Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.
Algorithms
Animals
Chemistry, Pharmaceutical
/ methods
Cluster Analysis
Computational Biology
/ methods
Diabetic Nephropathies
/ drug therapy
Drug Design
Epigenesis, Genetic
Gene Expression Profiling
/ methods
Gene Regulatory Networks
Global Health
Humans
Kidney Cortex
/ drug effects
Kidney Medulla
/ drug effects
Linear Models
Machine Learning
Male
Mice
Mice, Inbred DBA
MicroRNAs
/ genetics
Microarray Analysis
Oligonucleotide Array Sequence Analysis
Principal Component Analysis
Regression Analysis
Signal Transduction
Support Vector Machine
Systems Biology
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 12 2021
06 12 2021
Historique:
received:
03
03
2021
accepted:
12
11
2021
entrez:
7
12
2021
pubmed:
8
12
2021
medline:
28
1
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34873190
doi: 10.1038/s41598-021-02282-3
pii: 10.1038/s41598-021-02282-3
pmc: PMC8648918
doi:
Substances chimiques
MicroRNAs
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
23452Informations de copyright
© 2021. The Author(s).
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