Steroid Hormone Biosynthesis and Dietary Related Metabolites Associated with Excessive Daytime Sleepiness.

Background: Excessive daytime sleepiness (EDS) is a complex sleep problem that affects approximately 33% of the United States population. Although EDS usually occurs in conjunction with insufficient sleep, and other sleep and circadian disorders, recent studies have shown unique genetic markers and metabolic pathways underlying EDS. Here, we aimed to further elucidate the biological profile of EDS using large scale single- and pathway-level metabolomics analyses. Methods: Metabolomics data were available for 877 metabolites in 6,071 individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and EDS was assessed using the Epworth Sleepiness Scale (ESS) questionnaire. We performed linear regression for each metabolite on continuous ESS, adjusting for demographic, lifestyle, and physiological confounders, and in sex specific groups. Subsequently, gaussian graphical modelling was performed coupled with pathway and enrichment analyses to generate a holistic interactive network of the metabolomic profile of EDS associations. Findings: We identified seven metabolites belonging to steroids, sphingomyelin, and long chain fatty acids sub-pathways in the primary model associated with EDS, and an additional three metabolites in the male-specific analysis. The identified metabolites particularly played a role in steroid hormone biosynthesis. Interpretation: Our findings indicate that an EDS metabolomic profile is characterized by endogenous and dietary metabolites within the steroid hormone biosynthesis pathway, with some pathways that differ by sex. Our findings identify potential pathways to target for addressing the causes or consequences of EDS and related sleep disorders. Funding: Details regarding funding supporting this work and all studies involved are provided in the acknowledgments section. Research in context: Evidence before this study: There is a growing recognition of the paramount importance of sleep on health and cardiometabolic disease. Excessive daytime sleepiness (EDS), one of the key common sleep treatment targets, has been linked to increased risk of mortality, hypertension, cardiovascular disease, car accidents as well as decrease in life quality, and productivity. Despite its impact on health, much remains unknown about the biological mechanisms of EDS and if those mechanisms are independent from other sleep disorders. Recent genetic evidence that shows that EDS is associated with specific genetic biomarkers supports the need to further study the underlying biology of EDS.Added value of this study: Here, we used measurements of metabolites, the products and by-products of metabolism to identify the metabolomic profile of EDS. Metabolites are produced by the biological reactions within the body via proteins-themselves products of genes-and by the breakdown of external sources such as nutritional intake and breathing air pollutants. Therefore, metabolomics enables study of the effects of nutrition, environmental exposures, and genetics. In this study we aimed to identify the metabolites that were associated with excessive daytime sleepiness. Additionally, we mapped these metabolites into a publicly available online biological network of human metabolism pathways to obtain an understanding of our findings on a larger scale.Implications of all the available evidence: Identifying the metabolites and pathways related to daytime sleepiness provides insights into the biological mechanisms of EDS and suggests future research opportunities to identify targets for prevention, prediction, and treatments for EDS and potentially other sleep disorders coupled with sleepiness. In this study we found 7 such metabolites-some endogenously synthesised and some obtained from dietary sources-associated with EDS. The network analysis implicated the steroid hormone biosynthesis pathway as a shared pathway underlying those metabolites, and identified linkages to key metabolites related to sleep: melatonin and cortisol metabolism.

Multiset correlation and factor analysis enables exploration of multi-omics data.

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Large scale proteomic studies create novel privacy considerations.

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Kruppel-like factor 4 regulates endothelial inflammation.

The vascular endothelium plays a critical role in vascular homeostasis. Inflammatory cytokines and non-laminar blood flow induce endothelial dysfunction and confer a pro-adhesive and pro-thrombotic phenotype. Therefore, identification of factors that mediate the effects of these stimuli on endothelial function is of considerable interest. Kruppel-like factor 4 expression has been documented in endothelial cells, but a function has not been described. In this communication we describe the expression in vitro and in vivo of Kruppel-like factor 4 in human and mouse endothelial cells. Furthermore, we demonstrate that endothelial Kruppel-like factor 4 is induced by pro-inflammatory stimuli and shear stress. Overexpression of Kruppel-like factor 4 induces expression of multiple anti-inflammatory and anti-thrombotic factors including endothelial nitric-oxide synthase and thrombomodulin, whereas knockdown of Kruppellike factor 4 leads to enhancement of tumor necrosis factor alpha-induced vascular cell adhesion molecule-1 and tissue factor expression. The functional importance of Kruppel-like factor 4 is verified by demonstrating that Kruppel-like factor 4 expression markedly decreases inflammatory cell adhesion to the endothelial surface and prolongs clotting time under inflammatory states. Kruppel-like factor 4 differentially regulates the promoter activity of pro- and anti-inflammatory genes in a manner consistent with its anti-inflammatory function. These data implicate Kruppel-like factor 4 as a novel regulator of endothelial activation in response to pro-inflammatory stimuli.