Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models

ABSTRACT: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. 84 BL2 cell-line samples were hybridized to HGU133+2 Affymetrix GeneChips.

ORGANISM(S): Homo sapiens

SUBMITTER: Christian Kohler

PROVIDER: E-GEOD-68761 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.

Pirkl Martin M Hand Elisabeth E Kube Dieter D Spang Rainer R

Bioinformatics (Oxford, England) 20151117 6

<h4>Motivation</h4>Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling ...[more]

PMID: 26581413

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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 TGFÎ² - and TNFÎ± -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 TGFÎ² and TNFÎ± 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. Experiment Overall Design: Patients and tissue samples: Experiment Overall Design: Synovial membrane samples were obtained within 10 min following tissue excision upon joint replacement/synovectomy from RA and OA patients (n = 3 each). After removal, tissue samples were frozen and stored at -70Â°C. Informed patient consent was acquired and the study was approved by the ethics committees of the respective universities. RA patients were classified according to the American College of Rheumatology (ACR) criteria, OA patients according to the respective criteria for osteoarthritis. The preparation of primary semi-transformed synovial fibroblasts from RA and OA patients was performed as previously described (Zimmermann et al., Arthritis Res. 2001;3(1):72-6). Briefly, the tissue samples were minced and digested with trypsin/collagenase P. The resulting single cell suspension was cultured for seven days. Non-adherent cells were removed by medium exchange. SFB were then negatively purified using DynabeadsÂ® M 450 CD14 and subsequently cultured over 4 passages in DMEM containing 100 Î¼g/ml gentamycin, 100 Î¼g/ml penicillin/streptomycin, 20 mM HEPES, and 10% FCS. Experiment Overall Design: Cell stimulation and isolation of total RNA : Experiment Overall Design: At the end of the fourth passage, the SFB were stimulated by 10 ng/ml TGFÎ² or TNFÎ± in serum-free DMEM for 0, 1, 2, 4, and 12 h. At the end of each time point, medium was removed and the cells were digested with trypsin/versene (0.25%). Following centrifugation and washing with PBS, the cells were lysed with RLT buffer (Qiagen) and frozen at 70Â°C. Total RNA was isolated using the RNeasy Kit (Qiagen) according to the supplier's recommendation. Experiment Overall Design: Microarray data analysis: Experiment Overall Design: RNA probes were labelled according to the supplier's instructions (AffymetrixÂ®, Santa Clara, CA, USA). Analysis of gene expression was performed using U133 Plus 2.0 RNA microarrays. Hybridization and washing was performed according to the supplier's instructions. Microarrays were analyzed by laser scanning (Hewlett-Packard Gene Scanner). Background-corrected signal intensities were determined using the MAS 5.0 software (AffymetrixÂ®) and normalized among arrays to facilitate comparisons between different patients. For this purpose, arrays were grouped according to patient class the respective stimulus (TGFÎ² and TNFÎ±, n=6 each). The arrays in each group were normalized using quantile normalization. Experiment Overall Design: See publication for further details.

Dataset Information

Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models

Publications

Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.

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