Data_Sheet_1_ACE2 Netlas: In silico Functional Characterization and Drug-Gene Interactions of ACE2 Gene Network to Understand Its Potential Involvement in COVID-19 Susceptibility.xlsx
Angiotensin-converting enzyme-2 (ACE2) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed an integrative multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their statistically enriched biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains (p < 4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon (p < 4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within ± 10 kb of ACE2-network genes we identified miRNAs whose binding sites may be altered as a consequence of genetic variation. The identified miRNAs revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. The genetic variant associations in RORA, SLC12A6, and SLC6A19 genes were observed in genome-wide association study (GWAS) of COVID-19 susceptibility. We also report the GWAS-identified variant in 3p21.31 locus, serves as trans-QTL for RORA and RORC genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/.
History
References
- https://doi.org//10.1101/787903
- https://doi.org//10.2215/CJN.09730620
- https://doi.org//10.1016/S2213-8587(20)30238-2
- https://doi.org//10.1101/2020.06.19.20135095
- https://doi.org//10.1016/j.mehy.2020.110112
- https://doi.org//10.1101/2020.07.03.20145581
- https://doi.org//10.11131/2015/101185
- https://doi.org//10.1038/s41431-020-0636-6
- https://doi.org//10.1093/nar/gky399
- https://doi.org//10.1038/s41467-019-10649-4
- https://doi.org//10.5152/TJAR.2020.569
- https://doi.org//10.1038/nprot.2009.97
- https://doi.org//10.1371/journal.pgen.1008175
- https://doi.org//10.1016/j.jiph.2020.07.014
- https://doi.org//10.1056/NEJMoa2020283
- https://doi.org//10.1093/nar/gky311
- https://doi.org//10.1002/jcla.23535
- https://doi.org//10.3389/fimmu.2020.01636
- https://doi.org//10.1038/nmeth.2689
- https://doi.org//10.3389/fcvm.2020.582399
- https://doi.org//10.1093/nar/gky1095
- https://doi.org//10.1101/709998
- https://doi.org//10.1002/path.2162
- https://doi.org//10.1016/S2666-5247(20)30115-4
- https://doi.org//10.1016/j.cell.2020.02.052
- https://doi.org//10.1016/j.cels.2018.03.001
- https://doi.org//10.1136/gutjnl-2020-320926
- https://doi.org//10.1093/nar/gkh103
- https://doi.org//10.3390/cells8101162
- https://doi.org//10.1101/2020.05.06.081026
- https://doi.org//10.1099/jmm.0.001231
- https://doi.org//10.1093/gerona/glaa131
- https://doi.org//10.1101/gr.118992.110
- https://doi.org//10.1093/nar/gky509
- https://doi.org//10.1111/dom.14057
- https://doi.org//10.1111/irv.12758
- https://doi.org//10.1038/nrd4626
- https://doi.org//10.1016/j.rmed.2020.105941
- https://doi.org//10.1371/journal.pone.0007837
- https://doi.org//10.1007/s00018-020-03580-1
- https://doi.org//10.1038/s41467-020-19587-y
- https://doi.org//10.17235/reed.2020.7137/2020
- https://doi.org//10.1093/bioinformatics/btv536
- https://doi.org//10.1371/journal.pone.0207374
- https://doi.org//10.1183/13993003.02139-2014
- https://doi.org//10.1093/nar/gky1079
- https://doi.org//10.1101/2020.09.24.20200048
- https://doi.org//10.1038/s41575-020-0328-2
- https://doi.org//10.1093/bioinformatics/btu655
- https://doi.org//10.1111/aas.13673
- https://doi.org//10.1093/nar/gkl128
- https://doi.org//10.1056/NEJMoa2021436
- https://doi.org//10.1186/s12933-020-01035-2
- https://doi.org//10.1093/nar/gky1016
- https://doi.org//10.1001/jama.2020.6775
- https://doi.org//10.1016/j.stem.2020.11.009
- https://doi.org//10.1038/nature12961
- https://doi.org//10.1101/2020.09.04.20188318
- https://doi.org//10.1080/08830185.2020.1756284
- https://doi.org//10.1093/nar/gkw937
- https://doi.org//10.1101/447367
- https://doi.org//10.1007/s00726-020-02870-z
- https://doi.org//10.1016/j.cell.2020.02.058
- https://doi.org//10.1001/jama.2020.1585
- https://doi.org//10.1038/s41588-019-0481-0
- https://doi.org//10.1038/s41467-017-01261-5
- https://doi.org//10.1038/s41586-020-2008-3
- https://doi.org//10.1001/jamacardio.2020.3575
- https://doi.org//10.1038/s41467-018-05552-3
- https://doi.org//10.1038/s41598-020-67338-2
- https://doi.org//10.1038/s41586-020-2818-3
- https://doi.org//10.1093/nar/gkz888
- https://doi.org//10.1038/nmeth.3547
- https://doi.org//10.1038/s41586-020-2012-7
- https://doi.org//10.1038/s41598-017-12492-3
Usage metrics
Read the peer-reviewed publication
Categories
- Gene and Molecular Therapy
- Gene Expression (incl. Microarray and other genome-wide approaches)
- Genetics
- Genetically Modified Animals
- Livestock Cloning
- Developmental Genetics (incl. Sex Determination)
- Epigenetics (incl. Genome Methylation and Epigenomics)
- Biomarkers
- Genomics
- Genome Structure and Regulation
- Genetic Engineering
CC BY 4.0