Pigmentation pattern formation in butterflies: experiments and models

Butterfly pigmentation patterns are one of the most spectacular and vivid examples of pattern formation in biology. They have attracted much attention from experimentalists and theoreticians, who have tried to understand the underlying genetic, chemical and physical processes that lead to patterning. In this paper, we present a brief review of this field by first considering the generation of the localised, eyespot, patterns and then the formation of more globally controlled patterns. We present some new results applied to pattern formation on the wing of the mimetic butterfly Papilio dardanus.     

Extensive differential protein phosphorylation as intraerythrocytic Plasmodium falciparum schizonts develop into extracellular invasive merozoites

Pathology of the most lethal form of malaria is caused by Plasmodium falciparum asexual blood stages and initiated by merozoite invasion of erythrocytes. We present a phosphoproteome analysis of extracellular merozoites revealing 1765 unique phosphorylation sites including 785 sites not previously detected in schizonts. All MS data have been deposited in the ProteomeXchange with identifier PXD001684 ( http://proteomecentral.proteomexchange.org/dataset/PXD001684 ). The observed differential phosphorylation between extra and intraerythrocytic life‐cycle stages was confirmed using both phospho‐site and phospho‐motif specific antibodies and is consistent with the core motif [K/R]xx[pS/pT] being highly represented in merozoite phosphoproteins. Comparative bioinformatic analyses highlighted protein sets and pathways with established roles in invasion. Within the merozoite phosphoprotein interaction network a subnetwork of 119 proteins with potential roles in cellular movement and invasion was identified and suggested that it is coregulated by a further small subnetwork of protein kinase A (PKA), two calcium‐dependent protein kinases (CDPKs), a phosphatidyl inositol kinase (PI3K), and a GCN2‐like elF2‐kinase with a predicted role in translational arrest and associated changes in the ubquitinome. To test this notion experimentally, we examined the overall ubiquitination level in intracellular schizonts versus extracellular merozoites and found it highly upregulated in merozoites. We propose that alterations in the phosphoproteome and ubiquitinome reflect a starvation‐induced translational arrest as intracellular schizonts transform into extracellular merozoites.     

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.     

Variation in macronuclear genome content of three ciliates with extensive chromosomal fragmentation: a preliminary analysis

The genome architecture of ciliates, including features such as nuclear dualism and large-scale genome rearrangements, impacts gene and genome evolution in these organisms. To better understand the structure of macronuclear chromosomes in ciliates with extensively processed chromosomes, a sample of complete macronuclear chromosomes was sequenced from three ciliate species: Metopus es (Class [Cl]: Armophorea), Nyctotherus ovalis (Cl: Armophorea), and Chilodonella uncinata (Cl: Phyllopharyngea). By cloning whole macronuclear chromosomes into a plasmid vector, we generated nine clones from each of M. es and C. uncinata, and 37 clones from N. ovalis. Analysis of these macronuclear chromosomes provides insight into the evolution of genome features such as chromosome content, gene structure, and genetic code. Phylogenetic patterns can be found in telomere structure and codon usage, which are both more similar in M. es and N. ovalis than C. uncinata. In addition, we provide evidence of lateral transfer of a bacterial endo-beta-mannanase gene onto a M. es chromosome and report the discovery of a 42-bp conserved sequence motif within N. ovalis untranslated regions.     

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: