scholarly journals Network Pharmacology-Based Analysis of the Underlying Mechanism of Hyssopus cuspidatus Boriss. for Antiasthma: A Characteristic Medicinal Material in Xinjiang

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongchang Liu ◽  
Yan Mao ◽  
Zhengyi Gu ◽  
Jinhua He
Keyword(s):  
Signaling Pathways ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Underlying Mechanism ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Interleukin 17 ◽  
Ppi Network ◽  
Active Compounds ◽  
Kegg Pathway Analysis

Background. Hyssopus cuspidatus Boriss. (Shen Xiang Cao (SXC)), a traditional medicine herb in Xinjiang, has a long history of being used by minorities to treat asthma. However, its active antiasthmatic compounds and underlying mechanism of action are still unknown. The aim of this study was to investigate the bioactive compounds and explore the molecular mechanism of SCX in the treatment of asthma using network pharmacology. Methods. The compounds of SCX were collected by a literature search, and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction were used to predict targets and screen active compounds. Moreover, asthma-related targets were obtained based on DisGeNET, Herb, and GeneCards databases, and a protein-protein interaction (PPI) network was built by the STRING database. Furthermore, the topological analysis of the PPI and SXC-compound-target networks were analyzed and established by Cytoscape software. Finally, the RStudio software package was used for carrying out Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. AutoDock tools and AutoDock Vina were used to molecularly dock the active compounds and key targets. Results. A total of 8 active compounds and 258 potential targets related to SXC were predicted, and PPI network screened out key targets, including IL-6, JUN, TNF, IL10, and CXCL8. GO enrichment analysis involved cell responses to reactive oxygen species, oxidative stress, chemical stress, etc. In addition, KEGG pathway analysis showed that SXC effectively treated asthma through regulation of mitogen-activated protein kinases (MAPK) signaling pathways, interleukin 17 (IL-17) signaling pathways, toll-like receptor (TLR) signaling pathways, and tumor necrosis factor (TNF) signaling pathways. Conclusion. The preliminary study that was based on multiple compounds, multiple targets, and multiple pathways provides a scientific basis for further elucidating the molecules involved and the underlying antiasthma-related mechanisms of SXC.

scholarly journals Mechanisms of Berberine for the Treatment of Atherosclerosis Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xuejiao Xie ◽  
Xingyu Ma ◽  
Siyu Zeng ◽  
Wansi Tang ◽  
Liucheng Xiao ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Foam Cell Formation ◽  
Ppi Network ◽  
The Core ◽  
Ppi Networks ◽  
Kegg Pathway Analysis

Atherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. The processes of atherosclerosis include endothelial dysfunction, new endothelial layer formation, lipid sediment, foam cell formation, plaque formation, and plaque burst. Owing to the adverse effects of first-line medications, it is urgent to discover new medications to deal with atherosclerosis. Berberine is one of the most promising natural products derived from traditional Chinese medicine. However, the panoramic mechanism of berberine against atherosclerosis has not been discovered clearly. In this study, we used network pharmacology to investigate the interaction between berberine and atherosclerosis. We identified potential targets related to berberine and atherosclerosis from several databases. A total of 31 and 331 putative targets for berberine and atherosclerosis were identified, respectively. Then, we constructed berberine and atherosclerosis targets with PPI data. Berberine targets network with PPI data had 3204 nodes and 79437 edges. Atherosclerosis targets network with PPI data had 5451 nodes and 130891 edges. Furthermore, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. The core PPI network had 132 nodes and 3339 edges. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis in David database. GO analysis indicated that the biological processes were correlated with G1/S transition of mitotic cells cycle. KEGG pathway analysis found that the pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibited inflammation and cell proliferation in the treatment of atherosclerosis. Our study provided a valid theoretical foundation for future research.

scholarly journals Network Pharmacology Analysis of the active components and anticancer targets of Rhubarb

2021 ◽  
Author(s):  
Hu Junrui ◽  
Duan Yongqiang ◽  
Cui Gongning ◽  
Luo Qiang ◽  
Xi Shanshan ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Drug Targets ◽  
Target Genes ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Active Components ◽  
Active Compounds ◽  
Cellular Targets ◽  
Kegg Pathway Analysis

AbstractTo investigate the mechanisms and active components governing the anticancer activity of rhubarb.The TCMSP database was screened to identify the active components of rhubarb and Swiss target predictions were generated to predict their cellular targets. TTD and OMIM databases were used to predict tumor-related target genes. "Cytoscape" was used to construct drug targets. PPI network analysis, GO enrichment analysis and KEGG pathway analysis of the key targets were investigated using String and David databases. A total of 33 components and 116 corresponding targets were screened. Amongst them, the key active compounds in rhubarb included emodin, aloe emodin, β-sitosterol, emodin methyl ether and rhein, which were predicted to target TP53, AKT1, STAT3, PIK3CA, HRAS, and VEGFA. GO analysis revealed that the cellular targets clustered into 159 biological processes, including those involved in cellular composition (n=24) and molecular functions (n=42, P<0.01). KEGG pathway analysis revealed 85 (P < 0.01) pathways related to cancer. The active compounds in rhubarb target TP53, AKT1 and PIK3CA. Rhubarb therefore regulates cancer development through an array of biological pathways.

scholarly journals Exploration of the Mechanism and Active Targets of Chinese Herbal Formula, JTTZ, for the Treatment of Type 2 Diabetes with Obesity and Hyperlipidemia Based on Network Pharmacology

2021 ◽  
Author(s):  
Haiyu Zhang ◽  
Xuedong An ◽  
De Jin ◽  
Jiaxing Tian ◽  
Wenke Liu ◽  
...  
Keyword(s):  
Inflammatory Response ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Therapeutic Targets ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Bile Acid Metabolism ◽  
Ppi Networks ◽  
Kegg Pathway Analysis

Abstract BackgroundPrevious studies have indicated that the JTTZ formula exhibits clinical benefit in T2D with obesity and hyperlipidemia such as lowering blood glucose, blood lipids, weight, and ameliorating symptoms as well as regulating islet function. However, their mechanism of action remains unclear. T2D with obesity and hyperlipidemia is associated with a severely poor management duo to difficulty in achieving the clinical goals and lack of effective multi-targeted therapies. In this study, we explored its potential mechanisms and therapeutic targets by network pharmacology. MethodsThe active ingredients and targets of JTTZ were obtained in the TCMSP, TCMID, TCM Database@Taiwan, PubChem and Swiss Target Prediction. And the therapeutic targets were searched from TTD, DrugBank Database and DisGeNET. Then, topology analysis were used as secondary screens to identify key hubs of the network. Finally, the data was integrated by Cytoscape software to construct a common network module. PPI networks were visualized to identify the interaction of the candidate targets. GO and KEGG pathway analysis were implemented. Rerult: 110 active compounds and 166 candidate targets of JTTZ against T2D with obesity and hyperlipidemia were obtained to construct compound-targets network. And, the therapeutic targets AKT2, RELA, NFKB1 and GSK3B were identified. GO and KEGG pathway analysis indicated that the biological processes related to inflammatory response, insulin secretion, steroid and bile acid metabolism, and 13 pathways mainly including adipocytokine signaling pathway, cAMP signaling pathway and cGMP-PKG signaling pathway were enriched. ConclusionOur data established that JTTZ intervenes with adipose tissue dysfunction via regulating to the adipocytokine (leptin and adiponectin), AMPK signaling pathway, cAMP and cGMP-PKG signaling pathway, inhibits systematic inflammatory response by NF-κB and MAPK signaling pathway, and ameliorates insulin resistance through PI3K/AKT2 pathway, all of which could thus offer a promising therapeutic strategy. In addition, AKT2, RELA, NFKB1 and GSK3B were identified to be regarded as potential therapeutic targets as well.

scholarly journals Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6036 ◽  
Author(s):  
Xin Gao ◽  
Yinyi Chen ◽  
Mei Chen ◽  
Shunlan Wang ◽  
Xiaohong Wen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Aurora Kinase ◽  
Smooth Muscle Contraction ◽  
Ppi Network ◽  
Tissue Samples ◽  
Kegg Pathway Analysis ◽  
Key Genes

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

scholarly journals FKBP-related ncRNA-mRNA axis in breast cancer

2020 ◽  
Author(s):  
Hanchu Xiong ◽  
Zihan Chen ◽  
Wenwen Zheng ◽  
Jing Sun ◽  
Qingshuang Fu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factors ◽  
Differential Expression ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Annotation ◽  
Prognostic Analysis ◽  
Kegg Pathway Analysis

Abstract Background Breast cancer (BC) is a disease with morbidity ranking the first of women worldwidely. FK506-binding protein (FKBP) family has been demonstrated to possess various functions by interacting with different molecular targets in BC. However, a comprehensive ncRNA-mRNA regulatory axis of FKBP has not yet been reported. Methods FKBP related miRNAs were obtained from miRWalk database. Then, potential lncRNAs, transcription factors as well as mRNAs of screened differentially expressed miRNAs (DE-miRNAs) were analysed by using LncBase v.2, miRGen v3 and miRWalk database. Additionally, differential expression and prognostic analysis of lncRNAs were evaluated using TANRIC database. Next, GO annotation and KEGG pathway analysis were processed using DAVID database. Protein-Protein Interaction (PPI) network was established and hub genes were identified using STRING database. Finally, differential expression and prognostic analysis of hub genes were further conducted using UALCAN and bc-GenExMiner v4.2 database, respectively. Results Eleven DE-miRNAs, consisting of four FKBP4 related DE-miRNAs and seven FKBP5 related DE-miRNAs, were screened. 482 predicted lncRNAs were found for DE-miRNAs. Then, expression and prognostic results of nine of top twenty lncRNAs of BC were significantly identified. LINC00662 and LINC00963 expression were significantly associated with patients’ overall survival (OS). Then, nine potential upstream transcription factors were identified in motifs of DE-miRNAs. 320 target genes were identified for GO annotation and KEGG pathway analysis, which were mainly enriched in cysteine-type endopeptidase activity involved in apoptotic process. Construction and analysis in PPI network showed that RAB7A was selected as a hub gene with the toppest connectivity scores. Differential expression analysis of nine in top ten hub genes of BC were significantly identified. RAB7A and ARRB1 expression were significantly related with BC patients’ OS. Conclusions In current study, we firstly established a predicted FKBP-related ncRNA-mRNA regulatory network, thus exploring a comprehensive interpretation of molecular mechanisms and providing potential clues in seeking novel therapeutics for BC. In the future, much more experiments should be conducted to verify our findings.

scholarly journals Exploring the Mechanisms Underlying the Therapeutic Effect of Xixin Decoction on Alzheimer's Disease Based on Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Zhuo Zhang ◽  
Jiang-lin Xu ◽  
Ming-qing Wei ◽  
Ting Li ◽  
Jing Shi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Therapeutic Effect ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Active Compounds

Abstract Background and objective: Alzheimer’s disease (AD) has been a worldwide problem, not only the treatment but also the prevention. As a commonly used Chinese Herbal Formula, Xixin Decoction (XXD) has significant therapeutic effect on AD but without clear mechanism. This study was aimed to predict the main active compounds and targets of XXD in the treatment of AD and to explore the potential mechanism by using network pharmacology and molecular docking. Methods: The compounds of XXD were searched in the TCMSP and the TCMID database, and the active compounds were screened based on the ADME model and SwissADME platform. SwissTargetPrediction platform was used to search for the primary candidate targets of XXD. The common targets related to AD obtained by two databases (GeneCards and DisGeNET) were determined as candidate proteins involved in AD. To acquire the related targets of XXD in the treatment of AD, the target proteins related to AD were intersected with the predicted targets of XXD. Then these overlapping targets were imported into the STRING database to build PPI network including hub targets; Cytoscape 3.7.2 software was used to construct the topology analysis for the herb-compound-target network diagram while one of it’s plug-in called CytoNCA was used to calculate degree value to screen the main active compounds of XXD. GO and KEGG pathway enrichment analyses were conducted to explore the core mechanism of action and biological pathways associated with the decoction via Metascape platform. We used AutoDock Vina and PyMOL 2.4.0 softwares for molecular docking of hub targets and main compounds.Results: We determined 114 active compounds which meet the conditions of ADME screening, 973 drug targets, and 973 disease targets. However, intersection analysis screened out 208 shared targets. PPI network identified 9 hub targets, including TP53, PIK3CA, MAPK1, MAPK3, STAT3, AKT1, etc. The 10 main active compounds play a major role in treatment of AD by XXD. Hub targets were found to be enriched in 10 KEGG pathways, involving the Pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, Alzheimer's disease, Neuroactive ligand-receptor interaction, Dopaminergic synapse, Serotonergic synapse and MAPK signaling pathway. The docking results indicated that the 8 hub targets exhibit good binding activity with the 9 main active compounds of XXD.Conclusions: We found the advantages of multi-compounds-multi-targets-multi-pathways regulation to reveal the mechanism of XXD for treating AD based on network pharmacology and molecular docking. Our study provided a theorical basis for further clinical application and experimental research of XXD for anti-AD in the future.

scholarly journals Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Yanyan Tang ◽  
Ping Zhang
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

A Network Pharmacology Approach to Explore the Underlying Mechanism of Tufuling Qiwei Tangsan in Treating Psoriasis

2021 ◽  
Vol 16 ◽  
Author(s):  
Xiaolei Ma ◽  
Yinan Lu ◽  
Yang Lu ◽  
Zhili Pei
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Mechanism Of Action ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Mapk Signaling ◽  
Binding Activity ◽  
Network Pharmacology ◽  
Active Components ◽  
Active Compounds

Background: Tufuling Qiwei Tangsan (TQTS) is a commonly used Mongolian medicine preparation against psoriasis in China. However, its mechanism of action and molecular targets for the treatment of psoriasis is still unclear. Network pharmacology can reveal the synergistic mechanism of drugs at the molecular, target and pathway levels, and is suitable for the complex study of traditional Chinese medicine formulations. However, it is rarely involved in the application of Mongolian medicine with the same holistic concept of traditional Chinese medicine. Method: In this paper, the active compounds of TQTS were collected and their targets were identified. Psoriasis-related targets were obtained by analyzing the differential expressed genes between psoriasis patients and healthy individuals. Then, the network concerning the interactions of potential targets of TQTS with well-known psoriasis-related targets was built. The core targets were selected according to topological parameters. And the enrichment analysis was carried out to explore the mechanism of action of TQTS. Moreover, molecular docking was performed to study the interaction between the selected ligands and receptors related to psoriasis. Result and Conclusion: Eighty-five active compounds of TQTS were screened, with corresponding 270 targets, and 313 differentially expressed genes were identified. Additionally, enrichment analysis showed that the targets of TQTS for treating psoriasis were mainly concentrated in multiple biological processes, including apoptosis, growth factor response,etc., and related pathways including PI3K-Akt and MAPK signaling pathway, and so on. Genes such as NFKB1, TP53 and MAPK1 are the key genes in the gene pathway network of TQTS against psoriasis. The 4 main active components of TQTS have certain binding activity with 13 potential targets, and the stability of interaction with AKT1 is the best, which indicate the potential mechanism of TQTS on psoriasis.

Comprehensive analysis of long noncoding RNAs and mRNAs expression profiles and functional networks during chondrogenic differentiation of murine ATDC5 cells

2019 ◽  
Vol 51 (8) ◽  
pp. 778-790
Author(s):  
Wei Wang ◽  
Yu Ding ◽  
Yanhua Xu ◽  
Hefeng Yang ◽  
Wenjing Liu ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Pathway Analysis ◽  
Chondrogenic Differentiation ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Receptor Interaction ◽  
Messenger Rnas ◽  
Kegg Pathway Analysis

AbstractChondrogenic differentiation is a coordinated biological process orchestrated by various cell signaling pathways, involving complex pathways regulated at both transcriptional and post-transcriptional levels. Long noncoding RNAs (lncRNAs) are emerging as important regulators in the modulation of multiple cell processes. However, the potential roles of lncRNAs and their regulatory mechanisms in chondrogenic differentiation remain largely unclear. In this study, microarray was performed to detect the expression profiles of lncRNAs and messenger RNAs (mRNAs) during chondrogenic differentiation of murine chondrogenic cell line ATDC5. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore their functions. Coding-noncoding co-expression (CNC) and competing endogenous RNA (ceRNA) networks were also constructed with bioinformatics methods. The results revealed that 1009 lncRNAs and 1206 mRNAs were differentially regulated during chondrogenic differentiation. GO and KEGG pathway analysis indicated that the principal functions of the transcripts were associated with system development and extracellular matrix-receptor interaction, TGF-β signaling, and PI3K-Akt signaling pathways. The CNC network showed that lncRNA AK136902 was positively correlated with prostaglandin F receptor (FP). The ceRNA network covered 3 lncRNAs, 121 miRNAs and 241 edges. The upregulated lncRNA AK136902, AK016344, and ENSMUST00000180767 might promote chondrogenic differentiation by acting as ceRNAs. Knockdown of lncRNA AK136902 could inhibit the mRNA expression of FP and other chondrogenic related genes, including Aggrecan and Col2a1 during chondrogenic differentiation. Our results provide a new perspective on the modulation of lncRNAs during chondrogenic differentiation.

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