Regulatory Pathway Analysis by High-Throughput In Situ Hybridization

Automated in situ hybridization enables the construction of comprehensive atlases of gene expression patterns in mammals. Such atlases can become Web-searchable digital expression maps of individual genes and thus offer an entryway to elucidate genetic interactions and signaling pathways. Towards this end, an atlas housing ∼1,000 spatial gene expression patterns of the midgestation mouse embryo was generated. Patterns were textually annotated using a controlled vocabulary comprising >90 anatomical features. Hierarchical clustering of annotations was carried out using distance scores calculated from the similarity between pairs of patterns across all anatomical structures. This process ordered hundreds of complex expression patterns into a matrix that reflects the embryonic architecture and the relatedness of patterns of expression. Clustering yielded 12 distinct groups of expression patterns. Because of the similarity of expression patterns within a group, members of each group may be components of regulatory cascades. We focused on the group containing Pax6, an evolutionary conserved transcriptional master mediator of development. Seventeen of the 82 genes in this group showed a change of expression in the developing neocortex of Pax6-deficient embryos. Electromobility shift assays were used to test for the presence of Pax6-paired domain binding sites. This led to the identification of 12 genes not previously known as potential targets of Pax6 regulation. These findings suggest that cluster analysis of annotated gene expression patterns obtained by automated in situ hybridization is a novel approach for identifying components of signaling cascades

Effects of sleep deprivation on sleep recovery and EEG delta power changes during NREM sleep.

<p>(A) Time-course changes in NREM sleep (NREMS) and REM sleep (REMS) and the delta power of slow-wave activity (0.5–5 Hz) during NREM sleep are expressed as the 2 h means ± SEM during baseline recordings (closed circles) and sleep deprivation (SD, open circles) in NAB (n = 7), LAB (n = 10), HAB (n = 10) mice. SD began at the onset of the light period and lasted for 6 h. Two-way ANOVA among the three mouse lines revealed a significant effect of ‘line’ on EEG delta power in NREM sleep during the post-SD 6–12 h during the light period (<i>P</i><0.001) and on NREM and REM sleep and EEG delta power during the post-SD 13–18 h during the dark period (<i>P</i><0.05, <i>P</i><0.05, <i>P</i><0.001). * <i>P</i><0.05, ** <i>P</i><0.001, assessed by two-way ANOVA with the factors ‘line’ and ‘interval’ followed by Bonferroni's test.</p

Latency to NREM and REM sleep during baseline conditions and after sleep deprivation in mouse models of anxiety.

<p>Values are the means in min ± SEM. The letters indicate a significant between-line difference (<i>P</i><0.05) within the individual parameter (a: from NAB mice, b: from LAB mice, c: from HAB mice), assessed by one-way ANOVA with the factor ‘line’ followed by Bonferroni's test. Statistical differences in values after sleep deprivation relative to the corresponding baseline values within each line, analyzed by <i>t</i>-test (* <i>P</i><0.05, ** <i>P</i><0.001). NREMS, non-REM sleep; REMS, REM sleep; SD, sleep deprivation.</p

Sleep-wake distribution in the mouse model of trait anxiety.

<p>(A) Time-course changes in sleep-wake patterns and EEG delta power during NREM sleep of NAB (n = 7), LAB (n = 10) and HAB (n = 10) mice under baseline conditions. Data points represent 2 h means ± SEM of time spent in wake, NREM sleep (NREMS) and REM sleep (REMS). The delta power during NREM sleep is represented as the mean value ± SEM of normalized EEG power densities in the frequency range of 0.5–4.0 Hz restricted to NREM sleep. Two-way analysis of variance (ANOVA) revealed significant effects of ‘line’ and ‘time’ for all vigilance states and delta power (P<0.001) and their interaction for all vigilance states across 24 h (wake, P<0.001; NREMS, <i>P</i><0.001; REMS, <i>P</i><0.05). (B) Percentage of time spent in wake, NREM and REM sleep and the normalized EEG power of delta band during the 12 h light and dark period. Values are the 12 h means ± SEM. *<i>P</i><0.05, **<i>P</i><0.001, assessed by one-way ANOVA with the factor ‘line’ followed by Bonferroni's test. The light-dark (LD) differences were analyzed as well. Two-way ANOVA revealed significant effects of ‘line’ and ‘LD interval’ for all vigilance states (<i>P</i><0.001) and their interaction for all vigilance states (<i>P</i><0.05). The direct comparisons between lines of the 12 h LD dynamics in circadian amplitudes were performed by one-way ANOVA with the factor ‘line’ followed by Tukey's test (wake, NREMS, <i>P</i><0.001; REMS, <i>P</i><0.05).</p

EEG spectra during wakefulness, NREM and REM sleep in the mouse model of trait anxiety.

<p>NAB (n = 7), LAB (n = 10), HAB (n = 10). (A) Spectral distribution of EEG power densities averaged from all 4-s epochs scored as wake, NREM sleep (NREMS) and REM sleep (REMS) over 24 h of recording under baseline conditions. The power of every 0.25 Hz bin was first averaged across the individual vigilance state and then normalized as a group by calculating the percentage of each bin from the total power across 24 h (0.25–32 Hz). All figures show spectral distributions of the EEG power density in the frequency range of 0.25 to 25 Hz. Two-way ANOVA with the factors ‘frequency’ and ‘line’ followed by Bonferroni's test revealed significant effects of ‘frequency’ and an interaction between ‘frequency’ and ‘line’ for each vigilance state (<i>P</i><0.0001), whereas the ‘line’ effects were not significant. For REM sleep, the EEG theta peak frequency, between 6 and 9 Hz, is shown in the insertion. *<i>P</i><0.05, assessed by one-way ANOVA with the factor ‘line’ followed by Bonferroni's test. (B) Comparisons of EEG power density within the delta (0.5–5 Hz), theta (6 – 9 Hz), sigma (10 – 15 Hz), and beta (16 – 23 Hz) bands in the 12 h light and dark period for each vigilance state. Data are presented as the 12 h means ± SEM. The EEG power of each frequency band was normalized by total EEG power during 24 h. *<i>P</i><0.05, **<i>P</i><0.001, assessed by one-way ANOVA with the factor ‘line’ followed by Bonferroni's test.</p

Heterozygosity for the Mood Disorder-Associated Variant Gln460Arg Alters P2X7 Receptor Function and Sleep Quality

A single nucleotide polymorphism substitution from glutamine (Gln, Q) to arginine (Arg, R) at codon 460 of the purinergic P2X7 receptor (P2X7R) has repeatedly been associated with mood disorders. The P2X7R-Gln460Arg variant per se is not compromised in its function. However, heterologous expression of P2X7R-Gln460Arg together with wild-type P2X7R has recently been demonstrated to impair receptor function. Here we show that this also applies to humanized mice coexpressing both human P2X7R variants. Primary hippocampal cells derived from heterozygous mice showed an attenuated calcium uptake upon agonist stimulation. While humanized mice were unaffected in their behavioral repertoire under basal housing conditions, mice that harbor both P2X7R variants showed alterations in their sleep quality resembling signs of a prodromal disease stage. Also healthy heterozygous human subjects showed mild changes in sleep parameters. These results indicate that heterozygosity for the wild-type P2X7R and its mood disorder-associated variant P2X7R-Gln460Arg represents a genetic risk factor, which is potentially able to convey susceptibility to mood disorders.Fil: Metzger, Michael W.. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Walser, Sandra M.. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Dedic, Nina. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Aprile García, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Jakubcakova, Vladimira. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Adamczyk, Marek. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Webb, Katharine J.. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Uhr, Manfred. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Refojo, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Schmidt, Mathias V.. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Friess, Elisabeth. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Steiger, Axel. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Kimura, Mayumi. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Chen, Alon. Max Planck Institut Fur Psychiatrie; Alemania. Weizmann Institute of Science; IsraelFil: Holsboer, Florian. Max Planck Institut Fur Psychiatrie; AlemaniaFil: Arzt, Eduardo Simon. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Wurst, Wolfgang. Institute of Developmental Genetics; Alemania. Technische Universität München-Weihenstephan; Alemania. German Center for Neurodegenerative Diseases; Alemania. Universität München; AlemaniaFil: Deussing, Jan M.. Max Planck Institut Fur Psychiatrie; Alemani