“…The model uses a straightforward Michaelis-Menten description of Zn 2ϩ influx. It is recognized that numerous studies have indicated that multiple pathways may exist for both the influx and efflux of Zn 2ϩ in neurons (8). However, modeling influx in this way with efflux as a simple reversal of the influx process was adequate to match the experimental data.…”
To understand the mechanisms of neuronal Zn2+ homeostasis better, experimental data obtained from cultured cortical neurons were used to inform a series of increasingly complex computational models. Total metals (inductively coupled plasma-mass spectrometry), resting metallothionein, 65Zn2+ uptake and release, and intracellular free Zn2+ levels using ZnAF-2F were determined before and after neurons were exposed to increased Zn2+, either with or without the addition of a Zn2+ ionophore (pyrithione) or metal chelators [EDTA, clioquinol (CQ), and N, N, N′, N′-tetrakis(2-pyridylmethyl)ethylenediamine]. Three models were tested for the ability to match intracellular free Zn2+ transients and total Zn2+ content observed under these conditions. Only a model that incorporated a muffler with high affinity for Zn2+, trafficking Zn2+ to intracellular storage sites, was able to reproduce the experimental results, both qualitatively and quantitatively. This “muffler model” estimated the resting intracellular free Zn2+ concentration to be 1.07 nM. If metallothionein were to function as the exclusive cytosolic Zn2+ muffler, the muffler model predicts that the cellular concentration required to match experimental data is greater than the measured resting concentration of metallothionein. Thus Zn2+ buffering in resting cultured neurons requires additional high-affinity cytosolic metal binding moieties. Added CQ, as low as 1 μM, was shown to selectively increase Zn2+ influx. Simulations reproduced these data by modeling CQ as an ionophore. We conclude that maintenance of neuronal Zn2+ homeostasis, when challenged with Zn2+ loads, relies heavily on the function of a high-affinity muffler, the characteristics of which can be effectively studied with computational models.
“…The model uses a straightforward Michaelis-Menten description of Zn 2ϩ influx. It is recognized that numerous studies have indicated that multiple pathways may exist for both the influx and efflux of Zn 2ϩ in neurons (8). However, modeling influx in this way with efflux as a simple reversal of the influx process was adequate to match the experimental data.…”
To understand the mechanisms of neuronal Zn2+ homeostasis better, experimental data obtained from cultured cortical neurons were used to inform a series of increasingly complex computational models. Total metals (inductively coupled plasma-mass spectrometry), resting metallothionein, 65Zn2+ uptake and release, and intracellular free Zn2+ levels using ZnAF-2F were determined before and after neurons were exposed to increased Zn2+, either with or without the addition of a Zn2+ ionophore (pyrithione) or metal chelators [EDTA, clioquinol (CQ), and N, N, N′, N′-tetrakis(2-pyridylmethyl)ethylenediamine]. Three models were tested for the ability to match intracellular free Zn2+ transients and total Zn2+ content observed under these conditions. Only a model that incorporated a muffler with high affinity for Zn2+, trafficking Zn2+ to intracellular storage sites, was able to reproduce the experimental results, both qualitatively and quantitatively. This “muffler model” estimated the resting intracellular free Zn2+ concentration to be 1.07 nM. If metallothionein were to function as the exclusive cytosolic Zn2+ muffler, the muffler model predicts that the cellular concentration required to match experimental data is greater than the measured resting concentration of metallothionein. Thus Zn2+ buffering in resting cultured neurons requires additional high-affinity cytosolic metal binding moieties. Added CQ, as low as 1 μM, was shown to selectively increase Zn2+ influx. Simulations reproduced these data by modeling CQ as an ionophore. We conclude that maintenance of neuronal Zn2+ homeostasis, when challenged with Zn2+ loads, relies heavily on the function of a high-affinity muffler, the characteristics of which can be effectively studied with computational models.
“…The analyses of several eukaryotic genomes have led to the estimate that zinc may be required for the function of .3% of all proteins (Lander et al 2001). Zinc has also been implicated in signaling processes and may be a signaling molecule: zinc is concentrated in some synaptic vesicles and then released into the synapse where it might modulate neurotransmission (Frederickson and Bush 2001;Colvin et al 2003;Wall 2005;Yamasaki et al 2007). Zinc affects epidermal growth factor receptor/Ras-mediated signal transduction, thus playing a role in cell fate determination (Wu et al 1999;Bruinsma et al 2002;Samet et al 2003;Yoder et al 2004).…”
Zinc plays many critical roles in biological systems: zinc bound to proteins has structural and catalytic functions, and zinc is proposed to act as a signaling molecule. Because zinc deficiency and excess result in toxicity, animals have evolved sophisticated mechanisms for zinc metabolism and homeostasis. However, these mechanisms remain poorly defined. To identify genes involved in zinc metabolism, we conducted a forward genetic screen for chemically induced mutations that cause Caenorhabditis elegans to be resistant to high levels of dietary zinc. Nineteen mutations that confer significant resistance to supplemental dietary zinc were identified. To determine the map positions of these mutations, we developed a genomewide map of single nucleotide polymorphisms (SNPs) that can be scored by the high-throughput method of DNA pyrosequencing. This map was used to determine the approximate chromosomal position of each mutation, and the accuracy of this approach was verified by conducting three-factor mapping experiments with mutations that cause visible phenotypes. This is a generally applicable mapping approach that can be used to position a wide variety of C. elegans mutations. The mapping experiments demonstrate that the 19 mutations identify at least three genes that, when mutated, confer resistance to toxicity caused by supplemental dietary zinc. These genes are likely to be involved in zinc metabolism, and the analysis of these genes will provide insights into mechanisms of excess zinc toxicity.
“…Far less is known regarding the Zn 2 + redistribution in neurons that is required to provide Zn 2 + for accumulation into synaptic vesicles in the axon terminal ( Figure 4 ); however, the Zn 2 + network likely includes mitochondria, vesicles, and lysosomes (Colvin et al , 2003 ;Sensi et al , 2009 . This suggests that during times of increased or decreased Zn 2 + availability, MT-III modulates the availability of free Zn 2 + for incorporation into synaptic vesicles for neuronal activity.…”
Section: Brainmentioning
confidence: 99%
“…Zn 2 + is exocytosed as an extracellular signaling ion and interacts with postsynaptic targets to modulate synaptic transmission and plasticity and signal transduction (Sindreu and Storm , 2011 ). Zn 2 + availability has profound effects on neurological function (Colvin et al , 2003 ); therefore, intracellular levels are tightly regulated. Following each stimulatory event, Zn 2 + must be taken up by the neuron, redistributed within subcellular compartments, and accumulated in synaptic vesicles.…”
Zinc (Zn 2 + ) is the most abundant trace element in cells and is essential for a vast number of catalytic, structural, and regulatory processes. Mounting evidence indicates that like calcium (Ca 2 + ), intracellular Zn 2 + pools are redistributed for specifi c cellular functions. This occurs through the regulation of 24 Zn 2 + transporters whose localization and expression is tissue and cell specifi c. We propose that the complement and regulation of Zn 2 + transporters expressed within a given cell type refl ects the function of the cell itself and comprises a ' Zn 2 + network. ' Importantly, increasing information implicates perturbations in the Zn 2 + network with metabolic consequences and disease. Herein, we discuss our current understanding of Zn 2 + transporters from the perspective of a Zn 2 + network in four specifi c tissues with unique Zn 2 + requirements (mammary gland, prostate, pancreas, and brain). Delineating the entire Zn 2 + transporting network within the context of unique cellular Zn 2 + needs is important in identifying critical gaps in our knowledge and improving our understanding of the consequences of Zn 2 + dysregulation in human health and disease.
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