Development of model based on clock gene expression of human hair follicle cells to estimate circadian time

ABSTRACT

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, and NR1D2) 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.

KEYWORDS:

• Circadian clock
• circadian genes
• hair follicle
• machine learning
• circadian time estimation

Contributions

T.L, C.C, H.P.I, D.G, H.L conceived and planned the experiments. T.L, J.H.M performed the experiments. T.L, W.R.K, S.K performed data analysis. T.L, W.R.K, D.G, H.J prepared the manuscript. All authors discussed the results and commented on the paper.

Disclosure statement

The authors declare no competing financial interests.

Data availability statement

The data that support the findings of present study are only available on request from the corresponding author (D. G and H-J. Lee). The data are not publicly available due to them containing information that could compromise research participant privacy and consent.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

This study was supported by the Korea Health 21 R&D Project funded by the National Research Foundation of Korea (2017M3A9F1031220 and 2019R1A2C2084158)