Empirically Derived Integrated Stellar Yields of Fe-Peak Elements

We present here the initial results of a new study of massive star yields of Fe-peak elements. We have compiled from the literature a database of carefully determined solar neighborhood stellar abundances of seven iron-peak elements, Ti, V, Cr, Mn, Fe, Co, and Ni and then plotted [X/Fe] versus [Fe/H] to study the trends as functions of metallicity. Chemical evolution models were then employed to force a fit to the observed trends by adjusting the input massive star metallicity-sensitive yields of Kobayashi et al. Our results suggest that yields of Ti, V, and Co are generally larger as well as anticorrelated with metallicity, in contrast to the Kobayashi et al. predictions. We also find the yields of Cr and Mn to be generally smaller and directly correlated with metallicity compared to the theoretical results. Our results for Ni are consistent with theory, although our model suggests that all Ni yields should be scaled up slightly. The outcome of this exercise is the computation of a set of integrated yields, i.e., stellar yields weighted by a slightly flattened time-independent Salpeter initial mass function and integrated over stellar mass, for each of the above elements at several metallicity points spanned by the broad range of observations. These results are designed to be used as empirical constraints on future iron-peak yield predictions by stellar evolution modelers. Special attention is paid to the interesting behavior of [Cr/Co] with metallicity -- these two elements have opposite slopes -- as well as the indirect correlation of [Ti/Fe] with [Fe/H]. These particular trends, as well as those exhibited by the inferred integrated yields of all iron-peak elements with metallicity, are discussed in terms of both supernova nucleosynthesis and atomic physics.Comment: 27 pages, 6 figures; Accepted for Publication in the Astrophysical Journa

The Effects of Cholera Toxin on Cellular Energy Metabolism

Multianalyte microphysiometry, a real-time instrument for simultaneous measurement of metabolic analytes in a microfluidic environment, was used to explore the effects of cholera toxin (CTx). Upon exposure of CTx to PC-12 cells, anaerobic respiration was triggered, measured as increases in acid and lactate production and a decrease in the oxygen uptake. We believe the responses observed are due to a CTx-induced activation of adenylate cyclase, increasing cAMP production and resulting in a switch to anaerobic respiration. Inhibitors (H-89, brefeldin A) and stimulators (forskolin) of cAMP were employed to modulate the CTx-induced cAMP responses. The results of this study show the utility of multianalyte microphysiometry to quantitatively determine the dynamic metabolic effects of toxins and affected pathways

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Exome-Wide Association Study of Endometrial Cancer in a Multiethnic Population

Endometrial cancer (EC) contributes substantially to total burden of cancer morbidity and mortality in the United States. Family history is a known risk factor for EC, thus genetic factors may play a role in EC pathogenesis. Three previous genome-wide association studies (GWAS) have found only one locus associated with EC, suggesting that common variants with large effects may not contribute greatly to EC risk. Alternatively, we hypothesize that rare variants may contribute to EC risk. We conducted an exome-wide association study (EXWAS) of EC using the Infinium HumanExome BeadChip in order to identify rare variants associated with EC risk. We successfully genotyped 177,139 variants in a multiethnic population of 1,055 cases and 1,778 controls from four studies that were part of the Epidemiology of Endometrial Cancer Consortium (E2C2). No variants reached global significance in the study, suggesting that more power is needed to detect modest associations between rare genetic variants and risk of EC

Mouse CD94/NKG2A Is a Natural Killer Cell Receptor for the Nonclassical Major Histocompatibility Complex (MHC) Class I Molecule Qa-1b

Natural killer (NK) cells preferentially lyse targets that express reduced levels of major histocompatibility complex (MHC) class I proteins. To date, the only known mouse NK receptors for MHC class I belong to the Ly49 family of C-type lectin homodimers. Here, we report the cloning of mouse NKG2A, and demonstrate it forms an additional and distinct class I receptor, a CD94/NKG2A heterodimer. Using soluble tetramers of the nonclassical class I molecule Qa-1b, we provide direct evidence that CD94/NKG2A recognizes Qa-1b. We further demonstrate that NK recognition of Qa-1b results in the inhibition of target cell lysis. Inhibition appears to depend on the presence of Qdm, a Qa-1b-binding peptide derived from the signal sequences of some classical class I molecules. Mouse NKG2A maps adjacent to CD94 in the heart of the NK complex on mouse chromosome six, one of a small cluster of NKG2-like genes. Our findings suggest that mouse NK cells, like their human counterparts, use multiple mechanisms to survey class I expression on target cells

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Analysis of Qa-1bPeptide Binding Specificity and the Capacity of Cd94/Nkg2a to Discriminate between Qa-1–Peptide Complexes

The major histocompatibility complex class Ib protein, Qa-1b, serves as a ligand for murine CD94/NKG2A natural killer (NK) cell inhibitory receptors. The Qa-1b peptide-binding site is predominantly occupied by a single nonameric peptide, Qa-1 determinant modifier (Qdm), derived from the leader sequence of H-2D and L molecules. Five anchor residues were identified in this study by measuring the peptide-binding affinities of substituted Qdm peptides in experiments with purified recombinant Qa-1b. A candidate peptide-binding motif was determined by sequence analysis of peptides eluted from Qa-1 that had been folded in the presence of random peptide libraries or pools of Qdm derivatives randomized at specific anchor positions. The results indicate that Qa-1b can bind a diverse repertoire of peptides but that Qdm has an optimal primary structure for binding Qa-1b. Flow cytometry experiments with Qa-1b tetramers and NK target cell lysis assays demonstrated that CD94/NKG2A discriminates between Qa-1b complexes containing peptides with substitutions at nonanchor positions P4, P5, or P8. Our findings suggest that it may be difficult for viruses to generate decoy peptides that mimic Qdm and raise the possibility that competitive replacement of Qdm with other peptides may provide a novel mechanism for activation of NK cells