Sleep Disruption and Daytime Sleepiness Correlating with Disease Severity and Insulin Resistance in Non-Alcoholic Fatty Liver Disease: A Comparison with Healthy Controls

BACKGROUND & AIMS: Sleep disturbance is associated with the development of obesity, diabetes and hepatic steatosis in murine models. Hepatic triglyceride accumulation oscillates in a circadian rhythm regulated by clock genes, light-dark cycle and feeding time in mice. The role of the sleep-wake cycle in the pathogenesis of human non-alcoholic fatty liver disease (NAFLD) is indeterminate. We sought to detail sleep characteristics, daytime sleepiness and meal times in relation to disease severity in patients with NAFLD. METHODS: Basic Sleep duration and latency, daytime sleepiness (Epworth sleepiness scale), Pittsburgh sleep quality index, positive and negative affect scale, Munich Chronotype Questionnaire and an eating habit questionnaire were assessed in 46 patients with biopsy-proven NAFLD and 22 healthy controls, and correlated with biochemical and histological parameters. RESULTS: In NAFLD compared to healthy controls, time to fall asleep was vastly prolonged (26.9 vs. 9.8 min., p = 0.0176) and sleep duration was shortened (6.3 vs. 7.2 hours, p = 0.0149). Sleep quality was poor (Pittsburgh sleep quality index 8.2 vs. 4.7, p = 0.0074) and correlated with changes in affect. Meal frequency was shifted towards night-times (p = 0.001). In NAFLD but not controls, daytime sleepiness significantly correlated with liver enzymes (ALAT [r = 0.44, p = 0.0029], ASAT [r = 0.46, p = 0.0017]) and insulin resistance (HOMA-IR [r = 0.5, p = 0.0009]) independent of cirrhosis. In patients with fibrosis, daytime sleepiness correlated with the degree of fibrosis (r = 0.364, p = 0.019). CONCLUSIONS: In NAFLD sleep duration was shortened, sleep onset was delayed and sleep quality poor. Food-intake was shifted towards the night. Daytime sleepiness was positively linked to biochemical and histologic surrogates of disease severity. The data may indicate a role for sleep-wake cycle regulation and timing of food-intake in the pathogenesis of human NAFLD as suggested from murine models

Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution.

Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development.This work was supported by the following grants: R.L.—National Health and Medical Research Council (NHMRC) Project Grant 1130168, NHMRC Investigator Grant 1178460, Silvia and Charles Viertel Senior Medical Research Fellowship, Howard Hughes Medical Institute International Research Scholarship, and Australian Research Council (ARC) LE170100225; S.F.—NHMRC Ideas Grant 1184421; I.V.—ARC Future Fellowship FT170100359, UNSW Scientia Fellowship, and NHMRC Project Grant RG170137; S.B.—NHMRC-ARC Dementia Research Development Fellowship 1111206; C.P.—Raine Foundation Priming Grant RPG66-21; J.M.P.—Silvia and Charles Viertel Senior Medical Research Fellowship, ARC Future Fellowship FT180100674. This work was supported by a Cancer Research Trust grant ‘‘Enabling advanced single cell cancer genomics in WA’’ and Cancer Council WA enabling grant. Genomic data were generated at the ACRF Centre for Advanced Cancer Genomics and Genomics WA. Human brain tissue was received from the UMB Brain and Tissue Bank at the University of Maryland, part of the NIH NeuroBioBank. The glioblastoma sample was procured and provided by the AGOG biobank, funded by CINSW grant SRP-08-10. L.M. was a fellow of The Lorenzo and Pamela Galli Medical Research Trust. We thank Ankur Sharma and Greg Neely for valuable feedback. The graphical abstract and elements of Figure 1A were created with BioRender.S

A design method for cross-disciplinary coordination and innovation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1991.Includes bibliographical references (leaves 123-128).by John C. Pflueger.Ph.D

Age-Associated Sperm DNA Methylation Alterations: Possible Implications in Offspring Disease Susceptibility

<div><p>Recent evidence demonstrates a role for paternal aging on offspring disease susceptibility. It is well established that various neuropsychiatric disorders (schizophrenia, autism, etc.), trinucleotide expansion associated diseases (myotonic dystrophy, Huntington's, etc.) and even some forms of cancer have increased incidence in the offspring of older fathers. Despite strong epidemiological evidence that these alterations are more common in offspring sired by older fathers, in most cases the mechanisms that drive these processes are unclear. However, it is commonly believed that epigenetics, and specifically DNA methylation alterations, likely play a role. In this study we have investigated the impact of aging on DNA methylation in mature human sperm. Using a methylation array approach we evaluated changes to sperm DNA methylation patterns in 17 fertile donors by comparing the sperm methylome of 2 samples collected from each individual 9–19 years apart. With this design we have identified 139 regions that are significantly and consistently hypomethylated with age and 8 regions that are significantly hypermethylated with age. A representative subset of these alterations have been confirmed in an independent cohort. A total of 117 genes are associated with these regions of methylation alterations (promoter or gene body). Intriguingly, a portion of the age-related changes in sperm DNA methylation are located at genes previously associated with schizophrenia and bipolar disorder. While our data does not establish a causative relationship, it does raise the possibility that the age-associated methylation of the candidate genes that we observe in sperm might contribute to the increased incidence of neuropsychiatric and other disorders in the offspring of older males. However, further study is required to determine whether, and to what extent, a causative relationship exists.</p></div

Donor demographics.

<p>Donor demographics.</p

Hypermethylation of Circulating Free DNA in Cutaneous Melanoma

Changes in DNA methylation are well documented in cancer development and progression and are typically identified through analyses of genomic DNA. The capability of monitoring tumor-specific methylation changes in circulating tumor DNA (ctDNA) has the potential to improve the sensitivity of ctDNA for the diagnosis and prognosis of solid tumors. In this study we profiled the methylation of seven gene targets (all known to be hypermethylated in metastatic melanoma) within the plasma of patients with advanced melanoma using amplicon-based next generation sequencing of bisulfite-treated DNA. Hypermethylation of 6/7 gene targets, including paraoxonase 3 (PON3) was significantly elevated in patients with metastatic melanoma (n = 4) compared to healthy control samples (n = 5). In addition, the degree of hypermethylation of PON3 and MEOX2 were significantly correlated with ctDNA copy number in melanoma patients, confirming the utility of methylated ctDNA in the absence of tumor mutation data for genes such as BRAF, RAS or EGFR

(A) Comparison of MiSeq results to our array results at 21 representative regions.

<p>Because beta-values and fraction methylation are generated in a different manner (array vs. sequencing respectively) they are not directly comparable. For this reason we compared the fractional difference for each loci and each technology. This is accomplished by the following equation: fractional difference = (aged value/young value)−1. (B) the fractional difference between young and aged samples at 15 selected loci as measured by array in the 17 donor samples as well as in the independent cohort (19 samples from individuals > = 45 years of age and 47 samples from individuals <25 years of age taken from the general population). On average the fractional difference identified in the independent cohort (as measured by sequencing) was approximately 2.2 times greater in magnitude than was identified in the 17 donors.</p

(A) The magnitude of alterations in terms of amount of change per year (reported as slope magnitude) for all regional changes that occur at CpG islands, shores and outside of these regions (other).

<p>Average alterations per year were approximately 0.281%. (B) Average β-values for all significant windows (hypomethylation and hypermethylation events) for both aged and young. Average decrease in β-value was approximately 3.9% for intra-individual hypomethylation events and 3.2% for hypermethylation events. (C) the percent of regions of hypermethylation and hypomethylation at CpG islands, shores and outside of these regions (Other). Hypermethylation events were significantly more enriched at islands than were hypomethylation events based on a fisher exact test (p = 0.0056). Hypomethylation events were significantly more enriched at shores in comparison to hypermethylation events (p = 0.0015). Hypermethylation and hypomethylation events were similarly enriched in regions outside of islands and shores. (D) We also investigated the co-localization of nucleosomes (every region of known histone retention) as well as histone modifications (H3K4 methylation, and H3K27 methylation) with our windows of interest. Hypermethylation events were less frequently associated with all retained histones (nucleosomes) or loci with H3K27 methylation when compared to hypomethylation events based on Fisher's Exact Test (p = 0.002; p = 0.0107). Co-localization of hypermethylation or hypomethylation events with H3K4 methylation was statistically similar.</p

The frequency of disease associations within our gene set was analyzed and compared to the frequency of disease associations for all genes known to be associated with at least a single disease based on GAD annotation.

<p>Schizophrenia, bipolar disorder, diabetes mellitus and hypertension were selected, as there were at least 3 genes in our small set of identified genes that are associated with these diseases. Only bipolar disorder was more frequently associated with our identified genes than the background set of genes, based on chi-squared analysis with multiple comparison correction (Bonferonni) of the 117 age associated genes analyzed (p = 0.012), and schizophrenia also trended toward increased frequency (p = 0.07). However, these are not considered significant enrichments if considering all genes in the genome (omitting the filter for a disease connection). The frequency of genes associated with hypertension and diabetes mellitus in the two groups was statistically similar.</p

(A) Chromosomal loci of each altered region are depicted where blue marks represent hypomethylation events and red marks hypermethylation events.

<p>(B) The Correlation Maps app on the USeq platform was used to locate any specific chromosomal enrichment of altered methylation windows. Specifically, the application called any 100 kb region where at least two significantly altered methylation marks were found. All called chromosomal enrichment regions are displayed though none were found to be significantly enriched over the background.</p