Biology subjects

Laserson, U.

Publications and source records attributed to Laserson, U..

5 recordsLinked to original sources

bioRxiv2024

ML-driven design of 3' UTRs for mRNA stability

Using mRNA as a therapeutic has received enormous attention in the last few years, but instability of the molecule remains a hurdle to achieving long-lasting therapeutic levels of protein expression. In this study, we describe our approach for designing stable mRNA molecules by combining machine learning-driven sequence design with high-throughput experimental assays. We developed a high-throughput massively parallel reporter assay (MPRA) that, in a single experiment, measures the half-life of tens of thousands of unique mRNA sequences containing designed 3 UTRs. Over multiple design-build-test iterations, we have accumulated mRNA stability measurements for 180,000 unique genomic and synthetic 3 UTRs, representing the largest such dataset of sequences. We trained highly-accurate machine learning models to map from 3 UTR sequence to mRNA stability, and used them to guide the design of synthetic 3 UTRs that increase mRNA stability in cell lines. Finally, we validated the function of several ML-designed 3 UTRs in mouse models, resulting in up to 2-fold more protein production over time and 30-100-fold higher protein output at later time points compared to a commonly used benchmark. These results highlight the potential of ML-driven sequence design for mRNA therapeutics.

synthetic biology↗

bioRxiv2021

Prevalence, persistence, and genetics of antibody responses to protein toxins and virulence factors

Microbial exposures are crucial environmental factors that impact healthspan by sculpting the immune system and microbiota. Antibody profiling via programmable Phage ImmunoPrecipitation Sequencing (PhIP-Seq) provides a high-throughput, costeffective approach for multiplexed detection of exposure and response to thousands of microbial protein products. Here we designed and constructed a library of 95,601 56 amino acid peptide tiles spanning a subset of environmental proteins more likely to be associated with immune responses: those with "toxin" or "virulence factor" keyword annotations. PhIP-Seq was used to profile the circulating antibodies of ~1,000 individuals against this "ToxScan" library of 14,430 toxins and virulence factors from 1,312 genera of organisms. In addition to a detailed analysis of six commonly encountered human commensals and pathogens, we study the age-dependent stability of the ToxScan profile and use a genome-wide association study (GWAS) to find that the MHC-II locus modulates the selection of bacterial epitopes. We detect previously described anti-flagellin antibody responses in a Crohns disease cohort and identify a novel association between anti-flagellin antibodies and juvenile dermatomyositis (JDM). PhIP-Seq with the ToxScan library provides a new window into exposure and immune responses to environmental protein toxins and virulence factors, which can be used to study human health and disease at cohort scale.

immunology↗

bioRxiv2020

Epitope profiling reveals binding signatures of SARS-CoV-2 immune response and cross-reactivity with endemic HCoVs

A major goal of current SARS-CoV-2 vaccine efforts is to elicit antibody responses that confer protection. Mapping the epitope targets of the SARS-CoV-2 antibody response is critical for innovative vaccine design, diagnostics, and development of therapeutics. Here, we developed a phage display library to map antibody binding sites at high resolution within the complete viral proteomes of all human-infecting coronaviruses in patients with mild or moderate/severe COVID-19. The dominant immune responses to SARS-CoV-2 were targeted to regions spanning the Spike protein, Nucleocapsid, and ORF1ab. Some epitopes were identified in the majority of samples while others were rare, and we found variation in the number of epitopes targeted by different individuals. We also identified a set of cross-reactive sequences that were bound by antibodies in SARS-CoV-2 unexposed individuals. Finally, we uncovered a subset of enriched epitopes from commonly circulating human coronaviruses with significant homology to highly reactive SARS-CoV-2 sequences.

immunology↗

bioRxiv2020

Improved predictive models of peptide presentation on MHC I

Computational prediction of the peptides presented on MHC class I proteins is an important tool for studying T cell immunity. The data available to develop such predictors has expanded with the use of mass spec to identify naturally-presented MHC ligands. In addition to elucidating binding motifs, the identified ligands also reflect the antigen processing steps that occur prior to MHC binding. Here, we developed an integrated predictor of MHC I presentation that combines new models for MHC I binding and antigen processing. Considering only peptides first predicted by the binding model to bind strongly to MHC, the antigen processing model is trained to discriminate published mass spec-identified MHC I ligands from unobserved peptides. The integrated model outperformed the two individual components as well as NetMHCpan 4.0 and MixMHCpred 2.0.2 on held-out mass spec experiments. Our predictors are implemented in the MHCflurry package, version 1.6.0 (github.com/openvax/mhcflurry).

immunology↗

bioRxiv2018

Improved Analysis of Phage ImmunoPrecipitation Sequencing (PhIP-Seq) Data Using a Z-score Algorithm

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a massively multiplexed, phage-display based methodology for analyzing antibody binding specificities, with several advantages over existing techniques, including the uniformity and completeness of proteomic libraries, as well as high sample throughput and low cost. Data generated by the PhIP-Seq assay are unique in many ways. The only published analytical approach for these data suffers from important limitations. Here, we propose a new statistical framework with several improvements. Using a set of replicate mock immunoprecipitations (negative controls lacking antibody input) to generate background binding distributions, we establish a statistical model to quantify antibody-dependent changes in phage clone abundance. Our approach incorporates robust regression of experimental samples against the mock IPs as a means to calculate the expected phage clone abundance, and provides a generalized model for calculating each clones expected abundance-associated standard deviation. In terms of bias removal and detection sensitivity, we demonstrate that this z-score algorithm outperforms the previous approach. Further, in a large cohort of autoantibody-defined Sjogrens Syndrome (SS) patient sera, PhIP-Seq robustly identified Ro52, Ro60, and SSB/La as known autoantigens associated with SS. In an effort to identify novel SS-specific binding specificities, SS z-scores were compared with z-scores obtained by screening Ropositive sera from patients with systemic lupus erythematosus (SLE). This analysis did not yield any commonly targeted SS-specific autoantigens, suggesting that if they exist at all, their epitopes are likely to be discontinuous or post-translationally modified. In summary, we have developed an improved algorithm for PhIP-Seq data analysis, which was validated using a large set of sera with clinically characterized autoantibodies. This z-score approach will substantially improve the ability of PhIP-Seq to detect and interpret antibody binding specificities. The associated Python code is freely available for download here: https://github.com/LarmanLab/PhIP-Seq-Analyzer.

bioinformatics↗