Biology subjects

Hause, R. J.

Publications and source records attributed to Hause, R. J..

5 recordsLinked to original sources

bioRxiv2025

Helix: a structure-aware deep learning model for accurate prediction of A-to-I RNA editing by endogenous ADARs

Adenosine deaminase acting on RNA (ADAR) converts adenosine to inosine within double-stranded RNA (dsRNA) and can be co-opted for therapeutic RNA editing by introducing dsRNA substrates in trans using programmable guide RNAs (gRNAs). However, ADARs natural promiscuity necessitates sophisticated gRNA designs to achieve efficient and specific editing. Here we present Helix, a predictive model that achieves highly accurate, zero-shot per-adenosine editing predictions for any target sequence. Helixs performance arises from two architectural choices: a transformer framework that scales effectively with increasing and imbalanced training data; and a structure-aware attention mechanism that incorporates predicted RNA secondary structure, a key determinant of ADAR activity. Helixs predictive accuracy enables seamless integration with DeepREAD, our previously reported generative model, in a noisy-student distillation framework termed DeepHelix. This workflow supports both zero-shot gRNA design and the generation of complex, constraint-based designs. We demonstrate DeepHelixs utility by designing gRNAs that efficiently edit a therapeutically relevant adenosine and by leveraging its flexible design space to engineer species cross-reactive gRNAs to accelerate pre-clinical development.

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

Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing

Determining the pathogenicity of human genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes will likely require generalizable, scalable assays. Here we describe Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance in a single experiment. We apply VAMP-seq to quantify the abundance of 7,595 single amino acid variants of two proteins, PTEN and TPMT, in which functional variants are clinically actionable. We identify 1,079 PTEN and 805 TPMT single amino acid variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and our abundance data suggest that a PTEN variant accounting for ~10% of PTEN missense variants in melanomas functions via a dominant negative mechanism. Finally, we demonstrate that VAMP-seq can be applied to other genes, highlighting its potential as a generalizable assay for characterizing missense variants.

genetics↗

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

Using Large-Scale Mutagenesis To Guide Single Amino Acid Scanning Experiments

Alanine scanning mutagenesis is a widely-used method for identifying protein positions that are important for function or ligand binding. Alanine was chosen because it is physicochemically innocuous and constitutes a deletion of the side chain at the {beta}- carbon. Alanine is also thought to best represent the effects of other mutations; however, this assumption has not been formally tested. To determine whether alanine substitutions are always the best choice, we analyzed 34,373 mutations in fourteen proteins whose effects were measured using large-scale mutagenesis approaches. We found that several substitutions, including histidine and asparagine, are better at recapitulating the effects of other substitutions. Histidine and asparagine also correlated best with the effects of other substitutions in different structural contexts. Furthermore, we found that alanine is among the worst substitutions for detecting ligand interface positions, despite its frequent use for this purpose. Our work highlights the utility of large-scale mutagenesis data and can help to guide future single substitution mutational scans.

molecular biology↗