Yotsuyanagi, H. | Search bioRxiv

bioRxiv2023

Characterization of an EG.5.1 clinical isolate in vitro and in vivo

EG.5.1 is a subvariant of the SARS-CoV-2 Omicron XBB variant that is rapidly increasing in prevalence worldwide. EG.5.1 has additional substitutions in its spike protein (namely, Q52H and F456L) compared with XBB.1.5. However, the pathogenicity, transmissibility, and immune evasion properties of clinical isolates of EG.5.1 are largely unknown. In this study, we used wild-type Syrian hamsters to investigate the replicative ability, pathogenicity, and transmissibility of a clinical EG.5.1 isolate. Our data show that there are no obvious differences in growth ability and pathogenicity between EG.5.1 and XBB.1.5, and both EG.5.1 and XBB.1.5 are attenuated compared to a Delta variant isolate. We also found that EG.5.1 is transmitted more efficiently between hamsters compared with XBB.1.5. In addition, unlike XBB.1.5, we detected EG.5.1 virus in the lungs of four of six exposed hamsters, suggesting that the virus tropism of EG.5.1 is different from that of XBB.1.5 after airborne transmission. Finally, we assessed the neutralizing ability of plasma from convalescent individuals and found that the neutralizing activity against EG.5.1 was slightly, but significantly, lower than that against XBB.1.5 or XBB.1.9.2. This suggests that EG.5.1 effectively evades humoral immunity and that the amino acid differences in the S protein of EG.5.1 compared with that of XBB.1.5 or XBB.1.9.2 (i.e., Q52H, R158G, and F456L) alter the antigenicity of EG.5.1. Our data suggest that the increased transmissibility and altered antigenicity of EG.5.1 may be driving its increasing prevalence over XBB.1.5 in the human population.

microbiology↗

bioRxiv2017

TCR-peptide contact profile determines immunogenicity in pathogen/tumor-derived MHC-I epitopes

The existence of population-wide T cell immunity is widely recognized for multiple pathogen-derived immunodominant epitopes, despite the vast diversity and individualized nature of T cell receptor (TCR) repertoire. We thus hypothesized that population-wide epitope immunogenicity could be probabilistically defined by exploiting public TCR features. To gain a proof-of-concept, here we describe a machine learning framework yielding probabilistic estimates of immunogenicity, termed \"immunogenicity scores\", by utilizing features designed to mimic thermodynamic interactions between peptides bound to major histocompatibility complex (MHC) and TCR repertoire. Immunogenicity score dynamics among observed and computationally simulated single amino acid mutants delineated the landscape of position- and residue-specific mutational impacts, and even quantitatively estimated escaping potentials of known epitopes with remarkable positional specificity. This study illustrates that the population-wide aspect of adaptive immunity is predictable via non-individualized approach, possibly indicating antigen-guided convergence of human T cell reactivity.

immunology↗