Abstract
The recently emerged SARS-CoV-2 virus is responsible for the ongoing COVID-19 pandemic that has rapidly developed into a global public health threat. Patients severely affected with COVID-19 present distinct clinical features, including acute respiratory disorder, neutrophilia, cy-tokine storm, and sepsis. In addition, multiple pro-inflammatory cytokines are found in the plasma of such patients. Transcriptome sequencing of different specimens obtained from patients suffering from severe episodes of COVID-19 shows dynamics in terms of their immune responses. However, those host factors required for SARS-CoV-2 propagation and the underlying molecular mechanisms responsible for dysfunctional immune responses during COVID-19 infection remain elusive. In the present study, we analyzed the mRNA-long non-coding RNA (lncRNA) co-expression network derived from publicly available SARS-CoV-2-infected transcriptome data of human lung epithelial cell lines and bronchoalveolar lavage fluid (BALF) from COVID-19 patients. Through co-expression network analysis, we identified four differentially expressed lncRNAs strongly correlated with genes involved in various immune-related pathways crucial for cytokine signaling. Our findings suggest that the aberrant expression of these four lncRNAs can be associated with cytokine storms and anti-viral responses during severe SARS-CoV-2 infection of the lungs. Thus, the present study uncovers molecular interactions behind the cytokine storm activation potentially responsible for hyper-inflammatory responses in critical COVID-19 patients.
| Original language | English |
|---|---|
| Article number | 402 |
| Journal | Viruses |
| Volume | 13 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2021 |
Bibliographical note
Publisher Copyright:© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Funding
Funding: S.M. was supported by the Israeli Council for Higher Education through the PBC fellowship program for outstanding postdoctoral researchers from China and India. M.F.-M. was supported by the Israel Innovation Authority (Kamin grant #66824, 2019–2020) and COVID-19 Data Science Institute (DSI) grant, Bar-Ilan University (#247017, 2020).
| Funders | Funder number |
|---|---|
| COVID-19 Data Science Institute | |
| Israel Innovation Authority | 66824 |
| Bar-Ilan University | 247017 |
| Council for Higher Education | |
| Defence Science Institute |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- COVID-19
- Co-expression network
- Cytokine storm
- LncRNA
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