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

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)

Interactions of tropomyosin Tpm1.1 on a single actin filament: A method for extraction and processing of high resolution TIRF microscopy data.

Skeletal muscle tropomyosin (Tpm1.1) is an elongated, rod-shaped, alpha-helical coiled-coil protein that forms continuous head-to-tail polymers along both sides of the actin filament. In this study we use single molecule fluorescence TIRF microscopy combined with a microfluidic device and fluorescently labelled proteins to measure Tpm1.1 association to and dissociation from single actin filaments. Our experimental setup allows us to clearly resolve Tpm1.1 interactions on both sides of the filaments. Here we provide a semi-automated method for the extraction and quantification of kymograph data for individual actin filaments bound at different Tpm1.1 concentrations. We determine boundaries on the kymograph on each side of the actin filament, based on intensity thresholding, performing fine manual editing of the boundaries (if needed) and extracting user defined kinetic properties of the system. Using our analytical tools we can determine (i) nucleation point(s) and rates, (ii) elongation rates of Tpm1.1, (iii) identify meeting points after the saturation of filament, and when dissociation occurs, (iv) initiation point(s), (v) the final dissociation point(s), as well as (vi) dissociation rates. All of these measurements can be extracted from both sides of the filament, allowing for the determination of possible differences in behaviour on the two sides of the filament, and across concentrations. The robust and repeatable nature of the method allows quantitative, semi-automated analyses to be made of large studies of acto-tropomyosin interactions, as well as for other actin binding proteins or filamentous structures, opening the way for dissection of the dynamics underlying these interactions

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

In recent years, interest in the capability of virus particles as a treatment for cancer has increased. In this work, we present a mathematical model embodying the interaction between tumour cells and virus particles engineered to infect and destroy cancerous tissue. To quantify the effectiveness of oncolytic virotherapy, we conduct a local stability analysis and bifurcation analysis of our model. In the absence of tumour growth or viral decay, the model predicts that oncolytic virotherapy will successfully eliminate the tumour cell population for a large proportion of initial conditions. In comparison, for growing tumours and decaying viral particles there are no stable equilibria in the model; however, oscillations emerge for certain regions in our parameter space. We investigate how the period and amplitude of oscillations depend on tumour growth and viral decay. We find that higher tumour replication rates result in longer periods between oscillations and lower amplitudes for uninfected tumour cells. From our analysis, we conclude that oncolytic viruses can reduce growing tumours into a stable oscillatory state, but are insufficient to completely eradicate them. We propose that it is only with the addition of other anti-cancer agents that tumour eradication may be achieved by oncolytic virus

Modelling combined virotherapy and immunotherapy: strengthening the antitumour immune response mediated by IL-12 and GM-CSF expression

Combined virotherapy and immunotherapy has been emerging as a promising and effective cancer treatment for some time. Intratumoural injections of an oncolytic virus instigate an immune reaction in the host, resulting in an influx of immune cells to the tumour site. Through combining an oncolytic viral vector with immunostimulatory cytokines an additional antitumour immune response can be initiated, whereby immune cells induce apoptosis in both uninfected and virus infected tumour cells. We develop a mathematical model to reproduce the experimental results for tumour growth under treatment with an oncolytic adenovirus co-expressing the immunostimulatory cytokines interleukin 12 (IL-12) and granulocyte-monocyte colony stimulating factor (GM-CSF). By exploring heterogeneity in the immune cell stimulation by the treatment, we find a subset of the parameter space for the immune cell induced apoptosis rate, in which the treatment will be less effective in a short time period. Therefore, we believe the bivariate nature of treatment outcome, whereby tumours are either completely eradicated or grow unbounded, can be explained by heterogeneity in this immune characteristic. Furthermore, the model highlights the apparent presence of negative feedback in the helper T cell and APC stimulation dynamics, when IL-12 and GM-CSF are co-expressed as opposed to individually expressed by the viral vector

Metabolomics and Lipidomics Signatures of Insulin Resistance and Abdominal Fat Depots in People Living with Obesity

The liver, skeletal muscle, and adipose tissue are major insulin target tissues and key players in glucose homeostasis. We and others have described diverse insulin resistance (IR) phenotypes in people at risk of developing type 2 diabetes. It is postulated that identifying the IR phenotype in a patient may guide the treatment or the prevention strategy for better health outcomes in populations at risk. Here, we performed plasma metabolomics and lipidomics in a cohort of men and women living with obesity not complicated by diabetes (mean [SD] BMI 36.0 [4.5] kg/m2, n = 62) to identify plasma signatures of metabolites and lipids that align with phenotypes of IR (muscle, liver, or adipose tissue) and abdominal fat depots. We used 2-step hyperinsulinemic-euglycemic clamp with deuterated glucose, oral glucose tolerance test, dual-energy X-ray absorptiometry and abdominal magnetic resonance imaging to assess muscle-, liver- and adipose tissue- IR, beta cell function, body composition, abdominal fat distribution and liver fat, respectively. Spearman’s rank correlation analyses that passed the Benjamini–Hochberg statistical correction revealed that cytidine, gamma-aminobutyric acid, anandamide, and citrate corresponded uniquely with muscle IR, tryptophan, cAMP and phosphocholine corresponded uniquely with liver IR and phenylpyruvate and hydroxy-isocaproic acid corresponded uniquely with adipose tissue IR (p −4). Plasma cholesteryl sulfate (p = 0.00029) and guanidinoacetic acid (p = 0.0001) differentiated between visceral and subcutaneous adiposity, while homogentisate correlated uniquely with liver fat (p = 0.00035). Our findings may help identify diverse insulin resistance and adiposity phenotypes and enable targeted treatments in people living with obesity

Longitudinal Changes in Insulin Resistance in Normal Weight, Overweight and Obese Individuals

Background: Large cohort longitudinal studies have almost unanimously concluded that metabolic health in obesity is a transient phenomenon, diminishing in older age. We aimed to assess the fate of insulin sensitivity per se over time in overweight and obese individuals. Methods: Individuals studied using the hyperinsulinaemic-euglycaemic clamp at the Garvan Institute of Medical Research from 2008 to 2010 (n = 99) were retrospectively grouped into Lean (body mass index (BMI) < 25 kg/m2) or overweight/obese (BMI ≥ 25 kg/m2), with the latter further divided into insulin-sensitive (ObSen) or insulin-resistant (ObRes), based on median clamp M-value (M/I, separate cut-offs for men and women). Fifty-seven individuals participated in a follow-up study after 5.4 ± 0.1 years. Hyperinsulinaemic-euglycaemic clamp, dual-energy X-ray absorptiometry and circulating cardiovascular markers were measured again at follow-up, using the same protocols used at baseline. Liver fat was measured using computed tomography at baseline and proton magnetic resonance spectroscopy at follow-up with established cut-offs applied for defining fatty liver. Results: In the whole cohort, M/I did not change over time (p = 0.40); it remained significantly higher at follow-up in ObSen compared with ObRes (p = 0.02), and was not different between ObSen and Lean (p = 0.41). While BMI did not change over time (p = 0.24), android and visceral fat increased significantly in this cohort (ptime ≤ 0.0013), driven by ObRes (p = 0.0087 and p = 0.0001, respectively). Similarly, systolic blood pressure increased significantly over time (ptime = 0.0003) driven by ObRes (p = 0.0039). The best correlate of follow-up M/I was baseline M/I (Spearman’s r = 0.76, p = 1.1 × 10−7). Conclusions: The similarity in insulin sensitivity between the ObSen and the Lean groups at baseline persisted over time. Insulin resistance in overweight and obese individuals predisposed to further metabolic deterioration over time