Long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 are differentially expressed in severe COVID-19 patients: An integrated single-cell analysis

  1. Kai Huang
  2. Catherine Wang
  3. Christen Vagts
  4. Vanitha Raguveer
  5. Patricia W. Finn
  6. David L. Perkins

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Abstract

Hyperactive and damaging inflammation is a hallmark of severe rather than mild Coronavirus disease 2019 (COVID-19). To uncover key inflammatory differentiators between severe and mild COVID-19, we applied an unbiased single-cell transcriptomic analysis. We integrated two single-cell RNA-seq datasets with COVID-19 patient samples, one that sequenced bronchoalveolar lavage (BAL) cells and one that sequenced peripheral blood mononuclear cells (PBMCs). The combined cell population was then analyzed with a focus on genes associated with disease severity. The immunomodulatory long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 were highly differentially expressed between mild and severe patients in multiple cell types. Within those same cell types, the concurrent detection of other severity-associated genes involved in cellular stress response and apoptosis regulation suggests that the pro-inflammatory functions of these lncRNAs may foster cell stress and damage. Thus, NEAT1 and MALAT1 are potential components of immune dysregulation in COVID-19 that may provide targets for severity related diagnostic measures or therapy.

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  1. Version published to 10.1371/journal.pone.0261242
  2. ScreenIT

    SciScore for 10.1101/2021.03.26.21254445: (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
    The raw count matrices for BAL cells and PBMC cells were downloaded from the NCBI Gene Expression Omnibus
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    The function SCTransform from the Seurat package was applied to each dataset separately to regress out technical variability as well as the percentage of mitochondrial gene expression.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    The resulting proportions were plotted using the ggplot2 R package.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    (Wickham, 2016) Differential Expression: For each cell type, differentially expressed genes (DEGs) were calculated separately for BAL and PBMC cells using MAST with UMI count as a latent variable.
    MAST
    suggested: None
    Module gene ontology enrichment was computed topGO with default settings.
    topGO
    suggested: None
    (Alexa and Rahnenfuhrer, 2020) Module and gene level plots were generated using the R packages ggplot2, ComplexHeatmap, and Circlize.(Gu et al., 2016, 2014; Wickham, 2016)
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)

    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:
    Downregulation of this stress response gene has been shown to cause mitochondrial dysfunction and ROS production that can lead to cell death.(Santofimia-Castaño et al., 2018) Lastly, our observation that CTSL, a protein crucial for COVID-19 viral entry is upregulated across multiple cell types in severe patients provides a potential initial mechanism for the induction of the NEAT1 and MALAT1 mediated inflammatory state through increased efficiency of viral entry.(Bittmann et al., 2020) Limitations in our study include the small sample size, the variable clinical presentation and treatment. Additionally, time from presentation to sample collection varied across patients. The stratification of patients as severe or mild may also introduce unknown factors due to patient variability in presentation and classification. Additional studies with more subjects and stringent recruiting and sample collection would further elucidate these findings. We have demonstrated a clear ensemble of differential gene activity associated with severe disease in COVID-19 infection that revolves around the lncRNAs NEAT1 and MALAT1. Their specific activity changes in severe patients coupled with inflammasome promoting functions, suggest important roles in the COVID-19 hyperinflammatory process. These findings indicate that NEAT1 and MALAT1 may be candidates for treatment targeting or biological marker exploration.

    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.

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  3. Version published to 10.1101/2021.03.26.21254445 on medRxiv