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Wu, x.

Publications and source records attributed to Wu, x..

2 recordsLinked to original sources
bioRxiv2023

Dehydrozaluzanin C- derivative protects septic mice by alleviating over-activated inflammatory response and promotes the phagocytosis of macrophages

Host-directed therapy (HDT) is a new adjuvant strategy that interfere with host cell factors that are required by a pathogen for replication or persistence. In this study, we assessed the effect of dehydrozaluzanin C-derivative (DHZD), a modified compound from dehydrozaluzanin C (DHZC), as a potential HDT agent for severe infection. LPS-induced septic mouse model and Carbapenem resistant Klebsiella pneumoniae (CRKP) infection mouse model was used for testing in vivo. RAW264.7 cells, mouse primary macrophages, and DCs were used for in vitro experiments. Dexamethasone (DXM) was used as a positive control agent. DHZD ameliorated tissue damage (lung, kidney, and liver) and excessive inflammatory response induced by LPS or CRKP infection in mice. Also, DHZD improved the hypothermic symptoms of acute peritonitis induced by CRKP, inhibited heat-killed CRKP (HK-CRKP)-induced inflammatory response in macrophages, and upregulated the proportions of phagocytic cell types in lungs. In vitro data suggested that DHZD decreases LPS-stimulated expression of IL-6, TNF- and MCP-1 via PI3K/Akt/p70S6K signaling pathway in macrophages. Interestingly, the combined treatment group of DXM and DHZD had a higher survival rate and lower level of IL-6 than those of the DXM-treated group; the combination of DHZD and DXM played a synergistic role in decreasing IL-6 secretion in sera. Moreover, the phagocytic receptor CD36 was increased by DHZD in macrophages, which was accompanied by increased bacterial phagocytosis in a clathrin- and actin-dependent manner. This data suggests that DHZD may be a potential drug candidate for treating bacterial infections.

pathology↗
bioRxiv2023

Multi_CycGT: a DL-based multimodal model for membrane permeability prediction of cyclic peptides

As a highly versatile therapeutic modality, cyclic peptides have gained significant attention due to their exceptional binding affinity, minimal toxicity and capacity to target the surface of conventionally "undruggable" proteins. However, the development of cyclic peptides with therapeutic effects by targeting intracellular biological targets has been hindered by the issue of limited membrane permeability. In this paper, we have conducted an extensive benchmarking analysis of a proprietary dataset consisting of 6941 cyclic peptides, employing machine learning and deep learning models. In addition, we propose an innovative multimodal model called Multi_CycGT which combines a Graph Convolutional Network (GCN) and a Transformer to extract 1D and 2D features. These encoded features are then fused for the prediction of cyclic peptide permeability. The cross-validation experiments demonstrate that the proposed Multi_CycGT model achieved the highest level of accuracy on the test set, with an accuracy value of 0.8206 and an AUC value of 0.8650. This paper introduces a pioneering deep learning-based approach that demonstrates enhanced effectiveness in predicting the membrane permeability of cyclic peptides. It also represents the first attempt in this field. We hope that this work will help to accelerate the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

bioinformatics↗