Claire Jean-Quartier

Leucine Zipper EF Hand-containing Transmembrane Protein 1 (Letm1) and Uncoupling Proteins 2 and 3 (UCP2/3) Contribute to Two Distinct Mitochondrial Ca2+ Uptake Pathways

Cytosolic Ca2+ signals are transferred into mitochondria over a huge concentration range. In our recent work we described uncoupling proteins 2 and 3 (UCP2/3) to be fundamental for mitochondrial uptake of high Ca2+ domains in mitochondria-ER junctions. On the other hand, the leucine zipper EF hand-containing transmembrane protein 1 (Letm1) was identified as a mitochondrial Ca2+/H+ antiporter that achieved mitochondrial Ca2+ sequestration at small Ca2+ increases. Thus, the contributions of Letm1 and UCP2/3 to mitochondrial Ca2+ uptake were compared in endothelial cells. Knock-down of Letm1 did not affect the UCP2/3-dependent mitochondrial uptake of intracellularly released Ca2+ but strongly diminished the transfer of entering Ca2+ into mitochondria, subsequently, resulting in a reduction of store-operated Ca2+ entry (SOCE). Knock-down of Letm1 and UCP2/3 did neither impact on cellular ATP levels nor the membrane potential. The enhanced mitochondrial Ca2+ signals in cells overexpressing UCP2/3 rescued SOCE upon Letm1 knock-down. In digitonin-permeabilized cells, Letm1 exclusively contributed to mitochondrial Ca2+ uptake at low Ca2+ conditions. Neither the Letm1- nor the UCP2/3-dependent mitochondrial Ca2+ uptake was affected by a knock-down of mRNA levels of mitochondrial calcium uptake 1 (MICU1), a protein that triggers mitochondrial Ca2+ uptake in HeLa cells. Our data indicate that Letm1 and UCP2/3 independently contribute to two distinct, mitochondrial Ca2+ uptake pathways in intact endothelial cells.

Studying mitochondrial Ca2+ uptake – A revisit

Highlights ► Mitochondrial Ca2+ sequestration was tested by various techniques. ► Kinetics and Ca2+ sensitivity of mitochondrial Ca2+ uptake depends on the techniques chosen. ► By electrophysiology, 2 and 3 distinct Ca2+ inward currents were measured in HeLa and endothelial cell mitoplasts. ► Mitoplast Ca2+ inward currents differ in frequency of appearance and conductance ranging from 7.6 to 74.3 pS. ► Mitochondrial Ca2+ uptake routes/modes exists and might be cell specific.

Mitochondrial Ca 2+ uniporter (MCU)-dependent and MCU-independent Ca 2+ channels coexist in the inner mitochondrial membrane

A protein referred to as CCDC109A and then renamed to mitochondrial calcium uniporter (MCU) has recently been shown to accomplish mitochondrial Ca2+ uptake in different cell types. In this study, we investigated whole-mitoplast inward cation currents and single Ca2+ channel activities in mitoplasts prepared from stable MCU knockdown HeLa cells using the patch-clamp technique. In whole-mitoplast configuration, diminution of MCU considerably reduced inward Ca2+ and Na+ currents. This was accompanied by a decrease in occurrence of single channel activity of the intermediate conductance mitochondrial Ca2+ current (i-MCC). However, ablation of MCU yielded a compensatory 2.3-fold elevation in the occurrence of the extra large conductance mitochondrial Ca2+ current (xl-MCC), while the occurrence of bursting currents (b-MCC) remained unaltered. These data reveal i-MCC as MCU-dependent current while xl-MCC and b-MCC seem to be rather MCU-independent, thus, pointing to the engagement of at least two molecularly distinct mitochondrial Ca2+ channels.

Molecularly distinct routes of mitochondrial Ca2+ uptake are activated depending on the activity of the sarco/endoplasmic reticulum Ca2+ ATPase (SERCA).

Background: Mitochondria may utilize different proteins to decode high and low cytosolic Ca2+. Results: Inhibition of SERCA shifts mitochondrial Ca2+ uptake from being UCP3-dependent to Letm1-dependent. Conclusion: Depending on the mode of intracellular Ca2+ release, two different mitochondrial Ca2+ uptake pathways are engaged. Significance: The dissection of two molecularly distinct mitochondrial Ca2+ uptake routes depending on SERCA activity points to the complexity of the mitochondrial Ca2+ uptake machinery. The transfer of Ca2+ across the inner mitochondrial membrane is an important physiological process linked to the regulation of metabolism, signal transduction, and cell death. While the definite molecular composition of mitochondrial Ca2+ uptake sites remains unknown, several proteins of the inner mitochondrial membrane, that are likely to accomplish mitochondrial Ca2+ fluxes, have been described: the novel uncoupling proteins 2 and 3, the leucine zipper-EF-hand containing transmembrane protein 1 and the mitochondrial calcium uniporter. It is unclear whether these proteins contribute to one unique mitochondrial Ca2+ uptake pathway or establish distinct routes for mitochondrial Ca2+ sequestration. In this study, we show that a modulation of Ca2+ release from the endoplasmic reticulum by inhibition of the sarco/endoplasmatic reticulum ATPase modifies cytosolic Ca2+ signals and consequently switches mitochondrial Ca2+ uptake from an uncoupling protein 3- and mitochondrial calcium uniporter-dependent, but leucine zipper-EF-hand containing transmembrane protein 1-independent to a leucine zipper-EF-hand containing transmembrane protein 1- and mitochondrial calcium uniporter-mediated, but uncoupling protein 3-independent pathway. Thus, the activity of sarco/endoplasmatic reticulum ATPase is significant for the mode of mitochondrial Ca2+ sequestration and determines which mitochondrial proteins might actually accomplish the transfer of Ca2+ across the inner mitochondrial membrane. Moreover, our findings herein support the existence of distinct mitochondrial Ca2+ uptake routes that might be essential to ensure an efficient ion transfer into mitochondria despite heterogeneous cytosolic Ca2+ rises.

Integrated web visualizations for protein-protein interaction databases.

BackgroundUnderstanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.ResultsWe selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.ConclusionsOnly some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

The general disease group of malignant neoplasms depicts one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to the disease. Our idea is to create a user interface for linking a simulation on cancer modeling to relevant additional publicly and freely available data. We are not only providing a categorized list of open datasets and queryable databases for the different types of cancer and related information, we also identify a certain subset of temporal and spatial data related to tumor growth. Furthermore, we describe the integration possibilities into a simulation tool on tumor growth that incorporates the tumor’s kinetics.

In silico modeling for tumor growth visualization

BackgroundCancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research.ResultsWe aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscapeand http://styx.cgv.tugraz.at:8080/cpm-cytoscape/.ConclusionIn-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.

Tumor Growth Simulation Profiling

Cancer constitutes a condition and is referred to a group of numerous different diseases, that are characterized by uncontrolled cell growth. Tumors, in the broader sense, are described by abnormal cell growth and are not exclusively cancerous. The molecular basis involves a process of multiple steps and underlying signaling pathways, building up a complex biological framework. Cancer research is based on both disciplines of quantitative and life sciences which can be connected through Bioinformatics and Systems Biology. Our study aims to provide an enhanced computational model on tumor growth towards a comprehensive simulation of miscellaneous types of neoplasms. We create model profiles by considering data from selected types of tumors. Growth parameters are evaluated for integration and compared to the different disease examples.

Machine Learning for In Silico Modeling of Tumor Growth

The various interplaying variables of tumor growth remain key questions in cancer research, in particular what makes such a growth malignant and what are possible therapies to stop the growth and prevent re-growth. Given the complexity and heterogeneity of the disease, as well as the steadily growing set of publicly available big data sets, there is an urgent need for approaches to make sense out of these open data sets. Machine learning methods for tumor growth profiles and model validation can be of great help here, particularly, discrete multi-agent approaches.

Assessment of Mitochondrial Ca 2+ Uptake

Mitochondrial Ca(2+) uptake regulates mitochondrial function and contributes to cell signaling. Accordingly, quantifying mitochondrial Ca(2+) signals and elaborating the mechanisms that accomplish mitochondrial Ca(2+) uptake are essential to gain our understanding of cell biology. Here, we describe the benefits and drawbacks of various established old and new techniques to assess dynamic changes of mitochondrial Ca(2+) concentration ([Ca(2+)]mito) in a wide range of applications.