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The Coronavirus disease 2019 (COVID-19) pandemic is overwhelming the healthcare systems. Identification of systemic reactions underlying COVID-19 will lead to new biomarkers and therapeutic targets for monitoring and early intervention in this viral infection. We performed targeted metabolomics covering up to 630 metabolites within several key metabolic pathways in plasma samples of 20 hospitalized COVID-19 patients and 37 matched controls. Plasma metabolic signatures specifically differentiated severe COVID-19 from control patients. The identified metabolic signatures indicated distinct alterations in both lipid and amino acid metabolisms in COVID-19 compared to control patient plasma. Systems biology-based analyses identified sphingolipid, tryptophan, tyrosine, glutamine, arginine, and arachidonic acid metabolism as mostly impacted pathways in COVID-19 patients. Notably, gamma-aminobutyric acid (GABA) was significantly reduced in COVID-19 patients and GABA plasma levels allowed for stratification of COVID-19 patients with high sensitivity and specificity. The data reveal large metabolic disturbances in COVID-19 patients and suggest use of GABA as potential biomarker and therapeutic target for the infection. [ABSTRACT FROM AUTHOR]

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss. [ABSTRACT FROM AUTHOR]

Adipocytes store excess energy in the form of triglycerides and signal the levels of stored energy to the brain. Here we show that adipocyte-specific deletion of Arntl (also known as Bmal1), a gene encoding a core molecular clock component, results in obesity in mice with a shift in the diurnal rhythm of food intake, a result that is not seen when the gene is disrupted in hepatocytes or pancreatic islets. Changes in the expression of hypothalamic neuropeptides that regulate appetite are consistent with feedback from the adipocyte to the central nervous system to time feeding behavior. Ablation of the adipocyte clock is associated with a reduced number of polyunsaturated fatty acids in adipocyte triglycerides. This difference between mutant and wild-type mice is reflected in the circulating concentrations of polyunsaturated fatty acids and nonesterified polyunsaturated fatty acids in hypothalamic neurons that regulate food intake. Thus, this study reveals a role for the adipocyte clock in the temporal organization of energy regulation, highlights timing as a modulator of the adipocyte-hypothalamic axis and shows the impact of timing of food intake on body weight. [ABSTRACT FROM AUTHOR]

High personal UVR doses can be gained during leisure activities, causing intense self-resolving inflammation (sunburn) of unprotected skin. UVR activates release of membrane fatty acids and upregulates their metabolism by cyclooxygenases (COX) and lipoxygenases (LOX) to different eicosanoids. While COX-derived prostaglandin (PG)E2 is a potent mediator of sunburn vasodilatation, LOX-derived 15-hydroxyeicosatetraenoic acid (HETE) and its lipoxin metabolites may contribute to sunburn limitation. We explored the relationships between expression of these lipid mediators and the clinical and histological outcomes, comparing responses of individuals prone and more resistant to sunburn. An acute UVR exposure of 12 SED (standard erythema dose) was applied to buttock skin of 32 white Caucasians (n = 16 phototype I/II, n = 16 phototype III/IV), and over the subsequent 72 h assessments were made of skin erythema, immunohistochemical expression of leukocyte markers, COX-2, 12-LOX, 15-LOX and nitric oxide synthase (NOS), and eicosanoid levels by LC/ESI-MS/MS. Evidence of a significant inflammatory response was seen earlier in phototype I/II with regard to expression of erythema (4h, p ＜ 0.001), neutrophil infiltration (24 h, p = 0.01), epidermal COX-2 (24 h, p ＜ 0.05) and 12-LOX (24 h, p ＜ 0.01), and dermal eNOS (24 h, p ＜ 0.05) proteins, although CD3+ lymphocyte infiltration showed an earlier increase in phototype III/IV (24 h, p ＜ 0.05). Although erythema was equivalent at 72 h in both groups, phototype I/II showed higher PGE2 accompanied by elevated 15-HETE, and a strong positive correlation was seen between these mediators (n = 18, r = 0.805, p = 0.0001). Hence anti-inflammatory eicosanoid 15-HETE may temper the pro-inflammatory milieu in sunburn, having greater influence in those prone to sunburn than those more resistant, given the same high UVR exposure conditions. [ABSTRACT FROM AUTHOR]

The gut microbiome and lipid metabolism are both recognized as essential components in the maintenance of metabolic health. The mechanisms involved are multifactorial and (especially for microbiome) poorly defined. A strategic approach to investigate the complexity of the microbial influence on lipid metabolism would facilitate determination of relevant molecular mechanisms for microbiome-targeted therapeutics. E. coli is associated with obesity and metabolic syndrome and we used this association in conjunction with gnotobiotic models to investigate the impact of E. coli on lipid metabolism. To address the complexities of the integration of the microbiome and lipid metabolism, we developed transcriptomics-driven lipidomics (TDL) to predict the impact of E. coli colonization on lipid metabolism and established mediators of inflammation and insulin resistance including arachidonic acid metabolism, alterations in bile acids and dietary lipid absorption. A microbiome-related therapeutic approach targeting these mechanisms may therefore provide a therapeutic avenue supporting maintenance of metabolic health. Integrative Lipidomics: Microbiome regulation of lipid metabolism Microbes multifactorially impact host lipid metabolism bearing a significant impact in health and disease. A team led by Mojgan Masoodi and Scott Parkinson at Nestlé Institute of Health Sciences (NIHS) developed an integrative data driven approach for predictive lipidomics investigations of host-microbial impacts on lipid metabolism. Results of in-vivo studies with germ-free mice inoculated with E. coli and in-vitro studies demonstrated the multifactorial nature of the impact of E. coli on arachidonic acid metabolism in the ileum and altered host inflammation and lipid absorption. The findings provide insights into understanding the host-microbiome interactions and identifying microbiome-related solutions for maintaining health and tackling disease. The systems approach presented is applicable to investigate broad range of microbiome dependent and independent alterations in host lipid metabolism. [ABSTRACT FROM AUTHOR]