ArticleBMC systems biology2011
Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.BMC genomics · 2020Article
- Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data.Scientific reports · 2018Article
- Goals in Nutrition Science 2015-2020.Frontiers in nutrition · 2015Article
- Zinc proteome interaction network as a model to identify nutrient-affected pathways in human pathologies.Genes & nutrition · 2014Article
- A genome-wide methylation study of severe vitamin D deficiency in African American adolescents.The Journal of pediatrics · 2013Article
- Construction and analysis of the protein-protein interaction network related to essential hypertension.BMC systems biology · 2013Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNutritional systems biology offers the potential for comprehensive predictions that account for all metabolic changes with the intricate biological organization and the multitudinous interactions between the cellular proteins. Protein-protein interaction (PPI) networks can be used for an integrative description of molecular processes. Although widely adopted in nutritional systems biology, these networks typically encompass a single category of functional interaction (i.e., metabolic, regulatory or signaling) or nutrient. Incorporating multiple nutrients and functional interaction categories under an integrated framework represents an informative approach for gaining system level insight on nutrient metabolism.
resultsWe constructed a multi-level PPI network starting from the interactions of 200 vitamin-related proteins. Its final size was 1,657 proteins, with 2,700 interactions. To characterize the role of the proteins we computed 6 centrality indices and applied model-based clustering. We detected a subgroup of 22 proteins that were highly central and significantly related to vitamin D. Immune system and cancer-related processes were strongly represented among these proteins. Clustering of the centralities revealed a degree of redundancy among the indices; a repeated analysis using subsets of the centralities performed well in identifying the original set of 22 most central proteins.
conclusionsHierarchical and model-based clustering revealed multi-centrality hubs in a vitamin PPI network and redundancies among the centrality indices. Vitamin D-related proteins were strongly represented among network hubs, highlighting the pervasive effects of this nutrient. Our integrated approach to network construction identified promiscuous transcription factors, cytokines and enzymes - primarily related to immune system and cancer processes - representing potential gatekeepers linking vitamin intake to disease.
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