Core Subnetwork Research Articles

Network pharmacology, molecular docking, and molecular dynamics simulations shed light on the mechanism behind Gynostemma pentaphyllum's efficacy against osteosarcoma.

Osteosarcoma (OS) is the most common type of malignant bone tumor, that poses a serious threat to the lives and health of children and adolescents. Traditional Chinese medicines (TCM) have gained attention for treating OS because of their potent anti-cancer effects and fewer side effects. It is commonly understood that Gynostemma pentaphyllum (Thunb.) Makino (GP) exhibits inhibitory effects on most tumors. However, the knowledge of the systematic mechanisms involved is limited. In this study, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was searched to screen the effective ingredients and corresponding target genes of GP, and disease target databases were searched to identify relevant targets for OS. Venn analysis was used to visualize overlapping genes, which were further extracted using the protein-protein interaction network. R software was used to conduct gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis, molecular docking and molecular dynamics simulation further validate the binding efficacy of potential therapeutic targets to compound molecules. In total, 161 and 1981 proteins were identified as target genes of GP and OS, respectively, and 104 overlapping genes were identified. Through analysis of the core subnetwork, 12 hub genes were identified, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the PI3K/Akt signaling pathway was the most significant. Molecular docking and molecular dynamics simulations show that a high affinity between quercetin and these targets, especially with the combination of TNF free energy (Δ Gbind) minimum, MM/PBSA and MM/GBSA is 42.85 kcal/mol, respectively, and 45.29 kcal/mol. The active ingredients Rhamnazin and Quercetin in Gypenoylum play a therapeutic role in OS through several key targets and pathways. This study provides ideas and references for further research on drug development.

Identification of steroid-induced osteonecrosis of the femoral head biomarkers based on immunization and animal experiments

BackgroundSteroid-induced osteonecrosis of femoral head (SONFH) is a severe health risk, and this study aims to identify immune-related biomarkers and pathways associated with the disease through bioinformatics analysis and animal experiments.MethodUsing SONFH-related datasets obtained from the GEO database, we performed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to extract SONFH-related genes. A protein-protein interaction (PPI) network was then constructed, and core sub-network genes were identified. Immune cell infiltration and clustering analysis of SONFH samples were performed to assess differences in immune cell populations. WGCNA analysis was used to identify module genes associated with immune cells, and hub genes were identified using machine learning. Internal and external validation along with animal experiments were conducted to confirm the differential expression of hub genes and infiltration of immune cells in SONFH.ResultsDifferential expression analysis revealed 502 DEGs. WGCNA analysis identified a blue module closely related to SONFH, containing 1928 module genes. Intersection analysis between DEGs and blue module genes resulted in 453 intersecting genes. The PPI network and MCODE module identified 15 key targets enriched in various signaling pathways. Analysis of immune cell infiltration showed statistically significant differences in CD8 + t cells, monocytes, macrophages M2 and neutrophils between SONFH and control samples. Unsupervised clustering classified SONFH samples into two clusters (C1 and C2), which also exhibited significant differences in immune cell infiltration. The hub genes (ICAM1, NR3C1, and IKBKB) were further identified using WGCNA and machine learning analysis. Based on these hub genes, a clinical prediction model was constructed and validated internally and externally. Animal experiments confirmed the upregulation of hub genes in SONFH, with an associated increase in immune cell infiltration.ConclusionThis study identified ICAM1, NR3C1, and IKBKB as potential immune-related biomarkers involved in immune cell infiltration of CD8 + t cells, monocytes, macrophages M2, neutrophils and other immune cells in the pathogenesis of SONFH. These biomarkers act through modulation of the chemokine signaling pathway, Toll-like receptor signaling pathway, and other pathways. These findings provide valuable insights into the disease mechanism of SONFH and may aid in future drug development efforts.

MiR-3202 inhibits bronchopulmonary dysplasia-mediated apoptosis and oxidative stress in bronchial epithelial cells via targeting RAG1

BackgroundBPD is a refractory disease affecting preterm infants with alveolar dysplasia and declined pulmonary function. However, the molecular mechanism underlying BPD is largely unknown. To explore the pathogenic mechanism of BPD and to facilitate better diagnosis and treatment of this disease. MethodThe DEMs and DEGs in BPD vs. Control samples from the miRNA expression data in GSE108754 and mRNA expression data in the GSE108755 were screened, followed by the construction of the miRNA-mRNA regulatory network. DEGs PPI network and hub DEGs analysis were constructed by using the STRING database and Cytoscape software. Functional and pathway enrichment analyses were then performed for these DEGs and DEMs based on the ClusterProfiler package in the R and the miRWalk database. The k-mean algorithm is used to perform clustering analysis of DEGs. Cellular experiments (flow cytometry, western blot, RT-PCR, dual-luciferase reporter assay) were used to validate the results of bioinformatics. ResultsWe obtained 20 DEMs and 262 DEGs. A 15 DEMs-11 DEGs regulatory network was constructed. miR-3202-RAG1 is a core sub-network. Hyperoxia induced a cell model of BPD. The upregulation of RAG1 and downregulation of miR-3202 were observed in BPD cells. Furthermore, siRNA targeting RAG1 was transfected into BEAS-2B cells to inhibit its expression and miR-3202 mimics was transfected into the cells to increase its expression. Inhibition of RAG1 and elevation of miR-3202 inhibit cell apoptosis and reduce ROS level caused by hyperoxia. A double-luciferase reporter assay revealed that miR-3202 directly targets RAG1. ConclusionThe miRNA-3202/RAG1 axis contributes into BPD-induced cell apoptosis and ROS production. The present study provides a probable target for the treatment of BPD.

Screening the critical protein subnetwork to delineate potential mechanisms and protective agents associated with arsenic-induced cutaneous squamous cell carcinoma: A toxicogenomic study

Recent studies show that complex mechanisms are involved in arsenic-induced malignant transformation of cells. This study aimed to decipher molecular mechanisms associated with arsenic-induced cutaneous squamous cell carcinoma (cSCC) and suggest potential protective factors. RNA-seq-based differentially expressed genes between arsenic-exposed human keratinocytes (HaCaT) and controls were used to construct a protein-protein interaction (PPI) network and discover critical subnetwork-based mechanisms. Protective compounds against arsenic toxicity were determined and their target interactions in the core sub-network were identified by the comparative toxicogenomic database (CTD). The binding affinity between the effective factor and target was calculated by molecular docking. A total of 15 key proteins were screened out as critical arsenic-responsive subnetwork (FN1, IL-1A, CCN2, PECAM1, FGF5, EDN1, FGF1, PXDN, DNAJB9, XBP1, ERN1, PDIA4, DNAJB11, FOS, PDIA6) and 7 effective protective agents were identified (folic acid, quercetin, zinc, acetylcysteine, methionine, catechin, selenium). The GeneMANIA predicted detailed interactions of the subnetwork and revealed terms related to unfolded protein response as the main processes. FN1, IL1A and CCN2, as top significant genes, had good docking affinity with folic acid and quercetin, as selected key compounds. Integration of gene expression and protein-protein interaction related to arsenic exposure in cSCC explored the potential mechanisms and protective agents.

Cell type-specific connectome predicts distributed working memory activity in the mouse brain.

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

Subnetwork-Lossless Robust Watermarking for Hostile Theft Attacks in Deep Transfer Learning Models

Recently, considerable progress has been made in providing solutions to prevent intellectual property (IP) theft for deep neural networks (DNNs) in ideal classification or recognition scenarios. However, little work has been dedicated to protecting the IP of DNN models in the context of transfer learning. Moreover, knowledge transfer is usually achieved through knowledge distillation or cross-domain distribution adaptation techniques, which will easily lead to the failure of the IP protection due to the high risk of the underlying DNN watermark being corrupted. To address this issue, we propose a subnetwork-lossless robust DNN watermarking (SRDW) framework, which can exploit out-of-distribution (OOD) guidance data augmentation to boost the robustness of watermarking. Specifically, we accurately seek the most rational modification structure (i.e., core subnetwork) using the module risk minimization, and then calculate the contrastive alignment error and the corresponding hash value as the reversible compensation information for the restoration of carrier network. Experimental results show that our scheme has superior robustness against various hostile attacks, such as fine-tuning, pruning, cross-domain matching, and overwriting. In the absence of malicious jamming attacks, the core subnetwork can be recovered without any loss. Besides that, we investigate how embedding watermarks in batch normalization (BN) layers affect the generalization performance of the deep transfer learning models, which reveals that reducing the embedding modifications in BN layers can further promote the robustness to resist hostile attacks.

Comprehensive Analysis of a Competing Endogenous RNA Co-Expression Network in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is the main cause of mortality world widely. Non-coding RNAs (lncRNAs) and associated competitive endogenous RNAs (ceRNAs) networks were recently proved to lead to mRNA gene expression downregulation but were still unclear in COPD. This study aims to investigate and elucidate the mechanisms underlying the involvement of ceRNA co-expression networks in COPD pathogenesis. Obtained expression signature of data from the Gene Expression Omnibus database and compared the differentially expression of mRNAs and miRNAs between COPD patients and healthy smokers. Predicted the miRNA-lncRNA and miRNA-mRNA interaction using online library and employed CIBERSORT to measure the proportions of the 22 immune cells in the COPD and control groups. Established a ceRNA-network comprising 11 lncRNAs, 5 miRNAs, and 16 mRNAs. Using the weighted correlation network analysis method, we identified hub genes and hub miRNAs and obtained one core sub-network, XIST, FGD5-AS1, KCNQ1OT1, HOXA11-AS, LINC00667, H19, PRKCQ-AS1, NUTM2A-AS1/has-mir-454-3p/ZNF678, PRRG4. COPD patients had different proportions of immune cells than controls, and these variations were associated with the magnitude of pulmonary function parameters. The ceRNA-network, particularly the core sub-network, may be a putative goal for COPD, in which specific immune cells were involved.

Stage Analysis of Breast Cancer Metabolomics: A System Biology Approach.

Breast cancer (BC) is the most common malignancy in women worldwide. Altered miRNA profile can disturb the metabolic homeostatic via regulation of gene expression in BC. In the present study to evaluate which miRNA, regulate metabolic pathways according to their stage, we performed comprehensive analysis of BC expression (mRNA and miRNA) of a set of patients by comparing samples of solid tumor tissue and adjacent tissue. The mRNA and miRNA data of breast cancer were downloaded from the cancer genome database (TCGA) using TCGAbiolinks package. Differentially expressed (mRNAs and miRNAs) was determined by DESeq2 package and predict valid miRNA-mRNA pairs using multiMiR package. All analyses were performed using the R software. Compound-reaction-enzyme-gene network was constructed using the Metscape a plugin for Cytoscape software. Then, core subnetwork computed by CentiScaPe, another plugin for Cytoscape. In Stage I, hsa-miR-592, hsa-miR-449a and hsa-miR-1269a targeted HS3ST4, ACSL1 and USP9Y genes respectively. In stage II, hsa-miR-3662, Hsa-miR-429, and hsa-miR-1269a targeted GYS2, HAS3, ASPA, TRHDE, USP44, GDA, DGAT2, and USP9Y genes. In stage III, hsa-miR-3662 targeted TRHDE, GYS2, DPYS, HAS3, NMNAT2, ASPA genes. In stage IV, hsa-miR-429, has-miR-23c, and hsa-miR-449a targeted genes GDA, DGAT2, PDK4, ALDH1A2, ENPP2, and KL. Those miRNAs and their targets were identified as the discriminative elements for the four stages of breast cancer. The most notable differences between BC and normal tissue in four stages involved multiple pathways and metabolites include: carbohydrate metabolism (e.g., Amylose, N-acetyl-D-glucosamin, beta-D-Glucuronoside, ""g""-CEHC-glucuronide, ""a""-CEHC-glucuronide, Heparan-glucosamine, 5,6-Dihydrouracil, 5,6-Dihydrothymine), branch-chain amino acid metabolism (e.g., N-Acetyl-L-aspartate, N-Formyl-L-aspartate, N`-acetyl-L-asparagine), Retinal metabolism (e.g., Retinal, 9-`cis`-retinal, 13-`cis`-retinal) and (FAD, NAD) as central coenzymes of metabolism. Set of crucial microRNAs and targeted genes plus the related metabolites were introduced for four stages of BC that can be consider for therapeutic and diagnostic purposes in the different stages of disease.

Transcriptome Sequencing of lncRNAs, circRNAs, miRNAs, mRNAs, and Interaction Network Constructing in Acute Exacerbation of Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) patients who suffer from acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) are at increased risk of respiratory deterioration and death. Non-coding RNAs (ncRNAs) play a vital role in AE-IPF, but studies of crosstalk between transcripts of IPF based on Traditional Chinese Medicine (TCM) syndrome type are relatively few. The construction of long non-coding RNAs (lncRNA)/circular RNAs (circRNA)-microRNAs (miRNA)-mRNA interaction networks can promote understanding RNA interaction in different syndrome types of AE-IPF. The study aimed to identify the difference in RNA transcription expression between IPF patients with "lung heat and collateral stasis (LHCS)" and "lung deficiency with collateral stasis (LDCS)" syndromes, further to construct the potential RNA networks. Five IPF patients with LHCS and five IPF patients with LDCS were recruited in this study to perform RNA sequencing and miRNA sequencing. Further analysis was carried out on the differential expression profiles of lncRNAs, circRNAs, miRNAs, and mRNAs among patients with LHCS and LDCS. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The lncRNA/circRNA-miRNA-mRNA competing endogenous RNAs (ceRNAs) network was constructed, and the key regulatory molecules were analyzed. For LHCS and LDCS, we identified 69 lncRNAs, 150 circRNAs, 27 miRNAs, and 56 mRNAs. Differential expression analysis through GO and KEGG highlights that differentially expressed mRNAs have significant associations with pathways such as tight junction and Hepatitis C. Within the ceRNA network, all nodes have a direct or indirect association with LHCS progression. The hsa-miR-150-5p core sub-network is composed of 1 lncRNA, 6 circRNAs, 1 miRNA, and 5 mRNAs. From the ceRNA sub-network analysis, NR_120628/hsa-miR-150-5p/E2F3 and hsa-circ-0053515/hsa-miR-150-5p/E2F3 emerged as the pivotal ceRNA pairs. This study highlights that the NR_120628/hsa-miR-150-5p/E2F3 and hsa-circ-0053515/hsa-miR-150-5p/E2F3 axes could be central in the regulation of LHCS, providing valuable insights into potential directions for subsequent research on LHCS. Chinese clinical trial registry (CHiCTR23007405). Registered on July 27, 2023. https://www.chictr.org.cn/.

Parkinson’s disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson’s disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Characterizing data patterns with core–periphery network modeling

Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification is still a challenge problem. Meanwhile, data classification techniques can only work well when data features present high level of similarity in the feature space within each class. Such a hypothesis is not always satisfied, since, in real-world applications, we frequently encounter the following situation: On one hand, the data samples of some classes (usually representing the normal cases) present well defined patterns; on the other hand, the data features of other classes (usually representing abnormal classes) present large variance, i.e., low similarity within each class. Such a situation makes data classification a difficult task. In this paper, we present a novel solution to deal with the above mentioned problems based on the mesostructure of a complex network, built from the original data set. Specifically, we construct a core–periphery network from the training data set in such way that the normal class is represented by the core sub-network and the abnormal class is characterized by the peripheral sub-network. The testing data sample is classified to the core class if it gets a high coreness value; otherwise, it is classified to the periphery class. The proposed method is tested on an artificial data set and then applied to classify x-ray images for COVID-19 diagnosis, which presents high classification precision. In this way, we introduce a novel method to describe data pattern of the data “without pattern” through a network approach, contributing to the general solution of classification.

Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms

Objective: Shiwei Qingwen decoction (SWQWD) is effective in preventing COVID-19. This study examined the active components of SWQWD and its potential targets for preventing COVID-19. The study used network pharmacology and molecular docking technology to verify the role of SWQWD targets through animal experiments and explored the mechanisms that enhance immunity to alleviate mild COVID-19 symptoms. Methods: First, SWQWD- and COVID-19-related targets were retrieved from TCMSP, GeneCards, and OMIM databases. Second, protein–protein interaction networks were established using the String database. The drug active ingredient target network was constructed in Cytoscape to identify the core target proteins. Third, Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the SWQWD mechanism of action. Finally, the targets were validated by molecular docking in an acute lung injury (ALI) rat model. Results: The SWQWD compound target network contained 79 compounds and 277 targets, coinciding with the 73 targets of COVID-19. The most important gene in the core subnetwork was a tumor necrosis factor (TNF). The 3 most potent compounds, quercetin, kaempferol, and luteolin, can enter the active pockets of TNF and have potential therapeutic roles in COVID-19. Conclusion: Quercetin, kaempferol, and luteolin in SWQWD may enhance immunity by regulating multiple TNF signal pathways. After administering SWQWD, the content of tumor necrosis factor-α was significantly reduced in the bronchoalveolar lavage fluid (BALF) of ALI rats in comparison to the model group. We believe SWQWD is able to prevent and control COVID-19 through the target of TNF.

Prediction of herbal medicines based on immune cell infiltration and immune- and ferroptosis-related gene expression levels to treat valvular atrial fibrillation.

Inflammatory immune response is apparently one of the determinants of progressive exacerbation of valvular atrial fibrillation(VAF). Ferroptosis, an iron-dependent modality of regulated cell death, is involved in the immune regulation of cardiovascular disease. However, the relevant regulatory mechanisms of immune infiltration and ferroptosis in VAF have been less studied. In the current study, a highly efficient system for screening immunity- and ferroptosis-related biomarkers and immunomodulatory ability of herbal ingredients has been developed with the integration of intelligent data acquisition, data mining, network pharmacology, and computer-assisted target fishing. VAF patients showed higher infiltration of neutrophils and resting stage dendritic cells, while VSR patients showed higher infiltration of follicular helper T cells. In addition, six (e.g., PCSK2) and 47 (e.g., TGFBR1) ImmDEGs and one (SLC38A1) and four (TGFBR1, HMGB1, CAV1, and CD44) FerDEGs were highly expressed in patients with valvular sinus rhythm (VSR) and VAF, respectively. We further identified a core subnetwork containing 34 hub genes, which were intersected with ImmDEGs and FerDEGs to obtain the key gene TGFBR1. Based on TGFBR1, 14 herbs (e.g., Fructus zizyphi jujubae, Semen Juglandis, and Polygonum cuspidatum) and six herbal ingredients (curcumin, curcumine, D-glucose, hexose, oleovitamin A, and resveratrol) were predicted. Finally, TGFBR1 was found to dock well with curcumin and resveratrol, and it was further verified that curcumin and resveratrol could significantly reduce myocardial fibrosis. We believe that herbs rich in curcumin and resveratrol such as Rhizoma curcumae longae and Curcuma kwangsiensis, mitigate myocardial fibrosis to improve VAF by modulating the TGFβ/Smad signaling pathway. This strategy provides a prospective approach systemically characterizing phenotype-target-herbs relationships based on the tissue-specific biological functions in VAF and brings us new insights into the searching lead compounds from Chinese herbs.

Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments.

The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein–protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.

A global Anopheles gambiae gene co-expression network constructed from hundreds of experimental conditions with missing values

BackgroundGene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes.ResultsIn this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes.ConclusionAnalysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.

Low-carbon eco-city development policy in China: a bibliometric analysis of policy documents

PurposeThe rationality of the selection and application of policy instruments in current policy schemes for promoting the development of low-carbon eco-cities in China is examined and, in view of existing problems, effective suggestions for scientifically and reasonably organizing and designing policy instruments are proposed, which is of great significance to optimize the policy system for the development of low-carbon eco-cities in China and thereby promote sustainable urban development.Design/methodology/approachIn the study, using 117 policy documents focusing on the development of low-carbon eco-cities issued by the Chinese central government as well as the relevant ministries and commissions between 2010 and 2019 and comprehensively adopting bibliometric and content analysis methods, the contents of policy documents are quantitatively analyzed from three dimensions: policy instrument, policy area and policy target.FindingsThe study finds the following. Ecological environment and low-carbon development are the focus of policy attention. Policy-promulgating institutions have formed a core subnetwork, that is, an “iron triangle.” There exists a structural imbalance in the use of policy instruments, and the distribution of policy instruments in different policy areas exhibits significant differentiating characteristics. Driven by different policy targets, the proportion of the policy instrument types adopted by the government is constantly changing. According to these findings, suggestions are put forward to optimize China's low-carbon eco-city development policy.Originality/value(1) A three-dimensional policy analysis framework is conducted from the perspective of policy instrument, which is innovative in theory. (2) This paper finds the selective utilization rationality and evolution rule of China's low-carbon eco-city development policy instruments. (3) Recommendations on optimizing China's low-carbon eco-city development policies are discussed systematically, which plays a theoretical guiding role in optimizing the development policies of low-carbon eco-cities in China.

The network structure of the China bond market: Characteristics and explanations from trading factors

Based on the transaction records of financial institutions in the China interbank bond market, we construct a binary and directed trading network for each trading day. Topology analysis of network snapshots shows the network to be scale-free and disassortative, revealing a stable “core–peripheral” hierarchical structure. Further, the rich-core method is adopted to identify the core nodes and divide the overall network into three subnetworks. Relying on the exponential random graph model (ERGM), we find that the pledge rate is vital in shaping the network structure. Still, collateral has a relatively weak impact as compensation for default risk. Appropriate counterparties, including primary dealers, trading institutions sharing more common counterparties and institutions that have formed long-term trading relationships, are conducive to forming a network. We also find that the transaction cost factor has the least impact on the core subnetwork, and the counterparty factor is more important for the intermediate and peripheral subnetworks.

Bioactivity assessment of essential oils of Cymbopogon species using a network pharmacology approach.

Essential oils ofCymbopogonspecies have wide commercial applications in fragrance, perfumery, and pharmaceuticals as they exhibit a horizon of bioactivities. Here, essential oils of C. flexuosus and C. martinii were analysed to identify bioactive constituents and bioactivities using a network pharmacology approach. Essential oils were isolated using hydro-distillation in a mini Clevenger apparatus. Analysis of essential oils by GC-MS revealed 20 and 15 chemical constituents in C. flexuosus and C. martinii, respectively. An ingredient-target protein-pathway network was constructed comprising 10 oil constituents (citral, geraniol, geranyl acetate, limonene, linalool, α-terpineol, borneol, α-pinene, myrcene, and n-decanol), 14 target proteins, 51 related pathways, and 108 connections. Analyses of the network showed geraniol, geranyl acetate, limonene, linalool, and citral as major active constituents. A core sub-network constructed from the ingredient-target protein-pathway network revealed bioactivities including anti-cancer, anti-inflammatory and neuroprotective. The protein association network pointed out the major target proteins viz., THRB, FXR, ALOX15, and TSHR and pathways like metabolic, and neuroactive ligand-receptor interaction pathways of essential oil constituents. The target proteins and pathways provided insights into the mechanism of action of bioactive constituents. Based on the results of the study, geraniol was correlated with neuroprotective, citral to chemo-preventive, and limonene to anti-inflammatory activities. Thus, the study offers a new way for the assessment of the bioactivities of Cymbopogon species essential oils leading to the development of new biomedicines.

Deciphering functional biomolecule potential of marine diatoms through complex network approach

Marine diatoms are unique reservoirs of bioactive compounds having enormous applications in therapeutics. But high-throughput screening methods are needed to elucidate the interaction between numerous biomolecules and their targets, facilitating rapid screening for novel drug molecules. So, in the present study chemical constituents were extracted from five marine diatoms using un-targeted metabolite profiling and in-silico virtual screening bioinformatics was employed to predict their bioactivity and molecular targets. A total of 17 chemical constituents out of 51 showed interactions with 76 protein targets associated with 213 pathways. Ingredient-target-pathway network revealed oleic acid, linoleic acid and cholest-5-en-3-ol as major active constituents. Core subnetwork and protein association network showed involvement of these compounds in key metabolic pathways related to cell signaling, cell growth and metabolism of xenobiotics. Thus, the present study for the first time revealed the main active ingredients and their associated pathways from marine diatoms using complex network approach.

Pathway-based protein-protein association network to explore mechanism of α-glucosidase inhibitors from Scutellaria baicalensis Georgi against type 2 diabetes.

Natural products have been widely used in the treatment of type 2 diabetes (T2D). However, their mechanisms are often obscured due to multi‐components and multi‐targets. The authors constructed a pathway‐based protein–protein association (PPA) network for target proteins of 13 α‐glucosidase inhibitors (AGIs) identified from Scutellaria baicalensis Georgi (SBG), designed to explore the underlying mechanisms. This network contained 118 nodes and 1167 connections. An uneven degree distribution and small‐world property were observed, characterised by high clustering coefficient and short average path length. The PPA network had an inherent hierarchy as C(k)∼k −0.71. It also exhibited potential weak disassortative mixing pattern, coupled with a decreased function Knn (k) and negative value of assortativity coefficient. These properties indicated that a few nodes were crucial to the network. PGH2, GNAS, MAPK1, MAPK3, PRKCA, and MAOA were then identified as key targets with the highest degree values and centrality indices. Additionally, a core subnetwork showed that chrysin, 5,8,2′‐trihydroxy‐7‐methoxyflavone, and wogonin were the main active constituents of these AGIs, and that the serotonergic synapse pathway was the critical pathway for SBG against T2D. The application of a pathway‐based protein–protein association network provides a novel strategy to explore the mechanisms of natural products on complex diseases.