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.
Summary of results obtained in 2020:
In the 2020 phase, a series of datasets of interest (aging-related), used previously have been re-evaluated and part of them have been selected as being relevant to the current project. This subset has been expanded by a semi-automated search process, followed-up by manual curation and annotation. As a result, currently the list of datasets that will be analyzed in this project includes 78 transcriptomic datasets. Several of these have already been pre-processed in 2020, using the same protocol (normalization with the same methodology).
These steps have been performed using software tools that were already developed in our group and needed only to be adapted/updated to the needs of the current project.
At the end of this phase, all the activities in the project are according to the proposal's GANTT.
Summary of results obtained in 2021:
In this phase (2021), the collection and annotation of selected transcriptomics datasets from databases and scientific research for the study of aging and age-related diseases, has been carried out. Aging-related data has been analyzed considering multiple studies and different experimental designs (different tissues, different experimental/clinical conditions, etc), and a common transcriptional signature from them has been derived.
This signature was then used to agnostically search in the GEO database for other datasets (not necessarily aging-related) to identify similar datasets, of potential interest to the field. To validate this core aging signature, we have also run a more targeted search among datasets associated with age-related diseases. These datasets have been collected and manually annotated for Alzheimer's disease, Parkinson, Type 2 Diabetes, Atherosclerosis, Osteoporosis and Sarcopenia. An algorithm for similarity has been designed and implemented and based on the similarity results, a network graph has been constructed to navigate through the identified dataset similarities. These activities (the meta-analysis for aging/age-related pathologies data, the analysis for the similarity transition graph) will continue in the next phase (2022) as well.
Currently, all the activities in the project are on track and according to the proposal's initial GANTT.
Summary of results obtained in 2022:
In this phase (2022), the previously constructed network model, based on a matrix of similarities between transcriptomic datasets has been extended. Namely, the matrix / graph of transcriptomics comparisons / transitions, representing physiological ageing changes, as well as pathological changes from age-related diseases, was clustered into several groups of tightly related studies.
The main cluster, which seems to be relevant for brain ageing and neurodegenerative diseases was then selected and further analyzed. The transcriptional changes that form the signature of this cluster were meta-analyzed, and the results were evaluated for functional enrichment and topological characteristics.
Furthermore, the up- and down-regulated genes were then used in conjunction with the ConnectivityMap database to predict drugs or other chemical compounds that could potentially reverse the core changes in the cluster. Interestingly, some of the identified drugs have been previously associated with ageing-related processes, which supports the idea that this model could also serve as a tool for prediction of novel geroprotectors or other therapeutics, relevant to both ageing and age-related diseases.