Pseudotemporal whole blood transcriptional profiling of COVID-19 patients stratified by clinical severity reveals differences in immune responses and possible role of monoamine oxidase B

  1. Claire Broderick
  2. Irene Rivero Calle
  3. Alberto Gómez Carballa
  4. Jose Gómez-Rial
  5. Ho Kwong Li
  6. Ravi Mehta
  7. Heather Jackson
  8. Antonio Salas
  9. Federico Martinón-Torres
  10. Shiranee Sriskandan
  11. Michael Levin
  12. Myrsini Kaforou
  13. the BioAID Consortium and GEN-COVID Study Group

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with highly variable clinical outcomes. Studying the temporal dynamics of host whole blood gene expression during SARS-CoV-2 infection can elucidate the biological processes that underlie these diverse clinical phenotypes. We employed a novel pseudotemporal approach using MaSigPro to model and compare the trajectories of whole blood transcriptomic responses in patients with mild, moderate and severe COVID-19 disease. We identified 5,267 genes significantly differentially expressed (SDE) over pseudotime and between severity groups and clustered these genes together based on pseudotemporal trends. Pathway analysis of these gene clusters revealed upregulation of multiple immune, coagulation, platelet and senescence pathways with increasing disease severity and downregulation of T cell, transcriptional and cellular metabolic pathways. The gene clusters exhibited differing pseudotemporal trends. Monoamine oxidase B was the top SDE gene, upregulated in severe>moderate>mild COVID-19 disease. This work provides new insights into the diversity of the host response to SARS-CoV-2 and disease severity and highlights the utility of pseudotemporal approaches in studying evolving immune responses to infectious diseases.

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  1. ScreenIT

    SciScore for 10.1101/2022.05.26.22274729: (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
    2.1 Clinical cohort: Whole blood RNA-Sequencing (RNA-Seq) datasets arising from adults (age ≥18 years) presenting with SARS-CoV-2 infection in March to May 2020, were employed from Gene Expression Omnibus (adult COVID-19 and healthy controls from United Kingdom [UK]; adult COVID-19 and inflammatory bowel disease from Spain).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    To complement and validate the MaSigPro analysis, time-course differential expression analysis was performed in DESeq2, using the likelihood ratio test.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)

    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:
    To the best of our knowledge, this is the first reported analysis of pseudotemporal transcriptomic trends in SARS-CoV-2 infection with comparisons of severity phenotypes, however it has some limitations. There were differences in age and sex between the severity groups, with age and male sex increasing with severity, which is in keeping with the epidemiology of COVID-19 disease [61, 62]. Therefore it is possible that some of the SDE genes we have identified are driven by age or sex, rather than COVID-19 severity. However, given COVID-19 severity, age and sex are so closely intertwined, adjusting for these two variables could mask key drivers of severity, and thus our unadjusted analysis may be a more sensitive approach. This study combines data from UK and Spanish cohorts. Both cohorts were recruited and sampled during the first wave in early 2020, but there may have been differences between the two countries, for example in government advice for staying at home and clinical management. The complexity of this analysis required us to minimise potential interference of the transcriptome by variables such as COVID-19 treatments and coinfections. Therefore a strict set of pre-determined exclusion criteria were employed that resulted in just two thirds of the samples being included in the analysis. Thus the sample size in some of the later severity-pseudotime groups was modest. We only included samples for which “Sample Severity” and “Worst Severity” classification were the same. ...

    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.

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  2. Version published to 10.1101/2022.05.26.22274729v1 on medRxiv