通过生物信息学和蛋白质组学分析鉴定糖尿病神经病变诱导的大鼠坐骨神经中作为醛糖还原酶抑制剂的已知榕树黄酮类化合物

Dureshahwar Khan, Hemant D Une, Mubashir Mohammed, Jaiprakash N Sangshetti, Sanjay N Harke, Manoj Damale, Jitendra B Naik
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本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Known Flavonoids of Ficus carica L. as Aldose Reductase Inhibitors in Sciatic Nerve of Diabetic Neuropathy-induced Rats through Bioinformatics and Proteomics Analysis.

Introduction: The polyol pathway is responsible for the metabolism of almost one-third of the total glucose in people with chronic diabetes. Moreover, it causes complications in organs that rely on aldose reductase (AR) as an enzyme. The purpose of this research was to examine the in vitro and in vivo effects of a flavonoid-rich ethyl acetate fraction of a methanolic extract of Ficus carica Lam. leaves (FCEA) on the aldose reductase gene AKR1B1. The complicated relation of AR for target confirmation and analysis of the flavonoids of FCEA, quercetin, kaempferol, and chrysin was explored by building a flavonoid-protein complex network utilizing GeneCards®, String, and Cytoscape Networking.

Method: The examination of ADMET was carried out after docking on the active sites of AR. By the binding and scoring abilities, the analysis was carried out. The ADMET characteristics demonstrated that these flavonoids had excellent solubility, absorption, and oral bioavailability, and the results demonstrate that they have potential. An additional in-vivo investigation was conducted on rats using a model induced by streptozotocin (STZ). Hence, upon induction, the rats' sciatic nerves were removed and prepared for an RT-PCR analysis of the AKR1B1 gene.

Result: Compared to the diabetic normal group and the metformin group, rats treated with FCEA had lower levels of messenger RNA and AKR1B1 gene expression.

Conclusion: This proves that FCEA has effectively blocked AR. It is highly likely to suggest FCEA as a potent aldose reductase inhibitor, as it considerably reduces the mRNA level of AKR1B1 gene expression in the sciatic nerve of sick rats, according to a combined bioinformatics prediction and RT-PCR analysis.

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