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Front. Immunol. | doi: 10.3389/fimmu.2018.00393

Heparan sulfate induces necroptosis in murine cardiomyocytes – a Medical-In-Silico approach using machine learning

 Elisabeth Zechendorf1,  Phillip Vaßen2, Jieyi Zhang2,  Ahmed Hallawa3, Antons Martincuks4,  Oliver Krenkel1, 5, Gerhard Müller-Newen4,  Tobias Schuerholz6, Tim-Philipp Simon1, Gernot Marx1, Gerd Ascheid3, Anke Schmeink2*,  Guido Dartmann7*,  Chris Thiemermann8 and  Lukas Martin1, 8*
  • 1Department of Intensive Care and Intermediate Care, Uniklinik RWTH Aachen, Germany
  • 2Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Germany
  • 3Chair for Integrated Signal Processing Systems, RWTH Aachen University, Germany
  • 4Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Germany
  • 5Department of Medicine III, Uniklinik RWTH Aachen, Germany
  • 6Department of Anesthesia and Intensive Care, Universitätsmedizin Rostock, Germany
  • 7Research Area Distributed Systems, Trier University of Applied Sciences, Germany
  • 8William Harvey Research Institute, Queen Mary University of London, United Kingdom

Abstract
Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signalling pathways involved in apoptosis and necroptosis are linked to trauma or sepsis associated cardiomyopathy. The underling causative factors, however, are still debatable. Heparan sulfate fragments belong to the class of danger/damage-associated molecular patterns (DAMP) liberated from endothelial-bound proteoglycans by heparanase during tissue injury associated with trauma or sepsis. We hypothesized that heparan sulfate induces apoptosis or necroptosis in murine cardiomyocytes. By using a novel Medical-In-Silico approach which combines conventional cell culture experiments with machine learning algorithms, we aimed to reduce a significant part of the expensive and time-consuming cell culture experiments and data generation by using computational intelligence (refinement & replacement). Cardiomyocytes exposed to heparan sulfate showed an activation of the intrinsic apoptosis signal pathway via cytochrome C and the activation of caspase 3 (both p<0.001). Notably, the exposure of heparan sulfate resulted in the induction of necroptosis by tumour necrosis factor α and receptor interaction protein 3 (p<0.05; p<0.01) and, hence, an increased level of necrotic cardiomyocytes. In conclusion, using this novel Medical-In-Silico approach, our data suggests i) that heparan sulfate induces necroptosis in cardiomyocytes by phosphorylation (activation) of RIP3, ii) that heparan sulfate is a therapeutic target in trauma or sepsis associated cardiomyopathy and iii) indicate that this proof-of-concept is a first step towards simulating the extent of activated components in the pro-apoptotic pathway induced by heparan sulfate with only a small data-set gained from the in vitro experiments by using machine learning algorithms.

Keywords: Septic cardiomyopathy, necroptosis, Apoptosis, Petri nets, modeling, optimization, Small data

Received: 15 Nov 2017; Accepted: 12 Feb 2018.

Edited by:

Deirdre R. Coombe, Curtin University, Australia

Reviewed by:

Toshiyuki Murai, Osaka University, Japan
Michael J. Wise, University of Western Australia, Australia  

Copyright: © 2018 Zechendorf, Vaßen, Zhang, Hallawa, Martincuks, Krenkel, Müller-Newen, Schuerholz, Simon, Marx, Ascheid, Schmeink, Dartmann, Thiemermann and Martin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Anke Schmeink, RWTH Aachen University, Research Area Information Theory and Systematic Design of Communication Systems, Aachen, Germany, Anke.Schmeink@rwth-aachen.de
Prof. Guido Dartmann, Trier University of Applied Sciences, Research Area Distributed Systems, Trier, Germany, g.dartmann@umwelt-campus.de
Dr. Lukas Martin, Uniklinik RWTH Aachen, Department of Intensive Care and Intermediate Care, Aachen, Germany, lmartin@ukaachen.de