Presentation : Development of an age-specific genome-scale model of skeletal muscle metabolism

Research output: Contribution to conferenceOtherAcademic

72 Downloads (Pure)

Abstract

Skeletal myocytes are among the most metabolically active cell types, implicated in nutrient balance, contributing to the insulin-stimulated clearance of glucose from the blood, and secreting myokines that contribute in regulating inflammation and the ageing process. The loss of muscle mass and strength with age (sarcopenia), is a risk factor for cardiovascular and metabolic diseases, it increases the risk of falls, of developing frailty and disabilities, and results in an impairment in the quality of life and autonomy of an individual.
An active lifestyle is the most immediate and accessible treatment to prevent sarcopenia, with a considerable impact on the ageing process: PANINI is a European Training Network whose aim is understanding how lifestyle factors can influence healthy ageing.

In this context, we present the first age-specific genome-scale metabolic model of the skeletal muscle, a mathematical representation of the myocyte metabolic network in the elderly, built using RECON2, the human metabolic reconstruction, and gene expression data, gathered from older adults' muscle tissue biopsies.
This model will be used to analyze patient-specific data for potential mechanisms able to explain the different ageing paces of different individuals and to investigate the effectiveness of different nutritional and physical exercise regimes in stimulating post-exercise protein synthesis, which is often impaired in the elderly.
The aim is to identify an optimal and personalized lifestyle change intervention able to prevent the onset of sarcopenia.

Original languageEnglish
Publication statusPublished - 1 Sep 2017

Keywords

  • metabolic modelling
  • sarcopenia
  • aging

Fingerprint Dive into the research topics of 'Presentation : Development of an age-specific genome-scale model of skeletal muscle metabolism'. Together they form a unique fingerprint.

Cite this