China
Africa
Explore chapters and articles related to this topic
The Journey through the Gene: a Focus on Plant Anti-pathogenic Agents Mining in the Omics Era
Published in Mahendra Rai, Chistiane M. Feitosa, Eco-Friendly Biobased Products Used in Microbial Diseases, 2022
José Ribamar Costa Ferreira-Neto, Éderson Akio Kido, Flávia Figueira Aburjaile, Manassés Daniel da Silva, Marislane Carvalho Paz de Souza, Ana Maria Benko-Iseppon
Studying the Triterpenic Acid (TA) metabolism in the mint family (Lamiaceae), Aminfar et al. (2019) conducted a gene expression tracking by transcriptome mining, alongwith with a co expression network analysis and tissue-specific metabolite-expression analysis, to identify potential genes involved in TAs biosynthetic pathways. Based on the results, genes encoding squalene epoxidase and oxidosqualene cyclases were proposed for boosting triterpene production. Some TAs present an anti-HIV activity (Cassels and Asensio 2010). According to Wisecaver et al. (2017), global gene co expression networks are a largely unexplored valuable resource for discovering the genetic basis and architecture of natural plant products.
Omics and coagulation disorders in pregnancy
Published in Moshe Hod, Vincenzo Berghella, Mary E. D'Alton, Gian Carlo Di Renzo, Eduard Gratacós, Vassilios Fanos, New Technologies and Perinatal Medicine, 2019
Sara Ornaghi, Michael J. Paidas
Recent developments in high-throughput whole-genome sequencing make it feasible to assess the entire transcriptome with the potential to agnostically uncover biological processes driving complex phenotypes. As compared to univariate gene expression, analyses contrasting normal and adverse phenotypic outcomes, systems biology methods that apply a networks-based analysis of transcriptome-wide data better capture the complexity of intergene relationships and the pathways that lead to clinical diseases. Thus, systems biology methods, such as weighted gene coexpression network analysis (WGCNA), hold the opportunity to better define the coregulatory patterns that underlie complex phenotypes by facilitating systems-level characterization of expression changes by clustering highly correlated genes into coexpression modules of conserved biological function (77).
Toxicogenomics in Toxicologic Pathology
Published in Pritam S. Sahota, James A. Popp, Jerry F. Hardisty, Chirukandath Gopinath, Page R. Bouchard, Toxicologic Pathology, 2018
Arun R. Pandiri, David E. Malarkey, Mark J. Hoenerhoff
Newer bioinformatics approaches are being developed to understand the toxicity profiles using unsupervised network-based approaches. Sutherland et al. (2017) have used a novel bio-informatics approach called the weighted gene co-expression network analysis (WGCNA) to organize the transcriptomic data in an unsupervised manner into well-defined gene networks or modules with unique biological functions. The assumption is that these gene networks or modules are conserved across species and hence any discovery in the rodents would have translational relevance. Using rat hepatic transcriptomic data from Drug Matrix and TG-GATEs, they have integrated the co-expressed modules with standard pathology endpoints to develop a framework called the ‘toxicogenomic module associations with pathogenesis’ (TXG-MAP) to characterize mechanisms of drug-induced liver injury that are translatable to human liver diseases (Sutherland et al. 2017). Such toxicogenomic approaches that integrate transcriptomics and pathology are likely to yield biologically meaningful conclusions.
Identification of immune-associated prognostic biomarkers in lung adenocarcinoma on the basis of gene co-expression network
Published in Immunopharmacology and Immunotoxicology, 2023
Jianhai Zhang, Ange Chen, Zhang Xue, Chengzhi Liang
Weighted gene co-expression network analysis (WGCNA) is able to analyze expression patterns of genes in multiple samples. It mainly uses gene expression patterns to cluster similar genes into gene modules. By analyzing the correlation between specific traits and modules, candidate biomarkers or therapeutic targets can be identified [6]. In recent years, it has become increasingly common to use WGCNA to analyze public database data and mine cancer diagnostic and prognostic markers. By using WGCNA method, Yi Liao et al. [7] obtained green and blue modules of stem cell index based on mRNA expression, and finally constructed a 6-hub genes prognostic model for LUAD patients by protein-protein interaction network and other bioinformatics means. Tian et al. [8] mined the core modules and central genes related to breast cancer and found them being new sites for targeted therapy of breast cancer. Bao et al. [9] finally identified 16 potential targeted drugs for ovarian cancer treatment by drug-gene interaction analysis. These genes may be targets for these targeted drugs [9]. In a word, WGCNA method is suitable for screening genes with co-expression relationship.
Microbiota, not host origin drives ex vivo intestinal epithelial responses
Published in Gut Microbes, 2022
Kaline Arnauts, Padhmanand Sudhakar, Sare Verstockt, Cynthia Lapierre, Selina Potche, Clara Caenepeel, Bram Verstockt, Jeroen Raes, Séverine Vermeire, João Sabino, Catherine Verfaillie, Marc Ferrante
To complement differential gene expression analysis, we used weighted gene co-expression network analysis (WGCNA) to assess clusters of genes with similar expression patterns. Comparison of epithelial cells from UC and non-IBD controls in both inflamed and non-inflamed conditions, resulted in the identification of 11 co-expression clusters ranging in size from 77 to 2015 genes. In absence of microbiota, three clusters significantly correlated with UC non-inflamed epithelium as compared to non-IBD epithelium; of which one cluster significantly correlated with inflamed UC epithelium (turquoise cluster). The significant clusters identified in absence of microbiota were involved in tight junction and integrin signaling, and DNA damage response (BRCA1). The pink cluster was linked to the role of PKR in interferon induction and antiviral responses. In contrast, upon exposure to microbiota from the healthy volunteer only one significant correlation (pink cluster) was identified in inflamed conditions (Supplementary Figure 4). Again, the highest number of involved clusters was detected in the control condition, indicating that differences between UC and non-IBD epithelial cells were mainly driven by baseline characteristics and not induced by exposure to microbiota of UC patients or the healthy volunteer.
Identification of key pathways and genes in the progression of silicosis based on WGCNA
Published in Inhalation Toxicology, 2022
Jiaqi Lv, Jingwei Xiao, Qiang Jia, Xiangjing Meng, Zhifeng Yang, Shuangshuang Pu, Ming Li, Tao Yu, Yi Zhang, Haihua Wang, Li Liu, Zhongsheng Li, Xiao Chen, Haitao Yang, Yulu Li, Mengyun Qiao, Airu Duan, Hua Shao, Bin Li
First and foremost, weighted gene co-expression networks were successfully constructed. With a minimum module size 30, the module detection sensitivity deep split 2 and the height cutoff value of 0.25, seventeen gene modules with high similarity were explored by blockwiseModules function in R (Figure 5A). Importantly, it should be noted that genes in the grey module cannot be clustered into any other modules, so it was not included in following analyses. Based on the gene co-expression networks, eigengene adjacencies among modules were calculated and represented in the form of a heatmap, which provided a certain data basis for determining modules in further analyses (Figure 5B and C). Module preservation analysis for each group was shown in Supplementary Figure 7, suggesting valid evidence that most modules were well preserved in this study.