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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.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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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 |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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and TRß, which are located on different chromosomes. The TR genes and ß generate several T3-binding receptors, TR1 and TRß1–3, that mediate the effects of T3. A variant receptor protein, TR2, 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, TR1 deficiency yields an abnormal heart rate and lower body temperature (5), whereas TR2 deficient mice, which overexpress TR1, exhibit a mixture of hypo- and hyperthyroid features (7). Interestingly, mice devoid of both the TR1 and TR2 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 TR1 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.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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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. 1
. 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 3). The statistical analyses are detailed in Materials and Methods.
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. The identities of genes showing variation by a factor of 2 or more in selected categories are shown in Table 1 (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/).
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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 TR1 or products of a secondary phenomena resulting from long-term adaptation of the tissue to TRß deficiency. Genes in Table 1
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.
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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 2, marked in bold).
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, upper panel, and Table 3) or RPAs (Fig. 4, lower panel, and Table 3). 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.
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DISCUSSION |
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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. 2).
A K-means cluster analysis allowed the classification of T3 effects in relation to the extent of TRß dependence (Fig. 3). Both genes with a rapid or a late T3 response showed dependence of TRß (Fig. 3 and Table 1). 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 TR1 and TR2 in TRß-/- mice does not differ from that found in the WT controls (6, 13, 14) (see also Table 2), indicating that compensation by increased expression of TR1 does not occur.
The fact that T3 activated a large number of genes also in the absence of TRß suggests the involvement of TR1, although other explanations are also possible. Several possibilities that may explain unique and shared effects mediated by TRß and TR1 are as follows. First, TR1 may, in TRß-/- mice, substitute for the absent receptor. Specificity may also be determined by a differential promoter usage by the different receptors. TR1 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 TR1-/- mice or the development of a TR-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 2, 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 1). 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 , 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 1 and Fig. 3). The hypothyroid TRß-/- mice show an increased expression of a similar set of lipogenic genes (Table 2) 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- 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.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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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 1 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 3) 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. 4, 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. 4B). 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.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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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.
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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