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BackgroundActivated cardiac fibroblasts (CF) play a central role in cardiac fibrosis, a condition associated with most cardiovascular diseases. Conversion of quiescent into activated CF sustains heart integrity upon injury. However, permanence of CF in active state inflicts deleterious heart function effects. Mechanisms underlying this cell state conversion are still not fully disclosed, contributing to a limited target space and lack of effective anti-fibrotic therapies.Materials and methodsTo prioritize targets for drug development, we studied CF remodeling upon activation at transcriptomic and proteomic levels, using three different cell sources: primary adult CF (aHCF), primary fetal CF (fHCF), and induced pluripotent stem cells derived CF (hiPSC-CF).ResultsAll cell sources showed a convergent response upon activation, with clear morphological and molecular remodeling associated with cell-cell and cell-matrix interactions. Quantitative proteomic analysis identified known cardiac fibrosis markers, such as FN1, CCN2, and Serpine1, but also revealed targets not previously associated with this condition, including MRC2, IGFBP7, and NT5DC2.ConclusionExploring such targets to modulate CF phenotype represents a valuable opportunity for development of anti-fibrotic therapies. Also, we demonstrate that hiPSC-CF is a suitable cell source for preclinical research, displaying significantly lower basal activation level relative to primary cells, while being able to elicit a convergent response upon stimuli.

Deer velvet antlers are the young horns of male deer that are not ossified and densely overgrown. Velvet antler and its preparations have been widely used in the treatment of postmenopausal osteoporosis (PMOP) in recent years, although its mechanism of action in the human body remains unclear. To screen the effective ingredients and targets of velvet antler in the treatment of PMOP using network pharmacology and to explore the potential mechanisms of velvet antler action in such treatments, we screened the active ingredients and targets of velvet antler in the BATMAN-TCM database. We also screened the relevant targets of PMOP in the GeneCards and OMIM databases and then compared the targets at the intersection of both velvet antler and PMOP. We used Cytoscape 3.7.2 software to construct a network diagram of “disease-drug-components-targets” and a protein-protein interaction (PPI) network through the STRING database and screened out the core targets; the R language was then used to analyze the shared targets between antler and PMOP for GO-enrichment analysis and KEGG pathway-annotation analysis. Furthermore, we used the professional software Maestro 11.1 to verify the predictive analysis based on network pharmacology. Hematoxylin-eosin (H&E) staining and micro-CT were used to observe the changes in trabecular bone tissue, further confirming the results of network pharmacological analysis. The potentially effective components of velvet antler principally include 17β-E2, adenosine triphosphate, and oestrone. These components act on key target genes such as AKT1, IL6, MAPK3, TP53, EGFR, SRC, and TNF and regulate the PI3K/Akt-signaling and MAPK-signaling pathways. These molecules participate in a series of processes such as cellular differentiation, apoptosis, metabolism, and inflammation and can ultimately be used to treat PMOP; they reflect the overall regulation, network regulation, and protein interactions.

Purpose: To determine the molecular mechanism involved in the anti-migraine effect of Asari Radix et Rhizoma (ARR) using network pharmacology. Methods: The compounds present in ARR were identified through information retrieval from literature and public databases, and were screened based on absorption, distribution, metabolism, excretion and toxicity. Target genes related to the selected compounds and migraine were identified or predicted from public databases. Hub genes in ARR against migraine were identified through analysis of interactions in overlapping genes between compounds and migraine target genes, based on STRING database. Gene enrichment analysis of overlapping genes was performed using Database for Annotation, Visualization and Integrated Discovery. Results: A total of 138 compounds were selected as potential bioactive compounds in ARR. Target genes related to the selected compounds (611 genes) and migraine (278 genes) were obtained, including 71 overlapping genes. The hub genes in the anti-migraine effect of ARR were BDNF, IL6, COMT, APP and TNF. Gene enrichment analysis showed the top 10 biological processes or pathways involved in the mechanism of anti-migraine action of ARR. The tissue source of the overlapping genes was not limited to the brain. The results from gene enrichment analysis revealed that the effect of ARR on migraine was holistic, which is characteristic of traditional Chinese medicines. Conclusion: Network pharmacology has been used to decipher the molecular mechanism involved in the action of ARR against migraine. The results provide a scientific basis for the clinical effect of ARR on migraine.

This research utilized the systematic biological and proteomics strategies to explore the regulatory mechanism of Danshen Yin Modified (DSYM) on atherosclerosis (AS) biological network. The traditional Chinese medicine database and HPLC was used to find the active compounds of DSYM, Pharmmapper database was used to predict potential targets, and OMIM database and GeneCards database were used to collect AS targets. String database was utilized to obtain the other protein of proteomics proteins and the protein‐protein interaction (PPI) data of DSYM targets, AS genes, proteomics proteins and other proteins. The Cytoscape 3.7.1 software was utilized to construct and analyse the network. The DAVID database is used to discover the biological processes and signalling pathways that these proteins aggregate. Finally, animal experiments and proteomics analysis were used to further verify the prediction results. The results showed that 140 active compounds, 405 DSYM targets and 590 AS genes were obtained, and 51 differentially expressed proteins were identified in the DSYM‐treated ApoE‐/‐ mouse AS model. A total of 4 major networks and a number of their derivative networks were constructed and analysed. The prediction results showed that DSYM can regulate AS‐related biological processes and signalling pathways. Animal experiments have also shown that DSYM has a therapeutic effect on ApoE‐/‐mouse AS model (P

Zymogen granule protein 16B (ZG16B) has been identified in various cancers, while so far the association between ZG16B and breast cancer hasn’t been explored. Our aim is to confirm whether it can serve as a prognostic biomarker in breast cancer. In this study, Oncomine, Cancer Cell Line Encyclopedia (CCLE), Ualcan, and STRING database analyses were conducted to detect the expression level of ZG16B in breast cancer with different types. Kaplan–Meier plotter was used to analyze the prognosis of patients with high or low expression of ZG16B. We found that ZG16B was significantly upregulated in breast cancer. Moreover, ZG16B was closely associated with foregone biomarkers and crucial factors in breast cancer. In the survival analysis, high expression of ZG16B represents a favorable prognosis in patients. Our work demonstrates the latent capacity of ZG16B to be a biomarker for prognosis of breast cancer.

Aims: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches Background: Neurodevelopmental disorders (NNDs) are disabilities associated chiefly with the functioning of the neurological system and brain. Children with neurodevelopmental disorders have difficulties with speech, behaviour, learning and other neurological functions. Systems biology is a holistic approach to enciphering the complexity of biological systems and their interactions. It opens the way to a more successful discovery of novel therapeutics. Objective: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches. Methods: A list of genes was collected from NCBI database for Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases. STRING database and Cytoscape software was used for construction and interpreting molecular interaction in the network. 3D structure of target protein, was build and validated.The phytochemicals were identified through various research articles and filtered out by virtual screening through Molinspiration. Molecular docking analyses of known phytochemical with target proteins were performed usingAutoDock tool. Result: AKT1 for Autism, SNAP25 for Intellectual Disability (ID) and DRD4 for Attention Deficit Hyperactivity Disorder (ADHD) were identified as most potential drug target through network study. further the modelled structure of obtained target were undergo molecular docking study with kown phytochemicals. Based on lowest binding energy, Huperzine A for Autism and ID, Valerenic acid for ADHD found to be the most potential therapeutic molecules. Conclusion: Huperzine A against Autism and ID, Valerenic acid against ADHD found to be the most potential therapeutic molecules and expected to be effective in the treatment of NNDs. Phytochemicals do not have side effects so extract of these can be taken in preventive form too as these disorders occur during developmental stages of the child. Further the obtained molecule if experimentally validated would play promising role for the treatment of NDDs in human.

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

Essential gene prediction models built so far are heavily reliant on sequence-based features and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we applied our approach for the prediction of essential genes to organisms from the STRING database and hosted the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2700+ bacteria predicted using features derived from STRING protein-protein functional association networks. Housing a total of 3.5M+ genes, NetGenes offers various features like essentiality scores, annotations and feature vectors for each gene. NetGenes is available athttps://rbc-dsai-iitm.github.io/NetGenes/

Objective: To investigate the possible mechanism of Yiqing Capsules in the treatment of upper respiratory tract infection based on network pharmacology. Methods: The main active components of Yiqing Capsules were selected on TCMSP database; the targets of upper respiratory tract infection were selected on GeneCards database. The drug-compound-target network and PPi network were constructed through STRING database and soft Cytoscape 3.7.2. Soft R was used to perform GO enrichment analysis and KEGG pathway enrichment analysis of main targets. Results: According to the screening conditions, 48 active compounds and 171 related targets were obtained. GO enrichment analysis obtained 2333 items, KEGG pathway enrichment analysis obtained 2248 items, including Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection, Epstein-Barr virus infection, PI3K-Akt signaling pathway, etc. Conclusion: Yiqing capsules play a therapeutic role in upper respiratory tract infection through multi-target and multi-pathway.

Background Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. Results We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.

Introduction: Pain is valuable in diagnosis and also warning of the patients. Many molecular reagents are introduced which are related to pain. In this research, the pain-related genes are screened to identify the critical ones. Methods: First, the pain-related genes were pulling out from the STRING database, and Cytoscape software was used to make the interactome unit. Then the central genes and their neighbors were analyzed. Finally, the genes were clustered, and the essential genes were introduced. Results: After analyzing 159 genes of the network, FOS, IL6, TNF, TAC1, IL8, and KNG1 were identified as the essential genes. Further analysis revealed that 88 genes are directly connected to the central genes. More resolution led to ignoring TNF and IL8 and considering SCN-alpha and PAICS as additional critical nodes. Conclusion: Six critical genes related to pain were identified. They can be potentially considered as new drug targets. Further investigation is required to introduce the central genes as a pain killer.

Circular RNAs (circRNAs) along other complementary regulatory elements in ceRNAs networks possess valuable characteristics for both diagnosis and treatment of several human cancers including breast cancer (BC). In this study, we combined several systems biology tools and approaches to identify influential BC circRNAs, RNA binding proteins (RBPs), miRNAs, and related mRNAs to study and decipher the BC triggering biological processes and pathways.Rooting from the identified total of 25 co-differentially expressed circRNAs (DECs) between triple negative (TN) and luminal A subtypes of BC from microarray analysis, five hub DECs (hsa_circ_0003227, hsa_circ_0001955, hsa_circ_0020080, hsa_circ_0001666, and hsa_circ_0065173) and top eleven RBPs (AGO1, AGO2, EIF4A3, FMRP, HuR (ELAVL1), IGF2BP1, IGF2BP2, IGF2BP3, EWSR1, FUS, and PTB) were explored to form the upper stream regulatory elements. All the hub circRNAs were regarded as super sponge having multiple miRNA response elements (MREs) for numerous miRNAs. Then four leading miRNAs (hsa-miR-149, hsa-miR-182, hsa-miR-383, and hsa-miR-873) accountable for BC progression were also introduced from merging several ceRNAs networks. The predicted 7- and 8-mer MREs matches between hub circRNAs and leading miRNAs ensured their enduring regulatory capability. The mined downstream mRNAs of the circRNAs-miRNAs network then were presented to STRING database to form the PPI network and deciphering the issue from another point of view. The BC interconnected enriched pathways and processes guarantee the merits of the ceRNAs networks’ members as targetable therapeutic elements.This study suggested extensive panels of novel covering therapeutic targets that are in charge of BC progression in every aspect, hence their impressive role cannot be excluded and needs deeper empirical laboratory designs.

Background: Cervical carcinoma is one of the most common malignant gynecological tumors and is associated with high rates of morbidity and mortality. Early diagnosis and early treatment can reduce the mortality rate of cervical cancer. However, there is still no specific biomarkers for the diagnosis and detection of cervical cancer prognosis. Therefore, it is greatly urgent in searching biomarkers correlated with the diagnosis and prognosis of cervical cancer. Results: The mRNA and microRNA expression profile datasets ({"type":"entrez-geo","attrs":{"text":"GSE7803","term_id":"7803"}}GSE7803, {"type":"entrez-geo","attrs":{"text":"GSE9750","term_id":"9750"}}GSE9750, {"type":"entrez-geo","attrs":{"text":"GSE63514","term_id":"63514"}}GSE63514, and {"type":"entrez-geo","attrs":{"text":"GSE30656","term_id":"30656"}}GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrated bioinformatics. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained by R software. The protein–protein interaction (PPI) networks of the DEGs were performed from the STRING database and further visualized by Cytoscape software. A total of 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs validated to be associated with cervical cancer were obtained using HMDD online website and the target genes of DEMs were identified using the miRWalk2.0 online database. ESR1, PPP1R3C, NSG1, and TMPRSS11D were the gene targets of hsa-miR-21; the targets of hsa-miR-16 were GYS2, ENDOU, and KLF4. These targets were all downregulated in cervical cancer. Finally, we verified the expression of those targets in cervical tissues from TCGA and GTEx databases and analyzed their relationship with survival of cervical cancer patients. In the end, the expression of key genes in cervical cancer tissues was verified via experiment method, we found KLF4 and ESR1 were downregulated in tumor tissues. Conclusion: This study indicates that KLF4 and ESR1 are downregulated by the upregulated miR21 and miRNA16 in cervical cancer, respectively, using bioinformatics analysis, and the lower expression of KLF4 and ESR1 is closely related to the poor prognosis. They might be of clinical significance for the diagnosis and prognosis of cervical cancer, and provide effective targets for the treatment of cervical cancer.

Purpose: This study aimed to find risk factors for colon cancer progression with bioinformatics methods, and validated by clinical patients. Methods: Differentially expressed genes (DEGs) between colon cancer tissues and normal colon tissues were extracted from The Cancer Genome Atlas (TCGA) database using R software, amounted to 8,051. DEGs between pathologic stage I+II and stage III+IV amounted to 373, and were compared with DEGs of cancer/normal analyzed above to get the intersection of both. Ninety-six intersected DEGs were identified and defined as progressive DEGs of colon cancer. Then these 96 progressive DEGs were studied by Gene ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis using the DAVID database and visualizing by R software. A protein-protein interaction (PPI) network and functional modules were established using the STRING database. Further, an overall survival (OS) curve was drawn via the GEPIA website based on the CGA database and six progressive DEGs were found to be involved with OS of colon cancer patients. The Linkedomics website was used for detailed analysis of specific subsets of TNM. Results: Pregnancy specific glycoprotein (PSG), vitamin digestion, and absorption were confirmed to promote the progression of colon cancer. Furthermore, NTF4 was found to be associated with both OS and each subset of TNM; therefore, defined as a key risk factor for colon cancer progression. Further analysis of NTF4 expression using clinical data showed it acted as a key risk factor and diagnosis marker for colon cancer progression. Conclusion: NTF4 is a risk factor contributing to colon cancer progression and associated with overall survival.

The clinical interaction between stroke and periodontitis has been consistently studied and confirmed. Hence, forecasting potentially new protein interactions in this association using bioinformatic strategies presents potential interest. In this exploratory study, we conducted a protein-protein network interaction (PPI) search with documented encoded proteins for both stroke and periodontitis. Genes of interest were collected via GWAS database. The STRING database was used to predict the PPI networks, first in a sensitivity purpose (confidence cut-off of 0.7), and then with a highest confidence cut-off (0.9). Genes over-representation was inspected in the final network. As a result, we foresee a prospective protein network of interaction between stroke and periodontitis. Inflammation, pro-coagulant/pro-thrombotic state and ultimately atheroma plaque rupture is the main biological mechanism derived from the network. These pilot results may pave the way to future molecular and therapeutic studies to further comprehend the mechanisms between these two conditions.

MotivationSeveral protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence. However, this is a trade-off that is hard to achieve. In addition, most aligners require a considerable effort to use in practice and many researchers must choose an aligner without the opportunity to previously compare the performance of different aligners.ResultsWe developed PINAWeb, a user-friendly web-based tool to obtain and compare the results produced by the aligners: AligNet, HubAlign, L-GRAAL, PINALOG and SPINAL. PPINs can be uploaded either from the STRING database or from a user database. The source code of PINAWeb is freely available on GitHub to enable researchers to add other aligners, network databases or alignment score metrics. In addition, PINAWeb provides a report with the analysis for every alignment in terms of topological and functional information scores, as well as the visualization of the alignments’ comparison (agreement/differences) when more than one aligner are considered.Availabilityhttps://bioinfo.uib.es/~recerca/PINAWeb

Background: Genetic factors are important in spermatogenesis and fertility maintenance, and are potentially significant biomarkers for the early detection of infertility. However, further understanding of these biological processes is required. Methods: In the present study, we sought to identify associated genes by reanalyzing separate studies from Gene Expression Omnibus datasets ({"type":"entrez-geo","attrs":{"text":"GSE45885","term_id":"45885"}}GSE45885, {"type":"entrez-geo","attrs":{"text":"GSE45887","term_id":"45887"}}GSE45887 and {"type":"entrez-geo","attrs":{"text":"GSE9210","term_id":"9210"}}GSE9210) and validation datasets ({"type":"entrez-geo","attrs":{"text":"GSE4797","term_id":"4797"}}GSE4797, 145467, 108886, 6872). The differential genes were used the limma package in R language. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by the clusterprofier package. The protein-protein interaction network was constructed by the STRING database. The interaction between mRNA and TF was predicted by miRWalk web. At last, The Cancer Genome Atlas data were used to identify hub gene expression levels in GEPIA web. Results: The results showed that 27 shared genes associated with spermatogenesis. We effectively screen out two genes (KIF2C and TEKT2) and both validated by {"type":"entrez-geo","attrs":{"text":"GSE4797","term_id":"4797"}}GSE4797, 145467, 108886 and 6872. Among 27 shared genes, KIF2C and TEKT2 both down-regulated in spermatogenesis. The network of TF-miRNA-target gene was established, we found KIF2C-miRNAs (has-miR-3154, 6075, 6760-5p, 1251-5p, 186-sp)-TFs (EP300, SP1) might work in spermatogenesis. Conclusions: Our study might help to improve our understanding of the mechanisms in spermatogenesis and provide diagnostic biomarkers and therapeutics targets.

Background: Coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving disease with no effective drug treatment. Traditional Chinese medicines have been widely used to treat COVID-19 in China. Chansu and its major active constituent, bufalin, exert broad-spectrum antiviral effects. Although the clinical efficacy of Chansu injection for COVID-19 treatment has been confirmed, its mechanism of action remains unclear. Objectives: In this study, we used network pharmacology and molecular docking technology to explore the potential material basis and mechanism of action of Chansu injection for COVID-19 treatment. Methods: The main components of Chansu injection were determined using high-performance liquid chromatography (HPLC). The PharmMapper, SwissTargetPrediction, SEA, and TCMID databases were used to screen for the active ingredients and therapeutic targets of Chansu injection while the OMIM and GeneCards Suite databases were used to search for COVID-19-related targets. The STRING database was used for protein–protein interaction (PPI) network construction and topological analysis while DAVID was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the core targets. Molecular docking of the three Chansu injection components (i.e., cinobufagin, resibufogenin, and bufalin) to angiotensin-converting enzyme II, spike (S) protein, 3CL protease, and RNA-dependent RNA polymerase was also carried out. Results: The three Chansu injection compounds were identified using HPLC. A total of 236 drug-related targets and 16,611 disease-related targets were identified, and 77 common targets were determined through mapping. The PPI mapping results revealed 16 core targets obtained through topological analysis and screening. Further, GO and KEGG pathway enrichment analyses revealed that the PI3K and JAK–STAT signaling pathways are the major pathways regulated by Chansu injection in COVID-19 treatment. The molecular docking results suggest that the three Chansu injection components have high binding energies to the S protein. Conclusions: This study revealed that the potential mechanism of Chansu injection for COVID-19 treatment involves multiple targets and pathways, thereby providing a scientific basis for its clinical application and further research.

Objective. The mechanism of peach kernel-safflower in treating diabetic nephropathy (DN) was investigated using network pharmacology. Methods. Network pharmacology methodology was applied to screen the effective compounds of peach kernel-safflower in the SymMap and TCMSP databases. Potential targets were then screened in the ETCM, SEA, and SymMap databases to construct a compound-target network. This was followed by screening of DN targets in OMIM, Gene, and GeneCards databases. The common targets of drugs and diseases were selected for analysis in the STRING database, and the results were imported into Cytoscape 3.8.0 to construct a protein-protein interaction network. Next, GO and KEGG enrichment analyses were performed. Finally, Schrödinger molecular docking verified the reliability of the results. Results. A total of 23 effective compounds and 794 potential targets resulted from our screening process. Quercetin and luteolin were identified as the main effective ingredients in peach kernel-safflower. Furthermore, five key targets (VEGFA, IL6, TNF, AKT1, and TP53), AGE-RAGE, fluid shear stress and atherosclerosis, IL-17, and HIF-1 signaling pathways may be involved in the treatment of DN using peach kernel-safflower. Conclusions. This study embodies the complex network relationship of multicomponents, multitargets, and multipathways of peach kernel-safflower to treat DN and provides a basis for further research on its mechanism.

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/., Author summary We revisit at a large scale the question of inferring DDIs from PPIs. Compared to previous studies, we take a unified approach accross multiple sources of PPIs. This approach is a method for inferring new edges in a tripartite graph setting and can be compared to link prediction approaches in knowledge graphs. Aggregation of several sources is performed using an optimized weighted average of the individual scores calculated in each source. A huge dataset of over 84K DDIs is produced which far exceeds the previous datasets. We show that a significant portion of the PPIDM dataset covers a large number of PPIs from curated (IMEx) or non curated (STRING) databases. Such a reservoir of DDIs deserves further exploration and can be combined with high-throughput methods such as cross-linking mass spectrometry to identify plausible protein partners of proteins of interest.

Background. Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis. Methods. DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable. Results. We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667. Conclusion. We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

To investigate the mechanism of action of Rhubarb (Dahuang) in the treatment of colon cancer by network pharmacological method. TCMSP database was used to screen the active components and action targets of Rhubarb, Genecards database was used to screen the relevant action targets of colon cancer, Venny 2.1.0 software was used to make the intersection of Rhubarb and colon cancer, and the potential targets of Rhubarb for the treatment of colon cancer were obtained.Cytoscape 3.7.2 software and the STRING database were used to construct the component-target network of Rhubarb active components and colon cancer related targets. The important targets were obtained by analyzing the network, and the GO and KEGG pathway enrichment analysis was performed on them using Metascape database. Sixty active ingredients including EUPATIN, Mutatochrome, Physciondiglucoside, Procyanidin B-5,3’ -o-gallate, rhin were screened from Rhubarb, which could act on 61 colon cancer related targets.Among them, 13 targets, including PTGS2, IL1B and Bax, are important targets, which may be involved in various biological processes such as the response to the regulation of colon cancer nutritional level, the homeostasis of multicellular organisms and the regulation of peptide secretion through the regulation of PI3K and other pathways. Rhubarb can participate in the treatment of colon cancer through multi-target and multi-pathway.

Background Coronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD. Method We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein–protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed. Results A total of 2127 differentially expressed genes (DEGs) were identified in GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls. Conclusion We identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.

Background Due to the fact that pulmonary tuberculosis (PTB) is a highly infectious respiratory disease characterized by high herd susceptibility and hard to be treated, this study aimed to search novel effective biomarkers to improve the prognosis and treatment of PTB patients. Methods Firstly, bioinformatics analysis was performed to identify PTB-related differentially expressed genes (DEGs) from GEO database, which were then subjected to GO annotation and KEGG pathway enrichment analysis to initially describe their functions. Afterwards, clustering analysis was conducted to identify PTB-related gene clusters and relevant PPI networks were established using the STRING database. Results Based on the further differential and clustering analyses, 10 DEGs decreased during PTB development were identified and considered as candidate hub genes. Besides, we retrospectively analyzed some relevant studies and found that 7 genes (CCL20, PTGS2, ICAM1, TIMP1, MMP9, CXCL8 and IL6) presented an intimate correlation with PTB development and had the potential serving as biomarkers. Conclusions Overall, this study provides a theoretical basis for research on novel biomarkers of PTB, and helps to estimate PTB prognosis as well as probe into targeted molecular treatment.

Background:: Traditional Chinese medicine has accumulated rich resources and experience through clinical research to explore the prevention and treatment of chronic cerebral circulatory insufficiency, but current medicine lacks in-depth research and confirmation on the established protocols and mechanism of prescribed TCMs at the macro and micro levels. Objective:: To explore the prescription of Chinese medicines for the treatment of chronic cerebral circulation insufficiency (CCCI) and to explore the mechanism of core drugs. Methods:: 229 Chinese prescriptions for CCCI were collected from CNKI, CBM, VIP and WANFANG databases for this study. The frequency and association rules of drugs were analyzed and the core drugs by TCMISSV2.5 software was extracted. The active ingredients and targets were obtained by TCMSP, and genes of CCCI were collected from the DisGeNET, OMIM, DrugBank disease databases. The intersection targets of herbal medicine and disease were imported into the STRING database for PPI network. The key targets were screened by the network topology algorithm. The Systems Dock website was used to verify the molecular docking. The GOEAST and DAVID tools were used to perform GO and KEGG pathway analysis with the key target genes. Results:: 117 drugs involved in 229 prescriptions were identified, 2 core drugs were identified. We identified 8 active ingredients, which were mandenol, myricanone, perlolyrine, senkyunone, wallichilide, sitosterol, beta-sitosterol and stigmasterol. 371 herbal targets predicted and 335 disease targets. The enrichment analysis showed that the core herbal medicines could prevent CCCI by 15 key signaling pathways. Conclusion:: There are direct or indirect connections in key signaling pathways, which not only participate in energy metabolism, hormone regulation, signal transduction, but also play a role in the comprehensive intervention of nervous system, immune system, circulatory system and other systems, which is consistent with the comprehensive pathogenesis of CCCI induced by multiple factors.

Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes.

Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.

Mining disease-related genes contributes momentously to handling lung adenocarcinoma (LUAD). But genetic complexity and tumor heterogeneity severely get in the way. Fortunately, new light has been shed by dramatic progress of bioinformatic technology in the past decades. In this research, we investigated relationships between gene expression and clinical features of LUAD via integrative bioinformatic analysis. First, we applied limma and DESeq2 packages to analyze differentially expressed genes (DEGs) of LUAD from GEO database and TCGA project (tumor tissues versus normal tissues), and acquired 180 down-regulated DEGs and 52 up-regulated DEGs. Then, we investigated genetic and biological assignment of theses DEGs by Bioconductor packages and STRING database. We found these DEGs were distributed dispersedly among chromosomes, enriched observably in extracellular matrix-related processes, and weighted hierarchically in interaction network. Finally, we established DEGs-based statistical models for evaluating TNM stage and survival status of LUAD. And these models (logistic regression models for TNM parameter and Cox regression models for survival probability) all possessed fine predictive efficacy (C-indexes: T, 0.740; N, 0.687; M, 0.823; overall survival, 0.678; progression-free survival, 0.611). In summary, we have successfully established gene expression-based models for assessing clinical characteristics of LUAD, which will assist its pathogenesis investigation and clinical intervention.

Background Traditional Chinese medicine (TCM) encompasses numerous herbal formulas which play critical therapeutic roles through “multi-components, multi-targets and multi-pathways” mechanisms. Exploring the interaction among these mechanisms can certainly help to depict the core therapeutic function of herbal formulas. Xiaoyao decoction (XYD) is one of the most well-known traditional Chinese medicine formulas which has been widely applied to treat various diseases. In this study, taking XYD as an example, we proposed a network pharmacology-based method to identify the main therapeutic targets of this herbal concoctions. Methods Chemical data of XYD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID) and Compound Reference Database (CRD) and screened oral bioavailability attributes from SwissADME using Veber’s filter. Targets of sample chemicals were identified using the online tool similarity ensemble approach (SEA), and pathways were enriched using STRING database. On the basis of targets–pathways interactions from the enrichment, a “targets–pathways–targets” (TPT) network was constructed. In the TPT network, the importance of each target was calculated by the declining value of network efficiency, which represents the influential strength of a specific set-off target on the whole network. Network-based predictive results were statistically validated with existing experimental evidence. Results The TPT network was comprised of 279 nodes and 6549 edges. The declining value of network efficiency of the sample targets was significantly correlated with their involvement frequency in existing studies of XYD using Spearman’s test (p Conclusions The network pharmacology-based approach in the present study shows promising potential for identifying the main therapeutic targets from TCM formulas. This study provides valuable information for TCM researchers and clinicians for better understanding the main therapeutic targets and therapeutic roles of herbal decoctions in clinical settings.

Objective. To elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China. Methods. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets. Results. The ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis. Conclusion. In this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a “multicompound, multitarget, and multipathway” manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

BACKGROUND Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract. Surgical resection and tyrosine kinase inhibitors are defined as the main treatments but cannot cure patients with advanced GIST, which eventually develops into recurrence and acquired drug resistance. Therefore, it is necessary to identify prognostic biomarkers and new therapeutic targets for GISTs. CC chemokine receptor type 8 (CCR8) protein participates in regulation of immune responses. Recent studies on CCR8 in non-small cell lung cancer and colorectal cancer showed that it was highly expressed in tumor-infiltrating regulatory T cells and correlated with a poor prognosis. AIM To detect CCR8 expression in GIST tissues and analyze its relationships with clinicopathological features and prognosis in patients with GISTs. METHODS Tissue samples were used for the tissue microarrays construction. The microarrays were then subjected to immunohistochemical analyses to detect CCR8 expression. Next, Kaplan–Meier analysis was utilized to calculate the survival rate of patients with complete follow-up data, and the potential prognostic value of CCR8 was evaluated by Cox regression analysis. Finally, a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes single-gene enrichment chart of CCR8 was constructed using the STRING database. RESULTS CCR8-positive signals were detected as brown or brown-yellow particles by immunohistochemistry located in the cytoplasm. Among 125 tissue samples, 74 had CCR8 high expression and 51 had low or negative expression. Statistical analyses suggested CCR8 was significantly correlated with tumor size, mitotic index, AFIP-Miettinen risk classification and tumor location. Kaplan–Meier and multivariate analyses showed that patients with low or negative CCR8 expression, mitotic index < 5/high-power fields (HPF) and tumor diameter < 5 cm had a better prognosis. Based on the STRING database, CCR8 was significantly enriched in biological processes such as tumor immunity, T lymphocyte chemotaxis, migration and pathways like the nuclear factor-κB and tumor necrosis factor pathways as well as intestinal immune regulation networks. CONCLUSION CCR8 is a prognostic biomarker for malignant potential of GISTs, with high expression correlated with malignancy and poor prognosis.

Protein-protein interactions (PPIs) underlie the molecular mechanisms of most biological processes. Mitogen-activated protein kinases (MAPKs) can be dephosphorylated by MAPK-specific phosphatases such as PP2C, which are critical to transduce extracellular signals into adaptive and programmed responses. However, the experimental approaches for identifying PPIs are expensive, time-consuming, laborious and challenging. In response, many computational methods have been developed to predict PPIs. Yet, these methods have inherent disadvantages such as high false positive and negative results. Thus, it is crucial to develop in silico approaches for predicting PPIs efficiently and accurately. In this study, we identified PPIs among 16 BdMAPKs and 86 BdPP2Cs in B. distachyon using a novel docking approach. Further, we systematically investigated the docking site (D-site) of BdPP2C which plays a vital role for recognition and docking of BdMAPKs. D-site analysis revealed that there were 96 pairs of PPIs including all BdMAPKs and most BdPP2Cs, which indicated that BdPP2C may play roles in other signaling networks. Moreover, most BdPP2Cs have a D-site for BdMAPKs in our prediction results, which suggested that our method can effectively predict PPIs, as confirmed by their 3D structure. In addition, we validated this methodology with known Arabidopsis and yeast phosphatase-MAPK interactions from the STRING database. The results obtained provide a vital research resource for exploring an accurate network of PPIs between BdMAPKs and BdPP2Cs.

We conducted an in silico analysis to search for important genes in the pathogenesis of Caseous Lymphadenitis (CL), with prospects for use in formulating effective vaccines against this disease. For this, we performed a survey of proteins expressed by Corynebacterium pseudotuberculosis, using protein sequences collected from the NCBI GenPept database and the keywords "caseous lymphadenitis" and "Corynebacterium pseudotuberculosis" and "goats". A network was developed using the STRING 10 database, with a confidence score of 0.900. For every gene interaction identified, we summed the interaction score of each gene, generating a combined association score to obtain a single score named weighted number of links (WNL). Genes with the highest WNL were named "leader genes". Ontological analysis was extracted from the STRING database through Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A search in the GenPept database revealed 2,124 proteins. By using and plotting with STRING 10, we then developed an in silico network model comprised of 1,243 genes/proteins interconnecting through 3,330 interactions. The highest WNL values were identified in the rplB gene, which was named the leader gene. Our ontological analysis shows that this protein acts effectively mainly on Metabolic pathways and Biosynthesis of secondary metabolites. In conclusion, the in silico analyses showed that rplB has good potential for vaccine development. However, functional assays are needed to make sure that this protein can potentially induce both humoral and cellular immune responses against C. pseudotuberculosis in goats.