Differential Mitochondrial Adaptation in Primary Vascular Smooth Muscle Cells from a Diabetic Rat Model

Diabetes affects more than 330 million people worldwide and causes elevated cardiovascular disease risk. Mitochondria are critical for vascular function, generate cellular reactive oxygen species (ROS), and are perturbed by diabetes, representing a novel target for therapeutics. We hypothesized that adaptive mitochondrial plasticity in response to nutrient stress would be impaired in diabetes cellular physiology via a nitric oxide synthase- (NOS-) mediated decrease in mitochondrial function. Primary smooth muscle cells (SMCs) from aorta of the nonobese, insulin resistant rat diabetes model Goto-Kakizaki (GK) and the Wistar control rat were exposed to high glucose (25 mM). At baseline, significantly greater nitric oxide evolution, ROS production, and respiratory control ratio (RCR) were observed in GK SMCs. Upon exposure to high glucose, expression of phosphorylated eNOS, uncoupled respiration, and expression of mitochondrial complexes I, II, III, and V were significantly decreased in GK SMCs (p<0.05). Mitochondrial superoxide increased with high glucose in Wistar SMCs (p<0.05) with no change in the GK beyond elevated baseline concentrations. Baseline comparisons show persistent metabolic perturbations in a diabetes phenotype. Overall, nutrient stress in GK SMCs caused a persistent decline in eNOS and mitochondrial function and disrupted mitochondrial plasticity, illustrating eNOS and mitochondria as potential therapeutic targets

CMS physics technical design report : Addendum on high density QCD with heavy ions

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Raw and processed microscope images of fixed cells at baseline and following various experimental perturbations

The data included in this article comprise raw and processed images of fixed cells at baseline and subjected to various experimental perturbations. This dataset includes images of HUVEC cells fixed and subsequently incubated at either 37 °C or room temperature, primary rat vascular smooth muscle cells exposed to 25 mM glucose, and SH-SY5Y neurons exposed to hydrogen peroxide. Raw images appear exactly as they were captured on the microscope, while processed images show the binarization provided by software used for measurements of mitochondrial morphology. For in-depth discussion of the experiments and computational methods pertaining to this data, please refer to the corresponding research article titled “Fully automated software for quantitative measurements of mitochondrial morphology” (McClatchey et al., in press) [1]

Microvascular perfusion heterogeneity impairs oxygenation and contributes to peripheral vascular disease in metabolic syndrome

It has been established that development of the metabolic syndrome in obese Zucker rats (OZR) is associated with impaired fatigue-resistance of in situ skeletal muscle paralleling a blunted functional hyperemia. However, recent studies suggest that reduced bulk oxygen delivery to skeletal muscle with elevated metabolic demand is not sufficient to explain the compromised muscle performance. Using novel experimental data and recent insight into altered perfusion within the microcirculation of OZR, we developed a new simulation for tissue oxygenation with increasing metabolic demand in lean (LZR) and OZR skeletal muscle using physiologically-realistic data and relationships. As metabolic demand rose (via contraction frequency), blood flow to, and oxygen uptake by, in situ skeletal muscle increased in both strains, although the response was blunted in OZR. Oddly, venous blood oxygen tension (PvO2) draining the gastrocnemius muscle of OZR was elevated versus LZR across metabolic demands; a paradoxical response given assumptions of incrased microvascular residency time in skeletal muscle of OZR. Using a microvascular network model of multiple Krogh cylinders supplied by a network with homogeneous flow distribution at bifurcations (gamma=0.5), we were unable to simulate tissue oxygenation and PvO2 differences between LZR and OZR. However, with introduction of increasing perfusion asymmetry (gamma>0.5), changes to microvascular hematocrit and increased plasma skimming throughout the network, our ability to simulate the experimental results was much improved. As a result, we have demonstrated that increased perfusion asymmetry (gamma) within the skeletal muscle microcirculation is not only a defining characteristic of the metabolic syndrome, it is required to effectively model and understand alterations in blood-tissue oxygen exchange in this highly translationally relevant model of human disease risk

Phase Transitions in the Multi-cellular Regulatory Behavior of Pancreatic Islet Excitability

<div><p>The pancreatic islets of Langerhans are multicellular micro-organs integral to maintaining glucose homeostasis through secretion of the hormone insulin. β-cells within the islet exist as a highly coupled electrical network which coordinates electrical activity and insulin release at high glucose, but leads to global suppression at basal glucose. Despite its importance, how network dynamics generate this emergent binary on/off behavior remains to be elucidated. Previous work has suggested that a small threshold of quiescent cells is able to suppress the entire network. By modeling the islet as a Boolean network, we predicted a phase-transition between globally active and inactive states would emerge near this threshold number of cells, indicative of critical behavior. This was tested using islets with an inducible-expression mutation which renders defined numbers of cells electrically inactive, together with pharmacological modulation of electrical activity. This was combined with real-time imaging of intracellular free-calcium activity [Ca²⁺]_i and measurement of physiological parameters in mice. As the number of inexcitable cells was increased beyond ∼15%, a phase-transition in islet activity occurred, switching from globally active wild-type behavior to global quiescence. This phase-transition was also seen in insulin secretion and blood glucose, indicating physiological impact. This behavior was reproduced in a multicellular dynamical model suggesting critical behavior in the islet may obey general properties of coupled heterogeneous networks. This study represents the first detailed explanation for how the islet facilitates inhibitory activity in spite of a heterogeneous cell population, as well as the role this plays in diabetes and its reversal. We further explain how islets utilize this critical behavior to leverage cellular heterogeneity and coordinate a robust insulin response with high dynamic range. These findings also give new insight into emergent multicellular dynamics in general which are applicable to many coupled physiological systems, specifically where inhibitory dynamics result from coupled networks.</p></div

Experimental data showing how Boolean network model describes phase transitions in islet [Ca^2<b>+</b>]_i.

<p>A) Percent cells showing [Ca²⁺]_i elevations as a function of number of excitable cells, as determined by lack of GFP and thus Kir6.2^{[ΔN30,K185Q]} expression (i.e. P_exc = 1-%GFP), together with Boolean network model fit. Filled squares indicate experimental data, solid line represents mean of simulations that best fit data with <i>p</i> = 0.30 and <i>Sp</i> = 0.15 (χ² = 1.38), dashed lines represents 95% confidence intervals of the simulation fit. B) Representative [Ca²⁺]_i data for islets indicated in A, from regions of wild-type (I), ‘pre-critical’ (II), ‘critical’ (III) and ‘post-critical’ (IV) levels of P_exc. Left: Areas of activity are highlighted in red and scale bars represent 50 µm. Right: Representative time-courses of normalized FuraRed calcium dye fluorescence for cells within each islet, where vertical scale bar indicates 20% change in fluorescence. Red time-courses are determined to be active, black time-courses are determined to be inactive. See SI for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s009" target="_blank">Movies S1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s010" target="_blank">S2</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s011" target="_blank">S3</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s012" target="_blank">S4</a> of these data. C) Experimental data with Boolean network simulations for varying connectivity <i>p</i>. D) As in C for varying threshold of inactive cells <i>Sp</i>. E) Probability distribution of fitted <i>p</i> (linear scale) and <i>Sp</i> (log scale) parameters to data in A, along with heat map of 2D χ² distribution (log scale).</p

Boolean network model predictions.

<p>A) Schematic representation of the network model with limited connectivity. Note larger connected clusters have a higher probability of containing inexcitable cells. B) Example false-color maps displaying probability of activity, generated from a simulated network with <i>p</i> = 0.30 at P_exc = 95% (top) and P_exc = 60% (bottom). Note substantially increased likelihood of activity with the higher P_exc. Further description for how this was generated can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s001" target="_blank">figure S1</a>. C) Boolean network model predictions for the mean percent active cells as a function of proportion of excitable cells (P_exc) for varying coupling probabilities <i>p</i>, with a threshold fraction of inexcitable cells <i>Sp</i> = 0.15. D) Boolean network model predictions for the mean percent active cells as a function of coupling probability <i>p</i>, for varying proportion of excitable cells P_exc, where <i>Sp</i> = 0.15.</p

Phase transitions in endogenous β-cell network activity, as shown by the activity in a fully-coupled islet system as a function of the activity in the uncoupled islet system; where the latter represents the intrinsic excitability of the constituent cells.

<p>A) Experimentally measured transition from global activity to quiescence in wild-type islets treated with varying diazoxide concentrations, showing phase transition in activity as constituent cellular activity is reduced B) Simulated transition from global activity to quiescence upon normal gap junction conductance as K_ATP is uniformly activated across the islet in the dynamical oscillator model. C) Modelled transition from activity to quiescence within the Boolean lattice resistor network model as P_exc is reduced, for <i>p</i> = 0.3 and <i>Sp</i> = 0.5. Note in all cases; for islets lacking gap junction coupling, with zero gap junction conductance and for <i>p</i> = 0, the transition is trivially linear (blue dashed).</p

Coupled dynamical oscillator model describes experimental islet phase transitions.

<p>A) Percent cells showing [Ca²⁺]_i elevations in simulated islets as a function of fraction of excitable cells (P_exc), as set by the % cells lacking ATP-insensitivity. Solid line represents mean of simulation results generated from 5 random number seeds, dashed lines represents 95% confidence intervals of simulations. B) Representative simulated [Ca²⁺]_i time-courses for parameters indicated in A, from regions of wild-type (I), ‘pre-critical’ (II), ‘critical’ (III) and ‘post-critical’ (IV) behavior, as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi-1003819-g002" target="_blank">figure 2</a>. Vertical scale bar indicates 20% change in simulated [Ca²⁺]_i. Red time-courses are determined to be active, black time-courses are determined to be inactive. See SI for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s013" target="_blank">Movies S5</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s014" target="_blank">S6</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s015" target="_blank">S7</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s016" target="_blank">S8</a> of these data. C) Percent cells showing [Ca²⁺]_i elevations in simulated islet as a function of number of excitable cells (P_exc) for varying mean gap junction conductance values. Filled squares indicate experimental data from Kir6.2^{[ΔN30,K185Q]}-expressing islets in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi-1003819-g002" target="_blank">figure 2</a>.</p