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Endocrinology, doi:10.1210/en.2008-0350
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*LIOTHYRONINE
Endocrinology Vol. 149, No. 8 3989-4000
Copyright © 2008 by The Endocrine Society

Thyroid Hormone Action in the Adult Brain: Gene Expression Profiling of the Effects of Single and Multiple Doses of Triiodo-L-Thyronine in the Rat Striatum

Diego Diez, Carmen Grijota-Martinez, Patrizia Agretti, Giuseppina De Marco, Massimo Tonacchera, Aldo Pinchera, Gabriella Morreale de Escobar, Juan Bernal and Beatriz Morte

Instituto de Investigaciones Biomédicas (D.D., C.G.-M., G.M.d.E., J.B., B.M.), Consejo Superior de Investigaciones Científicas and Universidad Autónoma de Madrid (CSIC-UAM), and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER) (C.G.-M., G.M.d.E., J.B., B.M.), Instituto de Salud Carlos III, 28029 Madrid, Spain; and Department of Endocrinology and Metabolism (P.A., G.D.M., M.T., A.P.), Centro Eccellenza AmbiSEN, University of Pisa, 56124 Pisa, Italy

Address all correspondence and requests for reprints to: Beatriz Morte and Juan Bernal, Instituto de Investigaciones Biomédicas, Arturo Duperier 4, 28029 Madrid, Spain. E-mail: bmorte{at}iib.uam.es; or jbernal{at}iib.uam.es.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Thyroid hormones have profound effects on mood and behavior, but the molecular basis of thyroid hormone action in the adult brain is relatively unknown. In particular, few thyroid hormone-dependent genes have been identified in the adult brain despite extensive work carried out on the developing brain. In this work we performed global analysis of gene expression in the adult rat striatum in search for genomic changes taking place after administration of T3 to hypothyroid rats. The hormone was administered in two different schedules: 1) a single, large dose of 25 µg per 100 g body weight (SD) or 2) 1.5 µg per 100 g body weight once daily for 5 d (RD). Twenty-four hours after the single or last of multiple doses, gene expression in the striatum was analyzed using Codelink microarrays. SD caused up-regulation of 149 genes and down-regulation of 88 genes. RD caused up-regulation of 18 genes and down-regulation of one gene. The results were confirmed by hybridization to Affymetrix microarrays and by TaqMan PCR. Among the genes identified are genes involved in circadian regulation and the regulation of signaling pathways in the striatum. These results suggest that thyroid hormone is involved in regulation of striatal physiology at multiple control points. In addition, they may explain the beneficial effects of large doses of thyroid hormone in bipolar disorders.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
THE EFFECTS AND mechanisms of action of thyroid hormone on the developing brain have been extensively studied (1, 2). Thyroid hormone has multiple actions on brain maturation, resulting from regulation of neural cell migration and differentiation, synaptogenesis, and myelination. Hypothyroidism alters neuronal migration in the cerebral cortex and the cerebellum and impairs the differentiation of pyramidal cells, Purkinje cells, {gamma}-aminobutyric acid (GABA)ergic cell precursors, oligodendrocytes, astrocytes, and microglia. Most if not all of these actions of thyroid hormone are due to regulation of gene expression mediated by interaction with transcriptionally active nuclear receptors (3).

The molecular basis of thyroid hormone action in the mature brain is less well known, despite its importance in brain function. In adults thyroid hormone influences mood and behavior, and thyroid dysfunction very often leads to psychiatric disorders (4). High doses of T4 are effective in bipolar depression (5, 6). Despite these observations, very little is known on the mechanisms of action of thyroid hormone in the adult brain. Neurotransmitter systems are affected by deficiency or excess of thyroid hormones (7), and thyroid hormone influences neurogenesis in the subventricular and subgranular zones in the adult rat brain (8, 9). Thyroid hormone receptors are widely expressed in the adult brain, and particularly the TR{alpha}1 isoform has been implicated in the control of pathways regulating behavior (10). As in other tissues, it is most likely that the action of thyroid hormone in the adult brain is exerted through the control of gene expression. However, an important feature of thyroid hormone action is that the genes regulated by thyroid hormones in the developing brain are insensitive to thyroid hormone in the adult brain, with some exceptions (2).

The purpose of the present work was to fill an important gap in our knowledge of thyroid hormone action in the adult brain by the identification of thyroid hormone-responsive genes. For this study we selected the striatum for two main reasons. One reason was because the thyroid hormone regulated gene Nrgn (also known as RC3), is responsive to T3 in the striatum but not other regions of the adult brain (11). Another reason was the problems of sensitivity threshold due to the high complexity of the whole brain: the cellular complexity of the striatum is not as high as other regions of potential interest such as the cerebral cortex because a single cell type, the GABAergic medium-spiny projection neuron, represents more than 90% of the total cell population. It is worth mentioning in this context that the striatum was one of the regions showing altered metabolic activity after administration of T4 to bipolar patients (6).

We analyzed the effects of single and multiple doses of T3 administration to hypothyroid rats on striatal gene expression by microarray analysis. As a result, we identified novel gene targets of thyroid hormone. In terms of gene regulation, the effect of a single, large dose of T3 was more dramatic than that of multiple lower doses, probably indicating that amplified gene responses to T3, as occurs in liver (12), are also present in the adult brain. This observation is important to explain the beneficial effects of large doses of thyroid hormone in bipolar disorders.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Animals and treatment
Rats from the Wistar strain grown in our animal facilities were used. Protocols for animal handling were approved by the local institutional Animal Care Committee and followed the rules of the European Union. Animals were under temperature (22 ± 2 C) and light (12-h light, 12-h dark cycle; lights on at 0700 h) controlled conditions and had free access to food and water. Hypothyroidism was induced in adult male rats at postnatal day (P) 50 by surgical thyroidectomy, taking care to spare the parathyroid glands (9). To ensure complete thyroidectomy, the rats were given the antithyroid compound 2-mercapto-1-methylimidazole (Sigma Chemical Co., St. Louis, MO) 0.02% in the drinking water until the end of the experiment, on P75. Hypothyroid rats (referred to as TX) had low serum levels of both T4 (0.34 ± 0.13 ng/ml) and T3 (0.07 ± 0.05 ng/ml), compared with normal rats of the same age (24.8 ± 5.7 and 0.49 ± 0.07 ng/ml, respectively). Serum thyroid hormones were measured as described (13).

T3 (Sigma), dissolved in 0.05 M NaOH and diluted in saline containing 0.1% BSA, was administered ip to the TX rats according to two different schedules: in one group T3 was given as a single dose of 25 µg of T3 per 100 g body weight on P74, i.e. 24 h before the animals were killed (SD group). Plasma T3 concentration 24 h after the injection was 4.35 ± 1.93 ng/ml, corresponding to an estimated fractional occupancy of T3 nuclear receptors of 0.87, according to the formula: occupancy = plasma T3/(plasma T3 + 0.67) (14). The SD group was used to identify fast changes of gene expression taking place after T3 administration to TX rats, under conditions of near full saturation of nuclear receptors for 24 h. A second group of TX rats was also treated with T3 but for 5 d with single daily doses of 1.5 µg of T3 per 100 g body weight, starting on P70 (RD group). Plasma T3 concentration 24 h after the last injection was 0.66 ± 0.17 ng/ml and an estimated T3 receptor occupancy of 0.49. The effect of T3 treatment was monitored by Northern blotting analysis of liver type 1 deiodinase (D1) mRNA (15) and quantified by densitometry using the National Institutes of Health Image J software (http://rsb.info.nih.gov/ij/), with correction for the housekeeping gene Gapdh. Both treatment schedules resulted in similar inductions of D1 mRNA (TX: 0.40 ± 0.05; SD: 1.30 ± 0.20; RD: 1.15 ± 0.20, P < 0.05 for T3 treated vs. TX).

The animals, weighing about 200 g, were killed by decapitation under anesthesia with a mixture of ketamine and medetomidine (9) 24 h after the single T3 dose or the last of the multiple doses. The brain was rapidly removed and the striatum was isolated after separation from the internal capsule and kept frozen at –80 C until RNA preparation.

RNA analysis
Total RNA was isolated individually from each animal using the Trizol procedure (Invitrogen, Carlsbad, CA), with an additional step of chloroform extraction. The quality of RNA was analyzed using a BioAnalyzer (Agilent, Santa Clara, CA). cDNA was prepared from 250 ng of RNA using the high-capacity cDNA reverse transcription kit (Applied Biosystems, Foster City, CA). For quantitative PCR, a cDNA aliquot corresponding to 5 ng of the starting RNA was used, with Taqman Assay-on-Demand primers and the Taqman universal PCR master mix, No Amp Erase UNG (Applied Biosystems) on a 7900HT fast real-time PCR system (Applied Biosystems). The PCR program consisted in a hot start of 95 C for 10 min, followed by 40 cycles of 15 sec at 95 C and 1 min at 60 C. PCRs were performed in triplicates, using the 18S gene as internal standard and the 2-cycle threshold method for analysis (16).

Individual striatal RNA samples from six hypothyroid, four SD, and five RD rats were hybridized to separate Codelink microarrays (rat whole genome bioarray; GE Healthcare Europe GmbH, Munich, Germany). These arrays contain 33,849 probes, representing 14,519 unique sequences. A limited survey was also performed using RNA pools from five animals of each condition and hybridized to the Rat Expression Array 230A (product 511036 from Affymetrix, Santa Clara, CA), containing 10,417 unique sequences. There were 9749 sequences common to both platforms (see supplemental Fig. 1, published as supplemental data on The Endocrine Society’s Journals Online web site at http://endo.endojournals.org). All procedures were as recommended by the manufacturers. Codelink hybridizations were performed at the Instituto de Investigaciones Biomédicas, Madrid, whereas Affymetrix hybridizations were performed at the University of Pisa.

Analysis of the data from microarray hybridizations
The data were analyzed using the R software (17) and packages from the Bioconductor project (http://www.bioconductor.org/) (18, 19). The codelink (20) and affy (21) packages were used for reading and preprocessing the arrays, genefilter (22) for data filtering and limma (23) for statistical analysis. For Codelink arrays, raw intensities were exported for each array with the Codelink software and the files read into R. Background correction using the normexp method and quantile normalization was applied. Probes having a signal to noise ratio (SNR) below 1 in all samples were removed from further analysis. Gene-wise intensities were fitted to a linear model with the experimental group (TX, RD, and SD) as factor and contrasts RD-TX and SD-TX were computed to identify genes differentially expressed between the treatments and the reference group. Genes were selected as differentially expressed with Padjust < 0.05 (24). Data from the Affymetrix arrays were loaded into R and processed using the robust multichip average (25) method. Genes were selected based on fold change to compare the results with the Codelink data. For this task an absolute fold change of 1.6 was used. (0.7 in log2 scale).

We performed an analysis of enriched gene ontology (GO) (26) categories to look for affected biological processes, molecular functions, or pathways using the PANTHER resource (27). A gene universe was created by filtering from the list of all probes in the array those not expressed, i.e. with a SNR less than 1 in all samples. After that, we translated the probe identifiers to Entrez Gene identifiers (removing any probe without Entrez Gene) and filtered those lacking GO annotations. Finally, we removed all duplicated Entrez Gene identifications, obtaining 7,287 identifiers from the original 33,849 probes (supplemental Table 1. For the test we translated the list of differentially expressed genes to Entrez Gene and removed those not present in the universe.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Effects of T3 on gene expression in the adult striatum
To analyze the effects of thyroid hormone on gene expression in the adult rat striatum, T3 was administered to hypothyroid rats either acutely (SD) or as single daily doses (RD). RNA from individual rat striata was used to hybridize Codelink microarrays. Both the SD and RD treatments resulted in changes of gene expression. Figure 1AGo shows the hierarchical clustering (heat map) of all the individual data representing the differentially expressed sequences at a significance level of Padjust < 0.05. The complete, annotated heat map is shown as supplemental Fig. 2. The columns contain the data from individual samples, which clustered in three groups as a function of treatment. The rows show the relative intensity of the probes, centered around the mean intensity for all individual RNAs. The probes were grouped in two main clusters: one group (cluster a) contained sequences whose expression increased after T3. The second group (cluster b) contained sequences whose expression was decreased by T3. Visual inspection of the heat map also revealed that the effect of the SD was more pronounced than the RD, although in most cases there was also an effect but of lower intensity of the RD.


Figure 1
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  		FIG. 1. Effects of T3 on gene expression in the adult rat striatum. A, Two-dimensional representation of expression values (heat map) across all samples and differentially expressed genes. All intensities were normalized across the rows (subtracting the mean and dividing by the SD) to enable comparison between genes. The normalized log intensity values of the probes were centered to the median value of each probe set and colored on a range of +2 (red) and –2 (blue); yellow indicates intermediate value. Columns correspond to samples, whereas row corresponds to individual probe sets (in some cases several probe sets correspond to the same gene). Two main clusters, a and b, classify the differentially expressed genes as having higher or lower expression after T3 treatment, respectively. The horizontal arrow shows a cluster of genes showing higher expression after multiple doses of T3 (RD group) but not after a single dose (SD group). B, The profiles plot shown here contains the same information found in the heat map but with a different perspective. This allows us to focus on the general tendency of selected gene clusters. Signal intensities are normalized as in the heat map and expression values and colored based on the overall pattern. For each pattern, a loess (locally weighted polynomial regression) fit line describes the overall profile of the corresponding group. This tendency profile is plotted separately for genes up-regulated (red and green) and down-regulated (blue). The upper panel shows the expression values of each of the 222 up-regulated sequences and 112 down-regulated sequences selected after the T3 single dose (SD group) in the individual arrays for the three experimental groups. The lower panel corresponds to the expression values of each of the 25 up-regulated sequences and the one that was down-regulated after the T3 repeated dose (RD group). In the RD group, the up-regulated genes follow two trends: one with little change in the SD group (green line) and another that was also changed in the SD group (red line); there was only one gene decreased after RD (blue line). Most of the genes in cluster a of the heat map correspond to genes following the blue line in B. On the other hand, genes in cluster b in the heat map correspond to genes following the red line in B. Those genes indicated with the green line correspond to the cluster shown with an arrow in the heat map.

 
The comparisons between the effects of the SD vs. the RD are clearly seen in the plot profiles shown in Fig. 1BGo. These plots show the expression values along different arrays for the probes selected as differentially expressed in the two treatment groups. The signal intensities are scaled to the same range so that they can be compared. The tendency profile [Loess curve (28)] is plotted separately for up-regulated (Fig. 1BGo, red and green) and down-regulated (Fig. 1BGo, blue) genes. The upper panel shows the behavior in each group of the genes that changed after the single T3 dose (SD group). The lower panel shows the genes whose expression was changed after the multiple T3 doses (RD group). The data indicate that administration of a single high dose of T3 sets in motion large changes of gene expression, in contrast to the more discrete changes after treatment with the much lower, multiple doses.

Figure 2Go shows the MA scatter plots (Fig. 2Go, upper panels) and the Venn diagram (Fig. 2Go, lower panel). The MA plots relate the log2 of the fold change (M) of the signal after T3 treatment with its mean log-expression level (A) in the treated and untreated groups. The dotted horizontal lines represent a fold change of 2 for up-regulated genes or 50% for down-regulated genes. The blue dots represent the differentially expressed probe sets with Padjust < 0.05. In the SD group, 334 sequences were differentially expressed with respect to the hypothyroid animals. From them, 222 sequences were up-regulated and 112 sequences down-regulated. After subtracting 80 nonannotated sequences and 17 repeated probes, there were 149 up-regulated and 88 down-regulated genes. Many of the statistically significant, differentially expressed genes had absolute M values as low as 0.5, indicating moderate changes of gene expression. After filtering the data on the basis of fold change, there were 91 up-regulated genes and 53 down-regulated genes with absolute M values greater than 0.7.


Figure 2
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  		FIG. 2. Effects of single (SD) and multiple (RD) doses of T3 on gene expression after administration to TX animals. A, MA plots, relating the log2 of the fold change (M, in ordinate) vs. the mean of log intensity of the signal (A, in abscissa) of SD vs. TX and RD vs. TX. Red and orange dots represent probe sets having SNRs of 1 or below. Black dots are probe sets with SNR above 1. The blue dots represent the differentially expressed sequences, with Padjust < 0.05. B, Venn diagram showing the quantitative relation between the number of sequences differentially expressed in the SD and RD groups.

 
The RD caused changes of expression of 26 sequences. After subtracting the nonannotated and repeated sequences, 18 genes were up-regulated and one was down-regulated [Gja7, or connexin 45 (29)]. As shown in the intersection between the SD and RD in the Venn diagram, 17 sequences were up-regulated with both treatment schedules. This set of 17 sequences corresponded to 10 unique genes: the cannabinoid receptor Cnr1, an important G protein-coupled receptor (30), recently linked to hyperactivity of neonatal hypothyroid rats (31); angiotensin converting enzyme, Ace, a component of brain renin-angiotensin system (32); the sonic hedgehog transcriptional effector Gli1, linked to neuroprotection in the adult brain (33); the sonic hedgehog responsive gene Ith3 [inter-{alpha} trypsin inhibitor (34)]; the extraneuronal monoamine transporter Slc22a3, implicated in several neurological disorders (35); the voltage gated sodium channel β4-subunit, Scn4b, involved in electrical signaling and cell adhesion and defective in Huntington’s disease (36, 37); the small heat shock protein Hspb6 (38); Grifin, or galactin-related interfiber protein originally described in the lens (39); and Loc290651, similar to myoinositol 1-phosphate synthase A1, and RGD1305038_predicted, similar to serum-inducible kinase.

The mean expression of the eight sequences specifically increased by RD with P < 0.05 is represented as a green line in the plot profile of Fig. 1BGo. Seven of these sequences could be observed as a prominent cluster in the heat map (Fig. 1Go, horizontal arrow). Three of these genes are related to red blood cells (Loc287167, Hba1, and AlaS2); Ceacam1 encodes a cell adhesion molecule (40); Prph1 encodes the intermediate filament and Akt substrate peripherin (41, 42); and Cd52 encodes a surface antigen.

The complete set of genes that were differentially expressed in the SD and RD groups, selected on the basis of statistical significance is shown as supplemental Table 2. From these data, genes having a fold change of at least 1.6 in any of the two treatment groups are displayed in Table 1GoGoGo. This table shows the fold change in the SD and the RD groups and the A value. The A value corresponds to the mean of the log2 intensity values over all samples and is a measure of the average expression level. Values with Padjust < 0.05 are highlighted. The genes were categorized according to the biological processes or molecular function assigned by GO annotation (26). The GO annotation suggested that the differentially expressed genes were involved in a variety of biological processes.


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  		TABLE 1. Differentially expressed genes grouped by biological process (BP; GO)

 

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  		TABLE 1A. Cont.

 

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  		TABLE 1B. Cont.

 
To determine the statistical significance of these observations, we used PANTHER analysis to identify those categories significantly over- or downrepresented within the group of differentially expressed genes in relation to the universe of expressed genes in the striatum as found in the arrays (7287; see Materials and Methods). The analysis (supplemental Table 3) showed the pathway, biological process and molecular function categories with over- or downrepresented candidate genes with P < 0.05. Within the pathway category, we found three genes related to the circadian clock system (Per1, Per2, Nr1d2); heterotrimeric G protein signaling pathways (Gq{alpha} and Go{alpha}-mediated pathways: Rgs2, Rgs14, Rgs9, Rasgrp1, Arhgef3_predicted, Rhoc_predicted, Rap1ga1, Rasgrp2_predicted); oxidative stress response (Dusp1, Dusp5, Map2k3, Dupd1_predicted); phenylethylamine degradation (Aldh1a1, Doxl1); and MAPK pathway (Map2k3, Fos, Rps6ka4_predicted). Within the biological process and molecular function categories, we found, among others, genes involved in regulation of phosphate metabolism, signal transduction, tyrosine kinase signaling pathways, extracellular transport and import, cell structure, and neuronal activity.

Confirmation of microarray data
We used two approaches to confirm the data. First, we checked the reproducibility of the assay in a different platform. Reproducibility of microarray data across different platforms and laboratories is dramatically dependent on the criteria used to select the differentially expressed genes (43). In our studies we analyzed individual RNAs and applied the limma statistics to account for variability of the data and therefore selected the candidate genes on the basis of adjusted P value. On the other hand, it has been noted that selection on the basis on fold change achieves better reproducibility among different platforms and biological groups than a selection based on t statistics (44, 45).

Therefore, as a secondary screening for partial validation of the data and to select sequences for further confirmation by PCR, we performed an independent analysis using the Affymetrix platform. In this assay, we used pools of RNA instead of individual samples. Striatal RNA from five animals of each group were pooled and hybridized to the Affymetrix rat expression arrays 230A. Candidate genes were selected on the basis of an increase or decrease of at least 1.6-fold and compared with the candidate genes previously selected from the Codelink arrays. These comparisons were limited by the fact that some genes were uniquely present in one of the two different arrays (see supplemental Fig. 1 and supplemental Table 4). Fifty two differentially expressed genes from the Codelink platform were absent from the Affymetrix arrays, and 20 genes with an M value above 0.7 in the Affymetrix arrays were absent from the Codelink arrays. Among the latter, Nrgn and Tubb3, known to be dependent of the thyroid status (11, 46), were found as induced by SD and RD treatments in the Affymetrix arrays (not shown) but were not present as probes in the Codelink arrays.

From the genes common to both platforms (supplemental Table 5), five were up-regulated by both T3 treatment schedules (see below), and three were up-regulated by the RD only (Hba1, Alas2, Loc287167). From the SD treatments, 26 up-regulated and 14 down-regulated genes were confirmed in both platforms. Among the SD up-regulated genes, hr (or hairless), Rasd2 (also known as Rhes), and Klf9 (also known as BTEB) are already known to be thyroid hormone-regulated genes. Among the SD down-regulated genes, there were several early response genes (Egr1, Arc, Dusp1, Egr4, Fos, Nr4a1, Nr4a3, Egr2, and Homer1).

For biological confirmation using Taqman PCR, we used a different group of rats from the ones used for the arrays. The genes selected for confirmation included the five genes that were up-regulated by both SD and RD in both platforms: Ace (angiotensin converting enzyme), Hspb6 (heat shock protein B6), Cnr1 (cannabinoid receptor 1), Itih3 (inter-{alpha} trypsin inhibitor), and Scn4b (voltage-gated sodium channel IV). We also checked Rgs9 (regulator of G protein signaling 9), a striatum-specific gene (47), which shows a robust increase by SD; Klf9 (Kruppel-like factor, also known as basic transcription factor binding protein, or BTEB), a gene previously shown to be regulated by thyroid hormone in several cell types (48); Dbp (D-site binding protein); Rasgrp1 (also known as CalDAG-GEF1); and the early response genes Nr4a1(also known as NGFIB), Arc, Dusp1, Egr1 (also known as NGFIA), and Homer1. The results, shown in Fig. 3Go, show that there was a good correlation between the results from the arrays and the PCR, both in the direction and magnitude of expression changes.


Figure 3
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  		FIG. 3. Correlation between data from Codelink and Affymetrix hybridizations and Taqman PCR assays of selected differentially expressed genes from the SD and RD groups.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
In this article we provide, for the first time, a comprehensive list of genes that are differentially expressed by thyroid hormone in the adult striatum. Our approach involved the analysis of gene expression after administration of T3 to hypothyroid rats.

Most previous studies on the effects of T3 on brain gene expression have been limited to the postnatal period, and few genes were known to be sensitive to T3 in the adult brain. In one study, Haas et al. (49) used a limited set of 1224 neural-specific genes and found that hyperthyroidism induced only modest changes in the expression of 11 genes. This study is not comparable with ours, first because of the limited number of genes in the array, second because total brain was analyzed, and third because T3 was given at doses 10-fold higher than in this work and for 10 consecutive days so that the animals were hyperthyroid.

One of the earliest genes identified as regulated by T3 in the adult brain is Nrgn, a gene confirmed to be regulated at the transcriptional level in cultured neurons (50) and demonstrated to be a sensitive marker of T3 action. Nrgn was not recovered initially in the list of candidate genes because it was not present as a hybridizable probe in the arrays. However, it was present in the Affymetrix chips and was detected after the acute and multiple-dose treatments, in agreement with previous studies. Other genes present as candidate genes and previously shown to be regulated by thyroid hormones in other paradigms include Klf9 (48), Egr1 (51), the mitochondrial importer Tomm70 (52), the transcription factor Hr (53), the small G protein Rasd2 (also known as Rhes) (54), the transcription factor Nr4a1 (55), the cannabinoid receptor Cnr1 (31), and the dual-specificity phosphatase Dusp1 (56).

Early studies by Oppenheimer and colleagues (57) showed that the response of some genes, such as pituitary GH, followed a linear response in relation to nuclear occupancy after T3 administration. Expression of these genes did not increase above the baseline euthyroid level. For other genes, exemplified by liver enzymes, the response was nonlinear and amplified with further increases of receptor occupancy above the euthyroid level of about 50% (12).

Based on these studies, the dosage and timing of T3 administration was chosen by us so that two different situations were reached. The single dose was intended to result in near full saturation of nuclear receptors for 24 h. Twenty-four hours after injection, the fractional occupancy of nuclear receptors was calculated to be 0.86. In the case of multiple injections, the fractional occupancy 24 h after the last injection was 0.49, which is the physiological occupancy of T3 receptors in liver (12). The single injection protocol should likely select for fast, linear, and amplified responses after T3, whereas the multiple injection dosage should identify linear, steady-state responses. Therefore, it is not surprising that the number of candidate genes was larger after the acute injection of T3 than after the multiple daily doses. In the RD group, there were one down-regulated gene and eight up-regulated genes. The only down-regulated gene, Gja7, encodes a gap junction membrane channel protein expressed in several regions of the central nervous system but not in the striatum (58). This agrees with the almost no expression found in the RD-treated rats, indicating that thyroid hormone is involved in maintaining Gja7 repression under normal conditions. Three of the up-regulated genes were of red blood cell origin. The significance of this finding is uncertain. Interestingly, Hba1 has also been found previously to be regulated by T3 in the cerebellum in vivo and in primary cultures of cerebellar granular cells, presumably at the transcriptional level (56). The functional significance of the regulation of the rest of genes comprising this small set of candidate genes is uncertain because they do not share common functional properties.

Despite the above, the expression of many genes after the RD falls between the levels of the hypothyroid and acutely treated rats, indicating that the group of genes sensitive to each of the treatment schedules is qualitatively similar. Actually, we calculate that 52% of the differentially expressed genes in the SD were changed in the RD by at least 20%, and in the same direction. This justifies the functional analysis performed using the PANTHER resource from the list of all candidate genes found under the two treatment schedules. On the other hand, the overlap between the SD and RD suggests that the genes induced in both treatment schedules are regulated at the transcriptional level.

PANTHER analysis of the significance of represented pathways disclosed three genes of the circadian clock system. Although not present in the PANTHER analysis, another regulated gene, Dbp, has been described as a circadian gene (59). Also, other genes related to the wakefulness (Fos, Arc, Hsps, Egr1, Homer) (59) were sensitive to T3. Because at least one of these genes, Dbp, has been proposed as a candidate gene in bipolar disorders (60), it is tempting to speculate that the beneficial effects of large doses of thyroid hormones in bipolar disorders is mediated, at least in part, through regulation of the expression of these genes.

Another group of candidate genes have important roles in the physiology of striatal neurons. There are 314 genes of enriched expression in the striatum (61), from which 21 were differentially expressed after T3 treatment (supplemental Table 6). Some of these genes are defective in Huntington’s disease (37), such as Scn4b, Rasd2, Rasgrp1, Klf9, and Rgs9. The latter is also one of the 10 most enriched genes in the striatum in relation to other brain regions (61). Central to striatal neuron signaling is the regulation of dopamine and cAMP-regulated phosphoprotein (DARPP)-32 phosphorylation [Fig 4Go (62)]. Phospho-DARPP-32 levels are controlled by the cAMP-protein kinase A pathway, which promotes phosphorylation, and by the Ca2+-calcineurin pathway, which promotes dephosphorylation. Rgs9 and Rasd2 are involved in G protein signaling regulating the cAMP pathway (47, 63), whereas Nrgn is involved in the regulation of the Ca2+/calmodulin pathway (64). In addition, sodium channels and some of the early response genes, such as Arc, Homer, and Dusp1, are also regulated by phospho-DARPP-32. A large fraction of the candidate genes found in the screening are also involved in intracellular signaling cascades involving G protein and cation transport. Therefore, thyroid hormone is involved in maintaining an optimal signaling in the striatum by acting at multiple control points.


Figure 4
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  		FIG. 4. Role of thyroid hormone in the control of signaling in the striatum. Shown is a simplified scheme of signaling pathways in the striatum. DARPP-32 phosphorylation is under control of the cAMP pathway, which promotes phosphorylation, and by the Ca2+-calmodulin (CaM) pathway, which promotes dephosphorylation. cAMP production is under control of G protein-coupled membrane receptors such as dopamine receptors (D1, D2) and others not included in the figure, such as opiate receptors, adenosine, and serotonin receptors. Voltage-dependent Ca2+ channels (VDCC), N-methyl-D-aspartate (NMDA) glutamate receptors, dopamine 2 receptors (D2), and GABAA receptors (not shown) regulate intracellular Ca2+ concentration and CaM activation. Genes regulated by thyroid hormone are involved in different control points of the signaling cascade, as shown by the arabic numerals: 1, G protein-coupled receptors; 2, Ca2+ signaling and CaM activation; 3, MAPK pathways; 4, early gene transcription; 5, ion channels. PLC, Phospholipase C; PKA, protein kinase A; PP-2B, calcium and calmodulin-dependent protein phosphatase, or calcineurin; CaMKIV, calcium and calmodulin-dependent kinase IV; PP-1, protein phosphatase 1.

 
In conclusion, we have identified for the first time a set of genes whose expression is dependent on thyroid hormone in the adult striatum. We believe that this work opens the way for more detailed analysis of the effects of thyroid hormone in the adult brain and their beneficial therapeutic effects in affective disorders.


   Acknowledgments
 
The technical expertise of Eulalia Moreno and Ana Torrecilla is gratefully acknowledged.


   Footnotes
 
This work was supported by Grant BFU2005-01740 from the Ministry of Education and Science of Spain, the European Union Integrated Project CRESCENDO (Consortium for Research on Nuclear Receptors in Development and Aging) Grant LSHM-CT-2005-018652, and the Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III.

Present address for D.D.: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 6110011, Japan.

Disclosure Statement: The authors have nothing to disclose.

First Published Online May 8, 2008

Abbreviations: DARPP, Dopamine and cAMP-regulated phosphoprotein; D1, type 1 deiodinase; GABA, {gamma}-aminobutyric acid; GO, gene ontology; P, postnatal day; RD, repeated doses; SD, single dose; SNR, signal to noise ratio; TX, hypothyroid rats.

Received March 13, 2008.

Accepted for publication April 30, 2008.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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