Transcriptomic clustering of critically ill COVID-19 patients

  1. Cecilia López-Martínez
  2. Paula Martín-Vicente
  3. Juan Gómez de Oña
  4. Inés López-Alonso
  5. Helena Gil-Peña
  6. Elías Cuesta-Llavona
  7. Margarita Fernández-Rodríguez
  8. Irene Crespo
  9. Estefanía Salgado del Riego
  10. Raquel Rodríguez-García
  11. Diego Parra
  12. Javier Fernández
  13. Javier Rodríguez-Carrio
  14. Francisco José Jimeno-Demuth
  15. Alberto Dávalos
  16. Luis A. Chapado
  17. Eliecer Coto
  18. Guillermo M. Albaiceta
  19. Laura Amado-Rodríguez

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Abstract

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit.

Methods

In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings.

Results

We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results.

Conclusions

These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.

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  1. Version published to 10.1183/13993003.00592-2022
  2. ScreenIT

    SciScore for 10.1101/2022.03.01.22271576: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: Study design: This prospective observational study was reviewed and approved by the regional ethics committee (Comité de Ética de la Investigación Clínica del Principado de Asturias, ref 2020.188).
    Consent: Informed consent was obtained from each patient’s next of kin.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Recombinant DNA
    SentencesResources
    All the analyses were performed using R v4.1.1 27 and packages ggplot2 28, pROC 29 and survival 30, in addition to those previously cited.
    pROC 29
    suggested: RRID:Addgene_70284)
    Software and Algorithms
    SentencesResources
    . miRNA readouts were mapped using bowtie2 18, with an index built using the hg38 human reference genome.
    bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    Quantification of sequenced miRNAs was performed using miRDeep2 19 with reference human mature and hairpin miRNA sequences downloaded from miRBase (release 22, https://www.mirbase.org).
    https://www.mirbase.org
    suggested: (miRBase, RRID:SCR_003152)
    Analysis of differentially expressed genes: Gene raw counts obtained after pseudoalignment were compared between clusters using DESeq2 22.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Genes with an absolute log2 fold change above 2 and an adjusted p-value lower than 0.01 were used for Gene Set Enrichment Analysis (GSEA) using the clusterProfiler R package 23
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    Differentially expressed genes between clusters were also matched with the c-miRNAs expressed for each group using the MicroRNA Target Filter tool from Ingenuity Pathway Analysis (Qiagen Digital Insights), to identify predicted interactions.
    Ingenuity Pathway Analysis
    suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)
    Clinical data and gene counts were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/, accession number GSE157103).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    All the analyses were performed using R v4.1.1 27 and packages ggplot2 28, pROC 29 and survival 30, in addition to those previously cited.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our results have several limitations. First, the sample size is reduced, so we cannot discard the existence of additional clusters with other underlying pathogenetic mechanisms, or that different clustering parameters or strategies may yield different results. However, unbiased p-values associated to the identified clusters were high, and the results confirmed in a validation cohort, thus suggesting a robust classification. Second, cell populations were estimated by deconvolution of the bulk transcriptome and should be confirmed using single cell RNA seq or flow cytometry. Finally, it is unclear if applied treatments can modify the expression of the genes used for clustering, although we did not observe differences in the prescribed treatments between groups. Moreover, it is unclear if therapeutic immunomodulation may impact this transcriptomic profile or, ultimately, outcomes. In summary, our results show that transcriptomic clustering using peripheral blood RNA at ICU admission allows the identification of two groups of critically-ill COVID-19 with different immune profile and outcome. These findings could be useful for risk stratification of these patients and help to identify specific profiles that could benefit from personalized treatments aimed to modulate the inflammatory response or its consequences.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.03.01.22271576v1 on medRxiv