Effects of AMPK activation on insulin sensitivity and metabolism in leptin-deficient ob/ob mice

AMP-activated protein kinase (AMPK) is a heterotrimeric complex, composed of a catalytic subunit (α) and two regulatory subunits (β and γ), which act as a metabolic sensor to regulate glucose and lipid metabolism. A mutation in the γ3 subunit (AMPKγ3(R225Q)) increases basal AMPK phosphorylation, while concomitantly reducing sensitivity to AMP. AMPKγ3(R225Q) (γ3(R225Q)) transgenic mice are protected against dietary-induced triglyceride accumulation and insulin resistance. We determined whether skeletal muscle-specific expression of AMPKγ3(R225Q) prevents metabolic abnormalities in leptin-deficient ob/ob (ob/ob-γ3(R225Q)) mice. Glycogen content was increased, triglyceride content was decreased, and diacylglycerol and ceramide content were unaltered in gastrocnemius muscle from ob/ob-γ3(R225Q) mice, whereas glucose tolerance was unaltered. Insulin-stimulated glucose uptake in extensor digitorum longus muscle during the euglycemic-hyperinsulinemic clamp was increased in lean γ3(R225Q) mice, but not in ob/ob-γ3(R225Q) mice. Acetyl-CoA carboxylase phosphorylation was increased in gastrocnemius muscle from γ3(R225Q) mutant mice independent of adiposity. Glycogen and triglyceride content were decreased after leptin treatment (5 days) in ob/ob mice, but not in ob/ob-γ3(R225Q) mice. In conclusion, metabolic improvements arising from muscle-specific expression of AMPKγ3(R225Q) are insufficient to ameliorate insulin resistance and obesity in leptin-deficient mice. Central defects due to leptin deficiency may override any metabolic benefit conferred by peripheral overexpression of the AMPKγ3(R225Q) mutation

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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Neurodevelopmental disorders in children aged 2-9 years: Population-based burden estimates across five regions in India.

BACKGROUND: Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. METHODS AND FINDINGS: We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. CONCLUSIONS: The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions

Nutrient and energy sensing in skeletal muscle

Nutrient overload and physical inactivity often leads to the development of obesity and type 2 diabetes. Acute over-nutrition can induce insulin resistance, while physical exercise enhances skeletal muscle insulin sensitivity. Like every living cell, skeletal muscle senses nutrient and energy signals and to adjust metabolic flux. This thesis focuses on some of the key nutrient and energy sensing (exercise/contraction-induced) pathways in skeletal muscle that regulate metabolism. AMPK is a key energy sensing enzyme, composed of three different subunits, with several isoforms existing for each subunit. The role of the different AMPK subunits in the regulation of mTOR signaling was investigated. In EDL muscle from wild-type mice, AICAR (a chemical AMPK activator) completely inhibited insulin-mediated phosphorylation of S6K1 (Thr389), rpS6 (Ser235/236) and 4E-BP1 (Thr37/46). Thus, AMPK is negative regulator of mTOR signaling. The inhibitory effects of AICAR were partially blocked in skeletal muscle from alpha2 AMPK depleted (KO) and gamma3 AMPK KO mice, functional alpha2 AMPK and gamma3 AMPK subunits are required for the AICAR-mediated inhibition of mTOR signaling. Excessive amino acid availability impairs insulin action in skeletal muscle. In primary human myotubes, supra-physiological leucine concentrations reduced insulin-stimulated Akt phosphorylation, glucose uptake and glucose incorporation into glycogen. These results indicate nutrient overload induced insulin resistance. Depletion of S6K1 using siRNA enhanced basal glucose uptake and protected against the development insulin resistance in response to leucine. Study II highlights a direct role for S6K1 plays in insulin action and glucose metabolism. Several proteins are phosphorylated in skeletal muscle in response to acute exercise. The effect of cycling or resistance exercise on the phosphorylation of Akt substrates was determined using an antibody that recognizes a consensus Akt phosporylation motif (PAS). Proteins of 160 and 300 kDa were indentified as AS160 (TBC1D4) and filamin A, respectively. Acute endurance exercise increased phosphorylation of TBC1D4 and filamin A, with concomitant increase in phosphorylation of Akt Ser473, whereas acute resistance exercise was without effect. TBC1D4 and filamin A may provide link between acute exercise and metabolism in muscle. Hypoxia is useful model to study effects of exercise/muscle contraction. In paper III, hypoxia-induced glucose transport was partially impaired in EDL muscle from gamma3 AMPK KO mice, indicating a role for the gamma3 AMPK subunit in glucose metabolism. These effects were uncoupled from AMPK and TBC1D1/D4 signaling, suggesting that an AMPK-and TBC1D1/D4-independent mechanism contributes to glucose transport in skeletal muscle. An interaction between AMPK and CaMK is implicated, since the CaMK inhibitor KN-93 had a more potent effect to reduce hypoxia-induced glucose transport in gamma3 AMPK KO mice. Nitric oxide (NO) is implicated in exercise-induced signaling networks. Exposure of human skeletal muscle to an NO donor increased glucose uptake, with a concomitant increase in cGMP levels and alpha1-associated AMPK activity. Thus, NO/cGMP signaling may be part of a novel pathway that regulates skeletal muscle glucose uptake. In conclusion, AMPK and mTOR signaling play important roles in regulation of skeletal muscle metabolism. AMPK appears to have a heterotrimer-specific action on skeletal muscle metabolism. Furthermore, contraction/exercise responsive signaling pathways including CaMK, NO-cGMP and Akt are important in the regulation of skeletal muscle glucose uptake

Proteomics of Skeletal Muscle: Focus on Insulin Resistance and Exercise Biology

Skeletal muscle is the largest tissue in the human body and plays an important role in locomotion and whole body metabolism. It accounts for ~80% of insulin stimulated glucose disposal. Skeletal muscle insulin resistance, a primary feature of Type 2 diabetes, is caused by a decreased ability of muscle to respond to circulating insulin. Physical exercise improves insulin sensitivity and whole body metabolism and remains one of the most promising interventions for the prevention of Type 2 diabetes. Insulin resistance and exercise adaptations in skeletal muscle might be a cause, or consequence, of altered protein expressions profiles and/or their posttranslational modifications (PTMs). Mass spectrometry (MS)-based proteomics offer enormous promise for investigating the molecular mechanisms underlying skeletal muscle insulin resistance and exercise-induced adaptation; however, skeletal muscle proteomics are challenging. This review describes the technical limitations of skeletal muscle proteomics as well as emerging developments in proteomics workflow with respect to samples preparation, liquid chromatography (LC), MS and computational analysis. These technologies have not yet been fully exploited in the field of skeletal muscle proteomics. Future studies that involve state-of-the-art proteomics technology will broaden our understanding of exercise-induced adaptations as well as molecular pathogenesis of insulin resistance. This could lead to the identification of new therapeutic targets