Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19

Abstract

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene–drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

References

[1] Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. Jama. 2020;323:1239-1242.

[2] Xu L, Ma Y, Yuan J, et al. COVID-19 quarantine reveals that behavioral changes have an effect on myopia progression. Ophthalmology. 2021;128(11):1652-1654.

[3] Zhang B, Zhang Z, Koeken VA, et al. Altered and allele-specific open chromatin landscape reveals epigenetic and genetic regulators of innate immunity in COVID-19. Cell Genomics. 2023;3(2):100232.

[4] Gaziano L, Giambartolomei C, Pereira AC, et al. Actionable druggable genome-wide mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med. 2021;27(4):668-676.

[5] Chen KG, Park K, Spence JR. Studying SARS-CoV-2 infectivity and therapeutic responses with complex organoids. Nat Cell Biol. 2021;23(8):822-833.

[6] Hirt CK, Booij TH, Grob L, et al. Drug screening and genome editing in human pancreatic cancer organoids identifies drug-gene interactions and candidates for off-label therapy. Cell Genomics. 2022;2(2):100095.

[7] Xiang B, Deng C, Qiu F, et al. Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis. J Nanobiotechnol. 2021;19(1):1-21.

[8] Li K, Ouyang M, Zhan J, Tian R. CRISPR-based functional genomics screening in human-pluripotent-stem-cell-derived cell types. Cell. Genomics. 2023;3(5):100300.

[9] Han Y, Duan X, Yang L, et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature. 2021;589(7841):270-275.

[10] Luo C, Liu H, Xie F, et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genomics. 2022;2(3):100107.

[11] Monteil V, Kwon H, Prado P, et al. Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2. Cell. 2020;181(4):905-913. e907.

[12] Mallard TT, Linnér RK, Grotzinger AD, et al. Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities. Cell Genomics. 2022;2(6):100140.

[13] Pairo-Castineira E, Clohisey S, Klaric L, et al. Genetic mechanisms of critical illness in COVID-19. Nature. 2021;591(7848):92-98.

[14] Ellinghaus D, Degenhardt F, Bujanda L, et al. Genomewide association study of severe Covid-19 with respiratory failure. N Engl J Med. 2020;383(16):1522-1534.

[15] Ma Y, Huang Y, Zhao S, et al. Integrative genomics analysis reveals a 21q22. 11 locus contributing risk to COVID-19. Hum Mol Genet. 2021;30(13):1247-1258.

[16] Zheng J, Zhang Y, Zhao H, et al. Multi-ancestry mendelian randomization of omics traits revealing drug targets of COVID-19 severity. EBioMedicine. 2022;81:104112.

[17] Zhou S, Butler-Laporte G, Nakanishi T, et al. A neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat Med. 2021;27(4):659-667.

[18] Jagadeesh KA, Dey KK, Montoro DT, et al. Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat Genet. 2022;54(10):1479-1492.

[19] Ma Y, Qiu F, Deng C, et al. Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+ monocytes and memory CD8+ T cells involved in severe COVID-19. Genome Med. 2022;14(1):1-21.

[20] Delorey TM, Ziegler CG, Heimberg G, et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature. 2021;595(7865):107-113.

[21] Clough E, Barrett T. The Gene Expression Omnibus database. Methods Mol Biol. 2016;1418:93-110.

[22] Athar A, Füllgrabe A, George N, et al. ArrayExpress update–from bulk to single-cell expression data. Nucleic Acids Res. 2019;47(D1):D711-D715.

[23] Leinonen R, Sugawara H, Shumway M; Collaboration INSD. The sequence read archive. Nucleic Acids Res. 2010;39(Suppl 1):D19-D21.

[24] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841-842.

[25] Zheng GX, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8(1):1-12.

[26] Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.

[27] 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM. A global reference for human genetic variation. Nature. 2015;526(7571):68-74.

[28] Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2013;30(7):923-930.

[29] Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19:1-5.

[30] Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420.

[31] Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019;16(12):1289-1296.

[32] Lotfollahi M, Naghipourfar M, Luecken MD, et al. Mapping single-cell data to reference atlases by transfer learning. Nat Biotechnol. 2022;40(1):121-130.

[33] The COVID-19 host genetics initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur J Hum Genet. 2020;28(6):715-718.

[34] Turner SD. qqman: an R package for visualizing GWAS results using QQ and Manhattan plots. Biorxiv 2014:005165.

[35] Ma Y, Deng C, Zhou Y, et al. Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. Cell Genom. 2023;3(9):100383.

[36] Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30.

[37] Finucane HK, Reshef YA, Anttila V, et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet. 2018;50(4):621-629.

[38] DeTomaso D, Jones MG, Subramaniam M, Ashuach T, Ye CJ, Yosef N. Functional interpretation of single cell similarity maps. Nat Commun. 2019;10(1):4376.

[39] Geary RC. The contiguity ratio and statistical mapping. Incorporated Stat. 1954;5(3):115-146.

[40] Barbeira AN, Dickinson SP, Bonazzola R, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9(1):1825.

[41] Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 2019;15(1):e1007889.

[42] de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219.

[43] Ma X, Wang P, Xu G, Yu F, Ma Y. Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma. BMC Med Genomics. 2020;13(1):123.

[44] Dong Z, Ma Y, Zhou H, et al. Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets. BMC Pulm Med. 2020;20(1):270.

[45] Chung NC, Miasojedow B, Startek M, Gambin A. Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data. BMC Bioinformatics. 2019;20(15):1-11.

[46] Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41(Web Server issue):W77-W83.

[47] Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;43(Database issue):D1049-D1056.

[48] Bulik-Sullivan BK, Loh P-R, Finucane HK, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291-295.

[49] Ma Y, Zhou Y, Su J. Github 2023.

[50] Barbeira AN, Bonazzola R, Gamazon ER, et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 2021;22(1):49.

[51] Freshour SL, Kiwala S, Cotto KC, et al. Integration of the drug–gene interaction database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021;49(D1):D1144-D1151.

[52] Fang H, De Wolf H, Knezevic B, et al. A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat Genet. 2019;51(7):1082-1091.

[53] Mountjoy E, Schmidt EM, Carmona M, et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet. 2021;53(11):1527-1533.

[54] Borkowski B, Wiliński A, Szczesny W, Binderman Z. Mathematical analysis of synthetic measures based on radar charts. Math Model Anal. 2020;25(3):473-489.

[55] Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.

[56] Holloway EM, Wu JH, Czerwinski M, et al. Differentiation of human intestinal organoids with endogenous vascular endothelial cells. Dev Cell. 2020;54(4):516-528. e517.

[57] Cruceanu C, Dony L, Krontira AC, et al. Cell-type-specific impact of glucocorticoid receptor activation on the developing brain: a cerebral organoid study. Am J Psychiatry. 2022;179(5):375-387.

[58] Zhang MJ, Hou K, Dey KK, et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat Genet. 2022;54:1572-1580.

[59] Wang R, Lin D-Y, Jiang Y. EPIC: inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing. PLoS Genet. 2022;18(6):e1010251.

[60] Zhu R, Yan T, Feng Y, et al. Mesenchymal stem cell treatment improves outcome of COVID-19 patients via multiple immunomodulatory mechanisms. Cell Res. 2021;31(12):1244-1262.

[61] Xu R, Feng Z, Wang F-S. Mesenchymal stem cell treatment for COVID-19. EBioMedicine. 2022;77:103920.

[62] Ding S, Liang TJ. Is SARS-CoV-2 also an enteric pathogen with potential fecal–oral transmission? A COVID-19 virological and clinical review. Gastroenterology. 2020;159(1):53-61.

[63] Xu E, Xie Y, Al-Aly Z. Long-term gastrointestinal outcomes of COVID-19. Nat Commun. 2023;14(1):983.

[64] Sashindranath M, Nandurkar HH. Endothelial dysfunction in the brain: setting the stage for stroke and other cerebrovascular complications of COVID-19. Stroke. 2021;52(5):1895-1904.

[65] Fullard JF, Lee H-C, Voloudakis G, et al. Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19. Genome Med. 2021;13(1):1-13.

[66] Yuan J, Fan D, Xue Z, Qu J, Su J. Co-expression of mitochondrial genes and ACE2 in cornea involved in COVID-19. Invest Ophthalmol Vis Sci. 2020;61(12):13.

[67] Horowitz JE, Kosmicki JA, Damask A, et al. Genome-wide analysis provides genetic evidence that ACE2 influences COVID-19 risk and yields risk scores associated with severe disease. Nat Genet. 2022;54(4):382-392.

[68] Pathak GA, Singh K, Miller-Fleming TW, et al. Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization. Nat Commun. 2021;12(1):1-11.

[69] Roberts GH, Partha R, Rhead B, et al. Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects. Nat Genet. 2022;54(4):374-381.

[70] Downes DJ, Cross AR, Hua P, et al. Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus. Nat Genet. 2021;53(11):1606-1615.

[71] Kousathanas A, Pairo-Castineira E, Rawlik K, et al. Whole-genome sequencing reveals host factors underlying critical COVID-19. Nature. 2022;607(7917):97-103.

[72] Ma Y, Li MD. Establishment of a strong link between smoking and cancer pathogenesis through DNA methylation analysis. Sci Rep. 2017;7(1):1-13.

[73] Menche J, Sharma A, Kitsak M, et al. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015;347(6224):1257601.

[74] Auwul MR, Rahman MR, Gov E, Shahjaman M, Moni MA. Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19. Brief Bioinform. 2021;22(5):bbab120.

[75] Guan W-j, Liang W-h, Zhao Y, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. 2020;55(5):2000547.

[76] Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020;395(10229):1054-1062.

[77] Samuel RM, Majd H, Richter MN, et al. Androgen signaling regulates SARS-CoV-2 receptor levels and is associated with severe COVID-19 symptoms in men. Cell Stem Cell. 2020;27(6):876-889. e812.

[78] Kathiriya JJ, Wang C, Zhou M, et al. Human alveolar type 2 epithelium transdifferentiates into metaplastic KRT5+ basal cells. Nat Cell Biol. 2022;24(1):10-23.

[79] Gulati GS, Sikandar SS, Wesche DJ, et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science. 2020;367(6476):405-411.

[80] Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14(10):979-982.

[81] Rendeiro AF, Ravichandran H, Bram Y, et al. The spatial landscape of lung pathology during COVID-19 progression. Nature. 2021;593(7860):564-569.

[82] Li H, Li X, Wu Q, et al. Plasma proteomic and metabolomic characterization of COVID-19 survivors 6 months after discharge. Cell Death Dis. 2022;13(3):1-12.

[83] Sun J-K, Liu Y, Zou L, et al. Acute gastrointestinal injury in critically ill patients with COVID-19 in Wuhan, China. World J Gastroenterol. 2020;26(39):6087-6097.

[84] Zang R, Castro MFG, McCune BT, et al. TMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes. Sci Immunol. 2020;5(47):eabc3582.

[85] Wilen CB, Lee S, Hsieh LL, et al. Tropism for tuft cells determines immune promotion of norovirus pathogenesis. Science. 2018;360(6385):204-208.

[86] Baldridge MT, Nice TJ, McCune BT, et al. Commensal microbes and interferon-λ determine persistence of enteric murine norovirus infection. Science. 2015;347(6219):266-269.

[87] Jyothula SS, Peters A, Liang Y, et al. Fulminant lung fibrosis in non-resolvable COVID-19 requiring transplantation. EBioMedicine. 2022;86:104351.

[88] Leng L, Ma J, Zhang P-P, et al. Spatial region-resolved proteome map reveals mechanism of COVID-19-associated heart injury. Cell Rep. 2022;39(11):110955.

[89] Melms JC, Biermann J, Huang H, et al. A molecular single-cell lung atlas of lethal COVID-19. Nature. 2021;595(7865):114-119.

[90] Li Y, Wei L, He L, Sun J, Liu N. Interferon-induced transmembrane protein 3 gene polymorphisms are associated with COVID-19 susceptibility and severity: a meta-analysis. J Infect. 2022;84:825-833.

[91] Rofeal M, El-Malek FA. Ribosomal proteins as a possible tool for blocking SARS-COV 2 virus replication for a potential prospective treatment. Med Hypotheses. 2020;143:109904.

[92] Yang B, Fan J, Huang J, et al. Clinical and molecular characteristics of COVID-19 patients with persistent SARS-CoV-2 infection. Nat Commun. 2021;12(1):1-13.

[93] Boldrini M, Canoll PD, Klein RS. How COVID-19 affects the brain. JAMA Psychiatry. 2021;78(6):682-683.

[94] Ng J-H, Sun A, Je HS, Tan E-K. Unravelling pathophysiology of neurological and psychiatric complications of COVID-19 using brain organoids. Neuroscientist. 2021;29:30-40.

[95] Meinhardt J, Radke J, Dittmayer C, et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat Neurosci. 2021;24(2):168-175.

[96] Cantuti-Castelvetri L, Ojha R, Pedro LD, et al. Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity. Science. 2020;370(6518):856-860.

[97] Daly JL, Simonetti B, Klein K, et al. Neuropilin-1 is a host factor for SARS-CoV-2 infection. Science. 2020;370(6518):861-865.

[98] Liddelow SA, Guttenplan KA, Clarke LE, et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature. 2017;541(7638):481-487.

[99] Varga Z, Flammer AJ, Steiger P, et al. Endothelial cell infection and endotheliitis in COVID-19. The Lancet. 2020;395(10234):1417-1418.

[100] Wang J, Liu J, Luo M, et al. Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference. Iscience. 2022;25(11):105348.

[101] Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet. 2021;22(10):658-671.

[102] Anderson AG, Rogers BB, Loupe JM, et al. Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer's disease-specific cis-regulatory elements. Cell. Genomics. 2023;3(3):100263.

[103] Jeong R, Bulyk ML. Blood cell traits' GWAS loci colocalization with variation in PU. 1 genomic occupancy prioritizes causal noncoding regulatory variants. Cell Genomics. 2023;3:100327.

[104] Wu Y, Qi T, Wray NR, Visscher PM, Zeng J, Yang J. Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes. Cell Genomics. 2023;3:100344.

[105] Hung IF-N, Lung K-C, Tso EY-K, et al. Triple combination of interferon beta-1b, lopinavir–ritonavir, and ribavirin in the treatment of patients admitted to hospital with COVID-19: an open-label, randomised, phase 2 trial. Lancet. 2020;395(10238):1695-1704.

[106] Abdo-Cuza A, Castellanos-Gutiérrez R, Treto-Ramirez J, et al. Safety and efficacy of intranasal recombinant human interferon alfa 2b as prophylaxis for COVID-19 in patients on a hemodialysis program. J Renal Endocrinol. 2020;7(1):e05.

[107] Dey KK, Gazal S, van de Geijn B, et al. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. Cell Genomics. 2022;2(7):100145.

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