Biology subjects

Briggs, A. W.

Publications and source records attributed to Briggs, A. W..

6 recordsLinked to original sources

bioRxiv2026

Rett syndrome lifespan extension in mice via AI-guided ADAR editing

Rett syndrome is a severe neurodevelopmental disorder primarily caused by mutations in the MECP2 gene. A significant subset of severe cases are driven by nonsense mutations that generate premature stop codons, leading to loss of functional MeCP2 protein. Here, we describe a novel therapeutic strategy that uses endogenous adenosine deaminase acting on RNA (ADAR) enzymes to correct the R168X mutation at the RNA level. Using generative artificial intelligence trained on large empirical datasets, we engineered guide RNAs (gRNAs) that recruit endogenous ADAR to convert the mutant stop codon (UGA) into a tryptophan (UGG) to restore full-length MeCP2. Once incorporated into an optimized expression system based on endogenous small nuclear RNA regulatory elements and packaged into adeno-associated virus, these gRNAs enabled precise RNA editing at the target site with minimal off-target activity across the transcriptome while restoring full-length MeCP2 protein expression in patient-derived induced pluripotent stem cell neurons. Delivered intravenously to an R168X mouse model, the gRNAs achieved ~70% targeted RNA editing and substantially restored MeCP2 throughout the brain resulting in markedly improved Rett-like phenotypes and significantly extended lifespan. These findings demonstrate that AI-guided ADAR-mediated RNA editing is a precise and efficient technology for correcting nonsense mutations, with therapeutic potential for Rett syndrome and other genetic diseases.

neuroscience↗

bioRxiv2024

A Novel Engineered U7 Small Nuclear RNA Scaffold Greatly Increases in vitro and in vivo ADAR-Mediated Programmable RNA Base Editing

Custom RNA base editing using the endogenous human Adenosine Deaminase Acting on RNA (ADAR) enzyme presents a promising approach for precision therapeutics, alleviating concerns of permanent DNA damage or immunogenicity from1 foreign bacterial proteins such as CRISPR/Cas. ADAR can be directed to act on therapeutic RNA targets by antisense guide RNAs (gRNAs) that create a substrate for ADARs adenosine-to-inosine (effectively A-to-G) deamination activity. Delivery of gRNAs via a DNA expression construct provided by Adeno-Associated Virus (AAV) might allow life-long duration of the therapy. However, a major challenge for RNA editing using gene-encoded gRNAs and endogenous levels of ADAR is achieving sufficient gRNA activity inside cells, especially in therapeutic situations where AAV delivery may provide as low as one viral genome per cell. Here we show that embedding antisense gRNAs into a U7 small nuclear RNA (snRNA) framework and adding hnRNP A1 binding domains greatly increases the efficiency of custom RNA editing. This increased editing efficiency allows for detectable RNA editing from a single genomic insertion of gRNA construct per cell, which enabled a pooled library screen of 750+ gRNA variations to further improve the SmOPT U7 hairpin system. The screen revealed critical residues responsible for RNA editing and generated new SmOPT and U7 hairpin variants that further boosted RNA editing. The final design, combined with an improved synthetic U7 promoter, resulted in up to 76% targeted editing with a single integrated copy of construct per cell, representing a 10- to 100-fold increase over existing circular gRNA approaches. Using systemic in vivo AAV delivery, we achieved an unprecedented 75% RNA editing in the total brain of a mouse model of Hurler syndrome. Our novel SmOPT U7 system also improved published antisense oligos for DMD exon skipping, currently in clinical trials, by up to 25-fold in differentiated myoblasts, and therefore represents a universal scaffold for ADAR-based RNA editing as well as other antisense RNA therapies.

bioengineering↗

bioRxiv2024

Generative machine learning of ADAR substrates for precise and efficient RNA editing

Adenosine Deaminase Acting on RNA (ADAR) converts adenosine to inosine within certain double-stranded RNA structures. However, ADARs promiscuous editing and poorly understood specificity hinder therapeutic applications. We present an integrated approach combining high-throughput screening (HTS) with generative deep learning to rapidly engineer efficient and specific guide RNAs (gRNAs) to direct ADARs activity to any target. Our HTS quantified ADAR-mediated editing across millions of unique gRNA sequences and structures, identifying key determinants of editing outcomes. We leveraged these data to develop DeepREAD (Deep learning for RNA Editing by ADAR Design), a diffusion-based model that elucidates complex design rules to generate novel gRNAs outperforming existing design heuristics. DeepREADs gRNAs achieve highly efficient and specific editing, including challenging multi-site edits. We demonstrate DeepREADs therapeutic potential by designing gRNAs targeting the MECP2R168X mutation associated with Rett syndrome, achieving both allelic specificity and species cross-reactivity. This approach significantly accelerates the development of ADAR-based RNA therapeutics for diverse genetic diseases.

bioengineering↗

bioRxiv2018

T-cell receptor αβ chain pairing is associated with CD4+ and CD8+ lineage specification

While a highly diverse T-cell receptor (TCR) repertoire is the hallmark of a healthy adaptive immune system, relatively little is understood about how the CD4+ and CD8+ TCR repertoires differ from one another. We here utilize high-throughput single T-cell sequencing to obtain approximately 100,000 TCR {beta} chain pairs from human subjects, stratified into CD4+ and CD8+ lineages. We reveal that substantial information about T-cell lineage is encoded by V{beta} gene pairs and, to a lesser extent, by several other TCR features such as CDR3 length and charge. We further find that the strength of association between the {beta} chain and T-cell lineage is surprisingly weak, similar in strength to that of the chain. Using machine learning classifiers to predict T-cell lineage from TCR features, we demon-strate that {beta} chain pairs are significantly more informative than individual chains alone. These findings provide unprecedented insight into the CD4+ and CD8+ TCR repertoires and highlight the importance of {beta} chain pairing in TCR function and specificity.

immunology↗

bioRxiv2017

Single-cell sequencing reveals αβ chain pairing shapes the T cell repertoire

A diverse T cell repertoire is a critical component of the adaptive immune system, providing protection against invading pathogens and neoplastic changes, relying on the recognition of foreign antigens and neoantigen peptides by T cell receptors (TCRs). However, the statistical properties and function of the T cell pool in an individual, under normal physiological conditions, are poorly understood. In this study, we report a comprehensive, quantitative characterization of the T cell repertoire from over 1.9 million cells, yielding over 200,000 high quality paired {beta} sequences in 5 healthy human subjects. The dataset was obtained by leveraging recent biotechnology developments in deep RNA sequencing of lymphocytes via single-cell barcoding in emulsion. We report non-random associations and non-monogamous pairing between the and {beta} chains, lowering the theoretical diversity of the T cell repertoire, and increasing the frequency of public clones shared among individuals. T cell clone size distributions closely followed a power law, with markedly longer tails for CD8+ cytotoxic T cells than CD4+ helper T cells. Furthermore, clonality estimates based on paired chains from single T cells were lower than that from single chain data. Taken together, these results highlight the importance of sequencing {beta} pairs to accurately quantify lymphocyte receptor diversity.

immunology↗

bioRxiv2017

Tumor-infiltrating immune repertoires captured by single-cell barcoding in emulsion

Tumor-infiltrating lymphocytes (TILs) are critical to anti-cancer immune responses, but their diverse phenotypes and functions remain poorly understood and challenging to study. We therefore developed a single-cell barcoding technology for deep characterization of TILs without the need for cell-sorting or culture. Our emulsion-based method captures full-length, natively paired B-cell and T-cell receptor (BCR and TCR) sequences from lymphocytes among millions of input cells. We validated the method with 3 million B-cells from healthy human blood and 350,000 B-cells from an HIV elite controller, before processing 400,000 cells from an unsorted dissociated ovarian adenocarcinoma and recovering paired BCRs and TCRs from over 11,000 TILs. We then extended the barcoding method to detect DNA-labeled antibodies, allowing ultra-high throughput, simultaneous protein detection and RNA sequencing from single cells.

immunology↗