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

Taek Lee, Chul Hyun Cho, Woon Ryoung Kim, Joung Ho Moon, Soojin Kim, Dongho Geum, Hoh Peter In, Heon-Jeong Lee

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalChronobiology International
DOIs
Publication statusPublished - 2020

Keywords

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

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

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