Additional file 1 of Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis
Creators: name: Xiang, Bingyu
name: Deng, Chunyu
name: Qiu, Fei
name: Li, Jingjing
name: Li, Shanshan
name: Zhang, Huifang
name: Lin, Xiuli
name: Huang, Yukuan
name: Zhou, Yijun
name: Su, Jianzhong
name: Lu, Mingqin
name: Ma, Yunlong
Publisher: name: figshare
Year: 2021
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: Immunology
subject: FOS: Clinical medicine

description: Additional File 1: Figure S1. Circus plot showing the results of gene-level genetic association analysis of GWAS summary statistics on PBC. Figure S2. MAGMA-based functional annotation analysis for GWAS summary data. a) MAGMA-based pathway enrichment analysis based on GWAS summary data on PBC. b) Multidimensional scaling analysis of 41 significant pathways identified MAGMA-based pathway analysis. Figure S4. Results of MAGMA gene-property analysis based on the web-access tool of FUMA. Figure S2. Venn plot shows the overlap of results between MAGMA and S-MultiXcan analysis. Figure S5. Permutation analysis of 100,000 times for results from MAGMA and S-PrediXcan (liver and blood) compared with results from S-MultiXcan across multiple tissues. Figure S6. Differential gene expression analysis of 29 risk genes based on bulk RNA profiles of liver tissue (Dataset of GSE159676). Figure S7. Differential gene expression analysis of 29 risk genes based on bulk RNA profiles of blood tissue (Dataset of GSE119600). Figure S8. Differential gene expression analysis of 29 risk genes based on bulk RNA profiles in CD4+T cells (Dataset of GSE93170). Figure S9. Functional characterization of 29 risk associated with PBC. a) Schematic diagram showing the literature mining and GWAS catalog searching for novel PBC-risk genes. b) Phenotype-based enrichment analysis based on the GLAD4U database for 29 risk genes. Figure S10. Functional enrichment analysis of GO-term of biological process for 29 PBC-risk genes. Figure S11. Functional enrichment analysis of GO-term of cellular components for 29 PBC-risk genes. Figure S12. Functional enrichment analysis of GO-term of molecular function for 29 PBC-risk genes. Figure S13. Gene-drug interaction analysis for these identified PBC-associated risk genes. a) Drug-gene interaction analysis showing 10 druggable gene categories. b) Drug-gene subnetwork. Figure S14. Violin plot showing the difference in the expression level of Ormdl3 between cholangitis-affected mice and healthy controls. Figure S15. The expression of ORMDL3 examined in PBC livers and paracarcinoma of intrahepatic metastasis by immunohistochemistry. Figure S16. Box plots showing the effects of different genotypes (CC, CT, and TT) of rs9303277 on the expression of ORMDL3 among different immune cell types. Figure S17. Functional enrichment analysis of differential expressed genes between ORMDL3+ and ORMDL3- cholangiocytes. Figure S18. Identify signals contributing most to outgoing (sources) or incoming (targets) signaling of cell populations in liver tissues (GSE115469). Figure S19. The aggregated cell-tocell communication network among cell populations in liver tissues (GSE115469). Figure S20. The cellular communications of ORMDL3- Cholangiocyte with other cell populations in liver tissues (GSE115469). Figure S21. Dot plots showing that predicted cellular interactions of ORMDL3- Cholangiocyte with other cell populations in liver tissues (GSE115469). Figure S22. The inferred incoming (target) communication patterns of cell populations in liver tissue (GSE115469). Figure S23. Relative contribution of each ligand-receptor pair to that of VEGF signaling pathway.