Research Article

Analysis of Hypoxiamir-Gene Regulatory Network Identifies Critical MiRNAs Influencing Cell-Cycle Regulation Under Hypoxic Conditions

Author(s): Apoorv Gupta, Sugadev Ragumani, Yogendra Kumar Sharma, Yasmin Ahmad and Pankaj Khurana*

Volume 8, Issue 3, 2019

Page: [223 - 236] Pages: 14

DOI: 10.2174/2211536608666190219094204

Price: $65


Background: Hypoxia is a pathophysiological condition which arises due to low oxygen concentration in conditions like cardiovascular diseases, inflammation, ascent to higher altitude, malignancies, deep sea diving, prenatal birth, etc. A number of microRNAs (miRNAs), Transcription Factors (TFs) and genes have been studied separately for their role in hypoxic adaptation and controlling cell-cycle progression and apoptosis during this stress.

Objective: We hypothesize that miRNAs and TFs may act in conjunction to regulate a multitude of genes and play a crucial and combinatorial role during hypoxia-stress-responses and associated cellcycle control mechanisms.

Method: We collected a comprehensive and non-redundant list of human hypoxia-responsive miRNAs (also known as hypoxiamiRs). Their experimentally validated gene-targets were retrieved from various databases and a comprehensive hypoxiamiR-gene regulatory network was built.

Results: Functional characterization and pathway enrichment of genes identified phospho-proteins as enriched nodes. The phospho-proteins which were localized both in the nucleus and cytoplasm and could potentially play important role as signaling molecules were selected; and further pathway enrichment revealed that most of them were involved in NFkB signaling. Topological analysis identified several critical hypoxiamiRs and network perturbations confirmed their importance in the network. Feed Forward Loops (FFLs) were identified in the subnetwork of enriched genes, miRNAs and TFs. Statistically significant FFLs consisted of four miRNAs (hsa-miR-182-5p, hsa- miR-146b-5p, hsa-miR-96, hsa-miR-20a) and three TFs (SMAD4, FOXO1, HIF1A) both regulating two genes (NFkB1A and CDKN1A).

Conclusion: Detailed BioCarta pathway analysis identified that these miRNAs and TFs together play a critical and combinatorial role in regulating cell-cycle under hypoxia, by controlling mechanisms that activate cell-cycle checkpoint protein, CDKN1A. These modules work synergistically to regulate cell-proliferation, cell-growth, cell-differentiation and apoptosis during hypoxia. A detailed mechanistic molecular model of how these co-regulatory FFLs may regulate the cell-cycle transitions during hypoxic stress conditions is also put forth. These biomolecules may play a crucial and deterministic role in deciding the fate of the cell under hypoxic-stress.

Keywords: Body tissues, Feed Forward Loops (FFLs), hypoxia, hypoxiamiRs, network perturbations, regulating modules.

Graphical Abstract
Li Y, Padmanabha D, Gentile LB, Dumur CI, Beckstead RB, Baker KD. HIF- and non-HIF-regulated hypoxic responses require the estrogen-related receptor in Drosophila melanogaster. PLoS Genet 2013; 9(1): e1003230.
Semenza GL. HIF-1 and human disease: one highly involved factor. Genes Dev 2000; 14(16): 1983-91.
Gupta A, Sugadev R, Sharma YK, Yahmad Y, Khurana P. Role of miRNAs in hypoxia-related disorders. J Biosci 2018; 43(4): 739-49.
Majmundar AJ, Wong WJ, Simon MC. Hypoxia-inducible factors and the response to hypoxic stress. Mol Cell 2010; 40(2): 294-309.
Kunz M, Ibrahim SM. Molecular responses to hypoxia in tumor cells. Mol Cancer 2003; 2: 23.
Hubbi ME, Semenza GL. Regulation of cell proliferation by hypoxia-inducible factors. Am J Physiol Cell Physiol 2015; 309(12): C775-82.
Goda N, Ryan HE, Khadivi B, McNulty W, Rickert RC, Johnson RS. Hypoxia-inducible factor 1 alpha is essential for cell cycle arrest during hypoxia. Mol Cell Biol 2003; 23(1): 359-69.
Hu CJ, Wang LY, Chodosh LA, Keith B, Simon MC. Differential roles of hypoxia-inducible factor 1alpha (HIF-1alpha) and HIF-2alpha in hypoxic gene regulation. Mol Cell Biol 2003; 23(24): 9361-74.
Fandrey J, Gorr TA, Gassmann M. Regulating cellular oxygen sensing by hydroxylation. Cardiovasc Res 2006; 71(4): 642-51.
Kunz M, Moeller S, Koczan D, et al. Mechanisms of hypoxic gene regulation of angiogenesis factor Cyr61 in melanoma cells. The J Boil Chem 2003; 278(46): 45651-60.
Semenza GL. Targeting HIF-1 for cancer therapy. Nat Rev Cancer 2003; 3(10): 721-32.
Manalo DJ, Rowan A, Lavoie T, et al. Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-1. Blood 2005; 105(2): 659-69.
Wang V, Davis DA, Haque M, Huang LE, Yarchoan R. Differential gene up-regulation by hypoxia-inducible factor-1alpha and hypoxia-inducible factor-2alpha in HEK293T cells. Cancer Res 2005; 65(8): 3299-306.
Benita Y, Kikuchi H, Smith AD, Zhang MQ, Chung DC, Xavier RJ. An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia. Nucleic Acids Res 2009; 37(14): 4587-602.
Zhang HM, Kuang S, Xiong X, Gao T, Liu C, Guo AY. Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases. Brief Bioinform 2015; 16(1): 45-58.
Slomiany MG, Rosenzweig SA. Hypoxia-inducible factor-1-dependent and -independent regulation of insulin-like growth factor-1-stimulated vascular endothelial growth factor secretion. The J Pharmacol Exp Ther 2006; 318(2): 666-75.
Mizukami Y, Kohgo Y, Chung DC. Hypoxia inducible factor-1 independent pathways in tumor angiogenesis. Clin Cancer Res 2007; 13(19): 5670-4.
Fujisue Y, Nakagawa T, Takahara K, et al. Induction of erythropoietin increases the cell proliferation rate in a hypoxia-inducible factor-1-dependent and -independent manner in renal cell carcinoma cell lines. Oncol Lett 2013; 5(6): 1765-70.
Lee J, Lee J. Hypoxia-inducible Factor-1 (HIF-1)-independent hypoxia response of the small heat shock protein hsp-16.1 gene regulated by chromatin-remodeling factors in the nematode Caenorhabditis elegans. The J Boil Chem 2013; 288(3): 1582-9.
Joyce D, Albanese C, Steer J, Fu M, Bouzahzah B, Pestell RG. NF-kappaB and cell-cycle regulation: the cyclin connection. Cytokine Growth Factor Rev 2001; 12(1): 73-90.
Weiler-Mithoff EM, Friederich HC, Horn W, Issing K. Increase of oxygen partial pressure and acceleration of wound healing by tetrachlorodecaoxide. Zeitschrift fur Hautkrankheiten 1989; 64(3): 208-11.
Ledoux AC, Perkins ND. NF-kappaB and the cell cycle. Biochem Soc Trans 2014; 42(1): 76-81.
Cummins EP, Berra E, Comerford KM, et al. Prolyl hydroxylase-1 negatively regulates IkappaB kinase-beta, giving insight into hypoxia-induced NFkappaB activity. Proc Natl Acad Sci USA 2006; 103(48): 18154-9.
Gilmore TD. Introduction to NF-kappaB: players, pathways, perspectives. Oncogene 2006; 25(51): 6680-4.
Wouters BG, Koritzinsky M. Hypoxia signalling through mTOR and the unfolded protein response in cancer. Nat Rev Cancer 2008; 8(11): 851-64.
Kulshreshtha R, Ferracin M, Wojcik SE, et al. A microRNA signature of hypoxia. Mol Cell Biol 2007; 27(5): 1859-67.
Huang X, Le QT, Giaccia AJ. MiR-210--micromanager of the hypoxia pathway. Trends Mol Med 2010; 16(5): 230-7.
Chivukula RR, Mendell JT. Circular reasoning: microRNAs and cell-cycle control. Trends Biochem Sci 2008; 33(10): 474-81.
Bandi N, Zbinden S, Gugger M, et al. miR-15a and miR-16 are implicated in cell cycle regulation in a Rb-dependent manner and are frequently deleted or down-regulated in non-small cell lung cancer. Cancer Res 2009; 69(13): 5553-9.
Chen J, Wang DZ. microRNAs in cardiovascular development. J Mol Cell Cardiol 2012; 52(5): 949-57.
Chan YC, Khanna S, Roy S, Sen CK. miR-200b targets Ets-1 and is down-regulated by hypoxia to induce angiogenic response of endothelial cells. The J Biol Chem 2011; 286(3): 2047-56.
Tili E, Croce CM, Michaille JJ. MiR-155: on the crosstalk between inflammation and cancer. Int Rev Immunol 2009; 28(5): 264-84.
Li T, Li RS, Li YH, et al. miR-21 as an independent biochemical recurrence predictor and potential therapeutic target for prostate cancer. The J Urol 2012; 187(4): 1466-72.
Gulyaeva LF, Kushlinskiy NE. Regulatory mechanisms of microRNA expression. J Transl Med 2016; 14(1): 143.
Zhang G, Shi H, Wang L, et al. MicroRNA and transcription factor mediated regulatory network analysis reveals critical regulators and regulatory modules in myocardial infarction. PLoS One 2015; 10(8): e0135339.
Zhang G, Xu Z, Wang N. Network of microRNA, transcription factors, target genes and host genes in human mesothelioma. Exp Therapeut Med 2017; 13(6): 3039-46.
Khurana SR, Sarkar S, Singh SB. A network-based analysis of proteins involved in hypoxia-stress and identification of leader proteins. J Proteomics Enzymol 2016; 5(1)
Tsang J, Zhu J, van Oudenaarden A. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell 2007; 26(5): 753-67.
Liang C, Li Y, Luo J, Zhang Z. A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human. Bioinformatics 2015; 31(14): 2348-55.
Franco E, Galloway KE. Feedback loops in biological networks. Methods Mol Biol 2015; 1244: 193-214.
Mangan S, Alon U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 2003; 100(21): 11980-5.
Su N, Wang Y, Qian M, Deng M. Combinatorial regulation of transcription factors and microRNAs. BMC Syst Biol 2010; 4: 150.
Li G, Ross KE, Arighi CN, Peng Y, Wu CH, Vijay-Shanker K. miRTex: a text mining system for miRNA-gene relation extraction. PLOS Comput Biol 2015; 11(9): e1004391.
Griffiths-Jones S. miRBase: the microRNA sequence database. Methods Mol Biol 2006; 342: 129-38.
Ru Y, Kechris KJ, Tabakoff B, et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res 2014; 42(17): e133.
Chou CH, Chang NW, Shrestha S, et al. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 2016; 44(D1): D239-47.
Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 2006; 12(2): 192-7.
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 2009; 37(Database issue): D105-10.
Fontaine JF, Priller F, Barbosa-Silva A, Andrade-Navarro MA. Genie: literature-based gene prioritization at multi genomic scaleNucleic Acids Res 2011; 39(Web Server issue): W455-61
Khurana P, Sugadev R, Jain J, Singh SB. HypoxiaDB: a database of hypoxia-regulated proteinsDatabase: The J Biol Databases Curat 2013; 2013: bat074
Jeanquartier F, Jean-Quartier C, Holzinger A. Integrated web visualizations for protein-protein interaction databases. BMC Bioinformatics 2015; 16: 195.
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011; 27(3): 431-2.
Dennis G Jr, Sherman BT, Hosack DA, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003; 4(5): 3.
Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 2010; 5(11): e13984.
Schuierer S, Tranchevent LC, Dengler U, Moreau Y. Large-scale benchmark of endeavour using metacore maps. Bioinformatics 2010; 26(15): 1922-3.
Nishimura D. BioCarta. Biotech Softw Internet Rep 2004; 2(3): 117.
Pankaj K, Sugadev R, Sarkar S, Shashi BS. A comprehensive assessment of networks and pathways of hypoxia-associated proteins and identification of responsive protein modules. Netw Model Anal Health Inform Bioinform 2016; 5(1): 1-13.
Rezaei-Tavirani M, Rezaei-Tavirani M, Mansouri V, et al. Introducing crucial protein panel of gastric adenocarcinoma disease. Gastroenterol Hepatol From Bed To Bench 2017; 10(1): 21-8.
Safari-Alighiarloo N, Taghizadeh M, Tabatabaei SM, et al. Identification of new key genes for type 1 diabetes through construction and analysis of protein-protein interaction networks based on blood and pancreatic islet transcriptomes. J Diabetes 2017; 9(8): 764-77.
Hamed M, Spaniol C, Nazarieh M, Helms V. TFmiR: a web server for constructing and analyzing disease-specific transcription factor and miRNA co-regulatory networks. Nucleic Acids Res 2015; 43(W1): W283-8.
Matys V, Fricke E, Geffers R, et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res 2003; 31(1): 374-8.
Griffith OL, Montgomery SB, Bernier B, et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res 2008; 36(Database issue): D107-13.
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27(12): 1739-40.
Wang J, Lu M, Qiu C, Cui Q. TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res 2010; 38(Database issue): D119-22.
Zhou KR, Liu S, Sun WJ, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res 2017; 45(D1): D43-50.
Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014; 42(Database issue): D92-7.
Sengupta D, Bandyopadhyay S. Participation of microRNAs in human interactome: extraction of microRNA-microRNA regulations. Mol Biosyst 2011; 7(6): 1966-73.
Shalgi R, Lieber D, Oren M, Pilpel Y. Global and local architecture of the mammalian microRNA-transcription factor regulatory network. PLOS Comput Biol 2007; 3(7): e131.
Zhu H, Fan GC. Role of microRNAs in the reperfused myocardium towards post-infarct remodelling. Cardiovasc Res 2012; 94(2): 284-92.
Volm M, Koomagi R. Hypoxia-Inducible Factor (HIF-1) and its relationship to apoptosis and proliferation in lung cancer. Anticancer Res 2000; 20(3A): 1527-33.
Schmidt M, Fernandez de Mattos S, van der Horst A, et al. Cell cycle inhibition by FoxO forkhead transcription factors involves downregulation of cyclin D. Mol Cell Biol 2002; 22(22): 7842-52.
Papageorgis P, Cheng K, Ozturk S, et al. Smad4 inactivation promotes malignancy and drug resistance of colon cancer. Cancer Res 2011; 71(3): 998-1008.
Wierenga AT, Vellenga E, Schuringa JJ. Convergence of hypoxia and TGF beta pathways on cell cycle regulation in human hematopoietic stem/progenitor cells. PLoS One 2014; 9(3): e93494.
Zhang H, Akman HO, Smith EL, Zhao J, Murphy-Ullrich JE, Batuman OA. Cellular response to hypoxia involves signaling via Smad proteins. Blood 2003; 101(6): 2253-60.
Ijichi H, Otsuka M, Tateishi K, et al. Smad4-independent regulation of p21/WAF1 by transforming growth factor-beta. Oncogene 2004; 23(5): 1043-51.
Guttilla IK, White BA. Coordinate regulation of FOXO1 by miR-27a, miR-96, and miR-182 in breast cancer cells. The J Biol Chem 2009; 284(35): 23204-16.
Zhou CH, Zhang XP, Liu F, Wang W. Modeling the interplay between the HIF-1 and p53 pathways in hypoxia. Sci Rep 2015; 5: 13834.
Kano H, Arakawa Y, Takahashi JA, et al. Overexpression of RFT induces G1-S arrest and apoptosis via p53/p21(Waf1) pathway in glioma cell. Biochem Biophys Res Commun 2004; 317(3): 902-8.
Curran JE, Weinstein SR, Griffiths LR. Polymorphic variants of NFKB1 and its inhibitory protein NFKBIA, and their involvement in sporadic breast cancer. Cancer Lett 2002; 188(1-2): 103-7.
Hirata H, Ueno K, Shahryari V, et al. MicroRNA-182-5p promotes cell invasion and proliferation by down regulating FOXF2, RECK and MTSS1 genes in human prostate cancer. PLoS One 2013; 8(1): e55502.
Kouri FM, Hurley LA, Daniel WL, et al. miR-182 integrates apoptosis, growth, and differentiation programs in glioblastoma. Genes Dev 2015; 29(7): 732-45.
Katakowski M, Zheng X, Jiang F, Rogers T, Szalad A, Chopp M. MiR-146b-5p suppresses EGFR expression and reduces in vitro migration and invasion of glioma. Cancer Invest 2010; 28(10): 1024-30.
Cai T, Long J, Wang H, Liu W, Zhang Y. Identification and characterization of miR-96, a potential biomarker of NSCLC, through bioinformatic analysis. Oncol Rep 2017; 38(2): 1213-23.

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