Back

Develop a durable, memory-driven, CspZ-targeting Lyme disease vaccine by rationale adjuvant selection

McCarty, M.; Hernandez, S. A.; Malfetano, J.; Leao, A. C.; Villar, M. J.; Yang, X.; Chen, Y.-L.; Lee, J.; Liu, Z.; Strych, U.; Bottazzi, M. E.; Pal, U.; Strle, K.; Chen, W.-H.; Lin, Y.-P.

2026-01-09 immunology
10.64898/2026.01.08.698480 bioRxiv
Show abstract

Rational adjuvant selection is a systematic approach based on adjuvant-mediated immunomodulation to identify safe vaccine regimens that enhance protective immunity. Transmitted through ticks and caused by the bacterium Borrelia burgdorferi (Bb), Lyme disease (LD) is the most common vector-borne disease in the Northern hemisphere. There are no effective vaccines, making it suitable for testing the concept of rational adjuvant selection. Here, we formulated our previously developed and effective LD vaccine antigen, CspZ-YAC187S, with different adjuvants suitable for human use; we analyzed the immune response by transcriptomics and tested the vaccine efficacy after Bb infection. We identified Alum-CpG and Alum-Gal to elicit the highest titers of CspZ-YAC187S-dependent protective antibodies and robust levels of protection but through distinct mechanisms of immunomodulation. We demonstrated that immunization with Alum-CpG formulated CspZ-YAC187S provided up to nine months of protective bactericidal antibody titers, as well as recall-memory response to prevent LD after natural infection. Immunity was linked to elevated levels of IgG1 memory cells in the vaccine-triggered immune responses. This work thus identified a durable, memory immunity-driven LD vaccine, ultimately paving the road to understanding the mechanisms of rationale adjuvant selection for vaccine development.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.