Systematic Deciphering of Cancer Genome Networks

When growth regulatory genes are damaged in a cell, it may become cancerous. Current technological advances in the last decade have allowed the characterization of the whole genome of these cells by directly or indirectly measuring DNA changes. Complementary analyses were developed to make sense of the massive amounts of data generated. A large majority of these analyses were developed to construct interaction networks between genes from, primarily, expression array data. We review the current technologies and analyses that have developed in the last decade. We further argue that as cancer genomics evolves from single gene validations to gene network inferences, new analyses must be developed for the different technological platforms.     

Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradients is subject to the interplay of biotic interactions in complement to abiotic filtering and stochastic forces. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose using food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant–herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve species distribution and community forecasts. The trophic interaction network between butterfly larvae and host plant was phylogenetically structured and driven by host plant nitrogen content allowing forecasting the food web model to unknown interactions links. This combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may occur. Our combined approach points toward a promising direction for modeling the spatial variation in entire species interaction networks.     

Motor usage imprints microtubule stability along the shaft

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A regulatory gene network related to the porcine umami taste receptor (TAS1R1/TAS1R3)

Taste perception plays an important role in the mediation of food choices in mammals. The first porcine taste receptor genes identified, sequenced and characterized, TAS1R1 and TAS1R3, were related to the dimeric receptor for umami taste. However, little is known about their regulatory network. The objective of this study was to unfold the genetic network involved in porcine umami taste perception. We performed a meta‐analysis of 20 gene expression studies spanning 480 porcine microarray chips and screened 328 taste‐related genes by selective mining steps among the available 12 320 genes. A porcine umami taste‐specific regulatory network was constructed based on the normalized coexpression data of the 328 genes across 27 tissues. From the network, we revealed the ‘taste module’ and identified a coexpression cluster for the umami taste according to the first connector with the TAS1R1/TAS1R3 genes. Our findings identify several taste‐related regulatory genes and extend previous genetic background of porcine umami taste.     

Prediction of ground reaction forces during gait based on kinematics and a neural network model

Kinetic information during human gait can be estimated with inverse dynamics, which is based on anthropometric, kinematic, and ground reaction data. While collecting ground reaction data with a force plate is useful, it is costly and requires regulated space. The goal of this study was to propose a new, accurate methodology for predicting ground reaction forces (GRFs) during level walking without the help of a force plate. To predict GRFs without a force plate, the traditional method of Newtonian mechanics was used for the single support phase. In addition, an artificial neural network (ANN) model was applied for the double support phase to solve statically indeterminate structure problems. The input variables of the ANN model, which were selected to have both dependency and independency, were limited to the trajectory, velocity, and acceleration of the whole segment's mass centre to minimise errors. The predicted GRFs were validated with actual GRFs through a ten-fold cross-validation method, and the correlation coefficients (R) for the ground forces were 0.918 in the medial–lateral axis, 0.985 in the anterior–posterior axis, and 0.991 in the vertical axis during gait. The ground moments were 0.987 in the sagittal plane, 0.841 in the frontal plane, and 0.868 in the transverse plane during gait. The high correlation coefficients(R) are due to the improvement of the prediction rate in the double support phase. This study also proved the possibility of calculating joint forces and moments based on the GRFs predicted with the proposed new hybrid method. Data generated with the proposed method may thus be used instead of raw GRF data in gait analysis and in calculating joint dynamic data using inverse dynamics.     

Exploring the Potential Mechanism of Guchang Zhixie Wan for Treating Ulcerative Colitis by Comprehensive Network Pharmacological Approaches and Molecular Docking Validation as Well as Cell Experiments

Guchang Zhixie Wan (GZW) is a commonly used Chinese medicine for the treatment of ulcerative colitis (UC). This research explored the potential pharmacological mechanism of GZW in UC. The active ingredients, potential targets, and UC-related genes of GZW were retrieved from public databases. The pharmacological mechanisms including key components, potential targets and signal pathways were determined through bioinformatics analysis. The results of this study were verified through virtual molecular docking and cell experiments. Network analysis revealed that 26 active GZW compounds and 148 potential GZW target proteins were associated with UC. Quercetin, kaempferol and β-sitosterol were identified as the core active ingredients of GZW. IFNG, IL-1A, IL-1B, JUN, RELA, and STAT1 were indicated as key targets of GZW. These key targets have a strong affinity for quercetin, kaempferol, and β-sitosterol. GO and KEGG enrichment analysis showed that GZW target proteins are highly enriched in inflammatory, immune, and oxidative stress-related pathways. This study confirmed the therapeutic effect and revealed potential molecular mechanism of GZW on UC. And the protective effects of GZW on inflammatory bowel disease pathway were also revealed through STAT3/NF-κB/IL-6 pathway. The findings of this study enhanced our understanding of GZW in the treatment of UC and provided a feasible method for discovering potential drugs from traditional Chinese medicine formulations.