Notch signaling modulates the electrical behavior of cardiomyocytes.

Notch receptor signaling is active during cardiac development and silenced in myocytes after birth. Conversely, outward K+ Kv currents progressively appear in postnatal myocytes leading to shortening of the action potential (AP) and acquisition of the mature electrical phenotype. In the present study, we tested the possibility that Notch signaling modulates the electrical behavior of cardiomyocytes by interfering with Kv currents. For this purpose, the effects of Notch receptor activity on electrophysiological properties of myocytes were evaluated using transgenic mice with inducible expression of the Notch1 intracellular domain (NICD), the functional fragment of the activated Notch receptor, and in neonatal myocytes after inhibition of the Notch transduction pathway. By patch clamp, NICD-overexpressing cells presented prolonged AP duration and reduced upstroke amplitude, properties that were coupled with reduced rapidly activating Kv and fast Na+ currents, compared with cells obtained from wild-type mice. In cultured neonatal myocytes, inhibition of the proteolitic release of NICD with a γ-secretase antagonist increased transcript levels of the Kv channel-interacting proteins 2 (KChIP2) and enhanced the density of Kv currents. Collectively, these results indicate that Notch signaling represents an important regulator of the electrophysiological behavior of developing and adult myocytes by repressing, at least in part, repolarizing Kv currents. NEW & NOTEWORTHY We investigated the effects of Notch receptor signaling on the electrical properties of cardiomyocytes. Our results indicate that the Notch transduction pathway interferes with outward K+ Kv currents, critical determinants of the electrical repolarization of myocytes.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19.

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC 50 ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.