DEWNA: dynamic entropy weight network analysis and its application to the DNA-binding proteome in A549 cells with cisplatin-induced damage.

Cisplatin is one of the most commonly used chemotherapy drugs for treating solid tumors. As a genotoxic agent, cisplatin binds to DNA and forms platinum-DNA adducts that cause DNA damage and activate a series of signaling pathways mediated by various DNA-binding proteins (DBPs), ultimately leading to cell death. Therefore, DBPs play crucial roles in the cellular response to cisplatin and in determining cell fate. However, systematic studies of DBPs responding to cisplatin damage and their temporal dynamics are still lacking. To address this, we developed a novel and user-friendly stand-alone software, DEWNA, designed for dynamic entropy weight network analysis to reveal the dynamic changes of DBPs and their functions. DEWNA utilizes the entropy weight method, multiscale embedded gene co-expression network analysis and generalized reporter score-based analysis to process time-course proteome expression data, helping scientists identify protein hubs and pathway entropy profiles during disease progression. We applied DEWNA to a dataset of DBPs from A549 cells responding to cisplatin-induced damage across 8 time points, with data generated by data-independent acquisition mass spectrometry (DIA-MS). The results demonstrate that DEWNA can effectively identify protein hubs and associated pathways that are significantly altered in response to cisplatin-induced DNA damage, and offer a comprehensive view of how different pathways interact and respond dynamically over time to cisplatin treatment. Notably, we observed the dynamic activation of distinct DNA repair pathways and cell death mechanisms during the drug treatment time course, providing new insights into the molecular mechanisms underlying the cellular response to DNA damage.

Baicalin Ameliorates Depression-like Behaviors via Inhibiting Neuroinflammation and Apoptosis in Mice.

Depression is a common neuropsychiatric disease which brings an increasing burden to all countries globally. Baicalin, a flavonoid extracted from the dried roots of Scutellaria, has been reported to exert anti-inflammatory, antioxidant, and neuroprotective effects in the treatment of depression. However, the potential biological mechanisms underlying its antidepressant effect are still unclear. In the present study, we conducted extensive research on the potential mechanisms of baicalin's antidepressant effect using the methods of network pharmacology, including overlapped terms-based analysis, protein-protein interaction (PPI) network topology analysis, and enrichment analysis. Moreover, these results were further verified through molecular docking, weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and subsequent animal experiments. We identified forty-one genes as the targets of baicalin in the treatment of depression, among which AKT1, IL6, TP53, IL1B, and CASP3 have higher centrality in the more core position. Meanwhile, the roles of peripheral genes derived from direct potential targets were also observed. Our study suggested that biological processes, such as inflammatory reaction, apoptosis, and oxidative stress, may be involved in the therapeutic process of baicalin on depression. These mechanisms were validated at the level of structure, gene, protein, and signaling pathway in the present study. Taken together, these findings propose a new perspective on the potential mechanisms underlying baicalin's antidepressant effect, and also provide a new basis and clarified perspective for its clinical application.

Deciphering Immunotoxicity in Animal-Derived Biomaterials: A Genomic and Bioinformatics Approach.

Immunotoxicity evaluation has been crucial in preclinical testing for implantable animal-derived biomaterials due to their prolonged contact with the human body, which requires stringent safety assessments. By creating experimental models with varying levels of immunotoxicity, this study reveals the decisive role of decellularization treatment in diminishing the immunogenicity of materials, thus ensuring clinical safety. Employing cutting-edge differential gene expression analysis, the research not only accurately quantifies gene expression alterations in immune responses but also, through pathway enrichment analysis, identifies gene networks associated with oncogenesis. This offers novel insights into the mechanisms of immune responses following biomaterial implantation. Additionally, the study highlights the importance of developing highly sensitive immunotoxicity testing methods and validates the efficacy of high-throughput sequencing and bioinformatics tools in assessing biomaterial safety, providing robust scientific support for future preclinical evaluations.

System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson's disease: a network pharmacology and in-silico evaluation.

IMPHY000797 derivatives have been well known for their efficacy in various diseases. Moreover, IMPHY000797 derivatives have been found to modulate such genes involved in multiple neurological disorders. Hence, this study seeks to identify such genes and the probable molecular mechanism that could be involved in the pathogenesis of Parkinson's disease. The study utilized various biological tools such as DisGeNET, STRING, Swiss target predictor, Cytoscape, AutoDock 4.2, Schrodinger suite, ClueGo, and GUSAR. All the reported genes were obtained using DisGeNET, and further, the common genes were incorporated into the STRING to get the KEGG pathway, and all the data was converted to a protein/pathway network via Cytoscape. The clustering of the genes was performed for the gene-enriched data using two-sided hypergeometrics (p-value). The binding affinity of the IMPHY000797 was verified with the highest regulated 25 proteins via utilizing the "Monte Carlo iterated search technique" and the "Emodel and Glide score" function. Three thousand five hundred eighty-three genes were identified for Parkinson's disease and 31 genes for IMPHY000797 compound, among which 25 common genes were identified. Further, the "FOXO-signaling pathway" was identified to be a modulated pathway. Among the 25 proteins, the highest modulated genes and highest binding affinity were exhibited by SIRT3, FOXO1, and PPARGC1A with the compound IMPHY000797. Further, rat toxicity analysis provided the efficacy and safety of the compound. The study was required to identify the probable molecular mechanism, which needs more confirmation from other studies, which is still a significant hit-back.

Systems Biology Methods via Genome-Wide RNA Sequences to Investigate Pathogenic Mechanisms for Identifying Biomarkers and Constructing a DNN-Based Drug-Target Interaction Model to Predict Potential Molecular Drugs for Treating Atopic Dermatitis.

This study aimed to construct genome-wide genetic and epigenetic networks (GWGENs) of atopic dermatitis (AD) and healthy controls through systems biology methods based on genome-wide microarray data. Subsequently, the core GWGENs of AD and healthy controls were extracted from their real GWGENs by the principal network projection (PNP) method for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Then, we identified the abnormal signaling pathways by comparing the core signaling pathways of AD and healthy controls to investigate the pathogenesis of AD. Then, IL-1ß, GATA3, Akt, and NF-κB were selected as biomarkers for their important roles in the abnormal regulation of downstream genes, leading to cellular dysfunctions in AD patients. Next, a deep neural network (DNN)-based drug-target interaction (DTI) model was pre-trained on DTI databases to predict molecular drugs that interact with these biomarkers. Finally, we screened the candidate molecular drugs based on drug toxicity, sensitivity, and regulatory ability as drug design specifications to select potential molecular drugs for these biomarkers to treat AD, including metformin, allantoin, and U-0126, which have shown potential for therapeutic treatment by regulating abnormal immune responses and restoring the pathogenic signaling pathways of AD.

Physiological Parameters and Transcriptomic Levels Reveal the Response Mechanism of Maize to Deep Sowing and the Mechanism of Exogenous MeJA to Alleviate Deep Sowing Stress.

Deep sowing, as a method to mitigate drought and preserve soil moisture and seedlings, can effectively mitigate the adverse effects of drought stress on seedling growth. The elongation of the hypocotyl plays an important role in the emergence of maize seeds from deep-sowing stress. This study was designed to explore the function of exogenous methyl jasmonate (MeJA) in the growth of the maize mesocotyl and to examine its regulatory network. The results showed that the addition of a 1.5 µ mol L-1 MeJA treatment significantly increased the mesocotyl length (MES), mesocotyl and coleoptile length (MESCOL), and seedling length (SDL) of maize seedlings. Transcriptome analysis showed that exogenous MeJA can alleviate maize deep-sowing stress, and the differentially expressed genes (DEGs) mainly include ornithine decarboxylase, terpene synthase 7, ethylene responsive transcription factor 11, and so on. In addition, candidate genes that may regulate the length of maize hypocotyls were screened by Weighted Gene Co-expression Network Analysis (WGCNA). These genes may be involved in the growth of maize hypocotyls through transcriptional regulation, histones, ubiquitin protease, protein binding, and chlorophyll biosynthesis and play an important role in maize deep-sowing tolerance. Our research findings may provide a theoretical basis for determining the tolerance of maize to deep-sowing stress and the mechanism of exogenous hormone regulation of deep-sowing stress.

Elucidating gastric cancer mechanisms and therapeutic potential of Adociaquinone A targeting EGFR: A genomic analysis and Computer Aided Drug Design (CADD) approach.

Gastric cancer predominantly adenocarcinoma, accounts for over 85% of gastric cancer diagnoses. Current therapeutic options are limited, necessitating the discovery of novel drug targets and effective treatments. The Affymetrix gene expression microarray dataset (GSE64951) was retrieved from NCBI-GEO data normalization and DEGs identification was done by using R-Bioconductor package. Gene Ontology (GO) analysis of DEGs was performed using DAVID. The protein-protein interaction network was constructed by STRING database plugin in Cytoscape. Subclusters/modules of important interacting genes in main network were extracted by using MCODE. The hub genes from in the network were identified by using Cytohubba. The miRNet tool built a hub gene/mRNA-miRNA network and Kaplan-Meier-Plotter conducted survival analysis. AutoDock Vina and GROMACS MD simulations were used for docking and stability analysis of marine compounds against the 5CNN protein. Total 734 DEGs (507 up-regulated and 228 down-regulated) were identified. Differentially expressed genes (DEGs) were enriched in processes like cell-cell adhesion and ATP binding. Eight hub genes (EGFR, HSPA90AA1, MAPK1, HSPA4, PPP2CA, CDKN2A, CDC20, and ATM) were selected for further analysis. A total of 23 miRNAs associated with hub genes were identified, with 12 of them targeting PPP2CA. EGFR displayed the highest expression and hazard rate in survival analyses. The kinase domain of EGFR (PDBID: 5CNN) was chosen as the drug target. Adociaquinone A from Petrosia alfiani, docked with 5CNN, showed the lowest binding energy with stable interactions across a 50 ns MD simulation, highlighting its potential as a lead molecule against EGFR. This study has identified crucial DEGs and hub genes in gastric cancer, proposing novel therapeutic targets. Specifically, Adociaquinone A demonstrates promising potential as a bioactive drug against EGFR in gastric cancer, warranting further investigation. The predicted miRNA against the hub gene/proteins can also be used as potential therapeutic targets.

Exploring the protective effect and molecular mechanism of betulin in Alzheimer's disease based on network pharmacology, molecular docking and experimental validation.

Alzheimer's disease (AD) is a neurodegenerative disorder that impairs learning and memory, with high rates of mortality. Birch bark has been traditionally used in the treatment of various skin ailments. Betulin (BT) is a key compound of birch bark that exhibits diverse pharmacological benefits and therapeutic potential in AD. However, the therapeutic effects and molecular mechanisms of BT in AD remain unclear. The present study aimed to predict the potential therapeutic targets of BT in the treatment of AD, and to determine the specific underlying molecular mechanisms through network pharmacology analysis and experimental validation. PharmMapper was used to predict the target genes of BT, and four disease databases were searched to screen for AD targets. The intersection targets were identified using the jveen website. Drug­disease target protein­protein interaction networks and hub genes were obtained and visualized using the Search Tool for the Retrieval of Interacting Genes/Proteins database and Cytoscape. The Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and AutoDock was used for molecular docking analysis of BT and hub genes. Subsequently, the network­predicted mechanisms of BT in AD were verified in vitro. A total of 495 BT and 1,386 AD targets were identified, and 120 were identified as potential targets of BT in the treatment of AD. The results of the molecular docking analysis revealed a strong binding affinity between BT and the hub genes. In addition, enrichment analyses of GO and KEGG pathways indicated that the neuroprotective effects of BT mainly involved the 'PI3K­Akt signaling pathway'. The results of in vitro experiments demonstrated that pretreatment with BT for 2 h may ameliorate formaldehyde (FA)­induced cytotoxicity and morphological changes in HT22 cells, and decrease FA­induced Tau hyperphosphorylation and reactive oxygen species levels. Furthermore, the PI3K/AKT signaling pathway was activated and the expression levels of downstream proteins, namely GSK3ß, Bcl­2 and Bax, were modified following pre­treatment with BT. Overall, the results of network pharmacology and in vitro analyses revealed that BT may reduce FA­induced AD­like pathology by modulating the PI3K/AKT signaling pathway, highlighting it as a potential multi­target drug for the treatment of AD.

Identification of autophagy-related signatures in doxorubicin-induced cardiotoxicity.

PURPOSE: Doxorubicin is an antibiotic drug used clinically to treat infectious diseases and tumors. Unfortunately, it is cardiotoxic. Autophagy is a cellular self-decomposition process that is essential for maintaining homeostasis in the internal environment. Accordingly, the present study was proposed to characterize the autophagy-related signatures of doxorubicin-induced cardiotoxicity. METHODS: Datasets related to doxorubicin-induced cardiotoxicity were retrieved by searching the GEO database and differentially expressed genes (DEGs) were identified. DEGs were taken to intersect with autophagy-related genes to obtain autophagy-related signatures, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network were performed on them. Further, construction of miRNA-hub gene networks and identification of target drugs to reveal potential molecular mechanisms and therapeutic strategies. Animal models of doxorubicin-induced cardiotoxicity were constructed to validate differences in gene expression for autophagy-related signatures. RESULTS: PBMC and heart samples from the GSE37260 dataset were selected for analysis. There were 995 and 2357 DEGs in PBMC and heart samples, respectively, and they had 23 intersecting genes with autophagy-related genes. RT-qPCR confirmed the differential expression of 23 intersecting genes in doxorubicin-induced cardiotoxicity animal models in general agreement with the bioinformatics results. An autophagy-related signatures consisting of 23 intersecting genes is involved in mediating processes and pathways such as autophagy, oxidative stress, apoptosis, protein ubiquitination and phosphorylation. Moreover, Akt1, Hif1a and Mapk3 are hub genes in autophagy-associated signatures and their upstream miRNAs are mainly rno-miR-1188-5p, rno-miR-150-3p and rno-miR-326-3p, and their drugs are mainly CHEMBL55802, Carboxyamidotriazole and 3-methyladenine. CONCLUSION: This study identifies for the first-time autophagy-related signatures in doxorubicin's cardiotoxicity, which could provide potential molecular mechanisms and therapeutic strategies for doxorubicin-induced cardiotoxicity.

Network pharmacology-based investigation and experimental validation of the mechanism of metformin in the treatment of acute myeloid leukemia.

Metformin, a widely used anti-diabetic agent, has shown significant anti-cancer properties as reported in in various cancers, including acute myeloid leukemia. However, the detailed mechanisms by which metformin influences acute myeloid leukemia remain unrevealed. Employing a synergistic approach of network pharmacology and experimental validation, this study systematically identifies and analyzes potential metformin targets and AML-related genes. These findings are then cross-referenced with biomedical databases to construct a target-gene network, providing insights into metformin's pharmacodynamics in AML treatment. Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses are utilized. Results show metformin's effectiveness in inhibiting AML cell proliferation and inducing apoptosis through the AKT/HIF1A/PDK1 signaling pathway. This research provides insights into metformin's clinical application in AML treatment.

Integrating network pharmacology and bioinformatics to explore the mechanism of Xiaojian Zhongtang in treating major depressive disorder: An observational study.

Major depressive disorder (MDD) is a common mental illness. The traditional Chinese medicine compound Xiaojian Zhongtang (XJZT) has a good therapeutic effect on MDD, but the specific mechanism is not clear. The aim of this study is to explore the molecular mechanism of XJZT in the treatment of MDD through network pharmacology and bioinformatics. The traditional Chinese medicine system pharmacology database was used to screen the chemical components and targets of XJZT, while the online Mendelian inheritance in man, DisGeNET, Genecards, and therapeutic target database databases were used to collect MDD targets and identify the intersection targets of XJZT and MDD. A "drugs-components-targets" network was constructed using the Cytoscape platform, and the STRING was used for protein-protein interaction analysis of intersecting targets. Gene Ontology and Kyoto encyclopedia of genes and genomes analysis of intersecting targets was performed using the DAVID database. Obtain serum and brain transcriptome datasets of MDD from the gene expression omnibus database, and perform differentially expressed genes, weighted gene co-expression network analysis, gene set enrichment analysis, and receiver operating characteristic analysis. A total of 127 chemical components and 767 targets were obtained from XJZT, among which quercetin, kaempferol, and maltose are the core chemical components, and 1728 MDD targets were screened out, with 77 intersecting targets between XJZT and MDD. These targets mainly involve AGE-RAGE signaling pathway in diabetic complexes, epidermal growth factor receptor tyrosine kinase inhibitor resistance, and HIF-1 signaling pathway, and these core targets have strong binding activity with core components. In addition, 1166 differentially expressed genes were identified in the MDD serum transcriptome dataset, and weighted gene co-expression network analysis identified the most relevant gene modules (1269 genes), among which RAC-alpha serine/threonine-protein kinase (AKT1), D(4) dopamine receptor (DRD4), and kynurenine 3-monooxygenase (KMO) were target genes for the treatment of MDD with XJZT, these 3 genes are mainly related to the ubiquitin-mediated proteolysis, arachidonic acid (AA) metabolism, and Huntington disease pathways, and the expression of AKT1, DRD4, and KMO was also found in the MDD brain transcriptome dataset, which is significantly correlated with the occurrence of MDD. We have identified 3 key targets for XJZT treatment of MDD, including AKT1, KMO, and DRD4, and they can be regulated by the key components of XJZT, including quercetin, maltose, and kaempferol. This provides valuable insights for the early clinical diagnosis and development of therapeutic drugs for MDD.

Role and molecular mechanisms of HuangQiSiJunZi decoction for treating triple-negative breast cancer as explored via network pharmacology and bioinformatics analyses.

OBJECTIVE: In this study, we evaluated the molecular mechanisms of HuangQiSiJunZi Decoction (HQSJZD) for treating triple-negative breast cancer (TNBC) using network pharmacology and bioinformatics analyses. METHODS: Effective chemical components together with action targets of HQSJZD were selected based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Meanwhile, differentially expressed genes (DEGs) were extracted from TNBC sample data in The Cancer Genome Atlas (TCGA) database. Additionally, we built a protein-protein interaction (PPI) network and acquired hub genes. Gene Expression Omnibus(GEO) datasets were utilized to verify the accuracy of hub gene expression. Additionally, enrichment analyses were conducted on key genes. Furthermore, TNBC severity-related high-risk factors were screened through univariate together with multivariate Cox regressions; next, the logistic regression prediction model was built. Moreover, differential levels of 22 immune cell types in TNBC tissues compared with normal tissues were analyzed. The hub gene levels within pan-cancer and the human body were subsequently visualized and analyzed. Finally, quantitative PCR (RT-qPCR) was used to validate the correlation of the hub genes in TNBC cells. RESULTS: The study predicted 256 targets of active ingredients and 1791 DEGs in TNBC, and obtained 16 hub genes against TNBC. The prognostic signature based on FOS, MMP9, and PGR was independent in predicting survival. A total of seven types of immune cells, such as CD4 + memory T cells, showed a significant difference in infiltration (p < 0.05), and immune cells were related to the hub genes. The HPA database was adopted for hub gene analyses, and as determined, FOS was highly expressed in most human organs. The results of RT-qPCR validation for the FOS hub gene were consistent with those of bioinformatic analyses. CONCLUSION: HQSJZD might regulate the interleukin-17 and aging pathways via FOS genes to increase immune cell infiltration in TNBC tissues, and thus, may treat TNBC and improve the prognosis. The FOS genes are likely to be a new marker for TNBC.

Identification of competing endogenous RNA networks associated with circRNA and lncRNA in TCDD-induced cleft palate development.

2,3,7,8 -tetrachlorodibenzo-p-dioxin (TCDD) is a teratogen that can induce cleft palate formation, a common birth defect. Competing endogenous RNAs (ceRNAs), including circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), indirectly regulate gene expression via sharing microRNAs (miRNAs). Nevertheless, the mechanism by which they act as ceRNAs to regulate palatal development remains to be explored in greater detail. Here, the cleft palate model of C57BL/6 N pregnant mice was constructed by gavage of TCDD (64 ug/kg) on gestation day (GD) 10.5, and the palatal shelves were taken on gestation day (GD) 14.5 for whole-transcriptome sequencing to investigate the underlying mechanisms of the roles of circRNAs and lncRNAs as ceRNAs in cleft palate. Sequencing results revealed that 293 lncRNA, 589 circRNA, 47 miRNA, and 138 messenger RNA (mRNA) were significantly dysregulated, and the cytochrome P450 (CYP) enzymes and the aryl hydrocarbon receptor (AhR) pathway play key roles in the induction of cleft palate upon exposure to TCDD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed the function of TCDD function was mainly related to the metabolic processes of intracellular compounds, including the metabolic processes of cellular aromatic compounds and the metabolism of exogenous drugs by cytochrome P450, etc. Furthermore, quantitative reverse transcription polymerase chain reaction (qRT-PCR) indicated that the circRNA_1781/miR-30c-1-3p/PKIB and XR_380026.2/miR-1249-3p/DNAH10 ceRNA networks were hypothesized to be a hub involved in palatal development suggesting that the circRNA_1781/miR-30c-1-3p/PKIB and XR_380026.2/miR-1249-3p/DNAH10 ceRNA networks may be critical for palatogenesis, setting the foundation for the investigation of cleft palate.

Identifying new molecular signatures and potential therapeutics for idiopathic pulmonary fibrosis: a network medicine approach.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive collagen deposition and fibrosis of the lung parenchyma, leading to respiratory failure. The molecular mechanisms underlying IPF pathogenesis remain incompletely understood, hindering the development of effective therapeutic strategies. We have used a network medicine approach to comprehensively analyze molecular interactions and identify novel molecular signatures and potential therapeutics associated with IPF progression. Our integrative analysis revealed dysregulated molecular networks that are central to IPF pathophysiology. We have highlighted key molecular players and signaling pathways that are implicated in aberrant fibrotic processes. This systems-level understanding enables the identification of new biomarkers and therapeutic targets for IPF, providing potential avenues for precision medicine. Drug repurposing analysis revealed several drug candidates with anti-fibrotic, anti-inflammatory, and anti-cancer activities that could potentially slow fibrotic progression and improve patient outcomes. This study offers new insights into the molecular underpinnings of IPF and highlights network medicine approaches in uncovering complex disease mechanisms. The molecular signatures and therapeutic targets identified hold promise for developing precision therapies tailored to individual patients, ultimately advancing the management of this debilitating lung disease.

Integrating network pharmacology, molecular docking and experimental verification to explore the therapeutic effect and potential mechanism of nomilin against triple-negative breast cancer.

BACKGROUND: Nomilin is a limonoid compound known for its multiple biological activities, but its role in triple negative breast cancer (TNBC) remains unclear. This study aims to uncover the potential therapeutic effect of nomilin on TNBC and elucidate the specific mechanism of its action. METHODS: We employed weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the GeneCards database to identify potential targets for TNBC. Simultaneously, we utilized the Swiss Target Prediction, ChEMBL, and STITCH databases to identify potential targets of nomilin. The core targets and mechanisms of nomilin against TNBC were predicted through protein-protein interaction (PPI) network analysis, molecular docking, and enrichment analysis. The results of the network pharmacology were corroborated by conducting experiments. RESULTS: A total of 17,204 TNBC targets were screened, and 301 potential targets of nomilin were identified. Through the PPI network, eight core targets of nomilin against TNBC were pinpointed, namely BCL2, Caspase3, CyclinD1, EGFR, HSP90AA1, KRAS, PARP1, and TNF. Molecular docking, molecular dynamics simulation and proteome microarray revealed that nomilin exhibits strong binding activity to these core proteins. Enrichment analysis results indicated that the anti-TNBC effect of nomilin is associated with PI3K/Akt pathway. In vitro and in vivo experiments have demonstrated that nomilin inhibits TNBC cell proliferation and migration while promoting cell apoptosis through the PI3K/Akt pathway. CONCLUSION: For the first time, the research effectively discovered the objectives and mechanisms of nomilin in combating TNBC using network pharmacology, molecular docking, molecular dynamics simulation, proteome microarray and experimental confirmation, presenting a hopeful approach for treating TNBC.

The Chronic Toxicity of Endocrine-Disrupting Chemical to Daphnia magna: A Transcriptome and Network Analysis of TNT Exposure.

Endocrine-disrupting chemicals (EDCs) impair growth and development. While EDCs can occur naturally in aquatic ecosystems, they are continuously introduced through anthropogenic activities such as industrial effluents, pharmaceutical production, wastewater, and mining. To elucidate the chronic toxicological effects of endocrine-disrupting chemicals (EDCs) on aquatic organisms, we collected experimental data from a standardized chronic exposure test using Daphnia magna (D. magna), individuals of which were exposed to a potential EDC, trinitrotoluene (TNT). The chronic toxicity effects of this compound were explored through differential gene expression, gene ontology, network construction, and putative adverse outcome pathway (AOP) proposition. Our findings suggest that TNT has detrimental effects on the upstream signaling of Tcf/Lef, potentially adversely impacting oocyte maturation and early development. This study employs diverse bioinformatics approaches to elucidate the gene-level toxicological effects of chronic TNT exposure on aquatic ecosystems. The results provide valuable insights into the molecular mechanisms of the adverse impacts of TNT through network construction and putative AOP proposition.

Screening of differentially expressed RNAs and identifying a ceRNA axis during cadmium-induced oxidative damage in pancreatic ß cells.

Cadmium, a common metal pollutant, has been demonstrated to induce type 2 diabetes by disrupting pancreatic ß cells function. In this study, transcriptome microarray was utilized to identify differential gene expression in oxidative damage to pancreatic ß cells following cadmium exposure. The results indicated that a series of mRNAs, LncRNAs, and miRNAs were altered. Of the differentially expressed miRNAs, miR-29a-3p exhibited the most pronounced alteration, with an 11.62-fold increase relative to the control group. Following this, the target gene of miR-29a-3p was identified as Col3a1 through three databases (miRDB, miRTarbase and Tarbase), which demonstrated a decrease across the transcriptome microarray. The upstream target gene of miR-29a-3p was identified as NONMMUT036805, with decreased expression observed in the microarray. Finally, the expression trend of NONMMUT036805/miR-29a-3p/Col3a1 was reversed following NAC pretreatment. This was accompanied by a reduction in oxidative damage indicators, MDA/ROS/GSH-Px appeared to be negatively affected to varying degrees. In conclusion, this study has demonstrated that multiple RNAs are altered during cadmium exposure-induced oxidative damage in pancreatic ß cells. The NONMMUT036805/miR-29a-3p/Col3a1 axis has been shown to be involved in this process, which provides a foundation for the identification of potential targets for cadmium toxicity intervention.

Network medicine analysis for dissecting the therapeutic mechanism of consensus TCM formulae in treating hepatocellular carcinoma with different TCM syndromes.

Introduction: Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Traditional Chinese Medicine (TCM) is widely utilized as an adjunct therapy, improving patient survival and quality of life. TCM categorizes HCC into five distinct syndromes, each treated with specific herbal formulae. However, the molecular mechanisms underlying these treatments remain unclear. Methods: We employed a network medicine approach to explore the therapeutic mechanisms of TCM in HCC. By constructing a protein-protein interaction (PPI) network, we integrated genes associated with TCM syndromes and their corresponding herbal formulae. This allowed for a quantitative analysis of the topological and functional relationships between TCM syndromes, HCC, and the specific formulae used for treatment. Results: Our findings revealed that genes related to the five TCM syndromes were closely associated with HCC-related genes within the PPI network. The gene sets corresponding to the five TCM formulae exhibited significant proximity to HCC and its related syndromes, suggesting the efficacy of TCM syndrome differentiation and treatment. Additionally, through a random walk algorithm applied to a heterogeneous network, we prioritized active herbal ingredients, with results confirmed by literature. Discussion: The identification of these key compounds underscores the potential of network medicine to unravel the complex pharmacological actions of TCM. This study provides a molecular basis for TCM's therapeutic strategies in HCC and highlights specific herbal ingredients as potential leads for drug development and precision medicine.

An integrated bioinformatics approach reveals the potential role of microRNA-30b-5p and let-7a-5p during SARS CoV-2 spike-1 mediated neuroinflammation.

SARS-CoV-2 induced neuroinflammation contributing to neurological sequelae is one of the critical outcomes of long-COVID, however underlying regulatory mechanisms involved therein are poorly understood. We deciphered the profile of dysregulated microRNAs, their targets, associated pathways, protein-protein interactions (PPI), transcription factor-hub genes interaction networks, hub genes-microRNA co-regulatory networks in SARS-CoV-2 Spike-1 (S1) stimulated microglial cells along with candidate drug prediction using RNA-sequencing and multiple bioinformatics approaches. We identified 11 dysregulated microRNAs in the S1-stimulated microglial cells (p < 0.05). KEGG analysis revealed involvement of important neuroinflammatory pathways such as MAPK signalling, PI3K-AKT signalling, Ras signalling and axon guidance. PPI analysis further identified 11 hub genes involved in these pathways. Real time PCR validation confirmed a significant upregulation of microRNA-30b-5p and let-7a-5p; proinflammatory cytokines- IL-6, TNF-α, IL-1ß, GM-CSF; and inflammatory genes- PIK3CA and AKT in the S1-stimulated microglial cells, while PTEN and SHIP1 expression was decreased as compared to the non-stimulated cells. Drug prediction analysis further indicated resveratrol, diclofenac and rapamycin as the potential drugs based on their degree of interaction with hub genes. Thus, targeting of these microRNAs and/or their intermediate signalling molecules would be a prospective immunotherapeutic approach in alleviating SARS-CoV-2-S1 mediated neuroinflammation; and needs further investigations.

Towards an enhanced understanding of osteoanabolic effects of PTH-induced microRNAs on osteoblasts using a bioinformatic approach.

In this study, we used a bioinformatic approach to construct a miRNA-target gene interaction network potentially involved in the anabolic effect of parathyroid hormone analogue teriparatide [PTH (1-34)] on osteoblasts. We extracted a dataset of 26 microRNAs (miRNAs) from previously published studies and predicted miRNA target interactions (MTIs) using four software tools: DIANA, miRWalk, miRDB, and TargetScan. By constructing an interactome of PTH-regulated miRNAs and their predicted target genes, we elucidated signaling pathways regulating pluripotency of stem cells, the Hippo signaling pathway, and the TGF-beta signaling pathway as the most significant pathways in the effects of PTH on osteoblasts. Furthermore, we constructed intersection of MTI networks for these three pathways and added validated interactions. There are 8 genes present in all three selected pathways and a set of 18 miRNAs are predicted to target these genes, according to literature data. The most important genes in all three pathways were BMPR1A, BMPR2 and SMAD2 having the most interactions with miRNAs. Among these miRNAs, only miR-146a-5p and miR-346 have validated interactions in these pathways and were shown to be important regulators of these pathways. In addition, we also propose miR-551b-5p and miR-338-5p for further experimental validation, as they have been predicted to target important genes in these pathways but none of their target interactions have yet been verified. Our wet-lab experiment on miRNAs differentially expressed between PTH (1-34) treated and untreated mesenchymal stem cells supports miR-186-5p from the literature obtained data as another prominent miRNA. The meticulous selection of miRNAs outlined will significantly support and guide future research aimed at discovering and understanding the crucial pathways of osteoanabolic PTH-epigenetic effects on osteoblasts. Additionally, they hold potential for the discovery of new PTH target genes, innovative biomarkers for the effectiveness and safety of osteoporosis-affected treatment, as well as novel therapeutic targets.