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Blood transcriptome based biomarkers for human circadian phase
Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain’s circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5318160/ https://www.ncbi.nlm.nih.gov/pubmed/28218891 http://dx.doi.org/10.7554/eLife.20214 |
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author | Laing, Emma E Möller-Levet, Carla S Poh, Norman Santhi, Nayantara Archer, Simon N Dijk, Derk-Jan |
author_facet | Laing, Emma E Möller-Levet, Carla S Poh, Norman Santhi, Nayantara Archer, Simon N Dijk, Derk-Jan |
author_sort | Laing, Emma E |
collection | PubMed |
description | Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain’s circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We developed and validated a multivariate whole-blood mRNA-based predictor of melatonin phase which requires few samples. Transcriptome data were collected under normal, sleep-deprivation and abnormal sleep-timing conditions to assess robustness of the predictor. Partial least square regression (PLSR), applied to the transcriptome, identified a set of 100 biomarkers primarily related to glucocorticoid signaling and immune function. Validation showed that PLSR-based predictors outperform published blood-derived circadian phase predictors. When given one sample as input, the R(2) of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R(2) was 0.90. This blood transcriptome-based model enables assessment of circadian phase from a few samples. DOI: http://dx.doi.org/10.7554/eLife.20214.001 |
format | Online Article Text |
id | pubmed-5318160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-53181602017-02-22 Blood transcriptome based biomarkers for human circadian phase Laing, Emma E Möller-Levet, Carla S Poh, Norman Santhi, Nayantara Archer, Simon N Dijk, Derk-Jan eLife Computational and Systems Biology Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain’s circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We developed and validated a multivariate whole-blood mRNA-based predictor of melatonin phase which requires few samples. Transcriptome data were collected under normal, sleep-deprivation and abnormal sleep-timing conditions to assess robustness of the predictor. Partial least square regression (PLSR), applied to the transcriptome, identified a set of 100 biomarkers primarily related to glucocorticoid signaling and immune function. Validation showed that PLSR-based predictors outperform published blood-derived circadian phase predictors. When given one sample as input, the R(2) of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R(2) was 0.90. This blood transcriptome-based model enables assessment of circadian phase from a few samples. DOI: http://dx.doi.org/10.7554/eLife.20214.001 eLife Sciences Publications, Ltd 2017-02-20 /pmc/articles/PMC5318160/ /pubmed/28218891 http://dx.doi.org/10.7554/eLife.20214 Text en © 2017, Laing et al http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Laing, Emma E Möller-Levet, Carla S Poh, Norman Santhi, Nayantara Archer, Simon N Dijk, Derk-Jan Blood transcriptome based biomarkers for human circadian phase |
title | Blood transcriptome based biomarkers for human circadian phase |
title_full | Blood transcriptome based biomarkers for human circadian phase |
title_fullStr | Blood transcriptome based biomarkers for human circadian phase |
title_full_unstemmed | Blood transcriptome based biomarkers for human circadian phase |
title_short | Blood transcriptome based biomarkers for human circadian phase |
title_sort | blood transcriptome based biomarkers for human circadian phase |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5318160/ https://www.ncbi.nlm.nih.gov/pubmed/28218891 http://dx.doi.org/10.7554/eLife.20214 |
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