Department of Computer Science and Information Systems, Teesside University, Borough Road, Middlesbrough, UK.
BMC Bioinformatics. 2018 Nov 20;19(Suppl 14):415. doi: 10.1186/s12859-018-2383-z.
Ageing 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.
We 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.
We 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.
衰老可以分为两种方式，即 chronological ageing（生理年龄）和 biological ageing（生物年龄）。虽然生理年龄是衡量自出生以来所经过的时间的指标，但生物（也称为转录组学）年龄是通过比较个体与同一年龄段的其他个体之间时间和环境对个体的影响来定义的。最近的研究表明，转录组年龄与某些基因相关，这些基因的每个都有一个效应大小。使用这些效应大小，我们可以根据个体的年龄相关基因表达水平计算其转录组年龄。这种方法的局限性在于它没有考虑这些基因表达变化如何影响个体的新陈代谢，从而影响其可观察的细胞表型。
为了进一步了解转录组衰老，我们提出了一种基于多组学约束基模型和机器学习的方法。我们使用来自 499 名健康个体外周血单核细胞的正常化 CD4 T 细胞基因表达数据来创建个体代谢模型。然后，将这些模型与转录组年龄预测器和生理年龄结合使用，以提供对转录组年龄和生理年龄之间差异的新见解。结果，我们提出了一种新的代谢年龄预测器。
我们表明，与基于基因的方法相比，我们的多组学预测器提供了对转录组衰老的更详细分析，并为进一步了解人类细胞衰老机制提供了基础。
