BENviewer: a gene interaction network visualization server based on graph embedding model

BENviewer is a brand-new online gene interaction network visualization server based on graph embedding models. With mature graph embedding algorithms applied on several interaction network databases, it provides human-friendly 2D visualization based on more than 2000 biological pathways, and these results present not only genes involved but also tightness of interactions in an intuitive way. As a unique visualization server introducing graph embedding application for the first time, it is expected to help researchers gain deeper insights into biological networks beyond generating results explainable by existing knowledge. Additionally, operation flow for users is simplified to greater extent in its current version; meanwhile URL optimization contributes to data acquisition in batch for further analysis. BENviewer is freely available at http://www.bmeonline.cn/BENviewer, besides it is open-sourced at https://github.com/SKLB-lab/BENviewer, http://benviewer.bmeonline.cn.

Systematic Protein Level Regulation via Degradation Machinery Induced by Genotoxic Drugs

In this study we monitored protein dynamics in response to cisplatin, 5-fluorouracil, and irinotecan with different concentrations and administration modes using "reverse-phase" protein arrays (RPPAs) in order to gain comprehensive insight into the protein dynamics induced by genotoxic drugs. Among 666 protein time-courses, 38% exhibited an increasing trend, 32% exhibited a steady decrease, and 30% fluctuated within 24 h after drug exposure. We analyzed almost 12,000 time-course pairs of protein levels based on the geometrical similarity by correlation distance (dCor). Twenty-two percent of the pairs showed dCor > 0.8, which indicates that each protein of the pair had similar dynamics. These trends were disrupted by a proteasome inhibitor, MG132, suggesting that the protein degradation system was activated in response to the drugs. Among the pairs with high dCor, the average dCor of pairs with apoptosis-related protein was significantly higher than those without, indicating that regulation of protein levels was induced by the drugs. These results suggest that the levels of numerous functionally distinct proteins may be regulated by common degradation machinery induced by genotoxic drugs.

Myosin‑II heavy chain and formin mediate the targeting of myosin essential light chain to the division site before and during cytokinesis

MLC1 is a haploinsufficient gene encoding the essential light chain for Myo1, the sole myosin‑II heavy chain in the budding yeast Saccharomyces cerevisiae. Mlc1 defines an essential hub that coordinates actomyosin ring function, membrane trafficking, and septum formation during cytokinesis by binding to IQGAP, myosin‑II, and myosin‑V. However, the mechanism of how Mlc1 is targeted to the division site during the cell cycle remains unsolved. By constructing a GFP‑tagged MLC1 under its own promoter control and using quantitative live‑cell imaging coupled with yeast mutants, we found that septin ring and actin filaments mediate the targeting of Mlc1 to the division site before and during cytokinesis, respectively. Both mechanisms contribute to and are collectively required for the accumulation of Mlc1 at the division site during cytokinesis. We also found that Myo1 plays a major role in the septin‑dependent Mlc1 localization before cytokinesis, whereas the formin Bni1 plays a major role in the actin filament-dependent Mlc1 localization during cytokinesis. Such a two‑tiered mechanism for Mlc1 localization is presumably required for the ordered assembly and robustness of cytokinesis machinery and is likely conserved across species.

Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs

Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non-activated STAT5 proteins. Our theoretical prediction based on results obtained with MONETA was validated for KIT by in vitro experiments. MONETA is a versatile analytical and visualization tool entirely devoted to the understanding of the functioning/malfunctioning of allosteric regulation in proteins - a crucial basis to guide the discovery of next-generation allosteric drugs.

Dynamic hubs show competitive and static hubs non-competitive regulation of their interaction partners

Date hub proteins have 1 or 2 interaction interfaces but many interaction partners. This raises the question of whether all partner proteins compete for the interaction interface of the hub or if the cell carefully regulates aspects of this process? Here, we have used real-time rendering of protein interaction networks to analyse the interactions of all the 1 or 2 interface hubs of Saccharomyces cerevisiae during the cell cycle. By integrating previously determined structural and gene expression data, and visually hiding the nodes (proteins) and their edges (interactions) during their troughs of expression, we predict when interactions of hubs and their partners are likely to exist. This revealed that 20 out of all 36 one- or two- interface hubs in the yeast interactome fell within two main groups. The first was dynamic hubs with static partners, which can be considered as ‘competitive hubs’. Their interaction partners will compete for the interaction interface of the hub and the success of any interaction will be dictated by the kinetics of interaction (abundance and affinity) and subcellular localisation. The second was static hubs with dynamic partners, which we term ‘non-competitive hubs’. Regulatory mechanisms are finely tuned to lessen the presence and/or effects of competition between the interaction partners of the hub. It is possible that these regulatory processes may also be used by the cell for the regulation of other, non-cell cycle processes.

A multidimensional matrix for systems biology research and its application to interaction networks

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.