Metabolic EngineeringBioengineeringPeer-reviewed journal article
Metabolic engineering improves transduction efficiency and downstream vector isolation by altering the lipid composition of extracellular vesicle-enclosed AAV.
Paula EspinozaiD, Ming Liang ChengiD, Carrie Ng, Demitri de la Cruz, Elizabeth D Wasson, Deirdre M. McCarthyiD, Pradeep G. BhideiD, Casey A. MaguireiD, Miguel C. SantoscoyiD
Abstract
Adeno-associated viruses (AAV) are promising vectors for gene therapy due to their efficacy in vivo. However, there is room for improvement to address key limitations such as the pre-existing immunity to AAV in patients, high-dose toxicity, and relatively low efficiency for some cell types. This study introduces a metabolic engineering approach, using knockout of the enzyme phosphatidylserine synthase 1 (PTDSS1) to increase the abundance of extracellular vesicle-enclosed AAV (EV-AAV) relative to free AAV in the supernatant of producer cells, simplifying downstream purification processes. The lipid-engineered HEK293T-{Delta}PTDSS1 cell line achieved a 42.7-fold enrichment of EV-AAV9 compared to free AAV9 in the supernatant. The rational genetic strategy also led to a 300-fold decrease of free AAV in supernatant compared to wild-type HEK293T. The membrane-engineered EV-AAV9 (mEV-AAV9) showed unique envelope composition alterations, including cholesterol enrichment and improved transduction efficiency in human AC16 cardiomyocytes by 1.5-fold compared to conventional EV-AAV9 and by 11-fold compared to non-enveloped AAV9. Robust in-vivo transduction four weeks after intraparenchymal administration of mEV-AAV9 was observed in the murine brain. This study shows promise in the potential of lipid metabolic engineering strategies to improve the efficiency and process development of enveloped gene delivery vectors.
- organism: human
- organism: mouse
- assay: proteomics
- assay: metagenomics
- assay: metabolomics
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: protein folding & dynamics
- approach: experimental assay
- approach: genetic perturbation / CRISPR
- approach: microscopy & imaging analysis
- approach: statistical model
- Citations
- 6
- FWCI
- 1.26
- Field percentile
- top 21%
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