A population-based gene expression signature of molecular clock phase from a single epidermal sample

Genome Med. 2020 Aug 21;12(1):73. doi: 10.1186/s13073-020-00768-9.

Abstract

Background: For circadian medicine to influence health, such as when to take a drug or undergo a procedure, a biomarker of molecular clock phase is required--one that is easily measured and generalizable across a broad population. It is not clear that any circadian biomarker yet satisfies these criteria.

Methods: We analyzed 24-h molecular rhythms in human dermis and epidermis at three distinct body sites, leveraging both longitudinal (n = 20) and population (n = 154) data. We applied cyclic ordering by periodic structure (CYCLOPS) to order the population samples where biopsy time was not recorded. With CYCLOPS-predicted phases, we used ZeitZeiger to discover potential biomarkers of clock phase.

Results: Circadian clock function was strongest in the epidermis, regardless of body site. We identified a 12-gene expression signature that reported molecular clock phase to within 3 h (mean error = 2.5 h) from a single sample of epidermis--the skin's most superficial layer. This set performed well across body sites, ages, sexes, and detection platforms.

Conclusions: This research shows that the clock in epidermis is more robust than dermis regardless of body site. To encourage ongoing validation of this putative biomarker in diverse populations, diseases, and experimental designs, we developed SkinPhaser--a user-friendly app to test biomarker performance in datasets ( https://github.com/gangwug/SkinPhaser ).

Keywords: Circadian biomarkers; Circadian medicine; Dermis; Epidermis; Population rhythm; Skin.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Biomarkers
  • Circadian Clocks / genetics*
  • Circadian Rhythm / genetics*
  • Dermis / metabolism
  • Epidermis / metabolism*
  • Gene Expression Profiling / methods
  • Gene Expression Regulation*
  • Genome-Wide Association Study / methods
  • Humans
  • Organ Specificity
  • Transcriptome*

Substances

  • Biomarkers