University of Hamburg

About: University of Hamburg is a education organization based out in Hamburg, Germany. It is known for research contribution in the topics: Population & Laser. The organization has 45564 authors who have published 89286 publications receiving 2850161 citations. The organization is also known as: Hamburg University.

Gating of the voltage-dependent chloride channel CIC-0 by the permeant anion

Abstract: Chloride channels of the ClC family are important for the control of membrane excitability, cell volume regulation, and possibly transepithelial transport. Although lacking the typical voltage-sensor found in cation channels, gating of ClC channels is clearly voltage-dependent. For the prototype Torpedo channel ClC-0 (refs 11-15) we now show that channel opening is strongly facilitated by external chloride. Other less permeable anions can substitute for chloride with less efficiency. ClC-0 conductance shows an anomalous mole fraction behaviour with Cl-/NO3- mixtures, suggesting a multi-ion pore. Gating shows a similar anomalous behaviour, tightly linking permeation to gating. Eliminating a positive charge at the cytoplasmic end of domain D12 changes kinetics, concentration dependence and halide selectivity of gating, and alters pore properties such as ion selectivity, single-channel conductance and rectification. Taken together, our results strongly suggest that in these channels voltage-dependent gating is conferred by the permeating ion itself, acting as the gating charge.

Regulatory networks define phenotypic classes of human stem cell lines

Abstract: Hundreds of different human cell lines are grouped under the catch-all term 'stem cells'. They can be from embryos, fetuses or adults. And they can be pluripotent — able to produce a broad range of cells — or fated to produce a limited repertoire of cell types. Muller et al. set out to establish a 'stem cell diagnostic' to bring order to the characterization and classification of human stem cells, based on a database of transcriptional profiles derived from more than 150 cell samples. Bioinformatic analyses revealed that pluripotent stem cell lines share many properties and all possess a characteristic protein–protein network, dubbed 'PluriNet'. Other cell types, including brain-derived neural stem cell lines, are much more diverse. These results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks. Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells—typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation—to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine1,2 has highlighted the need for a general, reproducible method for classification of these cells3. We report here the creation and analysis of a database of global gene expression profiles (which we call the ‘stem cell matrix’) that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method4,5 to categorize a collection of ∼150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis6 we uncovered a protein–protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks.

Memristor-based neural networks

Abstract: The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (Some figures may appear in colour only in the online journal)