Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes

Ruchi Jain, Sathishkumar Ramaswamy, Divinlal Harilal, Mohammed Uddin, Tom Loney, Norbert Nowotny, Hanan Alsuwaidi, Rupa Varghese, Zulfa Deesi, Abdulmajeed Alkhajeh, Hamda Khansaheb, Alawi Alsheikh-Ali, Ahmad Abou Tayoun*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


Characterizing key molecular and cellular pathways involved in COVID-19 is essential for disease prognosis and management. We perform shotgun transcriptome sequencing of human RNA obtained from nasopharyngeal swabs of patients with COVID-19, and identify a molecular signature associated with disease severity. Specifically, we identify globally dysregulated immune related pathways, such as cytokine-cytokine receptor signaling, complement and coagulation cascades, JAK-STAT, and TGF- β signaling pathways in all, though to a higher extent in patients with severe symptoms. The excessive release of cytokines and chemokines such as CCL2, CCL22, CXCL9 and CXCL12 and certain interferons and interleukins related genes like IFIH1, IFI44, IFIT1 and IL10 were significantly higher in patients with severe clinical presentation compared to mild and moderate presentations. Differential gene expression analysis identified a small set of regulatory genes that might act as strong predictors of patient outcome. Our data suggest that rapid transcriptome analysis of nasopharyngeal swabs can be a powerful approach to quantify host molecular response and may provide valuable insights into COVID-19 pathophysiology.

Original languageEnglish
Pages (from-to)153-160
Number of pages8
JournalComputational and Structural Biotechnology Journal
Publication statusPublished - Jan 2021
Externally publishedYes


  • COVID-19
  • Disease severity
  • Expression signature
  • Nasopharyngeal swabs
  • Transcriptome sequencing

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications


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