The interplay between lncRNAs, RNA-binding proteins and viral genome during SARS-CoV-2 infection reveals strong connections with regulatory events involved in RNA metabolism and immune response

  1. Francisco J. Enguita
  2. Ana Lúcia Leitão
  3. J. Tyson McDonald
  4. Viktorija Zaksas
  5. Saswati Das
  6. Diego Galeano
  7. Deanne Taylor
  8. Eve Syrkin Wurtele
  9. Amanda Saravia-Butler
  10. Stephen B. Baylin
  11. Robert Meller
  12. D. Marshall Porterfield
  13. Douglas C. Wallace
  14. Jonathan C. Schisler
  15. Christopher E. Mason
  16. Afshin Beheshti

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Abstract

Viral infections are complex processes based on an intricate network of molecular interactions. The infectious agent hijacks components of the cellular machinery for its profit, circumventing the natural defense mechanisms triggered by the infected cell. The successful completion of the replicative viral cycle within a cell depends on the function of viral components versus the cellular defenses. Non-coding RNAs (ncRNAs) are important cellular modulators, either promoting or preventing the progression of viral infections. Among these ncRNAs, the long non-coding RNA (lncRNA) family is especially relevant due to their intrinsic functional properties and ubiquitous biological roles. Specific lncRNAs have been recently characterized as modulators of the cellular response during infection of human host cells by single stranded RNA viruses. However, the role of host lncRNAs in the infection by human RNA coronaviruses such as SARS-CoV-2 remains uncharacterized. In the present work, we have performed a transcriptomic study of a cohort of patients with different SARS-CoV-2 viral load. Our results revealed the existence of a SARS-CoV-2 infection-dependent pattern of transcriptional up-regulation in which specific lncRNAs are an integral component. To determine the role of these lncRNAs, we performed a functional correlation analysis complemented with the study of the validated interactions between lncRNAs and RNA-binding proteins (RBPs). This combination of in silico functional association studies and experimental evidence allowed us to identify a lncRNA signature composed of six elements - NRIR, BISPR, MIR155HG, FMR1-IT1, USP30-AS1, and U62317.2 - associated with the regulation of SARS-CoV-2 infection. We propose a competition mechanism between the viral RNA genome and the regulatory lncRNAs in the sequestering of specific RBPs that modulates the interferon response and the regulation of RNA surveillance by nonsense-mediated decay (NMD).

Abstract Figure

Model of interactions among lncRNA and cognate RNA-binding proteins in SARS-CoV-2 infection. According to our model, the viral genome can establish direct interactions with three core proteins (DDX3X, UPF1 and IGF2BP2) involved in mRNA metabolism and regulation of the interferon response, which are also components of a SARS-CoV-2 lncRNA-centered regulatory network. The competition between viral RNA and lncRNAs could act as a counteracting factor for the normal function of homeostatic lncRNA-centered regulatory networks, contributing to viral progression and replication. Black arrows depict physical interactions between network components; red arrows represent functional relationships.

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    SciScore for 10.1101/2022.03.26.485903: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data source and group stratification: The source data for this study was generated within the framework of COV-IRT consortium (www.cov-irt.org) and deposited at the Short Read Archive (SRA) database with the project reference PRJNA671371, corresponding to a previously published study [26].
    Short Read Archive
    suggested: None
    Analysis of RNAseq data: Raw Illumina sequence reads obtained by a pair-end sequencing strategy, were filtered, and trimmed with Trimmomatic software [34].
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Filtered sequence reads were dual-aligned with the reference SARS-CoV-2 genome from Wuhan (strain reference MN908947.3) and the human genome (genome build GRCh38 and GENCODE v33) using the STAR aligner [35].
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    STAR
    suggested: (STAR, RRID:SCR_004463)
    Data was normalized using the variance-stabilizing transform (vst) in the DESeq2 package [
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    In addition to the classical GO-term enrichment analysis, the redundant ontology terms were filtered by REVIGO software [41].
    REVIGO
    suggested: (REViGO, RRID:SCR_005825)
    Graphical analysis and representation of lncRNA-centered regulatory networks was performed by NAViGaTOR software [43]
    NAViGaTOR
    suggested: (TMA Navigator, RRID:SCR_005599)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2022.03.26.485903v1 on bioRxiv