Camptothecin for COVID-19

Nuclear bodies are structures in eukaryotic cells that lack a plasma membrane and are considered protein condensates, DNA, or RNA molecules. Known nuclear bodies include the nucleolus, Cajal bodies, and promyelocytic leukemia nuclear bodies. These bodies are involved in the concentration, exclusion, sequestration, assembly, modification, and recycling of specific components involved in the regulation of ribosome biogenesis, RNA transcription, and RNA processing. Additionally, nuclear bodies have been shown to participate in cellular processes such as the regulation of transcription of the cell cycle, mitosis, apoptosis, and the cellular stress response. The dynamics and functions of these bodies depend on the state of the cell. It is now known that both DNA and RNA viruses can direct their proteins to nuclear bodies, causing alterations in their composition, dynamics, and functions. Although many of these mechanisms are still under investigation, it is well known that the interaction between viral and nuclear body proteins is necessary for the success of the viral infection cycle. In this review, we concisely describe the interaction between viral and nuclear body proteins. Furthermore, we focus on the role of the nucleolus in RNA virus infections. Finally, we discuss the possible implications of the interaction of viral proteins on cellular transcription and the formation/degradation of non-coding RNAs.

As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS).

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.