculosis in a model of low-dose aerosol infection. We assessed IL-3 production by CD4+ T cells 4 wk after mycobacterial infection and found that mediastinal lymph node and lung contained IL-3–producing CD4+ T cells that are specific to the mycobacterial peptide ESAT-6. (Fig. 5H, 5I). Thus, transcriptome analysis of mycobacteria-specific CD4+ T cells resulted in the identification of a previously unrecognized subset characterized by the production of IL-3.
Using two different experimental models, we have shown in the current study that detection of activation-induced CD154 expression is a reliable method to identify and isolate Ag-specific CD4+ T cells for downstream applications. Our approach was based on modifications of a method reported previously that uses inclusion of fluorescent Abs to CD154 and monensin in the media during in vitro Ag restimulation of CD4+ T cells (8). We initially validated this method using adoptively transferred Tg CD4+ T cells specific to a peptide of OVA and then extended and confirmed our results in a second model examining endogenous mycobacteria-specific CD4+ T cells after BCG vaccination. In these models, we found that CD154 expression served as a reliable marker for ≥90% of the Ag-specific CD4+ T cells that could be reactivated by Ag exposure in vitro. Expression microarray analysis performed on mycobacteria-specific CD4+ T cells further supported the validity of using CD154 expression as a surrogate marker for Ag specificity. In addition, microarray analysis revealed that, although multiple functional Th subsets were present in the CD154+ population, Ag-specific regulatory T cells were likely to be excluded, consistent with previous reports in the literature (43–45).
Transcriptome analysis of Ag-specific CD4+ T cells highlighted several understudied molecules expressed by these cells. One of these is Ermn, the most highly expressed transcript in CD154+ cells relative to CD154− cells, which encodes a cytoskeletal protein Ermin. Expression of ERMN is believed to be restricted to oligodendrocytes, and it has been shown to bind and reorganize F-actin in these cells (31). ERMN belongs to the family of ERM proteins that functions upstream and downstream of Rho GTPases and probably controls process outgrowth and morphological changes during oligodendrocyte differentiation (31). Although the function of ERMN in CD4+ T cells or other immune cells is yet to be studied, it is possible that, similar to oligodendrocytes, the function of ERMN in these cells is related to cytoskeletal changes during activation. Another transcript that showed high levels of expression in CD154+ cells encoded ZEB2, a two-handed zinc-finger transcription factor that binds DNA at tandem, separated consensus E-box sites (32). Based on microarray intensities, expression of Zeb2 was highest among all transcription factors in CD154+ cells versus CD154− cells from BCG-vaccinated mice. Recent studies have shown that ZEB2 expression is important for the differentiation of effector and memory CD8+ T cells postinfection with lymphocytic choriomeningitis virus (32, 49). Although ZEB2 was shown to be downstream of T-bet, and these two factors coregulate many of the same genes, it appears that ZEB2 modulates these downstream genes independently. Given that ZEB2 plays a similar role in the maturation of NK cells (50) and is required for the development of plasmacytoid dendritic cells and monocytes (33), it would be relevant to assess whether ZEB2 has similar functions in the differentiation of CD4+ T cells.
Our experiments also showed that BCG vaccination and M. smegmatis infection induce IL-3–producing CD4+ T cells, which are related to Th1 cells. We pursued IL-3–producing CD4+ T cells because, in the microarray analysis, transcripts for this cytokine showed the most differential expression among cytokines that are not associated with a defined Th population. In addition, this cytokine has not been investigated in detail in the context of antimycobacterial immunity. IL-3 was originally described as a cytokine secreted by CD4+ T cells and has been shown to have proliferative effects on various cell types (51, 52). Studies on IL-3–knockout mice showed that IL-3 has minor nonredundant effects on hematopoiesis, but it plays a significant role in antiparasite immunity through its action on mast cells and basophils (46, 53–56). There is increasing interest in this cytokine given recent reports on the involvement of IL-3 in augmenting features of sepsis, systemic lupus erythematosus, and the experimental autoimmune encephalomyelitis model of multiple sclerosis (57–59).
Our results showed that M. tuberculosis infection by the aerosol route induced IL-3–producing CD4+ T cells in the lung. Although we perfused lungs prior to processing the tissue into single-cell suspensions, we cannot exclude the possibility that these cells reside within the lung vasculature, because simple perfusion may be inefficient at removing intravascular T cells (60). Further analysis of these cells after intravascular staining will allow us to better determine their location within the lung (61). This is important, because it has been shown that Th1 cells in the lung parenchyma are highly protective, whereas those in the lung vasculature are only weakly protective (62). If they are located within the lung parenchyma, their location within the granuloma and their relationship with other cells, especially those expressing the receptor to IL-3, will be important issues to address in future studies. Additionally, the question of whether IL-3–producing CD4+ T cells contribute significantly to immunity during infection with M. tuberculosis and whether IL-3–producing CD4+ T cells induced by BCG vaccination are recalled during an M. tuberculosis challenge will require further study. Given the role of IL-3 in antiparasite immunity, IL-3–producing CD4+ T cells may have effects that are deleterious for antituberculosis immunity, as has been suggested generally for Th2 cytokines (48). That IL-3–producing CD4+ T cells are induced by M. smegmatis, a nonpathogenic Mycobacterium, does not exclude this possibility. This deleterious effect could be mediated by mast cells, which are abundant at barrier surfaces, including respiratory epithelium (63). Mast cells have been shown to interact with M. tuberculosis, triggering the release of histamine, β-hexosaminidase, TNF-α, and IL-6 (64). In addition, activation of mast cells using compound 48/80 in M. tuberculosis–infected mice can lead to increased bacterial burdens, presumably by decreasing Th1 cytokines and increasing IL-10 (65). Basophils and eosinophils can also infiltrate inflamed lung and mediate activities that exacerbate pathology and promote mycobacterial growth (66–68).
In summary, our studies support the use of activation-induced expression of CD154 as a method to detect and isolate Ag-specific CD4+ T cells in murine models of infection and vaccination. This method has advantages over ICS when downstream applications require live cells or when high-quality RNA needs to be isolated for transcriptomic studies. This approach can also be used when tetramers or bifunctional Abs for cell surface cytokine-capture assays are not readily available. In addition, this method is useful when analyzing responses to complex Ag mixtures, such as extracts of whole micro-organisms or tumor cells, or when the choice of cytokine to be assayed is uncertain.
We thank the staff from the Einstein Genomics and Flow Cytometry Core facilities for assistance with microarray and cell sorting experiments, and we thank Pooja Arora for insightful discussions.
This work was supported by National Institutes of Health/National Institute of Allergy and Infectious Diseases Grants 1R21AI092448 (to S.A.P.) and 2P01AI063537 (to W.R.J., S.A.P., and J.C.). Core resources for flow cytometry and microarray analysis were supported by the Einstein Cancer Center (Grant CA13330). Support for C.T.J. was provided by National Institutes of Health Training Grant GM07491.
The microarray files presented in this article have been submitted to the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) under accession number GSE98182.
The online version of this article contains supplemental material.
Abbreviations used in this article:
The authors have no financial conflicts of interest.