ABSTRACT
Severe cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with elevated blood glucose levels and metabolic complications. However, the molecular mechanisms for how SARS-CoV-2 infection alters glycometabolic control are incompletely understood. Here, we connect the circulating protein GP73 with enhanced hepatic gluconeogenesis during SARS-CoV-2 infection. We first demonstrate that GP73 secretion is induced in multiple tissues upon fasting and that GP73 stimulates hepatic gluconeogenesis through the cAMP/PKA signaling pathway. We further show that GP73 secretion is increased in cultured cells infected with SARS-CoV-2, after overexpression of SARS-CoV-2 nucleocapsid and spike proteins and in lungs and livers of mice infected with a mouse-adapted SARS-CoV-2 strain. GP73 blockade with an antibody inhibits excessive glucogenesis stimulated by SARS-CoV-2 in vitro and lowers elevated fasting blood glucose levels in infected mice. In patients with COVID-19, plasma GP73 levels are elevated and positively correlate with blood glucose levels. Our data suggest that GP73 is a glucogenic hormone that likely contributes to SARS-CoV-2-induced abnormalities in systemic glucose metabolism.
Subject(s)
COVID-19/complications , COVID-19/virology , Glucose/metabolism , Hyperglycemia/etiology , Hyperglycemia/metabolism , Membrane Proteins/metabolism , SARS-CoV-2 , Animals , Biomarkers , Cyclic AMP-Dependent Protein Kinases/metabolism , Diet, High-Fat , Disease Models, Animal , Fasting , Gene Expression , Gluconeogenesis/drug effects , Gluconeogenesis/genetics , Host-Pathogen Interactions , Humans , Hyperglycemia/blood , Liver/metabolism , Liver/pathology , Membrane Proteins/antagonists & inhibitors , Membrane Proteins/blood , Membrane Proteins/genetics , Mice , Mice, Knockout , Organ Specificity/geneticsABSTRACT
The ongoing COVID-19 epidemic has spread worldwide since December 2019. Effective use of engineering controls can prevent its spread and thereby reduce its impact. As airborne transmission is an important mode of infectious respiratory disease transmission, mathematical models of airborne infection are needed to develop effective engineering control. We developed a new approach to obtain the spatial distribution for the probability of infection (PI) by combining the spatial flow impact factor (SFIF) method with the Wells-Riley model. Our method can be combined with the anti-problem approach, in order to determine the optimized arrangement of people and/or air purifiers in a confined space beyond the ability of previous methods. This method was validated by a CFD-integrated method, and an illustrative example is presented. We think our method can be helpful in controlling infection risk and making the best use of the space and equipment in built environments, which is important for preventing the spread of COVID-19 and other infectious respiratory diseases, and promoting the development of sustainable cities and society.