Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Dimitrakopoulou, Konstantina ^a   Vrahatis, Aristidis G ^a,b   Bezerianos, Anastasios ^a,c  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Cardiac aging; Integromics network; Meta analysis; microRNA synergism; Module

Indexed keywords

CYTOCHROME C OXIDASE; HYPERPOLARIZATION ACTIVATED CYCLIC NUCLEOTIDE GATED POTASSIUM CHANNEL 3; MICRORNA; MICRORNA 106A; MICRORNA 106B; MICRORNA 107; MICRORNA 125A; MICRORNA 125B; MICRORNA 128; MICRORNA 132; MICRORNA 148A; MICRORNA 152; MICRORNA 15B; MICRORNA 181B; MICRORNA 18A; MICRORNA 190B; MICRORNA 19A; MICRORNA 200B; MICRORNA 200C; MICRORNA 22; MICRORNA 34A; MICRORNA 363; POTASSIUM CHANNEL; PROTEOME; PROTON TRANSPORTING ADENOSINE TRIPHOSPHATE SYNTHASE; RAS PROTEIN; REDUCED NICOTINAMIDE ADENINE DINUCLEOTIDE DEHYDROGENASE; RIBOSOME PROTEIN; TRANSCRIPTOME; UNCLASSIFIED DRUG;

AGING; ARTICLE; BIOMICS; CARDIAC AGING; CARDIOVASCULAR PARAMETERS; COMPUTER PROGRAM; HUMAN; INFORMATION PROCESSING; INTEGROMICS; LIFESPAN; LONGEVITY; MATHEMATICAL ANALYSIS; NONHUMAN; PROTEIN EXPRESSION; PROTEIN INTERACTION; PROTEIN TARGETING; PROTEOMICS; REPRODUCIBILITY; SEQUENCE HOMOLOGY; ANIMAL; CARDIOVASCULAR DISEASE; GENE REGULATORY NETWORK; GENETICS; HEART; META ANALYSIS; MOUSE; PATHOLOGY; PATHOPHYSIOLOGY;

AGING; ANIMALS; CARDIOVASCULAR DISEASES; GENE REGULATORY NETWORKS; HEART; HUMANS; MICE; MICRORNAS; TRANSCRIPTOME;

EID: 84928238343     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-1256-3     Document Type: Article

References (98)

1. Odden MC, Coxson PG, Moran A, Lightwood JM, Goldman L, Bibbins-Domingo K, et al. The impact of the aging population on coronary heart disease in the United States. Am J Med. 2011;124:827-33. e5.
2. Dai DF, Chen T, Johnson SC, Szeto H, Rabinovitch PS. Cardiac aging: from molecular mechanisms to significance in human health and disease. Antioxid Redox Signal. 2012;16:1492-526.
3. Blagosklonny MV. Validation of anti-aging drugs by treating age-related diseases. Aging (Albany NY). 2009;1:281-8.
4. Ho JW, Stefani M, Dos Remedios CG, Charleston MA. A model selection approach to discover age-dependent gene expression patterns using quantile regression models. BMC Genomics. 2009;10 Suppl 3:S16.
5. Pan CL, Peng CY, Chen CH, McIntire S. Genetic analysis of age-dependent defects of the Caenorhabditis elegans touch receptor neurons. Proc Natl Acad Sci U S A. 2011;108:9274-9.
6. Zhang X, Azhar G, Wei JY. The expression of microRNA and microRNA clusters in the aging heart. PLoS One. 2012;7:e34688.
7. Smith-Vikos T, Slack FJ. MicroRNAs and their roles in aging. J Cell Sci. 2012;125:7-17.
8. Boon RA, Iekushi K, Lechner S, Seeger T, Fischer A, Heydt S, et al. MicroRNA-34a regulates cardiac ageing and function. Nature. 2013;495:107-10.
9. Li W, Chen L, Li W, Qu X, He W, He Y, et al. Unraveling the characteristics of microRNA regulation in the developmental and aging process of the human brain. BMC Med Genomics. 2013;6:55.
10. Xu J, Li Y, Li X, Li C, Shao T, Bai J, et al. Dissection of the potential characteristic of miRNA-miRNA functional synergistic regulations. Mol Biosyst. 2013;9:217-24.
11. Zhu W, Zhao Y, Xu Y, Sun Y, Wang Z, Yuan W, et al. Dissection of protein interactomics highlights microRNA synergy. PLoS One. 2013;8:e63342.
12. Kriete A. Robustness and aging-a systems-level perspective. Biosystems. 2013;112:37-48.
13. Managbanag JR, Witten TM, Bonchev D, Fox LA, Tsuchiya M, Kennedy BK, et al. Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS One. 2008;3:e3802.
14. Budovsky A, Abramovich A, Cohen R, Chalifa-Caspi V, Fraifeld V. Longevity network: construction and implications. Mech Ageing Dev. 2007;128:117-24.
15. Xue H, Xian B, Dong D, Xia K, Zhu S, Zhang Z, et al. A modular network model of aging. Mol Syst Biol. 2007;3:147.
16. Hou L, Huang J, Green CD, Boyd-Kirkup J, Zhang W, Yu X, et al. Systems biology in aging: linking the old and the young. Curr Genomics. 2012;13:558-65.
17. van den Akker EB, Passtoors WM, Jansen R, van Zwet EW, Goeman JJ, Hulsman M, et al. Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging. Aging Cell. 2014;13:216-25.
18. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K, et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012;13:R97.
19. Dempsey KM, Ali HH. Identifying aging-related genes in mouse hippocampus using gateway nodes. BMC Syst Biol. 2014;8:62.
20. Dai C, Li W, Liu J, Zhou XJ. Integrating many co-splicing networks to reconstruct splicing regulatory modules. BMC Syst Biol. 2012;6 Suppl 1:S17.
21. Ma X, Gao L, Tan K. Modeling disease progression using dynamics of pathway connectivity. Bioinformatics. 2014;30:2343-50.
22. Zeng T, Wang DC, Wang X, Xu F, Chen L. Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling. Drug Resist Updat. 2014;17:64-76.
23. Lovejoy WS, Loch CH. Minimal and maximal characteristic path lengths in connected sociomatrices. Social Networks. 2003;25:333-47.
24. Albert R, Jeong H, Barabási AL. Error and attack tolerance of complex networks. Nature. 2000;406:378-82.
25. Mihalik Á, Csermely P. Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation. PLoS Comput Biol. 2011;7:e1002187.
26. Jazbutyte V, Fiedler J, Kneitz S, Galuppo P, Just A, Holzmann A, et al. MicroRNA-22 increases senescence and activates cardiac fibroblasts in the aging heart. Age (Dordr). 2013;35:747-62.
27. Sataranatarajan K, Feliers D, Mariappan MM, Lee HJ, Lee MJ, Day RT, et al. Molecular events in matrix protein metabolism in the aging kidney. Aging Cell. 2012;11:1065-73.
28. Schraml E, Grillari J. From cellular senescence to age-associated diseases: the miRNA connection. Longev Healthspan. 2012;1:10.
29. Vacchi-Suzzi C, Hahne F, Scheubel P, Marcellin M, Dubost V, Westphal M, et al. Heart structure-specific transcriptomic atlas reveals conserved microRNA-mRNA interactions. PLoS One. 2013;8:e52442.
30. Faraonio R, Salerno P, Passaro F, Sedia C, Iaccio A, Bellelli R, et al. A set of miRNAs participates in the cellular senescence program in human diploid fibroblasts. Cell Death Differ. 2012;19:713-21.
31. Mancini M, Saintigny G, Mahé C, Annicchiarico-Petruzzelli M, Melino G, Candi E. MicroRNA-152 and -181a participate in human dermal fibroblasts senescence acting on cell adhesion and remodeling of the extra-cellular matrix. Aging (Albany NY). 2012;4:843-53.
32. Ucar A, Gupta SK, Fiedler J, Erikci E, Kardasinski M, Batkai S, et al. The miRNA-212/132 family regulates both cardiac hypertrophy and cardiomyocyte autophagy. Nat Commun. 2012;3:1078.
33. Panguluri SK, Tur J, Chapalamadugu KC, Katnik C, Cuevas J, Tipparaju SM. MicroRNA-301a mediated regulation of Kv4.2 in diabetes: identification of key modulators. PLoS One. 2013;8:e60545.
34. Saunders LR, Sharma AD, Tawney J, Nakagawa M, Okita K, Yamanaka S, et al. miRNAs regulate SIRT1 expression during mouse embryonic stem cell differentiation and in adult mouse tissues. Aging (Albany NY). 2010;2:415-31.
35. Hoekstra M, van der Lans CA, Halvorsen B, Gullestad L, Kuiper J, Aukrust P, et al. The peripheral blood mononuclear cell microRNA signature of coronary artery disease. Biochem Biophys Res Commun. 2010;394:792-7.
36. Wolfson M, Tacutu R, Budovsky A, Aizenberg N, Fraifeld VE. MicroRNAs: relevance to aging and Age-related diseases. Open Longevity Sci. 2008;2:66-75.
37. Wang N, Zhou Z, Liao X, Zhang T. Role of microRNAs in cardiac hypertrophy and heart failure. IUBMB Life. 2009;61:566-71.
38. Baseler WA, Thapa D, Jagannathan R, Dabkowski ER, Croston TL, Hollander JM. miR-141 as a regulator of the mitochondrial phosphate carrier (Slc25a3) in the type 1 diabetic heart. Am J Physiol Cell Physiol. 2012;303:C1244-51.
39. Han M, Yang Z, Sayed D, He M, Gao S, Lin L, et al. GATA4 expression is primarily regulated via a miR-26b-dependent post-transcriptional mechanism during cardiac hypertrophy. Cardiovasc Res. 2012;93:645-54.
40. Brett JO, Renault VM, Rafalski VA, Webb AE, Brunet A. The microRNA cluster miR-106b ~ 25 regulates adult neural stem/progenitor cell proliferation and neuronal differentiation. Aging (Albany NY). 2011;3:108-24.
41. Mogilyansky E, Rigoutsos I. The miR-17/92 cluster: a comprehensive update on its genomics, genetics, functions and increasingly important and numerous roles in health and disease. Cell Death Differ. 2013;20:1603-14.
42. van Almen GC, Verhesen W, van Leeuwen RE, van de Vrie M, Eurlings C, Schellings MW, et al. MicroRNA-18 and microRNA-19 regulate CTGF and TSP-1 expression in age-related heart failure. Aging Cell. 2011;10:769-79.
43. Song DW, Ryu JY, Kim JO, Kwon EJ. do Kim H. The miR-19a/b family positively regulates cardiomyocyte hypertrophy by targeting atrogin-1 and MuRF-1. Biochem J. 2014;457:151-62.
44. Rawal S, Manning P, Katare R. Cardiovascular microRNAs: as modulators and diagnostic biomarkers of diabetic heart disease. Cardiovasc Diabetol. 2014;13:44.
45. Hackl M, Brunner S, Fortschegger K, Schreiner C, Micutkova L, Mück C, et al. miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging. Aging Cell. 2010;9:291-6.
46. Pan W, Zhong Y, Cheng C, Liu B, Wang L, Li A, et al. MiR-30-regulated autophagy mediates angiotensin II-induced myocardial hypertrophy. PLoS One. 2013;8:e53950.
47. Bao Y, Lin C, Ren J, Liu J. MicroRNA-384-5p regulates ischemia-induced cardioprotection by targeting phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit delta (PI3K p110δ). Apoptosis. 2013;18:260-70.
48. Palacín M, Reguero JR, Martín M, Díaz Molina B, Morís C, Alvarez V, et al. Profile of microRNAs differentially produced in hearts from patients with hypertrophic cardiomyopathy and sarcomeric mutations. Clin Chem. 2011;57:1614-6.
49. Da Costa Martins PA, De Windt LJ. MicroRNAs in control of cardiac hypertrophy. Cardiovasc Res. 2012;93:563-72.
50. Mutharasan RK, Nagpal V, Ichikawa Y, Ardehali H. microRNA-210 is upregulated in hypoxic cardiomyocytes through Akt- and p53-dependent pathways and exerts cytoprotective effects. Am J Physiol Heart Circ Physiol. 2011;301:H1519-30.
51. Huang ZP, Chen J, Seok HY, Zhang Z, Kataoka M, Hu X, et al. MicroRNA-22 regulates cardiac hypertrophy and remodeling in response to stress. Circ Res. 2013;112:1234-43.
52. Divakaran V, Mann DL. The emerging role of microRNAs in cardiac remodeling and heart failure. Circ Res. 2008;103:1072-83.
53. Warsow G, Greber B, Falk SS, Harder C, Siatkowski M, Schordan S, et al. ExprEssence-revealing the essence of differential experimental data in the context of an interaction/regulation net-work. BMC Syst Biol. 2010;4:164.
54. Jiao X, Sherman BT, da Huang W, Stephens R, Baseler MW, Lane HC, et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics. 2012;28:1805-6.
55. Schulz TJ, Westermann D, Isken F, Voigt A, Laube B, Thierbach R, et al. Activation of mitochondrial energy metabolism protects against cardiac failure. Aging (Albany NY). 2010;2:843-53.
56. Ahuja P, Sdek P, MacLellan WR. Cardiac myocyte cell cycle control in development, disease, and regeneration. Physiol Rev. 2007;87:521-44.
57. de Magalhães JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875-81.
58. Haigis MC, Yankner BA. The aging stress response. Mol Cell. 2010;40:333-44.
59. Dobrzyn P, Pyrkowska A, Duda MK, Bednarski T, Maczewski M, Langfort J, et al. Expression of lipogenic genes is upregulated in the heart with exercise training-induced but not pressure overload-induced left ventricular hypertrophy. Am J Physiol Endocrinol Metab. 2013;304:E1348-58.
60. Wende AR, O'Neill BT, Bugger H, Riehle C, Tuinei J, Buchanan J, et al. Enhanced cardiac Akt/protein kinase B signaling contributes to pathological cardiac hypertrophy in part by impairing mitochondrial function via transcriptional repression of mitochondrion-targeted nuclear genes. Mol Cell Biol. 2015;35:831-46.
61. Kim HK, Thu VT, Heo HJ, Kim N, Han J. Cardiac proteomic responses to ischemia-reperfusion injury and ischemic preconditioning. Expert Rev Proteomics. 2011;8:241-61.
62. Mirisola MG, Longo VD. Conserved role of Ras-GEFs in promoting aging: from yeast to mice. Aging (Albany NY). 2011;3:340-3.
63. Thum T, Galuppo P, Wolf C, Fiedler J, Kneitz S, van Laake LW, et al. MicroRNAs in the human heart: a clue to fetal gene reprogramming in heart failure. Circulation. 2007;116:258-67.
64. Lee CK, Allison DB, Brand J, Weindruch R, Prolla TA. Transcriptional profiles associated with aging and middle age-onset caloric restriction in mouse hearts. Proc Natl Acad Sci U S A. 2002;99:14988-93.
65. Sun J, Yu EY, Yang Y, Confer LA, Sun SH, Wan K, et al. Stn1-Ten1 is an Rpa2-Rpa3-like complex at telomeres. Genes Dev. 2009;23:2900-14.
66. Jäger D, Holtz J, Redpath NT, Müller SP, Pönicke K, Heinroth-Hoffmann I, et al. The ageing heart: influence of cellular and tissue ageing on total content and distribution of the variants of elongation factor-2. Mech Ageing Dev. 2002;123:1305-19.
67. van Rooij E, Purcell AL, Levin AA. Developing microRNA therapeutics. Circ Res. 2012;10:496-507.
68. Shih H, Lee B, Lee RJ, Boyle AJ. The aging heart and post-infarction left ventricular remodeling. J Am Coll Cardiol. 2011;57:9-17.
69. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.
70. Xu J, Li CX, Li YS, Lv JY, Ma Y, Shao TT, et al. MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features. Nucleic Acids Res. 2011;39:825-36.
71. Ocorr K, Perrin L, Lim HY, Qian L, Wu X, Bodmer R. Genetic control of heart function and aging in Drosophila. Trends Cardiovasc Med. 2007;17:177-82.
72. Somel M, Guo S, Fu N, Yan Z, Hu HY, Xu Y, et al. MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res. 2010;20:1207-18.
73. Predmore BL, Alendy MJ, Ahmed KI, Leeuwenburgh C, Julian D. The hydrogen sulfide signaling system: changes during aging and the benefits of caloric restriction. Age (Dordr). 2010;32:467-81.
74. Robert V, Besse S, Sabri A, Silvestre JS, Assayag P, Nguyen VT, et al. Differential regulation of matrix metalloproteinases associated with aging and hypertension in the rat heart. Lab Invest. 1997;76:729-38.
75. Gayral S, Garnotel R, Castaing-Berthou A, Blaise S, Fougerat A, Berge E, et al. Elastin-derived peptides potentiate atherosclerosis through the immune Neu1-PI3Kγ pathway. Cardiovasc Res. 2014;102:118-27.
76. Houtkooper RH, Argmann C, Houten SM, Cantó C, Jeninga EH, Andreux PA, et al. The metabolic footprint of aging in mice. Sci Rep. 2011;1:134.
77. Amberg D, Leadsham JE, Kotiadis V, Gourlay CW. Cellular ageing and the actin cytoskeleton. Aging Res Yeast Subcellular Biochem. 2012;57:331-52.
78. Gourlay CW, Ayscough KR. The actin cytoskeleton in ageing and apoptosis. FEMS Yeast Res. 2005;5:1193-8.
79. Zhang X, Azhar G, Helms S, Zhong Y, Wei JY. Identification of a subunit of NADH-dehydrogenase as a p49/STRAP-binding protein. BMC Cell Biol. 2008;9:8.
80. Yaniv Y, Juhaszova M, Sollott SJ. Age-related changes of myocardial ATP supply and demand mechanisms. Trends Endocrinol Metab. 2013;24:495-505.
81. Ren JC, Rebrin I, Klichko V, Orr WC, Sohal RS. Cytochrome c oxidase loses catalytic activity and structural integrity during the aging process in Drosophila melanogaster. Biochem Biophys Res Commun. 2010;401:64-8.
82. Borrás C, Monleón D, López-Grueso R, Gambini J, Orlando L, Pallardó FV, et al. RasGrf1 deficiency delays aging in mice. Aging (Albany NY). 2011;3:262-76.
83. North BJ, Sinclair DA. The intersection between aging and cardiovascular disease. Circ Res. 2012;110:1097-108.
84. Gao J, Ade AS, Tarcea VG, Weymouth TE, Mirel BR, Jagadish HV, et al. Integrating and annotating the interactome using the MiMI plugin for cytoscape. Bioinformatics. 2009;25:137-8.
85. Mora A, Donaldson IM. iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database. BMC Bioinformatics. 2011;12:455.
86. Nunez YO, Truitt JM, Gorini G, Ponomareva ON, Blednov YA, Harris RA, et al. Positively correlated miRNA-mRNA regulatory networks in mouse frontal cortex during early stages of alcohol dependence. BMC Genomics. 2013;14:725.
87. Lee SY, Sohn KA, Kim JH. MicroRNA-centric measurement improves functional enrichment analysis of co-expressed and differentially expressed microRNA clusters. BMC Genomics. 2012;13 Suppl 7:S17.
88. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007;3:e201.
89. Park SK, Kim K, Page GP, Allison DB, Weindruch R, Prolla TA. Gene expression profiling of aging in multiple mouse strains: identification of aging biomarkers and impact of dietary antioxidants. Aging Cell. 2009;8:484-95.
90. Dimitrakopoulou K, Vrahatis AG, Dimitrakopoulos GN, Bezerianos A. Aging integromics: module-based markers of heart aging from multi-omics data. In proceedings of the 15th international conference on biomedical engineering: 4-7 december 2013; Singapore. IFMBE Proc Vol. 2014;43:104-7.
91. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498-504.
92. Meyer PE, Lafitte F, Bontempi G. minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics. 2008;9:461.
93. Luo W, Hankenson KD, Woolf PJ. Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information. BMC Bioinformatics. 2008;9:467.
94. Maraziotis IA, Dimitrakopoulou K, Bezerianos A. Growing functional modules from a seed protein via integration of protein interaction and gene expression data. BMC Bioinformatics. 2007;8:408.
95. Wang X, Wang Z, Ye Z. HKC: an algorithm to predict protein complexes in protein-protein interaction networks. J Biomed Biotechnol. 2011;2011:480294.
96. Kacprowski T, Doncheva NT, Albrecht M. NetworkPrioritizer: a versatile tool for network-based prioritization of candidate disease genes or other molecules. Bioinformatics. 2013;29:1471-3.
97. Pihur V, Datta S, Datta S. Finding common genes in multiple cancer types through meta-analysis of microarray experiments: a rank aggregation approach. Genomics. 2008;92:400-3.
98. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9:796-804.