Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.     

Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks

A novel extraction protocol is described with which metabolites, proteins and RNA are sequentially extracted from the same sample, thereby providing a convenient procedure for the analysis of replicates as well as exploiting the inherent biological variation of independent samples for multivariate data analysis. A detection of 652 metabolites, 297 proteins and clear RNA bands in a single Arabidopsis thaliana leaf sample was validated by analysis with gas chromatography coupled to a time of flight mass spectrometer for metabolites, two-dimensional liquid chromatography coupled to mass spectrometry for proteins, and Northern blot analysis for RNA. A subset of the most abundant proteins and metabolites from replicate analysis of different Arabidopsis accessions was merged to form an integrative dataset allowing both classification of different genotypes and the unbiased analysis of the hierarchical organization of proteins and metabolites within a real biochemical network.     

Molecular network and functional implications of macromolecular tRNA synthetase complex

Understanding the complex network and multi-functionality of proteins is one of the main objectives of post-genome research. Aminoacyl-tRNA synthetases (ARSs) are the family of enzymes that are essential for cellular protein synthesis and viability that catalyze the attachment of specific amino acids to their cognate tRNAs. However, a lot of evidence has shown that these enzymes are multi-functional proteins that are involved in diverse cellular processes, such as tRNA processing, RNA splicing and trafficking, rRNA synthesis, apoptosis, angiogenesis, and inflammation. In addition, mammalian ARSs form a macromolecular complex with three auxiliary factors or with the elongation factor complex. Although the functional meaning and physiological significance of these complexes are poorly understood, recent data on the molecular interactions among the components for the multi-ARS complex are beginning to provide insights into the structural organization and cellular functions. In this review, the molecular mechanism for the assembly and functional implications of the multi-ARS complex will be discussed.     

Integration,visualization and analysis of human interactome

Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of ‘omics’ fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways.     

PONDR-FIT: A meta-predictor of intrinsically disordered amino acids

Protein intrinsic disorder is becoming increasingly recognized in proteomics research. While lacking structure, many regions of disorder have been associated with biological function. There are many different experimental methods for characterizing intrinsically disordered proteins and regions; nevertheless, the prediction of intrinsic disorder from amino acid sequence remains a useful strategy especially for many large-scale proteomic investigations. Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed by combining the outputs of several individual disorder predictors. By eight-fold cross-validation, this meta-predictor, called PONDR-FIT, was found to improve the prediction accuracy over a range of 3 to 20% with an average of 11% compared to the single predictors, depending on the datasets being used. Analysis of the errors shows that the worst accuracy still occurs for short disordered regions with less than ten residues, as well as for the residues close to order/disorder boundaries. Increased understanding of the underlying mechanism by which such meta-predictors give improved predictions will likely promote the further development of protein disorder predictors. Access to PONDR-FIT is available at www.disprot.org.     

神经网络提高肝细胞癌磁共振波谱诊断正确率

通过评价31磷磁共振波谱(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)来辨别三种诊断类型:肝细胞癌,正常肝和肝硬化。运用反向传输神经网络(BP)和径向基函数神经网络(RBF)分析31P-MRS数据,分别建立神经网络模型,进行肝细胞癌的诊断分类以期提高识别率。实验结果证明,应用神经网络模型后,31P-MR波谱对活体肝细胞癌的诊断正确率从89.47%提高到97.3%,且BP更优于RBF。     

vGNM: a better model for understanding the dynamics of proteins in crystals

The dynamics of proteins are important for understanding their functions. In recent years, the simple coarse-grained Gaussian Network Model (GNM) has been fairly successful in interpreting crystallographic B-factors. However, the model clearly ignores the contribution of the rigid body motions and the effect of crystal packing. The model cannot explain the fact that the same protein may have significantly different B-factors under different crystal packing conditions. In this work, we propose a new GNM, called vGNM, which takes into account both the contribution of the rigid body motions and the effect of crystal packing, by allowing the amplitude of the internal modes to be variables. It hypothesizes that the effect of crystal packing should cause some modes to be amplified and others to become less important. In doing so, vGNM is able to resolve the apparent discrepancy in experimental B-factors among structures of the same protein but with different crystal packing conditions, which GNM cannot explain. With a small number of parameters, vGNM is able to reproduce experimental B-factors for a large set of proteins with significantly better correlations (having a mean value of 0.81 as compared to 0.59 by GNM). The results of applying vGNM also show that the rigid body motions account for nearly 60% of the total fluctuations, in good agreement with previous findings.     

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models—for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used. Action Editor: Nicolas Brunel     

Response of Net Ecosystem Productivity of Three Boreal Forest Stands to Drought

In 2000–03, continuous eddy covariance measurements of carbon dioxide (CO₂) flux were made above mature boreal aspen, black spruce, and jack pine forests in Saskatchewan, Canada, prior to and during a 3-year drought. During the 1st drought year, ecosystem respiration (R) was reduced at the aspen site due to the drying of surface soil layers. Gross ecosystem photosynthesis (GEP) increased as a result of a warm spring and a slow decrease of deep soil moisture. These conditions resulted in the highest annual net ecosystem productivity (NEP) in the 9 years of flux measurements at this site. During 2002 and 2003, a reduction of 6% and 34% in NEP, respectively, compared to 2000 was observed as the result of reductions in both R and GEP, indicating a conservative response to the drought. Although the drought affected most of western Canada, there was considerable spatial variability in summer rainfall over the 100-km extent of the study area; summer rainfalls in 2001 and 2002 at the two conifer sites minimized the impact of the drought. In 2003, however, precipitation was similarly low at all three sites. Due to low topographic position and consequent poor drainage at the black spruce site and the coarse soil with low water-holding capacity at the jack pine site almost no reduction in R, GEP, and NEP was observed at these two sites. This study shows that the impact of drought on carbon sequestration by boreal forest ecosystems strongly depends on rainfall distribution, soil characteristics, topography, and the presence of vegetation that is well adapted to these conditions. The online version of the original article can be found under doi:     

Once-daily fluticasone furoate/vilanterol 100/25 mcg versus twice daily combination therapies in COPD – mixed treatment comparisons of clinical efficacy

Background

Fluticasone furoate (FF)/vilanterol (VI) 100/25 mcg is a once-daily inhaled corticosteroid (ICS)/long-acting beta₂ agonist (LABA) treatment approved in the United States, Canada and Europe for the long-term maintenance therapy of COPD. We report data from mixed treatment comparisons (MTC) of once-daily FF/VI against established twice-daily ICS/LABA combination therapies on clinical efficacy outcomes.

Methods

Data from 33 parallel-group randomised controlled trials (RCTs) of ICS/LABAs, of ≥8 weeks’ duration in patients ≥12 years of age with COPD, identified by systematic review, were analysed using covariate-adjusted Bayesian hierarchical models for three efficacy outcomes. Lung function, assessed by change from baseline in forced expiratory volume in one second (FEV₁), was the outcome of primary interest (n = 28 studies). Secondary objectives were assessment of annual rate of moderate/severe exacerbations (n = 15) and patient-reported health status, measured by change from baseline in St George’s Respiratory Questionnaire (SGRQ) Total score (n = 20). Overall, 25 different treatments were included in the MTC; we report findings, including probabilities of non-inferiority, for comparisons of once-daily FF/VI 100/25 mcg with twice-daily fluticasone propionate (FP)/salmeterol (SAL) 500/50 mcg and budesonide (BUD)/formoterol (FORM) 400/12 mcg.

Results

For FEV₁, FF/VI 100/25 mcg demonstrated >99% probability of non-inferiority to FP/SAL 500/50 mcg and BUD/FORM 400/12 mcg using a 50 mL margin. For annual rate of moderate/severe exacerbations, FF/VI 100/25 mcg demonstrated 73% and 77% probability of non-inferiority to FP/SAL 500/50 mcg and BUD/FORM 400/12 mcg, respectively, using a 10% rate ratio margin. For SGRQ Total score, the corresponding probabilities of non-inferiority were 99% and 98%, respectively, on a 2-unit margin. Significant covariate effects were identified: increased age was associated with deterioration in FEV₁ and reduced exacerbation frequency; shorter study duration was associated with reduced exacerbation frequency.

Conclusions

FF/VI 100/25 mcg was comparable with corresponding doses of FP/SAL and BUD/FORM on lung function and health status outcomes. Non-inferiority on moderate/severe exacerbation rate was not demonstrated to the same degree of confidence, though observed rates were similar. Model limitations include a weak treatment network for the exacerbation analysis and variability across the included studies. Our data support previous RCT findings suggesting that the efficacy of FF/VI 100/25 mcg on lung function and health status in COPD is comparable with twice-daily ICS/LABAs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-015-0184-8) contains supplementary material, which is available to authorized users.