Intestinal Receptor of SARS-CoV-2 in Inflamed IBD Tissue Seems Downregulated by HNF4A in Ileum and Upregulated by Interferon Regulating Factors in Colon

  1. Bram Verstockt
  2. Sare Verstockt
  3. Saeed Abdu Rahiman
  4. Bo-jun Ke
  5. Kaline Arnauts
  6. Isabelle Cleynen
  7. João Sabino
  8. Marc Ferrante
  9. Gianluca Matteoli
  10. Séverine Vermeire

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Abstract

Background

Patients with inflammatory bowel disease [IBD] are considered immunosuppressed, but do not seem more vulnerable for COVID-19. Nevertheless, intestinal inflammation has shown to be an important risk factor for SARS-CoV-2 infection and prognosis. Therefore, we investigated the role of intestinal inflammation on the viral intestinal entry mechanisms, including ACE2, in IBD.

Methods

We collected inflamed and uninflamed mucosal biopsies from Crohn’s disease [CD] [n = 193] and ulcerative colitis [UC] [n = 158] patients, and from 51 matched non-IBD controls for RNA sequencing, differential gene expression, and co-expression analysis. Organoids from UC patients were subjected to an inflammatory mix and processed for RNA sequencing. Transmural ileal biopsies were processed for single-cell [sc] sequencing. Publicly available colonic sc-RNA sequencing data, and microarrays from tissue pre/post anti-tumour necrosis factor [TNF] therapy, were analysed.

Results

In inflamed CD ileum, ACE2 was significantly decreased compared with control ileum [p = 4.6E-07], whereas colonic ACE2 was higher in inflamed colon of CD/UC compared with control [p = 8.3E-03; p = 1.9E-03]. Sc-RNA sequencing confirmed this ACE2 dysregulation and exclusive epithelial ACE2 expression. Network analyses highlighted HNF4A as key regulator of ileal ACE2, and pro-inflammatory cytokines and interferon regulating factors regulated colonic ACE2. Inflammatory stimuli upregulated ACE2 in UC organoids [p = 1.7E-02], but not in non-IBD controls [p = 9.1E-01]. Anti-TNF therapy restored colonic ACE2 regulation in responders.

Conclusions

Intestinal inflammation alters SARS-CoV-2 coreceptors in the intestine, with opposing dysregulations in ileum and colon. HNF4A, an IBD susceptibility gene, seems an important upstream regulator of ACE2 in ileum, whereas interferon signalling might dominate in colon.

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  1. Version published to 10.1093/ecco-jcc/jjaa185
  2. ScreenIT

    SciScore for 10.1101/2020.06.24.169383: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All included patients had given written consent to participate in the Institutional Review Board approved IBD Biobank of University Hospitals Leuven, Belgium (B322201213950/S53684 and B322201110724/S52544).
    IRB: All included patients had given written consent to participate in the Institutional Review Board approved IBD Biobank of University Hospitals Leuven, Belgium (B322201213950/S53684 and B322201110724/S52544).
    Randomization19 Bulk transcriptomics: Inflamed biopsies were taken at the most affected site at the edge of an ulcerative surface, whereas uninflamed biopsies were taken randomly in macroscopic unaffected areas.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Raw sequencing data were aligned to the reference genome (GRCh37) using Hisat2
    Hisat2
    suggested: (HISAT2, RRID:SCR_015530)
    (version 2.1.0) 21 and absolute counts generated using HTSeq.
    HTSeq
    suggested: (HTSeq, RRID:SCR_005514)
    22 Counts were normalized and protein coding genes selected (Ensemble hg 19 reference build)23 using the DESeq2 package.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Pathway and upstream regulator analyses were performed using Ingenuity Pathway Analysis (IPA, QIAGEN, Aarhus, Denmark), with network visualization via Cytoscape (v3.8.0).28
    Ingenuity Pathway Analysis
    suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Annotation of the ileal data was performed using SingleR R package, with inbuilt Human Cell Atlas data as reference.
    SingleR
    suggested: None
    Quality control, clustering and dimensionality reduction of sc-RNA seq data was performed using Seurat R package (Version 3.1.5).32, 33 Data from each 10x run were integrated after performing SCTransform on each dataset, with percentage of mitochondrial genes set as a parameter to be regressed.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Single Cell Network Inference (SCENIC) analysis was performed using a python implementation of the SCENIC pipeline (PySCENIC) (Version 0.9.19).
    SCENIC
    suggested: (SCENIC, RRID:SCR_017247)
    All steps were performed using PLINK (v1.90b4.9).
    PLINK
    suggested: (PLINK, RRID:SCR_001757)

    Results from OddPub: Thank you for sharing your data.

    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 did not find any issues relating to the usage of bar graphs.

    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.

  3. Version published to 10.1101/2020.06.24.169383v1 on bioRxiv