scholarly journals Bioinformatics Analysis for the Antirheumatic Effects of Huang-Lian-Jie-Du-Tang from a Network Perspective

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Haiyang Fang ◽  
Yichuan Wang ◽  
Tinghong Yang ◽  
Yang Ga ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Therapeutic Effect ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Ppi Network ◽  
New Approach ◽  
Target Proteins ◽  
Multiple Components ◽  
Main Pathway ◽  
Tcm Theory ◽  
Therapeutic Mechanisms

Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear “heat” and “poison” that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target proteins with 3 types of anti-RA drugs, and several pathways in immune system and bone formation are significantly regulated by HLJDT’s components, suggesting the therapeutic effect of HLJDT on RA. By defining an antirheumatic effect score to quantitatively measure the therapeutic effect, we found that the score of each HLJDT’s component is very low, while the whole HLJDT achieves a much higher effect score, suggesting a synergistic effect of HLJDT achieved by its multiple components acting on multiple targets. At last, topological analysis on the RA-associated PPI network was conducted to illustrate key roles of HLJDT’s target proteins on this network. Integrating our findings with TCM theory suggests that HLJDT targets on hub nodes and main pathway in the Hot ZENG network, and thus it could be applied as adjuvant treatment for Hot-ZENG-related RA. This study may facilitate our understanding of antirheumatic effect of HLJDT and it may suggest new approach for the study of TCM pharmacology.

Bioinformatics-based Prediction of the mechanism and targets for BushenjieduDecoction in preventing relapse of acute leukemia

2020 ◽  
Author(s):  
Weilong Sun ◽  
Fujun Yang ◽  
Weipeng Shi ◽  
Xia Tao ◽  
Zhiwei Xi ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Traditional Chinese Medicines ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Chinese Medicines ◽  
Kegg Pathways ◽  
Long Time ◽  
Underlying Mechanisms

Abstract Background: Leukemia is a lethal myeloproliferative disorder, its’ relapse following chemotherapy is the major concern in clinical practice. For a long time, we found that traditional Chinese medicines such as Bushenjiedudecoction (BSJD) have significant effects on delaying relapse. However, the underlying mechanisms are not clear, which limits the clinical application of BSJD decoction. Methods: Therefore, we tried to make some explorations in this study. We isolated mesenchymal stem cells (MSC) after treated them with BSJD for proteomic analysis. And then 109 targets were screened out through analysis of the shared proteins of that affected by BSJD and those related to leukemia. Subsequently, the data were analyzed by GO functions, KEGG pathways, PPI network and topological analysis, and then some nodes were selected for animal experiment. Results: As a result, we demonstrated the effective targets of BSJD on MSC through bioinformatics analysis and explored the potential mechanism of BSJD from its influence on niches.These targets contains Hspb1、Dnmt1、Mmp2、Thbs1、Crebbp、Hmgb1、Acta2、Cdkn1b、Atg7、Tsc2 and Icam1. Afterwards, we confirmed BSJD reduced the gene expression of ICAM-1 through cultured MSC in vitro.Conclusions: We screened the potential targets of BSJD on MSC through proteomics and bioinformatics analysis, and selected some genes for experimental verification. These studies demonstrated the effect of BSJD on MSC. We hope that this research method could provide a new way of systematically studying the effects of traditional Chinese medicine on diseases.

scholarly journals High Throughput Bioinformatics-based Prediction of the targets for BushenjieduDecoction in preventing relapse of acute leukemia

2020 ◽  
Author(s):  
Weilong Sun ◽  
Fujun Yang ◽  
Weipeng Shi ◽  
Xia Tao ◽  
Zhiwei Xi ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Myeloproliferative Disorder ◽  
Traditional Chinese Medicines ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Chinese Medicines ◽  
Kegg Pathways ◽  
Long Time ◽  
Underlying Mechanisms

Abstract Leukemia is a lethal myeloproliferative disorder, its’ relapse following chemotherapy is the major concern in clinical practice. For a long time, we found that traditional Chinese medicines such as Bushenjiedudecoction (BSJD) have significant effects on delaying relapse. However, the underlying mechanisms are not clear, which limits the clinical application of BSJD decoction. Therefore, we tried to make some explorations in this study. We isolated mesenchymal stem cells (MSC) after treated them with BSJD for proteomic analysis. And then 109 targets were screened out through analysis of the shared proteins of that affected by BSJD and those related to leukemia. Subsequently, the data were analyzed by GO functions, KEGG pathways, PPI network and topological analysis, and then some nodes were selected for animal experiment. As a result, we demonstrated the effective targets of BSJD on MSC through bioinformatics analysis and explored the potential mechanism of BSJD from its influence on niches.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

scholarly journals Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hua Ma ◽  
Zhihui He ◽  
Jing Chen ◽  
Xu Zhang ◽  
Pingping Song
Keyword(s):  
Gastric Cancer ◽  
Protein Interactions ◽  
Clustering Algorithm ◽  
Cox Model ◽  
Topological Analysis ◽  
Lasso Regression ◽  
Ppi Network ◽  
Hub Genes ◽  
P53 Signaling ◽  
Analysis Methods

AbstractGastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.

scholarly journals TripletProt: Deep Representation Learning of Proteins based on Siamese Networks

2020 ◽  
Author(s):  
Esmaeil Nourani ◽  
Ehsaneddin Asgari ◽  
Alice C. McHardy ◽  
Mohammad R.K. Mofrad
Keyword(s):  
Functional Annotation ◽  
Cellular Localization ◽  
State Of The Art ◽  
Language Model ◽  
Representation Learning ◽  
Learning Problems ◽  
Ppi Network ◽  
New Approach ◽  
Protein Protein Interaction ◽  
Siamese Networks

AbstractWe introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. We evaluate TripletProt comprehensively in protein functional annotation tasks including sub-cellular localization (14 categories) and gene ontology prediction (more than 2000 classes), which are both challenging multi-class multi-label classification machine learning problems. We compare the performance of TripletProt with the state-of-the-art approaches including recurrent language model-based approach (i.e., UniRep), as well as protein-protein interaction (PPI) network and sequence-based method (i.e., DeepGO). Our TripletProt showed an overall improvement of F1 score in the above mentioned comprehensive functional annotation tasks, solely relying on the PPI network. TripletProt and in general Siamese Network offer great potentials for the protein informatics tasks and can be widely applied to similar tasks.

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

scholarly journals Evidence of TCM Theory in Treating the Same Disease with Different Methods: Treatment of Pneumonia with Ephedra sinica and Scutellariae Radix as an Example

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Liping Sun ◽  
Dandan Wang ◽  
Yan Xu ◽  
Wenxiu Qi ◽  
Yanbo Wang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Future Directions ◽  
Network Construction ◽  
Ephedra Sinica ◽  
Scutellariae Radix ◽  
Tcm Theory ◽  
Global Health Problem

Pneumonia is a serious global health problem and the leading cause of mortality in children. Antibiotics are the main treatment for bacterial pneumonia, but there are serious drug resistance problems. Traditional Chinese medicine (TCM) has been used to treat diseases for thousands of years and has a unique theory. This article takes the treatment of pneumonia with Ephedra sinica as a representative hot medicine and Scutellariae Radix as a representative cold medicine as an example. We explore and explain the theory of treating the same disease with different TCM treatments. Using transcriptomics and network pharmacology methods, GO, KEGG enrichment, and PPI network construction were carried out, demonstrating that Ephedra sinica plays a therapeutic role through the NF-κB and apoptosis signaling pathways targeting PLAU, CD40LG, BLC2L1, CASP7, and CXCL8. The targets of Scutellariae Radix through the IL-17 signaling pathway are MMP9, CXCL8, and MAPK14. Molecular docking technology was also used to verify the results. In short, our results provide evidence for the theory of treating the same disease with different treatments, and we also discuss future directions for traditional Chinese medicine.

scholarly journals Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huijing Zhu ◽  
Xin Zhu ◽  
Yuhong Liu ◽  
Fusong Jiang ◽  
Miao Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Candidate Genes ◽  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Kegg Pathway Analysis

Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.

scholarly journals Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Gene Ontology ◽  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Integrin Subunit ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

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