Muscular diacylglycerol metabolism and insulin resistance

Failure of insulin to elicit an increase in glucose uptake and metabolism in target tissues such as skeletal muscle is a major characteristic of non-insulin dependent type 2 diabetes mellitus. A strong correlation between intramyocellular triacylglycerol concentrations and the severity of insulin resistance has been found and led to the assumption that lipid oversupply to skeletal muscle contributes to reduced insulin action. However, the molecular mechanism that links intramyocellular lipid content with the generation of muscle insulin resistance is still unclear. It appears unlikely that the neutral lipid metabolite triacylglycerol directly impairs insulin action. Hence it is believed that intermediates in fatty acid metabolism, such as fatty acyl-CoA, ceramides or diacylglycerol (DAG) link fat deposition in the muscle to compromised insulin signaling. DAG is identified as a potential mediator of lipid-induced insulin resistance, as increased DAG levels are associated with protein kinase C activation and a reduction in both insulin-stimulated IRS-1 tyrosine phosphorylation and PI3 kinase activity. As DAG is an intermediate in the synthesis of triacylglycerol from fatty acids and glycerol, its level can be lowered by either improving the oxidation of cellular fatty acids or by accelerating the incorporation of fatty acids into triacylglycerol. This review discusses the evidence that implicates DAG being central in the development of muscular insulin resistance. Furthermore, we will discuss if and how modulation of skeletal muscle DAG levels could function as a possible therapeutic target for the treatment of type 2 diabetes mellitus

Postexercise changes in myocellular lipid droplet characteristics of young lean individuals are affected by circulatory nonesterified fatty acids

Intramyocellular lipid (IMCL) content is an energy source during acute exercise. Nonesterified fatty acid (NEFA) levels can compete with IMCL utilization during exercise. IMCL content is stored as lipid droplets (LDs) that vary in size, number, subcellular distribution, and in coating with LD protein PLIN5. Little is known about how these factors are affected during exercise and recovery. Here, we aimed to investigate the effects of acute exercise with and without elevated NEFA levels on intramyocellular LD size and number, intracellular distribution and PLIN5 coating, using high-resolution confocal microscopy. In a crossover study, 9 healthy lean young men performed a 2-h moderate intensity cycling protocol in the fasted (high NEFA levels) and glucose-fed state (low NEFA levels). IMCL and LD parameters were measured at baseline, directly after exercise and 4 h postexercise. We found that total IMCL content was not changed directly after exercise (irrespectively of condition), but IMCL increased 4 h postexercise in the fasting condition, which was due to an increased number of LDs rather than changes in size. The effects were predominantly detected in type I muscle fibers and in LDs coated with PLIN5. Interestingly, subsarcolemmal, but not intermyofibrillar IMCL content, was decreased directly after exercise in the fasting condition and was replenished during the 4 h recovery period. In conclusion, acute exercise affects IMCL storage during exercise and recovery, particularly in type I muscle fibers, in the subsarcolemmal region and in the presence of PLIN5. Moreover, the effects of exercise on IMCL content are affected by plasma NEFA levels.NEW & NOTEWORTHY Skeletal muscle stores lipids in lipid droplets (LDs) that can vary in size, number, and location and are a source of energy during exercise. Specifically, subsarcolemmal LDs were used during exercise when fasted. Exercising in the fasted state leads to postrecovery elevation in IMCL levels due to an increase in LD number in type I muscle fibers, in subsarcolemmal region and decorated with PLIN5. These effects are blunted by glucose ingestion during exercise and recovery

Towards a neural hierarchy of time scales for motor control

Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control

Individual variation in body temperature and energy expenditure in response to mild cold

Department of Human Biology, Maastricht University, 6200 MD Maastricht, The Netherlands. [email protected] We studied interindividual variation in body temperature and energy expenditure, the relation between these two, and the effect of mild decrease in environmental temperature (16 vs. 22 degrees C) on both body temperature and energy expenditure. Nine males stayed three times for 60 h (2000-0800) in a respiration chamber, once at 22 degrees C and twice at 16 degrees C, in random order. Twenty-four-hour energy expenditure, thermic effect of food, sleeping metabolic rate, activity-induced energy expenditure, and rectal and skin temperatures were measured. A rank correlation test with data of 6 test days showed significant interindividual variation in both rectal and skin temperatures and energy expenditures adjusted for body composition. Short-term exposure of the subjects to 16 degrees C caused a significant decrease in body temperature (both skin and core), an increase in temperature gradients, and an increase in energy expenditure. The change in body temperature gradients was negatively related to changes in energy expenditure. This shows that interindividual differences exist with respect to the relative contribution of metabolic and insulative adaptations to cold

Augmenting muscle diacylglycerol and triacylglycerol content by blocking fatty acid oxidation does not impede insulin sensitivity

A low fat oxidative capacity has been linked to muscle diacylglycerol (DAG) accumulation and insulin resistance. Alternatively, a low fat oxidation rate may stimulate glucose oxidation, thereby enhancing glucose disposal. Here, we investigated whether an ethyl-2-[6-(4-chlorophenoxy)hexyl]-oxirane-2-carboxylate (etomoxir)-induced inhibition of fat oxidation leads to muscle fat storage and insulin resistance. An intervention in healthy male subjects was combined with studies in human primary myotubes. Furthermore, muscle DAG and triacylglycerol (TAG), mitochondrial function, and insulin signaling were examined in etomoxir-treated C57bl6 mice. In humans, etomoxir administration increased glucose oxidation at the expense of fat oxidation. This effect was accompanied by an increased abundance of GLUT4 at the sarcolemma and a lowering of plasma glucose levels, indicative of improved glucose homeostasis. In mice, etomoxir injections resulted in accumulation of muscle TAG and DAG, yet improved insulin-stimulated GLUT4 translocation. Also in human myotubes, insulin signaling was improved by etomoxir, in the presence of increased intramyocellular lipid accumulation. These insulin-sensitizing effects in mice and human myotubes were accompanied by increased phosphorylation of AMP-activated protein kinase (AMPK). Our results show that a reduction in fat oxidation leading to accumulation of muscle DAG does not necessarily lead to insulin resistance, as the reduction in fat oxidation may activate AMPK

Calorie restriction-like effects of 30 days of resveratrol supplementation on energy metabolism and metabolic profile in obese humans

Resveratrol is a natural compound that affects energy metabolism and mitochondrial function and serves as a calorie restriction mimetic, at least in animal models of obesity. Here we treated 11 healthy, obese men with placebo and 150 mg/day resveratrol in a randomized double-blind cross-over study for 30 days. Resveratrol significantly reduced sleeping- and resting metabolic rate. In muscle, resveratrol activated AMPK, increased SIRT1 and PGC-1α protein levels, increased citrate synthase activity without change in mitochondrial content, and improved muscle mitochondrial respiration on a fatty acid-derived substrate. Furthermore, resveratrol elevated intramyocellular lipid levels, and decreased intrahepatic lipid content, circulating glucose, triglycerides, alanine-aminotransferase, and inflammation markers. Systolic blood pressure dropped and HOMA index improved after resveratrol. In the postprandial state, adipose tissue lipolysis and plasma fatty acid and glycerol decreased. In conclusion, we demonstrate that 30 days of resveratrol supplementation induces metabolic changes in obese humans, mimicking the effects of calorie restriction

Reassortment between Avian H5N1 and Human Influenza Viruses Is Mainly Restricted to the Matrix and Neuraminidase Gene Segments

Highly pathogenic avian influenza H5N1 viruses have devastated the poultry industry in many countries of the eastern hemisphere. Occasionally H5N1 viruses cross the species barrier and infect humans, sometimes with a severe clinical outcome. When this happens, there is a chance of reassortment between H5N1 and human influenza viruses. To assess the potential of H5N1 viruses to reassort with contemporary human influenza viruses (H1N1, H3N2 and pandemic H1N1), we used an in vitro selection method to generate reassortant viruses, that contained the H5 hemagglutinin gene, and that have a replication advantage in vitro. We found that the neuraminidase and matrix gene segments of human influenza viruses were preferentially selected by H5 viruses. However, these H5 reassortant viruses did not show a marked increase in replication in MDCK cells and human bronchial epithelial cells. In ferrets, inoculation with a mixture of H5N1-pandemic H1N1 reassortant viruses resulted in outgrowth of reassortant H5 viruses that had incorporated the neuraminidase and matrix gene segment of pandemic 2009 H1N1. This virus was not transmitted via aerosols or respiratory droplets to naïve recipient ferrets. Altogether, these data emphasize the potential of avian H5N1 viruses to reassort with contemporary human influenza viruses. The neuraminidase and matrix gene segments of human influenza viruses showed the highest genetic compatibility with HPAI H5N1 virus

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page