Die Erhaltung nativer Proteinstrukturen unter Ausschluss von Lösungsmittel: eine Untersuchung mit Hilfe der Kombination von Ionenmobilität mit Spektroskopie

Kann die Struktur kleinerer bis mittelgroßer Proteine beim Übergang aus der Lösung in die Gasphase bewahrt werden? Zwar haben sich eine Vielzahl von Studien dieser Frage gewidmet, jedoch steht eine eindeutige Antwort noch aus. Die Klärung dieses Problems ist gleichwohl wichtig, denn davon hängt es ab, ob die empfindlichen Methoden der nativen Massenspektrometrie Probleme der Strukturbiologie adressieren können. Mithilfe einer Kombination aus Ionenmobilitäts-Massenspektrometrie und Infrarotspektroskopie untersuchen wir sowohl Sekundär- als auch Tertiärstruktur von Proteinen, die unter milden Bedingungen aus der Lösung in die Gasphase gebracht werden. In dieser Studie wurden die Moleküle Myoglobin und β-Lactoglobulin untersucht, die prototypische Beispiele für helikale bzw. β-Faltblatt-reiche Proteine sind. Unsere Ergebnisse zeigen, dass für niedrige Ladungszustände und unter sanften Bedingungen Aspekte der nativen Sekundär- und Tertiärstuktur bewahrt werden können

EcID. A database for the inference of functional interactions in E. coli

The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es

EcID. A database for the inference of functional interactions in E. coli

The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es

Constitutive P2Y2 receptor activity regulates basal lipolysis in human adipocytes

White adipocytes are key regulators of metabolic homeostasis, which release stored energy as free fatty acids via lipolysis. Adipocytes possess both basal and stimulated lipolytic capacity, but limited information exists regarding the molecular mechanisms that regulate basal lipolysis. Here, we describe a mechanism whereby autocrine purinergic signaling and constitutive P2Y2 receptor activation suppresses basal lipolysis in primary human in vitro differentiated adipocytes. We found that human adipocytes possess cytoplasmic calcium tone due to ATP secretion and constitutive P2Y2 receptor activation. Pharmacological antagonism or knockdown of P2Y2 receptors increases intracellular cAMP levels and enhances basal lipolysis. P2Y2 receptor antagonism works synergistically with phosphodiesterase inhibitors in elevating basal lipolysis, but is dependent upon adenylate cyclase activity. Mechanistically, we suggest that the increased calcium tone exerts an anti-lipolytic effect by suppression of calcium-sensitive adenylate cyclase isoforms. We also observed that acute enhancement of basal lipolysis following P2Y2 receptor antagonism alters the profile of secreted adipokines leading to longer term adaptive decreases in basal lipolysis. Our findings reveal that basal lipolysis and adipokine secretion are controlled by autocrine purinergic signaling in human adipocytes

Uncertainty in energy yield estimation based on C-Si module roundrobin results.

Results of the European FP7 Sophia project roundrobin of c-Si module power measurements at STC and low irradiance and temperature coefficients were used to calculate annual energy yield at four sites. The deviation in the estimates solely due to the different measurement results is reported, neglecting the uncertainty in the meteorological data and losses unrelated to the performed measurements. While minimising the deviation in Pmax measurements remains the key challenge, the low irradiance and temperature coefficient contributions are shown to be significant. Propagating the measurement deviation in c-Si module measurements would suggest that expanded uncertainty in energy yield due to module characterization alone can be as high as ±3-4%

Results of the Sophia module intercomparison part-1: stc, low irradiance conditions and temperature coefficients measurements of C-Si technologies

The results of a measurement intercomparison between eleven European laboratories measuring PV energy relevant parameters are reported. The purpose of the round-robin was to assess the uncertainty analyses of the participating laboratories on c-Si modules and to establish a baseline for the following thin-film round-robin. Alongside the STC measurements, low irradiance conditions (200W/m2) and temperature coefficients measurements were performed. The largest measurement deviation from the median at STC was for HIT modules from -3.6% to +2.7% in PMAX, but in agreement with the stated uncertainties of the participants. This was not the case for low irradiance conditions and temperature coefficients measurements with some partners underestimating their uncertainties. Larger deviations from the median from -5% to +3% in PMAX at low irradiance conditions and -6.6% to +18.3% for the PMAX temperature coefficient were observed. The main sources of uncertainties contributing to the spread in measurements were the RC calibration, mismatch factor and capacitive effects at STC and low irradiance conditions as well as the additional light inhomogeneity for the latter. The uncertainty in the junction temperature and the temperature deviation across the module were the major contributors for temperature coefficients measurements

Improving protein function prediction methods with integrated literature data

<p>Abstract</p> <p>Background</p> <p>Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.</p> <p>Results</p> <p>We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.</p> <p>Conclusion</p> <p>Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.</p

Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach

Background: Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. Methodology/Principal Findings: We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/. Conclusions: We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.This work was funded by the BioSapiens (grant number LSHG-CT-2003-503265) and the Experimental Network for Functional Integration (ENFIN) Networks of Excellence (contract number LSHG-CT-2005-518254), by Consolider BSC (grant number CSD2007-00050) and by the project “Functions for gene sets” from the Spanish Ministry of Education and Science (BIO2007-66855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Identification of two novel powdery mildew resistance loci, Ren6 and Ren7, from the wild Chinese grape species Vitis piasezkii

Descriptive statistics of the phenotypic scores within the base mapping population 11-373. Powdery mildew symptoms in the field were evaluated in two subsequent years. Greenhouse, in vitro experiments and the qPCR-based molecular assay were carried out with three to four biological replicates of each seedling plant in 2014. (DOCX 14ย�kb

Modelling semantic transparency

We present models of semantic transparency in which the perceived trans- parency of English noun–noun compounds, and of their constituent words, is pre- dicted on the basis of the expectedness of their semantic structure. We show that such compounds are perceived as more transparent when the first noun is more frequent, hence more expected, in the language generally; when the compound semantic rela- tion is more frequent, hence more expected, in association with the first noun; and when the second noun is more productive, hence more expected, as the second ele- ment of a noun–noun compound. Taken together, our models of compound and con- stituent transparency lead us to two conclusions. Firstly, although compound trans- parency is a function of the transparencies of the constituents, the two constituents differ in the nature of their contribution. Secondly, since all the significant predictors in our models of compound transparency are also known predictors of processing speed, perceived transparency may itself be a reflex of ease of processing