Direct Interaction Network Research Articles

Deciphering the Role of Trehalose in Chroococcidiopsis sp. 029's High-Desiccation Resistance: Sequence Determination, Structural Modelling and Simulative Analysis of the 30S Ribosomal Subunit.

Desert strains of the genus Chroococcidiopsis are among the most desiccation-resistant cyanobacteria capable of anhydrobiosis. The accumulation of two sugars, sucrose and trehalose, facilitates the entrance of anhydrobiotes into a reversible state of dormancy by stabilizing cellular components upon water removal. This study aimed to evaluate, at the atomistic level, the role of trehalose in desiccation resistance by using as a model system the 30S ribosomal subunit of the desert cyanobacterium Chroococcidiopsis sp. 029. Molecular dynamic simulations provided atomistic evidence regarding its protective role on the 30S molecular structure. Trehalose forms an enveloping shell around the ribosomal subunit and stabilizes the structures through a network of direct interactions. The simulation confirmed that trehalose actively interacts with the 30S ribosomal subunit and that, by replacing water molecules, it ensures ribosomal structural integrity during desiccation, thus enabling protein synthesis to be carried out upon rehydration.

Disturbance-mediated changes to boreal mammal spatial networks in industrializing landscapes.

Compound effects of anthropogenic disturbances on wildlife emerge through a complex network of direct responses and species interactions. Land-use changes driven by energy and forestry industries are known to disrupt predator-prey dynamics in boreal ecosystems, yet how these disturbance effects propagate across mammal communities remains uncertain. Using structural equation modeling, we tested disturbance-mediated pathways governing the spatial structure of multipredator multiprey boreal mammal networks across a landscape-scale disturbance gradient within Canada's Athabasca oil sands region. Linear disturbances had pervasive direct effects, increasing site use for all focal species, except black bears and threatened caribou, in at least one landscape. Conversely, block (polygonal) disturbance effects were negative but less common. Indirect disturbance effects were widespread and mediated by caribou avoidance of wolves, tracking of primary prey by subordinate predators, and intraguild dependencies among predators and large prey. Context-dependent responses to linear disturbances were most common among prey and within the landscape with intermediate disturbance. Our research suggests that industrial disturbances directly affect a suite of boreal mammals by altering forage availability and movement, leading to indirect effects across a range of interacting predators and prey, including the keystone snowshoe hare. The complexity of network-level direct and indirect disturbance effects reinforces calls for increased investment in addressing habitat degradation as the root cause of threatened species declines and broader ecosystem change.

Skin proteomic screening and functional analysis of differential proteins associated with coat color in sheep (Ovis aries).

Coat color is an important characteristic and economic trait in domestic sheep. In this study, we explored the potential mechanisms and the signaling pathways involved in coat color regulation for sheep. Isobaric tags for relative and absolute quantification (iTRAQ) technology was used to catalog global protein expression profiles in skin of sheep with black versus white coat color. Immunofluorescence was used to observe the expression localization of differential protein. Western blot and quantitative real time polymerase chain reaction (qRT-PCR) were used to evaluate their role in the coat color formation of sheep. A total of 136 differential proteins were obtained in different coat colors, including 101 up-regulated and 35 down-regulated. Pigmentation function entries were enriched through gene ontology annotation. Tyrosine metabolism and platelet activation signaling pathway were extracted by Kyoto encyclopedia of genes and genomes analysis. Apolipoprotein A-1 (APOA1) and fibrinogen alpha chain (FGA) were found to be critical differential proteins by the interaction of differential proteins in the direct-interaction network diagram. Strikingly, twenty candidate differential proteins were screened, from which beta-actin (ACTB) protein showed higher expression in white sheep skin, while albumin (ALB), APOA1, MAOA (amine oxidase) and FGA proteins showed higher expression in black sheep skin, which was validated by immunofluorescence, western blot, and qRT-PCR. This study identified several novel proteins that may be involved in the coat color formation of sheep. The white and black sheep skin proteome profiles obtained provide a valuable resource for future research to understand the network of protein expression controlling skin physiology and melanogenesis in sheep.

Distributed Discrete-Time Convex Optimization With Closed Convex Set Constraints: Linearly Convergent Algorithm Design.

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

Quantitative Analysis of Urban Polycentric Interaction Using Nighttime Light Data: A Case Study of Shanghai, China

The urban polycentric structure is connected to the economy and enormously impacts socioeconomic development and policies. Unlike traffic data and big geographic data, remote sensing data have shown an accessible way to measure urban spatial interaction. However, most existing studies only focused on the interaction among cities rather than within cities. Meanwhile, the urban spatial interaction, which should be directional, was always expressed as an undirected graph. Therefore, this article developed a network-based radiation model using nighttime light remote sensing data and mapped a directed interaction network (inward and outward direction) among urban centers. Taking the region within the outer ring of Shanghai as an example, the taxi trajectory data were adopted to validate the result with the <i>R</i>² of 0.61. We discovered that: the urban polycentric interaction network is dumbbell-shaped with an east-west development corridor crossing the main center and connecting two main urban center clusters. The in-strength and out-strength interaction of each urban center have a similar distribution. The urban centers with higher in-strength and out-strength are mainly concentrated toward the main center, especially in the east-west direction. At the urban center level, the total inward interaction is slightly higher than the total outward interaction of most urban centers. Spatially, an unbalanced distribution was found. In summary, our proposed method effectively indicates the urban polycentric interaction and is applicable to other regions since it requires no arbitrary parameters and the input data (e.g., nighttime light data) is readily available.

Targeted and systemic insights into the crosstalk between DNA-dependent protein kinase catalytic subunit and receptors of estrogen, progesterone and epidermal growth factor in the context of cancer.

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) has emerged as a regulator of carcinogenesis. Increased expression of DNA-PKcs correlates with metastatic cancers. Here we review recently reported crosstalk of DNA-PKcs with estrogen (ER), progesterone (PR) and epidermal growth factor (EGFR) receptors. The reports show an extensive network of functional and direct interactions. Targeted studies focused on specific molecular mechanisms, and a systems biology network analysis shows unbiasedly engagement of various cellular functions. Feedforward regulation between expression and activities of DNA-PKcs and ER, DNA-PKcs-dependent phosphorylation of PR and an impact on PR-dependent transcription, and DNA-PKcs-promoted EGFR-dependent aggressiveness and metastases are examples of the results of targeted studies. Systems biology approach extracted many more genes and proteins engaged by DNA-PKcs in interaction with ER, PR, and EGFR. Examples are such regulators and predictors of breast tumorigenesis as BRCA1, TP53, and 18 genes of the MammaPrint signature. Reviewed here data suggest that the diagnostic value of DNA-PKcs in the context of ER, PR and EGFR signaling is defined by a network signature rather than by single markers. This review summarizes mechanisms of DNA-PKcs interaction with ER, PR, and EGFR, highlights tumor suppressors and oncogenes engaged by DNA-PKcs, and emphasizes the importance of diagnostic network-based signatures.

NetControl4BioMed: a web-based platform for controllability analysis of protein-protein interaction networks.

MotivationThere is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g. drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets.ResultsWe introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein–protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI and InnateDB, directed interaction data from InnateDB, Omnipath and SIGNOR, cell-line data from COLT and DepMap, and drug–target data from DrugBank.Availabilityand implementationThe application and data are available online at https://netcontrol.combio.org/. The source code is available at https://github.com/Vilksar/NetControl4BioMed under an MIT license.

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.

MotivationAccumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions.ResultsIn this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.Availability and implementationThe C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato.Supplementary information Supplementary data are available at Bioinformatics online.

Direct interaction network inference for compositional data via codaloss.

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss.

Direct protein-protein interaction network for insecticide resistance based on subcellular localization analysis in Drosophila melanogaster

In present study, we constructed the direct protein-protein interaction network of insecticide resistance based on subcellular localization analysis. Totally 177 of 528 resistance proteins were identified and they were located in 11 subcellular localizations. We further analyzed topological properties of the network and the biological characteristics of resistance proteins, such as k-core, neighborhood connectivity, instability index and aliphatic index. They can be used to predict the key proteins and potential mechanisms from macro-perspective. The problem of resistance has not been solved fundamentally, because the development of new insecticides can’t keep pace with the development speed of resistance, and the lack of understanding of molecular mechanism of resistance. As the further analysis to reduce data noise, we constructed the direct protein-protein interaction network of insecticide resistance based on subcellular localization analysis. The interaction between proteins located at the same subcellular location belongs to direct interactions, thus eliminating indirect interaction. Totally 177 of 528 resistance proteins were identified and they were located in 11 subcellular localizations. We further analyzed topological properties of the network and the biological characteristics of resistance proteins, such as k-core, neighborhood connectivity, instability index and aliphatic index. They can be used to predict the hub proteins and potential mechanisms from macro-perspective. This is the first study to explore the insecticide resistance molecular mechanism of Drosophila melanogaster based on subcellular localization analysis. It can provide the bioinformatics foundation for further understanding the mechanisms of insecticide resistance. It also provides a reference for the study of molecular mechanism of insecticide resistance of other insects.

Declining invasive grey squirrel populations may persist in refugia as native predator recovery reverses squirrel species replacement

Abstract Invasive species pose one of the most serious global threats to biodiversity. Investigations into the interactions of native and non‐native species focus on the impacts of single species, despite being embedded in a network of direct and indirect interactions between multiple species and their environments. We developed 1‐km2 resolution, single‐species and multi‐species occupancy models using quantitative camera trap data collected by citizen scientists at 332 sites in a regional survey comprising the 14,130 km2 of Northern Ireland. Recent research suggests that native red and invasive grey squirrels in Britain and Ireland are linked by resource and disease‐mediated competition, and by a shared enemy, the European pine marten. We demonstrate that the presence of the pine marten reverses red squirrel replacement by grey squirrels on a regional basis, with red squirrel occupancy positively affected by exposure to pine marten. In contrast, the grey squirrel has a strongly negative response to the presence of pine marten. Modelling habitat suitability of pine marten and both squirrel species suggests that, despite the potentially strong effect of a recovering population of pine martens in controlling grey squirrel distribution, the latter is likely to persist in urban refugia that are either inaccessible or avoided by the pine marten. Synthesis and applications. Sustainable recovery of both the red squirrel and the pine marten across the wider landscapes of Ireland, Scotland and Northern England seems probable, while in Southern England and Wales, although possible, chances are more remote. Increasing native woodland cover on a landscape scale to facilitate pine marten recovery may assist in realizing this scenario. Despite the ongoing recovery of the pine marten, and resultant declines in grey squirrels, isolated populations are likely to persist in urban areas. Human‐lead control of grey squirrel populations in urban refugia requires urgent funding and implementation to avoid the development of novel genotypes in grey squirrel source populations that enable the species to better avoid predation by the pine marten.

Magnitude and direction of stream-forest community interactions change with timescale.

Networks of direct and indirect biotic interactions underpin the complex dynamics and stability of ecological systems, yet experimental and theoretical studies often yield conflicting evidence regarding the direction (positive or negative) or magnitude of these interactions. We revisited pioneering data sets collected at the deciduous forested Horonai Stream and conducted ecosystem-level syntheses to demonstrate that the direction of direct and indirect interactions can change depending on the timescale of observation. Prior experimental studies showed that terrestrial prey that enter the stream from the adjacent forest caused positive indirect effects on aquatic invertebrates during summer by diverting fish consumption. Seasonal and annual estimates of secondary production and organic matter flows along food web pathways demonstrate that this seasonal input of terrestrial invertebrate prey increases production of certain fish species, reversing the indirect effect on aquatic invertebrates from positive at the seasonal timescale to negative at the annual timescale. Even though terrestrial invertebrate prey contributed 54% of the annual organic matter flux to fishes, primarily during summer, fish still consumed 98% of the aquatic invertebrate annual production, leading to top-down control that is not revealed in short-term experiments and demonstrating that aquatic prey may be a limiting resource for fishes. Changes in the direction or magnitude of interactions may be a key factor creating nonlinear or stabilizing feedbacks in complex systems, and these dynamics can be revealed by merging experimental and comparative approaches at different scales.

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions.

Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. OSN are very volatile because users enter and exit and frequently change their interactions. This makes the robustness of such systems difficult to measure and to control. To quantify robustness, we propose a coreness value obtained from the directed interaction network. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. For agents, we define a utility function that depends on their relative reputation and their costs for interactions. The decision of agents to leave the OSN depends on this utility. Our aim is to prevent drop-out cascades by influencing specific agents with low utility. We identify strategies to control agents in the core and the periphery of the OSN such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased.

Multiple Small RNAs Interact to Co-regulate Ethanol Tolerance in Zymomonas mobilis.

sRNAs represent a powerful class of regulators that influences multiple mRNA targets in response to environmental changes. However, very few direct sRNA–sRNA interactions have been deeply studied in any organism. Zymomonas mobilis is a bacterium with unique ethanol-producing metabolic pathways in which multiple small RNAs (sRNAs) have recently been identified, some of which show differential expression in ethanol stress. In this study, we show that two sRNAs (Zms4 and Zms6) are upregulated under ethanol stress and have significant impacts on ethanol tolerance and production in Z. mobilis. We conducted multi-omics analysis (combining transcriptomics and sRNA-immunoprecipitation) to map gene networks under the influence of their regulation. We confirmed that Zms4 and Zms6 bind multiple RNA targets and regulate their expressions, influencing many downstream pathways important to ethanol tolerance and production. In particular, Zms4 and Zms6 interact with each other as well as many other sRNAs, forming a novel sRNA–sRNA direct interaction network. This study thus uncovers a sRNA network that co-orchestrates multiple ethanol related pathways through a diverse set of mRNA targets and a large number of sRNAs. To our knowledge, this study represents one of the largest sRNA–sRNA direct interactions uncovered so far.

Network Pharmacology-Based Pharmacological Mechanism of the Chinese Medicine Rhizoma drynariae Against Osteoporosis.

Rhizoma drynariae is the main traditional Chinese medicine used for the treatment of osteoporosis, but its anti-osteoporotic targeting mechanism has not been fully elucidated due to the complexity of its active ingredients. In this study, the pharmacological mechanism of action of Rhizoma drynariae against osteoporosis was studied by integrating pharmacological concepts.The pharmacokinetic characteristics of selected major active constituents of Rhizoma drynariae and the SMILES structural similarity were used to predict related targets. A literature search was conducted to identify known osteoporosis treatment targets, which were then combined with the predicted targets to construct the direct or indirect target interaction network map of Rhizoma drynariae against osteoporosis. Finally, data on the key targets of the interactions, ranked according to relevant node parameters obtained through pathway enrichment analysis and screening of key targets and active ingredients of Rhizoma drynariae, were used to perform molecular docking simulation.We screened 16 active ingredients of Rhizoma drynariae, and 7 key targets with direct or indirect effects with a high frequency were obtained. These main pathways were found to play important roles in the PI3k-akt signaling pathway, osteoclast differentiation, Wnt signaling pathway, and estrogen signaling pathway. Molecular docking showed that most active ingredients of Rhizoma drynariae had strong binding efficiency with key targets.Based on network pharmacology, we conclude that Rhizoma drynariae plays an anti-osteoporotic role by directly or indirectly targeting multiple major signaling pathways and influencing the proliferation and differentiation of multiple types of cells.

Direct interaction network and differential network inference from compositional data via lasso penalized D-trace loss

The development of high-throughput sequencing technologies for 16S rRNA gene profiling provides higher quality compositional data for microbe communities. Inferring the direct interaction network under a specific condition and understanding how the network structure changes between two different environmental or genetic conditions are two important topics in biological studies. However, the compositional nature and high dimensionality of the data are challenging in the context of network and differential network recovery. To address this problem in the present paper, we proposed two new loss functions to incorporate the data transformations developed for compositional data analysis into D-trace loss for network and differential network estimation, respectively. The sparse matrix estimators are defined as the minimizer of the corresponding lasso penalized loss. Our method is characterized by its straightforward application based on the ADMM algorithm for numerical solution. Simulations show that the proposed method outperforms other state-of-the-art methods in network and differential network inference under different scenarios. Finally, as an illustration, our method is applied to a mouse skin microbiome data.

Compositional data network analysis via lasso penalized D-trace loss.

With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality. In this article, we first propose a novel loss function for compositional data called CD-trace based on D-trace loss. A sparse matrix estimator for the direct interaction network is defined as the minimizer of lasso penalized CD-trace loss under positive-definite constraint. An efficient alternating direction algorithm is developed for numerical computation. Simulation results show that CD-trace compares favorably to gCoda and that it is better than sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) (hereinafter S-E) in network recovery with compositional data. Finally, we test CD-trace and compare it to the other methods noted above using mouse skin microbiome data. The CD-trace is open source and freely available from https://github.com/coamo2/CD-trace under GNU LGPL v3. Supplementary data are available at Bioinformatics online.

Reversible SAHH inhibitor protects against glomerulonephritis in lupus-prone mice by downregulating renal \u03b1-actinin-4 expression and stabilizing integrin-cytoskeleton linkage

BackgroundGlomerulonephritis is one of the major complications and causes of death in systemic lupus erythematosus (SLE) and is characterized by glomerulosclerosis, interstitial fibrosis, and tubular atrophy, along with severe persistent proteinuria. DZ2002 is a reversible S-adenosyl-l-homocysteine hydrolase (SAHH) inhibitor with potent therapeutic activity against lupus nephritis in mice. However, the molecular events underlying the renal protective effects of DZ2002 remained unclear. This study is designed to uncover the molecular mechanisms of DZ2002 on glomerulonephritis of lupus-prone mice.MethodsWe conducted a twice-daily treatment of DZ2002 on the lupus-prone NZB/WF1 mice, and the progression of lupus nephritis and alteration of renal function were monitored. The LC-MS-based label-free quantitative (LFQ) proteomic approach was applied to analyze the kidney tissue samples from the normal C57BL/6 mice and the NZB/WF1 mice treated with DZ2002 or vehicle. KEGG pathway enrichment and direct protein-protein interaction (PPI) network analyses were used to map the pathways in which the significantly changed proteins (SCPs) are involved. The selected proteins from proteomic analysis were validated by Western blot analysis and immunohistochemistry in the kidney tissues.ResultsThe twice-daily regimen of DZ2002 administration significantly ameliorated the lupus nephritis and improved the renal function in NZB/WF1 mice. A total of 3275 proteins were quantified, of which 253 proteins were significantly changed across normal C57BL/6 mice and the NZB/WF1 mice treated with DZ2002 or vehicle. Pathway analysis revealed that 13 SCPs were involved in tight junction and focal adhesion process. Further protein expression validation demonstrated that DZ2002-treated NZB/WF1 mice exhibited downregulation of α-actinin-4 and integrin-linked kinase (ILK), as well as the restoration of β1-integrin activation in the kidney tissues compared with the vehicle-treated ones.ConclusionsOur study demonstrated the first evidence for the molecular mechanism of SAHH inhibitor on glomerulonephritis in SLE via the modulation of α-actinin-4 expression and focal adhesion-associated signaling proteins in the kidney.

Affine formation control for heterogeneous multi-agent systems with directed interaction networks

In this paper, affine formation control problems of heterogeneous multi-agent systems with linear dynamics are studied. As a novel method, the affine formation control has the ability to handle various movement constraints based on the affine transformation. A proportional-integral (PI)-type control scheme is proposed to ensure the effective affine formation control of the leader-following group with directed interaction graphs and successfully eliminate the steady-state errors. Sufficient conditions for the selection of control parameters are given and proved. Considering that the followers are subjected to the external bounded time-varying disturbances, the designed protocol shows good robustness and can guarantee uniformly ultimate boundedness of affine formation errors. Numerical simulations are carried out to verify the theoretical results.

2SCENT

In interaction networks nodes may interact continuously and repeatedly. Not only which nodes interact is important, but also the order in which interactions take place and the patterns they form. These patterns cannot be captured by solely inspecting the static network of who interacted with whom and how frequently, but also the temporal nature of the network needs to be taken into account. In this paper we focus on one such fundamental interaction pattern, namely a temporal cycle. Temporal cycles have many applications and appear naturally in communication networks. In financial networks, on the other hand, the presence of a temporal cycle could be indicative for certain types of fraud, and in biological networks, feedback loops are a prime example of this pattern type. We present 2SCENT, an efficient algorithms to find all temporal cycles in a directed interaction network. 2SCENT consist of a non-trivial temporal extension of a seminal algorithm for finding cycles in static graphs, preceded by an efficient candidate root filtering technique which can be based on Bloom filters to reduce the memory footprint. We tested 2SCENT on six real-world data sets, showing that it is up to 300 times faster than the only existing competitor and scales up to networks with millions of nodes and hundreds of millions of interactions. Results of a qualitative experiment indicate that different interaction networks may have vastly different distributions of temporal cycles, and hence temporal cycles are able to characterize an important aspect of the dynamic behavior in the networks.