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Adapted Boolean network models for extracellular matrix formation.

Wollbold J, Huber R, Pohlers D, Koczan D, Guthke R, Kinne RW, Gausmann U - BMC Syst Biol (2009)

Bottom Line: 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.This resulted in adapted networks for both cytokine stimulation conditions.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstr. 11a, 07745 Jena, Germany. johannes.wollbold@tu-dresden.de

ABSTRACT

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.

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Gene expression time courses following TGFβ1 or TNFα treatment. COL1A1 (A), JUNB (B), and SMAD7 (C) gene expression in response to TGFβ1 treatment (upper row); TNFα response (lower row) of NFKB1 (D), MMP1 (E), and SMAD7 (F). The average time course is shown as light red curve without symbols, the data for individual samples are depicted in other colours (OA1: blue, filled symbol; OA2: red, filled symbol; OA3: green, filled symbol; RA1: purple, filled symbol; RA2: blue, open symbol; RA3: yellow, open symbol). The time courses and the values calculated from the microarray experiments for all analysed genes are included in additional file 4.
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Figure 2: Gene expression time courses following TGFβ1 or TNFα treatment. COL1A1 (A), JUNB (B), and SMAD7 (C) gene expression in response to TGFβ1 treatment (upper row); TNFα response (lower row) of NFKB1 (D), MMP1 (E), and SMAD7 (F). The average time course is shown as light red curve without symbols, the data for individual samples are depicted in other colours (OA1: blue, filled symbol; OA2: red, filled symbol; OA3: green, filled symbol; RA1: purple, filled symbol; RA2: blue, open symbol; RA3: yellow, open symbol). The time courses and the values calculated from the microarray experiments for all analysed genes are included in additional file 4.

Mentions: Following pre-processing of the microarray data gained from U133 Plus 2.0 arrays, we extracted the data for probe sets related to our genes of interest (see Methods). The data are available in the GEO database (GSE13837 at [42]). For the following analyses we excluded values which exceeded the reliability threshold of p ≤ 0.05 for any patient at any time point (0, 1, 2, 4, 6, and 12 hours). In Figure 2, some selected examples for the influence of TGFβ1 and on gene expression are presented. The time courses of COL1A1 and JUNB expression are shown to illustrate the TGFβ1 response in SFB, and the TNFα response is illustrated by NFKB1 and MMP1 expression. SMAD7 expression data are also included for both treatments. The data and the respective diagrams for all genes and both treatments can be found in additional file 4.


Adapted Boolean network models for extracellular matrix formation.

Wollbold J, Huber R, Pohlers D, Koczan D, Guthke R, Kinne RW, Gausmann U - BMC Syst Biol (2009)

Gene expression time courses following TGFβ1 or TNFα treatment. COL1A1 (A), JUNB (B), and SMAD7 (C) gene expression in response to TGFβ1 treatment (upper row); TNFα response (lower row) of NFKB1 (D), MMP1 (E), and SMAD7 (F). The average time course is shown as light red curve without symbols, the data for individual samples are depicted in other colours (OA1: blue, filled symbol; OA2: red, filled symbol; OA3: green, filled symbol; RA1: purple, filled symbol; RA2: blue, open symbol; RA3: yellow, open symbol). The time courses and the values calculated from the microarray experiments for all analysed genes are included in additional file 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2734845&req=5

Figure 2: Gene expression time courses following TGFβ1 or TNFα treatment. COL1A1 (A), JUNB (B), and SMAD7 (C) gene expression in response to TGFβ1 treatment (upper row); TNFα response (lower row) of NFKB1 (D), MMP1 (E), and SMAD7 (F). The average time course is shown as light red curve without symbols, the data for individual samples are depicted in other colours (OA1: blue, filled symbol; OA2: red, filled symbol; OA3: green, filled symbol; RA1: purple, filled symbol; RA2: blue, open symbol; RA3: yellow, open symbol). The time courses and the values calculated from the microarray experiments for all analysed genes are included in additional file 4.
Mentions: Following pre-processing of the microarray data gained from U133 Plus 2.0 arrays, we extracted the data for probe sets related to our genes of interest (see Methods). The data are available in the GEO database (GSE13837 at [42]). For the following analyses we excluded values which exceeded the reliability threshold of p ≤ 0.05 for any patient at any time point (0, 1, 2, 4, 6, and 12 hours). In Figure 2, some selected examples for the influence of TGFβ1 and on gene expression are presented. The time courses of COL1A1 and JUNB expression are shown to illustrate the TGFβ1 response in SFB, and the TNFα response is illustrated by NFKB1 and MMP1 expression. SMAD7 expression data are also included for both treatments. The data and the respective diagrams for all genes and both treatments can be found in additional file 4.

Bottom Line: 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.This resulted in adapted networks for both cytokine stimulation conditions.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstr. 11a, 07745 Jena, Germany. johannes.wollbold@tu-dresden.de

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus