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A synthetic modular approach for modeling the role of the 3D microenvironment in tumor progression.

ABSTRACT: Here, we demonstrate the flexibility of peptide-functionalized poly(ethylene glycol) (PEG) hydrogels for modeling tumor progression. The PEG hydrogels were formed using thiol-ene chemistry to incorporate a matrix metalloproteinase-degradable peptide crosslinker (KKCGGPQG?IWGQGCKK) permissive to proteolytic remodeling and the adhesive CRGDS peptide ligand. Tumor cell function was investigated by culturing WM239A melanoma cells on PEG hydrogel surfaces or encapsulating cells within the hydrogels, and either as monocultures or indirect (non-contact) cocultures with primary human dermal fibroblasts (hDFs). WM239A cluster size and proliferation rate depended on the shear elastic modulus for cells cultured on PEG hydrogels, while growth was inhibited by coculture with hDFs regardless of hydrogel stiffness. Cluster size was also suppressed by hDFs for WM239A cells encapsulated in PEG hydrogels, which is consistent with cells seeded on top of hydrogels. Notably, encapsulated WM239A clusters and single cells adopted invasive phenotypes in the hDF coculture model, which included single cell and collective migration modes that resembled invasion from human melanoma patient-derived xenograft tumors encapsulated in equivalent PEG hydrogels. Our combined results demonstrate that peptide-functionalized PEG hydrogels provide a useful platform for investigating aspects of tumor progression in 2D and 3D microenvironments, including single cell migration, cluster growth and invasion.

SUBMITTER: Singh SP

PROVIDER: S-EPMC4671067 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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A synthetic modular approach for modeling the role of the 3D microenvironment in tumor progression.

Singh S P SP Schwartz M P MP Tokuda E Y EY Luo Y Y Rogers R E RE Fujita M M Ahn N G NG Anseth K S KS

Scientific reports 20151207

Here, we demonstrate the flexibility of peptide-functionalized poly(ethylene glycol) (PEG) hydrogels for modeling tumor progression. The PEG hydrogels were formed using thiol-ene chemistry to incorporate a matrix metalloproteinase-degradable peptide crosslinker (KKCGGPQG↓IWGQGCKK) permissive to proteolytic remodeling and the adhesive CRGDS peptide ligand. Tumor cell function was investigated by culturing WM239A melanoma cells on PEG hydrogel surfaces or encapsulating cells within the hydrogels, ...[more]

PMID: 26638791

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Project description:The functional interplay between tumor cells and their adjacent stroma has been suggested to play crucial roles in the initiation and progression of tumors and the effectiveness of chemotherapy. The extracellular matrix (ECM), a complex network of extracellular proteins, provides both physical and chemicals cues necessary for cell proliferation, survival, and migration. Understanding how ECM composition and biomechanical properties affect cancer progression and response to chemotherapeutic drugs is vital to the development of targeted treatments. 3D cell-derived-ECMs and esophageal cancer cell lines were used as a model to investigate the effect of ECM proteins on esophageal cancer cell lines response to chemotherapeutics. Immunohistochemical and qRT-PCR evaluation of ECM proteins and integrin gene expression was done on clinical esophageal squamous cell carcinoma biopsies. Esophageal cancer cell lines (WHCO1, WHCO5, WHCO6, KYSE180, KYSE 450 and KYSE 520) were cultured on decellularised ECMs (fibroblasts-derived ECM; cancer cell-derived ECM; combinatorial-ECM) and treated with 0.1% Dimethyl sulfoxide (DMSO), 4.2 µM cisplatin, 3.5 µM 5-fluorouracil and 2.5 µM epirubicin for 24 h. Cell proliferation, cell cycle progression, colony formation, apoptosis, migration and activation of signaling pathways were used as our study endpoints. The expression of collagens, fibronectin and laminins was significantly increased in esophageal squamous cell carcinomas (ESCC) tumor samples compared to the corresponding normal tissue. Decellularised ECMs abrogated the effect of drugs on cancer cell cycling, proliferation and reduced drug induced apoptosis by 20⁻60% that of those plated on plastic. The mitogen-activated protein kinase-extracellular signal-regulated kinase (MEK-ERK) and phosphoinositide 3-kinase-protein kinase B (PI3K/Akt) signaling pathways were upregulated in the presence of the ECMs. Furthermore, our data show that concomitant addition of chemotherapeutic drugs and the use of collagen- and fibronectin-deficient ECMs through siRNA inhibition synergistically increased cancer cell sensitivity to drugs by 30⁻50%, and reduced colony formation and cancer cell migration. Our study shows that ECM proteins play a key role in the response of cancer cells to chemotherapy and suggest that targeting ECM proteins can be an effective therapeutic strategy against chemoresistant tumors.

Project description:BACKGROUND: Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. RESULTS: We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. CONCLUSION: The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties.

Project description:BackgroundThe modular design of synthetic gene circuits via composable parts (DNA segments) and pools of signal carriers (molecules such as RNA polymerases and ribosomes) has been successfully applied to bacterial systems. However, eukaryotic cells are becoming a preferential host for new synthetic biology applications. Therefore, an accurate description of the intricate network of reactions that take place inside eukaryotic parts and pools is necessary. Rule-based modeling approaches are increasingly used to obtain compact representations of reaction networks in biological systems. However, this approach is intrinsically non-modular and not suitable per se for the description of composable genetic modules. In contrast, the Model Description Language (MDL) adopted by the modeling tool ProMoT is highly modular and it enables a faithful representation of biological parts and pools.ResultsWe developed a computational framework for the design of complex (eukaryotic) gene circuits by generating dynamic models of parts and pools via the joint usage of the BioNetGen rule-based modeling approach and MDL. The framework converts the specification of a part (or pool) structure into rules that serve as inputs for BioNetGen to calculate the part's species and reactions. The BioNetGen output is translated into an MDL file that gives a complete description of all the reactions that take place inside the part (or pool) together with a proper interface to connect it to other modules in the circuit. In proof-of-principle applications to eukaryotic Boolean circuits with more than ten genes and more than one thousand reactions, our framework yielded proper representations of the circuits' truth tables.ConclusionsFor the model-based design of increasingly complex gene circuits, it is critical to achieve exact and systematic representations of the biological processes with minimal effort. Our computational framework provides such a detailed and intuitive way to design new and complex synthetic gene circuits.

Dataset Information

A synthetic modular approach for modeling the role of the 3D microenvironment in tumor progression.

Publications

A synthetic modular approach for modeling the role of the 3D microenvironment in tumor progression.

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