Network Biology Approaches to Identify Molecular and Systems-Level Differences Between Salmonella Pathovars

Author(s):  
Marton Olbei ◽  
Robert A. Kingsley ◽  
Tamas Korcsmaros ◽  
Padhmanand Sudhakar
Keyword(s):  
2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Yeni Lim ◽  
Oran Kwon

Abstract Objectives Increasing attention has been paid to a range of botanical food supplement that help to maintain vascular health. Multiple components in botanical foods are expected to be working in concert with various targets. In a previous our animal study, Phellinus baumii and Salvia miltiorrhiza Bunge (PS) ameliorated endothelial and vascular dysfunction in a platelet activation rat model. This study aimed to provide the components, target molecules, phenotypes, signaling pathways, and investigate the mechanism of PS on vascular health. Methods Network biology analysis was based on the data from two clinical trials. The first clinical trial was performed in healthy subjects using high-fat-induced vascular dysfunction model. The second clinical trial was performed in healthy smokers. Differential markers obtained from clinical data, Affymetrix microarray, metabolomics, together with ingredient of PS, were mapped onto the network platform termed the context-oriented directed associations. A network of “component-target-phenotype-pathway” was constructed. Results The resulting vascular health network demonstrates that the components of PS are linked various target molecules for adhesion molecule production, platelet activation, endothelial inflammation, vascular dilation, and mitochondrial metabolism and detoxification, implicated with various metabolic pathways. Conclusions Using network biology methods, this study revealed the components and their target molecules, phenotypes, signaling pathways and provided wider information to support the synergistic mechanisms of PS on vascular health. Funding Sources This research was funded by the Bio & Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science & ICT and the BK21PLUS of the National Research Foundation.


2021 ◽  
Author(s):  
Priya Tolani ◽  
Srishti Gupta ◽  
Kirti Yadav ◽  
Suruchi Aggarwal ◽  
Amit Kumar Yadav

2016 ◽  
Vol 12 (4) ◽  
pp. e1004884 ◽  
Author(s):  
Victor Trevino ◽  
Alberto Cassese ◽  
Zsuzsanna Nagy ◽  
Xiaodong Zhuang ◽  
John Herbert ◽  
...  

2012 ◽  
Vol 27 (3) ◽  
pp. 202-209 ◽  
Author(s):  
Stephen Y. Chan ◽  
Kevin White ◽  
Joseph Loscalzo

2016 ◽  
Vol 12 (7) ◽  
pp. 824-835 ◽  
Author(s):  
Sung Jin Cho ◽  
Jihoo Lee ◽  
Hyun Jae Lee ◽  
Hyun-Young Jo ◽  
Mangalam Sinniah ◽  
...  

Genes ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 525 ◽  
Author(s):  
Samar Tareen ◽  
Michiel Adriaens ◽  
Ilja Arts ◽  
Theo de Kok ◽  
Roel Vink ◽  
...  

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.


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