Fibulin-5/DANCE is essential for elastogenesis in vivo.

The elastic fibre system has a principal role in the structure and function of various types of organs that require elasticity, such as large arteries, lung and skin. Although elastic fibres are known to be composed of microfibril proteins (for example, fibrillins and latent transforming growth factor (TGF)-beta-binding proteins) and polymerized elastin, the mechanism of their assembly and development is not well understood. Here we report that fibulin-5 (also known as DANCE), a recently discovered integrin ligand, is an essential determinant of elastic fibre organization. fibulin-5-/- mice generated by gene targeting exhibit a severely disorganized elastic fibre system throughout the body. fibulin-5-/- mice survive to adulthood, but have a tortuous aorta with loss of compliance, severe emphysema, and loose skin (cutis laxa). These tissues contain fragmented elastin without an increase of elastase activity, indicating defective development of elastic fibres. Fibulin-5 interacts directly with elastic fibres in vitro, and serves as a ligand for cell surface integrins alphavbeta3, alphavbeta5 and alpha9beta1 through its amino-terminal domain. Thus, fibulin-5 may provide anchorage of elastic fibres to cells, thereby acting to stabilize and organize elastic fibres in the skin, lung and vasculature.     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.     

The binary progenitor of Tycho Brahe's 1572 supernova

The brightness of type Ia supernovae, and their homogeneity as a class, makes them powerful tools in cosmology, yet little is known about the progenitor systems of these explosions. They are thought to arise when a white dwarf accretes matter from a companion star, is compressed and undergoes a thermonuclear explosion. Unless the companion star is another white dwarf (in which case it should be destroyed by the mass-transfer process itself), it should survive and show distinguishing properties. Tycho's supernova is one of only two type Ia supernovae observed in our Galaxy, and so provides an opportunity to address observationally the identification of the surviving companion. Here we report a survey of the central region of its remnant, around the position of the explosion, which excludes red giants as the mass donor of the exploding white dwarf. We found a type G0-G2 star, similar to our Sun in surface temperature and luminosity (but lower surface gravity), moving at more than three times the mean velocity of the stars at that distance, which appears to be the surviving companion of the supernova.     

Common variants on 1p36 and 1q42 are associated with cutaneous basal cell carcinoma but not with melanoma or pigmentation traits

To search for new sequence variants that confer risk of cutaneous basal cell carcinoma (BCC), we conducted a genome-wide SNP association study of 930 Icelanders with BCC and 33,117 controls. After analyzing 304,083 SNPs, we observed signals from loci at 1p36 and 1q42, and replicated these associations in additional sample sets from Iceland and Eastern Europe. Overall, the most significant signals were from rs7538876 on 1p36 (OR = 1.28, P = 4.4 x 10(-12)) and rs801114 on 1q42 (OR = 1.28, P = 5.9 x 10(-12)). The 1p36 locus contains the candidate genes PADI4, PADI6, RCC2 and ARHGEF10L, and the gene nearest to the 1q42 locus is the ras-homolog RHOU. Neither locus was associated with fair pigmentation traits that are known risk factors for BCC, and no risk was observed for melanoma. Approximately 1.6% of individuals of European ancestry are homozygous for both variants, and their estimated risk of BCC is 2.68 times that of noncarriers.     

The histidine triad nucleotide-binding protein 1 supports mu-opioid receptor–glutamate NMDA receptor cross-regulation

A series of pharmacological and physiological studies have demonstrated the functional cross-regulation between MOR and NMDAR. These receptors coexist at postsynaptic sites in midbrain periaqueductal grey (PAG) neurons, an area implicated in the analgesic effects of opioids like morphine. In this study, we found that the MOR-associated histidine triad nucleotide-binding protein 1 (HINT1) is essential for maintaining the connection between the NMDAR and MOR. Morphine-induced analgesic tolerance is prevented and even rescued by inhibiting PKC or by antagonizing NMDAR. However, in the absence of HINT1, the MOR becomes supersensitive to morphine before suffering a profound and lasting desensitization that is refractory to PKC inhibition or NMDAR antagonism. Thus, HINT1 emerges as a key protein that is critical for sustaining NMDAR-mediated regulation of MOR signaling strength. Thus, HINT1 deficiency may contribute to opioid-intractable pain syndromes by causing long-term MOR desensitization via mechanisms independent of NMDAR.     

An adaptive radiation model for the origin of new gene functions

Current models for the evolution of new gene functions after gene duplication presume that the duplication events themselves have no effect on fitness. But those duplications that result in new gene functions are likely to be positively selected from their inception. The evolution of new function may start with the amplification of an existing gene with some level of preadaptation for that function, followed by a period of competitive evolution among the gene copies, resulting in the preservation of the most effective variant and the 'pseudogenization' and eventual loss of the rest.     

Temporal aggregation and systematic sampling in structural time-series models

Given a structural time-series model specified at a basic time interval, this paper deals with the problems of forecasting efficiency and estimation accuracy generated when the data are collected at a timing interval which is a multiple of the time unit chosen to build the basic model. Results are presented for the simplest structural models, the trend plus error models, under the assumption that the parameters of the model are known. It is shown that the gains in forecasting efficiency and estimation accuracy for having data at finer intervals are considerable for both stock and flow variables with only one exception. No gain in forecasting efficiency is achieved in the case of a stock series that follows a random walk.     

Newly identified loci that influence lipid concentrations and risk of coronary artery disease

To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.     

Network modeling links breast cancer susceptibility and centrosome dysfunction

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.