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Published on: November 2, 2020
Attractors of hypertrophic cardiomyopathy using maximal cliques and attract methods
Ming-Jun Feng1, Hui-Min Chu1, Cai-Jie Shen2
1Department of Cardiology, Ningbo First Hospital, Ningbo 315000, Zhejiang Province, China.
Insights
This study identified key molecular pathways in hypertrophic cardiomyopathy (HCM). Proteasome, ribosome, and oxidative phosphorylation are implicated in HCM's pathophysiology, offering potential therapeutic targets.
Area of Science:
- Molecular Biology
- Systems Biology
- Cardiovascular Research
Background:
- Hypertrophic cardiomyopathy (HCM) is a complex genetic heart disease.
- Identifying molecular mechanisms underlying HCM is crucial for developing effective treatments.
Purpose of the Study:
- To identify differential attractor modules associated with hypertrophic cardiomyopathy (HCM).
- To integrate clustering-based maximal cliques algorithm and the Attract method for module discovery.
Main Methods:
- Recruited HCM microarray data from ArrayExpress.
- Constructed and re-weighted protein-protein interaction (PPI) networks for normal and HCM conditions using Spearman correlation coefficient (SCC).
- Applied maximal cliques and Attract method to identify differential attractor modules, followed by pathway enrichment analysis.
Main Results:
- Identified 926 and 1118 maximal cliques in normal and HCM PPI networks, respectively.
- Obtained 32 and 55 modules from normal and HCM networks, identifying 5 differential attractor module pairs.
- Pathway enrichment analysis revealed significant involvement of proteasome, ribosome, and oxidative phosphorylation pathways.
Conclusions:
- Proteasome, ribosome, and oxidative phosphorylation pathways are significantly altered in HCM.
- These pathways may play critical pathophysiological roles in the development of hypertrophic cardiomyopathy.
- Findings provide insights into molecular mechanisms and potential therapeutic targets for HCM.
Background:
Our study was designed to identify the differential attractor modules related with hypertrophic cardiomyopathy (HCM) by integrating clustering-based on maximal cliques algorithm and Attract method.
Methods:
We firstly recruited the HCM-related microarray data from ArrayExpress database. Next, protein-protein interaction (PPI) networks of normal and HCM were constructed and re-weighted using spearman correlation coefficient (SCC). Then, maximal cliques were found from the PPI networks through the clustering-based on maximal cliques approach. Afterwards, highly overlapped cliques were eliminated or merged according to the interconnectivity, and then modules were obtained. Subsequently, we used Attract method to identify differential attractor modules, following by the pathway enrichment analyses for genes in differential attractor modules.
Results:
After removing the cliques with nodes less than or equal to 4, 926 and 1118 maximal cliques in normal and HCM PPI networks were obtained for module analysis. Then, we obtained 32 and 55 modules from the PPI networks of normal and HCM, respectively. By comparing with normal condition, there were 5 module pairs with the same or similar gene composition. Significantly, based on attract method, we found that these 5 modules were differential attractors. Pathway enrichment analyses indicated that proteasome, ribosome and oxidative phosphorylation were the significant pathways.
Conclusions:
Proteasome, ribosome and oxidative phosphorylation might play pathophysiological roles in HCM.
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