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Multiomics Evaluation of Gastrointestinal and Other Clinical Characteristics of COVID-19

2020; Elsevier BV; Volume: 158; Issue: 8 Linguagem: Inglês

10.1053/j.gastro.2020.03.045

1528-0012

Mulong Du, Guoshuai Cai, Feng Chen, David C. Christiani, Zhengdong Zhang, Meilin Wang,

Cancer Immunotherapy and Biomarkers

Since December 2019, coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced a worldwide panic. Beyond the principal human-to-human transmission method by droplet and contact, there is still limited knowledge about possible alternate transmission methods to guide clinical care. Recent clinical studies have observed digestive symptoms in patients with COVID-19,1 possibly because of the enrichment and infection of SARS-CoV-2 in the gastrointestinal tract, mediated by virus receptor of angiotensin converting enzyme 2 (ACE2),2Hoffmann M. et al.Cell. 2020; 181: 271-280Abstract Full Text Full Text PDF PubMed Scopus (5101) Google Scholar which suggests the potential for a fecal-oral route of SARS-CoV-2 transmission.3Gu J. et al.Gastroenterology. 2020; 158: 1518-1519Abstract Full Text Full Text PDF PubMed Scopus (1058) Google Scholar,4Xiao F. et al.Gastroenterology. 2020; 158: 1831-1833Abstract Full Text Full Text PDF PubMed Scopus (2039) Google Scholar However, there is still a large gap in the biological knowledge of COVID-19. In this study, via a bulk-to-cell strategy focusing on ACE2, we performed an integrated omics analysis at the genome, transcriptome, and proteome levels in bulk tissues and single cells across species to decipher the potential routes for SARS-CoV-2 infection in depth. Clinical and epidemiologic data of patients with COVID-19 were collected from a continually updated resource.5Xu B. et al.Lancet Infect Dis. 2020; 20: 534Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar The transcriptome and proteome derived from bulk tissues and cells were accessed from multiple databases. A phenome-wide association study data set was supplied for genetic analysis on the ACE2 pathway. P values were calculated from t test and gene set analysis. More details are shown in Supplementary Methods. We constructed a user-friendly interface for the visualization of clinical symptoms of COVID-19 (https://mulongdu.shinyapps.io/map_covid/; Supplementary Figure 1). Fever was the most common symptom at onset of illness (>70%). Notably, 5.13% and 3.34% of patients had recorded digestive symptoms from Hubei and the outside (Supplementary Table 1), respectively; 1.67% of asymptomatic carriers were recorded with positive SARS-CoV-2. As shown in Figure 1, ACE2 was widely expressed across tissues. ACE2 was considered intestine specific because its expression was enriched more than 4-fold in the intestinal tract (Figure 1A) compared with other tissues. The protein detection supported the activity of ACE2 in digestive, excretory, and reproductive organs (Figure 1A). A similar expression pattern could be found in extra data sets (Figure 1B–D). However, ACE2 expression was mild in the lung. We further performed cell-specific analysis to decipher the expression pattern of ACE2 in target organs. ACE2 messenger RNA (mRNA) in the intestinal cell lines was significantly higher than in the lung cell lines (P = 2.88 × 10–6) (Figure 1E) but did not differ significantly between the kidney and lung cell lines. Moreover, the mean values of ACE2 protein in both the intestinal (0.31) and kidney (0.61) cell lines were higher than those in the lung (–0.26), although there were no significant differences among the cells (Figure 1F). In colonic tissue microenvironments, ACE2 exhibited significantly higher expression in epithelia than in stroma (P = 4.60 × 10–8 in treatment A; P = 8.81 × 10–7 in treatment B) (Figure 1G); however, intriguingly, there was no significant difference in ACE2 mRNA between aspirin intervention and placebo (Figure 1G). Subsequently, we carried out single-cell analysis to dissect the ACE2 expression pattern. ACE2 was enriched specifically in the enterocytes of mice small intestine epithelia (Supplementary Figure 2A and B), which was consistent with the findings in humans.6Zhang H. et al.bioRxiv. 2020:; (2020.01.30.927806. Available at:)https://www.biorxiv.org/content/10.1101/2020.01.30.927806v1Date accessed: May 22, 2020Google Scholar ACE2 was highly concentrated in epithelia at the renal proximal tubule in both humans and mice (Supplementary Figure 2C–E). We obtained 7 phenotypes related to the intestinal tract and kidney deposited in the phenome-wide association study data set and extracted genome-wide association study summary statistics with more than 3000 single-nucleotide polymorphisms assigned to 33 genes of the ACE2 pathway for the gene set analysis (Supplementary Table 2 and Supplementary Figure 3). In the pathway-based level, we found no significant genetic association of the ACE2 pathway with 7 phenotypes but a modest performance of the prediction model in nephrotic syndrome (area under the receiver operating characteristic curve, 0.607) (Supplementary Table 3). Partitioned to the gene-based level, the significant joint effect of each gene extended across different phenotypes (Supplementary Table 3). In the disorders of the digestive and excretory systems, TGFB1 was associated with colorectal cancer, ACE and MAPK3 with nephrotic syndrome, and KLK1 and KNG1 with urolithiasis. Similarly, in the blood test parameters, ACE2, along with ENPEP, exhibited a significant association only with Alb. On the basis of prior evidence suggesting that ACE2 mediated the entry of SARS-CoV-2 into cells,2Hoffmann M. et al.Cell. 2020; 181: 271-280Abstract Full Text Full Text PDF PubMed Scopus (5101) Google Scholar we previously found that ACE2 expression was significantly higher in smokers than in nonsmokers, especially in distinct lung cell types.7Cai G. et al.Am J Respir Crit Care Med. 2020; Google Scholar This was corroborated by the clinical observation that the patients with severe cases of COVID-19 were more likely to have a smoking history (22.1%) than those with nonsevere COVID-19 (13.1%).1Guan W.J. et al.N Engl J Med. 2020; 382: 1708-1720Crossref PubMed Scopus (20562) Google Scholar In this study, ACE2 was confirmed to be enriched in the epithelia of the intestinal tract; therefore, a mutual interaction potentially occurred such that SARS-CoV-2 disrupted ACE2 activity, infected the intestinal epithelium by its cytotoxicity, and shed into feces, resulting in gastrointestinal manifestations and/or positive SARS-CoV-2 in stool.4Xiao F. et al.Gastroenterology. 2020; 158: 1831-1833Abstract Full Text Full Text PDF PubMed Scopus (2039) Google Scholar,8Xu Y. et al.Nat Med. 2020; 26: 502-505Crossref PubMed Scopus (1078) Google Scholar Considering the physiologic renewal of intestinal epithelia every 4–5 days, our results warn that more attention must be given to the possibility of fecal-oral transmission of SARS-CoV-2, especially by asymptomatic carriers. The renal proximal tubule enriched in ACE2 indicated that viral shedding in the urine was feasible; however, no evidence supported the actual detection of SARS-CoV-2 in the urine.4Xiao F. et al.Gastroenterology. 2020; 158: 1831-1833Abstract Full Text Full Text PDF PubMed Scopus (2039) Google Scholar Nevertheless, renal impairment was common in patients with severe COVID-19,1Guan W.J. et al.N Engl J Med. 2020; 382: 1708-1720Crossref PubMed Scopus (20562) Google Scholar which could be supported by the potential that SARS-CoV-2 damaged renal tubular cells and induced the disruption of the ACE2 pathway referring to ACE2, ACE, ENPEP, TGFB1, THOP1, MAS1, and NLN involved in kidney dysfunction. In summary, ACE2 enriched in the intestinal tract and kidney—more specifically, in the epithelium—could mediate the entry of SARS-CoV-2 into cells to accumulate and cause cytotoxicity but does not respond to nonsteroidal anti-inflammatory drugs. It is reasonable to emphasize the monitoring of digestive and excretory system complications in patients with COVID-19 and the possibility of SARS-CoV-2 transmission via the fecal-oral route by individuals with suspected infection and asymptomatic carriers (Supplementary Figure 4). The authors acknowledge Professor David C. Christiani (Departments of Environmental Health and Department of Epidemiology, Harvard T.H. Chan School of Public Health) as the senior author of this study. The authors would like to thank Dr Duo Peng (Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health) for his helpful advice and comments on the notion of this project and editing of the manuscript. The authors would like to thank Junyi Xin, Shuai Ben, and Silu Chen (Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China), Peng Huang (Department of Epidemiology, Key Laboratory of Infectious Diseases, Nanjing Medical University, Nanjing, China), Qiang Cao (Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China), and Lijuan Lin (Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China) for their invaluable contributions to this article. Mulong Du, PhD (Conceptualization: Equal; Methodology: Lead; Writing – original draft: Lead); Guoshuai Cai, PhD (Formal analysis: Lead; Investigation: Equal; Writing – review & editing: Supporting); Feng Chen, PhD (Supervision: Supporting; Visualization: Supporting; Writing – review & editing: Supporting); David C. Christiani, MD (Supervision: Equal; Writing – review & editing: Supporting); Zhengdong Zhang, PhD (Conceptualization: Equal; Supervision: Lead); Meilin Wang, PhD (Conceptualization: Equal; Funding acquisition: Equal; Supervision: Supporting) The clinical and epidemiologic data of patients with COVID-19 were collected from a continually updated resource (https://tinyurl.com/s6gsq5y),1Xu B. et al.Lancet Infect Dis. 2020; 20: 534Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar including 26,357 cases worldwide. The analyzed data set was downloaded on Feb 23, 2020, which enrolled 14,729 confirmed cases from Hubei, Wuhan province, and 11,628 cases from the outside of Hubei around the world. The visualization of symptoms of COVID-19 cases worldwide was built using plotly and shiny packages in the R library (R Core Team, Vienna, Austria). The consensus normalized expression value of ACE2 mRNA was extracted from the combined transcriptomics data sets by Human Protein Atlas,2Uhlen M. et al.Science. 2015; 347: 1260419Crossref PubMed Scopus (9189) Google Scholar Genotype Tissue Expression,3Nat Genet. 2013; 45: 580-585Crossref PubMed Scopus (5350) Google Scholar and Functional Annotation of The Mammalian Genome,4Yu N.Y. et al.Nucleic Acids Res. 2015; 43: 6787-6798Crossref PubMed Scopus (81) Google Scholar which enrolled 55 tissue types and 6 blood cell types. Briefly, the Human Protein Atlas provided transcriptome and proteome profiles across approximately 40 kinds of cells, tissues, and organs in the human body and grouped genes into categories describing the tissue specificity. The Genotype Tissue Expression project collected samples from 54 nondiseased tissue sites from nearly 1000 individuals. The Functional Annotation of The Mammalian Genome project supplied transcriptome data for every major human organ and more than 200 cancer cell lines. The Cancer Genome Atlas and Gene Expression Omnibus also supplied transcriptome profiles derived from bulk tissues. We extracted ACE2 mRNA in normal tissues from both The Cancer Genome Atlas, spanning 33 cancer types deposited in the Gene Expression Profiling Interactive Analysis platform,5Tang Z. et al.Nucleic Acids Res. 2017; 45: W98-W102Crossref PubMed Scopus (6435) Google Scholar and the Gene Expression Omnibus, including major organs (GSE2361,6Ge X. et al.Genomics. 2005; 86: 127-141Crossref PubMed Scopus (215) Google Scholar GSE7905,7Dezso Z. et al.BMC Biol. 2008; 6: 49Crossref PubMed Scopus (158) Google Scholar and GSE715718Thomas S.S. et al.Genom Data. 2015; 6: 154-158Crossref PubMed Scopus (7) Google Scholar). We further grouped cells into the subtypes intestine, kidney, and lung according to the cell progenitor using the Cancer Cell Line Encyclopedia data set and performed differential expression analysis for ACE2 mRNA and protein across each subgroup using the t test. The Cancer Cell Line Encyclopedia project provided omics data, including transcriptome9Barretina J. et al.Nature. 2012; 483: 603-607Crossref PubMed Scopus (5504) Google Scholar and proteome,10Nusinow D.P. et al.Cell. 2020; 180: 387-402Abstract Full Text Full Text PDF PubMed Scopus (475) Google Scholar covering 1100 cell lines. We used the Single Cell Expression Atlas (https://www.ebi.ac.uk/gxa/sc/home) and Kidney Interactive Transcriptomics (http://humphreyslab.com/SingleCell/) to visualize the expression pattern of ACE2 mRNA in each cell cluster of intestinal tract and kidney. Briefly, single-cell RNA sequencing of intestinal and kidney tissues were carried out using the InDrop, DropSeq, or 10× Chromium platforms, and the distribution of ACE2 was shown in the cell subgroups derived from both humans (including 4524 nuclei in complete kidney tissues11Wu H. et al.Cell Stem Cell. 2018; 23: 869-881Abstract Full Text Full Text PDF PubMed Scopus (362) Google Scholar and 4259 nuclei in kidney epithelia12Wu H. et al.J Am Soc Nephrol. 2018; 29: 2069-2080Crossref PubMed Scopus (255) Google Scholar) and mice (including 1522 cells in small intestinal epithelia13Haber A.L. et al.Nature. 2017; 551: 333-339Crossref PubMed Scopus (954) Google Scholar and 11,395 cells and nuclei in kidney epithelia14Wu H. et al.J Am Soc Nephrol. 2019; 30: 23-32Crossref PubMed Scopus (386) Google Scholar). The key pathways containing ACE2 were accessed from the Kyoto Encyclopedia of Genes and Genomes (Renin-angiotensin system; hsa04614) and PathCards (ACE Inhibitor Pathway, Pharmacodynamics SuperPath). Considering that TMPRSS2 could cooperate with ACE2 to enable the virus to enter the host cells,15Hoffmann M. et al.Cell. 2020; 181: 271-280Abstract Full Text Full Text PDF PubMed Scopus (13712) Google Scholar we ultimately included 33 genes in the ACE2 network for further genetic analysis (Supplementary Table 2), among which ACE2, ATP6AP2, and AGTR2 were located on chromosome X. The ACE2 pathway network was constructed using STRING (https://string-db.org/) (Supplementary Figure 3). Genomic information for the candidate genes was extracted from the National Center for Biotechnology Information assembly by GRCh 37 (Supplementary Table 2). The 1000 Genomes Projects (phase 3) provided the genetic information for each gene, along with the following quality control criteria for selecting genetic variants underlying east Asian ancestry (CHB: Han Chinese in Beijing, China; JPT: Japanese in Tokyo, Japan): minor allele frequency >0.01, P value for Hardy-Weinberg equilibrium >1.0 × 10–6, and genotyping call rate >95%. We initially used the largest Asian phenome-wide association study database, the Japanese Encyclopedia of Genetic Associations by Riken,8Thomas S.S. et al.Genom Data. 2015; 6: 154-158Crossref PubMed Scopus (7) Google Scholar,9Barretina J. et al.Nature. 2012; 483: 603-607Crossref PubMed Scopus (5504) Google Scholar to evaluate the genetic effect of the ACE2 pathway on the clinical symptoms of COVID-19 and the disease process that occurs in ACE2-enriched tissues. More than 200 traits or diseases with related genome-wide association studies were deposited in the Japanese Encyclopedia of Genetic Associations by Riken, of which the summary statistics were calculated by logistic or linear regression analysis for the association between each single nucleotide polymorphism and phenotypes. When mapping the phenotypes related to the intestinal tract and kidney deposited in the phenome-wide association study data set, we used colorectal cancer, nephrotic syndrome, and urolithiasis for candidate digestive and excretory system diseases. In addition, we considered the blood test parameters, including the albumin/globulin ratio and the Albumin, C-reactive protein, and total protein concentrations, as reflections of patients' health conditions in terms of the clinical symptoms of COVID-19.18Chen N. et al.Lancet. 2020; Google Scholar To avoid unexplainable and weak effects of a single genetic variant on a phenotype, we thus performed gene set analysis using Multi-marker Analysis of genomic Annotation (MAGMA),19de Leeuw C.A. et al.PLoS Comput Biol. 2015; 11e1004219Crossref PubMed Scopus (1574) Google Scholar which aggregated genetic variants into candidate gene or pathway sets, to evaluate and predict the phenotypes susceptibility. We also used SummaryAUC to evaluate the performance of polygenic risk prediction models in binary outcomes underlying summary statistics.20Song L. et al.Bioinformatics. 2019; 35: 4038-4044Crossref PubMed Scopus (11) Google Scholar All public databases involving human participants were approved by the ethics committees of original studies, and this study was approved by the institutional review board of Nanjing Medical University (NJMUIRB -2020-020).Supplementary Figure 2Single-cell RNA sequencing data showing the ACE2 expression pattern in the intestinal tract and kidney. (A) tSNE plot of small intestinal epithelial cell subgroups. Cells were partitioned into 9 groups: early enterocyte progenitor, enterocyte, late enterocyte progenitor, stem cell, tuft cell, endocrine cell, goblet cell, Paneth cell, and transit amplifying cell. (B) Cells expressing ACE2 expression are colored blue, indicating enrichment of ACE2 in intestinal enterocytes. (C) tSNE plot of cell subgroups for each kidney data set. Cells from human and mouse kidney tissues were grouped by kidney anatomy: ascending limb (AL), collecting duct–principal cell (CD-PC), connecting tubule (CNT), distal convoluted tubule (DCT/DT), descending limb (DL), endothelial cell (EC), intercalated cell (IC), loop of Henle (LH), mesangial cell (MC), macrophage (MΦ), podocyte (Pod/P), and proximal tubule (PT). (D) Cells expressing ACE2 are colored red, indicating enrichment of ACE2 in the proximal tubule of the kidney. (E) The abundance of ACE2 expression across each cell subgroup in human and mouse kidneys. avg, average; exp, expression; CPM, counts per million; pct, percent; tSNE, t-distributed stochastic neighbor embedding.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Figure 3Network of genes in the ACE2 pathway. The network was constructed by STRING (https://string-db.org/) with the default parameters.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Figure 4Schematic of the bulk-to-cell strategy for evaluating SARS-CoV-2 infection, accumulation, and transmission across hosts. The diagram was constructed with BioRender (https://biorender.com/).View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Table 1Clinical Characteristics of Patients Infected With SARS-CoV-2CharacteristicsPatients in HubeiPatients outside of Hubein = 39%n = 359%Age, y <1800.00123.34 18–2900.004412.26 30–3912.567320.33 40–4925.137320.33 50–59410.266518.11 60–691128.215615.60 70–79923.08143.90 ≥801128.2151.39Sex Male2666.6720155.99 Female1333.3314841.23Countries Belgium10.28 Cambodia10.28 China25671.31 France30.84 Germany10.28 Italy10.28 Japan5214.48 Malaysia71.95 Nepal10.28 Philippines10.28 Russia20.56 Singapore92.51 South Korea92.51 Thailand51.39 United States30.84 Vietnam71.95Symptoms Fever3179.4926674.09 Cough2153.8512835.65 Fatigue512.82164.46 Digestive (diarrhea, nausea, vomiting/emesis, anorexia)25.13123.34 Asymptomatic00.006a3 patients were from Japan, and 3 were from Malaysia.1.67a 3 patients were from Japan, and 3 were from Malaysia. Open table in a new tab Supplementary Table 2Information on 33 Genes in the ACE2 PathwayGene nameGene IDChromosomePositionStrandREN59721204123944-204135465–AGT1831230838269-230850336–AGTR11853148415658-148460790+CPA313593148583043-148614874+MME43113154797436-154901518+KNG138273186435098-186462199+ENPEP20284111397229-111484493+NR3C243064148999915-149365850–NLN57486565018023-65125111+LNPEP4012596271346-96365115+PREP55506105725442-105850999–MAS141426160320218-160329339+NOS348467150688144-150711687+CYP11B215858143991975-143999259–MRGPRD1165121168747490-68748455–PRCP55471182535409-82612733–CMA112151424974712-24977471–CTSG15111425042724-25045466–BDKRB26241496671016-96710666+BDKRB16231496721641-96735304+ANPEP2901590328126-90358119–MAPK355951630125426-30134630–ACE16361761554422-61575741+THOP17064192785464-2813599+TGFB170401941836812-41859831–KLK138161951322402-51327043–KLK238171951376689-51383823+CTSA54762044519591-44527459+TMPRSS271132142836236-42880085–MAPK155942222113946-22221970–ACE259272X15579156-15620192–ATP6AP210159X40440141-40465889+AGTR2186X115301958-115306225+ID, identification. Open table in a new tab Supplementary Table 3Gene Set Analysis to Evaluate the Genetic Effect of the ACE2 Pathway on the 7 PhenotypesSourcesPhenotypesPathway nameNumber of genes/SNPsSample size, NPpathway basedAUC (variance)DiseaseColorectal cancerACE2 pathway33202,807.9200.542 (1.21 × 10-5)Nephrotic syndromeACE2 pathway33212,453.9330.607 (8.24 × 10-5)UrolithiasisACE2 pathway33212,453.7430.537 (1.29 × 10-5)Blood testAlbuminACE2 pathway33102,223.306NAAlbumin/globulin ratioACE2 pathway3398,626.349C-reactive proteinACE2 pathway3375,391.365Total proteinACE2 pathway33113,509.541Gene nameNumber of SNPsDiseaseColorectal cancerTGFB123202,807.028NANephrotic syndromeACE37212,453.012Nephrotic syndromeMAPK33212,453.012UrolithiasisKLK113212,453.001UrolithiasisKNG1146212,453.002Blood testAlbuminENPEP130102,223.020NAAlbuminACE218102,223.029Albumin/globulin ratioACE3398,626.012Albumin/globulin ratioTGFB12398,626.013Albumin/globulin ratioTHOP13698,626.020Albumin/globulin ratioMAS11398,626.032Albumin/globulin ratioNLN25398,626.037C-reactive proteinNR3C262475,391.021Total proteinMRGPRD1113,509.008Total proteinCTSA16113,509.012Total proteinTHOP136113,509.012AUC, area under the receiver operating characteristic curve (calculated by SummaryAUC); NA, not available; SNP, single nucleotide polymorphism. Open table in a new tab ID, identification. AUC, area under the receiver operating characteristic curve (calculated by SummaryAUC); NA, not available; SNP, single nucleotide polymorphism.