The MALDI TOF E2/E3 ligase assay as universal tool for drug discovery in the ubiquitin pathway

AbstractIn many diseases, components of the ubiquitin system - such as E2/E3 ligases and deubiquitylases - are dysregulated. The ubiquitin system has therefore become an emergent target for the treatment of a number of diseases, including cancer, neurodegeneration and autoimmunity. Despite of the efforts in this field, primary screenings of compound libraries to individuate new potential therapeutic molecules targeting the ubiquitin pathway have been strongly limited by the lack of robust and fast high-throughput assays. Here we report the first label-free high-throughput screening (HTS) assay for ubiquitin E2 conjugating enzymes and E3 ligases based on Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI TOF) mass spectrometry. The MALDI TOF E2/E3 assay allows us to test E2 conjugating enzymes and E3 ligases for their ubiquitin transfer activity, to identify E2/E3 active pairs, inhibitor potency and specificity and to screen compound librariesin vitrowithout synthesis of chemical or fluorescent probes. We demonstrate that the MALDI TOF E2/E3 assay is a universal tool for drug discovery screening in the ubiquitin pathway as it is suitable for working with all E3 ligase families and requires a reduced amount of reagents, compared to standard biochemical assays.</jats:p

On boosting kernel regression

In this paper we propose a simple multistep regression smoother which is constructed in an iterative manner, by learning the Nadaraya-Watson estimator with L-2 boosting. We find, in both theoretical analysis and simulation experiments, that the bias converges exponentially fast. and the variance diverges exponentially slow. The first boosting step is analysed in more detail, giving asymptotic expressions as functions of the smoothing parameter, and relationships with previous work are explored. Practical performance is illustrated by both simulated and real data

Modelling Knowledge Integration Process in Early Contractor Involvement Procurement at Tender Stage - A Western Australian Case Study

Purpose This paper aims to disseminate the knowledge integration process modelling throughout the phases of the early contractor involvement (ECI) procurement methodology, to optimise the benefit of ECI procurement method. The development of the model was aimed at taking advantage from the associated benefits of integrating knowledge and of ECI procurement. ECI provides contractors with an alternative means to tendering, designing and constructing projects. Thus, this paper explores knowledge interconnectivity and its integration involving numerous disciplines with various stakeholders to benefit from the collaborative environment of ECI. Design/methodology/approach The methodology implemented in the research includes a thorough literature review to establish the characteristics of the ECI tender stage as well as the characteristics of knowledge to be integrated in an ECI setting. Following this, an embedded case study research methodology was used involving three healthcare ECI projects undertaken by a Western Australian commercial contractor through 20 semi-structured interviews and project archival study, followed by the development of knowledge integration process models throughout the ECI process of the studied cases. Findings The research findings provide the basis to develop a knowledge integration process model throughout the ECI stages. The tender stage was found to be the most crucial stage for knowledge integration, particularly from the main contractor’s perspective to impart change and to influence the project outcome. The outcome of this research identifies the richness and interconnectivity of knowledge throughout the knowledge integration process in an ECI project starting from the intra-organisational knowledge integration process followed by the inter-organisational process of knowledge integration. This inside-out perspective of knowledge integration also revealed the need for mapping the implementation of knowledge integration from instrumental to incremental approach throughout the ECI stages in optimising the intended benefits of integrating knowledge. Originality/value This paper reports the development of a knowledge integration process model with the view to optimise the management effectiveness of integrating knowledge in ECI projects. Although knowledge integration and ECI can be considered existing and widely accepted concepts, the novelty of this research lies in the specific use of the knowledge integration process to analyse the knowledge flow, transformation and, hence, management in ECI projects. As it has been acknowledged that knowledge integration is beneficial but also a complex process, the methodology implemented here in modelling the process can be used as the basis to model knowledge integration in other ECI projects to further capitalise from ECI as a collaborative procurement method

A weather-type statistical downscaling framework for oceanwave climate

Wave climate characterization at different time scales (long-term historical periods, seasonal prediction, and future projections) is required for a broad number of marine activities. Wave reanalysis databases have become a valuable source of information covering time periods of decades. A weather-type approach is proposed to statistically downscale multivariate wave climate over different time scales from the reanalysis long-term period. The model calibration is performed using historical data of predictor (sea level pressure) and predictand (sea-state parameters) from reanalysis databases. The storm activity responsible for the predominant swell composition of the local wave climate is included in the predictor definition. N-days sea level pressure fields are used as predictor. K-means algorithm with a postorganization in a bidimensional lattice is used to obtain weather patterns. Multivariate hourly sea states are associated with each pattern. The model is applied at two locations on the east coast of the North Atlantic Ocean. The validation proves the model skill to reproduce the seasonal and interannual variability of monthly sea-state parameters. Moreover, the projection of wave climate onto weather types provides a multivariate wave climate characterization with a physically interpretable linkage with atmospheric forcings. The statistical model is applied to reconstruct wave climate in the last twentieth century, to hindcast the last winter, and to project wave climate under climate change scenarios. The statistical approach has been demonstrated to be a useful tool to analyze wave climate at different time scales.The work was partly funded by the project iMar21 (CTM2010-15009) from the Spanish Government and the FP7 European projects CoCoNet (287844) and Mermaid (288710)

Spectral Ocean Wave Climate Variability Based on Atmospheric Circulation Patterns

Traditional approaches for assessing wave climate variability have been broadly focused on aggregated or statistical parameters such as significant wave height, wave energy flux, or mean wave direction. These studies, although revealing the major general modes of wave climate variability and trends, do not take into consideration the complexity of the wind-wave fields. Because ocean waves are the response to both local and remote winds, analyzing the directional full spectra can shed light on atmospheric circulation not only over the immediate ocean region, but also over a broad basin scale. In this work, the authors use a pattern classification approach to explore wave climate variability in the frequency–direction domain. This approach identifies atmospheric circulation patterns of the sea level pressure from the 31-yr long Climate Forecast System Reanalysis (CFSR) and wave spectral patterns of two selected buoys in the North Atlantic, finding one-to-one relations between each synoptic pattern (circulation type) and each spectral wave energy distribution (spectral type). Even in the absence of long-wave records, this method allows for the reconstruction of longterm wave spectra to cover variability at several temporal scales: daily, monthly, seasonal, interannual, decadal, long-term trends, and future climate change projections.The authors are grateful to Puertos del Estado (Spanish Ministry of Public Works and Infrastructures) for providing us the instrumental buoy data. This work was partially funded by the project IMAR21 (CT M2010-15009) from the Spanish Government

Welfare Effects of the Euro Cash Changeover: Do Assumptions Really Matter?

Manski's partial identification allows less restrictive, therefore, more credible assumptions than the assumption of random treatment assignment to solve the evaluation problem. In this article the theory of partial identification is applied to the welfare effect of the euro cash changeover. When evaluating the impact of the euro cash changeover on individual welfare, Wunder et al. (2008) face the evaluation problem. Instead of arguing for a comparability of both treatment groups used (i.e. the British and the German Population), partial identification as a more robust technique is used for evaluating the effect of the euro cash changeover. Imposing less restrictive assumptions leaves out an answer about the direction of the welfare effect

Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature

<p>Abstract</p> <p>Background</p> <p>Given the large number of genes purported to be prognostic for breast cancer, it would be optimal if the genes identified are not confounded by the continuously changing systemic therapies. The aim of this study was to discover and validate a breast cancer prognostic expression signature for distant metastasis in untreated, early stage, lymph node-negative (N-) estrogen receptor-positive (ER+) patients with extensive follow-up times.</p> <p>Methods</p> <p>197 genes previously associated with metastasis and ER status were profiled from 142 untreated breast cancer subjects. A "metastasis score" (MS) representing fourteen differentially expressed genes was developed and evaluated for its association with distant-metastasis-free survival (DMFS). Categorical risk classification was established from the continuous MS and further evaluated on an independent set of 279 untreated subjects. A third set of 45 subjects was tested to determine the prognostic performance of the MS in tamoxifen-treated women.</p> <p>Results</p> <p>A 14-gene signature was found to be significantly associated (p < 0.05) with distant metastasis in a training set and subsequently in an independent validation set. In the validation set, the hazard ratios (HR) of the high risk compared to low risk groups were 4.02 (95% CI 1.91–8.44) for the endpoint of DMFS and 1.97 (95% CI 1.28 to 3.04) for overall survival after adjustment for age, tumor size and grade. The low and high MS risk groups had 10-year estimates (95% CI) of 96% (90–99%) and 72% (64–78%) respectively, for DMFS and 91% (84–95%) and 68% (61–75%), respectively for overall survival. Performance characteristics of the signature in the two sets were similar. Ki-67 labeling index (LI) was predictive for recurrent disease in the training set, but lost significance after adjustment for the expression signature. In a study of tamoxifen-treated patients, the HR for DMFS in high compared to low risk groups was 3.61 (95% CI 0.86–15.14).</p> <p>Conclusion</p> <p>The 14-gene signature is significantly associated with risk of distant metastasis. The signature has a predominance of proliferation genes which have prognostic significance above that of Ki-67 LI and may aid in prioritizing future mechanistic studies and therapeutic interventions.</p

Can Biomarkers Identify Women at Increased Stroke Risk? The Women's Health Initiative Hormone Trials

Objective: The Women's Health Initiative hormone trials identified a 44% increase in ischemic stroke risk with combination estrogen plus progestin and a 39% increase with estrogen alone. We undertook a case-control biomarker study to elucidate underlying mechanisms, and to potentially identify women who would be at lower or higher risk for stroke with postmenopausal hormone therapy (HT). Design: The hormone trials were randomized, double-blind, and placebo controlled. Setting: The Women's Health Initiative trials were conducted at 40 clinical centers in the United States. Participants: The trials enrolled 27,347 postmenopausal women, aged 50-79 y. Interventions: We randomized 16,608 women with intact uterus to conjugated estrogens 0.625 mg with medroxyprogesterone acetate 2.5 mg daily or placebo, and 10,739 women with prior hysterectomy to conjugated estrogens 0.625 mg daily or placebo. Outcome Measures: Stroke was ascertained during 5.6 y of follow-up in the estrogen plus progestin trial and 6.8 y of follow-up in the estrogen alone trial. Results: No baseline clinical characteristics, including gene polymorphisms, identified women for whom the stroke risk from HT was higher. Paradoxically, women with higher baseline levels of some stroke-associated biomarkers had a lower risk of stroke when assigned to estrogen plus progestin compared to placebo. For example, those with higher IL-6 were not at increased stroke risk when assigned to estrogen plus progestin (odds ratio 1.28) but were when assigned to placebo (odds ratio 3.47; p for difference = 0.02). Similar findings occurred for high baseline PAP, leukocyte count, and D-dimer. However, only an interaction of D-dimer during follow-up interaction with HT and stroke was marginally significant (p = 0.03). Conclusions: Biomarkers did not identify women at higher stroke risk with postmenopausal HT. Some biomarkers appeared to identify women at lower stroke risk with estrogen plus progestin, but these findings may be due to chance