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A Pilot Characterization of the Human Chronobiome
Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719427/ https://www.ncbi.nlm.nih.gov/pubmed/29215023 http://dx.doi.org/10.1038/s41598-017-17362-6 |
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author | Skarke, Carsten Lahens, Nicholas F. Rhoades, Seth D. Campbell, Amy Bittinger, Kyle Bailey, Aubrey Hoffmann, Christian Olson, Randal S. Chen, Lihong Yang, Guangrui Price, Thomas S. Moore, Jason H. Bushman, Frederic D. Greene, Casey S. Grant, Gregory R. Weljie, Aalim M. FitzGerald, Garret A. |
author_facet | Skarke, Carsten Lahens, Nicholas F. Rhoades, Seth D. Campbell, Amy Bittinger, Kyle Bailey, Aubrey Hoffmann, Christian Olson, Randal S. Chen, Lihong Yang, Guangrui Price, Thomas S. Moore, Jason H. Bushman, Frederic D. Greene, Casey S. Grant, Gregory R. Weljie, Aalim M. FitzGerald, Garret A. |
author_sort | Skarke, Carsten |
collection | PubMed |
description | Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data from remote sensors, physiological and multi-omics analyses to assess the feasibility of detecting time dependent signals - the chronobiome – despite the “noise” attributable to the behavioral differences of free-living human volunteers. The majority (62%) of sensor readouts showed time-specific variability including the expected variation in blood pressure, heart rate, and cortisol. While variance in the multi-omics is dominated by inter-individual differences, temporal patterns are evident in the metabolome (5.4% in plasma, 5.6% in saliva) and in several genera of the oral microbiome. This demonstrates, despite a small sample size and limited sampling, the feasibility of characterizing at scale the human chronobiome “in the wild”. Such reference data at scale are a prerequisite to detect and mechanistically interpret discordant data derived from patients with temporal patterns of disease expression, to develop time-specific therapeutic strategies and to refine existing treatments. |
format | Online Article Text |
id | pubmed-5719427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57194272017-12-08 A Pilot Characterization of the Human Chronobiome Skarke, Carsten Lahens, Nicholas F. Rhoades, Seth D. Campbell, Amy Bittinger, Kyle Bailey, Aubrey Hoffmann, Christian Olson, Randal S. Chen, Lihong Yang, Guangrui Price, Thomas S. Moore, Jason H. Bushman, Frederic D. Greene, Casey S. Grant, Gregory R. Weljie, Aalim M. FitzGerald, Garret A. Sci Rep Article Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data from remote sensors, physiological and multi-omics analyses to assess the feasibility of detecting time dependent signals - the chronobiome – despite the “noise” attributable to the behavioral differences of free-living human volunteers. The majority (62%) of sensor readouts showed time-specific variability including the expected variation in blood pressure, heart rate, and cortisol. While variance in the multi-omics is dominated by inter-individual differences, temporal patterns are evident in the metabolome (5.4% in plasma, 5.6% in saliva) and in several genera of the oral microbiome. This demonstrates, despite a small sample size and limited sampling, the feasibility of characterizing at scale the human chronobiome “in the wild”. Such reference data at scale are a prerequisite to detect and mechanistically interpret discordant data derived from patients with temporal patterns of disease expression, to develop time-specific therapeutic strategies and to refine existing treatments. Nature Publishing Group UK 2017-12-07 /pmc/articles/PMC5719427/ /pubmed/29215023 http://dx.doi.org/10.1038/s41598-017-17362-6 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Skarke, Carsten Lahens, Nicholas F. Rhoades, Seth D. Campbell, Amy Bittinger, Kyle Bailey, Aubrey Hoffmann, Christian Olson, Randal S. Chen, Lihong Yang, Guangrui Price, Thomas S. Moore, Jason H. Bushman, Frederic D. Greene, Casey S. Grant, Gregory R. Weljie, Aalim M. FitzGerald, Garret A. A Pilot Characterization of the Human Chronobiome |
title | A Pilot Characterization of the Human Chronobiome |
title_full | A Pilot Characterization of the Human Chronobiome |
title_fullStr | A Pilot Characterization of the Human Chronobiome |
title_full_unstemmed | A Pilot Characterization of the Human Chronobiome |
title_short | A Pilot Characterization of the Human Chronobiome |
title_sort | pilot characterization of the human chronobiome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719427/ https://www.ncbi.nlm.nih.gov/pubmed/29215023 http://dx.doi.org/10.1038/s41598-017-17362-6 |
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