Quantitative live-cell imaging and computational modeling shed new light on endogenous WNT/CTNNB1 signaling dynamics

WNT/CTNNB1 signaling regulates tissue development and homeostasis in all multicellular animals, but the underlying molecular mechanism remains incompletely understood. Specifically, quantitative insight into endogenous protein behavior is missing. Here, we combine CRISPR/Cas9-mediated genome editing and quantitative live-cell microscopy to measure the dynamics, diffusion characteristics and absolute concentrations of fluorescently tagged, endogenous CTNNB1 in human cells under both physiological and oncogenic conditions. State-of-the-art imaging reveals that a substantial fraction of CTNNB1 resides in slow-diffusing cytoplasmic complexes, irrespective of the activation status of the pathway. This cytoplasmic CTNNB1 complex undergoes a major reduction in size when WNT/CTNNB1 is (hyper)activated. Based on our biophysical measurements, we build a computational model of WNT/CTNNB1 signaling. Our integrated experimental and computational approach reveals that WNT pathway activation regulates the dynamic distribution of free and complexed CTNNB1 across different subcellular compartments through three regulatory nodes: the destruction complex, nucleocytoplasmic shuttling, and nuclear retention.

Acute Effects of Morning Light on Plasma Glucose and Triglycerides in Healthy Men and Men with Type 2 Diabetes

Ambient light intensity is signaled directly to hypothalamic areas that regulate energy metabolism. Observational studies have shown associations between ambient light intensity and plasma glucose and lipid levels, but human data on the acute metabolic effects of light are scarce. Since light is the main signal indicating the onset of the diurnal phase of physical activity and food intake in humans, we hypothesized that bright light would affect glucose and lipid metabolism. Therefore, we determined the acute effects of bright light on plasma glucose and lipid concentrations in 2 randomized crossover trials: (1) in 8 healthy lean men and (2) in 8 obese men with type 2 diabetes. From 0730 h, subjects were exposed to either bright light (4000 lux) or dim light (10 lux) for 5 h. After 1 h of light exposure, subjects consumed a 600-kcal mixed meal. Primary endpoints were fasting and postprandial plasma glucose levels. In healthy men, bright light did not affect fasting or postprandial plasma glucose levels. However, bright light increased fasting and postprandial plasma triglycerides. In men with type 2 diabetes, bright light increased fasting and postprandial glucose levels. In men with type 2 diabetes, bright light did not affect fasting triglyceride levels but increased postprandial triglyceride levels. We show that ambient light intensity acutely affects human plasma glucose and triglyceride levels. Our findings warrant further research into the consequences of the metabolic effects of light for the diagnosis and prevention of hyperglycemia and dyslipidemia.

Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis

Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze-thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2(IV)(7-3)). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786-7794, ref 1) produced a data-matrix containing quantitative information on 2,559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low t-test significance (p-value > 0.05) and a low absolute fold ratio (<2) between the two levels of each factor were removed. The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA). Permutation tests were used to identify which of the preanalytical factors influenced the abundance of the measured peptides most significantly. The most important preanalytical factors affecting peptide intensity were (1) the hemolysis level, (2) stopping trypsin digestion with acid, and (3) the trypsin/protein ratio. This provides guidelines for the experimentalist to keep the ratio of trypsin/protein constant and to control the trypsin reaction by stopping it with acid at an accurately set pH. The hemolysis level cannot be controlled tightly as it depends on the status of a patient's blood (e.g., red blood cells are more fragile in patients undergoing chemotherapy) and the care with which blood was sampled (e.g., by avoiding shear stress). However, its level can be determined with a simple UV spectrophotometric measurement and samples with extreme levels or the peaks affected by hemolysis can be discarded from further analysis. The loadings of the ASCA model led to peptide peaks that were most affected by a given factor, for example, to hemoglobin-derived peptides in the case of the hemolysis level. Peak intensity differences for these peptides were assessed by means of extracted ion chromatograms confirming the results of the ASCA model.