Expression of ACE2, TMPRSS2 and CTSL in human airway epithelial cells under physiological and pathological conditions: Implications for SARS-CoV-2 infection

  1. Junping Yin
  2. Brigitte Kasper
  3. Frank Petersen
  4. Xinhua Yu

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Abstract

SARS-CoV-2 enters into human airway epithelial cells via membrane fusion or endocytosis, and this process is dependent on ACE2, TMPRSS2, and cathepsin L. In this study, we examined the expression profiles of the three SARS-CoV-2 entry-related genes in primary human airway epithelial cells isolated from donors with different physiological and pathological backgrounds such as smoking, COPD, asthma, lung cancer, allergic rhinitis, cystic fibrosis, or viral infections. By reanalyzing 54 GEO datasets comprising transcriptomic data of 3428 samples, this study revealed that i) smoking is associated with an increased expression of ACE2 and TMPRSS2 and a decreased expression of cathepsin L; ii) infection of rhinovirus as well as poly(I:C) stimulation leads to high expression of all three SARS-CoV-2 entry-related genes; iii) expression of ACE2 and cathepsin L in nasal epithelial cells are decreased in patients with asthma and allergic rhinitis. In conclusion, this study implicates that infection of respiratory viruses, cigarette smoking and allergic respiratory diseases might affect the susceptibility to and the development of COVID-19.

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    SciScore for 10.1101/2020.08.06.240796: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data retrieval: An exhaustive search of the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was performed to identify eligible data on 1st June 2020.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Meta analysis: All meta-analysis were performed using “meta” package in RStudio.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. ScreenIT

    SciScore for 10.1101/2020.08.06.240796: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Methods Data retrieval An exhaustive search of the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/) was performed to identify eligible data on 1st June 2020.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    For agilent microarray data, raw expression profiling files were uploaded into RStudio using package “limma” 60 .
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    Cohen J.
    Cohen J
    suggested: (Lymphoma Research Foundation, RRID:SCR_004410)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    First of all, the key word “Airway epithelial cells” was used for the search without any limitations. In a second step, non-human datasets, non-series datasets and non-gene expression array datasets were filtered out. Finally, datasets from step two were reviewed carefully, and the following datasets were excluded: (a) datasets of cell line(s), (b) redundant dataset, (c) datasets containing only one group and (d) datasets containing less than 5 samples per group. Data correction, normalization For affymetrix microarrays, CEL files were uploaded into RStudio (Version 1.3.959, based on R version 4.0.1) using package “affy” 58 . Subsequently, background correction and normalization were applied to the raw data using “Robust Multichip Average (RMA)” method 59 . For agilent microarray data, raw expression profiling files were uploaded into RStudio using package “limma” 60 . By using the “limma” package, background correction and normalization were performed using “normexp” and “quantile” methods, respectively. Background corrected, normalized and log2 transformed signal intensities of ACE2, TMPRSS2 and cathepsin L were outputted and used for further analysis. Meta analysis All meta-analysis were performed using “meta” package in RStudio. Standardised mean difference (SMD) was utilized to assess the effect size of a factor on the expression of targeted genes, and 95% confidence intervals (CIs) of SMD was calculated 61 . According to the guideline proposed by Cohen62, the magnitude of the SMD is interpreted as below: small, SMD = 0.2; medium, SMD = 0.5; and large, SMD = 0.8. Fixed or random effect model was applied to pool the effect size depending on the heterogeneity across the datasets determined by inconsistency (I2) statistics and Cochrane's Q test. random effect model was applied when there was significant heterogeneity among datasets (I2 value > 50% or P value of Q test < 0.05), otherwise fixed effect model was utilized 63, 64. Statistics All statistical analyses were conducted using RStudio. Statistical significance between two groups was calculated using paired or unpaired Student’s t test depending on the samples. For the study containing two categorical factors, we utilized two-way ANOVA to determine the statistical difference followed by Tukey’s test for post hoc analysis. Multiple linear regression model was generated to evaluate the correlation of lung function index of COPD patients and healthy controls with gene expression of ACE2. A P value<0.05 was considered as statistical significance. Fundings This study was supported by the Deutsche Forschungsgemeinschaft via Exzellenzcluster 2167, DFG-27260646 and GRK1727 “Modulation of Autoimmunity” as well as the Bundesministerium für Bildung und Forschung (BMBF) via the German Center for Lung Research (DZL). Author Contribution Study design: X.Y., Literature search and data analysis: J.Y., Data interpretation: B.K. F.P. X.Y., Writing: B.K. F.P. X.Y. Competing financial interests The authors declare no competing financial interests. Reference List 1. Huang,C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497-506 (2020). 2. Zhu,N. et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med.(2020). 3. WHO characterizes COVID-19 as a pandemic. 2020. Ref Type: Internet Communication 4. COVID-19 Coronavirus Pandemic. 2020. Ref Type: Internet Communication 5. Lu,R. et al. 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Decreased Interferon Alpha/Beta Signature Associated with Human Lung Tumorigenesis. J. Interferon Cytokine Res. 35, 963-968 (2015). 42. Tsay,J.C. et al. Molecular characterization of the peripheral airway field of cancerization in lung adenocarcinoma. PLoS. One. 10, e0118132 (2015). 43. Clarke,L.A., Sousa,L., Barreto,C., & Amaral,M.D. Changes in transcriptome of native nasal epithelium expressing F508del-CFTR and intersecting data from comparable studies. Respir. Res. 14, 38 (2013). 44. Giovannini-Chami,L. et al. Distinct epithelial gene expression phenotypes in childhood respiratory allergy. Eur. Respir. J. 39, 1197-1205 (2012). 45. Wagener,A.H. et al. The impact of allergic rhinitis and asthma on human nasal and bronchial epithelial gene expression. PLoS. One. 8, e80257 (2013). 46. Kicic,A. et al. Decreased fibronectin production significantly contributes to dysregulated repair of asthmatic epithelium. Am. J. Respir. Crit Care Med. 181, 889-898 (2010). 47. Bochkov,Y.A. et al. Rhinovirus-induced modulation of gene expression in bronchial epithelial cells from subjects with asthma. Mucosal. Immunol. 3, 69-80 (2010). 48. Bosco,A., Wiehler,S., & Proud,D. Interferon regulatory factor 7 regulates airway epithelial cell responses to human rhinovirus infection. BMC. Genomics 17, 76 (2016). 49. Proud,D. et al. Gene expression profiles during in vivo human rhinovirus infection: insights into the host response. Am. J. Respir. Crit Care Med. 178, 962-968 (2008). 50. Wagener,A.H. et al. dsRNA-induced changes in gene expression profiles of primary nasal and bronchial epithelial cells from patients with asthma, rhinitis and controls. Respir. Res. 15, 9 (2014). 51. Ziegler,C.G.K. et al. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell 181, 1016-1035 (2020). 52. Guo,F.R. Active smoking is associated with severity of coronavirus disease 2019 (COVID-19): An update of a meta-analysis. Tob. Induc. Dis. 18, 37 (2020). 53. Zheng,Z. et al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J. Infect.(2020). 54. Pillay,T.S. Gene of the month: the 2019-nCoV/SARS-CoV-2 novel coronavirus spike protein. J. Clin. Pathol. 73, 366-369 (2020). 55. Camiolo,M.J., Gauthier,M., Kaminski,N., Ray,A., & Wenzel,S.E. Expression of SARSCoV-2 Receptor ACE2 and Coincident Host Response Signature Varies by Asthma Inflammatory Phenotype. J. Allergy Clin. Immunol.(2020). 56. Jackson,D.J. et al. Association of respiratory allergy, asthma, and expression of the SARS-CoV-2 receptor ACE2. J. Allergy Clin. Immunol.(2020). 57. Bashir,H. et al. Association of rhinovirus species with common cold and asthma symptoms and bacterial pathogens. J. Allergy Clin. Immunol. 141, 822-824 (2018). 58. Gautier,L., Cope,L., Bolstad,B.M., & Irizarry,R.A. affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20, 307-315 (2004). 59. Irizarry,R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4, 249-264 (2003). 60. Ritchie,M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). 61. Faraone,S.V. Interpreting estimates of treatment effects: implications for managed care. P. T. 33, 700-711 (2008). 62. 63. 64. Cohen J. Statistical Power Analysis for the Behavioral Sciences.1988). He,R.Q. et al. Downregulated miR-23b-3p expression acts as a predictor of hepatocellular carcinoma progression: A study based on public data and RT-qPCR verification. Int. J. Mol. Med. 41, 2813-2831 (2018). Higgins,J.P. & Thompson,S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539-1558 (2002). Fig. 1. Flow chart of GEO databases screening and selection process. Fig. 2. Expression of ACE2, TMPRSS2 and cathepsin L (CTSL) in airway epithelial cells of healthy current smokers (CS) and never smokers (NS). a) Violin plot of expression levels of ACE2, TMPRSS2 and CTSL in 12 datasets which contain current smokers and never smoker. Mean and standard deviation (SD) of each group are presented as dot and line, respectively. Statistical difference was calculated by Student’s t test. *p<0.05, **p<0.01, ***p<0.001. b) Forest plot of 12 datasets examining expression of ACE2, TMPRSS2 and CTSL in current smokers and never smokers. The x-axis indicates the standardized mean difference (SMD), while the y-axis shows GEO datasets and cell types. Each square in the plots represents the SMD in corresponding datasets and the 95% confidence interval (CI) is shown by the error bar. The size of each square represents the weight of the individual dataset in the meta-analysis. The diamonds in the bottom represent the SMD of the meta-analysis. The SMD, 95% CI and P values of meta-analysis are depicted. BEC, bronchial epithelial cell, NEC, nasal epithelial cell, SAEC, small airway epithelial cell, LAEC, large airway epithelial cell, TAEC, trachea airway epithelial cell. Fig. 3. Expression of ACE2, TMPRSS2 and cathepsin L (CTSL) in airway epithelial cells of healthy subjects and COPD patients. a) Violin plot of expression levels of ACE2, TMPRSS2 and CTSL in 3 datasets which contain healthy never smokers and patients with COPD. Statistical difference was calculated by Student’s t test. *p<0.05, **p<0.01, ***p<0.001. b) Forest plot of 3 datasets examining expression of ACE2, TMPRSS2 and CTSL in healthy never smokers and patients with COPD. The SMD, 95% CI and P values of meta-analysis are depicted. c) Violin plot of expression levels of ACE2, TMPRSS2 and CTSL in 5 datasets which contain healthy smokers and patients with COPD. d) Forest plot of 5 datasets examining expression of ACE2, TMPRSS2 and CTSL in healthy smokers and patients with COPD. CS, Current smokers, FS, Former smokers, SM, Smokers. Fig. 4. Decreased expression of ACE2 and cathepsin L (CTSL) in nasal epithelial cells of patients with allergic respiratory diseases. a) Violin plot of expression levels of ACE2, TMPRSS2 and CTSL in two datasets which contain healthy controls, patients with allergic rhinitis and patients with asthma. Statistical difference was calculated by Student’s t test. *p<0.05 and **p<0.01. b) Forest plot of datasets examining expression of ACE2, TMPRSS2 and CTSL in nasal epithelial cells of healthy controls and patients with allergic rhinitis or asthma. The x-axis indicates the standardized mean difference (SMD), while the y-axis shows GEO datasets and cell types. The SMD, 95% CI and P values of meta-analysis are depicted. Fig. 5. Expression of ACE2, TMPRSS2 and cathepsin L (CTSL) in airway epithelial cells after rhinoviral infection or TLR3 activation. Expression kinetics of of ACE2 (a), TMPRSS2 (e), and CTSL (i) in airway epithelial cells of healthy subjects experimentally infected with rhinovirus or saline-treated controls (data from GSE11348). Expression of ACE2 (b), TMPRSS2 (f), and CTSL (j) in airway epithelial cells isolated from healthy subjected and stimulated in vitro with or without rhinovirus in presence or absence of IRFsiRNA (data from GSE70190). Expression of ACE2 (c), TMPRSS2 (g), and CTSL (k) in airway epithelial cells isolated from healthy subjected or patients with asthma and stimulated in vitro with rhinovirus or saline control (data from GSE13396). Expression of ACE2 (d), TMPRSS2 (h), and CTSL (l) in airway epithelial cells isolated from healthy subjected or patients with asthma and stimulated in vitro with poly(I:C) or saline control (data from GSE13396). Statistical analysis was performed using two-way ANOVA, Tukey’s test for post hoc analysis was performed after two-way ANOVA analysis. *, p<0.05, **, p<0.01, ***, p<0.001. HC, healthy control, NS, not significant.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.06.240796v1 on bioRxiv