32 research outputs found
Multiplex networks of cortical and hippocampal neurons revealed at different timescales
A Novel Antibody-Based Biomarker for Chronic Algal Toxin Exposure and Sub-Acute Neurotoxicity
The neurotoxic amino acid, domoic acid (DA), is naturally produced by marine phytoplankton and presents a significant threat to the health of marine mammals, seabirds and humans via transfer of the toxin through the foodweb. In humans, acute exposure causes a neurotoxic illness known as amnesic shellfish poisoning characterized by seizures, memory loss, coma and death. Regular monitoring for high DA levels in edible shellfish tissues has been effective in protecting human consumers from acute DA exposure. However, chronic low-level DA exposure remains a concern, particularly in coastal and tribal communities that subsistence harvest shellfish known to contain low levels of the toxin. Domoic acid exposure via consumption of planktivorous fish also has a profound health impact on California sea lions (Zalophus californianus) affecting hundreds of animals yearly. Due to increasing algal toxin exposure threats globally, there is a critical need for reliable diagnostic tests for assessing chronic DA exposure in humans and wildlife. Here we report the discovery of a novel DA-specific antibody response that is a signature of chronic low-level exposure identified initially in a zebrafish exposure model and confirmed in naturally exposed wild sea lions. Additionally, we found that chronic exposure in zebrafish caused increased neurologic sensitivity to DA, revealing that repetitive exposure to DA well below the threshold for acute behavioral toxicity has underlying neurotoxic consequences. The discovery that chronic exposure to low levels of a small, water-soluble single amino acid triggers a detectable antibody response is surprising and has profound implications for the development of diagnostic tests for exposure to other pervasive environmental toxins
Chronic low-level domoic acid exposure alters gene transcription and impairs mitochondrial function in the CNS.
Multiplex networks of cortical and hippocampal neurons revealed at different timescales.
Recent studies have emphasized the importance of multiplex networks--interdependent networks with shared nodes and different types of connections--in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy--an information theoretic quantity that can be used to measure linear and nonlinear interactions--to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons ("hubs") were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons
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Repeated low level domoic acid exposure increases CA1 VGluT1 levels, but not bouton density, VGluT2 or VGAT levels in the hippocampus of adult mice
Domoic acid (DA) is a neurotoxin produced during harmful algal blooms that accumulates in marine organisms that serve as food resources for humans. While acute DA neurotoxicity can cause seizures and hippocampal lesions, less is known regarding how chronic, subacute DA exposure in adulthood impacts the hippocampus. With more frequent occurrences of harmful algal blooms, it is important to understand the potential impact of repeated, low-level DA exposure on human health. To model repeated, low-dose DA exposure, adult mice received a single low-dose (0.75 ± 0.05 μg/g) of DA or vehicle weekly for 22 consecutive weeks. Quantitative immunohistochemistry was performed to assess the effects of repeated, low-level DA exposure on hippocampal cells and synapses. Vesicular glutamate transporter 1 (VGluT1) immunoreactivity within excitatory boutons in CA1 of DA-exposed mice was increased. Levels of other vesicular transporter proteins (i.e., VGluT2 and the vesicular GABA transporter (VGAT)) within boutons, and corresponding bouton densities, were not significantly altered in CA1, CA3, or dentate gyrus. There were no significant changes in neuron density or glial fibrillary acidic protein (GFAP) immunoreactivity following chronic, low-dose exposure. This suggests that repeated low doses of DA, unlike high doses of DA, do not cause neuronal loss or astrocyte activation in hippocampus in adult mice. Instead, these findings demonstrate that repeated exposure to low levels of DA leads to subtle changes in VGluT1 expression within CA1 excitatory boutons, which may alter glutamatergic transmission in CA1 and disrupt behaviors dependent on spatial memory
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Chronic low-level exposure to the common seafood toxin domoic acid causes cognitive deficits in mice
The consumption of one meal of seafood containing domoic acid (DA) at levels high enough to induce seizures can cause gross histopathological lesions in hippocampal regions of the brain and permanent memory loss in humans and marine mammals. Seafood regulatory limits have been set at 20mgDA/kg shellfish to protect human consumers from symptomatic acute exposure, but the effects of repetitive low-level asymptomatic exposure remain a critical knowledge gap. Recreational and Tribal-subsistence shellfish harvesters are known to regularly consume low levels of DA. The aim of this study was to determine if chronic low-level DA exposure, at doses below those that cause overt signs of neurotoxicity, has quantifiable impacts on cognitive function. To this end, female C57BL/6NJ mice were exposed to asymptomatic doses of DA (≈0.75mg/kg) or vehicle once a week for several months. Spatial learning and memory were tested in a radial water maze paradigm at one, six and 25 weeks of exposure, after a nine-week recovery period following cessation of exposure, and at three old age time points (18, 24 and 28 months old). Mice from select time points were also tested for activity levels in a novel cage environment using a photobeam activity system. Chronic low-level DA exposure caused significant spatial learning impairment and hyperactivity after 25 weeks of exposure in the absence of visible histopathological lesions in hippocampal regions of the brain. These cognitive effects were reversible after a nine-week recovery period with no toxin exposure and recovery was sustained into old age. These findings identify a new potential health risk of chronic low-level exposure in a mammalian model. Unlike the permanent cognitive impacts of acute exposure, the chronic low-level effects observed in this study were reversible suggesting that these deficits could potentially be managed through cessation of exposure if they also occur in human seafood consumers
Hub sharing was limited to adjacent time scales.
<p>(<b>A</b>) We classified each neuron as a hub, non-hub, or unconnected neuron at each time scale. A neuron was considered to be a shared hub or shared non-hub for two time scales if its status as a hub or non-hub was consistent across those time scales. Hubs were defined using a degree threshold set by the likelihood to have a given number of connections in a random network (0.05 in this illustrative diagram and 10<sup>−4</sup> in the full analysis). (<b>B</b>) We calculated the amount of hub and non-hub sharing (see <i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115764#s4" target="_blank">Materials and Methods</a></i>) for each pair of time scales and grouped the results into neighboring (4 or less) and distant (greater than 4) time scales. <i><u>We found that hubs were only shared at a significant level for neighboring time scales, while non-hubs were broadly shared across all time scales</u></i> (multiple comparisons correct Mann-Whitney Test (1, 2, and 3 dots: p<0.05, 0.01, and 0.001 respectively), error bar: standard error of the mean). For each data set, we subtracted the mean sharing values for 500 trials with neuron identities randomized and neuron hub, non-hub, or unconnected status held constant. This null model approximates the amount of sharing expected based only on the number of hubs, non-hubs, and unconnected neurons in the data set, as well as the effect of ignoring the multiplex properties of the networks and considering the time scales to be truly independent networks. We also calculated the mean sharing value of (<b>C</b>) hubs and (<b>E</b>) non-hubs across each pair of time scales for cortical and hippocampal networks. In (B), neighboring time scale pairs are up and to the left of the white line, while distant time scale pairs are down and to the right of the white line. (<b>D and F</b>) Finally, we calculated the multiple comparisons corrected Mann-Whitney Test p-values between sharing results from data and sharing results from the null model.</p
Binning structure for short time scales on example spike trains.
<p>Note that the time scales overlapped to some degree to capture interactions with all delays and that time scales greater than 1 possessed delays to prevent short time scale interactions from influencing long time scale measurements.</p
Hippocampal structures were preserved throughout culturing. Photographs of cortico-hippocampal organotypic cultures.
<p>(<b>A</b>) A bright field image of an example organotypic culture at DIV1. The hippocampal structure is visible without staining. Blue arrows indicate the location of the edge of the recording array. (<b>B</b>) NeuN staining of the culture after data taking and tissue fixation at DIV16. There are missing neurons in CA3 as consistent with a previous report <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115764#pone.0115764-Zimmer1" target="_blank">[111]</a>, but the overall layer structure is well conserved. (<b>C</b>) Overlaid photograph of A and B. Positions and dimensions of the hippocampal structures are well conserved during the incubation period. (<b>D</b>) Overlaid photograph of B, the outline of the array (yellow rectangle), and the estimated locations of the recorded neurons. Light blue circles are manually identified hippocampal neurons and red circles are neurons recorded outside the hippocampal structure. Locations of the recorded neurons match with the granule cell layer and the cell body layer. For complete details on culture preparation, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115764#pone.0115764-Ito2" target="_blank">[48]</a>.</p
Connectivity statistics.
<p>(<b>A</b>) TE p-value histogram for real and randomized data. Real data show many more pairs of neurons with low p-values compared to randomized data. In this analysis, the p-value threshold was set at less than 0.001. The extrema correspond to the time scale with the largest or smallest percentage value for a given p-value. (<b>B</b>) Number of found connections. The number of connections found in each network was at least 4 times larger than expected by chance, with most networks containing 10 to 100 times more connections than expected by chance. (<b>C</b>) Effective N values. The effective N for each network is the number of connected nodes in the network. The mean ± STD in the original data (sub-networks) was 241±103 (42±7) for cortical networks and 128±54 (42±7) for hippocampal networks. (<b>D</b>) Effective k values. The effective k for each network is the average number of connections (degree) per neuron. The mean ± STD in the original data (sub-networks) was 22±30 (3.7±0.7) for cortical networks and 11±18 (3.7±0.8) for hippocampal networks. Note that the sub-network procedure significantly reduced the variability of the effective N and k values, as well as the differences in effective N and k between tissue types.</p