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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
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Link clustering explains non-central and contextually essential genes in protein interaction networks
Inhae Kim1, Heetak Lee1, Kwanghwan Lee1
1Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea.
Scientific Reports
|August 14, 2019
Summary
Link clustering identifies contextual essential genes (EGs) that change importance across conditions. These non-central EGs, unlike central ones, offer potential therapeutic targets in pathogenic states.
Area of Science:
- Systems biology
- Network biology
- Genomics
Background:
- Essential genes (EGs) can alter their functional importance across different biological contexts.
- Identifying context-specific EGs is crucial for pinpointing therapeutic targets, especially in pathogenic conditions.
- Protein-protein interaction (PPI) networks are valuable for understanding gene function and essentiality.
Purpose of the Study:
- To introduce and validate link clustering as a metric for identifying contextual EGs within PPI networks.
- To investigate the characteristics of EGs identified by link clustering compared to centrality measures.
- To explore the association between link clustering and gene essentiality across various biological contexts.
Main Methods:
- Analysis of human and yeast protein-protein interaction (PPI) networks.
- Application of link clustering algorithms to identify non-central EGs.
- Comparison of link clustering with traditional centrality measures for predicting gene essentiality.
- Assessment of gene expression levels and evolutionary conservation for different EG categories.
Main Results:
- 29-47% of EGs in human and yeast PPI networks were better characterized by link clustering than by centrality.
- Non-central EGs demonstrated a higher propensity to change essentiality across human cell lines and between species.
- Non-central EGs displayed intermediate levels of gene expression and evolutionary conservation compared to central EGs and non-essential genes.
- Non-central EGs significantly impacted lower hierarchical community structures within the networks.
Conclusions:
- Link clustering effectively identifies contextual essential genes (EGs) that are non-central in PPI networks.
- Non-central EGs exhibit dynamic essentiality and possess unique molecular and evolutionary features.
- This approach provides a novel strategy for discovering potential therapeutic targets by focusing on context-specific EGs.
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