Asymmetric Assembly and Self-Adjuvanted Antigen Delivery Platform for Improved Antigen Uptake and Antitumor Effect.

The development of effective tumor vaccines is an important direction in the field of cancer prevention/immunotherapy. Efficient antigen delivery is essential for inducing effective antitumor responses for tumor vaccines. Lumazine synthase (BLS) from Brucella spp. is a decameric protein with delivery and adjuvant properties, but its application in tumor vaccines is limited. Here, we developed an antigen delivery platform by combining a BLS asymmetric assembly and the Plug-and-Display system of SpyCatcher/SpyTag. An asymmetric assembly system consisting of BLSke and BLSdr was developed to equally assemble two molecules. Then, the MHC-I-restricted ovalbumin peptide (OVA(257-264) SIINFEKL) was conjugated with BLSke, and a cell-penetrating peptide (CPP) KALA was conjugated with BLSdr using the SpyCatcher/SpyTag system. KALA modification enhanced internalization of OVA peptides by DCs as well as promoted the maturation of DCs and the cross-presentation of SIINFEKL. Moreover, the immunotherapy of a KALA-modified vaccine suppressed tumor growth and enhanced CD8+ T cell responses in E.G7-OVA tumor-bearing mice. In the prophylactic model, KALA-modified vaccination showed the most significant protective effect and significantly prolonged the survival period of tumor challenged mice. In conclusion, the asymmetric assembly platform equally assembles two proteins or peptides, avoiding their spatial or functional interference. This asymmetric assembly and Plug-and-Display technology provide a universal platform for rapid development of personalized tumor vaccines.

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice.

Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.