Identification of Novel STAT6-Regulated Proteins in Mouse B Cells by Comparative Transcriptome and Proteome Analysis

Lavanya Mokada-Gopal, Alexander Boeser, Christian H. K. Lehmann, Friedel Drepper, Diana Dudziak, Bettina Warscheid and David Voehringer
J Immunol May 1, 2017, 198 (9) 3737-3745; DOI: https://doi.org/10.4049/jimmunol.1601838

Abstract

The transcription factor STAT6 plays a key role in mediating signaling downstream of the receptors for IL-4 and IL-13. In B cells, STAT6 is required for class switch recombination to IgE and for germinal center formation during type 2 immune responses directed against allergens or helminths. In this study, we compared the transcriptomes and proteomes of primary mouse B cells from wild-type and STAT6-deficient mice cultured for 4 d in the presence or absence of IL-4. Microarray analysis revealed that 214 mRNAs were upregulated and 149 were downregulated >3-fold by IL-4 in a STAT6-dependent manner. Across all samples, ∼5000 proteins were identified by label-free quantitative liquid chromatography/mass spectrometry. A total of 149 proteins was found to be differentially expressed >3-fold between IL-4–stimulated wild-type and STAT6−/− B cells (75 upregulated and 74 downregulated). Comparative analysis of the proteome and transcriptome revealed that expression of these proteins was mainly regulated at the transcriptional level, which argues against a major role for posttranscriptional mechanisms that modulate the STAT6-dependent proteome. Nine proteins were selected for confirmation by flow cytometry or Western blot. We show that CD30, CD79b, SLP-76, DEC205, IL-5Rα, STAT5, and Thy1 are induced by IL-4 in a STAT6-dependent manner. In contrast, Syk and Fc receptor–like 1 were downregulated. This dataset provides a framework for further functional analysis of newly identified IL-4–regulated proteins in B cells that may contribute to germinal center formation and IgE switching in type 2 immunity.

Introduction

B cells constitute the humoral arm of the adaptive immune response by their ability to differentiate into Ab-secreting plasma cells. Type 2 immune responses against allergens and helminths elicit the secretion of IL-4 and IL-13 from Th2 cells that promote class switch recombination (CSR) in B cells to IgE and IgG1 Ab isotypes. The transcription factor STAT6 mediates downstream signaling from the receptors for IL-4 and IL-13, which both contain the IL-4R α-chain (IL-4Rα) (1). The cytoplasmic tail of IL-4Rα gets phosphorylated at several tyrosine residues (Y) upon ligand binding, among which p-Y575, p-Y603, and p-Y631 are important docking sites for the Src homology 2 (SH2) domain of STAT6. STAT6 is composed of a central DNA-binding domain, an SH2 domain, an SH3 domain, and a C-terminal transactivation domain. Upon binding to IL-4Rα, Janus kinases (mainly JAK1 and JAK3) phosphorylate STAT6 at Y641, which leads to formation of homodimers via SH2–p-Y641 interaction. STAT6 dimers translocate to the nucleus, bind to spaced palindromic sequences (consensus sequence: TTCN3–4GAA, also named GAS-4), and regulate gene expression (2).

In B cells, STAT6 was shown to be required for CSR to IgE and IgG1 (3–5). In addition, we recently showed that B cell–intrinsic STAT6 is required for germinal center formation during type 2 immune responses (6). Oligonucleotide microarray analysis of IL-4–stimulated B cells from wild-type (WT) and STAT6−/− mice led to the identification of ∼70 genes that were regulated by STAT6, and about half of them were actually suppressed by STAT6 (7). To further reveal direct STAT6 binding sites in the genome, chromatin immunoprecipitation sequencing analysis was performed with the human Burkitt lymphoma cell line Ramos. This study identified 556 putative STAT6 target sites in the genome of this cell line (8). A similar study with primary mouse T cells revealed >4000 STAT6 binding sites and transcriptional regulation of ∼1200 genes (9). Using a STAT6 RNA interference approach, the expression of 453 genes was affected by decreased STAT6 expression in human T cells (10). Furthermore, STAT6 was found to regulate the activity of enhancers by epigenetic modifications of histones and deposition of the acetyltransferase p300 (11). In general, these studies demonstrated that STAT6 regulates the expression of several hundred genes in T and B cells. However, changes in transcription may not translate into changes at the protein level, because mRNAs are subject to regulation by microRNAs and other factors that interfere with translation. Furthermore, the variable stability and half-life of proteins make it difficult to predict changes at the protein level from transcriptome analysis. There is no information available on the IL-4/STAT6–regulated proteome in primary B cells; hence, it is unclear to what extent changes in the transcriptome actually translate into changes in the proteome. Without this information, it is difficult to judge whether specific therapeutic intervention of mRNA transcription or stability (e.g., by microRNA or short hairpin RNA approaches) would be successful in lowering the protein levels. Furthermore, for proteins with a very low turnover, even a minor increase in mRNA levels could result in a major increase at the protein level. Therefore, it is important to investigate mRNA and protein expression levels in parallel.

In this study, we compared the IL-4/STAT6–regulated transcriptome and proteome in primary B cells isolated from WT and STAT6−/− mice. B cells were purified from the spleen and stimulated in vitro with anti-CD40 and LPS or anti-IgM–F(ab′)2 in the presence or absence of IL-4. Transcriptome analysis was performed with oligonucleotide microarrays. Global relative quantification of proteins was achieved by gel-enhanced label-free liquid chromatography/mass spectrometry (LC/MS). Hierarchical clustering and principal component analysis revealed that IL-4–induced changes in the transcriptome were almost completely dependent on STAT6. In contrast, the quantitative proteome analysis revealed that the expression of many IL-4–regulated proteins changes, even in the absence of STAT6. The top 75 proteins with changes in abundance levels induced by IL-4 in a STAT6-dependent manner were also found to be regulated at the transcriptional level. Most of these proteins were not previously known to be regulated by STAT6 in B cells. We confirmed the MS-based quantitative proteome data by flow cytometric and Western blot analysis of select proteins. This study provides a framework for further functional characterization of STAT6-regulated proteins in B cells that might be involved in germinal center formation and CSR.

Materials and Methods

Mice

BALB/c (WT) mice were purchased from Charles River (Sulzfeld, Germany). STAT6−/− mice (4) were described previously and were used on a BALB/c background. Mice were kept under specific pathogen–free conditions and used at 8–12 wk of age. All experiments were performed in accordance with German animal protection law and European Union guidelines 86/809 and were approved by the Federal Government of Lower Franconia.

B cell cultures

Untouched B cells from WT and STAT6−/− mice were isolated from the spleen by negative selection EasySep Mouse B Cell Isolation Kit (STEMCELL Technologies, Vancouver, Canada). A total of 2 × 106 purified B cells was cultured in RPMI 1640 (PAN-Biotech, Aidenbach, Germany) supplemented with 10% FCS (Life Technologies by Thermo Fisher Scientific, Darmstadt, Germany), 25 mM HEPES, 10 mM 2-ME, 2 mM L-Glutamine (both from Life Technologies by Thermo Fisher Scientific), and 100 μg/ml Penicillin/Streptomycin (Biochrom, Berlin, Germany) and stimulated with IL-4 (25 ng/ml; R&D Systems, Minneapolis, MN), anti-CD40 Ab (FGK4.5, 10 μg/ml; Bio X Cell, West Lebanon, NH), and LPS (2 μg/ml; Sigma-Aldrich, St. Louis, MO) for 4 d. Fresh medium was added on day 3. For confirmation experiments shown in Figs. 4B and 5B, LPS was replaced by goat anti-mouse IgM-F(ab′)2 (10 μg/ml; Jackson ImmunoResearch Laboratories, West Grove, PA).

Flow cytometric analysis of B cell cultures

B cell cultures were harvested on day 4 and incubated at 4°C for 5 min with Fc-block (anti-CD16/CD32, clone 2.4G2; Bio X Cell), followed by 20 min with fluorescently labeled Abs diluted in FACS buffer (PBS, 2% FCS, and 0.1% NaN3). Cells were washed once in FACS buffer before analysis. The following Abs (purchased from eBioscience, San Diego, CA, unless stated otherwise) were used: allophycocyanin-labeled IgG1 (A85-1; BD Biosciences, Heidelberg, Germany), PBe450-labeled B220 (RA3-6B2), allophycocyanin-labeled CD90.2 (30H12), PE-labeled IL-5Rα (T21), PE-labeled CD79b (HM79-12), PE-labeled CD30 (mCD30.1), allophycocyanin-labeled DEC205 (NLDC-145), allophycocyanin-labeled Fc receptor–like 1 (FcRL1) (745916; R&D Systems), and eFluor 780–labeled Fixable Viability Dye. Flow cytometric analysis was performed using a FACSCanto II (BD Biosciences), and data were analyzed by FlowJo software (v10).

SDS-PAGE

A total of 2 × 106 B cells was harvested from B cell cultures on day 4 and lysed in 80–90 μl of RIPA lysis buffer (1% Nonidet P-40 [Sigma-Aldrich], 50 mM Tris [pH 7.4], 0.15 M NaCl [both from Carl Roth, Karlsruhe, Germany], 1 mM EDTA [pH 8], and 0.25% deoxycholic acid [both from Sigma-Aldrich]). Phosphatase Inhibitor Cocktail Tablets and Protease Cocktail Tablets (both from Roche Diagnostics, Rotkreuz, Switzerland) were added during cell lysis. Protein samples (20 μg) were resuspended in a 6× sample buffer (20% SDS, 5% 2-ME [both from Carl Roth], 30% glycerol [AppliChem, Darmstadt, Germany], and some grains of Bromophenol blue [Carl Roth]). NuPAGE 4–12% Bis-Tris Protein Gels (Life Technologies by Thermo Fisher Scientific) were used for electrophoresis. Gels were washed several times in water and then fixed with fixation buffer (50% methanol, 2% phosphoric acid) for 30 min. Fixed gels were washed three times for 10 min in water and then incubated for 30 min in staining buffer consisting of 34% methanol, 2% phosphoric acid, and 17% ammonium sulfate before adding Coomassie 250 G to stain the gel for 16 h.

Western blot analysis

A total of 10 μg of protein from total B cell lysates was suspended in 6× sample buffer, separated by 12% Mini-Protean TGX stain free SDS-PAGE, and transferred onto Immuno-Blot PVDF Membranes (both from Bio-Rad, Munich, Germany). Membranes were incubated with Abs against SLP-76 (product no. 4958, 1:1000), Syk (product no. 2712, 1:1000; both from Cell Signaling Technology, Danvers, MA), STAT5a/b (cat. no. AF2168, 1 μg/ml; R&D Systems), or β-actin (13E5, 1:1000; Cell Signaling Technology). HRP-conjugated Donkey Anti-Rabbit IgG was used as secondary Ab (1:5000; Jackson ImmunoResearch) and visualized with the SignalFire ECL Western blotting substrate (Cell Signaling Technology), according to the provided protocol.

Label-free quantitative LC/MS

Gel lanes were cut into 10 slices, which were destained and subjected to alkylation of thiol groups by iodoacetamide and in-gel digestion using trypsin, as described (12). Peptide mixtures were analyzed by ultra-HPLC tandem MS (MS/MS) using an Ultimate 3000 RSLCnano online coupled to a Q Exactive Plus mass spectrometer (both from Thermo Scientific). Samples were washed for 5 min on a 5 × 0.3-mm PepMap C18 μ-precolumn, followed by separation on a 50 cm × 75-μm C18 reversed-phase nano LC column (Acclaim PepMap, 2 μm particle size, 100 Å pore size; both from Thermo Scientific) at 40°C using a linear gradient from 3 to 34% (v/v) acetonitrile in 2% (v/v) DMSO and 0.1% formic acid in 30 min and a subsequent increase to 82% acetonitrile in 5 min at a flow rate of 250 nl/min. For electrospray ionization, a fused silica emitter (New Objective) and a Nanospray Flex ion source (Thermo Scientific) in positive mode, at a voltage of 1.5 kV and a transfer capillary temperature of 200°C, were used. MS survey scans were recorded between m/z 375 and 1700 at a resolution of 70,000, an automatic gain control target of 3 × 106 ions, and a maximum ion injection time of 60 ms. The top 12 most intense multiple charged precursor ions exceeding the intensity threshold of 5.8 × 103 were subjected to MS/MS experiments using higher-energy collisional dissociation (normalized collision energy 28) with an automatic gain control of 1 × 105 ions, a maximum injection time of 120 ms, and a resolution of 35,000. Previously fragmented precursor ions were excluded from additional MS/MS scans for 45 s.

LC/MS data analysis

LC/MS raw data were analyzed using Andromeda/MaxQuant software (version 1.5.3.12) (13, 14) and protein sequences from the proteome set of mouse from the UniProt database (54,489 entries, including isoforms; version 2015_08) and the set of common contaminants provided by MaxQuant. Up to three missed sites for proteolytic cleavage by trypsin were accepted. Acetylation of protein N termini and oxidation of methionine were considered as variable modifications, whereas carbamidomethylation of cysteine was considered as a fixed modification. A false discovery rate of 1% was applied to peptide and protein levels. For protein identification, at least one unique peptide with a minimal length of 6 aa was required. For quantification, the “match between runs” option was enabled, and the algorithm for label-free quantification (LFQ) was applied with a minimal ratio count of two. Perseus software (version 1.5.2.6) (15) was used for statistical analysis of the protein abundance data. In brief, entries marked as identified only by site, reverse entries, or potential contaminants were removed. Reproducibility of datasets across five biological replicates per condition was verified, as reflected by Pearson correlation coefficients generally > +0.95. Data from one sample (replicate one of WT without IL-4) showed poor correlation < +0.7 with other replicates and were excluded from further analysis. The dataset was filtered for at least two nonzero LFQ intensity values. Log10 values of LFQ intensities were calculated. Missing values were imputed from a normal distribution (width of 0.3, downshifted by 1.8 SD), thereby modeling proteins of low expression under certain conditions. Mean log10 LFQ intensities of biological replicates were computed, and a two-sample two-tailed Student t test was applied to identify candidate proteins with significantly different abundances (p < 0.05) between conditions. MS proteomics data have been submitted to the ProteomeXchange Consortium via the PRIDE (16) partner repository with the dataset identifier PXD005224.

Microarray analysis

Total RNA from three biological replicates of each B cell culture described above was extracted using an RNeasy Mini kit (Miltenyi Biotec, Bergisch-Gladbach, Germany). RNA quality was assessed using an Agilent 2100 Bioanalyzer platform; all samples revealed RNA integrity number values between 9.6 and 9.9, indicating a high RNA purity and integrity. A total of 100 ng of each RNA sample was used to generate Cy3-labeled cRNA, which was then hybridized to 8x60K whole mouse genome oligonucleotide microarrays (Agilent Technologies). Labeling and hybridization were performed by Miltenyi Biotec. Normalization (Robust Multichip Average) and background subtraction for gMedianSignal were done using the Biobase package (17) in R language (http://www.R-project.org). After log2 transformation, a linear modeling approach (limma package, R) (18) was used to identify differentially expressed genes within the experimental groups. We generally applied a p value cutoff of 0.05 and a fold change cutoff of two or three, as mentioned in the Results and graphs. The microarray dataset has been submitted to the Gene Expression Omnibus database under accession number GSE84075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84075).

Results

Identification of IL-4– and STAT6-regulated mRNA transcripts in primary mouse B cells

The transcription factor STAT6 gets phosphorylated within minutes after IL-4R stimulation, and major changes in transcriptional profiles can already be observed a few hours after stimulation (7). For this study, we were interested in IL-4–induced and STAT6-dependent changes in mRNA and protein levels a few days after IL-4 exposure to mimic the in vivo situation of a helminth infection in which we showed that B cell–intrinsic STAT6 is essential for germinal center formation; this response requires a few days to establish. To identify the IL-4– and STAT6-regulated transcriptome in B cells, we first purified untouched B cells from the spleen of naive WT and STAT6-deficient (STAT6−/−) mice on a BALB/c background. STAT6 deficiency had no obvious impact on B cell development, because the numbers and ratio of splenic marginal zone and follicular B cells were comparable between WT and STAT6−/− mice (Supplemental Fig. 1). Purified B cells from three individual WT and STAT6−/− mice were then cultured for 4 d in vitro with medium containing LPS and anti-CD40 in the presence or absence of IL-4, as described in Materials and Methods. We used anti-CD40 to mimic the signal from Th cells (CD40L) and included LPS to increase the number and viability of B cells during the 4-d culture period that was especially critical to perform the proteome analysis described in the next paragraph (Supplemental Fig. 2). All cultures contained ∼96% B cells (B220hiCD138−) and 2% plasma cells (B220loCD138+) at 4 d after culture (Supplemental Fig. 3). Total RNA was isolated from these four different cultures and subjected to microarray analysis. The comparison of gene expression profiles between WT and STAT6−/− samples that had been cultured in the absence of IL-4 (Fig. 1A, lower right panel) or between STAT6−/− samples cultured in the presence or absence of IL-4 (Fig. 1A, lower left panel) revealed that only ∼20 mRNAs were differentially expressed >3-fold. However, 462 mRNAs were up- or downregulated >3-fold by IL-4 in B cells from WT mice (Fig. 1A, upper left panel), and similar numbers were observed for the comparison of IL-4–stimulated WT and IL-4–stimulated STAT6−/− samples (Fig. 1A, upper right panel). To further investigate whether the IL-4–regulated genes in WT B cells are dependent on STAT6 signaling, we compared the differentially expressed gene sets between WT+IL-4 versus WT and WT+IL-4 versus STAT6−/−+IL-4 samples. The genes that were regulated >3-fold by IL-4 were indeed largely dependent on STAT6 signaling, because 290 of 462 genes were overlapping (compare the red circles with the intersection between the red and blue circles in Fig. 1B). The top 100 IL-4–regulated and STAT6-dependent genes are shown in Supplemental Fig. 4; the entire dataset is available from the Gene Expression Omnibus database (accession number GSE84075).

FIGURE 1.
FIGURE 1.

Microarray analysis of STAT6-regulated genes in B cells. (A) Dot plots show pairwise comparisons of the transcriptomes from splenic B cells of WT and STAT6−/− mice cultured for 4 d in vitro with LPS and anti-CD40 in the presence or absence of IL-4. The plots illustrate the ratio of log2 spot intensity (x-axis) and average spot intensity log2 (y-axis) based on the average values from three biological replicates per condition and a p value < 0.05. The blue lines indicate the cut-off for transcripts that were differentially expressed ≥2-fold. The numbers indicate the number of transcripts that were differentially expressed 2-fold (no parentheses) or 3-fold (in parentheses). Comparison of WT B cells cultured in the presence (WT+IL-4) or absence (WT) of IL-4 (upper left panel). Comparison of WT and STAT6−/− B cells that were cultured in the presence of IL-4 (upper right panel). Comparison of STAT6−/− B cells cultured in the presence (STAT6−/− + IL-4) or absence (STAT6−/−) of IL-4 (lower left panel). Comparison of WT and STAT6−/− B cells cultured without IL-4 (lower right panel). (B) Venn diagrams show the number of transcripts that were up- or downregulated >3-fold by IL-4 in WT B cells (WT+IL-4 versus WT) in comparison with IL-4–induced and STAT6-dependent transcripts (WT+IL-4 versus STAT6−/−+IL-4). The overlap contains transcripts that were regulated by IL-4 in WT B cells in a STAT6-dependent manner. (C) The 1000 most differentially expressed genes among all samples were subjected to hierarchical cluster analysis. Red indicates upregulated mRNA transcripts, and blue indicates downregulated mRNA transcripts. (D) Principal component analysis for microarray data showing a single colored dot for each sample.

Next, we performed unsupervised hierarchical cluster analysis of the 1000 most differentially expressed genes among all 12 samples described above. The three WT+IL-4 replicate samples showed a gene-expression profile that was very distinct from all other samples (Fig. 1C). This could also be visualized by principal component analysis (Fig. 1D). These findings illustrate that the IL-4–regulated transcriptome in primary B cells is largely dependent on STAT6 signaling.

Quantitative proteome analysis of IL-4–stimulated WT and STAT6−/− B cells

Because mRNAs are regulated by several posttranscriptional events that modulate their stability, it is difficult to predict differences at the protein level from transcriptome data. Furthermore, the turnover of proteins is also tightly regulated, which further complicates reliable predictions. Therefore, we investigated the IL-4–induced and STAT6-dependent proteome in B cells by label-free quantitative MS analysis, as described in Materials and Methods. We used five biological replicates from the four different B cell cultures described above. In total, we identified 4960 distinct proteins; 252 proteins were up- or downregulated >2-fold by IL-4 stimulation in WT B cells (Fig. 2A, upper left panel).

FIGURE 2.
FIGURE 2.

Label-free quantitative MS to identify IL-4/STAT6–regulated proteins in B cells. (A) Volcano plots show the logarithmic (log10) ratio of protein intensities in pairwise comparisons of WT and STAT6−/− B cells cultured in the presence or absence of IL-4 (x-axis; indicates the difference in expression) plotted against the −log10 p values of the t test across biological replicates. The horizontal lines represent the cut-off for p < 0.05, the vertical lines are thresholds for proteins that were differentially expressed by ≥2-fold. (B) Venn diagrams show the number of proteins that were up- or downregulated >3-fold by IL-4 in WT B cells (left circles) or by the deficiency of STAT6 under stimulation with IL-4 (right circles). The overlap contains proteins that were regulated by IL-4 in WT B cells in a STAT6-dependent manner.

Restricting the data set to proteins that were regulated >3-fold reduced the number to 115 (59 upregulated and 56 downregulated). Analysis of the IL-4–regulated and STAT6-dependent proteome (comparison of WT+IL-4 versus STAT6−/−+IL-4) revealed that 274 proteins were differentially expressed >2-fold, and 75 proteins were upregulated and 74 proteins were downregulated >3-fold (Fig. 2A, upper right panel). IL-4 also induced some changes in the proteome of B cells from STAT6−/− mice, but almost no differences were observed when comparing the proteomes of WT and STAT6−/− B cells that had not been cultured in the presence of IL-4 (Fig. 2A, lower panels). The majority of proteins regulated >3-fold by IL-4 in WT B cells were STAT6 dependent, as shown by the overlap of protein sets in Fig. 2B (43 of 59 upregulated and 32 of 56 downregulated). However, we also found 74 proteins that were differentially expressed >3-fold between WT+IL-4 versus STAT6−/−+IL-4 but not between WT+IL-4 versus WT.

Correlation between the transcriptome and proteome data

To further investigate whether the STAT6-dependent proteins that were up- or downregulated >3-fold are also regulated at the mRNA level, we correlated the microarray data with the proteome data. To our knowledge, such a direct comparison of STAT6-dependent gene expression at the mRNA and protein levels has not been described before. The added value of this comparison is reflected by the ability to determine whether changes in protein levels are based on changes in transcription or are due to posttranscriptional mechanisms that regulate protein turnover. This information can be important, for example, for potential therapeutic interventions aiming at lowering protein levels by targeting specific mRNAs. Differential expression of essentially all proteins for which a correlation could be identified in the microarray data set showed that the expression was regulated in the same direction at the transcriptional level (Fig. 3). This indicates that posttranscriptional and posttranslational mechanisms play a minor role in the IL-4–regulated proteome in B cells during the 4-d timeframe of stimulation used in this experimental setup.

FIGURE 3.
FIGURE 3.

Alphabetical list of the most differently expressed STAT6-regulated proteins and their correlation to mRNA transcript levels. The list shows fold changes (FC) in IL-4–induced (left columns) or IL-4–repressed (right columns) and STAT6-regulated proteins in comparison with the FC in corresponding mRNA transcripts of datasets from WT+IL-4 and STAT6−/−+IL-4 B cells. Differences at the protein level are indicated in red/dark green (up/downregulated >10-fold), orange/light green (up/downregulated >5-fold), and yellow/olive (up/downregulated >3-fold). Differences at the mRNA transcript level are indicated in red/dark green (up/downregulated >4-fold), orange/light green (up/downregulated >2-fold), and yellow/olive (up/downregulated >1.5-fold). Red arrows indicate the proteins selected for confirmation in Figs. 4 and 5. nf, not found.

The list of IL-4–induced and STAT6-dependent proteins that were regulated >3-fold includes components of the MHC class II complex (H2-DM), cytokine receptor signaling (IL-5Rα, Socs2, STAT5), BCR complex (CD79b), and surface receptors (Tnfrsf8 [also known as CD30], Ly75 [DEC205], and others) (Fig. 3). Interestingly, the classical T cell marker Thy1 (CD90) appeared to be the most strongly IL-4–induced STAT6-dependent protein (76-fold upregulation at the protein level and 249-fold upregulation at the mRNA level). The signaling adaptor Lcp2 (SLP-76) is another well-characterized T cell–associated protein that we identified to be upregulated >40-fold by IL-4 in B cells. The list of IL-4–suppressed and STAT6-dependent proteins includes the inhibitory receptors CD72 and FcRL1, many GTP-binding proteins, Irf9, STAT4, and the tyrosine kinase Syk, which is known to be important for signaling through the BCR (Fig. 3).

Confirmation of the STAT6-dependent expression of selected proteins

We selected several up- or downregulated proteins from the list in Fig. 3 for confirmation by flow cytometry or Western blot analysis. Purified splenic B cells from WT and STAT6−/− mice were cultured for 4 d in medium containing anti-CD40 and LPS or anti-IgM F(ab′)2 in the presence or absence of IL-4. The IL-4–regulated and STAT6-dependent expression of CD30, CD79b, Thy1.2, IL-5Rα, DEC205, and FcRL1 could be confirmed by flow cytometry (Fig. 4). Likewise, Western blot analysis confirmed the IL-4–induced and STAT6-dependent upregulation of STAT5a/b and SLP-76, whereas Syk was downregulated by IL-4 in B cells from WT mice but not STAT6−/− mice (Fig. 5).

FIGURE 4.
FIGURE 4.

Confirmation of STAT6-regulated proteins by flow cytometry. Flow cytometric analysis for selected STAT6-regulated proteins in WT and STAT6−/− B cells cultured for 4 d with LPS and anti-CD40 (A) or anti-IgM–F(ab′)2 and anti-CD40 (B) in the presence or absence (control) of IL-4. Line graph overlays show the expression level of indicated surface markers on B cell cultures from WT or STAT6−/− mice. The results are representative of three (A) and two (B) independent experiments.

FIGURE 5.
FIGURE 5.

Confirmation of STAT6-regulated proteins by Western blot. Western blot analysis shows the expression of SLP-76, Syk, and STAT5a/b in B cells from WT and STAT6−/− mice cultured for 4 d with LPS and anti-CD40 (A) or anti-IgM–F(ab′)2 and anti-CD40 (B) in the presence (IL-4) or absence of IL-4. β-actin was used as loading control.

Discussion

The transcription factor STAT6 plays a critical role in B cells during type 2 immune responses by mediating germinal center formation (6) and CSR to IgE and IgG1 (3, 4, 19, 20). To identify STAT6-dependent proteins that may be required for these functions, we performed comparative transcriptome and proteome analyses. We identified 290 genes that were regulated >3-fold at the transcriptional level by IL-4 in a STAT6-dependent manner. This number is approximately four times higher than previously described in a similar in vitro culture system of primary mouse B cells (7). However, the difference could be explained by the fact that the previous study used another microarray platform and analyzed the samples 24 h after stimulation, whereas our cultures were analyzed on day 4. Furthermore, we included anti-CD40 Abs in the culture medium to mimic the signal from Th cells; this stimulus was missing in the previous study. We found that the expression of >400 mRNAs was up- or downregulated >3-fold by IL-4 in B cells from WT mice, and most of them were STAT6 dependent, whereas only 17 mRNAs were regulated by IL-4 in B cells from STAT6−/− mice. This illustrates that STAT6 is the major signaling component for the IL-4–regulated transcriptome in B cells, although the cytoplasmic tail of the IL-4R α-chain contains many docking sites for other signaling molecules, including IRS-1/2, which further activate the PI3K/Akt and MAPK pathways (1). By label-free quantitative MS analysis, we could identify ∼5000 proteins, and the expression of essentially all IL-4–regulated and STAT6-dependent proteins was also found to be regulated at the transcriptional level, excluding a substantial contribution of posttranscriptional mechanisms shaping the proteome. A previous proteomic study of the STAT6-regulated proteome used two-dimensional gel electrophoresis of total mouse mononuclear cells that had been metabolically labeled and stimulated by anti-CD3 and IL-4 for 24 h. In this study, only 21 differentially expressed proteins could be identified, and their cellular origin remained undefined (21). Our list of 75 up- and 74 downregulated proteins contains many interesting candidates that may regulate the function of IL-4–stimulated B cells.

We selected several up- and downregulated proteins to confirm the quantitative MS data by flow cytometry and Western blot analysis. Among the selected proteins was CD79b (also known as Igβ), which was reported to be upregulated by IL-4, leading to increased expression levels of the BCR and stronger BCR-induced ERK phosphorylation (22). CD30 is a member of the TNFR superfamily that is not expressed on resting lymphocytes but was shown to be upregulated by IL-4 in T cells (23). In this study, we discovered that CD30 is also induced by IL-4 in B cells; it remains to be analyzed whether this receptor modulates B cell functions during type 2 immune responses.

The STAT6-dependent upregulation of IL-5Rα confirms previous data (24). Signaling from IL-5R is mediated, in part, by STAT5, which we identified as another IL-4–induced protein in B cells. Two independent studies further showed that IL-5 promotes the differentiation of IL-4–stimulated B cells to Ab-secreting cells in vitro (25, 26). Therefore, upregulation of IL-5Rα and STAT5 by STAT6 signaling may explain why IL-4–induced and IgE-switched germinal center B cells rapidly differentiate into plasma cells (27).

The most strongly IL-4–induced and STAT6-dependent protein was Thy1, a phosphatidylinositol-linked membrane glycoprotein that is constitutively and highly expressed in T cells, innate lymphoid cells, neurons, and some other cell types, including basophils and pluripotent hematopoietic stem cells (28, 29). The biological function of Thy1 is poorly understood, but it may likely be involved in signal transduction from the plasma membrane, because it was shown that thymocytes of Thy1-deficient mice exhibit augmented TCR signaling and impaired T cell differentiation (30). Other studies showed that LPS+IL-4–stimulated B cells, but not LPS-stimulated B cells, upregulate Thy1 (31, 32). We confirm and extend these findings by showing that STAT6 expression in B cells is required for this effect. The biological role of Thy1 on IL-4–stimulated B cells remains to be analyzed.

Unexpectedly, we observed that the signaling adaptor protein SLP-76 was also induced by IL-4 in a STAT6-dependent manner. SLP-76 is required for T cell and platelet development but has no apparent role in B cell development (33, 34). Rather, normal B cell development depends on the expression of a similar adaptor protein named SLP-65 or BLNK (35). However, it was shown that SLP-76 is expressed in mouse B cells where it binds to the inhibitory receptor CD22 (36, 37). The consequences of IL-4–induced upregulation of SLP-76 in B cells for survival or CSR remain to be analyzed. Several target proteins, probably including SLP-76, are phosphorylated by the tyrosine kinase Syk, which we found to be downregulated by IL-4 in a STAT6-dependent manner in this study. IL-4 is generally regarded as a cytokine that enhances BCR signaling and B cell proliferation (38). Therefore, it would be interesting to investigate whether downregulation of Syk is required for this effect.

We further revealed that IL-4 induced the upregulation of DEC205 in B cells. This receptor is highly expressed on dendritic cells where it is used for phagocytosis (39, 40). DEC205 can be targeted for tolerance induction of CD8 T cells via cross-presentation (41). Whether DEC205-expressing B cells have a similar function remains to be analyzed. The surface receptor FcRL1 (CD307a) was described to be primarily expressed on B cells (42). FcRL1 is a type I transmembrane glycoprotein with three extracellular Ig domains and two ITAM motifs in the cytoplasmic tail (42). This indicates that it could serve as an activating receptor. FcRL1 was found to be upregulated on B cells from patients with acute hepatitis B virus infection (43); however, its biological function has not been characterized.

Taken together, we performed comparative transcriptome and proteome analyses to identify novel IL-4–regulated and STAT6-dependent proteins in mouse B cells. This provides the framework to further investigate the biological functions of these proteins during germinal center formation, CSR, and differentiation into memory B cells and plasma cells in the context of type 2 immune responses. A better understanding of these processes could help to develop new therapeutic options for treatment of allergic diseases and helminth infection.

Disclosures

The authors have no financial conflicts of interest.

Acknowledgments

We thank Alexander Matthies and Kirstin Castiglione for technical assistance and members of Collaborative Research Center TRR 130 for helpful discussions.

Footnotes

  • This work was supported primarily by Collaborative Research Center TRR 130 of the Deutsche Forschungsgemeinschaft (Project P20 to D.V. and Project C02 to B.W.) and CRC1181 (project A7 to D.D.). Additional support was provided by intramural funding to C.H.K.L. (IZKF-J54) and D.D. (IZKF-A65). Research in the Warscheid group was additionally funded by the Excellence Initiative of the German Federal and State Governments (EXC 294 BIOSS Centre for Biological Signalling Studies).

  • The mass spectrometry proteomics data presented in this article have been submitted to the ProteomeXchange Consortium under accession number PXD005224. The microarray dataset presented in this article has been submitted to the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84075) under accession number GSE84075.

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article:

    CSR
    class switch recombination
    FcRL1
    Fc receptor–like 1
    LC/MS
    liquid chromatography/mass spectrometry
    LFQ
    label-free quantification
    MS
    mass spectrometry
    MS/MS
    tandem MS
    SH2
    Src homology 2
    WT
    wild-type.

  • Received October 27, 2016.
  • Accepted March 3, 2017.

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