一种主动免疫训练策略以产生针对病毒逃逸的预适应性免疫

INTRODUCTION: RNA viruses rapidly evolve to evade host immunity, posing a significant challenge to conventional vaccines. Natural infection, however, provides a blueprint for broad immunity by training the immune system against a diverse antigenic landscape. This study addresses the problem of creating a vaccine that mimics this complex training process.

OBJECTIVES: We aimed to develop and validate a novel vaccination paradigm, "Proactive Immune Training" (PIT), designed to enhance cross-reactive neutralization breadth against variants within the predicted evolutionary space.

METHODS: Using SARS-CoV-2 as a model, we constructed a 600-mutant mRNA library (m-library) based on 30 predicted high-frequency mutation sites. This library was encapsulated in proprietary lipid nanoparticles (LNPs) to form the PIT vaccine. We then evaluated a sequential prime-boost regimen consisting of a single-antigen (SA) vaccine followed by a PIT boost (SA + PIT) in BALB/c mice, assessing humoral and cellular immunity, and performing deep sequencing of B-cell and T-cell receptor repertoires.

RESULTS: The SA + PIT regimen induced significantly broader and more potent neutralizing antibodies against a range of SARS-CoV-2 variants compared to conventional regimens. This enhanced protection was mechanistically linked to a profound diversification of the T-cell and B-cell receptor repertoires, a hallmark of superior immune adaptability.

CONCLUSION: The PIT strategy successfully guides the immune system to generate a broad and adaptive immunological response. This proactive, training-based paradigm offers a powerful and versatile framework for developing next-generation vaccines against highly variable pathogens.