Disruption of central and peripheral circadian clocks and circadian controlled estrogen receptor rhythms in night shift nurses in working environments.

Chronic disruption of circadian rhythms by night shift work is associated with an increased breast cancer risk. However, little is known about the impact of night shift on peripheral circadian genes (CGs) and circadian-controlled genes (CCGs) associated with breast cancer. Hence, we assessed central clock markers (melatonin and cortisol) in plasma, and peripheral CGs (PER1, PER2, PER3, and BMAL1) and CCGs (ESR1 and ESR2) in peripheral blood mononuclear cells (PBMCs). In day shift nurses (n = 12), 24-h rhythms of cortisol and melatonin were aligned with day shift-oriented light/dark schedules. The mRNA expression of PER2, PER3, BMAL1, and ESR2 showed 24-h rhythms with peak values in the morning. In contrast, night shift nurses (n = 10) lost 24-h rhythmicity of cortisol with a suppressed morning surge but retained normal rhythmic patterns of melatonin, leading to misalignment between cortisol and melatonin. Moreover, night shift nurses showed disruption of rhythmic expressions of PER2, PER3, BMAL1, and ESR2 genes, resulting in an impaired inverse correlation between PER2 and BMAL1 compared to day shift nurses. The observed trends of disrupted circadian markers were recapitulated in additional day (n = 20) and night (n = 19) shift nurses by measurement at early night and midnight time points. Taken together, this study demonstrated the misalignment of cortisol and melatonin, associated disruption of PER2 and ESR2 circadian expressions, and internal misalignment in peripheral circadian network in night shift nurses. Morning plasma cortisol and PER2, BMAL1, and ESR2 expressions in PBMCs may therefore be useful biomarkers of circadian disruption in shift workers.

Investigation of metabolites for estimating blood deposition time.

Trace deposition timing reflects a novel concept in forensic molecular biology involving the use of rhythmic biomarkers for estimating the time within a 24-h day/night cycle a human biological sample was left at the crime scene, which in principle allows verifying a sample donor's alibi. Previously, we introduced two circadian hormones for trace deposition timing and recently demonstrated that messenger RNA (mRNA) biomarkers significantly improve time prediction accuracy. Here, we investigate the suitability of metabolites measured using a targeted metabolomics approach, for trace deposition timing. Analysis of 171 plasma metabolites collected around the clock at 2-h intervals for 36 h from 12 male participants under controlled laboratory conditions identified 56 metabolites showing statistically significant oscillations, with peak times falling into three day/night time categories: morning/noon, afternoon/evening and night/early morning. Time prediction modelling identified 10 independently contributing metabolite biomarkers, which together achieved prediction accuracies expressed as AUC of 0.81, 0.86 and 0.90 for these three time categories respectively. Combining metabolites with previously established hormone and mRNA biomarkers in time prediction modelling resulted in an improved prediction accuracy reaching AUCs of 0.85, 0.89 and 0.96 respectively. The additional impact of metabolite biomarkers, however, was rather minor as the previously established model with melatonin, cortisol and three mRNA biomarkers achieved AUC values of 0.88, 0.88 and 0.95 for the same three time categories respectively. Nevertheless, the selected metabolites could become practically useful in scenarios where RNA marker information is unavailable such as due to RNA degradation. This is the first metabolomics study investigating circulating metabolites for trace deposition timing, and more work is needed to fully establish their usefulness for this forensic purpose.

[Seasons, circadian rhythms, sleep and suicidal behaviors vulnerability]. / Saisons, rythmes circadiens, sommeil et vulnérabilité aux conduites suicidaires.

INTRODUCTION: Suicidal behaviors are common in the general population and are so a major public health problem. In order to improve suicide prevention and to reduce the mortality by suicide, it appears essential to better identify suicide risk factors. Seasonality, circadian rhythms and sleep abnormalities have been already associated with numerous psychiatric disorders. This review aimed to characterize the associations between seasonality, circadian rhythms, sleep and suicidal behaviors including suicide attempts and completed suicides. METHODS: We conducted a literature search between 1973 and 2015 in PubMed databases using the following terms: ("suicide" OR "suicidality" OR "suicide attempts" OR "suicidal behavior") AND ("circadian rhythms" OR "seasons" OR "sleep"). RESULTS: Many studies confirm a specific seasonality for suicide with a higher peak of suicides in spring for both sex and a lower peak in autumn especially for women. This distribution seems to correlate with depressive symptoms (especially for the autumn peak), gender and different types of suicide. Regarding gender and type of suicide differences, males more commonly commit violent suicide with a higher rate of suicides in spring. Suicide behaviors appear to be influenced by climatic and biological factors like sunshine, daylight cycles, temperature, air pollutants, viruses, parasites and aeroallergens. Circadian variations exist in suicide rates depending on age with a morning peak for elder and an evening peak for youth. In addition, completed suicide peak in early morning whereas suicide attempts peak rather in later afternoon. Several biomarkers dysregulation like melatonin, serotonin and cortisol may be implicated in suicide circadian variations. Furthermore, specific sleep disorders like insomnia, nightmares and sleep deprivation are common risk factors of suicide and possibly independently of the presence of depressive symptoms. Finally, the efficacy of chronotherapeutics (such as luminotherapy, dark therapy, sleep deprivation and melatonin drugs) has been suggested in the reduction of suicidal behaviors. CONCLUSION: The suicide seasonality is very well documented showing a main peak in spring and another one in autumn. A suicide circadian distribution also exists depending of the suicidal behavior intensity and of the age. Numerous sleep disorders are also suicide risk factors and can be treated with chronotherapeutics. A better identification of seasonality, circadian rhythms and sleep abnormalities in suicidal behaviors could allow a better prevention in suicidal attempts and a reduction in death by suicide.

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

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 ).