Dataset Information

Formation of Multi-Component Extracellular Matrix Protein Fibers.

ABSTRACT: The extracellular matrix (ECM) consists of polymerized protein monomers that form a unique fibrous network providing stability and structural support to surrounding cells. We harnessed the fibrillogenesis mechanisms of naturally occurring ECM proteins to produce artificial fibers with a heterogeneous protein makeup. Using ECM proteins as fibril building blocks, we created uniquely structured multi-component ECM fibers. Sequential incubation of fibronectin (FN) and laminin (LAM) resulted in self-assembly into locally stacked fibers. In contrast, simultaneous incubation of FN with LAM or collagen (COL) produced molecularly stacked multi-component fibers because both proteins share a similar assembly mechanism or possess binding domains specific to each other. Sequential incubation of COL on FN fibers resulted in fibers with sandwiched layers because COL molecules bind to the external surface of FN fibers. By choosing proteins for incubation according to the interplay of their fibrillogenesis mechanisms and their binding domains (exposed when they unfold), we were able to create ECM protein fibers that have never before been observed.

SUBMITTER: Ahn S

PROVIDER: S-EPMC5790006 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Formation of Multi-Component Extracellular Matrix Protein Fibers.

Ahn Seungkuk S Lee Keel Yong KY Parker Kevin Kit KK Shin Kwanwoo K

Scientific reports 20180130 1

The extracellular matrix (ECM) consists of polymerized protein monomers that form a unique fibrous network providing stability and structural support to surrounding cells. We harnessed the fibrillogenesis mechanisms of naturally occurring ECM proteins to produce artificial fibers with a heterogeneous protein makeup. Using ECM proteins as fibril building blocks, we created uniquely structured multi-component ECM fibers. Sequential incubation of fibronectin (FN) and laminin (LAM) resulted in self- ...[more]

PMID: 29382927

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Project description:Background Rheumatoid arthritis (RA) is a chronic inflammatory disease, characterized by joint destruction and perpetuated by the synovial membrane (SM). In the inflamed SM, activated synovial fibroblasts (SFB) form the major cell type promoting development and progression of the disease by an abnormal expression/secretion of pro-inflammatory cytokines, tissue-degrading enzymes resulting in a predominant degradation of the extra-cellular matrix (ECM), and collagens causing joint fibrosis. We developed a new procedure, based on human knowledge and formal concept analysis (FCA), to simulate and analyze the temporal behaviour of regulatory and signaling networks. It was applied to a regulatory network (containing 18 genes from 5 functional groups) representing ECM formation and destruction in TGFβ - and TNFα -stimulated SFB. Results For the modelling of SFB-controlled ECM turnover in rheumatic diseases, Boolean network architecture was used as well as extensive literature information and revision by experimental gene expression data from stimulated SFB. In course of revision, the additional experimental information resulted in different biologically reasonable changes, yielding two Boolean networks that describe TGFβ and TNFα effects, respectively. The final simulations were further analyzed by the attribute exploration algorithm of FCA, integrating again the observed time series in a more fine-grained and automated manner. The generated temporal rules clearly reveal subtle regulatory relationships between different genes, co-expression patterns and converse gene expression regulation in rheumatic diseases. Conclusion The developed Boolean network based method for the dynamical analysis of regulatory and signaling networks represents a reliable systems biological solution for the improved understanding of complex regulatory pathways and the interactions among different genes in disease. The resulting knowledge base can be used for further analysis of the ECM system in human fibroblasts and may be queried to predict the functional consequences of observed (e.g. in diseases as RA) or hypothetical (e.g. for therapeutic purposes) gene expression disturbances.

Project description:BACKGROUND: Due to the rapid data accumulation on pathogenesis and progression of chronic inflammation, there is an increasing demand for approaches to analyse the underlying regulatory networks. For example, rheumatoid arthritis (RA) is a chronic inflammatory disease, characterised by joint destruction and perpetuated by activated synovial fibroblasts (SFB). These abnormally express and/or secrete pro-inflammatory cytokines, collagens causing joint fibrosis, or tissue-degrading enzymes resulting in destruction of the extra-cellular matrix (ECM).We applied three methods to analyse ECM regulation: data discretisation to filter out noise and to reduce complexity, Boolean network construction to implement logic relationships, and formal concept analysis (FCA) for the formation of minimal, but complete rule sets from the data. RESULTS: First, we extracted literature information to develop an interaction network containing 18 genes representing ECM formation and destruction. Subsequently, we constructed an asynchronous Boolean network with biologically plausible time intervals for mRNA and protein production, secretion, and inactivation. Experimental gene expression data was obtained from SFB stimulated by TGFbeta1 or by TNFalpha and discretised thereafter. The Boolean functions of the initial network were improved iteratively by the comparison of the simulation runs to the experimental data and by exploitation of expert knowledge. This resulted in adapted networks for both cytokine stimulation conditions. The simulations were further analysed by the attribute exploration algorithm of FCA, integrating the observed time series in a fine-tuned and automated manner. The resulting temporal rules yielded new contributions to controversially discussed aspects of fibroblast biology (e.g., considerable expression of TNF and MMP9 by fibroblasts stimulation) and corroborated previously known facts (e.g., co-expression of collagens and MMPs after TNFalpha stimulation), but also revealed some discrepancies to literature knowledge (e.g., MMP1 expression in the absence of FOS). CONCLUSION: The newly developed method successfully and iteratively integrated expert knowledge at different steps, resulting in a promising solution for the in-depth understanding of regulatory pathways in disease dynamics. The knowledge base containing all the temporal rules may be queried to predict the functional consequences of observed or hypothetical gene expression disturbances. Furthermore, new hypotheses about gene relations were derived which await further experimental validation.

Dataset Information

Formation of Multi-Component Extracellular Matrix Protein Fibers.

Publications

Formation of Multi-Component Extracellular Matrix Protein Fibers.

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