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Abnormalities in circadian rhythms have been reported in people with mood disorders, but these abnormalities are marked by considerable inter-individual variability. This study aimed to identify pathophysiological subgroups on the basis of circadian markers and evaluate how these subgroups relate to psychiatric profiles. Thirty-five young adults (18-31 years old) receiving clinical care for unipolar depressive disorders and 15 healthy controls took part to this study. The Hamilton Rating Scale for Depression and the Young Mania rating scale were used to evaluate the severity of mood symptoms in participants with depressive disorders. All participant underwent ambulatory sleep monitoring with actigraphy for about 12 days before attending a laboratory-based chronobiological assessment which included repeated salivary samples to determine dim light melatonin onset (DLMO) and continuous core body temperature (CBT) monitoring using an ingestible temperature sensor. Cluster analyses were conducted across all participants to identify subgroups with consistent circadian timing profiles based on DLMO and the nocturnal minima of CBT. Two clusters were identified: 'delayed' and 'conventional timing' circadian phase. Descriptive analyses showed that the delayed cluster was characterised by abnormal time relationships between circadian phase markers and the sleep-wake cycle. Importantly, individuals from the delayed cluster had worse depression severity (t(28) = -2.7, p = 0.011) and hypomanic symptoms (Z = -2.2, p = 0.041) than their peers with conventional circadian timing. These findings suggest that delayed and disorganised circadian rhythms may be linked to worse psychiatric profiles in young people with depressive disorders.
Pubmed ID: 30301878
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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