IL-6-induced trans-signalling is more potent than classic signalling in human hepatoma cells
To elaborate the underlying molecular mechanisms of the different cellular responses to IL-6 trans- and classic signalling we made use of the specific trans-signalling inducer Hy-IL-6. Hy-IL-6 is a fusion protein of IL-6 and sIL-6Rα [14]. We verified the applicability of Hy-IL-6 to induce trans-signalling in lieu of IL-6 and sIL-6Rα. Using the law of mass action and considering the dissociation constant KD = 0.5 nM of the IL-6:IL-6Rα complex [48], we calculated how many IL-6:sIL-6Rα complexes are formed for a given number of IL-6 and sIL-6Rα. Next, we compared signalling induced by either IL-6:sIL-6Rα complex or an equimolar amount of Hy-IL-6. HepG2 cells, that express both gp130 and membrane-bound IL-6Rα, were stimulated for 30 min with 0.17 nM IL-6 + 100 nM sIL-6Rα, forming 0.17 nM IL-6:sIL-6Rα complex. For comparison HepG2 cells were stimulated with 0.17 nM Hy-IL-6 or left untreated. Phosphorylation of STAT3 was analysed by intracellular flow cytometry. No difference in STAT3 activation in response to trans-signalling induced by either Hy-IL-6 or the IL-6:sIL-6Rα complex was obvious (Additional file 1: Figure S1). Hence, Hy-IL-6 can act as a substitute for equimolar amounts of IL-6:sIL-6Rα complexes.
We next investigated whether the strength of IL-6-induced STAT3 activation in response to classic signalling is different to that in response to trans-signalling. We therefore compared the kinetics of STAT3 phosphorylation, SOCS3 mRNA and SOCS3 protein expression in HepG2 cells stimulated with IL-6 (0.08 nM (Fig. 1a) or 0.17 nM (Fig. 1b)) to initiate classic signalling, or with Hy-IL-6 (0.08 nM (Fig. 1a) or 0.17 nM (Fig. 1b)) to initiate trans-signalling. STAT3 phosphorylation and SOCS3 protein expression were analysed by quantitative Western blotting (Additional file 1: Figure S6) and SOCS3 mRNA was quantified by qRT-PCR. IL-6-induced trans- and classic signalling result in transient phosphorylation of STAT3. However, trans-signalling-induced STAT3 activation (red) is more pronounced than classic signalling-induced STAT3 phosphorylation (blue). SOCS3 mRNA and protein expression follow the peak of STAT3 phosphorylation. Both SOCS3 mRNA and protein expression are higher in response to trans-signalling than to classic signalling (Fig. 1).
In summary, STAT3 phosphorylation is markedly elevated in response to trans-signalling compared to classic signalling, resulting also in higher SOCS3 mRNA and protein expression levels.
Set-based modelling supports the hypothesis that intracellular signalling initiated by IL-6 trans- or classic signalling does not differ
So far, it is not understood whether the observed differences of STAT3 activation in response to trans- and classic signalling (Fig. 1) are caused by different strength of receptor activation or by different mechanisms and dynamics of signalling downstream to receptor activation. To test these alternative hypotheses we made use of the described set-based modelling and model validation approach [43]. This method enables testing whether a model can reasonably be parameterised despite uncertain experimental data. In other words, set-based modelling allows invalidating a model that is not capable of reproducing given experimental data, and therefore to invalidate the underlying model hypothesis (Fig. 2 AI, blue line). Notably, the solution of a set-based model relies on a feasibility problem (FP) and several mathematical relaxation steps (Fig. 2 AII, Additional file 1: Text S1). These relaxation steps lead to the identification of valid parameter sets. These sets, however, may contain false positive parameters that result in model trajectories which do not match to experimental data (Fig. 2 AIII). Strictly speaking, only a model invalidation, but not a model validation is possible [43]. Once a model is found to be not invalid, an exclusion of false positive parameter sets is performed using an outer-bounding algorithm (Fig. 2 AIII). Furthermore, analyses such as Monte Carlo sampling are applied to determine valid parameterisations resulting in trajectories that match the experimental data (orange cross and trajectory Fig. 2 AIII).
Our goal was to test the hypothesis that topologies and kinetics downstream of receptor activation are identical in trans- and classic signalling. To do so, we used set-based modelling. We considered three different dynamic computational models (Fig. 2b). While the first model combines trans- and classic signalling, the second and third models describe trans- and classic signalling by two separated models (see Material and Methods for modelling assumptions, Additional file 1: Text S2.1 and Additional file 1: Tables S1 and S2 for detailed model descriptions). We modelled the specifics of classic signalling by considering that IL-6 first binds to IL-6Rα followed by binding of the IL-6:IL-6Rα complex to gp130. In case of trans-signalling Hy-IL-6 associates directly with gp130.
To validate/invalidate the according hypotheses, we developed the following workflow (Fig. 2c). First, parameter estimation is performed for the three initial models (Fig. 2b) using set-based parameter estimation. Subsequently, the estimated parameter ranges serve as inputs to reduced models lacking SOCS3 negative feedback. The aim of this second step is to further confine the initial parameter ranges using again set-based parameter estimation. Next, the results of both set-based parameter estimation rounds are merged which results in calibrated models with reduced parameter ranges.
To finally test whether our calibrated models cannot be invalidated and hence support our initial hypothesis, a yes/no workflow is applied (Fig. 2c). Notably, the results of the yes/no workflow applied in this study are depicted in bold black arrows in Fig. 2c, while alternative workflows are given by dotted arrows. We first ask whether the obtained parameter ranges for the three calibrated models (Fig. 2c box 1) overlap. In case the ranges are disjoint, the initial hypothesis is deemed invalid. In case the parameter ranges overlap, we next ask whether the model combining both, trans- and classic signalling, yields the smallest and the same parameter ranges as at least one of the models describing trans-signalling only or classic signalling only (box 2). If this question is neglected, a Monte Carlo sampling analysis is subsequently performed for all three models to check whether at least individual parameter sets can be found that overlap between all three models (boxes 2a and b). In case the parameter sets are disjoint, we can state that our initial hypothesis is invalid. If in contrast, the obtained parameter sets overlap, we deem this hypothesis as not invalid.
Above, we asked whether the model which combines both, trans- and classic signalling yields the same parameter ranges as the models describing only trans-signalling and only classic signalling. If this applies, we can state that the model which combines both, trans- and classic signalling constrains parameter ranges best (Fig. 2c box 3) and can be used for further Monte Carlo sampling analyses (box 3a). If finally, parameterisations are determined, such that the model is capable to represent all experimental data (box 3b), we cannot invalidate the hypothesis that trans- and classic signalling-induced Jak/STAT signalling employ the same pathway topology downstream of receptor activation (box 3c). Subsequently, the developed and not invalid model can be used for further analyses, while in negative case, the hypothesis is deemed invalid and the model is rejected.
To perform the proposed workflow, we employed the experimental data presented in Fig. 1. Additionally, we determined the number of IL-6Rα and gp130 molecules on the cell surface by a bead-based FACS assay as 2099 ± 347 molecules/cell surface (2.2 nM ± 0.4 nM) and 16,198 ± 2965 molecules/cell surface (16.8 nM ± 3.1 nM), respectively (Fig. 2d, Additional file 1: Figure S5). Furthermore, the absolute number of STAT3 in HepG2 cells was measured by quantitative Western blotting as 9.2 × 105 ± 4.2 × 105 molecules/cell (958 nM ± 445 nM), (Fig. 2d and Additional file 1: Figure S2). Based on the algebraic eqs. (S6)–(S8) presented in Additional file 1: Text S2.1.1 the amounts determined for STAT3, IL-6Rα and gp130 were used as start values to calculate the model quantities of the [IL-6:IL-6Rα] complex, of the non-activated hexameric receptor complex [Rcomplex], and of unphosphorylated STAT3 [STAT3]. Furthermore, initial boundaries of all parameters were specified in a global range of 10− 9-103 covering all biologically-justified parameter values (Additional file 1: Table S3). We further considered the range of 0.5–50 nM for the dissociation constant of the IL-6:IL-6Rα complex (KD1 = \( \frac{p_2}{p_1} \)) [11, 49,50,51,52] and the range of 0.01–0.05 nM for the dissociation constants of the IL-6:IL-6Rα:gp130 complex \( \left({\mathrm{K}}_{\mathrm{D}2}=\frac{{\mathrm{p}}_4^{\mathrm{cl}}}{{\mathrm{p}}_3^{\mathrm{cl}}}\right) \) and of the Hy-IL-6:gp130 complex \( \left({\mathrm{K}}_{\mathrm{D}3}=\frac{{\mathrm{p}}_4^{\mathrm{tr}}}{{\mathrm{p}}_3^{\mathrm{tr}}}\right) \) [11, 50] (Fig. 2b).
Based on these constraints, we used our set-based parameter estimation workflow (Fig. 2c) to test whether the hypothesis that intracellular signalling mechanisms do not differ between trans- and classic signalling. We started with the initial model that combines both, trans- and classic signalling. The first round of set-based modelling provided restrictions on the model parameters p3tr, p4tr, and p7-p12, while other parameters could not (p5, p6 and p13) or only marginally be restricted (p1, p2, p3cl, p4cl; Fig. 2e dark grey bars compared to initial parameter intervals in green and Additional file 1: Table S3, fifth column). Of note, none of the parameter sets was found to be empty so that the model could not be invalidated at this step.
To further validate the results, we additionally implemented the model into the Data2Dynamics software package [44], and performed identifiability analyses. As result, parameters for which we estimated tight outer bounds (p3tr, p4tr, p9-p12) were identifiable in contrast to the remaining parameters. Hence, this different approach supports the results from set-based modelling.
Including both, trans- and classic signalling simultaneously in a single model may constrain the parameter boundaries in comparison to specific models on trans- and classic signalling. Thus, in a second step, we separated trans- and classic signalling in two models and used set-based modelling to estimate parameter ranges for the two models individually. The results for parameter estimation of these two models are depicted in Fig. 2e (blue bars for classic signalling only; red bars for trans-signalling only) and Additional file 1: Table S4. Again, all parameter sets were found to be non-empty. We could restrict 14 (p1, p2, p3cl, p4cl, p3tr, p4tr, p7, p8, p9, pdelay1, p10, p11, pdelay2, p12) out of 17 model parameter ranges. However, for the remaining three parameters (p5, p6 and p13) no restrictions could be made (Fig. 2e).
In summary the performed set-based parameter estimation did not render our models invalid and enabled us to restrict most of the unknown parameters. This result counts for all three initial models.
Decoupling of fast and slow processes within Jak/STAT signalling improves parameter restriction
The so far unrestricted parameters are important to describe the initial and fast activation of the pathway. Thus, we analysed these model parameters in reduced models that decouple the early and fast receptor activation from the subsequent slow reactions, which include the synthesis of SOCS3 protein and the SOCS3-dependent negative feedback. We exploit the fact that biochemical parameters are independent of the network topology and used the estimated parameter ranges obtained by analysing the initial models (Fig. 2e, Additional file 1: Table S3, fifth column and Additional file 1: Table S4, second and fifth columns) as input values for the reduced models (Fig. 2c). As before, one reduced model described trans- and classic signalling and two additional reduced models described either trans- or classic signalling (Fig. 3a). By setting the parameters p11, pdelay2, p12, and p13 to zero we assumed the production of SOCS3 protein - and hence the resulting negative feedback - to be blocked (blue part in Fig. 3a). To match these assumptions experimentally, we analysed the kinetics of Jak/STAT signalling in HepG2 cells while blocking the synthesis of SOCS3 protein with cycloheximide (CHX). CHX blocked IL-6 and Hy-IL-6-induced SOCS3 protein expression and consequently cytokine-induced STAT3 phosphorylation was strongly increased (Fig. 3b). For kinetic analyses HepG2 cells were stimulated with IL-6 or Hy-IL-6 (0.08 nM and 0.17 nM) in the presence of CHX (Fig. 3c, d). Whereas IL-6 and Hy-IL-6-induced phosphorylation of STAT3 and SOCS3 mRNA expression were transient in the absence of CHX (Fig. 1), phosphorylation of STAT3 was sustained in the presence of CHX and reached a plateau after 60 min of stimulation (Fig. 3c, d left panels and Additional file 1: Figure S7). Cytokine-induced expression of SOCS3 mRNA rose continuously until the end of the experiment (Fig. 3c, d right panels). Notably, also in the absence of the SOCS3 feedback, trans-signalling was stronger than classic signalling.
These additional experimental data were used for parameter estimations of the reduced models. Compared to the analyses of the initial models, ranges of parameters p1, p2, p3cl, p4cl, p7, and p8 could be further reduced for all three models lacking the SOCS3 feedback loop (Fig. 4a, compare light colours (w/o SOCS3 feedback) with the corresponding dark colours (with SOCS3 feedback); Additional file 1: Table S3 sixth column and Additional file 1: Table S4 third and sixth columns). Notably, ranges for parameters p5, p6, and p13 could neither be restricted using the initial models and corresponding data, nor using the reduced models with the additional data.
We next merged the results from first and second set-based parameter estimation results by choosing the smallest obtained parameter ranges from both rounds and thereby obtained so called calibrated models (Fig. 2c, Additional file 1: Table S3, seventh column and Additional file 1: Table S4, fourth and seventh columns).
Subsequently, we followed the flow chart as given in Fig. 2c to test (non-)invalidity of our initial hypothesis, that signalling mechanisms downstream of receptor activation do not differ between trans- and classic signalling. The obtained parameter sets for the calibrated models describing trans- and classic signalling, trans-signalling only, and classic signalling overlap (compare grey, blue and red bars in Fig. 4a) (Fig. 2c box 1). Furthermore, the parameter ranges estimated from the model describing only trans-signalling yielded the same ranges than from the model describing both, trans- and classic signalling (compare grey and red bars in Fig. 4a) (Fig. 2c box 2). In contrast, parameter ranges estimated from the model describing only classic signalling were less restricted than parameter ranges estimated from the two other models. Hence, neither the individual model for trans- nor the individual model for classic signalling enable further parameter restrictions than the combined model. Consequently, we concluded that the model which combines both, trans- and classic signalling, constrains the parameter ranges best and can be used for further analyses (Fig. 2c box 3).
Monte Carlo sampling and set-based refinements of parameter ranges
Using the set-based modelling approach, the ranges of the parameters of the calibrated model that describes both, trans- and classic signalling were restricted optimally using set-based modelling and the given experimental data (Fig. 4a, dotted bars). However, to finally test whether we can (in)validate our initial hypothesis, we analysed whether defined parameter sets within the restricted parameter ranges exist that enable us to reproduce our experimental data (Fig. 2c box 3). We therefore applied Monte Carlo sampling to the calibrated model that describes both, trans- and classic signalling (Fig. 2c box 3a). The estimated parameter ranges (Fig. 4a, dotted bars) served as outer bounds for Monte-Carlo sampling. Out of 150,000 parameterisations, we derived the 150 parameterisations with lowest square deviation between our model predictions and the experimental data (Fig. 4a, Additional file 1: Table S3, seventh column). Exemplary parameterisations out of these 150 parameterisations are depicted as magenta crosses in Fig. 4a. These 150 parameterisations allowed predictions, which are in line with the experimental data (Fig. 2c box 3a; Fig. 4b). Specifically, in Fig. 4b model predictions for the kinetics of trans- and classic signalling-induced STAT3 phosphorylation, SOCS3 mRNA expression, and SOCS3 protein expression for up to 90 min are depicted in dark and light grey corridors, respectively. These corridors result from simulations of the model with the determined 150 best parameterisations. Experimental data (as shown in Figs. 1 and 3c,d) are given in red for trans-signalling and blue for classic signalling. As the model was capable to represent all experimental data using the obtained parameterisations, we could not invalidate the model. Thus, we were not forced to reject our initial hypothesis that signalling mechanisms downstream of receptor activation do not differ between trans- and classic signalling (Fig. 2c box 3c).
As can be seen in Fig. 4a for most of the parameters, the parameterisations derived by Monte Carlo sampling did not cover the complete parameter ranges restricted by set-based parameter estimation. This, however, is no proof for the non-existence of valid parameter solutions in other regions of the restricted parameter ranges. That is why, we next confirmed our results from Monte Carlo sampling independently. Specifically, we aimed to demonstrate, that regions where no samples were determined by Monte Carlo sampling are indeed invalid, i.e. do not contain parameters that sufficiently describe the experimental data. To do so, we used an iterative procedure that is built upon successive refinements of the lower and upper parameter bounds (Additional file 1: Table S3, seventh column). Starting with parameter p1, we moved the previously estimated lower and upper bounds (Fig. 4a, dotted bars) of p1 inwards, while testing at each step if the model is deemed invalid. By this, we obtained refined and tightened parameter bounds for p1. Of note, boundaries for p2 were automatically restricted after refining p1 as the ratio of p1 to p2 represents the dissociation constant of the IL-6:IL-6Rα complex. We proceeded with parameter p3cl similar as to p1. As result, also parameter p4cl could be further restricted. The procedure was repeated for the remaining parameters resulting in a further refinement of the estimated parameter ranges depicted as black horizontal lines in Fig. 4a (Additional file 1: Table S3, seventh column in brackets). These refined ranges comprised all determined parameterisations. Of note, all results from Monte Carlo sampling are within the refined parameter ranges. Thus, our results from Monte Carlo sampling could be confirmed.
In summary, Monte Carlo sampling and a subsequent refinement of parameter ranges allowed us to develop a mathematical model of IL-6-induced trans- and classic signalling with tight and valid parameter ranges that sufficiently reproduced experimental data.
We finally challenged the predictive capacity of our model. We therefore calculated the dose-dependent phosphorylation of STAT3 expected after 30 min of stimulation with either IL-6 or Hy-IL-6. For experimental validation, we stimulated HepG2 cells with 13 different equimolar concentrations of Hy-IL-6 and IL-6 for 30 min and monitored STAT3 phosphorylation by intracellular flow cytometry. As control we included the experimental conditions used in Fig. 4b (stimulation with 0.08 nM and 0.17 nM cytokine for 30 min). Both, trans- and classic signalling induced a dose-dependent phosphorylation of STAT3. Notably, trans-signalling was stronger than classic signalling for all cytokine concentrations tested. Model predictions were in line with these experimental results, that were not used for parameterisation, which proves the predictive power of our calibrated model (Fig. 4c).
In summary, we established a parameterised predictive computational model that describes the differences between trans- and classic signalling without proposing differences in canonical intracellular signalling.
Model prediction reveals that differences between trans- and classic signalling are caused by the ratio of gp130 to IL-6Rα on the cell surface
As we could not invalidate the hypothesis that topology and kinetics of Jak/STAT signalling downstream of receptor activation are the same for trans- and classic signalling, we next asked which components of the signalling pathway are responsible for the observed differences in STAT3 activation in response to trans- and classic signalling. To analyse whether the amount of receptors on the cell surface affects the ratio of classic to trans-signalling we varied the start values of gp130 and membrane-bound IL-6Rα. With these different input variables we performed model predictions using the obtained 150 best Monte Carlo parameter samples and our final predictive calibrated model for trans- and classic signalling. We predicted the ratio of trans-signalling to classic signalling-induced STAT3 phosphorylation after 30 min of cytokine stimulation as model output. A ratio of 1 implies that both signalling modes lead to equally strong STAT3 activation (red line in Fig. 5) whereas a ratio > 1 indicates that trans-signalling is stronger than classic signalling.
The mean concentrations of IL-6RαTotal, STAT3Total and gp130 were determined as 2.2 nM, 958 nM and 16.9 nM, respectively (see Fig. 2d). First, we fixed IL-6RαTotal and STAT3Total to their mean concentrations and varied the mean value of gp130Total ± one order of magnitude (1.69 nM to 169 nM, Fig. 5a). Notably, for endogenous gp130 concentrations (white area) the model well rendered the high ratio of trans- to classic signalling shown experimentally. For increasing amounts of gp130 the ratio of trans- to classic signalling further increased, while the ratio of trans- to classic signalling decreased for lower amounts of gp130.
Next, we kept the concentration of gp130Total and STAT3Total constant but varied the amount of IL-6RαTotal ± one order of magnitude (0.22 nM to 22 nM, Fig. 5b). The white area depicts the concentration of endogenous IL-6Rα ± STD. As shown experimentally trans-signalling was two to three times stronger than classic signalling under these conditions. Interestingly, for higher concentrations of IL-6RαTotal the difference between STAT3 phosphorylation during trans- and classic signalling was completely ablated.
From these observations we concluded that the ratio of IL-6Rα to gp130 on the cell surface crucially determines the strength of STAT3 activation in response to trans- and classic signalling. Limited expression of membrane-bound IL-6Rα restricts STAT3 activation in response to classic signalling but does not limit trans-signalling because soluble IL-6Rα compensates for limited expression of membrane-bound IL-6Rα. If gp130 expression is lower than IL-6Rα expression, gp130 acts as a bottleneck for trans- and classic signalling, hence, trans-signalling cannot surpass classic signalling. In line with this hypothesis HepG2 cells, where trans-signalling is stronger than classic signalling, express eight times more gp130 than IL-6Rα (Fig. 2d).
The results from modelling do not argue for classic or trans-signalling-specific signal transduction downstream of the respective activated receptor complex. To substantiate this hypothesis, we applied our model to predict the influence of the extent of STAT3 expression on the ratio of trans- to classic signalling-induced STAT3 activation. We predicted STAT3 phosphorylation for STAT3Total ranging from 95.8 nM to 9580 nM (Fig. 5c) and fixed expression of IL-6RαTotal and gp130Total to endogenous amounts. As shown before, STAT3Total concentrations in the range of endogenous STAT3 expression are predicted to result in STAT3 phosphorylation, that is two to three times stronger in response to trans-signalling than in response to classic signalling. With increasing amounts of STAT3Total this difference decreased slightly. Yet, STAT3 phosphorylation in response to trans-signalling was still two times stronger than in response to classic signalling – even at a concentration of 9580 nM STAT3Total. This supports our hypothesis that intracellular signalling is not causative for the differences between trans- and classic signalling.
In summary, our model analyses let us hypothesize that the ratio of gp130 to IL-6Rα determines the differences between trans- and classic signalling.
Experimental validation of the impact of receptor ratios on the differences between trans- and classic signalling
To challenge our hypothesis that the receptor ratio determines the difference between trans- and classic signalling, we set up additional experiments in HepG2 cells that stably overexpress IL-6Rα (HepG2-IL-6Rα) on the cell surface. We quantified IL-6Rα and gp130 expression by FACS analysis (Fig. 6a). In contrast to HepG2 cells, that express approximately eight times more gp130 than IL-6Rα (Fig. 2d), HepG2-IL-6Rα cells express approximately 100-times more IL-6Rα than gp130. Of note, STAT3 expression in HepG2 and HepG2-IL-6Rα cells was similar (Fig. 2d and Fig. 6a; Fig. 5c blue area). According to our model predictions HepG2-IL-6Rα cells reflect a situation in which the ratio of classic to trans-signalling equals to one (Fig. 5a and b blue areas). To validate these predictions experimentally, we stimulated HepG2-IL-6Rα cells with IL-6 or Hy-IL-6 (0.17 nM) and analysed STAT3 phosphorylation, SOCS3 mRNA expression, and protein expression in response to trans- and classic signalling (Fig. 6b, Additional file 1: Figure S8A). In accordance with model predictions there is no difference between trans- and classic signalling in HepG2-IL-6Rα cells. Furthermore, HepG2-IL-6Rα cells were stimulated with 13 different equimolar concentrations of Hy-IL-6 and IL-6 for 30 min. Dose-dependent STAT3 phosphorylation was equal in response to trans- and classic signalling (Fig. 6c). Finally, HepG2-IL-6Rα cells were stimulated with IL-6 or Hy-IL-6 (0.17 nM) and treated with CHX for blocking SOCS3 protein synthesis. STAT3 phosphorylation as well as SOCS3 mRNA expression were similar in response to trans- and classic signalling also in the absence of SOCS3 feedback (Fig. 6d, Additional file 1: Figure S8B).
In summary, in line with the prediction by our model, trans- and classic signalling in HepG2-IL-6Rα cells resulted in equal kinetics and dose-dependent activation of Jak/STAT signalling. Data-driven modelling and experimental data revealed that the stronger activity of IL-6-induced trans-signalling in HepG2 cells can sufficiently be explained by the ratio of gp130 to IL-6Rα on the cell surface.
Strength of trans- and classic signalling translates into strength of cell proliferation
So far, our analyses focussed on IL-6-induced signalling, but have not investigated the downstream effects of STAT3 activation which, besides others, result in proliferation of blood cells [53]. Murine pre B cells (Ba/F3) are a convenient and established cellular system for studying cytokine-induced proliferation. Ba/F3 cells proliferate in response to IL-3. Ba/F3 cells stably expressing gp130 and IL-6Rα proliferate in response to IL-6 [45]. Thus, to determine how trans- and classic signalling translate into cell proliferation, we analysed trans- and classic signalling-induced proliferation of Ba/F3-gp130-IL-6Rα cells. The cells were treated with increasing equimolar amounts of Hy-IL-6 and IL-6 to induce trans- and classic signalling, respectively. Proliferation was analysed after 48 h and found to be equal in response to trans- and classic signalling (Fig. 7a). To test whether this correlates with equal dynamics of cellular signalling in response to trans- and classic signalling, we analysed the kinetics of Jak/STAT signalling in response to 0.17 nM Hy-IL-6 or IL-6 (Fig. 7b, Additional file 1: Figure S9). In response to both stimuli, STAT3 phosphorylation increases up to 15 min and subsequently decreases slowly to a steady state reached about 75 min post start of stimulation. Dose-dependent STAT3 phosphorylation in response to stimulation with either IL-6 or Hy-IL-6 for 30 min is documented in Fig. 7c. Again, no differences in IL-6- and Hy-IL-6-induced STAT3 phosphorylation were obvious.
These results correspond to equal IL-6 trans- and classic signalling-induced STAT3 activation observed in HepG2-IL-6Rα cells. Based on these similarities and our modelling results (Fig. 5) we hypothesized that Ba/F3-gp130-IL-6Rα cells express more IL-6Rα than gp130. Indeed, quantifying the number of IL-6Rα and gp130 molecules in Ba/F3-gp130-IL-6Rα revealed 40 times more IL-6Rα than gp130 molecules on the cell surface (Fig. 7d). These results further strengthen the hypothesis that differences between trans- and classic signalling are primarily caused by the IL-6Rα to gp130 ratio.
Pharmacological inhibition of intracellular IL-6-induced signalling does not discriminate between trans- and classic signalling
IL-6-induced trans-signalling is associated with severe inflammatory diseases, whereas classic signalling contributes to the anti-inflammatory activities of IL-6 [3, 4]. These observations have encouraged the development of approaches to specifically block trans-signalling. We aimed to test, whether three Jak inhibitors (Baricitinib, Ruxolitinib, Tofacitinib) [54] differentially block IL-6-induced trans- and classic signalling. We demonstrated that the inhibitors interfere with STAT3 activation by applying the inhibitors to cells stimulated with IL-6 or Hy-IL-6 (Fig. 8a). To compare their inhibitory potential against IL-6-induced trans- and classic signalling, IC50 values for inhibition of trans- and classic signalling-induced growth of Ba/F3-gp130-IL-6Rα cells were determined (Fig. 8b). All three inhibitors inhibited growth of Ba/F3-gp130-IL-6Rα cells in a dose-dependent manner. However, none of the Jak inhibitors discriminated between cell proliferation initiated by IL-6 classic or trans-signalling.
From these experiments, we conclude that interfering in the first step of intracellular signalling is not appropriate to specifically target trans-signalling. These findings further strengthen the hypothesis that differences observed between IL-6-induced trans- and classic signalling are not caused by differences in intracellular signalling. Instead, the response towards IL-6 trans- and IL-6 classic signalling is crucially determined by the ratio of IL-6 receptor α to gp130 expression.