Food for thought: the importance of glucose and other energy substrates for sustaining brain function under varying levels of activity.

The brain requires a constant and substantial energy supply to maintain its main functions. For decades, it was assumed that glucose was the major if not the only significant source of energy for neurons. This view was supported by the expression of specific facilitative glucose transporters on cerebral blood vessels, as well as neurons. Despite the fact that glucose remains a key energetic substrate for the brain, growing evidence suggests a different scenario. Thus astrocytes, a major type of glial cells that express their own glucose transporter, play a critical role in coupling synaptic activity with glucose utilization. It was shown that glutamatergic activity triggers an enhancement of aerobic glycolysis in this cell type. As a result, lactate is provided to neurons as an additional energy substrate. Indeed, lactate has proven to be a preferential energy substrate for neurons under various conditions. A family of proton-linked carriers known as monocarboxylate transporters has been described and specific members have been found to be expressed by endothelial cells, astrocytes and neurons. Moreover, these transporters are subject to fine regulation of their expression levels and localization, notably in neurons, which suggests that lactate supply could be adjusted as a function of their level of activity. Considering the importance of energetics in the aetiology of several neurodegenerative diseases, a better understanding of its cellular and molecular underpinnings might have important implications for the future development of neuroprotective strategies.

Glycogen metabolism as a marker of astrocyte differentiation.

Glycogen is a hallmark of mature astrocytes, but its emergence during astrocytic differentiation is unclear. Differentiation of E14 mouse neurospheres into astrocytes was induced with fetal bovine serum (FBS), Leukemia Inhibitory Factor (LIF), or Ciliary Neurotrophic Factor (CNTF). Cytochemical and enzymatic analyses showed that glycogen is present in FBS- or LIF- but not in CNTF-differentiated astrocytes. Glycogenolysis was induced in FBS- and LIF-differentiated astrocytes but glycogen resynthesis was observed only with FBS. Protein targeting to glycogen mRNA expression appeared with glial fibrillary acidic protein and S100beta in FBS and LIF conditions but not with CNTF. These results show that glycogen metabolism constitutes a useful marker of astrocyte differentiation.

Low predictive value of elevated blood pressure during childhood and adolescence

Background: Screening of elevated blood pressure (BP) in children has been advocated to early identify hypertension. However, identification of children with sustained elevated BP is challenging due to the high BP variability. The value of an elevated BP measure during childhood and adolescence for the prediction of future elevated BP is not well described. Objectives: We assessed the positive (PPV) and negative (NPV) predictive value of high BP for sustained elevated BP in cohorts of children of the Seychelles, a rapidly developing island state in the African region. Methods: Serial school-based surveys of weight, height, and BP were conducted yearly between 1998-2006 among all students of the country in four school grades (kindergarten [G0, mean age (SD): 5.5 (0.4) yr], G4 [9.2 (0.4) yr], G7 [12.5 (0.4) yr] and G10 (15.6 (0.5) yr]. We constituted three cohorts of children examined twice at 3-4 years interval: 4,557 children examined at G0 and G4, 6,198 at G4 and G7, and 6,094 at G7 and G10. The same automated BP measurement devices were used throughout the study. BP was measured twice at each exam and averaged. Obesity and elevated BP were defined using the CDC (BMI_95th sex-, and age-specific percentile) and the NHBPEP criteria (BP_95th sex-, age-, and height specific percentile), respectively. Results: Prevalence of obesity was 6.1% at G0, 7.1% at G4, 7.5% at G7, and 6.5% at G10. Prevalence of elevated BP was 10.2% at G0, 9.9% at G4, 7.1% at G7, and 8.7% at G10. Among children with elevated BP at initial exam, the PPV of keeping elevated BP was low but increased with age: 13% between G0 and G4, 19% between G4 and G7, and 27% between G7 and G10. Among obese children with elevated BP, the PPV was higher: 33%, 35% and 39% respectively. Overall, the probability for children with normal BP to remain in that category 3-4 years later (NPV) was 92%, 95%, and 93%, respectively. By comparison, the PPV for children initially obese to remain obese was much higher at 71%, 71%, and 62% (G7-G10), respectively. The NPV (i.e. the probability of remaining at normal weight) was 94%, 96%, and 98%, respectively. Conclusion: During childhood and adolescence, having an elevated BP at one occasion is a weak predictor of sustained elevated BP 3-4 years later. In obese children, it is a better predictor.

Selective effects of PPARgamma agonists and antagonists on human pre-adipocyte differentiation.

Aim: The insulin sensitizer rosiglitazone (RTZ) acts by activating peroxisome proliferator and activated receptor gamma (PPAR gamma), an effect accompanied in vivo in humans by an increase in fat storage. We hypothesized that this effect concerns PPARgamma(1) and PPARgamma(2) differently and is dependant on the origin of the adipose cells (subcutaneous or visceral). To this aim, the effect of RTZ, the PPARgamma antagonist GW9662 and lentiviral vectors expressing interfering RNA were evaluated on human pre-adipocyte models. Methods: Two models were investigated: the human pre-adipose cell line Chub-S7 and primary pre-adipocytes derived from subcutaneous and visceral biopsies of adipose tissue (AT) obtained from obese patients. Cells were used to perform oil-red O staining, gene expression measurements and lentiviral infections. Results: In both models, RTZ was found to stimulate the differentiation of pre-adipocytes into mature cells. This was accompanied by significant increases in both the PPARgamma(1) and PPARgamma(2) gene expression, with a relatively stronger stimulation of PPARgamma(2). In contrast, RTZ failed to stimulate differentiation processes when cells were incubated in the presence of GW9662. This effect was similar to the effect observed using interfering RNA against PPARgamma(2). It was accompanied by an abrogation of the RTZ-induced PPARgamma(2) gene expression, whereas the level of PPARgamma(1) was not affected. Conclusions: Both the GW9662 treatment and interfering RNA against PPARgamma(2) are able to abrogate RTZ-induced differentiation without a significant change of PPARgamma(1) gene expression. These results are consistent with previous results obtained in animal models and suggest that in humans PPARgamma(2) may also be the key isoform involved in fat storage.

Differences in glucose transporter gene expression between rat pancreatic alpha- and beta-cells are correlated to differences in glucose transport but not in glucose utilization.

Glucose exerts inverse effects upon the secretory function of islet alpha- and beta-cells, suppressing glucagon release and increasing insulin release. This diverse action may result from differences in glucose transport and metabolism between the two cell types. The present study compares glucose transport in rat alpha- and beta-cells. beta-Cells transcribed GLUT2 and, to a lesser extent, GLUT 1; alpha-cells contained GLUT1 but no GLUT2 mRNA. No other GLUT-like sequences were found among cDNAs from alpha- or beta-cells. Both cell types expressed 43-kDa GLUT1 protein which was enhanced by culture. The 62-kDa beta-cell GLUT2 protein was converted to a 58-kDa protein after trypsin treatment of the cells without detectable consequences upon glucose transport kinetics. In beta-cells, the rates of glucose transport were 10-fold higher than in alpha-cells. In both cell types, glucose uptake exceeded the rates of glucose utilization by a factor of 10 or more. Glycolytic flux, measured as D-[5(3)H]glucose utilization, was comparable in alpha- and beta-cells between 1 and 10 mmol/liter substrate. In conclusion, differences in glucose transporter gene expression between alpha- and beta-cells can be correlated with differences in glucose transport kinetics but not with different glucose utilization rates.

Measurements of glial metabolic fluxes with C-11-acetate using positron emission and an adapted NMR-based metabolic modeling approach

Objectives: Acetate brain metabolism has the particularity to occur specifically in glial cells. Labeling studies, using acetate labeled either with 13C (NMR) or 11C (PET), are governed by the same biochemical reactions and thus follow the same mathematical principles. In this study, the objective was to adapt an NMR acetate brain metabolism model to analyse [1-11C]acetate infusion in rats. Methods: Brain acetate infusion experiments were modeled using a two-compartment model approach used in NMR.1-3 The [1-11C]acetate labeling study was done using a beta scintillator.4 The measured radioactive signal represents the time evolution of the sum of all labeled metabolites in the brain. Using a coincidence counter in parallel, an arterial input curve was measured. The 11C at position C-1 of acetate is metabolized in the first turn of the TCA cycle to the position 5 of glutamate (Figure 1A). Through the neurotransmission process, it is further transported to the position 5 of glutamine and the position 5 of neuronal glutamate. After the second turn of the TCA cycle, tracer from [1-11C]acetate (and also a part from glial [5-11C]glutamate) is transferred to glial [1-11C]glutamate and further to [1-11C]glutamine and neuronal glutamate through the neurotransmission cycle. Brain poster session: oxidative mechanisms S460 Journal of Cerebral Blood Flow & Metabolism (2009) 29, S455-S466 Results: The standard acetate two-pool PET model describes the system by a plasma pool and a tissue pool linked by rate constants. Experimental data are not fully described with only one tissue compartment (Figure 1B). The modified NMR model was fitted successfully to tissue time-activity curves from 6 single animals, by varying the glial mitochondrial fluxes and the neurotransmission flux Vnt. A glial composite rate constant Kgtg=Vgtg/[Ace]plasma was extracted. Considering an average acetate concentration in plasma of 1 mmol/g5 and the negligible additional amount injected, we found an average Vgtg = 0.08±0.02 (n = 6), in agreement with previous NMR measurements.1 The tissue time-activity curve is dominated by glial glutamate and later by glutamine (Figure 1B). Labeling of neuronal pools has a low influence, at least for the 20 mins of beta-probe acquisition. Based on the high diffusivity of CO2 across the blood-brain barrier; 11CO2 is not predominant in the total tissue curve, even if the brain CO2 pool is big compared with other metabolites, due to its strong dilution through unlabeled CO2 from neuronal metabolism and diffusion from plasma. Conclusion: The two-compartment model presented here is also able to fit data of positron emission experiments and to extract specific glial metabolic fluxes. 11C-labeled acetate presents an alternative for faster measurements of glial oxidative metabolism compared to NMR, potentially applicable to human PET imaging. However, to quantify the relative value of the TCA cycle flux compared to the transmitochondrial flux, the chemical sensitivity of NMR is required. PET and NMR are thus complementary.

A common biological basis of obesity and nicotine addiction.

Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34,216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 × 10(-7)). These findings replicate in a second large data set (N=127,274, thereof 76,242 smokers) for both SI (P=1.2 × 10(-5)) and CPD (P=9.3 × 10(-5)). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity.

Glucose uptake, utilization, and signaling in GLUT2-null islets.

We previously reported that pancreatic islet beta-cells from GLUT2-null mice lost the first phase but preserved the second phase of glucose-stimulated insulin secretion (GSIS). Furthermore, we showed that the remaining secretory activity required glucose uptake and metabolism because it can be blocked by inhibition of oxidative phosphorylation. Here, we extend these previous studies by analyzing, in GLUT2-null islets, glucose transporter isoforms and glucokinase expression and by measuring glucose usage, GSIS, and glucose-stimulated insulin mRNA biosynthesis. We show that in the absence of GLUT2, no compensatory expression of either GLUT1 or GLUT3 is observed and that glucokinase is expressed at normal levels. Glucose usage by isolated islets was increased between 1 and 6 mmol/l glucose but was not further increased between 6 and 20 mmol/l glucose. Parallel GSIS measurements showed that insulin secretion was not stimulated between 2.8 and 6 mmol/l glucose but was increased by >4-fold between 6 and 20 mmol/l glucose. Stimulation by glucose of total protein and insulin biosynthesis was also markedly impaired in the absence of GLUT2. Finally, we re-expressed GLUT2 in GLUT2-null beta-cells using recombinant lentiviruses and demonstrated a restoration of normal GSIS. Together, these data show that in the absence of GLUT2, glucose can still be taken up by beta-cells, albeit at a low rate, and that this transport activity is unlikely to be attributed to GLUT1 or GLUT3. This uptake activity, however, is limiting for normal glucose utilization and signaling to secretion and translation. These data further demonstrate the key role of GLUT2 in murine beta-cells for glucose signaling to insulin secretion and biosynthesis.

Plasma PCSK9 concentrations during an oral fat load and after short term high-fat, high-fat high-protein and high-fructose diets.

BACKGROUND: PCSK9 (Proprotein Convertase Subtilisin Kexin type 9) is a circulating protein that promotes hypercholesterolemia by decreasing hepatic LDL receptor protein. Under non interventional conditions, its expression is driven by sterol response element binding protein 2 (SREBP2) and follows a diurnal rhythm synchronous with cholesterol synthesis. Plasma PCSK9 is associated to LDL-C and to a lesser extent plasma triglycerides and insulin resistance. We aimed to verify the effect on plasma PCSK9 concentrations of dietary interventions that affect these parameters. METHODS: We performed nutritional interventions in young healthy male volunteers and offspring of type 2 diabetic (OffT2D) patients that are more prone to develop insulin resistance, including: i) acute post-prandial hyperlipidemic challenge (n=10), ii) 4 days of high-fat (HF) or high-fat/high-protein (HFHP) (n=10), iii) 7 (HFruc1, n=16) or 6 (HFruc2, n=9) days of hypercaloric high-fructose diets. An acute oral fat load was also performed in two patients bearing the R104C-V114A loss-of-function (LOF) PCSK9 mutation. Plasma PCSK9 concentrations were measured by ELISA. For the HFruc1 study, intrahepatocellular (IHCL) and intramyocellular lipids were measured by 1H magnetic resonance spectroscopy. Hepatic and whole-body insulin sensitivity was assessed with a two-step hyperinsulinemic-euglycemic clamp (0.3 and 1.0 mU.kg-1.min-1). FINDINGS: HF and HFHP short-term diets, as well as an acute hyperlipidemic oral load, did not significantly change PCSK9 concentrations. In addition, post-prandial plasma triglyceride excursion was not altered in two carriers of PCSK9 LOF mutation compared with non carriers. In contrast, hypercaloric 7-day HFruc1 diet increased plasma PCSK9 concentrations by 28% (p=0.05) in healthy volunteers and by 34% (p=0.001) in OffT2D patients. In another independent study, 6-day HFruc2 diet increased plasma PCSK9 levels by 93% (p<0.0001) in young healthy male volunteers. Spearman's correlations revealed that plasma PCSK9 concentrations upon 7-day HFruc1 diet were positively associated with plasma triglycerides (r=0.54, p=0.01) and IHCL (r=0.56, p=0.001), and inversely correlated with hepatic (r=0.54, p=0.014) and whole-body (r=-0.59, p=0.0065) insulin sensitivity. CONCLUSIONS: Plasma PCSK9 concentrations vary minimally in response to a short term high-fat diet and they are not accompanied with changes in cholesterolemia upon high-fructose diet. Short-term high-fructose intake increased plasma PCSK9 levels, independent on cholesterol synthesis, suggesting a regulation independent of SREBP-2. Upon this diet, PCSK9 is associated with insulin resistance, hepatic steatosis and plasma triglycerides.

Differentially phased leaf growth and movements in Arabidopsis depend on coordinated circadian and light regulation.

In contrast to vastly studied hypocotyl growth, little is known about diel regulation of leaf growth and its coordination with movements such as changes in leaf elevation angle (hyponasty). We developed a 3D live-leaf growth analysis system enabling simultaneous monitoring of growth and movements. Leaf growth is maximal several hours after dawn, requires light, and is regulated by daylength, suggesting coupling between growth and metabolism. We identify both blade and petiole positioning as important components of leaf movements in Arabidopsis thaliana and reveal a temporal delay between growth and movements. In hypocotyls, the combination of circadian expression of PHYTOCHROME INTERACTING FACTOR4 (PIF4) and PIF5 and their light-regulated protein stability drives rhythmic hypocotyl elongation with peak growth at dawn. We find that PIF4 and PIF5 are not essential to sustain rhythmic leaf growth but influence their amplitude. Furthermore, EARLY FLOWERING3, a member of the evening complex (EC), is required to maintain the correct phase between growth and movement. Our study shows that the mechanisms underlying rhythmic hypocotyl and leaf growth differ. Moreover, we reveal the temporal relationship between leaf elongation and movements and demonstrate the importance of the EC for the coordination of these phenotypic traits.

Body fat levels in children and adolescents: effects on the prevalence of obesity

BACKGROUND AND AIMS: There is little information regarding the effect of different definitions of obesity on nutritional epidemiology. The aim was thus to assess: (a) the values of percentage of body fat (%BF) by gender and age; (b) the prevalence of obesity according to different %BF cut-offs; and (c) the sensitivity and specificity of BMI according to different %BF cut-offs used to define obesity. METHODS: Cross-sectional study on 2494 boys and 2519 girls aged 10­18 years from the Lisbon area. %BF was measured using a hand-held device. In a sub sample of 211 boys and 724 girls %BF was assessed using skin folds. RESULTS: %BF levels were higher in girls and decreased with age in both genders. Prevalence of obesity varied considerably according to the %BF cut-off used: in boys, it ranged from 4.7% (age-specific 95th percentile) to 26.5% (fixed 25% cut-off), whereas by BMI it was 5.3%. In girls, prevalence of obesity ranged from 0.4% (age-specific BMI-derived %BF values) to 25.4% (fixed 30% cut-off), whereas by BMI it was 4.7%. The specificity of BMI criteria was over 95% irrespective of the %BF cut-off used; conversely, most sensitivities were below 40%. Sensitivities over 50% were obtained for the age-specific BMI-derived %BF values in boys and the age-specific 95th %BF percentile in both genders. Using %BF derived from the skin fold measurements leads to similar results. CONCLUSIONS: Prevalence of obesity varies considerably according to the %BF cut-off used. BMI cut-offs have a low sensitivity but a high specificity. Age- and gender-specific cut-offs for %BF should be used to define pediatric obesity.

Association between growth and blood pressure during the life course in children and adolescents

Background: Little is known on the relative importance of growth at different periods between birth and adolescence on blood pressure (BP). Objective: To assess the association between birth weight, change in body weight (growth) and BP across the entire span of childhood and adolescence. Methods: School-based surveys were conducted annually between 1998 and 2006 among all children in four school grades (kindergarten, 4th, 7th, and 10th year of compulsory school) in the Seychelles, Indian Ocean. Height and weight and BP were measured. Three cohorts of children examined twice were analyzed: 1606 children surveyed at age 5.5 and 9.1, 2557 at age 9.2 and 12.5, and 2065 at age 12.5 and 15.5, respectively. Weights at birth and at one year were extracted from medical files. Weights were expressed as Z-scores and growth was defined as a change in weight Z-scores (corresponding to weight centile crossing). The association between BP (at age 5.5, 9.2, 12.5, and 15.5) and weight at different times was assessed by linear regression. Using results of regression models of BP on all successive weights, life course plots were drawn by plotting regression coefficients against age at which weight was measured. The figure shows a life course plot of systolic BP in boys aged 15.5. Results: Without adjustment for current weight (at the time of BP measurement), birth weight was not associated with current BP, irrespective of age, excepted for girls at age 15.5 for whom a modest positive association was found. When adjusted for current weight, birth weight was negatively and modestly associated with current BP. BP was strongly associated with current weight, irrespective of age. Life course plots showed that BP was strongly associated with growth during the few preceding years but not with growth during earlier years, except for growth during the first year of life which tended to be associated with systolic BP. Conclusions: Our findings suggest that BP during childhood and adolescence is mainly determined by current body weight and recent growth.