Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Dec 15:12:789317.
doi: 10.3389/fimmu.2021.789317. eCollection 2021.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Aliakbar Hasankhani  1 , Abolfazl Bahrami  1   2 , Negin Sheybani  3 , Behzad Aria  4 , Behzad Hemati  5 , Farhang Fatehi  1 , Hamid Ghaem Maghami Farahani  1 , Ghazaleh Javanmard  1 , Mahsa Rezaee  6 , John P Kastelic  7 , Herman W Barkema  7
Affiliations

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Aliakbar Hasankhani et al. Front Immunol. .

Abstract

Background: The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods: RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results: Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion: This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Keywords: COVID-19 pandemic; WGCNA; hub-high traffic genes; immunopathogenesis; systems biology; systems immunology; therapeutic targets in infectious diseases.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The stringent step-by-step pipeline of the RNA-seq data analysis and differential co-expression network approach in this study.
Figure 2
Figure 2
Preprocessing of weighted gene co-expression network analysis. (A) Sample clustering to detect outliers in healthy samples as reference set. All samples had a standardized connectivity score > −2.5. (B) Gene hierarchical clustering dendrogram of 21 detected modules based on a dissimilarity (1-TOM) measure. Branches represent modules that are marked with a specific color. The y-axis represents the co-expression distance, the x-axis represents genes and the grey module represents background genes.
Figure 3
Figure 3
The preservation status of the respective modules. (A) MedianRank preservation results. The y axis represents medianRank values and x axis represent module size. Each point with a specific color represents the corresponding module. (B) Zsummary preservation results. The y axis represents Zsummary values and x axis represents the module size. Each point with a specific color represents the corresponding module. Modules with medianRank ≥ 8 (the blue dashed line) or Zsummary ≤ 10 (the red dashed line) were considered non-preserved between healthy controls and COVID-19 samples.
Figure 4
Figure 4
The top GO biological processes of the non-preserved modules. The y axis and the x axis represent significant enriched GO terms and module name, respectively. Color and size of each point represent adjusted p value and number of genes for each term, respectively.
Figure 5
Figure 5
PPI network based on the co-expressed hub genes of the blue module. This module had the most biological associations with the immunopathogenesis of COVID-19. Large circles and orange octagons represent hub-high traffic genes and TFs, respectively.
Figure 6
Figure 6
Module-trait relationships analysis. (A) Module-trait relationships (MTRs) between detected modules and disease severity of COVID-19. Module-trait relationships MTRs are obtained by calculating the correlation between the traits and the module eigengenes. The red and blue colors indicate strong positive correlation and strong negative correlation, respectively. Rows represent module eigengene (ME) and columns indicate disease severity of COVID-19. Asterisks corresponds significant highly-correlated values. (B) Eigengene adjacency heatmap indicate relationship among all the modules.
Figure 7
Figure 7
The Gene, Gene Ontology and pathway, related modules involved in the disease severity of COVID-19.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, et al. . Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med (2020) 382(18):1708–20. doi: 10.1056/NEJMoa2002032 - DOI - PMC - PubMed
    1. Cucinotta D, Vanelli M. WHO Declares COVID-19 a Pandemic. Acta BioMed (2020) 91(1):157–60. doi: 10.23750/abm.v91i1.9397 - DOI - PMC - PubMed
    1. Sohrabi C, Alsafi Z, O'Neill N, Khan M, Kerwan A, Al-Jabir A, et al. . World Health Organization Declares Global Emergency: A Review of the 2019 Novel Coronavirus (COVID-19). Int J Surg (2020) 76:71–6. doi: 10.1016/j.ijsu.2020.02.034 - DOI - PMC - PubMed
    1. Auwul MR, Rahman MR, Gov E, Shahjaman M, Moni MA. Bioinformatics and Machine Learning Approach Identifies Potential Drug Targets and Pathways in COVID-19. Briefings Bioinf (2021). doi: 10.1093/bib/bbab120 - DOI - PMC - PubMed
    1. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. . Epidemiological and Clinical Characteristics of 99 Cases of 2019 Novel Coronavirus Pneumonia in Wuhan, China: A Descriptive Study. Lancet (2020) 395(10223):507–13. doi: 10.1016/S0140-6736(20)30211-7 - DOI - PMC - PubMed

Substances