Gene Transcriptional Signatures in Healthy Ageing and Related Pathologies

As a result of the project implementation (2020-2022), all proposed activities (A1-A10) were carried out successfully and all deliverables (D1-D7) were obtained.
Briefly, 1) a series of publicly available transcriptomics datasets were collected, evaluated (to account for different experimental conditions, different tissues, etc.), and selected if being of interest for the study of ageing, or for age-related diseases (such as Alzheimer's disease, Parkinson's disease, Type 2 Diabetes, Atherosclerosis, Osteoporosis, and Sarcopenia). All selected datasets were then bioinformatically processed, including data normalization (using the same normalization method for all studies) and computation of differential expression for each gene.
For each of the available tissues (whole blood, bone marrow, brain, eye, leukocytes, lymphocytes, liver, muscle, skin, stem cells), a common "transcriptional signature" was derived - in other words, a pattern of core transcriptional changes found in that tissue. Analyzing the transcriptional signatures for each tissue, it was observed that major differences between age-related changes exist across tissues, but there was also a small number of molecular components working in tandem across multiple tissues - which could constitute a more universal basis for ageing and age-related diseases.

Next, to be able to compare profiles of gene expression changes, a script was developed that calculates a similarity score, based on a "Gene Set Enrichment Analysis" type algorithm. This algorithm was further used for two types of searches in the GEO database: 1) an agnostic search, without prior knowledge of what a relevant dataset might be, and 2) a targeted search to directly analyze the molecular links between aging and age-related pathologies.
These similarity scores allowed for the construction of a similarity matrix, which was then represented by an unoriented graph/network. This network model, which includes both physiological transitions in ageing but also changes caused by age-related pathologies, allowed us to select several subsets of core transitions which were then the input for meta-analyses. Within the project, 2 subsets were chosen; first, a subset chosen manually as central in the initial similarity network, and then a subset representing the largest cluster (relevant in particular to brain aging and neurodegeneration). The signatures obtained in the end, that is, those common changes for the comparisons/transitions in the dataset subsets were functionally and topologically analyzed. Subsequently, the signatures were used to identify drugs or other chemical compounds that could reverse the changes. Interestingly, among the proposed drugs a number of compounds already known to have a role in aging or related processes were also revealed (either from in vitro studies or animal models). This result can be considered as a partial validation of the potential of the methodology developed in the project.

Overall, the activities in the TE "GeT-SHARP" project have led to the development of some tools and of a bioinformatics model that can help both fundamental research - in the study of aging and of ageing-associated pathologies, but also in applied research - giving the possibility to achieve some predictions for modulatory drugs or genetic therapies. As such, we are aiming to disseminate these results through the publication of one or more scientific articles. This dissemination will include both the description of the methodology, as well as the results obtained with the help of the created model. Such an article is currently under development.