Biology subjects

Wasdin, P. T.

Publications and source records attributed to Wasdin, P. T..

5 recordsLinked to original sources

bioRxiv2025

Glycan-reactive antibodies isolated from human HIV-1 vaccine trial participants show broad pathogen cross-reactivity

HIV-1 continues to pose a significant global health challenge, requiring ongoing research into effective prevention and treatment strategies. Understanding the B cell repertoire that can be engaged upon vaccination in humans is crucial for the development of future preventive vaccines. In this study, PBMCs from HIV-negative participants in the multivalent HVTN124 human HIV-1 vaccine clinical trial were interrogated for HIV-reactive B cells using LIBRA-seq, a high-throughput B cell mapping technology. We report the discovery of glycan-reactive antibodies capable of neutralizing diverse heterologous HIV-1 virus strains. Further, isolated antibodies showed broad cross-reactivity against antigens from a variety of other pathogens, while remaining mostly negative on autoreactivity assays. The emerging class of glycan- reactive virus-neutralizing antibodies with exceptional breadth of pathogen cross- reactivity may present an effective target for vaccination at the population level.

immunology↗

bioRxiv2024

Generation of antigen-specific paired heavy-light chain antibody sequences using large language models

The traditional process of antibody discovery is limited by inefficiency, high costs, and low success rates. Recent approaches employing artificial intelligence (AI) have been developed to optimize existing antibodies and generate antibody sequences in a target-agnostic manner. In this work, we present MAGE (Monoclonal Antibody GEnerator), a sequence-based Protein Language Model (PLM) fine-tuned for the task of generating paired human variable heavy and light chain antibody sequences against targets of interest. We show that MAGE can generate novel and diverse antibody sequences with experimentally validated binding specificity against SARS-CoV-2, an emerging avian influenza H5N1, and respiratory syncytial virus A (RSV-A). MAGE represents a first-in-class model capable of designing human antibodies against multiple targets with no starting template.

bioinformatics↗

bioRxiv2023

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq

Structured AbstractO_ST_ABSMotivationC_ST_ABSLIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in LIBRA-seq antigen count data is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies. ResultsIn this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of technical noise. We apply this method to a dataset of nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method used in LIBRA-seq, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow. Availability and ImplementationJupyter notebooks detailing model fitting and figure generation in Python are available at https://github.com/perrywasdin/mixture_model_denoising. ContactEmail: Ivelin.Georgiev@Vanderbilt.edu Supplementary InformationSupplementary figures are provided in the attached PDF.

bioinformatics↗

bioRxiv2023

Development of LIBRA-seq for the Guinea Pig Model System as a Tool for the Evaluation of Antibody Responses to Multivalent HIV-1 Vaccines

Consistent elicitation of serum antibody responses that neutralize diverse clades of HIV-1 remains a primary goal of HIV-1 vaccine research. Prior work has defined key features of soluble HIV-1 Envelope (Env) immunogen cocktails that influence the neutralization breadth and potency of multivalent vaccine-elicited antibody responses including the number of Env strains in the regimen. We designed immunization groups that consisted of different numbers of Env strains to be used in a cocktail immunization strategy: the smallest cocktail (group 2) consisted of a set of two Env strains, which were a subset of the three Env strains that made up group 3, which in turn were a subset of the six Env strains that made up group 4. Serum neutralizing titers were broadest in guinea pigs that were immunized with a cocktail of three Envs compared to cocktails of two and six, suggesting that multivalent Env immunization provides a benefit but may be detrimental when the cocktail size is too large. We then adapted the LIBRA-seq platform for antibody discovery to be compatible with guinea pigs, and isolated several tier 2 neutralizing monoclonal antibodies. Three antibodies isolated from two separate guinea pigs were similar in their gene usage and CDR3s, establishing evidence for a guinea pig public clonotype elicited through vaccination. Taken together, this work investigated multivalent HIV-1 Env immunization strategies and provides a novel methodology for screening guinea pig B cell receptor antigen specificity at a high throughput level using LIBRA-seq. IMPORTANCEMultivalent vaccination with soluble Env immunogens is at the forefront of HIV-1 vaccination strategies, but little is known about the influence of the number of Env strains included in vaccine cocktails. Our results suggest that adding more strains is sometimes beneficial but may be detrimental when the number of strains is too high. Additionally, we adapted the LIBRA-seq platform to be compatible with guinea pig samples and isolated several tier 2 neutralizing monoclonal antibodies, some of which share V and J gene usage and >80% CDR3 identity, thus establishing the existence of public clonotypes in guinea pigs elicited through vaccination.

immunology↗

bioRxiv2023

Convergent Sequence Features of Antiviral B Cells

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, building diverse antigen-specific antibody repertoires. In recent years, these repertoires have become an important source for novel antibody-based antiviral therapeutics, yet there is still limited understanding of the determinants of antibody-antigen specificity. Here, we generated a large dataset mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against twenty viral antigens representing diverse pathogens of biomedical significance. Analysis revealed antigen-specific patterns in variable gene usage, gene pairing, and somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals. These results help define the characteristics of human antibody repertoires simultaneously against an unprecedented number and diversity of viral targets. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

immunology↗