https://orcid.org/0000-0002-0576-8698
![Sample our Bioscience journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5925/TOC-Subject-Sample-2021-Bioscience.jpg"})
Abstract
Background and Purpose
Insulin resistance (IR) is one of the factors that results in metabolic syndrome, type 2 diabetes mellitus and different aspects of cardiovascular diseases. Moringa oleifera seeds (MOS), traditionally used as an antidiabetic food and traditional medicine in tropical Asia and Africa, have exhibited potential effects in improving IR. To systematically explore the pharmacological mechanism of the anti-IR effects of MOS, we adopted a network pharmacology approach at the molecular level.
Methods
By incorporating compound screening and target prediction, a feasible compound-target-pathway network pharmacology model was established to systematically predict the potential active components and mechanisms of the anti-IR effects of MOS. Biological methods were then used to verify the results of the network pharmacology analysis.
Results
Our comprehensive systematic approach successfully identified 32 bioactive compounds in MOS and 44 potential targets of these compounds related to IR, as well as 37 potential pathways related to IR. Moreover, the network pharmacology analysis revealed that glycosidic isothiocyanates and glycosidic benzylamines were the major active components that improved IR by acting on key targets, such as SRC, PTPN1, and CASP3, which were involved in inflammatory responses and insulin-related pathways. Further biological research demonstrated that the anti-IR effects of MOS were mediated by increasing glucose uptake and modulating the expression of SRC and PTPN1.
Conclusion
Our study successfully predicts the active ingredients and potential targets of MOS for improving IR and helps to illustrate mechanism of action at a systemic level. This study not only provides new insights into the chemical basis and pharmacology of MOS but also demonstrates a feasible method for discovering potential drugs from traditional medicines.
Acknowledgment
The authors greatly appreciate the technical support about MOE from Prof. Dongsheng Cao (Central South University, Changsha, China) during the conduct of this study.
Disclosure
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper and report no conflicts of interest for this work.