Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of monkey aorta to examine aging and gender effects

ABSTRACT: Although increased vascular stiffness is more prominent in aging males than females, and males are more prone to vascular disease with aging, no study has investigated the genes potentially responsible for gender differences in vascular aging. We tested the hypothesis that the transcriptional adaptation to aging differs in males and females using a monkey model, which is not only physiologically and phylogenetically closer to humans than the more commonly studied rodent models, but also is not afflicted with the most common forms of vascular disease that accompany the aging process in humans, e.g., atherosclerosis, hypertension, and diabetes. Gene expressions of monkey aorta from four groups (YF, OF, YM, OM) were detected to find out the molecular mechanism of aorta stiffness. Experiment Overall Design: Four groups were used in this experiment. 6 samples per group.The transcriptional profile of the aorta was compared by high-density microarrays between young and old males or females. About 600 genes were expressed differentially when comparing old versus young animals. Analysis of the different groups was further performed by gene ontology.We also analyzed whether the transcriptional regulation described above might correspond to specific transcription factors

ORGANISM(S): Macaca fascicularis

SUBMITTER: hongyu qiu

PROVIDER: E-GEOD-6599 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Aging is the primary risk factor of neurodegenerative disorders such as Alzheimer's disease (AD). However, the molecular events occurring during brain aging are extremely complex and still largely unknown. For a better understanding of these age-associated modifications, animal models as close as possible to humans are needed. We thus analyzed the transcriptome of the temporal cortex of the primate Microcebus murinus using human oligonucleotide microarrays (Affymetrix). Gene expression profiles were assessed in the temporal cortex of 6 young adults, 10 healthy old animals and 2 old, "AD-like" animals that presented b-amyloid plaques and cortical atrophy, which are pathognomonic signs of AD in humans. Gene expression data of the 14,911 genes that were detected in at least 3 samples were analyzed. By SAM (significance analysis of microarrays), we identified 47 genes that discriminated young from healthy old and "AD-like" animals. These findings were confirmed by principal component analysis (PCA). ANOVA of the expression data from the three groups identified 695 genes (including the 47 genes previously identified by SAM and PCA) with significant changes of expression in old and "AD-like" in comparison to young animals. About one third of these genes showed similar changes of expression in healthy aging and in M-bM-^@M-^\AD-likeM-bM-^@M-^] animals, whereas more than two thirds showed opposite changes in these two groups in comparison to young animals. Hierarchical clustering analysis of the 695 markers indicated that each group had distinct expression profiles which characterized each group, especially the "AD-like" group. Functional categorization showed that most of the genes that were up-regulated in healthy old and down-regulated in "AD-like" animals belonged to metabolic pathways, particularly protein synthesis. These data suggest the existence of compensatory mechanisms during physiological brain aging that disappear in M-bM-^@M-^\AD-likeM-bM-^@M-^] animals. These results open the way to new exploration of physiological and M-bM-^@M-^\AD-likeM-bM-^@M-^] aging in primates. Microcebus murinus were divided in 3 groups: the first group included 6 young adults (4 females and 2 males), the second included 10 healthy old animals (7 females and 3 males) and the third one was composed by 2 "AD-like" old females. Since Microcebus murinus microarrays do not exist, we decided to use Affymetrix human genome chips (HG U133 plus 2), since studies have illustrated the feasibility of detecting non-human primate brain transcripts using human genome chips.

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Transcription profiling of monkey aorta to examine aging and gender effects

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