Physiogenomic analysis of weight loss induced by dietary carbohydrate restriction

BACKGROUND: Diets that restrict carbohydrate (CHO) have proven to be a successful dietary treatment of obesity for many people, but the degree of weight loss varies across individuals. The extent to which genetic factors associate with the magnitude of weight loss induced by CHO restriction is unknown. We examined associations among polymorphisms in candidate genes and weight loss in order to understand the physiological factors influencing body weight responses to CHO restriction. METHODS: We screened for genetic associations with weight loss in 86 healthy adults who were instructed to restrict CHO to a level that induced a small level of ketosis (CHO ~10% of total energy). A total of 27 single nucleotide polymorphisms (SNPs) were selected from 15 candidate genes involved in fat digestion/metabolism, intracellular glucose metabolism, lipoprotein remodeling, and appetite regulation. Multiple linear regression was used to rank the SNPs according to probability of association, and the most significant associations were analyzed in greater detail. RESULTS: Mean weight loss was 6.4 kg. SNPs in the gastric lipase (LIPF), hepatic glycogen synthase (GYS2), cholesteryl ester transfer protein (CETP) and galanin (GAL) genes were significantly associated with weight loss. CONCLUSION: A strong association between weight loss induced by dietary CHO restriction and variability in genes regulating fat digestion, hepatic glucose metabolism, intravascular lipoprotein remodeling, and appetite were detected. These discoveries could provide clues to important physiologic adaptations underlying the body mass response to CHO restriction

The role of parental achievement goals in predicting autonomy-supportive and controlling parenting

Although autonomy-supportive and controlling parenting are linked to numerous positive and negative child outcomes respectively, fewer studies have focused on their determinants. Drawing on achievement goal theory and self-determination theory, we propose that parental achievement goals (i.e., achievement goals that parents have for their children) can be mastery, performance-approach or performance-avoidance oriented and that types of goals predict mothers' tendency to adopt autonomy-supportive and controlling behaviors. A total of 67 mothers (aged 30-53 years) reported their goals for their adolescent (aged 13-16 years; 19.4 % girls), while their adolescent evaluated their mothers' behaviors. Hierarchical regression analyses showed that parental performance-approach goals predict more controlling parenting and prevent acknowledgement of feelings, one autonomy-supportive behavior. In addition, mothers who have mastery goals and who endorse performance-avoidance goals are less likely to use guilt-inducing criticisms. These findings were observed while controlling for the effect of maternal anxiety

White Matter Development in Early Puberty: A Longitudinal Volumetric and Diffusion Tensor Imaging Twin Study

White matter microstructure and volume show synchronous developmental patterns in children. White matter volume increases considerably during development. Fractional anisotropy, a measure for white matter microstructural directionality, also increases with age. Development of white matter volume and development of white matter microstructure seem to go hand in hand. The extent to which the same or different genetic and/or environmental factors drive these two aspects of white matter maturation is currently unknown. We mapped changes in white matter volume, surface area and diffusion parameters in mono- and dizygotic twins who were scanned at age 9 (203 individuals) and again at age 12 (126 individuals). Over the three-year interval, white matter volume (+6.0%) and surface area (+1.7%) increased, fiber bundles expanded (most pronounced in the left arcuate fasciculus and splenium), and fractional anisotropy increased (+3.0%). Genes influenced white matter volume (heritability ∼85%), surface area (∼85%), and fractional anisotropy (locally 7% to 50%) at both ages. Finally, volumetric white matter growth was negatively correlated with fractional anisotropy increase (r = –0.62) and this relationship was driven by environmental factors. In children who showed the most pronounced white matter growth, fractional anisotropy increased the least and vice-versa. Thus, white matter development in childhood may reflect a process of both expansion and fiber optimization

Physiogenomic comparison of human fat loss in response to diets restrictive of carbohydrate or fat

<p>Abstract</p> <p>Background</p> <p>Genetic factors that predict responses to diet may ultimately be used to individualize dietary recommendations. We used physiogenomics to explore associations among polymorphisms in candidate genes and changes in relative body fat (Δ%BF) to low fat and low carbohydrate diets.</p> <p>Methods</p> <p>We assessed Δ%BF using dual energy X-ray absorptiometry (DXA) in 93 healthy adults who consumed a low carbohydrate diet (carbohydrate ~12% total energy) (LC diet) and in 70, a low fat diet (fat ~25% total energy) (LF diet). Fifty-three single nucleotide polymorphisms (SNPs) selected from 28 candidate genes involved in food intake, energy homeostasis, and adipocyte regulation were ranked according to probability of association with the change in %BF using multiple linear regression.</p> <p>Results</p> <p>Dieting reduced %BF by 3.0 ± 2.6% (absolute units) for LC and 1.9 ± 1.6% for LF (p < 0.01). SNPs in nine genes were significantly associated with Δ%BF, with four significant after correction for multiple statistical testing: rs322695 near the retinoic acid receptor beta (<it>RARB</it>) (p < 0.005), rs2838549 in the hepatic phosphofructokinase (<it>PFKL</it>), and rs3100722 in the histamine N-methyl transferase (<it>HNMT</it>) genes (both p < 0.041) due to LF; and the rs5950584 SNP in the angiotensin receptor Type II (<it>AGTR2</it>) gene due to LC (p < 0.021).</p> <p>Conclusion</p> <p>Fat loss under LC and LF diet regimes appears to have distinct mechanisms, with <it>PFKL </it>and <it>HNMT </it>and <it>RARB </it>involved in fat restriction; and <it>AGTR2 </it>involved in carbohydrate restriction. These discoveries could provide clues to important physiologic mechanisms underlying the Δ%BF to low carbohydrate and low fat diets.</p

Metal-Free ALS Variants of Dimeric Human Cu,Zn-Superoxide Dismutase Have Enhanced Populations of Monomeric Species

Amino acid replacements at dozens of positions in the dimeric protein human, Cu,Zn superoxide dismutase (SOD1) can cause amyotrophic lateral sclerosis (ALS). Although it has long been hypothesized that these mutations might enhance the populations of marginally-stable aggregation-prone species responsible for cellular toxicity, there has been little quantitative evidence to support this notion. Perturbations of the folding free energy landscapes of metal-free versions of five ALS-inducing variants, A4V, L38V, G93A, L106V and S134N SOD1, were determined with a global analysis of kinetic and thermodynamic folding data for dimeric and stable monomeric versions of these variants. Utilizing this global analysis approach, the perturbations on the global stability in response to mutation can be partitioned between the monomer folding and association steps, and the effects of mutation on the populations of the folded and unfolded monomeric states can be determined. The 2- to 10-fold increase in the population of the folded monomeric state for A4V, L38V and L106V and the 80- to 480-fold increase in the population of the unfolded monomeric states for all but S134N would dramatically increase their propensity for aggregation through high-order nucleation reactions. The wild-type-like populations of these states for the metal-binding region S134N variant suggest that even wild-type SOD1 may also be prone to aggregation in the absence of metals

Increased Litterfall in Tropical Forests Boosts the Transfer of Soil CO2 to the Atmosphere

Aboveground litter production in forests is likely to increase as a consequence of elevated atmospheric carbon dioxide (CO2) concentrations, rising temperatures, and shifting rainfall patterns. As litterfall represents a major flux of carbon from vegetation to soil, changes in litter inputs are likely to have wide-reaching consequences for soil carbon dynamics. Such disturbances to the carbon balance may be particularly important in the tropics because tropical forests store almost 30% of the global soil carbon, making them a critical component of the global carbon cycle; nevertheless, the effects of increasing aboveground litter production on belowground carbon dynamics are poorly understood. We used long-term, large-scale monthly litter removal and addition treatments in a lowland tropical forest to assess the consequences of increased litterfall on belowground CO2 production. Over the second to the fifth year of treatments, litter addition increased soil respiration more than litter removal decreased it; soil respiration was on average 20% lower in the litter removal and 43% higher in the litter addition treatment compared to the controls but litter addition did not change microbial biomass. We predicted a 9% increase in soil respiration in the litter addition plots, based on the 20% decrease in the litter removal plots and an 11% reduction due to lower fine root biomass in the litter addition plots. The 43% measured increase in soil respiration was therefore 34% higher than predicted and it is possible that this ‘extra’ CO2 was a result of priming effects, i.e. stimulation of the decomposition of older soil organic matter by the addition of fresh organic matter. Our results show that increases in aboveground litter production as a result of global change have the potential to cause considerable losses of soil carbon to the atmosphere in tropical forests

A Chemocentric Approach to the Identification of Cancer Targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided

WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369–378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87–114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8–24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler’s and Cowan’s basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey’s model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems

Accurate peak list extraction from proteomic mass spectra for identification and profiling studies

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential technique in proteomics both to identify the proteins of a biological sample and to compare proteomic profiles of different samples. In both cases, the main phase of the data analysis is the procedure to extract the significant features from a mass spectrum. Its final output is the so-called peak list which contains the mass, the charge and the intensity of every detected biomolecule. The main steps of the peak list extraction procedure are usually preprocessing, peak detection, peak selection, charge determination and monoisotoping operation.</p> <p>Results</p> <p>This paper describes an original algorithm for peak list extraction from low and high resolution mass spectra. It has been developed principally to improve the precision of peak extraction in comparison to other reference algorithms. It contains many innovative features among which a sophisticated method for managing the overlapping isotopic distributions.</p> <p>Conclusions</p> <p>The performances of the basic version of the algorithm and of its optional functionalities have been evaluated in this paper on both SELDI-TOF, MALDI-TOF and ESI-FTICR ECD mass spectra. Executable files of MassSpec, a MATLAB implementation of the peak list extraction procedure for Windows and Linux systems, can be downloaded free of charge for nonprofit institutions from the following web site: <url>http://aimed11.unipv.it/MassSpec</url></p