Differential gene expression by RNA-Seq in Sigma-2 Receptor/TMEM97 knockout cells reveals its role in complement activation and SARS-CoV-2 viral uptake

  1. Aladdin Riad
  2. Yann Aubert
  3. Chenbo Zeng
  4. Thomas J. A. Graham
  5. E. James Petersson
  6. Brian C. Capell
  7. Robert H. Mach

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Abstract

Our lab has recently shown that the Sigma-2 Receptor/Transmembrane Protein 97 (sigma-2R/TMEM97) interacts with the low-density lipoprotein receptor (LDLR) and facilitates the enhanced uptake of various ligands including lipoproteins and intrinsically disordered proteins. TMEM97 has been recently been shown to interact with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteins, highlighting its potential involvement with viral entry into the cell. We hypothesized that sigma-2R/TMEM97 may play a role in facilitating viral uptake, and with the regulation of inflammatory and thrombotic pathways that are involved with viral infection. In this study, we identified the top differentially expressed genes upon the knockout of sigma-2R/TMEM97, and analyzed the genes involved with the inflammatory and thrombotic cascades, effects that are observed in patients infected with SARS-CoV-2. We found that the ablation of sigma-2R/TMEM97 resulted in an increase in Complement Component 4 Binding Protein (C4BP) proteins, at both the translational and transcriptional levels. We also showed that sigma-2R/TMEM97 interacts with the cellular receptor for SARS-CoV-2, the human angiotensin-converting enzyme 2 (ACE2) receptor, forming a protein complex, and that disruption of this complex results in the inhibition of viral uptake. The results of this study suggest that sigma-2R/TMEM97 may be a novel therapeutic target to inhibit SARS-CoV-2 viral uptake, as well as to decrease inflammatory and thrombotic effects through the modulation of the complement cascade.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.14.435180: (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
    Cells were blocked with 10% Goat Serum (50062Z Thermo Scientific) for one hour then incubated with rabbit anti-TMEM97 primary antibody (Novus NBP1-30436) 1:200 in PLA buffer diluent and mouse anti-RFP (Invitrogen MA515257) overnight, washed 3 times with PLA Wash buffer B 3 times, proceeded with PLA assay according to manufacturer (Duolink In Situ PLA Far Red kit Sigma DUO92105)
    anti-TMEM97
    suggested: (Novus Cat# NBP1-30436, RRID:AB_10003636)
    anti-RFP
    suggested: (Thermo Fisher Scientific Cat# MA5-15257, RRID:AB_10999796)
    Experimental Models: Cell Lines
    SentencesResources
    Cell Culture: HeLa cell sigma-2R/TMEM97 knockout cell lines were generated as previously described 6,7.
    HeLa
    suggested: None
    Software and Algorithms
    SentencesResources
    FASTQ files were aligned to the human genome (hg19) using STAR aligner 41 (version 2.7.1a) requiring the marking of multi-mappers and duplicates unique mappers (--bamRemoveDuplicatesType UniqueIdentical).
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Impact of sigma2-R/TMEM97 KO on HeLa cells transcriptome was assessed using Rsubread /
    Rsubread
    suggested: (Rsubread, RRID:SCR_016945)
    Paired-end fragments were counted at the meta-feature level (i.e., genes) with the featuresCounts utility 43 implemented in Rsubread, using in-built human genome reference annotation (NCBI RefSeq hg19), requiring duplicate reads to be ignored and successful alignment of both ends from the same fragment before assignment of the fragment to a meta-feature.
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Differential gene expression between TMEM97 KO cells and control cells was assessed using edgeR gene-wise negative binomial generalized linear model with quasi-likelihood testing method (glmQLfit).
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    IC50 was determined using GraphPad Prism 9.0.
    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: 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.
    • No funding statement was detected.
    • 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. Version published to 10.1101/2021.03.14.435180v1 on bioRxiv