Time-restricted feeding improves adaptation to chronically alternating light-dark cycles

Disturbance of the circadian clock has been associated with increased risk of cardio-metabolic disorders. Previous studies showed that optimal timing of food intake can improve metabolic health. We hypothesized that time-restricted feeding could be a strategy to minimize long term adverse metabolic health effects of shift work and jetlag. In this study, we exposed female FVB mice to weekly alternating light-dark cycles (i.e. 12 h shifts) combined with ad libitum feeding, dark phase feeding or feeding at a fixed clock time, in the original dark phase. In contrast to our expectations, long-term disturbance of the circadian clock had only modest effects on metabolic parameters. Mice fed at a fixed time showed a delayed adaptation compared to ad libitum fed animals, in terms of the similarity in 24 h rhythm of core body temperature, in weeks when food was only available in the light phase. This was accompanied by increased plasma triglyceride levels and decreased energy expenditure, indicating a less favorable metabolic state. On the other hand, dark phase feeding accelerated adaptation of core body temperature and activity rhythms, however, did not improve the metabolic state of animals compared to ad libitum feeding. Taken together, restricting food intake to the active dark phase enhanced adaptation to shifts in the light-dark schedule, without significantly affecting metabolic parameters

In vitro inhibition of porcine cytochrome P450 by 17β -estradiol and 17α-estradiol

Sexually mature pigs are known to possess high concentrations of testicular steroids, which have been shown to change the activities of cytochrome P450 in vitro. The aim of the present study was to evaluate the regulation of CYP1A and CYP2E1 activity by the steroids dihydrotestosterone (DHT), 3β-androstenol, 17β-estradiol and 17α-estradiol. Catalytic activities of 7-ethoxyresorufin O-deethylase (EROD) and 7-methoxyresorufin O-demethylase (MROD) were used as markers of CYP1A activities, while p-nitrophenol hydroxylase (PNPH) was used as a marker of CYP2E1 activities. Of the steroids tested, only 17β-estradiol and 17α-estradiol inhibited EROD and MROD activities. This inhibition was observed when a steroid concentration of 100 µM was used, while lower concentrations showed no inhibitory effect. PNPH activities were inhibited only by 100 µM of 17β-estradiol. The significance of these results in vivo is unknown because inhibition was only found when concentrations of estrogens higher than physiological levels were used. Nevertheless, the results provided further evidence on the important role of estrogens in regulation of porcine cytochrome P450 activities

Solution structure of the two iron rubredoxin of pseudomonas oleovarans determined by NMR spectroscopy and solution x-ray scattering and interactions with rubredoxin reductase.

No description supplie

In vivo and in silico dynamics of the development of Metabolic Syndrome

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available

<i>In vivo</i> development of the Metabolic Syndrome results in different phenotypes.

<p>Experimentally observed metabolic parameters upon dietary induction in male E3L.CETP mice over the time course of three months is displayed in two ways: in the left panels the data are expressed as mean ± standard deviation (error bars) for the low-fat diet (LFD; n = 8; light blue), high-fat diet (HFD; n = 12 (pooled from two groups n = 7 for the full time period, n = 5 until 2 months of dietary induction; dark blue) and high-fat diet with 0.25% cholesterol (HFD+C; n = 8; green) groups, whereas in the right panels the data of the animals on HFD+C are depicted for each animal individually. Individuals in this cohort were subdivided into two groups based on the plasma triglyceride (TG) and plasma total cholesterol (TC) levels. The dyslipidemic Metabolic Syndrome phenotypes are depicted in red (MetS_DLP; mice with high plasma TG and simultaneous high plasma TC at t = 3 months) and the non-dyslipidemic Metabolic Syndrome phenotypes in gray (MetS_non-DLP; mice with low plasma TG and simultaneous low plasma TC at t = 3 months). Differences between groups were determined using one-way ANOVA test. When significant differences were found, Fisher’s LSD test was used as a post hoc test to determine the differences between two independent groups: * P<0.05; ** P<0.01; *** P<0.001 HFD as compared to LFD ^# P<0.05; ^## P<0.01; ^### P<0.001 HFD+C as compared to HFD.</p

Metabolic flux trajectory analysis depicts differences among phenotypes and dyslipidemia development.

<p>Trajectory analysis reveals decreased dietary cholesterol absorption from the intestinal lumen in the non-dyslipidemic Metabolic Syndrome phenotype (a) and increased hepatic activity in the dyslipidemia Metabolic Syndrome phenotype (b-f). The median metabolic flux trajectories (calculated from the top 10% best trajectories from n = 1,000) are depicted with a solid line for the hepatic dietary cholesterol absorption from the intestinal lumen (a), hepatic (V)LDL-TG uptake from the plasma (b), hepatic fatty acid uptake from the plasma (c), hepatic bile acid synthesis from cholesterol (d), hepatic <i>de novo</i> lipogenesis (e), and hepatic β-oxidation (f). The shaded area depicts the 10% range of trajectories around the median. The low-fat diet cohort is depicted in light blue; the high-fat cohort in dark blue; the non-dyslipidemic Metabolic Syndrome phenotype in gray and the dyslipidemic Metabolic Syndrome phenotype in red. The experimental hepatic <i>de novo</i> lipogenesis (e) data are shown as black error bars that represent mean ± standard deviation.</p

Schematic representation of the computational model MINGLeD.

<p>MINGLeD describes the metabolic pathways of glucose and lipids to describe the development of MetS. This multi-compartment framework encompasses pathways in dietary absorption, hepatic, peripheral and intestinal lipid metabolism, hepatic and plasma lipoprotein metabolism and plasma, hepatic and peripheral carbohydrate metabolism. The metabolite pools in the different tissue compartments are displayed in the frames; the corresponding metabolic fluxes are represented using the arrows. The dashed arrows represent the dietary inflow in terms of the different macronutrients derived from the experimental data. AA, amino acid; ACAT, Acyl-coenzyme A:cholesterol acyltransferase; ACoA, Acetyl CoA; BA, bile acid; C, cholesterol; CE, cholesteryl ester; CEH, cholesterol ester hydrolase; CETP, cholesteryl ester transfer protein; CM, chylomicron; DNL, <i>de novo</i> lipogenesis; (F)C, (free) cholesterol; (F)FA, (free) fatty acid; G, glucose; G6P, glucose-6-phosphate; GNG, gluconeogenesis; HDL, high density lipoprotein; TG, triglyceride; TICE, transintestinal cholesterol absorption; (V)LDL, (very) low density lipoprotein.</p

MINGLeD describes metabolic phenotypes of male E3L.CETP mice upon different diets and time points.

<p>The metabolic phenotypes are depicted for three different diets (with HFD+C composed of two subgroups that emerged after two months of dietary induction) at four different time points. Model fits (colored error bars: mean ± standard deviation) of MINGLeD calibrated to the phenotype snapshots (raw, individual mouse data shown in gray) separately. Only acceptable model simulations were included, which was classified as having a weighted sum of squared errors (see Eq 1 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006145#pcbi.1006145.s004" target="_blank">S3 Note</a>) below 100.</p

Onset and development of the Metabolic Syndrome reveals two distinct phenotypes of dyslipidemic status.

<p>The classical hallmarks of MetS are depicted by simulations of the individual trajectories (the top 10% best were selected from n = 1,000) for the low-fat diet (LFD) are shown in light blue; high-fat diet (HFD) in dark blue, the non-dyslipidemic Metabolic Syndrome phenotypes (MetS_non-DLP) in gray and the dyslipidemic Metabolic Syndrome phenotypes (MetS_DLP) in red. The color intensity reflects the density of the trajectories: the darker, the more probable the simulated solution. Experimental <i>in vivo</i> data are shown as black error bars that represent mean ± standard deviation. The 5^th column shows an overlay of the mean of the trajectories for each of the subgroups showing the development of increased triglycerides and cholesterol levels in the plasma in the dyslipidemic MetS phenotypes between two and three months.</p

Metabolic Syndrome development is associated with hepatic steatosis in both dyslipidemic and non-dyslipidemic phenotypes.

<p>The mean trajectories of the liver lipid profiles (calculated from the top 10% best trajectories from n = 1,000) are depicted for the hepatic triglyceride pool (a), hepatic free cholesterol pool (b) and the hepatic cholesteryl ester pool (c). Experimental data was obtained at the end of the study and is depicted by the black error bars representing mean ± standard deviation for each of the groups. The data from the LFD cohort is used as initial value, assuming no hepatic lipid accumulation to have occurred in this control group. Differences between groups were determined using one-way ANOVA test. When significant differences were found, Fisher’s LSD test was used as a post hoc test to determine the differences between two independent groups: * P<0.05; ** P<0.01; *** P<0.001 as compared to LFD ^# P<0.05; ^## P<0.01; ^### P<0.001 as compared to HFD ^$ P<0.05; ^$$ P<0.01; ^$$$ P<0.001 as compared to HFD+C (MetS_non-DLP).</p