Main Regards

Much has been discovered in recent decades about the changes that occur in the immune system throughout life, and how they influence healthy ageing in people. These changes are grouped in what is called immunosenescence. Despite such advances, there are still several open questions related to the identification of specific transcriptional programs that undergo changes throughout life and how the dynamics between these processes can be responsible for the remodelling of the immune system, leading to a decline in immune function in the elderly. As explained by (NIKOLICH-ŽUGICH, 2018), there is no comprehensive understanding of which of the declining immune functions are primary problems, biological processes that are disrupted early in life, and which of them are secondary, that is, an attempt to return to homeostasis that was lost due to primary problems.

‌ In this context, this work applies systems biology methods to study the transcriptional remodelling that occurs in peripheral blood with increasing age. With this, we intend to increase our understanding related to immune changes observed throughout life and how these can be related to immunosenescence. For this, we have developed new methodologies to identify transcripts and biological processes that may be related to immunosenescence or ageing. We pooled 1807 blood samples collected from healthy individuals aged between 15 and 96 years. The expression data from individuals of the same age were integrated into a representative sample of this age, creating a new dataset, here called ageCollapsed, which represents the temporal evolution in the expression levels of 8348 transcripts annotated in the older versions. of the human genome (hg38).

‌ With this massive amount of data, given both the high coverage of the human genome and a large number of samples, highly age-related transcripts were found, called AgingGenes, and biological processes of the immune system altered with age, represented by a network of co-expression called AgingNet. Through the expression profile of AgingGenes and AgingNet components, moments of important transcriptional disturbances were identified through change point detection analysis.