Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

AJ Venkatakrishnan; Arjun Puranik; Akash Anand; David Zemmour; Xiang Yao; Xiaoying Wu; Ramakrishna Chilaka; Dariusz K Murakowski; Kristopher Standish; Bharathwaj Raghunathan; Tyler Wagner; Enrique Garcia-Rivera; Hugo Solomon; Abhinav Garg; Rakesh Barve; Anuli Anyanwu-Ofili; Najat Khan; Venky Soundararajan

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.24.005702 doi: bioRxiv preprint Surprisingly, cells from respiratory tissues were notably absent among the populations with highest ACE2 expression by scRNA-seq ( Figure 2B ). This observation was corroborated by complementary gene expression analysis of over 250,000 bulk RNA-seq samples from GTEx 23, 24 and the Gene Expression Omnibus (GEO) along with protein expression analysis from healthy tissue proteomics and immunohistochemistry (IHC) datasets [25] [26] [27] , where lung and other respiratory tissues consistently show lower ACE2 expression compared to the digestive tract and kidney (Figure S3A-D) . However, the respiratory transmission of COVID-19 along with the disease symptomatology and well-documented viral shedding in respiratory secretions 28 strongly indicates that SARS-CoV-2 indeed infects and replicates within these tissues. This would suggest that ACE2 is likely expressed in the respiratory epithelium, and so we prioritized the respiratory and digestive tracts for further knowledge synthesis-augmented scRNA-seq analysis.