Meta-transcriptomic analysis reveals the gene expression and novel conserved sub-genomic RNAs in SARS-CoV-2 and MERS-CoV

  1. Lin Lyu
  2. Ru Feng
  3. Mingnan Zhang
  4. Qiyu Gong
  5. Yinjing Liao
  6. Yanjiao Zhou
  7. Xiaokui Guo
  8. Bing Su
  9. Yair Dorsett
  10. Lei Chen

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

Fundamental to viral biology is identification and annotation of viral genes and their function. Determining the level of coronavirus gene expression is inherently difficult due to the positive stranded RNA genome and the identification of sub-genomic RNAs (sgRNAs) that are required for expression of most viral genes. In the COVID-19 epidemic so far, few genomic studies have looked at viral sgRNAs and none have systematically examined the sgRNA profiles of large numbers of SARS-CoV2 datasets in conjuction with data for other coronaviruses.

Results

We developed a bioinformatic pipeline to analyze the sgRNA profiles of coronaviruses and applied it to 588 individual samples from 20 independent studies, covering more than 10 coronavirus species. Our result showed that SARS-CoV, SARS-CoV-2 and MERS-CoV each had a core sgRNA repertoire generated via a canonical mechanism. Novel sgRNAs that encode peptides with evolutionarily conserved structures were identified in several coronaviruses and were expressed in vitro and in vivo . Two novel peptides may have direct functional relevance to disease, by alluding interferon responses and disrupting IL17E (IL25) signaling. Relevant to coronavirus infectivity and transmission, we also observed that the level of Spike sgRNAs were significantly higher in-vivo than in-vitro , while the opposite held true for the Nucleocapside protein.

Conclusions

Our results greatly expanded the predicted number of coronaviruses proteins and identified potential viral peptide suggested to be involved in viral virulence. These methods and findings shed new light on coronavirus biology and provides a valuable resource for future genomic studies of coronaviruses.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.16.043224: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.16.043224 on bioRxiv