Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12864-015-1256-3
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dc.titleIntegromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism
dc.contributor.authorDimitrakopoulou, K
dc.contributor.authorVrahatis, A.G
dc.contributor.authorBezerianos, A
dc.date.accessioned2020-10-27T10:50:54Z
dc.date.available2020-10-27T10:50:54Z
dc.date.issued2015
dc.identifier.citationDimitrakopoulou, K, Vrahatis, A.G, Bezerianos, A (2015). Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism. BMC Genomics 16 (1) : 147. ScholarBank@NUS Repository. https://doi.org/10.1186/s12864-015-1256-3
dc.identifier.issn14712164
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/181413
dc.description.abstractBackground: 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. © 2015 Dimitrakopoulou et al.; licensee BioMed Central.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectcytochrome c oxidase
dc.subjecthyperpolarization activated cyclic nucleotide gated potassium channel 3
dc.subjectmicroRNA
dc.subjectmicroRNA 106a
dc.subjectmicroRNA 106b
dc.subjectmicroRNA 107
dc.subjectmicroRNA 125a
dc.subjectmicroRNA 125b
dc.subjectmicroRNA 128
dc.subjectmicroRNA 132
dc.subjectmicroRNA 148a
dc.subjectmicroRNA 152
dc.subjectmicroRNA 15b
dc.subjectmicroRNA 181b
dc.subjectmicroRNA 18a
dc.subjectmicroRNA 190b
dc.subjectmicroRNA 19a
dc.subjectmicroRNA 200b
dc.subjectmicroRNA 200c
dc.subjectmicroRNA 22
dc.subjectmicroRNA 34a
dc.subjectmicroRNA 363
dc.subjectpotassium channel
dc.subjectproteome
dc.subjectproton transporting adenosine triphosphate synthase
dc.subjectRas protein
dc.subjectreduced nicotinamide adenine dinucleotide dehydrogenase
dc.subjectribosome protein
dc.subjecttranscriptome
dc.subjectunclassified drug
dc.subjectmicroRNA
dc.subjecttranscriptome
dc.subjectaging
dc.subjectArticle
dc.subjectbiomics
dc.subjectcardiac aging
dc.subjectcardiovascular parameters
dc.subjectcomputer program
dc.subjecthuman
dc.subjectinformation processing
dc.subjectintegromics
dc.subjectlifespan
dc.subjectlongevity
dc.subjectmathematical analysis
dc.subjectnonhuman
dc.subjectprotein expression
dc.subjectprotein interaction
dc.subjectprotein targeting
dc.subjectproteomics
dc.subjectreproducibility
dc.subjectsequence homology
dc.subjectanimal
dc.subjectcardiovascular disease
dc.subjectgene regulatory network
dc.subjectgenetics
dc.subjectheart
dc.subjectmeta analysis
dc.subjectmouse
dc.subjectpathology
dc.subjectpathophysiology
dc.subjectAging
dc.subjectAnimals
dc.subjectCardiovascular Diseases
dc.subjectGene Regulatory Networks
dc.subjectHeart
dc.subjectHumans
dc.subjectMice
dc.subjectMicroRNAs
dc.subjectTranscriptome
dc.typeArticle
dc.contributor.departmentLIFE SCIENCES INSTITUTE
dc.description.doi10.1186/s12864-015-1256-3
dc.description.sourcetitleBMC Genomics
dc.description.volume16
dc.description.issue1
dc.description.page147
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