Back

Lipid anchor engineering controls cell-penetrating arginine-rich peptide presentation for efficient siEGFR liposomal delivery to triple-negative breast cancer cells

Bialecki, P.; Braccia, S.; Makowski, T.; Piorecka, K.; Falcigno, L.; Bellavita, R.; Falanga, A.; Bryszewska, M.; Robaszkiewicz, A.; Galdiero, S.; Pedziwiatr-Werbicka, E.

2026-08-25 biophysics
10.64898/2026.08.20.745705 bioRxiv
Show abstract

Understanding the physicochemical factors that govern siRNA nanocarrier assembly is essential for the rational design of effective delivery systems. By optimizing various lipid compositions, cholesterol content and PEG length we created a peptide-functionalized cationic liposomal platform made of DOPE/TAP lipids with cholesterol-anchored nona-arginine (R9-Chol) for siRNA complexation, intracellular transport and effective silencing of the target EGFR gene. Analysis of {zeta}-potential and dynamic light scattering allowed to rationally design formulation of stable, monodisperse nanoscale lipoplexes with a positive surface charge. With fluorescence polarization, circular dichroism and agarose gel electrophoresis we found an optimal siRNA:liposome complexation ratio of 1:77, which protected siRNA from ribonuclease-mediated degradation. Morphological imaging confirmed a shift from discrete vesicular structures to organized multilamellar lipoplexes, consistent with electrostatically driven self-assembly. In cellular studies, the optimized nanocarrier promoted efficient uptake of fluorescent siRNA in MDA-MB-231 cells and achieved functional delivery of anti-EGFR, leading to substantially reduced expression of the target gene at both transcript and protein levels. This work offers mechanistic understanding of peptide-assisted lipid:siRNA assembly and positions R9-functionalized DOPE/TAP liposomes as a promising platform for siRNA delivery.

Matching journals

The top 13 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.