Machine learning identifies a compact gene set for monitoring the circadian clock in human blood.
Machine learning identifies a compact gene set for monitoring the circadian clock in human blood.
复制标题
机器学习识别出一个紧凑的基因集，用于监测人类血液中的生物钟。
DOI：
10.1186/s13073-017-0406-4
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发表时间：
2017-02-28
期刊：
影响因子：
12.3
通讯作者：
Hughey JJ
中科院分区：
文献类型：
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作者：
Hughey JJ
生物钟及其产生的日常节奏对人类健康至关重要，但经常被现代环境所扰乱。与此同时，昼夜节律可能会影响疗效和毒性的治疗和代谢反应的食物摄入。开发昼夜节律功能障碍的治疗方法，以及优化其他健康状况的每日治疗时间，将需要一种简单而准确的方法来监测生物钟的分子状态。在这里，我们使用了一种最近开发的称为ZeitZeiger的方法，从人类血液中的全基因组基因表达来预测昼夜节律时间（CT，根据昼夜节律钟的一天中的时间）。在对来自三个公开数据集的60个个体的498个样本进行交叉验证时，ZeitZeiger预测了单个样本的CT，中位绝对误差为2.1小时。在所有498个样本上训练的预测器使用了15个基因，其中只有两个是核心生物钟的一部分。然后将ZeitZeiger应用于来自相同三个数据集的475个额外样本，我们量化了血液中的生物钟如何受到睡眠-觉醒和明暗周期的各种扰动的影响。最后，我们扩展了ZeitZeiger（1）通过基于已知时间间隔的多个样本进行预测来处理个体内变异，以及（2）通过基于来自相应个体的样本进行个性化预测来处理个体间变异。这些策略中的每一种都将CT的预测提高了约20%。我们的研究结果是迈向精确昼夜节律医学的重要一步。此外，我们对ZeitZeiger的可推广扩展可能适用于越来越多的生物数据集，这些数据集包含每个个体的多个观察结果。本文的在线版本（doi：10.1186/s13073-017-0406-4）包含补充材料，可供授权用户使用。
The circadian clock and the daily rhythms it produces are crucial for human health, but are often disrupted by the modern environment. At the same time, circadian rhythms may influence the efficacy and toxicity of therapeutics and the metabolic response to food intake. Developing treatments for circadian dysfunction, as well as optimizing the daily timing of treatments for other health conditions, will require a simple and accurate method to monitor the molecular state of the circadian clock. Here we used a recently developed method called ZeitZeiger to predict circadian time (CT, time of day according to the circadian clock) from genome-wide gene expression in human blood. In cross-validation on 498 samples from 60 individuals across three publicly available datasets, ZeitZeiger predicted CT in single samples with a median absolute error of 2.1 h. The predictor trained on all 498 samples used 15 genes, only two of which are part of the core circadian clock. By then applying ZeitZeiger to 475 additional samples from the same three datasets, we quantified how the circadian clock in the blood was affected by various perturbations to the sleep–wake and light–dark cycles. Finally, we extended ZeitZeiger (1) to handle intra-individual variation by making predictions based on multiple samples taken a known time apart, and (2) to handle inter-individual variation by personalizing predictions based on samples from the respective individual. Each of these strategies improved prediction of CT by ~20%. Our results are an important step towards precision circadian medicine. In addition, our generalizable extensions to ZeitZeiger may be applicable to the growing number of biological datasets that contain multiple observations per individual. The online version of this article (doi:10.1186/s13073-017-0406-4) contains supplementary material, which is available to authorized users.