Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols

Oncolytic virotherapy is an experimental cancer treatment that uses genetically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. While PEG modification appeared to improve plasma retention and initial infectivity it also increased the virus particle arrival time. We derive a mathematical model that describes the interaction between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy is explored.Comment: 15 pages, 6 figure

Kinetic evidence for unique regulation of GLUT4 trafficking by insulin and AMP-activated protein kinase activators in L6 myotubes.

In L6 myotubes, redistribution of a hemagglutinin (HA) epitope-tagged GLUT4 (HA-GLUT4) to the cell surface occurs rapidly in response to insulin stimulation and AMP-activated protein kinase (AMPK) activation. We have examined whether these separate signaling pathways have a convergent mechanism that leads to GLUT4 mobilization and to changes in GLUT4 recycling. HA antibody uptake on GLUT4 in the basal steady state reached a final equilibrium level that was only 81% of the insulin-stimulated level. AMPK activators (5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR) and A-769662) led to a similar level of antibody uptake to that found in insulin-stimulated cells. However, the combined responses to insulin stimulation and AMPK activation led to an antibody uptake level of approximately 20% above the insulin level. Increases in antibody uptake due to insulin, but not AICAR or A-769662, treatment were reduced by both wortmannin and Akt inhibitor. The GLUT4 internalization rate constant in the basal steady state was very rapid (0.43 min(-1)) and was decreased during the steady-state responses to insulin (0.18 min(-1)), AICAR (0.16 min(-1)), and A-769662 (0.24 min(-1)). This study has revealed a nonconvergent mobilization of GLUT4 in response to activation of Akt and AMPK signaling. Furthermore, GLUT4 trafficking in L6 muscle cells is very reliant on regulated endocytosis for control of cell surface GLUT4 levels

Glycemic effects and safety of L-Glutamine supplementation with or without sitagliptin in type 2 diabetes patients-a randomized study.

BACKGROUND AND AIMS: L-glutamine is an efficacious glucagon-like peptide (GLP)-1 secretagogue in vitro. When administered with a meal, glutamine increases GLP-1 and insulin excursions and reduces postprandial glycaemia in type 2 diabetes patients. The aim of the study was to assess the efficacy and safety of daily glutamine supplementation with or without the dipeptidyl peptidase (DPP)-4 inhibitor sitagliptin in well-controlled type 2 diabetes patients. METHODS: Type 2 diabetes patients treated with metformin (n = 13, 9 men) with baseline glycated hemoglobin (HbA1c) 7.1±0.3% (54±4 mmol/mol) received glutamine (15 g bd)+ sitagliptin (100 mg/d) or glutamine (15 g bd) + placebo for 4 weeks in a randomized crossover study. RESULTS: HbA1c (P = 0.007) and fructosamine (P = 0.02) decreased modestly, without significant time-treatment interactions (both P = 0.4). Blood urea increased (P<0.001) without a significant time-treatment interaction (P = 0.8), but creatinine and estimated glomerular filtration rate (eGFR) were unchanged (P≥0.5). Red blood cells, hemoglobin, hematocrit, and albumin modestly decreased (P≤0.02), without significant time-treatment interactions (P≥0.4). Body weight and plasma electrolytes remained unchanged (P≥0.2). CONCLUSIONS: Daily oral supplementation of glutamine with or without sitagliptin for 4 weeks decreased glycaemia in well-controlled type 2 diabetes patients, but was also associated with mild plasma volume expansion. TRIAL REGISTRATION: ClincalTrials.gov NCT00673894

Binding of Transcription Factor GabR to DNA Requires Recognition of DNA Shape at a Location Distinct from its Cognate Binding Site

Mechanisms for transcription factor recognition of specific DNA base sequences are well characterized and recent studies demonstrate that the shape of these cognate binding sites is also important. Here, we uncover a new mechanism where the transcription factor GabR simultaneously recognizes two cognate binding sites and the shape of a 29 bp DNA sequence that bridges these sites. Small-angle X-ray scattering and multi-angle laser light scattering are consistent with a model where the DNA undergoes a conformational change to bend around GabR during binding. In silico predictions suggest that the bridging DNA sequence is likely to be bendable in one direction and kinetic analysis of mutant DNA sequences with biolayer interferometry, allowed the independent quantification of the relative contribution of DNA base and shape recognition in the GabR–DNA interaction. These indicate that the two cognate binding sites as well as the bendability of the DNA sequence in between these sites are required to form a stable complex. The mechanism of GabR–DNA interaction provides an example where the correct shape of DNA, at a clearly distinct location from the cognate binding site, is required for transcription factor binding and has implications for bioinformatics searches for novel binding sites

The Distance Between:An algorithmic approach to comparing stochastic models to time-series data

While mean-field models of cellular operations have identified dominant processes at the macroscopic scale, stochastic models may provide further insight into mechanisms at the molecular scale. In order to identify plausible stochastic models, quantitative comparisons between the models and the experimental data are required. The data for these systems have small sample sizes and time-evolving distributions. The aim of this study is to identify appropriate distance metrics for the quantitative comparison of stochastic model outputs and time-evolving stochastic measurements of a system. We identify distance metrics with features suitable for driving parameter inference, model comparison, and model validation, constrained by data from multiple experimental protocols.In this study, stochastic model outputs are compared to synthetic data across three scales: that of the data at the points the system is sampled during the time course of each type of experiment; a combined distance across the time course of each experiment; and a combined distance across all the experiments. Two broad categories of comparators at each point were considered, based on the empirical cumulative distribution (ECDF) of the data and of the model outputs: discrete based measures such as the Kolmogorov-Smirnov distance, and integrated measures such as the Wasserstein-1 distance between the ECDFs.It was found that the discrete based measures were highly sensitive to parameter changes near the synthetic data parameters, but were largely insensitive otherwise, whereas the integrated distances had smoother transitions as the parameters approached the true values. The integrated measures were also found to be robust to noise added to the synthetic data, replicating experimental error. The characteristics of the identified distances provides the basis for the design of an algorithm suitable for fitting stochastic models to real world stochastic data

High-throughput analysis of the dynamics of recycling cell surface proteins.

International audienceRecycling via the plasma membrane is a key feature that is shared by many membrane proteins. Using a combination of indirect immunofluorescence labeling and fluorescence detection using a fluorescence multiwell plate reader, we exploited the possibilities of quantitatively measuring the trafficking kinetics of transmembrane proteins. Parameters that can be studied include dynamic appearance/presence at the cell surface, recycling via the cell surface, and internalization. For the insulin-responsive glucose transporter GLUT4 (glucose transporter number 4), details are presented on how to quantitatively measure insulin-induced GLUT4 translocation toward the plasma membrane (transition state) and to analyze cell surface recycling of GLUT4 in basal and insulin-stimulated cells (steady state)

Insulin Increases Cell Surface GLUT4 Levels by Dose Dependently Discharging GLUT4 into a Cell Surface Recycling Pathway

The insulin-responsive glucose transporter GLUT4 plays an essential role in glucose homeostasis. A novel assay was used to study GLUT4 trafficking in 3T3-L1 fibroblasts/preadipocytes and adipocytes. Whereas insulin stimulated GLUT4 translocation to the plasma membrane in both cell types, in nonstimulated fibroblasts GLUT4 readily cycled between endosomes and the plasma membrane, while this was not the case in adipocytes. This efficient retention in basal adipocytes was mediated in part by a C-terminal targeting motif in GLUT4. Insulin caused a sevenfold increase in the amount of GLUT4 molecules present in a trafficking cycle that included the plasma membrane. Strikingly, the magnitude of this increase correlated with the insulin dose, indicating that the insulin-induced appearance of GLUT4 at the plasma membrane cannot be explained solely by a kinetic change in the recycling of a fixed intracellular GLUT4 pool. These data are consistent with a model in which GLUT4 is present in a storage compartment, from where it is released in a graded or quantal manner upon insulin stimulation and in which released GLUT4 continuously cycles between intracellular compartments and the cell surface independently of the nonreleased pool

Insulin stimulates the entry of GLUT4 into the endosomal recycling pathway by a quantal mechanism.

International audienceThe insulin-sensitive glucose transporter GLUT4 mediates the uptake of glucose into adipocytes and muscle cells. In this study we have used a novel 96-well plate fluorescence assay to study the kinetics of GLUT4 trafficking in 3T3-L1 adipocytes. We have found evidence for a graded release mechanism whereby GLUT4 is released into the plasma membrane recycling system in a nonkinetic manner as follows: the kinetics of appearance of GLUT4 at the plasma membrane is independent of the insulin concentration; a large proportion of GLUT4 molecules do not participate in plasma membrane recycling in the absence of insulin; and with increasing insulin there is an incremental increase in the total number of GLUT4 molecules participating in the recycling pathway rather than simply an increased rate of recycling. We propose a model whereby GLUT4 is stored in a compartment that is disengaged from the plasma membrane recycling system in the basal state. In response to insulin, GLUT4 is quantally released from this compartment in a pulsatile manner, leaving some sequestered from the recycling pathway even in conditions of excess insulin. Once disengaged from this location we suggest that in the continuous presence of insulin this quanta of GLUT4 continuously recycles to the plasma membrane, possibly via non-endosomal carriers that are formed at the perinuclear region

High-throughput analysis of the dynamics of recycling cell surface proteins.

International audienceRecycling via the plasma membrane is a key feature that is shared by many membrane proteins. Using a combination of indirect immunofluorescence labeling and fluorescence detection using a fluorescence multiwell plate reader, we exploited the possibilities of quantitatively measuring the trafficking kinetics of transmembrane proteins. Parameters that can be studied include dynamic appearance/presence at the cell surface, recycling via the cell surface, and internalization. For the insulin-responsive glucose transporter GLUT4 (glucose transporter number 4), details are presented on how to quantitatively measure insulin-induced GLUT4 translocation toward the plasma membrane (transition state) and to analyze cell surface recycling of GLUT4 in basal and insulin-stimulated cells (steady state)