Research Article

Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses

Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, United Kingdom
Department of Applied Mathematics, School of Mathematics, University of Leeds, United Kingdom
Department of Biology and Centre of Cellular Nanoanalytics, University of Osnabrück, Germany
Université de Lille, INSERM UMR1277 CNRS UMR9020–CANTHER and Institut pour la Recherche sur le Cancer de Lille (IRCL), France
Department of Statistics, School of Mathematics, University of Leeds, United Kingdom
Univ. Lille, Univ. LilleInserm, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, France
T-6 Theoretical Division, Los Alamos National Laboratory, United States

Apr 19, 2021

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Abstract
Introduction
Results
Discussion
Materials and methods
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Abstract

Cytokines elicit pleiotropic and non-redundant activities despite strong overlap in their usage of receptors, JAKs and STATs molecules. We use IL-6 and IL-27 to ask how two cytokines activating the same signaling pathway have different biological roles. We found that IL-27 induces more sustained STAT1 phosphorylation than IL-6, with the two cytokines inducing comparable levels of STAT3 phosphorylation. Mathematical and statistical modeling of IL-6 and IL-27 signaling identified STAT3 binding to GP130, and STAT1 binding to IL-27Rα, as the main dynamical processes contributing to sustained pSTAT1 levels by IL-27. Mutation of Tyr613 on IL-27Rα decreased IL-27-induced STAT1 phosphorylation by 80% but had limited effect on STAT3 phosphorgylation. Strong receptor/STAT coupling by IL-27 initiated a unique gene expression program, which required sustained STAT1 phosphorylation and IRF1 expression and was enriched in classical Interferon Stimulated Genes. Interestingly, the STAT/receptor coupling exhibited by IL-6/IL-27 was altered in patients with systemic lupus erythematosus (SLE). IL-6/IL-27 induced a more potent STAT1 activation in SLE patients than in healthy controls, which correlated with higher STAT1 expression in these patients. Partial inhibition of JAK activation by sub-saturating doses of Tofacitinib specifically lowered the levels of STAT1 activation by IL-6. Our data show that receptor and STATs concentrations critically contribute to shape cytokine responses and generate functional pleiotropy in health and disease.

Introduction

IL-27 and IL-6 both have intricate functions regulating inflammatory responses (O'Shea and Plenge, 2012). IL-27 is a hetero-dimeric cytokine comprised of p28 and EBI3 subunits (Pflanz et al., 2002). IL-27 exerts its activities by binding GP130 and IL-27Rα receptor subunits in the surface of responsive cells, triggering the activation of the JAK1/STAT1/STAT3 signaling pathway. IL-27 elicits both pro- and anti-inflammatory responses, although the later activity seems to be the dominant one (Yoshida and Hunter, 2015). IL-27 stimulation inhibits RORγt expression, thereby suppressing Th-17 commitment and limiting subsequent production of pro-inflammatory IL-17 (Stumhofer et al., 2006; Diveu et al., 2009). Moreover, IL-27 induces a strong production of anti-inflammatory IL-10 on (Tbet⁺ and FoxP3^-) Tr-1 cells (Fitzgerald et al., 2007; Stumhofer et al., 2007; Pot et al., 2011) further contributing to limit the inflammatory response. IL-6 engages a hexameric receptor complex comprised each of two copies of IL-6Rα, GP130 and IL-6 (Boulanger et al., 2003), triggering the activation, as IL-27 does, of the JAK1/STAT1/STAT3 signaling pathway. However, opposite to IL-27, IL-6 is known as a paradigm pro-inflammatory cytokine (Rose-John, 2018; Hunter and Jones, 2015). IL-6 inhibits lineage differentiation to Treg cells (Korn et al., 2008) while promoting that of Th-17 cells (Kimura and Kishimoto, 2010; Jones et al., 2010), thus supporting its pro-inflammatory role. How IL-27 and IL-6 elicit opposite immunomodulatory activities despite activating almost identical signaling pathways is currently not completely understood.

The relative and absolute STAT activation levels seem to have a diverse set of roles, which lead to a strong signaling and functional plasticity by cytokines. Although IL-6 robustly activates STAT3, it is capable to mount a considerable STAT1 response as well (Rolvering et al., 2017). Moreover, in the absence of STAT3, IL-6 induces a strong STAT1 response comparable to IFNγ – a prototypic STAT1 activating cytokine (Costa-Pereira et al., 2002). Likewise, the absence of STAT1 potentiates the STAT3 response for IL-27, which normally elicits a strong STAT1 response, rendering it to mount an IL-6-like response (Rolvering et al., 2017). This suggests a competition of STAT1/3 for phospho-tyrosine motifs at the cytoplasmic domain of cytokine receptors regulated by their different binding rate constants (on and off rates). Indeed, different STAT1 or STAT3 binding affinities have been assessed in vitro for the phospho-tyrosines on GP130 (Wiederkehr-Adam et al., 2003). Furthermore, negative feedback mechanisms, controlled by SOCSs and phosphatases, have been described as critical players influencing STAT1 and STAT3 phosphorylation kinetics and thereby shaping their signal integration for GP130-utilizing cytokines (Schmitz et al., 2000; Yasukawa et al., 2003; Croker et al., 2003; Brender et al., 2007). Yet, how all these molecular components are integrated by a given cell to produce the desired response is still an open question. Among the IL-6/IL-12 cytokine family, IL-27 exhibits a unique STAT activation pattern. The majority of GP130-engaging cytokines activate preferentially STAT3, with activation of STAT1 an accessory or balancing component in the signaling pathway (Camporeale, 2012; Regis et al., 2008). IL-27, however, triggers STAT1 and STAT3 activation with high potency (Lucas et al., 2003). Indeed, different studies have shown that IL-27 responses rely on either STAT1 (Kamiya et al., 2004; Takeda et al., 2003; Neufert et al., 2007) or STAT3 activation (Stumhofer et al., 2007; Owaki et al., 2008). Moreover, recent transcriptomics studies showed that in the absence of STAT3, IL-6 and IL-27 lost more than 75% of target gene induction. Yet, STAT1 was the main factor driving the specificity of the IL-27 versus the IL-6 response, highlighting a critical interplay of STAT1 and STAT3 engagement (Hirahara et al., 2015).

While the biological responses induced by IL-27 and IL-6 have been extensively studied (Yoshida and Hunter, 2015; Hunter and Jones, 2015), the very initial steps of signal activation and kinetic integration by these two cytokines have not been comprehensively analysed. Since the different biological outcomes elicited by IL-27 and IL-6 are most likely encoded in the early events of cytokine stimulation, here we specifically aimed to identify the molecular determinants underlying functional selectivity by IL-27 in human T-cells. We asked how a defined cytokine stimulus is propagated in time over multiple layers of signaling to produce the desired response. To this end, we probed IL-27 and IL-6 signaling at different scales, ranging from cell surface receptor assembly and early STAT1/3 effector activation to an unbiased and quantitative multi-omics approach: phospho-proteomics after early cytokine stimulation, kinetics of transcriptomic changes and alteration of the T-cell proteome upon prolonged cytokine exposure.

IL-6 and IL-27 induced similar levels of assembly of their respective receptor complexes, which resulted in comparable phosphorylation of STAT3 by the two cytokines. IL-27, on the other hand, triggered a more sustained STAT1 phosphorylation. To decipher the molecular events which determine sustained STAT1 phosphorylation by IL-27, we mathematically model the STAT1 and STAT3 signaling kinetics induced by each of these cytokines. We identified differential binding of STAT1 and STAT3 to IL-27Rα and GP130, respectively, as the main factors contributing to a sustained STAT1 activation by IL-27. At the transcriptional level, IL-27 triggered the expression of a unique gene program, which strictly required the cooperative action between sustained pSTAT1 and IRF1 expression to drive the induction of an interferon-like gene signature that profoundly shaped the T-cell proteome. Interestingly, our mathematical models of IL-6 and IL-27 signaling predicted that changes in receptor and STAT expression could fundamentally change the magnitude and timescale of the IL-6 and IL-27 responses. We found high levels of STAT1 expression in SLE patients when compared to healthy donors, which correlated with biased STAT1 responses induced by IL-6 and IL-27 in these patients. Strikingly, we could specifically inhibit STAT1 activation by IL-6 using suboptimal doses of the JAK inhibitor Tofacitinib. This could provide a new strategy to specifically target individual STATs engaged by cytokines.

Results

IL-27 induces a more sustained STAT1 activation than HypIL-6 in human Th-1 cells

IL-6 and IL-27 are critical immunomodulatory cytokines. Although IL-6 engages a hexameric surface receptor comprised of two molecules of IL-6Rα and two molecules of GP130 to trigger the activation of STAT1 and STAT3 transcription factors (Figure 1a), IL-27 binds GP130 and IL-27Rα to trigger activation of the same STATs molecules (Figure 1a). Despite sharing a common receptor subunit, GP130, and activating similar signaling pathways, these two cytokines exhibit non-redundant immunomodulatory activities, with IL-6 eliciting a potent pro-inflammatory response and IL-27 acting more as an anti-inflammatory cytokine. Here, we set to investigate the molecular rules that determine the functional specificity elicited by IL-6 and IL-27 using human Th-1 cells as a model experimental system. Due to the challenging recombinant expression of the human IL-27, we have recombinantly produced a murine single-chain variant of IL-27 (p28 and EBI3) which cross-reacts with the human receptors and triggers potent signaling, comparable to the signaling output produced by commercial human IL-27 (Oniki et al., 2006; Figure 1—figure supplement 1a). In addition, we have used a linker-connected single-chain fusion protein of IL-6Rα and IL-6 termed HyperIL-6 (HypIL-6) (Fischer et al., 1997) to diminish IL-6 signaling variability due to changes in IL-6Rα expression during T cell activation (Oberg et al., 2006).

Figure 1 with 3 supplements see all

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Cytokine receptor activation by IL-27 and (Hyp)IL-6: (a) Cartoon model of stepwise assembly of the IL-27 and HypIL-6-induced receptor complex and subsequent activation of STAT1 and STAT3.

(b) Dose-dependent phosphorylation of STAT1 and STAT3 as a response to IL-27 and HypIL-6 stimulation in TH-1 cells, normalized to maximal IL-27 stimulation. Data was obtained from three biological replicates with each two technical replicates, showing mean ±std dev. (c) Phosphorylation kinetics of STAT1 and STAT3 followed after stimulation with saturating concentrations of IL-27 (2 nM) and HypIL-6 (20 nM) or unstimulated TH-1 cells, normalized to maximal IL-27 stimulation. Data was obtained from five biological replicates with each two technical replicates, showing mean ±std dev. (d) Top: Phosphorylation kinetics of STAT1 and STAT3 followed after stimulation with HypIL-6 (20 nM) or left unstimulated, comparing wt RPE1 and RPE1 GP130KO reconstituted with high levels of mXFPm-GP130 (=10 x [GP130]). Data was normalized to maximal stimulation levels of each treatment. Left: cell surface GP130 levels comparing RPE1 GP130KO, wt RPE1 and RPE1 GP130KO stably expressing mXFPm-GP130 measured by flow cytometry. Data was obtained from one biological replicate with each two technical replicates, showing mean ±std dev. Bottom right: cell surface levels of GP130 measured by flow cytometry for indicated cell lines. (e) Cartoon model of cell surface labeling of mXFP-tagged receptors by dye-conjugated anti-GFP nanobodies (NB) and identification of receptor dimers by single molecule dual-color co-localization. (f) Raw data of dual-color single-molecule TIRF imaging of mXFPe-IL-27Rα^NB-RHO11 and GP130^NB-DY649 after stimulation with IL-27. Particles from the insets (IL-27Ra: red and GP130: blue) were followed by single molecule tracking (150 frames ~ 4.8 s) and trajectories > 10 steps (320 ms) are displayed. Receptor heterodimerization was detected by co-localization/co-tracking analysis. (g) Relative number of co-trajectories observed for heterodimerization of IL-27Rα and GP130 as well as homodimerization of GP130 for unstimulated cells or after indicated cytokine stimulation. Each data point represents the analysis from one cell with a minimum of 23 cells measured for each condition. Two-tailed Student’s T-test: *p<0.05, **p≤0.01,***p≤0.001; n.s., not significant. (h) Stoichiometry of the IL-27–induced receptor complex revealed by bleaching analysis. Left: Intensity traces of mXFPe-IL-27Rα^NB-RHO11 and GP130^NB-DY649 were followed until fluorophore bleaching. Middle: Merged imaging raw data for selected timepoints. Right: overlay of the trajectories for IL-27Rα (red) and GP130 (blue).

CD4 +T cells from human buffy coat samples were isolated by magnetic activated cell sorting (MACS) and grew under Th-1-polarizing conditions. Th-1 cells were then used to study in vitro signaling by IL-27 and IL-6 (Figure 1—figure supplement 1b). We took advantage of a barcoding methodology allowing high-throughput multiparameter flow cytometry to perform detailed dose/response and kinetics studies induced by HypIL-6 and IL-27 in Th-1 cells (Krutzik et al., 2011; Figure 1—figure supplement 1b). Dose-response experiments with IL-27 and HypIL-6 on Th-1 cells showed concentration-dependent phosphorylation of STAT1 and STAT3. Phosphorylation of STAT1/3 was more sensitive to activation by IL-27 with an EC₅₀ of ~20 pM compared to ~400 pM for HypIL-6 (Figure 1b). Despite this difference in sensitivity, both cytokines yielded the same activation amplitude for pSTAT3. For pSTAT1, however, we observed a significantly reduced maximal amplitude for HypIL-6 relative to IL-27 (Figure 1b). We next performed kinetic studies to assess whether the poor STAT1 activation by HypIL-6 was a result from different activation kinetics. For STAT3, we saw the peak of phosphorylation after ~15–30 min, followed by a gradual decline. Both cytokines exhibited an almost identical sustained pSTAT3 profile, with ~20% of activation still seen after 3 hr of continuous stimulation. Interestingly, IL-27 did not only activate STAT1 with higher amplitude but also more sustained than HypIL-6 (Figure 1c). This could be better appreciated when pSTAT1 levels were normalized to maximal MFI for each cytokine, with IL-27 inducing clearly a more sustain phosphorylation of STAT1 than HypIL-6 (Figure 1—figure supplement 1c). The same phenotype was observed in other T-cell subsets of activated PBMCs (Figure 1—figure supplement 1d). As cell surface GP130 levels are significantly reduced upon T-cell activation (Betz and Müller, 1998), we next investigated whether the transient STAT1 activation profile induced by HypIL-6 resulted from limited availability of GP130. For that we generated a RPE1 cell clone stably expressing 10 times higher levels of GP130 in its surface (Figure 1d, right panel). Stimulation of this RPE1 clone with HypIL-6 resulted in a more sustained activation of STAT3, with very little effect on STAT1 activation kinetics when compared to RPE1 wild-type cells, suggesting that GP130 receptor density does not contribute to the transient STAT1 activation kinetics elicited by HypIL-6 (Figure 1d).

Ligand-induced cell-surface receptor assembly by IL-27 and HypIL-6

We next investigated whether IL-27 and HypIL-6 elicited differential cell surface receptor engagement that could explain their distinct signaling output. For that, we measured the dynamics of receptor assembly in the plasma membrane of live cells by simultaneous dual-color total internal reflection fluorescence (TIRF) imaging. RPE1 cells were chosen as a model experimental system since they do not express endogenous IL-27Rα (Figure 1—figure supplement 2a). We generated an RPE1 clone, stably expressing IL-27Rα which responds to IL-27 and HypIL-6 stimulation. Importantly, this reconstituted experimental system mimicked the pSTAT1/3 activation kinetics of T-cells (Supp. Fig. 2b), albeit exhibiting similar pSTAT1 amplitudes for IL-27 and HypIL-6, most likely because of the high endogenous levels of GP130 in RPE1 cells. We used previously described RPE1 GP130 KO cells (Figure 1—figure supplement 2; Martinez-Fabregas et al., 2019) to transfect and express tagged variants of IL-27Rα and GP130, to allow quantitative site-specific fluorescence cell surface labeling by dye-conjugated nanobodies (NBs) (Figure 1e) as recently described in Gorby et al., 2020. For both IL-27Rα and GP130, we found a random distribution and unhindered lateral diffusion of individual receptor monomers (Figure 1f). Single-molecule co-localization combined with co-tracking analysis was then used to identify correlated motion of IL-27Rα and GP130 which was taken as a readout for receptor heterodimer formation (Ruprecht et al., 2010; Figure 1f, Video 1). In the resting state, we did not observe pre-assembly of IL-27Rα and GP130. However, after stimulation with IL-27 we found substantial heterodimerization (Figure 1f and g, Figure 1—figure supplement 3a, Video 1 and 2). At elevated laser intensities, bleaching analysis of individual complexes confirmed a one-to-one (1:1) complex stoichiometry of IL-27Rα and GP130, whereas single-molecule Förster resonance energy transfer (FRET) further corroborated close molecular proximity of the two receptor chains (Figure 1h). We also observed association and dissociation events of receptor heterodimers, pointing to a dynamic equilibrium between monomers and dimers as proposed for other heterodimeric cytokine receptor systems (Moraga et al., 2015a; Wilmes et al., 2015; Video 3).

Video 1

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posterframe for video — Single-molecule co-tracking as a readout for dimerization of cytokine receptors.

Cell surface labeling of mXFPe-IL-27Rα by eNB^RHO11 (left, top) and mXFPm-GP130 by mNB^DY649 (left, bottom) after stimulation with IL-27 (20 nM). In the overlay of the zoomed section of both spectral channels (mXFPe-IL-27Rα^RHO11: Red, mXFPm-GP130^DY649: Blue), yellow lines indicate co-locomotion of IL-27Rα and GP130 (≥10 steps). Acquisition frame rate: 30 Hz, Playback: real time.

Video 2

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Video 3

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Video 4

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To measure homodimerization of GP130 by HypIL-6, we stochastically labeled GP130 with equal concentrations of the same NB species conjugated to either of the two dyes (Wilmes et al., 2020). We saw strong homodimerization of GP130 after stimulation with HypIL-6 (Figure 1g, Figure 1—figure supplement 3a, Video 4). Homodimerization was confirmed either by single-color dual-step bleaching or dual-color single-step bleaching as shown for other homodimeric cytokine receptors (Figure 1—figure supplement 3b; Pflanz et al., 2004). For both cytokine receptor systems, we saw a cytokine-induced reduction of the diffusion mobility, which has been ascribed to increased friction of receptor dimers diffusing in the plasma membrane. However, we note that HypIL-6 stimulation impaired diffusion of GP130 more strongly than IL-27 did, possibly indicating faster receptor internalization (Figure 1—figure supplement 3c). Based on the dimerization data, we were able to calculate the two-dimensional equilibrium dissociation constants ( $K_{D}^{2 D}$ ) assuming mass action kinetics for a dynamic monomer-dimer equilibrium: for IL-27-induced heterodimerization of IL-27Rα and GP130, we calculated a two-dimensional constant, denoted by $K_{D}^{2 D},$ of approximately 0.81 μm⁻². In activated T-cells with high levels and a significant excess of IL-27Rα over GP130, this $K_{D}^{2 D}$ ensures strong receptor assembly by IL-27 (Diegelmann et al., 2012). $K_{D}^{2 D}$ for GP130 homodimerization by HypIL-6 was approximately 0.21 μm⁻². This higher affinity is most likely due to the two high-affinity binding sites engaged in the hexameric receptor complex (Boulanger et al., 2003). However, in T-cells the expression of GP130 can be particularly low, thus, probably limiting HypIL-6-induced signaling. Taken together, these experiments marked ligand-induced receptor assembly as the initial step triggering downstream signaling for both IL-27 and HypIL-6, with no obvious differences in their receptor activation mechanism which could support the observed more sustained STAT1 activation elicited by IL-27.

Mathematical and statistical analysis of HypIL-6 and IL-27-induced STAT kinetic responses

To gain further insight into the molecular rules and kinetics that define IL-27 sustained STAT1 phosphorylation, we developed two mathematical models of the initial steps of HypIL-6 and IL-27 receptor-mediated signaling, respectively. A diagram which describes the molecular reactions in each model is shown in Figure 2—figure supplement 1a, and the complete model reaction scheme is given in Figure 2—figure supplement 1b–h. The mathematical model for each cytokine considers the following events: (i) cytokine association and dissociation to a receptor chain (Figure 2a, Figure 2—figure supplement 1b and c, top panel), (ii) cytokine-induced dimer association and dissociation (Figure 2—figure supplement 1b and c, bottom panel), (iii) STAT1 (or STAT3) binding and unbinding to dimer (Figure 2—figure supplement 1d and e), (iv) STAT1 (or STAT3) phosphorylation when bound to dimer (Figure 2—figure supplement 1d and e), (v) internalization/degradation of complexes (Figure 2—figure supplement 1f and g), and (vi) dephosphorylation of free STAT1 (or STAT3) (Figure 2—figure supplement 1h). Details of model assumptions, model parameters and parameter inference have been provided in the Materials and methods under the sections Mathematical models and Bayesian inference. The ordinary differential equations for each mathematical model are given in Supplementary Information 1, where we have assumed mass action kinetics.

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Mathematical modeling results in RPE1 and Th-1 cells.

(a) Simplified cartoon model of IL-27/HypIL-6 signal propagation layers and coverage of the mathematical modeling approach. (b) Model selection results showing the relative probabilities of each hypothesis, for different values of the distance threshold, $δ^{*}$ , in RPE1 cells. (c) Pointwise median and 95% credible intervals of the predictions from the mathematical model, calibrated with the experimental data, using the posterior distributions for the parameters from the ABC-SMC. For the experimental data, phosphorylation kinetics of pSTAT1 and pSTAT3 were followed in RPE1 IL-27Rα after stimulation with saturating concentrations of IL-27 (2 nM) and HypIL-6 (10 nM). (d) Kernel density estimates of the posterior distributions for the parameters $p \in {r_{1, j}^{+}, r_{1, j}^{-}, r_{2, j}^{+}, r_{2, j}^{-}, k_{i a}^{+}, k_{i a}^{-}, k_{i b}^{+}, k_{i b}^{-}, q, d_{i}, β_{j}, [R_{1} (0)], [R_{2} (0)], [S_{1} (0)], [S_{3} (0)]}$ in the mathematical models where $j \in {6,27}$ and $i \in {1,3}$ . A '+' in the parameter notation indicates that this is a forward rate constant (binding) and a '–' in the notation indicates that this is a reverse rate constant (dissociation). In the first row, a '6' in the notation indicates that this is a reaction rate in the HypIL-6 system and a '27' indicates that this is a reaction rate in the IL-27 system.

We first wanted to explore if there existed a potential feedback mechanism in the mechanisms of receptor complexes internalization/degradation over time. Negative feedback mechanisms have been reported for cytokine receptor signaling, while positive feedback loops are unlikely to happen at the timescale of the given experiments (3 hr). To this end, and for each cytokine model, we considered two hypotheses: hypothesis one assumes that receptor complexes (Figure 2—figure supplement 1f and g) are internalized with rate proportional to the concentration of the species in which they are contained (e.g. different dimer types), and hypothesis 2, that receptor complexes are internalized with rate proportional to the product of the concentration of the species in which they are contained and the sum of the concentrations of free cytoplasmic phosphorylated STAT1 and STAT3. Hypothesis 2 is consistent with a negative feedback mechanism in which pSTAT molecules translocate to the nucleus, where they increase the translation of negative feedback proteins such as SOCS3. In order not to increase the complexity of the mathematical models with additional variables and parameters, we chose to include the feedback mechanism in hypothesis two implicitly, rather than by considering new equations for species such as SOCS3. Details of the two model hypotheses are described in the Materials and methods (Mathematical models).

We first carried out a structural identifiability analysis (Castro and de Boer, 2020) for the IL-27 and HypIL-6 mathematical models under both hypotheses, to determine which of the model parameters could be independently inferred. We found that all model parameters and initial concentrations were structurally identifiable, given the data and the known experimental initial concentrations. We made use of the RPE1 experimental data set to carry out Bayesian model selection for the two different hypotheses. We found that hypothesis 1 could explain the data better than hypothesis 2, with a probability of 99%. This result can be seen in Figure 2b, in which we plot, for different values of the distance threshold, $δ$ , between the mathematical model output and the data (see Mathematical models and Bayesian inference in Materials and methods, for details), the relative probability of each hypothesis, where hypothesis 1 is denoted $H_{1}$ and hypothesis 2 is denoted $H_{2}$ . It can be observed that for smaller values of the distance threshold, which indicate data and mathematical model results are closer, the relative probability of hypothesis 1 is higher than that of hypothesis 2.

We then made use of this result to explore the mathematical models for both cytokines under hypothesis 1, in particular we performed parameter calibration. To this end (and as described in Materials and methods under Mathematical models and Bayesian inference), we carried out Bayesian inference (ABC-SMC Toni et al., 2009) together with the mathematical models (hypothesis 1) and the experimental data sets to quantify the reaction rates (Figure 2—figure supplement 1) and initial molecular concentrations (see Table 2 and Table 3). The Bayesian parameter calibration of the two models of cytokine signaling allows one to quantify the observed kinetics of pSTAT1/3 phosphorylation induced by HypIL-6 and IL-27 in RPE1 and Th-1 cells (Figure 2c). Substantial differences in STAT association rates to and dissociation rates from the dimeric complexes were inferred to critically contribute to defining pSTAT1/3 kinetics. Figure 2d shows the kernel density estimates (KDEs) for the posterior distributions of the rate constants and initial concentrations in the models. $k_{i a}^{+}$ denotes the rate at which STATi binds to GP130 and $k_{i b}^{+}$ denotes the rate at which STATi binds to IL-27Rα, for $i \in \{1,3\}$ . Our results indicate that STAT1 and STAT3 exhibit different binding preferences toward IL-27Rα and GP130. While STAT1 exhibits stronger binding to IL-27Rα than GP130 ( $k_{1 b}^{+} > k_{1 a}^{+}$ ), STAT3 exhibits stronger binding to GP130 than IL-27Rα, ( $k_{3 a}^{+} > k_{3 b}^{+}$ ) in agreement with previous observations (17). To further quantify these differences in the posterior distributions for the STAT binding rates, Table 1 provides summary statistics for each of these rates. For example, from the table we see that there is a difference of at least one order of magnitude between the means and medians of the pairs of STAT/receptor binding parameters ( $k_{i a}^{+}$ compared with $k_{i b}^{+}$ for $i \in {1,3}$ ). Finally, we note that, although there exist some significant correlations between posterior distributions for pairs of parameters in the analysis, for all four pairs of STAT/receptor interaction parameters (second row of Figure 2d), the absolute value of the correlation coefficient is less than 0.25, and thus, the posterior distributions are representative of the rates they correspond to.

Table 1

Summary statistics of the posterior distributions for the STAT/receptor binding and dissociation rates.

Parameter	Mean	Median
$k_{1 a}^{+}$	$1.1 \times 10^{- 1}$	$2.2 \times 10^{- 2}$
$k_{1 a}^{-}$	$4.2 \times 10^{- 1}$	$1.8 \times 10^{- 1}$
$k_{1 b}^{+}$	$1.2 \times 10^{0}$	$3.4 \times 10^{- 1}$
$k_{1 b}^{-}$	$1.2 \times 10^{0}$	$7.2 \times 10^{- 1}$
$k_{3 a}^{+}$	$1.1 \times 10^{0}$	$3.7 \times 10^{- 1}$
$k_{3 a}^{-}$	$1.4 \times 10^{0}$	$8.0 \times 10^{- 1}$
$k_{3 b}^{+}$	$6.8 \times 10^{- 2}$	$7.5 \times 10^{- 4}$
$k_{3 b}^{-}$	$1.2 \times 10^{0}$	$4.3 \times 10^{- 1}$

IL-27Rα cytoplasmic domain is required for sustained pSTAT1 kinetics

The Bayesian inference carried out with the experimental data and the mathematical models clearly indicated statistically significant differences in the binding rates of STAT1/STAT3 to GP130 and IL-27Rα, to account for the different phosphorylation kinetics exhibited by HypIL-6 and IL-27. Thus, we next investigated whether the more sustained STAT1 activation by IL-27 resulted from its specific engagement of IL-27Rα. For that, we used RPE1 cells, which do not express IL-27Rα (Figure 1—figure supplement 2a), to systematically dissect the contribution of the IL-27Rα cytoplasmic domain to the differential pSTAT activation by IL-27. IL-27Rα’s intracellular domain is very short and only encodes two Tyr susceptible to be phosphorylated in response to IL-27 stimulation, that is, Tyr543 and Ty613 (Figure 3a). We mutated these two Tyr to Phe to analyse their contribution to IL-27 induced signaling. We stably expressed WT IL-27Rα as well as different IL-27Rα Tyr mutants in RPE1 cells with comparable cell surface expression levels (Figure 3b). As the endogenous GP130 expression levels remain unaltered, all generated clones exhibited very comparable responses to HypIL-6 (Figure 3b, bottom panels). IL-27 triggered comparable levels of STAT1 and STAT3 activation in RPE1 cells reconstituted with IL-27Rα WT and IL-27Rα Y543F mutant, suggesting that this Tyr residue does not contribute to signaling by this cytokine (Figure 3b and Figure 3—figure supplement 1a). In RPE1 cells reconstituted with the IL-27Rα Y613F or Y543F-Y613F mutants, IL-27 stimulation resulted in 80% of the STAT3 activation, but only 20% of the STAT1 activation levels induced by this cytokine relative to IL-27Rα WT (Figure 3b; Pradhan et al., 2010). These observations suggest a tight coupling of STAT phosphorylation to one of the receptor chains; namely, IL-27Rα with pSTAT1 and GP130 with pSTAT3, respectively. We next tested how the cytoplasmic domains of GP130 and IL-27Rα shape the pSTAT kinetic profiles. Thus, we generated a stable RPE1 clone expressing a chimeric construct comprised of the extracellular and transmembrane domain of IL-27Rα but the cytoplasmic domain of GP130 (Figure 3c, Figure 3—figure supplement 1b). Again, as both cell lines express unaltered endogenous GP130 levels, they exhibited comparable responses to HyIL-6 (Figure 3c). Strikingly, this domain-swap resulted in a transient pSTAT1 kinetic response by IL-27 comparable to HypIL-6 stimulation. STAT3 activation on the other hand remained unaltered suggesting that the cytoplasmic domain of IL-27Rα is essential for a sustained pSTAT1 response but not for pSTAT3.

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IL-27Rα cytoplasmic domain is required for sustained pSTAT1 kinetics.

(a) Representation of the cytoplasmic domain of IL-27Rα with its highlighted tyrosine residues Y543 and Y613. (b) STAT1 and STAT3 phosphorylation kinetics of RPE1 clones stably expressing wt and mutant IL-27Rα after stimulation with IL-27 (10 nM, top panels) or after stimulation with HypIL-6 (20 nM, bottom panels), normalized to maximal levels of wt IL-27Rα stimulated with IL-27 (top) or HypIL-6 (bottom). Data was obtained from three experiments with each two technical replicates, showing mean ± std dev. Bottom right: cell surface levels variants measured by flow cytometry for indicated IL-27Rα cell lines. (c) Cytoplasmic domain of IL-27Rα is required for sustained pSTAT1 activation. Left: Cartoon representation of receptor complexes. Right: STAT1 and STAT3 phosphorylation kinetics of RPE1 clones stably expressing wt IL-27Rα and IL-27Rα-GP130 chimera after stimulation with IL-27 (10 nM, top panels) or after stimulation with HypIL-6 (20 nM, bottom panels). Data was normalized to maximal levels for each cytokine and cell line. Data was obtained from two experiments with each two technical replicates, showing mean ±std dev. (d) Phosphatases do not account for differential pSTAT1/3 activity induced by IL-27 and HypIL-6. Left: Schematic representation of workflow using JAK inhibitor Tofacitinib. Right: MFI ratio of Tofacitinib-treated and non-treated RPE1 mXFPe-IL-27Rα cells for pSTAT1 and pSTAT3 after stimulation with IL-27 (10 nM) and HypIL-6 (20 nM). Data was obtained from two experiments with each two technical replicates, showing mean ± std dev.

Two plausible scenarios could explain the observed pSTAT1/3 activation differential by HypIL-6 and IL-27: (i) IL-27Rα-JAK2 complex phosphorylates STAT1 faster than GP130-JAK1 complex or (ii) pSTAT1 is more quickly dephosphorylated in the IL-6/GP130 receptor homodimer. In the latter case, pSTAT deactivation by constitutively expressed phosphatases could be an additional factor of regulation. Indeed, SHP-2 has been described to bind to GP130 and shape IL-6 responses (Kim et al., 1998). However, our Bayesian inference results (together with the mathematical models and the experimental data) identified the STAT/receptor association rates as the only rates that could account for the greater and more sustained activation of STAT1 by IL-27. We note (as described in the Materials and methods) that the phosphorylation rate, denoted by q, of STAT1 and STAT3 when bound to a dimer (homo- or hetero-) has been assumed to be independent of the STAT type and the receptor chain. Moreover, the model also included dephosphorylation of free pSTAT molecules, and predicted that the rates at which these reactions occur ( $d_{1}$ and $d_{3}$ ) had rather similar posterior distributions, hence arguing against the potential role of phosphatases to specifically target STAT1 upon HypIL-6 stimulation. To distinguish between the two plausible scenarios, we next determined the rates of pSTAT1/3 dephosphorylation by blocking JAK activity upon cytokine stimulation making use of the JAK inhibitor Tofacitinib in RPE1 cells. Tofacitinib was added 15 min after stimulation with either cytokine and pSTAT1 and pSTAT3 levels were measured at the indicated times. JAK inhibition markedly shortened the pSTAT1/3 activation profiles induced by both cytokines (Figure 3d, Figure 3—figure supplement 1c). The relative dephosphorylation rates could then be determined by the signal intensity ratio of +/- Tofacitinib. Even though pSTAT1 levels were more affected by JAK inhibition than those of pSTAT3, the observed relative changes were nearly identical for IL-27 and HypIL-6. These findings were also confirmed for Th-1 cells (Figure 3—figure supplement 1d and e) and indicate, that selective phosphatase activity cannot serve as an explanation for the pSTAT1/3 differential by HypIL-6 and IL-27, in agreement with our mathematical modeling predictions. Similarly, we tested whether neosynthesis of feedback inhibitors such as SOCS3 (Croker et al., 2003) would selectively impair signaling by HypIL-6 but not by IL-27. To this end we pre-treated cells with Cycloheximide (CHX) and followed the pSTAT1/3 kinetics induced by the two cytokines (Figure 3—figure supplement 2a and b). CHX treatment resulted in more sustained pSTAT3 activity for both cytokines. To our surprise, STAT1 phosphorylation by IL-27 was even more sustained while pSTAT1 levels induced by IL-6 remained unaffected. These observations exclude that feedback inhibitors selectively impair STAT1 activation kinetics by HypIL-6 and thus do not account for the faster STAT1 dephosphorylation kinetics observed under HypIL-6 stimulation. Overall, our data from the chimera and mutant experiments, which were not used in the Bayesian parameter calibration, provide additional support: they validate the mathematical models of HypIL-6 and IL-27 signaling and point to the differential association/dissociation of STAT1 and STAT3 to IL-27Rα and GP130, respectively, as the main factor defining STAT phosphorylation kinetics in response to HypIL-6 and IL-27 stimulation.

Unique and overlapping effects of IL-27 and HypIL-6 on the Th-1 phosphoproteome

Thus far, we have investigated the differential activation of STAT1/STAT3 induced by HypIL-6 and IL-27. Next, we asked whether IL-27 and IL-6 induced the activation of additional and specific intracellular signaling programs that could contribute to their unique biological profiles. To this end, we investigated the IL-27 and HypIL-6 activated signalosome using quantitative mass-spectrometry-based phospho-proteomics. MACS-isolated CD4+ were polarized into Th-1 cells and expanded in vitro for stable isotope labeling by amino acids in cell culture (SILAC). Cells were then stimulated for 15 min with saturating concentrations of IL-27 (10 nM), HypIL-6 (20 nM) or left untreated. Samples were enriched for phosphopeptides (Ti-IMAC), subjected to mass spectrometry and raw files analysed by MaxQuant software (Figure 4—figure supplement 1). In total we could quantify ~6400 phosphopeptides from 2600 proteins, identified across all conditions (unstimulated, IL-27, HypIL-6) for at least two out of three tested donors. For IL-27 and HypIL-6 we detected similar numbers of significantly upregulated (87 vs. 78) and downregulated (155 vs. 140) phosphorylation events (Figure 4a) and systematically categorized them in context with their cellular location and ascribed biological functions (Figure 4—figure supplement 1b and c; Huang et al., 2009b). The two cytokines shared approximately half of the upregulated and one third of the downregulated phospho-peptides (Figure 4—figure supplement 2a) but also exhibited differential target phosphorylation (Figure 4b and Figure 4—figure supplement 2b). As expected, we found multiple members of the STAT protein family among the top phosphorylation hits by the two cytokines, validating our study (Figure 4b and c). In line with our previous observations, we detected the same relative amplitudes for tyrosine phosphorylated STAT3 and STAT1. In addition to tyrosine-phosphorylation, we detected robust serine-phosphorylation on S727 for STAT1 and STAT3 (Figure 4c). While pS-STAT1 activity correlated with pY-STAT1 with IL-27 being more potent than HypIL-6, this was not the case for STAT3. Despite an identical pY-STAT3 phosphorylation profile, HypIL-6 induced a ~50% higher pS-STAT3 relative to IL-27 (Figure 4c). These results were corroborated, following the phosphorylation kinetics of pS-STAT1 and pS-STAT3 by flow-cytometry (Figure 4d). Interestingly, while IL-27 and IL-6 have been described to also activate other signaling pathways, such as MAPK, p38, and AKT (Diegelmann et al., 2012), we did not observe this in our phospho-target screening.

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Unique and overlapping effects of IL-27 and HypIL-6 on the phosphoproteome of Th-1 cells.

(a) Volcano plot of the phospho-sites regulated (p value ≤ 0.05, fold change ≥+1.5 or≤−1.5) by IL-27 (left) and HypIL-6 (right). Cells were stimulated for 15 min at saturating concentrations (IL-27: 10 nM, HypIL-6: 20 nM). Data was obtained from three biological replicates. (b) Heatmap representation (examples) of shared and differentially up- (left) and downregulated (right) phospho-sites after IL-27 and HypIL-6 stimulation. Data represents the mean (log₂) fold change of three biological replicates. (c) Tyrosine and Serine phosphorylation of selected STAT proteins after stimulation with IL-27 (red) and HypIL-6 (blue). Two-tailed Student’s T-test: *p<0.05, **p≤0.01,***p≤0.001; n.s., not significant. (d) pS727-STAT1 and pS727-STAT3 phosphorylation kinetics in Th-1 cells after stimulation with IL-27 or HypIL-6, normalized to maximal IL-27 stimulation. Data was obtained from three biological replicates with each two technical replicates, showing mean ±std dev. (e) GO analysis ‘biological processes’ of the phospho-sites regulated by IL-27 (red) and HypIL-6 (blue) represented as bubble-plots. (f) Phosphorylation of target proteins associated with STAT3/CDK transcription initiation complex after stimulation with IL-27 (blue) and HypIL-6 (red) and schematic representation of transcription regulation of RNA polymerase II with identified phospho-sites (red flags). Two-tailed Student’s T-test: *p<0.05, **p≤0.01,***p≤0.001; n.s., not significant.

Given the overlapping phospho-proteomic changes, gene ontology (GO) analysis associated several sets of phosphopeptides with biological processes that were mostly shared between both cytokines (Figure 4e, Figure 4—figure supplement 2c). A large set of phospho-peptides was linked to transcription initiation (including JAK/STAT signaling) or mRNA modification (Figure 4e). Interestingly, IL-27 stimulation was associated to negative regulation of RNA polymerase II, whereas a positive regulation was detected for HypIL-6. A closer look into the functional regulation of RNA-pol II activity by the two cytokines revealed that multiple proteins involved in this process were differentially regulated by HypIL-6 and IL-27 (Figure 4f). While positive regulators of RNA-pol II transcription, such as Negative Elongation Factor A (NELFA), PPM1G, RCHY1 and POL2RA, were much more phosphorylated in response to HypIL-6 than IL-27, negative regulators of RNA-pol II transcription, such as LARP7, were much more engaged by IL-27 treatment than by HypIL-6 (Figure 4f). Interestingly, in a previous study we linked RNA-pol II regulation with the levels of STAT3 S727phosphorylation induced by HypIL-6 via recruitment of CDK8 to STAT3-dependent genes (Bancerek et al., 2013). Our phospho-proteomic analysis thus, suggests that IL-27 and HypIL-6 recruit different transcriptional complexes that ultimately could contribute to provide gene expression specificity by the two cytokines. Additionally, we identified several interesting IL-27-specific phosphorylation targets. One example was Ubiquitin Protein Ligase E3 Component N-Recognin 5 (UBR5). Phosphorylated UBR5 leads to ubiquitination and subsequent degradation of Rorγc (Rutz et al., 2015), the key transcription factor required for Th-17 lineage commitment, thus limiting Th-17 differentiation (Figure 4—figure supplement 2d). A second example is PAK2, which phosphorylates and stabilizes FoxP3 leading to higher levels of T_Reg cells (Figure 4—figure supplement 2d; O'Hagan et al., 2017). Moreover, IL-27 stimulation led to a very strong phosphorylation of BCL2-associated agonist of cell death (BAD), a critical regulator of T-cell survival and a well-known substrate of the PAK2 kinase (Ye and Field, 2012). Overall, our data show a large overlap between the IL-6 and IL-27 signaling program, with a strong focus on JAK/STAT signaling. However, IL-27 engages additional signaling intermediaries that could contribute to its unique immuno-modulatory activities. Further studies will be required to assess how these IL-27 specific signaling pockets contribute to shape IL-27 responses.

Kinetic decoupling of gene induction programs depends on sustained STAT1 activation and IRF1 expression by IL-27

Next, we investigated how the different kinetics of STAT activation induced by HypIL-6 and IL-27 ultimately modulated gene expression by these two cytokines. To this end, we performed RNA-seq analysis of Th-1 cells stimulated with HypIL-6 or IL-27 for 1 hr, 6 hr and 24 hr to obtain a dynamic perspective of gene regulation. We identified ~12500 shared genes that could be quantified for all three donors and throughout all tested experimental conditions. In a first step, we compared how similar the gene programs induced by HypIL-6 and IL-27 were. Principal component analysis (PCA) was run for a subset of genes, found to be significantly up- (total ~250) or downregulated (total ~950) by either of the experimental conditions (p value≤0.05, fold change ≥+2 or ≤−2). At one hour of stimulation HypIL-6 and IL-27 induced very similar gene programs, with the two cytokines clustering together in the PCA analysis regardless of whether we focused on the subsets of upregulated or downregulated genes (Figure 5a). However, the similarities between the two cytokines changed dramatically in the course of continuous stimulation. While the two cytokines induced the downregulation of comparable gene programs at 6 hr and 24 hr stimulation, as denoted by the close clustering in the PCA analysis (Figure 5a, right panel) and the fraction of shared genes (~40%, Figure 5b, Figure 5—figure supplement 1a–c, Figure 5—figure supplement 2a), this was not observed for upregulated genes. Although the two cytokines induced comparable gene upregulation programs after 1 hr of stimulation (~80% shared genes), this trend almost completely disappeared at later stimulation times (Figure 5a and b, Figure 5—figure supplement 2b). This is well-reflected by the absolute numbers of up- or downregulated genes observed for IL-27 and HypIL-6 (Figure 5c). Stimulation with both cytokines yielded a similar trend of gene downregulation (Figure 5c, right panel). However, while HypIL-6 stimulation resulted in a spike of gene upregulation at 1 hr that quickly disappeared at later stimulation times, IL-27 stimulation was capable to increase the number of upregulated genes beyond 6 hr of stimulation and maintains it even after 24 hr (Figure 5c, left panel). This ‘kinetic decoupling’ of gene induction seems to have a striking functional relevance. Gene set enrichment analysis (GSEA) (Liao et al., 2019) identified several reactome pathways to be enriched for IL-27 over the course of stimulation – most of them linked with Interferon signaling and immune responses (Figure 5d). In contrast, for HypIL-6 stimulation no pathway enrichment was detected. Most importantly, the vast majority of IL-27-induced genes that were associated to these pathways belonged to genes upregulated by IL-27 treatment and that have been previously linked to STAT1 activation (Satoh and Tabunoki, 2013; Rusinova et al., 2013; Figure 5—figure supplement 2c). Although HypIL-6 treatment resulted in the induction of some of these genes, their expression was very transient in time, in agreement with the short STAT1 activation kinetic profile exhibited by HypIL-6 (Figure 5—figure supplement 2b and c).

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Kinetic decoupling of gene induction programs depends on sustained STAT1 activation by IL-27.

(a) Principal component analysis for genes found to be significantly upregulated (left) or downregulated (right) for at least one of the tested conditions (time and cytokine). Data was obtained from three biological replicates. (b) Kinetics of gene induction shared between IL-27 and HypIL-6 (relative to IL-27) for upregulated genes (red) or downregulated genes (green). (c) Kinetics of gene numbers induced after IL-27 and HypIL-6 stimulation for upregulated genes (left) and downregulated genes (right). (d) GSEA reactome analysis of selected pathways with significantly altered gene induction in response to IL-27 or HypIL-6 stimulation. Data represents the mean (log₂) fold change of three biological replicates. (e) Cluster analysis comparing the gene induction kinetics after IL-27 or HypIL-6 stimulation. Gene induction heatmaps for example genes as well as induction kinetics (mean) are shown for highlighted gene clusters. Data represents the mean (log₂) fold change of three biological replicates.

Next, we performed cluster analysis to find further similarities and discrepancies between the gene expression programs engaged by HypIL-6 and IL-27 (Figure 5e). Since genes downregulated by IL-27 and HypIL-6 showed overall good similarity throughout the whole kinetic series, we mainly focused on differences in upregulated gene induction. We identified three functionally relevant gene clusters. The first gene cluster corresponds to genes that are transiently and equally induced by HypIL-6 and IL-27. These genes peak after one hour and return to basal levels after 6 hr and 24 hr of stimulation (Figure 5e). Interestingly, this cluster contains classical IL-6-induced and STAT3-dependent genes, such as members of the NFκB and Jun/Fos transcriptional complex (Suh et al., 2008), as well as the feedback inhibitor Suppressor Of Cytokine Signaling 3 (SOCS3) (Villarino et al., 2006) and T-cell early activation marker CD69. (Figure 5e). A second cluster of genes corresponded to genes that were persistently activated by IL-27 but only transiently by HypIL-6 (Figure 5e). Among these genes we found classical STAT1-dependent genes, such as SOCS1, Programmed Cell Death Ligand 1 (PDL1 = CD274) (Hirahara et al., 2012) and members of the interferon-induced protein with tetratricopeptide repeats (IFIT) family. The third cluster of genes corresponded to genes exhibiting strong and sustained activation by IL-27 after 6 hr and 24 hr stimulation but no activation by HypIL-6 at all. This ‘2nd wave’ of gene induction by IL-27 was almost exclusively comprised of classical Interferon Stimulated Genes (ISGs) (Figure 5—figure supplement 2c), such as STAT1 and 2, Guanylate Binding Protein 1 (GBP1), GBP2, 4 and 5, and IRF8 and 9.

It is worth mentioning, that genes in the third cluster appear to require persistent STAT1 activation (Hu et al., 2002; Francois-Newton et al., 2012) and were the basis for the IFN signature identified in our reactome pathway analysis. Still, we were surprised about the magnitude of this 2nd gene wave. Even though IL-27 exerts a sustained pSTAT1 kinetic profile, pSTAT1 levels were down to ~10% of maximal amplitude after 3 hr of stimulation. We reasoned that additional factors could further amplify the STAT1 response for IL-27 but not for HypIL-6. Within the 1st wave of STAT1-dependent genes, we also spotted the transcription factor Interferon Response Factor 1 (IRF1), that was continuously induced throughout the kinetic series in response to IL-27 but only transiently spiking after 1 hr of HypIL-6 stimulation (Figure 5e). IRF1 expression was shown to prolong pSTAT1 kinetics (Zenke et al., 2018) and to be required for IL-27-dependent Tr-1 differentiation and function (Karwacz et al., 2017). We confirmed the kinetics of IRF1 protein expression by flow cytometry and showed higher and more sustained protein levels after IL-27 stimulation relative to HypIL-6 (Figure 5—figure supplement 3a). Next, we tested in our RPE1 IL-27Rα cell system, whether siRNA-mediated knockdown of IRF1 would alter the gene induction profiles of certain STAT1 or STAT3-dependent marker genes. In RPE1 IL-27Rα cells, IRF1 protein levels were peaking around 6 hr after stimulation with IL-27 and transfection with IRF1-targeting siRNA knocked down expression by >80% (Figure 5—figure supplement 3b). Importantly, knockdown of IRF1 did not alter the overall kinetics of pSTAT1 and pSTAT3 activation (Figure 5—figure supplement 3c). Induction of STAT1-dependent genes STAT1, GBP5, and OAS1 as well as STAT3-dependent gene SOCS3 were followed by RT qPCR (Figure 5—figure supplement 3d). Interestingly, up to 6 hr of stimulation, the gene induction curves were identical for control- and IRF1-siRNA treated cells. Later than 6 hr – that is, when IRF1 protein levels are peaking – the gene induction was decreased between 40 and 70% in absence of IRF1. Strikingly, expression of SOCS3, a classical STAT3-dependent reporter gene was transient and independent on IRF1 levels, highlighting that IRF1 selectively amplifies STAT1-dependent gene induction. Taken together our data support a scenario whereby IL-27 by exhibiting a kinetic decoupling of STAT1 and STAT3 activation is capable of triggering independent gene expression waves, which ultimately contribute to shape its distinct biology.

IL-27-induced STAT1 response drives global proteomic changes in Th-1 cells

Next, we aimed to uncover how the distinct gene expression programs engaged by HypIL-6 and IL-27 ultimately relate to alterations of the Th-1 cell proteome. For that, we continuously stimulated SILAC -labeled Th-1 cells for 24 hr with saturating doses of IL-27 and HypIL-6 and compared quantitative proteomic changes to unstimulated controls (Figure 6a). We quantified ~3600 proteins present in all three biological replicates and in all tested conditions (unstimulated/IL-27/HypIL-6). Both cytokines downregulated a similar number of proteins (IL-27: 57, HypIL-6: 52) (Figure 6b) with approximately half of them being shared by the two cytokines, mimicking our observations in the RNA-seq studies (Figure 6c, Figure 6—figure supplement 1). With 68 upregulated proteins, IL-27 was almost twice as potent as HypIL-6 (35 proteins) with very little overlap.

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IL-27-induced STAT1 response drives global proteomic changes in Th-1 cells.

(a) Workflow for quantitative SILAC proteomic analysis of Th-1 cells continuously stimulated (24 hr) with IL-27 (10 nM), HypIL-6 (20 nM) or left untreated. (b) Global proteomic changes in Th-1 cells induced by IL-27 (left) or HypIL-6 (right) represented as volcano plots. Proteins significantly up- or downregulated are highlighted in red (p value ≤ 0.05, fold change ≥+1.5 or≤−1.5). Significantly altered ISG-encoded proteins by IL-27 are highlighted in yellow. Data was obtained from three biological replicates. (c) Venn diagrams comparing unique upregulated (left) and downregulated (right) proteins by IL-27 (blue) and HypIL-6 (red) as well as shared altered proteins. ISG-encoded proteins are highlighted in yellow. (d) Heatmaps of the top 30 up- and downregulated proteins by IL-27 compared to HypIL-6. Data representation of the mean (log₂) fold change of three biological replicates. (e) Heatmap representation and enrichment plot of proteins identified by GSEA reactome pathway enrichment analysis ‘Cytokine signaling and immune system’ induced by IL-27. Data representation of the mean (log₂) fold change of three biological replicates. (f) Correlation of IL-27 and HypIL-6-induced RNA-seq transcript levels (≥+2 or ≤−2 fold change) with quantitative proteomic data (≥+1.5 or≤−1.5 fold change). Data representation of the mean (log₂) fold change of three biological replicates.

Among the upregulated proteins by IL-27 but not HypIL-6, we detected several proteins with described immune-modulatory functions on T-cells. One of these proteins was Transforming Growth Factor β (TGF-β), which is a key regulator with pleiotropic functions on T-cells (Yoshimura et al., 2010). TGF-β has been identified to synergistically act with IL-27 to induce IL-10 secretion from Tr-1 cells – thus accounting for one of the key anti-inflammatory functions of IL-27 (Awasthi et al., 2007). On the other hand, we also found SELPLG-encoded protein RSGL-1 which is critically required for efficient migration and adhesion of Th-1 cells to inflamed intestines (Brown et al., 2012; Matsumoto et al., 2007). Interestingly, we found LARP7 moderately upregulated by IL-27. This negative regulator for RNA pol II was also identified in our phospho-target screening and selectively engaged by IL-27 (Figure 4f). IL-27 and HypIL-6 share ~60% of downregulated proteins, but without strong functional patterns. Both cytokines downregulated several proteins related to mitotic cell cycle (LIG1, CSNK2B, PSMB1) mRNA processing and splicing (NCBP2, PCBP2, NUDT21) (Slenter et al., 2018).

Strikingly, a significant number (~40%) of proteins upregulated by IL-27 belong to the group of ISGs (Figure 6b and c, Figure 6—figure supplement 1b). This particular set of proteins including STAT1, STAT2, MX Dynamin like GTPase 1 (MX1), Interferon Stimulated Gene 20 (ISG20) or Poly(ADP-Ribose) Polymerase Family Member 9 (PARP9) was not markedly altered by HypIL-6. Of note: the overall expression patterns of the most significantly altered proteins are congruent to the gene induction patterns observed after 6 hr and 24 hr (Figure 6d and e, Supp. Fig. 10b). Similar to this, GSEA reactome analysis identified again pathways associated with interferon signaling and cytokine/immune system but failed to detect any significant functional enrichment by HypIL-6 (Figure 6e, Figure 6—figure supplement 1b & c). Finally, we correlated RNAseq-based gene induction patterns with detected proteomic changes. To our surprise, we only found a relatively low number of shared hits. However, the identified proteins belong exclusively to a group upregulated by IL-27 (Figure 6f). They are all located in the ‘2nd gene wave’ cluster and all of them are regulated by ISGs (Figure 5e). Taken together these results provide compelling evidence that sustained pSTAT1 activation by IL-27 accounts for its gene induction and proteomic profiles, thus, giving a mechanistic explanation for the diverse biological outcomes of IL-27 and IL-6. Our observations are in good agreement with previous findings in cancer cells, showing that particularly the involvement of STAT1 activation is responsible for proteomic remodeling by IL-27 (Petretto et al., 2016).

Receptor and STAT concentrations determine the nature of the IL-6/IL-27 response

Our data suggest that STAT molecules compete for binding to a limited number of phospho-Tyr motifs in the intracellular domains of cytokine receptors. A direct consequence derived from this hypothesis is that cells can adjust and change their responses to cytokines by altering their concentrations of specific STATs or receptors molecules. To assess to what degree immune cells differ in their expression of cytokine receptors and STATs, we investigated levels of IL-6Rα, GP130, IL-27Rα, STAT1, and STAT3 protein expression across different immune cell populations making use of the Immunological Proteomic Resource (ImmPRes - http://immpres.co.uk) database. Strikingly, the level of expression of these proteins change dramatically across the populations studied (Figure 7a), suggesting that these cells could potentially produce very different responses to HypIL-6 and IL-27 stimulation.

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Receptor and STAT concentrations determine the nature of the cytokine response.

(a) Copy numbers of indicated proteins determined for different T-cell subsets using mass-spectrometry-based proteomics (ImmPRes - http://immpres.co.uk). (b) Model predictions for varying levels of STAT1 and STAT3 (left panel) or IL-27Rα and GP130 (right panel) for phosphorylation kinetics of STAT1 and STAT3. (c) Gene expression profiles determined by RNAseq analysis comparing indicated genes of a cohort of SLE risk patients with a cohort of healthy controls. Data obtained from: *Proc Natl Acad Sci U S A* 115, 12565–12572. Two-tailed Student’s T-test: *p<0.05, **p≤0.01,***p≤0.001; n.s., not significant. (d) Dose-dependent phosphorylation of STAT1 and STAT3 as a response to IL-27 and HypIL-6 stimulation in naive and IFNα2-primed (2 nM, 24 hr) Th-1 cells, normalized to maximal IL-27 stimulation (ctrl). Data was obtained from four biological replicates with each two technical replicates, showing mean ±std dev. (e) Phosphorylation of STAT1 (left) and STAT3 (right) as a response to IL-27 (2 nM, 15 min) and HypIL-6 (10 nM, 15 min) stimulation in healthy control (ctrl) and SLE patient CD4 +T cells. Data was obtained from five healthy control donors (Diveu et al., 2009) and six SLE patients. Two-tailed Student’s T-test: *p<0.05, **p≤0.01,***p≤0.001; n.s., not significant. (f) Tofacitinib titration to inhibit STAT1 and STAT3 phosphorylation by IL-27 (top, 2 nM, 15 min) HypIL-6 (bottom, 10 nM, 15 min) in Th-1 cells (left) and RPE1 IL-27Rα cells (right). Data was obtained from three donors with each two technical replicates (Th-1) and two biological experiments (RPE1 IL-27Rα cells) with each two technical replicates, showing mean ±std dev.

In order to quantify (and predict) how changes in expression levels of different proteins modify the kinetics of pSTAT, we made use of the two mathematical models of HypIL-6 and IL-27 stimulation and the parameters inferred with Bayesian methods. Our mathematical models could accurately reproduce the experimental results generated across our study, that is, signaling by the IL-27Rα chimeric and IL-27Rα-Y616F mutant receptors and dose/response studies (Figure 7—figure supplement 1a–c), making use of the posterior parameter distributions generated from the Bayesian parameter calibration. Having developed mathematical models which are able to accurately explain the experimental data (Figure 3—figure supplement 1c and d) and reproduce independent experiments (Figure 3b and c), we then sought to use the models to predict pSTAT signaling kinetics under different concentration regimes of receptors and STATs. To simplify the simulations, we focused our analysis in GP130 and STAT1 proteins, two of the proteins that greatly vary in the different immune populations (Figure 7a). As baseline values for the concentrations $[G P 130 (0)]$ , $[I L 27 R α (0)]$ , $[S T A T 1 (0)]$ and $[S T A T 3 (0)]$ we used approximately the median values from the posterior distributions for each parameter: $[G P 130 (0)] = 25 n M$ , $[I L 27 R α (0)] = 50 n M$ and $[S T A T 1 (0)] = [S T A T 3 (0)] = 500 n M$ . To see the effect of varying GP130 concentrations on pSTAT signaling, we decreased the initial concentration of GP130 and simulated the model using the accepted parameters sets from the ABC-SMC to inform the other parameter values. A tenfold reduction on GP130 concentration $([G P 130 (0)] = 2.5 n M)$ resulted in a striking loss in pSTAT1 levels induced by HypIL-6, with very little effect on pSTAT3 levels induced by this cytokine (Figure 7b). pSTAT1/3 kinetics induced by IL-27, however, was not affected by this decrease in GP130 concentration (Figure 7b). Interestingly, the HypIL-6 signaling profile predicted by our model at low GP130 concentrations strongly resemble the one induced by HypIL-6 in Th-1 cells (Figure 1c), where very low levels of GP130 are found, further confirming the robustness of the predictions generated by our mathematical models. When the concentration of STAT1 was increased by a factor of ten $[S T A T 1 (0)] = 5000 n M$ , both HypIL-6 and IL-27 induced significantly higher levels of pSTAT1 activation (Figure 7b). pSTAT3 levels were not affected for HypIL-6 stimulation but were decreased for IL-27 stimulation (Figure 7b), further indicating the competitive nature of the binding of STAT1 and STAT3 to IL-27Rα and GP130. Overall, our mathematical model predicts that changes on GP130 and STAT1 expression produce a substantial remodeling of the HypIL-6 and IL-27 signalosome, which ultimately could lead to aberrant responses.

STAT1 protein levels in SLE patients modify HypIL-6 and IL-27 signaling responses

STAT1 is a classical IFN responsive gene and STAT1 levels are highly increased in environments rich in IFNs (Wong et al., 1998). Thus, we next ask whether STAT1 levels would be increased in SLE patients, an example of disease where IFNs have been shown to correlate with a poor prognosis, making use of available gene expression datasets (Tokuyama et al., 2018). We did not find differences in the expression of GP130, IL-6Rα or IL-27Rα in SLE patients (Figure 7—figure supplement 2a). However, we detected a considerable increase in the levels of STAT1 and STAT3 transcripts in these patients when compared to healthy controls, with the increase on STAT1 expression being significantly more pronounced (Figure 7c). Since our mathematical model predicted that increases in STAT1 expression could significantly change cytokine-induced cellular responses by HypIL-6 and IL-27, we next experimentally tested this prediction. For that, we primed Th-1 cells with IFNα2 overnight to increase total STAT1 levels (and to a lower extent STAT3) in these cells (Figure 7—figure supplement 2b). While both HypIL-6 and IL-27 induced comparable levels of pSTAT3 in primed and non-primed Th-1 cells, levels of pSTAT1 induced by the two cytokines were significantly upregulated in primed Th-1 cells, resulting in a bias STAT1 response and confirming our model predictions (Figure 7d). We next investigated whether this bias STAT1 activation by HypIL-6 and IL-27 observed in IFNα2-primed Th-1 cells was also present in SLE patients. For that we collected PBMCs from six SLE patients or five age-matched healthy controls and measured STAT1 and STAT3 expression, as well as pSTAT1 and pSTAT3 induction by HyIL-6 and IL-27 after 15 min treatments in CD4 T cells. Importantly, comparable results to those obtained with IFN-primed Th-1 cells were obtained, with signaling bias toward pSTAT1 in CD4 +T cells from SLE patients stimulated with HypIL-6 and IL-27 (Figure 7e, Figure 7—figure supplement 2c & d), further supporting the fact that STAT concentrations play a critical role in defining cytokine responses in autoimmune disorders.

Our data show that STAT1 and STAT3 compete for phospho-Tyr motifs in GP130, with STAT3 having an advantage resulting from its tighter affinity to GP130. Finally, we asked whether crippling JAK activity by using sub-saturating doses of JAK inhibitors could differentially affect STAT1 and STAT3 activation by HypIL-6 and therefore rescue the altered cytokine responses found in SLE patients. To test this, RPE1 and Th-1 cells were stimulated with saturated concentrations of HypIL-6 and titrating the concentrations of Tofacitinib, a clinically approved JAK inhibitor. Strikingly, Tofacitinib inhibited HypIL-6 induced pSTAT1 more efficiently than pSTAT3 in both RPE1 IL-27Rα cells and Th-1 cells (Figure 7f). At 50 nM concentration, Tofacitinib inhibited pSTAT1 levels induced by HypIL-6 by 60%, while only inhibited pSTAT3 levels by 30% (Figure 7f) – an effect that we did not observe for IL-27 stimulation. Overall, our results show that the changes in STATs concentration found in autoimmune disorders shape cytokine signaling responses and could contribute to disease progression.

Discussion

Cytokine pleiotropy is the ability of a cytokine to exert a wide range of biological responses in different cell types. This functional pleiotropy has made the study of cytokine biology extremely challenging given the strong crosstalk and shared usage of key components of their signaling pathways, leading to a high degree of signaling plasticity, yet still allowing functional selectivity (Garbers et al., 2012; Kang et al., 2020). Here, we aimed to identify the underlying determinants that define cytokine functional selectivity by comparing IL-27 and IL-6 at multiple scales – ranging from cell surface receptors to proteomic changes. We show that IL-27 triggers a more sustained STAT1 phosphorylation than IL-6, via a high-affinity STAT1/IL-27Rα interaction centered around Tyr613 on IL-27Rα. This in turn results in a more sustained IRF1 expression induced by IL-27, which leads to the upregulation of a second wave of gene expression unique to IL-27 and comprised of classical ISGs. We go one step further and show that this strong receptor/STAT coupling is altered in autoimmune disorders where STATs concentrations are often dysregulated. Increased expression of STAT1 in SLE patients’ biases HypIL-6 and IL-27 responses toward STAT1 activation, further contributing to the worsening of the disease. By using suboptimal doses of the JAK inhibitor Tofacitinib, we show that specific STAT proteins engaged by a given cytokine can be targeted.

The tight coupling of one receptor subunit to one particular STAT that we have identified in our study is a rather unusual phenomenon for heterodimeric cytokine receptor complexes, which has been first suggested by Owaki et al., 2008. Generally, the entire signaling output driven by a cytokine-receptor complex emanates from a dominant receptor subunit, which carries several Tyr residues susceptible of being phosphorylated (Umeshita-Suyama et al., 2000; Nadeau et al., 1999). This in turn results in competition between different STATs for binding to shared phospho-Tyr motifs in the dominant receptor chain, leading to different kinetics of STAT phosphorylation as observed for IL-6 stimulation (Rolvering et al., 2017; Figure 1b). Moreover, this localized signaling quantum allows phosphatases and feedback regulators – induced upon cytokine stimulation – to act in synergy to reset the system to its basal state, generating a very synchronous and coordinated signaling wave. Although very effective, this molecular paradigm presents its limitations. STAT competition for the same pool of phospho-Tyr makes the system very sensitive to changes in STAT concentration. IFNγ-primed cells, which exhibit increased STAT1 levels, trigger an IFNγ-like STAT1 response upon IL-6 stimulation (Costa-Pereira et al., 2002). IL-10 anti-inflammatory properties are lost in cells with high levels of STAT1 expression, as a result of a pro-inflammatory environment rich in IFNs (Sharif et al., 2004). Indeed, we show that STAT1 transcripts levels are increased in Crohn’s disease and SLE patients and they contributed to alter IL-6 responses. Strikingly, IL-27 appears to have evolved away from this general model of cytokine signaling activation. Our results show that STAT1 activation by IL-27 is tightly coupled to IL-27Rα, while STAT3 activation by this cytokine mostly depends on GP130. This decoupled STAT1 and STAT3 activation by IL-27 is possible thanks to the presence of a putative high-affinity STAT1 binding site on IL-27Rα that resembles the one present in IFNγR1 (Pflanz et al., 2004). As a result of this, IL-27 can trigger sustained and independent phosphorylation of both STAT1 and STAT3. This IL-27 feature allows it to induce robust responses in dynamic immune environments. Indeed, our mathematical models of cytokine signaling, and Bayesian inference, together with the experimental observations show that changes in receptor concentration minimally affected pSTAT1/3 induced by IL-27, while they fundamentally alter IL-6 responses. Overall, our data show that cytokine responses are versatile and adapt to the continuously changing cell proteome, highlighting the need to measure cytokine receptors and STATs expression levels, in addition to cytokine levels, in disease environments to better understand and predict altered responses elicited by dysregulated cytokines.

In recent years, it has become apparent that the stability of the cytokine-receptor complex influences signaling identity by cytokines (Richter et al., 2017). Short-lived complexes activate less efficiently those STAT molecules that bind with low-affinity phospho-Tyr motif in a given cytokine receptor (Martinez-Fabregas et al., 2019). Our current results further support this kinetic discrimination mechanism for STAT activation. Our statistical inference identified differences in STAT recognition to the cytokine receptor phospho-Tyr motifs as one of the major determinants of STAT phosphorylation kinetics. This parameter alone was sufficient to explain transient and sustained STAT1 phosphorylation induced by IL-6 and IL-27, respectively, without the need to invoke the action of phosphatases or negative feedback regulators such as SOCSs. Indeed, our results indicate that the rate of STAT1 dephosphorylation is similar between the IL-6 and IL-27 systems, suggesting that phosphatases do not contribute to these early kinetic differences. Moreover, blocking protein translation, and therefore the upregulation of negative feedback regulators by IL-6 treatment did not result in a more sustained STAT1 phosphorylation by IL-6, again indicating that the transient kinetics of STAT1 phosphorylation by IL-6 is encoded at the receptor level and does not require further regulation. However, recent reports have found that the amplitude of STAT1 phosphorylation in response to IL-6 is regulated by levels of PTPN2 expression, suggesting that phosphatases can play additional roles in shaping IL-6 responses beyond controlling the kinetics of STAT activation (Twohig et al., 2019). STAT1 phosphorylation levels by IL-27 on the other hand were significantly more sustained in the absence of protein translation, suggesting that negative feedback mechanisms are required to downmodulate signaling emanating from high-affinity STAT-receptor interactions. Overall, our results suggest that while phosphatases and negative feedback regulators play an important role in maintaining cytokine signaling homeostasis (Heinrich et al., 2003), the kinetics of STAT activation appears to be already encoded at the level of receptor engagement, thus ensuring maximal efficiency and signal robustness.

Cytokine signaling plasticity can occur at the level of receptor activation. In the past years, a scenario has emerged suggesting that the absolute number of signaling active receptor complexes is a critical determinant for signal output integration. Accordingly, specific biological responses were shown to be tuned either by abundance of cell surface receptors (Levin et al., 2011; Moraga et al., 2009) or by the level of receptor assembly (Martinez-Fabregas et al., 2019; Wilmes et al., 2015; Ho et al., 2017). Here, we show for the first time that IL-27-induced dimerization of IL-27Rα and GP130 at the cell surface of live cells – in good agreement with previous studies on heterodimeric cytokine receptor systems (Wilmes et al., 2015; Richter et al., 2017). For IL-27, the receptor subunits IL-27Rα and GP130 can be expressed at different ratios as seen for naive vs. activated T-cells (Charlot-Rabiega et al., 2011) as well as intestinal cells (Diegelmann et al., 2012). On T-cells, particularly after activation, IL-27Rα is expressed in strong excess over GP130, rendering GP130 as the limiting factor for receptor complex assembly (Pflanz et al., 2004). Interestingly, we observe that in addition to a faster kinetic of STAT1 phosphorylation, HypIL-6 treatment induces a lower maximal amplitude in pSTAT1 activation in T cells. This is in stark contrast to our results in RPE1 cells, where high abundance of GP130 (~3000–4000 copies of cell surface GP130) is found. In these cells, both cytokines elicited similar amplitudes of STAT1 phosphorylation. Our results suggest that surface receptor density in synergy with STATs binding dynamics to phospho-Tyr motif on cytokine receptors act to define the amplitude and kinetics of STAT activation in response to cytokine stimulation.

The distinct STAT1 and STAT3 kinetic profiles induced by IL-6 and IL-27 are the prerequisite for time-correlated decoupling of genetic programs: a ‘shared GP130/STAT3-dependent wave’ and an IL-27-‘unique IL-27Rα/STAT1-dependent wave’. However, pSTAT1 levels induced by IL-27 at 3 hr were down to ~10% of maximal amplitude, suggesting that additional factors would be required to amplify the initial STAT1 response elicited by IL-27. We observed that IL-27 induces the expression of an early wave of classical STAT1-dependent genes, which is also shared by IL-6. However, while IL-27 induces the upregulation of these genes throughout the entire duration of the experiment, IL-6 only resulted in a transient spike. We reasoned that this additional factor required for IL-27 signal amplification would be among these early STAT1-dependent genes. Among this set of genes we found the transcription factor IRF1, which had been shown to act as a feedback amplificant for pSTAT1 activity (Zenke et al., 2018). Importantly, IRF1 protein levels have been shown to be upregulated in response to IL-27 and IFNγ but not to IL-6 stimulation in hepatocytes (Bender et al., 2009). IRF1 plays a key role in chromatin accessibility which is critically required for IL-27-induced differentiation of Tr-1 cells and subsequent IL-10 secretion (Karwacz et al., 2017). Here, we could prove that the contribution of IRF1 on STAT1- but not STAT3-dependent genes is a generic feature of IL-27 signaling. This readily explains the significant transcriptomic overlap of IL-27 with type I (Imamichi et al., 2012) or type II interferons (Rolvering et al., 2017) after long-term stimulation with these cytokines. Along this line, it is not surprising that IL-27 – beyond its well-described effects on T-cell development – can also mount a considerable antiviral response as shown in hepatic cells and PBMCs (Fakruddin et al., 2007; Frank et al., 2010). Our results suggest that by modulating the kinetics of STAT phosphorylation, cytokines can modulate the expression of accessory transcription factors, such as IRF1, that act in synergy with STATs to fine-tune gene expression and provide functional diversity.

Data availability

Python (version 3.7) codes for the ABC-SMC model selection and parameter inference can be found in the public repository https://github.com/PollyJeffrey/Cytokine_modelling (copy archived at swh:1:rev:9c3e0ddc7a96eac941baad560d1541d660b0515d; Wilmes, 2021), along with the results of the analysis. Phospho-proteomic and proteomic datasets were uploaded to the Proteome Exchange platform with accession numbers PXD024657 and PXD024188 respectively. RNA-seq dataset was uploaded in the GSE database with accession number GSE164479.

Target	For	Rev	Size
β-actin	CATGTACGTTGCTATCCAGGC	CTCCTTAATGTCACGCACGAT	250 bp
STAT1	CTAGTGGAGTGGAAGCGGAG	CACCACAAACGAGCTCTGAA	252 bp
GBP5	TCCTCGGATTATTGCTCGGC	CCTTTGCGCTTCAGCCTTTT	309 bp
OAS1	GAAGGCAGCTCACGAAACC	AGGCCTCAGCCTCTTGTG	114 bp
SOCS3	GTCCCCCCAGAAGAGCCTATTA	TTGACGGTCTTCCGACAGAGAT	118

Parameter	Description	Unit
$r_{1,6}^{+}, r_{1,27}^{+}$	Rate of receptor-ligand binding	nM⁻¹s⁻¹
$r_{1,6}^{-}, r_{1,27}^{-}$	Rate of receptor-ligand dissociation	s⁻¹
$r_{2,6}^{+}, r_{2,27}^{+}$	Rate of monomer-monomer binding (or dimer formation)	nM⁻¹s⁻¹
$r_{2,6}^{-}, r_{2,27}^{-}$	Rate of dimer dissociation	s⁻¹
$k_{i a}^{+}$	Rate of STATi binding to GP130	nM⁻¹s⁻¹
$k_{i b}^{+}$	Rate of STATi binding to IL-27Rα	nM⁻¹s⁻¹
$k_{i a}^{-}$	Rate of STATi dissociating GP130	s⁻¹
$k_{i b}^{-}$	Rate of STATi dissociating IL-27Rα	s⁻¹
$q$	Rate of STATi phosphorylation on a dimer	s⁻¹
$d_{i}$	Rate of pSTATi dephosphorylation	s⁻¹
$β_{6}, β_{27}$	Rate of receptor internalization/degradation under hypothesis 1	s⁻¹
$γ_{6}, γ_{27}$	Rate of receptor internalization/degradation under hypothesis 2	nM⁻¹s⁻¹
$[R_{1} (0)]$	Initial concentration of GP130	nM
$[R_{2} (0)]$	Initial concentration of IL-27Rα	nM
$[S_{i} (0)]$	Initial concentration of STATi	nM

Parameter	Prior distribution	Reference
$r_{1,6}^{+}$	$10^{r} for r \sim N (- 3, 1.5)$	*
$r_{1,6}^{-}$	$10^{r} for r \sim N (- 3.9, 1.96)$	*
$r_{1,27}^{+}$	$10^{r} for r \sim N (- 2.34, 1.17)$	*
$r_{1,27}^{-}$	$10^{r} for r \sim N (- 2.82, 1.41)$	*
$r_{2, j}^{+}$ for j∈{6,27}	$10^{r} for r \sim U n i f (- 2, 3)$	Kozer et al., 2013
$r_{2, j}^{-}$ for j∈{6,27}	$10^{r} for r \sim U n i f (- 3, 1)$	Kozer et al., 2013
$k_{i a}^{+}$ , $k_{i b}^{+}$ for i∈{1,3}	$10^{r} for r \sim U n i f (- 7, 1)$	**
$k_{i a}^{-}$ , $k_{i b}^{-}$ for i∈{1,3}	$10^{r} for r \sim U n i f (- 2, 1)$	**
$q$	$10^{r} for r \sim U n i f (- 3, 2)$	Assumed
$d_{i}$ for i∈{1,3}	$10^{r} for r \sim U n i f (- 5, - 2)$	***
$β_{j}$ for j∈{6,27}	$10^{r} for r \sim U n i f (- 5, - 1)$	†
$γ_{j}$ for j∈{6,27}	$10^{r} for r \sim U n i f (- 5, - 1)$	†
$[R_{1} (0)]$	$N (12.7, 6.35)$	‡
$[R_{2} (0)]$	$N (33.8,16.9)$	‡
$[S_{1} (0)]$	$N (300,100)$	Itzhak et al., 2016
$[S_{3} (0)]$	$N (400,100)$	Itzhak et al., 2016

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Cytokine receptor activation by IL-27 and (Hyp)IL-6: (a) Cartoon model of stepwise assembly of the IL-27 and HypIL-6-induced receptor complex and subsequent activation of STAT1 and STAT3.

Single-molecule co-tracking as a readout for dimerization of cytokine receptors.

Dynamics of IL-27-induced receptor assembly.

Ligand-induced heterodimerization of IL-27Rα and GP130.

Ligand-induced homodimerization of GP130.

Mathematical modeling results in RPE1 and Th-1 cells.

Summary statistics of the posterior distributions for the STAT/receptor binding and dissociation rates.

IL-27Rα cytoplasmic domain is required for sustained pSTAT1 kinetics.

Unique and overlapping effects of IL-27 and HypIL-6 on the phosphoproteome of Th-1 cells.

Kinetic decoupling of gene induction programs depends on sustained STAT1 activation by IL-27.

IL-27-induced STAT1 response drives global proteomic changes in Th-1 cells.

Receptor and STAT concentrations determine the nature of the cytokine response.

Parameter mathematical notation, description, and units, where i∈{1,3} so that STAT i corresponds to STAT1 or STAT3.

Prior distribution assigned to each parameter and initial molecular concentration.

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Stephan Wilmes

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Polly-Anne Jeffrey

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Jonathan Martinez-Fabregas

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Maximillian Hafer

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Paul K Fyfe

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Elizabeth Pohler

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Silvia Gaggero

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Martín López-García

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Grant Lythe

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Charles Taylor

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Thomas Guerrier

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David Launay

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Suman Mitra

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Jacob Piehler

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Carmen Molina-París

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Ignacio Moraga

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Parameter mathematical notation, description, and units, where $i \in {1,3}$ so that STAT $i$ corresponds to STAT1 or STAT3.