MTGO: PPI Network Analysis Via Topological and Functional Module Identification

Explore the active ingredients and potential mechanism of action on Actinidia arguta leaves against T2DM by integration of serum pharmacochemistry and network pharmacology

BACKGROUND

Actinidia arguta leaves (AAL) are traditionally consumed as a vegetable and as tea in folk China and Korea. Previous studies have reported the anti-diabetic effect of AAL, but its bioactive components and mechanism of action are still unclear.

AIM OF THE STUDY

This study aims to identify the hypoglycemic active components of AAL by combining serum pharmacochemistry and network pharmacology and to elucidate its possible mechanism of action.

METHODS

Firstly, the effective components in mice serum samples were characterized by UPLC-Q/TOF-MSE. Furthermore, based on these active ingredients, network pharmacology analysis was performed to establish an "H-C-T-P-D" interaction network and reveal possible biological mechanisms. Finally, the affinity between serum AAL components and the main proteins in the important pathways above was investigated through molecular docking analysis.

RESULTS

Serum pharmacochemistry analysis showed that 69 compounds in the serum samples were identified, including 23 prototypes and 46 metabolites. The metabolic reactions mainly included deglycosylation, dehydration, hydrogenation, methylation, acetylation, glucuronidation, and sulfation. Network pharmacology analysis showed that the key components quercetin, pinoresinol diglucoside, and 5-O-trans-p-coumaroyl quinic acid butyl ester mainly acted on the core targets PTGS2, HRAS, RELA, PRKCA, and BCL2 targets and through the PI3K-Akt signaling pathway, endocrine resistance, and MAPK signaling pathway to exert a hypoglycemic effect. Likewise, molecular docking results showed that the three potential active ingredients had good binding effects on the five key targets.

CONCLUSION

This study provides a basis for elucidating the pharmacodynamic substance basis of AA against T2DM and further exploring the mechanism of action.

CTHRC1 is a prognostic biomarker correlated with immune infiltration in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, characterized by high morbidity, high mortality, and poor prognosis. Collagen triple helix repeat containing 1 (CTHRC1) has been shown to be highly expressed in various cancers. However, its biological functions, potential role as a biomarker, and its relationship with immune infiltrates in HNSCC remain unclear. Our principal objective was to analyze CTHRC1 expression, its prognostic implications, biological functions, and its effects on the immune system in HNSCC patients using bioinformatics analysis.

METHODS

The expression matrix was obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). CTHRC1 expression in HNSCC was analyzed between tumor and adjacent normal tissues, different stages were compared, and its impact on clinical prognosis was assessed using Kaplan-Meier analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA) were employed for enrichment analysis. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to analyze protein-protein interactions. Pearson correlation tests were used to investigate the association between CTHRC1 expression and immune checkpoints. The correlation between CTHRC1 and immune infiltration was investigated using CIBERSORT, TIMER, and ESTIMATE.

RESULTS

Compared to adjacent normal tissues, CTHRC1 was found to be highly overexpressed in tumors. Increased expression of CTHRC1 was more evident in the advanced stage of HNSCC and predicted a poor prognosis. Most genes related to CTHRC1 in HNSCC were enriched in physiological functions of Extracellular matrix(ECM) and tumor. Furthermore, several immune checkpoints, such as TNFSF4 and CD276 have been shown to be associated with CTHRC1 expression. Notably, the level of CTHRC1 expression correlated significantly with immune infiltration levels, particularly activated macrophages in HNSCC.

CONCLUSIONS

High expression of CTHRC1 predicts poor prognosis and is associated with immune infiltration in HNSCC, confirming its utility as a tumor marker for HNSCC.

TRIAL REGISTRATION

Not applicable. All data are from public databases and do not contain any clinical trials.

Exploring the efficacy and molecular mechanism of Danhong injection comprehensively in the treatment of idiopathic pulmonary fibrosis by combining meta-analysis, network pharmacology, and molecular docking methods

BACKGROUND

Danhong injection, a compound injection of Chinese herbal medicine, has been widely used in idiopathic pulmonary fibrosis (IPF) at present as an adjuvant treatment. However, the clinical efficacy and molecular mechanism of IPF are still unclear. This study will evaluate and explore the clinical efficacy and molecular mechanism of Danhong injection in the treatment of IPF.

METHODS

In meta-analysis, the computer was used to search 8 databases (PubMed, EMbase, CENTRAL, MEDLINE, CBM, CNKI, WanFang, and VIP) to collect the RCTs, and RevMan 5.3 and Stata 14.0 were used for statistical analysis. It has been registered on PROSPERO: CRD42020221096. In network pharmacology, the main chemical components and targets of the chemical components of Danhong injection were obtained in TCMSP and Swiss Target Prediction databases. The main targets of IPF were obtained through Gencards, Disgenet, OMIM, TTD, and DRUGBANK databases. The String platform was used to construct PPI networks. Cytoscape 3.8.2 was used to construct the "Danhong components - IPF targets-pathways" network. The molecular docking verification was conducted by Auto Dock.

RESULTS

Twelve RCTs were finally included with a total of 896 patients. The meta-analysis showed that Danhong injection could improve the clinical efficiency ([OR] = 0.25, 95% CI [0.15, 0.41]), lung function, arterial blood gas analysis, inflammatory cytokines, and serum cytokines associated with pulmonary fibrosis of IPF patients, respectively (P < .05). The core active components of Danhong injection on IPF were Luteolin, Quercetin, and Kaempferol, and the core targets were PTGS2, AR, ESR1, PPARG, and RELA. Danhong injection mainly improved IPF through PD-L1 expression and PD-1 checkpoint path in cancer, pathways in cancer, PI3K-Akt signaling pathway, etc.

CONCLUSION

These results provided scientific basis for the clinical use of Danhong injection for the treatment of IPF, and provided a new direction to explore the potential mechanism of action of Danhong injection.

Molecular network mechanism in cerebral ischemia-reperfusion rats treated with human urine stem cells

Objective

To explore the effect of human urine-derived stem cells (husc) in improving the neurological function of rats with cerebral ischemia-reperfusion (CIR), and report new molecular network by bioinformatics, combined with experiment validation.

Methods

After CIR model was established, and husc were transplanted into the lateral ventricle of rats，neurological severe score (NSS) andgene network analysis were performed. Firstly, we input the keywords "Cerebral reperfusion" and "human urine stem cells" into Genecard database and merged data with findings from PubMed so as to get their targets genes, and downloaded them to make Venny intersection plot. Then, Gene ontology (GO) analysis, kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and protein-protein interaction (PPI) were performed to construct molecular network of core genes. Lastly, the expressional level of core genes was validated via quantitative real-time polymerase chain reaction (qRT-PCR), and localized by immunofluorescence.

Results

Compared with the Sham group, the neurological function of CIR rats was significantly improved after the injection of husc into the lateral ventricle; at 14 days, P = 0.028, which was statistically significant. There were 258 overlapping genes between CIR and husc, and integrated with 252 genes screened from PubMed and CNKI. GO enrichment analysis were mainly involved neutrophil degranulation, neutrophil activation in immune response and platelet positive regulation of degranulation, Hemostasis, blood coagulation, coagulation, etc. KEGG pathway analysis was mainly involved in complement and coagulation cascades, ECM-receptor. Hub genes screened by Cytoscape consist ofCD44, ACTB, FN1, ITGB1, PLG, CASP3, ALB, HSP90AA1, EGF, GAPDH. Lastly, qRT-PCR results showed statistic significance (P < 0.05) in ALB, CD44 and EGF before and after treatment, and EGF immunostaining was localized in neuron of cortex.

Conclusion

husc transplantation showed a positive effect in improving neural function of CIR rats, and underlying mechanism is involved in CD44, ALB, and EGF network.

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein-protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

Identification of Autophagy-Related Candidate Genes in the Early Diagnosis of Alzheimer's Disease and Exploration of Potential Molecular Mechanisms

This study aimed to identify autophagy-related candidate genes for the early diagnosis of Alzheimer's disease (AD) and elucidate their potential molecular mechanisms. Differentially expressed genes (DEGs) and phenotype-associated significant module genes were obtained using the "limma" package and weighted gene co-expression network analysis (WGCNA) based on hippocampal tissue datasets from AD patients and control samples. The intersection between the list of autophagy-related genes (ATGs), DEGs, and module genes was further investigated to obtain AD-autophagy-related differential expression genes (ATDEGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to identify hub genes, and a second intersection was performed with important module genes from the protein-protein interaction (PPI) network to obtain co-hub genes. Finally, a diagnostic model was constructed by receiver operating characteristic (ROC) analysis to determine the candidate genes with high diagnostic efficacy in the external validation set. Moreover, immune infiltration analysis was performed on AD patient brain tissues and explore the correlation between candidate genes and immune cells. We further analyzed the expression level of candidate genes in the SH-SY5Y cells with Aβ25-35 (25 µM). Among the 17 identified AD-ATDEGs, ATP6V1E1 stood out with area under the curve (AUC) values of 0.869, 0.817, and 0.714 in the external validation set, underscoring its high diagnostic efficacy in both hippocampal and peripheral blood contexts for AD patients. Meanwhile, ATP6V1E1 expression was positively correlated with effector memory CD4 + T cells, while negatively correlated with natural killer T cells and activated CD4 + T cells. Results from quantitative PCR (qPCR) and immunofluorescence assays indicated a reduction in ATP6V1E1 expression, aligning with our database analysis findings. In summary, ATP6V1E1 as a candidate gene provides a new perspective for the early identification and pathogenesis of AD.

Prediction of influential proteins and enzymes of certain diseases using a directed unimodular hypergraph

Protein-protein interaction (PPI) analysis based on mathematical modeling is an efficient means of identifying hub proteins, corresponding enzymes and many underlying structures. In this paper, a method for the analysis of PPI is introduced and used to analyze protein interactions of diseases such as Parkinson's, COVID-19 and diabetes melitus. A directed hypergraph is used to represent PPI interactions. A novel directed hypergraph depth-first search algorithm is introduced to find the longest paths. The minor hypergraph reduces the dimension of the directed hypergraph, representing the longest paths and results in the unimodular hypergraph. The property of unimodular hypergraph clusters influential proteins and enzymes that are related thereby providing potential avenues for disease treatment.

Evaluation of the Cellular Resistance Process in Treated Cells Via Extracorporeal Photopheresis

Introduction: Extracorporeal photopheresis (ECP) is a therapeutic method applied against some diseases such as cancers. Using 8-methoxypsoralen (8-MOP) and UVA radiation in ECP is associated with achievement in the treatment of patients with leukemic cutaneous T-cell lymphoma (CTCL). Evaluation of cellular resistance versus ECP is the aim of this study. Methods: Data were downloaded from the Gene Expression Omnibus (GEO) database and were analyzed via the GEO2R program. The significant DEGs were assessed via protein-protein interaction (PPI) network analysis by using the STRING database and Cytoscape software. The critical genes were evaluated via gene ontology by using the ClueGO application of Cytoscape software. The identified biological processes were determined and analyzed. Results: Fifty-seven significant DEGs were determined. The main connected component of the PPI network including 32 queried significant DEGs plus 50 first neighbors was constructed. Nineteen histones as critical nodes were assessed via gene ontology, and "nucleosome organization" was pointed out as the crucial biological process. Finally, 15 histones from H2A, H2B, and H3 histone families were identified as the key genes that are involved in the resistance property of the treated cells. Conclusion: In conclusion, 15 members of H2A, H2B, and H3 families (especially H2A family) were considered as the origin of resistance versus ECP treatment. It is concluded that sensitivity to ECP treatment depends on gross molecular events which are involved in the functions of histones.

Overexpression of C19orf48 correlates with poor prognosis in breast cancer

As one of the most commonly diagnosed cancers in women around the world, breast cancer has been detailed studied. This study aimed to identify the expression of c19orf48 in several kinds of cancers including liver, lung and breast cancers etc. The driving factors behind it were analysed and it found that the amplification of c19orf48 may relate with the elevated expression. At the same time, the correlation between the expression of it and the survival time in breast cancer patients was explored. It was found that the c19orf48 expression at transcriptional level elevated in breast cancer tissue samples compared with the normal. It was inferred that the c19orf48 play its oncogenic role in development of breast cancer by involving in cell-cycle related biological process. In conclusion, c19orf48 may be a useful and predictive biomarker for the prognosis of breast cancer patients. To the best of our knowledge, this is the first report describing the expression of c19orf48, the potential driving factor led to this and its effect.

Study on the mechanism of Baihe Dihuang decoction in treating menopausal syndrome based on network pharmacology

Menopausal syndrome (MS) refers to a series of symptoms with autonomic nervous system dysfunction caused by decreased sex hormones before and after menopause. Baihe Dihuang (BHDH) decoction positively affects MS, but its mechanism remains unclear. This study aimed to reveal the underlying mechanism through network pharmacology. The components of the BHDH Decoction were found through HERB, while corresponding targets were obtained from the HERB, Drug Bank, NPASS, Targetnet, and Swisstarget databases. The MS targets were obtained from GeneCards and OMIM. STRING was used to construct the protein-protein interaction networks. OmicShare tools were used for Gene Ontology and Kyoto encyclopedia of genes and genomes analyses. Finally, Autodock Vina 1.1.2 software (https://vina.scripps.edu/downloads/) was used for molecular alignment to verify whether the main active ingredients and key targets had good binding activity. We screened out 27 active ingredients and 251 effective targets of BHDH Decoction, 3405 MS-related targets, and 133 intersection targets between BHDH Decoction and MS. Protein-protein interaction network identified tumor protein P53, Serine/threonine-protein kinase AKT, epidermal growth factor receptor, Estrogen Receptor 1, and jun proto-oncogene as critical targets. Gene ontology analysis showed that these targets were mainly involved in the cellular response to chemical stimulus, response to oxygen-containing compound, cellular response to endogenous stimulus, response to an organic substance, and response to chemical, etc. Kyoto encyclopedia of genes and genomes pathways were mainly enriched in endocrine resistance, pathways in cancer, and the ErbB signaling pathway, etc. Molecular docking results showed that emodin and stigmasterol are strongly associated with Serine/threonine-protein kinase AKT, Estrogen Receptor 1, epidermal growth factor receptor, sarcoma gene, and tumor protein P53. This study preliminarily revealed the multi-component, multi-target, and multi-channel mechanism of BHDH Decoction in treating MS. It provides a reference for in vitro and in vivo research and clinical application of BHDH Decoction in the treatment of MS.

Capturing the hierarchically assorted modules of protein-protein interactions in the organized nucleome

Nuclear proteins are major constituents and key regulators of nucleome topological organization and manipulators of nuclear events. To decipher the global connectivity of nuclear proteins and the hierarchically organized modules of their interactions, we conducted two rounds of cross-linking mass spectrometry (XL-MS) analysis, one of which followed a quantitative double chemical cross-linking mass spectrometry (in vivoqXL-MS) workflow, and identified 24,140 unique crosslinks in total from the nuclei of soybean seedlings. This in vivo quantitative interactomics enabled the identification of 5340 crosslinks that can be converted into 1297 nuclear protein-protein interactions (PPIs), 1220 (94%) of which were non-confirmative (or novel) nuclear PPIs compared with those in repositories. There were 250 and 26 novel interactors of histones and the nucleolar box C/D small nucleolar ribonucleoprotein complex, respectively. Modulomic analysis of orthologous Arabidopsis PPIs produced 27 and 24 master nuclear PPI modules (NPIMs) that contain the condensate-forming protein(s) and the intrinsically disordered region-containing proteins, respectively. These NPIMs successfully captured previously reported nuclear protein complexes and nuclear bodies in the nucleus. Surprisingly, these NPIMs were hierarchically assorted into four higher-order communities in a nucleomic graph, including genome and nucleolus communities. This combinatorial pipeline of 4C quantitative interactomics and PPI network modularization revealed 17 ethylene-specific module variants that participate in a broad range of nuclear events. The pipeline was able to capture both nuclear protein complexes and nuclear bodies, construct the topological architectures of PPI modules and module variants in the nucleome, and probably map the protein compositions of biomolecular condensates.

Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method

INTRODUCTION

Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way.

METHODS

We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs.

RESULTS

We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks.

CONCLUSION

We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.

Exploration of the anti-liver injury active components of Shaoyao Gancao decoction by network pharmacology and experiments in vivo

BACKGROUND

Shaoyao Gancao decoction (SGD), a classic traditional Chinese herbal formula, has been widely used to treat febrile diseases in the clinic for centuries. In recent years, a growing number of studies have found that SGD has a favorable anti-liver injury effect.

PURPOSE

In this study, we want to know the potential active components of SGD treatment in liver injury.

STUDY DESIGN

A novel method combining computer simulation and in vivo experiment was established for the first time and used to investigate this problem.

METHODS

A network pharmacology was used to explore the active components of SGD treatment in liver injury, and preliminarily verified the results of network pharmacology through molecular docking. To further understand the active compounds of SGD in the treatment of liver injury, we compared the prototypes and metabolites of SGD in healthy rats and rats with liver injury after oral administration. In addition, a UPLC-MS/MS method was developed and successfully applied to investigate the pharmacokinetics of 9 compounds of SGD in healthy and liver injury rats.

RESULTS

It showed that SGD exerted protective effects against liver injury by the active components of liquiritin and albiflorin, etc. The values of the AUC_0-t, AUC_0-∞, t_1/2, T_max were significantly different after oral administration of SGD in healthy and liver injury rats. This indicates that the pharmacokinetic study in the pathological state of liver injury can provide more valuable information for guiding clinical medication.

CONCLUSION

In this study, the integration of network pharmacology and experiments in vivo provides a novel strategy to explore active components of TCMs to treat diseases.

Crosstalkr: An open-source R package to facilitate drug target identification

In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).

Identification and Validation of Immune-Related Genes Diagnostic for Progression of Atherosclerosis and Diabetes

Background

Atherosclerosis and type 2 diabetes mellitus contribute to a large part of cardiovascular events, but the underlying mechanism remains unclear. In this study, we focused on identifying the linking genes of the diagnostic biomarkers and effective therapeutic targets associated with these two diseases.

Methods

The transcriptomic datasets of atherosclerosis and type 2 diabetes mellitus were obtained from the GEO database. Differentially expressed genes analysis was performed by R studio software, and differential analysis including functional enrichment, therapeutic small molecular agents prediction, and protein-protein interaction analysis were applied to the common shared differentially expressed genes. Hub genes were identified and further validated using an independent dataset and clinical samples. Furthermore, we measured the expression correlations, immune cell infiltration, and diagnostic capability of the three key genes.

Results

We screened out 28 up-regulated and six down-regulated common shared differentially expressed genes. Functional enrichment analysis showed that cytokines and immune activation were involved in the development of these two diseases. Six small molecules with the highest absolute enrichment value were identified. Three critical genes (CD4, PLEK, and THY1) were further validated both in validation sets and clinical samples. The gene correlation analysis showed that CD4 was strongly positively correlated with PLEK, and ROC curves confirmed the good discriminatory capacity of CD4 and PLEK in two diseases.

We have established the co-expression network between atherosclerosis lesions progressions and type 2 diabetes mellitus, and identified CD4 and PLEK as key genes in the two diseases, which may facilitate both development of diagnosis and therapeutic strategies.

Molecular Mechanism of the Role of Apigenin in the Treatment of Hyperlipidemia: A Network Pharmacology Approach

The therapeutic effect of apigenin (APG) on hyperlipidemia was investigated using network pharmacology combined with molecular docking strategy, and the potential targets of APG in the treatment of hyperlipidemia were explored. Genetic Ontology Biological Process (GOBP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of common targets were performed. Then, molecular docking was used to predict the binding mode of APG to the target. Finally, Sprague Dawley rats were used to establish a hyperlipidemia model. The expression levels of insulin (INS) and vascular endothelial growth factor A (VEGFA) mRNA in each group were detected by quantitative reverse transcription-polymerase chain reaction. Network pharmacological studies revealed that the role of APG in the treatment of hyperlipidemia was through the regulation of INS, VEGFA, tumor necrosis factor, epidermal growth factor receptor, matrix metalloprotein 9, and other targets, as well as through the regulation of the hypoxia-inducible factor 1 (HIF-1) signaling pathway, fluid shear stress, and atherosclerosis signaling pathways, vascular permeability; APG also participated in the regulation of glucose metabolism and lipid metabolism, and acted on vascular endothelial cells, and regulated vascular tone. Molecular docking showed that APG binds to the target with good efficiency. Experiments showed that after APG treatment, the expression levels of INS and VEGFA mRNA in the model group were significantly decreased (p<0.01). In conclusion, APG has multiple targets and affects pathways involved in the treatment of hyperlipidemia by regulating the HIF-1 signaling pathway, fluid shear stress, and the atherosclerosis pathway.

DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer

BACKGROUND

Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection.

RESULTS

In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods.

CONCLUSIONS

The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

Application of network composite module analysis and verification to explore the bidirectional immunomodulatory effect of Zukamu granules on Th1 / Th2 cytokines in lung injury

ETHNOPHARMACOLOGICAL RELEVANCE

Zukamu granules (ZKMG), as the preferred drug for the treatment of colds in Uygur medical theory, has been used for 1500 years. It is also widely used in China and included in the National Essential Drugs List (2018 edition). It has unique anti-inflammatory, antitussive and analgesic effects.

AIM OF THE STUDY

Aiming at the research of traditional Chinese medicine (TCM) with the characteristics of overall regulation of body diseases and the immune regulation mechanism with the concept of integrity, this paper put forward the integrated application of network composite module analysis and animal experiment verification to study the immune regulation mechanism of TCM.

MATERIALS AND METHODS

The active components and targets of ZKMG were predicted, and network module analysis was performed to explore their potential immunomodulatory mechanisms. Then acute lung injury (ALI) mice and idiopathic pulmonary fibrosis (IPF) rats were used as pathological models to observe the effects of ZKMG on the pathological conditions of infected ALI and IPF rats, determine the contents of Th1, Th2 characteristic cytokines and immunoglobulins, and study the intervention of GATA3/STAT6 signal pathway.

RESULTS

The results of network composite module analysis showed that ZKMG contained 173 pharmacodynamic components and 249 potential targets, and four key modules were obtained. The immunomodulatory effects of ZKMG were related to T cell receptor signaling pathway. The validation results of bioeffects that ZKMG could carry out bidirectional immune regulation on Th1/Th2 cytokines in the stage of ALI and IPF, so as to play the role of regulating immune homeostasis and organ protection.

CONCLUSIONS

The network composite module analysis and verification method is an exploration to study the immune regulation mechanism of TCM by combining the network module prediction analysis with animal experiments, which provides a reference for subsequent research.

Identification of Taohong Siwu Decoction in Treating Chronic Glomerulonephritis Using Network Pharmacology and Molecular Docking

Background: Based on network pharmacology and molecular docking technology, the pharmacological mechanism of Taohong Siwu Decoction (THSWD) in the treatment of chronic glomerulonephritis (CGN) was analyzed to provide a theoretical basis for the subsequent development of new drugs and the clinical application of Traditional Chinese Medicine (TCM). Methods: Active ingredients of drugs and disease target genes were obtained from Traditional Chinese Medicine Database and Analysis Platform (TCMSP) database and GeneCards database. The “drug component target” network of THSWD was constructed using Cytoscape version 3.8.2 software. The protein interaction was analyzed using STRING platform, the protein–protein interaction (PPI) network was constructed, and the potential protein function modules in the network were mined. Metascape platform was used to analyze “drug component target” and its biological processes and pathways. The clusterProfiler R package was called to perform kyoto encyclopedia of genes and genomes (KEGG) pathway and gene ontology (GO) function enrichment analysis on CGN-related targets regulated by THSWD. Molecular docking verification was performed by AutoDock Vina software. Results: THSWD has 205 target genes and 45 active components, 104 of which are cross with the CGN inflammatory gene. Its main active ingredients, stigmasterol, kaempferol, and sitosterol, have positive relationships with the inflammatory targets of CGN, tumor necrosis factor (TNF), IL-6, AKT1, and MAPK14. THSWD modulates the biological pathway of CGN and mainly acts on TNF-α signal pathway, interleukin-17 signal pathway, etc., whose main functions are response to lipid sugar, heme binding, G protein-coupled amine receptor activity, etc. The results of molecular docking showed that the main active compounds could bind to the core targets and showed good affinity. Conclusion: The molecular mechanism of THSWD in the treatment of CGN from the perspective of network pharmacology are components such as beta-sitosterol, kaempferol, and quercetin and key action targets such as TNF, IL-6, AKT1 protein kinase, and MAPK14 protein kinase play a synergistic role in autoimmune, infection, and inflammatory response-related pathways.

The Mechanism of Crocetin Targeting Cardiovascular Disease Based on Network Pharmacology Constrained by Spectral Experiments

The network pharmacology under conditions is a recent development trend. We use network pharmacology methods to analyze the mechanism of crocetin (CRO) that regulates cardiovascular diseases. In this work, the spectral experimental data of CRO-Protein interaction is first time combined with constraint conditions to solve the problems of targeting redundancy and lack of verification. CRO targets and cardiovascular disease targets were obtained by the target database. The STRING platform was used for PPI analysis. The GO and KEGG pathways of the target were analyzed using the Metascape platform; The core functional targets of CRO were screened by molecular docking techniques and the spectra of CRO and human serum albumin (HSA). Under the collaborative constraint conditions, the core targets of CRO that regulate cardiovascular diseases are ADRA1A, ADRA1B, CHRM1, CHRM2, GABRA1, and PTGS2; This study incorporates spectroscopy and molecular docking as constraints into the network pharmacological analysis, which significantly improves the credibility of network pharmacological analysis compared with unconstrained conditions. This method provides theoretical references for the in-depth study of the mechanism between active substances and protein targets for other medicines in network pharmacology.

Mechanism Investigation of Wuwei Shexiang Pills on Gouty Arthritis via Network Pharmacology, Molecule Docking, and Pharmacological Verification

Background

Gout is a common crystal-related arthritis caused by the deposition of monosodium urates (MSU). Tibetan medicine Wuwei Shexiang Pills (WSP) has been demonstrated to exhibit anti-inflammatory, antihyperuricemia, and antigout activities. However, the underlying mechanism is unknown.

Objectives

To explore the mechanisms of Wuwei Shexiang Pills on gouty arthritis via network pharmacology, molecule docking, and pharmacological verification.

Methods

The ingredients and targets of WSP were obtained by searching and screening in BATMAN-TCM and SwissADME. The targets involving the gout were acquired from public databases. The shared targets were put onto STRING to construct a PPI network. Furthermore, Metascape was applied for the GO and KEGG enrichment analysis to predict the biological processes and signaling pathways. And molecular docking was performed to validate the binding association between the key ingredients and the relative proteins of TNF signaling. Based on the serum pharmacology, the predicted antigout mechanism of WSP was validated in MSU-induced THP-1 macrophages. The levels of inflammatory cytokines and mRNA were measured by ELISA and qRT-PCR, respectively, and MAPK, NF-κB, and NLRP3 signaling-associated proteins were determined by western blot and immunofluorescence staining.

Results

48 bioactive ingredients and 165 common targets were found in WSP. The data showed that 5-Cis-Cyclopentadecen-1-One, 5-Cis-Cyclotetradecen-1-One, (−)-isoshyobunone, etc. were potential active ingredients. TNF signaling, HIF-1 signaling, and Jak-STAT signaling were predicted to be the potential pathways against gout. The molecule docking analysis found that most ingredients had a high affinity for p65, NLRP3, IL-1β, TNF-α, and p38. The data from in vitro experiment showed that WSP suppressed the production and gene expression of inflammatory cytokines. Furthermore, WSP could inhibit the activation of MAPK, NF-κB, and NLRP3 signaling pathways.

Conclusion

Our finding suggested that the antigout effect of WSP could be achieved by inhibiting MAPK, NF-κB, and NLRP3 signaling pathways. WSP might be a candidate drug for gouty treatment.

Exploration of Hub Genes in Retinopathy of Prematurity Based on Bioinformatics Analysis of the Oxygen-Induced Retinopathy Model

Retinopathy of prematurity (ROP) is a major blindness-causing disease that is characterized by an arrest of normal vascular development and neovascularization of the retina. Previous studies have shown that genetic factors may be associated with the development and severity of ROP. However, the genes and mechanisms underlying ROP remain unclear. We aimed to identify hub genes in ROP and drugs related to these genes by integrative analysis. The expression profiles of GSE158799 and GSE135844 were acquired from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Then, an integrative analysis was performed including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, transcription factor (TF)-gene, and miRNA-gene networks analysis. Moreover, we verified hub genes and identified potential drugs. 225 common DEGs were identified. Biological function analysis indicated that angiogenesis, cell surface, cell adhesion, extracellular matrix, and focal adhesion genes were enriched among DEGs. The PI3K/Akt signalingpathway, focal adhesion, and extracellular matrix (ECM)-receptor interaction were markedly enriched in the KEGG pathway analysis. Finally, 5 hub genes related to the nosogenesis of ROP were identified and found to be targeted by VEGFA inhibitors, TLR4 antagonists, and sunitinib. The present study showed that VEGFA, ACTA2, MKI67, CD68, and TLR4 are potential hub genes involved in the pathogenesis of ROP. Moreover, TLR4 antagonists and sunitinib may be new candidate drugs for ROP therapy, in addition to VEGFA inhibitors.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Investigation into Chronic Low-Dose Ionizing Radiation Effect on Gene Expression Profile of Human HUVECs Cells

Introduction: Understanding the molecular mechanism of chronic low-dose ionizing radiation (LDIR) effects on the human body is the subject of many research studies. Several aspects of cell function such as cell proliferation, apoptosis, inflammation, and tumorigenesis are affected by LDIR. Detection of the main biological process that is targeted by LIDR via network analysis is the main aim of this study. Methods: GSE66720 consisting of gene expression profiles of human umbilical vein endothelial cells (HUVECs) (a suitable cell line to be investigated), including irradiated and control cells, was downloaded from Gene Expression Omnibus (GEO). The significant differentially expressed genes (DEGs) were determined and analyzed via protein-protein interaction (PPI) network analysis to find the central individuals. The main cell function which was related to the central nodes was introduced. Results: Among 64 queried DEGs, 48 genes were recognized by the STRING database. C-X-C motif chemokine ligand 8 (CXCL8), intercellular adhesion molecule 1 (ICAM1), Melanoma growth-stimulatory activity/growth-regulated protein α (CXCL1), vascular cell adhesion molecule 1 (VCAM-1), and nerve growth factor (NGF) were introduced as hub nodes. Conclusion: Findings indicate that inflammation is the main initial target of LDIR at the cellular level which is associated with alteration in the other essential functions of the irradiated cells.

Key pathways and genes in hepatitis B virus-related liver inflammation: Expression profiling and bioinformatics analysis

Chronic hepatitis B virus infection has become a major public health issue worldwide, which can lead to liver inflammation, fibrosis, and hepatocellular carcinoma. According to the inflammation activity, liver tissues can be divided into 5 grades (G0-G4). However, the mechanism of the development of liver inflammation remains unclear. In our study, expression profiling by microarray and bioinformatics technology was used to systemically identify differentially expressed genes (DEGs) between low grades (G0-G1) and high (G2-G4) grades of liver inflammation. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and protein-protein interaction network construction were performed for further identification of the key functions, pathways, and hub genes that might play important roles in the inflammation development. A total of 1982 DEGs were identified, consisting of 1220 downregulated genes and 762 upregulated genes. GO analysis revealed the DEGs were mainly enriched in GO terms that related to neutrophil activation and degranulation. MAPK1, ITGA2, CDK2, TGFB1, CDKN2A, MTOR, IL6, PCNA, OAS2, and EP300 were hub genes that had the highest centricity and might be potential markers for inflammation development. This study identified the differentially expressed genes between different grades of inflammation, which would enlighten the study that focuses on the mechanism of liver inflammation development.

Functional networks of the human bromodomain-containing proteins

Background: Bromodomains are a structurally conserved epigenetic reader domain that bind to acetylated lysine residues in both histone and non-histone proteins. Bromodomain-containing proteins (BRD proteins) often function as scaffolding proteins in the assembly of multi-protein complexes to regulate diverse biological processes. BRD proteins have been classified based on biological and functional similarity, however the functions of many BRD proteins remains unknown. PPI network analysis is useful for revealing organizational roles, identifying functional clusters, and predicting function for BRD proteins. Results: We used available data to construct protein-protein interaction networks (PPINs) to study the properties of the human bromodomain protein family. The network properties of the BRD PPIN establishes that the BRD proteins serve as hub proteins that are enriched near the global center to form an inter-connected PPIN. We identified dense subgraphs formed by BRD proteins and find that different BRD proteins share topological similarity and functional associations. We explored the functional relationships through clustering and Hallmark pathway gene set enrichment analysis and identify potential biological roles for different BRD proteins. Conclusion: In our network analysis we confirmed that BRD proteins are conserved central nodes in the human PPI network and function as scaffolds to form distinctive functional clusters. Overall, this study provides detailed insight into the predictive functions of BRD proteins in the context of functional complexes and biological pathways.

Active components and molecular mechanism of Syringa oblata Lindl. in the treatment of endometritis based on pharmacology network prediction

Antibiotic treatment of endometritis was limited by the inevitable antibiotic residues and risk of bacterial resistance. Therefore, the development of safe and effective strategies for endometritis treatment is urgently needed. Syringa oblata Lindl. (SOL) showed great pharmacological potential against endometritis. However, the active components and underlying mechanism of SOL for endometritis treatment remain indeterminate. In our study, the active components and possible molecular mechanism of SOL against endometritis were predicted through computer data mining and biological networks construction. It was predicted that the main active components of SOL were luteolin, kaempferol, oleanolic acid, and rutin, and their anti-endometritis effect was mainly attributed to the TLRs/NF-κB signaling pathway. Furthermore, a green and efficient deep eutectic solvent combined with ultrasound-assisted extraction (DES-UAE) was performed and optimized to obtain high contents of total flavonoid, rutin, and luteolin. The four predicted active components in the SOL extracts were qualitatively and quantitatively analyzed by LC/MS and HPLC. Finally, the pharmacological effects of SOL and active components have been verified by Staphylococcus aureus-endometritis models in mice. H&E staining and bacterial load in uterus tissues assays initially validated the pharmacodynamic effects of SOL, and quantitative real-time PCR (RT-qPCR) and ELISA results confirmed that SOL and four active components could ameliorate the uterus injury caused by Staphylococcus aureus, the mechanism of action is related to the TLRs/NF-κB signaling pathway.

Exploration of novel ligands to target C-C Motif Chemokine Receptor 2 (CCR2) as a promising pharmacological treatment against traumatic brain injury

It is widely reported that the overexpression of the C-C Motif Chemokine Receptor 2 (CCR2) has negative implications in neuroinflammatory diseases such as traumatic brain injury (TBI), although promising drugs to tackle this have been less forthcoming. As of 2016, only 2 drugs specifically targeting this receptor have made their way to market, with unsuccessful outcome unfortunately, suggesting that the search for more specific and precise ligands is utterly necessary. In this paper we hypothesized that by targeting Glu291, Met295, Trp98, Leu45 and Val189 amino acids, essential in the binding of CCR2 with C-C Motif Chemokine Ligand 2 (CCL2), the endogenous substrate, mitigates its activity in TBI. We used a pharmacophore model to screen for suitable ligands that may bind to CCR2, which returned 871 ligands. Docking and molecular dynamics results uncovered that two ligands (A102) and (A435) contained several of those important residues and showed a stability and compactness when in complex with CCR2, with these results confirmed by MMGBSA calculations with A102 recording a better interaction compared to A435. Finally, a PPI network was built to explore downstream signaling being regulated by both ligands in TBI, showing amyloid precursor protein (APP) as a key target and neuroactive-ligand receptor interaction (1.80E-27) the top functional annotated category. In conclusion, for the first time we report novel ligands A102 and A435 targeting CCR2 as a potential new pharmacological approach to target inflammation post-TBI.

Serial-Omics and Molecular Function Study Provide Novel Insight into Cucumber Variety Improvement

Cucumbers are rich in vitamins and minerals. The cucumber has recently become one of China’s main vegetable crops. More specifically, the adjustment of the Chinese agricultural industry’s structure and rapid economic development have resulted in increases in the planting area allocated to Chinese cucumber varieties and in the number of Chinese cucumber varieties. After complete sequencing of the “Chinese long” genome, the transcriptome, proteome, and metabolome were obtained. Cucumber has a small genome and short growing cycle, and these traits are conducive to the application of molecular breeding techniques for improving fruit quality. Here, we review the developments and applications of molecular markers and genetic maps for cucumber breeding and introduce the functions of gene families from the perspective of genomics, including fruit development and quality, hormone response, resistance to abiotic stress, epitomizing the development of other omics, and relationships among functions.

Identification of key sex-specific pathways and genes in the subcutaneous adipose tissue from pigs using WGCNA method

Background

Adipose tissues (ATs), including visceral ATs (VATs) and subcutaneous ATs (SATs), are crucial for maintaining energy and metabolic homeostasis. SATs have been found to be closely related to obesity and obesity-induced metabolic disease. Some studies have shown a significant association between subcutaneous fat metabolism and sexes. However, the molecular mechanisms for this association are still unclear. Here, using the pig as a model, we investigated the systematic association between the subcutaneous fat metabolism and sexes, and identified some key sex-specific pathways and genes in the SATs from pigs.

Results

The results revealed that 134 differentially expressed genes (DEGs) were identified in female and male pigs from the obese group. A total of 17 coexpression modules were detected, of which six modules were significantly correlated with the sexes (P < 0.01). Among the significant modules, the greenyellow module (cor = 0.68, P < 9e-06) and green module (cor = 0.49, P < 0.003) were most significantly positively correlated with the male and female, respectively. Functional analysis showed that one GO term and four KEGG pathways were significantly enriched in the greenyellow module while six GO terms and six KEGG pathways were significantly enriched in the green module. Furthermore, a total of five and two key sex-specific genes were identified in the two modules, respectively. Two key sex-specific pathways (Ras-MAPK signaling pathway and type I interferon response) play an important role in the SATs of males and females, respectively.

Conclusions

The present study identified some key sex-specific pathways and genes in the SATs from pigs, which provided some new insights into the molecular mechanism of being involved in fat formation and immunoregulation between pigs of different sexes. These findings may be beneficial to breeding in the pig industry and obesity treatment in medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01054-w.

Clinicopathological value of the upregulation of cyclin-dependent kinases regulatory subunit 2 in osteosarcoma

Background

Cyclin-dependent kinase subunit 2 (CKS2) is a member of cyclin dependent kinase subfamily and the relationship between CKS2 and osteosarcoma (OS) remains to be further analyzed.

Methods

80 OS and 41 non-tumor tissue samples were arranged to perform immunohistochemistry (IHC) to evaluate CKS2 expression between OS and non-tumor samples. The standard mean deviation (SMD) was calculated based on in-house IHC and tissue microarrays, and exterior high-throughput datasets for further verification of CKS2 expression trend in OS. The effect of CKS2 expression on clinicopathological parameters of OS patients, and single-cell in OS tissues was analyzed through public high-throughput datasets and functional enrichment analysis was conducted for co-expression genes of CKS2 in accordance with weighted correlation network analysis.

Results

A total of 217 OS samples and 87 non-tumor samples (including tissue and cell line) were obtained from in-house IHC, microarrays and exterior high-throughput datasets. The analysis of integrated expression status demonstrated up-regulation of CKS2 in OS (SMD = 1.57, 95%CI [0.27–2.86]) and the significant power of CKS2 expression in distinguishing OS samples from non-tumor samples (AUC = 0.97 95%CI [0.95–0.98]). Clinicopathological analysis of GSE21257 indicated that OS patients with higher CKS2 expression was more likely to suffer OS metastasis. Although Kaplan–Meier curves showed no remarkable difference of overall survival rate between OS patients with high and low-CKS2, CKS2 was found up-regulated in proliferating osteosarcoma cells. Co-expression genes of CKS2 were mainly assembled in function and pathways such as cell cycle, cell adhesion, and intercellular material transport.

Conclusions

In summary, up-regulation of CKS2 expression in OS tissue was found through multiple technical approaches. In addition, scRNA-seq and co-expression analysis showed that CKS2 may have an impact on important biological process linked with cell cycle, cell adhesion, and intercellular material transport. Present study on CKS2 in OS indicated a promising prospect for CKS2 as a biomarker for OS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01234-8.

Systems pharmacology-based drug discovery and active mechanism of natural products for coronavirus pneumonia (COVID-19): An example using flavonoids

BACKGROUND

Recently, the value of natural products has been extensively considered because these resources can potentially be applied to prevent and treat coronavirus pneumonia 2019 (COVID-19). However, the discovery of nature drugs is problematic because of their complex composition and active mechanisms.

METHODS

This comprehensive study was performed on flavonoids, which are compounds with anti-inflammatory and antiviral effects, to show drug discovery and active mechanism from natural products in the treatment of COVID-19 via a systems pharmacological model. First, a chemical library of 255 potential flavonoids was constructed. Second, the pharmacodynamic basis and mechanism of action between flavonoids and COVID-19 were explored by constructing a compound-target and target-disease network, targets protein-protein interaction (PPI), MCODE analysis, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.

RESULTS

In total, 105 active flavonoid components were identified, of which 6 were major candidate compounds (quercetin, epigallocatechin-3-gallate (EGCG), luteolin, fisetin, wogonin, and licochalcone A). 152 associated targets were yielded based on network construction, and 7 family proteins (PTGS, GSK3β, ABC, NOS, EGFR, and IL) were included as central hub targets. Moreover, 528 GO items and 178 KEGG pathways were selected through enrichment of target functions. Lastly, molecular docking demonstrated good stability of the combination of selected flavonoids with 3CL Pro and ACEⅡ.

CONCLUSION

Natural flavonoids could enable resistance against COVID-19 by regulating inflammatory, antiviral, and immune responses, and repairing tissue injury. This study has scientific significance for the selective utilization of natural products, medicinal value enhancement of flavonoids, and drug screening for the treatment of COVID-19 induced by SARS-COV-2.

Identification of Immune Markers in Dilated Cardiomyopathies with Heart Failure by Integrated Weighted Gene Coexpression Network Analysis

Dilated cardiomyopathy (DCM), a heterogeneous cardiomyopathy, is a major cause of heart failure and heart transplant. Currently, immunotherapy is believed to be an effective treatment method for DCM. However, individual differences are so obvious that the clinical effect is not satisfactory. In order to find immune-related biomarkers of DCM to guide treatment and improve clinical efficacy, we downloaded a GSE120895 dataset from the Gene Expression Omnibus (GEO) database using CIBERSORT and WGCNA algorithms in RStudio and visualizing the protein–protein interaction (PPI) network for key modules by Cytoscape, and finally obtained six hub genes. A GSE17800 dataset was downloaded from the GEO dataset to verify the diagnostic values of hub genes, MYG1, FLOT1, and ATG13, which were excellent. Our study revealed unpublished potential immune mechanisms, biomarkers, and therapeutic targets of DCM.

The molecular mechanism of Ligusticum wallichii for improving idiopathic pulmonary fibrosis: A network pharmacology and molecular docking study

BACKGROUND

At present, there was no evidence that any drugs other than lung transplantation can effectively treat Idiopathic Pulmonary Fibrosis (IPF). Ligusticum wallichii, or Chinese name Chuan xiong has been widely used in different fibrosis fields. Our aim is to use network pharmacology and molecular docking to explore the pharmacological mechanism of the Traditional Chinese medicine (TCM) Ligusticum wallichii to improve IPF.

MATERIALS AND METHODS

The main chemical components and targets of Ligusticum wallichii were obtained from TCMSP, Swiss Target Prediction and Phammapper databases, and the targets were uniformly regulated in the Uniprot protein database after the combination. The main targets of IPF were obtained through Gencards, OMIM, TTD and DRUGBANK databases, and protein interaction analysis was carried out by using String to build PPI network. Metascape platform was used to analyze its involved biological processes and pathways, and Cytoscape3.8.2 software was used to construct "component-IPF target-pathway" network. And molecular docking verification was conducted through Auto Dock software.

RESULTS

The active ingredients of Ligusticum wallichii were Myricanone, Wallichilide, Perlolyrine, Senkyunone, Mandenol, Sitosterol and FA. The core targets for it to improve IPF were MAPK1, MAPK14, SRC, BCL2L1, MDM2, PTGS2, TGFB2, F2, MMP2, MMP9, and so on. The molecular docking verification showed that the molecular docking affinity of the core active compounds in Ligusticum wallichii (Myricanone, wallichilide, Perlolyrine) was <0 with MAPK1, MAPK14, and SRC. Perlolyrine has the strongest molecular docking ability, and its docking ability with SRC (-6.59 kJ/mol) is particularly prominent. Its biological pathway to improve IPF was mainly acted on the pathways in cancer, proteoglycans in cancer, and endocrine resistance, etc.

CONCLUSIONS

This study preliminarily identified the various molecular targets and multiple pathways of Ligusticum wallichii to improve IPF.

Downregulation of ALDH6A1 is a New Marker of Muscle Insulin Resistance in Type 2 Diabetes Mellitus

Purpose

Skeletal muscle insulin resistance (IR) is an important etiology of type 2 diabetes mellitus (T2DM); however, its molecular mechanism is yet to be fully defined. This study attempted to identify the gene expression patterns and molecular disorders in T2DM patients’ skeletal muscle samples.

Methods

First, the difference in genetic expression among GSE25462 data was analyzed. Next, PPI network analysis of differential genes was carried out, after which the maladjustment module was identified. Then, an enrichment analysis and gene set enrichment analysis (GSEA) were carried out. Finally, the transcription factors that regulate the modular genes by raid were predicted.

Results

Most differentially expressed genes were found to be able to form an interaction network and cluster into 9 modules. These modular genes were shown to possess a significant correlation with immune inflammation and metabolic response. Importantly, the top 15 genes of area under receiver operating characteristic curve (AUC) were identified, and the expression of 10 genes by GSE12643, GSE18732 and GSE29221 was confirmed. The expression and AUC value of ALDH6A1 were then verified according to three sets of data, where ALDH6A1 was found to be negatively correlated with follicular helper T cells. However, among the predicted transcription regulators, HDAC was shown to have a better regulatory effect.

Conclusion

The findings highlight that the dysregulation of ALDH6A1 expression in IR of T2DM may serve as a potential therapeutic target. ALDH6A1 is involved in the immune inflammation and metabolic pathways.

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

From communities to protein complexes: A local community detection algorithm on PPI networks

Identifying protein complexes in protein-protein interaction (ppi) networks is often handled as a community detection problem, with algorithms generally relying exclusively on the network topology for discovering a solution. The advancement of experimental techniques on ppi has motivated the generation of many Gene Ontology (go) databases. Incorporating the functionality extracted from go with the topological properties from the underlying ppi network yield a novel approach to identify protein complexes. Additionally, most of the existing algorithms use global measures that operate on the entire network to identify communities. The result of using global metrics are large communities that are often not correlated with the functionality of the proteins. Moreover, ppi network analysis shows that most of the biological functions possibly lie between local neighbours in ppi networks, which are not identifiable with global metrics. In this paper, we propose a local community detection algorithm, (lcda-go), that uniquely exploits information of functionality from go combined with the network topology. lcda-go identifies the community of each protein based on the topological and functional knowledge acquired solely from the local neighbour proteins within the ppi network. Experimental results using the Krogan dataset demonstrate that our algorithm outperforms in most cases state-of-the-art approaches in assessment based on Precision, Sensitivity, and particularly Composite Score. We also deployed lcda, the local-topology based precursor of lcda-go, to compare with a similar state-of-the-art approach that exclusively incorporates topological information of ppi networks for community detection. In addition to the high quality of the results, one main advantage of lcda-go is its low computational time complexity.

CREB1K292 and HINFPK330 as Putative Common Therapeutic Targets in Alzheimer's and Parkinson's Disease

Integration of omics data and deciphering the mechanism of a biological regulatory network could be a promising approach to reveal the molecular mechanism involved in the progression of complex diseases, including Alzheimer's and Parkinson's. Despite having an overlapping mechanism in the etiology of Alzheimer's disease (AD) and Parkinson's disease (PD), the exact mechanism and signaling molecules behind them are still unknown. Further, the acetylation mechanism and histone deacetylase (HDAC) enzymes provide a positive direction toward studying the shared phenomenon between AD and PD pathogenesis. For instance, increased expression of HDACs causes a decrease in protein acetylation status, resulting in decreased cognitive and memory function. Herein, we employed an integrative approach to analyze the transcriptomics data that established a potential relationship between AD and PD. Data preprocessing and analysis of four publicly available microarray datasets revealed 10 HUB proteins, namely, CDC42, CD44, FGFR1, MYO5A, NUMA1, TUBB4B, ARHGEF9, USP5, INPP5D, and NUP93, that may be involved in the shared mechanism of AD and PD pathogenesis. Further, we identified the relationship between the HUB proteins and transcription factors that could be involved in the overlapping mechanism of AD and PD. CREB1 and HINFP were the crucial regulatory transcription factors that were involved in the AD and PD crosstalk. Further, lysine acetylation sites and HDAC enzyme prediction revealed the involvement of 15 and 27 potential lysine residues of CREB1 and HINFP, respectively. Our results highlighted the importance of HDAC1(K292) and HDAC6(K330) association with CREB1 and HINFP, respectively, in the AD and PD crosstalk. However, different datasets with a large number of samples and wet lab experimentation are required to validate and pinpoint the exact role of CREB1 and HINFP in the AD and PD crosstalk. It is also possible that the different datasets may or may not affect the results due to analysis parameters. In conclusion, our study potentially highlighted the crucial proteins, transcription factors, biological pathways, lysine residues, and HDAC enzymes shared between AD and PD at the molecular level. The findings can be used to study molecular studies to identify the possible relationship in the AD-PD crosstalk.

The Identification of the Biomarkers of Sheng-Ji Hua-Yu Formula Treated Diabetic Wound Healing Using Modular Pharmacology

Sheng-Ji Hua-Yu (SJHY) formula has been proved to reduce the severity of diabetic wound healing without significant adverse events in our previous clinical trials. However, based on multi-target characteristics, the regulatory network among herbs, ingredients, and hub genes remains to be elucidated. The current study aims to identify the biomarkers of the SJHY formula for the treatment of diabetic wound healing. First, a network of components and targets for the SJHY formula was constructed using network pharmacology. Second, the ClusterONE algorithm was used to build a modular network and identify hub genes along with kernel pathways. Third, we verified the kernel targets by molecular docking to select hub genes. In addition, the biomarkers of the SJHY formula were validated by animal experiments in a diabetic wound healing mice model. The results revealed that the SJHY formula downregulated the mRNA expression of Cxcr4, Oprd1, and Htr2a, while upregulated Adrb2, Drd, Drd4, and Hrh1. Besides, the SJHY formula upregulated the kernel pathways, neuroactive ligand-receptor interaction, and cAMP signaling pathway in the skin tissue homogenate of the diabetic wound healing mice model. In summary, this study identified the potential targets and kernel pathways, providing additional evidence for the clinical application of the SJHY formula for the treatment of diabetic wound healing.

High Expression of IKZF2 in Malignant T Cells Promotes Disease Progression in Cutaneous T Cell Lymphoma

Cutaneous T cell lymphoma is a generally indolent disease derived from skin-homing mature T cells. However, in advanced stages, cutaneous T cell lymphoma may manifest aggressive clinical behaviour and lead to a poor prognosis. The mechanism of disease progression in cutaneous T cell lymphoma remains unknown. This study, based on a large clinical cohort, found that IKZF2, an essential transcription factor during T cell development and differentiation, showed stage-dependent overexpression in the malignant T cells in mycosis fungoides lesions. IKZF2 is specifically over-expressed in advanced-stage mycosis fungoides lesions, and correlates with poor prognosis. Mechanistically, overexpression of IKZF2 promotes cutaneous T cell lymphoma progression via inhibiting malignant cell apoptosis and may contribute to tumour immune escape by downregulating major histocompatibility complex II molecules and up-regulating the production of anti-inflammatory cytokine interleukin-10 by malignant T cells. These results demonstrate the important role of IKZF2 in high-risk cutaneous T cell lymphoma and pave the way for future targeted therapy.

Investigation of hub gene associated with the infection of Staphylococcus aureus via weighted gene co-expression network analysis

Introduction

Staphylococcus aureus is a gram-positive bacterium that causes serious infection. With the increasing resistance of bacteria to current antibiotics, it is necessary to learn more about the molecular mechanism and cellular pathways involved in the Staphylococcus aureus infection.

Methods

We downloaded the GSE33341 dataset from the GEO database and applied the weighted gene co-expression network analysis (WGCNA), from which we obtained some critical modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were applied to illustrate the biological functions of genes in these modules. We constructed the protein-protein interaction (PPI) network by Cytoscape and selected five candidate hub genes. Five potential hub genes were validated in GSE30119 by GraphPad Prism 8.0. The diagnostic values of these genes were calculated and present in the ROC curve based on the GSE13670 dataset. Their gene functions were analyzed by Gene Set Enrichment Analysis (GSEA).

Results

A co-expression network was built with 5000 genes divided into 11 modules. The genes in green and turquoise modules demonstrated a high correlation. According to the KEGG and GO analyses, genes in the green module were closely related to ubiquitination and autophagy. Subsequently, we picked out the top five hub genes in the green module. And UBB was determined as the hub gene in the GSE30119 dataset. The expression level of UBB, ASB, and MKRN1 could significantly differentiate between Staphylococcus aureus infection and healthy controls based on the ROC curve. The GSEA analysis indicated that lower expression levels of UBB were associated with the P53 signal pathway.

Conclusions

We identified some hub genes and significant signal enrichment pathways in Staphylococcus aureus infection via bioinformatics analysis, which may facilitate the development of potential clinical therapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02392-y.

Modular pharmacology-based approach to identify hub genes and kernel pathways of taodan granules treated psoriasis

ETHNOPHARMACOLOGICAL RELEVANCE

Taodan granules (TDG) have been observed to decrease interleukins, or psoriasis area and severity index (PASI) score for psoriasis vulgaris, without significant adverse events. However, the regulatory network remains elucidated.

AIM OF THE STUDY

The objective is to identify critical genes and kernel pathways of TDG treated psoriasis.

MATERIALS AND METHODS

Firstly, construct a network of components-targets of TDG using network pharmacology. Secondly, the ClusterONE algorithm was used to build a modular network and identify critical genes and corresponding pathways. Thirdly, the critical genes and kernel pathways were verified in imiquimod (IMQ) induced psoriasis-like mice model.

RESULTS

The results validated that TDG downregulated the mRNA expression of MMP2 (degree = 5, P < 0.05), IL6 (degree = 9, P < 0.05), TNF (degree = 14, P < 0.05), CCL2 (degree = 8, P < 0.05), CXCL2 (degree = 8, P < 0.05), IL1B (degree = 9, P < 0.05), and JUN (degree = 9, P < 0.05), while upregulated IL10 (degree = 8) expression. Besides, TDG were observed to regulate IL17 signaling pathway and TNF signaling pathway (size = 18), via the skin tissue homogenate of psoriasis-like mice.

CONCLUSION

In summary, this study identified the potential targets and pathways, providing additional evidence for the clinical application of TDG treated psoriasis.

Study on the Mechanism of Liuwei Dihuang Pills in Treating Parkinson's Disease Based on Network Pharmacology

Background

Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks.

Methods

The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD-related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH- and PD-related targets and Cytoscape software to construct the drug-component-target network. Moreover, STRING and Cytoscape software was used to analyze protein-protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity.

Results

After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug-disease targets were obtained. We consequently constructed a drug-component-targets network and a PPI-drug-disease-targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta-sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets.

Conclusion

LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta-sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD.

Dissecting the Pathogenesis of Diabetic Retinopathy Based on the Biological ceRNA Network and Genome Variation Disturbance

Background

Diabetic retinopathy (DR) is the most important manifestation of diabetic microangiopathy. It is essential to explore the gene regulatory relationship and genomic variation disturbance of biological networks in DR progression.

Methods

In this study, we constructed a comprehensive lncRNA-mRNA ceRNA network of DR procession (CLMN) and explored its topological characteristics.

Results

Modular and functional analysis indicated that the organization of CLMN performed fundamental and specific functions in diabetes and DR pathology. The differential expression of hub ceRNA nodes and positive correlation reveals the highly connected ceRNA regulation and important roles in the regulating of DR pathology. A large proportion of SNPs in the TFBS, DHS, and enhancer regions of lncRNAs will affect lncRNA transcription and further cause expression variation. Some SNPs were found to disrupt the lncRNA functional elements such as miRNA target binding sites. These results indicate the complex nature of genotypic effects in the disturbing of CLMN and further contribute to gene expression variation and different disease phenotypes.

Conclusion

The identification of individual genomic variations and analysis of biological network disturbance by these genomic variations will help provide more personalized treatment plans and promote the development of precision medicine for DR.

A Potential Three-Gene-Based Diagnostic Signature for Hypertension in Pregnancy

Background

Hypertensive disorders of pregnancy affect approximately 5–10% of all pregnancies, and this study aims to identify potential diagnostic signatures.

Methods

We downloaded the mRNA profiles of GSE75010 (placenta samples) and GSE48424 (blood samples) datasets with or without hypertension in pregnancy from the Gene Expression Omnibus database. Differential expression analysis was performed on the placenta samples using limma package of R language. GO terms and KEGG pathways enrichment analyses were performed on the placenta samples by the clusterProfiler package of R language. Infiltrating immune cell proportion of the placenta samples was evaluated using CIBERSORT software. The key genes involved in hypertension in pregnancy were screened from protein–protein interaction (PPI) network constructed based on the differentially expressed genes (DEGs). The logistic regression model was constructed by the glm package of R language, and receiver operating characteristic (ROC) curve was plotted to determine the accuracy of the model.

Results

For the placenta samples, a total of 104 DEGs were identified, and 39 GO terms and 7 KEGG pathways were significantly enriched based on these 104 genes. Furthermore, the analysis of infiltrating immune cells indicated that the difference in the amount of immune cells might be the potential cause of hypertension in pregnancy. The logistic regression model was constructed based on three optimal genes (LEP, PRL and IGFBP1) screened from PPI network and could efficiently separate patients with hypertension in pregnancy from healthy subjects.

Conclusion

A predictive model based on three potential genes LEP, PRL and IGFBP1 was obtained, suggesting that these genes might be potential diagnostic signatures for hypertension in pregnancy.

Gland context networks: A novel approach for improving prostate cancer identification

Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis. Here we propose a novel methodology that can be used for systematically defining contextual features regarding prostate glands. This is done by defining a Gland Context Network (GCN), a representation of the prostate sample containing information about the spatial relationship between glands as well as the similarity between their appearance. We show that such a network can be used for establishing contextual features at any spatial scale, therefore providing information that is not easily obtained from traditional shape and textural features. Furthermore, it is shown that even basic features derived from a GCN can lead to state-of-the-art classification performance regarding PCa. All in all, GCNs can assist in defining more effective approaches for PCa grading.