Modeling the Kinetics of Integrin Receptor Binding to Hepatic Extracellular Matrix Proteins

Hudson, Shanice V.; Dolin, Christine E.; Poole, Lauren G.; Massey, Veronica; Wilkey, Daniel W.; Beier, Juliane I.; Merchant, Michael L.; Frieboes, Hermann B.; Arteel, Gavin E.

doi:10.1038/s41598-017-12691-y

Cited by 22 publications

(23 citation statements)

References 48 publications

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“…While integrin αv is expressed by parenchymal cells and HSCs, most integrin β3 was expressed on HSCs [ 29 ]. Furthermore, while integrin αv forms complexes with several β subunits (β1, β3, β5, β6, β8), integrin β3 forms complexes with only two type α subunits, αv and αIIb [ 30 ]. It might be thought that amount of integrin αvβ3 is similar to integrin β3 rather than integrin αv.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of hepatic integrin αvβ3 expression in non-alcoholic steatohepatitis (NASH) model mouse by 18F-FPP-RGD2 PET

et al. 2018

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BackgroundActivated hepatic stellate cells (HSCs), which express integrin αvβ3, are a major fibrogenic factor in NASH pathophysiology. 18F-labeled cyclic arginine-glycine-aspartic acid penta-peptide (18F-FPP-RGD2) has been used as a PET probe for tumors expressing integrin αvβ3. The aim of this study was to assess the potential of PET with 18F-FPP-RGD2 to detect hepatic integrin αvβ3 expression in non-alcoholic steatohepatitis (NASH) model mice.ResultsThirty-two male C57BL/6 mice aged 6 weeks were fed a choline-deficient, l-amino acid-defined, high-fat diet (CDAHFD) for 3 and 8 weeks. 18F-FPP-RGD2 PET imaging of the liver was performed at 3 and 8 weeks after CDAHFD feeding. After PET scanning, levels of hepatic integrin αvβ, 3α-smooth muscle actin (α-SMA), and collagen type 1 alpha 1(col1a1) were measured. Histopathological analysis of hepatic steatosis, inflammation, and fibrosis, as well as blood biochemistry analysis, was also performed. CDAHFD for 3 and 8 weeks produced a moderate-to-severe steatosis and inflammation of the liver in mice. NAFLD activity score (NAS) in mice fed the CDAHFD for 3 and 8 weeks were more than 4 indicating NASH or borderline NASH pathology. Fibrosis was observed only in mice fed the CDAHFD for 8 weeks. PET imaging showed that the hepatic standardized uptake value, SUV80–90 min, was increased with prolonged CDAHFD feeding compared with the respective controls (CDAHFD 3 weeks 0.32 ± 0.06 vs 0.48 ± 0.05, p < 0.01; CDAHFD 8 weeks 0.35 ± 0.04 vs 0.75 ± 0.07, p < 0.01, respectively). Prolonged CDAHFD feeding increased hepatic mRNA and protein levels of integrin αv and β3 at 3 and 8 weeks. Hepatic 18F-FPP-RGD2 uptake and amount of integrin αv and β3 protein were well correlated (r = 0.593, p < 0.05 and r = 0.835, p < 0.001, respectively). Hepatic 18F-FPP-RGD2 uptake also showed a positive correlation with Sirius red-positive area.ConclusionsThe hepatic uptake of 18F-FPP-RGD2 correlated well with integrin αv and β3 expression and histological fibrosis in a mouse model of NASH, suggesting the predictability of fibrosis in NASH pathology.

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Section: Discussionmentioning

confidence: 99%

Evaluation of hepatic integrin αvβ3 expression in non-alcoholic steatohepatitis (NASH) model mouse by 18F-FPP-RGD2 PET

et al. 2018

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“…A recent study demonstrated a similar concept by modelling the effect of ECM concentration and composition upon the kinetics of integrin binding and clustering (Hudson et al, ). In the same way, the concentration‐dependent dynamics and substrate specificity of RTK signalling in response to changing ECM conditions are key computational parameters required for future model development.…”

Section: Potential Applications For Modelling Of Matrix Signallingmentioning

confidence: 99%

The extracellular matrix as a key regulator of intracellular signalling networks

Hastings

Skhinas

Fey

et al. 2018

British J Pharmacology

161

120

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The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub-network in computational models of cellular decision making, with a focus on its role in diseases such as cancer. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.

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“…Previous modeling has evaluated growth of liver metastases 10–12 , development of liver fibrosis 13,14 , and treatment of liver metastases via nanotherapy 15,16 . Recently, the kinetics of integrin receptor binding to hepatic ECM proteins were modeled 17 . However, the interaction of liver metastases, ECM and tumor-associated macrophages has not been extensively evaluated in a unified modeling framework.…”

Section: Introductionmentioning

confidence: 99%

Computational/experimental evaluation of liver metastasis post hepatic injury: interactions with macrophages and transitional ECM

Hudson

Miller

Mahlbacher

et al. 2019

Sci Rep

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The complex interactions between subclinical changes to hepatic extracellular matrix (ECM) in response to injury and tumor-associated macrophage microenvironmental cues facilitating metastatic cell seeding remain poorly understood. This study implements a combined computational modeling and experimental approach to evaluate tumor growth following hepatic injury, focusing on ECM remodeling and interactions with local macrophages. Experiments were performed to determine ECM density and macrophage-associated cytokine levels. Effects of ECM remodeling along with macrophage polarization on tumor growth were evaluated via computational modeling. For primary or metastatic cells in co-culture with macrophages, TNF-α levels were 5× higher with M1 vs. M2 macrophages. Metastatic cell co-culture exhibited 10× higher TNF-α induction than with primary tumor cells. Although TGFβ1 induction was similar between both co-cultures, levels were slightly higher with primary cells in the presence of M1. Simulated metastatic tumors exhibited decreased growth compared to primary tumors, due to high local M1-induced cytotoxicity, even in a highly vascularized microenvironment. Experimental analysis combined with computational modeling may provide insight into interactions between ECM remodeling, macrophage polarization, and liver tumor growth.

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Modeling the Kinetics of Integrin Receptor Binding to Hepatic Extracellular Matrix Proteins

Cited by 22 publications

References 48 publications

Evaluation of hepatic integrin αvβ3 expression in non-alcoholic steatohepatitis (NASH) model mouse by 18F-FPP-RGD2 PET

Evaluation of hepatic integrin αvβ3 expression in non-alcoholic steatohepatitis (NASH) model mouse by 18F-FPP-RGD2 PET

The extracellular matrix as a key regulator of intracellular signalling networks

Computational/experimental evaluation of liver metastasis post hepatic injury: interactions with macrophages and transitional ECM

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