PEGylated surfaces for the study of DNA–protein interactions by atomic force microscopy

DNA–protein interactions are vital to cellular function, with key roles in the regulation of gene expression and genome maintenance. Atomic force microscopy (AFM) offers the ability to visualize DNA–protein interactions at nanometre resolution in near-physiological buffers, but it requires that the DNA be adhered to the surface of a solid substrate. This presents a problem when working in biologically relevant protein concentrations, where proteins may be present in large excess in solution; much of the biophysically relevant information can therefore be occluded by non-specific protein binding to the underlying substrate. Here we explore the use of PLLx-b-PEGy block copolymers to achieve selective adsorption of DNA on a mica surface for AFM studies. Through varying both the number of lysine and ethylene glycol residues in the block copolymers, we show selective adsorption of DNA on mica that is functionalized with a PLL10-b-PEG113/PLL1000–2000 mixture as viewed by AFM imaging in a solution containing high concentrations of streptavidin. We show – through the use of biotinylated DNA and streptavidin – that this selective adsorption extends to DNA–protein complexes and that DNA-bound streptavidin can be unambiguously distinguished in spite of an excess of unbound streptavidin in solution. Finally, we apply this to the nuclear enzyme PARP1, resolving the binding of individual PARP1 molecules to DNA by in-liquid AFM

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity