Allen Yujie Jiang

Prime editing is a versatile clinical genome editing method that enables precise substitutions, small insertions and deletions at specified locations in the genomes of living systems including human cells. Although non-viral lipid nanoparticle (LNP) delivery of RNA in vivo has become a preferred method for gene editing in animals and patients, its application to complex, three-component prime editing systems has yielded low editing efficiencies. Here we developed a systematic prime editing LNP (PE-LNP) optimization platform that addresses key bottlenecks in cargo design that limit editing efficiency. This generalizable workflow yielded PE-LNPs that can achieve 49% average in vivo prime editing in the bulk mouse liver with a single dose of 2 mg kg ⁻¹ . We applied our workflow to the correction of PAH R408W, a cause of phenylketonuria, in a mouse model and achieved prime editing efficiencies and serum phenylalanine levels anticipated to be curative. We also show that PE-LNPs minimize off-target editing compared with DNA delivery methods, induce only transient elevation of liver enzymes and can be dosed repeatedly to improve editing efficiencies. These PE-LNP systems provide an attractive alternative to viral delivery by offering transient expression that minimizes off-target editing, no observed long-term toxicity and high levels of non-viral in vivo liver prime editing.

Although protein engineering and laboratory evolution have been used to optimize prime editors, we show that previous changes that improve prime editor efficiency also compromise protein stability and expression level, limiting performance. To address these limitations, we apply structure-informed artificial intelligence-guided methods such as the inverse-folding network ProteinMPNN to redesign the reverse transcriptase (RT) domains of engineered and evolved prime editors while preserving regions essential for catalysis. Redesigned RTs are extensively mutated, with 30-163 amino acid substitutions, and exhibit enhanced folding stability and soluble expression and up to twofold higher intracellular prime editor protein levels following mRNA delivery. Redesigned PE8 prime editors demonstrate enhanced editing efficiencies across multiple ex vivo contexts, including in several human primary cell types and via several delivery modalities. In mice, editing efficiency is up to 2.9-fold higher than that of state-of-the-art PE6, PE7 and PEmax prime editors. These findings demonstrate a generalizable approach for augmenting laboratory evolution to improve genome editing agents.

The performance of prime-editing (PE) systems has been improved by systematic engineering of their protein and small RNA components but the structured RNA motifs appended to the 3′ end of PE guide RNAs (pegRNAs)—a key determinant of pegRNA stability and editing efficiency—have not been extensively studied. We introduce PE-PRISM, a high-throughput pooled screen to identify and optimize these 3′ RNA motifs in human cells. Here, using PE-PRISM, we evaluated 2,858 RNA motifs across four iterative libraries, including natural and engineered pseudoknots, G-quadruplexes and reverse transcriptase recruitment elements. We applied structure-guided mutagenesis and combinatorial variant screening to refine hits, culminating in the engineered and evolved pseudoknot variants tevo2.0, eHAV and eSBRMV1-A. In a screen correcting 847 pathogenic ClinVar variants, the top-performing motifs improved PE efficiency over the widely used tevopreQ ₁ motif for >90% of edits. They also increased PE efficiencies for correcting disease-associated mutations in primary human cells and in vivo in mouse brain and liver.

Zellweger spectrum disorder (ZSD) is caused by biallelic loss-of-function variants in PEX genes required for peroxisome biogenesis, which is critical for normal cellular metabolism and signalling. The PEX1 -p.G843D (c.2528G>A) allele, present in approximately 30% of individuals with ZSD, frequently results in chronic liver disease that can progress to cirrhosis, hepatocellular carcinoma and degraded neurological health. Here we report the development and application of an adenine base editing strategy to correct an established homozygous Pex1 -p.G844D ZSD mouse model that manifests liver pathologies and metabolic dysfunction found in patients. Through intravenous delivery of AAV9 encoding ABE8e-V106W into both neonatal and 4-week-old mice, we achieved up to 60% pathogenic allele correction in the bulk liver. By restoring peroxisome function, base editing eliminated bulk accumulation of very long-chain and branched-chain fatty acids, and toxic C27-bile acid intermediates. Increased levels of phytanic acid, a branched-chain fatty acid that becomes harmful when accumulated, were normalized in blood, liver and brain tissue. Treatment of homozygous Pex1 -p.G844D mice resulted in the progressive, dose-dependent normalization of liver transcriptomes and histopathology, accompanied by gains in body weight. Non-viral lipid nanoparticle delivery of ABE8e-V106W mRNA to 4-week-old mice also yielded correction of the Pex1 -p.G844D allele in 27% of bulk liver cells. In patient-derived fibroblasts, base editing corrected >80% of PEX1 -p.G843D alleles and restored peroxisome homeostasis. Genome-wide experimental and computational off-target analyses found minimal off-target editing in the mouse or human genome. Collectively, these findings suggest that liver base editing over a range of ages may benefit individuals with ZSD and provides a foundation for developing precision gene correction treatments that address the root cause of a wide range of peroxisomal disorders.

Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger RNA delivery technology. Here, to advance the identification of ionizable lipids beyond current methods, which rely on experimental screening and/or rational design, we introduce lipid optimization using neural networks, a deep-learning strategy for ionizable lipid design. We created a dataset of >9,000 lipid nanoparticle activity measurements and used it to train a directed message-passing neural network for prediction of nucleic acid delivery with diverse lipid structures. Lipid optimization using neural networks predicted RNA delivery in vitro and in vivo and extrapolated to structures divergent from the training set. We evaluated 1.6 million lipids in silico and identified two structures, FO-32 and FO-35, with local mRNA delivery to the mouse muscle and nasal mucosa. FO-32 matched the state of the art for nebulized mRNA delivery to the mouse lung, and both FO-32 and FO-35 efficiently delivered mRNA to ferret lungs. Overall, this work shows the utility of deep learning for improving nanoparticle delivery.

To unlock the full promise of messenger (mRNA) therapies, expanding the toolkit of lipid nanoparticles is paramount. However, a pivotal component of lipid nanoparticle development that remains a bottleneck is identifying new ionizable lipids. Here we describe an accelerated approach to discovering effective ionizable lipids for mRNA delivery that combines machine learning with advanced combinatorial chemistry tools. Starting from a simple four-component reaction platform, we create a chemically diverse library of 584 ionizable lipids. We screen the mRNA transfection potencies of lipid nanoparticles containing those lipids and use the data as a foundational dataset for training various machine learning models. We choose the best-performing model to probe an expansive virtual library of 40,000 lipids, synthesizing and experimentally evaluating the top 16 lipids flagged. We identify lipid 119-23, which outperforms established benchmark lipids in transfecting muscle and immune cells in several tissues. This approach facilitates the creation and evaluation of versatile ionizable lipid libraries, advancing the formulation of lipid nanoparticles for precise mRNA delivery.

Inhaled delivery of mRNA has the potential to treat a wide variety of diseases. However, nebulized mRNA lipid nanoparticles (LNPs) face several unique challenges including stability during nebulization and penetration through both cellular and extracellular barriers. Here we develop a combinatorial approach addressing these barriers. First, we observe that LNP formulations can be stabilized to resist nebulization-induced aggregation by altering the nebulization buffer to increase the LNP charge during nebulization, and by the addition of a branched polymeric excipient. Next, we synthesize a combinatorial library of ionizable, degradable lipids using reductive amination, and evaluate their delivery potential using fully differentiated air–liquid interface cultured primary lung epithelial cells. The final combination of ionizable lipid, charge-stabilized formulation and stability-enhancing excipient yields a significant improvement in lung mRNA delivery over current state-of-the-art LNPs and polymeric nanoparticles.

The expanding applications of nonviral genomic medicines in the lung remain restricted by delivery challenges. Here, leveraging a high-throughput platform, we synthesize and screen a combinatorial library of biodegradable ionizable lipids to build inhalable delivery vehicles for messenger RNA and CRISPR–Cas9 gene editors. Lead lipid nanoparticles are amenable for repeated intratracheal dosing and could achieve efficient gene editing in lung epithelium, providing avenues for gene therapy of congenital lung diseases.

The emergency use authorizations (EUAs) of two mRNA-based severe acute respiratory syndrome coronavirus (SARS-CoV)-2 vaccines approximately 11 months after publication of the viral sequence highlights the transformative potential of this nucleic acid technology. Most clinical applications of mRNA to date have focused on vaccines for infectious disease and cancer for which low doses, low protein expression and local delivery can be effective because of the inherent immunostimulatory properties of some mRNA species and formulations. In addition, work on mRNA-encoded protein or cellular immunotherapies has also begun, for which minimal immune stimulation, high protein expression in target cells and tissues, and the need for repeated administration have led to additional manufacturing and formulation challenges for clinical translation. Building on this momentum, the past year has seen clinical progress with second-generation coronavirus disease 2019 (COVID-19) vaccines, Omicron-specific boosters and vaccines against seasonal influenza, Epstein–Barr virus, human immunodeficiency virus (HIV) and cancer. Here we review the clinical progress of mRNA therapy as well as provide an overview and future outlook of the transformative technology behind these mRNA-based drugs.