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BMC Genomics. 2015 Mar 4;16:147. doi: 10.1186/s12864-015-1256-3.

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

Author information

1
Department of Medical Physics, School of Medicine, University of Patras, Patras, 26500, Greece. kondim@upatras.gr.
2
Department of Medical Physics, School of Medicine, University of Patras, Patras, 26500, Greece. vrachatis@ceid.upatras.gr.
3
Department of Computer Engineering and Informatics, University of Patras, Patras, 26500, Greece. vrachatis@ceid.upatras.gr.
4
Department of Medical Physics, School of Medicine, University of Patras, Patras, 26500, Greece. bezer@upatras.gr.
5
Singapore Institute for Neurotechnology (SINAPSE), Center of Life Sciences, National University of Singapore, Singapore, 117456, Singapore. bezer@upatras.gr.

Abstract

BACKGROUND:

The avalanche of integromics and panomics approaches shifted the deciphering of aging mechanisms from single molecular entities to communities of them. In this orientation, we explore the cardiac aging mechanisms - risk factor for multiple cardiovascular diseases - by capturing the micronome synergism and detecting longevity signatures in the form of communities (modules). For this, we developed a meta-analysis scheme that integrates transcriptome expression data from multiple cardiac-specific independent studies in mouse and human along with proteome and micronome interaction data in the form of multiple independent weighted networks. Modularization of each weighted network produced modules, which in turn were further analyzed so as to define consensus modules across datasets that change substantially during lifespan. Also, we established a metric that determines - from the modular perspective - the synergism of microRNA-microRNA interactions as defined by significantly functionally associated targets.

RESULTS:

The meta-analysis provided 40 consensus integromics modules across mouse datasets and revealed microRNA relations with substantial collective action during aging. Three modules were reproducible, based on homology, when mapped against human-derived modules. The respective homologs mainly represent NADH dehydrogenases, ATP synthases, cytochrome oxidases, Ras GTPases and ribosomal proteins. Among various observations, we corroborate to the involvement of miR-34a (included in consensus modules) as proposed recently; yet we report that has no synergistic effect. Moving forward, we determined its age-related neighborhood in which HCN3, a known heart pacemaker channel, was included. Also, miR-125a-5p/-351, miR-200c/-429, miR-106b/-17, miR-363/-92b, miR-181b/-181d, miR-19a/-19b, let-7d/-7f, miR-18a/-18b, miR-128/-27b and miR-106a/-291a-3p pairs exhibited significant synergy and their association to aging and/or cardiovascular diseases is supported in many cases by a disease database and previous studies. On the contrary, we suggest that miR-22 has not substantial impact on heart longevity as proposed recently.

CONCLUSIONS:

We revised several proteins and microRNAs recently implicated in cardiac aging and proposed for the first time modules as signatures. The integromics meta-analysis approach can serve as an efficient subvening signature tool for more-oriented better-designed experiments. It can also promote the combinational multi-target microRNA therapy of age-related cardiovascular diseases along the continuum from prevention to detection, diagnosis, treatment and outcome.

PMID:
25887273
PMCID:
PMC4367845
DOI:
10.1186/s12864-015-1256-3
[Indexed for MEDLINE]
Free PMC Article

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