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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2017-02-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/20214 |
| _version_ | 1828168919303585792 |
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| author | Emma E Laing Carla S Möller-Levet Norman Poh Nayantara Santhi Simon N Archer Derk-Jan Dijk |
| author_facet | Emma E Laing Carla S Möller-Levet Norman Poh Nayantara Santhi Simon N Archer Derk-Jan Dijk |
| author_sort | Emma E Laing |
| collection | DOAJ |
| 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 R2 of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R2 was 0.90. This blood transcriptome-based model enables assessment of circadian phase from a few samples. |
| first_indexed | 2024-04-12T02:42:43Z |
| format | Article |
| id | doaj.art-1faf2ce4b4fe4419a4c48b1fdb79f13f |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:42:43Z |
| publishDate | 2017-02-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-1faf2ce4b4fe4419a4c48b1fdb79f13f2022-12-22T03:51:16ZengeLife Sciences Publications LtdeLife2050-084X2017-02-01610.7554/eLife.20214Blood transcriptome based biomarkers for human circadian phaseEmma E Laing0https://orcid.org/0000-0002-2095-2442Carla S Möller-Levet1Norman Poh2Nayantara Santhi3Simon N Archer4Derk-Jan Dijk5Department of Microbial Sciences, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomBioinformatics Core Facility, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomDepartment of Computer Science, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United KingdomSurrey Sleep Research Centre, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomSurrey Sleep Research Centre, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomSurrey Sleep Research Centre, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomDiagnosis 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 R2 of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R2 was 0.90. This blood transcriptome-based model enables assessment of circadian phase from a few samples.https://elifesciences.org/articles/20214biomarkertranscriptomicsmachine learningsleep disordersneurodegenerationchronotherapy |
| spellingShingle | Emma E Laing Carla S Möller-Levet Norman Poh Nayantara Santhi Simon N Archer Derk-Jan Dijk Blood transcriptome based biomarkers for human circadian phase eLife biomarker transcriptomics machine learning sleep disorders neurodegeneration chronotherapy |
| 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 | biomarker transcriptomics machine learning sleep disorders neurodegeneration chronotherapy |
| url | https://elifesciences.org/articles/20214 |
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