Smoking increases expression of the SARS-CoV-2 spike protein-binding long ACE2 isoform in bronchial epithelium.

After more than two years the COVID-19 pandemic, that is caused by infection with the respiratory SARS-CoV-2 virus, is still ongoing. The risk to develop severe COVID-19 upon SARS-CoV-2 infection is increased in individuals with a high age, high body mass index, and who are smoking. The SARS-CoV-2 virus infects cells of the upper respiratory tract by entering these cells upon binding to the Angiotensin-converting enzyme 2 (ACE2) receptor. ACE2 is expressed in various cell types in the lung but the expression is especially high in goblet and ciliated cells. Recently, it was shown that next to its full-length isoform, ACE2 also has a short isoform. The short isoform is unable to bind SARS-CoV-2 and does not facilitate viral entry. In the current study we investigated whether active cigarette smoking increases the expression of the long or the short ACE2 isoform. We showed that in active smokers the expression of the long, active isoform, but not the short isoform of ACE2 is higher compared to never smokers. Additionally, it was shown that the expression of especially the long, active isoform of ACE2 was associated with secretory, club and goblet epithelial cells. This study increases our understanding of why current smokers are more susceptible to SARS-CoV-2 infection, in addition to the already established increased risk to develop severe COVID-19.

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients.

Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host-pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.