A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes’ interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19

  1. Jun Ding
  2. David Earl Hostallero
  3. Mohamed Reda El Khili
  4. Gregory Joseph Fonseca
  5. Simon Milette
  6. Nuzha Noorah
  7. Myriam Guay-Belzile
  8. Jonathan Spicer
  9. Noriko Daneshtalab
  10. Martin Sirois
  11. Karine Tremblay
  12. Amin Emad
  13. Simon Rousseau

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Abstract

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.

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  1. Version published to 10.1371/journal.pcbi.1008810
  2. ScreenIT

    SciScore for 10.1101/2020.07.01.20144121: (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
    We also collected the protein-protein interactions (with interaction scores) between all human host proteins from the HIPPIE database78 We obtained a list of Highly/Lowly (H/L) expressed genes under different health conditions that potentially associated with COVID1910,28,29.
    HIPPIE
    suggested: (HIPPIE, RRID:SCR_014651)
    For the knowledge-guided mode, we used three biological networks to augment the GO enrichment analysis: the experimentally verified protein-protein interaction (PPI) and the co-expression networks from the STRING database34 as well as the HumanNet Integrated network 35.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    HumanNet
    suggested: (HumanNet, RRID:SCR_016146)
    Using these inputs, foRWaRD first generates a heterogeneous network comprising of gene-gene edges and disease-gene edges, with normalized edge weights representing the strength of the gene-gene interaction and the strength of evidence for gene-disease interaction (e.g. from the DisGeNET database).
    DisGeNET
    suggested: (DisGeNET, RRID:SCR_006178)
    Software Availability: The software GeneList2COVID19 is written in Python, available as an open source tool at GitHub (https://github.com/phoenixding/genelist2covid19).
    Python
    suggested: (IPython, RRID:SCR_001658)
    An implementation of the software foRWaRD is available in Python and is freely available on GitHub (https://github.com/ddhostallero/foRWaRD).
    foRWaRD
    suggested: (Grant Forward, RRID:SCR_013998)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In terms of limitation, GeneList2COVID19 is dependent on the prior knowledge of the protein interactome within the host, and between the host and virus. Currently, we have a well-established protein-protein interactome for the human species. However, the interactome between the SARS-CoV-2 proteins and human proteins is relatively limited23, since such an interactome is far from complete. For example, there are no reported interactions for ACE2 and TMPRSS2, which are critical to SARS-CoV-2 infection. We provided an option (-v) in GeneList2COVID19 to utilize any new host-viral protein interactome data when it becomes available. While, GeneList2COVID19 is good at telling whether an input gene list is associated with COVID-19, it cannot test the mechanistic hypothesis generated. It can provide the network that connects the genes in the list of the SARS-COV-2 proteins, but it cannot determine which nodes/edges in the network is more critical (and when they are activated). At this stage, the most useful information is derived from considering the entire network. As the availability of COVID-19 related “-omics” data increases, we will extend the method into a joint-model that integrates all those omics data for a more comprehensive, high-definition network model that can provide additional and more precise insights for the role of genes in COVID-19. The second computational algorithm provided, foRWaRD (https://github.com/ddhostallero/foRWaRD), is also limited by the requirement of k...

    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 10.1101/2020.07.01.20144121v1 on medRxiv