Potansiyel COVID-19 ilişkili mikroRNA’ların bilgisayar ortamında araştırılması

Year 2024, Volume: 49 Issue: 1, 170 - 180, 29.03.2024

Seyedehsadaf Asfa Didem Ökmen , Athanasia Pavlopoulou

https://doi.org/10.17826/cumj.1415977

Abstract

Amaç: Koronavirüs SARS-CoV-2'nin neden olduğu küresel COVID-19 pandemisi, artan aşılama oranlarına rağmen varlığını sürdürmekte ve her hafta yeni vakalar rapor edilmektedir. Transkripsiyon sonrası seviyede gen ekspresyonunu düzenleyen kodlamayan RNA türleri olan mikroRNA'lar, SARS-CoV-2 yaşam döngüsünde, patofizyolojisinde ve konağın antikoronaviral yanıtlarında çok önemli bir rol oynarlar. Bu çalışmada, COVID-19 ile ilişkili genleri düzenlemesi muhtemel, işlevsel olarak ilişkili miRNA'ları in silico araştırıp bulmak amaçlanmıştır.
Gereç ve Yöntem: Bu çalışmada, COVID-19'un epigenetik belirleyicilerini bulmaya yönelik veri tabanı araştırması, gen seti zenginleştirme analizi ve internet tabanlı mikroRNA’ya yönelik hedef tahmin yöntemlerini içeren bütünleştirici bir biyoinformatik yaklaşım kullanılmıştır.
Bulgular: Karmaşık bir mikroRNA-hedef gen ağı oluşturularak, potansiyel olarak hedeflenen ve COVID-19 ile ilgili önemli genleri düzenleyen yüksek düzeyde etkileşime sahip 8 mikroRNA'dan oluşan bir dizi tespit edildi. Bu miRNA'lar ve bunlara karşılık gelen genler SARS-CoV-2 enfeksiyonuna verilen yanıtta rol oynayabilir.
Sonuç: İşlevsel olarak ilişkili 8 miRNA, COVID-19 tanısı için önemli bulgular olabilirler.

Keywords

biyoinformatik, COVID-19 enfeksiyonu ile ilişkili genler, antivirüs mikroRNA, protein-protein etkileşimleri, gen seti zenginleştirme analiz, ; mikroRNA-gen ağı

In silico investigation of potential COVID-19-associated microRNA signatures

Abstract

Purpose: The global pandemic COVID-19, caused by the coronavirus SARS-CoV-2, is persistent despite the increasing vaccination rates, with new cases being reported per week. MicroRNAs, that is, non-coding RNA species that regulate gene expression at the post-transcriptional level, play a pivotal role in the SARS-CoV-2 life cycle, pathophysiology and host’s anticoronaviral responses. The objective of this study was the in silico discovery of functionally associated miRNAs that likely co-regulate COVID-19-related genes
Materials and Methods: In the present study, an integrative bioinformatics approach was employed, including database searching, gene set enrichment analysis, network-based and microRNA target prediction methods, towards the discovery of epigenetic determinants of COVID-19.
Results: An intricate microRNA-target gene network was constructed, and a set of 8 highly interacting microRNAs, that potentially co-target and co-regulate key COVID-19-related genes, was detected. These miRNAs and their corresponding genes are likely involved in the host’s response to SARS-CoV-2 infection.
Conclusion: The 8 functionally associated miRNAs could constitute a signature for COVID-19 diagnosis.

Keywords

COVID19, bioinformatics, host response genes, microRNAs

References

• WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/.
• Mahase E. Covid-19: New "Pirola" variant BA.2.86 continues to spread in UK and US. BMJ. 2023;382:2097.
• Satapathy P, Kumar P, Gupta JK, Rabaan AA, Al Kaabi NA, Mohanty D et al. The emergence and implications of SARS-CoV-2 omicron subvariant BA.2.86 on global health. Int J Surg. 2024.
• Abdolreza E, Fereshteh E, Armin JM, Amir S. EG.5 (Eris) and BA.2.86 (Pirola) two new subvariants of SARS-CoV-2: a new face of old COVID-19. Infection. 2024.
• Zhang L, Kempf A, Nehlmeier I, Cossmann A, Richter A, Bdeir N et al. SARS-CoV-2 BA.2.86 enters lung cells and evades neutralizing antibodies with high efficiency. Cell. 2024.
• Wang X, Lu L, Jiang S. SARS-CoV-2 evolution from the BA.2.86 to JN.1 variants: unexpected consequences. Trends Immunol. 2024;45:81-4.
• Khan SA, Bhuiyan MA, Dewan SMR. JN.1: The present public health concern pertains to the emergence of a novel variant of COVID-19. Environ Health Insights. 2024;18:11786302241228958.
• Diamond MS, Kanneganti TD. Innate immunity: the first line of defense against SARS-CoV-2. Nat Immunol. 2022;23:165-76.
• Gustine JN, Jones D. Immunopathology of Hyperinflammation in COVID-19. Am J Pathol. 2021;191:4-17.
• Tufan A, Avanoglu Guler A, Matucci-Cerinic M. COVID-19, immune system response, hyperinflammation and repurposing antirheumatic drugs. Turk J Med Sci. 2020;50:620-32.
• Chen YM, Zheng Y, Yu Y, Wang Y, Huang Q, Qian F et al. Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage. EMBO J. 2020;39:e105896.
• Krynytska I, Marushchak M, Birchenko I, Dovgalyuk A, Tokarskyy O. COVID-19-associated acute respiratory distress syndrome versus classical acute respiratory distress syndrome (a narrative review). Iran J Microbiol. 2021;13:737-47.
• Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27:601-15.
• Sanyaolu A, Okorie C, Marinkovic A, Patidar R, Younis K, Desai P et al. Comorbidity and its impact on patients with COVID-19. SN Compr Clin Med. 2020;2:1069-76.
• Ambros V. The functions of animal microRNAs. Nature. 2004;431:350-5.
• Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281-97.
• Abedi F, Rezaee R, Hayes AW, Nasiripour S, Karimi G. MicroRNAs and SARS-CoV-2 life cycle, pathogenesis, and mutations: biomarkers or therapeutic agents? Cell Cycle. 2021;20:143-53.
• Arghiani N, Nissan T, Matin MM. Role of microRNAs in COVID-19 with implications for therapeutics. Biomed Pharmacother. 2021;144:112247.
• Farr RJ, Rootes CL, Rowntree LC, Nguyen THO, Hensen L, Kedzierski L et al. Altered microRNA expression in COVID-19 patients enables identification of SARS-CoV-2 infection. PLoS Pathog. 2021;17:e1009759.
• Yang CY, Chen YH, Liu PJ, Hu WC, Lu KC, Tsai KW. The emerging role of miRNAs in the pathogenesis of COVID-19: Protective effects of nutraceutical polyphenolic compounds against SARS-CoV-2 infection. Int J Med Sci. 2022;19:1340-56.
• Liang Y, Fang D, Gao X, Deng X, Chen N, Wu J et al. Circulating microRNAs as emerging regulators of COVID-19. Theranostics. 2023;13:125-47.
• Panda M, Kalita E, Singh S, Kumar K, Rao A, Prajapati VK. MiRNA-SARS-CoV-2 dialogue and prospective anti-COVID-19 therapies. Life Sci. 2022;305:120761.
• Khan MA, Sany MRU, Islam MS, Islam A. Epigenetic regulator mirna pattern differences among SARS-CoV, SARS-CoV-2, and SARS-CoV-2 world-wide isolates delineated the mystery behind the epic pathogenicity and distinct clinical characteristics of pandemic COVID-19. Front Genet. 2020;11:765.
• Singh M, Chazal M, Quarato P, Bourdon L, Malabat C, Vallet T et al. A virus-derived microRNA targets immune response genes during SARS-CoV-2 infection. EMBO Rep. 2022;23:e54341.
• Bartoszewski R, Dabrowski M, Jakiela B, Matalon S, Harrod KS, Sanak M et al. SARS-CoV-2 may regulate cellular responses through depletion of specific host miRNAs. Am J Physiol Lung Cell Mol Physiol. 2020;319:L444-L55.
• Li C, Wang R, Wu A, Yuan T, Song K, Bai Y et al. SARS-COV-2 as potential microRNA sponge in COVID-19 patients. BMC Med Genomics. 2022;15:94.
• Ahmad W, Gull B, Baby J, Panicker NG, Khader TA, Akhlaq S et al. Differentially-regulated miRNAs in COVID-19: A systematic review. Rev Med Virol. 2023;33:e2449.
• Fayyad-Kazan M, Makki R, Skafi N, El Homsi M, Hamade A, El Majzoub R et al. Circulating miRNAs: Potential diagnostic role for coronavirus disease 2019 (COVID-19). Infect Genet Evol. 2021;94:105020.
• Fernandez-Pato A, Virseda-Berdices A, Resino S, Ryan P, Martinez-Gonzalez O, Perez-Garcia F et al. Plasma miRNA profile at COVID-19 onset predicts severity status and mortality. Emerg Microbes Infect. 2022;11:676-88.
• Latini A, Vancheri C, Amati F, Morini E, Grelli S, Matteucci C et al. Expression analysis of miRNA hsa-let7b-5p in naso-oropharyngeal swabs of COVID-19 patients supports its role in regulating ACE2 and DPP4 receptors. J Cell Mol Med. 2022;26:4940-48.
• Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics. 1998;14:656-64.
• Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S et al. The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.1-1.30.33.
• Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605-12.
• Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498-504.
• Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8:11.
• Kirov S, Ji R, Wang J, Zhang B. Functional annotation of differentially regulated gene set using WebGestalt: a gene set predictive of response to ipilimumab in tumor biopsies. Methods Mol Biol. 2014;1101:31-42.
• Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47:W199-W205.
• Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995;57:289-300
• Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M et al. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013;41:W169-73.
• Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4:e05005.
• Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278-84.
• Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020;48:D127-31.
• Liu W, Wang X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol. 2019;20:18.
• Yang Y, Fu X, Qu W, Xiao Y, Shen HB. MiRGOFS: a GO-based functional similarity measurement for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA-disease association. Bioinformatics. 2018;34:3547-56.
• Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics. 2010;26:976-8.
• Zhang W, Zhang Y, Min Z, Mo J, Ju Z, Guan W et al. COVID19db: a comprehensive database platform to discover potential drugs and targets of COVID-19 at whole transcriptomic scale. Nucleic Acids Res. 2022;50:D747-D57.
• Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56-68.
• Kontou PI, Pavlopoulou A, Dimou NL, Pavlopoulos GA, Bagos PG. Network analysis of genes and their association with diseases. Gene. 2016;590:68-78.
• Lauw FN, Pajkrt D, Hack CE, Kurimoto M, van Deventer SJ, van der Poll T. Proinflammatory effects of IL-10 during human endotoxemia. J Immunol. 2000;165:2783-9.
• Dinarello CA. Interleukin-18, a proinflammatory cytokine. Eur Cytokine Netw. 2000;11:483-6.
• Callahan V, Hawks S, Crawford MA, Lehman CW, Morrison HA, Ivester HM et al. The pro-inflammatory chemokines CXCL9, CXCL10 and CXCL11 are upregulated following SARS-CoV-2 Infection in an AKT-Dependent Manner. Viruses. 2021;13:1062.
• Coperchini F, Chiovato L, Croce L, Magri F, Rotondi M. The cytokine storm in COVID-19: An overview of the involvement of the chemokine/chemokine-receptor system. Cytokine Growth Factor Rev. 2020;53:25-32.
• Hu B, Huang S, Yin L. The cytokine storm and COVID-19. J Med Virol. 2021;93:250-56.
• Montazersaheb S, Hosseiniyan Khatibi SM, Hejazi MS, Tarhriz V, Farjami A, Ghasemian Sorbeni F et al. COVID-19 infection: an overview on cytokine storm and related interventions. Virol J. 2022;19:92.
• Ozbek M, Toy HI, Takan I, Asfa S, Arshinchi Bonab R, Karakulah G et al. a counterintuitive neutrophil-mediated pattern in COVID-19 patients revealed through transcriptomics analysis. Viruses. 2022;15:104.
• Remy KE, Mazer M, Striker DA, Ellebedy AH, Walton AH, Unsinger J et al. Severe immunosuppression and not a cytokine storm characterizes COVID-19 infections. JCI Insight. 2020;5:e140329.
• Hariharan A, Hakeem AR, Radhakrishnan S, Reddy MS, Rela M. The role and therapeutic potential of nf-kappa-b pathway in severe COVID-19 Patients. Inflammopharmacology. 2021;29:91-100.
• Asgari S, Pousaz LA. Human genetic variants identified that affect COVID susceptibility and severity. Nature. 2021;600:390-91.
• Huffman JE, Butler-Laporte G, Khan A, Pairo-Castineira E, Drivas TG, Peloso GM et al. Multi-ancestry fine mapping implicates OAS1 splicing in risk of severe COVID-19. Nat Genet. 2022;54:125-27.
• Zhou S, Butler-Laporte G, Nakanishi T, Morrison DR, Afilalo J, Afilalo M et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat Med. 2021;27:659-67.
• Scialo F, Daniele A, Amato F, Pastore L, Matera MG, Cazzola M et al. ACE2: the major cell entry receptor for SARS-CoV-2. Lung. 2020;198:867-77.
• Aboudounya MM, Heads RJ. COVID-19 and Toll-Like Receptor 4 (TLR4): SARS-CoV-2 may bind and activate TLR4 to Increase ACE2 expression, facilitating entry and causing hyperinflammation. Mediators Inflamm. 2021;2021:8874339.
• Mukherjee S. Toll-like receptor 4 in COVID-19: friend or foe? Future Virol. 2022;17:415–17.
• Chandrashekar DS, Athar M, Manne U, Varambally S. Comparative transcriptome analyses reveal genes associated with SARS-CoV-2 infection of human lung epithelial cells. Sci Rep. 2021;11:16212.
• Santa Cruz A, Mendes-Frias A, Oliveira AI, Dias L, Matos AR, Carvalho A et al. Interleukin-6 Is a biomarker for the development of fatal severe acute respiratory syndrome coronavirus 2 pneumonia. Front Immunol. 2021;12:613422.
• Chen LYC, Hoiland RL, Stukas S, Wellington CL, Sekhon MS. Confronting the controversy: interleukin-6 and the COVID-19 cytokine storm syndrome. Eur Respir J. 2020;56:2003006.
• Geronikolou SA, Takan I, Pavlopoulou A, Mantzourani M, Chrousos GP. Thrombocytopenia in COVID‑19 and vaccine‑induced thrombotic thrombocytopenia. Int J Mol Med. 2022;49:35.
• Borsini A, Di Benedetto MG, Giacobbe J, Pariante CM. Pro- and anti-inflammatory properties of interleukin (IL6) in vitro: relevance for major depression and for human hippocampal neurogenesis. Int J Neuropsychopharmacol. 2020;23:738-50.
• Scheller J, Chalaris A, Schmidt-Arras D, Rose-John S. The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochim Biophys Acta. 2011;1813:878-88.
• Galbraith MD, Kinning KT, Sullivan KD, Baxter R, Araya P, Jordan KR et al. Seroconversion stages COVID19 into distinct pathophysiological states. Elife. 2021;10:e65508.
• Chatterjee P, Chiasson VL, Bounds KR, Mitchell BM. Regulation of the anti-inflammatory cytokines interleukin-4 and interleukin-10 during pregnancy. Front Immunol. 2014;5:253.
• Choudhury A, Mukherjee S. Taming the storm in the heart: exploring different therapeutic choices against myocardial inflammation in COVID-19. Recent Adv Antiinfect Drug Discov. 2021;16:89-93.
• Swanson L, Katkar GD, Tam J, Pranadinata RF, Chareddy Y, Coates J et al. TLR4 signaling and macrophage inflammatory responses are dampened by GIV/Girdin. Proc Natl Acad Sci U S A. 2020;117:26895-906.
• Mohd Zawawi Z, Kalyanasundram J, Mohd Zain R, Thayan R, Basri DF, Yap WB. prospective roles of tumor necrosis factor-alpha (tnf-alpha) in COVID-19: prognosis, therapeutic and management. Int J Mol Sci. 2023;24:6142.
• Martinez-Gomez LE, Martinez-Armenta C, Medina-Luna D, Ordonez-Sanchez ML, Tusie-Luna T, Ortega-Pena S et al. Implication of myddosome complex genetic variants in outcome severity of COVID-19 patients. J Microbiol Immunol Infect. 2023;56:939-50.
• Assou S, Ahmed E, Morichon L, Nasri A, Foisset F, Bourdais C et al. The transcriptome landscape of the in vitro human airway epithelium response to SARS-CoV-2. Int J Mol Sci. 2023;24:12017.
• Zinani OQH, Keseroglu K, Ozbudak EM. Regulatory mechanisms ensuring coordinated expression of functionally related genes. Trends Genet. 2022;38:73-81.
• Arshinchi Bonab R, Asfa S, Kontou P, Karakulah G, Pavlopoulou A. Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach. PeerJ. 2022;10:e14149.
• Vastrad B, Vastrad C, Tengli A. Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods. 3 Biotech. 2020;10:422.
• Li C, Hu X, Li L, Li JH. Differential microRNA expression in the peripheral blood from human patients with COVID-19. J Clin Lab Anal. 2020;34:e23590.
• Katopodis P, Randeva HS, Spandidos DA, Saravi S, Kyrou I, Karteris E. Host cell entry mediators implicated in the cellular tropism of SARS‑CoV‑2, the pathophysiology of COVID‑19 and the identification of microRNAs that can modulate the expression of these mediators (Review). Int J Mol Med. 2022;49:20.
• Milenkovic D, Ruskovska T, Rodriguez-Mateos A, Heiss C. Polyphenols could prevent SARS-CoV-2 infection by modulating the expression of mirnas in the host cells. Aging Dis. 2021;12:1169-82.