Using Systems Biology to understand Immunosenescen
  • Using Systems Biology to Understand Immunosenescence
  • Background
    • Introduction
    • 1.1. Aging in Society and in the Individual
    • 1.2. Aging and its Molecular Mechanisms
    • 1.3.The Remodeling of the Immune System: Immunosenescence
    • 1.4. Changes in the Immune System Related to Immunosenescence
    • 1.5 Chronic Inflammation During Aging: Inflammaging
    • 1.6 The Immune Risk Phenotype (IRP)
    • 1.6. Systems Biology
  • Objectives
  • Methods
    • Overall Methodology
    • 3.1 Survey of Studies
    • 3.2. Reannotation of Probes in Microarrays
    • 3.3. Data acquisition and pre-processing
    • 3.4. Creation of age-representative samples: AgeCollapsed
    • 3.5. Detection of Highly Age-Related Transcripts: AgingGenes
    • 3.6. Lifetime Co-Expressed Transcript Analysis: AgingNet
    • 3.7. Detection of Change Points in Age-Related Modules
  • Results
    • 4.1. Survey and Data Acquisition
    • 4.2. Reannotation of Platforms
    • 4.3. AgeCollapsed Pre-Processing and Creation
    • 4.4. Assessment of the Agreement of the Relationships of Transcripts with Age between the Sexes
    • 4.5. AgingGenes and AgingNet Reviews
    • 4.6 Aging Co-Expression Network: AgingNet
  • Discussion
    • Main Regards
    • AgingGenes
    • Análise de Co-Expressão: AgingNet
  • Conclusions
    • Final Regards
  • Citations
    • References
  • Appendix
    • Supplementary Files
Powered by GitBook
On this page
  1. Conclusions

Final Regards

PreviousAnálise de Co-Expressão: AgingNetNextReferences

Last updated 3 years ago

Was this helpful?

CtrlK

Was this helpful?

The methodologies developed in this study allowed to find transcriptional disturbances that match the morphological characteristics already described in the literature and, mainly, to identify new transcripts and biological processes never before related to immunosenescence.

‌ Furthermore, it was found that there are at least two major moments during life when the immune system undergoes changes that can lead to the establishment of immunosenescence. The first of them reside early in life, around the age of 30, where disturbances related to the transduction of signals involved mainly in lymphocyte signalling, control of regulatory T cells and cell proliferation are noticed. Alterations continue to happen until the mid-55s, when they intensify, causing a clear rupture in the expression profile of the components studied here, called a transcriptional inflexion point. These changes may be directly responsible for the physiological decline observed in the elderly.

‌ Such methodologies can be adapted to higher quality transcriptomics data, such as RNA-Seq, in which it would be possible to identify new transcripts more precisely, the emergence of age-related isoforms and study the dynamics of cellular subpopulations of the immune system throughout life. Furthermore, they can also be applied to other levels of biological information, such as metabolomics and proteomics, to better understand the relationships between the components of the immune system. Thus, it is expected that the findings of this study have the potential to help understand the mechanisms involved in immunosenescence and underlying ageing, and in the future make possible the creation of interventions capable of beneficially regulating the remodelling of the immune system during life, allowing the extension of life with quality.