GIANT: a cytoscape plugin for modular networks

Pilot-scale study of innovative mechanically-enhanced dynamic composting for treating kitchen waste

This study introduced a novel mechanically-enhanced dynamic composting (MEDC) method for treating kitchen waste (KW) through partial-mixing and stratified fermentation. A pilot test varied aeration frequencies (AF) to refine control parameters and explore the maturation mechanism. Results showed that a moderate AF (10 min/4 h) achieved optimal efficiency, with a compost germination index of 123 % within 15 d. Moderate AF enhanced the growth of Corynebacterium_1 (25.4 %) and Saccharomonospora (10.5 %) during the low-temperature stage and Bacillus growth (91.3 %) during the maturation stage. Moreover, it enhanced microbial interactions (with an average degree of 19.9) and promoted substrate degradation and transformation, expediting heating and maturation. Multivariate dimensionality reduction analysis showed the MEDC accomplished rapid composting through stratified composting, dividing the reactor into distinct functional zones: feeding, low-temperature, high-temperature, and maturation. This enabled efficient microorganism enrichment and material degradation, expediting KW decomposition and maturation. This study offers a promising alternative for accelerated KW composting.

Colonization characteristics of fungi in Polygonum hydropipe L. and Polygonum lapathifolium L. and its effect on the content of active ingredients

Polygonum hydropiper, is a plant of the Persicaria genus, which is commonly used to treat various diseases, including gastrointestinal disorders, neurological disorders, inflammation, and diarrhea. However, because of different local standards of P. hydropiper, people often confuse it with Polygonum lapathifolium L. and other closely related plants. This poses a serious threat to the safety and efficacy of the clinical use of P. hydropiper. This study aims to determine the six active ingredients of P. hydropiper and P. lapathifolium. Then the endophytic fungi and rhizosphere soil of the two species were sequenced by Illumina Miseq PE300. The results show significant differences between the community composition of the leaves, stems, and roots of the P. hydropiper and the P. lapathifolium in the same soil environment. Of the six secondary metabolites detected, five had significant differences between P. hydropiper and P. lapathifolium. Then, we evaluated the composition of the significantly different communities between P. hydropiper and P. lapathifolium. In the P. hydropiper, the relative abundance of differential communities in the leaves was highest, of which Cercospora dominated the differential communities in the leaves and stem; in the P. lapathifolium, the relative abundance of differential community in the stem was highest, and Cladosporium dominated the differential communities in the three compartments. By constructing the interaction network of P. hydropiper and P. lapathifolium and analyzing the network nodes, we found that the core community in P. hydropiper accounted for 87.59% of the total community, dominated by Cercospora; the core community of P. lapathifolium accounted for 19.81% of the total community, dominated by Sarocladium. Of these core communities, 23 were significantly associated with active ingredient content. Therefore, we believe that the community from Cercospora significantly interferes with recruiting fungal communities in P. hydropiper and affects the accumulation of secondary metabolites in the host plant. These results provide an essential foundation for the large-scale production of P. hydropiper. They indicate that by colonizing specific fungal communities, secondary metabolic characteristics of host plants can be helped to be shaped, which is an essential means for developing new medicinal plants.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike-ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike-ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

One for All, All for One: The Peculiar Dynamics of TNF-Receptor-Associated Factor (TRAF2) Subunits

TNF Receptor-Associated Factor 2 (TRAF2) is a homo-trimer belonging to the TNF-receptor-associated factor family (TRAFs). The TRAF2 oligomeric state is crucial for receptor binding, the interaction with other proteins (involved in the TNFR signaling), and the interaction with biological membranes. In this study, we present a computational analysis of the Molecular Dynamics of TRAF2-C (a truncated and soluble TRAF2 form) to identify patterns in the interactions between the three chains. We have performed a canonical analysis of the motion applied to molecular dynamics starting from the available crystal structure to identify correlated motions in TRAF2 dynamics. We have computed the displacement matrix, providing a frame-by-frame displacement for each residue in the dynamic. We provide the results in terms of the correlation matrix, which represents a detailed map of the correlated motions of residues. Eventually, we computed the so-called dynamical clusters, based on the Principal Component Analysis (PCA) of the motion (displacement) and the k means application on the first two principal components space. The results clearly indicate that, most of the time, two chains move in a strongly correlated motion, while the third chain follows a freer motion. A detailed analysis of the correlation matrix also shows that a few specific interface residues characterize the interaction of the more independent subunit with the other two. These findings suggest that the equilibrium between the trimer and the dissociated species (dimers and monomers) might be finely tuned by controlling a few critical residues in the protein quaternary structure, probably facilitating the regulation of oligomerization and dissociation in vivo.

Earthworms reduce the dissemination potential of antibiotic resistance genes by changing bacterial co-occurrence patterns in soil

Globally distributed earthworms affect compositions of soil compounds, microbial community structures, as well as antibiotic resistance genes (ARGs). Compared to their surroundings, earthworm gut is a simpler environment which could filter out microbes when soil passes through it. However, little is known about how earthworms affect the dissemination of ARGs in soil, and the understanding of the relationship between microbe-microbe interactions and ARGs is still lacking. Here, we designed a microcosm experiment with earthworm addition, and determined bacterial and fungal community compositions based on amplicon sequencing. We also examined mobile genetic elements (MGEs) and ARGs in earthworm gut and soils using high-throughput qPCR. The results showed significant differences of bacterial, fungal and ARG patterns between gut and soil. Earthworms indirectly impacted the patterns of ARGs in soils by affecting bacterial communities and soil properties, which play key roles in the distribution of ARGs and MGEs. The absolute abundances of MGEs in earthworm gut were significantly lower than those in soils, and earthworms reduce the absolute abundance of MGEs in soils. Earthworms changed the microbial co-occurrence patterns, and reduced bacterial connectivity, which were significantly and positively correlated with MGE abundance. These results highlight the importance of earthworm on the distribution and dissemination of ARGs in soils.

3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

MOTIVATION

The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders.

RESULTS

Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies.

AVAILABILITYAND IMPLEMENTATION

3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protistan-Bacterial Microbiota Exhibit Stronger Species Sorting and Greater Network Connectivity Offshore than Nearshore across a Coast-to-Basin Continuum

Little is known regarding how community assembly and species association vary with habitat and depth. Here, we examined the assembly and association of protistan and bacterial communities across a coast-shelf-slope-basin gradient of the South China Sea using high-throughput sequencing of the V3 and V4 regions of the rRNA gene transcript. Our study revealed that homogenizing dispersal and drift exerted an influence on protistan communities comparable to that on bacterial communities. In contrast, selection and dispersal limitation exerted contrasting effects on the two microbial communities. Community assembly was governed to a greater degree by selection than by dispersal limitation in the bacterial community, and this was much lower in the protistan community. Moreover, this organismal assembly pattern was robust with habitat and depth. However, the relative importance of selection to dispersal limitation varied with habitat and depth in both communities, where horizontally it was higher offshore than nearshore and vertically it was lower in the bottom or deep chlorophyll maximum (DCM) than on the surface. The offshore possessed more microbial network complexity and more associations among microbial taxa than the nearshore, and vertically, the bottom possessed more complexity than the surface and the DCM. Moreover, temperature is strongly associated with the composition and co-occurrence of microbial communities, implying that temperature plays a dominant role in the selection of the protistan-bacterial microbiome across a coast-to-basin continuum. This study contributes to our understanding of the assembly mechanism and species association of protistan-bacterial microbiota across multiple habitats and depths.

IMPORTANCE Microbial organisms play a crucial role in global nutrient cycling. Few studies have attempted to simultaneously investigate the community assembly of microeukaryotes and prokaryotes and their association patterns in oceanic waters. This is especially true regarding how they vary with habitats and depths despite the fact that they are essential for developing a more holistic understanding of marine ecosystems. This study revealed the differential actions of selection and dispersal limitation and species association across a coast-to-basin continuum on the marine protistan-bacterial microbiome. Moreover, temperature was identified as a crucial factor driving the structure and co-occurrence of protistan and bacterial communities. The results emphasize that the differences in community assembly and association patterns between nearshore and offshore of the main constituents of the ocean microbiota should be considered to understand their current and future configurations. This is especially crucial in the context of climate change, as the response of ocean microbiota to nearshore and offshore temperature changes remains unknown.

The comparison of microbial communities in thyroid tissues from thyroid carcinoma patients

Thyroid carcinoma is a common endocrine organ cancer associated with abnormal hormone secretion, leading to the disorder of metabolism. The intestinal microbiota is vital to maintain digestive and immunologic homeostasis. The relevant information of the microbial community in the gut and thyroid, including composition, structure, and relationship, is unclear in thyroid carcinoma patients. A total of 93 samples from 25 patients were included in this study. The results showed that microbial communities existed in thyroid tissue; gut and thyroid had high abundance of facultative anaerobes from the Proteobacteria phyla. The microbial metabolism from the thyroid and gut may be affected by the thyroid carcinoma cells. The cooccurrence network showed that the margins of different thyroid tissues were unique areas with more competition; the stabilization of microcommunities from tissue and stool may be maintained by several clusters of species that may execute different vital metabolism processes dominantly that are attributed to the microenvironment of cancer.

Integrated Biophysical Modeling of the SARS-CoV-2 Spike Protein Binding and Allosteric Interactions with Antibodies

Structural and biochemical studies of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoproteins and complexes with highly potent antibodies have revealed multiple conformation-dependent epitopes highlighting conformational plasticity of spike proteins and capacity for eliciting specific binding and broad neutralization responses. In this study, we used coevolutionary analysis, molecular simulations, and perturbation-based hierarchical network modeling of the SARS-CoV-2 spike protein complexes with a panel of antibodies targeting distinct epitopes to explore molecular mechanisms underlying binding-induced modulation of dynamics and allosteric signaling in the spike proteins. Through coevolutionary analysis of the SARS-CoV-2 spike proteins, we identified highly coevolving hotspots and functional clusters that enable a functional cross-talk between distant allosteric regions in the SARS-CoV-2 spike complexes with antibodies. Coarse-grained and all-atom molecular dynamics simulations combined with mutational sensitivity mapping and perturbation-based profiling of the SARS-CoV-2 receptor-binding domain (RBD) complexes with CR3022 and CB6 antibodies enabled a detailed validation of the proposed approach and an extensive quantitative comparison with the experimental structural and deep mutagenesis scanning data. By combining in silico mutational scanning, perturbation-based modeling, and network analysis of the SARS-CoV-2 spike trimer complexes with H014, S309, S2M11, and S2E12 antibodies, we demonstrated that antibodies can incur specific and functionally relevant changes by modulating allosteric propensities and collective dynamics of the SARS-CoV-2 spike proteins. The results provide a novel insight into regulatory mechanisms of SARS-CoV-2 S proteins showing that antibody-escaping mutations can preferentially target structurally adaptable energy hotspots and allosteric effector centers that control functional movements and allosteric communication in the complexes.

Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches

The rapidly growing body of structural and biochemical studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examined molecular mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic analysis of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained molecular simulations, protein stability analysis, and perturbation-based modeling of residue interaction networks, we examined how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the experimentally confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and determine topography of signal communication pathways operating through state-specific cascades of control switch points. This analysis provides a plausible strategy for allosteric probing of the conformational equilibrium and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.

Disclosing Allostery Through Protein Contact Networks

Proteins are located in the twilight zone between chemistry and biology, where a peculiar kind of complexity starts. Proteins are the smallest 'devices' showing a sensible adaptation to their environment by the production of appropriate behavior when facing a specific stimulus. This fact qualifies (from the 'effector' side) proteins as nanomachines working as catalysts, motors, or switches. However (from the sensor side), the need to single out the 'specific stimulus' out of thermal noise qualifies proteins as information processing devices. Allostery corresponds to the modification of the configuration (in a broad sense) of the protein molecule in response to a specific stimulus in a non-strictly local way, thereby connecting the sensor and effector sides of the nanomachine. This is why the 'disclosing' of allostery phenomenon is at the very heart of protein function; in this chapter, we will demonstrate how a network-based representation of protein structure in terms of nodes (aminoacid residues) and edges (effective contacts between residues) is the natural language for getting rid of allosteric phenomena and, more in general, of protein structure/function relationships.

Contrasting Patterns of the Bacterial Communities in Melting Ponds and Periglacial Rivers of the Zhuxi glacier in the Tibet Plateau

Since the early 21^st century, global climate change has been inducing rapid glacier retreat at an unprecedented rate. In this context, the melt ponds impart increasing unique footprints on the periglacial rivers due to their hydrodynamic connection. Given that bacterial communities control numerous ecosystem processes in the glacial ecosystem, exploring the fate of bacterial communities from melt ponds to periglacial rivers yields key knowledge of the biodiversity and biogeochemistry of glacial ecosystems. Here, we analyzed the bacterial community structure, diversity, and co-occurrence network to reveal the community organization in the Zhuxi glacier in the Tibet Plateau. The results showed that the bacterial communities in melt ponds were significantly lower in alpha-diversity but were significantly higher in beta-diversity than those in periglacial rivers. The rare sub-communities significantly contributed to the stability of the bacterial communities in both habitats. The co-occurrence network inferred that the mutually beneficial relationships predominated in the two networks. Nevertheless, the lower ratio of positive to negative edges in melt ponds than periglacial rivers implicated fiercer competition in the former habitat. Based on the significantly higher value of degree, betweenness, and modules, as well as shorter average path length in melt ponds, we speculated that their bacterial communities are less resilient than those of periglacial rivers.

A Small RNA-Mediated Regulatory Network in Arabidopsis thaliana Demonstrates Connectivity Between phasiRNA Regulatory Modules and Extensive Co-Regulation of Transcription by miRNAs and phasiRNAs

Gene regulation involves the orchestrated action of multiple regulators to fine-tune the expression of genes. Hierarchical interactions and co-regulation among regulators are commonly observed in biological systems, leading to complex regulatory networks. Small RNA (sRNAs) have been shown to be important regulators of gene expression due to their involvement in multiple cellular processes. In plants, microRNA (miRNAs) and phased small interfering RNAs (phasiRNAs) correspond to two well-characterized types of sRNAs involved in the regulation of posttranscriptional gene expression, although information about their targets and interactions with other gene expression regulators is limited. We describe an extended sRNA-mediated regulatory network in Arabidopsis thaliana that provides a reference frame to understand sRNA biogenesis and activity at the genome-wide level. This regulatory network combines a comprehensive evaluation of phasiRNA production and sRNA targets supported by degradome data. The network includes ~17% of genes in the A. thaliana genome, representing ~50% annotated gene ontology (GO) functional categories. Approximately 14% of genes with GO annotations corresponding to regulation of gene expression were found to be under sRNA control. The unbiased bioinformatic approach used to produce the network was able to detect 107 PHAS loci (regions of phasiRNA production), 5,047 active phasiRNAs (~70% of which were non-canonical), and reconstruct 17 regulatory modules resulting from complex regulatory interactions between different sRNA-regulatory pathways. Known regulatory modules like miR173-TAS-PPR/TPR and miR390-TAS3-ARF/F-box were faithfully reconstructed and expanded, illustrating the accuracy and sensitivity of the methods and providing confidence for the validity of findings of previously unrecognized modules. The network presented here includes a 2X increase in the number of identified PHAS loci, a large complement (~70%) of non-canonical phasiRNAs, and the most comprehensive evaluation of sRNA cleavage activity in A. thaliana to date. Structural analysis showed similarities to networks of other biological systems and demonstrated connectivity between phasiRNA regulatory modules with extensive co-regulation of transcripts by miRNAs and phasiRNAs. The described regulatory network provides a reference that will facilitate global analyses of individual plant regulatory programs such as those that control homeostasis, development, and responses to biotic and abiotic environmental changes.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

MORO: a Cytoscape app for relationship analysis between modularity and robustness in large-scale biological networks

BACKGROUND

Although there have been many studies revealing that dynamic robustness of a biological network is related to its modularity characteristics, no proper tool exists to investigate the relation between network dynamics and modularity.

RESULTS

Accordingly, we developed a novel Cytoscape app called MORO, which can conveniently analyze the relationship between network modularity and robustness. We employed an existing algorithm to analyze the modularity of directed graphs and a Boolean network model for robustness calculation. In particular, to ensure the robustness algorithm's applicability to large-scale networks, we implemented it as a parallel algorithm by using the OpenCL library. A batch-mode simulation function was also developed to verify whether an observed relationship between modularity and robustness is conserved in a large set of randomly structured networks. The app provides various visualization modes to better elucidate topological relations between modules, and tabular results of centrality and gene ontology enrichment analyses of modules. We tested the proposed app to analyze large signaling networks and showed an interesting relationship between network modularity and robustness.

CONCLUSIONS

Our app can be a promising tool which efficiently analyzes the relationship between modularity and robustness in large signaling networks.

GH32 family activity: a topological approach through protein contact networks

The application of Protein Contact Networks methodology allowed to highlight a novel response of border region between the two domains to substrate binding. Glycoside hydrolases (GH) are enzymes that mainly hydrolyze the glycosidic bond between two carbohydrates or a carbohydrate and a non-carbohydrate moiety. These enzymes are involved in many fundamental and diverse biological processes in plants. We have focused on the GH32 family, including enzymes very similar in both sequence and structure, each having however clear specificities of substrate preferences and kinetic properties. Structural and topological differences among proteins of the GH32 family have been here identified by means of an emerging approach (Protein Contact network, PCN) based on the formalization of 3D structures as contact networks among amino-acid residues. The PCN approach proved successful in both reconstructing the already known functional domains and in identifying the structural counterpart of the properties of GH32 enzymes, which remain uncertain, like their allosteric character. The main outcome of the study was the discovery of the activation upon binding of the border (cleft) region between the two domains. This reveals the allosteric nature of the enzymatic activity for all the analyzed forms in the GH32 family, a character yet to be highlighted in biochemical studies. Furthermore, we have been able to recognize a topological signature (graph energy) of the different affinity of the enzymes towards small and large substrates.

GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research

Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface.

Organization principles of biological networks: An explorative study

The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure-function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models. A clear link between topological structure and biological function did emerge in terms of class membership prediction (average 67% of correct predictions, p<0.0001) with a varying degree of 'peculiarity' across classes going from a very low (25%) recognition efficiency for neural and brain networks to the extremely high (80%) peculiarity of amino acid-amino acid interaction (AAI) networks. We recognized four main dimensions (principal components) as main organization principles of biological networks. These components allowed for an efficient description of network architectures and for the identification of 'not-physiological' (in this case cancer metabolic networks acting as test set) wiring patterns. We highlighted as well the need of developing new theoretical generative models for biological networks overcoming the limitations of present mathematical graph idealizations.

Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation

Spatial memory depends on the hippocampus, which is particularly vulnerable to aging. This vulnerability has implications for the impairment of navigation capacities in older people, who may show a marked drop in performance of spatial tasks with advancing age. Contemporary understanding of long-term memory formation relies on molecular mechanisms underlying long-term synaptic plasticity. With memory acquisition, activity-dependent changes occurring in synapses initiate multiple signal transduction pathways enhancing protein turnover. This enhancement facilitates de novo synthesis of plasticity related proteins, crucial factors for establishing persistent long-term synaptic plasticity and forming memory engrams. Extensive studies have been performed to elucidate molecular mechanisms of memory traces formation; however, the identity of plasticity related proteins is still evasive. In this study, we investigated protein turnover in mouse hippocampus during long-term spatial memory formation using the reference memory version of radial arm maze (RAM) paradigm. We identified 1592 proteins, which exhibited a complex picture of expression changes during spatial memory formation. Variable linear decomposition reduced significantly data dimensionality and enriched three principal factors responsible for variance of memory-related protein levels at (1) the initial phase of memory acquisition (165 proteins), (2) during the steep learning improvement (148 proteins), and (3) the final phase of the learning curve (123 proteins). Gene ontology and signaling pathways analysis revealed a clear correlation between memory improvement and learning phase-curbed expression profiles of proteins belonging to specific functional categories. We found differential enrichment of (1) neurotrophic factors signaling pathways, proteins regulating synaptic transmission, and actin microfilament during the first day of the learning curve; (2) transcription and translation machinery, protein trafficking, enhancement of metabolic activity, and Wnt signaling pathway during the steep phase of memory formation; and (3) cytoskeleton organization proteins. Taken together, this study clearly demonstrates dynamic assembly and disassembly of protein-protein interaction networks depending on the stage of memory formation engrams.

Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach

Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein–protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node’s ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein–protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.

A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression

Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David’s Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg’s Hub Centrality and Bonacich’s Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive behavioral characterization of group-living animals with the utilization of novel statistical methods to further our understanding of the neurobiological basis of social behavior at the individual, relationship and group levels.