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Human blood plasma circadian lipidome misalignment due to night shift simulation

ABSTRACT: Blood plasma was collected from a total of 14 subjects participating in a between-groups, in-laboratory study. Healthy volunteers (ages 22-34, BMI average 25.7) were assigned to a 3 day simulated night-shift schedule (6 male, 1 female) or a simulated day-shift (i.e., control, 4 male, 3 female) schedule. For each group, this was followed by a 24-h constant routine protocol, during which blood was drawn at 3-h intervals to measure plasma lipid profiles using LC-MS/MS.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Thomas Metz

PROVIDER: MSV000086152 | MassIVE |

REPOSITORIES: MassIVE

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Publications

Simulated Night-Shift Schedule Disrupts the Plasma Lipidome and Reveals Early Markers of Cardiovascular Disease Risk.

Kyle Jennifer E JE Bramer Lisa M LM Claborne Daniel D Stratton Kelly G KG Bloodsworth Kent J KJ Teeguarden Justin G JG Gaddameedhi Shobhan S Metz Thomas O TO Van Dongen Hans P A HPA

Nature and science of sleep 20220521

<h4>Introduction</h4>The circadian system coordinates daily rhythms in lipid metabolism, storage and utilization. Disruptions of internal circadian rhythms due to altered sleep/wake schedules, such as in night-shift work, have been implicated in increased risk of cardiovascular disease and metabolic disorders. To determine the impact of a night-shift schedule on the human blood plasma lipidome, an in-laboratory simulated shift work study was conducted.<h4>Methods</h4>Fourteen healthy young adult ...[more]

PMID: 35645584

Similar Datasets

GNPS - Human blood plasma circadian lipidome misalignment due to night shift simulation

Project description:Blood plasma was collected from a total of 14 subjects participating in a between-groups, in-laboratory study. Healthy volunteers (ages 22-34, BMI average 25.7) were assigned to a 3 day simulated night-shift schedule (6 male, 1 female) or a simulated day-shift (i.e., control, 4 male, 3 female) schedule. For each group, this was followed by a 24-h constant routine protocol, during which blood was drawn at 3-h intervals to measure plasma lipid profiles using LC-MS/MS.

| MSV000086152 | GNPS

The effect of simulated night shift work on the circadian regulation of the human transcriptome

Project description:Eight healthy human subjects were enrolled in a 6-day simulated shift work protocol. Blood samples were collected during the two 24-hour measurement periods. Blood samples were collected every 4 hours during both measurement periods. Subjects entered the lab on Day 1. At the start of Day 2, the first 24-hour measurement period was started. Subjects slept according to their habitual sleep/wake schedule, followed by a 16-hour constant posture procedure. On days 3-6, the sleep period was delayed by 10 hours. Following the third night on this schedule, subjects underwent another 24-hour measurement period. During both measurement periods, 7 blood samples were collected and PBMCs were isolated. mRNA was extracted, labelled, and hybridized to microarrays.

2018-05-08 | GSE107537 | GEO

The phase-shifting effect of bright light exposure on circadian rhythmicity in the human transcriptome

Project description:Eleven healthy human subjects were enrolled in a 6-day simulated shift work protocol. Blood samples were collected during the two 24-hour measurement periods. Blood samples were collected every 4 hours during both measurement periods. Subjects entered the lab on Day 1. At the start of Day 2, the first 24-hour measurement period was started. Subjects slept according to their habitual sleep/wake schedule, followed by a 16-hour constant posture procedure. On days 3-6, the sleep period was delayed by 10 hours. Participants in the control group were exposed to dim light throughout the waking periods; participants in the bright group were exposed to bright light (~6,000lux) for 8 hours during the waking period. Following the third night on this schedule, subjects underwent another 24-hour measurement period. During both measurement periods, blood samples were collected and PBMCs were isolated. mRNA was extracted, labelled, and hybridized to microarrays.

2019-02-14 | GSE122725 | GEO

Human serum proteomics reveal molecular signature after one night of sleep deprivation

Project description:Study Objectives: Sleep deprivation is highly prevalent and caused by conditions such as night shift work or illnesses like obstructive sleep apnea. Compromised sleep is proposed to play a role in several cardiovascular, immune related and neurodegenerative disorders. We recently published human serum proteome changes after a simulated night shift. This study aimed to further explore changes in the human blood serum after 6h of sleep deprivation at night by proteomics and systems biological databases. Methods: Human blood serum samples from 8 self-declared healthy females were analyzed using mass spectrometry and high-pressure liquid chromatography. Each subject was their own control, and two samples were taken from each subject, the first one after 6h of sleep at night and the second one after 6h of sleep deprivation the following night. Biological databases and bioinformatic software were used for systems biological analyzes and comparative analysis with other published sleep-related datasets. Results: Of 494 proteins, 66 were found to be differentially expressed after 6h of sleep deprivation at night. Functional enrichment analysis revealed associations of these proteins with several biological functions related to the regulation of cellular processes like protein- and ion-binding connected to platelet degranulation and blood coagulation, as well as associations with different curated gene sets. Conclusions: This study presents serum proteomic changes after 6h of sleep deprivation, supports previous findings that only 6h of sleep deprivation affects several biological processes and revealed a molecular signature of protein changes related to pathological conditions like altered coagulation and platelet function, impaired lipid and immune function and cancer. Keywords: Human blood serum, proteomics, sleep deprivation, cellular stress, functional enrichment analysis

2024-06-27 | PXD045729 | Pride

Characterisation of microRNAs present in amniotic fluid and maternal blood plasma in heifers carrying male and female embryos

Project description:We studied the miRNA expression profile of heifers carrying male and female embryos in amniotic fluid and blood plasma at day 39 post-insemination, to determine the possible roles of miRNAs during the sex determination process.

2021-09-03 | E-MTAB-10130 | biostudies-arrayexpress

Proteomics of human peripheral blood mononuclear cells under normal circadian cycle and under a simulated night shift

Project description:Proteomic data from human peripheral blood mononuclear cells (PBMCs). Samples taken over a 24 hour constant light and diet following 3 days of normal day shift cycle or a night shift - circadian misaligned schedule where sleep and wake cycles were reversed. Time points (in clock time) 1, 7, 14, 21 hours; 6 volunteers for each condition. Samples were digested with trypsin, then analyzed by LC-MS/MS. Data was searched with MS-GF+ using PNNL's DMS processing pipeline.

2023-10-05 | MSV000093054 | MassIVE

PROTEOMIC CHARACTERIZATION OF DRY BLOOD SPOTS OF HEALTHY WOMEN DURING SIMULATION THE MICROGRAVITY EFFECTS USING DRY IMMERSION

Project description:This study is unique in that it was the first time that it was the female response to microgravity simulated by 3-day dry immersion that was analyzed. The result of the choice of the object of research fell on dry blood spots, since the technique of capillary blood sampling is less invasive and allows sampling with a greater frequency. This allows researchers to analyze the changes caused by dry immersion in stages and observe the unfolding of the adaptive response taking into account the time of exposure to the experimental conditions.

2022-02-17 | PXD027654 | Pride

Watanabe2018_State Transition Model with Treatment and Costs

Project description:Model with functions depending on Age, Male, BP (Blood Pressure). There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: Age/1000; Healthy to Sick: According to function F1 depending on Age and Male and BP; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male. Pre-Transition Rules: Age increased by 1 and BP by Age/10 each simulation cycle. Post-Transition Rules: Treatment = BP>140 , becomes 1 when BP crosses 140 threshold; BP =BP-Treatment*10 , meaning a drop of 10 once treatment is applied; CostThisYear = Age + \Treatment*10 , cost depends on age and if treatment was taken; Cost= Cost + CostThisYear , it accumulates cost over time. Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,...,50 for each individual), BP =120, Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10 and their ages and costs in each state. A stratified report by male and female and young – up to age 30 and old above age 30 is produced.

2019-01-30 | MODEL1803120006 | BioModels

Eucalyptus xylem diurnal time series

Project description:The daily cycle of night and day affects the behaviour and physiology of almost all living things. At the molecular level, many genes show daily changes in expression levels. To determine whether changes in transcript abundance occur in wood forming tissues of Eucalyptus trees we used a cDNA microarray to examine gene expression levels at roughly four hour intervals throughout the day.

2009-05-13 | GSE11731 | GEO

Watanabe2018_Simple markov model

Project description:Simple Markov model. There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3. The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male. Initial conditions: Healthy = (50 Male, 50 Female), Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10.

2019-01-30 | MODEL1803120004 | BioModels

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