Back

miniVec: A miniaturized plasmid backbone supporting antibiotic-free and additive-free fermentation with enhanced yield and functionality

Shi, S. J.; Lin, Y.; Ye, J. Z.; Kwok, A. Z.; Want, K. Z.; Cai, J. Z.; Hu, M. M.; Liu, A. Y.; Li, K. J.; Guo, B. Y.; Xia, H. H.; Huang, E. P.; Chen, J. X.; Hong, J. F.; Zheng, C. K.; Lahn, B. T.

2025-10-03 bioengineering
10.1101/2025.10.02.679775 bioRxiv
Show abstract

Plasmids are a foundational research reagent and a key material in biopharmaceutical manufacturing. Plasmids require a backbone for propagation in E. coli, which typically contains an antibiotic resistance gene alongside a replication origin. As plasmids increasingly enter clinical applications, concerns are raised on the safety risks of antibiotic resistance genes. Additionally, these protein-coding genes occupy long stretches of DNA that incur significant metabolic burdens on host cells, which negatively impacts plasmid manufacturability and functionality, leading to high production cost and compromised clinical efficacy. Here, we describe miniVec, a novel miniaturized plasmid backbone devoid of protein-coding sequence, and instead expresses a small RNA to provide constant selective pressure capable of sustaining high plasmid copy numbers in plain culture media devoid of antibiotics or other chemical additives. This simplifies large-scale fermentation and greatly increases plasmid yield. Notably, miniVec confers enhanced functionality in a variety of applications such as chemical transfection, electroporation, virus packaging, transposon-or CRISPR-mediated genome integration, and in vivo naked DNA transfection and vaccination, while exhibiting no detectable immunogenicity or toxicity. These advantages establish miniVec as the next-generation plasmid platform for clinical applications, featuring improved safety that aligns with regulatory expectations, enhanced manufacturability leading to much higher yield and dramatic cost reduction, and augmented functionality in diverse applications.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.