Biology subjects

Birnbaum, M. E.

Publications and source records attributed to Birnbaum, M. E..

29 records · Page 2Linked to original sources

bioRxiv2022

An unbiased characterization of the HLA-E and CD94/NKG2x peptide repertoire reveals peptide ligands that skew NK cell activation

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E and NK receptor peptide repertoire has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design.

immunology↗

bioRxiv2022

Identification of highly cross-reactive mimotopes for a public T cell response in murine melanoma

While immune checkpoint blockade results in durable responses for some patients, many others have not experienced such benefits. These treatments rely upon reinvigorating specific T cell-antigen interactions. However, it is often unknown what antigens are being recognized by T cells or how to potently induce antigen-specific responses in a broadly applicable manner. Here, we characterized the CD8+ T cell response to a murine model of melanoma following combination immunotherapy to determine the basis of tumor recognition. Sequencing of tumor-infiltrating T cells revealed a repertoire of highly homologous TCR sequences that were particularly expanded in treated mice and which recognized an antigen from an endogenous retrovirus. While vaccination against this peptide failed to raise a protective T cell response in vivo, engineered antigen mimotopes induced a significant expansion of CD8+ T cells cross-reactive to the original antigen. Vaccination with mimotopes resulted in killing of antigen-loaded cells in vivo yet showed modest survival benefit in a prophylactic vaccine paradigm. Together, this work demonstrates the identification of a dominant tumor-associated antigen and generation of mimotopes which can induce robust functional T cell responses that are cross-reactive to the endogenous antigen across multiple individuals.

immunology↗

bioRxiv2022

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. This approach identifies binders missed by computational prediction, highlighting the potential for systemic computational errors given even state-of-the-art training data, and underlines design considerations for epitope identification experiments. This platform serves as a framework for examining relationships between viral conservation and MHC binding, and can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of this approach for determining high-confidence peptide-MHC binding.

immunology↗

bioRxiv2022

Leukemia-intrinsic determinants of CAR-T response revealed by in vivo genome-wide CRISPR screening.

CAR-T therapy is a promising new treatment modality for B-cell malignancies. However, the majority of patients inevitably go on to experience disease relapse through largely unknown means. To investigate leukemia-intrinsic CAR-T resistance mechanisms, we performed genome-wide CRISPR-Cas9 loss-of-function screens in an immunocompetent murine model of B-cell acute lymphoblastic leukemia (B-ALL) utilizing a novel, modular guide RNA library. We identified IFN{gamma}/JAK/STAT signaling and components of antigen processing and presentation pathway as key mediators of resistance to CAR-T therapy in vivo, but not in vitro. Transcriptional characterization of this model demonstrated an upregulation of these pathways in CAR-T treated relapsed tumors, and examination of data from CAR-T treated patients with B-ALL revealed an association between poor outcomes and increased expression of JAK/STAT/MHC-I in leukemia cells. Overall, our data identify an unexpected mechanism of resistance to CAR-T therapy in which tumor cell interaction with CAR-T cells in vivo induces expression of an adaptive T-cell resistance program in tumor cells.

cancer biology↗

bioRxiv2021

Antigen identification and high-throughput interaction mapping by reprogramming viral entry

Deciphering immune recognition is critical for understanding a broad range of diseases and for the development of effective vaccines and immunotherapies. Efforts to do so are limited by a lack of technologies capable of simultaneously capturing the complexity of adaptive immune receptor repertoires and the landscape of potential antigens. To address this, we present RAPTR (Receptor-Antigen Pairing by Targeted Retroviruses). RAPTR combines viral pseudotyping and molecular engineering approaches to enable one-pot library on library interaction screens by displaying antigens on the surface of lentiviruses and encoding their identity in the viral genome. Antigen-specific viral infection of cells allows readout of both antigen and receptor identities via single-cell sequencing. The resulting system is modular, scalable, and compatible with any cell type, making it easily implemented. These techniques provide a suite of new tools for targeted viral entry, molecular engineering, and interaction screens with broad potential applications.

immunology↗

bioRxiv2021

CARPOOL: A library-based platform to rapidly identify next generation chimeric antigen receptors

CD19-targeted CAR therapies have successfully treated B cell leukemias and lymphomas, but many responders later relapse or experience toxicities. CAR intracellular domains (ICDs) are key to converting antigen recognition into anti-tumor effector functions. Despite the many possible immune signaling domain combinations that could be included in CARs, almost all CARs currently rely upon CD3{zeta}, CD28, and/or 4-1BB signaling. To explore the signaling potential of CAR ICDs, we generated a library of 700,000 CD19 CAR molecules with diverse signaling domains and developed a high throughput screening platform to enable optimization of CAR signaling for anti-tumor functions. Our strategy identifies CARs with novel signaling domain combinations that elicit distinct T cell behaviors from a clinically available CAR, including enhanced proliferation and persistence, lower exhaustion, potent cytotoxicity in an in vitro tumor rechallenge condition, and comparable tumor control in vivo. This approach is readily adaptable to numerous disease models, cell types, and selection conditions, making it a promising tool for rapidly improving adoptive cell therapies and expanding their utility to new disease indications.

immunology↗

bioRxiv2020

Extracting and Interpreting the Effects of Higher Order Sequence Features on Peptide MHC Binding

Understanding the factors contributing to peptide MHC (pMHC) affinity is critical for the study of immune responses and the development of novel therapeutics. Developments in yeast display platforms have enabled the collection of pMHC binding data for vast libraries of peptides. However, methods for interpreting this data are still at an early stage. In this work we propose an approach for extracting peptide sequence features that affect pMHC binding from such datasets. In the process we develop the theoretical framework for fitting and interpreting these features. We demonstrate that these features accurately capture the kinetics underlying pMHC binding, and can be used to predict pMHC binding well enough to rival the current state of the art. We then analyze the extracted factors and show that they correlate with our current structural understanding of MHC molecules. Finally, we discuss the implication these factors have on the complexity of peptide engineering.

immunology↗

bioRxiv2020

Vaccination reshapes the virus-specific T cell repertoire in unexposed adults

How baseline T cell characteristics impact human T cell responses to novel pathogens remains unknown. Here, we address this question by studying the CD4+ T cell response in unexposed individuals to live attenuated yellow fever virus (YFV) vaccine. We quantified virus-specific population dynamics over time using class II peptide-MHC tetramers. Our data revealed that, even in the absence of known viral exposure, memory phenotype T cells were found in the majority of virus-specific precursors in healthy adults. Pre-existing memory T cells can be divided into two groups; abundant pre-vaccine populations that underwent limited overall expansion and rare cells that generated naive-like responses and preferentially contributed to the memory repertoire after vaccination. Single cell T cell receptor (TCR) sequencing was used to track the evolution of immune responses to different epitopes and showed an association between preservation of unexpanded TCRs before exposure and the robustness of post-vaccine responses. Instead of a further increase in pre-established TCR clones, vaccination boosted the representation of rare TCRs. Thus, vaccine restructures the abundance and clonal hierarchy of virus-specific T cells. Our results link T cell precursor states to post-exposure responses, identifying peripheral education of virus-specific repertoire as a key component of effective vaccination. {circ}YFV-specific precursors contain abundant memory phenotype cells in healthy adults. {circ}Precursor frequency and phenotype are linked to post-immune response. {circ}Vaccination recruits rare virus-specific populations. {circ}Vaccination overrides pre-established clonal hierarchy

immunology↗

bioRxiv2020

Machine learning optimization of peptides for presentation by class II MHCs

T cells play a critical role in normal immune responses to pathogens and cancer and can be targeted to MHC-presented antigens via interventions such as peptide vaccines. Here, we present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying the peptides anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization successfully improved peptide binding.

immunology↗

bioRxiv2019

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Here we present a machine learning method that can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. We also demonstrate that machine learning can improve target-specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data.\n\nSignificanceAntibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing. We demonstrate that with sufficient target-specific training data machine learning can suggest novel antibody variable domain sequences that are superior to those observed during training. Our machine learning method does not require any target structural information. We further show that data from disparate antibody campaigns can be combined by machine learning to improve antibody specificity.

immunology↗

bioRxiv2018

Longitudinal immunosequencing in healthy people reveals persistent T cell receptors rich in public receptors

BackgroundThe adaptive immune system maintains a diversity of T cells capable of recognizing a broad array of antigens. Each T cells specificity and affinity for antigens is determined by its T cell receptors (TCRs), which together across all T cells form a repertoire of tens of millions of unique receptors in each individual. Although many studies have examined how TCR repertoires change in response to disease or drugs, few have explored the temporal dynamics of the TCR repertoire in healthy individuals.\n\nResultsHere we report immunosequencing of TCR {beta} chains (TCR{beta}) from the blood of three healthy individuals at eight time points over one year. TCR{beta} repertoires from samples of all T cells and memory T cells clearly clustered by individual, confirming that TCR{beta} repertoires are specific to individuals across time. This individuality was absent from TCR{beta}s from naive T cells, suggesting that these differences result from an individuals antigen exposure history. Many characteristics of the TCR{beta} repertoire (e.g., alpha diversity, clonality) were stable across time, although we found evidence of T cell expansion dynamics even within healthy individuals. We further identified a subset of \"persistent\" TCR{beta}s present across all time points, and these receptors were rich in clonal and public receptors.\n\nConclusionsOur results revealed persistent receptors that may play a key role in immune system maintenance. They further highlight the importance of longitudinal sampling of the immune system and provide a much-needed baseline for TCR{beta} dynamics in healthy individuals. Such a baseline should help improve interpretation of changes in the TCR{beta} repertoire during disease or treatment.

immunology↗