Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information

<p>Abstract</p> <p>Background</p> <p>As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence.</p> <p>Results</p> <p>We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the selected features, a decision tree method can achieve 82.6% overall accuracy with 0.607 Matthews Correlation Coefficient (MCC) in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the decision tree method achieves 82.6% overall accuracy with 0.604 MCC. We also evaluated the proposed method on all SAPs obtained from the Swiss-Prot, the method achieves 0.42 MCC with 73.2% overall accuracy. This method allows users to make reliable predictions when protein structures are not available. Different from previous studies, in which only a small set of features were arbitrarily chosen and considered, here we used an automated method to systematically discover useful features from a large set of features well-annotated in public databases.</p> <p>Conclusion</p> <p>The proposed method is a useful tool for the classification of SAPs, especially, when the structure of the protein is not available.</p

Rational design of a conformation-specific antibody for the quantification of A beta oligomers

The accurate quantification of the amounts of small oligomeric assemblies formed by the amyloid β (Aβ) peptide represents a major challenge in the Alzheimer’s field. There is therefore great interest in the development of methods to specifically detect these oligomers by distinguishing them from larger aggregates. The availability of these methods will enable the development of effective diagnostic and therapeutic interventions for this and other diseases related to protein misfolding and aggregation. We describe here a single-domain antibody able to selectively quantify oligomers of the Aβ peptide in isolation and in complex protein mixtures from animal models of disease

Introduction to the Conceptualisation of Environmental Citizenship for Twenty-First-Century Education

The EU’s growth strategy (Europe 2020) and the European vision for green, circular and low-carbon economy in line with the EU 2050 (EU-roadmap 2050) give par- ticular attention to citizens’ participation and engagement and therefore to Environmental Citizenship. Environmental Citizenship has been an influential con- cept in many different arenas such as economy, policy, philosophy, corporation management and marketing, which could also be better exploited and established in the field of education. Environmental Citizenship is recognized as an important aspect in addressing global environmental problems such as climate change (Stern 2011; Ockwell et al. 2009) whilst providing support to pro-environmental organisa- tions and individuals, contributing also to public pressure for political action (sign- ing petitions, writing to politicians and newspapers). Many varied definitions of Environmental Citizenship can be found within the literature. Some of them are quite similar, and important overlaps can be observed; however, others can be quite different with contradictions in their philosophy and approach. According to Dobson (2010), Environmental Citizenship refers to pro-environmental behaviour, in public and in private, driven by a belief in fairness of the distribution of environmental goods, in participation and in the co-creation of sustainability policy. It is about the active participation of citizens in moving towards sustainability. Education and especially environmental discourses in science education have a lot to contribute in adopting and promoting Environmental Citizenship. However, the conceptualisation of Environmental Citizenship in educational context remains an imperative need. The under-explored (until now) potential for pro-environmental behaviour change through Environmental Citizenship should be further emphasised (Dobson 2010) and can contribute greatly to a more sustainable world.info:eu-repo/semantics/publishedVersio

The relationship between SF-6D utility scores and lifestyle factors across three life-stages: Evidence from the Australian Longitudinal Study on Women’s Health

Purpose: To investigate how SF-6D utility scores change with age between generations of women, and to quantify the relationship of SF-6D with lifestyle factors across life-stages. Methods: Up to seven waves of self-reported, longitudinal data were drawn for the 1973-78 (young, N=13772), 1946-51 (mid-age, N=12792), 1921-26 (older, N=9972) cohorts from the Australian Longitudinal Study on Women’s Health. Mixed effects models were employed for analysis. Results: Young and mid-age women had similar average SF-6D scores at baseline (0.63-0.64), which remained consistent over 16 year period. However, older women had lower scores at baseline at 0.57 which steadily declined over 15 years. Across cohorts, low education attainment, greater difficulty in managing on income, obesity, physical inactivity, heavy smoking, non-drinking and increasing stress levels were associated with lower SF-6D scores. The magnitude of effect varied between cohorts. SF-6D scores were lower amongst young women with high risk drinking behaviours than low-risk drinkers. Mid-age women who were underweight, never married, or underwent surgical menopause also reported lower SF-6D scores. Older women who lived in remote areas, who were ex-smokers, or were underweight reported lower SF-6D scores. Conclusion: The SF-6D utility score is sensitive to differences in lifestyle factors across adult lifestages. Gradual loss of physical functioning may explain the steady decline in health for older women. Key factors associated with SF-6D include physical activity, body mass index, menopause status, smoking, alcohol use and stress. Factors associated with poorer SF-6D scores vary in type and magnitude at different life stages

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies

Improving the prediction of disease-related variants using protein three-dimensional structure

Background: Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.Results: In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.Conclusion: This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available. \ua9 2011 Capriotti; licensee BioMed Central Ltd

A mathematical and computational review of Hartree-Fock SCF methods in Quantum Chemistry

We present here a review of the fundamental topics of Hartree-Fock theory in Quantum Chemistry. From the molecular Hamiltonian, using and discussing the Born-Oppenheimer approximation, we arrive to the Hartree and Hartree-Fock equations for the electronic problem. Special emphasis is placed in the most relevant mathematical aspects of the theoretical derivation of the final equations, as well as in the results regarding the existence and uniqueness of their solutions. All Hartree-Fock versions with different spin restrictions are systematically extracted from the general case, thus providing a unifying framework. Then, the discretization of the one-electron orbitals space is reviewed and the Roothaan-Hall formalism introduced. This leads to a exposition of the basic underlying concepts related to the construction and selection of Gaussian basis sets, focusing in algorithmic efficiency issues. Finally, we close the review with a section in which the most relevant modern developments (specially those related to the design of linear-scaling methods) are commented and linked to the issues discussed. The whole work is intentionally introductory and rather self-contained, so that it may be useful for non experts that aim to use quantum chemical methods in interdisciplinary applications. Moreover, much material that is found scattered in the literature has been put together here to facilitate comprehension and to serve as a handy reference.Comment: 64 pages, 3 figures, tMPH2e.cls style file, doublesp, mathbbol and subeqn package

Global Array-Based Transcriptomics from Minimal Input RNA Utilising an Optimal RNA Isolation Process Combined with SPIA cDNA Probes

Technical advances in the collection of clinical material, such as laser capture microdissection and cell sorting, provide the advantage of yielding more refined and homogenous populations of cells. However, these attractive advantages are counter balanced by the significant difficultly in obtaining adequate nucleic acid yields to allow transcriptomic analyses. Established technologies are available to carry out global transcriptomics using nanograms of input RNA, however, many clinical samples of low cell content would be expected to yield RNA within the picogram range. To fully exploit these clinical samples the challenge of isolating adequate RNA yield directly and generating sufficient microarray probes for global transcriptional profiling from this low level RNA input has been addressed in the current report. We have established an optimised RNA isolation workflow specifically designed to yield maximal RNA from minimal cell numbers. This procedure obtained RNA yield sufficient for carrying out global transcriptional profiling from vascular endothelial cell biopsies, clinical material not previously amenable to global transcriptomic approaches. In addition, by assessing the performance of two linear isothermal probe generation methods at decreasing input levels of good quality RNA we demonstrated robust detection of a class of low abundance transcripts (GPCRs) at input levels within the picogram range, a lower level of RNA input (50 pg) than previously reported for global transcriptional profiling and report the ability to interrogate the transcriptome from only 10 pg of input RNA. By exploiting an optimal RNA isolation workflow specifically for samples of low cell content, and linear isothermal RNA amplification methods for low level RNA input we were able to perform global transcriptomics on valuable and potentially informative clinically derived vascular endothelial biopsies here for the first time. These workflows provide the ability to robustly exploit ever more common clinical samples yielding extremely low cell numbers and RNA yields for global transcriptomics

Education for Environmental Citizenship and Responsible Environmental Behaviour

The notion of Environmental Citizenship embodies behaviour – an actively involved citizen who exercises his/her environmental rights and obligations in the private and public spheres. Education for Environmental Citizenship implies behavioural change; its goal is to facilitate an individual’s intellectual growth (cognitive domain) and emotional capacity (affective domain) that may lead to a critical and actively engaged individual. Human behaviour is overwhelmingly sophisticated, and what shapes pro-environmental behaviour is complex and context specific. Furthermore, empirical research indicates a discrepancy between possessing environmental knowledge and environmentally supportive attitudes and behaving pro-environmentally. The point of departure of this chapter is that the social and psychological study of behaviour has much to inform the study of environmental behaviour and, deriving from this, to inform regarding the type of education towards behaviour/action in the goal of sustainable socioecological transformation. The chapter focuses on internal (psychosocial) factors. It presents selected models regarding factors influencing behavioural decisions that are acknowledged as influential theoretical frameworks for investigating pro-environmental behaviour, as well as various theories that inform these models. These are categorised into knowledge-based models; attitude-, value- and norm-oriented models; skills, self-efficacy and situational factors; and new approaches to environmental behaviour models. The chapter concludes with suggestions for Education for Environmental Citizenship deriving from the various models