Functional characterization of SARS-CoV-2 infection suggests a complex inflammatory response and metabolic alterations

  1. Lucía Trilla-Fuertes
  2. Ricardo Ramos
  3. Natalia Blanca-López
  4. Elena López-Camacho
  5. Laura Martín-Pedraza
  6. Pablo Ryan Murua
  7. Mariana Díaz-Almirón
  8. Carlos Llorens
  9. Toni Gabaldón
  10. Andrés Moya
  11. Juan Ángel Fresno Vara
  12. Angelo Gámez-Pozo

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Abstract

Covid-19, caused by the SARS-CoV-2 virus, has reached the category of a worldwide pandemic. Even though intensive efforts, no effective treatments or a vaccine are available. Molecular characterization of the transcriptional response in Covid-19 patients could be helpful to identify therapeutic targets. In this study, RNAseq data from peripheral blood mononuclear cell samples from Covid-19 patients and healthy controls was analyzed from a functional point of view using probabilistic graphical models. Two networks were built: one based on genes differentially expressed between healthy and infected individuals and another one based on the 2,000 most variable genes in terms of expression in order to make a functional characterization. In the network based on differentially expressed genes, two inflammatory response nodes with different tendencies were identified, one related to cytokines and chemokines, and another one related to bacterial infections. In addition, differences in metabolism, which were studied in depth using Flux Balance Analysis, were identified. SARS-CoV2-infection caused alterations in glutamate, methionine and cysteine, and tetrahydrobiopterin metabolism. In the network based on 2,000 most variable genes, also two inflammatory nodes with different tendencies between healthy individuals and patients were identified. Similar to the other network, one was related to cytokines and chemokines. However, the other one, lower in Covid-19 patients, was related to allergic processes and self-regulation of the immune response. Also, we identified a decrease in T cell node activity and an increase in cell division node activity. In the current absence of treatments for these patients, functional characterization of the transcriptional response to SARS-CoV-2 infection could be helpful to define targetable processes. Therefore, these results may be relevant to propose new treatments.

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  1. Version published to 10.1101/2020.06.22.164384v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.22.164384: (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
    Processing of RNA sequencing data: Before processing fragments per kilobase of exon model per million of reads (FPKM) data, we checked their quality using FastQC (v0.11.9, Brabaham,
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Reads longer than 100 nt showed the presence of Illumina adapter sequences which were removed by trimming using Prinseq [30]so all samples were matched to 2×100 format.
    Prinseq
    suggested: (PRINSEQ, RRID:SCR_005454)
    Then, reads were mapped against the human genome (GRCh38.96) using TopHat, using an estimated paired-end inner size of 25 and finally FPKM data were obtained using CuffDiff.
    TopHat
    suggested: (TopHat, RRID:SCR_013035)
    After FPKM processing, Perseus v1.6.5 software was used to filter RNAseq data [32].
    Perseus
    suggested: (Perseus, RRID:SCR_015753)
    These gene ontology analyses were performed in DAVID webtool v8 using “Homo sapiens” as background and KEGG, Biocarta and GOTERM-FAT as categories [33].
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    The same analysis pipeline was used to characterize the differential genes defined by CuffDiff, i.e. a network was built using the genes defined as significantly differential between healthy controls and patients.
    CuffDiff
    suggested: (Cuffdiff, RRID:SCR_001647)
    Network visualization was done in Cytoscape [35].
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Finally, FBA was solved using COBRA Toolbox library v2.0 [41] and MATLAB.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    Our study had some limitations. Probably the most important one was that the reduced number of samples limited the statistical power and the information that could be obtained by functional analyses. A larger number of samples will be useful to deepen into the molecular characterization of this disease. Also, a study based on a larger cohort stratified according the severity of the disease could be of much interest as it may help define how functional modules vary in relation to the virulence of the infection. In this study, some previously not described relevant processes in SARS-CoV-2 pathogenesis such as bacterial inflammatory response processes, tetrahydrobiopterin metabolism or allergic processes, were proposed. In the absence of treatments for these patients, molecular characterization of the disease could be helpful to improve the understanding of the mechanisms of the disease and to define targetable processes. The application of these type of analyses in larger cohorts may be useful not just to determine therapeutic targets but also to define predictors of immune response to infection. Therefore, these results may be relevant to propose new therapeutic treatments in the future.

    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 10.1101/2020.06.22.164384v1 on bioRxiv