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BMC Genomics

, 16:899

Human and rodent genomics

Abstract

BackgroundMammalian aging is a highly complex process, a full mechanistic understanding of which is still lacking. One way to help understand the molecular changes underlying aging is through a comprehensive analysis of the transcriptome, the primary determinant of age-related phenotypic diversity. Previous studies have relied on microarray analysis to examine gene expression profiles in different tissues of aging organisms. However, studies have shown microarray-based transcriptional profiling is less accurate and not fully capable of capturing certain intricacies of the global transcriptome.

MethodsHere, using directional whole transcriptome RNA-sequencing of aged mouse liver we have identified a comprehensive high-resolution profile of differentially expressed liver transcripts comprised of canonical protein-coding transcripts, transcript isoforms, and non-coding RNA transcripts, including pseudogenes, long non-coding RNAs and small RNA species.

ResultsResults show extensive age-related changes in every component of the mouse liver transcriptome and a pronounced increase in inter-individual variation. Functional annotation of the protein-coding mRNAs and isoforms indicated broad alterations in immune response, cell activation, metabolic processes, and RNA modification. Interestingly, multiple lncRNAs Meg3, Rian, Mirg from the Dlk-Dio3 microRNA locus were found up-regulated in aging liver, classifying this locus as a putative regulatory hotspot locus in aging liver. Moreover, integration of the altered non-coding RNAs and protein-coding transcripts into interaction networks of age-related change revealed inflammation, cellular proliferation, and metabolism as the dominant aging phenotypes in mouse liver.

ConclusionsOur analyses provide the first comprehensive dissection of the transcriptional landscape in aging mouse liver.

KeywordsGene expression Aging RNA-seq Transcriptome Non-coding RNA Variation Liver Electronic supplementary materialThe online version of this article doi:10.1186-s12864-015-2061-8 contains supplementary material, which is available to authorized users.

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Author: Ryan R. White - Brandon Milholland - Sheila L. MacRae - Mingyan Lin - Deyou Zheng - Jan Vijg

Source: https://link.springer.com/







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