Topology Of Interaction Networks Research Articles

Microenvironment drives flower visitors’ activity in a population of the bishop’s cap cactus (Astrophytum myriostigma)

Biotic and environmental conditions could affect the plant-flower visitor dynamics and, consequently, the fruit set in angiosperms. The aims of this study were: 1) to test the effect of microenvironment on activity of flower visitors in a population of Astrophytum myriostigma and 2) to describe the plant-flower visitor network’s structure. Data were recorded during two synchronous flowerings. The effect of microenvironment on flower visitors’ activity was tested through generalized linear models, and the interaction network structure was described. Frequency and duration of visits were mainly affected by atmospheric pressure and dew point. There were fewer flower visitors on plants located close to nurses and rocks. The interaction network topology tends to have a nested structure. Due to pollinators’ decline, these findings help understand the drivers limiting the flower visitors’ activity. The interactions between flower visitors and plants could be affected by atmospheric pressure alterations triggered by climate change.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world’s largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management—analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

Network pharmacology‑based investigation of potential targets of triptonodiol acting on non-small-cell lung cancer

BackgroundTriptonodiol is a very promising antitumor drug candidate extracted from the Chinese herbal remedy Tripterygium wilfordii Hook. F., and related studies are underway.MethodsTo explore the mechanism of triptonodiol for lung cancer treatment, we used network pharmacology, molecular docking, and ultimately protein validation. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis were performed through the David database. Molecular docking was performed using PyMoL2.3.0 and AutoDock Vina software. After screening, the major targets of triptonodiol were identified for the treatment of lung cancer. Target networks were established, Protein–protein interaction (PPI) network topology was analyzed, then KEGG pathway enrichment analysis was performed. Useful proteins were screened by survival analysis, and Western blot analysis was performed.ResultsTriptonodiol may regulate cell proliferation, drug resistance, metastasis, anti-apoptosis, etc., by acting on glycogen synthase kinase 3 beta (GSK3B), protein kinase C (PKC), p21-activated kinase (PAK), and other processes. KEGG pathway enrichment analysis showed that these targets were associated with tumor, erythroblastic oncogene B (ErbB) signaling, protein phosphorylation, kinase activity, etc. Molecular docking showed that the target protein GSK has good binding activity to the main active component of triptonodiol. The protein abundance of GSK3B was significantly downregulated in non-small-cell lung cancer cells H1299 and A549 treated with triptonodiol for 24 h.ConclusionThe cellular-level studies combined with network pharmacology and molecular docking approaches provide new ideas for the development and therapeutic application of triptonodiol, and identify it as a potential GSK inhibitor.

[Chemical constituents and mechanism of Chuanzhi Tongluo Capsules based on UPLC-Q-Exactive Orbitrap-MS and network pharmacology].

The chemical constituents of Chuanzhi Tongluo Capsules were analyzed and identified using ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) to clarify the pharmacological substance basis. In addition, network pharmacology was employed to explore the mechanism of Chuanzhi Tongluo Capsules in the treatment of cerebral infarction. Gradient elution was performed using acetonitrile and 1% acetic acid in water as the mobile phase. Mass spectrometry was performed in positive and negative ion modes. Xcalibur 4.2 software was used for compound analysis, including accurate mass-to-charge ratio and MS/MS fragment information, combined with the comparison of reference standards and literature data. A total of 152 compounds were identified, including 32 organic acids, 35 flavonoids and their glycosides, 33 diterpenes, 13 phthalides, 12 triterpenes and triterpene saponins, 23 nitrogen-containing compounds, and 4 other compounds, and their fragmentation patterns were analyzed. SwissTargetPrediction, GeneCards, DAVID, and other databases were used to predict and analyze the core targets and mechanism of Chuanzhi Tongluo Capsules. Protein-protein interaction(PPI) network topology analysis identified 10 core targets, including TNF, VEGFA, EGFR, IL1B, and CTNNB1. KEGG enrichment analysis showed that Chuanzhi Tongluo Capsules mainly exerted their effects through the regulation of lipid and atherosclerosis, glycoproteins in cancer, MicroRNAs in cancer, fluid shear stress, and atherosclerosis-related pathways. Molecular docking was performed between the key constituents and core targets, and the results demonstrated a strong binding affinity between the key constituents of Chuanzhi Tongluo Capsules and the core targets. This study comprehensively elucidated the chemical constituents of Chuanzhi Tongluo Capsules and explored the core targets and mechanism in the treatment of cerebral infarction based on network pharmacology, providing a scientific reference for the study of the pharmacological substance basis and formulation quality standards of Chuanzhi Tongluo Capsules.

Transitions and its indicators in mutualistic meta-networks: effects of network topology, size of metacommunities and species dispersal

Gradual changes in the environment could cause dynamical ecological networks to suddenly shift from one state to an alternative state. When this happens ecosystem functions and services provided by ecological networks get disrupted. We, however, know very little about how the topology of such interaction networks can play a role in the transition of ecological networks when spatial interactions come into play. In the event of such unwanted transitions, little is known about how statistical metrics used to inform such impending transitions, measured at the species-level or at the community-level could relate to network architecture and the size of the metacommunity. Here, using hundred and one empirical plant-pollinator networks in a spatial setting, I evaluated the impact of network topology and spatial scale of species interactions on transitions, and on statistical metrics used as predictors to forecast such transitions. Using generalized Lotka-Volterra equations in a meta-network framework, I show that species dispersal rate and the size of the metacommunity can impact when a transition can occur. In addition, forecasting such unwanted transitions of meta-networks using statistical metrics of instability was also consequently dependent on the topology of the network, species dispersal rate, and the size of the metacommunity. The results indicated that the plant-pollinator meta-networks that could exhibit stronger statistical signals before collapse than others were dependent on their network architecture and on the spatial scale of species interactions.

Gene enrichment analysis and protein–protein interaction network topology delineates S-Phase kinase-associated protein 1 and catenin beta-1 as potential signature genes linked to glioblastoma prognosis

Background: Glioblastoma (GBM) is the most malignant and accounts for 60% of brain tumors in adults. Current therapy for GBM involves surgical removal of the tumor followed by radiotherapy with concomitant adjuvant therapy temozolomide. Despite improvements in therapy, patient survival remains low. The exact etiology of a brain tumor is uncertain, and numerous unknown genes are involved in the progression of GBM. The aim of the present study was to evaluate various genes involved in GBM through bioinformatic approach. Methods: In the present study, gene expression profile of GSE68424 was retrieved from the GEO database to explore the genes in GBM. Results: Analysis of expression profile data revealed that 33 genes were upregulated and 1189 genes were downregulated based on the log2 fold change cut-off criteria. The genes S-Phase kinase-associated protein 1 (SKP1) and Catenin beta-1 (CTNNB1) have been linked to GBM prognosis. Conclusion: SKP1 and CTNNB1 were identified as a candidate gene for GBM study as a result of these findings. Catenin beta-1 was the protein with the highest closeness centrality value and is the key component of canonical Wnt signaling downstream pathway. More study is needed to establish the molecular function of SKP1 and CTNNB1 in GBM development, as well as the biomarker's specificity and sensitivity.

Fault tolerance in qudit circuit design

The efficient decomposition of multicontrolled gates is a significant factor in quantum compiling, both in circuit depth and $T$-gate count. Recent work has demonstrated that qudits have the potential to reduce resource requirements from linear to logarithmic depth and to avoid fractional phase rotations. Here, we argue, based on the scaling of decoherence in high-index states, that circuit depth is not the primary factor, and that both the choice of entangling gate and interaction network topology act together to determine the spread of errors and the ultimate failure rate in a circuit. We further show that for certain linear-depth circuits, additional error mitigation is possible via selective application of resources.

Prediction of protein-protein interactions based on ensemble residual convolutional neural network

With the development of bio-medical big data, the prediction of protein-protein interactions (PPIs) with the help of deep learning (DL) has attracted much attention for the study of intermolecular mechanism, drug design, human disease treatment. Given that the experiment-based methods can be difficult, reliable and DL-based approaches are needed. In this paper, we develop the EResCNN, an effective predictor to predict PPIs based on ensemble residual convolutional neural network. First, the fused feature representation is captured by concatenating the vectors obtained via pseudo amino acid composition (PseAAC), auto covariance descriptor (AC), pseudo position-specific scoring matrix (PsePSSM), encoding based on grouped weight (EBGW), multivariate mutual information (MMI) and conjoint triad (CT). Then the high-level information can be obtained using the convolution and pooling of the residual convolutional neural network (RCNN) via layer-by-layer learning. At last, we ensemble RCNN, XGBoost, random forest, LightGBM and extremely randomized trees to build the EResCNN model. The predictive results indicate that EResCNN achieves better performance with the ACC values of 95.34%, 87.89% and 98.61% on S. cerevisiae, H. pylori and Human-Y. pestis datasets, respectively. We also apply EResCNN to the datasets of H. sapiens, M. musculus, C. elegans and E. coli for cross-species prediction. Especially, we find EResCNN can infer significant PPIs network on one-core network, Wnt-related signal pathway network, cancer-specific network and multi-core network, which could provide some references for signal pathway research, disease-related gene mining, and interaction network topology.

Analysis of Traditional Chinese Medicine Syndromes and Treatment Laws of Diabetic Kidney Disease and the Action Mechanism of High-Frequency Chinese Herbs in the Treatment of Diabetic Kidney Disease Based on Real-World Study

Abstract Objective Our objective was to analyze the traditional Chinese medicine (TCM) syndrome and treatment laws of diabetic kidney disease (DKD) and the action mechanism of high-frequency Chinese herbs in the treatment of DKD based on real-world study. Methods The data of patients with DKD who had been treated in the First Hospital Affiliated to Henan University of Chinese Medicine from January 1, 2014 to December 31, 2021 were retrospectively analyzed through the hospital information management system. The contents of the cases were statistically analyzed using IBM SPSS Statistics 25 software, and the laws of DKD treatment were summarized. Network pharmacology and molecular docking were used to analyze the action mechanism of high-frequency Chinese herbs in the treatment of DKD. Results The data of a total of 1,201 patients with DKD were included, involving 72 kinds of TCM syndromes. Nine disease nature elements and six disease location elements were extracted, involving 405 Chinese herbs. The top five high-frequency Chinese herbs were Baizhu (Atractylodis Macrocephalae Rhizoma), Fuling (Poria), Huangqi (Astragali Radix), Chuanxiong (Chuanxiong Rhizoma), and Danshen (Salviae Miltiorrhizae Radix et Rhizoma). Thirty kinds of Chinese herbs with the frequency of ≥100 were mainly deficiency-tonifying herbs and blood-activating and stasis-eliminating herbs. The medicinal properties were mainly warm and mild, and the medicinal flavors were sweet and bitter mostly. For the meridian tropism, the main meridian tropism of these herbs is spleen meridian and lung meridian. The clustering method aggregated the 30 commonly used Chinese herbs into six categories. A total of 58 effective active ingredients of high-frequency Chinese herbs and 164 related targets were screened based on the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP); 1,434 targets of DKD and 90 potential targets of high-frequency Chinese herbs for DKD were obtained. “Active ingredient-potential target” network topology analysis indicated that quercetin, luteolin, 7-O-methylisoxitol, hederagenin, and 4-methylene miltirone were the five core chemical components of high-frequency Chinese herbs in the treatment of DKD. Protein–protein interaction network topology analysis indicated that protein kinase B1, interleukin-6, tumor necrosis factor, vascular endothelial growth factor A, tumor protein P53 were the core protein targets. Twenty signaling pathways were obtained by Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Molecular docking showed that luteolin, 4-methylene miltirone, and osthole had strong binding to AKT1. Conclusion The differentiation and treatment of DKD with TCM follows the principles of “taking kidney as the root, considering both the liver and spleen, tonifying qi and nourishing yin, promoting blood circulation and resolving blood stasis.” High-frequency Chinese herbs for the treatment of DKD have multicomponent, multitarget, and multipath characteristics.

Protein function annotation based on heterogeneous biological networks

BackgroundAccurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data.ResultsIn this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively.ConclusionsWe demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

Bacterial plasmid-associated and chromosomal proteins have fundamentally different properties in protein interaction networks

Plasmids facilitate horizontal gene transfer, which enables the diversification of pathogens into new anatomical and environmental niches, implying that plasmid-encoded genes can cooperate well with chromosomal genes. We hypothesise that such mobile genes are functionally different to chromosomal ones due to this ability to encode proteins performing non-essential functions like antimicrobial resistance and traverse distinct host cells. The effect of plasmid-driven gene gain on protein–protein interaction network topology is an important question in this area. Moreover, the extent to which these chromosomally- and plasmid-encoded proteins interact with proteins from their own groups compared to the levels with the other group remains unclear. Here, we examined the incidence and protein–protein interactions of all known plasmid-encoded proteins across representative specimens from most bacteria using all available plasmids. We found that plasmid-encoded genes constitute ~ 0.65% of the total number of genes per bacterial sample, and that plasmid genes are preferentially associated with different species but had limited taxonomical power beyond this. Surprisingly, plasmid-encoded proteins had both more protein–protein interactions compared to chromosomal proteins, countering the hypothesis that genes with higher mobility rates should have fewer protein-level interactions. Nonetheless, topological analysis and investigation of the protein–protein interaction networks' connectivity and change in the number of independent components demonstrated that the plasmid-encoded proteins had limited overall impact in > 96% of samples. This paper assembled extensive data on plasmid-encoded proteins, their interactions and associations with diverse bacterial specimens that is available for the community to investigate in more detail.

The Study on the Action Mechanism of the Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) Couplet Herbs on Membranous Nephropathy Based on Network Pharmacology

Abstract Objective Our objective was to explore the action mechanism of the Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs in the treatment of membranous nephropathy (MN) based on network pharmacology. Methods The active ingredients and targets of Jinyingzi (Rosae Laevigatae Fructus) and Qianshi (Euryales Semen) were screened by systematic pharmacology database and analysis platform. Online Human Mendelian Genetic database and GeneCards database were used to retrieve MN-related targets. The active ingredient-related targets and MN disease targets were introduced into Venny 2.1, and Wayne diagram was drawn. The intersection targets were the potential targets of the Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs in the treatment of MN. The protein interaction network of potential targets was constructed, and the core targets were screened with String platform. Metascape platform was used for functional enrichment analysis of gene ontology (GO) and pathway enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG). The “herb-active ingredient-target-pathway” networks were drawn by using Cytoscape software, and the key components, targets, and signaling pathways were screened. Results A total of 8 active ingredients and 193 related targets in Jinyingzi (Rosae Laevigatae Fructus) and Qianshi (Euryales Semen) were screened out; a total of 1,621 targets of MN disease and 105 potential targets for the treatment of MN were obtained in the treatment with Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs; 40 core targets were screened by protein–protein interaction network topology analysis; a total of 1,978 results were obtained by GO function enrichment analysis, and 206 signal pathways were obtained by KEGG pathway enrichment analysis and screening. The network topology analysis of “herb-active ingredient-target-pathway” showed that the key components included quercetin, kaempferol, β-sitosterol, etc.; the key targets included protein kinase Bα (AKT), mitogen-activated protein kinase 1 (MAPK1), B-cell lymphoma-2 (BCL2), prostaglandin-endoperoxide synthase 2 (PTGS2), etc.; the key pathways included advanced glycation end product/receptor of AGE signaling pathway, phosphatidyl inositol 3-kinase (PI3K)/AKT signaling pathway, MAPK signaling pathway, hypoxia-inducible factor-1 signaling pathway, Ras signaling pathway, nuclear factor kappa-B (NF-κB) signaling pathway, Toll-like receptors signaling pathway, p53 signaling pathway and vascular endothelial growth factor signaling pathway in the late stage of diabetic complications. Conclusion The Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs can regulate PI3K/AKT, MAPK, NF-κB signaling pathways in MN by targeting proteins of AKT1, MAPK8, PTGS2 through key components of quercetin, β-sitosterol, and kaempferol, so as to inhibit the overexpression of inflammatory factors in renal tissues, regulate inflammatory response, and improve renal function.

Identification of T cell-related biomarkers for breast cancer based on weighted gene co-expression network analysis

Breast cancer is the most frequent malignancy worldwide, with immunotherapy and targeted therapy being key strategies to improving the prognosis. We downloaded mRNA expression dataset of breast cancer from The Cancer Genome Atlas (TCGA) database, and divided preprocessed genes into 12 modules based on gene expression profile by weighted gene co-expression network analysis (WGCNA). The StromalScore, ImmuneScore and ESTIMATEScore of samples were assessed. The Kaplan–Meier curve showed that ImmuneScore was notably correlated with breast cancer patient’s prognosis. By analyzing the connectivity between module eigengenes and clinical traits, the gene module closely related to ImmuneScore was obtained. Further, through intramodular gene connectivity and protein–protein interaction network topology analysis of module genes, hub genes (HLA-E, HLA-DPB1 and HLA-DRB1) in immune-related module were screened out. Finally, bioinformatics analysis displayed that HLA-DPB1 and HLA-DRB1 were notably overexpressed and HLA-E was underexpressed in breast cancer tissues. TIMER database analysis showed that three hub gene levels were significantly correlated with infiltration levels of CD8+ T cells and CD4+ T cells. Meanwhile, Pearson correlation analysis revealed positive correlation between three hub genes and those of immune checkpoint genes (LAG3, PD-1, PD-L1). Additionally, prognosis could be effectively evaluated by HLA-DPB1 and HLA-DRB1 levels, and differentially activated signalling pathways between high- and low-expression groups of HLA-E and HLA-DPB1 were obtained by gene set enrichment analysis. To conclude, this study identified three T cell-related biomarkers for breast cancer based on TCGA-BRCA dataset, and the screened genes could provide references for breast cancer immunotherapy.

Mechanism of Huo-Xiang-Zheng-Qi in Preventing and Treating COVID-19: A Study Based on Network Pharmacology and Molecular Docking Techniques

Objective: The Chinese herbal formula Huo-Xiang-Zheng-Qi (HXZQ) is effective in preventing and treating coronavirus disease 19 (COVID-19) infection; however, its mechanism remains unclear. This study used network pharmacology and molecular docking techniques to investigate the mechanism of action of HXZQ in preventing and treating COVID-19. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to search for the active ingredients and targets of the 10 traditional Chinese medicines (TCMs) of HXZQ prescription (HXZQP). GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmacogenomics Knowledge Base (PharmGKB), Therapeutic Target Database (TTD), and DrugBank databases were used to screen COVID-19-related genes and intersect them with the targets of HXZQP to obtain the drug efficacy targets. Cytoscape 3.8 software was used to construct the drug-active ingredient–target interaction network of HXZQP and perform protein–protein interaction (PPI) network construction and topology analysis. R software was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, AutoDock Vina was utilized for molecular docking of the active ingredients of TCM and drug target proteins. Results: A total of 151 active ingredients and 250 HXZQP targets were identified. Among these, 136 active ingredients and 67 targets of HXZQP were found to be involved in the prevention and treatment of COVID-19. The core proteins identified in the PPI network were MAPK1, MAPK3, MAPK8, MAPK14, STAT3, and PTGS2. Using GO and KEGG pathway enrichment analysis, HXZQP was found to primarily participate in biological processes such as defense response to a virus, cellular response to biotic stimulus, response to lipopolysaccharide, PI3K-Akt signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, and other signaling pathways closely related to COVID-19. Molecular docking results reflected that the active ingredients of HXZQP have a reliable affinity toward EGFR, MAPK1, MAPK3, MAPK8, and STAT3 proteins. Conclusion: Our study elucidated the main targets and pathways of HXZQP in the prevention and treatment of COVID-19. The study findings provide a basis for further investigation of the pharmacological effects of HXZQP.

Identification of Immune-Related Markers in Hepatocellular Carcinoma Based on Gene Co-expression Network.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death throughout the world, with constant increasing morbidity and mortality. Existing studies suggest that immunotherapy is beneficial to the treatment of advanced HCC. At present, it is imperative to identify biomarkers suitable for HCC immunotherapy. In this paper, the mRNA expression data of HCC were downloaded from The Cancer Genome Atlas, and Stromal Score, Immune Score and ESTIMATE Score of each sample were calculated. Weighted gene co-expression network analysis clustered the pretreated genes into eight modules. The brown module that was remarkably associated with Immune Score was identified by module eigengene-immune trait analysis, in which genes were mainly enriched in immune-related pathways. Four hub genes (CCR5, CD53, ITGB2, and TYROBP) related to tumor immunity, were screened out by intramodular gene connectivity combined with protein-protein interaction network topology analysis. Kaplan-Meier survival analysis presented a correlation between high expression of CCR5 and CD53, and better prognoses of HCC patients. TIMER analysis revealed a positive correlation between expression of each hub gene and immune cell infiltration level, and a positive correlation between the expression of each hub gene and the expression of immunosuppressive factors CTLA4 and PDCD1. Gene set enrichment analysis displayed that there was an evident difference in the activation of cytokines and the activation of immune signal transduction-related pathways between high- and low-expression groups of each hub gene. In conclusion, this study identified four potential genetic markers associated with HCC immunity and assessed their association with HCC patient's prognosis and immune microenvironment. The study results are expected to provide theoretical guidance for immunotherapy of HCC patients.

Optimizing higher-order network topology for synchronization of coupled phase oscillators

Networks in nature have complex interactions among agents. One significant phenomenon induced by interactions is synchronization of coupled agents, and the interactive network topology can be tuned to optimize synchronization. Previous studies showed that the optimized conventional network with pairwise interactions favors a homogeneous degree distribution of nodes for undirected interactions, and is always structurally asymmetric for directed interactions. However, the optimal control on synchronization for prevailing higher-order interactions is less explored. Here, by considering the higher-order interactions in a hypergraph and the Kuramoto model with 2-hyperlink interactions, we find that the network topology with optimized synchronizability may have distinct properties. For undirected interactions, optimized networks with 2-hyperlink interactions by simulated annealing tend to become homogeneous in the nodes’ generalized degree. We further rigorously demonstrate that for directed interactions, the structural symmetry can be preserved in the optimally synchronizable network with 2-hyperlink interactions. The results suggest that controlling the network topology of higher-order interactions leads to synchronization phenomena beyond pairwise interactions.

The Antitumor Effect and Mechanism of Total Flavonoids From Coreopsis Tinctoria Nutt (Snow Chrysanthemum) on Lung Cancer Using Network Pharmacology and Molecular Docking

Coreopsis tinctoria Nutt (C. tinctoria), also known as Snow Chrysanthemum, is rich in polyphenols and flavonoids. It has important pharmacological effects such as lowering blood lipids, regulating blood glucose, and anti-tumor effect. However, its anti-tumor mechanism has not yet been investigated thoroughly. This study aimed to explore the anti-tumor effect of total flavonoids extracted from C. tinctoria (CTFs) on lung cancer and the possible mechanism. The components of CTFs were analyzed using Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The active components of CTFs were screened according to oral bioavailability (OB) and drug-likeness (DL). Totally, 68 components of CTFs were identified and 23 active components were screened. Network pharmacological analysis on the active components identified 288 potential targets associated with lung cancer. After protein-protein interaction (PPI) network topology analysis, 17 key protein targets including Akt1, MAPK1, TP53, Bcl-2, Caspase-3, Bax, GSK3B and CCND1 were screened. The molecular docking results showed that the active components of CTFs had good binding activity with key targets. GO and KEGG analysis of candidate targets found that the main enrichment was in PI3K/Akt-mediated intrinsic apoptotic pathways. Finally, according to the results of network pharmacology, the potential molecular mechanism of CTFs intervention in lung cancer was validated experimentally in vitro and in vivo. The experimental validation results demonstrated that the antitumor activity of CTFs on lung cancer may be related to inhibiting the PI3K-Akt signaling pathway and activating the mitochondrial-mediated apoptosis pathway.

Assessing the anti-inflammatory effects of quercetin using network pharmacology and in vitro experiments.

The present study aimed to investigate the anti-inflammatory effects of quercetin and the associated mechanisms involved. ELISA, reverse transcription-quantitative PCR and western blot analysis were performed to determine the anti-inflammatory effects of quercetin in lipopolysaccharide (LPS)-stimulated RAW264.7 cells. The molecular mechanisms of quercetin were investigated using network pharmacology, molecular docking technology and in vitro experiments. The results revealed that quercetin reduced the LPS-induced production of TNF-α, IL-6 and IL-1β in RAW264.7 macrophages. Protein-protein interaction network topology analysis indicated that Akt was the target of quercetin. Kyoto Encyclopedia of Genes and Genomes analysis indicated that quercetin may regulate the PI3K/Akt signaling pathway to exert its anti-inflammatory effects. Furthermore, the molecular docking results indicated that quercetin had a good affinity for the active sites of Akt. Western blot analysis confirmed that quercetin inhibited the phosphorylation of Akt, with an efficacy stronger than that of an Akt inhibitor. Taken together, Akt served as a target as part of the mechanism of the anti-inflammatory effect of quercetin. This result lays a foundation for the clinical application of quercetin in the treatment of inflammatory diseases.

Why body size matters: how larger fish ontogeny shapes ecological network topology

Ontogenetic development can strongly shape species interactions. Yet, rarely is stage‐structure considered when analyzing species interaction networks, particularly networks that can account for more than feeding relationships. Here, we assess 1) if body size or trophic level regulate the importance of species' ontogeny on their interactions and 2) how including relevant stage‐structure affects the topology of species interaction networks. We use a count‐based inferential method to create networks from adult and juvenile fish count data and test stage‐structure importance by comparing a model that includes stage‐structure for all species against models that include stage‐structure only for larger fishes and only for piscivorous fishes during network construction. While the inferential method we use cannot differentiate between different types of interactions, it can account for different interaction types within a network as a pairwise interaction is inferred when one species influences the abundance of another. Next, we use graphlet‐based techniques to test if including stage‐structure alters overall network topology and a linear model to measure if adult‐juvenile size differences drive interaction differences at a species‐level. We find that the model that includes stage‐structure only for larger fishes outperforms other stage‐structured models including the model with only piscivore stage‐structure, and that larger differences in body size among juveniles and adults lead to greater interaction dissimilarities. Moreover, we find topological differences between inferred networks that only include adults and those that account for the stage‐structure of larger species. Overall, our study demonstrates how stage‐structured topological changes can be measured using inferred interaction networks and illustrates how larger species' juveniles fundamentally shape the structure of stream fish communities.

Structure-based identification of sensor species for anticipating critical transitions

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure-that is, the network topology of plant-animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of "sensor species," whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant-pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.