Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

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Fibrosis protein-protein interactions from Google matrix analysis of MetaCore network

10.1101/2021.10.20.465138 ◽

2021 ◽

Author(s):

Ekaterina Kotelnikova ◽

Klaus M. Frahm ◽

Dima L. Shepelyansky ◽

Oksana Kunduzova

Keyword(s):

Heart Failure ◽

Protein Interactions ◽

Cardiac Fibrosis ◽

Matrix Analysis ◽

Great Promise ◽

Protein Protein Interactions ◽

Matrix Algorithms ◽

Google Matrix ◽

End Stage Heart Failure ◽

End Stage

Protein-protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here we use the MetaCore network and the Google matrix algorithms for prediction of protein-protein interactions dictating cardiac fibrosis, a primary causes of end-stage heart failure. The developed algorithms allow to identify interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

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Google matrix analysis of bi-functional SIGNOR network of protein–protein interactions

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2020.125019 ◽

2020 ◽

Vol 559 ◽

pp. 125019

Author(s):

Klaus M. Frahm ◽

Dima L. Shepelyansky

Keyword(s):

Protein Interactions ◽

Matrix Analysis ◽

Protein Protein Interactions ◽

Google Matrix

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Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions

10.1101/750695 ◽

2019 ◽

Author(s):

Klaus M. Frahm ◽

Dima L. Shepelyansky

Keyword(s):

Protein Interactions ◽

Biological Networks ◽

Linear Response Theory ◽

Matrix Analysis ◽

Global Network ◽

Protein Protein Interactions ◽

Effective Interactions ◽

Ising Spin ◽

Google Matrix ◽

Frame Work

AbstractMotivationDirected protein networks with only a few thousand of nodes are rather complex and do not allow to extract easily the effective influence of one protein to another taking into account all indirect pathways via the global network. Furthermore, the different types of activation and inhibition actions between proteins provide a considerable challenge in the frame work of network analysis. At the same time these protein interactions are of crucial importance and at the heart of cellular functioning.ResultsWe develop the Google matrix analysis of the protein-protein network from the open public database SIGNOR. The developed approach takes into account the bi-functional activation or inhibition nature of interactions between each pair of proteins describing it in the frame work of Ising-spin matrix transitions. We also apply a recently developed linear response theory for the Google matrix which highlights a pathway of proteins whose PageRank probabilities are most sensitive with respect to two proteins selected for the analysis. This group of proteins is analyzed by the reduced Google matrix algorithm which allows to determine the effective interactions between them due to direct and indirect pathways in the global network. We show that the dominating activation or inhibition function of each protein can be characterized by its magnetization. The results of this Google matrix analysis are presented for three examples of selected pairs of proteins. The developed methods work rapidly and efficiently even for networks with several million of nodes and can be applied to various biological networks.AvailabilityThe Google matrix data and executive code of described algorithms are available at http://www.quantware.ups-tlse.fr/QWLIB/google4signornet/

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Statistical properties of the MetaCore network of protein-protein interactions

10.1101/2021.04.02.438245 ◽

2021 ◽

Author(s):

Ekaterina Kotelnikova ◽

Klaus Michael Frahm ◽

José Lages ◽

Dima L Shepelyansky

Keyword(s):

Protein Interactions ◽

Statistical Properties ◽

Matrix Analysis ◽

Directed Network ◽

Protein Protein Interactions ◽

Google Matrix ◽

The World ◽

Functional Nature ◽

Average Action ◽

Effective Network

The MetaCore commercial database describes interactions of proteins and other chemical molecules and clusters in the form of directed network between these elements, viewed as nodes. The number of nodes goes beyond 40 thousands with almost 300 thousands links between them. The links have essentially bi-functional nature describing either activation or inhibition actions between proteins. We present here the analysis of statistical properties of this complex network applying the methods of the Google matrix, PageRank and CheiRank algorithms broadly used in the frame of the World Wide Web, Wikipedia, the world trade and other directed networks. We specifically describe the Ising PageRank approach which allows to treat the bi-functional type of protein-protein interactions. We also show that the developed reduced Google matrix algorithm allows to obtain an effective network of interactions inside a specific group of selected proteins. This method takes into account not only direct protein-protein interactions but also recover their indirect nontrivial couplings appearing due to summation over all the pathways passing via the global bi-functional network. The developed analysis allows to espablish an average action of each protein being more oriented to activation or inhibition. We argue that the described Google matrix analysis represents an efficient tool for investigation of influence of specific groups of proteins related to specific diseases.

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Localization of endothelial nitric oxide synthase in the normal and failing human atrial myocardium

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100166397 ◽

1996 ◽

Vol 54 ◽

pp. 786-787

Author(s):

Chi-Ming Wei ◽

Margarita Bracamonte ◽

Shi-Wen Jiang ◽

Richard C. Daly ◽

Christopher G.A. McGregor ◽

...

Keyword(s):

Nitric Oxide ◽

Heart Failure ◽

Nitric Oxide Synthase ◽

Endothelial Nitric Oxide Synthase ◽

Cardiac Tissues ◽

Mammalian Tissues ◽

End Stage Heart Failure ◽

End Stage ◽

Oxide Synthase ◽

Endothelial Nitric Oxide

Nitric oxide (NO) is a potent endothelium-derived relaxing factor which also may modulate cardiomyocyte inotropism and growth via increasing cGMP. While endothelial nitric oxide synthase (eNOS) isoforms have been detected in non-human mammalian tissues, expression and localization of eNOS in the normal and failing human myocardium are poorly defined. Therefore, the present study was designed to investigate eNOS in human cardiac tissues in the presence and absence of congestive heart failure (CHF).Normal and failing atrial tissue were obtained from six cardiac donors and six end-stage heart failure patients undergoing primary cardiac transplantation. ENOS protein expression and localization was investigated utilizing Western blot analysis and immunohistochemical staining with the polyclonal rabbit antibody to eNOS (Transduction Laboratories, Lexington, Kentucky).

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