Adipose Tissue Extracellular Matrix and Vascular Abnormalities in Obesity and Insulin Resistance

Context: Insulin resistance is associated with inflammation, fibrosis, and hypoxia in adipose tissue. Objective: This study was intended to better characterize the extracellular matrix (ECM) and vascularity of insulin-resistant adipose tissue. Design: Adipose expression of collagens, elastin, and angiogenic factors was assessed using realtime RT-PCR and immunohistochemistry (IHC) in abdominal sc adipose tissue. Adipocyte-macrophage coculture experiments examined the effects of polarized macrophages on adipose ECM gene expression, and the effects of collagens were measured in an angiogenesis assay. Participants and Setting: A total of 74 nondiabetic subjects participated at a University Clinical Research Center. Interventions: Interventions included baseline adipose biopsy and measurement of insulin sensitivity. Main Outcome Measures: Outcome measures included characterization of vascularity and ECM in adipose tissue. Results: CD31 (an endothelial marker) mRNA showed no significant correlation with body mass index or insulin sensitivity. In a subgroup of 17 subjects (nine obese, eight lean), CD31-positive capillary number in obese was decreased by 58%, whereas larger vessels were increased by 70%, accounting for the lack of change in CD31 expression with obesity. Using IHC, obese (compared with lean) subjects had decreased elastin and increased collagen V expression, and adipocytes cocultured with M2 macrophages had reduced elastin and increased collagen V expression. In obese subjects, collagen V was colocalized with large blood vessels, and the addition of collagen V to an angiogenesis assay inhibited endothelial budding. Conclusions: The adipose tissue from obese/insulin- resistant subjects has fewer capillaries and morelarge vessels as compared with lean subjects.The ECM of adipose tissue may play an important role in regulating the expandability as well as angiogenesis of adipose tissue. (J Clin Endocrinol Metab 96: E1990–E1998, 2011

The Lipogenic Enzymes DGAT1, FAS, and LPL in Adipose Tissue: Effects of Obesity, Insulin Resistance, and TZD Treatment

Acyl-coenzyme A:diacylglycerol transferase (DGAT), fatty acid synthetase (FAS), and LPL are three enzymes important in adipose tissue triglyceride accumulation. To study the relationship of DGAT1, FAS, and LPL with insulin, we examined adipose mRNA expression of these genes in subjects with a wide range of insulin sensitivity (SI). DGAT1 and FAS (but not LPL) expression were strongly correlated with SI. In addition, the expression of DGAT1 and FAS (but not LPL) were higher in normal glucose-tolerant subjects compared with subjects with impaired glucose tolerance (IGT) (P \u3c 0.005). To study the effects of insulin sensitizers, subjects with IGT were treated with pioglitazone or metformin for 10 weeks, and lipogenic enzymes were measured in adipose tissue. After pioglitazone treatment, DGAT1 expression was increased by 33 ± 10% (P \u3c 0.05) and FAS expression increased by 63 ± 8% (P \u3c 0.05); however, LPL expression was not altered. DGAT1, FAS, and LPL mRNA expression were not significantly changed after metformin treatment. The treatment of mice with rosiglitazone also resulted in an increase in adipose expression of DGAT1 by 2- to 3-fold, as did the treatment of 3T3 F442A adipocytes in vitro with thiazolidinediones. These data support a more global concept suggesting that adipose lipid storage functions to prevent peripheral lipotoxicity

Economic Models Involving Time Fractal

In this article, the price adjustment equation has been proposed and studied in the frame of fractal calculus which plays an important role in market equilibrium. Fractal time has been recently suggested by researchers in physics due to the self-similar properties and fractional dimension. We investigate the economic models from the viewpoint of local and non-local fractal Caputo derivatives. We derive some novel analytical solutions via the fractal Laplace transform. In fractal calculus, a useful local fractal derivative is a generalized local derivative in the standard computational sense, and the non-local fractal Caputo fractal derivative is a generalization of the non-local fractional Caputo derivative. The economic models involving fractal time provide a new framework that depends on the dimension of fractal time. The suggested fractal models are considered as a generalization of standard models that present new models to economists for fitting the economic data. In addition, we carry out a comparative analysis to understand the advantages of the fractal calculus operator on the basis of the additional fractal dimension of time parameter, denoted by $alpha$, which is related to the local derivative, and we also indicate that when this dimension is equal to $1$, we obtain the same results in the standard fractional calculus as well as when $alpha$ and the nonlocal memory effect parameter, denoted by $gamma$, of the nonlocal fractal derivative are both equal to $1$, we obtain the same results in the standard calculus

Increasing Adipocyte Lipoprotein Lipase Improves Glucose Metabolism in High Fat Diet-Induced Obesity

Lipid accumulation in liver and skeletal muscle contributes to co-morbidities associated with diabetes and obesity. We made a transgenic mouse in which the adiponectin (Adipoq) promoter drives expression of lipoprotein lipase (LPL) in adipocytes to potentially increase adipose tissue lipid storage. These mice (Adipoq-LPL) have improved glucose and insulin tolerance as well as increased energy expenditure when challenged with a high fat diet (HFD). To identify the mechanism(s) involved, we determined whether the Adipoq-LPL mice diverted dietary lipid to adipose tissue to reduce peripheral lipotoxicity, but we found no evidence for this. Instead, characterization of the adipose tissue of the male mice after HFD challenge revealed that the mRNA levels of peroxisome proliferator-activated receptor-γ (PPARγ) and a number of PPARγ-regulated genes were higher in the epididymal fat pads of Adipoq-LPL mice than control mice. This included adiponectin, whose mRNA levels were increased, leading to increased adiponectin serum levels in the Adipoq-LPL mice. In many respects, the adipose phenotype of these animals resembles thiazolidinedione treatment except for one important difference, the Adipoq-LPL mice did not gain more fat mass on HFD than control mice and did not have increased expression of genes in adipose such as glycerol kinase, which are induced by high affinity PPAR agonists. Rather, there was selective induction of PPARγ-regulated genes such as adiponectin in the adipose of the Adipoq-LPL mice, suggesting that increasing adipose tissue LPL improves glucose metabolism in diet-induced obesity by improving the adipose tissue phenotype. Adipoq-LPL mice also have increased energy expenditure

Omega-3 Fatty Acids Reduce Adipose Tissue Macrophages in Human Subjects with Insulin Resistance

Fish oils (FOs) have anti-inflammatory effects and lower serum triglycerides. This study examined adipose and muscle inflammatory markers after treatment of humans with FOs and measured the effects of ω-3 fatty acids on adipocytes and macrophages in vitro. Insulin-resistant, nondiabetic subjects were treated with Omega-3-Acid Ethyl Esters (4 g/day) or placebo for 12 weeks. Plasma macrophage chemoattractant protein 1 (MCP-1) levels were reduced by FO, but the levels of other cytokines were unchanged. The adipose (but not muscle) of FO-treated subjects demonstrated a decrease in macrophages, a decrease in MCP-1, and an increase in capillaries, and subjects with the most macrophages demonstrated the greatest response to treatment. Adipose and muscle ω-3 fatty acid content increased after treatment; however, there was no change in insulin sensitivity or adiponectin. In vitro, M1-polarized macrophages expressed high levels of MCP-1. The addition of ω-3 fatty acids reduced MCP-1 expression with no effect on TNF-α. In addition, ω-3 fatty acids suppressed the upregulation of adipocyte MCP-1 that occurred when adipocytes were cocultured with macrophages. Thus, FO reduced adipose macrophages, increased capillaries, and reduced MCP-1 expression in insulin-resistant humans and in macrophages and adipocytes in vitro; however, there was no measureable effect on insulin sensitivity. Diabetes 62:1709–1717, 201

Coupled Systems of Differential-Algebraic and Kinetic Equations with Application to the Mathematical Modelling of Muscle Tissue

We consider a coupled system composed of a linear differential-algebraic equation (DAE) and a linear large-scale system of ordinary differential equations where the latter stands for the dynamics of numerous identical particles. Replacing the discrete particles by a kinetic equation for a particle density, we obtain in the mean-field limit the new class of partially kinetic systems. We investigate the influence of constraints on the kinetic theory of those systems and present necessary adjustments. We adapt the mean-field limit to the DAE model and show that index reduction and the mean-field limit commute. As a main result, we prove Dobrushin's stability estimate for linear systems. The estimate implies convergence of the mean-field limit and provides a rigorous link between the particle dynamics and their kinetic description. Our research is inspired by mathematical models for muscle tissue where the macroscopic behaviour is governed by the equations of continuum mechanics, often discretised by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory. The latter represents a kinetic equation that characterises the state of the actin-myosin bindings in the muscle filaments. Linear partially kinetic systems are a simplified version of such models, with focus on the constraints.Comment: 32 pages, 18 figure

Spatio-Temporal Dependence of the Signaling Response in Immune-Receptor Trafficking Networks Regulated by Cell Density: A Theoretical Model

Cell signaling processes involve receptor trafficking through highly connected networks of interacting components. The binding of surface receptors to their specific ligands is a key factor for the control and triggering of signaling pathways. In most experimental systems, ligand concentration and cell density vary within a wide range of values. Dependence of the signal response on cell density is related with the extracellular volume available per cell. This dependence has previously been studied using non-spatial models which assume that signaling components are well mixed and uniformly distributed in a single compartment. In this paper, a mathematical model that shows the influence exerted by cell density on the spatio-temporal evolution of ligands, cell surface receptors, and intracellular signaling molecules is developed. To this end, partial differential equations were used to model ligand and receptor trafficking dynamics through the different domains of the whole system. This enabled us to analyze several interesting features involved with these systems, namely: a) how the perturbation caused by the signaling response propagates through the system; b) receptor internalization dynamics and how cell density affects the robustness of dose-response curves upon variation of the binding affinity; and c) that enhanced correlations between ligand input and system response are obtained under conditions that result in larger perturbations of the equilibrium . Finally, the results are compared with those obtained by considering that the above components are well mixed in a single compartment

Targeted Drug Delivery by Gemtuzumab Ozogamicin: Mechanism-Based Mathematical Model for Treatment Strategy Improvement and Therapy Individualization

Gemtuzumab ozogamicin (GO) is a chemotherapy-conjugated anti-CD33 monoclonal antibody effective in some patients with acute myeloid leukemia (AML). The optimal treatment schedule and optimal timing of GO administration relative to other agents remains unknown. Conventional pharmacokinetic analysis has been of limited insight for the schedule optimization. We developed a mechanism-based mathematical model and employed it to analyze the time-course of free and GO-bound CD33 molecules on the lekemic blasts in individual AML patients treated with GO. We calculated expected intravascular drug exposure (I-AUC) as a surrogate marker for the response to the drug. A high CD33 production rate and low drug efflux were the most important determinants of high I-AUC, characterizing patients with favorable pharmacokinetic profile and, hence, improved response. I-AUC was insensitive to other studied parameters within biologically relevant ranges, including internalization rate and dissociation constant. Our computations suggested that even moderate blast burden reduction prior to drug administration enables lowering of GO doses without significantly compromising intracellular drug exposure. These findings indicate that GO may optimally be used after cyto-reductive chemotherapy, rather than before, or concomitantly with it, and that GO efficacy can be maintained by dose reduction to 6 mg/m2 and a dosing interval of 7 days. Model predictions are validated by comparison with the results of EORTC-GIMEMA AML19 clinical trial, where two different GO schedules were administered. We suggest that incorporation of our results in clinical practice can serve identification of the subpopulation of elderly patients who can benefit most of the GO treatment and enable return of the currently suspended drug to clinic

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p