[Screening for Characteristic Genes of Different Traditional Chinese Medicine Syndromes of Psoriasis Vulgaris: A Study Based on Bioinformatics and Machine Learning]. / ç”Ÿç‰©ä¿¡æ¯å­¦å’Œæœºå™¨å­¦ä¹ ç­›é€‰é“¶å±‘ç— ä¸­åŒ»è¯åž‹ç‰¹å¾åŸºå› .

Objective: To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndromes, including blood-heat syndrome (BHS), blood stasis syndrome (BSS), and blood-dryness syndrome (BDS), through bioinformatics and machine learning and to provide a scientific basis for the clinical diagnosis and treatment of PV of different TCM syndrome types. Methods: The GSE192867 dataset was downloaded from Gene Expression Omnibus (GEO). The limma package was used to screen for the differentially expressed genes (DEGs) of PV, BHS, BSS, and BDS in PV patients and healthy populations. In addition, KEGG (Kyoto Encyclopedia of Genes and Genes) pathway enrichment analysis was performed. The DEGs associated with PV, BHS, BSS, and BDS were identified in the screening and were intersected separately to obtain differentially characterized genes. Out of two algorithms, the support vector machine (SVM) and random forest (RF), the one that produced the optimal performance was used to analyze the characteristic genes and the top 5 genes were identified as the key characteristic genes. The receiver operating characteristic (ROC) curves of the key characteristic genes were plotted by using the pROC package, the area under curve (AUC) was calculated, and the diagnostic performance was evaluated, accordingly. Results: The numbers of DEGs associated with PV, BHS, BSS, and BDS were 7699, 7291, 7654, and 6578, respectively. KEGG enrichment analysis was focused on Janus kinase (JAK)/signal transducer and activator of transcription (STAT), cyclic adenosine monophosphate (cAMP), mitogen-activated protein kinase (MAPK), apoptosis, and other pathways. A total of 13 key characteristic genes were identified in the screening by machine learning. Among the 13 key characteristic genes, malectin (MLEC), TUB like protein 3 (TULP3), SET domain containing 9 (SETD9), nuclear envelope integral membrane protein 2 (NEMP2), and BTG anti-proliferation factor 3 (BTG3) were the key characteristic genes of BHS; phosphatase 15 (DUSP15), C1q and tumor necrosis factor related protein 7 (C1QTNF7), solute carrier family 12 member 5 (SLC12A5), tripartite motif containing 63 (TRIM63), and ubiquitin associated protein 1 like (UBAP1L) were the key characteristic genes of BSS; recombinant mouse protein (RRNAD1), GTPase-activating protein ASAP3 Protein (ASAP3), and human myomesin 2 (MYOM2) were the key characteristic genes of BDS. Moreover, all of them showed high diagnostic efficacy. Conclusion: There are significant differences in the characteristic genes of different PV syndromes and they may be potential biomarkers for diagnosing TCM syndromes of PV.

Investigating the potential of Juglans regia phytoconstituents for the treatment of cervical cancer utilizing network biology and molecular docking approach.

The fourth most frequent type of cancer in women and the leading cause of mortality for females worldwide is cervical cancer. Traditionally, medicinal plants have been utilized to treat various illnesses and ailments. The molecular docking method is used in the current study to look into the phytoconstituents of Juglans regia's possible anticancer effects on cervical cancer target proteins. This work uses the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to find differentially expressed genes. Furthermore, protein-protein interactions of differentially expressed genes were constructed using network biology techniques. The top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11) are then determined by computing topological parameters with Cytohubba. In addition, molecular docking research was performed on Juglans regia phytocompounds that were extracted from the IMPPAT database versus hub genes that had been identified. Utilizing molecular dynamics, simulation confirmed that prioritized docked complexes with low binding energies were stable.

Molecular insights into gastric cancer: The impact of TGFBR2 and hsa-mir-107 revealed by microarray sequencing and bioinformatics.

BACKGROUND: Gastric carcinoma (GC) remains a significant therapeutic challenge, garnering widespread attention. Oxymatrine (OMT), an active component of the traditional Chinese medicine compound Kushen injection (CKI), has shown promising results in combination with chemotherapy for the treatment of GC. However, the molecular mechanisms underlying OMT's therapeutic effects in GC have yet to be elucidated. METHODS: The transcriptomic expression data of HGC-27 post-OMT intervention were obtained through microarray sequencing, while the miRNA and mRNA sequencing data for GC patients were sourced from the TCGA database. The mechanism of OMT intervention in GC is analyzed in multiple aspects, including Protein-Protein Interactions (PPI), Competitive Endogenous RNA (ceRNA) networks, correlation and co-expression analyses, immune infiltration, and clinical implications. RESULTS: By analyzing key modules, five critical mRNAs were identified, and their interacting miRNAs were predicted to construct a ceRNA network. Among these, TGFBR2 and hsa-miR-107 have correlations or co-expression relationships with other genes in the network. They are differentially expressed in most other cancers, associated with prognosis, and have diagnostic value. TGFBR2 also exhibits immune infiltration phenomena, and its high expression is linked to poor patient prognosis. Low expression of hsa-miR-107 is associated with poor patient prognosis. OMT may act on the TGFß/Smad signaling pathway or negatively regulate the WNT signaling pathway through the hsa-miR-107/BTRC axis, thereby inhibiting the onset and progression of GC. CONCLUSION: The mechanisms of OMT intervention in GC are diverse, TGFBR2 and hsa-miR-107 may serve as prognostic molecular biomarkers or potential therapeutic targets.

Biomarkers and coptis chinensis activity for rituximab-resistant diffuse large B-cell lymphoma: Combination of bioinformatics analysis, network pharmacology and molecular docking.

BACKGROUND: Rituximab resistance is one of the great challenges in the treatment of diffuse large B-cell lymphoma (DLBCL), but relevant biomarkers and signalling pathways remain to be identified. Coptis chinensis and its active ingredients have antitumour effects; thus, the potential bioactive compounds and mechanisms through which Coptis chinensis acts against rituximab-resistant DLBCL are worth exploring. OBJECTIVE: To elucidate the core genes involved in rituximab-resistant DLBCL and the potential therapeutic targets of candidate monomers of Coptis chinensis. METHODS: Using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the Similarity Ensemble Approach and Swiss Target Prediction, the main ingredients and pharmacological targets of Coptis chinensis were identified through database searches. Through the overlap between the pharmacological targets of Coptis chinensis and the core targets of rituximab-resistant DLBCL, we identified the targets of Coptis chinensis against rituximab-resistant DLBCL and constructed an active compound-target interaction network. The targets and their corresponding active ingredients of Coptis chinensis against rituximab-resistant DLBCL were molecularly docked. RESULTS: Berberine, quercetin, epiberberine and palmatine, the active components of Coptis chinensis, have great potential for improving rituximab-resistant DLBCL via PIK3CG. CONCLUSION: This study revealed biomarkers and Coptis chinensis-associated molecular functions for rituximab-resistant DLBCL.

Statistical integration of multi-omics and drug screening data from cell lines.

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder.

Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github.com/22n9n23/SGAE-MDA.

A multi-epitope subunit vaccine based on CU/ZN-SOD, OMP31 and BP26 against Brucella melitensis infection in BALB/C mice.

Brucellosis, a zoonosis caused by Brucella, is highly detrimental to both humans and animals. Most existing vaccines are live attenuated vaccines with safety flaws for people and animals. Therefore, it is advantageous to design a multi-epitope subunit vaccine (MEV) to prevent Brucella infection. To this end, we applied a reverse vaccinology approach. Six cytotoxic T cell (CTL) epitopes, seven T helper cell (HTL) epitopes, and four linear B cell epitopes from CU/ZN-SOD, Omp31, and BP26 were obtained. We linked the CTL, HTL, B-cell epitopes, the appropriate CTB molecular adjuvant, and the universal T helper lymphocyte epitope, PADRE, with linkers AAY, GPPGG, and KK, respectively. This yielded a 412-amino acid MEV construct, which we named MEVcob. The immunogenicity, stability, safety, and feasibility of the construct were evaluated by bioinformatics tools (including the AlphaFold2 prediction tool, the AlphaFold2 tool, NetMHC-I pan 4.0 server, IEDB MHC-I server, ABCpred service, and C-ImmSim server); the physicochemical properties, secondary and tertiary structures, and binding ability of MEVocb to toll-like receptor 4 (TLR4) was analyzed. Then, codon adaptation and computer cloning studies were performed. MEVocb is highly immunogenic in immunostimulation experiments, The proteins translated by these sequences were relatively stable, exhibiting a high antigenic index. Furthermore, mouse experiments confirmed that the MEVocb construct could raise IFN-γ, IgG, IgG2a, IgG1, IL-2, TNF-α levels in mice, indicating that induced a specific humoral and cellular immune response in BALB/c mice. This vaccine induced a statistically significant level of protection in BALB/c mice when challenged with Brucella melitensis 043 in Xinjiang. Briefly, we utilized immunoinformatic tools to design a novel multi-epitope subunit candidate vaccine against Brucella. This vaccine aims to induce host immune responses and confer specific protective effects. The study results offer a theoretical foundation for the development of a novel Brucella subunit vaccine.

Potential molecular targets and drugs for basement membranes-related intervertebral disk degeneration through bioinformatics analysis and molecular docking.

BACKGROUND: Through bioinformatics analysis to identify the hub genes of Intervertebral disc degeneration (IVDD) associated with basement membranes (BMs) and find out the potential molecular targets and drugs for BMs-related annulus fibrosus (AF) degeneration based on bioinformatic analysis and molecular approach. METHODS: Intervertebral disc degeneration (IVDD) related targets were obtained from GeneCards, DisGenet and OMIM databases. BMs related genes were obtained from Basement membraneBASE database. The intersection targets were identified and subjected to protein-to-protein interaction (PPI) construction via STRING. Hub genes were identified and conducted Gene ontology (GO) and pathway enrichment analysis through MCODE and Clue GO in Cytospace respectively. DSigDB database was retrieved to predict therapeutic drugs and molecular docking was performed through PyMOL, AutoDock 1.5.6 to verify the binding energy between the drug and the different expressed hub genes. Finally, GSE70362 from GEO database was obtained to verify the different expression and correlation of each hub gene for AF degeneration. RESULTS: We identified 41 intersection genes between 3 disease targets databases and Basement membraneBASE database. PPI network revealed 25 hub genes and they were mainly enriched in GO terms relating to glycosaminoglycan catabolic process, the TGF-ß signaling pathway. 4 core targets were found to be significant via comparison of microarray samples and they showed strong correlation. The molecular docking results showed that the core targets have strong binding energy with predicting drugs including chitosamine and retinoic acid. CONCLUSIONS: In this study, we identified hub genes, pathways, potential targets, and drugs for treatment in BMs-related AF degeneration and IVDD.

Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors.

MOTIVATION: Next Generation Sequencing technologies make it possible to detect rare genetic variants in individual patients. Currently, more than a dozen software and web services have been created to predict the pathogenicity of variants related with changing of amino acid residues. Despite considerable efforts in this area, at the moment there is no ideal method to classify pathogenic and harmless variants, and the assessment of the pathogenicity is often contradictory. In this article, we propose to use peptides structural formulas of proteins as an amino acid residues substitutions description, rather than a single-letter code. This allowed us to investigate the effectiveness of chemoinformatics approach to assess the pathogenicity of variants associated with amino acid substitutions. RESULTS: The structure-activity relationships analysis relying on protein-specific data and atom centric substructural multilevel neighborhoods of atoms (MNA) descriptors of molecular fragments appeared to be suitable for predicting the pathogenic effect of single amino acid variants. MNA-based Naïve Bayes classifier algorithm, ClinVar and humsavar data were used for the creation of structure-activity relationships models for 10 proteins. The performance of the models was compared with 11 different predicting tools: 8 individual (SIFT 4G, Polyphen2 HDIV, MutationAssessor, PROVEAN, FATHMM, MVP, LIST-S2, MutPred) and 3 consensus (M-CAP, MetaSVM, MetaLR). The accuracy of MNA-based method varies for the proteins (AUC: 0.631-0.993; MCC: 0.191-0.891). It was similar for both the results of comparisons with the other individual predictors and third-party protein-specific predictors. For several proteins (BRCA1, BRCA2, COL1A2, and RYR1), the performance of the MNA-based method was outstanding, capable of capturing the pathogenic effect of structural changes in amino acid substitutions. AVAILABILITY AND IMPLEMENTATION: The datasets are available as supplemental data at Bioinformatics online. A python script to convert amino acid and nucleotide sequences from single-letter codes to SD files is available at https://github.com/SmirnygaTotoshka/SequenceToSDF. The authors provide trial licenses for MultiPASS software to interested readers upon request.

Brief literature review and comprehensive bioinformatics analytics unravel the potential mechanism of curcumin in the treatment of periodontitis.

OBJECTIVE: Periodontitis is a chronic oral disease prevalent worldwide, and natural products are recommended as adjunctive therapy due to their minor side effects. Curcumin, a widely used ancient compound, has been reported to possess therapeutic effects in periodontitis. However, the exact mechanism underlying its activity remains unclear. In this context, the present study aimed to conduct computational simulations to uncover the potential mechanism of action of Curcumin in the treatment of periodontitis. MATERIALS AND METHODS: Single-cell analysis was conducted using a dataset (i.e., GSE164241) curated from the Gene Expression Omnibus (GEO) database through an R package "Seurat package." Bulk RNA sequencing data were curated from GSE10334 and GSE16134 and processed by R package "Limma." Then, the marker genes in the single-cell transcriptome and differentially expressed genes (DEGs) in the bulk transcriptome were integrated. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were also carried out to reveal their functionalities. Key targets were mined from their protein-protein interaction (PPI) network topologically. Afterward, molecular docking was performed. The top-ranked pose was subjected to molecular dynamics simulations to investigate the stability of the docking result. RESULTS: FOS, CXCL1, CXCL8, and IL1B, were filtered after a series of selected processes. The results of molecular modeling suggested that except for IL1B, the Vena Scores of the rest exceeded -5 kcal/mol. Furthermore, the molecular dynamic simulation indicated that the binding of the CXCL8-Curcumin complex was stable over the entire 100 ns simulation. CONCLUSION: The present study unlocked the binding modes of CXCL1, FOS, and CXCL8 with the Curcumin molecule, which were relatively stable, especially for CXCL8, hindering its promising potential to serve as the critical targets of Curcumin in periodontitis treatment.

Key Risk Genes Identified From the Postmortem Brain of Patients With Major Depressive Disorder and Their Potential Clinical Applications.

BACKGROUND: Major depressive disorder (MDD) is a type of emotional dysfunction, and its pathogenesis has not been fully elucidated. Specifically, the key molecules in depression-related brain regions involved in this disease and their contributions to this disease are currently unclear. METHODS: GSE53987 and GSE54568 were selected from the Gene Expression Omnibus database. The data were standardized to identify the common differentially expressed genes (DEGs) in the cortex of MDD patients in the 2 datasets. The DEGs were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The STRING database was used to build protein-protein interaction networks, and the cytoHubba plugin was used to identify hub genes. Furthermore, we selected another blood transcriptome dataset that included 161 MDD and 169 control samples to explore the changes in the screened hub genes. Mice were subjected to 4 weeks of chronic unpredictable mild stress to establish an animal model of depression, and the expression of these hub genes in tissues of the prefrontal cortex was then detected by quantitative real time polymerase chain reaction (qRT-PCR). We subsequently predicted the possible posttranscriptional regulatory networks and traditional Chinese medicine according to the hub genes using a few online databases. RESULTS: The analysis identified 147 upregulated genes and 402 downregulated genes were identified in the cortex of MDD patients compared with that of the controls. Enrichment analyses revealed that DEGs were predominantly enriched in synapse-related cell functions, linoleic acid metabolism, and other pathways. Protein-protein interaction analysis identified 20 hub genes based on the total score. The changes in KDM6B, CUX2, NAAA, PHKB, NFYA, GTF2H1, CRK, CCNG2, ACER3, and SLC4A2 in the peripheral blood of MDD patients were consistent with those in the brain. Furthermore, the prefrontal cortex of mice with depressive-like behaviors showed significantly increased Kdm6b, Aridb1, Scaf11, and Thoc2 expression and decreased Ccng2 expression compared with that of normal mice, which was consistent with the results found for the human brain. Potential therapeutic candidates, such as citron, fructus citri, leaves of Panax Notoginseng, sanchi flower, pseudoginseng, and dan-shen root, were selected via traditional Chinese medicine screening. CONCLUSIONS: This study identified several novel hub genes in specific brain regions involved in the pathogenesis of MDD, which may not only deepen our understanding of depression but may also provide new ideas for its diagnosis and treatment.

Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine.

Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1, DTL, KIF14, and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

Identification of prognostic hub genes and therapeutic targets for selenium deficiency in chicks model through transcriptome profiling.

Selenium deficiency is a prevalent micronutrient deficiency that poses a major health concern worldwide. This study aimed to shed light on the molecular mechanisms underlying selenium deficiency using a chick model. Chickens were divided into control and selenium deficient groups. Plasma samples were collected to measure selenium concentration and transcriptome analyse were performed on oviduct samples. The results showed that selenium deficiency led to a significant reduction in plasma selenium levels and altered the expression of 10,266 differentially expressed genes (DEGs). These DEGs primarily regulated signal transduction and cell motility. The molecular function includes GTPase regulatory activity, and KEGG pathway analysis showed that they were mainly involved in the signal transduction. By using Cytoscape and CancerGeneNet tool, we identified 8 modules and 10 hub genes (FRK, JUN, PTPRC, ACTA2, MST1R, SDC4, SDC1, CXCL12, MX1 and EZR) associated with receptor tyrosine kinase pathway, Wnt and mTOR signaling pathways that may be closely related to cancer. These hub genes could be served as precise diagnostic and prognostic candidate biomarkers of selenium deficiency and potential targets for treatment strategies in both animals and humans. This study sheds light on the molecular basis of selenium deficiency and its potential impact on public health.

An Interaction Network Driven Approach for Identifying Cervical, Endometrial, Vulvar Carcinomic Biomarkers and Their Multi-targeted Inhibitory Agents from Few Widely Available Medicinal Plants.

Differently expressed genes (DEGs) across cervical (CC), endometrial (EC), and vulvar carcinoma (VC) may serve as potential biomarkers for these progressive tumor conditions. In this study, DEGs of cervical (CC), endometrial (EC), and vulvar carcinoma (VC) were identified by microarray analysis. The interaction network between the identified 124 DEGs was constructed and analyzed to identify the hub genes and genes with high stress centrality. DEGs, namely, CDK1 and MMP9, were found to show highest degree and highest stress centrality respectively from the gene interaction network of 124 nodes and 1171 edges. DEG CDK1 is found to be overlapping in both cervical and endometrial carcinomic conditions while DEG MMP9 is found in vulvar carcinomic condition. Further, as it is studied that many phytochemicals play an important role as medicinal drugs, we have identified phytochemicals from few widely available medicinal plants and performed comprehensive computational study to identify a multi-targeted phytochemical against the identified DEGs, which are crucially responsible for the progression of these carcinomic conditions. Virtual screening of the phytochemicals against the target DEG protein structures with PDB IDs 4Y72 and 1GKC resulted in identifying the multi-targeted phytochemical against both the proteins. The molecular docking and dynamics simulation studies reveal that luteolin can act as a multi-targeted agent. Thus, the interactional and structural insights of luteolin toward the DEG proteins signify that it can be further explored as a multi-targeted agent against the cervical, endometrial, and vulvar carcinoma.

Network-Based Method to Investigate the Promoted Cell Apoptosis Mechanisms of Oridonin in OSCC through the RNA-Transcriptome.

The morbidity of oral cancer is high in the world. Oridonin is a traditional Chinese medicine that can effectively inhibit oral squamous cell carcinoma (OSCC) growth, but its mechanism remains unclear. Our previous data showed that oridonin inhibited CAL-27 cell proliferation and promoted apoptosis. Herein, we explored the mechanism and target of oridonin in human OSCC through RNA sequencing and integration of multiple bioinformatics analysis strategies. Differences in gene expression can be analyzed with RNA sequencing. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), gene set enrichment analysis (GSEA), Disease Ontology (DO), and other enrichment analyses were used to evaluate differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks were built via the STRING database. It was found that tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, and nuclear factor-kappa B (NF-kappaB) signaling pathway were associated with the therapeutic effects of oridonin in OSCC. Three key genes (BIRC3, TNFSF10, and BCL6) were found to associate with cell apoptosis in OSCC cells treated with oridonin. Quantitative PCR assays verified the expression of apoptosis-related DEGs: TNFSF10, BIRC3, AIFM2, BCL6, BCL2L2, and Bax. Western blots were employed for verifying proteins expression associated with DEGs: cleaved caspase 3, Bax, Bcl-w, anti-cIAP2, and anti-TRAIL. In conclusion, our findings reveal the molecular pathways and targets by which oridonin can treat and induce cytotoxic effects in OSCC: by affecting the signaling including TNF, NF-κB, and cytokine-cytokine receptor interaction and by regulating the key gene BIRC3, TNFSF10, and BCL6. It should be noted that further clinical trial validation is very necessary. Combined with current research trends, our existing research may provide innovative research drugs for the treatment of OSCC.

A novel strategy for dynamic modeling of genome-scale interaction networks.

MOTIVATION: The recent availability of omics data allows the construction of holistic maps of interactions between numerous role-playing biomolecules. However, these networks are often static, ignoring the dynamic behavior of biological processes. On the other hand, dynamic models are commonly constructed on small scales. Hence, the construction of large-scale dynamic models that can quantitatively predict the time-course cellular behaviors remains a big challenge. RESULTS: In this study, a pipeline is proposed for the automatic construction of large-scale dynamic models. The pipeline uses a list of biomolecules and their time-course trajectories in a given phenomenon as input. First, the interaction network of the biomolecules is constructed. To state the underlying molecular events of each interaction, it is translated into a map of biochemical reactions. Next, to define the kinetics of the reactions, an ordinary differential equation (ODE) is generated for each involved biomolecule. Finally, the parameters of the ODE system are estimated by a novel large-scale parameter approximation method. The high performance of the pipeline is demonstrated by modeling the response of a colorectal cancer cell line to different chemotherapy regimens. In conclusion, Systematic Protein Association Dynamic ANalyzer constructs genome-scale dynamic models, filling the gap between large-scale static and small-scale dynamic modeling strategies. This simulation approach allows for holistic quantitative predictions which are critical for the simulation of therapeutic interventions in precision medicine. AVAILABILITY AND IMPLEMENTATION: Detailed information about the constructed large-scale model of colorectal cancer is available in supplementary data. The SPADAN toolbox source code is also available on GitHub (https://github.com/PooyaBorzou/SPADAN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-accuracy protein model quality assessment using attention graph neural networks.

Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.

PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach.

BACKGROUND: Cell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases. RESULTS: PriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research. CONCLUSIONS: PriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.

Fasciola gigantica vaccine construct: an in silico approach towards identification and design of a multi-epitope subunit vaccine using calcium binding EF-hand proteins.

Continuous attempts have been made to pinpoint candidate vaccine molecules and evaluate their effectiveness in order to commercialise such vaccines for the treatment of tropical fascioliasis in livestock. The pathophysiology of fascioliasis can be related to liver damage brought on by immature flukes that migrate and feed, as well as immunological reactions to chemicals produced by the parasites and alarm signals brought on by tissue damage. Future research should, in our opinion, concentrate on the biology of invasive parasites and the resulting immune responses, particularly in the early stages of infection. The goal of the current study was to use the calcium-binding proteins from F. gigantica to create a multi-epitope subunit vaccine. The adjuvant, B-cell epitopes, CTL epitopes, and HTL epitopes that make up the vaccine construct are all connected by certain linkers. The antigenicity, allergenicity, and physiochemical properties of the vaccine construct were examined. The vaccine construct was docked with toll-like receptor 2, and simulations of the molecular dynamics of the complex's stability, interaction, and dynamics were run. After performing in silico cloning and immunosimulation, it was discovered that the construct was suitable for further investigation. New vaccination technologies and adjuvant development are advancing our food safety procedures since vaccines are seen as safe and are accepted by the user community. This research is also applicable to the F. hepatica system.

Study on the mechanism of Jin Gui Shen Qi Pill in the treatment of erectile dysfunction based on bioinformatics analysis.

Erectile dysfunction (ED) is a male disease, which is easy to cause disharmony in sexual life. However, at present, there are few drugs with small side effects in clinic. Jin Gui Shen Qi Pill (JGSQP) is a traditional Chinese medicine compound with obvious clinical effect in treating ED. Therefore, it is imperative to explore clinical drugs based on inhibiting the pathological characteristics of ED. First, the active ingredients and action targets in JGSQP were screened by applying Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SWISS Target Prediction. Further, a systematic pharmacological analysis platform for traditional Chinese medicine, and the ED targets were screened by applying Gene Cards and Online Mendelian Inheritance in Man databases to construct drug active ingredient-target-disease mapping, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis. Finally, Molecular docking and molecular dynamics simulations were used to screen the active ingredients of JGSQP acting on PDE-5, and analyze the ligand-receptor interaction relationship and binding free energy. The results showed that there were 212 potential targets of JGSQP for ED disease, and GO analysis revealed that the main pathways were positive regulation of DNA-binding transcription factor activity, regulation of vascular diameter, and negative regulation of vascular diameter, etc. KEGG analysis revealed that the main pathways were HIF-1 signaling pathway, prolactin signaling pathway, fluid shear stress, and atherosclerosis, etc. PPI network analysis revealed that the core targets TGFB1 and EGFR have important roles. Molecular docking and molecular dynamics simulations showed that the main components acting on PDE-5 were MOL000546, MOL011169, MOL000279, MOL000273 and Sildenafil. MOL000546 was able to bind stably to PDE-5. The multi-component, multi-target, and multi-pathway action characteristics of JGSQP were confirmed by network pharmacology, which predicted the possible mechanism of action of JGSQP in the treatment of ED and provided a theoretical reference for further experimental validation.