Testosterone for COVID-19

Background During COVID-19 pandemic, men had reduced serum testosterone and higher mortality rate than women. Variations in high density lipoprotein (HDL) levels were detected in severe COVID-19 individuals. We evaluated the response of testicular macrophages, steroidogenic activity and lipid metabolism of Leydig cells in SARS-CoV-2-infected K18-hACE2 mice. Methods Testes were analyzed under light and electron microscope. Immunolocalization of human angiotensin converting enzyme (hACE2) and viral proteins (spike and nucleocapsid) were evaluated in association with the expression of viral recognition receptor , Rigi . Steroidogenesis was evaluated by the expression of steroidogenic factor-1 ( Sf1 ), and the immunolocalization of steroidogenic proteins and testosterone. Pro-inflammatory (TNF-α, IL-1β, IL-6), anti-inflammatory (IL-10) cytokines, macrophages (CD68 and CD163) and macrophage inhibitory factor (MIF) were detected by immunolocalization and Western blot. The expression of lipid metabolism genes ( Srebp, Dgat1 and Scarb1 ) were investigated by RT-qPCR. Results In the infected animals, the Leydig cells showed enhanced immunolocalization of hACE2, spike and nucleocapsid. The expression of Rigi , pro-inflammatory cytokines and number of macrophages increased, confirming viral infection. Sf1 expression, steroidogenic proteins and testosterone were reduced whereas the expression of Dgat1, Srebp and Scarb1 increased. Lipid droplets-enriched Leydig cells and viral particles in lipids were observed. The infected Leydig cells also showed enhanced pro-inflammatory cytokines immunolabeling. Conclusion SARS-CoV-2 infects Leydig cells, activates its immune response and impairs steroidogenesis. The virus uses the steroidogenic machinery and induces lipid metabolism pathways for its survival and replication in these cells. These findings support the low testosterone and HDL levels in men with severe COVID-19.

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts’ curation and drug–target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

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.