##common.pageHeaderLogo.altText##

Original article

Vol. 152 No. 4748 (2022)

Potential core genes associated with COVID-19 identified via weighted gene co-expression network analysis

  • Chao Wu
  • Zuowei Wu
  • Yang Chen
  • Xing Huang
  • Bole Tian
Cite this as:
Swiss Med Wkly. 2022;152:40033
Published
30.11.2022

Summary

AIMS: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus belonging to the Coronaviridae family that causes coronavirus disease (COVID-19). This disease rapidly reached pandemic status, presenting a serious threat to global health. However, the detailed molecular mechanism contributing to COVID-19 has not yet been elucidated.   METHODS: The expression profiles, including the mRNA levels, of samples from patients infected with SARS-CoV-2 along with clinical data were obtained from the GSE152075 dataset in the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules, which were then implemented to evaluate the relationships between fundamental modules and clinical traits. The differentially expressed genes (DEGs), gene ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were evaluated using R software packages.   RESULTS: A total of 377 SARS-CoV-2-infected samples and 54 normal samples with available clinical and genetic data were obtained from the GEO database. There were 1444 DEGs identified between the sample types, which were used to screen out 11 co-expression modules in the WGCNA. Six co-expression modules were significantly associated with three clinical traits (SARS-CoV-2 positivity, age, and sex). Among the DEGs in two modules significantly correlated with SARS-CoV-2 positivity, enrichment was observed in the biological process of viral infection strategies (viral translation) in the GO analysis. The KEGG signalling pathway analysis demonstrated that the DEGs in the two modules were commonly enriched in oxidative phosphorylation, ribosome, and thermogenesis pathways. Moreover, a five-core gene set (RPL35A, RPL7A, RPS15, RPS20, and RPL17) with top connectivity with other genes was identified in the SARS-CoV-2 infection modules, suggesting that these genes may be indispensable in viral transcription after infection.   CONCLUSION: The identified core genes and signalling pathways associated with SARS-CoV-2 infection can significantly supplement the current understanding of COVID-19. The five core genes encoding ribosomal proteins may be indispensable in viral protein biosynthesis after SARS-CoV-2 infection and serve as therapeutic targets for COVID-19 treatment. These findings can be used as a basis for creating a hypothetical model for future experimental studies regarding associations of SARS-CoV-2 infection with ribosomal protein function.

References

  1. Li X, Zai J, Wang X, Li Y. Potential of large "first generation" human-to-human transmission of 2019-nCoV. J Med Virol. 2020 Apr;92(4):448-454. eng. Epub 2020/01/31. https://doi.org/10.1002/jmv.25693. Cited in: Pubmed; PMID 31997390.
  2. Hemmat N, Derakhshani A, Bannazadeh Baghi H, Silvestris N, Baradaran B, De Summa S. Neutrophils, Crucial, or Harmful Immune Cells Involved in Coronavirus Infection: A Bioinformatics Study. Front Genet. 2020 Jun;11:641.
  3. van Doremalen N, Bushmaker T, Morris DH, Holbrook MG, Gamble A, Williamson BN, et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N Engl J Med. 2020 Apr;382(16):1564–7.
  4. Zou L, Ruan F, Huang M, Liang L, Huang H, Hong Z, et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med. 2020 Mar;382(12):1177–9.
  5. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents. 2020 Mar;55(3):105924.
  6. Wang C, Liu Z, Chen Z, Huang X, Xu M, He T, Zhang Z. The establishment of reference sequence for SARS-CoV-2 and variation analysis. J Med Virol. 2020 Jun;92(6):667-674. eng. Epub 2020/03/14. https://doi.org/10.1002/jmv.25762. Cited in: Pubmed; PMID 32167180.
  7. Khailany RA, Safdar M, Ozaslan M. Genomic characterization of a novel SARS-CoV-2. Gene Rep. 2020 Apr 16;19:100682. eng. Epub 2020/04/18. https://doi.org/10.1016/j.genrep.2020.100682. Cited in: Pubmed; PMID 32300673.
  8. Hui DS, E IA, Madani TA, Ntoumi F, Kock R, Dar O, Ippolito G, McHugh TD, Memish ZA, Drosten C, Zumla A, Petersen E. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis. 2020 Feb;91:264-266. eng. Epub 2020/01/19. https://doi.org/10.1016/j.ijid.2020.01.009. Cited in: Pubmed; PMID 31953166.
  9. Calligari P, Bobone S, Ricci G, Bocedi A. Molecular Investigation of SARS-CoV-2 Proteins and Their Interactions with Antiviral Drugs. Viruses. 2020 Apr 14;12(4). eng. Epub 2020/04/17. https://doi.org/10.3390/v12040445. Cited in: Pubmed; PMID 32295237.
  10. Prajapat M, Sarma P, Shekhar N, Avti P, Sinha S, Kaur H, Kumar S, Bhattacharyya A, Kumar H, Bansal S, Medhi B. Drug targets for corona virus: A systematic review. Indian J Pharmacol. 2020 Jan-Feb;52(1):56-65. eng. Epub 2020/03/24. https://doi.org/10.4103/ijp.IJP_115_20. Cited in: Pubmed; PMID 32201449.
  11. Chen Y, Guo Y, Pan Y, Zhao ZJ. Structure analysis of the receptor binding of 2019-nCoV. Biochem Biophys Res Commun. 2020 Feb;525(1):S0006-291X(20)30339-9.
  12. Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, Lou Y, Gao D, Yang L, He D, Wang MH. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int J Infect Dis. 2020 Mar;92:214-217. eng. Epub 2020/02/03. https://doi.org/10.1016/j.ijid.2020.01.050. Cited in: Pubmed; PMID 32007643.
  13. Guzzi PH, Mercatelli D, Ceraolo C, Giorgi FM. Master Regulator Analysis of the SARS-CoV-2/Human Interactome. J Clin Med. 2020 Apr 1;9(4). eng. Epub 2020/04/05. https://doi.org/10.3390/jcm9040982. Cited in: Pubmed; PMID 32244779.
  14. Pei G, Chen L, Zhang W. WGCNA Application to Proteomic and Metabolomic Data Analysis. Methods Enzymol. 2017;585:135-158. eng. Epub 2017/01/23. https://doi.org/10.1016/bs.mie.2016.09.016. Cited in: Pubmed; PMID 28109426.
  15. Yin L, Cai Z, Zhu B, Xu C. Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA. Genes (Basel). 2018 Feb 14;9(2). eng. Epub 2018/02/15. https://doi.org/10.3390/genes9020092. Cited in: Pubmed; PMID 29443924.
  16. Udyavar AR, Hoeksema MD, Clark JE, Zou Y, Tang Z, Li Z, Li M, Chen H, Statnikov A, Shyr Y, Liebler DC, Field J, Eisenberg R, Estrada L, Massion PP, Quaranta V. Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer. BMC Syst Biol. 2013;7 Suppl 5(Suppl 5):S1. eng. Epub 2014/02/26. . Cited in: Pubmed; PMID 24564859. https://doi.org/10.1186/1752-0509-7-S5-S1.
  17. Shi Z, Derow CK, Zhang B. Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression. BMC Syst Biol. 2010 May 27;4:74. eng. Epub 2010/05/29. https://doi.org/10.1186/1752-0509-4-74. Cited in: Pubmed; PMID 20507583.
  18. Liu X, Hu AX, Zhao JL, Chen FL. Identification of Key Gene Modules in Human Osteosarcoma by Co-Expression Analysis Weighted Gene Co-Expression Network Analysis (WGCNA). J Cell Biochem. 2017 Nov;118(11):3953-3959. eng. Epub 2017/04/12. https://doi.org/10.1002/jcb.26050. Cited in: Pubmed; PMID 28398605.
  19. Chen PY, Cripps AW, West NP, Cox AJ, Zhang P. A correlation-based network for biomarker discovery in obesity with metabolic syndrome. BMC Bioinformatics. 2019 Dec 10;20(Suppl 6):477. eng. Epub 2019/12/12. https://doi.org/10.1186/s12859-019-3064-2. Cited in: Pubmed; PMID 31823713.
  20. Xia WX, Yu Q, Li GH, Liu YW, Xiao FH, Yang LQ, Rahman ZU, Wang HT, Kong QP. Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA. PeerJ. 2019;7:e6555. eng. Epub 2019/03/20. https://doi.org/10.7717/peerj.6555. Cited in: Pubmed; PMID 30886771.
  21. Lieberman NA, Peddu V, Xie H, Shrestha L, Huang ML, Mears MC, et al. In vivo antiviral host transcriptional response to SARS-CoV-2 by viral load, sex, and age. PLoS Biol. 2020 Sep;18(9):e3000849.
  22. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9:559. eng. Epub 2008/12/31. https://doi.org/10.1186/1471-2105-9-559. Cited in: Pubmed; PMID 19114008.
  23. Shi K, Bing ZT, Cao GQ, Guo L, Cao YN, Jiang HO, Zhang MX. Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis. Int J Ophthalmol. 2015;8(2):269-74. eng. Epub 2015/05/06. https://doi.org/10.3980/j.issn.2222-3959.2015.02.10. Cited in: Pubmed; PMID 25938039.
  24. Li X, He Y, Hao C, Li X, Li X. Weighted gene correlation network analysis reveals novel regulatory modules associated with recurrent early pregnancy loss. Biosci Rep. 2020 Jun 26;40(6). eng. Epub 2020/05/14. . Cited in: Pubmed; PMID 32401299. https://doi.org/10.1042/BSR20193938.
  25. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012 May;16(5):284-7. eng. Epub 2012/03/30. https://doi.org/10.1089/omi.2011.0118. Cited in: Pubmed; PMID 22455463.
  26. Villanueva RA, Chen ZJ. ggplot2: Elegant graphics for data analysis. Taylor & Francis; 2019.
  27. Zhang W, Xu J, Li Y, Zou X. Integrating network topology, gene expression data and GO annotation information for protein complex prediction. J Bioinform Comput Biol. 2019 Feb;17(1):1950001. eng. Epub 2019/02/26. . Cited in: Pubmed; PMID 30803297. https://doi.org/10.1142/S021972001950001X.
  28. Thomas PD, Hill DP, Mi H, Osumi-Sutherland D, Van Auken K, Carbon S, Balhoff JP, Albou LP, Good B, Gaudet P, Lewis SE, Mungall CJ. Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems. Nat Genet. 2019 Oct;51(10):1429-1433. eng. Epub 2019/09/25. https://doi.org/10.1038/s41588-019-0500-1. Cited in: Pubmed; PMID 31548717.
  29. Li Y, Dong W, Shi Y, Deng F, Chen X, Wan C, Zhou M, Zhao L, Fu ZF, Peng G. Rabies virus phosphoprotein interacts with ribosomal protein L9 and affects rabies virus replication. Virology. 2016 Jan 15;488:216-24. eng. Epub 2015/12/15. https://doi.org/10.1016/j.virol.2015.11.018. Cited in: Pubmed; PMID 26655239.
  30. Abbas W, Dichamp I, Herbein G. The HIV-1 Nef protein interacts with two components of the 40S small ribosomal subunit, the RPS10 protein and the 18S rRNA. Virol J. 2012 Jul 10;9:103. eng. Epub 2012/06/08. . Cited in: Pubmed; PMID 22672539. https://doi.org/10.1186/1743-422X-9-103.
  31. Rofeal M, Abd El-Malek F. Ribosomal proteins as a possible tool for blocking SARS-COV 2 virus replication for a potential prospective treatment. Elsevier; 2020. https://doi.org/10.1016/j.mehy.2020.109904.
  32. Lv H, Dong W, Qian G, Wang J, Li X, Cao Z, Lv Q, Wang C, Guo K, Zhang Y. uS10, a novel Npro-interacting protein, inhibits classical swine fever virus replication. J Gen Virol. 2017 Jul;98(7):1679-1692. eng. Epub 2017/07/20. https://doi.org/10.1099/jgv.0.000867. Cited in: Pubmed; PMID 28721853.
  33. Totura AL, Whitmore A, Agnihothram S, Schäfer A, Katze MG, Heise MT, Baric RS. Toll-Like Receptor 3 Signaling via TRIF Contributes to a Protective Innate Immune Response to Severe Acute Respiratory Syndrome Coronavirus Infection. mBio. 2015 May 26;6(3):e00638-15. eng. Epub 2015/05/28. https://doi.org/10.1128/mBio.00638-15. Cited in: Pubmed; PMID 26015500.
  34. Horng JC, Moroz V, Rigotti DJ, Fairman R, Raleigh DP. Characterization of large peptide fragments derived from the N-terminal domain of the ribosomal protein L9: definition of the minimum folding motif and characterization of local electrostatic interactions. Biochemistry. 2002 Nov 12;41(45):13360-9. eng. Epub 2002/11/06. https://doi.org/10.1021/bi026410c. Cited in: Pubmed; PMID 12416980.
  35. Beyer AR, Bann DV, Rice B, Pultz IS, Kane M, Goff SP, Golovkina TV, Parent LJ. Nucleolar trafficking of the mouse mammary tumor virus gag protein induced by interaction with ribosomal protein L9. J Virol. 2013 Jan;87(2):1069-82. eng. Epub 2012/11/09. . Cited in: Pubmed; PMID 23135726. https://doi.org/10.1128/JVI.02463-12.
  36. Abbas W, Dichamp I, Herbein G. The HIV-1 Nef Protein Interacts with two components of the 40S small ribosomal subunit, the RPS10 protein and the 18S rRNA. Virology Journal. 2012 2012/07/10;9(1):103. https://doi.org/10.1186/1743-422X-9-103.
  37. Robledo S, Idol RA, Crimmins DL, Ladenson JH, Mason PJ, Bessler M. The role of human ribosomal proteins in the maturation of rRNA and ribosome production. RNA. 2008 Sep;14(9):1918–29.
  38. Colombo P, Yon J, Fried M. The organization and expression of the human L7a ribosomal protein gene. Biochim Biophys Acta. 1991 Dec;1129(1):93–5.
  39. Fumagalli S, Ivanenkov VV, Teng T, Thomas G. Suprainduction of p53 by disruption of 40S and 60S ribosome biogenesis leads to the activation of a novel G2/M checkpoint. Genes Dev. 2012 May;26(10):1028–40.
  40. Wang M, Parshin AV, Shcherbik N, Pestov DG. Reduced expression of the mouse ribosomal protein Rpl17 alters the diversity of mature ribosomes by enhancing production of shortened 5.8S rRNA. RNA. 2015 Jul;21(7):1240–8.
  41. Rospert S. Ribosome function: governing the fate of a nascent polypeptide. Curr Biol. 2004 May;14(10):R386–8.
  42. Li W, Moore MJ, Vasilieva N, Sui J, Wong SK, Berne MA, et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature. 2003 Nov 27;426(6965):450-4. eng. Epub 2003/12/04. https://doi.org/10.1038/nature02145. Cited in: Pubmed; PMID 14647384.
  43. Kuba K, Imai Y, Rao S, Gao H, Guo F, Guan B, et al. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury. Nat Med. 2005 Aug;11(8):875-9. eng. Epub 2005/07/12. https://doi.org/10.1038/nm1267. Cited in: Pubmed; PMID 16007097.
  44. Xu X, Chen P, Wang J, Feng J, Zhou H, Li X, Zhong W, Hao P. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Science China Life Sciences. 2020 2020/03/01;63(3):457-460. https://doi.org/10.1007/s11427-020-1637-5.
  45. Rabaan AA, Al-Ahmed SH, Haque S, Sah R, Tiwari R, Malik YS, et al. SARS-CoV-2, SARS-CoV, and MERS-COV: A comparative overview. Infez Med. 2020 Ahead Of Print Jun 1;28(2):174-184. eng. Epub 2020/04/11. Cited in: Pubmed; PMID 32275259.
  46. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020 Mar;579(7798):270-273. eng. Epub 2020/02/06. https://doi.org/10.1038/s41586-020-2012-7. Cited in: Pubmed; PMID 32015507.
  47. Zhao Y, Zhao Z, Wang Y, Zhou Y, Ma Y, Zuo W. Single-cell RNA expression profiling of ACE2, the receptor of SARS-CoV-2. bioRxiv. 2020:2020.01.26.919985. https://doi.org/10.1101/2020.01.26.919985.