Design, Assessment, and in vivo Evaluation of a Computational Model Illustrating the Role of CAV1 in CD4+ T-lymphocytes

Caveolin-1 (CAV1) is a vital scaffold protein heterogeneously expressed in both healthy and malignant tissue. We focus on the role of CAV1 when overexpressed in T-cell leukemia. Previously, we have shown that CAV1 is involved in cell-to-cell communication, cellular proliferation, and immune synapse formation; however, the molecular mechanisms have not been elucidated. We hypothesize that the role of CAV1 in immune synapse formation contributes to immune regulation during leukemic progression, thereby warranting studies of the role of CAV1 in CD4+ T-cells in relation to antigen-presenting cells. To address this need, we developed a computational model of a CD4+ immune effector T-cell to mimic cellular dynamics and molecular signaling under healthy and immunocompromised conditions (i.e., leukemic conditions). Using the Cell Collective computational modeling software, the CD4+ T-cell model was constructed and simulated under CAV1+/+, CAV1+/−, and CAV1−/− conditions to produce a hypothetical immune response. This model allowed us to predict and examine the heterogeneous effects and mechanisms of CAV1 in silico. Experimental results indicate a signature of molecules involved in cellular proliferation, cell survival, and cytoskeletal rearrangement that were highly affected by CAV1 knock out. With this comprehensive model of a CD4+ T-cell, we then validated in vivo protein expression levels. Based on this study, we modeled a CD4+ T-cell, manipulated gene expression in immunocompromised versus competent settings, validated these manipulations in an in vivo murine model, and corroborated acute T-cell leukemia gene expression profiles in human beings. Moreover, we can model an immunocompetent versus an immunocompromised microenvironment to better understand how signaling is regulated in patients with leukemia.

CAV1 acts as a scaffolding molecule thereby likely contributing to diverse events in the cell through CAV1-mediated recruitment of signaling complexes to the plasma membrane. Moreover, T-cell activation through the TCR and competent immune synapse formation are necessary for a healthy immune response. Misregulation of these processes can lead to deleterious effects, including cancer progression and a phenotype known as tumorinduced immunosuppression (16,17). As CD4 + T-cells are vital for proper adaptive immune function, and CAV1 plays a role in immune synapse formation, we chose to further investigate the CAV1-mediated pathways in a CD4 + T-cell.
To better understand the intricate biology of CAV1 signaling in CD4 + T-cells, the development of a comprehensive in silico model is warranted. Through such a model, further identification of molecules associated with CAV1 signaling can occur.

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Importantly, such a model allows for real-time simulations using computer software in an effort to identify specific mechanisms in the cell. In order to generate such a comprehensive and dynamic model, a systems biology approach is required (18). This approach provides a potentially greater understanding of the complex cellular functions that occur in living systems, allowing the use of computer models to conduct thousands of virtual experiments as well as make methodical predictions regarding proteins of interest (18)(19)(20)(21).
Herein, we describe the construction and validation of a fully functional in silico CD4 + T-cell model using the Cell Collective, a web-based, open-source dynamic modeling platform that allows scientists to construct computational models in a nonmathematical fashion (20,22). From this in silico starting position, comprehensive simulations were performed, allowing for predictions and hypotheses to be drawn for further in vitro/in vivo experimentation. To our knowledge, this is the first time a dynamic model of a CD4 + T-cell has been created to observe the downstream effects of CAV1 +/+ (wild type), CAV1 +/− (heterozygous), and CAV1 −/− (knock down) upon cell signaling and intracellular networks as validated by in silico simulations and in vivo investigations.

COMPUTATIONAL MODEL CONSTRUCTION WITH CELL COLLECTIVE
The presented model was constructed using Cell Collectivea collaborative and interactive platform for modeling biological/biochemical systems (20,22). The mathematical framework behind Cell Collective is based on a common qualitative (discrete) modeling technique where the regulatory mechanism of each node is described with a logical function [for more comprehensive information on logical modeling, see Ref. (23,24)]. Cell Collective allows users to construct and simulate large-scale computational models of various biological processes based on qualitative interaction information extracted from previously published literature. The initial version of the model was structured after the previously published models (25,26). The individual components and local interactions in the presented final model were retrieved manually from published literature. The model was subsequently validated against well-known experimentally demonstrated T-cell dynamics (see Model Validation), as well as new experiments presented in this paper. The Cell Collective's Knowledge Base was used to catalog and annotate every interaction and regulatory mechanism (e.g., tyrosine phosphorylation on Y316) as mined from the primary literature. The model is freely available for simulations and further contributions by others directly in the platform. The model can be also downloaded in the SBML format (24) to be used within other software tools.

MODEL VALIDATION
The model was constructed using local (e.g., protein-protein interaction) information from the primary literature. In other words, during the construction phase of the model, there was no attempt to determine the local interactions based on any other larger phenotypes or phenomena. However, after the model was completed, verification of the accuracy of the model involved testing it for the ability to reproduce complex input-output phenomena that have been observed in the laboratory. To do this, the T-cell model was simulated under a multitude of cellular conditions and analyzed in terms of input-output dose-response curves to determine whether the model behaves as expected [ Figure 2; Ref. (27)(28)(29)(30)(31)(32)(33)], including various downstream effects as a result of activation of the TCR, G-protein-coupled receptor, cytokine, and integrin pathways. A total of 20 phenomena were used for the validation phase (data not shown).

IN SILICO SIMULATIONS
The Cell Collective platform was utilized to perform all simulations for the CD4 + T-cell model. Virtual extracellular environments, composed of 20 CD4 + T-cell stimuli, were optimized for each in silico experiment based on immunocompetent versus immunocompromised (diseased) settings [ Table 1; Ref. (9, 13, 30,  Frontiers in Immunology | Tumor Immunity  described (36). The aforementioned simulations were run under six separate conditions including wild-type tissue, diseased tissue (diseases A and B), wild-type blood, and diseased blood (diseases A and B). Wild-type and diseased blood simulations are not included due to inconclusive data. Each experimental environment was simulated under (1) healthy cellular conditions, (2) CAV1 knocked out, (3) CAV1 activated 50% of the time (CAV1 +/− ) and (4) CAV1 activated at random levels between 0 and 100. (To be able to artificially control the activity levels of CAV1 under environments 3 and 4, an external species, "CAV1 Activator," was built into the model to activate CAV1 independently of the activity levels of its direct upstream regulators).

MOUSE MAINTENANCE
Animals were housed in pathogen-free animal facilities, and all experimental protocols were reviewed and approved per the Institutional Animal Care and Use Committee at the University of Nebraska Medical Center/University of Nebraska at Omaha (IACUC# 13-056-08-EP). C57Bl/6J and B6.Cg-Cav1 tm1Mls /J mice were purchased from the Jack-son Laboratory (Bar Harbor, ME, USA). Post-natal day 54 (±5 days) mice were used for all experiments.

HISTOLOGICAL STAINING
Spleen and lymph node tissues were sectioned and stained at the University of Nebraska Medical Center's Tissue Science Facility. Spleen and lymph node tissues were sectioned, preserved in 10% formalin, and embedded onto slides in paraffin. Specifically, all tissues were sectioned after at least 48 h in fixative and stained with hematoxylin and eosin using standard protocol. For immunohistochemical staining (IHC), tissues were deparrafinized in xylene for 3 min and rehydrated in decreasing concentrations of ethanol (100-50%). Antigen retrieval was performed by boiling sections in a solution of Sodium Citrate with 0.05% Tween-20. Blocking against non-specific binding and endogenous peroxidases was performed by incubation with 5% bovine serum albumin (BSA; Invitrogen) and 0.3% hydrogen peroxide, respectively. Primary antibody incubation was conducted for 90 min at room temperature in phosphate buffered saline (PBS; DIBCO). The following antibodies were used: Rac1 (Merck KGaA, Darmstadt, Germany); CD28 (BD Pharmingen); GATA3 (BD Pharmingen); CD26 (Abcam); BCL10 (Cell Applications, Inc.). Horse radish peroxidase-conjugated secondary antibodies (Cell Signaling Technology; BD Pharmingen; Abcam) were incubated for 1 h at room temperature. Slides were developed with a working solution 3,3-diaminobenzidine for 10 min at room temperature, followed by rinsing with distilled water and mounting the coverslip with Permount (Thermo Fisher Scientific).

GENE EXPRESSION PROFILING
Microarray data were downloaded from the Gene Expression Omnibus, accession number GSE55851 (37). Data contained whole genome expression profiling of CD4 + T-cells, sorted based on a CADM1/CD7 phenotype. Samples were collected from Frontiers in Immunology | Tumor Immunity patients diagnosed with Adult T-cell leukemia-lymphoma (ATL) subtypes: asymptomatic (n = 2), smoldering (n = 2), chronic (n = 1), acute (n = 2), and healthy controls (n = 3). A minimum of two samples were taken from each patient for microarray analyses. Molecules of interest, as established utilizing the Cell Collective, were selected within the microarray data and analyzed by fold-change from normal controls between ATL subtypes. Fold-change values were subjected to uncentered, average-linkage correlation using Cluster 3.0, and Java Tree-View as described previously (9). Further, Pearson regression analyses were conducted to discern correlation among molecules of interest in relation to CAV1 expression across ATL subtypes.

IN SILICO MODELING OF A CD4 + T-CELL
The completed CD4 + T-cell model consists of 188 nodes representing components of various signaling pathways and corresponding protein-to-protein, protein-phosphorylation, and kinase interactions (Figure 1A). These interactions correlate with preliminary data generated using Osprey software used to model CAV1 protein-to-protein interaction types ( Figure 1B). Each method of analysis (both the Cell Collective and Osprey) implicates a role for CAV1 in phosphorylation, signal transduction, transport, and cytoskeletal arrangement. The regulation of these events by CAV1 is highly complex and dynamic, as illustrated in Figure 1C.
To verify that the local interactions built into the model were able to accurately mimic complex phenomena that have been produced in the laboratory, 20 validations were conducted via simulations in Cell Collective (six representative validations are shown in Figures 2A-F). As an example, simulated TCR activation by an APC leads to Erk activation, and the subsequent downstream effect of cellular proliferation as represented by the literature (Figure 2A, Figure S1 in Supplemental Material).

IDENTIFICATION OF MOST AFFECTED PROTEINS IN SILICO
Experiments were simulated using the in silico model (as described in Table 1) to make rational predictions about how the system would function in the laboratory. Following 1,000 iterations, www.frontiersin.org we observed the protein products most affected by CAV1 +/+ , CAV1 +/− , and CAV1 −/− in immounocompetent versus immunocompromised conditions (Figures 3A-C). In order to test the validity of the model, we chose to investigate expression levels of the proteins most affected in the knock down genotype as compared to a wild-type system. Specifically proteins disregulated by CAV1 −/− across all three conditions (WT, Disease A, and Disease B) included CD26, CARMA1, FYN, SHC1, SOS, SHP2, NOS2A, BCL10, and GRB2 ( Figure 3C). Based on findings from the model as well as preliminary in vitro data, we hypothesized that CAV1 expression regulates Ras-related C3 botulinum toxin substrate 1 (RAC1), B-cell lymphoma/leukemia 10 (BCL10), GATA-binding protein 3 (GATA3), CD26, and CD28 (Figures 1C and 3C).
The abovementioned results are indicative of CAV1-mediate regulation of a variety of cellular functions, notably those that are downstream of TCR and integrin pathways of which CAV1 serves as the scaffold. Given these results, we were able to make predictions about protein expression in relation to CAV1 and test them in the laboratory using in vitro and in vivo experiments. For example, we observed that CAV1 is involved in the integrin signaling pathway that ultimately activates the mitogen-activated protein kinase (MAPK) cascade ( Figure 2); therefore, we can predict that cellular proliferation will be decreased if CAV1 is knocked down.

VERIFICATION OF IN SILICO PREDICTIONS IN VIVO
To determine differences in morphology between CAV1 −/− and wild-type mice, we examined tissues (including lymph nodes and spleen) stained with hematoxylin and eosin and observed no differences were observed in tissue architecture. Lymphoid organs (lymph nodes and spleen) were selected for their robustness of CD4 + T-cells, and liver was used as a control for tissue histology (i.e., to ensure mice were disease free). Furthermore, immunohistochemistry was utilized to biologically validate in silico predictions from our CD4 + T-cell model.
Top hit proteins: GATA3, RAC1, CD26, and BCL10 were selected for IHC to validate the model (Figure 4). RAC1 and GATA3 showed upregulation in the lymph node tissue of the CAV1 −/− mice. We observed CD26 and BCL10 to be upregulated in both the lymph node and spleen tissues of the CAV1 −/− mice. We also stained for CD28, as it is a well-characterized costimulatory protein involved in T-cell activation. CD28 showed no differential expression between the wild-type and CAV1 −/− mice.
Based on predictions from the model (Figure 3C), we investigated the differential expression, hierarchal clustering (Figure 5A), and regression analyses (Figure 5B) of top hit proteins using microarray data from ATL cases (GSE55851). Comparison of ATL subtypes for identification of potential molecular signatures in relation to CAV1 expression reveal seven molecules, including CAV1, clustered together based upon gene expression profiles following hierarchal clustering (Figure 5A). We observed a positive correlation (R = 0.78), with distinct signatures displayed between healthy, asymptomatic, smoldering, chronic, and acute patients (Figure 5B). Pearson regression analyses were conducted between each molecule of interest in relation to CAV1 expression across ATL subtypes ( Figure 5B). Specifically, we FIGURE 4 | Immunohistochemistry from murine model validation of in silico-predicted differentially expressed molecules with CAV1 knockout. Molecules downstream of CAV1 that were predicted to be affected with CAV1 knockout using the Cell Collective were validated using lymphoid tissue histology from wild-type (WT) C57Bl/6 mice and CAV1 −/− mice. The corresponding hematoxylin and eosin preparations are included in the top panel for morphological orientation; all tissues were sectioned subsequently.  (Figure 3); therefore, it is translated to in vivo, leukemic conditions.

DISCUSSION
Herein, we present a comprehensive, computational model of a CD4 + T-cell, including CAV1 regulatory pathways. This model incorporates experimentally validated interactions to posit the role of CAV1 in healthy CD4 + cells and CD4 + cells in the context of T-cell leukemia/lymphoma (i.e., when the immune response is skewed). CD4 + T-cells are a vital component of the immune system, as they protect against cancer, infection, and play a role in autoimmunity. CAV1 has been shown to be upregulated in numerous types of malignancies. Consequently, we built a CD4 + T-cell model to better understand basic T-cell biology and to address the role(s) of CAV1 within immunocompetent versus immunocompromised conditions. Inclusively, we investigated the role of CAV1 in the regulation of cellular processes, including cell cycle progression, cell proliferation, actin polymerization, and immune synapse formation. This model was successfully constructed using the Cell Collective platform that allows users to build cellular models capable of mimicking actual cellular systems in the laboratory (19)(20)(21)(22)38). The accuracy of the model was then successfully validated through the comparison of simulations with well-established, global input-output relationships as previously observed experimentally. Most importantly, new in silico predictions were validated in vitro/in vivo using both murine models and gene expression profiles from patients with a T-cell leukemia due to the previously observed role of Cav1 in lymphocytes (39)(40)(41)(42)(43)(44)(45)(46).
Using Cell Collective, we were able to perform virtual experiments in order to make predictions as to how a CD4 + T-cell would behave when the expression levels of CAV1 were altered and when CAV1 was knocked down. These experiments provided insight as to which proteins, and ultimately which cellular functions, might be regulated by CAV1. Based on in silico results, we observed that BCL10, CD26, FYN, CARMA1, SHC1, SOS, SHP2, NOS2A, GRB2, and GATA3 are strongly influenced by CAV1 expression (Figure 3C). Consequently, the expression of these molecules in vivo was investigated using cluster analyses of microarray data to measure gene expression ( Figure 5). Finally, four key proteins were selected for further verification of the predictions of the model using immunohistochemistry on mouse tissue with and without Cav1 (Figure 4).
In addition to the four key proteins, CD28 was chosen because it is known to regulate T-cell activation independent of CAV1 expression (15). RAC1 and GATA3 expression were upregulated in the lymph node tissue of the CAV1 −/− mice. These results were expected based on studies showing the role of CAV1 in lymphoid tissue (9). We observed both CD26 and BCL10 upregulation, especially in the germinal centers of the lymph node and spleen tissue of the CAV1 −/− mice. These validations of the in silico predictions show that the CD4 + T-cell model is biologically relevant. These validations of the in silico predictions show that the CD4 + T-cell model is biologically relevant. Therefore, we suggest that based on www.frontiersin.org the validations in vivo the T-cell in silico model is predictive of biological processes.
We then translated the in silico predictions to gene expression profiles of patients with a T-cell malignancy (i.e., ATL). Of the top 15 most differentially expressed molecules in CAV1-mediated pathways, the top 4 most highly correlated molecules to CAV1 expression were BCL10, DEC2, SHP2, and GATA3 (R value > 0.74; Figure 5B). Obstinately, SOS1, FYN, SOS2, and CD26 were negatively correlated with CAV1 (R < 0.74; Figure 5B). Additionally, we observed unique clustering of these 15 conserved molecules across subtypes of ATL patients. Interestingly, there was a distinguishable differential expression of MALT1 in asymptomatic ATL cases as compared with healthy individuals (Figure 5A). Therefore, further investigation into the plausibility of these correlated molecules being used for diagnostic purposes is warranted. We hope that our other ongoing studies will shed light as to their role in ATL. In short, our data regarding the role of CAV1 in cell signaling (as demonstrated using an in silico software and subsequently validated experimentally) corroborate that of the existing literature.
The usefulness of the in silico approach combined with that of in vivo/in vitro approaches provide rapid information as to the cell signaling networks in healthy and leukemic cells. There are currently many therapies being used to treat leukemia that target specific proteins in order to inhibit cellular pathways. These treatment modalities are advanced when comprehensive, molecular models allow the researcher to observe the direct mechanism of action of gene targeting as well as downstream consequences of gene/protein knockdown. The CD4 + T-cell model will hopefully be able to provide insight to for both T-cell biology (as demonstrated herein) as well as possible targets for lymphocytic leukemia treatments. The importance of in silico approaches combined with immunoinformatics as well as in vivo validation cannot be understated (18). With the often-prohibitive cost of drug design, it is imperative to use computational approaches to derive and test hypotheses. Current therapy regimens for T-cell malignancies can be modeled in silico initially in an effort to understand mechanisms and potential outcomes.
A comprehensive model of the CD4 + cells has the potential to provide substantial insight into cancer treatment, immunotherapy, and cellular biology. This twofold approach incorporating in silico and in vivo investigations has the potential to translate diagnostics and therapeutic targets from bench to bedside.

AUTHOR CONTRIBUTIONS
Brittany D. Conroy, Tyler A. Herek, and Timothy D. Shew are equally contributing first authors. Brittany D. Conroy wrote the paper, built the model, and did the simulations and verifications. Timothy D. Shew and Tyler A.
Herek did bioinformatics studies, immunohistochemistry, verifications, and wrote the paper. Joshua J. Larson assisted in bioinformatics studies and mouse work. Laura Allen and Matthew Latner built the model, ran validations, and performed data analyses. Paul H. Davis provided suggestions for experimental design and helped implement experiments. Christine E. Cutucache and Tomáš Helikar conceptualized and designed the experiment, assisted in implementation, oversaw evaluation, and wrote the paper.

ACKNOWLEDGMENTS
We thank Andrew Pulfer for his work in helping to construct the initial CD4 + T-cell model. Thanks to the Tissue Core Facility at the University of Nebraska Medical Center for assistance with histology. Additionally, we thank Nebraska NASA for Fellowships (Brittany D. Conroy and Christine E. Cutucache), the University of Nebraska at Omaha's Sponsored Programs Office for internal funding through an UCRCA grant (Tomáš Helikar and Christine E. Cutucache), and thanks to Dr. George Haddix and the Nebraska University Foundation.