Genome-wide screening for DNA variants associated with reading and language traits

Reading and language abilities are heritable traits that are likely to share some genetic influences with each other. To identify pleiotropic genetic variants affecting these traits, we first performed a genome-wide association scan (GWAS) meta-analysis using three richly characterized datasets comprising individuals with histories of reading or language problems, and their siblings. GWAS was performed in a total of 1862 participants using the first principal component computed from several quantitative measures of reading- and language-related abilities, both before and after adjustment for performance IQ. We identified novel suggestive associations at the SNPs rs59197085 and rs5995177 (uncorrected P≈10 for each SNP), located respectively at the CCDC136/FLNC and RBFOX2 genes. Each of these SNPs then showed evidence for effects across multiple reading and language traits in univariate association testing against the individual traits. FLNC encodes a structural protein involved in cytoskeleton remodelling, while RBFOX2 is an important regulator of alternative splicing in neurons. The CCDC136/FLNC locus showed association with a comparable reading/language measure in an independent sample of 6434 participants from the general population, although involving distinct alleles of the associated SNP. Our datasets will form an important part of on-going international efforts to identify genes contributing to reading and language skills. Genome-wide association scan meta-analysis for reading and language ability. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0:a quantitative molecular docking study

Background: HLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 - 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pKa of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0. Results: The X-ray structure of the peptide - HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides. Conclusions: The higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His 79β. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins. © 2012 Patronov et al.; licensee BioMed Central Ltd.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Evaluating the Distribution of Terrestrial Dissolved Organic Matter in a Complex Coastal Ecosystem Using Fluorescence Spectroscopy

The coastal zone of the Florida Keys features the only living coral reef in the continental United States and as such represents a unique regional environmental resource. Anthropogenic pressures combined with climate disturbances such as hurricanes can affect the biogeochemistry of the region and threaten the health of this unique ecosystem. As such, water quality monitoring has historically been implemented in the Florida Keys, and six spatially distinct zones have been identified. In these studies however, dissolved organic matter (DOM) has only been studied as a quantitative parameter, and DOM composition can be a valuable biogeochemical parameter in assessing environmental change in coastal regions. Here we report the first data of its kind on the application of optical properties of DOM, in particular excitation emission matrix fluorescence with parallel factor analysis (EEM-PARAFAC), throughout these six Florida Keys regions in an attempt to assess spatial differences in DOM sources. Our data suggests that while DOM in the Florida Keys can be influenced by distant terrestrial environments such as the Everglades, spatial differences in DOM distribution were also controlled in part by local surface runoff/fringe mangroves, contributions from seasgrass communities, as well as the reefs and waters from the Florida Current. Application of principal component analysis (PCA) of the relative abundance of EEM-PARAFAC components allowed for a clear distinction between the sources of DOM (allochthonous vs. autochthonous), between different autochthonous sources and/or the diagenetic status of DOM, and further clarified contribution of terrestrial DOM in zones where levels of DOM were low in abundance. The combination between EEM-PARAFAC and PCA proved to be ideally suited to discern DOM composition and source differences in coastal zones with complex hydrology and multiple DOM sources.

Heritability and genetic correlations of personality traits in a wild population of yellow-bellied marmots (Marmota flaviventris)

This article is protected by copyright. All rights reserved. Acknowledgments We thank all the marmoteers that helped collect data over the years, and specifically Amanda Lea for help with the pedigree and Leon Chong for help in the field. The comments of two anonymous reviewers helped us improve our original MS. M.B.P. was funded by two U.S. Department of Education GAANN Fellowships, an NSF GK-12 Fellowship, and the UCLA Department of Ecology and Evolutionary Biology. J.G.A.M. was supported by a Marie-Curie Fellowship. D.T.B was supported by the National Geographic Society, UCLA (Faculty Senate and the Division of Life Sciences), a Rocky Mountain Biological Laboratory research fellowship and by the NSF (IDBR-0754247 and DEB-1119660 to D.T.B., as well as DBI 0242960 and 0731346 to the Rocky Mountain Biological Laboratory).

Upper-Pleistocene terrace deposits in Mediterranean climate: geomorphological and source-rock control on mineral and geochemical signatures (Betic Cordillera, SE Spain)

Mineral and chemical composition of alluvial Upper-Pleistocene deposits from the Alto Guadalquivir Basin (SE Spain) were studied as a tool to identify sedimentary and geomorphological processes controlling its formation. Sediments located upstream, in the north-eastern sector of the basin, are rich in dolomite, illite, MgO and KB2BO. Downstream, sediments at the sequence base are enriched in calcite, smectite and CaO, whereas the upper sediments have similar features to those from upstream. Elevated rare-earth elements (REE) values can be related to low carbonate content in the sediments and the increase of silicate material produced and concentrated during soil formation processes in the neighbouring source areas. Two mineralogical and geochemical signatures related to different sediment source areas were identified. Basal levels were deposited during a predominantly erosive initial stage, and are mainly composed of calcite and smectite materials enriched in REE coming from Neogene marls and limestones. Then the deposition of the upper levels of the alluvial sequences, made of dolomite and illitic materials depleted in REE coming from the surrounding Sierra de Cazorla area took place during a less erosive later stage of the fluvial system. Such modification was responsible of the change in the mineralogical and geochemical composition of the alluvial sediments.

Assessment of the EEE-4 oral test: a discourse analysis based on complex networks.

With the development of information technology, the theory and methodology of complex network has been introduced to the language research, which transforms the system of language in a complex networks composed of nodes and edges for the quantitative analysis about the language structure. The development of dependency grammar provides theoretical support for the construction of a treebank corpus, making possible a statistic analysis of complex networks. This paper introduces the theory and methodology of the complex network and builds dependency syntactic networks based on the treebank of speeches from the EEE-4 oral test. According to the analysis of the overall characteristics of the networks, including the number of edges, the number of the nodes, the average degree, the average path length, the network centrality and the degree distribution, it aims to find in the networks potential difference and similarity between various grades of speaking performance. Through clustering analysis, this research intends to prove the network parameters’ discriminating feature and provide potential reference for scoring speaking performance.

Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis – moving out of the laboratory and into the food supply chain

Major food adulteration and contamination events occur with alarming regularity and are known to be episodic, with the question being not if but when another large-scale food safety/integrity incident will occur. Indeed, the challenges of maintaining food security are now internationally recognised. The ever increasing scale and complexity of food supply networks can lead to them becoming significantly more vulnerable to fraud and contamination, and potentially dysfunctional. This can make the task of deciding which analytical methods are more suitable to collect and analyse (bio)chemical data within complex food supply chains, at targeted points of vulnerability, that much more challenging. It is evident that those working within and associated with the food industry are seeking rapid, user-friendly methods to detect food fraud and contamination, and rapid/high-throughput screening methods for the analysis of food in general. In addition to being robust and reproducible, these methods should be portable and ideally handheld and/or remote sensor devices, that can be taken to or be positioned on/at-line at points of vulnerability along complex food supply networks and require a minimum amount of background training to acquire information rich data rapidly (ergo point-and-shoot). Here we briefly discuss a range of spectrometry and spectroscopy based approaches, many of which are commercially available, as well as other methods currently under development. We discuss a future perspective of how this range of detection methods in the growing sensor portfolio, along with developments in computational and information sciences such as predictive computing and the Internet of Things, will together form systems- and technology-based approaches that significantly reduce the areas of vulnerability to food crime within food supply chains. As food fraud is a problem of systems and therefore requires systems level solutions and thinking.

Molecular Typing, Virulence Traits and Antimicrobial Resistance of Diabetic Foot Staphylococci

Diabetes mellitus is a major chronic disease that continues to increase significantly. One of the most important and costly complications of diabetes are foot infections that may be colonized by pathogenic and antimicrobial resistant bacteria, harboring several virulence factors, that could impair its successful treatment. Staphylococcus aureus is one of the most prevalent isolate in diabetic foot infections, together with aerobes and anaerobes.

Exploring complex chemical systems: insights from mass spectrometry, electrochemistry and continuous cell culture

The work presented herein covers a broad range of research topics and so, in the interest of clarity, has been presented in a portfolio format. Accordingly, each chapter consists of its own introductory material prior to presentation of the key results garnered, this is then proceeded by a short discussion on their significance. In the first chapter, a methodology to facilitate the resolution and qualitative assessment of very large inorganic polyoxometalates was designed and implemented employing ion-mobility mass spectrometry. Furthermore, the potential of this technique for ‘mapping’ the conformational space occupied by this class of materials was demonstrated. These claims are then substantiated by the development of a tuneable, polyoxometalate-based calibration protocol that provided the necessary platform for quantitative assessments of similarly large, but unknown, polyoxometalate species. In addition, whilst addressing a major limitation of travelling wave ion mobility, this result also highlighted the potential of this technique for solution-phase cluster discovery. The second chapter reports on the application of a biophotovoltaic electrochemical cell for characterising the electrogenic activity inherent to a number of mutant Synechocystis strains. The intention was to determine the key components in the photosynthetic electron transport chain responsible for extracellular electron transfer. This would help to address the significant lack of mechanistic understanding in this field. Finally, in the third chapter, the design and fabrication of a low-cost, highly modular, continuous cell culture system is presented. To demonstrate the advantages and suitability of this platform for experimental evolution investigations, an exploration into the photophysiological response to gradual iron limitation, in both the ancestral wild type and a randomly generated mutant library population, was undertaken. Furthermore, coupling random mutagenesis to continuous culture in this way is shown to constitute a novel source of genetic variation that is open to further investigation.

High resolution mass spectrometry for quantitative analysis and untargeted screening of algal toxins in mussels and passive samplers

Measurement of marine algal toxins has traditionally focussed on shellfish monitoring while, over the last decade, passive sampling has been introduced as a complementary tool for exploratory studies. Since 2011, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been adopted as the EU reference method (No.15/2011) for detection and quantitation of lipophilic toxins. Traditional LC-MS approaches have been based on low-resolution mass spectrometry (LRMS), however, advances in instrument platforms have led to a heightened interest in the use of high-resolution mass spectrometry (HRMS) for toxin detection. This work describes the use of HRMS in combination with passive sampling as a progressive approach to marine algal toxin surveys. Experiments focused on comparison of LRMS and HRMS for determination of a broad range of toxins in shellfish and passive samplers. Matrix effects are an important issue to address in LC-MS; therefore, this phenomenon was evaluated for mussels (Mytilus galloprovincialis) and passive samplers using LRMS (triple quadrupole) and HRMS (quadrupole time-of-flight and Orbitrap) instruments. Matrix-matched calibration solutions containing okadaic acid and dinophysistoxins, pectenotoxin, azaspiracids, yessotoxins, domoic acid, pinnatoxins, gymnodimine A and 13-desmethyl spirolide C were prepared. Similar matrix effects were observed on all instruments types. Most notably, there was ion enhancement for pectenotoxins, okadaic acid/dinophysistoxins on one hand, and ion suppression for yessotoxins on the other. Interestingly, the ion selected for quantitation of PTX2 also influenced the magnitude of matrix effects, with the sodium adduct typically exhibiting less susceptibility to matrix effects than the ammonium adduct. As expected, mussel as a biological matrix, quantitatively produced significantly more matrix effects than passive sampler extracts, irrespective of toxin. Sample dilution was demonstrated as an effective measure to reduce matrix effects for all compounds, and was found to be particularly useful for the non-targeted approach. Limits of detection and method accuracy were comparable between the systems tested, demonstrating the applicability of HRMS as an effective tool for screening and quantitative analysis. HRMS offers the advantage of untargeted analysis, meaning that datasets can be retrospectively analysed. HRMS (full scan) chromatograms of passive samplers yielded significantly less complex data sets than mussels, and were thus more easily screened for unknowns. Consequently, we recommend the use of HRMS in combination with passive sampling for studies investigating emerging or hitherto uncharacterised toxins.