Biology subjects

Milighetti, M.

Publications and source records attributed to Milighetti, M..

2 recordsLinked to original sources

bioRxiv2022

Large clones of pre-existing T cells drive early immunity against SARS-COV-2 and LCMV infection.

We analyzed the dynamics of the earliest T cell response to SARS-COV-2. A wave of TCRs strongly but transiently expand during infection, frequently peaking the same week as the first positive PCR test. These expanding TCR CDR3s were enriched for sequences functionally annotated as SARS-COV-2 specific. Most epitopes recognized by the expanding TCRs were highly conserved between SARS-COV-2 strains, but not with circulating human coronaviruses. Many expanding CDR3s were also present at high precursor frequency in pre-pandemic TCR repertoires. A similar set of early response TCRs specific for lymphocytic choriomeningitis virus epitopes were also found at high frequency in the pre-infection naive repertoire. High frequency naive precursors may allow the T cell response to respond rapidly during the crucial early phases of acute viral infection. One-Sentence SummaryHigh frequency naive precursors underly the rapid T cell response during the crucial early phases of acute viral infection.

immunology↗

bioRxiv2021

Predicting T cell receptor antigen specificity from structural features derived from homology models of receptor-peptide-major histocompatibility complexes.

1The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

immunology↗