Patterns of Liver Gene Expression Governed by TRß

Amilcar Flores-Morales, Hjalmar Gullberg, Leandro Fernandez, Nina Ståhlberg, Norman H. Lee, Björn Vennström and Gunnar Norstedt

Departments of Molecular Medicine (A.F.-M., N.S., G.N.) and Cell and Molecular Biology (H.G., B.V.), Karolinska Institute, Stockholm 17176, Sweden; Health Science Center (L.F.), Pharmacology Section, Las Palmas de Gran Canaria University, 35080 Las Palmas, Spain; and Department of Functional Genomics (N.H.L.), Institute for Genomic Research, Rockville, Maryland 20850

Address all correspondence and requests for reprints to: Amilcar Flores-Morales, Center of Molecular Medicine (CMM), L8:01, Karolinska Hospital, 17176 Stockholm, Sweden. E-mail: Amilcar.Flores{at}molmed.ki.se.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Several metabolic processes in the liver are regulated by thyroid hormone (T3). Gene expression profiles of livers from normal and TRß-deficient mouse strains should allow the classification of rapid and sustained effects of T3, as well as identification of target genes that are dependent on TRß. The immediate and long-term T3 regulation of about 4000 genes in livers from hypo- and hyperthyroid wild-type and TRß-deficient mice was analyzed using cDNA microarrays. T3 was found to regulate more than 200 genes, and among these, more than 100 were previously not described. Sixty percent of all these genes show dependence on the TRß gene for T3 regulation, indicating that TR{alpha}1 may have previously unknown functions in the liver. Analysis of the gene expression patterns showed a clear functional distinction between rapid (2 h) actions of T3 and late effects, seen after 5 d of sustained T3 treatment. Many metabolic actions were rapidly executed, whereas effects on mitochondrial function, for example, were seen after the sustained T3 treatment. As compared with wild-type controls, TRß-/-mice exhibited elevated expression of some target genes and reduced levels of others, indicating that both direct and indirect gene regulation by TRs in liver is complex and involves both ligand-dependent and -independent actions by the major TR isoforms.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
THYROID HORMONE (T3) is essential for normal development, differentiation, and maintenance of metabolic balance in mammals. T3 exerts its pleiotropic actions through binding to specific receptors that belong to the nuclear hormone receptor superfamily (1). T3 receptors (TRs) are encoded by two separate genes, TR{alpha} and TRß, which are located on different chromosomes. The TR genes {alpha} and ß generate several T3-binding receptors, TR{alpha}1 and TRß1–3, that mediate the effects of T3. A variant receptor protein, TR{alpha}2, does not bind T3 and its exact functions are unclear. The different receptor isoforms contain highly homologous DNA binding, ligand binding, and transactivation domains and, as a consequence, they bind similar thyroid hormone response elements in DNA and exhibit similar ligand-dependent transactivation activity in vitro (2). TRs can either suppress or induce expression of a target gene and recently, Feng et al. (3) reported that more than half of the target genes in liver are negatively regulated by T3.

The different TR isoforms mediate receptor-specific physiological activities despite their structural similarities (4). Mice with a targeted disruption of the TRß gene show hyperthyroxinemia and an impaired T3-induced repression of the pituitary TSH gene. These mice also show impairment of the auditory system and resistance to hypercholesterolemia under hypothyroid conditions but do not exhibit major impairment in growth or additional alterations in neurological functions (4, 5, 6). On the other hand, TR{alpha}1 deficiency yields an abnormal heart rate and lower body temperature (5), whereas TR{alpha}2 deficient mice, which overexpress TR{alpha}1, exhibit a mixture of hypo- and hyperthyroid features (7). Interestingly, mice devoid of both the TR{alpha}1 and TR{alpha}2 isoforms become progressively hypothyreotic, exhibit growth retardation, diminished body temperature, and delayed maturation of bone and intestine, and die within 5 wk after birth (8). The different patterns of the expression of the different TRs may account for their phenotypic differences. Nevertheless, there are few tissues that exclusively express one form of the receptor. Instead, several forms are coexpressed at a variable ratio, allowing the possibility for overlaps in receptor actions (9).

The effects of T3 in the liver involve T4 turnover, regulation of triglycerides, and cholesterol metabolism, for example, as well as lipoprotein homeostasis (10). The hormone also modulates other cellular processes such as cell proliferation (11) and mitochondrial respiration (12). TRß is the prevalent TR in liver representing 85% of the T3-binding activity (13). TRß-deficient mice express similar amounts of TR{alpha}1 as the wild-type (WT) animals but show resistance to some of the T3 actions. TRß-deficient mice fail to lower serum cholesterol levels in response to T3 (10) and express low levels of spot14 (13) and 5'-deiodinase-1 (14).

To extend the understanding of T3 regulation of central metabolic processes in liver, we determined the immediate and the long-term expression of about 4,000 genes in liver samples from hypothyroid wild-type (WT) and TRß-deficient mice using DNA microarray technology. Our results identified a large number of T3-regulated genes not previously described in the liver and demonstrated the difference between early and late effects of T3 in this target tissue. A high degree of dependence on TRß for the actions of T3 was also observed.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
T3 treatment of hypothyroid mice is expected to trigger a complex gene-regulatory response in liver, with genes being up- or down-regulated with variable kinetics. The predominance of TRß over TR{alpha}1 in the liver predicts a major role for TRß. To test these hypotheses we compared gene expression patterns of hypothyroid WT and TRß-/- mice with those treated with T3 for 2 h and 5 d as well as between hypothyroid TRß-/- and WT mice. This allowed us to identify rapidly responsive genes including a subset that is TRß dependent, and genes that respond to sustained hormonal treatment and which therefore may be indirectly regulated. A summary of the experimental design is shown in Fig. 1Go. In brief, mRNA was isolated from livers of hypothyroid WT and TRß-/- mice before and after T3 treatment. Subsequently, global changes in gene expression induced by T3 were analyzed using a DNA microarray containing 4000 cDNAs of rat or mouse origin. We have chosen to denote genes as T3 regulated or TRß dependent if their level of expression is changed by 70% or more in a statistically significant fashion. This level of change has previously been shown to be valid when compared with other direct methods, such as Northern Blot, ribonuclease protection assay (RPA), and RT-PCR (15, 16, 17) (see also Table 3Go). The statistical analyses are detailed in Materials and Methods.



View larger version (12K):
[in this window]
[in a new window]
 
Figure 1. Design of Pairwise Comparisons

The actions of T3 on liver gene expression were evaluated at two different time points: 2 h and 5 d in both TRß-/- and in WT hypothyroid animals. Liver mRNA was isolated from WT and TRß-/- mice that were either hypothyroid or hormone treated for either 2 h or 5 d. mRNA was labeled by reverse transcription in the presence of Cy3- or Cy5-dUTP and was used to perform altogether five pairwise comparisons of gene expression profiles as indicated by the arrows. Solid arrowheads indicate Cy5 labeling and open arrowheads indicate Cy3 labeling.

 

View this table:
[in this window]
[in a new window]
 
Table 3. Comparison of Gene Expression of Individual Genes in Either TRß-/- Mice or WT Controls Determined by Northern Blot1 or RNAse Protection Analysis2 (A) vs. Microarrays3 (B)

 
Of the 4000 cDNA clones analyzed, around 1900 showed hybridization, and more than 250 showed reactivity to T3 in at least one of the time points analyzed. When measured after 2 h, T3 down-regulated 72 transcripts in WT livers by more than 70% whereas 73 were up-regulated to the same extent. In contrast, only 84 genes responded to T3 in TRß-deficient mice to the same extent, suggesting that 50% of the genes rapidly regulated by T3 in liver require TRß. Certain genes responded to T3 in livers from TRß-/- mice, suggesting a partial or complete independence of TRß. The distribution of T3-regulated genes within functional categories is shown in Fig. 2Go. The identities of genes showing variation by a factor of 2 or more in selected categories are shown in Table 1GoGo (the complete list can be found in Table 4, which is published as supplemental data on The Endocrine Society’s Journals Online web site, http://mend.endojournals.org/).



View larger version (23K):
[in this window]
[in a new window]
 
Figure 2. Functional Classification of T3-Regulated Genes in Liver from WT and TRß-/- Mice

The experimental design is described in Materials and Methods and in Fig. 1Go. T3 regulation, defined as more than 70% variation, was then determined by DNA microarray technology comparing cDNA from T3-treated to cDNA from hypothyroid mice. The length of the bars represents the number of T3-regulated genes in each functional category. Within each functional category, different colors indicate the number of genes that were regulated 2 h after a single injection of 5 µg T3 and 5 µg T4 (2 h), genes regulated after 5 d of one daily 5-µg T3 injection (5d), and those genes regulated after both 2 h and 5 d of hormone injections (2 h and 5 d).

 

View this table:
[in this window]
[in a new window]
 
Table 1. T3-Regulated Genes in Livers of WT and TRß (-/-) Hypothyroid Mice

 

View this table:
[in this window]
[in a new window]
 
Table 1A. Continued

 
To determine the extent of TRß dependence in the WT animals, we performed a cluster analysis of expression data using the K-means algorithm. Before the algorithm was applied, data were selected on the basis of fold change as explained above. A database was assembled with the fold change in gene expression in each of the experiments. The data for each gene were represented as a four-dimensional vector with each point representing ratios from one type of experimental situation. To study the extent of TRß contribution independently of the magnitude of the changes in expression levels, the expression data were transformed to place all the measurements within the same range (see also Materials and Methods). K-means clustering was performed using a dot product calculation to determine the distance between vectors. Figure 3Go shows the clusters A, B, C, E, F, K, L, M, and N of the 15 clusters generated. Clusters A–C indicate T3 up-regulated genes that are highly or partially TRß dependent in immediate (A) or long-term (B and C) hormonal responses. Genes down-regulated by T3 in a TRß-dependent fashion are found in E and F. Noteworthy is the existence of clusters that represent genes that are regulated by T3 in liver independently of TRß receptor expression (clusters K, L, M, and N). These genes may represent direct targets of TR{alpha}1 or products of a secondary phenomena resulting from long-term adaptation of the tissue to TRß deficiency. Genes in Table 1Go are annotated in relation to the cluster analysis. The complete cluster analysis can be found in Fig. 5, which is published as supplemental data on The Endocrine Society’s Journals Online web site.



View larger version (28K):
[in this window]
[in a new window]
 
Figure 3. TRß Dependence of T3-Regulated Genes in Liver

A K-means cluster analysis was performed with expression data normalized so that 1 U represents maximal possible induction/repression. The expression data used were the mean of three independent hybridizations from a similar number of labeled cDNA samples. Left panel, The cluster output is shown with the fold change indicated colorimetrically; green color indicates down-regulation by T3 and red color indicates up-regulation. Individual genes are represented along the vertical axis, whereas experimental conditions are represented horizontally. Right panel, Nine clusters (A, B, C, E, F, K, L, M, and N) are shown. The mean normalized expression values were calculated for each cluster. Significant differences between T3 effects in WT and TRß -/- are indicated. [(*) P < 0.00001].

 
TRs can bind to specific DNA response elements independently of ligand occupancy. To test whether the genes defined above as TRß dependent were also regulated by the unliganded receptor, we determined the expression ratios of genes from hypothyroid TRß(-/-) and hypothyroid WT animals. Table 2Go shows that 45 genes were differentially expressed (70% up- or down-regulated): 22 were increased and 23 were decreased in TRß-/- mice as compared with WT. The changes in gene expression detected in hypothyroid TRß -/- mice resembled those induced by T3 in WT mice since about 80% of those genes were previously characterized as T3 responsive. This group includes genes up-regulated by T3 in a TRß-dependent fashion (Table 2Go, marked in bold).


View this table:
[in this window]
[in a new window]
 
Table 2. Ligand-Independent Effects of TRß

 
To verify our microarray analysis, we measured RNA levels either by Northern blots (Fig. 4Go, upper panel, and Table 3Go) or RPAs (Fig. 4Go, lower panel, and Table 3Go). Probes for nine different genes were used, and the expression levels were put in relation to control values. A comparison of these data with the microarray results for the corresponding genes gave a correlation coefficient of 0.92. This result confirms the validity of the microarray data.



View larger version (58K):
[in this window]
[in a new window]
 
Figure 4. Confirmatory Analysis of Gene Expression Data

Effect of T3 deprivation (HT) and hormone treatment for either 2 h (2h) or 5 days (5d), of TRß-/- and corresponding WT mice, on the abundance of mRNA for spot14, malic enzyme, Nudix7, glucose-6-phosphatase, squalene synthase, CD36/fatty acid transporter, GAPDH, FAS, and farnesyl pyrophosphate synthase (FPS). The upper panel is a representative mRNA expression analysis by Northern blot on RNA pools. Each pool was obtained by combining RNA prepared from four to six livers as described in Materials and Methods. GAPDH was added as a control for equal loading of RNA. The lower panel represents expression values obtained by RPA/solution hybridization assays. The values are expressed in arbitrary units (A.U) calculated as the measured radioactivity for the specific gene in relation to the measurement of GAPDH. The values are the average of four to six ratio determinations corresponding to an equal number of liver samples in each treatment.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Eleven percent of the hepatic genes (201 of 1900) measured in our array responded to T3 in hypothyroid WT mice, and about 100 of these have no prior record of being influenced by T3. The fraction of T3-regulated genes was somewhat higher than the 8% previously reported (18), which may be due to differences in experimental design such as the aim to define the functional differences between rapid (2 h) and sustained (5 d) T3 effects. The distinction between these effects is of interest because the fast responding genes are likely to be direct targets whereas many of the late responding ones may be indirectly regulated as a result of intermediary transcription factors, mRNA stability changes, or other physiological long-term effects caused by T3 treatment.

Changes in Gene Expression Patterns Induced by T3
The T3-responsive hepatic genes were categorized to include all major liver functions. Differences were evident among categories in relation to their temporal responsiveness to T3. For example, the effects of T3 on lipogenic genes were in general rapid, and in some cases transient, whereas the effects of T3 on genes for the mitochondrial respiratory chain, transcription factors, and protein turnover were of a long-term nature (Fig. 2Go).

A K-means cluster analysis allowed the classification of T3 effects in relation to the extent of TRß dependence (Fig. 3Go). Both genes with a rapid or a late T3 response showed dependence of TRß (Fig. 3Go and Table 1Go). However, genes belonging to certain metabolic class were not limited to specific clusters, with the exception of cluster A, which contains rapidly responsive genes for lipogenic enzymes dependent on TRß. Moreover, the dependance on TRß could be either direct or an indirect effect of the hormone. The differences in T3 action between the TRß-/- and WT mice are likely to be due to the specific contribution of TRß, as it is the most abundant receptor isoform in liver. The content of TR{alpha}1 and TR{alpha}2 in TRß-/- mice does not differ from that found in the WT controls (6, 13, 14) (see also Table 2Go), indicating that compensation by increased expression of TR{alpha}1 does not occur.

The fact that T3 activated a large number of genes also in the absence of TRß suggests the involvement of TR{alpha}1, although other explanations are also possible. Several possibilities that may explain unique and shared effects mediated by TRß and TR{alpha}1 are as follows. First, TR{alpha}1 may, in TRß-/- mice, substitute for the absent receptor. Specificity may also be determined by a differential promoter usage by the different receptors. TR{alpha}1 and TRß, however, exhibit little difference in binding to response elements or in activation of reporter genes (2) and therefore interaction, through, for example, their respective N-terminal domains, with other gene regulatory factors could provide such specificity. Alternatively, the TR isoforms may differ in expression in the different cell types of the liver. Answering these questions will have to await further investigations using either TR{alpha}1-/- mice or the development of a TR{alpha}-specific ligand.

One of our objectives was to find genes that are rapidly and directly induced by TRß. Accordingly, a rapid gene regulation by T3, a significantly reduced T3-dependent gene regulation in TRß -/- animals, and an increased expression in hypothyroid TRß -/- compared with WT (because unliganded TR suppresses gene expression), would be the hallmark of such target genes. Based on these criteria, 13 candidates were identified (Table 2Go, in bold). Although this is as yet a prediction, it is interesting to note that three of these genes are known to be regulated through well defined TREs (19, 20, 21).

Putative Liver Functions of T3-Regulated Genes
Although the interpretations must be both cautious and preliminary, attempts to translate information of gene expression patterns into function and phenotype are of interest and should be discussed. In this study, we have chosen to limit this to the effects of T3 on metabolism and other selected liver functions.

T3 has insulin-like actions on lipogenesis in liver (22) and antiinsulin effects on liver glucose output. The expression patterns reveal regulation of several genes directly or indirectly involved in fatty acid synthesis (Table 1Go). De novo fatty acid synthesis requires the supply of nicotinamide adenine dinucleotide phosphate, reduced form (NADPH), and acetyl-coenzyme A (CoA) to be used by the cytosolic enzyme fatty acid synthase (FAS). Nicotinamide adenine dinucleotide phosphate, reduced form, synthesis would be promoted by the observed T3 induction of 6-phosphogluconate dehydrogenase (6PGDH) and glucose-6-phosphate dehydrogenase (G6PDH), enzymes belonging to the oxidative branch of pentose phosphate pathway (23) and by the induction of malic enzyme (13). T3 also reduced the expression of key glycolytic enzymes (e.g. fructose 1, 6 biphosphatase, pyruvate kinase, and aldolase B), an effect that could result in the accumulation of glucose-6-phosphate (G6P) to be used in the pentose phosphate pathway. Increased supply of G6P could also be achieved by the T3 regulation of the adrenergic pathway, as indicated by the T3 induction of two key enzymes (cyclohydrolase I and aromatic L-amino acid decarboxylase) in the catecholamine synthesis pathway. Furthermore, T3 negatively regulated phosphodiesterase and induced phosphatidylinositol-4-phosphate 5-kinase {alpha}, type II, involved in the synthesis of the second messenger IP3 (24). These effects, combined with up-regulation of the ß-adrenergic receptor (3), could result in an increased hepatic responsiveness to adrenergic receptor stimulation. We have also reproduced the previously described induction of glucose-6-phosphatase by T3 (3).

The regulation by T3 of key players in the lipogenic and glucogenic pathways, e.g. spot 14, FAS, 6PGDH, glucose-6-phosphate dehydrogenase, G6P, and stearyl-CoA desaturase, was rapid and transient. Such regulation of lipogenic genes may help to explain the apparent contradiction between the lipogenic actions of T3 in liver and the reduced levels of lipoproteins/lipids achieved by long-term hormonal treatment (10). Furthermore, the T3 regulation of FAS, G6P, 6PGDH, phosphofructokinase, and other key enzymes within the lipogenic pathway were dependent of the TRß form of the receptor for their regulation (Table 1Go and Fig. 3Go). The hypothyroid TRß-/- mice show an increased expression of a similar set of lipogenic genes (Table 2Go) and have, as compared with controls, reduced levels of lipoproteins in serum after induction of hypothyroidism (10). Therefore, no linear relationship between the TRß-dependent expression of liver lipogenic enzymes and T3 regulation of serum cholesterol levels is apparent. Nevertheless, it is possible that a lipogenic stimulus regulates lipoprotein synthesis or assembly (25).

Our results show that apolipoproteins C1 and A-IV were down-regulated by T3. Because overexpression of C1 leads to combined hyperlipidemia, reduction of its expression may mediate some of the beneficial effects of the hormone on serum lipoprotein patterns (26). The fact that ApoC1 is suppressed by TRß may contribute to the resistance of TRß -/- mice to reduce their cholesterol levels after T3 treatment. The gene expression pattern also suggests that T3 could suppress production of hepatic triglycerides by reducing glycerol 3-phosphate, used in esterification of fatty acids. This effect could derive from the T3 actions on the glycolytic pathway (see above) and its known activation of the mitochondrial glycerol phosphate shuttle (27).

Our demonstration that T3 treatment inhibits the two key members of the desaturase complex, steraryl-CoA desaturase and reduced nicotinamide adenine dinucleotide (NADH) cytochrome b5 reductase, indicates that the lipid composition of lipoproteins and therefore also lipoprotein function may be altered. This is fully in concordance with the known ability of T3 to reduce the ratio of unsaturated to saturated fatty acids in very low density lipoprotein (28) particles.

T3 reduces serum cholesterol and a previous study showed that 7-{alpha} hydroxylase (CYP7A), a major regulator of cholesterol degradation and bile acid synthesis, is increased by T3 in a TRß-dependent manner (10). Here we show that the enzymes squalene synthetase, farnesyl pyrophosphate synthetase, and squalene monooxygenase are induced by T3, in a fashion similar to hydroxymethylglutaryl-CoA reductase. These enzymes belong to the biosynthetic cholesterol pathway and support the concept that T3 can increase both synthesis and degradation of cholesterol.

Previous experiments on TRß -/- mice showed that T3 regulation of energy expenditure in hypothyroid animals is independent of TRß (13). However, the late T3 effects seen by us included genes encoding proteins involved in the respiratory chain, such as subunits of reduced nicotinamide adenine dinucleotide-ubiquinone Q reductase, cytochrome c, and the F1F0 ATP synthetase. Some of these genes showed a reduced expression in TRß-/- mice, indicating that TRß may contribute to the regulation of mitochondrial oxidative phosphorylation in liver. De novo lipid synthesis, membrane phospholipid assembly, mitochondrial DNA replication, and transcription of both nuclear and mitochondrial genes have been linked to the known stimulation of mitochondriogenesis by T3 (29, 30, 31, 32). Our results indicate that the late effects on nuclear encoded genes for mitochondrial proteins may be an indirect action of T3. This is further supported by a recent report (33) on mitochondrial transcription being mediated by an indirect T3 induction of NRF-1 and PGC-1, known regulators of mitochondrial biogenesis (34).

T3 Effects Related to Proliferation and Signaling
No obvious expression pattern of genes involved in cell proliferation was seen, although several novel effects of the hormone were observed. Noteworthy is that several genes involved in proliferation were either induced (e.g. N-ras) or repressed by T3 (epidermal growth factor, ERK-1, and MAPK phosphatase). Other genes, associated with tissue differentiation or antiproliferative mechanisms, likewise showed an increased (PML: promoter of acute promyelocytic leukemia) or reduced expression (PC3/BTG2, a p53-regulated protein with antiproliferative actions) (35). Obviously, both proliferation and differentiation programs are tightly balanced to ensure the integrity of liver tissue after T3 treatment. Other signaling molecules were also either induced or repressed by T3, in a TRß-dependent fashion. The regulation of various small GTPases and some of their regulatory proteins involved in cellular trafficking, cellular motility, and cytoskeleton rearrangement (36) represents novel actions of T3.

Some of the T3-regulated genes are also controlled by GH (15, 16). Although it is known that T3 both regulates GH release and can influence GH effects at its site of action, it is noteworthy that expression of STAT5b, a transcription factor that mediates many GH effects in the liver (37), was rapidly induced by T3 in a TRß-dependent manner. This observation is in full accordance with the well known dependence on T3 of certain GH actions in liver (12).

In summary, the use of DNA microarray technology on mice with a targeted mutation in the TRß gene allowed us to substantially increase our knowledge on T3 actions in liver. Clearly, T3 regulates the expression of functionally different sets of genes in temporally distinct ways. Importantly, the use of TRß-/- animals allowed us to dissect this complex pattern and define a number of T3-responsive genes that are dependent on TRß in vivo. One can envision similar experimental strategies to define the contribution of specific transcription factors to the in vivo actions of multiple hormones and trophic factors.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Animals and Experimental Design
Male mice aged 2–3.5 months were used. The studies were approved by the Institutional Animal Care and Use Committee. TRß-deficient mice were genotyped by Southern blot or PCR analysis using PCR primers specific for the mutant TRß allele, as described previously (6). The genetic background of the TRß(-/-) mice is a hybrid of 129/Sv x C57Bl/6J. The mice, their housing conditions, and the procedures for inducing hypo- and hyperthyroidism were recently described in great detail (38). The mice were divided into groups, each consisting of four to six WT and four to six TRß-/- animals. At the onset of the experiment, all groups of animals were provided a low-iodine diet (LID) (R584, AnalyCen Nordic AB, Lidköping, Sweden) for 14 d to accustom them to the synthetic chow. The mice were then made hypothyroid by inclusion of 0.05% methimazole (MMI) and 1% potassium perchlorate in the drinking water (LID+MMI) for 21 d when on the LID diet. This treatment lowered serum-free T3 to about 2 pmol/liter. From d 35, one group of animals were injected daily with 5 µg T3 for an additional 5-d period to induce hyperthyroidism (24 h after the last T3 injection serum-free T3 was, on average, about 15 pmol/liter). Another group of animals was injected with 5 µg T3 and 5 µg T4; on d 35, the animals in this group were killed 2 h after the T3/T4 injection. At the time of kill, trunk blood was collected after decapitation and T3 analyses were performed later. We used a subset of the animal samples described previously (10), and, consequently, the corresponding serum levels of T3 and T4 in the animals subjected to the different treatments were similar.

Generation of cDNA Microarrays
Approximately 4000 cDNA clones were selected from the TIGR Rat Gene Index (www.tigr.org) and our own collection of rat and mouse cDNA libraries enriched with hepatic genes of known function (15). Procedures for array fabrication, including quality controls and sequence verification, have been described elsewhere (15, 16).

RNA Preparation, cDNA Labeling, Purification, and Hybridization
Livers were collected after decapitation and immediately frozen on solid CO2. Total RNA was isolated from individual livers using TRIzol Reagent (Life Technologies, Inc., Gaithersburg, MD), according to the protocol supplied by the manufacturer. The quality of the RNA samples was examined on a denaturing agarose gel. Equal amounts of total RNA from five animals in the same experimental group were pooled before mRNA purification. mRNA was purified from 1 mg of total RNA using 35 mg oligo(dT)-cellulose (Amersham Pharmacia Biotech, Uppsala, Sweden) as previously described (16).

The protocol employed for probe labeling and purification was essentially as described previously (15). Two micrograms of mRNA were used from each group of animals for each experiment. Labeled cDNA was produced by oligo-dT-primed reverse transcription reaction using SuperScript II (Life Technologies, Inc.). Oligo dT primers and cyanine (Cy)-labeled nucleotides were obtained from Amersham Pharmacia Biotech. In the first set of experiments, each hybridization compared Cy3-labeled cDNA reverse transcribed from mRNA isolated from livers of young male hypothyroid mice (WT) with Cy5-labeled cDNA isolated from livers of similar animals treated with T3 for 2 h or 5 d. In another set of experiments, Cy3-labeled cDNA reverse transcribed from mRNA isolated from livers of young male hypothyroid mice with a target mutation of TRß gene were compared with Cy5-labeled cDNA isolated from livers of similar animals treated with T3 for 2 h or 5 d. Finally the gene expression in livers of TRß(-/-) hypothyroid animals (Cy5 labeled) was compared with the gene expression of WT animals.

The labeled and purified cDNA was added to the array at a final volume of 15 µl in hybridization buffer (0.75 M NaCl, 75 mM Na citrate, 0.2% SDS, 10 µg poly(A) RNA, 10 µg yeast tRNA). The array was covered by a plastic 22 x 22-mm cover slip (Grace Biolabs, Bend, OR) and put in a sealed hybridization chamber (Corning, Inc., Corning, NY). After the hybridization, which took place at 65 C for 15–18 h, the array was washed and dried.

Image Analysis, Data Acquisition, and Statistical Evaluation
The array was scanned using a GMS 418 scanner (Affymetrix, Santa Clara, CA). Image analysis was performed using the GenePix Pro software (Axon Instruments, Foster City, CA). Automatic flagging was used to localize absent or very weak spots (<2 times above background), which were excluded from analysis. Normalization between the two fluorescent images was performed, as described previously (15). The average coefficient of variation of ratio measurements (calculated as SD/mean) for replicate experiments was 10% (0.1 ± 0.09) in the data set generated during this study. The variability of the triplicate analysis was estimated using SAM software (39). SAM is a statistical technique for finding significantly regulated genes in a set of microarray experiments. For each gene i in the array, SAM computes the T-statistic di, a score derived from the changes of genes expression in relation to the SD of repeated measurements for that gene. A threshold can be set based on di to identify potentially significant changes in gene expression. The threshold can be adjusted based on an associated false discovery rate (FDR) value: the percentage of genes expected to be wrongly identified as differentially expressed when a certain threshold (d value) is set. To each of the genes in the array a q value is assigned. This value is similar to the familiar P value and measures the lowest FDR at which the gene is called significant. According to the SAM analysis, the list of T3-regulated genes (Table 1Go and Table 4, which is published as supplemental data) has an associated FDR of less than 1%, meaning that of the 250 genes identified less than 3 are expected to be falsely classified as regulated. To the statistically based criteria we have added a further requirement based on the absolute changes in expression ratios. Only genes with average changes of 70% were counted as differentially expressed. This level of expression changes has been shown by us (15, 16 and Table 3Go) and others (17) to be reproducible when other direct methods, such as Northern Blot, RPA, and RT-PCR, are used to estimate gene expression. Although lower levels of changes in gene expression may have important biological consequences, insufficient information exists regarding its reproducibility by independent methodologies.

As most of the array elements are rat cDNA clones and the RNAs analyzed were of mouse origin, we performed an homology search where rat cDNA sequences were compared with the mouse EST division of the GenBank database using the BLAST program (40). The identity of the mouse clone with the highest sequence homology to the corresponding rat clone is shown in the supplemental data, together with the e-value and the percentage of identity. For each of the mouse genes reported here as being regulated based on a rat probe (array element), a highly homologous mouse clone could be identified (e-value < e-20). Hence, the high level of homology between rat and mouse genes used in this study justifies the use of rat cDNA clones to measure expression in tissues of mouse origin. According to the BLAST results, the possibility of misidentifying a mouse gene as being differentially regulated based on an orthologous rat probe is very small and comparable to the likelihood of cross-hybridization between two distinct genes within the same species (rat). Moreover, significant cross-hybridization with homologies above 75% has been previously reported using a similar technology (44), which further support the use rat probes to measure orthologous mouse genes.

Confirmatory Gene Expression Analysis
The expression of several of the genes identified as differentially expressed by the cDNA microarray analysis was measured using Northern blots or RPA analysis. The expression of spot14, glucose-6-phosphatase, Nudix7, Malic enzyme, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was done both on liver RNA pools from four to six mice and on individual samples with similar results (Fig. 4Go, upper panel). The measurement of squalene synthase and CD36/fatty acid transporter was performed only in RNA pools. The expression of FAS and farnesyl pyrophosphate synthase was measured by RPA/solution hybridization assays in individual liver samples (Fig. 4BGo). Probe labeling, hybridization and quantitation were performed essentially as described previously (10, 16). Expression values were normalized against GAPDH content and used to calculate the expression ratios corresponding to the measurements obtained by the cDNA microarray analysis.

K-Means Clustering and Statistical Analysis
Clustering analysis was performed essentially as described elsewhere (41). The fold ratio of gene expression for each of the experiments analyzed was scored and filtered as explained above and represents the average of three determinations. D-dimensional vectors (d = number of experiments included) were created for each of the N genes included from the data set selected as being regulated. The N, D-dimensional vectors were normalized to the unit sphere using the Cluster program (42) and used as input into K-means clustering algorithms. Gene clusters were generated using J-express (43). Mean expression patterns were calculated from the normalized gene vectors in the clusters, and the statistical significance was determined using an unequal variance t test. All the computer programs used are freely available at www.microarrays.org.


    ACKNOWLEDGMENTS
 
We thank Dr. Douglas Forrest for providing the TRß-/- mice and for his helpful comments on the manuscript.


    FOOTNOTES
 
A.F.-M. is supported by the University of Applied Sciences (UDCA), Colombia. A.F.-M. and G.N. are supported by the Swedish Medical Research Council; H.G. and B.V. are supported by The Swedish Cancer Society; L.F. is supported by the Consejería de Educación del Gobierno Autónomo de Canarias, Ministerio de Educación y Cultura (PM98-0033), Ministerio de Sanidad y Consumo (FIS 1/1000), and Colegio Oficial de Médicos de Las Palmas; and N.H.L. is supported by a National Heart, Lung and Blood Institute Grant, HL-59781.

Abbreviations: CoA, Coenzyme A; Cy, cyanine; FAS, fatty acid synthase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; G6P, glucose-6-phosphate; LID, low-iodine diet; MMI, methimazole; 6PGDH, 6-phosphogluconate dehydrogenase; RPA, ribonuclease protection assay; WT, wild-type.

Received for publication December 10, 2001. Accepted for publication February 20, 2002.


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Lazar MA 1993 Thyroid hormone receptors: multiple forms, multiple possibilities. Endocr Rev 14:184–193[Medline]
  2. Zhang J, Lazar MA 2000 The mechanism of action of thyroid hormones. Annu Rev Physiol 62:439–466[CrossRef][Medline]
  3. Feng X, Jiang Y, Meltzer P, Yen PM 2000 Thyroid hormone regulation of hepatic genes in vivo detected by complementary DNA microarray. Mol Endocrinol 14:947–955[Abstract/Free Full Text]
  4. Forrest D, Vennstrom B 2000 Functions of thyroid hormone receptors in mice. Thyroid 10:41–52[Medline]
  5. Wikstrom L, Johansson C, Salto C, Barlow C, Campos Barros A, Baas F, Forrest D, Thoren P, Vennstrom B 1998 Abnormal heart rate and body temperature in mice lacking thyroid hormone receptor {alpha}1. EMBO J 17:455–461[Abstract/Free Full Text]
  6. Forrest D, Hanebuth E, Smeyne RJ, Everds N, Stewart CL, Wehner JM, Curran T 1996 Recessive resistance to thyroid hormone in mice lacking thyroid hormone receptor ß: evidence for tissue-specific modulation of receptor function. EMBO J 15:3006–3015[Abstract]
  7. Salto C, Kindblom JM, Johansson C, Wang Z, Gullberg H, Nordstrom K, Mansen A, Ohlsson C, Thoren P, Forrest D, Vennstrom B 2001 Ablation of TR{alpha}2 and a concomitant overexpression of {alpha}1 yields a mixed hypo- and hyperthyroid phenotype in mice. Mol Endocrinol 15:2115–2128[Abstract/Free Full Text]
  8. Fraichard A, Chassande O, Plateroti M, Roux JP, Trouillas J, Dehay C, Legrand C, Gauthier K, Kedinger M, Malaval L, Rousset B, Samarut J 1997 The T3R{alpha} gene encoding a thyroid hormone receptor is essential for post-natal development and thyroid hormone production. EMBO J 16:4412–4420[Abstract/Free Full Text]
  9. Forrest D, Sjoberg M, Vennstrom B 1990 Contrasting developmental and tissue-specific expression of {alpha} and ß thyroid hormone receptor genes. EMBO J 9:1519–1528[Abstract]
  10. Gullberg H, Rudling M, Forrest D, Angelin B, Vennstrom B 2000 Thyroid hormone receptor ß-deficient mice show complete loss of the normal cholesterol 7{alpha}-hydroxylase (CYP7A) response to thyroid hormone but display enhanced resistance to dietary cholesterol. Mol Endocrinol 14:1739–1749[Abstract/Free Full Text]
  11. Pibiri M, Ledda-Columbano GM, Cossu C, Simbula G, Menegazzi M, Shinozuka H, Columbano A 2001 Cyclin D1 is an early target in hepatocyte proliferation induced by thyroid hormone (T3). FASEB J 15:1006–1013[Abstract/Free Full Text]
  12. Mutvei A, Husman B, Andersson G, Nelson BD 1989 Thyroid hormone and not growth hormone is the principle regulator of mammalian mitochondrial biogenesis. Acta Endocrinol (Copenh) 121:223–228[Medline]
  13. Weiss RE, Murata Y, Cua K, Hayashi Y, Seo H, Refetoff S 1998 Thyroid hormone action on liver, heart, and energy expenditure in thyroid hormone receptor ß-deficient mice. Endocrinology 139:4945–4952[Abstract/Free Full Text]
  14. Amma LL, Campos-Barros A, Wang Z, Vennstrom B, Forrest D 2001 Distinct tissue-specific roles for thyroid hormone receptors ß and {alpha}1 in regulation of type 1 deiodinase expression. Mol Endocrinol 15:467–475[Abstract/Free Full Text]
  15. Tollet-Egnell P, Flores-Morales A, Stahlberg N, Malek RL, Lee N, Norstedt G 2001 Gene expression profile of the aging process in rat liver: normalizing effects of growth hormone replacement. Mol Endocrinol 15:308–318[Abstract/Free Full Text]
  16. Flores-Morales A, Stahlberg N, Tollet-Egnell P, Lundeberg J, Malek RL, Quackenbush J, Lee NH, Norstedt G 2001 Microarray analysis of the in vivo effects of hypophysectomy and growth hormone treatment on gene expression in the rat. Endocrinology 142:3163–3176[Abstract/Free Full Text]
  17. Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JC, Trent JM, Staudt LM, Hudson Jr J, Boguski MS, Lashkari D, Shalon D, Botstein D, Brown PO 1999 The transcriptional program in the response of human fibroblasts to serum. Science 283:83–87[Abstract/Free Full Text]
  18. Oppenheimer JH, Schwartz HL, Mariash CN, Kinlaw WB, Wong NC, Freake HC 1987 Advances in our understanding of thyroid hormone action at the cellular level. Endocr Rev 8:288–308[Medline]
  19. Xiong S, Chirala SS, Hsu MH, Wakil SJ 1998 Identification of thyroid hormone response elements in the human fatty acid synthase promoter. Proc Natl Acad Sci USA 95:12260–12265[Abstract/Free Full Text]
  20. Desvergne B, Petty KJ, Nikodem VM 1991 Functional characterization and receptor binding studies of the malic enzyme thyroid hormone response element. J Biol Chem 266:1008–1013[Abstract/Free Full Text]
  21. Liu HC, Towle HC 1994 Functional synergism between multiple thyroid hormone response elements regulates hepatic expression of the rat S14 gene. Mol Endocrinol 8:1021–1037[Abstract]
  22. Mariash CN, Kaiser FE, Schwartz HL, Towle HC, Oppenheimer JH 1980 Synergism of thyroid hormone and high carbohydrate diet in the induction of lipogenic enzymes in the rat. Mechanisms and implications. J Clin Invest 65:1126–1134[Medline]
  23. Baquer NZ, Cascales M, McLean P, Greenbaum AL 1976 Effects of thyroid hormone deficiency on the distribution of hepatic metabolites and control of pathways of carbohydrate metabolism in liver and adipose tissue of the rat. Eur J Biochem 68:403–413[Abstract]
  24. Boronenkov IV, Anderson RA 1995 The sequence of phosphatidylinositol-4-phosphate 5-kinase defines a novel family of lipid kinases. J Biol Chem 270:2881–2884[Abstract/Free Full Text]
  25. Baum CL, Teng BB, Davidson NO 1990 Apolipoprotein B messenger RNA editing in the rat liver. Modulation by fasting and refeeding a high carbohydrate diet. J Biol Chem 265:19263–19270[Abstract/Free Full Text]
  26. Shachter NS, Ebara T, Ramakrishnan R, Steiner G, Breslow JL, Ginsberg HN, Smith JD 1996 Combined hyperlipidemia in transgenic mice overexpressing human apolipoprotein Cl. J Clin Invest 98:846–855[Abstract/Free Full Text]
  27. Heimberg M, Olubadewo JO, Wilcox HG 1985 Plasma lipoproteins and regulation of hepatic metabolism of fatty acids in altered thyroid states. Endocr Rev 6:590–607[Abstract]
  28. Schroeder F, Wilcox HG, Keyes WG, Heimberg M 1982 Effects of thyroid status on the structure of the very low density lipoprotein secreted by the perfused liver. Endocrinology 110:551–562[Abstract]
  29. Wrutniak-Cabello C, Casas F, Cabello G 2001 Thyroid hormone action in mitochondria. J Mol Endocrinol 26:67–77[Abstract/Free Full Text]
  30. Brand MD, Steverding D, Kadenbach B, Stevenson PM, Hafner RP 1992 The mechanism of the increase in mitochondrial proton permeability induced by thyroid hormones. Eur J Biochem 206:775–781[Abstract]
  31. Bangur CS, Howland JL, Katyare SS 1995 Thyroid hormone treatment alters phospholipid composition and membrane fluidity of rat brain mitochondria. Biochem J 305:29–32[Medline]
  32. Garstka HL, Facke M, Escribano JR, Wiesner RJ 1994 Stoichiometry of mitochondrial transcripts and regulation of gene expression by mitochondrial transcription factor A. Biochem Biophys Res Commun 200:619–626[CrossRef][Medline]
  33. Weitzel JM, Radtke C, Seitz HJ 2001 Two thyroid hormone-mediated gene expression patterns in vivo identified by cDNA expression arrays in rat. Nucleic Acids Res 29:5148–5155[Abstract/Free Full Text]
  34. Wu Z, Puigserver P, Andersson U, Zhang C, Adelmant G, Mootha V, Troy A, Cinti S, Lowell B, Scarpulla RC, Spiegelman BM 1999 Mechanisms controlling mitochondrial biogenesis and respiration through the thermogenic coactivator PGC-1. Cell 98:115–124[Medline]
  35. Guardavaccaro D, Corrente G, Covone F, Micheli L, D’Agnano I, Starace G, Caruso M, Tirone F 2000 Arrest of G(1)-S progression by the p53-inducible gene PC3 is Rb dependent and relies on the inhibition of cyclin D1 transcription. Mol Cell Biol 20:1797–1815[Abstract/Free Full Text]
  36. Hildebrand JD, Taylor JM, Parsons JT 1996 An SH3 domain-containing GTPase-activating protein for Rho and Cdc42 associates with focal adhesion kinase. Mol Cell Biol 16:3169–3178[Abstract]
  37. Wood TJ, Sliva D, Lobie PE, Pircher TJ, Gouilleux F, Wakao H, Gustafsson JA, Groner B, Norstedt G, Haldosen LA 1995 Mediation of growth hormone-dependent transcriptional activation by mammary gland factor/Stat 5. J Biol Chem 270:9448–9453[Abstract/Free Full Text]
  38. Gothe S, Wang Z, Ng L, Kindblom JM, Barros AC, Ohlsson C, Vennstrom B, Forrest D 1999 Mice devoid of all known thyroid hormone receptors are viable but exhibit disorders of the pituitary-thyroid axis, growth, and bone maturation. Genes Dev 13:1329–1341[Abstract/Free Full Text]
  39. Tusher VG, Tibshirani R, Chu G 2001 Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98:5116–5121[Abstract/Free Full Text]
  40. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ 1990 Basic local alignment search tool. J Mol Biol 215:403–410[CrossRef][Medline]
  41. Soukas A, Cohen P, Socci ND, Friedman JM 2000 Leptin-specific patterns of gene expression in white adipose tissue. Genes Dev 14:963–980[Abstract/Free Full Text]
  42. Eisen MB, Spellman PT, Brown PO, Botstein D 1998 Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868[Abstract/Free Full Text]
  43. Dysvik B, Jonassen I 2001 J-Express: exploring gene expression data using Java. Bioinformatics 17:369–370[Abstract]
  44. Spellman PT, Sherlock G, Zhang MQ, Iyer, VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B 1998 Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cervisiae by microarray hybridization. Mol Biol Cell 9:3273–3297[Abstract/Free Full Text]