Deep RNA sequencing of intensive care unit patients with COVID-19

  1. Alger M. Fredericks
  2. Maximilian S. Jentzsch
  3. William G. Cioffi
  4. Maya Cohen
  5. William G. Fairbrother
  6. Shivam J. Gandhi
  7. Elizabeth O. Harrington
  8. Gerard J. Nau
  9. Jonathan S. Reichner
  10. Corey E. Ventetuolo
  11. Mitchell M. Levy
  12. Alfred Ayala
  13. Sean F. Monaghan

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Abstract

COVID-19 has impacted millions of patients across the world. Molecular testing occurring now identifies the presence of the virus at the sampling site: nasopharynx, nares, or oral cavity. RNA sequencing has the potential to establish both the presence of the virus and define the host’s response in COVID-19. Single center, prospective study of patients with COVID-19 admitted to the intensive care unit where deep RNA sequencing (> 100 million reads) of peripheral blood with computational biology analysis was done. All patients had positive SARS-CoV-2 PCR. Clinical data was prospectively collected. We enrolled fifteen patients at a single hospital. Patients were critically ill with a mortality of 47% and 67% were on a ventilator. All the patients had the SARS-CoV-2 RNA identified in the blood in addition to RNA from other viruses, bacteria, and archaea. The expression of many immune modulating genes, including PD-L1 and PD-L2, were significantly different in patients who died from COVID-19. Some proteins were influenced by alternative transcription and splicing events, as seen in HLA-C, HLA-E, NRP1 and NRP2. Entropy calculated from alternative RNA splicing and transcription start/end predicted mortality in these patients. Current upper respiratory tract testing for COVID-19 only determines if the virus is present. Deep RNA sequencing with appropriate computational biology may provide important prognostic information and point to therapeutic foci to be precisely targeted in future studies.

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  1. Version published to 10.1038/s41598-022-20139-1
  2. ScreenIT

    SciScore for 10.1101/2021.01.11.21249276: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All participants, or their appropriate surrogate, provided informed consent as approved by the Institutional Review Board (Approval #: 411616).
    IRB: All participants, or their appropriate surrogate, provided informed consent as approved by the Institutional Review Board (Approval #: 411616).
    Randomizationnot detected.
    BlindingComputational Biology and Statistical Analysis: All computational analysis was done blinded to the clinical data.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Clinical data including COVID specific therapies was collected prospectively from the electronic medical record and participants were followed until hospital discharge or death.
    COVID
    suggested: (CovidNLP, RRID:SCR_018513)
    The data was assessed for quality control using FastQC.[9] RNA sequencing data was aligned to the human genome utilizing the STAR aligner.[10] Reads that aligned to the human genome were separated and are now referred to as ‘mapped’ reads.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    The unmapped reads were further analyzed with Kraken2[12] using the PlusPFP index[13] to identify other bacterial, fungal, archaeal and viral pathogens.
    Kraken2
    suggested: None

    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:
    A limitation to this form of analysis is that the PCA cannot identify a specific gene or event most responsible for outcomes; it uses all 380,000 data points. Accurate assessment of prognosis using sequencing technology might be valuable to inform end of life care discussions in the ICU. Despite the limitations of this single-center study with a small patient number, we were still able to document that deep RNA sequencing and appropriate computational analysis yields valuable insight into the pathogenesis and host response of COVID-19 in critically ill patients. Novel drug targets were identified from SARS-CoV-2 RNA and the host response, including RNA dependent RNA polymerase, the N protein, and the PD-1 immune checkpoint pathway. The presence of pathogen RNA in the blood suggests co-infection should be reconsidered. Most importantly, PCA of the entropy of >380,000 events allowed use to group patients into those likely to die versus those likely to live, and this may be helpful in family discussions with critically ill patients. Translating these results to clinical practice will improve the diagnosis, assessment of prognosis, and therapy of COVID-19.

    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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  3. Version published to 10.1101/2021.01.11.21249276v1 on medRxiv