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Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

Titel:	Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.
Autor/in / Beteiligte Person:	Abedi, M ; Marateb, HR ; Mohebian, MR ; Aghaee-Bakhtiari, SH ; Nassiri, SM ; Gheisari, Y
Link:	Volltext (PDF) View record at Springer (Volltext)
Zeitschrift:	Scientific reports, Jg. 11 (2021-12-06), Heft 1, S. 23452
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2021
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/s41598-021-02282-3
Schlagwort:	Algorithms Animals Chemistry, Pharmaceutical methods Cluster Analysis Computational Biology methods Drug Design Epigenesis, Genetic Gene Expression Profiling methods Gene Regulatory Networks Global Health Humans Kidney Cortex drug effects Kidney Medulla drug effects Linear Models Male Mice Mice, Inbred DBA MicroRNAs genetics Microarray Analysis Oligonucleotide Array Sequence Analysis Principal Component Analysis Regression Analysis Signal Transduction Support Vector Machine Diabetic Nephropathies drug therapy Machine Learning Systems Biology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Research Support, Non-U.S. Gov't Language: English [Sci Rep] 2021 Dec 06; Vol. 11 (1), pp. 23452. <i>Date of Electronic Publication: </i>2021 Dec 06. MeSH Terms: Machine Learning* ; Systems Biology* ; Diabetic Nephropathies / *drug therapy ; Algorithms ; Animals ; Chemistry, Pharmaceutical / methods ; Cluster Analysis ; Computational Biology / methods ; Drug Design ; Epigenesis, Genetic ; Gene Expression Profiling / methods ; Gene Regulatory Networks ; Global Health ; Humans ; Kidney Cortex / drug effects ; Kidney Medulla / drug effects ; Linear Models ; Male ; Mice ; Mice, Inbred DBA ; MicroRNAs / genetics ; Microarray Analysis ; Oligonucleotide Array Sequence Analysis ; Principal Component Analysis ; Regression Analysis ; Signal Transduction ; Support Vector Machine References: Gembillo, G. et al. 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Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.