Host-pathogen systems biology: logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction.
BACKGROUND
The hepatocyte growth factor (HGF) stimulates mitogenesis, motogenesis, and morphogenesis in a wide range of tissues, including epithelial cells, on binding to the receptor tyrosine kinase c-Met. Abnormal c-Met signalling contributes to tumour genesis, in particular to the development of invasive and metastatic phenotypes. The human microbial pathogen Helicobacter pylori can induce chronic gastritis, peptic ulceration and more rarely, gastric adenocarcinoma. The H. pylori effector protein cytotoxin associated gene A (CagA), which is translocated via a type IV secretion system (T4SS) into epithelial cells, intracellularly modulates the c-Met receptor and promotes cellular processes leading to cell scattering, which could contribute to the invasiveness of tumour cells. Using a logical modelling framework, the presented work aims at analysing the c-Met signal transduction network and how it is interfered by H. pylori infection, which might be of importance for tumour development.


RESULTS
A logical model of HGF and H. pylori induced c-Met signal transduction is presented in this work. The formalism of logical interaction hypergraphs (LIH) was used to construct the network model. The molecular interactions included in the model were all assembled manually based on a careful meta-analysis of published experimental results. Our model reveals the differences and commonalities of the response of the network upon HGF and H. pylori induced c-Met signalling. As another important result, using the formalism of minimal intervention sets, phospholipase Cgamma1 (PLCgamma1) was identified as knockout target for repressing the activation of the extracellular signal regulated kinase 1/2 (ERK1/2), a signalling molecule directly linked to cell scattering in H. pylori infected cells. The model predicted only an effect on ERK1/2 for the H. pylori stimulus, but not for HGF treatment. This result could be confirmed experimentally in MDCK cells using a specific pharmacological inhibitor against PLCgamma1. The in silico predictions for the knockout of two other network components were also verified experimentally.


CONCLUSIONS
This work represents one of the first approaches in the direction of host-pathogen systems biology aiming at deciphering signalling changes brought about by pathogenic bacteria. The suitability of our network model is demonstrated by an in silico prediction of a relevant target against pathogen infection.
Franke
Raimo
R
Institute of Experimental Internal Medicine, Otto von Guericke University, Leipziger Str, 44, 39120 Magdeburg, Germany. Raimo.Franke@med.ovgu.de
Müller
Melanie
M
Wundrack
Nicole
N
Gilles
Ernst-Dieter
ED
Klamt
Steffen
S
Kähne
Thilo
T
Naumann
Michael
M
D000942:Antigens, Bacterial / Q000378:metabolism*; D001426:Bacterial Proteins / Q000378:metabolism*; D015153:Blotting, Western; D003198:Computer Simulation; D016481:Helicobacter Infections / Q000378:metabolism*; D016480:Helicobacter pylori; D017228:Hepatocyte Growth Factor / Q000378:metabolism*; D006801:Humans; D048052:Mitogen-Activated Protein Kinase 3 / Q000378:metabolism; D008954:Models, Biological; D019859:Proto-Oncogene Proteins c-met / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D000942:Antigens, Bacterial; D001426:Bacterial Proteins; C080866:cagA protein, Helicobacter pylori; D017228:Hepatocyte Growth Factor; D019859:Proto-Oncogene Proteins c-met; D048052:Mitogen-Activated Protein Kinase 3