Hypertrophy, increased ejection fraction, and reduced Na-K-ATPase activity in phospholemman-deficient mice

Phospholemman (FXYD1), a 72-amino acid transmembrane protein abundantly expressed in the heart and skeletal muscle, is a major substrate for phosphorylation in the cardiomyocyte sarcolemma. Biochemical, cellular, and electrophysiological studies have suggested a number of possible roles for this protein, including ion channel modulator, taurine-release channel, Na(+)/Ca(2+) exchanger modulator, and Na-K-ATPase-associated subunit. We have generated a phospholemman-deficient mouse. The adult null mice exhibited increased cardiac mass, larger cardiomyocytes, and ejection fractions that were 9% higher by magnetic resonance imaging compared with wild-type animals. Notably, this occurred in the absence of hypertension. Total Na-K-ATPase activity was 50% lower in the phospholemman-deficient hearts. Expression (per unit of membrane protein) of total Na-K-ATPase was only slightly diminished, but expression of the minor alpha(2)-isoform, which has been specifically implicated in the control of contractility, was reduced by 60%. The absence of phospholemman thus results in a complex response, including a surprisingly large reduction in intrinsic Na-K-ATPase activity, changes in Na-K-ATPase isoform expression, increase in ejection fraction, and increase in cardiac mass. We hypothesize that a primary effect of phospholemman is to modulate the Na-K-ATPase and that its reduced activity initiates compensatory responses.     

Machine Learning for Detecting Gene-Gene Interactions

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.     

Correlated Non-native Species Richness of Birds, Mammals, Herptiles and Plants: Scale Effects of Area, Human Population and Native Plants

Several extrinsic factors (area, native species diversity, human population size and latitude) significantly influence the non-native species richness of plants, over several orders of magnitude. Using several data sets, I examine the role of these factors in non-native species richness of several animal groups: birds, mammals and herptiles (amphibians, reptiles). I also examine if non-native species richness is correlated among these groups. I find, in agreement with Sax [2001, Journal of Biogeography 28: 139–150], that latitude is inversely correlated with non-native species richness of many groups. Once latitude is accounted for, area, human population size and native plant species richness are shown to be important extrinsic factors influencing non-native animal species. Of these extrinsic factors, human population size and native plant species richness are the best predictors of non-native animal species richness. Area, human population size and native plant species richness are highly intercorrelated, along with non-native species richness of all taxa. Indeed a factor analysis shows that a single multivariate axis explains over half of the variation for all variables among the groups. One reason for this covariation is that humans tend to most densely occupy the most productive and diverse habitats where native plant species richness is very high. It is thus difficult to disentangle the effects of human population size and native species richness on non-native species richness. However, it seems likely that these two factors may combine to increase non-native species richness in a synergistic way: high native species richness reflects greater habitat variety available for non-native species, and dense human populations (that preferentially occupy areas rich in native species) increase non-native species importation and disturbance of local habitats.     

Novel role of p66Shc in ROS-dependent VEGF signaling and angiogenesis in endothelial cells

p66Shc, a longevity adaptor protein, is demonstrated as a key regulator of reactive oxygen species (ROS) metabolism involved in aging and cardiovascular diseases. Vascular endothelial growth factor (VEGF) stimulates endothelial cell (EC) migration and proliferation primarily through the VEGF receptor-2 (VEGFR2). We have shown that ROS derived from Rac1-dependent NADPH oxidase are involved in VEGFR2 autophosphorylation and angiogenic-related responses in ECs. However, a role of p66Shc in VEGF signaling and physiological responses in ECs is unknown. Here we show that VEGF promotes p66Shc phosphorylation at Ser36 through the JNK/ERK or PKC pathway as well as Rac1 binding to a nonphosphorylated form of p66Shc in ECs. Depletion of endogenous p66Shc with short interfering RNA inhibits VEGF-induced Rac1 activity and ROS production. Fractionation of caveolin-enriched lipid raft demonstrates that p66Shc plays a critical role in VEGFR2 phosphorylation in caveolae/lipid rafts as well as downstream p38MAP kinase activation. This in turn stimulates VEGF-induced EC migration, proliferation, and capillary-like tube formation. These studies uncover a novel role of p66Shc as a positive regulator for ROS-dependent VEGFR2 signaling linked to angiogenesis in ECs and suggest p66Shc as a potential therapeutic target for various angiogenesis-dependent diseases.     

Survival trends of cancer amongst the south Asian and non-south Asian population under 30 years of age in Yorkshire, UK

Introduction: Several studies have shown differences in survival trends between ethnic groups across adults with cancer in the UK. It is unclear whether these differences exist exclusively in the older adult population or whether they begin to emerge in children and young adults. Methods: Subjects (n = 3534) diagnosed with cancer under 30 years of age in Yorkshire between 1990 and 2005 were analysed. Differences in survival rates for diagnostic subgroups were estimated by ethnic group (south Asian or not) using Kaplan–Meier estimation and Cox regression. Results: When compared to non-south Asians (all other ethnic groups excluding south Asians) a significant increased risk of death was seen for south Asians with leukaemia (hazard ratio (HR) = 1.75; 95% confidence interval (CI) = 1.11–2.76) and lymphoma (HR = 2.05; 95% CI = 1.09–3.87), whereas south Asians with solid tumours other than central nervous system tumours had a significantly reduced risk of death(HR = 0.50; 95% CI = 0.28–0.89). This was independent of socioeconomic deprivation. Conclusion: We found evidence of poorer survival outcomes for south Asians compared to non-south Asian children and young adults with leukaemia and lymphoma, but better outcomes for south Asian children and young adults with other solid tumours. This needs to be explained, and carefully addressed in the on-going development of cancer services.     

Stable isotope resolved metabolomics analysis of ribonucleotide and RNA metabolism in human lung cancer cells

We have developed a simple NMR-based method to determine the turnover of nucleotides and incorporation into RNA by stable isotope resolved metabolomics (SIRM) in A549 lung cancer cells. This method requires no chemical degradation of the nucleotides or chromatography. During cell growth, the free ribonucleotide pool is rapidly replaced by de novo synthesized nucleotides. Using [U-¹³C]-glucose and [U-¹³C,¹⁵N]-glutamine as tracers, we showed that virtually all of the carbons in the nucleotide riboses were derived from glucose, whereas glutamine was preferentially utilized over glucose for pyrimidine ring biosynthesis, via the synthesis of Asp through the Krebs cycle. Incorporation of the glutamine amido nitrogen into the N3 and N9 positions of the purine rings was also demonstrated by proton-detected ¹⁵N NMR. The incorporation of ¹³C from glucose into total RNA was measured and shown to be a major sink for the nucleotides during cell proliferation. This method was applied to determine the metabolic action of an anti-cancer selenium agent (methylseleninic acid or MSA) on A549 cells. We found that MSA inhibited nucleotide turnover and incorporation into RNA, implicating an important role of nucleotide metabolism in the toxic action of MSA on cancer cells.