33 research outputs found

    A Novel Antibody-Based Biomarker for Chronic Algal Toxin Exposure and Sub-Acute Neurotoxicity

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    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

    Multiplex networks of cortical and hippocampal neurons revealed at different timescales.

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    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

    Hub sharing was limited to adjacent time scales.

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    <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>βˆ’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.

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    <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.

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    <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
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