European Recommendations for Primary Prevention of Congenital Anomalies: A Joined Effort of EUROCAT and EUROPLAN Projects to Facilitate Inclusion of This Topic in the National Rare Disease Plans

Congenital anomalies (CA) are the paradigm example of rare diseases liable to primary prevention actions due to the multifactorial etiology of many of them, involving a number of environmental factors together with genetic predispositions. Yet despite the preventive potential, lack of attention to an integrated preventive strategy has led to the prevalence of CA remaining relatively stable in recent decades. The 2 European projects, EUROCAT and EUROPLAN, have joined efforts to provide the first science-based and comprehensive set of recommendations for the primary prevention of CA in the European Union. The resulting EUROCAT-EUROPLAN 'Recommendations on Policies to Be Considered for the Primary Prevention of Congenital Anomalies in National Plans and Strategies on Rare Diseases' were issued in 2012 and endorsed by EUCERD (European Union Committee of Experts on Rare Diseases) in 2013. The recommendations exploit interdisciplinary expertise encompassing drugs, diet, lifestyles, maternal health status, and the environment. The recommendations include evidence-based actions aimed at reducing risk factors and at increasing protective factors and behaviors at both individual and population level. Moreover, consideration is given to topics specifically related to CA (e.g. folate status, teratogens) as well as of broad public health impact (e.g. obesity, smoking) which call for specific attention to their relevance in the pre- and periconceptional period. The recommendations, reported entirely in this paper, are a comprehensive tool to implement primary prevention into national policies on rare diseases in Europe.

GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1-2 September 2014

G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent progress and highlight key areas of future research needed to accelerate GPCR drug discovery. Several key points emerged. Firstly, structures for all three major classes of GPCRs have now been solved and there is increasing coverage across the GPCR phylogenetic tree. This is likely to be substantially enhanced with data from x-ray free electron sources as they move beyond proof of concept. Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation. Thirdly, there will almost certainly be some GPCRs that will remain difficult targets because they exhibit complex ligand dependencies and have many metastable states rendering them difficult to resolve by crystallographic methods. Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour. This is particularly important because it has ramifications for how we interpret pre-clinical data. In summary, collaborative efforts between industry and academia have delivered significant progress in terms of structure and understanding of GPCRs and will be essential for resolving problems associated with the more difficult targets in the future.

Antitussive drugs--past, present, and future

Cough remains a serious unmet clinical problem, both as a symptom of a range of other conditions such as asthma, chronic obstructive pulmonary disease, gastroesophageal reflux, and as a problem in its own right in patients with chronic cough of unknown origin. This article reviews our current understanding of the pathogenesis of cough and the hypertussive state characterizing a number of diseases as well as reviewing the evidence for the different classes of antitussive drug currently in clinical use. For completeness, the review also discusses a number of major drug classes often clinically used to treat cough but that are not generally classified as antitussive drugs. We also reviewed a number of drug classes in various stages of development as antitussive drugs. Perhaps surprising for drugs used to treat such a common symptom, there is a paucity of well-controlled clinical studies documenting evidence for the use of many of the drug classes in use today, particularly those available over the counter. Nonetheless, there has been a considerable increase in our understanding of the cough reflex over the last decade that has led to a number of promising new targets for antitussive drugs being identified and thus giving some hope of new drugs being available in the not too distant future for the treatment of this often debilitating symptom.

Genetics of Diabetic Nephropathy: a Long Road of Discovery

The global prevalence of diabetic nephropathy is rising in parallel with the increasing incidence of diabetes in most countries. Unfortunately, up to 40 % of persons diagnosed with diabetes may develop kidney complications. Diabetic nephropathy is associated with substantially increased risks of cardiovascular disease and premature mortality. An inherited susceptibility to diabetic nephropathy exists, and progress is being made unravelling the genetic basis for nephropathy thanks to international research collaborations, shared biological resources and new analytical approaches. Multiple epidemiological studies have highlighted the clinical heterogeneity of nephropathy and the need for better phenotyping to help define important subgroups for analysis and increase the power of genetic studies. Collaborative genome-wide association studies for nephropathy have reported unique genes, highlighted novel biological pathways and suggested new disease mechanisms, but progress towards clinically relevant risk prediction models for diabetic nephropathy has been slow. This review summarises the current status, recent developments and ongoing challenges elucidating the genetics of diabetic nephropathy.

Price Discovery in the Dual-Platform US Treasury Market

Inter-dealer trading in US Treasury securities is almost equally divided between two electronic trading platforms that have only slight differences in terms of their relative liquidity and transparency. BrokerTec is more active in the trading of 2-, 5-, and 10-year T-notes while eSpeed has more active trading in the 30-year bond. Over the period studied, eSpeed provides a more pre-trade transparent platform than BrokerTec. We examine the contribution to ‘price discovery’ of activity in the two platforms using high frequency data. We find that price discovery does not derive equally from the two platforms and that the shares vary across term to maturity. This can be traced to differential trading activities and transparency of the two platforms.

Discovery of Main-Belt Comet P/2006 VW139 by Pan-STARRS1

We describe the discovery of comet-like activity in main-belt asteroid (300163) 2006 VW139 (later re-designated as Comet P/2006 VW139) by Pan-STARRS1. We also detail follow-up photometric, spectroscopic, and dynamical analyses of the object.

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

PS1-10afx at z = 1.388: Pan-STARRS1 Discovery of a New Type of Superluminous Supernova

We present the Pan-STARRS1 discovery of PS1-10afx, a unique hydrogen-deficient superluminous supernova (SLSN) at redshift z = 1.388. The light curve peaked at z P1 = 21.7 mag, making PS1-10afx comparable to the most luminous known SNe, with Mu = -22.3 mag. Our extensive optical and near-infrared observations indicate that the bolometric light curve of PS1-10afx rose on the unusually fast timescale of ~12 days to the extraordinary peak luminosity of 4.1 × 1044 erg s-1 (M bol = -22.8 mag) and subsequently faded rapidly. Equally important, the spectral energy distribution is unusually red for an SLSN, with a color temperature of ~6800 K near maximum light, in contrast to previous hydrogen-poor SLSNe, which are bright in the ultraviolet (UV). The spectra more closely resemble those of a normal SN Ic than any known SLSN, with a photospheric velocity of ~11, 000 km s-1 and evidence for line blanketing in the rest-frame UV. Despite the fast rise, these parameters imply a very large emitting radius (gsim 5 × 1015 cm). We demonstrate that no existing theoretical model can satisfactorily explain this combination of properties: (1) a nickel-powered light curve cannot match the combination of high peak luminosity with the fast timescale; (2) models powered by the spindown energy of a rapidly rotating magnetar predict significantly hotter and faster ejecta; and (3) models invoking shock breakout through a dense circumstellar medium cannot explain the observed spectra or color evolution. The host galaxy is well detected in pre-explosion imaging with a luminosity near L*, a star formation rate of ~15 M ⊙ yr-1, and is fairly massive (~2 × 1010 M ⊙), with a stellar population age of ~108 yr, also in contrast to the young dwarf hosts of known hydrogen-poor SLSNe. PS1-10afx is distinct from known examples of SLSNe in its spectra, colors, light-curve shape, and host galaxy properties, suggesting that it resulted from a different channel than other hydrogen-poor SLSNe.

Gene expression and epigenetic discovery screen reveal methylation of SFRP2 in prostate cancer

Aberrant activation of Wnts is common in human cancers, including prostate. Hypermethylation associated transcriptional silencing of Wnt antagonist genes SFRPs (Secreted Frizzled-Related Proteins) is a frequent oncogenic event. The significance of this is not known in prostate cancer. The objectives of our study were to (i) profile Wnt signaling related gene expression and (ii) investigate methylation of Wnt antagonist genes in prostate cancer. Using TaqMan Low Density Arrays, we identified 15 Wnt signaling related genes with significantly altered expression in prostate cancer; the majority of which were upregulated in tumors. Notably, histologically benign tissue from men with prostate cancer appeared more similar to tumor (r = 0.76) than to benign prostatic hyperplasia (BPH; r = 0.57, p < 0.001). Overall, the expression profile was highly similar between tumors of high (≥ 7) and low (≤ 6) Gleason scores. Pharmacological demethylation of PC-3 cells with 5-Aza-CdR reactivated 39 genes (≥ 2-fold); 40% of which inhibit Wnt signaling. Methylation frequencies in prostate cancer were 10% (2/20) (SFRP1), 64.86% (48/74) (SFRP2), 0% (0/20) (SFRP4) and 60% (12/20) (SFRP5). SFRP2 methylation was detected at significantly lower frequencies in high-grade prostatic intraepithelial neoplasia (HGPIN; 30%, (6/20), p = 0.0096), tumor adjacent benign areas (8.82%, (7/69), p < 0.0001) and BPH (11.43% (4/35), p < 0.0001). The quantitative level of SFRP2 methylation (normalized index of methylation) was also significantly higher in tumors (116) than in the other samples (HGPIN = 7.45, HB = 0.47, and BPH = 0.12). We show that SFRP2 hypermethylation is a common event in prostate cancer. SFRP2 methylation in combination with other epigenetic markers may be a useful biomarker of prostate cancer.

Discovery of DNA hypermethylation using a DHPLC screening strategy

Promoter hypermethylation is recognized as a hallmark of human cancer, in addition to conventional mechanisms of gene inactivation. As such, many new technologies have been developed over the past two decades to uncover novel targets of methylation and decipher complex epigenetic patterns. However, many of these are either labor intensive or provide limited data, confined to oligonucleotide hybridization sequences or enzyme cleavage sites and cannot be easily applied to screening large sets of sequences or samples. We present an application of denaturing high performance liquid chromatography (DHPLC), which relies on bisulfite modification of genomic DNA, for methylation screening. We validated DHPLC as a methylation screening tool using GSTP1, a well known target of methylation in prostate cancer. We developed an in silico approach to identify potential targets of promoter hypermethylation in prostate cancer. Using DHPLC, we screened two of these targets LGALS3 and SMAD4 for methylation. We show that DHPLC has an application as a fast, sensitive, quantitative and cost effective method for screening novel targets or DNA samples for DNA methylation.

Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study

BACKGROUND: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome.

METHODS: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone.

FINDINGS: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions.

INTERPRETATION: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.

SN 2011dh: DISCOVERY OF A TYPE IIb SUPERNOVA FROM A COMPACT PROGENITOR IN THE NEARBY GALAXY M51

On 2011 May 31 UT a supernova (SN) exploded in the nearby galaxy M51 (the Whirlpool Galaxy). We discovered this event using small telescopes equipped with CCD cameras and also detected it with the Palomar Transient Factory survey, rapidly confirming it to be a Type II SN. Here, we present multi-color ultraviolet through infrared photometry which is used to calculate the bolometric luminosity and a series of spectra. Our early-time observations indicate that SN 2011dh resulted from the explosion of a relatively compact progenitor star. Rapid shock-breakout cooling leads to relatively low temperatures in early-time spectra, compared to explosions of red supergiant stars, as well as a rapid early light curve decline. Optical spectra of SN 2011dh are dominated by H lines out to day 10 after explosion, after which He I lines develop. This SN is likely a member of the cIIb (compact IIb) class, with progenitor radius larger than that of SN 2008ax and smaller than the eIIb (extended IIb) SN 1993J progenitor. Our data imply that the object identified in pre-explosion Hubble Space Telescope images at the SN location is possibly a companion to the progenitor or a blended source, and not the progenitor star itself, as its radius (~1013 cm) would be highly inconsistent with constraints from our post-explosion spectra.