Long-term potentiation through calcium-mediated N-Cadherin interaction is tightly controlled by the three-dimensional architecture of the synapse

Poster presentation: Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. Paris, France. 13-18 July 2013. The synaptic cleft is an extracellular domain that is capable of relaying a presynaptically received electrical signal by diffusive neurotransmitters to the postsynaptic membrane. The cleft is trans-synaptically bridged by ring-like shaped clusters of pre- and postsynaptically localized calcium-dependent adhesion proteins of the N-Cadherin type and is possibly the smallest intercircuit in nervous systems [1]. The strength of association between the pre- and postsynaptic membranes can account for synaptic plasticity such as long-term potentiation [2]. Through neuronal activity the intra- and extracellular calcium levels are modulated through calcium exchangers embedded in the pre- and postsynaptic membrane. Variations of the concentration of cleft calcium induces changes in the N-Cadherin-zipper, that in synaptic resting states is rigid and tightly connects the pre- and postsynaptic domain. During synaptic activity calcium concentrations are hypothesized to drop below critical thresholds which leads to loosening of the N-Cadherin connections and subsequently "unzips" the Cadherin-mediated connection. These processes may result in changes in synaptic strength [2]. In order to investigate the calcium-mediated N-Cadherin dynamics at the synaptic cleft, we developed a three-dimensional model including the cleft morphology and all prominent calcium exchangers and corresponding density distributions [3-6]. The necessity for a fully three-dimensional model becomes apparent, when investigating the effects of the spatial architecture of the synapse [7], [8]. Our data show, that the localization of calcium channels with respect to the N-Cadherin ring has substantial effects on the time-scales on which the Cadherin-zipper switches between states, ranging from seconds to minutes. This will have significant effects on synaptic signaling. Furthermore we see, that high-frequency action potential firing can only be relayed to the Calcium/N-Cadherin-system at a synapse under precise spatial synaptic reorganization

1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

Investigation of cellular and network dynamics in the brain by means of modeling & simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling & simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in level of detail to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing spatial aspects of the cells. For single cell or small-world networks, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the 3D morphology of cells and organelles into 3D space and time-dependent simulations. Every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. We present a hybrid simulation approach, that makes use of reduced 1D-models using e.g. the NEURON which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed 3D-morphology of neurons and organelles. To couple 1D- & 3D-simulations, we present a geometry and membrane potential mapping framework, with which graph-based morphologies, e.g. in swc-/hoc-format, are mapped to full surface and volume representations of the neuron; membrane potential data from 1D-simulations are used as boundary conditions for full 3D simulations. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved highly detailed 3D-modeling approaches. The new framework is applied to investigate electrically active neurons and their intracellular spatio-temporal Calcium Dynamics

A novel mesh generator for the numerical simulation of multi-scale physics in neurons

Computational Neuroscience deals with spatio-temporal scales which vary considerably.For example interactions at synaptic contact regions occur on the scale of nanometers and nanoseconds to milliseconds (micro-scale) whereas networks of neurons can measure up to millimeters and signals are processed on the scale of seconds (macro-scale). Whole-cell calcium dynamics models (meso-scale) mediate between the multiple spatio-temporal scales. Of crucial importance is the calcium propagation mediated by the highly complex endoplasmic reticulum network. Most models do not account for the intricate intracellular architecture of neurons and consequently cannot resolve the interplay between structure and calcium-mediated function. To incorporate the detailed cellular architecture in intracellular Calcium models, a novel mesh generation methodology has been developed to allow for the efficient generation of computational meshes of neurons with a three-dimensionally resolved endoplasmic reticulum. Mesh generation routines are compiled into a versatile and fully automated reconstruct-and-simulation toolbox for multi-scale physics to be utilized on high-performance or regular computing infrastructures. First-principle numerical simulations on the neuronal reconstructions reveal that intracellular Calcium dynamics are effected by morphological features of the neurons, for instance a change of endoplasmic reticulum diameter leads to a significant spatio-temporal variability of the calcium signal at the soma.Math & Science Educatio

Tumour stroma-derived lipocalin-2 promotes breast cancer metastasis

Tumour cell-secreted factors skew infiltrating immune cells towards a tumour-supporting phenotype, expressing pro-tumourigenic mediators. However, the influence of lipocalin-2 (Lcn2) on the metastatic cascade in the tumour micro-environment is still not clearly defined. Here, we explored the role of stroma-derived, especially macrophage-released, Lcn2 in breast cancer progression. Knockdown studies and neutralizing antibody approaches showed that Lcn2 contributes to the early events of metastasis in vitro. The release of Lcn2 from macrophages induced an epithelial–mesenchymal transition programme in MCF-7 breast cancer cells and enhanced local migration as well as invasion into the extracellular matrix, using a three-dimensioanl (3D) spheroid model. Moreover, a global Lcn2 deficiency attenuated breast cancer metastasis in both the MMTV–PyMT breast cancer model and a xenograft model inoculating MCF-7 cells pretreated with supernatants from wild-type and Lcn2-knockdown macrophages. To dissect the role of stroma-derived Lcn2, we employed an orthotopic mammary tumour mouse model. Implantation of wild-type PyMT tumour cells into Lcn2-deficient mice left primary mammary tumour formation unaltered, but specifically reduced tumour cell dissemination into the lung. We conclude that stroma-secreted Lcn2 promotes metastasis in vitro and in vivo, thereby contributing to tumour progression. Our study highlights the tumourigenic potential of stroma-released Lcn2 and suggests Lcn2 as a putative therapeutic target

Macrophage-Derived Iron-Bound Lipocalin-2 Correlates with Renal Recovery Markers Following Sepsis-Induced Kidney Damage

During the course of sepsis in critically ill patients, kidney dysfunction and damage are among the first events of a complex scenario toward multi-organ failure and patient death. Acute kidney injury triggers the release of lipocalin-2 (Lcn-2), which is involved in both renal injury and recovery. Taking into account that Lcn-2 binds and transports iron with high affinity, we aimed at clarifying if Lcn-2 fulfills different biological functions according to its iron-loading status and its cellular source during sepsis-induced kidney failure. We assessed Lcn-2 levels both in serum and in the supernatant of short-term cultured renal macrophages (M phi) as well as renal tubular epithelial cells (TEC) isolated from either Sham-operated or cecal ligation and puncture (CLP)-treated septic mice. Total kidney iron content was analyzed by Perls' staining, while Lcn-2-bound iron in the supernatants of short-term cultured cells was determined by atomic absorption spectroscopy. Lcn-2 protein in serum was rapidly up-regulated at 6 h after sepsis induction and subsequently increased up to 48 h. Lcn-2-levels in the supernatant of TEC peaked at 24 h and were low at 48 h with no change in its iron-loading. In contrast, in renal M phi Lcn-2 was low at 24 h, but increased at 48 h, where it mainly appeared in its iron-bound form. Whereas TEC-secreted, iron-free Lcn-2 was associated with renal injury, increased M phi-released iron-bound Lcn-2 was linked to renal recovery. Therefore, we hypothesized that both the cellular source of Lcn-2 as well as its iron-load crucially adds to its biological function during sepsis-induced renal injury

pyPESTO: A modular and scalable tool for parameter estimation for dynamic models

Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. We present pyPESTO, a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto)

Density visualization pipeline: a tool for cellular and network density visualization and analysis

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the “mass” of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data

<p>This archive contains Supplementary code to the manuscript <em>Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data</em> by Domagoj Doresic, Stephan Grein, and Jan Hasenauer.</p&gt