Ex vivo red blood cell hemolysis assay for the evaluation of pH-responsive endosomolytic agents for cytosolic delivery of biomacromolecular drugs.

Phospholipid bilayers that constitute endo-lysosomal vesicles can pose a barrier to delivery of biologic drugs to intracellular targets. To overcome this barrier, a number of synthetic drug carriers have been engineered to actively disrupt the endosomal membrane and deliver cargo into the cytoplasm. Here, we describe the hemolysis assay, which can be used as rapid, high-throughput screen for the cytocompatibility and endosomolytic activity of intracellular drug delivery systems. In the hemolysis assay, human red blood cells and test materials are co-incubated in buffers at defined pHs that mimic extracellular, early endosomal, and late endo-lysosomal environments. Following a centrifugation step to pellet intact red blood cells, the amount of hemoglobin released into the medium is spectrophotometrically measured (405 nm for best dynamic range). The percent red blood cell disruption is then quantified relative to positive control samples lysed with a detergent. In this model system the erythrocyte membrane serves as a surrogate for the lipid bilayer membrane that enclose endo-lysosomal vesicles. The desired result is negligible hemolysis at physiologic pH (7.4) and robust hemolysis in the endo-lysosomal pH range from approximately pH 5-6.8.

A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility.

A central goal of human genetics is to identify susceptibility genes for common human diseases. An important challenge is modelling gene-gene interaction or epistasis that can result in nonadditivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as a machine learning alternative to parametric logistic regression for detecting interactions in the absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modelling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher's Exact Test rather than a predetermined threshold. The advantage of this approach is that only statistically significant genotype combinations are considered in the MDR analysis. We use simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire.

A simple and computationally efficient sampling approach to covariate adjustment for multifactor dimensionality reduction analysis of epistasis.

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR's ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.