Target Database Research Articles

Investigating the multitarget pharmacological mechanism of Rhodiola wallichiana var. cholaensis acting on angina pectoris using combined network pharmacology and molecular docking.

Rhodiola wallichiana var. cholaensis (RW) is one of the traditional Chinese medicinal materials, which is used to treat angina pectoris (AP). However, the possible underlying mechanisms remains unclear. The aim of this study was to explore RW in the treatment of AP and to identify the potential mechanism of the core compounds. In this study, systematic and comprehensive network pharmacology and molecular docking were used for the first time to explore the potential pharmacological mechanisms of RW on AP. First, the relative compounds were obtained by mining the literature, and potential targets of these compounds using target prediction were collected. We then built the AP target database using the DigSee and GeneCards databases. Based on the data, overlapping targets and hub genes were identified with Maximal Clique Centrality (MCC) algorithm in Cytoscape, cytoHubba. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) analysis were performed to screen the hub targets by topology. Molecular docking was utilized to investigate the receptor-ligand interactions on Autodock Vina and visualized in PyMOL. A total of 218 known RW therapeutic targets were selected. Systematic analysis identified nine hub targets (VEGFA, GAPDH, TP53, AKT1, CASP3, STAT3, TNF, MAPK1 and JUN) mainly involved in the complex treatment effects associated with the protection of the vascular endothelium, as well as the regulation of glucose metabolism, cellular processes, inflammatory responses, and cellular signal transduction. Molecular docking indicated that the core compounds had good affinity with the core targets. The results of this study preliminarily identify the potential targets and signaling pathways of RW in AP therapy and lay a promising foundation for further experimental studies and clinical trials.

Intelligent System for Providing Migration Through Dynamic Data Deserialization

The purpose of research. Timely provision of data transfer between information systems allows you to quickly exchange resources. However, applications may have different data formats and structures. Therefore, the aim of the research was to develop models, methods and algorithms for a system of asynchronous deserialization of a data string, providing an increase in the efficiency of determining data models by generating strongly typed objects.Methods. The way to deserialize models from data involves line-by-line decomposition of a JSON-file line with the definition of key-value types and their correlation with the data model: character, string, number, boolean value. After that, the web controller conducts asynchronous generation of the class and its objects. To classify string values, serialized string value classifiers are used. For asynchronous generation of objects, a system of “contracts” of models and algorithms for executing and converting these models are used.Results. The deserializer consists of a system of four model analysis controllers and a value generation algorithm. A simple single model deserialization model allows the model to be mapped to relational database table headers to enable model migration between systems. The generated objects are represented by static data types, which ensures that they can be written to any DBMS system using built-in tools. A complex model represents a block of values as a system of different models. Software has been developed for connecting source and target databases, which allows you to migrate data from the created models. Generated values are represented as full-fledged objects and can be used to create a web interface for applications, edit data models, and manage the model system.Conclusion. Experimental studies on deserialization of models from a JSON string containing complex model classes showed an average accuracy of determining the data type of models in 92% of cases, in particular when determining the types of values "character" and "string". The created models are presented in the form of a data table and can be used to ensure the migration of these models.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

BackgroundMetabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.MethodsRNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.ResultsThis study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.ConclusionThe metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

Exploring the potential mechanism and molecular targets of Taohong Siwu Decoction against deep vein thrombosis based on network pharmacology and analysis docking.

This study aims to investigate the mechanism of Taohong Siwu Decoction (THSWD) against deep vein thrombosis (DVT) using network pharmacology and molecular docking technology. We used the Traditional Chinese Medicine Systems Pharmacology database and reviewed literature to identify the main chemical components of THSWD. To find targets for DVT, we consulted GeneCards, Therapeutic Target Database, and PharmGKB databases. We used Cytoscape 3.8.2 software to construct herb-disease-gene-target networks. Additionally, we integrated drug targets and disease targets on the STRING platform to create a protein-protein interaction network. Then, we conducted Kyoto Encyclopedia of Genes and Genomes and gene ontology analysis. Finally, We employed the molecular docking method to validate our findings. We identified 56 potential targets associated with DVT and found 61 effective components. beta-sitosterol, quercetin, and kaempferol were the most prominent among these components. Our analysis of the protein-protein interaction network revealed that IL6, L1B, and AKT1 had the highest degree of association. Gene ontology analysis showed that THSWD treatment for DVT may involve response to inorganic substances, negative regulation of cell differentiation, plasma membrane protein complex, positive regulation of phosphorylation, and signaling receptor regulator activity. Kyoto Encyclopedia of Genes and Genomes analysis indicated that lipid and atherosclerosis, pathways in cancer, as well as the PI3K-Akt pathway are the main signal pathways involved. Molecular docking results demonstrated strong binding affinity between beta-sitosterol, quercetin, kaempferol, and AKT1 proteins as well as IL1B and IL6 proteins. The main targets for THSWD treatment of DVT may include AKT1, IL1B, and IL6. Beta-sitosterol, quercetin, and kaempferol may be the active ingredients responsible for producing this effect. These compounds may slow down the progression of DVT by regulating the inflammatory response through the PI3K/Akt pathway.

Identification and validation of a prognostic risk-scoring model for AML based on m7G-associated gene clustering.

Acute myeloid leukemia (AML) patients still suffer from poor 5-year survival and relapse after remission. A better prognostic assessment tool is urgently needed. New evidence demonstrates that 7-methylguanosine (m7G) methylation modifications play an important role in AML, however, the exact role of m7G-related genes in the prognosis of AML remains unclear. The study obtained AML expression profiles and clinical information from TCGA, GEO, and TARGET databases. Using the patient data from the TCGA cohort as the training set. Consensus clustering was performed based on 29 m7G-related genes. Survival analysis was performed by KM curves. The subgroup characteristic gene sets were screened using WGCNA. And tumor immune infiltration correlation analysis was performed by ssGSEA. The patients were classified into 3 groups based on m7G-related genebased cluster analysis, and the differential genes were screened by differential analysis and WGCNA. After LASSO regression analysis, 6 characteristic genes (including CBR1, CCDC102A, LGALS1, RD3L, SLC29A2, and TWIST1) were screened, and a prognostic risk-score model was constructed. The survival rate of low-risk patients was significantly higher than that of high-risk patients (p < 0.0001). The area under the curve values at 1, 3, and 5 years in the training set were 0.871, 0.874, and 0.951, respectively, indicating that this predictive model has an excellent predictive effect. In addition, after univariate and multivariate Cox regression screening, histograms were constructed with clinical characteristics and prognostic risk score models to better predict individual survival. Further analysis showed that the prognostic risk score model was associated with immune cell infiltration. These findings suggest that the scoring model and essential risk genes could provide potential prognostic biomarkers for patients with acute myeloid leukemia.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Unravels the Molecular Feature of Tumor-Associated Macrophage of Acute Myeloid Leukemia.

The association between acute myeloid leukemia (AML) and macrophage remains to be deeply explored. Gene expression profiles and clinical variable characteristics of AML patients were collected from TCGA, GEO, and TARGET databases. Consensus clustering was employed to construct the macrophage-related clusters. The macrophage-related index (MRI) was constructed using the LASSO and multivariate Cox analysis. The GSE71014 and TARGET datasets were utilized as external validation sets. Single-cell sequencing data for AML (GSE116256) was adopted to analyze modeled gene expression levels in cells. Two macrophage-related clusters with different prognostic and immune infiltration characteristics were constructed in AML. Cluster B had a poorer prognosis, more cancer-promoting pathway enrichment, and an immunosuppressive microenvironment. Relied on the MRI, patients of different groups showed different levels of immune infiltration, different mutations, and prognoses. LGALS1 and BCL2A1 may play roles in promoting cancer in AML, while ELANE may have a significant effect on suppressing cancer. Macrophage-related genes (MRGs) had significant impacts on the occurrence and progression of AML. MRI may better evaluate the prognosis and immune features of AML patients.

Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan–Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association.

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

HSPD1 Supports Osteosarcoma Progression through Stabilizing ATP5A1 and thus Activation of AKT/mTOR Signaling.

Malignant transformation is concomitant with excessive activation of stress response pathways. Heat shock proteins (HSPs) are stress-inducible proteins that play a role in folding and processing proteins, contributing to the non-oncogene addiction of stressed tumor cells. However, the detailed role of the HSP family in osteosarcoma has not been investigated. Bulk and single-cell transcriptomic data from the GEO and TARGET databases were used to identify HSPs associated with prognosis in osteosarcoma patients. The expression level of HSPD1 was markedly increased in osteosarcoma, correlating with a negative prognosis. Through in vitro and in vivo experiments, we systematically identified HSPD1 as an important contributor to the regulation of proliferation, metastasis, and apoptosis in osteosarcoma by promoting the epithelial-mesenchymal transition (EMT) and activating AKT/mTOR signaling. Subsequently, ATP5A1 was determined as a potential target of HSPD1 using immunoprecipitation followed by mass spectrometry. Mechanistically, HSPD1 may interact with ATP5A1 to reduce the K48-linked ubiquitination and degradation of ATP5A1, which ultimately activates the AKT/mTOR pathway to ensure osteosarcoma progression and EMT process. These findings expand the potential mechanisms by which HSPD1 exerts biological effects and provide strong evidence for its inclusion as a potential therapeutic target in osteosarcoma.

“Multispacecraft to Multidebris” Space Debris Removal Strategy Based on Target Allocation Method

The combination of increased amount of “space junk”, lack of precise tracking information and control of items orbiting Earth, and debris from past collisions posed important threats to human space missions. In this study, a strategy enabling “multispacecraft to multidebris” debris removal task is proposed. By analyzing the current distribution of space debris, 282 large debris and rocket bodies are selected as target database. The traditional-density-based spatial clustering of applications with noise algorithm is innovatively modified by defining the speed increment as threshold parameter for dividing target debris into different clusters. Then, the Hungarian-algorithm-based target allocation strategy is used to assign multispacecraft to different debris clusters for the removal mission. Simulations verify the effectiveness of the proposed “multispacecraft to multidebris” strategy that is able to remove as much as 83.33% of the total 282 target debris.

UBR5 Significantly Correlates with Osteosarcomas Prognosis and Immune Exhaustion Characteristic in the Tumor Microenvironment.

Ubiquitin ligases (E3s) play an important role in multiple cancers. The open-accessed expression profile and clinical information was downloaded from the TARGET database. The analysis was performed using R software. In this study, we comprehensively investigated the role of E3s in osteosarcomas (OS). We found that among all these E3s, UBR5 is a risk factor for OS. Considering that UBR5 has not been reported in previous studies focused on OS, we selected it for further analysis. Interestingly, we found that UBR5 had no significant effect on immune cell infiltration but a remarkable effect on immune function. Moreover, we divided the patients into "immune activation" and "immune exhaustion" types. KM survival curves indicated that the patients in the "immune exhaustion" types had a worse survival performance. Further, we identified the molecules involved in immune function and significantly correlated with UBR5. The biological enrichment analysis and prognosis model were then conducted based on these genes. Results indicated that the patients in the high-risk group had a worse survival performance, and underlying biological differences between high and low-risk patients were also explored. Ultimately, the effect pattern of UBR5 in pan-cancer was also explored. In summary, our study comprehensively explored the role of UBR5 in OS, as well as its effect on the immune microenvironment, which might be an underlying therapy target.

The enhancement of immunoactivity induced by immunogenic cell death through serine/threonine kinase 10 inhibition: a potential therapeutic strategy.

Immunogenic cell death (ICD) is capable of activating the anti-tumor immune response of the organism; however, it is concurrently a complex process involving multiple factors. The specific factors that impact the occurrence of ICD remain undefined. Through cluster analysis, patient specimens retrieved from the TARGET, TCGA, and GEO AML databases were categorized into two subtypes based on the expression levels of ICD-related genes: ICD-high and ICD-low. We compared the prognostic survival outcomes, pathway enrichment analysis, and immune cell infiltration between these two subtypes. Additionally, we identified factors related to AML development from multiple databases and verified the role of these factors both in vivo and in vitro in activating the immune response during the occurrence of ICD. In the ICD-high subtype, there was a notable increase in the abundance of immune cell populations, along with the enrichment of pathways pertinent to the activation of various immune cells. Despite these immunological enhancements, this subgroup demonstrated a poorer prognosis. This phenomenon was consistently observed across various additional AML datasets, leading us to hypothesize that elevated expression of ICD genes does not invariably correlate with a favorable prognosis. Notably, STK10 exhibited elevated expression in AML, was associated with a poor prognosis, and showed synchronous expression patterns with ICD genes. Inhibition of STK10 led to the activation of ICD and the induction of an antitumor response. Moreover, when combined with other ICD inducers, it produced a synergistic anti-tumor effect. Our results reveal the impact of STK10 on ICD and underscore its key role in initiating ICD.

A statistical characteristics preserving watermarking scheme for time series databases

Database watermarking is one of the most effective methods to protect the copyright of databases. However, traditional database watermarking has a potential drawback: watermark embedding will change the distribution of data, which may affect the use and analysis of databases. Considering that most analyses are based on the statistical characteristics of the target database, keeping the consistency of the statistical characteristics is the key to ensuring analyzability. Since statistical characteristics analysis is performed in groups, compared with traditional relational databases, time series databases (TSDBs) have obvious time-grouping characteristics and are more valuable for analysis. Therefore, this paper proposes a robust watermarking algorithm for time series databases, effectively ensuring the consistency of statistical characteristics. Based on the time-group characteristics of TSDBs, we propose a three-step watermarking method, which is based on linear regression, error compensation, and watermark verification, named RCV. According to the properties of the linear regression model and error compensation, the proposed watermark method generates a series of data that have the same statistical characteristics. Then, the verification mechanism is performed to validate the generated data until it conveys the target watermark message. Compared with the existing methods, our method achieves superior robustness and preserves constant statistical properties better.r.

The Pharmacological Mechanisms Underlying the Protective Effect of Ginsenoside Rg3 against Heart Failure.

Heart failure represents the terminal stage of various cardiovascular diseases. This study aims to explore the pharmacological mechanisms underlying the protective effect of Ginsenoside Rg3 against heart failure. Potential targets of Ginsenoside Rg3 were identified using SwissTargetPrediction and the Comparative Toxicogenomics Database, while heart failure-related genes were retrieved from the Comparative Toxicogenomics Database, Therapeutic Target Database, DisGeNET, and PharmGKB. Overlapping of Ginsenoside Rg3 targets with heart failure-related genes identified drug-disease interaction genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the drug-disease interaction genes to elucidate their biological functions. A protein-protein interaction network was constructed using the drug-disease interaction genes, and the hub genes were identified by topological analysis. Additionally, we validate the expression of IL-6 and TNF by real-time PCR. The intersection of Ginsenoside Rg3 targets and heart failure-related genes yielded 15 drug-disease interaction genes. Enrichment analysis highlighted the involvement of inflammation-related GO terms and KEGG pathways, such as positive regulation of interleukin-8 and -6 production, regulation of immune effector process, cytokine receptor binding, cytokine activity, adipocytokine signaling pathway, and IL-17 signaling pathway, which are implicated in the cardioprotective effect. Topological analysis revealed four hub genes: STAT3, CASP3, TNF, and IL-6. The application of Ginsenoside Rg3 significantly reversed the elevated levels of IL-6 and TNF in the isoproterenol-treated H9c2 cell line. Our findings suggest that the cardioprotective effect of Ginsenoside Rg3 may be mediated through its anti-inflammation properties. Further research is required to elucidate and validate the detailed cardioprotective mechanisms of Ginsenoside Rg3.

Molecular mechanism of Xuebijing in treating pyogenic liver abscess complicated with sepsis.

Xuebijing (XBJ) can alleviate the inflammatory response, improve organ function, and shorten the intensive care unit (ICU) stay in patients with pyogenic liver abscess (PLA) complicated with sepsis, but the molecular mechanisms have not been elucidated. This study aimed to explore the molecular mechanism of XBJ in treating PLA complicated with sepsis using a network pharmacology approach. The active ingredients and targets of XBJ were retrieved from the ETCM database. Potential targets related to PLA and sepsis were retrieved from the GeneCards, PharmGKB, DisGeNet, Online Mendelian Inheritance in Man (OMIM), Therapeutic Targets Database (TTD), and DrugBank databases. The targets of PLA complicated with sepsis were mapped to the targets of XBJ to identify potential treatment targets. Protein-protein interaction networks were analyzed using the STRING database. Potential treatment targets were imported into the Metascape platform for Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Molecular docking was performed to validate the interactions between active ingredients and core targets. XBJ was found to have 54 potential treatment targets for PLA complicated with sepsis. Interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor (TNF) were identified as core targets. KEGG enrichment analysis revealed important pathways, including the interleukin-17 (IL-17) signaling pathway, the TNF signaling pathway, the nuclear factor-kappa B (NF-κB) signaling pathway, and the Toll-like receptor (TLR) signaling pathway. Molecular docking experiments indicated stable binding between XBJ active ingredients and core targets. XBJ may exert therapeutic effects on PLA complicated with sepsis by modulating signaling pathways, such as the IL-17, TNF, NF-κB, and TLR pathways, and targeting IL-1β, IL-6, and TNF.

Curcumin Alleviates Osteoarthritis Through the p38MAPK Pathway: Network Pharmacological Prediction and Experimental Confirmation.

Osteoarthritis (OA) is a common degenerative disease worldwide. While curcumin has shown therapeutic effects on OA, its mechanism remains unknown. This study aimed to investigate the molecular mechanism of curcumin in treating OA through network pharmacology and both in vivo and in vitro experiments. Curcumin-related targets were obtained using the HERB and DrugBank databases. GeneCards and DisGeNET were used to build a target database for OA. The STRING database was employed to construct protein-protein interaction networks and analyze related protein interactions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology enrichment analyses of core targets were performed using Metascape. In addition, Autodock software was utilized for molecular docking validation of curcumin and disease targets. Further validation of the main findings was conducted through in vitro and in vivo experiments. In the in vitro experiments, an inflammation model was constructed through nitric oxide donor (SNP) stimulation of chondrocytes. Subsequently, the regulatory effects of curcumin on core targets and signaling pathways were validated using Western blotting and immunofluorescence staining techniques. In the in vivo experiments, an OA model was established by performing medial meniscectomy on male Sprague-Dawley rats. The therapeutic effects were evaluated using enzyme-linked immunosorbent assays, histologic staining, and micro-computed tomography (micro-CT) techniques. Core targets of curcumin relevant to OA therapy included tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1β, IL-6, matrix metalloproteinase 9 (MMP-9), B-cell lymphoma 2 (BCL-2), and caspase-3. The major biological processes involved oxidative stress and apoptotic processes, among others. The p38 mitogen-activated protein kinase (p38/MAPK) pathway was identified as the most likely pathway involved. In vitro experiments showed that curcumin significantly reduced oxidative stress levels, inhibited the expression of inflammatory factors IL-6 and Cyclooxygenase-2 (COX-2) and downregulated the expression of MMP-9 and MMP-1. In addition, curcumin was found to regulate the expression of BCL-2 and caspase-3 through the p38/MAPK pathway, inhibiting chondrocyte apoptosis. In vivo animal experiments demonstrated that curcumin significantly reduced the expression of OA-related factors (IL-1, IL-6, and TNF-α). Histological analysis and micro-CT results revealed that curcumin treatment significantly increased cartilage thickness, improved cartilage morphology, structure, and function, inhibited cartilage degradation, and enhanced the resorption of subchondral bone in the knee joints of rats with OA. Curcumin regulates oxidative stress and maintains mitochondrial function, thereby protecting chondrocyte guard. In addition, curcumin attenuates the inflammatory response of chondrocytes by inhibiting the phosphorylation of P38MAPK, slowing down the breakdown of the extrachondral matrix while preventing apoptosis of chondrocytes. Additionally curcumin attenuated cartilage degradation and bone damage while helping to boost bone density.

Ilmenite Content of Different Genesis of Productive Sediments of the Trostyanytsky Deposit

The article presents information on the geological structure and ore content of the Trostyanytsky ilmenite deposit located in the northern part of the Volodarsk-Volynskyi massif of the Korostensky pluton bedrock. The deposit is localized in the weathering crust of the basement crystalline rocks and continental deluvial-alluvial and alluvial sediments of the Middle Jurassic-Lower Cretaceous, which were formed by erosion and redeposition of eluvium. Weathering crust together with deluvial-alluvial and alluvial sediments formed the productive deposits of the Trostyanytsky deposit. On the basis of the created target database containing conditional coordinates of wells, their description and testing results, mapping constructions (isohypses of the roof, sole, isopachites of the thickness of the weathering crust of crystalline rocks of the basement, alluvial sands and redeposited kaolins of the Middle Jurassic-Lower Cretaceous) were carried out; lateral distribution of the average ilmenite content in the weathering crust, in the Middle Jurassic-Lower Cretaceous fluvial deposits and in productive sediments, distribution of ilmenite content in the vertical section of wells). These mapping constructions were created based on the results of preliminary (1999) and detailed (2008) geological exploration. The direction and strength of correlations between the topography of the sole and the surface, topography of the sole and the thickness of ilmenite-bearing formations, sole relief and ilmenite content, sediment thickness and ilmenite content in the weathering crust, alluvial deposits of the Middle Jurassic-Lower Cretaceous and in productive deposits. The obtained results made it possible to clarify the structural and material parameters of the ore-bearing rocks, the lateral and vertical distribution of ore bearing in different genetic and different age's formations.

AutoTarget: Disease-Associated druggable target identification via node representation learning in PPI networks

Drug target discovery, a pivotal early stage in drug development, is resource-intensive and crucial for ensuring drug efficacy. This study presents AutoTarget, a novel computational pipeline designed to identify disease-associated druggable targets by applying node representation learning to protein–protein interaction (PPI) networks. AutoTarget uses node2vec + for node classification, incorporating neighborhood context and structural equivalence in PPI networks derived from the STRING database. Data from the Therapeutic Target Database (TTD) and DisGeNET were integrated to identify known drug targets and gene-disease associations, respectively. Each protein is embedded into a 128-dimensional vector space, capturing local network structures and enabling the identification of structurally equivalent proteins. A Naïve Bayes classifier, trained on these embeddings, achieved a recall of 0.90 and an F1 score of 0.79 in predicting potential drug targets. AutoTarget identified 3,979 novel potential druggable target proteins out of 19,333 proteins in the PPI network, which were mapped to 23,363 diseases using DisGeNET. This creates a comprehensive resource for disease-specific drug target exploration. Case studies on triple-negative breast cancer and obesity demonstrated AutoTarget’s capability to identify both established and emerging targets, such as CD44, MAPK3, and GIP. Visualization of embedding vectors using t-SNE revealed clear separations between functional protein families, including nuclear proteins, growth factor receptors, and the G proteins within the kinase proteins. This supports the method’s ability to capture biologically relevant information. However, limitations were noted, including the inability to distinguish between different types of disease-associated proteins based solely on network features. Overall, this study advances the application of machine learning and network theory for identifying druggable targets across a wide range of diseases. AutoTarget provides researchers with a valuable tool for expediting the discovery of novel druggable targets, potentially streamlining the drug discovery process. The AutoTarget code and database are publicly available to facilitate further research.

Deciphering the Anticancer Arsenal of Piper longum: Network Pharmacology and Molecular Docking Unveil Phytochemical Targets Against Lung Cancer.

Introduction: Lung cancer, characterized by uncontrolled cellular proliferation within the lung tissues, is the predominant cause of cancer-related fatalities worldwide. The traditional medicinal herb Piper longum has emerged as a significant contender in oncological research because of its documented anticancer attributes, suggesting its potential for novel therapeutic development. Methods: This study adopted network pharmacology and omics methodology to elucidate the anti-lung cancer potential of P. longum by identifying its bioactive constituents and their corresponding molecular targets. Results: Through a comprehensive literature review and the Integrated Medicinal Plant Phytochemistry and Therapeutics database (IMPPAT), we identified 33 bioactive molecules from P. longum. Subsequent analyses employing tools such as SwissTargetPrediction, SuperPred, and DIGEP-Pred facilitated the isolation of 676 potential targets, among which 72 intersected with 666 lung cancer-associated genetic markers identified through databases including the Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards. Further validation through protein-protein interaction (PPI) networks, gene ontology, pathway analyses, boxplots, and overall survival metrics underscored the therapeutic potential of compounds such as 7-epi-eudesm-4(15)-ene-1β, demethoxypiplartine, methyl 3,4,5-trimethoxycinnamate, 6-alpha-diol, and aristolodione. Notably, our findings reaffirm the relevance of lung cancer genes, such as CTNNB1, STAT3, HIF1A, HSP90AA1, and ERBB2, integral to various cellular processes and pivotal in cancer genesis and advancement. Molecular docking assessments revealed pronounced affinity between 6-alpha-diol and HIF1A, underscoring their potential as therapeutic agents for lung cancer. Conclusion: This study not only highlights the bioactive compounds of P. longum but also reinforces the molecular underpinnings of its anticancer mechanism, paving the way for future lung cancer therapeutics.

Doxycycline hydrochloride inhibits the progress of malignant rhabdoid tumor of kidney by targeting MMP17 and MMP1 through PI3K-Akt signaling pathway

ObjectiveTo identify therapeutic targets for malignant rhabdoid tumors of kidney (MRTK) and to investigate the effects and underlying mechanism of doxycycline hydrochloride on these tumors. MethodsGene expression and clinical data of MRTK were retrieved from the TARGET database. Differentially expressed genes (DEGs) and prognostic-related genes (PRGs) were selected through a combination of statistical analyses. The functional roles of MMP17 and MMP1 were elucidated through RNA overexpression and intervention experiments. Furthermore, in vitro and in vivo studies provided evidence for the inhibitory effect of doxycycline hydrochloride on MRTK. Additionally, transcriptome sequencing was employed to investigate the underlying molecular mechanisms. Results3507 DEGs and 690 PRGs in MRTK were identified. Among these, we focused on 41 highly expressed genes associated with poor prognosis and revealed their involvement in extracellular matrix regulatory pathways. Notably, MMP17 and MMP1 stood out as particularly influential genes. When these genes were knocked out, a significant inhibition of proliferation, invasion and migration was observed in G401 cells. Furthermore, our study explored the impact of the matrix metalloproteinase inhibitor, doxycycline hydrochloride, on the malignant progression of G401 both in vitro and in vivo. Combined with sequencing data, the results indicated that doxycycline hydrochloride effectively inhibited MRTK progression, due to its ability to suppress the expression of MMP17 and MMP1 through the PI3K-Akt signaling pathway. ConclusionDoxycycline hydrochloride inhibits the expression of MMP17 and MMP1 through the PI3K-Akt signaling pathway, thereby inhibiting the malignant progression of MRTK in vivo and in vitro.