Abstract
Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor β (TGF-β) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-β induced EMT to better understand the operational hierarchy of this complex molecular program. These models incorporate mass action kinetics within an ordinary differential equation (ODE) framework to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-β exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was maintained following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-β and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-β and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-β treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.
Author Summary Tissue formation and remodeling requires a complex and dynamic balance of interactions between epithelial cells, which reside on the surface, and mesenchymal cells that reside in the tissue interior. During embryonic development, wound healing, and cancer, epithelial cells transform into a mesenchymal cell to form new types of tissues. It is important to understand this process so that it can be controlled to generate beneficial effects and limit pathological differentiation. Much research over the past 20 years has identified many different molecular species that are relevant, but these have mainly been studied one at a time. In this study, we developed and implemented a novel computational strategy to interrogate all of the known players in this transformation process to identify which are the major bottlenecks. We determined that NFATc1 and pSP1 are essential for promoting epithelial or mesenchymal differentiation, respectively. We then predicted the existence of a partially transformed cell that exhibits both epithelial and mesenchymal characteristics. We found this partial cell type develops a network of invasive but stunted vascular structures that may be a unique cell target for understanding cancer progression and angiogenesis.
Introduction
The epithelial to mesenchymal transition (EMT) is a broadly participating, evolutionarily conserved differentiation program essential for tissue morphogenesis, remodeling and pathological processes such as cancer Thiery (2003). During EMT polarized, tightly adhered epithelial cell monolayers are transformed into non-interacting motile mesenchymal cells that simultaneously degrade and synthesize extracellular matrix (ECM) components and invade into the underlying tissue space Stahl & Felsen (2001). EMT is the fundamental initiator of developmental processes such as embryonic gastrulation and valvulo-genesis Eisenberg & Markwald (1995) (also Kalluri J Clin Invest 2009, Thiery Cell 2009). Transforming growth factor β (TGF-β) family members are important inducers of both developmental and pathological EMT Xu et al. (2009), Zavadil & Böttinger (2005). Decades of research has focused on identifying molecular regulators of EMT, but almost all on a single gene and in a nearly binary yes/no level of qualitative understanding. Medici and coworkers recently identified a core signaling program by which TGF-β isoforms induce EMT across a variety of cell lines Medici et al. (2006, 2008). This program involves carefully orchestrated rounds of gene expression driven by the Smad and Snail families of transcription factors as well as other key factors such as lymphoid enhancer-binding factor 1 (LEF-1), nuclear factor of activated T-cells, cytoplasmic 1 (NFATc1), and specificity protein 1 (Sp1). Coregulators such as β-catenin, NF-κB, and the ErbB family of receptor tyrosine kinases however also participate in EMT regulation, but the degree of each’s influence is difficult to ascertain in isolation Hardy et al. (2010), Huber et al. (2004), Jiang et al. (2007), Kim et al. (2002). EMT also exhibits complex temporal dynamics that are often intractable in gain/loss of function studies. Elucidating the master regulatory architecture controlling EMT therefore requires inclusion of these complex overlapping and non-binary behaviors.
Systems biology and mathematical modeling are essential tools for understanding complex developmental programs like EMT Ahmed & Nawshad (2007). Previous computational models of TGF-β induced differentiation focused on single biological factors or EMT in single cells. For example, Chung et al., constructed a model of TGF-β receptor activation and Smad signaling using ordinary differential equations and mass-action kinetics. Their model suggested that a reduction of functional TGF-β receptors in cancer cells may lead to an attenuated Smad2 signal Chung et al. (2009). Similarly, Vilar et al. suggested that specific changes in receptor trafficking patterns could lead to phenotypes that favor tumorigenesis Vilar et al. (2006). Although these models provided insight into the role of receptor dynamics, EMT induction involves many other components, including competing second messengers and interconnected transcriptional regulatory loops. Integrating these additional scales of molecular signaling while maintaining the capacity for robust prediction requires a new and expanded computational and experimental strategy. Data-driven systems approaches Cirit & Haugh (2012) or logical model formulations Morris et al. (2011) are emerging paradigms that constrain model complexity through the in-corporation of training and validation data. These are interesting techniques because the data informs model structure (which can be expanded as more data becomes available). Alternatively, Bailey proposed more than a decade ago that a qualitative understanding of a complex biological system should not require complete definition of its structural and parametric content Bailey (2001). Shortly thereafter, Sethna and coworkers showed that complex model behavior is often controlled by only a few parameter combinations, a characteristic seemingly universal to multi-parameter models referred to as “sloppiness” Machta et al. (2013). Thus, reasonable model predictions are often possible with only limited parameter information. Taking advantage of this property, we developed sloppy techniques for parameter identification using ensembles of deterministic models Song et al. (2010). Furthermore, we proposed that the sloppy behavior of biological networks may also be seen as a source of cell-to-cell Lequieu et al. (2011) or even patient-to-patient heterogeneity Luan et al. (2010). Recently, Bayesian parameter identification techniques have also been used to explore cell-to-cell heterogeneity Hasenauer et al. (2011), Kalita et al. (2011), where a population of cells could be viewed as a dynamic ensemble of context-specific biochemical networks Creixell et al. (2012).
In this study, we developed a family of mechanistic models describing the induction of EMT by TGF-β isoforms in the presence and absence of vascular endothelial growth factor A (VEGF-A). We incorporated mass action kinetics within an ordinary differential equation (ODE) framework to describe the EMT interaction network containing 995 gene, protein or mRNA components interconnected through 1700 interactions. A family of model parameters was estimated using 41 molecular data sets generated in DLD1 colon carcinoma, MDCKII and A375 melanoma cells using the Pareto optimal ensemble technique (POETs) multiobjective optimization algorithm. POETs identified more than 15,000 likely TGF-β induced EMT models, from which we selected approximately 1100 models for further analysis. Analysis of the model population suggested that both MCF10A and DLD1 cells could exhibit phenotypic heterogeneity if treated simultaneously with TGF-β1/2 and VEGF-A. This heterogeneity was characterized by a significant fraction of the population being in a “hybrid state” having both high E-cadherin and high Vimentin expression. We tested these predictions using qRT-PCR and flow-cytometry studies in a variety of experimental conditions. Validation studies confirmed that upwards of 45% of the cellular population could be put into the hybrid state in the presence of both TGF-β1/2 and VEGF-A. Moreover, this response depended upon both activation of Sp1 by MAPK and NFATc1 transcriptional activity consistent with the predicted molecular signaling. Lastly, the hybrid populations of both DLD1 and MCF10A cells exhibited different functional behavior than those from either TGF-β or VEGF-A treatment. The extent of ductal branch formation significantly increased with MCF10A cells in the hybrid phenotype, compared with cells treated with VEGF-A alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and reveal a novel signaling axis, which possibly regulates carcinoma progression through an EMT versus tubulogenesis response.
Results
The model population captured key features of TGF-β induced EMT The EMT model architecture, based upon curated molecular connectivity, described the expression of 80 genes following exposure to TGF-β isoforms and VEGF-A (Fig. 1). The EMT model contained 995 molecular species interconnected by 1700 interactions. Model equations were formulated using mass-action kinetics within an ordinary differential equation (ODE) framework. ODEs and mass action kinetics are common tools to model biochemical pathways Chen et al. (2009), Schoeberl et al. (2002), Tasseff et al. (2011). However, while ODE models can simulate complex intracellular behavior, they require estimates for model parameters which are often difficult to obtain. The EMT model had 1756 unknown model parameters, 1700 kinetic constants and 56 non-zero initial conditions. As expected, these parameters were not uniquely identifiable given the training data Gadkar et al. (2005). Thus, instead of identifying a single best fit (but uncertain) model, we estimated a sloppy population of models (each consistent with the training data) by simultaneously minimizing the difference between model simulations and 41 molecular data sets using the Pareto Optimal Ensemble Technique (POETs). The training data were generated in DLD1 colon carcinoma, MDCKII, and A375 melanoma cells following exposure to TGF-β isoforms Medici et al. (2008). We organized these data sets into 11 objective functions which were simultaneously minimized by POETs. Additionally, we used 12 molecular data sets generated in HK-2 cells following VEGF-A exposure to train VEGF-A responsive model processes Lian et al. (2011). To guard against overfitting, we augmented the multiobjective optimization with leave-one-out cross validation to independently estimate both the training and prediction error for each objective. Thus, we generated 11 different model ensembles. Lastly, we compared model predictions with independent data sets not used during training (both at the molecular and model population levels) to evaluate the predictive power of the parameter ensemble. Additional details of the signaling architecture included in the model are presented in the materials and methods and the supplement.
POETs generated a population of probable signaling models which captured the multiple phases of EMT induction (Fig. 2). POETs sampled well over 106 probable models during each stage of the cross-validation, using a combination of both local and global random sampling. This sampling generated approximately 15,000 highly probable models from which we selected N ≃ 1100 models for further analysis. The selected models all had the same possible molecular connectivity, but different values for model parameters and extrinsic factors such as RNA polymerase or ribosome abundance. Model selection was based upon Pareto rank, the prediction and training error across all objectives and model to model correlation (supplemental materials). The model population recapitulated key signaling events following TGF-β exposure. We subdivided the response to TGF-β exposure into two phases. First, TGF-β1/2 signaling initiated a program which downregulated E-cadeherin expression in a MAPK dependent manner while simultaneously upregulating TGF-β3 expression. Second, TGF-β3 secretion initiated an autocrine feedback which upregulated the expression of mesenchymal markers such as Vimentin and key upstream transcription factors such as LEF-1 in a SMAD dependent manner. Each phase involved the hierarchal expression and/or post-translational modification of several key transcription factors. During the first phase, stimulation with TGF-β1/2 (10 a.u.) activated both the SMAD and MAPK pathways. MAPK activation resulted in the phosphorylation of the transcription factor activator protein 1 (AP-1), which in-turn upregulated the expression of Snail, a well established transcriptional repressor (Fig. 2A). Snail expression was MAPK-dependent; the MEK inhibitor U0126 blocked AP-1 activation and Snail expression following TGF-β1/2 exposure (Fig. 2A, Lane 3). Similar results were obtained for Slug expression, confirming initial activation through the MAPK pathway (data not shown). Overexpression of either Snail or Slug upregulated TGF-β3 expression (Fig. 2C) while simultaneously downregulating E-cadeherin expression (Fig. 2F). During the second phase, TGF-β3 secretion and the subsequent autocrine signaling resulted in the upregulation of mesenchymal marker expression. The TGF-β3 induced gene expression program involves a complex hierarchy of transcriptional and post-translational regulatory events. Absence of E-cadherin indirectly promoted TGF-β3 expression through the β-catenin/TCF4 complex following Snail or Slug expression (Fig. 2C, Lane 2 or 3). Conversely, over-expression of E-cadherin inhibited the TGF-β3 autocrine production by sequestering cytosolic β-catenin, thereby blocking EMT (Fig. 2C, Lane 4 or 5). TGF-β3 signaled through through the Smad pathway to regulate LEF-1 expression and down-stream target EMT genes (Fig. 2G). TGF-β3 (10 a.u.) in combination with downstream inhibitors (DN-Smad4 and DN-LEF-1) completely inhibited Vimentin expression, while elevating E-cadherin expression (Fig. 2H,I).
The predictive power of the ensemble was tested using both cross validation and by comparing simulations with data sets not used for model training. In whole, 78% of our training objectives were statistically significant (at a 95% confidence interval) compared to a randomized parameter family (N = 100) generated from the best-fit nominal set (starting point for the optimization). Conversely, we predicted approximately 60% of the training objectives, at a 95% confidence interval compared to randomized parameters. The model also captured the temporal gene expression responses of E-cadherin, pSmad2, and LEF-1 to within one-standard deviation (up to the 48 hr time-point) (Fig. 2J-L). This data was not used for model training. The high predictability can be attributed to the combination of the leave-one-out cross validation scheme, diverse objective functions, and robustness of the POETs algorithm. Taken together, the model captured the key signaling events revealed by Medici et al. Medici et al. (2008) that drive the phenotypic conversion. A listing of data used for training is included in the supplement (Fig. S5 and Fig. S6).
Identification of a novel LEF-1 regulator During model identification, we found that consistent TGF-β induced EMT required an additional regulatory protein. This protein, which we called hypothetical regulator 1 (YREG1), was required to mediate between SNAIL/SLUG transcriptional activity and the upregulation of LEF-1 expression following TGF-β1/2 exposure. SNAIL/SLUG are well known transcriptional repressors Dhasarathy et al. (2011), Hemavathy et al. (2000a,b), although there are a few studies which suggest that at least SNAIL can also act as a transcriptional activator Guaita et al. (2002). In the model, we assumed the expression of SNAIL/SLUG was likely regulated by AP1/SP1 Jackstadt et al. (2013). Thus, upon receiving a TGF-β1/2 signal, the model predicted enhanced SNAIL/SLUG expression, consistent with experimental observations. TGF-β1/2 stimulation also induces LEF-1 expression. However, literature evidence suggested that LEF-1 expression was not strongly dependent upon AP1/SP1 activity Eastman & Gross-chedl (1999). Thus, either SNAIL/SLUG are acting as inducers (contrary to substantial biochemical evidence) or, they are repressing the expression of an intermediate repressor. Given the biochemical evidence supporting SNAIL/SLUG as repressors, we created YREG1 a hypothetical intermediate repressor whose expression is downregulated by SNAIL/SLUG. The literature data therefore suggested that YREG1 had two transcriptional targets, LEF-1 and TGF-β3. By adding this regulator, our simulations became consistent with training and literature data. Medici et al. suggested a similar idea where feedback between β-catenin and LEF-1 was likely, although this feedback had yet to be identified Medici et al. (2008). Low levels of YREG1 expression were used in all simulations to regulate the formation of the β-catenin-LEF-1 complex. To test the potency of YREG1, we conducted knockdown and over-expression simulations following the addition of TGF-β1/2 (Fig. S8). In the absence of YREG1, most of the population failed to consistently respond to TGF-β1/2 exposure compared to the wild-type (Fig. S8A). Conversely, YREG1 overexpression revealed an exclusively epithelial phenotype following TGF-β1/2 stimulation (Fig. S8B). Overexpression of YREG1 repressed LEF-1 and TGF-β3 expression, thereby not allowing free β-catenin to form the β-catenin-LEF-1 complex which promotes mesenchymal gene expression, or SMAD activity following from autocrine TGF-β3 signaling. Likewise, the abundance of the pSmad2/4-LEF-1 complex was also reduced in cells overexpression YREG1, which blocked the repression of E-cadherin. Taken together, we found that low YREG1 expression was necessary for stabilizing EMT, while elevated YREG1 levels limited the extent of EMT induction.
TGF-β1/2 and VEGF-A exposure promotes phenotype heterogeneity through NFATc and phosphorylated Sp1 While we captured the central tendency of many of the molecular features of EMT induction following TGF-β1/2 exposure, an often neglected but important emergent feature of developmental and pathological programs is population heterogeneity Park et al. (2010). We (and others) previously hypothesized that deterministic model ensembles can interrogate population behavior, at least at a course grained level Lequieu et al. (2011). We tested this hypothesis by analyzing the response of the population of EMT models to extracellular cues and then comparing this response to flow cytometry studies. We used robustness coefficients to quantify the response of the individual members of the ensemble to TGF-β1/2 stimulation. We have previously used robustness coefficients to systematically quantify response of a system to structural or operational perturbations, for example gene deletions or the addition of a growth factor or hormone Lequieu et al. (2011), Song et al. (2010), Tasseff et al. (2010, 2011). Robustness coefficients quantify shifts in molecular marker abundance resulting from molecular of environmental perturbations relative to an unperturbed control state. Robustness coefficients ≫ 1 indicate that marker abundance increased, while robustness coefficients ≪ 1 indicates marker abundance decreased relative to an unperturbed control. A value of ~ 1 indicates approximately no change in marker abundance following the perturbation. We calculated robustness coefficients for each member of the ensemble (N ≃ 1100) for two downstream phenotypic markers, Vimentin (mesenchymal) and E-cadherin (epithelial) following the addition of TGF-β1/2 alone (Fig. 3), and VEGF-A in combination with NFATc inhibitors (Fig. 4). The absence of TGF-β1/2 or VEGF-A stimulation was used as the baseline for the robustness calculations.
We identified model subpopulations that exhibited different behaviors following exposure to TGF-β1/2 (Fig. 3A, labeled P1-P4). Analysis of the molecular signatures of these subpopulations suggested the abundance, localization and state of the Sp1, AP-1 and NFATc transcription factors controlled population heterogeneity. The behavior of the majority of models (>70%) was similar to subpopulation one (P1) or subpopulation two (P2) in Fig 3. These models showed the classically expected behavior, a switch from an epithelial to mesenchymal phenotype following TGF-β1/2 exposure. Models near P1 had evaluated nuclear localized phosphorylated Sp1, relative to non-induced cells (and models near P2). Elevated Sp1 activity decreased E-cadherin expression through Slug-mediated inhibition, which in turn increased Vimentin expression through TGF-β3 autocrine signaling and the liberation of β-catenin. Near P2, Sp1 transcriptional activity was lower than P1, leading to only modestly increased Vimentin expression and E-cadherin repression following TGF-β1/2 stimulation. Near subpopulation three (P3), reduced levels of nuclear phosphorylated AP-1, Sp1, and NFAT (resulting from the loss of ERK kinase activity) were responsible for Vimentin repression relative to the control. However, the most biologically interesting behavior was exhibited by subpopulation four (P4). Models near P4 had elevated Sp1 and NFAT transcriptional activity, which increased both Vimentin and E-cadherin expression. Analysis of these hypothetical cells suggested they had abnormal signaling; deregulated NFAT expression and nuclear localization promoted E-cadherin expression while TGF-β1/2 induced Sp1 action promoted Vimentin expression. Analysis of the connectivity and information flow through the signaling architecture suggested that Sp1 and NFAT action could be manipulated independently by simultaneous TGF-β1/2 and VEGF-A stimulation (Fig. S1).
To test this hypothesis, we simulated the response of the network to TGF-β1/2 and VEGF-A treatment with and without NFATc inhibitors (Fig. 4). As expected, stimulation with VEGF-A (50 a.u.) maintained an epithelial population (Q4-43.6%), while TGF-β1/2 (10 a.u.) exposure shifted the population from an epithelial (Q4-5.5%) to a mesenchymal (Q1-45.6%) phenotype (Fig. 4A and Fig. 4B). On the other hand, combined stimulation with TGF-β1/2 (10 a.u.) and VEGF-A (50 a.u.) increased both E-cadherin and Vimentin expression (Q2-45.3%), resulting in a hybrid phenotype with both epithelial and mesenchymal characteristics (Fig. 4C). To better understand this hybrid response, we quantified the simulated protein levels for E-cadherin, Vimentin, phosphorylated nuclear Sp1, nuclear NFATc1, α-smooth muscle actin (α-SMA) and Slug as a function of condition (Fig. S2A-C). Vimentin expression was correlated with high levels of nuclear phosphorylated Sp1, following TGF-β1/2 exposure. Conversely, elevated E-cadherin expression depended upon the activity of NFAT transcription factors downstream of VEGF-A stimulation. To further isolate the role of NFAT on this hybrid state, we simulated the inhibition of NFAT transcriptional activity across all conditions (all else being equal). NFAT inhibition in combination with VEGF-A treatment blocked all E-cadherin positive sets (Fig. 4D). Likewise, TGF-β1/2 treatment in combination with NFATc inhibition also resulted in the loss of E-cadherin expression (Fig. 4E). Lastly, NFATc inhibition in combination with simultaneous TGF-β1/2 and VEGF-A exposure repressed nearly all E-cadherin expression, shifting nearly the entire population towards a mesenchymal phenotype (Fig. 4F). Taken together, high levels of nuclear localized phosphorylated Sp1 correlated with Vimentin expression, while NFATc transcriptional activity was predicted to be critical for maintaining E-cadherin expression.
Combined TGF-β2 and VEGF-A exposure drives heterogeneity in MCF10A and DLD1 cells The EMT model simulations suggested the transcriptional activity of NFATc and Sp1 could be independently tuned to generate a hybrid cell population with both epithelial and mesenchymal characteristics. To test this hypothesis, we exposed either quiescent epithelial (MCFA10, (Fig. 5)) or transformed epithelial cells (DLD1, (Fig. S3)) to combinations of TGF-β1/2 and/or VEGF-A. As expected, treatment with TGF-β1/2 (10ng/ml) increased Slug and Vimentin expression, while repressing E-cadherin expression both at the transcript and protein levels in MCF10A (Fig. 5A-B) and DLD1 cells (Fig. S4C, Fig S3 D,E). Both MCF10A (Fig. 5C) and DLD1 cells (Fig. S3E,G) transitioned from quiescent cobblestone morphology to spread spindle shapes, consistent with EMT. As predicted, we found increased nuclear localization of phosphorylated Sp1 following TGF-β1/2 stimulation in both MCF10A (Fig. 5B,C) and DLD1 cells (Fig. S3E,F). Consistent with model predictions, VEGF-A (50ng/ml) treatment increased the abundance of NFATc1 and E-cadherin at both the transcript and protein level in both MCF10A (Fig. 5A) and DLD1 (Fig. S3A) cells. We also found that NFATc1 nuclear localization significantly increased in both MCF10 and DLD1 treated with VEGF-A independently of the abundance of nuclear localized phosphorylated Sp1 levels (Fig. 5B,C Fig.S3C,E). Interestingly, combining VEGF-A (50ng/ml) with TGF-β1/2 (10ng/ml) resulted in significantly elevated expression of both E-cadherin and Vimentin at the transcript and protein levels in both MCF10A and DLD1 cells (Fig 5A,B; Fig S3D,E; Fig S4C). NFATc1 expression increased, while Sp1 expression was similar to the TGF-β1/2 case alone (Fig. 5A-B, Fig S3D,E; Fig S4C)), supporting their independent regulation. The expression of Slug, and Vimentin significantly increased, while E-cadherin levels were increased in MCF10A cells (Fig 5A) and maintained at control levels in DLD1 cells (Fig. S3D). As further predicted, nuclear co-localization of both NFATc1 and phosphorylated Sp1 were apparent in MCF10A and DLD1 cells treated with both ligands (Fig. 5B,C Fig S3E,F). Taken together, combined VEGF-A and TGF-β1/2 treatment elicited a hybrid phenotype expressing both mesenchymal and epithelial characteristics in both MCF10A and DLD1 cells. This phenotype was driven by the transcriptional activity of two key transcription factors, Sp1 and NFATc, which could be modulated independently by TGF-β1/2 and VEGF-A exposure.
Our robustness analysis predicted that NFATc transcriptional activity was critical to maintaining E-cadherin expression in the presence of both VEGF-A and TGF-β1/2. We experimentally tested this hypothesis by exposing both MCF10A (Fig. 5E,F) and DLD1 cells (Fig. S4) to combinations of VEGF-A and TGF-β1/2 in the presence or absence of VIVIT, a soluble peptide inhibitor of NFATc transcriptional activity Aramburu et al. (1999). Treatment with VEGF-A (50ng/ml) and VIVIT (10μM) in MCF10A cells significantly reduced E-cadherin expression compared to VEGF-A alone (Fig 5D,E). Co-treatment with VIVIT and TGF-β1/2 did not enhance EMT capacity of MCF10A cells above that of TGF-β1/2 alone (Fig 5A,B,E). Likewise, VIVIT in combination with both TGF-β1/2 and VEGF-A resulted in a loss of E-cadherin gene and protein expression, while Slug and Vimentin levels remained increased (Fig. 5D,E). Quantitative flow cytometry confirmed these results in both MCF10A (Fig. 5F) and DLD1 cells (Fig. S4C). Both epithelial cell lines initially had high levels of E-cadherin expression, and low vimentin abundance (Q1-99.5%), but both MCF10A and DLD1 cells shifted from an epithelial to mesenchymal phenotype (Q1-33.4%, Q4-42.8%) following TGF-β1/2 exposure. As expected, NFATc nuclear localization was repressed with VIVIT treatment regardless of ligand stimulation, while the abundance of nuclear phosphorylated Sp1 increased for both TGF-β1/2 and TGF-β1/2 + VIVIT conditions (Fig. 5D,E). Combined TGF-β1/2 and VEGF-A increased both Vimentin and E-cadherin expression (Q1-42.1%, Q2-52.3%) compared to TGF-β1/2 alone. Together, these results demonstrate that NFATc and phosphorylated Sp1 are critical for regulating E-cadherin and Vimentin expression during phenotype heterogeneity in MCF10A and DLD1.
Ductal branching during acini formation is dependent upon phenotype heterogeneity in MCF10A and DLD1 cells We finally employed established three-dimensional (3D) in vitro models of invasion, migration, compaction, and tubulogenesis Dhimolea et al. (2010) to determine the functional consequences of the hybrid phenotype (Fig. 6). MCF10A and DLD1 cells were aggregated via hanging drop, placed on the surface of a collagen gel, and cultured for 72 hrs under various biochemical treatments. TGF-β1/2 stimulation significantly enhanced cell matrix invasion and matrix compaction, while in contrast VEGF-A stimulation promoted surface migration but no invasion or compaction (Fig. 6B-D). Interestingly, combined TGF-β1/2 and VEGF-A stimulation significantly increased cell migration potential above that of VEGF-A alone while maintaining 3D matrix compaction, though with decreased magnitude compared to TGF-β1/2 alone. Inhibition of NFATc transcriptional activity by VIVIT decreased migration following treatment with VEGF-A alone (Fig. 6B). Co-treatment of VIVIT significantly decreased migration, while complementarily increasing invasion and compaction, when MCF10A cells were stimulated with both VEGF-A and TGF-β1/2 (Fig. 6B-D). The responses of DLD1 cells followed a similar trend to MCF10A, although the magnitudes of migration, invasion, and compaction were less. Cell circularity within 3D gels strongly and negatively correlated with both invasion and compaction regardless of treatment (Fig. 6E). Circularity refers to the morphology of the cells. In general, a quiescent epithelial cells assumes a circular morphology in culture, while an active mesenchymal cell is highly elongated. The circularity index, a common means of quantifying cell morphology, relates cell area to perimeter. A perfect circle has a circularity index equal to 1.0, while a straight line has a circularity index equal to 0.0, see Butcher et al. Butcher et al. (2004). TGF-β1/2 treatment alone resulted in irregular and spindle shaped morphology, while VEGF-A exposure promoted round quiescent cells (Fig. 6A). Combined VEGF-A and TGF-β1/2 promoted morphology between these extremes. VIVIT mediated NFATc inhibition significantly reduced the circularity index, similar to TGF-β1/2 treatment (Fig. 6F). VEGF-A treatment also induced the formation of tubular structures (acini), but the number of tubular branches relative to total acini was significantly increased upon combined TGF-β1/2 and VEGF-A. No tubular structures were identified within the DLD1 constructs during the 7 day tubulogenesis endpoints, supporting that MCF10A and DLD1 cells have some cell-type specific EMT sensitivity despite their underlying competency for acquiring a heterogeneous phenotype. This suggests that initial EMT sensitivity of a cell influences downstream functional response from TGF-b and VEGFA stimulation. Together, these results establish that VEGF-A and TGF-β1/2 ligand concentrations potentiate between acini and ductal branch formation in 3D culture, and are dependent upon NFATc activity.
Discussion
In this study, we developed a family of mechanistic models describing the induction of EMT by TGF-β isoforms in the presence and absence of VEGF-A. The signaling architecture encoded in the model, which contained 995 molecular species interconnected by 1700 interactions, described the expression of 80 genes in response to growth factor stimulation. This simulation incorporates an unprecedented level of detail compared to previous models, but as a consequence created a large number of unknown model parameters. Because these parameters could not be estimated uniquely apriori, we estimated an ensemble of likely parameters using the POETs multiobjective optimization framework. The model population was trained and cross-validated to prescribe biological significance using 41 data sets generated in DLD1 colon carcinoma, MDCKII, and A375 melanoma cell lines Medici et al. (2008). POETs generated > 15,000 probable parameter sets using this data, from which we selected N ≃ 1100 for subsequent analysis. Analysis of this population predicted possible phenotypic modes (and their associated signaling) that cells could exhibit when stimulated with TGF-β and/or VEGF-A. The most novel hypothesis generated from the analysis was that cells could operate in a hybrid state defined by both epithelial and mesenchymal traits when stimulated simultaneously with TGF-β and VEGF-A. We tested this hypothesis in MCF10A and DLD1 cells stimulated with combinations of TGF-β and VEGF-A. As expected, in the presence of TGF-β or VEGF-A alone, MCF10A and DLD1 cells were either mesenchymal or epithelial, respectively. However, with both TGF-β and VEGF-A, MCF10A and DLD1 cells exhibited a hybrid phenotype, having both epithelial and mesenchymal characteristics. Furthermore, we found that functional traits such as tubulogenesis and ductal branching were different for cells in this hybrid phenotype. Together, this study established a predictive model of EMT induction, determined that deterministic model ensembles could predict population heterogeneity, and proved the existence of a unique hybrid phenotype resulting from the simultaneous integration of extracellular growth factor signals.
Cells routinely process a multitude of signals simultaneously, especially when coordinating developmental or pathological programs. For example, oncogenic cells integrate both mechanical and chemical cues in their local microenvironment during tumorigenesis, including cytokines VEGF and TGF-β Hong et al. (2013). VEGF-A mediates pathological angiogenic remodeling of tumors Nagy et al. (2007), while TGF-β can elicit both protective and oncogenic responses Ferrara (2002), Willis & Borok (2007). While much research has tested signaling pathways individually, far less is understood about combinatorial stimulation, such as with both VEGF-A and TGF-β. Recent in vitro and in vivo evidence has suggested that epithelial cells can exhibit heterogeneous phenotypes in addition to classically defined epithelial or mesenchymal states Polyak & Weinberg (2009), Strauss et al. (2011). For example, expression profiling in human epithelial cancer cell lines demonstrated a spectrum of phenotypes, including some that expressed both E-cadherin and Vimentin simultaneously Neve et al. (2006), Welch-Reardon et al. (2014). Zajchowski et al., speculated that these expression profiles were somehow important for maintaining epithelial properties, while simultaneously allowing other functional behavior such as proliferation and migration Zajchowski et al. (2001). Whether and how heterogeneous phenotypes arise and participate in cancer progression, as well as their response to pharmacological inhibition are fundamental questions that should receive increased attention. In this study, we determined that a hybrid phenotype could be obtained through combined treatment with VEGF-A and TGF-β, both common factors localized in the tumor microenvironment. Furthermore, our systematic simulation-experimentation strategy identified that the transcriptional activity of Sp1 and NFATc were the critical factors controlling this phenotypic heterogeneity. Several studies have highlighted the importance of NFATc as a key transcription factor involved in cell growth, survival, invasion, angio-genesis and cancer Mancini & Toker (2009). For example, proliferation and anchorage-independent growth of pancreatic tumor cells is dependent on calcineurin and NFATc1 activity, consistent with the high levels of nuclear NFATc1 found in pancreatic tumors Singh et al. (2010). Likewise, our results found that VEGF-A was a potent inducer of NFATc1 expression, which may be required for epithelial cell migration and tubulogene-sis. Although specific NFATc isoforms were not distinguished in the model, our simulations suggested that NFATc transcriptional activity was capable of maintaining epithelial traits, even during TGF-β induced EMT. Experimentally, we found that E-cadherin expression was dependent upon NFATc dephosphorylation in response to simultaneous VEGF-A and TGF-β1/2 treatment. Thus, these results support the hypothesis that NFATc activity plays a critical role in maintaining cell-cell contacts, even during partial EMT.
Epithelial cells reproduce tissue-like organization when grown in a three-dimensional extracellular matrix (ECM) environment, and therefore are an attractive model to study morphogenic mechanisms. It is well established that MCF10A cells form structures that closely resemble acini (multi-lobed cluster of cells) in three-dimensional in vitro cultures Debnath et al. (2003). It has been postulated that a cellular response reminiscent of partial EMT underlies this process, stimulating further branching and formation of acini Pearson & Hunter (2007). Normally well controlled process such as tubulogenesis can be co-opted by cancer cells to break away from a primary lesion and invade through the surrounding stroma O’Brien et al. (2004). However, by retaining a transient hybrid EMT-like state, clusters of these tube-forming tumor cells can reform at a high rate after invasion, possibly explaining why invasive human carcinomas frequently appear to be cellular collections with varying degrees of gland-like differentiation Debnath & Brugge (2005). In this study, we showed that our predicted hybrid phenotype generated by simultaneous treatment of epithelial cells with VEGF-A and TGF–β possessed altered migration and invasion, which enhanced tubular branching. A salient feature of this behavior, however, was the retention of cell-cell contacts that allowed cells to migrate without completely dissociating from their neighbors. Thus, our results support a mechanism in which hybrid cells can maintain some functional characteristics of epithelial cells such as cell-cell adhesion, which are normally lost in a fully differentiated mesenchymal state. The tumor microenvironment contains many soluble signals simultaneously, including VEGF and TGF–β. Thus, it is likely that some cancerous epithelial cells could exhibit hybrid EMT phenotypic states. This may explain why fibroblastoid morphology, a classical feature of EMT, is not commonly observed in human carcinomas Debnath & Brugge (2005). This study focused on the combinatorial effects of two very different ligand families present together in the tumor environment. Additional modeling studies are required to unravel the global response of epithelial cells to the full spectrum of chemical, substrate, and mechanical cues. The simulation strategy presented here is readily adaptable to larger species sets, with the major advantage that experimentally testable hypotheses can be generated regarding how signals get integrated to produce global cellular response. Furthermore, by simulating multiple ensembles of parameter sets, subpopulations across a constellation of phenotypes can be created and mined for common and/or divergent signaling characteristics. This is a significant advantage over forced convergence to a single unique solution and thereby generating a potentially non-physiological homogeneous population.
The deterministic population of EMT models predicted heterogeneous behavior that was qualitatively consistent with experimental studies. There is a diversity of algorithmic approaches to estimate model parameters Moles et al. (2003), as well as many strategies to integrate model identification with experimental design Rodriguez-Fernandez et al. (2013), Villaverde & Banga (2014). However, despite these advances, the identification of models describing intracellular network behavior remains challenging. There are different schools of thought to deal with this challenge. One school has focused on model reduction. Data-driven approaches Cirit & Haugh (2012), boolean Choi et al. (2012) or other logical model formulations Morris et al. (2011), Terfve et al. (2012) are emerging paradigms that constrain model complexity by the availability of the training and validation data. Other techniques such as constraints based modeling, which is commonly used to model metabolic networks, have also been applied to model transcriptional networks, although primarily in lower eukaryotes and prokaryotes Hyduke & Palsson (2010). These techniques (and many others, see review Wayman & Varner (2013)) are certainly exciting, with many interesting properties. However, we used the traditional approach of mass action kinetics within an ordinary differential equation framework. The identification problem for the EMT model was massively underdetermined. This is not uncommon for differential equation models, especially those that are highly mechanistic. Of course, we could have discarded mechanism or reduced the model scope to decrease the complexity of the identification problem. However, a central criticism leveled by biologists is that model simplification is often done at the cost of biological reality, or done for reasons of computational expediency Sainani (2012). To avoid this criticism, we systematically identified an ensemble of likely models each consistent with the training data, instead of a single but uncertain best fit model. Previously, we (and others) have suggested that deterministic ensembles could model heterogeneous populations in situations where stochastic computation was not feasible Lequieu et al. (2011). Population heterogeneity using deterministic model families has previously been explored for bacterial growth in batch cultures Lee et al. (2009). In that case, distributions were generated because the model parameters varied over the ensemble, i.e., extrinsic noise led to population heterogeneity. In this study, parameters controlling physical interactions such as disassociation rates or the rate of assembly or degradation of macromolecular machinery such as ribosomes were widely distributed over the ensemble. Population heterogeneity can also arise from intrinsic thermal fluctuations, which are not captured by a deterministic population of models Swain et al. (2002). Thus, deterministic ensembles, provide a coarse-grained or extrinsic-only ability to simulate population diversity. Despite this limitation, our prediction of phenotypic heterogeneity (and the underlying signaling events responsible for the heterogeneity) was consistent with experimental observations. This suggested that deterministic ensembles could simulate disease or developmental processes in which heterogeneity plays an important role, without having to resort to stochastic simulation.
A common criticism of ODE modeling has been the poorly characterized effect of structural and parametric uncertainty. In this study, parametric uncertainty was addressed by developing an ensemble of probable models instead of a single best-fit but uncertain model using multiobjective optimization. While computationally complex, multiobjective optimization is an important tool to address qualitative conflicts in training data that arise from experimental error or cell line artifacts Handl et al. (2007). On the other hand, structural uncertainty is defined as uncertainty in the biological connectivity. The EMT model connectivity was assembled from an extensive literature review. However, several potentially important signaling mechanisms were not included. First, we identified a potential gap in biological knowledge surrounding the regulation of LEF-1 expression, that was filled by the addition of the hypothetical YREG1 transcriptional repressor. The LEF-1 transcription factor is expressed in tissues that undergo EMT during embryogenesis Nawshad & Hay (2003), Vega et al. (2004), and has been suggested to promote an invasive phenotype in cancer cells Cano et al. (2000), Kim et al. (2002). Low levels of YREG1 were important for stabilizing the interaction between LEF-1 and β-catenin, while elevated levels inhibited EMT by downregulating LEF-1 transcriptional activity. Recent evidence has established a complex role of Amino terminal Enhancer of Split (AES) and Groucho/TLE on suppressing LEF-1 activity. AES opposes LEF-1 transcriptional activation while Groucho/TLE binds with LEF-1 for a histone deacetylase repression. In addition, β-catenin directly displaces Groucho/TLE repressors from TCF/LEF-1 in Wnt-mediated transcription activation Arce et al. (2009), Grumolato et al. (2013). Our model agrees with this newly discovered feedback system, as YREG1 regulates LEF-1 activity leading to EMT stabilization. Secondly, we should revisit the role of GSK-3β. GSK-3β is an important regulator which controls the abundance of both Snail and β-catenin through the ubiquitin-proteasome pathway Larue & Bellacosa (2005), Zhou et al. (2004). Specifically to our model, expression of Snail increases through 72 hrs. In contrast, experimental data has shown that activity of Snail peaks at 24 hrs which may be controlled by the GSK-3β complex Medici et al. (2006). Recent evidence has also suggested an essential role of NF-κB in epithelial transformation. NF-κB may influence Snail expression through the AKT pathway and directly stabilize Snail activity Wu et al. (2009). This is particularly important for integrating inflammation pathways, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), which have been linked to EMT in pathological conditions Sullivan et al. (2009). Other pathways such as Notch have also been shown to act synergistically with TGF-β to express Slug in the developing embryo Niessen et al. (2008). Lastly, while we have modeled classical protein signaling, we have not considered the role of regulatory RNAs on EMT. There is growing evidence that microRNAs (miRNAs) play a strong role in EMT, where several miRNAs, for example miR-21 and miR-31 are strongly associated with TGF-β exposure Bullock et al. (2012). Addressing missing structural components like these, could generate more insight into TGF-β signaling and its role in phenotypic transformation.
Materials and Methods
The simulation code and parameter ensemble used in this study can be downloaded from GitHub (https://github.com/jeffreyvarner/TGFb-VEGFA-Modelv1.git).
Signaling network connectivity The EMT model described the gene expression program resulting from TGF–β and VEGF-A signaling in a prototypical epithelial cell. The TGF–β-EMT network contained 995 nodes (proteins, mRNA or genes) interconnected by 1700 interactions. The network connectivity was curated from more than 40 primary literature sources in combination with on-line databases Jensen et al. (2009), Linding et al. (2007). The model interactome was not specific to a single epithelial cell line. Rather, we assembled canonical pathways involved in TGF–β and VEGF-A signaling, defaulting to human connectivity when possible. Using a canonical architecture allowed us to explore general features of TGF–β induced EMT without cell line specific artifacts. On the other hand, because of the canonical architecture, we evaluated the simulation conclusions in several cell lines to test the generality of our conclusions.
Our signaling network reconstruction was based on Medici et al. who identified the pathways though which MDCKII, DLD1 colon carcinoma, and A375 melanoma cells transition towards a mesenchymal phenotype Medici et al. (2008). Sequential activation of MAPK and Smad pathways were initiated upon addition of TGFβ1/2. Briefly, TGFβ2 signals through the RAS-RAF-MEK-ERK pathway to up-regulate Snail and Slug expression Medici et al. (2006). Snail, a known repressor of junctional proteins, inhibits the expression of E-cadherin Cano et al. (2000). This initial repression of E-cadherin leads to a release of β-catenin from the cell membrane. Cystolic β-catenin can then translocate to the nucleus and form transcriptional complexes with TCF-4 to drive TGFβ3 expression Medici et al. (2008). TGFβ3 signals to the cells interior by binding to type II receptors, which form heterodimers with type I receptors (ALK5) Derynck & Zhang (2003). This activates the receptors serine/threonine kinase activity to phosphorylate and activate the receptor Smads 2/3 Massagué et al. (2005). Phosphorylated Smads 2/3 (pSmad2/3) form heterodimers with partner Smad4 and translocate to the nucleus. pSmads complexes up-regulate other transcription factors, such as LEF-1. The pSmad2/4/LEF-1 has been shown to directly repress the E-cadherin gene Nawshad et al. (2007). LEF-1 also binds with β-catenin to upregulate mesenchymal proteins such as fibronectin Medici et al. (2011). The EMT gene expression program was initiated by the binding of TGF–β isoforms to TGF–β surface receptors. Binding of extracellular TGF–β1/2 with TGF–β surface receptors I/II (TGF–βR-I/II) initiates the assembly of adapter complexes which starts the downstream signaling program. In the model, TGF–β1/2 binds TGF–βR-I/II followed by the recruitment of activin receptor-like kinase 1 (ALK1) and TGF–β surface receptor III (TGF–βR-III) to form the activated receptor complex Derynck & Zhang (2003). Alternatively, we also included activin receptor-like kinase 5 (ALK5) recruitment in combination with Endoglin and TGF–βR-III as a second (redundant) activated receptor complex Gatza et al. (2010). Complex assembly activates the serine/threonine kinase activity on the receptor, leading to the recruitment and phosphorylation of Smad partners Massagué et al. (2005). Phosphorylated Smads2/3 (pSmad2/3) form heterodimers with partner Smad4 and then translocate to the nucleus where they act as both transcriptional activators and repressors. Nuclear pSmad2/3-Smad4 form transcriptional complexes with several genes in the model including lymphoid enhancer-binding factor 1 (LEF-1), Nuclear factor of activated T-cells, cytoplasmic 1 (NFACT1), and Specificity Protein 1 (SP1). On the other hand, nuclear pSmad2/3-Smad4 represses (in combination with the LEF-1 protein) the expression of E-cadherin (Cdh1) Nawshad et al. (2007) and Cadherin 5, type 2 (VE-Cadherin encoded by Cdh5). Repression of E-cadherin expression is the central event in the transition from an epithelial to a mesenchymal phenotype Cano et al. (2000). However, this transition is not solely driven by transcriptional events. At the protein level, the repression of E-cadherin leads to a release of β-catenin from cell membrane. Cystolic β-catenin then translocates to the nucleus and forms transcriptionally-active complexes with immunoglobulin transcription factor 2 (TCF-4) to drive TGF-β3 expression Medici et al. (2008). Simultaneously, ERK1/2-mediated phosphorylation of the AP1 and Sp1 transcription factors can also regulate transcriptional complexes involving NFAT, Slug, and Smads. Lastly, canonical pathways for processing extracellular VEGF-A, BMP and Wnt signals, in addition to the PI3K pathway were also included in the model. Additional information about the interactions included in the model, along with the Systems Biology Markup Language (SBML) file encoding these interactions are included in the supplemental materials.
Formulation, solution and analysis of the EMT model equations EMT was modeled using mass-action kinetics within an ordinary differential equation (ODE) framework:
The quantity x denotes the vector describing the abundance of protein, mRNA, and other species in the model (995 × 1). The stoichiometric matrix S encodes the signaling architecture considered in the model (995 × 1700). Each row of S describes a signaling component while each column describes a particular interaction. The (i, j) element of S, denoted by σij, describes how species i is involved with interaction j. If σij > 0, species i is produced by interaction j. Conversely, If σij < 0, then species i is consumed in interaction j. Lastly, if σij = 0, then species i is not involved in interaction j. The term r (x, k) denotes the vector of interactions rates (1700 × 1). We modeled each network interaction (gene expression, translation and biochemical transformations) using elementary rate laws where all reversible interactions were split into two irreversible steps (supplemental materials). Thus, the rate expression for interaction q was given by:
The set {Rq} denotes reactants for reaction q, while σjq denotes the stoichiometric coefficient (element of the matrix S) governing species j in reaction q. The quantity kq denotes the rate constant (unknown) governing reaction q. Model equations were generated in the C-programming language using the UNIVERSAL code generator, starting from an text-based input file (supplemental materials). UNIVERSAL, an open source Objective-C/Java code generator, is available as a Google Code project (http://code.google.com/p/universal-code-generator/). Model equations were solved using the CVODE solver in the SUNDIALS library Hindmarsh et al. (2005) on an Apple workstation (Apple, Cupertino, CA) as previously described Tasseff et al. (2011).
Estimation of model parameters using multiobjective optimization. The EMT model had 1756 unknown parameters (1700 kinetic constants and 56 non-zero initial conditions) which were not uniquely identifiable given the training data. Instead, we estimated a population of likely models (each consistent with the training data) using 41 data sets generated in DLD1 colon carcinoma, MDCKII, and A375 melanoma cells taken from Medici et al. Medici et al. (2008). We used the Pareto Optimal Ensemble Technique (POETs) multiobjective optimization framework in combination with leave-one-out cross-validation to estimate an ensemble of model parameters Song et al. (2010). Cross-validation was used to calculate both training and prediction error during the parameter estimation procedure Kohavi (1995). The 41 intracellular protein and mRNA data-sets used for identification were organized into 11 objective functions. These 11 objective functions were then partitioned, where each partition contained ten training objectives and one validation objective. The training and validation data were Western blots. Thus, all model simulations were in arbitrary units. However, POETs does allow a soft constraint on the order of magnitude of the model concentration scale. In this study, we assumed the natural model concentration scale was pmol/L. We did not place a lower bound on model states. However, based on the pmol/L natural scale, we treated all values less than 10−3 as zero (or no expression).
Robustness coefficients. Robustness coefficients were calculated as shown previously Lequieu et al. (2011), Tasseff et al. (2011). Robustness coefficients denoted by α (i, j,to,tf): quantify the response of a marker to a structural or operational perturbation to the network architecture. Here to and tf denote the initial and final simulation time respectively, while i and j denote the indices for the marker and the perturbation respectively. A value of α (i, j, to, tf) > 1, indicates increased marker abundance, while α (i, j, to, tf) < 1 indicates decreased marker abundance following perturbation j. If α (i, j, to, tf) ~ 1 the jth perturbation does not influence the abundance of marker i. Robustness coefficients were calculated for each member of the ensemble (N ≃ 1100).
Cell culture and experimental interrogation DLD1 colon carcinoma, MCF10A, and HUVEC were acquired from the American Tissue Culture Collection (Manassas, VA). Cells were grown in culture with RPMI 1640 medium with 10% fetal bovine serum and 1% penicillin/streptomycin for DLD1, EBM-2 supplemented with EGM-2, 5% fetal bovine serum, and 1% penicillin/streptomycin for HUVEC, or MGEM 2 supplemented with insulin, bovine pituitary extract, cholera toxin, hEGF, hydrocortisone, 5% horse serum, and 1% penicillin/streptomycin for MCF10A. Cells were serum starved for 24 hours and removed from all experimental conditions. Recombinant VEGFA165 was also removed from culture medium prior to experimentation. Recombinant human TGF–β2 (R & D Systems, Minneapolis, MN) was added to the culture medium at a concentration of 10 ng/ml and recombinant VEGFA165 at a concentration of (5ng/ml, 50ng/ml) for all relative experiments. NFAT inhibitor (VIVIT peptide) (EMDBiosciences, Darmstadt, Germany), was added to the culture medium at a concentration of 10µM for all relative experiments. Cells were passaged 1:3 or 1:4 every 3-6 d and used between passages 4 and 8.
RT-PCR RNA extractions were performed using a Qiagen total RNA purification kit (Qi-agen, Valencia, CA) and RNA was reverse transcribed to cDNA using the SuperScript III RT-PCR kit with oligo(dT) primer (Invitrogen). Sufficient quality RNA was determined by an absorbance ratio A260/A280 of 1.8-2.1, while the quantity of RNA was determined by measuring the absorbance at 260nm (A260). Real-time PCR experiments were conducted using the SYBR Green PCR system (Biorad, Hercules, CA) on a Biorad CFX96 cycler, with 40 cycles per sample. Cycling temperatures were as follows: denaturing, 95C; annealing, 60C; and extension, 70C. Primers were designed to detect GAPDH, E-cadherin, vimentin, Slug, Sp1, and NFATc1 in cDNA clones: Sp1 (F-TTG AAA AAG GAG TTG GTG GC, R-TGC TGG TTC TGT AAG TTG GG, Accession NG030361.1), NFATc1 (F-GCA TCA CAG GGA AGA CCG TGT C, R-GAA GTT CAA TGT CGG AGT TTC TGA G, Accession NG029226.1). GAPDH, E-cadherin, vimentin, and Slug primers were taken from previously published literature Medici et al. (2008).
Antibody Staining Samples were fixed in 4% PFA overnight at 4C. Samples were then washed for 15 minutes on a rocker 3 times with PBS, permeabilized with 0.2% Triton-X 100 (VWR International, Radnor, PA) for 10 minutes, and washed another 3 times with PBS. Samples were incubated overnight at 4C in a 1% BSA (Rockland Immunochemicals, Inc., Gilbertsville, PA) blocking solution followed by another 4C overnight incubation with either rabbit anti-human E-cadherin 1:100 (Abcam, ab53033), mouse anti-human phospho-Sp1 1:100 (Abcam, ab37707), mouse anti-human vimentin 1:100 (Invitrogen, V9), and rabbit anti-human NFATc1 (Santa Cruz, sc-7294) 1:100. After 3 washes for 15 minutes with PBS, samples were exposed to Alexa Fluor 488 or 568 conjugated (Invitrogen), species specific secondary antibodies at 1:100 in 1% BSA for 2 hours at room temperature. Three more washes with PBS for 15 minutes were followed by incubation with either DRAQ5 far red nuclear stain (Enzo Life Sciences, Plymouth Meeting, PA) at 1:1000.
FACS Flow cytometry for E-cadherin 1:100 (Abcam) and vimentin 1:100 expressing cells was performed. Briefly, cells were trypsinized, fixed with 4% PFA for 10 min and then preserved in 50% methanol/PBS. Cells were kept in the -20C until antibody staining was preformed. Samples were divided into multiple aliquots in order to stain the proteins separately and compensate for secondary antibody non-specific binding. Cells were incubated for 24 hrs at 4 C in primary antibody diluted in either PBS (extracellular) or 0.2% saponin-PBS (intracellular). Cells were then washed 3 times with PBS and incubated with appropriate secondary antibodies and imaged using a Coulter Epics XL-MCL Flow Cytometer (Coulter). All samples were compensated using appropriate background subtraction and all samples were normalized using 7500 cells per flow condition.
Three-Dimensional Culture and Tubulogenesis Assays For invasion/migration assays, cells were resuspended in culture media, and allowed to aggregate overnight in hanging drop culture (20μL; 20,000 cells). The spherical aggregates were placed on the surface of neutralized type I collagen hydrogels (1.5mg/mL) and allowed to adhere for 2 hrs before adding treatments. Cultures were maintained for 72 hrs, after which they were fixed in 4% PFA and slowly rehydrated using PBS. For compaction assays, cells were pelleted via centrifugation and resuspended within a neutralized collagen hydrogel (1.5mg/mL) solution at a density of 400,000 cells/mL. 250μL of gel was inoculated into culture wells, which solidified after 60min. Treatments were then added within 800μL of the culture medium without serum. Gels were liberated from the surfaces of the culture wells the next day and cultured free floating for an additional 3-7 days, exchanging serum free media with appropriate factors every 48 hrs.
Tubulogenesis was defined as a typical nonmalignant acini structure. This includes a polarized epithelial cell, hollow lumen, and the basal sides of the cell are surrounded by ECM proteins (Fig. 6A, Controls or VEGF treated). Previous work has shown that change in the morphological characteristics of nontumorigenic MCF10A epithelial acini occur over time and exploiting them to growth in 3D culture can be quantified. For example, using image segmentation, Chang et al. Chang et al. (2007) examined the elongation of the MCF10A acini at 6, 12, and 96 hours after a particular treatment. Polizzotti et al. Polizzotti et al. (2012) also suggested a computational method to quantify acini structure based on morphological characteristics in nonmalignant, noninvasive, and invasive conditions. Adapted from these approaches, we first fluorescently labeled our cultures and captured the acini structures by 3D confocal microscopy. Next individual acini structures in the images were segmented by imageJ and labeled. We then extracted the number of ductal branches. Ductal branching was defined as any elongated cell cluster extending away from the total acini structure, which was manually segmented and counted using ImageJ. A total of 5 images for each condition were used, and approximately 12 acini were analyzed in each image. Total branching was normalized to the amount of acini present, and provides an overall general assessment to the extent of acini remodeling.
Statistics Results are expressed as mean ± standard error, n≥6. Data was analyzed with the GraphPad Prism version 4.00 for Windows (GraphPad Software, San Diego, CA) and SAS (Statistical Analysis Software, Cary, NC). A one-way ANOVA with Tukey’s post hoc was used to compare differences between means and data was transformed when necessary to obtain equal sample variances. Differences between means were considered significant at p≺0.05.