The project aims to analyze and compare the age-related transcriptomics signatures in variuos tisues, both in healthy and pathological individuals, in order to identify shared or unique aging signature that drive aging or age-related diseases.
Aging is considered a major risk factor for the development of late-onset pathologies such as atherosclerosis, cancer, type 2 diabetes and neurodegenerative diseases. This suggests that age-related changes in gene expression should, to some extent, resemble the changes in gene expression observed in the above diseases. While the presence of common differentially- expressed genes is essential, this is not sufficient evidence to assume a common molecular basis for aging and ARDs. The important point is whether these genes display similar expression profiles as a whole. However, up until now, this question has not been fully addressed. In this study, we propose to analyze the age-related signatures of different human tissues (brain, muscle, lungs, kidney, and skin), compare them in order to get insights on whether the signatures are tissue-specific or whether there is a “common” aging signature across tissues. Additionally, we aim to search for similar-to-aging gene expression profiles among genetic screens of age-related pathologies. The identified pathological conditions with signatures similar to age-related transcriptional profiles of various tissues and cell types could then be used to build a dual aging-diseases centric model. This model could be extremely useful both at a theoretical level, for a better understanding of the mechanisms behind aging and diseases, as well as at an applicative level, for early-stage detection of late-onset diseases and related pathological conditions.
The overall goal of the proposed research is to integratively study aging and age-related diseases and their common links at a genetic/molecular level. More specifically, the objectives are: 1) Building a joint model of age-related gene expression changes in different human tissues; 2) Investigating the particular genetic signatures and molecular pathways shared between aging and various age-related conditions with profiles similar to healthy aging; 3) Constructing an integrative model that describes the changes in gene expression in aging and in age-related diseases.
Upon the completion of the project, the following outcomes should be achieved: 1) the group will have a list of aging signatures and a ranked list of datasets based on their gene expression similarity to aging; 2) the group will build a gene network model aimed at explaining how the molecular components that determine the similarity to aging interact between themselves; 3) the group will have a graph model of physiological and pathological transitions occurring with age which could allow to make hypotheses and inferences about the aging process.