A computational strategy for metabolic network construction based on the overlapping ratio: Study of patients' metabolic responses to different dialysis patterns. (August 2021) - Huang, Xin; Wang, Zeyu; Su, Benzhe; He, Xinyu; Liu, Bing; Kang, Baolin
Graphical abstract: Highlights: MNC-OR was proposed to identify metabolic signals for disease treatment effect research. MNC-OR is a user-friendly and robust biomedical data analysis algorithm. Marked plasma metabolite changes between predialysis and postdialysis for both HFD and HD therapies were found by MNC-OR. Abstract: Background: Uremia is a worldwide epidemic disease and poses a serious threat to human health. Both maintenance hemodialysis (HD) and maintenance high flux hemodialysis (HFD) are common treatments for uremia and are generally used in clinical applications. In-depth exploration of patients' metabolic responses to different dialysis patterns can facilitate the understanding of pathological alterations associated with uremia and the effects of different dialysis methods on uremia, which may be used for future personalized therapy. However, due to variations of multiple factors (i.e., genetic, epigenetic and environment) in the process of disease treatments, identification of the similarities and differences in plasma metabolite changes in uremic patients in response to HD and HFD remains challenging. Methods: In this study, a computational strategy for metabolic network construction based on the overlapping ratio (MNC-OR) was proposed for disease treatment effect research. In MNC-OR, the overlapping ratio was introduced to measure metabolic reactions and to construct metabolic networks for analysis of different treatment options. Then, MNC-OR was employed to analyze HD-pattern-dependent changes in plasma metabolites to explore the pathological alterations associated with uremia and the effectiveness of different dialysis patterns (i.e., HD and HFD) on uremia. Based on the networks constructed by MNC-OR, two network analysis techniques, namely, similarity analysis and difference analysis of network topology, were used to find the similarity and differences in metabolic signals in patients under treatment with either HD or HFD, which can facilitate the understanding of pathological alterations associated with uremia and provide the guidance for personalized dialysis therapy. Results: Similarity analysis of network topology suggested that abnormal energy metabolism, gut metabolism and pyrimidine metabolism might occur in uremic patients, and maintenance of both HFD and HD therapies have beneficial effects on uremia. Then, difference analysis of network topology was employed to extract the crucial information related to HD-pattern-dependent changes in plasma metabolites. Experimental results indicated that the amino acid metabolism was closer to the normal status in HFD-treated patients; however, in HD-treated patients, the ability of antioxidation showed greater reduction, and the protein O-GlcNAcylation level was higher. Our findings demonstrate the potential of MNC-OR for explaining the metabolic similarities and differences of patients in response to different dialysis methods, thereby contributing to the guidance of personalized dialysis therapy.