Hypoxia is one of the most important factors that limit the effect of radiotherapy, and the abundant H2O2 in tumor tissues will also aggravate hypoxia-induced radiotherapy resistance. Delivering catalase to decompose H2O2 into oxygen is an effective strategy to relieve tumor hypoxia and radiotherapy resistance. However, low stability limits catalase’s in vivo application, which is one of the most common limitations for almost all proteins’ internal utilization. Here, we develop catalase containing E. coli membrane vesicles (EMs) with excellent protease resistance to relieve tumor hypoxia for a long time. Even treated with 100-fold of protease, EMs showed higher catalase activity than free catalase. After being injected into tumors post 12 h, EMs maintained their hypoxia relief ability while free catalase lost its activity. Our results indicate that EMs might be an excellent catalase delivery for tumor hypoxia relief. Combined with their immune stimulation features, EMs could enhance radiotherapy and induce antitumor immune memory effectively.

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and physical interactions between proteins and ligands, which are considered essential to understanding the binding mechanism. This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein–ligand binding affinities. Specifically, we design a heterogeneous interaction layer that unifies covalent and noncovalent interactions into the message passing phase to learn node representations more effectively. The heterogeneous interaction layer also follows fundamental biological laws, including invariance to translations and rotations of the complexes, thus avoiding expensive data augmentation strategies. GIGN achieves state-of-the-art performance on three external test sets. Moreover, by visualizing learned representations of protein–ligand complexes, we show that the predictions of GIGN are biologically meaningful.

Understanding the structure of the protein-ligand complex is crucial to drug development. However, existing virtual structure measurement methods are mainly docking and its derived methods combined with deep learning, which have restricted performance and efficiency due to their sampling and scoring methodology. Here we show the complex structure can be directly predicted using our proposed LigPose based on geometric deep learning in an end-to-end manner. By representing the ligand and the protein as a complete graph, LigPose optimizes the 3-D structure of the complexes with their atom coordinates in the Euclidean space. LigPose achieved state-of-the-art performance on two major tasks in drug development, i.e., complex structure prediction and affinity estimation, indicating a promising paradigm of predicting the protein-ligand complex structures in drug development, with improved capacity far beyond popular docking tools.

Depression is a common mental disorder in modern society. A traditional Chinese medicine Huanglian-Wendan decoction with potential anti-inflammation is used as a clinical antidepressant. Our previous study showed central and peripheral inflammatory responses in a rat model of depression developed by chronic unpredictable mild stress (CUMS). Here, we investigated the anti-inflammatory activity and mechanism of Huanglian-Wendan decoction in CUMS rats. LC-MS/MS and HPLC were performed to determine the major compounds in water extract of this decoction. This study showed that Huanglian-Wendan decoction significantly increased sucrose consumption and reduced serum levels of interleukin-1 beta (IL-1β), IL-6, and alanine aminotransferase (ALT) in CUMS rats. Moreover, this decoction inhibited nuclear entry of nuclear factor-kappa B (NF-κB) with the reduction of phosphorylated protein of NF-κB (p-NF-κB) and inhibitor of NF-κB alpha (p-IκBα) and downregulated protein of nod-like receptor family pyrin domain-containing 3 (NLRP3), apoptosis-associated speck-like protein containing CARD (ASC), cysteinyl aspartate-specific proteinase-1 (Caspase-1), and IL-1β in liver and brain regions of CUMS rats. These findings demonstrated that Huanglian-Wendan decoction had antidepressant activity with hepatoprotection in CUMS rats coinciding with its anti-inflammation in both periphery and central. The inhibitory modulation of NF-κB and NLRP3 inflammasome activation by Huanglian-Wendan decoction may mediate its antidepressant action.

Background: To investigate the clinical signi cance of CX3 chemokine ligand 1(CX3CL1) and CX3CR1 in patients with bone metastasis from lung cancer. The expression levels of CX3CL1 and CX3CR1 mRNA and protein in primary lung cancer and lung cancer bone metastasis were detected by qRT-PCR and Western blot. Methods: 100 patients with lung cancer were divided into a boneless metastasis group (50 patients with bone metastasis) and a bone metastasis group (50 patients without distant metastasis). The bone transfer component was graded by Soloway classi cation (0 to III). The expression levels of serum CX3CL1-CX3CR1 axis was detected by enzyme-linked immunosorbent assay (ELISA). RT-qPCR and Western Blot were used to verify the transfection e ciency. The scratching assay was used to detect the migration of CX3CL1 to 95-D cells after down-regulating the expression of CX3CR1. Results: The expression levels of CX3CL1 and CX3CR1 mRNA and protein in the primary lung cancer and lung cancer bone metastasis were signi cantly higher than those in the adjacent tissues (P<0.0001). The levels of serum CX3CL1 and CX3CR1 in bone metastasis group were signi cantly higher than those in boneless metastasis group and healthy control group (P<0.05). In the bone metastasis group, the levels of serum CX3CL1 and CX3CR1 was signi cantly positively correlated with the degree of disease progression (P<0.01). Conclusions: The expression level of serum CX3CL1-CX3CR1 axis is expected to be an auxiliary reference index for monitoring bone metastasis of lung cancer.