Muscle Carnosine Is Associated with Cardiometabolic Risk Factors in Humans

Background Carnosine is a naturally present dipeptide abundant in skeletal muscle and an over-the counter food additive. Animal data suggest a role of carnosine supplementation in the prevention and treatment of obesity, insulin resistance, type 2 diabetes and cardiovascular disease but only limited human data exists. Methods and Results Samples of vastus lateralis muscle were obtained by needle biopsy. We measured muscle carnosine levels (high-performance liquid chromatography), % body fat (bioimpedance), abdominal subcutaneous and visceral adiposity (magnetic resonance imaging), insulin sensitivity (euglycaemic hyperinsulinemic clamp), resting energy expenditure (REE, indirect calorimetry), free-living ambulatory physical activity (accelerometers) and lipid profile in 36 sedentary non-vegetarian middle aged men (45±7 years) with varying degrees of adiposity and glucose tolerance. Muscle carnosine content was positively related to % body fat (r = 0.35, p = 0.04) and subcutaneous (r = 0.38, p = 0.02) but not visceral fat (r = 0.17, p = 0.33). Muscle carnosine content was inversely associated with insulin sensitivity (r = -0.44, p = 0.008), REE (r = -0.58, p<0.001) and HDL-cholesterol levels (r = -0.34, p = 0.048). Insulin sensitivity and physical activity were the best predictors of muscle carnosine content after adjustment for adiposity. Conclusion Our data shows that higher carnosine content in human skeletal muscle is positively associated with insulin resistance and fasting metabolic preference for glucose. Moreover, it is negatively associated with HDL-cholesterol and basal energy expenditure. Intervention studies targeting insulin resistance, metabolic and cardiovascular disease risk factors are necessary to evaluate its putative role in the prevention and management of type 2 diabetes and cardiovascular disease

EIF2S3 Mutations Associated with Severe X-Linked Intellectual Disability Syndrome MEHMO

Impairment of translation initiation and its regulation within the integrated stress response (ISR) and related unfolded-protein response has been identified as a cause of several multisystemic syndromes. Here, we link MEHMO syndrome, whose genetic etiology was unknown, to this group of disorders. MEHMO is a rare X-linked syndrome characterized by profound intellectual disability, epilepsy, hypogonadism and hypogenitalism, microcephaly, and obesity. We have identified a C-terminal frameshift mutation (Ile465Serfs) in the EIF2S3 gene in three families with MEHMO syndrome and a novel maternally inherited missense EIF2S3 variant (c.324T>A; p.Ser108Arg) in another male patient with less severe clinical symptoms. The EIF2S3 gene encodes the gamma subunit of eukaryotic translation initiation factor 2 (eIF2), crucial for initiation of protein synthesis and regulation of the ISR. Studies in patient fibroblasts confirm increased ISR activation due to the Ile465Serfs mutation and functional assays in yeast demonstrate that the Ile465Serfs mutation impairs eIF2gamma function to a greater extent than tested missense mutations, consistent with the more severe clinical phenotype of the Ile465Serfs male mutation carriers. Thus, we propose that more severe EIF2S3 mutations cause the full MEHMO phenotype, while less deleterious mutations cause a milder form of the syndrome with only a subset of the symptoms

Identification of a Novel β-Cell Glucokinase (GCK) Promoter Mutation (−71G>C) That Modulates GCK Gene Expression Through Loss of Allele-Specific Sp1 Binding Causing Mild Fasting Hyperglycemia in Humans

OBJECTIVE: Inactivating mutations in glucokinase (GCK) cause mild fasting hyperglycemia. Identification of a GCK mutation has implications for treatment and prognosis; therefore, it is important to identify these individuals. A significant number of patients have a phenotype suggesting a defect in glucokinase but no abnormality of GCK. We hypothesized that the GCK beta-cell promoter region, which currently is not routinely screened, could contain pathogenic mutations; therefore, we sequenced this region in 60 such probands. RESEARCH DESIGN AND METHODS: The beta-cell GCK promoter was sequenced in patient DNA. The effect of the identified novel mutation on GCK promoter activity was assessed using a luciferase reporter gene expression system. Electrophoretic mobility shift assays (EMSAs) were used to determine the impact of the mutation on Sp1 binding. RESULTS: A novel -71G>C mutation was identified in a nonconserved region of the human promoter sequence in six apparently unrelated probands. Family testing established cosegregation with fasting hyperglycemia (> or = 5.5 mmol/l) in 39 affected individuals. Haplotype analysis in the U.K. family and four of the Slovakian families demonstrated that the mutation had arisen independently. The mutation maps to a potential transcriptional activator binding site for Sp1. Reporter assays demonstrated that the mutation reduces promoter activity by up to fourfold. EMSAs demonstrated a dramatic reduction in Sp1 binding to the promoter sequence corresponding to the mutant allele. CONCLUSIONS: A novel beta-cell GCK promoter mutation was identified that significantly reduces gene expression in vitro through loss of regulation by Sp1. To ensure correct diagnosis of potential GCK-MODY (maturity-onset diabetes of the young) cases, analysis of the beta-cell GCK promoter should be included

Mutations in HNF1A Result in Marked Alterations of Plasma Glycan Profile

A recent genome-wide association study identified hepatocyte nuclear factor 1-α (HNF1A) as a key regulator of fucosylation. We hypothesized that loss-of-function HNF1A mutations causal for maturity-onset diabetes of the young (MODY) would display altered fucosylation of N-linked glycans on plasma proteins and that glycan biomarkers could improve the efficiency of a diagnosis of HNF1A-MODY. In a pilot comparison of 33 subjects with HNF1A-MODY and 41 subjects with type 2 diabetes, 15 of 29 glycan measurements differed between the two groups. The DG9-glycan index, which is the ratio of fucosylated to nonfucosylated triantennary glycans, provided optimum discrimination in the pilot study and was examined further among additional subjects with HNF1A-MODY (n = 188), glucokinase (GCK)-MODY (n = 118), hepatocyte nuclear factor 4-α (HNF4A)-MODY (n = 40), type 1 diabetes (n = 98), type 2 diabetes (n = 167), and nondiabetic controls (n = 98). The DG9-glycan index was markedly lower in HNF1A-MODY than in controls or other diabetes subtypes, offered good discrimination between HNF1A-MODY and both type 1 and type 2 diabetes (C statistic ≥ 0.90), and enabled us to detect three previously undetected HNF1A mutations in patients with diabetes. In conclusion, glycan profiles are altered substantially in HNF1A-MODY, and the DG9-glycan index has potential clinical value as a diagnostic biomarker of HNF1A dysfunction

Time-lapse gravity monitoring of CO2 migration based on numerical modeling of a faulted storage complex

In this study, the performance of both surface and borehole time-lapse gravity monitoring to detect CO leakage from a carbon storage site is evaluated. Several hypothetical scenarios of CO migration in a leaky fault, and thief zones at different depths at the Kimberlina site (California, USA) constitute the basis of the approach. The CO displacement is simulated using the TOUGH2 simulator applied to a detailed geological model of the site. The gravity responses to these CO plumes are simulated using forward modeling with sensors at ground surface and in vertical boreholes. Results of inversion on one scenario are also presented. The surface-based gravity responses obtained for the different leakage scenarios demonstrate that leakage can be detected at the surface in all the scenarios but the time to detection is highly variable (10–40 years) and dependent on the detection threshold considered. Borehole measurements of the vertical component of gravity provide excellent constraints in depth when they are located in proximity of the density anomaly associated with the presence of CO , thus discriminating multiple leaks in different thief zones. Joint inversion of surface and borehole data can bring valuable information of the occurrence of leakages and their importance by providing a reasonable estimate of mass of displaced fluids. This study demonstrates the importance of combining multiphase flow simulations with gravity modeling in order to define if and when gravity monitoring would be applicable at a given storage site. 2 2 2 2

Melanocortin-4 Receptor Gene Mutations in Obese Slovak Children

Summary The most common etiology of non-syndromic monogenic obesit

Magnetotelluric Investigations of the Kīlauea Volcano, Hawaii

In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100&nbsp;kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3&nbsp;km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively

Deep learning multiphysics network for imaging CO2 saturation and estimating uncertainty in geological carbon storage

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO2 saturation. The network is trained on pairs of CO2 label models and multiphysics data so that it can directly image CO2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO2 saturation images. Using realistic CO2 label models and multiphysics data derived from the Kimberlina CO2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring