Distinct mechanisms for TMPRSS2 expression explain organ-specific inhibition of SARS-CoV-2 infection by enzalutamide

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly become a global public health threat. The efficacy of several repurposed drugs has been evaluated in clinical trials. Among these drugs, a second-generation antiandrogen agent, enzalutamide, was proposed because it reduces the expression of transmembrane serine protease 2 (TMPRSS2), a key component mediating SARS-CoV-2-driven entry, in prostate cancer cells. However, definitive evidence for the therapeutic efficacy of enzalutamide in COVID-19 is lacking. Here, we evaluated the antiviral efficacy of enzalutamide in prostate cancer cells, lung cancer cells, human lung organoids and Ad-ACE2-transduced mice. Tmprss2 knockout significantly inhibited SARS-CoV-2 infection in vivo. Enzalutamide effectively inhibited SARS-CoV-2 infection in human prostate cells, however, such antiviral efficacy was lacking in human lung cells and organoids. Accordingly, enzalutamide showed no antiviral activity due to the AR-independent TMPRSS2 expression in mouse and human lung epithelial cells. Moreover, we observed distinct AR binding patterns between prostate cells and lung cells and a lack of direct binding of AR to TMPRSS2 regulatory locus in human lung cells. Thus, our findings do not support the postulated protective role of enzalutamide in treating COVID-19 through reducing TMPRSS2 expression in lung cells.

Article activity feed

  1. SciScore for 10.1101/2020.09.11.293035: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The primary antibodies included anti-β-Actin (Sigma-Aldrich, A3854, 1:5,000), anti-AR (Abcam, ab108341, 1:2,000), anti-TMPRSS2 (Abcam, ab92323, 1:1,000), and anti-ACE2 (Proteintech, 21115-1-AP, 1:1000) antibodies.
    anti-β-Actin
    suggested: None
    A3854
    suggested: (Sigma-Aldrich Cat# A3854, RRID:AB_262011)
    anti-ACE2
    suggested: (Proteintech Cat# 21115-1-AP, RRID:AB_10732845)
    21115-1-AP
    suggested: (Proteintech Cat# 21115-1-AP, RRID:AB_10732845)
    The following primary antibodies were used: anti-FLAG (Abmart, M20008F, 1:250) and anti-AR (Abcam, ab108341, 1:200).
    anti-FLAG
    suggested: (Advanced Targeting Systems Cat# IT-59-250, RRID:AB_10971726)
    M20008F
    suggested: None
    anti-AR
    suggested: (RayBiotech Cat# 130-10082-200, RRID:AB_11219819)
    The monoclonal antibody against the RBD domain of SARS-CoV-2 S protein (anti-S) was cloned from a convalescent SARS-CoV-2 individual and produced by transiently transfecting HEK293F cells.
    anti-S
    suggested: None
    The following primary antibodies were used: anti-SPC (Sigma-Aldrich, ab3786, 1:200), anti-AR (Abcam, ab108341, 1:200), and anti-TMPRSS2 (Abcam, ab92323, 1:250).
    anti-SPC
    suggested: None
    ab3786
    suggested: (Millipore Cat# AB3786, RRID:AB_91588)
    anti-TMPRSS2
    suggested: (Abcam Cat# ab92323, RRID:AB_10585592)
    Experimental Models: Cell Lines
    SentencesResources
    The monoclonal antibody against the RBD domain of SARS-CoV-2 S protein (anti-S) was cloned from a convalescent SARS-CoV-2 individual and produced by transiently transfecting HEK293F cells.
    HEK293F
    suggested: RRID:CVCL_6642)
    Pseudovirus production: SARS-CoV-2 pseudovirus was produced by cotransfection of 293T cells with pNL4-3.luc.RE and PCDNA3.1 encoding the SARS-CoV-2 S protein using Vigofect transfection reagent (Vigorous Biotechnology, T001).
    293T
    suggested: None
    SARS-CoV-2 were propagated in Vero cells.
    Vero
    suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)
    R package Diffbind was used to identify overlapped peaks between AR ChIP-seq-generated peaks in LNCaP cells and ATAC-seq-generated peaks in all four cell lines, respectively.
    LNCaP
    suggested: CLS Cat# 300265/p761_LNCaP, RRID:CVCL_0395)
    We next performed GSEA to determine whether hallmark androgen response genes show significantly differences between AR-positive prostate cancer cells (LNCaP, VCaP and 22RV1) and AR-positive lung cancer cells (A549, H1437 and H2126)44.
    A549
    suggested: None
    Software and Algorithms
    SentencesResources
    Sequences of primers are listed as followed: ChIP-seq library preparation: ChIP-seq was performed as previously described36.
    ChIP-seq
    suggested: (ChIP-seq, RRID:SCR_001237)
    DeepTools was further applied for heatmap visualization with the function of computeMatrix and plotHeatmap.
    DeepTools
    suggested: (Deeptools, RRID:SCR_016366)
    plotHeatmap
    suggested: None
    In brief, after raw reads were trimmed with TrimGalore-0.5.0, Bowtie2 was used for mapping the reads to the hg19 genome37.
    Bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    SAMtools was further utilized for bam file sorting and indexing.
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    R package Diffbind was used to identify overlapped peaks between AR ChIP-seq-generated peaks in LNCaP cells and ATAC-seq-generated peaks in all four cell lines, respectively.
    Diffbind
    suggested: (DiffBind, RRID:SCR_012918)
    To further identify specific prostate-open peaks, we employed the intersect function in bedtools 41 to exclude peaks that emerged in any of the three lung cell lines in LNCaP cells.
    bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    Statistical analysis: Two-tailed Student’s t-tests were performed in GraphPad Prism 7 to compare differences between two groups.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    However, our study had limitations. Microenvironmental components, including immune cells, nerve cells and stromal cells, are involved in viral infection and subsequent replication33–35. Although we employed Ad-ACE2-transduced in vivo mouse models, our models did not consider the human lung microenvironment. Since stromal cells in multiple human organs, including the lungs, are also characterized by AR expression, we cannot exclude the possibility that enzalutamide might display antiviral activity by altering the expression of some essential cytokines or chemokines in stromal cells. It was also noting that SARS-CoV-2 could still infect the lungs of Tmprss2-KO mice with lower effectivity. Besides, when transduced with Ad-ACE2, both TMPRSS2-negative prostate cells PC3 (data not shown) and lung cells H23 were permissive for robust SARS-CoV-2-driven entry (Extended Data Fig. 5k). These findings implied that besides TMPRSS2, other factors may also play a crucial role in promoting SARS-CoV-2 infection. Further studies to identify these factors and their precise functions in mediating SARS-CoV-2 infection will be really necessary. Finally, we took advantage of multiple models of human prostate and lung cells, patients-derived benign lung organoids and Ad-ACE2-transduced Tmprss2-KO and WT mice to comprehensively confirm the pivotal function of TMPRSS2 in SARS-CoV-2 infection. Our findings validated that enzalutamide significantly inhibits SARS-CoV-2 infection in AR and TMPRSS2 double...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04475601RecruitingEnzalutamide Treatment in COVID-19
    NCT04456049Not yet recruitingRandomized Open Label Phase II Study to Evaluate the Efficac…


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 16, 11 and 14. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.


    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.