Formulating cell line-specific models of IGF1R signaling

We modeled IGF1-IGF1R interactions based on the harmonic oscillator (HO) mechanism proposed by Kiselyov et al. [44] and supported by subsequent structural analyses [47–49]. IGF1R molecules exist in pre-formed dimeric complexes, each of which contain two IGF1 binding pockets that are considered to be functionally equivalent (Fig 1A) [50]. Each binding pocket contains two dissimilar IGF1 binding sites, termed Site 1 (S₁) and Site 2 (S₂). A binding pocket can accommodate at most one IGF1 molecule, so a dimeric complex can simultaneously engage at most two IGF1 molecules. According to the HO mechanism, an IGF1 molecule first engages S₁ or S₂ in one of the two binding pockets of a dimer. Sites S₁ and S₂ are known to have differing affinities for ligand binding [47,51], such that there exists a primary route toward receptor crosslinking whereby the high affinity site is bound first, as well as a secondary route whereby the ligand binds the lower affinity site first [44]. After binding one site (most likely the high affinity site), the other IGF1 binding surface may then engage the remaining site in the pocket. The two sites in the pocket are crosslinked when both binding surfaces of one IGF1 molecule simultaneously occupy S₁ and S₂ (Fig 1B). Crosslinking stabilizes the active conformation, wherein transmembrane and intracellular regions of the receptor are close together [48,52], making the receptor competent for autophosphorylation of intracellular phosphotyrosine sites. When one pocket is crosslinked, the S₁ and S₂ sites of the other pocket are located too far apart to permit ligand engagement of both sites.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. IGF1R phosphotyrosine contact map in HeLa S3 cells.

(A) Ligand-free and inactive IGF1R. Prior to crosslinking of dissimilar sites S₁ and S₂ of each IGF1R protomer in a dimer, the two kinase domains are not in contact and are thus unable to autophosphorylate receptor tyrosines. (B) IGF1R phosphotyrosine interactions in HeLa S3 cells. Each IGF1R in an IGF1R dimer contains six autophosphorylation tyrosine sites, labeled “Y” followed by the site’s position within the polypeptide chain (according to UniProt numbering). The kinase domains of each IGF1R subunit are labeled accordingly and encompass the region spanning positions 999 to 1274. Autophosphorylation and dephosphorylation of activated (IGF1-crosslinked) receptors in a dimer occur at the tyrosine sites indicated. Signaling proteins containing SH2 and/or PTB domains are recruited to their cognate tyrosine sites in a phosphorylated IGF1R dimer as indicated by the colored lines that begin at a tyrosine site and end at the protein that is recruited to that site.

https://doi.org/10.1371/journal.pcbi.1006706.g001

Recent crystallographic data have led to the proposal of an alternative mechanism for IGF1-IGF1R binding, which has been termed the induced fit mechanism [51]. In this picture of IGF1 binding, receptors are generally present in a closed conformation, wherein incoming ligand cannot interact with either S₁ or S₂ sites with high affinity. In contrast, the HO mechanism assumes that a proportion of receptors are present in an open conformation, with ligand capable of binding to either S₁ or S₂. Receptor activation via the induced fit mechanism is initiated when ligand interacts with the binding pocket in a metastable manner. The structural data suggest that this pre-complex includes IGF1 interaction with S₁ and putative residues contained in S₂. Once the pre-complex is formed, the receptor may relax and shift to an open conformation in which IGF1 can crosslink S₁ and S₂. The induced fit mechanism was proposed after analysis of crystal structures of unbound IGF1R as well as IGF1 in complex with S₁ in IGF1R. However, the crystallization chaperone is derived from the monoclonal antibody (mAb) 24–60, which is known to inhibit IGF1 binding to IGF1R [51,53]. Therefore, it is unclear how the reported structures supporting the induced fit mechanism reflect physiological conditions, and further study is needed to determine the in vivo conformations of IGF1R important for interaction with IGF1.

For the models presented here, we consider that IGF1 molecules are capable of reversibly associating with and crosslinking one of the two binding pockets, modulating IGF1R receptor transition between inactive and active conformations. Ultimately, our model preserves the major features of both the HO and induced fit mechanisms, each of which entails two steps to achieve activation of IGF1R. In either mechanism, IGF1 initially engages IGF1R, either by binding one site (HO mechanism) or by forming a pre-complex (induced fit mechanism). Then, the ligand-receptor complex is stabilized in a conformation competent for autophosphorylation, either via completion of crosslinking of the bound IGF1 (HO mechanism) or by conformational change of ligand and receptor (induced fit mechanism).

IGF1R possesses six autophosphorylation sites: Y973, Y980, Y1161, Y1165, Y1166, and Y1346 (Fig 1B). Note that this list of sites does not correspond to sites considered in some previous studies [46,54], as we excluded Y1280 and Y1281. The literature suggests that Y1280 and Y1281 are not in fact autophosphorylated (as indicated in some databases), but rather, are important in coordinating binding to the surrounding serines [55,56]. Fig 1B illustrates the mapping of IGF1R signaling proteins to the phosphotyrosines that they are capable of binding. The possible IGF1-IGF1R interactions shown in Fig 2A give rise to nine distinct dimeric complexes, of which three represent crosslinked receptors (Fig 2B). Tyrosine phosphorylation (Fig 2C) and dephosphorylation (Fig 2D) are each modeled as a one-step process (i.e., a first-order reaction). For all six tyrosines, we apply the same pseudo first-order phosphorylation rate constant (0.5 s^-1) and dephosphorylation rate constant (0.1 s^-1). These simplifications are acceptable based on the common assumption that neither tyrosine kinases nor phosphatases are saturated. The concentration of IGF1R considered in the model is in the nM range; RTKs have been observed to become saturated when concentrations are in the μM range [57]. The half-life of a phosphorylated tyrosine residue is on the order of seconds [58], which indicates excess phosphatase activity. Once a tyrosine site is phosphorylated, it can recruit its respective SH2/PTB domain-containing binding partner(s) (Fig 2E). Dephosphorylation is only permitted if a site is not occupied by a signaling protein.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Model for IGF1 interaction with IGF1R.

The model considers IGF1R to be present in preformed dimers, where each dimer contains two ligand binding pockets. Within each pocket, there are two sites, which we refer to as sites S₁ (light pink) and S₂ (light blue). S₁ and S₂ are bound by IGF1 (black rectangle) as signified by direct contact. Adjacent S₁ and S₂ sites are crosslinked when they are bound by the same IGF1 molecule, and, when crosslinking occurs, the kinase domains of the two receptor subunits come into contact. The IGF1R dimer is thus activated and can catalyze autophosphorylation. (A) The cyclic reaction scheme of IGF1-IGF1R binding presented here is derived from the model of Kiselyov et. al. [44]. (B) IGF1-IGF1R complexes that arise from the reactions of the cyclic reaction scheme. (C) Phosphorylation of one of the six IGF1R tyrosine sites, Y980. (D) Dephosphorylation of the Y980 site of IGF1R. (E) Recruitment of a representative IGF1R binding partner, IRS1 (pink structure), to phosphorylated Y980 via the IRS1 PTB domain (cyan structure), and subsequent dissociation.

https://doi.org/10.1371/journal.pcbi.1006706.g002

IGF1-IGF1R interactions are characterized by a cyclic reaction scheme with eight reactions (Fig 2A). The kinetics of forward (binding) reactions are characterized by the rate constants a₁, a₂, a'₁, and a'₂, where a'₁, and a'₂ characterize the rate of crosslinking of S₁ and S₂. The corresponding dissociation rate constants are d₁, d₂, d'₁, and d'₂. We take the view that the oscillatory movements of IGF1R dimers are spontaneous, meaning driven purely by thermal fluctuations without any dissipation of energy (e.g., no consumption of ATP). Thus, the IGF1-IGF1R cyclic reaction scheme must satisfy the principle of detailed balance [59,60]. In other words, the rate constants of the cyclic reaction scheme cannot be specified independently, and are related by the following constraint: (1) To estimate the values of the rate constants, we used BioNetFit [61] and IGF1-IGF1R ligand dissociation data and equilibrium binding data presented in Ref. [44]. We estimated the values of a₁, a₂, a'₁, d₁, d₂, d'₁, and d'₂. The remaining rate constant, a'₂, was determined by the detailed balance constraint of Eq 1. We did not assume d₁ = d'₁ or d₂ = d'₂. Kiselyov et al. found improved fits without the assumption that these pairs of dissociation rate constants are equivalent, but concluded that the inclusion of the equivalency constraint only marginally detracted from the quality of the fit. We found that, with the inclusion of the detailed balance constraint, acceptable fits (χ² ≤ 3.5) could not be achieved with d₁ = d'₁ and d₂ = d'₂. The curves generated with our best-fit parameter estimates are plotted in Fig 3. Note that the assignment of S₁ and S₂ is arbitrary, such that equivalent fits and simulation results are obtained when swapping the parameters of the two paths (i.e., swapping a₁ with a₂, d₁ with d₂, a'₁ with a'₂, and d'₁ with d'₂).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. Model parameterization.

Parameter values for IGF1-IGF1R binding were obtained by fitting to data from competition experiments and ligand dissociation experiments presented by Kiselyov et al. [44]. Experimental data are shown as points with error bars and the smooth curves were obtained via numerical simulation. For (A), we simulated to steady-state for a range of concentrations of unlabeled IGF1. For (B), we simulated a two-hour incubation with radiolabeled IGF1, then free ligand was washed away (i.e., the abundance of free IGF1 was set to zero), and we simulated the response to ligand addition for either 20 or 60 minutes, as indicated, for a range of concentrations of unlabeled IGF1.

https://doi.org/10.1371/journal.pcbi.1006706.g003

From our best-fit parameters in Table 1, we estimate site-specific K_D values to be 13 nM and 180 nM, comparable to the 9 nM and 490 nM estimated by Kiselyov et al. [44] and the value of 39 nM for IGF1 binding IGF1RΔβ (an ectodomain-only construct) measured by Xu et al. [51]. Furthermore, we estimated apparent K_D values using the simplification presented by Ullah et al. [62]. With this approach, the two-step pathway comprising initial ligand binding followed by crosslinking is approximated as a one-step reaction. We determined an apparent K_D value of 0.61 nM using the best-fit parameter set. This estimate is similar to the 0.12 nM reported by Kiselyov et al. [44] and subsequently used to fit an induced fit binding model in the study of Xu et al. [51].

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Comparison of parameter values for IGF1-IGF1R binding.

We estimated rate constants (defined as indicated in Fig 2) by fitting to IGF1-IGF1R binding data [44] using the BioNetFit software package [61]. We calculated the 95% confidence interval for each parameter using a bootstrapping procedure (see Material and Methods). The parameter a'₂ was taken to be given by Eq 1 and the values of the other rate constants.

https://doi.org/10.1371/journal.pcbi.1006706.t001

The IGF1R model with a natural formulation of rules has tremendous combinatorial complexity, which presents a significant challenge when performing simulations. There are three possible configurations in which a receptor dimer can be crosslinked (Fig 2B). Every receptor monomer has six tyrosine sites, or 12 sites in each receptor dimer, each of which can be unphosphorylated, phosphorylated but unbound, or phosphorylated and bound to an SH2/PTB domain-containing protein. With nine possible configurations of IGF1 bound to IGF1R (Fig 2B), a coarse estimation of the total number of possible unique species (avoiding double counting of symmetrical species) is ≈ 9 × (3¹² + 3⁶)/2 = 8×10⁵. It is impractical to generate and simulate the full reaction network between these species. To address this issue, we have optimized the formulation of rules, through a process we term restructuration, to obtain a rule set that implies an equivalent network with minimal state redundancy. We have outlined the approach used to restructure the IGF1R signaling model in Materials and Methods, and provided a detailed overview of restructuration in S1 File. After restructuring, the IGF1R model contains 577 species, or < 0.07% of the number of species implied by the natural formulation.

IGF1R recruitment of signaling proteins in HeLa S3 cells

Which signaling proteins are most often recruited to IGF1R depends on many factors, including relative protein expression levels (S1 Table), relative binding affinities (S2 Table), and competition for each of the six autophosphorylation sites. We first analyzed signaling protein binding in a HeLa S3 cellular background, parameterized with proteomics data from Kulak et al. [63]. Out of 18 possible binding partners, only ITK and ZAP70 are not expressed in HeLa S3 cells (S1 Table).

Fig 4A shows the model prediction for the temporal recruitment of SH2/PTB-domain containing proteins in HeLa S3 cells in response to 1 nM IGF1 stimulation. Fig 4B shows the steady-state recruitment at various doses of IGF1 stimulation. Simulations indicate strong recruitment (17–22% of IGF1R bound at steady-state with 1 nM stimulation) of SHC1, CRKL, ABL2, and STAT1 and modest recruitment (7–12% of IGF1R bound) of VAV2, YES1, VAV2, and RASA1.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 4. Predicted recruitment of signaling proteins to IGF1R in HeLa S3 cells.

The scale represents the normalized amount of each of the indicated proteins bound in simulations of either (A) time-dependent recruitment at 1 nM IGF1 stimulation, or (B) steady-state recruitment at varying IGF1 doses. (C) Comparison of rank ordering of IGF1R binding partners in HeLa S3 cells across five prediction methods. The top (first) ranked protein indicates the protein predicted to be bound to IGF1R with the highest abundance. Bar length indicates the amount of deviation from the ranking obtained by numerical simulations for estimates obtained by the analytical approach, copy number, by K_D, or the ratio of copy number to K_D. A negative deviation indicates that the method estimated a lower rank (less binding) than the prediction from numerical simulations; a positive deviation indicates a higher rank (more binding) than in numerical simulations. Calculation and evaluation of rank ordering are described in Materials and Methods.

https://doi.org/10.1371/journal.pcbi.1006706.g004

The adapter protein SHC1 is predicted to be the most highly recruited protein in HeLa S3 cells. On IGF1R, SHC1 binds only to pY980, a site that also recruits IRS1 and ZAP70. As ZAP70 is not expressed in HeLa S3 cells, the only competitor for SHC1 in this cellular background is IRS1. The strong recruitment of SHC1 is due to both its high copy number (10⁵/cell) (two orders of magnitude higher than that of IRS1, see S1 Table) and its strong affinity for this site (20-fold stronger than IRS1, see S2 Table).

Next, we consider STAT1, CRKL, and ABL2, which all display strong IGF1R recruitment. STAT1 and CRKL are the two most abundant binding partners in HeLa S3 cells, with relatively high concentrations (10⁵ copies per cell), whereas ABL2 is the ninth most abundant signaling protein, with an abundance on the order of 10⁴ copies per cell. STAT1 binds pY1161, which also recruits PIK3R3, SRC, YES1, SYK, and BLK. Despite these five competitors, recruitment of STAT1 is strong because the five competing proteins have relatively low expression levels (S1 Table). Like STAT1, CRKL competes with multiple proteins that are expressed at relatively low levels for binding to pY1166 and pY973. High recruitment of ABL2 can be explained by the strong affinity of ABL2 for pY973, which is approximately 40-fold greater than that of CRKL.

Predictions from simulations vs. predictions from simple ranking approaches

We observe that the rank order predicted by numerical simulations is distinct from that obtained through ranking by K_D, copy number, or the copy number/K_D ratio (Fig 4C), suggesting that competition, protein abundances, and binding affinities all contribute to the relative recruitment of downstream partners in an unintuitive manner. We further evaluated whether simple methods could return the same predictions obtained from simulations by deriving analytical expressions of equilibrium binding (S3 File, Eq. S1–S9). These expressions roughly estimate how the rank ordering of binding partners is related to factors affecting binding partner recruitment, including copy number and binding affinity. This approach is similar to that proposed by Maslov and Ispolatov [64].

To derive analytical expressions for estimating recruitment, we exploit a key simplification that assumes each IGF1R binds a single partner at a time. In the rule-based model, we allow for recruitment of multiple binding partners to different pY sites, which is consistent with the findings of a recent study where multi-site phosphorylation of EGFR was directly observed through and single-molecule fluorescence microscopy [65]. However, another study using related technology found that ligand-activated EGFR is infrequently multi-phosphorylated for the sites probed [66]. Thus, RTKs may in some cases bind only a single downstream signaling partner at a time, so an acceptable analytical approximation for these cases may be obtained by the application of this assumption.

The concentration of protein bound to IGF1R can be expressed as a function of the total protein concentration, the free, active IGF1R concentration, and the measured binding affinity between the protein and the receptor. Because each active receptor is assumed to bind only one signaling protein at a time, this relationship can be represented by the following equation (derived in S3 File as Eq. S9): (2)

In Eq 2, represents the concentration of the i^th protein species that is bound by IGF1R, represents the total concentration in the system of the i^th protein species, K_i is the apparent dissociation constant for partner X_i, and R is the concentration of free, active IGF1R. To account for competitive binding, we need to calculate the free active IGF1R concentration, R, from the following balance equation for the total active IGF1R (R^Tot), see S3 File. Eqs 2 and 3 provide a means to rank IGF1R binding partners.

(3)

There are six inconsistencies in rank order when comparing predictions from numerical and analytical methods (Fig 4C). Differences between the analytical and numerical rankings are minimized as the concentration of free active IGF1R in the analytical model increases, which suggests that these discrepancies arise from the assumption that each active RTK can bind only one binding partner at a time, described in more detail in S3 File. We also compared quantitative predictions from numerical simulations to those from the analytical approximation (S3 Fig). There is a strong positive correlation between the steady-state number of bound molecules predicted with each of the methods (Pearson’s r = 0.93 with a p value of 10⁻⁸ for HeLa S3, obtained using R software’s cor.test function), which provides support for use of the analytical approach for generating rough predictions. However, the degree of agreement between the approaches depends on the specific protein considered. Overall, we conclude that it is difficult to capture the predictions of the rule-based model solely via simple metrics like copy number or binding affinity, or even with our analytical expressions.

Determining rankings of IGF1R recruitment of signaling proteins across diverse cell lines

We investigated whether competition among the signaling proteins could be influenced by cell type-specific differences in protein copy number. In addition to the HeLa S3 cell model discussed above, we parameterized 44 other models to represent diverse cell types. Eleven human cell lines were studied by Geiger et al. [67]. An additional 31 models were formulated for cell lines within the NCI-60 panel derived from human tumors and maintained by the National Cancer Institute. We developed models for only the NCI-60 cell lines in which IGF1R was detected [68]. HeLa Kyoto proteomics data were from Hein et al. [69]. We also considered one non-human cell line, the immortal murine myoblast cell line C2C12 [70], to allow for comparison with available binding data [71].

For each cell line model, we simulated the steady-state pattern of recruitment of signaling proteins to IGF1R in response to stimulation with 1 nM IGF1. Across all cell lines, the most highly recruited proteins were predicted to be CRKL, STAT1, RASA1, and YES1 (Fig 5). The ranking from numerical simulations for each cell line is provided in S2 File along with predicted ranks from four simple ranking metrics: analytical approximation, copy number, equilibrium dissociation constant (K_D), and copy number divided by K_D.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 5. Summary of rank ordering of IGF1R binding partners across 45 cell line-specific models.

(A) The average rank across all models is shown for each binding partner. Error bars indicate the standard deviation in rank. The size of a data point indicates the number of cell lines in which the corresponding protein was expressed, as shown in the legend. (B) For the four binding proteins with the highest average rank, we show the percentage of cell lines in which the protein was ranked first, second, third, fourth, fifth, or lower than fifth.

https://doi.org/10.1371/journal.pcbi.1006706.g005

YES1 was the top-ranked binding partner in 38% of cell lines (Fig 5B). It has been suggested that YES1’s SH2 domain is remarkably promiscuous [45], such that the high recruitment predicted by our model may not accurately reflect recruitment in vivo. YES1 and other proteins in the SRC class of tyrosine kinases have been found to be highly connected, a factor which may contribute to oncogenic potential [46]. However, promiscuity does not necessarily indicate that YES1 is not a legitimate IGF1R binding partner. Indeed, the literature offers evidence that YES1’s SH2 domain does exhibit specificity. For example, YES1 reportedly binds to pY1161 and pY1165 with K_D values of approximately 0.9 μM, but was not strongly recruited to the other IGF1R tyrosine sites [46]. Similarly, YES1 is recruited to three of the eight sites in the INSR with K_D values less than 1 μM. In ErbB-family RTKs, including EGFR, ErbB2, ErbB3, and ErbB4, YES1 was either not detected as an interacting partner or bound weakly, with a K_D greater than 2 μM [11]. These observations suggest that YES1 is a bona fide binding partner relevant for IGF1R signaling, but further investigation is needed.

Hanke et al. [71] analyzed protein recruitment to IGF1R in the mouse myoblast cell line C2C12 via a pulldown assay. To our knowledge, Hanke et al.’s study provides the best data available to aid in evaluating predictions from our IGF1R binding model. Their procedure, in which peptides (15-mers) spanning each of the murine IGF1R phosphotyrosine residues were incubated with cell lysate, allows binding partners to compete for individual phosphotyrosines. The ratios of proteins detected with and without each phosphorylated tyrosine were reported. Unfortunately, this normalized recruitment data cannot provide a ranking of the most highly recruited binding partners, especially because each phosphotyrosine was considered separately. However, the pulldown data does provide a picture of detectable binding partners, which can be compared to model predictions. The most highly recruited protein in C2C12 simulations was RASA1, a GTPase-activating protein (GAP) for RAS. This protein was also detected in the pulldown assay, with a reported 8-fold increase in binding between phosphorylated and unphosphorylated Y1283 in murine IGF1R. Other proteins that were predicted to bind in simulations and detected by the pulldown assay include the PI3K regulatory α and β subunits (PIK3R1 and PIK3R2), which displayed 15-fold and 9-fold increase in binding, respectively, to pY1352 over Y1352.

Differences between the C2C12 model predictions and pulldown assay results arise because our models were designed to include only detectable interactions between human IGF1R and SH2/PTB domain-containing proteins. We kept binding affinities consistent across all cell line-specific models, including in simulations with the murine C2C12 cell line, an assumption based on previous findings that binding affinities are often evolutionarily conserved across eukaryotic organisms [72]. However, this approach resulted in exclusion from the model of many binding partners detected in the murine pulldown assay. For instance, human PLCG2, not PLCG1, has a detectable affinity for IGF1R [45,46], so only PLCG2 was included as a potential binding partner in the model, yet expression of PLCG2 in C2C12 was negligible [70]. Therefore, the model parameterized with protein copy numbers for C2C12 cannot predict binding of either PLCG1 or PLCG2. Likewise, GRB2, SHP2, PIK3CA, and PIK3CB were captured in the murine pulldown assay, but were not included in the models due to lack of detectable IGF1R binding in humans [45,46]. Thus, in this case human binding affinity data are not sufficient for predicting all binding partners in mouse myoblasts. The discrepancies between model predictions and experimental evidence could perhaps be rectified if K_D data for murine SH2/PTB domain interactions with IGF1R were to become available. However, pulldown assays are not ideally suited for the task of evaluating rank order predictions. One intriguing experimental approach that could be useful for this purpose is mass-spectrometry (MS)-based phosphoproteomics, which was recently used by Jadwin et al. [17] to measure phosphorylation of specific phosphosites in full-length EGFR. Most pY sites are bound to an SH2/PTB-domain containing protein, so quantifying which sites are highly phosphorylated could validate predictions of which partners are highly bound.

To determine whether cell line-specific IGF1R recruitment is indicative of tissue of origin, we performed hierarchical clustering to group cell lines according to similarity in protein recruitment profiles (Fig 6). In some cases, we do observe that similar tissue types clustered together. The HeLa S3 cell lines profiled by Kulak et al. [63] and the HeLa Kyoto cell line profiled by Hein et al. [69] were grouped together. Five out of six melanomas were co-clustered: M14, SKMEL28, UACC257, MALME3M, and SKMEL5. All of these cell lines had high recruitment of STAT1 and moderate recruitment of YES1. We also observe co-clustering of ovarian cancer cell lines OVCAR5, IGROV1, and OVCAR4, which had high predicted recruitment of YES1.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 6. Hierarchical clustering of cell lines according to predicted signaling protein recruitment.

Heatmap colors indicate the ratio, derived from simulation results, of the number of bound molecules of a given protein (corresponding to protein name on the x-axis) divided by the total number of IGF1R molecules. As multiple copies of a protein can be bound to one IGF1R molecule, the ratio can be greater than one. Cell lines indicated (on the y-axis) include the tissue of origin or type of cancer followed by an underscore and the cell line name. OVAR = ovarian cancer, NSCLC = non-small cell lung cancer, MELAN = melanoma, CERV = cervical cancer, CNS = central nervous system cancer, LEUK = leukemia. For those cell lines evaluated in multiple studies (A549, HeLa S3, and MCF7), the proteomics data from Geiger et al. is indicated with “_G” following the cell line name, the data for the NCI-60 panel from Gholami et al. with the suffix “_N”, and the data from Kulak et al. with “_K”.

https://doi.org/10.1371/journal.pcbi.1006706.g006

In examining the cell line clusters, we note that the source of the data contributes to similarity in predicted recruitment profiles. Eight out of the 11 cell lines profiled by Geiger et al. [67] are grouped together: RKO, U2OS, GAMG, LnCap, K562, Jurkat, HEK293, and HepG2. These cell lines stem from disparate tumor or tissue types, but they were all predicted to have very high recruitment of CRKL (more than one copy of CRKL bound to each molecule of IGF1R) coupled with moderately high recruitment of either SRC, SHC1, or RASA1. Similarly, of the seven cell lines that were profiled with higher coverage by Gholami et al. [68], four were clustered together: the ovarian carcinoma SKOV3, non-small cell lung cancer H460, the prostatic adenocarcinoma PC3, and the acute lymphoblastic leukemia CCRFCEM. This result indicates that, on the basis of IGF1R signaling, these four cell lines are predicted to be more similar to one another than to cell lines from the same tissue type. However, we suspect that clustering is highlighting an artifact in the data. Most of the NCI-60 cell lines have only low coverage proteomics data available; therefore, copy numbers derived from these data may not accurately represent the protein abundance profiles of these cell lines. Thus, while it is clear that IGF1R binding partner recruitment is influenced by protein copy number, the quality of the proteomics data used to parameterize the models is critical in using simulations to generate reliable predictions. We will set aside concerns with the quality of the available proteomics datasets for the sake for demonstrating the capabilities of cell line-specific models.

Correlations and anti-correlations in IGF1R-recruitment of proteins

To further interrogate the mutual dependency among signaling proteins, we developed population-level models for IGF1R signaling. The population-level models are designed to account for natural cell-to-cell variability in protein levels present in a clonal population of cells [73]. This analysis produces a distribution of recruitment profiles across 5,000 cells in each simulated population, thereby allowing us to ascertain whether the recruitment of any pair of binding proteins is correlated. Fig 7 shows a matrix of the correlations between each pair of binding partners for the HeLa S3, U251, MCF7, and GAMG population-level models (correlations for all other cell lines are in S4 and S5 Figs). The degree of positive or negative correlation between any two proteins may arise from direct competition for receptor binding or via indirect effects, as discussed below.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 7. Correlation matrix among the signaling proteins for IGF1R recruitment.

The color map shows the Pearson’s correlation coefficient among pairs of proteins studied in the population-level models for HeLa S3, U251, MCF7, and GAMG cell lines. Red indicates a negative correlation, blue a positive correlation, and white no correlation.

https://doi.org/10.1371/journal.pcbi.1006706.g007

In the HeLa S3 model, the moderate negative correlation (Pearson’s r = -0.34) between VAV2 and STAT1 arises from their direct competition for pY1161. Both proteins are present at relatively large and comparable abundances (10⁵ copies per cell), bind only to pY1161 with similar affinities, and therefore effectively antagonize each other. Other cell lines with a negative correlation between VAV2 and STAT1 included the leukemias CCRFCEM and SR, the melanoma M14, the non-small cell lung cancers H226 and H460, and the prostatic adenocarcinoma PC3. Negative correlations are also observed with discrepancies in binding affinity or copy number between competing pairs. The weak negative correlation between CRKL and ABL2 in HeLa S3 cells (Pearson’s r = -0.23) can be explained by competition for binding to pY973; these proteins have similar abundances, but ABL2 binds pY973 with approximately 40-fold higher affinity.

Slight variations in protein copy number contribute to differing patterns of correlations across cell lines. Cell line-specific differences are especially evident when considering competition for pY1161, to which nine different signaling proteins can bind. The pair of proteins displaying high negative correlations in the largest number of cell lines was YES1 and STAT1, which compete for binding pY1161 and were negatively correlated in 19 cell lines, including MCF7 (Fig 7). The average Pearson’s r for YES1/STAT1 was -0.55±0.18 across these 19 cell lines. In each of these cell lines, the abundances of YES1 and STAT1 were within an order of magnitude of one another, and other proteins that bind pY1161 were either not expressed or expressed at a low level in comparison to YES1/STAT1. In 14 cell lines, where STAT1 had low expression, and SRC expression was comparable to that of YES1, strong negative correlations between YES1 and SRC were evident, with an average Pearson’s r = -0.67±0.21 across these 14 cell lines (see correlations for MCF7 and GAMG in Fig 7).

Interestingly, positive correlations between binding partners are observed in several of the cell line-specific population-level simulations, despite there being no cooperative binding explicitly considered in the models. These positive correlations arise through an indirect mechanism. In four cells lines, including the gliomas U251 and SNB19, the renal cell carcinoma TK10, and the colorectal carcinoma HCC2998, a pattern of positive correlations (Pearson’s r ≥ 0.4) was identified for every pairwise combination of STAT1, VAV2, and YES1 (see Fig 7 for U251). Furthermore, we identified strong negative correlations between SRC and STAT1, VAV2, and YES1 in these cell lines. These four proteins compete for binding to pY1161, but in these cellular backgrounds, SRC is expressed much more highly (10⁷ copies per cell) than STAT1, VAV2, or YES1 (10²–10⁶ copies per cell). Thus, stochastic decreases in expression of higher abundance competitors results in a net increase in the opportunity for binding lower abundance proteins, observable as a positive correlation between lower-abundance binding partners. In other words, positive correlations arise when a pair or group of proteins are similarly affected by a mutual competitor.

Formulation of a generic model

We first developed a generic model for early events in IGF1R signaling that includes the following processes: IGF1 binding to IGF1R, autophosphorylation of IGF1R, dephosphorylation of phosphotyrosine sites, and reversible recruitment of signaling proteins with SH2 and PTB domains to IGF1R via interactions with phosphotyrosines.

The generic model was specified using the BioNetGen language (BNGL) [30]. All model specification files are available in S1 Compressed/ZIP File Archive. Rules were written for binding interactions between six phosphorylated tyrosines in IGF1R and the SH2/PTB domains of IGF1R binding partners characterized in the studies of Gordus et al. [45] and Koytiger et al. [46]. The following is an example of a rule for the binding of the PTB domain of one such binding partner, SHC1, to phosphorylated tyrosine residue 980 of IGF1R:

SHC1(PTB)+IGF1R(Y980~P)<->SHC1(PTB!1).IGF1R(Y980~P!1) ka,kd

The above rule includes two rate constants, one for binding (ka) and one for unbinding (kd), where mass-action kinetics are assumed. The kinase domain of each IGF1R monomer in a dimer was considered to be active if the dimer was crosslinked by IGF1. Phosphorylation and dephosphorylation of the receptor were modeled as first-order reactions.

We use the HUGO Gene Nomenclature Committee [103] approved gene symbols when referring to signaling proteins, where here the gene symbol in uppercase, non-italic font indicates a gene product. K_D values for binding of signaling proteins are from Gordus et al. [45] and Koytiger et al. [46], with the exception of IRS1, for which no detectable IGF1R binding was found in the aforementioned studies. As there is much evidence to suggest that IRS1 does interact directly with IGF1R [87,88], we approximated a K_D value based on quantification of interactions with INSR. Farooq et al. [89] found that the affinity of SHC1 recruitment to INSR at pY972 was approximately 20-fold stronger than that of IRS1. We assumed the same relation holds between SHC1 and IRS1 binding to IGF1R’s pY980, and set a K_D for IRS1 equal to 20-fold higher than that of SHC1 binding. Association rate constants (k_a) for signaling protein binding were set to 1×10⁶ M^-1s^-1, and dissociation rate constants were calculated using the formula k_d = k_a×K_D. Some proteins have multiple SH2 domains reported as binding to the same tyrosine site, such as the two SH2 domains in PIK3R2 reported to bind pY1346 [46]. For these proteins, we assumed that the domain with the higher binding affinity would dominate binding and accordingly used the lower of the K_D values in the model. We do not consider the possibility of two-point attachment of proteins with multiple SH2/PTB domains to adjacent phosphotyrosines in IGF1R.

As a simplification, we do not consider receptor trafficking. The ligand dissociation curves to which we fit our model were obtained at low temperatures, which is expected to reduce recycling [44].

The models formulated in this study are not intended to capture the dynamics of later IGF1R signaling events. We do not consider any interactions between the binding partners of IGF1R themselves, nor do we attempt to predict the activation and subsequent activity of the binding partners after recruitment to IGF1R.

Model restructuration

We used three strategies to restructure the natural model formulation and reduce state redundancy: decoupling, bunching, and scaling (S2 Fig). These strategies are described in further detail in S1 File.

1. Decoupling. The state of each phosphotyrosine site can be decoupled from the others because the phosphorylation dynamics of each site are assumed to be independent. If the state of one tyrosine site is not affected by the states of other sites, we can consider each of the six individual tyrosine sites as separate entities.

2. Bunching. The configurations of the S₁ and S₂ binding sites in a dimeric IGF1 receptor can be considered together as one binding pocket. Bunching the S₁ and S₂ sites is acceptable because the possible states for one site depend on the state of the other site in the pocket; both sites cannot simultaneously be bound to separate ligands. Specifically, the configuration of the S₁-S₂ binding pocket can either be 1) both sites are free, 2) one of the S₁-S₂ pair is bound to a ligand, or 3) both S₁ and S₂ sites in the pocket are bound to the same ligand (crosslinked). The same argument applies to the other binding pocket in the dimeric receptor. Using this representation, we grouped the individual states of four S_i’s into states of two pairs of binding pockets.

3. Scaling. After bunching IGF1 binding sites and decoupling tyrosine sites, the model accounts for six forms of receptor, with two identical tyrosine sites associated with each dimeric receptor form. Because the two sites in the same dimer form are assumed to act independently, one can construct a model as if there are six forms of receptor, each of which has only one tyrosine site. To preserve mass-action kinetics, one must increase the abundance of the ligand and receptor each by a factor of two and reduce the second-order rate constants characterizing interactions between the ligands and receptors (a₁ and a₂) by a factor of two.

Restructuration implies a model form that is equivalent to the natural formulation of a model, but with the elimination of many redundant states. As such, our restructured model is equivalent to the original model from which it was derived. We discuss this equivalency mathematically in S1 File, and through numerical simulation in S1 Fig.

Parameter estimation

Parameter estimation was performed with the BioNetFit software package [61]. BioNetFit is a tool designed to fit BNGL-formatted models to data through use of an evolutionary algorithm. Evolutionary algorithms are population-based metaheuristics inspired by nature that involve independent evaluation of parameter sets across parameter space and perturbation of the parameters over multiple population generations. While fitting the IGF1R model to experimental data, we found and fixed a number of minor bugs in the BioNetFit code base. The modified source code for the version used in this work, v1.01, is available at https://github.com/RuleWorld/BioNetFit. To ensure that the initial set of parameter values effectively sample parameter space where distinct parameters may span multiple orders of magnitude, we normalize the parameters through the use of multiplicative factors so that each parameter can be perturbed on the same scale (see S2 Compressed/ZIP File Archive). Finally, we used a bootstrapping procedure that is embedded in BioNetFit to characterize the uncertainty of the fitted parameters. This procedure samples the experimental data points with replacement and refits the model to the bootstrap sample [104]. The 2.5% and 97.5% percentiles of observed parameters from 2,000 independent fits to bootstrap samples were used to create the 95% confidence interval. To obtain the confidence interval for parameter a'₂, we calculated the value of a'₂ using Eq 1 for each of the 2,000 bootstrapping fits, and calculated the confidence interval using percentiles in the same manner as for the other parameters.

Formulation of cell line-specific models

The generic IGF1R signaling model can be explicitly parameterized as a cell line-specific model. To define a cell line-specific model, we incorporate signaling protein copy numbers consistent with the corresponding cell type from datasets available in the literature (S1 and S2 Tables). To make a model representing IGF1R signaling in a HeLa S3 cell, we specify abundances of the signaling proteins consistent with their reported copy numbers in HeLa S3 cells [63]. Hein et al. [69] provided absolute copy numbers for HeLa Kyoto cells. Deshmukh et al. [70] reported normalized relative abundances for C2C12 cells. Geiger et al. [67] quantified protein abundances in A549, GAMG, HEK293, HeLa S3, HEPG2, Jurkat, K562, LnCap, MCF7, RKO, and U2OS cell lines. Models for the NCI-60 cell lines were parameterized with data from Gholami et al. [68]. We used the higher coverage deep proteomics data for the seven cell lines for which these data were available: MCF7, M14, COLO205, CCRFCEM, U251, H460, PC3, SKOV3, and RXF393. Of the remaining NCI-60 cell lines, 24 had IGF1R expression that was nonzero. The proteomics profiling data were used to parameterize models for these cell lines. To calculate copy numbers from relative abundances, we assumed the same copy number of IGF1R as reported by Kulak et al. [63] and scaled the other copy numbers accordingly. This assumption is acceptable because we are interested in determining only the relative rank ordering of binding partner recruitment. With the exception of the protein copy numbers, the model assumes other parameters of the generic model remain the same across different cell types.

Formulation of population-level models

We expanded the IGF1R signaling models into population-level models, which account for intrinsic and extrinsic contributions to cell-to-cell variability in expression of IGF1R and IGF1R-binding proteins across populations of clonal cells. In the population-level models, the copy number of any protein i in cell k is a value sampled from a log normal distribution: X_k,i = e^μi+σiN. Here, μ_i is the mean and σ_i is the standard deviation. The mean μ_i represents the natural logarithm of the population-averaged copy number of the corresponding protein used in single-cell IGF1R signaling models. The population averaged copy numbers are listed in S1 Table. We assumed the same standard deviation σ_i = 0.2 for all proteins, a value consistent with experimentally determined cell-to-cell variability in protein copy number [105,106]. N is a random variable sampled from the standard normal distribution. Scripts used to generate the population models are provided in S3 Compressed/ZIP File Archive.

Simulations

For the restructured model, BioNetGen was used to generate a system of ODEs. We numerically integrated the resulting system of equations using CVODE/SUNDIALS [107,108], which is included in the BioNetGen package. For the natural model formulation, because of the intractability of generating the large reaction network described by model rules, we performed network-free simulation using NFsim [35].

Clustering

Recruitment data were obtained from steady-state simulations in individual cell line models. We normalized recruitment of each protein as the ratio of the absolute number of bound copies of the protein over the total amount of IGF1R. Some proteins can bind to IGF1R in multiple locations, so the ratio can be greater than one. Hierarchical clustering of cell lines and binding proteins according to normalized recruitment was performed with Python 2.7 and the clustermap function of Seaborn [109]. Clustering linkage was per Ward’s method [110].

Ranking and evaluation of RTK-protein interactions

Ranks in Fig 4 and S2 File were determined using five different metrics, which are based on either 1) copy number information only, 2) K_D values only, 3) ratios of copy number to K_D values only, 4) the rule-based model simulation for single cells, which includes protein copy numbers, site-specific K_D values, and accounts for competition for phosphotyrosine sites, and 5) the analytical approximation, which includes copy number, K_D values and a simplified treatment of competition in which only one binding partner can interact with an IGF1R dimer at one time (S3 File).

To calculate correlations between the recruitment of signaling protein pairs, we considered a population of 5,000 cells for each of the cell line-specific population-level models. In the models, the cells were stimulated with 1 nM IGF1. We created the correlation matrices in Fig 7, S4 Fig and S5 Fig based on steady-state recruitment of proteins to IGF1R. For a given protein i∈{1, …, m}, where m = 18, we collected its steady-state IGF1R binding Y_k,i in cell k∈{1, …, n}, where n = 5,000. We calculated the mean receptor recruitment of the protein as and the standard deviation follows as .

Finally, the Pearson's correlation coefficient between each pair of proteins i and j is , where . We used SciPy to calculate correlation coefficients from population-level data.