Das Suchergebnis hat sich seit Ihrer Suchanfrage verändert. Eventuell werden Dokumente in anderer Reihenfolge angezeigt.
  • Treffer 1 von 7
Zurück zur Trefferliste

Heparan sulfate induces necroptosis in murine cardiomyocytes: A medical-in silico approach combining in vitro experiments and machine learning

  • Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the underling causative factors are still debatable. Heparan sulfate (HS) fragments belong to the class of danger/damage-associated molecular patterns liberated from endothelial-bound proteoglycans by heparanase during tissue injury associated with trauma or sepsis. We hypothesized that HS induces apoptosis or necroptosis in murine cardiomyocytes. By using a novel Medical-In silico approach that 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 and replacement). Cardiomyocytes exposed to HS 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 HS resulted in the induction of necroptosis by tumor 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 suggest (i) that HS induces necroptosis in cardiomyocytes by phosphorylation (activation) of receptor-interacting protein 3, (ii) that HS is a therapeutic target in trauma- or sepsis-associated cardiomyopathy, and (iii) indicate that this proof-of-concept is a first step toward simulating the extent of activated components in the pro-apoptotic pathway induced by HS with only a small data set gained from the in vitro experiments by using machine learning algorithms.

Volltext Dateien herunterladen

Metadaten exportieren

Metadaten
Verfasserangaben:Elisabeth Zechendorf, Phillip Vaßen, Jieyi Zhang, Ahmed Hallawa, Antons Martincuks, Oliver Krenkel, Gerhard Müller-Newen, Tobias Schuerholz, Tim-Philipp Simon, Gernot Marx, Gerd Ascheid, Anke Schmeink, Guido Dartmann, Christoph Thiemermann, Lukas Martin
URN:urn:nbn:de:hbz:tr5-904
DOI:https://doi.org/10.3389/fimmu.2018.00393
Titel des übergeordneten Werkes (Deutsch):Frontiers in Immunology
Verlag:Frontiers Media
Dokumentart:Wissenschaftlicher Artikel (Fachzeitschriften)
Sprache:Englisch
Datum des OPUS-Uploads:31.08.2022
Datum der Erstveröffentlichung:20.03.2018
Veröffentlichende Hochschule:Hochschule Trier
Datum der Freischaltung:05.09.2022
Freies Schlagwort / Tag:Petri nets; apoptosis; modeling; necroptosis; optimization; septic cardiomyopathy; small data
GND-Schlagwort:Herzmuskelkrankheit; Apoptosis; Maschinelles Lernen
Jahrgang:9
Aufsatznummer:393
Seitenzahl:12
Erste Seite:1
Letzte Seite:12
Einrichtungen:FB Umweltplanung/-technik (UCB)
DDC-Klassifikation:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International