Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms ass...Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.展开更多
Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the ne...Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.展开更多
In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPI...In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPIs)provides a foundation for constructing protein interaction networks(PINs),which can furnish a new perspective for understanding cellular organizations,processes,and functions at network level.In this paper,we present a comprehensive survey on three main characteristics of PINs:centrality,modularity,and dynamics.1)Different centrality measures,which are used to calculate the importance of proteins,are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information;2)Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced;3)The dynamics of proteins,PPIs and sub-networks are discussed,respectively.Finally,the main applications of PINs in the complex diseases are reviewed,and the challenges and future research directions are also discussed.展开更多
Bats are the second-most diverse group of mammals in the world,and bat flies are their main parasites.However,significant knowledge gaps remain regarding these antagonistic interactions,especially since diverse factor...Bats are the second-most diverse group of mammals in the world,and bat flies are their main parasites.However,significant knowledge gaps remain regarding these antagonistic interactions,especially since diverse factors such as seasonality and host sex can affect their network structures.Here,we explore the influence of such factors by comparing species richness and composition of bat flies on host bats,as well as specialization and modularity of bat–bat fly interaction networks between seasons and adult host sexes.We captured bats and collected their ectoparasitic flies at 10 sampling sites in the savannahs of AmapáState,northeastern region of the Brazilian Amazon.Despite female bats being more parasitized and recording greater bat fly species richness in the wet season,neither relationship was statistically significant.The pooled network could be divided into 15 compartments with 54 links,and all subnetworks comprised>12 compartments.The total number of links ranged from 27 to 48(for the dry and wet seasons,respectively),and female and male subnetworks had 44 and 41 links,respectively.Connectance values were very low for the pooled network and for all subnetworks.Our results revealed higher bat fly species richness and abundance in the wet season,whereas specialization and modularity were higher in the dry season.Moreover,the subnetwork for female bats displayed higher specialization and modularity than the male subnetwork.Therefore,both seasonality and host sex contribute in different ways to bat–bat fly network structure.Future studies should consider these factors when evaluating bat–bat fly interaction networks.展开更多
Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies....Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.展开更多
Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing gen...Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments.With the advances of the high-throughput techniques,a large number of protein-protein interactions have been produced.Therefore,to address this issue,several methods based on protein interaction network have been proposed.In this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction networks.Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)annotations.SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account.The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and RWR.The experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing genes.Importantly,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes.展开更多
Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interactio...Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.展开更多
Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive atte...Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.展开更多
Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes...Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.展开更多
Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16...Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.展开更多
AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed...AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.展开更多
Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network ...Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.展开更多
Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do ...Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.展开更多
In network environments,before meaningful interactions can begin,trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy.Con...In network environments,before meaningful interactions can begin,trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy.Consequently,privacy protection and trust establishment become important in network interactions.In order to protect privacy while facilitating effective interactions,we propose a trust-based privacy protection method.Our main contributions in this paper are as follows:(1)We introduce a novel concept of k-sensitive privacy as a measure to assess the potential threat of inferring privacy;(2)According to trust and k-sensitive privacy evaluation,our proposed method can choose appropriate interaction patterns with lower degree of inferring privacy threat;(3)By considering interaction patterns for privacy protection,our proposed method can overcome the shortcomings of some current privacy protection methods which may result in low interaction success rate.Simulation results show that our method can achieve effective interactions with less privacy loss.展开更多
Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has ...Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.展开更多
To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia （CML） under the guidance of protein-protein interaction network, the molecular docking technique and in vitro c...To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia （CML） under the guidance of protein-protein interaction network, the molecular docking technique and in vitro cell experiment were chosen. CML-related genes were obtained from the online mendelian inheritance in man database （OMIM）, then String 10. 0 was used for text mining and constructing the CML protein-protein interaction network. The interaction data were input in Cytoscape 3. 4. 0 software. Plug-in CentiScaPe 2. 1 was used for implement topology analysis. Small active substances of Indigo Naturalis were obtained from a third-party database, which were optimized by Chemoffice 8. 0 and Sybyl 8. 1, then small molecular ligand library was obtained. The molecular docking was carried out by Surflex-Dock module, the key target was received after scoring. Protein-protein interaction network of CML was constructed, which was consisted of 425 nodes （ proteins） and 2 799 sides （ interactions）. The key gene J.AK2 was got. CML is a polygenic disease and JAK2 is likely to be a key node.展开更多
E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that m...E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.展开更多
Objective Oral squamous cell carcinoma(OSCC)is an aggressive cancer with a high mortality rate.San-Zhong-Kui-Jian-Tang(SZKJT),a Chinese herbal formula,has long been used as an adjuvant therapy in cancer clinical pract...Objective Oral squamous cell carcinoma(OSCC)is an aggressive cancer with a high mortality rate.San-Zhong-Kui-Jian-Tang(SZKJT),a Chinese herbal formula,has long been used as an adjuvant therapy in cancer clinical practice.Although its therapeutic effects and molecular mechanisms in OSCC have been previously elucidated,the potential interactions and mechanisms between the active phytochemicals and their therapeutic targets are still lacking.Methods The present study employed network pharmacology and topology approaches to establish a“herbal ingredients–active phytochemicals–target interaction”network to explore the potential therapeutic targets of SZKJT-active phytochemicals in the treatment of OSCC.The role of the target proteins in oncogenesis was assessed via GO and KEGG enrichment analyses,and their interactions with the active phytochemicals of SZKJT were calculated via molecular docking and dynamic simulations.The pharmacokinetic properties and toxicity of the active phytochemicals were also predicted.Results A total of 171 active phytochemicals of SZKJT fulfilled the bioavailability and drug-likeness screening criteria,with the flavonoids quercetin,kaempferol,and naringenin having the greatest potential.The 4 crucial targets of these active phytochemicals are PTGS2,TNF,BCL2,and CASP3,which encode cyclooxygenase-2,tumor necrosis factor(TNF),BCL-2 apoptosis regulator,and caspase-3,respectively.The interactions between phytochemicals and target proteins were predicted to be thermodynamically feasible and stable via molecular docking and dynamics simulations.Finally,the results revealed that the IL-6/JAK/STAT3 pathway and TNF signaling via NF-κB are the two prominent pathways targeted by SZKJT.Conclusion In summary,this study provides computational data for in-depth exploration of the mechanism by which SZKJT activates phytochemicals to treat OSCC.展开更多
Objective To evaluate the in vitro anti-diabetic effects of Bryonia dioica roots extracts,in-cluding water-acetone extracts and their ethyl acetate and butanol fractions,and chloroform-methanol extracts.Methods The to...Objective To evaluate the in vitro anti-diabetic effects of Bryonia dioica roots extracts,in-cluding water-acetone extracts and their ethyl acetate and butanol fractions,and chloroform-methanol extracts.Methods The total phenolic,flavonoid,flavonol,and saponin contents in the Bryonia dioica root extracts(chloroform-methanol extracts,water-acetone extracts and their ethyl acetate and butanol fractions)were determined using colorimetric methods with Folin-Ciocalteu,aluminum trichloride,and vanillin reagents,respectively.The in vitro anti-diabetic activity was evaluated by measuring the half-maximal inhibitory concentration(IC_(50))values of these root extracts againstα-amylase andα-glucosidase activities,evaluating their effects onα-amy-lase kinetics,quantifying the inhibition of bovine serum albumin(BSA)glycation using fluo-rometry to assess advanced glycation end products(AGE)production,and determining glu-cose uptake by isolated rat hemidiaphragm.Additionally,molecular docking analysis was conducted to investigate the binding affinity and interaction types between Bryonia dioica lig-ands(cucurbitacin B,bryogénin,vitexin,and isovitexin)and target enzymes,and a phyto-chemical-targets interaction network was constructed.Results Forα-amylase inhibition,ethyl acetate fraction demonstrated the most potent activi-ty(IC_(50)=145.95μg/mL),followed by chloroform-methanol extract(IC_(50)=300.86μg/mL).Water-acetone root extracts and their ethyl acetate and butanol fractions inhibited theα-glucosidase activity with IC50 values ranging from 562.88 to 583.90μg/mL.Both ethyl acetate and butanol fractions strongly inhibited non-enzymatic BSA glycation(IC_(50)=318.26 and 323.12μg/mL,respectively).The incubation of isolated rat hemidiaphragms with the ethyl acetate fraction(5 mg/mL)significantly increased glucose uptake(35.16%;P<0.0001),exceeding the effects of insulin(29.27%),chloroform-methanol extract(24.07%),and catechin(15.27%).Molecular docking revealed that cucurbitacin B exhibited the strongest docking scores againstα-amylase(-16.4 kcal/mol),andα-glucosidase(-14.2 kcal/mol).Compared with other ligands,isovitexin formed the maximum number of hydrogen bonds with theα-amylase active site residues(Asp300,Asp197,and Glu233),α-glucosidase residues(Ser13,Arg44,Met86,Gly10,Asp39,and Tyr131)and other residues(Arg195,Trp59,His299,and Tyr62).Network analysis identified 36 overlapping targets between Bryonia dioica phyto-chemicals and type 2 diabetes mellitus-associated genes,with cucurbitacins and polyphenols interacting withα-amylase,α-glucosidase,and Glut4 translocation pathway targets.Conclusion Bryonia dioica root extracts demonstrated promising in vitro anti-diabetic activi-ty through multiple mechanisms,including the inhibitory effect on digestive enzymes,pro-tein antiglycation potential,and enhancement of glucose uptake,suggesting their potential as a source for anti-diabetic drugs development.展开更多
基金supported by the earmarked fund for the Modern Agro-industry Technology Research System(No.CARS-49)the Natural Science Foundation of Shan-dong Province(No.ZR2019BC052)the National Natural Science Foundation of China(No.42006077).
文摘Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.
文摘Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.
基金This work was supported in part by the National Natural Science Foundation of China(Grants Nos.61832019,61622213)the Fundamental Research Funds for the Central Universities,CSU(2282019SYLB004)Hunan Provincial Science and Technology Program(2019CB1007).
文摘In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPIs)provides a foundation for constructing protein interaction networks(PINs),which can furnish a new perspective for understanding cellular organizations,processes,and functions at network level.In this paper,we present a comprehensive survey on three main characteristics of PINs:centrality,modularity,and dynamics.1)Different centrality measures,which are used to calculate the importance of proteins,are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information;2)Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced;3)The dynamics of proteins,PPIs and sub-networks are discussed,respectively.Finally,the main applications of PINs in the complex diseases are reviewed,and the challenges and future research directions are also discussed.
基金P.M.was supported by a master’s scholarship and currently,is supported by doctoral scholarships from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior(CAPES)Brazil(process number 88887.662021/2022-00)+4 种基金B.S.X.was supported by doctoral scholarships from CAPES,Brazil.W.D.C.was supported by post-doctoral funding(PNPD/CAPES)until early 2020.Currently,W.D.C.is supported by“Ayudas Maria Zambrano”(CA3/RSUE/2021-00197)funded by the Spanish Ministry of UniversitiesG.L.U.was supported by Paraiba State Research Foundation(FAPESQ)under a doctoral scholarship from Grant No.518/18 and by PDPG-Amazônia Legal(process number 88887.834037/2023-00)G.G.was supported by CNPq(process number 306216/2018)Universidade Federal de Mato Grosso do Sul.J.J.T.received a research productivity scholarship from CNPq(process number 316281/2021-22).
文摘Bats are the second-most diverse group of mammals in the world,and bat flies are their main parasites.However,significant knowledge gaps remain regarding these antagonistic interactions,especially since diverse factors such as seasonality and host sex can affect their network structures.Here,we explore the influence of such factors by comparing species richness and composition of bat flies on host bats,as well as specialization and modularity of bat–bat fly interaction networks between seasons and adult host sexes.We captured bats and collected their ectoparasitic flies at 10 sampling sites in the savannahs of AmapáState,northeastern region of the Brazilian Amazon.Despite female bats being more parasitized and recording greater bat fly species richness in the wet season,neither relationship was statistically significant.The pooled network could be divided into 15 compartments with 54 links,and all subnetworks comprised>12 compartments.The total number of links ranged from 27 to 48(for the dry and wet seasons,respectively),and female and male subnetworks had 44 and 41 links,respectively.Connectance values were very low for the pooled network and for all subnetworks.Our results revealed higher bat fly species richness and abundance in the wet season,whereas specialization and modularity were higher in the dry season.Moreover,the subnetwork for female bats displayed higher specialization and modularity than the male subnetwork.Therefore,both seasonality and host sex contribute in different ways to bat–bat fly network structure.Future studies should consider these factors when evaluating bat–bat fly interaction networks.
基金support from the Shanghai Science and Technology Development Program (Grant Nos. 03DZ14024 & 07ZR14010)the 863 High Technology Foundation of China (Grant No. 2006AA02A310)+1 种基金US NIH 1R01AI064806-01A2, 5R21DK082706U.S. Department of Energy, the Office of Science (BER) (Grant No. DE-FG02- 07ER64422)
文摘Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.
基金supported in part by the National Natural Science Foundation of China(61370024,61428209,61232001)Program for New Century Excellent Talents in University(NCET-12-0547)
文摘Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments.With the advances of the high-throughput techniques,a large number of protein-protein interactions have been produced.Therefore,to address this issue,several methods based on protein interaction network have been proposed.In this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction networks.Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)annotations.SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account.The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and RWR.The experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing genes.Importantly,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes.
基金National Natural Science Foundation of China,No.42361040。
文摘Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.
基金the National Natural Science Foundation of China(Nos.11861045 and 62162040)。
文摘Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.
基金National Natural Science Foundation of China,No.31971180 and No.11474013.
文摘Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
基金This study was supported by the National Water Pollution Control and Treatment Science and Technology Major Project(2017ZX07101-002).
文摘Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.
文摘AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.
基金National Natural Science Foundation of China(No.42171448)Key Laboratory of National Geographic Census and Monitoring,Ministry of Nature Resources(No.2020NGCMZD03)。
文摘Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.
基金This work is supported by the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103).
文摘Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.
基金research funding from the Beijing Education Commission under Grant No. KM201010005027National Natural Science Foundation of China under Grant No. 61074128National Social Science Foundation of China under Grant No. 07CTQ010
文摘In network environments,before meaningful interactions can begin,trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy.Consequently,privacy protection and trust establishment become important in network interactions.In order to protect privacy while facilitating effective interactions,we propose a trust-based privacy protection method.Our main contributions in this paper are as follows:(1)We introduce a novel concept of k-sensitive privacy as a measure to assess the potential threat of inferring privacy;(2)According to trust and k-sensitive privacy evaluation,our proposed method can choose appropriate interaction patterns with lower degree of inferring privacy threat;(3)By considering interaction patterns for privacy protection,our proposed method can overcome the shortcomings of some current privacy protection methods which may result in low interaction success rate.Simulation results show that our method can achieve effective interactions with less privacy loss.
基金Project supported by the National Natural Science Foundation of China(No.11172158)
文摘Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.
文摘To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia （CML） under the guidance of protein-protein interaction network, the molecular docking technique and in vitro cell experiment were chosen. CML-related genes were obtained from the online mendelian inheritance in man database （OMIM）, then String 10. 0 was used for text mining and constructing the CML protein-protein interaction network. The interaction data were input in Cytoscape 3. 4. 0 software. Plug-in CentiScaPe 2. 1 was used for implement topology analysis. Small active substances of Indigo Naturalis were obtained from a third-party database, which were optimized by Chemoffice 8. 0 and Sybyl 8. 1, then small molecular ligand library was obtained. The molecular docking was carried out by Surflex-Dock module, the key target was received after scoring. Protein-protein interaction network of CML was constructed, which was consisted of 425 nodes （ proteins） and 2 799 sides （ interactions）. The key gene J.AK2 was got. CML is a polygenic disease and JAK2 is likely to be a key node.
文摘E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.
文摘Objective Oral squamous cell carcinoma(OSCC)is an aggressive cancer with a high mortality rate.San-Zhong-Kui-Jian-Tang(SZKJT),a Chinese herbal formula,has long been used as an adjuvant therapy in cancer clinical practice.Although its therapeutic effects and molecular mechanisms in OSCC have been previously elucidated,the potential interactions and mechanisms between the active phytochemicals and their therapeutic targets are still lacking.Methods The present study employed network pharmacology and topology approaches to establish a“herbal ingredients–active phytochemicals–target interaction”network to explore the potential therapeutic targets of SZKJT-active phytochemicals in the treatment of OSCC.The role of the target proteins in oncogenesis was assessed via GO and KEGG enrichment analyses,and their interactions with the active phytochemicals of SZKJT were calculated via molecular docking and dynamic simulations.The pharmacokinetic properties and toxicity of the active phytochemicals were also predicted.Results A total of 171 active phytochemicals of SZKJT fulfilled the bioavailability and drug-likeness screening criteria,with the flavonoids quercetin,kaempferol,and naringenin having the greatest potential.The 4 crucial targets of these active phytochemicals are PTGS2,TNF,BCL2,and CASP3,which encode cyclooxygenase-2,tumor necrosis factor(TNF),BCL-2 apoptosis regulator,and caspase-3,respectively.The interactions between phytochemicals and target proteins were predicted to be thermodynamically feasible and stable via molecular docking and dynamics simulations.Finally,the results revealed that the IL-6/JAK/STAT3 pathway and TNF signaling via NF-κB are the two prominent pathways targeted by SZKJT.Conclusion In summary,this study provides computational data for in-depth exploration of the mechanism by which SZKJT activates phytochemicals to treat OSCC.
文摘Objective To evaluate the in vitro anti-diabetic effects of Bryonia dioica roots extracts,in-cluding water-acetone extracts and their ethyl acetate and butanol fractions,and chloroform-methanol extracts.Methods The total phenolic,flavonoid,flavonol,and saponin contents in the Bryonia dioica root extracts(chloroform-methanol extracts,water-acetone extracts and their ethyl acetate and butanol fractions)were determined using colorimetric methods with Folin-Ciocalteu,aluminum trichloride,and vanillin reagents,respectively.The in vitro anti-diabetic activity was evaluated by measuring the half-maximal inhibitory concentration(IC_(50))values of these root extracts againstα-amylase andα-glucosidase activities,evaluating their effects onα-amy-lase kinetics,quantifying the inhibition of bovine serum albumin(BSA)glycation using fluo-rometry to assess advanced glycation end products(AGE)production,and determining glu-cose uptake by isolated rat hemidiaphragm.Additionally,molecular docking analysis was conducted to investigate the binding affinity and interaction types between Bryonia dioica lig-ands(cucurbitacin B,bryogénin,vitexin,and isovitexin)and target enzymes,and a phyto-chemical-targets interaction network was constructed.Results Forα-amylase inhibition,ethyl acetate fraction demonstrated the most potent activi-ty(IC_(50)=145.95μg/mL),followed by chloroform-methanol extract(IC_(50)=300.86μg/mL).Water-acetone root extracts and their ethyl acetate and butanol fractions inhibited theα-glucosidase activity with IC50 values ranging from 562.88 to 583.90μg/mL.Both ethyl acetate and butanol fractions strongly inhibited non-enzymatic BSA glycation(IC_(50)=318.26 and 323.12μg/mL,respectively).The incubation of isolated rat hemidiaphragms with the ethyl acetate fraction(5 mg/mL)significantly increased glucose uptake(35.16%;P<0.0001),exceeding the effects of insulin(29.27%),chloroform-methanol extract(24.07%),and catechin(15.27%).Molecular docking revealed that cucurbitacin B exhibited the strongest docking scores againstα-amylase(-16.4 kcal/mol),andα-glucosidase(-14.2 kcal/mol).Compared with other ligands,isovitexin formed the maximum number of hydrogen bonds with theα-amylase active site residues(Asp300,Asp197,and Glu233),α-glucosidase residues(Ser13,Arg44,Met86,Gly10,Asp39,and Tyr131)and other residues(Arg195,Trp59,His299,and Tyr62).Network analysis identified 36 overlapping targets between Bryonia dioica phyto-chemicals and type 2 diabetes mellitus-associated genes,with cucurbitacins and polyphenols interacting withα-amylase,α-glucosidase,and Glut4 translocation pathway targets.Conclusion Bryonia dioica root extracts demonstrated promising in vitro anti-diabetic activi-ty through multiple mechanisms,including the inhibitory effect on digestive enzymes,pro-tein antiglycation potential,and enhancement of glucose uptake,suggesting their potential as a source for anti-diabetic drugs development.
