ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways

  1. Agatha Treveil
  2. Balazs Bohar
  3. Padhmanand Sudhakar
  4. Lejla Gul
  5. Luca Csabai
  6. Marton Olbei
  7. Martina Poletti
  8. Matthew Madgwick
  9. Tahila Andrighetti
  10. Isabelle Hautefort
  11. Dezso Modos
  12. Tamas Korcsmaros

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Abstract

The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.

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  1. Version published to 10.1371/journal.pcbi.1008685
  2. Version published to 10.1101/2020.06.23.167254v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.23.167254: (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
    Implementation: The workflow consists of modular R and Python scripts which can be run separately or through the provided Python wrapper script.
    Python
    suggested: (IPython, RRID:SCR_001658)
    To run everything, it is necessary that the user has R, Python3 and Cytoscape installed.
    Python3
    suggested: None
    We downloaded raw counts tables from a transcriptomics study of SARS-CoV-2 infected (MOI 2, 24 hour incubation) NHBE cells (Normal Human Bronchial/tracheal Epithelial cell line) with uninfected controls (Blanco-Melo et al. 2020) via Gene Expression Omnibus (accession GSE147507) (Edgar et al. 2002; Barrett et al. 2013).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    All networks were visualised in Cytoscape (v3.7.2).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, one limitation of the current workflow is that creation of Cytoscape visualisations and clustering analysis require the user to install and open the Cytoscape app. If this is not possible, for example because the scripts are not being run on a machine with a graphical interface, these steps are skipped. Furthermore, only basic visualisation is possible programmatically, due to challenges applying one visualisation strategy to all possible output networks, especially with regard to the function-based networks. In addition to accessibility through a default emphasis on SARS-CoV-2, a key strength of this workflow is the ability to use different input datasets: including different a priori molecular interactions, viral-human binding protein interactions and expressed/differentially expressed gene lists. This allows extensive customisation and permits rapid implementation to the most cutting-edge data soon after publication. Running the workflow across different transcriptomics datasets will allow comparison of intracellular viral responses between different cell types, different species and across different conditions (such as severe vs asymptomatic infection). For example, application of the workflow to transcriptomics data from specific immune cell-types, such as macrophages, will likely uncover different host affected signalling pathways and key TFs based on the infected cell-type. This, in turn, could increase our understanding of the role of different immune populat...

    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

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  4. Version published to 10.1101/2020.06.23.167254v1 on bioRxiv