Global gene expression analysis combined with a genomics approach for the identification of signal transduction networks involved in postnatal mouse myocardial proliferation and development

Wang,Ruoxin; Su,Chao; Wang,Xinting; Fu,Qiang; Gao,Xingjie; Zhang,Chunyan; Yang,Jie; Yang,Xi; Wei,Minxin

doi:10.3892/ijmm.2017.3234

Global gene expression analysis combined with a genomics approach for the identification of signal transduction networks involved in postnatal mouse myocardial proliferation and development

Authors:
- Ruoxin Wang
- Chao Su
- Xinting Wang
- Qiang Fu
- Xingjie Gao
- Chunyan Zhang
- Jie Yang
- Xi Yang
- Minxin Wei
View Affiliations

Affiliations: Department of Cardiovascular Surgery, Tianjin Medical University General Hospital, Tianjin 300070, P.R. China, Department of Biochemistry and Molecular Biology, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, P.R. China, Department of Immunology, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
Published online on: November 3, 2017 https://doi.org/10.3892/ijmm.2017.3234
Pages: 311-321
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Mammalian cardiomyocytes may permanently lose their ability to proliferate after birth. Therefore, studying the proliferation and growth arrest of cardiomyocytes during the postnatal period may enhance the current understanding regarding this molecular mechanism. The present study identified the differentially expressed genes in hearts obtained from 24 h‑old mice, which contain proliferative cardiomyocytes; 7‑day‑old mice, in which the cardiomyocytes are undergoing a proliferative burst; and 10‑week‑old mice, which contain growth‑arrested cardiomyocytes, using global gene expression analysis. Furthermore, myocardial proliferation and growth arrest were analyzed from numerous perspectives, including Gene Ontology annotation, cluster analysis, pathway enrichment and network construction. The results of a Gene Ontology analysis indicated that, with increasing age, enriched gene function was not only associated with cell cycle, cell division and mitosis, but was also associated with metabolic processes and protein synthesis. In the pathway analysis, ‘cell cycle’, proliferation pathways, such as the ‘PI3K‑AKT signaling pathway’, and ‘metabolic pathways’ were well represented. Notably, the cluster analysis revealed that bone morphogenetic protein (BMP)1, BMP10, cyclin E2, E2F transcription factor 1 and insulin like growth factor 1 exhibited increased expression in hearts obtained from 7‑day‑old mice. In addition, the signal transduction pathway associated with the cell cycle was identified. The present study primarily focused on genes with altered expression, including downregulated anaphase promoting complex subunit 1, cell division cycle (CDC20), cyclin dependent kinase 1, MYC proto-oncogene, bHLH transcription factor and CDC25C, and upregulated growth arrest and DNA damage inducible α in 10-week group, which may serve important roles in postnatal myocardial cell cycle arrest. In conclusion, these data may provide important information regarding myocardial proliferation and development.

View Figures

View References

1	Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Després JP, Fullerton HJ, et al Writing Group Members; American Heart Association; Statistics Committee: Stroke Statistics Subcommittee: Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 133:e38–e360. 2016. View Article : Google Scholar
2	Murry CE, Reinecke H and Pabon LM: Regeneration gaps: Observations on stem cells and cardiac repair. J Am Coll Cardiol. 47:1777–1785. 2006. View Article : Google Scholar : PubMed/NCBI
3	Linzbach AJ: Heart failure from the point of view of quantitative anatomy. Am J Cardiol. 5:370–382. 1960. View Article : Google Scholar : PubMed/NCBI
4	Poss KD, Wilson LG and Keating MT: Heart regeneration in zebrafish. Science. 298:2188–2190. 2002. View Article : Google Scholar : PubMed/NCBI
5	Porrello ER, Mahmoud AI, Simpson E, Hill JA, Richardson JA, Olson EN and Sadek HA: Transient regenerative potential of the neonatal mouse heart. Science. 331:1078–1080. 2011. View Article : Google Scholar : PubMed/NCBI
6	Naqvi N, Li M, Calvert JW, Tejada T, Lambert JP, Wu J, Kesteven SH, Holman SR, Matsuda T, Lovelock JD, et al: A proliferative burst during preadolescence establishes the final cardiomyocyte number. Cell. 157:795–807. 2014. View Article : Google Scholar : PubMed/NCBI
7	Mollova M, Bersell K, Walsh S, Savla J, Das LT, Park SY, Silberstein LE, Dos Remedios CG, Graham D, Colan S, et al: Cardiomyocyte proliferation contributes to heart growth in young humans. Proc Natl Acad Sci USA. 110:1446–1451. 2013. View Article : Google Scholar : PubMed/NCBI
8	Gan J, Sonntag HJ, Tang MK, Cai D and Lee KK: Integrative Analysis of the Developing Postnatal Mouse Heart Transcriptome. PLoS One. 10:e01332882015. View Article : Google Scholar : PubMed/NCBI
9	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
10	Wright GW and Simon RM: A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics. 19:2448–2455. 2003. View Article : Google Scholar : PubMed/NCBI
11	Yang H, Crawford N, Lukes L, Finney R, Lancaster M and Hunter KW: Metastasis predictive signature profiles pre-exist in normal tissues. Clin Exp Metastasis. 22:593–603. 2005. View Article : Google Scholar
12	Clarke R, Ressom HW, Wang A, Xuan J, Liu MC, Gehan EA and Wang Y: The properties of high-dimensional data spaces: Implications for exploring gene and protein expression data. Nat Rev Cancer. 8:37–49. 2008. View Article : Google Scholar :
13	Eisen MB, Spellman PT, Brown PO and Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95:14863–14868. 1998. View Article : Google Scholar : PubMed/NCBI
14	Gene Ontology Consortium: The Gene Ontology (GO) project in 2006. Nucleic Acids Res. 34:D322–D326. 2006. View Article : Google Scholar
15	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: The Gene Ontology Consortium: Gene ontology: Tool for the unification of biology. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
16	Dupuy D, Bertin N, Hidalgo CA, Venkatesan K, Tu D, Lee D, Rosenberg J, Svrzikapa N, Blanc A, Carnec A, et al: Genome-scale analysis of in vivo spatiotemporal promoter activity in Caenorhabditis elegans. Nat Biotechnol. 25:663–668. 2007. View Article : Google Scholar : PubMed/NCBI
17	Schlitt T, Palin K, Rung J, Dietmann S, Lappe M, Ukkonen E and Brazma A: From gene networks to gene function. Genome Res. 13:2568–2576. 2003. View Article : Google Scholar : PubMed/NCBI
18	Kanehisa M, Goto S, Kawashima S, Okuno Y and Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res. 32:D277–D280. 2004. View Article : Google Scholar :
19	Yi M, Horton JD, Cohen JC, Hobbs HH and Stephens RM: WholePathwayScope: A comprehensive pathway-based analysis tool for high-throughput data. BMC Bioinformatics. 7:302006. View Article : Google Scholar : PubMed/NCBI
20	Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C and Romero R: A systems biology approach for pathway level analysis. Genome Res. 17:1537–1545. 2007. View Article : Google Scholar : PubMed/NCBI
21	Ramoni MF, Sebastiani P and Kohane IS: Cluster analysis of gene expression dynamics. Proc Natl Acad Sci USA. 99:9121–9126. 2002. View Article : Google Scholar : PubMed/NCBI
22	Miller LD, Long PM, Wong L, Mukherjee S, McShane LM and Liu ET: Optimal gene expression analysis by microarrays. Cancer Cell. 2:353–361. 2002. View Article : Google Scholar : PubMed/NCBI
23	Dysvik B and Jonassen I: J-Express: exploring gene expressiondata using Java. Bioinformatics. 17:369–370. 2001. View Article : Google Scholar : PubMed/NCBI
24	Jansen R, Greenbaum D and Gerstein M: Relating whole-genome expression data with protein-protein interactions. Genome Res. 12:37–46. 2002. View Article : Google Scholar : PubMed/NCBI
25	Li C and Li H: Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics. 24:1175–1182. 2008. View Article : Google Scholar : PubMed/NCBI
26	Wei Z and Li H: A Markov random field model for network-based analysis of genomic data. Bioinformatics. 23:1537–1544. 2007. View Article : Google Scholar : PubMed/NCBI
27	Zhang JD and Wiemann S: KEGGgraph: A graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics. 25:1470–1471. 2009. View Article : Google Scholar : PubMed/NCBI
28	Spirin V and Mirny LA: Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA. 100:12123–12128. 2003. View Article : Google Scholar : PubMed/NCBI
29	Burton PB, Raff MC, Kerr P, Yacoub MH and Barton PJ: An intrinsic timer that controls cell-cycle withdrawal in cultured cardiac myocytes. Dev Biol. 216:659–670. 1999. View Article : Google Scholar
30	Tane S, Kubota M, Okayama H, Ikenishi A, Yoshitome S, Iwamoto N, Satoh Y, Kusakabe A, Ogawa S, Kanai A, et al: Repression of cyclin D1 expression is necessary for the maintenance of cell cycle exit in adult mammalian cardiomyocytes. J Biol Chem. 289:18033–18044. 2014. View Article : Google Scholar : PubMed/NCBI
31	Tane S, Ikenishi A, Okayama H, Iwamoto N, Nakayama KI and Takeuchi T: CDK inhibitors, p21(Cip1) and p27(Kip1), participate in cell cycle exit of mammalian cardiomyocytes. Biochem Biophys Res Commun. 443:1105–1109. 2014. View Article : Google Scholar : PubMed/NCBI
32	Lin Z, Zhou P, von Gise A, Gu F, Ma Q, Chen J, Guo H, van Gorp PR, Wang DZ and Pu WT: Pi3kcb links Hippo-YAP and PI3K-AKT signaling pathways to promote cardiomyocyte proliferation and survival. Circ Res. 116:35–45. 2015. View Article : Google Scholar :
33	Marshall-Clarke S, Reen D, Tasker L and Hassan J: Neonatal immunity: How well has it grown up. Immunol Today. 21:35–41. 2000. View Article : Google Scholar : PubMed/NCBI
34	Rochais F, Sturny R, Chao CM, Mesbah K, Bennett M, Mohun TJ, Bellusci S and Kelly RG: FGF10 promotes regional foetal cardiomyocyte proliferation and adult cardiomyocyte cell-cycle re-entry. Cardiovasc Res. 104:432–442. 2014. View Article : Google Scholar : PubMed/NCBI
35	Thomas PS, Rajderkar S, Lane J, Mishina Y and Kaartinen V: AcvR1-mediated BMP signaling in second heart field is required for arterial pole development: Implications for myocardial differentiation and regional identity. Dev Biol. 390:191–207. 2014. View Article : Google Scholar : PubMed/NCBI
36	Chen H, Shi S, Acosta L, Li W, Lu J, Bao S, Chen Z, Yang Z, Schneider MD, Chien KR, et al: BMP10 is essential for maintaining cardiac growth during murine cardiogenesis. Development. 131:2219–2231. 2004. View Article : Google Scholar : PubMed/NCBI
37	Ebelt H, Hufnagel N, Neuhaus P, Neuhaus H, Gajawada P, Simm A, Müller-Werdan U, Werdan K and Braun T: Divergent siblings: E2F2 and E2F4 but not E2F1 and E2F3 induce DNA synthesis in cardiomyocytes without activation of apoptosis. Circ Res. 96:509–517. 2005. View Article : Google Scholar : PubMed/NCBI
38	Bieniek J, Childress C, Swatski MD and Yang W: COX-2 inhibitors arrest prostate cancer cell cycle progression by downregulation of kinetochore/centromere proteins. Prostate. 74:999–1011. 2014. View Article : Google Scholar : PubMed/NCBI
39	Yeh C, Li A, Chuang JZ, Saito M, Cáceres A and Sung CH: IGF-1 activates a cilium-localized noncanonical Gβγ signaling pathway that regulates cell-cycle progression. Dev Cell. 26:358–368. 2013. View Article : Google Scholar : PubMed/NCBI
40	Berger J, Tarakci H, Berger S, Li M, Hall TE, Arner A and Currie PD: Loss of Tropomodulin4 in the zebrafish mutant träge causes cytoplasmic rod formation and muscle weakness reminiscent of nemaline myopathy. Dis Model Mech. 7:1407–1415. 2014. View Article : Google Scholar : PubMed/NCBI
41	Segers VF and Lee RT: Stem-cell therapy for cardiac disease. Nature. 451:937–942. 2008. View Article : Google Scholar : PubMed/NCBI
42	Passier R, van Laake LW and Mummery CL: Stem-cell-based therapy and lessons from the heart. Nature. 453:322–329. 2008. View Article : Google Scholar : PubMed/NCBI
43	Bearzi C, Rota M, Hosoda T, Tillmanns J, Nascimbene A, De Angelis A, Yasuzawa-Amano S, Trofimova I, Siggins RW, Lecapitaine N, et al: Human cardiac stem cells. Proc Natl Acad Sci USA. 104:14068–14073. 2007. View Article : Google Scholar : PubMed/NCBI
44	Kattman SJ, Witty AD, Gagliardi M, Dubois NC, Niapour M, Hotta A, Ellis J and Keller G: Stage-specific optimization of activin/nodal and BMP signaling promotes cardiac differentiation of mouse and human pluripotent stem cell lines. Cell Stem Cell. 8:228–240. 2011. View Article : Google Scholar : PubMed/NCBI
45	Horn MA and Trafford AW: Aging and the cardiac collagen matrix: Novel mediators of fibrotic remodelling. J Mol Cell Cardiol. 93:175–185. 2016. View Article : Google Scholar :
46	Lindsey ML, Iyer RP, Jung M, DeLeon-Pennell KY and Ma Y: Matrix metalloproteinases as input and output signals for post-myocardial infarction remodeling. J Mol Cell Cardiol. 91:134–140. 2016. View Article : Google Scholar : PubMed/NCBI
47	Vanhoutte D and Heymans S: TIMPs and cardiac remodeling: 'Embracing the MMP-independent-side of the family'. J Mol Cell Cardiol. 48:445–453. 2010. View Article : Google Scholar
48	Zhang S, Chang L, Alfieri C, Zhang Z, Yang J, Maslen S, Skehel M and Barford D: Molecular mechanism of APC/C activation by mitotic phosphorylation. Nature. 533:260–264. 2016. View Article : Google Scholar : PubMed/NCBI
49	Yamada K, Tamamori-Adachi M, Goto I, Iizuka M, Yasukawa T, Aso T, Okazaki T and Kitajima S: Degradation of p21Cip1 through anaphase-promoting complex/cyclosome and its activator Cdc20 (APC/CCdc20) ubiquitin ligase complex-mediated ubiquitylation is inhibited by cyclin-dependent kinase 2 in cardiomyocytes. J Biol Chem. 286:44057–44066. 2011. View Article : Google Scholar : PubMed/NCBI
50	Forester CM, Maddox J, Louis JV, Goris J and Virshup DM: Control of mitotic exit by PP2A regulation of Cdc25C and Cdk1. Proc Natl Acad Sci USA. 104:19867–19872. 2007. View Article : Google Scholar : PubMed/NCBI
51	Franckhauser C, Mamaeva D, Heron-Milhavet L, Fernandez A and Lamb NJ: Distinct pools of cdc25C are phosphorylated on specific TP sites and differentially localized in human mitotic cells. PLoS One. 5:e117982010. View Article : Google Scholar : PubMed/NCBI
52	Villa Del Campo C, Lioux G, Carmona R, Sierra R, Muñoz-Chápuli R, Clavería C and Torres M: Myc overexpression enhances of epicardial contribution to the developing heart and promotes extensive expansion of the cardiomyocyte population. Sci Rep. 6:353662016. View Article : Google Scholar : PubMed/NCBI
53	Hollander MC and Fornace AJ Jr: Genomic instability, centrosome amplification, cell cycle checkpoints and Gadd45a. Oncogene. 21:6228–6233. 2002. View Article : Google Scholar : PubMed/NCBI
54	Zhan Q, Antinore MJ, Wang XW, Carrier F, Smith ML, Harris CC and Fornace AJ Jr: Association with Cdc2 and inhibition of Cdc2/Cyclin B1 kinase activity by the p53-regulated protein Gadd45. Oncogene. 18:2892–2900. 1999. View Article : Google Scholar : PubMed/NCBI
55	Sarkisian MR and Siebzehnrubl D: Abnormal levels of Gadd45alpha in developing neocortex impair neurite outgrowth. PLoS One. 7:e442072012. View Article : Google Scholar : PubMed/NCBI