Abstract
BackgroundAgeing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype.ResultsWe propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor.ConclusionsWe show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells.
Highlights
Ageing can be classified in two different ways, chronological ageing and biological ageing
The mitochondrial theory of ageing states that oxidative damage caused by reactive oxygen species (ROS) produced by the mitochondria contributes to ageing by causing damage to mitochondrial DNA, lipids and proteins, which leads to cell death [6, 7]
This paper aims to improve on the current understanding of ageing by modelling how age-associated gene expression changes metabolic processes, enabling the identification of metabolic age predictors, selected using machine learning techniques
Summary
Ageing can be classified in two different ways, chronological ageing and biological ageing. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and their observable cellular phenotype. The mitochondrion is the primary organelle responsible for metabolic cellular respiration; it takes in oxygen and nutrients and converts them into energy in the form of adenosine age-associated diseases can be linked to age-associated changes in metabolic subsystems [1,2,3] Identifying these metabolic links has recently led to the discovery of ageassociated biomarkers [4, 5]. The mitochondrial theory of ageing states that oxidative damage caused by reactive oxygen species (ROS) produced by the mitochondria contributes to ageing by causing damage to mitochondrial DNA, lipids and proteins, which leads to cell death [6, 7]
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