Article Figures & Data
Figures
- FIG 1
(A) ACE2 and 6 related putative SARS-CoV-2 receptors on epithelial cells. ACE2, DPP4, and ANPEP are peptidases. (B) The 7 putative SARS-CoV-2 receptors were inflated in StringDB by adding up to 100 directly interacting proteins with direct association to the 7 input proteins using 1st shell interactions and StringDB default settings. (C and D) The main gene ontology of this inflated network is “Neutrophil Degranulation” (C) (http://amigo.geneontology.org/amigo/term/GO:0043312) and includes 8 neutrophil-enriched genes isolated in panel D. The gene ontology of these proteins was examined in the Amigo database. (E) One hundred thirteen genes were differentially regulated in RNA-seq data sets of human alveolar adenocarcinoma (A549) and human bronchial epithelial cells infected with SARS-CoV-2 in vitro (study details, data sets, and statistical analysis can be found in reference 14). (F) Following virus infection, most significantly upregulated genes were related to inflammatory and interferon responses. (G and H) Six classic neutrophil chemokines were upregulated in these cells following infection with SARS-CoV-2, depicted in panel G, as well as C3 and related complement pathway genes depicted in panel H. (I) Eighteen neutrophil-enriched and neutrophil chemotaxis genes were upregulated in an RNA-seq data set of BALF cells collected from 2 COVID-19 patients versus 3 healthy BALF donors (study details, data sets, and statistical analysis can be found in reference 16). (J and K) Putative druggable targets with likely neutrophil proinflammatory function derived from the analysis. Approved and experimental drugs with validated pharmacological evidence are presented as interaction networks. Protein-drug interactions were retrieved from DGIdb (v3.02; http://www.dgidb.org/search_interactions) and were curated (DrugBank [https://www.drugbank.ca/]) to exclude nonvalidated and false-positive interactions. All protein-protein interaction networks were developed in Cytoscape with cumulative protein-protein interaction scores computed in StringDB (v11) (https://string-db.org/) using default interaction sources (experimental evidence, coexpression, gene fusion, cooccurrence, curated databases, and references in scientific literature text-mining).
