Global State Measures of the Dentate Gyrus Gene Expression System Predict Antidepressant-Sensitive Behaviors

Background Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance. Methods We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. Results Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. Conclusions System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments

Genomic Imprinting of the X-linked Gene Transketolase-like 1 in Mouse and Human

Genomic imprinting is defined as differential allele expression based on parental origin. Imprinting of X-linked genes has been hypothesized to contribute to the 4-fold male:female sex bias in autism. This hypothesis emerged from studies of Turner syndrome, where girls with a maternal X (45, X m) show greater propensity to social impairment and have a higher rate of autism compared to paternal X (45, Xp) females and the general population. Through the use of murine models, the X-linked Xlr3/4 locus and Rhox5 have been identified as imprinted. However, no imprinted orthologs of these genes have been found in humans. Therefore, the search was expanded to identify X-linked genes that are imprinted in both mouse and human. ^ Allele-specific quantitative real-time PCR was used to examine expression of candidate genes in developing brain. As a result, we have identified Transketolase-like 1 (Tktl1) as an X-linked imprinted gene. Tktl1 codes for a transketolase enzyme, which operates in the pentose phosphate pathway (PPP). One function of the PPP is maintaining glutathione in a reduced state by reduction of NADP to NADPH. Since aberrant glutathione levels have been found associated with autistic spectrum conditions, the effect of Tktl1 expression on the state of glutathione was studied in the developing brain. In addition, Tktl1 allows for another opportunity to determine the mechanism of X-linked genomic imprinting, which is of current interest to our laboratory.

Total Levels of Hippocampal Histone Acetylation Predict Normal Variability in Mouse Behavior

<div><p>Background</p><p>Genetic, pharmacological, and environmental interventions that alter total levels of histone acetylation in specific brain regions can modulate behaviors and treatment responses. Efforts have been made to identify specific genes that are affected by alterations in total histone acetylation and to propose that such gene specific modulation could explain the effects of total histone acetylation levels on behavior — the implication being that under naturalistic conditions variability in histone acetylation occurs primarily around the promoters of specific genes.</p><p>Methods/Results</p><p>Here we challenge this hypothesis by demonstrating with a novel flow cytometry based technique that normal variability in open field exploration, a hippocampus-related behavior, was associated with total levels of histone acetylation in the hippocampus but not in other brain regions.</p><p>Conclusions</p><p>Results suggest that modulation of total levels of histone acetylation may play a role in regulating biological processes. We speculate in the discussion that endogenous regulation of total levels of histone acetylation may be a mechanism through which organisms regulate cellular plasticity. Flow cytometry provides a useful approach to measure total levels of histone acetylation at the single cell level. Relating such information to behavioral measures and treatment responses could inform drug delivery strategies to target histone deacetylase inhibitors and other chromatin modulators to places where they may be of benefit while avoiding areas where correction is not needed and could be harmful.</p></div

Measuring the Maturity of the Fast-Spiking Interneuron Transcriptional Program in Autism, Schizophrenia, and Bipolar Disorder

<div><h3>Background</h3><p>Emerging evidence suggests that fast-spiking (FS) interneurons are disrupted in multiple neuropsychiatric disorders including autism, schizophrenia, and bipolar disorder. FS cells, which are the primary source of synaptic inhibition, are critical for temporally organizing brain activity, regulating brain maturation, and modulating critical developmental periods in multiple cortical systems. Reduced expression of parvalbumin, a marker of mature FS cells, has been reported in individuals with schizophrenia and bipolar disorder and in mouse models of schizophrenia and autism. Although these results suggest that FS cells may be immature in neuropsychiatric disease, this possibility had not previously been formally assessed.</p> <h3>Methods</h3><p>This study used time-course global expression data from developing FS cells to create a maturation index that tracked with the developmental age of purified cortical FS cells. The FS cell maturation index was then applied to global gene expression data from human cortex to estimate the maturity of the FS cell developmental program in the context of various disease states. Specificity of the index for FS cells was supported by a highly significant correlation of maturation index measurements with parvalbumin expression levels that withstood correction for multiple covariates.</p> <h3>Conclusions</h3><p>Results suggest the FS cell developmental gene expression program is immature in autism, schizophrenia, and bipolar disorder. More broadly, the current study indicates that cell-type specific maturation indices can be used to measure the maturity of developmental programs even in data from mixed cell types such as those found in brain homogenates.</p> </div

FS cell index applied to typically developing prefrontal cortex.

<p>Panel (a) is a linear regression of FS cell index measurements from developing human prefrontal cortex versus post-gestational age (log2 years). Panel shows that the FS cell index increases with age. Panel (b) is a linear regression of the FS cell index versus parvalbumin expression levels in developing prefrontal cortex. Panel shows that parvalbumin levels were closely related to FS cell index measurements even in data from heterogeneous tissue. Parvalbumin expression levels were excluded from the calculation of the FS cell index.</p

FS cell index versus parvalbumin expression levels in gene expression studies of neuropsychiatric disease.

<p>Panels (a–c) are linear regression plots of the FS cell index versus parvalbumin expression levels in 3 separate studies of global gene expression in autism(a), schizophrenia(b), and bipolar disorder(c). Panels show that the relationship between FS cell index measurements and parvalbumin levels is robust across studies and micoarray platforms. Parvalbumin expression levels were excluded from the calculation of the FS cell index.</p

FS cell maturation index in developing FS cells.

<p>Panel (a) is a line plot of FS cell postnatal age (x-axis) versus the FS cell index (y-axis) in purified developing FS cells. Panel shows that the FS cell index increases with age. Panel (b) is a linear regression of parvalbumin expression levels versus the FS cell index. Index measurements and parvalbumin levels were highly correlated in developing FS cells (R² = 0.95). Parvalbumin expression levels were excluded from the calculation of the FS cell index.</p

Multiple linear regression - Human prefrontal cortex development.

<p>FS = FS cell index.</p><p>CSP = Corticospinal projection neuron index.</p><p>AST = Astrocyte index.</p><p>The first 3 tables show simple linear regression of 3 maturation indices (FS cell index, Corticospinal projection neuron index, and Astrocyte index) on parvalbumin expression levels in data from developing human prefrontal cortex. In all cases there was a highly significant relationship between index measurements and parvalbumin expression levels (p<0.0001). The fourth table shows results when all three indices were entered in the same model. Only the FS cell index remained significantly associated with parvalbumin expression levels (p<0.002).</p

FS cell index in psychiatric disorders.

<p>Panels (a–c) are bar graphs of the FS cell index in the cortex of controls and individuals with disease. Panels show that the FS cell index was decreased in the cortex in autism, schizophrenia, and bipolar disorder. Panels (d–f) are bar graphs of parvalbumin levels in the cortex of controls and individuals with disease. Panels show that parvalbumin levels were decreased in the cortex in autism, schizophrenia, and bipolar disorder. Parvalbumin expression levels were excluded from the calculation of the FS cell index. ** p<0.01, ***p<0.001 n = number of subjects.</p