Hippocampal CA1 Transcriptional Profile of Sleep Deprivation: Relation to Aging and Stress

<div><h3>Background</h3><p>Many aging changes seem similar to those elicited by sleep-deprivation and psychosocial stress. Further, sleep architecture changes with age suggest an age-related loss of sleep. Here, we hypothesized that sleep deprivation in young subjects would elicit both stress and aging-like transcriptional responses.</p> <h3>Methodology/Principal Findings</h3><p>F344 rats were divided into control and sleep deprivation groups. Body weight, adrenal weight, corticosterone level and hippocampal CA1 transcriptional profiles were measured. A second group of animals was exposed to novel environment stress (NES), and their hippocampal transcriptional profiles measured. A third cohort exposed to control or SD was used to validate transcriptional results with Western blots. Microarray results were statistically contrasted with prior transcriptional studies. Microarray results pointed to sleep pressure signaling and macromolecular synthesis disruptions in the hippocampal CA1 region. Animals exposed to NES recapitulated nearly one third of the SD transcriptional profile. However, the SD -aging relationship was more complex. Compared to aging, SD profiles influenced a significant subset of genes. mRNA associated with neurogenesis and energy pathways showed agreement between aging and SD, while immune, glial, and macromolecular synthesis pathways showed SD profiles that opposed those seen in aging.</p> <h3>Conclusions/Significance</h3><p>We conclude that although NES and SD exert similar transcriptional changes, selective presynaptic release machinery and Homer1 expression changes are seen in SD. Among other changes, the marked decrease in Homer1 expression with age may represent an important divergence between young and aged brain response to SD. Based on this, it seems reasonable to conclude that therapeutic strategies designed to promote sleep in young subjects may have off-target effects in the aged. Finally, this work identifies presynaptic vesicular release and intercellular adhesion molecular signatures as novel therapeutic targets to counter effects of SD in young subjects.</p> </div

Expression patterns for significant genes are shown.

<p><b>Left:</b> Artificially constructed templates (<b>A. Sustained; B. Transient; C. Delayed, and D. Linear)</b> were used to partition genes into specified patterns. The treatment group mean expression value for each significant gene was correlated with each of the four templates and the gene was assigned to the template with the highest |R|. Positive correlations are considered ‘increased’ with SD, negative correlations are considered ‘decreased’. <b>Center:</b> Heatmap for 10 representative genes assigned to each template and direction are shown. Data are expressed in standardized units and color coded (lower, color scale) by standard deviations from the mean. <b>Right:</b> Averaged results for all genes in each template are graphed (positive  =  solid green; negative  =  dashed orange; # genes in each pattern reported). Note: error bars plotted but obscured by symbols.</p

SD targets molecules associated with the glutamatergic synapse.

<p>We developed an <i>a priori</i> defined list of genes (101) reported to play a role in glutamatergic neurotransmission. 46 were significantly altered with SD (35 decreasing, 15 increasing. A high proportion of downregulated messages were associated with presynaptic neurotransmitter release and cell adhesion. As a process, macromolecular synthesis appears increased. Genes also found to change with age are noted with an (*- agreed; † opposed). Abbreviations: <i>Add1</i>- adducin 1 α; <i>Agrn</i>- agrin; <i>Ddah1</i>- dimethyl arginine dimethyl aminohydrolase; <i>Glud1</i>- glutamate dehydrogenase 1; Glutamate transporters (<i>Slc1a1</i>- excitatory amino acid transporter 3; <i>Grip2</i>- glutamate receptor interacting protein 2; <i>Slc1a2</i>- excitatory amino acid transporter 2, <i>Slc1a3</i>- excitatory amino acid transporter 1); <i>Kv1.1</i>- shaker K+ channel; <i>Nsf</i>- n-ethylamide sensitive factor; <i>Psen1</i>- presenilin 1; <i>Pscd1</i>- pleckstrin homology; <i>Sec15</i>- secretory factor 15; <i>Snca</i>- alpha synuclein.</p

Sleep deprivation reduces body weight, increases corticosterone, and causes detectable changes in hippocampal gene expression.

<p>A. Body weight was significantly reduced in all SD animals from both cohorts. Weights measured at the end of the study are expressed as a percentage of the body weight measured at the beginning of the study, a 4 day span (1-way ANOVA [* F_{2, 49} = 29.1, p  = 4.8×10⁻⁹]). B. Adrenal weights measured from animals in cohort 1(HC n  = 9; 24SD n  = 9; 72SD n  = 10) were not significantly increased (p  = 0.22). C. Corticosterone levels from animals in cohort 2 (HC n  = 12; 24SD n  = 9; 72SD n  = 13) were significantly increased at 24SD (1-way ANOVA [F_2,31 = 5.93, p  = 0.0066]; *post-hoc Tukeys). D. Of 8799 total probe sets, 2167 were rated present and had unique gene symbol level annotations. These were tested by 1-way ANOVA across HC, 24SD, and 72SD groups. A total of 679 genes were rated significant (p≤0.05). The False discovery rate (FDR) procedure estimates that 8.4% of these results are significant due to the error of multiple testing.</p

SD and aging influence a similar set of genes.

<p>A. Unlike NES (Fig. 4), the significant overlap between aging and SD (* p  = 0.028, binomial test) contained many genes whose change with age was opposite to that in SD. <b>B.</b> 158/214 genes in the overlap were manually assigned to one of 10 heuristic categories. Because of this approach, no statistical overrepresentation p-values are possible. The number of genes from within each quadrant of the overlap (agreed with SD, aging up, aging down; disagreed in SD- aging up, aging down) are shown. Genes in each category are listed in Results. <b>C.</b> Functional categorization genes regulated exclusively by Aging (upper) or Sleep Deprivation (lower) are separated based on direction of change (Left: Upregulated; Right: Downregulated).</p

Novel environment stress (NES) elicited a transcriptional response highly similar to that seen with SD.

<p>A separate cohort of animals exposed to NES were subjected to hippocampal microarray analysis. Of the 2167 filtered genes, 405 were identified as significant in the stress study (p≤0.05; 1-way ANOVA; FDR  = 0.18). <b>A.</b> Venn diagram comparing stress (black circle) and SD (SD- white circle) array results reveals a highly significant overlap of 189 genes significant in both studies (* p  = 1.55×10⁻⁸; binomial test). Directional analysis revealed that 96% (182/189; 81 upregulated; 101 downregulated) of these overlapping genes agreed in direction of change. <b>B.</b> NES was powered by 21 microarrays, while SD was powered by 53. Because of this discrepancy, the greater number of genes found in SD could reflect increased discovery power, rather than a stronger effect of SD. To test this, we iteratively selected subsets of 21 arrays from the 53 used in the SD study and tested for significance. This was repeated 1000 times and in each iteration, the number of genes significant (p≤0.05, 1-way ANOVA) were counted. The results from all 1000 iterations are plotted as a frequency histogram (open circles). This was well-fit by a Gaussian function (heavy black line, p<0.0001, R² = 0.91) with a peak of 476.4- meaning that, on average if only 21 chips had been used in the SD study, we would predict that 476 genes would be found significant. Using the fit function, and the observation that 405 genes were found in the NES study, we fail to support the hypothesis that SD finds more genes than NES (p  = 0.24; integrated area under the curve- gray). <b>C.</b> Gene Ontology Analysis for genes in each region/direction of change within the Venn diagram.</p