Abstract
The human biological system uses ‘inter-organ’ communication to achieve a state of homeostasis. This communication occurs through the response of receptors, located on target organs, to the binding of secreted ligands from source organs. Albeit years of research, the roles these receptors play in tissues is only partially understood. This work presents a new methodology based on the enrichment analysis scores of co-expression networks fed into support vector machines (SVMs) and k-NN classifiers to predict the tissue-specific metabolic roles of receptors. The approach is primarily based on the detection of coordination patterns of receptors expression. These patterns and the enrichment analysis scores of their co-expression networks were used to analyse ~ 700 receptors and predict metabolic roles of receptors in subcutaneous adipose. To facilitate supervised learning, a list of known metabolic and non-metabolic receptors was constructed using a semi-supervised approach following literature-based verification. Our approach confirms that pathway enrichment scores are good signatures for correctly classifying the metabolic receptors in adipose. We also show that the k-NN method outperforms the SVM method in classifying metabolic receptors. Finally, we predict novel metabolic roles of receptors. These predictions can enhance biological understanding and the development of new receptor-targeting metabolic drugs.
Highlights
The human biological system uses ‘inter-organ’ communication to achieve a state of homeostasis
Ligand–receptor secretion and binding are accomplished by molecules, i.e., ligands, secreted into the blood stream from source organs that bind to receptors located on both the cell surface and within the cells of target organs
The insulin receptor is included in its own pathway, the KEGG insulin signalling pathway
Summary
The human biological system uses ‘inter-organ’ communication to achieve a state of homeostasis. This work presents a new methodology based on the enrichment analysis scores of co-expression networks fed into support vector machines (SVMs) and k-NN classifiers to predict the tissue-specific metabolic roles of receptors. The approach is primarily based on the detection of coordination patterns of receptors expression These patterns and the enrichment analysis scores of their co-expression networks were used to analyse ~ 700 receptors and predict metabolic roles of receptors in subcutaneous adipose. Ligand–receptor secretion and binding are accomplished by molecules, i.e., ligands, secreted into the blood stream from source organs that bind to receptors located on both the cell surface and within the cells of target organs This complex network of whole-body ligand–receptor interactions serves as the information transducer of these feedback loops. Using this data and focusing on metabolic receptors and adipose tissue, we ask several questions: (1) Is expression of genes coded to receptors widely correlated within tissues? And in adipose in specific? (2) How can we use this data to infer the metabolic roles of receptors in tissues and to detect new metabolic receptors, not thought of as being members of a specific classically defined metabolic system? Together, answers to these questions can begin to delineate a comprehensive view of the metabolic network signalling
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