Most genetic diseases arise from recessive point mutations that require correction, rather than disruption, of the pathogenic allele to benefit patients. Base editing has the potential to directly repair point mutations and provide therapeutic restoration of gene function. Mutations of transmembrane channel-like 1 gene (TMC1) can cause dominant or recessive deafness. We developed a base editing strategy to treat Baringo mice, which carry a recessive, loss-of-function point mutation (c.A545G; resulting in the substitution p.Y182C) in Tmc1 that causes deafness. Tmc1 encodes a protein that forms mechanosensitive ion channels in sensory hair cells of the inner ear and is required for normal auditory function. We found that sensory hair cells of Baringo mice have a complete loss of auditory sensory transduction. To repair the mutation, we tested several optimized cytosine base editors (CBEmax variants) and guide RNAs in Baringo mouse embryonic fibroblasts. We packaged the most promising CBE, derived from an activation-induced cytidine deaminase (AID), into dual adeno-associated viruses (AAVs) using a split-intein delivery system. The dual AID-CBEmax AAVs were injected into the inner ears of Baringo mice at postnatal day 1. Injected mice showed up to 51% reversion of the Tmc1 c.A545G point mutation to wild-type sequence (c.A545A) in Tmc1 transcripts. Repair of Tmc1 in vivo restored inner hair cell sensory transduction and hair cell morphology and transiently rescued low-frequency hearing 4 weeks after injection. These findings provide a foundation for a potential one-time treatment for recessive hearing loss and support further development of base editing to correct pathogenic point mutations.

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models’ capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.

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In vitro selection queries large combinatorial libraries for sequence-defined polymers with target binding and reaction catalysis activity. While the total sequence space of these libraries can extend beyond 1022 sequences, practical considerations limit starting sequences to ≤~1015 distinct molecules. Selection-induced sequence convergence and limited sequencing depth further constrain experimentally observable sequence space. To address these limitations, we integrate experimental and machine learning approaches to explore regions of sequence space unrelated to experimentally derived variants. We perform in vitro selections to discover highly side-chain-functionalized nucleic acid polymers (HFNAPs) with potent affinities for a target small molecule (daunomycin KD = 5–65 nM). We then use the selection data to train a conditional variational autoencoder (CVAE) machine learning model to generate diverse and unique HFNAP sequences with high daunomycin affinities (KD = 9–26 nM), even though they are unrelated in sequence to experimental polymers. Coupling in vitro selection with a machine learning model thus enables direct generation of active variants, demonstrating a new approach to the discovery of functional biopolymers.