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. 2022 Feb:31:100990.
doi: 10.1016/j.mgene.2021.100990. Epub 2021 Oct 26.

MicroRNAs based regulation of cytokine regulating immune expressed genes and their transcription factors in COVID-19

Manoj Khokhar  1 , Sojit Tomo  1 , Purvi Purohit  1
Affiliations

MicroRNAs based regulation of cytokine regulating immune expressed genes and their transcription factors in COVID-19

Manoj Khokhar et al. Meta Gene. 2022 Feb.

Abstract

Background: Coronavirus disease 2019 is characterized by the elevation of a broad spectrum of inflammatory mediators associated with poor disease outcomes. We aimed at an in-silico analysis of regulatory microRNA and their transcription factors (TF) for these inflammatory genes that may help to devise potential therapeutic strategies in the future.

Methods: The cytokine regulating immune-expressed genes (CRIEG) were sorted from literature and the GEO microarray dataset. Their co-differentially expressed miRNA and transcription factors were predicted from publicly available databases. Enrichment analysis was done through mienturnet, MiEAA, Gene Ontology, and pathways predicted by KEGG and Reactome pathways. Finally, the functional and regulatory features were analyzed and visualized through Cytoscape.

Results: Sixteen CRIEG were observed to have a significant protein-protein interaction network. The ontological analysis revealed significantly enriched pathways for biological processes, molecular functions, and cellular components. The search performed in the miRNA database yielded ten miRNAs that are significantly involved in regulating these genes and their transcription factors.

Conclusion: An in-silico representation of a network involving miRNAs, CRIEGs, and TF, which take part in the inflammatory response in COVID-19, has been elucidated. Thus, these regulatory factors may have potentially critical roles in the inflammatory response in COVID-19 and may be explored further to develop targeted therapeutic strategies and mechanistic validation.

Keywords: AHR, Aryl hydrocarbon receptor; ARDS, acute respiratory distress syndrome; BAL, Bronchoalveolar Lavage; CC, Cellular components; CCL, Chemokine (C-C motif) ligands; CCL2, C-C motif chemokine 2; CCL3, C-C motif chemokine 3; CCL4, C-C motif chemokine 4; CCR, CC chemokine receptor; CEBPA, CCAAT/enhancer-binding protein alpha; COVID-19; COVID-19, Coronavirus Disease 2019; CREM, cAMP responsive element modulator; CRIEGs, Cytokine regulating immune expressed genes; CSF2, Granulocyte-macrophage colony-stimulating factor; CSF3, Granulocyte colony-stimulating factor; CXCL10, C-X-C motif chemokine 10; CXCL2, Chemokine (C-X-C motif) ligand 2; CXCL8, Interleukin-8; CXCR, C-X-C chemokine receptor; Cytokine storm; Cytokines; DDIT3, DNA damage-inducible transcript 3 protein; DEGs, Differentially expressed genes; E2F1, Transcription factor E2F1; EGR1, Early growth response protein 1; EP300, Histone acetyltransferase p300; ESR1, Estrogen receptor, Nuclear hormone receptor; ETS2, Protein C-ets-2; FOXP3, Forkhead box protein P3; GO, Gene Ontology; GSEs, Gene Series Expressions; HDAC1, Histone deacetylase 1; HDAC2, Histone deacetylase 2; HSF1, Heat shock factor protein 1; IL-6, interleukin-6; IL10, Interleukin-10; IL17A, Interleukin-17A; IL1B, Interleukin-1; IL2, Interleukin-2; IL6, Interleukin-6; IL7, Interleukin-7; IL9, Interleukin-9; IP-10, Interferon-Inducible Protein 10; IRF1, Interferon regulatory factor 1; Immuno-interactomics; JAK-STAT, Janus kinase (JAK)-signal transducer and activator; JAK2, Tyrosine-protein kinase JAK2; JUN, Transcription factor AP-1; KEGG, Kyoto Encyclopedia of Genes and Genomes; KLF4, Krueppel-like factor 4; MicroRNA, SARS-CoV-2; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; NFAT5, Nuclear factor of activated T-cells 5; NFKB1, Nuclear factor NF-kappa-B p105 subunit; NFKBIA, NF-kappa-B inhibitor alpha; NR1I2, Nuclear receptor subfamily 1 group I member 2; PDM, peripheral blood mononuclear cell; REL, Proto-oncogene c-Rel; RELA, Transcription factor p65; RUNX1, Runt-related transcription factor 1; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; SIRT1, NAD-dependent protein deacetylase sirtuin-1; SP1, Transcription factor Sp1; SPI1, Transcription factor PU.1; STAT1, Signal transducer and activator of transcription 1-alpha/beta; STAT3, Signal transducer and activator of transcription 3; TLR3, Toll-like receptor 3 (TLR3); TNF, Tumor necrosis factor; TNF-α, Tumor Necrosis Factor-Alpha; VDR, Vitamin D3 receptor; XBP1, X-box-binding protein 1; ZFP36, mRNA decay activator protein ZFP36; ZNF300, Zinc finger protein 300, heme oxygenase-1 (HO-1); miEAA, miRNA Enrichment Analysis and Annotation t.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flow Chart of the data processing and Analysis.
Fig. 2
Fig. 2
(A). Boxplot is representing the distribution of the values of the selected Samples. The gene expression profile after 12 h (Green), after 24 h (violet) after 36 h (pink) in Human Bronchial Epithelial Cells (2B4 cells) infected with SARS-CoV. (B). A mean-variance trend plot is applicable to check the mean-variance relationship of the DEGs data after fitting a linear model. (C). A mean difference (MD) plot displays log2 fold change versus average log2 expression values of DEGs. (D) A volcano plot shows statistical significance (−log10 P-value) versus magnitude of change (log2 fold change) DEGs.
Fig. 3
Fig. 3
Each CRIEG and their Transcription factor and common targeting MicroRNAs interaction network (A)CCL4; (B)CCL2; (C)CCL3; (D)IL17A; (E)CXCL8; (F)IL2; (G)CXCL10; (H)IL10; (I) IL1B; (J)TNF; (K)JAK2; (L) IL6. (Cyan colored Ellipse shaped Node: CRIEGs; Brick colored diamond shaped node: Transcription factor of CRIEGs; Pink colored rectangle shaped Node: MicroRNAs; Each node inter-connected with another node by the edges).
Fig. 4
Fig. 4
Protein-Protein Interaction between Cytokine storm genes.
Fig. 5
Fig. 5
(A). Venn diagram of common KEGG pathways involved in CRIEGs, TF of CRIEGs and MicoRNAs; (B) Common 17 KEGG pathways; (C) KEGG Pathways regulating CRIEGs, TF of CRIEGs and MicoRNAs; (D) Disease Category (DC) for MicroRNAs enrichments; (E) Gene Ontology (GO) for MicroRNAs enrichments; (F) RNA localization in cellular components; (G) Gene Ontology (GO) for CRIEGs (1) Cellular Component; (2) Biological Process (3) Molecular function; (H) Gene Ontology (GO) for TFs of CRIEGs (1) Cellular Component; (2) Biological Process (3) Molecular function.
Fig. 6
Fig. 6
The CRIEGs and Transcription factors of CRIEG and MicroRNAs interaction network. (Brick colour node: CRIEGs; Dark blue colour node: Transcription factor of CRIEGs; Pink colour node: CRIEGs and their transcription factor targeting microRNAs). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
See this image and copyright information in PMC

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References

    1. Arboleda J.F., Fernandez G.J., Urcuqui-Inchima S. Vitamin D-mediated attenuation of miR-155 in human macrophages infected with dengue virus: Implications for the cytokine response. Infect. Genet. Evol. 2019;69:12–21. doi: 10.1016/j.meegid.2018.12.033. - DOI - PubMed
    1. Bala S., Tilahun Y., Taha O., Alao H., Kodys K., Catalano D., Szabo G. Increased microRNA-155 expression in the serum and peripheral monocytes in chronic HCV infection. J. Transl. Med. 2012;10:151. doi: 10.1186/1479-5876-10-151. - DOI - PMC - PubMed
    1. Blanco-Melo D., Nilsson-Payant B.E., Liu W.-C., Uhl S., Hoagland D., Møller R., Jordan T.X., Oishi K., Panis M., Sachs D., Wang T.T., Schwartz R.E., Lim J.K., Albrecht R.A., tenOever B.R. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell. 2020;181:1036–1045.e9. doi: 10.1016/j.cell.2020.04.026. - DOI - PMC - PubMed
    1. Carrasco Pro S., Dafonte Imedio A., Santoso C.S., Gan K.A., Sewell J.A., Martinez M., Sereda R., Mehta S., Fuxman Bass J.I. Global landscape of mouse and human cytokine transcriptional regulation. Nucleic Acids Res. 2018;46:9321–9337. doi: 10.1093/nar/gky787. - DOI - PMC - PubMed
    1. Centa A., Fonseca A.S., da Silva Ferreira S.G., Azevedo M.L.V., de Paula C.B.V., Nagashima S., Machado-Souza C., dos Santos Miggiolaro A.F.R., Pellegrino Baena C., de Noronha L., Cavalli L.R. Deregulated miRNA expression is associated with endothelial dysfunction in post-mortem lung biopsies of COVID-19 patients. Am. J. Phys. Lung Cell. Mol. Phys. 2021;320:L405–L412. doi: 10.1152/ajplung.00457.2020. - DOI - PMC - PubMed
-