Biology subjects

Purcell, S.

Publications and source records attributed to Purcell, S..

7 recordsLinked to original sources

bioRxiv2023

Spindle Chirp and other Sleep Oscillatory Features in Young Children with Autism

ObjectivesTo determine whether spindle chirp and other sleep oscillatory features differ in young children with and without autism. MethodsAutomated processing software was used to re-assess an extant set of polysomnograms representing 121 children (91 with autism [ASD], 30 typically-developing [TD]), with an age range of 1.35-8.23 years. Spindle metrics, including chirp, and slow oscillation (SO) characteristics were compared between groups. SO and fast and slow spindle (FS, SS) interactions were also investigated. Secondary analyses were performed assessing behavioural data associations, as well as exploratory cohort comparisons to children with non-autism developmental delay (DD). ResultsPosterior FS and SS chirp was significantly more negative in ASD than TD. Both groups had comparable intra-spindle frequency range and variance. Frontal and central SO amplitude were decreased in ASD. In contrast to previous manual findings, no differences were detected in other spindle or SO metrics. The ASD group displayed a higher parietal coupling angle. No differences were observed in phase-frequency coupling. The DD group demonstrated lower FS chirp and higher coupling angle than TD. Parietal SS chirp was positively associated with full developmental quotient. ConclusionsFor the first time spindle chirp was investigated in autism and was found to be significantly more negative than in TD in this large cohort of young children. This finding strengthens previous reports of spindle and SO abnormalities in ASD. Further investigation of spindle chirp in healthy and clinical populations across development will help elucidate the significance of this difference and better understand this novel metric.

neuroscience↗

bioRxiv2023

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross- validated regularized regression to link sleep EEG features to cognitive performance in cross- sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics accounted for more covariance between sleep EEG and cognition than health variables, and consequently reduced this association by a greater degree, but even with the strictest covariate sets a statistically significant association was present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r=0.283), with the strongest effect ascribed to spindle- frequency activity. This association becomes weaker after adjusting for demographic (r=0.186) and health variables (r=0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

neuroscience↗

bioRxiv2021

Glycocalyx engineering with heparan sulfate mimetics attenuates Wnt activity during adipogenesis to promote glucose uptake and metabolism.

Adipose tissue (AT) plays a crucial role in maintaining me tabolic homeostasis by storing lipids and glucose from circulation as intracellular fat. As peripheral tissues like AT become insulin resistant, decompensation of blood glucose levels occurs causing type 2 diabetes (T2D). Currently, glycocalyx modulating as a pharmacological treatment strategy to improve glucose homeostasis in T2D patients is underexplored. Here, we show a novel role for cell surface heparan sulfate (HS) in establishing glucose uptake capacity and metabolic utilization in differentiated adipocytes. Using a combination of chemical and genetic interventions, we identified that HS modulates this metabolic phenotype by attenuating levels of Wnt signaling during adipogenesis. By engineering the glycocalyx of preadipocytes with exogenous synthetic HS mimetics, we were able to enhance glucose clearance capacity after differentiation through modulation of Wnt ligand availability. These findings establish the cellular glycocalyx as a possible new target for therapeutic intervention in T2D patients by enhancing glucose clearance capacity independent of insulin secretion. SIGNIFICANCEMetabolic disorders associated with the Western-style diet, such as type 2 diabetes, are among the main drivers of mortality in the US and globally, with more than 380 million people currently affected by this disease worldwide. However, treatment options for type 2 diabetes are currently limited to management of caloric uptake and expenditure, with none able to reverse the condition long-term. The ability to reprogram adipose tissues to improve their overall capacity to clear glucose may provide one such opportunity. Here we provide evidence that glycocalyx remodeling in pre-adipocytes with heparan sulfate mimetics will alter their differentiation program by modulating Wnt signaling to produce adipocytes with increased glucose uptake and utilization.

cell biology↗

bioRxiv2019

Characterization of single gene copy number variants in schizophrenia

Genetic studies of schizophrenia (SCZ) have now implicated numerous genomic loci that contribute to risk including several copy number variants (CNV) of large effect and hundreds of associated loci of small effect. However, in only a few cases has a specific gene been clearly identified. Rare CNV that affect only a single gene offer a potential avenue to discovering specific SCZ risk genes. Here, we use CNV generated from exome-sequencing of 4,913 SCZ cases and 6,188 controls in a homogenous Swedish cohort to assess the contribution of single-gene deletions and duplications to SCZ risk. As previously seen, we found an excess of rare deletions (p = 0.0004) and duplications (p = 0.0006) in SCZ cases compared to controls. When limiting to only single-gene CNV we identified nominally significant excess of deletions (p = 0.04) and duplications (p = 0.03). In an effort to increase the number of single-gene CNV, we reduced strict filtering criteria but required support from two independent CNV calling methods to create an expanded set that showed a significant burden of deletions in 11 out of 22 gene sets previously implicated in SCZ and in the combined set of genes across those sets (p = 0.008). Finally, for the significantly enriched set of voltage-gated calcium channels, we performed an extensive validation of all deletions generated from exome-sequencing as well as any deletion with evidence from previously analyzed genotyping arrays. In total, 4 exonic, single-gene deletions validated in cases and none in controls (p = 0.039), of which all were identified by exome-sequencing. Broadly, these results point to the potential contribution of single-gene CNV to SCZ and the added value of a deeper dive into CNV calls from exome-sequencing.

genetics↗

bioRxiv2018

Epigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American specific associations

Study ObjectivesExcessive daytime sleepiness (EDS) is a consequence of inadequate sleep, or of a primary disorder of sleep-wake control. Population variability in prevalence of EDS and susceptibility to EDS are likely due to genetic and biological factors as well as social and environmental influences. Epigenetic modifications (such as DNA methylation-DNAm) are potential influences on a range of health outcomes. Here, we explored the association between DNAm and daytime sleepiness quantified by the Epworth Sleepiness Scale (ESS).\n\nMethodsWe performed multi-ethnic and ethnic-specific epigenome-wide association studies for DNAm and ESS in 619 individuals from the Multi-Ethnic Study of Atherosclerosis. Replication was assessed in the Cardiovascular Health Study (CHS). Genetic variants in genes proximal to ESS-associated DNAm were analyzed to identify methylation quantitative trait loci and followed with replication of genotype-sleepiness associations in the UK Biobank.\n\nResults61 methylation sites were associated with ESS (FDR [≤] 0.1) in African Americans only, including an association in KCTD5, a gene strongly implicated in sleep. One association (cg26130090) replicated in CHS African Americans (p-value 0.0004). We identified a sleepiness-associated methylation site in the gene RAI1, a gene associated with sleep and circadian phenotypes. In a follow-up analysis, a genetic variant within RAI1 associated with both DNAm and sleepiness score. The variants association with sleepiness was replicated in the UK Biobank.\n\nConclusionsOur analysis identified methylation sites in multiple genes that may be implicated in EDS. These sleepiness-methylation associations were specific to African Americans. Future work is needed to identify mechanisms driving ancestry-specific methylation effects.\n\nStatement of SignificanceExcessive daytime sleepiness is associated with negative health outcomes such as reduction in quality of life, increased workplace accidents, and cardiovascular mortality. There are race/ethnic disparities in excessive daytime sleepiness, however, the environmental and biological mechanisms for these differences are not yet understood. We performed an association analysis of DNA methylation, measured in monocytes, and daytime sleepiness within a racially diverse study population. We detected numerous DNA methylation markers associated with daytime sleepiness in African Americans, but not in European and Hispanic Americans. Future work is required to elucidate the pathways between DNA methylation, sleepiness, and related behavioral/environmental exposures.

genomics↗

bioRxiv2018

Biological and clinical insights from genetics of insomnia symptoms

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.\n\nOne Sentence SummaryWe identify 57 genomic regions associated with insomnia pointing to the involvement of phototransduction and ubiquitination and potential causal links to CAD and depression.

genomics↗

bioRxiv2017

Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.

Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

genetics↗