Marvin N. Steijaert

Synaptic Transmission from Horizontal Cells to Cones Is Impaired by Loss of Connexin Hemichannels

In the vertebrate retina, horizontal cells generate the inhibitory surround of bipolar cells, an essential step in contrast enhancement. For the last decades, the mechanism involved in this inhibitory synaptic pathway has been a major controversy in retinal research. One hypothesis suggests that connexin hemichannels mediate this negative feedback signal; another suggests that feedback is mediated by protons. Mutant zebrafish were generated that lack connexin 55.5 hemichannels in horizontal cells. Whole cell voltage clamp recordings were made from isolated horizontal cells and cones in flat mount retinas. Light-induced feedback from horizontal cells to cones was reduced in mutants. A reduction of feedback was also found when horizontal cells were pharmacologically hyperpolarized but was absent when they were pharmacologically depolarized. Hemichannel currents in isolated horizontal cells showed a similar behavior. The hyperpolarization-induced hemichannel current was strongly reduced in the mutants while the depolarization-induced hemichannel current was not. Intracellular recordings were made from horizontal cells. Consistent with impaired feedback in the mutant, spectral opponent responses in horizontal cells were diminished in these animals. A behavioral assay revealed a lower contrast-sensitivity, illustrating the role of the horizontal cell to cone feedback pathway in contrast enhancement. Model simulations showed that the observed modifications of feedback can be accounted for by an ephaptic mechanism. A model for feedback, in which the number of connexin hemichannels is reduced to about 40%, fully predicts the specific asymmetric modification of feedback. To our knowledge, this is the first successful genetic interference in the feedback pathway from horizontal cells to cones. It provides direct evidence for an unconventional role of connexin hemichannels in the inhibitory synapse between horizontal cells and cones. This is an important step in resolving a long-standing debate about the unusual form of (ephaptic) synaptic transmission between horizontal cells and cones in the vertebrate retina.

Hemichannel-Mediated and pH-Based Feedback from Horizontal Cells to Cones in the Vertebrate Retina

Background Recent studies designed to identify the mechanism by which retinal horizontal cells communicate with cones have implicated two processes. According to one account, horizontal cell hyperpolarization induces an increase in pH within the synaptic cleft that activates the calcium current (Ca2+-current) in cones, enhancing transmitter release. An alternative account suggests that horizontal cell hyperpolarization increases the Ca2+-current to promote transmitter release through a hemichannel-mediated ephaptic mechanism. Methodology/Principal Findings To distinguish between these mechanisms, we interfered with the pH regulating systems in the retina and studied the effects on the feedback responses of cones and horizontal cells. We found that the pH buffers HEPES and Tris partially inhibit feedback responses in cones and horizontal cells and lead to intracellular acidification of neurons. Application of 25 mM acetate, which does not change the extracellular pH buffer capacity, does lead to both intracellular acidification and inhibition of feedback. Because intracellular acidification is known to inhibit hemichannels, the key experiment used to test the pH hypothesis, i.e. increasing the extracellular pH buffer capacity, does not discriminate between a pH-based feedback system and a hemichannel-mediated feedback system. To test the pH hypothesis in a manner independent of artificial pH-buffer systems, we studied the effect of interfering with the endogenous pH buffer, the bicarbonate/carbonic anhydrase system. Inhibition of carbonic anhydrase allowed for large changes in pH in the synaptic cleft of bipolar cell terminals and cone terminals, but the predicted enhancement of the cone feedback responses, according to the pH-hypothesis, was not observed. These experiments thus failed to support a proton mediated feedback mechanism. The alternative hypothesis, the hemichannel-mediated ephaptic feedback mechanism, was therefore studied experimentally, and its feasibility was buttressed by means of a quantitative computer model of the cone/horizontal cell synapse. Conclusion We conclude that the data presented in this paper offers further support for physiologically relevant ephaptic interactions in the retina.

Chloride currents in cones modify feedback from horizontal cells to cones in goldfish retina.

•  The GABAergic pathway modulates feedback between retinal horizontal cells (HCs) and cone photoreceptors, but is not mediating negative feedback, as previously hypothesized. •  Opening of GABA‐gated chloride channels in cone photoreceptors reduces the amplitude of feedback responses generated by HCs. •  Activation of a different presynaptic chloride current, the calcium‐dependent chloride current, in individual cones has a similar effect on feedback as application of GABA. •  Modulation of the strength of feedback from HCs seems to be a general consequence of activation of presynaptic chloride currents in cones. •  This puts the functional role of these currents in a new perspective; GABA acts as a slow and global neuromodulator enhancing feedback in the light‐ and attenuating feedback in the dark‐adapted retina, whereas the calcium‐dependent chloride current modulates feedback fast and locally to tune the size of feedback to local light conditions.

Stochastic Analysis of Amino Acid Substitution in Protein Synthesis

We present a formal analysis of amino acid replacement during mRNA translation. Building on an abstract stochastic model of arrival of tRNAs and their processing at the ribosome, we compute probabilities of the insertion of amino acids into the nascent polypeptide chain. To this end, we integrate the probabilistic model checker Prism in the Matlab environment. We construct the substitution matrix containing the probabilities of an amino acid replacing another. The resulting matrix depends on various parameters, including availability and concentration of tRNA species, as well as their assignment to individual codons. We draw a parallel with the standard mutation matrices like Dayhoff and PET91, and analyze the mutual replacement of biologically similar amino acids.

Computing the stochastic dynamics of phosphorylation networks.

Cells of all organisms share the ability to respond to various extracellular signals. Depending on the cell type and the organism, these signals may include hormones secreted by other cells or changes in nutrient concentrations. The signals are processed by an intricate network of protein-protein interactions, including phosphorylation and de-phosphorylation events. As some signaling proteins are only present in low concentrations, random fluctuations may affect the dynamics of the network. The mathematical modeling of networks with significant random fluctuations requires the use of stochastic methods. The stochastic dynamics of a chemical reaction system are described by the Chemical Master Equation. Often the numerical evaluation of this equation is problematic. The first problem is that many systems have an infinite number of possible states; leaving simulations of individual trajectories as the only alternative. To circumvent this problem, we focus on a class of systems that have a finite state space. More specifically, we focus on networks of phosphorylation cycles without taking into account the synthesis and degradation of proteins. The second problem is that memory requirements cause a practical limit to the size of systems that can be evaluated. In this paper, we discuss how these limitations can be overcome using parallel computation and methods dealing efficiently with the available memory. These methods were implemented in a parallel C++ program. We discuss two networks for which the stochastic dynamics were evaluated using this program: a single phosphorylation cycle and an oscillating MAP-kinase cascade.

Computing with Feedforward Networks of Artificial Biochemical Neurons

Phosphorylation cycles are a common motif in biological intracellular signaling networks. A phosphorylaton cycle can be modeled as an artificial biochemical neuron, which can be considered as a variant of the artificial neurons used in neural networks. In this way the artificial neural network metaphor can be used to model and study intracellular signaling networks. The question what types of computations can occur in biological intracellular signaling networks leads to the study of the computational power of networks of artificial biochemical neurons. Here we consider the computational properties of artificial biochemical neurons, based on mass-action kinetics. We also study the computational power of feedforward networks of such neurons. As a result, we give an algebraic characterization of the functions computable by these networks.

Single-variable reaction systems: deterministic and stochastic models.

Biochemical reaction networks are often described by deterministic models based on macroscopic rate equations. However, for small numbers of molecules, intrinsic noise can play a significant role and stochastic methods may thus be required. In this work, we analyze the differences and similarities between a class of macroscopic deterministic models and corresponding mesoscopic stochastic models. We derive expressions that provide a clear and intuitive view upon the behavior of the stochastic model. In particular, these expressions show the dependence of both the dynamics and the stationary distribution of the stochastic model on the number of molecules in the system. As expected, most properties of the stochastic model correspond well with those in the deterministic model if the number of molecules is large enough. However, for some properties, both models are inconsistent, even if the number of molecules in the stochastic model tends to infinity. Throughout this paper, we use a bistable autophosphorylation cycle as a running example. For such a bistable system, we give an explicit proof that the rate of convergence to the stationary distribution (or the second eigenvalue of the transition matrix) depends exponentially on the number of molecules.