Anti-Malignant Ascites Effect of Total Diterpenoids from Euphorbiae Ebracteolatae Radix Is Attributable to Alterations of Aquaporins via Inhibiting PKC Activity in the Kidney

A novel strategy with in vivo characterization, extraction, isolation and activity evaluation for discovery of absorbed anti-inflammatory oligosaccharides from Zhu-Ling decoction

Zhu-Ling decoction (ZLD), a classical traditional Chinese medicine (TCM) formula, is used for the treatment of chronic kidney diseases. However, the structure and activity of absorbed oligosaccharides (OSs) in ZLD are not clear. In this study, a novel strategy with in vivo characterization, extraction, isolation, activity evaluation was established and applied to identify absorbed anti-inflammatory OSs in ZLD. The results revealed that 30 OSs (22 reducing and 8 non-reducing OSs) and 11 OSs (7 reducing and 4 non-reducing OS) were characterized from ZLD in vitro and in vivo by using UPLC/Q-TOF-MS with PMP derivatization, respectively. Among them, a series of -1 → 3-β-D-Glcp-OSs were isolated and identified by HPLC-HILIC-UVD-ELSD, SPHPLC-HILIC-RID, monosaccharide composition, MS and 1D/2D-NMR spectroscopy, including laminaritriose, laminaritetraose, laminaripentaose, laminarihexaose, laminariheptaose, laminarioctaose and laminarinonaose. Moreover, the 4 non-reducing absorbed OSs were identified by comparison with reference standards, including sucrose, trehalose, raffinose and stachyose. Among them, laminaritriose, laminaritetraose and laminaripentaose significantly inhibited TNF-α and IL-6 levels in LPS-induced HK-2 cell and exerted significant anti-inflammatory effects via the NF-κB and Akt/mTOR signaling pathways. Together, our work provides a novel strategy for discovery of absorbed anti-inflammatory OSs and broadens new horizons for the discovery of in vivo pharmacodynamic substances in TCM formulas.

Perfluorooctanoic acid (PFOA) exposure induces renal filtration and reabsorption disorders via down-regulation of aquaporins

Perfluorooctanoic acid (PFOA) exposure is associated with kidney dysfunction, however the exact mechanisms by which PFOA induces nephrotoxicity and the specific involvement of aquaporins (AQPs) in kidney tissue remains unclear. In this study, adult male Sprague-Dawley (SD) rats were exposed to PFOA by oral gavage for 28 days and compared with controls. Body weight, water intake and urine volume were recorded daily. At the end of the experiment, blood and kidney samples were collected, and serum urea, creatine and uric acid levels were assessed. The renal expression levels of water channel proteins AQP1, AQP3, AQP2 and p-AQP2 (Ser256) were observed by immunohistochemical staining, and the corresponding transcription levels were detected by Western blot and qRT-PCR. The results showed that PFOA exposure inhibited weight gain and increased water intake, urine volume, kidney weight and renal visceral index. PASM staining and transmission electron microscopy revealed pathological thickening of the glomerular capsule and basement membrane. Serum urea levels were increased, while serum creatine levels were decreased compared to controls. Additionally, the expression levels of AQP1, AQP3, AQP2 and p-AQP2 in kidney tissues were decreased, and the phosphorylation of AQP2 at Ser256 was inhibited. In conclusion, we demonstrate that PFOA exposure can damage the renal filtration barrier and reduce the expression level of AQPs in renal tissues, leading to renal filtration and reabsorption disorders.

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity

Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently. Deep learning algorithms, in particular, have shown promise in terms of swiftly screening effective leads from huge compound libraries and producing novel compounds with desirable features. We used a deep neural network to search for potent β-microtubule inhibitors in natural goods. Eleutherobin, bruceine D (BD), and phorbol 12-myristate 13-acetate (PMA) are three highly effective natural compounds that have been found as β-microtubule inhibitors. In conclusion, this paper describes the use of deep learning to screen for effective β-microtubule inhibitors. This research also demonstrates the promising possibility of employing deep learning to develop drugs from natural products for a wider range of disorders.