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Updated: May 22, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
PPI network identifies interacting pathogenic signaling pathways in Candida albicans
Deepanjan Chattopadhyay1, Sanjib Das1, Paromita Saha Mondal1
1Department of Molecular Biology and Biotechnology, University of Kalyani, Kalyani 741235, Nadia, West Bengal, India. sahatanima@yahoo.co.in.
None:
Candida albicans, an opportunistic and systemic infection causing fungus, causes skin, nail, and mucosal layer lesions in healthy individuals and hospital borne catheter-related and nosocomial infections. This particular fungus exists in two distinct stages in its life cycle: yeast and hyphae. In this study, 20 signaling pathways associated with 177 proteins from C. albicans were identified to construct a PPI network. The core part of the network consisted of 165 proteins. Network topology analyses revealed that the formed PPI network is biologically robust and scale-free, with significant interactions between proteins through 19 252 shortest pathways. In this network, the top 10 hub proteins (RAS1, CDC42, HOG1, CPH1, STE11, EFG1, CEK1, HSP90, TEC1 and CST20) were identified using network analysis, which seem to be the most important proteins involved in different pathways for the development of pathogenesis and virulence. Modular analysis of the network resulted in top six sub-networks, three of which shared eight hub proteins. Ontology and functional enrichment analyses revealed that the majority of the proteins were associated with regulation of transcription by RNA polymerase II, plasma membrane and nucleic acid binding in biological processes, and cellular components and molecular functions, respectively. Enrichment analysis indicated that the proteins were mostly involved in oxidative phosphorylation and purine metabolism signaling pathways. We determined the complex web of signaling pathway involving proteins via PPI network analysis to unravel and decipher protein interactions within C. albicans to understand the complex pathogenesis processes for targeted therapeutic interferences using novel bioinformatics strategies.
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