Drug Repurposing for Various Courses of COVID-19 Based on Single-cell RNA Sequencing Data - PowerPoint Presentation

1. Drug Repurposing for Various Courses of COVID-19 Based on Single-cell RNA Sequencing DataKai Guo1*, Zhihan Wang1,2*, Pan Gao1*, Qinqin Pu1, Min Wu1$, Changlong Li2$, and Junguk Hur1$1 Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND 58202, USA2 West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan 610041, China

2. BackgroundCOVID-19-27 million infections-891 thousand deaths(WHO: 8 September 2020)Drug repurposing (Pushpakom S. Nat Rev Drug Discov, 2019) -less time-consuming -cost-effective -let alone the existing pharmaceutical supply chainsWHO

3. Materials & MethodsScRNA-seq data analysis 3 mild & 3 severe cases (lung BALF) and 3 healthy control (lung tissues) were downloaded from the NCBI Gene Expression Omnibus database (GSE145926) (Liao M. Nature medicine. 2020).A total of 43,914 cells collected for the analyses. MAST in Seurat v3 was used to perform differential analysis (DEGs: log2FC > 0.25 and adjusted p-value < 0.05). Drug repurposing using the LINCS drug-perturbation dataUpregulated and downregulated DEGs were against the LINCS database using the Connectivity Map Linked User Environment (CLUE) platform (Subramanian A. Cell. 2017).Drug connectivity score (CS) ≤ -90.Adverse drug reactions analysisFrom the SEP-L1000 database (https://maayanlab.net/SEP-L1000/).

4. WorkflowWorkflow of drug repurposing for treating different durations of COVID-19. Input publicly available scRNA-seq data and transcriptomic data of BALF in COVID-19 patients against the LINCS database by using the CLUE platform. Candidates are selected which can reverse expression of upregulated DEGs upon drug treatment and are compared by connectivity score and the number in major cell subtypes cross healthy, mild, and severe groups.

5. Results

6. The UMAP presentation of single-cell atlas of BALFs showing 6 major cell types.

7. GroupDrugDescriptionB cellsCD4+ T cellsCD8+ T cellsEpithelial cellsMacrophagesNK cellsPhase*M vs HflubendazoleTubulin inhibitor++++-+M vs HazacitidineDNA methyltransferase inhibitor-++++-S vs HABT-737BCL inhibitor+++-++S vs Hbrefeldin-aProtein synthesis inhibitor+++-++S vs HindirubinCDK inhibitor+++-++S vs HTPCA-1IKK inhibitor+++--+S vs HlopinavirHIV protease inhibitor+---++Phase 4S vs MfostamatinibSYK inhibitor++++++Phase 2S vs MVER-155008HSP inhibitor++++++Table 1. A list of potential drugs for treating COVID-19 based on LINCS database and DEGs Asterisk (*) represents the clinical trial for its efficacy in COVID-19 disease. M vs H, between mild and healthy samples; S vs H, between severe and healthy samples; S vs M, between severe and mild samples.

8. Common drug candidates. Venn Diagram showing the overlap among the drug candidates for treating COVID-19 between three sets across control, mild, and severe COVID-19 groups (A) and heatmap showing the 25 drugs shared by at least two sets (B).

9. Heatmap of drug-ADR association. On-label (A) and off-label (B) ADRs are illustrated in heatmaps. White color means no association between drug and ADRs.

10. Conclusion Based on the study, we thoroughly investigated potential candidates for the treatment in COVID-19 progression and predicted some possible adverse effects. The findings can guide additional repurposing studies, tailored for different stages of disease progression. AcknowledgementsThe work was partially supported by the National Institutes of Health grants P20GM113123 to JH, and R01AI138203 and AI109317 to MW. Figures were created by modifying illustrations provided by Sevier Medical Art (SMART) and Vecteezy.com.