Visualisation of nuclear calcium signals using rekombinant calcium indicators in in vivo systems

The generation of calcium signals following synaptic activity is a fundamental property of neurons that controls many neuronal processes including cell survival, learning and memory. In cultured neurons, increases in nuclear calcium concentrations are known to be critical for the activation of gene expression mediated by the transcription factor CREB. CREB has been implicated in transcription-dependent plasticity (late phase LTP). It is still unclear whether nuclear calcium signals are also responsible for CREB mediated gene transcription in vivo. The goal of the study was to visualise nuclear calcium signals in vivo. Investigating the importance of nuclear Ca2+ signalling in the intact brain is impeded by the restrictions and complexities of in vivo experimentation. However, D. melanogaster provides an excellent system to explore the role nuclear Ca2+ in an intact behaving animal. Since Ca2+ signalling of the nucleus is of particular interest transgenic flies expressing the nuclear localised Ca2+ indicator UAS GCaMP NLS to visualise nuclear Ca2+ signals and the nuclear Ca2+/CaM inhibitor UAS 2xM13 myc to interfere with nuclear Ca2+ signalling were generated. Using these flies, it could be shown that nuclear Ca2+ signalling could play a role in LTM. Further, using rAAV-mediated gene transfer the nuclear Ca2+ indicator rAAV GCaMP 2.0 NLS was expressed in CA1 pyramidal neurons of juvenile rats two weeks after in vivo injection. In acute slices from these juvenile rats an increase in nuclear Ca2+ concentration was visualised evoked by LTP inducing electrical stimulation. Using this method a correlation between LTP and increase in nuclear Ca2+ concentration could be shown. It is still unclear whether nuclear Ca2+ signals are necessary to induce LTP in acute slice. The major challenge of Ca2+ imaging in vivo either in flies or in rodents based on difficulties to detect signals through the intransparent skulls. The freshwater polyp H. vulgaris is completely transparent. Therefore, Ca2+ imaging of transgenic hydras expressing the Ca2+-indicators hyGCaMP or hyGCaMP NLS was done. Increase in nuclear Ca2+ signals was observed in ectodermal cells expressing hyGCaMP NLS. At the moment, the role of Ca2+ signals, especially of nuclear Ca2+ signals in hydra is unknown

Estimating Information Processing in a Memory System: The Utility of Meta-analytic Methods for Genetics

<div><p>Genetic studies in <i>Drosophila</i> reveal that olfactory memory relies on a brain structure called the mushroom body. The mainstream view is that each of the three lobes of the mushroom body play specialized roles in short-term aversive olfactory memory, but a number of studies have made divergent conclusions based on their varying experimental findings. Like many fields, neurogenetics uses null hypothesis significance testing for data analysis. Critics of significance testing claim that this method promotes discrepancies by using arbitrary thresholds (α) to apply reject/accept dichotomies to continuous data, which is not reflective of the biological reality of quantitative phenotypes. We explored using estimation statistics, an alternative data analysis framework, to examine published fly short-term memory data. Systematic review was used to identify behavioral experiments examining the physiological basis of olfactory memory and meta-analytic approaches were applied to assess the role of lobular specialization. Multivariate meta-regression models revealed that short-term memory lobular specialization is not supported by the data; it identified the cellular extent of a transgenic driver as the major predictor of its effect on short-term memory. These findings demonstrate that effect sizes, meta-analysis, meta-regression, hierarchical models and estimation methods in general can be successfully harnessed to identify knowledge gaps, synthesize divergent results, accommodate heterogeneous experimental design and quantify genetic mechanisms.</p></div

Forest plot of <i>rut</i> restoration in all lobes of the mushroom body.

<p>Each data set is identified by the source article and figure panel. The subgroups are different driver lines, the red diamond indicates the overall estimated value range for the proportional change relative to control.</p

Forest plot of the effect on STM of elevating flies from permissive to restrictive temperatures.

<p>This figure is a detailed version of the same plot in the previous figure, but uses proportional reductions instead of percentage changes. The source article and figure panel identifies each data set. The subgroups are different driver lines, the red diamond indicates the overall estimated value range for the proportional change relative to control.</p

Forest plot of <i>rut</i> restoration in the αβ and γ lobes.

<p>Each data set is identified by the source article and figure panel. The subgroups are different driver lines, the red diamond indicates the overall estimated value range for the proportional change relative to control.</p

Forest plot of experiments using <i>shi</i>^<i>ts</i> to inactivate neurotransmission from the αβ + γ lobes.

<p>Each data set is identified by the source article and figure panel. The subgroups are different driver lines, the red diamond indicates the overall estimated value range for the proportional change relative to control.</p

Characteristics of included experiments.

<p>All experiments are listed and identified by their study, figure panel and genotype/s. We name the most precise genotype possible based on the information given in the original article. Odor pair, range experimental temperature or temperature range, the nature of the conditioning shock and the relative humidity (RH) are also listed. The time delay between training and testing is listed in minutes; those labelled ‘0*’ were reported as following training ‘immediately.’ Shock is listed in volts; current type is omitted if not reported in the original study. Cells containing a dash indicate that the information was not found in the original article.</p

The extent of drivers’ Kenyon cell expression accounts for the majority of short-term olfactory memory effects.

<p>The estimated Kenyon cell counts for drivers were taken from Aso et al. 2009. The memory effect sizes are derived from nested, weighted, multivariate meta-regression models that adjusted for confounding variables that contributed to heterogeneity. <b>A.</b> Bubble plot of <i>rut</i> restoration; the cell count of driver lines accounts for 84% of the variance of the learning effects of rut restoration (p < 0.0001). Each bubble’s area indicates that estimate’s weight in the regression model; the blue fit line has a slope of 0.023% per cell [95CI 0.016, 0.030]. The grey line indicates the level of no rescue, i.e. the learning level of <i>rut</i> mutants. <b>B.</b> For <i>shi</i>^<i>ts</i> inactivation, 88% of the learning variance is attributable to the number of cells encompassed by the driver (p < 0.0001). The blue fit line has a slope of -0.034% per cell [95CI -0.046, -0.0216]; the grey line indicates the level of no effect, i.e. the learning expected from the effect of heat alone. <b>C.</b> Learning effect per cell in mushroom body sub-regions from <i>rut</i> restoration in different lobes and combinations, adjusted for heterogeneity effects. Error bars are confidence intervals; there are no statistical differences between rut lobe categories. <b>D.</b> The <i>shi</i>^<i>ts</i> learning effect per cell in two lobes and their combination. There are no statistical differences between <i>shi</i>^<i>ts</i> lobe categories.</p

Meta-analyses of <i>rutabaga</i> mutant lines and targeted transgenic restoration.

<p>Short-term memory data are expressed as percentages. <b>A.</b> A summary forest plot of learning changes observed in 340 experiments with <i>rut</i> mutant lines, with subgroups showing the differences between the various <i>rut</i> alleles and strains. Learning is expressed as a percentage change relative to wild type. The red diamond on the bottom line indicates that the overall impairment in learning in the <i>rut</i> hypomorphs relative to wild type controls is -60% [95CI -56%, -64%]. The complete forest plot is given in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005718#pgen.1005718.g003" target="_blank">Fig 3</a>. <b>B.</b> Summary estimates from the <i>rut</i> mutant meta-analysis and five meta-analyses of lobular restoration experiments. Learning is displayed as a percentage of wild type learning. The markers indicate the proportion of learning relative to wild type expressed as a percentage; error bars are 95% confidence intervals. To the right of the markers are numbers for the amount of rescue (R =) relative to the rut hypomorphs. N(E) and N(C) are the experimental and control iterations respectively. Except for the α′β′ lobes (p = 0.17), all lobe categories showed a statistically significant partial rescue of learning (αβ p = 0.029, γ p<1 x 10–45, αβ+γ p = 1.1 x 10–16, all lobes p<1 x 10–45) when compared with rut learning.</p