Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep.

Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the genetics of sleep and the circadian clock over the years. For researchers venturing into this fascinating area, this review thus includes fundamental features of circadian rhythm and genetic variables impacting sleep-wake cycles. Sleep/wake pathway medication exposure and susceptibility are influenced by genetic variations, and the responsiveness of sleep-related medicines is influenced by several functional polymorphisms. This review highlights the features of the circadian timing system and then a genetic perspective on wakefulness and sleep, as well as the relationship between sleep genetics and sleep disorders. Neurotransmission genes, as well as circadian and sleep/wake receptors, exhibit functional variability. Experiments on animals and humans have shown that these genetic variants impact clock systems, signaling pathways, nature, amount, duration, type, intensity, quality, and quantity of sleep. In this regard, the overview covers research on sleep genetics, the genomic properties of several popular model species used in sleep studies, homologs of mammalian genes, sleep disorders, and related genes. In addition, the study includes a brief discussion of sleep, narcolepsy, and restless legs syndrome from the viewpoint of a model organism. It is suggested that the understanding of genetic clues on sleep function and sleep disorders may, in future, result in an evidence-based, personalized treatment of sleep disorders.

DNA Methylation Modification Patterns Identify Distinct Prognosis and Responses to Immunotherapy and Targeted Therapy in Renal Cell Carcinoma.

BACKGROUND: Considering the remarkable heterogeneity of biological features of renal cell carcinoma (RCC), the current clinical classification that only relies on classic clinicopathological features is in urgent need of improvement. Herein, we aimed to conduct DNA methylation modification patterns in RCC. METHODS: We retrospectively curated multiple RCC cohorts, comprising TCGA-KIRC, TCGA-KICH, TCGA-KIRP, and E-MTAB-1980. DNA methylation modification patterns were proposed with an unsupervised clustering algorithm based on 20 DNA methylation regulators. Immunological features were characterized using tumor-infiltrating immune cells and immunomodulators. Sensitivity to immuno- or targeted therapy was estimated with submap and Genomics of Drug Sensitivity in Cancer (GDSC). DNA methylation score (DMS) was developed with principal component analysis. RESULTS: Three DNA methylation modification patterns were conducted across RCC patients, namely C1, C2 and C3. Among them, C3 displayed the most remarkable survival advantage. The three patterns presented in agreement with immune phenotypes: immune-desert, immune-excluded, and immune-inflamed, respectively. These patterns displayed distinct responses to anti-PD-1 and targeted drugs. DMS enabled the quantification of DNA methylation status individually as an alternative tool for prognostic estimation. CONCLUSIONS: The DNA methylation molecular patterns we proposed are an innovative complement to the traditional classification of RCC, which might contribute to precision medicine.