OLAH connects fatty acid metabolism to the severity of respiratory viral disease.

Respiratory virus infections may cause profound respiratory illness, yet the factors that underlie disease severity are not well understood. In this issue of Cell, Jia, Crawford, et al.1 identify the role of oleoyl-ACP-hydrolase (OLAH) in mediating life-threatening inflammation associated with viral respiratory disease severity.

Optimized microfluidic formulation and organic excipients for improved lipid nanoparticle mediated genome editing.

mRNA-based gene editing platforms have tremendous promise in the treatment of genetic diseases. However, for this potential to be realized in vivo, these nucleic acid cargos must be delivered safely and effectively to cells of interest. Ionizable lipid nanoparticles (LNPs), the most clinically advanced non-viral RNA delivery system, have been well-studied for the delivery of mRNA but have not been systematically optimized for the delivery of mRNA-based CRISPR-Cas9 platforms. In this study, we investigated the effect of microfluidic and lipid excipient parameters on LNP gene editing efficacy. Through in vitro screening in liver cells, we discovered distinct trends in delivery based on phospholipid, cholesterol, and lipid-PEG structure in LNP formulations. Combination of top-performing lipid excipients produced an LNP formulation that resulted in 3-fold greater gene editing in vitro and facilitated 3-fold greater reduction of a therapeutically-relevant protein in vivo relative to the unoptimized LNP formulation. Thus, systematic optimization of LNP formulation parameters revealed a novel LNP formulation that has strong potential for delivery of gene editors to the liver to treat metabolic disease.

Machine learning guided structure function predictions enable in silico nanoparticle screening for polymeric gene delivery.

Developing highly efficient non-viral gene delivery reagents is still difficult for many hard-to-transfect cell types and, to date, has mostly been conducted via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development of devices or therapeutics by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a dataset of synthetic biodegradable polymers, poly(beta-amino ester)s (PBAEs), which have shown exciting promise for therapeutic gene delivery in vitro and in vivo. The data set includes polymer properties as inputs as well as polymeric nanoparticle transfection performance and nanoparticle toxicity in a range of cells as outputs. This data was used to train and evaluate several state-of-the-art machine learning algorithms for their ability to predict transfection and understand structure-function relationships. By developing an encoding scheme for vectorizing the structure of a PBAE polymer in a machine-readable format, we demonstrate that a random forest model can satisfactorily predict DNA transfection in vitro based on the chemical structure of the constituent PBAE polymer in a cell line dependent manner. Based on the model, we synthesized PBAE polymers and used them to form polymeric gene delivery nanoparticles that were predicted in silico to be successful. We validated the computational predictions in two cell lines in vitro, RAW 264.7 macrophages and Hep3B liver cancer cells, and found that the Spearman's R correlation between predicted and experimental transfection was 0.57 and 0.66 respectively. Thus, a computational approach that encoded chemical descriptors of polymers was able to demonstrate that in silico computational screening of polymeric nanomedicine compositions had utility in predicting de novo biological experiments. STATEMENT OF SIGNIFICANCE: Developing highly efficient non-viral gene delivery reagents is difficult for many hard-to-transfect cell types and, to date, has mostly been explored via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development for therapeutic or biomanufacturing purposes by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a large compiled PBAE DNA gene delivery nanoparticle dataset across many cell types to develop predictive models for transfection and nanoparticle cytotoxicity. We develop a novel computational pipeline to encode PBAE nanoparticles with chemical descriptors and demonstrate utility in a de novo experimental context.

High-throughput and high-content bioassay enables tuning of polyester nanoparticles for cellular uptake, endosomal escape, and systemic in vivo delivery of mRNA.

Nanoparticle-based mRNA therapeutics hold great promise, but cellular internalization and endosomal escape remain key barriers for cytosolic delivery. We developed a dual nanoparticle uptake and endosomal disruption assay using high-throughput and high-content image-based screening. Using a genetically encoded Galectin 8 fluorescent fusion protein sensor, endosomal disruption could be detected via sensor clustering on damaged endosomal membranes. Simultaneously, nucleic acid endocytosis was quantified using fluorescently tagged mRNA. We used an array of biodegradable poly(beta-amino ester)s as well as Lipofectamine and PEI to demonstrate that this assay has higher predictive capacity for mRNA delivery compared to conventional polymer and nanoparticle physiochemical characteristics. Top nanoparticle formulations enabled safe and efficacious mRNA expression in multiple tissues following intravenous injection, demonstrating that the in vitro screening method is also predictive of in vivo performance. Efficacious nonviral systemic delivery of mRNA with biodegradable particles opens up new avenues for genetic medicine and human health.

Poly(Beta-Amino Ester) Nanoparticles Enable Nonviral Delivery of CRISPR-Cas9 Plasmids for Gene Knockout and Gene Deletion.

The CRISPR-Cas9 system is a powerful gene-editing tool with wide-ranging applications, but the safe and efficient intracellular delivery of CRISPR components remains a challenge. In this study, we utilized biodegradable poly(beta-amino ester) nanoparticles to codeliver plasmid DNA encoding Cas9 and short guide RNA (sgRNA), respectively, to enable gene knockout following a CRISPR-mediated cleavage at one genomic site (1-cut edit), as well as gene deletion following DNA cleavage at two sites flanking a region of interest (2-cut edits). We designed a reporter system that allows for easy evaluation of both types of edits: gene knockout can be assessed by a decrease in near-infrared fluorescent protein (iRFP) fluorescence, whereas deletion of an expression stop cassette turns on a red-enhanced nanolantern fluorescence/luminescence dual reporter. Nanoparticles enabled up to 70% gene knockout due to small indels, as well as 45% gain-of-function expression after a 600-bp deletion edit. The efficiency of 2-cut edits is more sensitive than 1-cut edits to Cas9 and the sgRNA expression level. We demonstrate promising biodegradable nanoparticle formulations for gene editing. Our findings also provide new insights into the screening and transfection requirements for different types of gene edits, which are applicable for designing nonviral delivery systems for the CRISPR-Cas9 platform.