Inferring Quercetin mediated miRNA-TF-gene Regulatory circuit using meta-analysis of Gene expression data

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Inferring Quercetin mediated miRNA-TF-gene Regulatory circuit using meta-analysis of Gene expression data

Author Affiliations

¹Department of Agricultural Biotechnology, Assam Agricultural University, Jorhat, Assam-785013, INDIA

Int. Res. J. Biological Sci., Volume 3, Issue (2), Pages 40-47, February,10 (2014)

Abstract

Both transcription factors (TFs) and microRNAs (miRNAs) are the key regulatory elements of genes at transcriptional and post-transcriptional levels. Though the mode of action these two elements vary significantly from each other, studies have shown that they can act upon genes in a combinatory network. In this study, we report an interaction map representing the combined regulatory effect of human TFs and miRNAs on the differentially expressed genes (DEGs) obtained from the meta-analysis of gene expression data based on quercetin mediated effect in human. Bioconductor packages were used with the help of R programming language to normalize and process three datasets: GSE7259, GSE15162 and GSE13899. Wilcoxon’s t-test was performed with the help of JAVA Multi-Experiment Viewer program (MeV) and the results revealed a total of 605 unique DEGs at p 0.05. Custom perl scripts searched 8 most common and frequent DEGs. BiNGO plug-in in Cytoscape analyzed the 8 DEGs and found 26 unique GO terms associated with 8 biological processes, 14 cellular components and 4 molecular functions. Bioinformatics tools identified 18 TFs for 7 DEGs except ATRX. The tools, miRanda and TargetScan identified 200 and 249 unique miRNAs targeting 8 DEGs and 9 TFs. The miRNA-TF-gene interaction map was constructed with the help of Cytoscape. The information obtained in this study provides insights into the dynamic characteristics of quercetin mediated miRNA-TF-gene circuit.

References

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Moreau Y., Aerts S., De M.B., De S.B. and Dabrowski M., Comparison and meta-analysis of microarray data: from the bench to the computer desk, Trends Genet.,19, 570-577 (2003)
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[ref2] Vaquerizas J.M., Kummerfeld S.K., Teichmann S.A. and Luscombe N.M., A census of human transcription factors: function, expression and evolution, Nat Rev Genet.,10,252-63 (2009)

[ref3] Lee T.I., Rinaldi N.J., Robert F., Odom D.T., Bar-Joseph Z., Gerber G.K., Hannett N.M., Harbison C.T., Thompson C.M., Simon I., Zeitlinger J., Jennings E.G., Murray H.L., Gordon D.B., Ren B., Wyrick J.J., Tagne J.B., Volkert T.L., Fraenkel E., Gifford D.K. and Young RA, Transcriptional regulatory networks in Saccharomyces cerevisiae, Science.,298, 799-804 (2002)

[ref4] Bartel D.P., MicroRNAs: genomics, biogenesis, mechanism, and function, Cell.,116, 281-297 (2004)

[ref5] Sun J., Gong X., Purow B. and Zhao Z., Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma, PLoS Comput Biol.,8,e1002488 (2012)

[ref6] Le T.D., Liu L., Liu B., Tsykin A., Goodall G.J., Satou K. and Li J., Inferring microRNA and transcription factor regulatory networks in heterogeneous data, BMC Bioinformatics.,14, 92 (2013)

[ref7] Sharmila G., Bhat F.A., Arunkumar R., Elumalai P., Raja S.P., Senthilkumar K. and Arunakaran J., Chemopreventive effect of quercetin, a natural dietary flavonoid on prostate cancer in in vivo model, Clin Nutr.,pii:S0261-5614, 00235-5 (2013)

[ref8] Dihal A.A., Tilburgs C., van Erk M.J., Rietjens I.M., Woutersen R.A. and Stierum R.H., Pathway and single gene analyses of inhibited Caco-2 differentiation by ascorbate-stabilized quercetin suggest enhancement of cellular processes associated with development of colon cancer, Mol Nutr Food Res.,51, 1031-1045 (2007)

[ref9] Boomgaarden I., Egert S., Rimbach G., Wolffram S., Müller M.J. and Döring F., Quercetin supplementation and its effect on human monocyte gene expression profiles in vivo, Br J Nutr.,104, 336-345 (2010)

[ref10] Brown P.O. and Botstein D., Exploring the new world of the genome with DNA microarrays, Nat. Genet.,21, 33-37 (1999)

[ref11] Moreau Y., Aerts S., De M.B., De S.B. and Dabrowski M., Comparison and meta-analysis of microarray data: from the bench to the computer desk, Trends Genet.,19, 570-577 (2003)

[ref12] Barrett T., Suzek T.O., Troup D.B., Wilhite S.E., Ngau W.C., Ledoux P., Rudnev D., Lash A.E., Fujibuchi W. and Edgar R., NCBI GEO: mining millions of expression profiles--database and tools, Nucleic Acids Res.,33, D562-566 (2005)

[ref13] de Magalhaes J.P., Curado J. and Church G.M., Meta-analysis of age-related gene expression profiles identifies common signatures of aging, Bioinformatics.,25, 875-881 (2009)

[ref14] Gentleman R., Carey V.J., Huber W., Irizarray R. and Dudoit S. eds., Statistics for Biology and Health: Bioinformatics and Computational Biology Solutions Using R and Bioconductor, New York, New York, USA, Springer Publishing, 229-248, (2005)

[ref15] Gautier L., Cope L., Bolstad B.M. and Irizarry R.A., affy–analysis of Affymetrix Gene Chip data at the probe level, Bioinformatics.,20, 307-315, (2003)

[ref16] Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis K.J., Scherf U. and Speed T.P., Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics.,4,249-264, (2003)

[ref17] Chu V.T., Gottardo R., Raftery A.E., Bumgarner R.E. and Yeung K.Y., MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis, Genome Biol.,9, R118 (2008)

[ref18] Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., 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)

[ref19] Maere S., Heymans K. and Kuiper M., BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks, Bioinformatics.,21, 3448-3449, (2005)

[ref20] Qiu C., Wang D., Wang E. and Cui Q., An upstream interacting context based framework for the computational inference of microRNA functions, Mol Biosyst.,8, 1492-1498, (2012)

[ref21] Jiang W., Zhang Y., Meng F., Lian B., Chen X., Yu X., Dai E., Wang S., Liu X., Li X., Wang L. and Li X., Identification of active transcription factor and miRNA regulatory pathways in Alzheimer's disease, Bioinformatics.,29 (20), 2596-602 (2013)

[ref22] Iwama H., Coordinated networks of microRNAs and transcription factors with evolutionary perspectives, Adv Exp Med Biol.,774, 169-187, (2013)

[ref23] Aguda B.D., Modeling microRNA-transcription factor networks in cancer, Adv Exp Med Biol., 774, 149-167 (2013)

[ref24] Tseng G.C., Ghosh D. and Feingold E., Comprehensive literature review and statistical considerations for microarray meta-analysis, Nucleic Acids Res.,40, 3785-3799, (2012)

[ref25] Moreau Y., Aerts S., De M.B., De S.B. and Dabrowski M., Comparison and meta-analysis of microarray data: from the bench to the computer desk, Trends Genet.,19, 570-577 (2003)

[ref26] Meyer F.R., Grausgruber H., Binter C., Mair G.E., Guelly C., Vogl C. and Steinborn R., Cross-platform microarray meta-analysis for the mouse jejunum selects novel reference genes with highly uniform levels of expression, PLoS One.,8, e63125, (2013)

[ref27] Villa C., Fenoglio C., De R.M., Clerici F., Marcone A., Benussi L., Ghidoni R., Gallone S., Cortini F., Serpente M., Cantoni C., Fumagalli G., Martinelli B.F., Cappa S., Binetti G., Franceschi M., Rainero I., Giordana M.T., Mariani C., Bresolin N., Scarpini E. and Galimberti D., Role of hnRNP-A1 and miR-590-3p in neuronal death: genetics and expression analysis in patients with Alzheimer disease and frontotemporal lobar degeneration, Rejuvenation Res., 14, 275-281, (2011)

[ref28] Deniaud E., Baguet J., Chalard R., Blanquier B., Brinza L., Meunier J., Michallet M.C., Laugraud A., Ah-Soon C., Wierinckx A., Castellazzi M., Lachuer J., Gautier C., Marvel J. and Leverrier Y., Overexpression of transcription factor Sp1 leads to gene expression perturbations and cell cycle inhibition, PLoS One., 4,e7035 (2009)

[ref29] Majid S.M., Liss A.S., You M. and Bose H.R., The suppression of SH3BGRL is important for v-Rel-mediated transformation, Oncogene., 25, 756-768, (2006)