Vlachou Denise, Veretennikova Maria, Usselmann Laura, Vasilyev Vadim, Ott Sascha, Bjarnason Georg A, Dallmann Robert, Levi Francis, Rand David A
Mathematics Institute & Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom.
Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom.
PLoS Comput Biol. 2024 Feb 29;20(2):e1011779. doi: 10.1371/journal.pcbi.1011779. eCollection 2024 Feb.
Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.
最近的研究表明，昼夜节律时钟会影响包括癌症和心脏病在内的多种疾病的发病、进展及治疗结果。因此，需要有工具来测量患者体内分子昼夜节律时钟及其下游靶点的功能状态。此外，生物钟是一个多维度的随机振荡器，而作为一个有噪声的多基因动态系统，用于分析它的工具很少。在本文中，我们考虑了TimeTeller背后的方法，这是一种机器学习工具，它将生物钟作为一个有噪声的多基因动态系统进行分析，并旨在通过对生物钟的多维状态进行建模，从单个转录组中估计昼夜节律时钟功能。我们将其应用于小鼠、狒狒和人类的微阵列及RNA测序数据，展示了它在时钟系统评估方面的潜力，并展示了如何可视化和量化生物钟的全局结构，通过时钟功能障碍对个体转录组样本进行定量分层，以及在个体、条件和组织之间对生物钟进行全局比较，从而突出了它在推进昼夜节律医学方面的潜在相关性。
