Determination of the substrate repertoire of ADAMTS2, 3, and 14 significantly broadens their functions and identifies extracellular matrix organization and TGF‐β signaling as primary targets

2016 ◽  
Vol 30 (5) ◽  
pp. 1741-1756 ◽  
Author(s):  
Mourad Bekhouche ◽  
Cedric Leduc ◽  
Laura Dupont ◽  
Lauriane Janssen ◽  
Frederic Delolme ◽  
...  
2013 ◽  
Vol 383 (1) ◽  
pp. 39-51 ◽  
Author(s):  
Michael R. Dohn ◽  
Nathan A. Mundell ◽  
Leah M. Sawyer ◽  
Julie A. Dunlap ◽  
Jason R. Jessen

2020 ◽  
Author(s):  
Zhengzhong Gu ◽  
Xiaohan Cui ◽  
Xudong Wang

Abstract Background: Prognostic prediction models have been developed to detect new biomarkers of gastric cancer (GC). The identification of new biomarkers could provide theoretical foundations for the application of molecular targeted therapy in advanced GC. The aim of this study was to construct a prognostic prediction model for stomach adenocarcinoma (STAD) based on The Cancer Genome Atlas (TCGA) database. Methods: First, we used the "limma" package to screen differentially expressed genes (DEGs) based on TCGA database. Gene ontology (GO) analysis was performed using the "ClusterProfiler" package. The interactions between proteins and the relationships between differentially expressed genes and clinical features were analyzed by protein-protein interaction (PPI) network analysis and weighted gene coexpression network analysis (WGCNA), respectively. Then, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to identify differentially enriched pathways. The GenVisR package and CIBERSORT were used to identify mutations and assess immune infiltration. Finally, the expression of COL3A1 in STAD tissues was verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting.Results: Six differentially expressed genes were screened out, namely, COL3A1, ADAMTS12, BGN, FNDC1, AEBP1 and HTRA3. The enrichment results showed that differentially expressed genes were involved in multiple pathways in STAD, such as those related to the extracellular matrix, extracellular structure organization, and extracellular matrix organization. The differentially expressed genes were related to immune infiltration via the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathways. The western blotting and RT-qPCR results suggested that COL3A1 was overexpressed in STAD tissues compared with normal tissues.Conclusion: COL3A1, ADAMTS12, BGN, FNDC1, AEBP1 and HTRA3 could play important roles in the tumorigenesis and progression of STAD via various pathways, including those involving the extracellular matrix, extracellular structure organization, and extracellular matrix organization. COL3A1, ADAMTS12, BGN, FNDC1, AEBP1, and HTRA3 act as oncogenes in most cancers and may be biomarkers. Additionally, the identification of COL3A1 as a candidate biomarker provides a direction for further research on the role of tumor immunity in gastric cancer.


Pain ◽  
2019 ◽  
Vol 160 (4) ◽  
pp. 932-944 ◽  
Author(s):  
Marc Parisien ◽  
Alexander Samoshkin ◽  
Shannon N. Tansley ◽  
Marjo H. Piltonen ◽  
Loren J. Martin ◽  
...  

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e101929 ◽  
Author(s):  
Robbie D. Schultz ◽  
Emily E. Bennett ◽  
E. Ann Ellis ◽  
Tina L. Gumienny

2002 ◽  
Vol 20 (7) ◽  
pp. 563-571 ◽  
Author(s):  
Claudia B.N. Mendes de Aguiar ◽  
Ricardo Castilho Garcez ◽  
Marcio Alvarez‐Silva ◽  
Andréa Gonçalves Trentin

2021 ◽  
Author(s):  
Yu Di

Abstract Object: Understanding hub genes associated with gastric adenocarcinoma (GC) development could lead to effective advances to diagnose and treat the diseases. In order to discover possible signal pathways and hub genes for the disease, we utilized the bioinformatics tools to analyze its mechanism.Method: The gene chip of GSE7997was download from the GEO Datasets. Expression data of gastric cancer and its adjacent normal tissues were compared and the DEGs were acquired. The clusterProfiler and KEGG.db R packages were used for the analysis of its gene ontology process and KEGG pathways. What’s more, the PPI network was constructed by the STRING website. The hub genes were acquired by the plugin of the Cytohubba in Cytoscape. Finally, these genes were examined by the TCGA datasets and potential drugs were explored by Connectivity map.Results: The up regulated DEGs was mainly associated with the process of an extracellular matrix organization, an extracellular matrix organization, Collagen catabolic process, and multicellular organismal catabolic process. The down regulated DEGs have mainly associated with the process of digestion, cellular response to zinc ion, digestive system process, and organic hydroxy compound metabolic process. The up regulated DEGs was mainly located on PI3K-AKTsignal pathways, human papillomavirus infection,Cytokine-cytokine receptor interaction, and focal adhesion process. The down regulated genes were mainly associated with protein digestion and absorption, mineral absorption, and the pancreatic secretion. Cytohubba had found hub genes of COL1A2, COL5A1, COL4A1, COL5A2, COL6A3, COL11A1, SERPINH1, FN1, and down-regulated COL2A1.These genes were associated with the process of transforming growth factor, extracellular matrix, cell adhesion, wound healing and so on. As examined by the TCGA datasets, these altered genes were associated with overall survivaland no disease progress time (P<0.05). Finally, we got the small molecule drugs of fenofibrate, benzbromarone, semustine, chloroquine, ondansetron, hydroxyachillin, megestrol, ciclopirox and monastrol for gastric cancer by Connectivity map (P<0.05).Conclusion: As mentioned above, we got 9 hub genes and tested by the TCGA datasets of gastric adenocarcinoma. They were associated with overall survival and disease free progressive time in gastric cancer patients. The bioinformatical analysis of the disease may enhance the understanding of the mechanism of disorders.


2021 ◽  
Author(s):  
Yiling Hong ◽  
Xu Dong ◽  
Lawrence Chang ◽  
Mariann Chang ◽  
Chen Xie ◽  
...  

Western Pacific Amyotrophic Lateral Sclerosis and Parkinsonism dementia Complex (ALS-PDC) is a neurodegenerative disease linked to the traditional consumption of cycad seeds by the Chamorro people of Guam. Little is known about the etiological role of cycad toxin in ALS-PDC. Patient derived induced pluripotent stem cells were derived from age and sex matched affected and unaffected patient lymphoid cells then differentiated into cerebral organoids. After three months, the ALS-PDC affected organoids were smaller, their neurons had less extensive neurite outgrowth, and the organoids had more reactive astrocytes and M1 microglia, fewer resting and M2 microglia, and more open extracellular space. Most of these phenomena could be recapitulated by exposing unaffected organoids to β-methylamino L-alanine (BMAA), a toxic amino acid produced by cyanobacteria living with cycad plants. Furthermore, ALS-PDC affected organoids exhibited an exacerbated neuroinflammatory response to BMAA exposure via activation of caspase1/NLRP3 inflammasome. A genome-wide transcriptome analysis of the organoids showed that the most down regulated pathways were taurine, alanine, aspartate, and glutamate metabolism; protein digestion; and absorption. The most down-regulated biological processes were type I interferon signaling, regulation of neuron differentiation and extracellular matrix organization. Our results suggested that the etiology of ALS-PDC is due to metabolic disorders that shifted microglia to a more proinflammatory M1 state instead of a non-inflammatory, repairing M2 state, which exacerbated inflammation and reduced extracellular matrix strength. Supplementation of transforming growth factor beta to ALS/PDC affected organoids increased the expression of interferon-induced transmembrane proteins (IFITMs) and restored M2 microglia populations and extracellular matrix organization. Organoids containing networks of neurons, astrocytes, microglia derived from iPSC with our protocol provides an excellent cellular model for neurodegenerative disease modeling.


1986 ◽  
Vol 195 (4) ◽  
pp. 265-275 ◽  
Author(s):  
Wiel M. Kühtreiber ◽  
Jos Bent ◽  
Adrie W. C. Dorresteijn ◽  
Arjan Graaf ◽  
Jo A. M. Biggelaar ◽  
...  
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