multi omics study revealing tissue dependent putative mechanisms of sars cov 2 drug CORD-Papers-2021-10-25 (Version 1)

Title: Multi-omics study revealing tissue-dependent putative mechanisms of SARS-CoV-2 drug targets on viral infections and complex diseases
Abstract: Drug target prioritisation for new targets and drug repurposing of existing drugs for COVID-19 treatment are urgently needed for the current pandemic. Here we pooled 353 candidate drug targets of COVID-19 from clinical trial registries and the literature and estimated their putative causal effects in 11 SARS-CoV-2 related tissues on 622 complex human diseases. By constructing a disease atlas of drug targets for COVID-19, we prioritise 726 target-disease associations as evidence of causality using robust Mendelian randomization (MR) and colocalization evidence (http://epigraphdb.org/covid-19/ctda/). Triangulating these MR findings with historic drug trial information and the druggable genome, we ranked and prioritised three genes DHODH, ITGB5 and JAK2 targeted by three marketed drugs (Leflunomide, Cilengitide and Baricitinib) which may have repurposing potential with careful risk assessment. This study evidences the value of our integrative approach in prioritising and repurposing drug targets, which will be particularly applicable when genetic association studies of COVID-19 are available in the near future.
Published: 5/11/2020
DOI: 10.1101/2020.05.07.20093286
DOI_URL: http://doi.org/10.1101/2020.05.07.20093286
Author Name: Zheng, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zheng_j
Author Name: Zhang, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_y
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Baird, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/baird_d
Author Name: Liu, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_x
Author Name: Wang, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_l
Author Name: Zhang, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_h
Author Name: Davey Smith, G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/davey_smith_g
Author Name: Gaunt, T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gaunt_t
sha: c4a113578de7f9b2c083980eda1b5580465ce2cf
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.05.07.20093286 http://medrxiv.org/cgi/content/short/2020.05.07.20093286v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: JAK2 human ITGB5 SARS-CoV-2 Cilengitide DHODH tissues Baricitinib COVID-19 target-disease http://epigraphdb.org/covid-19/ctda/ Leflunomide genome-side EpiGraphDB mouse clinical-phase Supplementary Table 7 TableS1 https://www.finngen.fi/fi B JAK2 LD r 2 SARS-CoV-2 stomach SARS-CoV organs https://www.decode.com/ hepatitis B diseases/phenotypes lung COVID-19; 3 COVID-19interacting proteins intestine gallbladder IVW SRP19 http://epigraphdb.org/covid-19/ctda/ COVID- 19 COVID- COVID-19 herpes simplex virus beta blood GRCh37.p13 POR github.com/MRCIEU/TwoSampleMR 372,482 target-disease Cilengitide proteome/transcriptome-wide DHODH Druggable hay target-disease plasma protein-phenotype pQTL kidney tissues TableS13 P<1x10 colon GTEx V8 heel bone mineral CTDA colon transverse COVID-19 (18) lipid LD Azithromycin kidney Supplementary Table 3 testis E2.3 TLR9 UK Biobank low-density lipoprotein cholesterol FinnGen cardiac tests= 45,590 kidney tubules kidney tubulointerstitial Supplementary Table 9 gastrointestinal tract alimentary tract extracellular proteins TableS11 OpenTargets NCT04320277 Baricitinib LDL-C Tissue p2=1×10 -4 Chloroquine ACE2 https://www.whocc.no/atc_ddd_index/ DGI phenome-wide cyclin G-associated kinase human cell lines Hydroxychloroquine SARS-CoV-2 receptor angiotensin converting enzyme II kidney glomerular ChEMBL Leflunomide medRxiv DNA NEU1 DeCODE plasma SAIGE UK KPNA1 glioblastoma COVID-19 patients TableS14 cis kidney cortex N=3,301 http://api.epigraphdb.org/. tissue COVID-19 Target-Disease Atlas TableS6 cancers eQTLGen Type 1 tissues 7 tissues colon sigmoid hay fever/allergic rhinitis https://www.leelabsg.org/resources TFRC p1=1× intestine terminal ileum human TableS2 SARS-CoV-2 proteins hepatitis LDL-C. ruxolitinib omics/tissues https://covid-19genehostinitiative.net/ TwoSampleMR R score=0.75 ITGB5 CTID UK Biobank (42 N=31,684 P<1.1x10 UK Biobank, China Kadoorie Biobank (43 bowel Crohn's
Extracted Text Content in Record: First 5000 Characters:Drug target prioritization for new targets and drug repurposing of existing drugs for COVID-19 treatment are urgently needed for the current pandemic. COVID-19 drugs targeting human proteins will potentially result in less drug resistance but could also exhibit unintended effects on other complex diseases. Here we pooled 353 candidate drug targets of COVID-19 from clinical trial registries and the literature and estimated their putative causal effects in 11 SARS-CoV-2 related tissues on 622 complex human diseases. By constructing a disease atlas of drug targets for COVID-19, we prioritise 726 target-disease associations with evidence of causality using robust Mendelian randomization (MR) and colocalization evidence (http://epigraphdb.org/covid-19/ctda/). Triangulating these MR findings with historic drug trial information and the druggable genome, we ranked and prioritised three genes DHODH, ITGB5 and JAK2 targeted by three marketed drugs (Leflunomide, Cilengitide and Baricitinib) which may have repurposing potential with careful risk assessment. We retrieved drug targets potentially relevant to COVID-19 from three resources (Figure 1 ): 1) target genes of 11 drugs under trials for COVID-19 treatment from ClinicalTrials.gov (Supplementary Table 1) ; 2) 332 human proteins interacting with SARS-CoV-2 proteins in human cell lines (8) ; 3) 44 genes associated with SARS-CoV in a mouse model (9) . After removing duplicates, 380 unique targets were selected (Supplementary Table 2 ). The genetic predictors (instruments) for the 380 target genes in 11 SARS-CoV-2 related tissues were extracted from 4 recent expression and protein quantitative trait locus (eQTL and pQTL respectively) studies (3)(10)(11) (12) . From these studies 1493 instruments for 353 drug targets were available for our proteome/transcriptome-wide association analysis (Supplementary Table 3 ), while the remaining 27 targets have no robustly associated genetic variants. The target-disease atlas of the 353 drug targets was built using two-sample MR (13)(14) and colocalization analysis (15) , evaluating evidence for their causal effects on 49 viral infection phenotypes, 501 complex diseases and 72 disease related phenotypes (Supplementary Table 4) . 45 ,590 target-disease associations were tested in plasma proteome and transcriptome in whole blood (P< 1.1x10 -6 at a Bonferroni-corrected threshold). Where data was available, we also tested the tissue-specific effects of gene expression of the same targets on the outcome phenotypes. Overall, 372,482 target-disease associations were estimated in the 11 COVID-19 relevant tissues (see the list of tissues in Methods). Overall, we observed 833 target-disease associations with robust MR evidence in the 11 tested tissues. 726 of the 833 (87.2%) associations also showed strong colocalization evidence (colocalization probability > 70%) (Supplementary Table 5 , 6, 7 and 8), making these the most reliable findings of this study. Of these, 366 associations were obtained using a single cis instrument in a Wald ratio model (16) , 327 were obtained using a single trans instrument and 33 were estimated using multiple instruments in an inverse variance weighted (IVW) model (17) . The remaining 107 (12.8%) associations had evidence from MR but did not have strong evidence of colocalization (probability<70%; Supplementary Table 9 ), emphasizing the importance of this approach to address confounding by linkage disequilibrium (LD) in phenome-wide association studies. Findings from our target-disease atlas can be used to conduct hypothesis-driven investigations of tissue-dependent effects of target expression on certain diseases. Figure 2 illustrates the effects of target expression in different tissues on four diseases from our atlas: Crohn's disease (A); hypertension (B); hay fever, allergic rhinitis or eczema (C); and diabetes (D). Tissue-specific associations were observed between 11 to 17 of the target genes and these four diseases (Supplementary Table 10 ). Since these target genes that encode COVID-19interacting proteins also appear to have causal effects on these diseases, it would be important to carefully assess their potential beneficial and/or adverse effects on these complex diseases in any future drug target prioritization for COVID-19. In the analysis of the association between the 353 drug targets and 49 viral infection phenotypes, we identified 2 associations with robust MR (P<1.1x10 -6 ) and colocalization evidence (probability >70%) using gene expression data in whole blood, including NEU1 associated with . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 11, 2020. . chronic hepatitis and DPY19L1 associated with viral enteritis. Three additional drug targets were suggestively associated with 3 viral
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