A Coarse-Grained Simulation Framework to Study Polyethylenimine-DNA Nanoparticles in Gene Delivery

Abstract

Cellular DNAs contain genetic information vital for the functioning of the cells. Corruption of this information, or genetic disorders, can lead to various diseases. A potential treatment is to deliver exogenous DNAs with the correct genetic information to malignant cells to achieve a therapeutic response. However, DNAs are prone to degradation and are not efficient in overcoming cellular barriers, thus requiring specialized gene carriers. Among the non-viral gene carriers, the polymer polyethylenimine (PEI) has shown potential. Adding PEI to DNA forms nanoparticles (NPs) that protect DNA from degradation and help it overcome cellular barriers such as cellular uptake, endosomal escape, and nuclear trafficking. The efficacy of gene delivery depends on the properties of PEIs and NPs but their relationship is not well understood. Current experimental studies are limited because molecules inside cells cannot be observed with infinite precision, whereas current molecular simulations have not modeled large systems relevant for gene delivery. This dissertation studies various steps of PEI-DNA gene delivery using large-scale coarsegrained (CG) molecular dynamics simulations. Three main studies have been performed. The first study includes the development of a CG forcefield for PEIs that capture its diverse molecular properties (degree of branching, molecular weight, and protonation ratio) and interaction with DNA. In the second study, the molecular aggregation mechanism behind PEI-DNA NP formation was explored using CG simulations with a large number of PEIs and DNAs at different N/P ratios. The aggregation was found to be dependent on the diffusion of PEIs, DNAs, and NPs and their electrostatic interactions. The N/P ratio was found to be an important control parameter for electrostatic interactions that can alter NP properties such as size, size distribution, shape, and rate of NP growth. Furthermore, for a high N/P ratio, a two-step addition of PEIs was found to make the NPs smaller and more spherical, which has the potential to increase the efficacy of cellular uptake. The third study performed large-scale CG simulations to determine the effects of endosomal acidification on PEI-DNA NPs, an inevitable step in gene delivery. Simulations of endosomal acidification revealed that NP undergoes structural changes. NPs prepared at low N/P ratio underwent further aggregation, whereas at high N/P ratio they dissociated. Dissociation of NPs increased the osmotic pressure and reduced the NP’s size that respectively help endosomal escape and nuclear trafficking. These findings support the observation of the strong efficacy of gene delivery at a high N/P ratio. The structural changes in the NP during dissociation were explained using a free energy landscape of PEIs, which revealed dissociation to be driven by repulsion between PEIs bound to the same DNA pair and repulsion between DNAs. These observations suggest a PEI with moderate molecular weight and degree of branching can increase NP dissociation and thereby the efficacy of gene delivery. To assist the comparison of molecular simulations with experimental fluorescence microscopy used to study gene delivery, a new in-silico fluorescence microscopy technique was developed. The new technique converted molecular simulation trajectories into images that are comparable to the images obtained from experimental fluorescence microscopy. The crossvalidation of in-silico images, experimental images, and molecular simulations bridged their analysis and generated new information such as determining the occurrence of NP dissociation in experimental images that were not originally reported. Comparison of in-silico images and molecular simulations can also determine equivalence of properties for future comparison between experiments and simulations. Furthermore, the comparison can be used to assess and develop image analysis algorithms for experimental images. Overall, this dissertation developed a framework for performing and analyzing large-scale CG simulations of different steps in PEI-DNA gene delivery and its comparison with experiments.