scholarly journals Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Hugo V. S. Rody ◽  
Luis E. A. Camargo ◽  
Silvana Creste ◽  
Marie-Anne Van Sluys ◽  
Loren H. Rieseberg ◽  
...  
Keyword(s):  
Plant Resistance ◽  
Biological Network ◽  
Quantitative Resistance ◽  
Model Systems ◽  
Primary Metabolism ◽  
Transcriptome Data ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Signal Perception ◽  
Biological Network Analysis

We assembled a dual-layered biological network to study the roles of resistance gene analogs (RGAs) in the resistance of sugarcane to infection by the biotrophic fungus causing smut disease. Based on sugarcane-Arabidopsis orthology, the modeling used metabolic and protein-protein interaction (PPI) data from Arabidopsis thaliana (from Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioGRID databases) and plant resistance curated knowledge for Viridiplantae obtained through text mining of the UniProt/SwissProt database. With the network, we integrated functional annotations and transcriptome data from two sugarcane genotypes that differ significantly in resistance to smut and applied a series of analyses to compare the transcriptomes and understand both signal perception and transduction in plant resistance. We show that the smut-resistant sugarcane has a larger arsenal of RGAs encompassing transcriptionally modulated subnetworks with other resistance elements, reaching hub proteins of primary metabolism. This approach may benefit molecular breeders in search of markers associated with quantitative resistance to diseases in non-model systems.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

scholarly journals Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Binbin Xie ◽  
Yiran Li ◽  
Rongjie Zhao ◽  
Yuzi Xu ◽  
Yuhui Wu ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Search Tool ◽  
Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

scholarly journals Integration of a Systems Biological Network Analysis and QTL Results for Biomass Heterosis in Arabidopsis thaliana

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e49951 ◽  
Author(s):  
Sandra Andorf ◽  
Rhonda C. Meyer ◽  
Joachim Selbig ◽  
Thomas Altmann ◽  
Dirk Repsilber
Keyword(s):  
Arabidopsis Thaliana ◽  
Network Analysis ◽  
Biological Network ◽  
Biological Network Analysis

Discovery of genes associated with sulfate-reducing bacteria biofilm using text mining and biological network analysis

2021 ◽  
Author(s):  
Abhilash Kumar Tripathi ◽  
Priya Saxena ◽  
Payal Thakur ◽  
Shailabh Rauniyar ◽  
Vinoj Gopalakrishnan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Biological Network ◽  
Sulfate Reducing Bacteria ◽  
Sulfate Reducing ◽  
Biological Network Analysis ◽  
Reducing Bacteria

scholarly journals Predicting disease associations via biological network analysis

2014 ◽  
Vol 15 (1) ◽  
pp. 304 ◽  
Author(s):  
Kai Sun ◽  
Joana P Gonçalves ◽  
Chris Larminie ◽  
Nataša Pržulj
Keyword(s):  
Network Analysis ◽  
Biological Network ◽  
Biological Network Analysis ◽  
Disease Associations

scholarly journals Elucidation of the Mechanisms and Molecular Targets of Lianhuaqingwen for Treatment of COVID-19 Based on Network Pharmacology

2021 ◽  
Vol 9 (4) ◽  
pp. 111-122
Author(s):  
Yan Luo ◽  
Si-ting Gao ◽  
Jun-xiong Cheng ◽  
Wei-jian Xiong ◽  
Wen-Fu Cao
Keyword(s):  
Target Genes ◽  
Molecular Targets ◽  
Core Gene ◽  
Mechanisms Of Action ◽  
Network Pharmacology ◽  
Pathway Network ◽  
Functional Annotations ◽  
Gene Pathway ◽  
Protein Protein Interaction ◽  
Ppi Networks

Lianhuaqingwen (LH) is the widely used in the treatment of Coronavirus disease 2019 (COVID-19). However, its mechanisms of action and molecular targets for treatment of COVID-19 are not clear. The active compounds of LH were collected and their targets were identified through the network pharmacology. The mechanism of compound multi components and multi targets and its relationship with disease are analyzed. COVID-19 targets were obtained by analyzing with TCMSP. In total, 282 active ingredients and 510 targets of LH were identified. Twenty-one target genes associated with LH and COVID-19. Protein-protein interaction (PPI) data were then obtained and PPI networks of LH putative targets and COVID-19-related targets were visualized and merged to identify the candidate targets for LH against COVID-19. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out. The gene-pathway network was constructed to screen the crucial target genes. The functional annotations of target genes were found to be related to immune regulation, host defense, inflammatory reaction and autoimmune diseases and so on. Twenty pathways including immunology, cancer, and cell processing were significantly enriched. Quercetin and luteolin might be the crucial ingredients. IL6 was the core gene and other several genes including IL1B, STAT1, IFNGR1, and NCF1 were the key genes in the gene-pathway network of LH for treatment of COVID-19. The results indicated that LH’s effects against COVID-19 might relate to regulation of immunological function through the specific biological processes and the related pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of complex herbal formulations.

scholarly journals CRISPR-based editing reveals edge-specific effects in biological networks

2018 ◽  
Author(s):  
Yi Li ◽  
Chance M. Nowak ◽  
Daniel Withers ◽  
Alexander Pertsemlidis ◽  
Leonidas Bleris
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Cellular Survival ◽  
Specific Effects ◽  
Biological Network Analysis ◽  
Multiple Edges ◽  
Cellular Phenotypes ◽  
Microrna Pathway ◽  
The Impact

AbstractUnraveling the properties of biological networks is central to understanding normal and disease cellular phenotypes. Networks consist of functional elements (nodes) that form a variety of diverse connections (edges) with each node being a hub for multiple edges. Herein, in contrast to node-centric network perturbation and analysis approaches, we present a high-throughput CRISPR-based methodology for delineating the role of network edges. Ablation of network edges using a library targeting 93 miRNA target sites in 71 genes reveals numerous edges that control, with variable importance, cellular survival under stress. To compare the impact of removing nodes versus edges in a biological network, we dissect a specific p53-microRNA pathway. In summary, we demonstrate that network edges are critical to the function and stability of biological networks. Our results introduce a novel genetic screening opportunity via edge ablation and highlight a new dimension in biological network analysis.

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