Search bioRxiv⌕ Search

Biology subjects

Rosa, N.

Publications and source records attributed to Rosa, N..

3 recordsLinked to original sources

Otenabant is a Selective Antagonist of Human PIEZO1

Background and purposePIEZO1 mechanosensitive cation channels translate mechanical cues into intracellular Ca2+ and Na+ elevations, enabling cells to respond to physical alterations in their environment. PIEZO1 contributes to red blood cells (RBC) volume homeostasis and gain-of-function PIEZO1 mutations cause hereditary xerocytosis (HX), a rare mostly compensated hemolytic anemia, and aberrant channel activation exacerbates sickling and vascular dysfunction in sickle cell disease. Despite strong genetic and physiological evidence supporting PIEZO1 as a therapeutic target, potent and selective inhibitors are limited, and existing compounds show modest specificity or poorly explored mechanisms. Improved pharmacological tools are needed. Experimental approachWe conducted a high-throughput screen of FDA-approved drugs to identify PIEZO1 inhibitors. Compounds were tested at concentrations of 10 {micro}M in a monocytic cell line, using intracellular Ca2+ elevations evoked by the PIEZO1 agonist Yoda1 as read-out. The inhibitory activity of the best hit was validated and compared to existing PIEZO1 inhibitors using electrophysiological analysis, orthogonal PIEZO1-dependent assays across cell lines and human RBCs. As functional proof, we investigated the impact of three PIEZO1 inhibitors on RBC deformability by ektacytometry, after Yoda1 pre-stimulation. Key resultsThis screen identified Otenabant, a selective Cannabinoid Receptor Type 1 (CB1) antagonist, as a potent PIEZO1 inhibitor. Otenabant dose-dependently inhibited Ca2+ elevations mediated by endogenous or exogenously expressed human PIEZO1, but was ineffective against mouse Piezo1, revealing species-specific channel differences. Otenabant inhibited mechanosensitive currents elicited by shear stress in fibroblasts and by repeated poking in PIEZO1-expressing HEK-293 cells, altering the currents activation and inactivation kinetics, and prevented Yoda1-induced hyperpolarization in RBCs. Otenabant was able to reverse the negative impact of Yoda1 on RBC deformability. Conclusions and implicationsThese findings demonstrate the utility of Yoda-based screening for discovering PIEZO1 antagonists and identify Otenabant as a promising chemical scaffold for developing selective PIEZO1 inhibitors with therapeutic potential.

pharmacology and toxicology↗

Knowledge Innovation Ecosystem for the Promotion of User-Centre Health Innovations: Living Lab Methodology and Lessons Learned Through the Proposal of Standard Good Practices

Living Labs, experiencing a global surge in popularity over the past years, demands standardized guidance through the development of widely accepted good practices. While challenging due to the complex and evolving nature of Living Labs, this task remains essential. These knowledge innovation ecosystems facilitate a diverse array of interconnected and interacting end-users and stakeholder partners who engage collaboratively to co-create, embed, and/or leverage end-user-centric breakthroughs at one or more innovation phases within a real-world context. Based on the development of six Living Labs in the health domain, this study proposes a more general yet critical set of Living Labs good practices, emphasizing the importance of strong initial marketing and promotion strategies for Living Labs open calls, enforcing gender equality, carefully selecting stakeholders, devising and implementing effective framework strategies for end-user engagement and value creation, ensuring value creation for all Living Labs partners, prolonging the long-term viability of the Living Lab project, promoting and disseminating impactful actions and results, fostering environmental sustainability, and processing results data for Living Lab performance evaluation.

scientific communication and education↗

Moving beyond MARCO

The use of imaging systems in protein crystallisation means that the experimental setups no longer require manual inspection to determine the outcome of the trials. However, it leads to the problem of how best to find images which contain useful information about the crystallisation experiments. The adoption of a deeplearning approach in 2018 enabled a four-class machine classification system of the images to exceed human accuracy for the first time. Underpinning this was the creation of a labelled training set which came from a consortium of several different laboratories. The MARCO classification model does not have the same accuracy on local data as it does on images from the original test set; this can be somewhat mitigated by retraining the ML model and including local images. We have characterized the image data used in the original MARCO model, and performed extensive experiments to identify training settings most likely to enhance the local performance of a MARCO-dataset based ML classification model.

biophysics↗