Gene microarray integrated with iTRAQ-based proteomics for the discovery of NLRP3 in LPS-induced inflammatory response of bovine mammary epithelial cells

2019 ◽  
Vol 86 (4) ◽  
pp. 416-424 ◽  
Author(s):  
Yu Sun ◽  
Lian Li ◽  
Chengmin Li ◽  
Genlin Wang ◽  
Guangdong Xing

AbstractMastitis, a major infectious disease in dairy cows, is characterized by an inflammatory response to pathogens such as Escherichia coli and Staphylococcus aureus. To better understand the immune and inflammatory response of the mammary gland, we stimulated bovine mammary gland epithelial cells (BMECs) with E. coli-derived lipopolysaccharide (LPS). Using transcriptomic and proteomic analyses, we identified 1019 differentially expressed genes (DEGs, fold change ≥2 and P-value < 0.05) and 340 differentially expressed proteins (DEPs, fold change ≥1.3 and P-value < 0.05), of which 536 genes and 162 proteins were upregulated and 483 genes and 178 proteins were downregulated following exposure to LPS. These differentially expressed genes were associated with 172 biological processes; 15 Gene Ontology terms associated with response to stimulus, 4 associated with immune processes, and 3 associated with inflammatory processes. The DEPs were associated with 51 biological processes; 2 Gene Ontology terms associated with response to stimulus, 1 associated with immune processes, and 2 associated with inflammatory processes. Meanwhile, several pathways involved in mammary inflammation, such as Toll-like receptor, NF-κB, and NOD-like receptor signaling pathways were also represented. NLRP3 depletion significantly inhibited the expression of IL-1β and PTGS2 by blocking caspase-1 activity in LPS-induced BMECs. These results suggest that NLR signaling pathways works in coordination with TLR4/NF-κB signaling pathways via NLRP3-inflammasome activation and pro-inflammatory cytokine secretion in LPS-induced mastitis. The study highlights the function of NLRP3 in an inflammatory microenvironment, making NLRP3 a promising therapeutic target in Escherichia coli mastitis.

2021 ◽  
Author(s):  
Cailin xue ◽  
Peng gao ◽  
Xudong zhang ◽  
Xiaohan cui ◽  
Lei jin ◽  
...  

Abstract Background: Abnormal methylation of DNA sequences plays an important role in the development and progression of pancreatic cancer (PC). The purpose of this study was to identify abnormal methylation genes and related signaling pathways in PC by comprehensive bioinformatic analysis of three datasets in the Gene Expression Omnibus (GEO). Methods: Datasets of gene expression microarrays (GSE91035, GSE15471) and gene methylation microarrays (GSE37480) were downloaded from the GEO database. Aberrantly methylated-differentially expressed genes (DEGs) were analysis by GEO2R software. GO and KEGG enrichment analyses of selected genes were performed using DAVID database. A protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Core module analysis was performed by Mcode in Cytoscape. Hub genes were obtained by CytoHubba app. in Cytoscape software. Results: A total of 267 hypomethylation-high expression genes, which were enriched in biological processes of cell adhesion, biological adhesion and regulation of signaling were obtained. KEGG pathway enrichment showed ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway. The top 5 hub genes of PPI network were EZH2, CCNA2, CDC20, KIF11, UBE2C. As for hypermethylation-low expression genes, 202 genes were identified, which were enriched in biological processes of cellular amino acid biosynthesis process and positive regulation of PI3K activity, etc. The pathways enriched were the pancreatic secretion and biosynthesis of amino acids pathways, etc. The five significant hub genes were DLG3, GPT2, PLCB1, CXCL12 and GNG7. In addition, five genes, including CCNA2, KIF11, UBE2C, PLCB1 and GNG7, significantly associated with patient's prognosis were also identified. Conclusion: Novel genes with abnormal expression were identified, which will help us further understand the molecular mechanism and related signaling pathways of PC, and these aberrant genes could possibly serve as biomarkers for precise diagnosis and treatment of PC.


2021 ◽  
Author(s):  
Ning Fan ◽  
Shuo Yuan ◽  
Yong Hai ◽  
Peng Du ◽  
Jian Li ◽  
...  

Abstract BackgroundInflammatory processes exacerbated by IL-1β are believed to be key mediators of disc degeneration and low back pain. However, the underlying mechanism remains unclear. We performed a bioinformatics analysis to identify the key genes that were differentially expressed between degenerative intervertebral disc cells with and without exposure to interleukin (IL)-1β, and explore the related signaling pathways and interaction networks.MethodsThe microarray data were downloaded from the Gene Expression Omnibus (GSE 27494). Then, analyses of the gene ontology, signaling pathways, and interaction networks for the differentially expressed genes (DEGs) were conducted using tools including the Database for Annotation, Visualization, and Integrated Discovery (DAVID), Metascape, Gene Set Enrichment Analysis (GSEA), Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, the Venn method, and packages of the R computing language.ResultsA total of 260 DEGs were identified, including 161 upregulated genes and 99 down-regulated genes. Gene Ontology (GO) annotation analysis showed that these DEGs were mainly associated with the extracellular region, chemotaxis, taxis, cytokine activity, and cytokine receptor binding. A Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed that these DEGs were mainly involved in the interactions of cytokine-cytokine receptor interaction, rheumatoid arthritis, tumor necrosis factor (TNF) signaling pathway, salmonella infection, and chemokine signaling pathway. The interaction network analysis indicated that 10 hub genes, including CXCL8, CXCL1, CCL20, CXCL2, CXCL5, CXCL3, CXCL6, C3, PF4, and GPER1 may play key roles in intervertebral disc degeneration.ConclusionsBioinformatic analysis showed that CXCL8 and other 9 key genes may play a role in the development of disc degeneration induced by inflammatory reactions, and can be used to identify the potential therapeutic target genes.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared


2021 ◽  
Author(s):  
Zhiyun Hao ◽  
Yuzhu Luo ◽  
Jiqing Wang ◽  
Jon Hickford ◽  
Huitong Zhou ◽  
...  

In our previous studies, microRNA-432 (miR-432) was found to be one of differentially expressed miRNAs in ovine mammary gland between the two breeds of lactating sheep with different milk production...


Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.


Sign in / Sign up

Export Citation Format

Share Document