Understanding the SARS-CoV-2 Virus Neutralizing Antibody Response: Lessons to Be Learned from HIV and Respiratory Syncytial Virus.

The SARS-CoV-2 pandemic commenced in 2019 and is still ongoing. Neither infection nor vaccination give long-lasting immunity and, here, in an attempt to understand why this might be, we have compared the neutralizing antibody responses to SARS-CoV-2 with those specific for human immunodeficiency virus type 1 (HIV-1) and respiratory syncytial virus (RSV). Currently, most of the antibodies specific for the SARS-CoV-2 S protein map to three broad antigenic sites, all at the distal end of the S trimer (receptor-binding site (RBD), sub-RBD and N-terminal domain), whereas the structurally similar HIV-1 and the RSV F envelope proteins have six antigenic sites. Thus, there may be several antigenic sites on the S trimer that have not yet been identified. The epitope mapping, quantitation and longevity of the SARS-CoV-2 S-protein-specific antibodies produced in response to infection and those elicited by vaccination are now being reported for specific groups of individuals, but much remains to be determined about these aspects of the host-virus interaction. Finally, there is a concern that the SARS-CoV-2 field may be reprising the HIV-1 experience, which, for many years, used a virus for neutralization studies that did not reflect the neutralizability of wild-type HIV-1. For example, the widely used VSV-SARS-CoV-2-S protein pseudotype has 10-fold more S trimers per virion and a different configuration of the trimers compared with the SARS-CoV-2 wild-type virus. Clarity in these areas would help in advancing understanding and aid countermeasures of the SARS-CoV-2 pandemic.

Flexible Distance-Based TCR Analysis in Python with tcrdist3.

Paired- and single-chain T cell receptor (TCR) sequencing are now commonly used techniques for interrogating adaptive immune responses. TCRs targeting the same epitope frequently share motifs consisting of critical contact residues. Here we illustrate the key features of tcrdist3, a new Python package for distance-based TCR analysis through a series of three interactive examples. In the first example, we illustrate how tcrdist3 can integrate sequence similarity networks, gene-usage plots, and background-adjusted CDR3 logos to identify TCR sequence features conferring antigen specificity among sets of peptide-MHC-multimer sorted receptors. In the second example, we show how the TCRjoin feature in tcrdist3 can be used to flexibly query receptor sequences of interest against bulk repertoires or libraries of previously annotated TCRs based on matching of similar sequences. In the third example, we show how the TCRdist metric can be leveraged to identify candidate polyclonal receptors under antigenic selection in bulk repertoires based on sequence neighbor enrichment testing, a statistical approach similar to TCRNET and ALICE algorithms, but with added flexibility in how the neighborhood can be defined.