Tumor Temporal Proteome Profiling Reveals the Immunological Triple Offensive Induced by Synthetic Anti-Cancer Salmonella.

The engineered "obligate" anaerobic Salmonella typhimurium strain YB1 shows a prominent ability to repress tumor growth and metastasis, which has great potential as a novel cancer immunotherapy. However, the antitumor mechanism of YB1 remains unelucidated. To resolve the proteome dynamics induced by the engineered bacteria, we applied tumor temporal proteome profiling on murine bladder tumors after intravenous injection of either YB1 or PBS as a negative control. Our data suggests that during the two weeks treatment of YB1 injections, the cured tumors experienced three distinct phases of the immune response. Two days after injection, the innate immune response was activated, particularly the complement and blood coagulation pathways. In the meantime, the phagocytosis was initiated. The professional phagocytes such as macrophages and neutrophils were recruited, especially the infiltration of iNOS+ and CD68+ cells was enhanced. Seven days after injection, substantial amount of T cells was observed at the invasion margin of the tumor. As a result, the tumor shrunk significantly. Overall, the temporal proteome profiling can systematically reveal the YB1 induced immune responses in tumor, showing great promise for elucidating the mechanism of bacteria-mediated cancer immunotherapy.

Antiviral drugs arbidol and interferon alpha-1b contribute to reducing the severity of COVID-19 patients: a retrospective cohort study.

OBJECTIVES: The aim of this study was to evaluate the role of antiviral drugs in reducing the risk of developing severe illness in patients with moderate COVID-19 pneumonia. METHODS: This retrospective cohort study included 403 adult patients with moderate COVID-19 pneumonia who were admitted to Shenzhen Third People's Hospital, China. The antiviral drugs arbidol, interferon alpha-1b, lopinavir-ritonavir and ribavirin were distributed to the patients for treatment. The primary endpoint of this study was the time to develop severe illness. RESULTS: Of the 462 patients admitted, 403 had moderate COVID-19 symptoms at hospital admission and were included in this study. 90 of the 403 (22.3%) patients progressed to severe illness. The use of arbidol was associated with a lower severity rate 3.5% compared to control group 30.5%, p-value < 0.0001; the adjusted hazard ratio was 0.28 (95% CI: 0.084-0.90, p = 0.033). The use of interferon alpha-1b was associated with a lower severity rate 15.5% compared to control group 29.3%, with p-value < 0.0001; the adjusted hazard ratio was 0.30 (95% CI: 0.15-0.58, p =  0.0005). The use of lopinavir-itonavir and ribavirin did not show significant differences in adjusted regression models. Early use of arbidol within 7 days of symptom onset was significantly associated with a reduced recovery time of - 5.2 days (IQR - 3.0 to - 7.5, p = 4e-06) compared with the control group. CONCLUSION: Treatment with arbidol and interferon alpha-1b contributes to reducing the severity of illness in patients with moderate COVID-19 pneumonia. Early use of arbidol may reduce patients' recovery time.

Multi-PLI: interpretable multi-task deep learning model for unifying protein-ligand interaction datasets.

The assessment of protein-ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods based on deep learning, including structure- and sequence-based models, have achieved impressive performance on several different datasets. However, their application still suffers from a generalizability issue because of insufficient data, especially for structure based models, as well as a heterogeneity problem because of different label measurements and varying proteins across datasets. Here, we present an interpretable multi-task model to evaluate protein-ligand interaction (Multi-PLI). The model can run classification (binding or not) and regression (binding affinity) tasks concurrently by unifying different datasets. The model outperforms traditional docking and machine learning on both binary classification and regression tasks and achieves competitive results compared with some structure-based deep learning methods, even with the same training set size. Furthermore, combined with the proposed occlusion algorithm, the model can predict the important amino acids of proteins that are crucial for binding, thus providing a biological interpretation.

dbHDPLS: A database of human disease-related protein-ligand structures.

Protein-ligand complexes perform specific functions, most of which are related to human diseases. The database, called as human disease-related protein-ligand structures (dbHDPLS), collected 8833 structures which were extracted from protein data bank (PDB) and other related databases. The database is annotated with comprehensive information involving ligands and drugs, related human diseases and protein-ligand interaction information, with the information of protein structures. The database may be a reliable resource for structure-based drug target discoveries and druggability predictions of protein-ligand binding sites, drug-disease relationships based on protein-ligand complex structures. It can be publicly accessed at the website: http://DeepLearner.ahu.edu.cn/web/dbDPLS/.