Back

Gut Delivery of Pentameric GLP-1 Using Genetically Engineered Bacillus subtilis for Diabetes and Obesity Treatment

Ye, N.; Di Pierro, F.; Zerbinati, N.; Tanda, M. L.; Duan, C.; Zupet, J.; Li, J.

2025-10-07 bioengineering
10.1101/2025.10.07.680898 bioRxiv
Show abstract

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance and {beta}-cell dysfunction. Glucagon-like peptide-1 (GLP-1) has emerged as a pivotal therapeutic target due to its roles in promoting insulin secretion, regulating appetite, and enhancing lipid metabolism. However, GLP-1-based therapies face significant challenges, including high production costs, complex delivery methods, and rapid in vivo degradation. To address these limitations, we developed a novel gut delivery strategy using Bacillus subtilis PY79 as a host for the expression of pentameric GLP-1. The genetically-modified strain, B. subtilis JH389, is demonstrated to maintain the same probiotic and antibiotic-sensitivity features belonged to the original strain PY79, and was deposited at the BCCM/LMG collection as B. subtilis LMG P-34037. The pentameric GLP-1 was designed to be digested by intestinal trypsin and to release active GLP-1 monomers capable of (i) resisting inactivativation by DPP-4 and trypsin, (ii) crossing the intestinal mucosa and (iii) entering the bloodstream. SDS-PAGE and Western blot confirmed the expression of pentameric GLP-1 in B. subtilis JH389, and fecal sample analysis demonstrated the strain gut engraftment. The administration of B. subtilis JH389 increased serum GLP-1 concentration, while also reducing blood glucose levels in a mouse model. Our in vitro and in vivo studies highlights the therapeutic potential of the JH389 strain and support the feasibility of using genetically engineered bacteria as a cost-effective and efficient delivery platform for GLP-1, paving the way for future applications in diabetes and metabolic disorder treatments.

Matching journals

The top 6 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.