Characteristic genes in THP‑1 derived macrophages infected with Mycobacterium tuberculosis H37Rv strain identified by integrating bioinformatics methods

Zhang,Yu‑Wei; Lin,Yan; Yu,Hui‑Yuan; Tian,Ruo‑Nan; Li,Fan

doi:10.3892/ijmm.2019.4293

Characteristic genes in THP‑1 derived macrophages infected with Mycobacterium tuberculosis H37Rv strain identified by integrating bioinformatics methods

Authors:
- Yu‑Wei Zhang
- Yan Lin
- Hui‑Yuan Yu
- Ruo‑Nan Tian
- Fan Li
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Affiliations: Department of Pathogen Biology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, Jilin 130021, P.R. China, School of Bethune Medical, Jilin University, Changchun, Jilin 130021, P.R. China
Published online on: July 30, 2019 https://doi.org/10.3892/ijmm.2019.4293
Pages: 1243-1254
Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Mycobacterium tuberculosis (M. tb) is a highly successful pathogen that has co‑existed with humans for 1,000's of years. As the cornerstone of the immune system, macrophages are a key part of innate immunity. They ingest and degrade foreign substances including aging cells and microorganisms, coordinate the inflammatory process, and are the first line of defense against M. tb infection. Recent advances in cellular mycobacteriology have indicated that M. tb uses an remarkably complex strategy to disrupt macrophage function, in order to counteract the antimicrobial mechanisms of the innate and adaptive immune responses, thereby achieving immune escape. With the popularity of microarray technology, a variety of public platforms have provided a variety of gene expression data associated with physiological and disease conditions. Meta‑analysis can systematically and quantitatively analyze multiple independent data concerning the same disease, greatly improving the statistical significance and credibility of the gene expression data analysis performed. In the present study, 6 microarray expression datasets of human acute monocytic leukemia THP‑1 cell line infected by M. tb H37Rv strain were collected from the GEO database. A total of 4 high‑quality datasets were identified using meta‑analysis methods in R language, and 306 differentially expressed genes with statistical significance were obtained. Then, a protein‑protein interaction (PPI) network of these differentially expressed genes was constructed on the Search Tool for the Retrieval of Interacting Genes/Proteins Database online tool and visualized by Cytoscape v. 3.6.1 software. Using CentiScape and MCODE plugin in the Cytoscape software to mine the functional modules associated with M. tb infection process, 32 characteristic genes were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed on the 32 characteristic genes, and it was demonstrated that these genes were primarily associated with the type I interferon (IFN) pathway. In the established model of THP‑1‑derived macrophages infected by M. tb, the actual differential expression levels of IFN‑stimulated gene 15 (ISG15), 2'‑5‑oligoadenylate synthetase like (OASL), IFN regulatory factor 7 (IRF7) and DExD/H‑box helicase 58 (DDX58), the first 4 genes of the 32 characteristic genes, were verified by reverse transcription quantitative polymerase chain reaction. The results were consistent with the results of microarray analysis. The association between ISG15, OASL and IRF7 and TB infection was also verified. Although a number of studies have identified that the type I IFN pathway may assist M. tb to achieve immune escape, the present study used a meta‑analysis of microarray data and PPI network analysis to examine some of the novel genes identified in the IFN pathway. The results furthered the understanding of the molecular mechanisms of the TB immune response and provided a novel perspective for future therapeutic goals.

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1	Goldberg MF, Saini NK and Porcelli SA: Evasion of innate and adaptive immunity by Mycobacterium tuberculosis. Microbiol Spectr. 5:2014.
2	Comas I, Coscolla M, Luo T, Borrell S, Holt KE, Kato-Maeda M, Parkhill J, Malla B, Berg S, Thwaites G, et al: Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet. 45:1176–1182. 2013. View Article : Google Scholar : PubMed/NCBI
3	Organization WH: Global Tuberculosis Report. 2018, https://www.who.int/tb/publications/global_report/en/. Accessed September 18, 2018.
4	Aderem A and Underhill DM: Mechanisms of phagocytosis in macrophages. Annu Rev Immunol. 17:593–623. 1999. View Article : Google Scholar : PubMed/NCBI
5	Weiss G and Schaible UE: Macrophage defense mechanisms against intracellular bacteria. Immunol Rev. 264:182–203. 2015. View Article : Google Scholar : PubMed/NCBI
6	Akira S, Uematsu S and Takeuchi O: Pathogen recognition and innate immunity. Cell. 124:783–801. 2006. View Article : Google Scholar : PubMed/NCBI
7	Bhatt K and Salgame P: Host innate immune response to Mycobacterium tuberculosis. J Clin Immunol. 27:347–362. 2007. View Article : Google Scholar : PubMed/NCBI
8	Hmama Z, Peña-Díaz S, Joseph S and Av-Gay Y: Immunoevasion and immunosuppression of the macrophage by Mycobacterium tuberculosis. Immunol Rev. 264:220–232. 2015. View Article : Google Scholar : PubMed/NCBI
9	Meena LS and Rajni: Survival mechanisms of pathogenic Mycobacterium tuberculosis H37Rv. FEBS J. 277:2416–2427. 2010. View Article : Google Scholar : PubMed/NCBI
10	Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A and Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA. 101:9309–9314. 2004. View Article : Google Scholar : PubMed/NCBI
11	Rung J and Brazma A: Reuse of public genome-wide gene expression data. Nat Rev Genet. 14:89–99. 2013. View Article : Google Scholar
12	Wang X, Kang DD, Shen K, Song C, Lu S, Chang LC, Liao SG, Huo Z, Tang S, Ding Y, et al: An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics. 28:2534–2536. 2012. View Article : Google Scholar : PubMed/NCBI
13	Smid M, Dorssers LC and Jenster G: Venn mapping: Clustering of heterologous microarray data based on the number of co-occurring differentially expressed genes. Bioinformatics. 19:2065–2071. 2003. View Article : Google Scholar : PubMed/NCBI
14	DeConde RP, Hawley S, Falcon S, Clegg N, Knudsen B and Etzioni R: Combining results of microarray experiments: A rank aggregation approach. Stat Appl Genet Mol Biol. 5:Article152006. View Article : Google Scholar : PubMed/NCBI
15	Xia J, Fjell CD, Mayer ML, Pena OM, Wishart DS and Hancock RE: INMEX-a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res. 41:W63–W70. 2013. View Article : Google Scholar : PubMed/NCBI
16	Xia J, Gill EE and Hancock RE: NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 10:823–844. 2015. View Article : Google Scholar : PubMed/NCBI
17	Wang R, Cai Y, Zhang B and Wu Z: A 16-gene expression signature to distinguish stage I from stage II lung squamous carcinoma. Int J Mol Med. 41:1377–1384. 2018.
18	Shao K, Shen LS, Li HH, Huang S and Zhang Y: Systematic-analysis of mRNA expression profiles in skeletal muscle of patients with type II diabetes: The glucocorticoid was central in pathogenesis. J Cell Physiol. 233:4068–4076. 2018. View Article : Google Scholar
19	Tuo Y, An N and Zhang M: Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods. Mol Med Rep. 17:4281–4290. 2018.PubMed/NCBI
20	Tseng GC, Ghosh D and Feingold E: Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res. 40:3785–3799. 2012. View Article : Google Scholar : PubMed/NCBI
21	Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:D447–D452. 2015. View Article : Google Scholar
22	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
23	Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar
24	Spandidos A, Wang X, Wang H and Seed B: PrimerBank: A resource of human and mouse PCR primer pairs for gene expression detection and quantification. Nucleic Acids Res. 38:D792–D799. 2010. View Article : Google Scholar :
25	Stanley SA, Raghavan S, Hwang WW and Cox JS: Acute infection and macrophage subversion by Mycobacterium tuberculosis require a specialized secretion system. Proc Natl Acad Sci USA. 100:13001–13006. 2003. View Article : Google Scholar : PubMed/NCBI
26	Hsu T, Hingley-Wilson SM, Chen B, Chen M, Dai AZ, Morin PM, Marks CB, Padiyar J, Goulding C, Gingery M, et al: The primary mechanism of attenuation of bacillus Calmette-Guerin is a loss of secreted lytic function required for invasion of lung interstitial tissue. Proc Natl Acad Sci USA. 100:12420–12425. 2003. View Article : Google Scholar : PubMed/NCBI
27	Guinn KM, Hickey MJ, Mathur SK, Zakel KL, Grotzke JE, Lewinsohn DM, Smith S and Sherman DR: Individual RD1-region genes are required for export of ESAT-6/CFP-10 and for virulence of Mycobacterium tuberculosis. Mol Microbiol. 51:359–370. 2004. View Article : Google Scholar : PubMed/NCBI
28	Stanley SA, Johndrow JE, Manzanillo P and Cox JS: The type I IFN response to infection with Mycobacterium tuberculosis requires ESX-1-mediated secretion and contributes to pathogenesis. J Immunol. 178:3143–3152. 2007. View Article : Google Scholar : PubMed/NCBI
29	de Veer MJ, Holko M, Frevel M, Walker E, Der S, Paranjape JM, Silverman RH and Williams BR: Functional classification of interferon-stimulated genes identified using microarrays. J Leukoc Biol. 69:912–920. 2001.PubMed/NCBI
30	Taylor KE and Mossman KL: Recent advances in understanding viral evasion of type I interferon. Immunology. 138:190–197. 2013. View Article : Google Scholar :
31	Manca C, Tsenova L, Bergtold A, Freeman S, Tovey M, Musser JM, Barry CE III, Freedman VH and Kaplan G: Virulence of a Mycobacterium tuberculosis clinical isolate in mice is determined by failure to induce Th1 type immunity and is associated with induction of IFN-alpha/beta. Proc Natl Acad Sci USA. 98:5752–5757. 2001. View Article : Google Scholar
32	Flynn JL and Chan J: What's good for the host is good for the bug. Trends Microbiol. 13:98–102. 2005. View Article : Google Scholar : PubMed/NCBI
33	Ordway D, Henao-Tamayo M, Harton M, Palanisamy G, Troudt J, Shanley C, Basaraba RJ and Orme IM: The hypervirulent Mycobacterium tuberculosis strain HN878 induces a potent TH1 response followed by rapid down-regulation. J Immunol. 179:522–531. 2007. View Article : Google Scholar : PubMed/NCBI
34	Antonelli LR, Gigliotti Rothfuchs A, Gonçalves R, Roffê E, Cheever AW, Bafica A, Salazar AM, Feng CG and Sher A: Intranasal Poly-IC treatment exacerbates tuberculosis in mice through the pulmonary recruitment of a pathogen-permissive monocyte/macrophage population. J Clin Invest. 120:1674–1682. 2010. View Article : Google Scholar : PubMed/NCBI
35	Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA, Oni T, Wilkinson KA, Banchereau R, Skinner J, Wilkinson RJ, et al: An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 466:973–977. 2010. View Article : Google Scholar : PubMed/NCBI
36	Desvignes L, Wolf AJ and Ernst JD: Dynamic roles of type I and type II IFNs in early infection with Mycobacterium tuberculosis. J Immunol. 188:6205–6215. 2012. View Article : Google Scholar : PubMed/NCBI
37	McNab FW, Ewbank J, Rajsbaum R, Stavropoulos E, Martirosyan A, Redford PS, Wu X, Graham CM, Saraiva M, Tsichlis P, et al: TPL-2-ERK1/2 signaling promotes host resistance against intracellular bacterial infection by negative regulation of type I IFN production. J Immunol. 191:1732–1743. 2013. View Article : Google Scholar : PubMed/NCBI
38	Dorhoi A, Yeremeev V, Nouailles G, Weiner J III, Jörg S, Heinemann E, Oberbeck-Müller D, Knaul JK, Vogelzang A, Reece ST, et al: Type I IFN signaling triggers immunopathology in tuberculosis-susceptible mice by modulating lung phagocyte dynamics. Eur J Immunol. 44:2380–2393. 2014. View Article : Google Scholar : PubMed/NCBI
39	Der SD, Zhou A, Williams BR and Silverman RH: Identification of genes differentially regulated by interferon alpha, beta, or gamma using oligonucleotide arrays. Proc Natl Acad Sci USA. 95:15623–15628. 1998. View Article : Google Scholar : PubMed/NCBI
40	Chaussabel D, Semnani RT, McDowell MA, Sacks D, Sher A and Nutman TB: Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasites. Blood. 102:672–681. 2003. View Article : Google Scholar : PubMed/NCBI
41	Hermann M and Bogunovic D: ISG15: In sickness and in health. Trends Immunol. 38:79–93. 2017. View Article : Google Scholar
42	Chua PK, McCown MF, Rajyaguru S, Kular S, Varma R, Symons J, Chiu SS, Cammack N and Nájera I: Modulation of alpha interferon anti-hepatitis C virus activity by ISG15. J Gen Virol. 90:2929–2939. 2009. View Article : Google Scholar : PubMed/NCBI
43	Zhang D and Zhang DE: Interferon-stimulated gene 15 and the protein ISGylation system. J Interferon Cytokine Res. 31:119–130. 2011. View Article : Google Scholar :
44	Speer SD, Li Z, Buta S, Payelle-Brogard B, Qian L, Vigant F, Rubino E, Gardner TJ, Wedeking T, Hermann M, et al: ISG15 deficiency and increased viral resistance in humans but not mice. Nat Commun. 7:114962016. View Article : Google Scholar : PubMed/NCBI
45	Sooryanarain H, Rogers AJ, Cao D, Haac MER, Karpe YA and Meng XJ: ISG15 modulates type I interferon signaling and the antiviral response during Hepatitis E virus replication. J Virol. 91:e00621–17. 2017. View Article : Google Scholar : PubMed/NCBI
46	Kimmey JM, Campbell JA, Weiss LA, Monte KJ, Lenschow DJ and Stallings CL: The impact of ISGylation during Mycobacterium tuberculosis infection in mice. Microbes Infect. 19:249–258. 2017. View Article : Google Scholar : PubMed/NCBI
47	Keller C, Hoffmann R, Lang R, Brandau S, Hermann C and Ehlers S: Genetically determined susceptibility to tuberculosis in mice causally involves accelerated and enhanced recruitment of granulocytes. Infect Immun. 74:4295–4309. 2006. View Article : Google Scholar : PubMed/NCBI
48	Sambarey A, Devaprasad A, Mohan A, Ahmed A, Nayak S, Swaminathan S, D'Souza G, Jesuraj A, Dhar C, Babu S, et al: Unbiased identification of blood-based biomarkers for pulmonary tuberculosis by modeling and mining molecular interaction networks. EBioMedicine. 15:112–126. 2017. View Article : Google Scholar : PubMed/NCBI
49	Sadler AJ and Williams BR: Interferon-inducible antiviral effectors. Nat Rev Immunol. 8:559–568. 2008. View Article : Google Scholar : PubMed/NCBI
50	Hancks DC, Hartley MK, Hagan C, Clark NL and Elde NC: Overlapping patterns of rapid evolution in the nucleic acid sensors cGAS and OAS1 suggest a common mechanism of pathogen antagonism and escape. PLoS Genet. 11:e10052032015. View Article : Google Scholar : PubMed/NCBI
51	Choi UY, Kang JS, Hwang YS and Kim YJ: Oligoadenylate synthase-like (OASL) proteins: Dual functions and associations with diseases. Exp Mol Med. 47:e1442015. View Article : Google Scholar : PubMed/NCBI
52	Lee MS, Kim B, Oh GT and Kim YJ: OASL1 inhibits translation of the type I interferon-regulating transcription factor IRF7. Nat Immunol. 14:346–355. 2013. View Article : Google Scholar : PubMed/NCBI
53	Lee MS, Park CH, Jeong YH, Kim YJ and Ha SJ: Negative regulation of type I IFN expression by OASL1 permits chronic viral infection and CD8(+) T-cell exhaustion. PLoS Pathog. 9:e10034782013. View Article : Google Scholar
54	de Toledo-Pinto TG, Ferreira AB, Ribeiro-Alves M, Rodrigues LS, Batista-Silva LR, Silva BJ, Lemes RM, Martinez AN, Sandoval FG, Alvarado-Arnez LE, et al: STING-dependent 2′-5′ oligoadenylate synthetase-like production is required for intracellular Mycobacterium leprae survival. J Infect Dis. 214:311–320. 2016. View Article : Google Scholar : PubMed/NCBI
55	Leisching G, Pietersen RD, van Heerden C, van Helden P, Wiid I and Baker B: RNAseq reveals hypervirulence-specific host responses to M. tuberculosis infection. Virulence. 8:848–858. 2017. View Article : Google Scholar :
56	Cheng Y and Schorey JS: Mycobacterium tuberculosis-induced IFN-β production requires cytosolic DNA and RNA sensing pathways. J Exp Med. 215:2919–2935. 2018. View Article : Google Scholar : PubMed/NCBI
57	Lin Y, Duan Z, Xu F, Zhang J, Shulgina MV and Li F: Construction and analysis of the transcription factor-microRNA co-regulatory network response to Mycobacterium tuberculosis: A view from the blood. Am J Transl Res. 9:1962–1976. 2017.