Development of model based on clock gene expression of human hair follicle cells to estimate circadian time
Development of model based on clock gene expression of human hair follicle cells to estimate circadian time
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DOI：
10.1080/07420528.2020.1777150
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发表时间：
2020-07-12
影响因子：
2.8
通讯作者：
Lee, Heon-Jeong
中科院分区：
文献类型：
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作者：
Lee, Taek;Cho, Chul-Hyun;Lee, Heon-Jeong
考虑到昼夜节律失调对人类病理生理和行为的影响，能够检测个体的内源性昼夜节律时间是很重要的。我们开发了一个基于机器学习过程的内源性时钟估计模型(ECEM)，该模型使用了10个昼夜节律基因的表达。连续两天于08：00、11：00、15：00、19：00、23：00h采集18例健康受试者毛囊细胞，获得10个昼夜节律基因的表达模式。ECEM采用昼夜节律函数的逆形式设计(即昼夜节律时间=F(基因))，并用留一法交叉验证(LOOCV)评估ECEM的准确性。结果，6个基因(PER1、PER3、CLOCK、CRY2、NPAS2和NR1D2)被选为最佳模型，其实际时间与预测时间的误差范围为3.24小时。
Considering the effects of circadian misalignment on human pathophysiology and behavior, it is important to be able to detect an individual's endogenous circadian time. We developed an endogenous Clock Estimation Model (eCEM) based on a machine learning process using the expression of 10 circadian genes. Hair follicle cells were collected from 18 healthy subjects at 08:00, 11:00, 15:00, 19:00, and 23:00 h for two consecutive days, and the expression patterns of 10 circadian genes were obtained. The eCEM was designed using the inverse form of the circadian gene rhythm function (i.e., Circadian Time = F(gene)), and the accuracy of eCEM was evaluated by leave-one-out cross-validation (LOOCV). As a result, six genes (PER1, PER3, CLOCK, CRY2, NPAS2, andNR1D2)were selected as the best model, and the error range between actual and predicted time was 3.24 h. The eCEM is simple and applicable in that a single time-point sampling of hair follicle cells at any time of the day is sufficient to estimate the endogenous circadian time.