Co-expression analysis to identify key modules and hub genes associated with COVID19 in Platelets

  1. Ahmed B. Alarabi
  2. Attayeb Mohsen
  3. Kenji Mizuguchi
  4. Fatima Z. Alshbool
  5. Fadi T. Khasawneh

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is a highly contagious virus that causes a severe respiratory disease known as Corona virus disease 2019 (COVID19). Indeed, COVID19 increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The pathophysiological processes underlying COVID19-induced thrombosis are complex, and remain poorly understood. To this end, platelets play important roles in regulating our cardiovascular system, including via contributions to coagulation and inflammation. There is an ample of evidence that circulating platelets are activated in COVID19 patients, which is a primary driver of the thrombotic outcome observed in these patients. However, the comprehensive molecular basis of platelet activation in COVID19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID19. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID19 disease at the molecular level, which might aid in defining new targets for treatment of COVID19–induced thrombosis.

key points

  • Co-expression analysis of platelet RNAseq from COVID19 patients show distinct clusters of genes (modules) that are highly correlated to COVID19 disease.

  • Identifying these modules might help in understanding the mechanism of thrombosis in COVID19 patients

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.09.01.458644: (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
    Data Preprocessing and Differentially Expressed Genes Screening: RNAseq data were downloaded from BioProject accession #PRJNA6344897.
    BioProject
    suggested: (NCBI BioProject, RRID:SCR_004801)
    The Kallisto program was employed for pseudoalignment of reads and quantification to obtain the counts and the transcript per million (TPM)19.
    Kallisto
    suggested: (kallisto, RRID:SCR_016582)
    Log2CPM (log transformed counts per million) was used for the differential expression analysis by employing Voom normalization20 and Limma R package21 TPM normalized and filtered to exclude low variance transcripts (≤ 0.001)22 was used for the weighted gene co-expression network analysis.
    Limma
    suggested: (LIMMA, RRID:SCR_010943)

    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:
    The present study has certain limitations that should be noted. Firstly, the analysis focused on only one dataset, due to limited access to platelet gene expression data that were collected from COVID19 patients. Therefore, additional datasets should be analyzed, if available, to validate our findings and/or obtain more representative results. Also, the number of samples was 15, which may be associated with some noise, albeit it is the minimum number of samples recommended for co-expression analysis by WGCNA. Finally, any limitations in the original study, from which the data was obtained will also be reflected in the results of this study. In conclusion, our co-expression analysis of a platelet RNAseq dataset from COVID19 patients and healthy controls revealed 16 modules, amongst which the yellow, black, and magenta were identified as the most critical in COVID19 disease. Additionally, 9 hub genes were determined to potentially serve key roles in pathophysiological mechanisms of COVID19 in the context of platelet biology. The positively associated yellow and black modules were identified to be involved in platelet degranulation, energy metabolism, and mitochondria. The negatively associated magenta module was associated with interactive pathways of apoptosis. These data should help expand our understanding of the underlying mechanisms of thrombosis in COVID19 disease and help promote and guide future experimental studies to investigate the roles of the protein coding genes i...

    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.

    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 10.1101/2021.09.01.458644v1 on bioRxiv