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3 Publications visible to you, out of a total of 3

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BACKGROUND: The EF-hand Ca 2+ sensor protein S100A1 has been identified as a molecular regulator and enhancer of cardiac performance. The ability of S100A1 to recognize and modulate the activity ofancer of cardiac performance. The ability of S100A1 to recognize and modulate the activity of targets such as SERCA2a (sarcoplasmic reticulum Ca 2+ ATPase) and RyR2 (ryanodine receptor 2) in cardiomyocytes has mostly been ascribed to its hydrophobic C-terminal α-helix (residues 75–94). We hypothesized that a synthetic peptide consisting of residues 75 through 94 of S100A1 and an N-terminal solubilization tag (S100A1ct) could mimic the performance-enhancing effects of S100A1 and may be suitable as a peptide therapeutic to improve the function of diseased hearts. METHODS: We applied an integrative translational research pipeline ranging from in silico computational molecular modeling and in vitro biochemical molecular assays as well as isolated rodent and human cardiomyocyte performance assessments to in vivo safety and efficacy studies in small and large animal cardiac disease models. RESULTS: We characterize S100A1ct as a cell-penetrating peptide with positive inotropic and antiarrhythmic properties in normal and failing myocardium in vitro and in vivo. This activity translates into improved contractile performance and survival in preclinical heart failure models with reduced ejection fraction after S100A1ct systemic administration. S100A1ct exerts a fast and sustained dose-dependent enhancement of cardiomyocyte Ca 2+ cycling and prevents β-adrenergic receptor–triggered Ca 2+ imbalances by targeting SERCA2a and RyR2 activity. In line with the S100A1ct-mediated enhancement of SERCA2a activity, modeling suggests an interaction of the peptide with the transmembrane segments of the sarcoplasmic Ca 2+ pump. Incorporation of a cardiomyocyte-targeting peptide tag into S100A1ct (cor-S100A1ct) further enhanced its biological and therapeutic potency in vitro and in vivo. CONCLUSIONS: S100A1ct is a promising lead for the development of novel peptide-based therapeutics against heart failure with reduced ejection fraction.

Authors: Dorothea Kehr, Julia Ritterhoff, Manuel Glaser, Lukas Jarosch, Rafael E. Salazar, Kristin Spaich, Karl Varadi, Jennifer Birkenstock, Michael Egger, Erhe Gao, Walter J. Koch, Max Sauter, Marc Freichel, Hugo A. Katus, Norbert Frey, Andreas Jungmann, Cornelius Busch, Paul J. Mather, Arjang Ruhparwar, Martin Busch, Mirko Völkers, Rebecca C. Wade, Patrick Most

Date Published: 21st Nov 2024

Publication Type: Journal

Abstract (Expand)

Zusammenfassung Angesichts der umwälzenden Auswirkungen, die künstliche Intelligenz (KI) auf Wissenschaft, Medizin und darüber hinaus hat, betrachten wir hier das Potenzial von KI für die Entdeckungenzial von KI für die Entdeckung neuer Medikamente gegen Herzkrankheiten. Wir definieren KI im weitesten Sinne als den Einsatz von maschinellem Lernen, einschließlich Statistik und Deep Learning, um Muster in Datensätzen zu erkennen, die für Vorhersagen genutzt werden können. Jüngste Durchbrüche in der Fähigkeit, sehr große Datenmengen zu berücksichtigen, haben einen Boom in der KI-gestützten Arzneimittelentdeckung sowohl in der Wissenschaft als auch in der Industrie ausgelöst. Viele neue Unternehmen verfügen bereits über Arzneimittel-Pipelines, die bis in die klinische Erprobung reichen, aber noch keine Medikamente gegen Herzkrankheiten enthalten. Wir beschreiben hier den Einsatz von KI für die Entdeckung von niedermolekularen Medikamenten und Biologika, einschließlich therapeutischer Peptide, sowie für die Vorhersage von Wirkungen wie Kardiotoxizität. Der konzertierte Einsatz von KI zusammen mit physikbasierten Simulationen und experimentellen Rückkopplungsschleifen wird notwendig sein, um das Potenzial der KI für die Arzneimittelentdeckung und die Entwicklung von Präzisionsarzneimitteln für Herzkrankheiten voll auszuschöpfen.

Authors: Manuel Glaser, Julia Ritterhof, Patrick Most, Rebecca C. Wade

Date Published: 20th Nov 2023

Publication Type: Journal

Abstract (Expand)

Abstract Background With the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treateder, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans ( Pomphorhynchus laevis, Neoechinorhynchus agilis , Neoechinorhynchus buttnerae ) from four fish species (common barbel, European eel, thinlip mullet, tambaqui). Results The workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel. Conclusions The combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7 .

Authors: Hanno Schmidt, Katharina Mauer, Manuel Glaser, Bahram Sayyaf Dezfuli, Sören Lukas Hellmann, Ana Lúcia Silva Gomes, Falk Butter, Rebecca C. Wade, Thomas Hankeln, Holger Herlyn

Date Published: 1st Dec 2022

Publication Type: Journal

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