bioRxivBioengineeringPreprint
Engineering Escherichia coli Nissle as safe chassis for delivery of therapeutic peptides
Aarón Pantoja‐AnglesiD, Zahir AliiD, Sherin AbdelrahmaniD, Cynthia Olivia Baldelamar-JuáreziD, Shahid ChaudharyiD, Misjudeen Raji, Luis F. RiveraiD, Lingyun ZhaoiD, Charlotte A. E. HauseriD, Magdy M. MahfouziD
Abstract
Synthetic biology enables the integration of sophisticated genetic programs into microorganisms, transforming them into potent vehicles for therapeutic applications. Engineering strategies for microorganisms are rapidly evolving, offering promising solutions for cancer therapy, microbiome modulation, digestive health support, and beyond. Developing novel tools to engineer safe, nonpathogenic microbial platforms is essential for advancing clinical therapies. In this work, we present an innovative engineering approach for the probiotic Escherichia coli Nissle (EcN), aimed at creating a safe and efficient chassis for the bioproduction of therapeutics. The EcN endogenous pM1 and pM2 plasmids were cured and re-engineered to introduce a CRISPR-Cas12 chromosome shredding device and a therapeutic-producing genetic circuit, thereby generating a nonproliferative therapeutic-delivery system. Next, we build an AI-based bioinformatic pipeline to predict Anticancer-Cell-Penetrating Peptides (ACCPP) candidates. As a proof-of-concept, a selected ACCPP was produced in the engineered EcN chromosome-shredded (CS) chassis. This strategy yields a robust and controllable platform for the safe production and delivery of therapeutics, paving the way for the future development of microbial therapies and their clinical applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=57 SRC="FIGDIR/small/673081v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@2430d4org.highwire.dtl.DTLVardef@1ce2borg.highwire.dtl.DTLVardef@8686cdorg.highwire.dtl.DTLVardef@1fc23fb_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: E. coli
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- Citations
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