Position-Scanning Peptide Libraries as Particle Immunogens for Improving CD8+ T-Cell Responses.
Xuedan HeShiqi ZhouBreandan QuinnWei-Chiao HuangDushyant JahagirdarMichael VegaJoaquin OrtegaMark D LongFumito ItoScott I AbramsJonathan F LovellPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2021)
Short peptides reflecting major histocompatibility complex (MHC) class I (MHC-I) epitopes frequently lack sufficient immunogenicity to induce robust antigen (Ag)-specific CD8+ T cell responses. In the current work, it is demonstrated that position-scanning peptide libraries themselves can serve as improved immunogens, inducing Ag-specific CD8+ T cells with greater frequency and function than the wild-type epitope. The approach involves displaying the entire position-scanning library onto immunogenic nanoliposomes. Each library contains the MHC-I epitope with a single randomized position. When a recently identified MHC-I epitope in the glycoprotein gp70 envelope protein of murine leukemia virus (MuLV) is assessed, only one of the eight positional libraries tested, randomized at amino acid position 5 (Pos5), shows enhanced induction of Ag-specific CD8+ T cells. A second MHC-I epitope from gp70 is assessed in the same manner and shows, in contrast, multiple positional libraries (Pos1, Pos3, Pos5, and Pos8) as well as the library mixture give rise to enhanced CD8+ T cell responses. The library mixture Pos1-3-5-8 induces a more diverse epitope-specific T-cell repertoire with superior antitumor efficacy compared to an established single mutation mimotope (AH1-A5). These data show that positional peptide libraries can serve as immunogens for improving CD8+ T-cell responses against endogenously expressed MHC-I epitopes.
Keyphrases
- amino acid
- monoclonal antibody
- high resolution
- double blind
- open label
- wild type
- placebo controlled
- acute myeloid leukemia
- magnetic resonance
- magnetic resonance imaging
- electron microscopy
- phase iii
- randomized controlled trial
- bone marrow
- highly efficient
- machine learning
- phase ii
- mass spectrometry
- electronic health record
- artificial intelligence
- data analysis