Plasma cell-free RNA characteristics in COVID-19 patients

  1. Yanqun Wang
  2. Jie Li
  3. Lu Zhang
  4. Hai-Xi Sun
  5. Zhaoyong Zhang
  6. Jinjin Xu
  7. Yonghao Xu
  8. Yu Lin
  9. Airu Zhu
  10. Yuxue Luo
  11. Haibo Zhou
  12. Yan Wu
  13. Shanwen Lin
  14. Yuzhe Sun
  15. Fei Xiao
  16. Ruiying Chen
  17. Liyan Wen
  18. Wei Chen
  19. Fang Li
  20. Rijing Ou
  21. Yanjun Zhang
  22. Tingyou Kuo
  23. Yuming Li
  24. Lingguo Li
  25. Jing Sun
  26. Kun Sun
  27. Zhen Zhuang
  28. Haorong Lu
  29. Zhao Chen
  30. Guoqiang Mai
  31. Jianfen Zhuo
  32. Puyi Qian
  33. Jiayu Chen
  34. Huanming Yang
  35. Jian Wang
  36. Xun Xu
  37. Nanshan Zhong
  38. Jingxian Zhao
  39. Junhua Li
  40. Jincun Zhao
  41. Xin Jin

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Abstract

The pathogenesis of COVID-19 is still elusive, which impedes disease progression prediction, differential diagnosis, and targeted therapy. Plasma cell-free RNAs (cfRNAs) carry unique information from human tissue and thus could point to resourceful solutions for pathogenesis and host-pathogen interactions. Here, we performed a comparative analysis of cfRNA profiles between COVID-19 patients and healthy donors using serial plasma. Analyses of the cfRNA landscape, potential gene regulatory mechanisms, dynamic changes in tRNA pools upon infection, and microbial communities were performed. A total of 380 cfRNA molecules were up-regulated in all COVID-19 patients, of which seven could serve as potential biomarkers (AUC > 0.85) with great sensitivity and specificity. Antiviral ( NFKB1A , IFITM3 , and IFI27 ) and neutrophil activation ( S100A8 , CD68 , and CD63 )–related genes exhibited decreased expression levels during treatment in COVID-19 patients, which is in accordance with the dynamically enhanced inflammatory response in COVID-19 patients. Noncoding RNAs, including some microRNAs (let 7 family) and long noncoding RNAs ( GJA9 - MYCBP ) targeting interleukin (IL6/IL6R), were differentially expressed between COVID-19 patients and healthy donors, which accounts for the potential core mechanism of cytokine storm syndromes; the tRNA pools change significantly between the COVID-19 and healthy group, leading to the accumulation of SARS-CoV-2 biased codons, which facilitate SARS-CoV-2 replication. Finally, several pneumonia-related microorganisms were detected in the plasma of COVID-19 patients, raising the possibility of simultaneously monitoring immune response regulation and microbial communities using cfRNA analysis. This study fills the knowledge gap in the plasma cfRNA landscape of COVID-19 patients and offers insight into the potential mechanisms of cfRNAs to explain COVID-19 pathogenesis.

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  1. Version published to 10.1101/gr.276175.121
  2. ScreenIT

    SciScore for 10.1101/2021.07.19.21260139: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics approval and patient recruitment: This study had been approved by The First Affiliate Hospital of Guangzhou Medical University Ethics Committee, and the institutional review board of BGI; written informed consents had been obtained from all patients and healthy donor participated in this study.
    Consent: Ethics approval and patient recruitment: This study had been approved by The First Affiliate Hospital of Guangzhou Medical University Ethics Committee, and the institutional review board of BGI; written informed consents had been obtained from all patients and healthy donor participated in this study.
    Sex as a biological variableBased on the knowledge that blood cells are the major contributor of cfDNA in most clinical scenarios (19, 68) and to date there is no clinical/genetic evidence of ovary injuries in COVID-19 patients (in fact, a large proportion of the COVID-19 patients are male in our cohort), we utilized the orientation-aware cfDNA fragmentation pattern around blood cell- and ovary-specific open chromatin regions from all COVID-19 blood samples as positive and negative signals, respectively, to train a classification model for injury assessment of other tissues.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    CfDNA sequencing and data processing: We used SOAPnuke (v1.5.0) (66) software to trim sequencing adapters, filter low quality and high ratio Ns in the raw reads with default parameters.
    SOAPnuke
    suggested: (SOAPnuke, RRID:SCR_015025)
    The preprocessed reads were then aligned to the human reference genome (NCBI build GRCh38) using BWA (67) software with default parameters.
    BWA
    suggested: (BWA, RRID:SCR_010910)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    On the other hand, there are various limitations in this study. Firstly, the clinical diagnosis for many COVID-19 patients and tissues are not available due to the limited medical resources during the outbreak of the pandemic. Secondly, we could only perform qualitative analyses without comprehensive statistical analyses for tissue injuries. Hence, to provide more meritorious information to the clinic, it is worthwhile to validate the results using larger and more thorough datasets in the following studies. In addition, it would be favorable to explore the feasibility of other analyses, such as nucleosome positioning (14, 36) and promoter coverage patterns (61) , for quantitative measurement of organ injuries in following works. As a summary, through analysis of cfDNA in COVID-19 patients, we report alterations and dynamics of cfDNA characteristics during treatment, as well as organ-specific signals in cfDNA, demonstrating that cell-free DNA could serve as valuable analytes for effective disease monitoring and tissue injury assessment of COVID-19 patients.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 37. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

    About SciScore

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  3. Version published to 10.1101/2021.07.19.21260139v1 on medRxiv