Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Prediction of protein structural classes for low-homology sequences based on predicted secondary structure

<p>Abstract</p> <p>Background</p> <p>Prediction of protein structural classes (<it>α</it>, <it>β</it>, <it>α </it>+ <it>β </it>and <it>α</it>/<it>β</it>) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.</p> <p>Results</p> <p>We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the <it>chaos game representation </it>is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using <it>recurrence quantification analysis</it>, <it>K-string based information entropy </it>and <it>segment-based analysis</it>. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at <url>http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/</url>.</p> <p>Conclusion</p> <p>The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of <it>α </it>helices and <it>β </it>strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.</p

Measurement of the proton form factor by studying e+e−→ppˉe^{+} e^{-}\rightarrow p\bar{p}

Using data samples collected with the BESIII detector at the BEPCII collider, we measure the Born cross section of $e^{+}e^{-}\rightarrow p\bar{p}$ at 12 center-of-mass energies from 2232.4 to 3671.0 MeV. The corresponding effective electromagnetic form factor of the proton is deduced under the assumption that the electric and magnetic form factors are equal $(|G_{E}|= |G_{M}|)$. In addition, the ratio of electric to magnetic form factors, $|G_{E}/G_{M}|$, and $|G_{M}|$ are extracted by fitting the polar angle distribution of the proton for the data samples with larger statistics, namely at $\sqrt{s}=$ 2232.4 and 2400.0 MeV and a combined sample at $\sqrt{s}$ = 3050.0, 3060.0 and 3080.0 MeV, respectively. The measured cross sections are in agreement with recent results from BaBar, improving the overall uncertainty by about 30\%. The $|G_{E}/G_{M}|$ ratios are close to unity and consistent with BaBar results in the same $q^{2}$ region, which indicates the data are consistent with the assumption that $|G_{E}|=|G_{M}|$ within uncertainties.Comment: 13 pages, 24 figure

Observation of the isospin-violating decay J/ψ→ϕπ0f0(980)J/\psi \to \phi\pi^{0}f_{0}(980)

Using a sample of 1.31 billion $J/\psi$ events collected with the BESIII detector at the BEPCII collider, the decays $J/\psi \to \phi \pi^{+}\pi^{-}\pi^{0}$ and $J/\psi \to \phi \pi^{0}\pi^{0}\pi^{0}$ are investigated. The isospin violating decay $J/\psi \to \phi \pi^{0} f_{0}(980)$ with $f_{0}(980) \to \pi\pi$, is observed for the first time. The width of the $f_{0}(980)$ obtained from the dipion mass spectrum is found to be much smaller than the world average value. In the $\pi^{0} f_{0}(980)$ mass spectrum, there is evidence of $f_1(1285)$ production. By studying the decay $J/\psi \to \phi\eta'$, the branching fractions of $\eta' \to \pi^{+}\pi^{-}\pi^{0}$ and $\eta' \to \pi^{0}\pi^{0}\pi^{0}$, as well as their ratio, are also measured.Comment: 10 pages, 10 figures, published in Phys. Rev.

An amplitude analysis of the π0π0\pi^{0}\pi^{0} system produced in radiative J/ψJ/\psi decays

An amplitude analysis of the $\pi^{0}\pi^{0}$ system produced in radiative $J/\psi$ decays is presented. In particular, a piecewise function that describes the dynamics of the $\pi^{0}\pi^{0}$ system is determined as a function of $M_{\pi^{0}\pi^{0}}$ from an analysis of the $(1.311\pm0.011)\times10^{9}$ $J/\psi$ decays collected by the BESIII detector. The goal of this analysis is to provide a description of the scalar and tensor components of the $\pi^0\pi^0$ system while making minimal assumptions about the properties or number of poles in the amplitude. Such a model-independent description allows one to integrate these results with other related results from complementary reactions in the development of phenomenological models, which can then be used to directly fit experimental data to obtain parameters of interest. The branching fraction of $J/\psi \to \gamma \pi^{0}\pi^{0}$ is determined to be $(1.15\pm0.05)\times10^{-3}$, where the uncertainty is systematic only and the statistical uncertainty is negligible.Comment: Submitted to Phys. Rev. D 19 pages, 4 figure

An allosteric role for receptor activity-modifying proteins in defining GPCR pharmacology

G protein-coupled receptors are allosteric proteins that control transmission of external signals to regulate cellular response. Although agonist binding promotes canonical G protein signalling transmitted through conformational changes, G protein-coupled receptors also interact with other proteins. These include other G protein-coupled receptors, other receptors and channels, regulatory proteins and receptor-modifying proteins, notably receptor activity-modifying proteins (RAMPs). RAMPs have at least 11 G protein-coupled receptor partners, including many class B G protein-coupled receptors. Prototypic is the calcitonin receptor, with altered ligand specificity when co-expressed with RAMPs. To gain molecular insight into the consequences of this protein–protein interaction, we combined molecular modelling with mutagenesis of the calcitonin receptor extracellular domain, assessed in ligand binding and functional assays. Although some calcitonin receptor residues are universally important for peptide interactions (calcitonin, amylin and calcitonin gene-related peptide) in calcitonin receptor alone or with receptor activity-modifying protein, others have RAMP-dependent effects, whereby mutations decreased amylin/calcitonin gene-related peptide potency substantially only when RAMP was present. Remarkably, the key residues were completely conserved between calcitonin receptor and AMY receptors, and between subtypes of AMY receptor that have different ligand preferences. Mutations at the interface between calcitonin receptor and RAMP affected ligand pharmacology in a RAMP-dependent manner, suggesting that RAMP may allosterically influence the calcitonin receptor conformation. Supporting this, molecular dynamics simulations suggested that the calcitonin receptor extracellular N-terminal domain is more flexible in the presence of receptor activity-modifying protein 1. Thus, RAMPs may act in an allosteric manner to generate a spectrum of unique calcitonin receptor conformational states, explaining the pharmacological preferences of calcitonin receptor-RAMP complexes. This provides novel insight into our understanding of G protein-coupled receptor-protein interaction that is likely broadly applicable for this receptor class

Energy compensation following consumption of sugar-reduced products: a randomized controlled trial

PURPOSE: Consumption of sugar-reformulated products (commercially available foods and beverages that have been reduced in sugar content through reformulation) is a potential strategy for lowering sugar intake at a population level. The impact of sugar-reformulated products on body weight, energy balance (EB) dynamics and cardiovascular disease risk indicators has yet to be established. The REFORMulated foods (REFORM) study examined the impact of an 8-week sugar-reformulated product exchange on body weight, EB dynamics, blood pressure, arterial stiffness, glycemia and lipemia. METHODS: A randomized, controlled, double-blind, crossover dietary intervention study was performed with fifty healthy normal to overweight men and women (age 32.0 ± 9.8 year, BMI 23.5 ± 3.0 kg/m2) who were randomly assigned to consume either regular sugar or sugar-reduced foods and beverages for 8 weeks, separated by 4-week washout period. Body weight, energy intake (EI), energy expenditure and vascular markers were assessed at baseline and after both interventions. RESULTS: We found that carbohydrate (P < 0.001), total sugars (P < 0.001) and non-milk extrinsic sugars (P < 0.001) (% EI) were lower, whereas fat (P = 0.001) and protein (P = 0.038) intakes (% EI) were higher on the sugar-reduced than the regular diet. No effects on body weight, blood pressure, arterial stiffness, fasting glycemia or lipemia were observed. CONCLUSIONS: Consumption of sugar-reduced products, as part of a blinded dietary exchange for an 8-week period, resulted in a significant reduction in sugar intake. Body weight did not change significantly, which we propose was due to energy compensation

Smoking, Green Tea Consumption, Genetic Polymorphisms in the Insulin-Like Growth Factors and Lung Cancer Risk

Insulin-like growth factors (IGFs) are mediators of growth hormones; they have an influence on cell proliferation and differentiation. In addition, IGF-binding protein (IGFBP)-3 could suppress the mitogenic action of IGFs. Interestingly, tea polyphenols could substantially reduce IGF1 and increase IGFBP3. In this study, we evaluated the effects of smoking, green tea consumption, as well as IGF1, IGF2, and IGFBP3 polymorphisms, on lung cancer risk. Questionnaires were administered to obtain the subjects' characteristics, including smoking habits and green tea consumption from 170 primary lung cancer cases and 340 healthy controls. Genotypes for IGF1, IGF2, and IGFBP3 were identified by polymerase chain reaction. Lung cancer cases had a higher proportion of smoking, green tea consumption of less than one cup per day, exposure to cooking fumes, and family history of lung cancer than controls. After adjusting the confounding effect, an elevated risk was observed in smokers who never drank green tea, as compared to smokers who drank green tea more than one cup per day (odds ratio (OR) = 13.16, 95% confidence interval (CI) = 2.96–58.51). Interaction between smoking and green tea consumption on lung cancer risk was also observed. Among green tea drinkers who drank more than one cup per day, IGF1 (CA)19/(CA)19 and (CA)19/X genotypes carriers had a significantly reduced risk of lung cancer (OR = 0.06, 95% CI = 0.01–0.44) compared with IGF1 X/X carriers. Smoking-induced pulmonary carcinogenesis could be modulated by green tea consumption and their growth factor environment

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN