The Journal of Membrane Biology

Grewer, C.; Rauen, T.

doi: 10.1007/s00232-004-0731-6pmid: 15834685

Glutamate is the major excitatory neurotransmitter in the mammalian CNS. The spatiotemporal profile of the glutamate concentration in the synapse is critical for excitatory synaptic signalling. The control of this spatiotemporal concentration profile requires the presence of large numbers of synaptically localized glutamate transporters that remove pre-synaptically released glutamate by uptake into neurons and adjacent glia cells. These glutamate transporters are electrogenic and utilize energy stored in the transmembrane potential and the Na+/K+-ion concentration gradients to accumulate glutamate in the cell. This review focuses on the kinetic and electrogenic properties of glutamate transporters, as well as on the molecular mechanism of transport. Recent results are discussed that demonstrate the multistep nature of the transporter reaction cycle. Results from pre-steady-state kinetic experiments suggest that at least four of the individual transporter reaction steps are electrogenic, including reactions associated with the glutamate-dependent transporter halfcycle. Furthermore, the kinetic similarities and differences between some of the glutamate transporter subtypes and splice variants are discussed. A molecular mechanism of glutamate transport is presented that accounts for most of the available kinetic data. Finally, we discuss how synaptic glutamate transporters impact on glutamate receptor activity and how transporters may shape excitatory synaptic transmission.

Sjö, A.; Magnusson, K.E.; Peterson, K.H.

doi: 10.1007/s00232-004-0728-1pmid: 15834686

Alpha-dystrobrevin (α-DB) has been described primarily as a cytoplasmic component of the dystrophin-glycoprotein complex in skeletal muscle cells. Isoforms of α-DB show different localization in cells and tissues; at basolateral membranes in epithelial cells, dystrobrevins mediate contact with the extracellular matrix, peripheral and transmembrane proteins and the filamentous actin cytoskeleton. Beside their structural role, α-DBs are assumed to be important in cell signalling and cell differentiation. We have primarily assessed the role of α-DB in two epithelial cell lines (MDCK I, HT 29), which represent different developmental stages and exhibit distinct permeability characteristics. Using a polyclonal anti-α-DB antibody, we have investigated its expression, localization and association with tight junction (TJ)- associated proteins (ZO-1, occludin) before and after protein kinase C (PKC) activation with phorbol myristate acetate. Distinct subsets of α-DB isoforms were detected in the two cell lines by immunoblotting. In both cell lines there was submembranous localization of α-DB both apically and basolaterally, shown with confocal imaging. PKC activation caused a reorganization of TJ, which was parallel to increased localization of α-DB to TJ areas, most pronounced in MDCK I cells. Moreover, actin and ZO-1 co-immunoprecipitated with a-DB, as displayed with immunoblotting. Our findings suggest that a-dystrobrevin specifically is associated with the tight junctions during their reorganization.

Marunaka, Y.; Niisato, N.; Miyazaki, H.

doi: 10.1007/s00232-004-0729-0pmid: 15834687

The present study provides a new concept of the spare receptor. Model [A]: 1) Several receptors connect with an effector; 2) if an agonist occupies one of the receptors connecting with one effector, the effector fully functions. When the number of receptors connecting with one effector is “m”, the relationship between the functional effectors (E) and the concentration of agonists ([a]) is as follows: where R t is the total number of receptors and K d is the agonist dissociation constant from the receptor. Model [B]: 1) Several receptors connect with an effector; 2) only when agonists occupy all of the receptors connecting with one effector, the effector functions. The relationship between E and [a] is as follows: If m=1, equations (I) and (II) are exactly the same as the Michaelis-Menten equation. If m is larger than 1, the apparent saturation in the effector efficiency becomes larger in Model [A], and smaller in Model [B], respectively. The dissociation of the fractional efficiency of effectors from the fractional binding of agonists to receptors becomes larger as m becomes larger in both models. Further, we propose a variable model, including the concept of agonist-occupancy-dependent stability in the functional conformation change of the effector; only when more than j pieces of receptors connecting with one effector are occupied by agonists, the effector functions (Model [M]). The relationship between E and [a] is as follows:

Fischbarg, J.; Diecke, F.P.J.

doi: 10.1007/s00232-004-0730-7pmid: 15834688

To predict the behavior of a transporting epithelium by intuitive means can be complex and frustrating. As the number of parameters to be considered increases beyond a few, the task can be termed impossible. The alternative is to model epithelial behavior by mathematical means. For that to be feasible, it has been presumed that a large amount of experimental information is required, so as to be able to use known values for the majority of kinetic parameters. However, in the present case, we are modeling corneal endothelial behavior beginning with experimental values for only five of eleven parameters. The remaining parameter values are calculated assuming cellular steady state and using algebraic software. With that as base, as in preceding treatments but with a distribution of channels/transporters suited to the endothelium, temporal cell and tissue behavior are computed by a program written in Basic that monitors changes in chemical and electrical driving forces across cell membranes and the paracellular pathway. We find that the program reproduces quite well the behaviors experimentally observed for the translayer electrical potential difference and rate of fluid transport, (a) in the steady state, (b) after perturbations by changes in ambient conditions HCO 3 − , Na+, and Cl− concentrations), and (c) after challenge by inhibitors (ouabain, DIDS, Na+- and Cl−-channel inhibitors). In addition, we have used the program to compare predictions of translayer fluid transport by two competing theories, electro-osmosis and local osmosis. Only predictions using electro-osmosis fit all the experimental data.