Exploring hepatic hormone actions using a compilation of gene expression profiles
© Ståhlberg et al; licensee BioMed Central Ltd. 2005
Received: 22 October 2004
Accepted: 13 June 2005
Published: 13 June 2005
Microarray analysis is attractive within the field of endocrine research because regulation of gene expression is a key mechanism whereby hormones exert their actions. Knowledge discovery and testing of hypothesis based on information-rich expression profiles promise to accelerate discovery of physiologically relevant hormonal mechanisms of action. However, most studies so-far concentrate on the analysis of actions of single hormones and few examples exist that attempt to use compilation of different hormone-regulated expression profiles to gain insight into how hormone act to regulate tissue physiology. This report illustrates how a meta-analysis of multiple transcript profiles obtained from a single tissue, the liver, can be used to evaluate relevant hypothesis and discover novel mechanisms of hormonal action. We have evaluated the differential effects of Growth Hormone (GH) and estrogen in the regulation of hepatic gender differentiated gene expression as well as the involvement of sterol regulatory element-binding proteins (SREBPs) in the hepatic actions of GH and thyroid hormone.
Little similarity exists between liver transcript profiles regulated by 17-α-ethinylestradiol and those induced by the continuos infusion of bGH. On the other hand, strong correlations were found between both profiles and the female enriched transcript profile. Therefore, estrogens have feminizing effects in male rat liver which are different from those induced by GH. The similarity between bGH and T3 were limited to a small group of genes, most of which are involved in lipogenesis. An in silico promoter analysis of genes rapidly regulated by thyroid hormone predicted the activation of SREBPs by short-term treatment in vivo. It was further demonstrated that proteolytic processing of SREBP1 in the endoplasmic reticulum might contribute to the rapid actions of T3 on these genes.
This report illustrates how a meta-analysis of multiple transcript profiles can be used to link knowledge concerning endocrine physiology to hormonally induced changes in gene expression. We conclude that both GH and estrogen are important determinants of gender-related differences in hepatic gene expression. Rapid hepatic thyroid hormone effects affect genes involved in lipogenesis possibly through the induction of SREBP1 proteolytic processing.
The completion of human and rodent genome sequences [1–3] has brought the post-genomic era to the field of endocrine research. Detailed genetic maps of the main endocrine models can now be used to study the molecular basis of endocrine disease and the molecular mechanisms of hormone actions. The possibility to explore expression data of thousands of genes across multiple experimental paradigms promise to rapidly increase our understanding of biological systems [4, 5]. The acquisition of experimental data at a genomic scale requires high throughput technologies such as DNA microarray analysis. Microarrays enable the simultaneous assessment of expression levels of tens of thousands of gene products in an ease to perform assay. Microarrays are especially attractive to the field of endocrine research because regulation of gene expression is an important mechanism whereby hormones exert their physiological actions. This is obvious in the case of steroid and thyroid hormones, which use intracellular receptors belonging to the nuclear receptor family of transcription factors . Peptide hormones also regulate gene expression after activating complex cascades of intracellular signaling events upon binding to transmembrane receptors . If the relation between hormones and the expression of different genes could be annotated, the abundant knowledge concerning endocrine physiology might be used to clarify the biological function of those genes. On the other hand, because expression profiles are rich in information, they are suitable to study the complex and pleitropic actions of hormones.
Here we analyzed a compilation of rat liver expression profiles from experiments designed to study gender and hormone actions in order to provide novel insight into the mechanisms of action of specific hormones. The dataset used in this study comprises the actions of thyroid hormone (T3), 17-α-ethinylestradiol and GH in liver. The data is freely available from the Endocrinology Gene Expression Database – http://www.cmm.ki.se/EndoGED and have also been deposited in Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/. Using this collection of microarray data, we analyzed the differential contribution of estrogens and GH to the regulation of gender differentiated liver gene expression. We also compared the actions of GH and T3 in liver and found a small overlap comprising genes involved in lipogenesis suggesting the common regulation of SREBP transcription factors. The regulation of SREBP1 by GH and thyroid hormone was analyzed.
Exploring hormonal regulation of gene expression in liver
Experiments included in the study.
Tester sample (hormone, time, dose)
c.i. of vehicle
c.i. of bGH, 1 week, 5 μg/h
c.i. of vehicle
c.i. of hGH, 3 weeks, 0.34 μg/g body weight/day
bGH added to medium, 24 hours, 100 ng/ml
17-α-ethinylestradiol, 1 day, 5 mg/kg body weight
injection of T3 & TT4, 2 hours, 5 μg T3 + 5 μg T4
The primers, amplicon sizes and annealing temperatures used for gene expression measurements by real-time PCR.
Comparisons of GH response in liver and in primary hepatocytes
As shown in figure 1b, the correlation between GH treatment and thyroid hormone or estrogen treatment in young males was rather low. This is not surprising since both hormones have distinct liver functions not always overlapping those of GH. Differences in expression can also arise from the choice of treatment duration, dose and mode of hormone treatment and this could result in the underestimation of commonly regulated genes. This ambiguity can only be resolved by measuring more expression profiles in experiments specifically designed to study hormonal interactions.
GH and estrogen contribute to gender differences in hepatic gene expression
Genes with a gender-differentiated expression pattern that were also affected in male rats continuously infused with GH.
Female predominant and up-regulated by continuous infusion of GH
similar to TNF ligand superfamily member 12
carboxylesterase 2 (intestine, liver)
Cytochrom P450 15-beta gene
similar to Alcohol sulfotransferase (Hydroxysteroid sulfotransferase)
rat senescence marker protein 2A gene, exons 1 and 2
HRAS like suppressor
sulfotransferase, hydroxysteroid preferring 2
arginine vasopressin receptor 1A
tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide
guanosine diphosphate dissociation inhibitor 1
Cytochrome P450, subfamily IIC6
similar to stromal interaction molecule 1
Atp5g3: ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c (subunit 9) isoform 3
Male predominant and down-regulated by continuous infusion of GH
carbonic anhydrase 3
solute carrier family 22, member 8
alpha-2u globulin PGCL1
similar to nucleoporin 37; nucleoporin Nup37
similar to Gelsolin precursor, plasma
hydroxysteroid 11-beta dehydrogenase 1
phytanoyl-CoA hydroxylase (Refsum disease)
phosphoribosyl pyrophosphate synthetase 2
ectonucleotide pyrophosphatase/phosphodiesterase 2
similar to another partner for ARF 1
putative homeodomain transcription factor 1
similar to Putative lysophosphatidic acid acyltransferase
similar to RIKEN cDNA 6330575P11
glutathione S-transferase, mu 1
Male predominant but up-regulated by continuous infusion of GH
ATP citrate lyase
fatty acid binding protein 5, epidermal
fatty acid synthase
Genes with a gender differentiated expression pattern that were also affected in male rats treated with 17-α ethinyl-estradiol (EE).
Female predominant and up-regulated by ethinylestradiol
hydroxysteroid sulfotransferase subunit
Rat hydroxysteroid sulfotransferase a (STa) mRNA, complete cds
Rat hydroxysteroid sulfotransferase mRNA, complete cds
Rattus norvegicus mRNA for putative alpha 1B-glycoprotein (ORF1)
Similar to Kruppel-like factor 7 (ubiquitous)
Rattus norvegicus Cytochrom P450 15-beta gene (Cyp2c12)
R. norvegicus rab GDI alpha mRNA
cytochrome P450, 2c39
Rat cytochrome P450 PB1 (PB1 allele) mRNA
ESTs, Highly similar to tetrahydrofolylpolyglutamate synthase
R. norvegicus mRNA for V1a arginine vasopressin receptor
R. norvegicus mRNA for C-CAM2a isoform
Amphoterin induced gene and ORF 3
ESTs, Weakly similar to Ser/ Thr protein phosphatase 5
Female predominant and down-regulated by ethinylestradiol
Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide
Rat alcohol dehydrogenase (ADH) mRNA, complete cds
glutathione reductase mRNA
glutathione peroxidase I
Male predominant and down-regulated by ethinylestradiol
carbonic anhydrase III
CYP3A2 (testosterone 6-Beta-hydroxylase)
Similar to nucleoporin 37
Hemoglobin, alpha 1 (Hba1)
2-alpha globin; alpha-2-globin chain; hemoglobin alpha chain
platelet-derived endothelial cell growth factor 1
peroxisomal phytanoyl-CoA hydroxylase (PHYH)
Rat ATP citrate-lyase mRNA, complete cds
Peroxisomal Ca-dependent solute carrier-like protein
Adenylate kinase 4
Glutamic-oxaloacetic transaminase 1, soluble
fatty acid synthase (EC 126.96.36.199)
pyruvate kinase (L-type)
Similar to another partner for ARF 1
apolipoprotein A – IV
choline kinase R; choline kinase R1
Rat p450Md mRNA for cytochrome P-450
Male predominant and up-regulated by ethinylestradiol
Glucose-6-phosphatase catalytic subunit 3
Hydroxysteroid dehydrogenase, 11 beta type 1
T3 and GH regulate lipogenic genes in liver
T3 and GH have overlapping effects on gene expression in liver.
cytochrome P450 4A3
malic enzyme 1
3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1
fatty acid binding protein 5, epidermal
thyroid hormone responsive protein
similar to Igh-6 protein
similar to coenzyme A diphosphatase
cytosolic acyl-CoA thioesterase 1
sterol-C5-desaturase (fungal ERG3, delta-5-desaturase)-like
FXYD domain-containing ion transport regulator 2
signal transducer and activator of transcription 5B
hemoglobin beta chain complex
farensyl diphosphate synthase
hemoglobin, alpha 1
hypothetical protein LK44
similar to HT021
peptidyl arginine deiminase, type 4
similar to Glutathione S-transferase, theta 3
fatty acid synthase
fatty acid elongase 1
similar to putative NAD(P)H steroid dehydrogenase
fatty acid desaturase 2
hypothetical protein RMT-7
2,3-oxidosqualene: lanosterol cyclase
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 5
multiple inositol polyphosphate histidine phosphatase 1
similar to hemoglobin alpha chain
Na+ dependent glucose transporter 1
insulin-like growth factor binding protein 2
protein O-mannosyltransferase 1
adipose differentiation-related protein
3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1
similar to Alcohol sulfotransferase (Hydroxysteroid sulfotransferase) (ST) (ST-60)
opioid receptor, sigma 1
growth response protein (CL-6)
2-4-dienoyl-Coenzyme A reductase 2, peroxisomal
aldehyde dehydrogenase family 3, subfamily A2
stearoyl-Coenzyme A desaturase 1
hypoxia induced gene 1
kinesin family member 1B
inositol 1,4,5-triphosphate receptor 1
fatty acid Coenzyme A ligase, long chain 5
phospholipase A2, group VI
similar to myotonic dystrophy protein kinase
GTP cyclohydrolase 1
farnesyl diphosphate farnesyl transferase 1
nasal embryonic LHRH factor
hemoglobin Y, beta-like embryonic chain
enoyl coenzyme A hydratase 1
fatty acid binding protein 4
isocitrate dehydrogenase 1
Here, we have attempted an analysis of a compilation of expression profiles to gain insight into the hormonal regulation of liver gene expression. We demonstrated that a positive correlation exists between the effects of GH treatment in primary hepatocytes cultured on matrigel and those detected in vivo. Nevertheless, the correlation was not very high despite the care taken of cultivating the hepatocytes on matrigel to avoid de-differentiation. We know from previous studies that hepatocytes cultured on matrigel express GH receptors, that GH signalling through the JAK2/STAT5 pathway is functional and that GH induces IGF-1, a well- known GH regulated gene in vivo . Therefore, the differences between the in vivo and in vitro models are likely due to structural and systemic factors found in intact liver which would be required for the full extension of GH actions. On the other hand, our data demonstrate that primary hepatocytes cultured on matrigel do provide a model to study some GH activated mechanisms; those directly related to the activation of the GH receptor and its signaling molecules. The newly described GH regulated genes in hepatocytes (Table 3): phytanoyl-CoA hydroxylase (Phyh), hydroxysteroid 11-beta dehydrogenase 1 (Hsd11b1), the catalytic subunit of protein phosphatase 3, alpha isoform (Ppp3ca) and fatty acid translocase/CD36 antigen (FAT/CD36) constitute new target genes that could be used to study the basis of transcriptional regulation by GH in hepatocytes.
Our global assessment of gene expression, demonstrates that estrogen and to a larger extent, the female-like continuous pattern of GH secretion are important for the maintenance of the gender differences in liver gene expression (Tables 4 and 5). These data support the existence of a cross talk in the hepatic actions of GH and estrogens for the regulation of a subset of the female-enriched genes. The exact mechanism of this cross talk cannot be extracted from the data, but previous findings offer a possible explanation. It is well known that the liver expresses relatively low levels of estrogen receptor and that some estrogen-induced effects in liver, including the expression of the estrogen receptor itself, may be secondary to its feminizing effect on GH pituitary secretion [20, 21]. The role of GH secretory patterns in determining gender differences in rat liver expression of genes involved in sterol and drug metabolism has been described before [16, 22]. Here and in recent studies by Ahluwalia et al  and Stahlberg et al , the significance of this regulation is demonstrated at a more comprehensive level and novel hepatic gender-regulated genes are identified. We don't know yet how gender determines the transcription of these genes. To the date, only a few gender-predominant and GH-regulated transcription factors have been identified, including female predominant HNF-6  and male predominant STAT5b . Nevertheless, an analysis of promoter sequences of the genes identified in Table 4 has failed to identify any significant overrepresentation of HNF-6 or STAT5 consensus binding sites (data not shown). Therefore, further analyses are required to better understand the molecular mechanism behind the feminizing actions of GH in liver.
Both GH and thyroid hormone are required for longitudinal growth . The coordinate actions of these hormones are achieved through multiple mechanisms. Thyroid hormone is a key activator of GH secretion in the pituitary gland while GH promotes the formation of T3 from less active T4 in peripheral tissues . Through the comparisons of expression profiles, we could identify a small group of genes that were up-regulated by both hormones. Most of the genes were assigned to the category of lipid metabolism by unbiased classification based on the current gene ontology. This is in agreement to the known lipogenic effects of both hormones in liver [7, 10, 28]. The correlation between GH and T3 effects was small which could be due to important differences in experimental design as well as to mechanistic differences. Therefore, the study of the long term effects of T3 treatment has the potential to identify more overlapping effects.
The effects of T3 described herein are produced after 2 hours of treatment and are likely to be direct hormonal effects on the liver. Therefore, we analyzed the proximal promoter of genes rapidly regulated by T3 to gain further insight into the mechanisms of hepatic T3 actions. Interestingly, we were not able to localize a significant overrepresentation of TREs in the close promoters of the T3-regulated genes. On the other hand, we found a clear overrepresentation of putative SREBP binding sites; in agreement with the lipogenic nature of the T3-regulated genes. Further analysis did not detect any transcriptional induction of SREBPs by T3. Instead, we showed that T3 decrease the concentration of ER-bound SREBP1, probably due to induction of its proteolytic processing. Regulation of SREBP activity by T3 seems to be complex and involves multiple mechanisms. The thyroid hormone receptor (TR) and active SREBP1c can cooperate to activate the transcription of a single gene even when their response elements are situated very far apart . Moreover, direct interaction between TR alpha and the active form of SREBP1 has been demonstrated when their binding sites are closely located in the promoter of the chicken acetyl-CoA carboxylase-α gene . Direct interactions between TR and ER-resident SREBPs are unlikely to be responsible for the effect observed in this study since the two proteins have different intracellular localizations . Nevertheless, this possibility can not be completely discarded since 10% of TRs are found in the cytosol both in the absence or presence of T3 . Non-genomic effects of T3 such as the activation of PI-3 kinase, MEK and STAT transcription factors have been reported in several systems . Whether the rapid induction of SREBP1 processing by T3 is due to a non-genomic mechanism of T3 action, or not, remains to be demonstrated. SREBP proteolyticactivation in the Golgi is regulated by its interaction with SCAP and Insig-1 and -2 . When sterols are present at high concentrations, the SCAP/SREBP complex is retained in the ER. When the sterol concentration is reduced, SCAP does not interact with Insig and the SCAP/SREBP complex exits the ER and is delivered to the Golgi where it is proteolytically cleaved. A role of SCAP as a monitor of the composition of the cytoplasmic leaflet of the ER membrane has been proposed . Since T3 has been shown to bind the outer half of the lipid bilayer in reconstituted microsomes , there is a hypothetical possibility that T3 could bind ER membranes and regulate SCAP activity. It is important to notice that the concentration of the nuclear (short) form of SREBP1 was also reduced upon T3 treatment. Why this occurs simultaneously to the transcriptional induction of SREBP target genes is unknown but a recent publication indicates that transcriptionally active SREBP1 is rapidly targeted for proteosomal degradation . If this mechanism is at play, one would expect the effects of T3 on lipogenic genes expression to be transient. This is indeed the case, most of the T3 effects on lipogenic genes can not be found after 5 days of hormonal treatment . Future studies will clarify the importance of SREBPs for thyroid hormone liver actions.
In summary, we have analyzed six different experiments concerning the hepatic actions of GH, T3, estrogen and gender. We could conclude that GH and estrogen are both important determinants of gender-related differences in hepatic gene expression, that GH and T3 have overlapping effects on the regulation of several lipogenic genes, and that some T3 effects in the liver may be mediated through the induction of proteolytic processing of SREBP1. Through EndoGED and its web-based interface, we have made available a large data set of transcript profiles related to the actions of several hormones in different in vivo and in vitro models. This resource is of special interest for the endocrine researcher offering the possibility of in depth exploration of hormonal transcriptional actions and interactions. In the same way, here exemplified by the analysis of hormone actions in liver, the actions of other hormones can be explored to generate testable hypotheses of relevance to endocrine research.
We have studied the effects of different hormones on liver gene expression patterns in various rat and mouse models. The experiments included in this study are listed in Table 1. They were conducted separately in different groups of animals, and by different persons in the laboratory. Some of the animal experiments have been described in previous publications: infusion of bovine growth hormone (bGH) in young male rats , infusion of human growth hormone (hGH) in old male rats , comparison of female and male rats , and the rapid effects of thyroid hormone (T3) treatment of hypothyroid mice . Total hepatic RNA from individual animals was isolated using Trizol (Invitrogen, CA) and microarray hybridizations were performed on arrays larger than the ones described in the previously published studies. At least 4 statistically independent microarray measurements were used to characterize each physiological situation; with the exception of the experiment where thyroid hormone actions were studied where RNA pooled from 5 different mice was used. Other experiments included in this study have not previously been described. Primary hepatocytes were isolated from young male rats and cultured on matrigel as described previously. The cells were grown in serum-free William's media E (Invitrogen, CA) supplemented with 55 μg/ml ascorbic acid (Sigma-Aldrich, MO), 100 IU/ml streptomycin and 1 μg/ml insulin (Sigma-Aldrich, MO) for two days before adding 100 ng/ml bGH (National Hormone and Peptide Program, A.F. Parlow, USA) to the media. GH-treated and untreated cells were harvested 24 hours later in Trizol (Invitrogen, CA), and RNA was purified according to the manufacturer's protocol. The procedure was repeated with cells isolated from different rats. In another experiment, 3 months old male rats were injected with 17-α-ethinylestradiol (5 mg/kg body weight; Sigma-Aldrich, MO) or vehicle 24 hours before sacrifice. Total RNA was isolated using Trizol (Invitrogen, CA). All animal experiments used in these studies were approved by the local ethical committee.
cDNA microarrays, probe preparation and hybridization
The cDNA microarrays used in this study were produced in our lab, as described previously . They have, however, been extended to comprise about 6200 clones, including clones from the TIGR Rat Gene Index, Research Genetics (Invitrogen, CA), and our own obtained through differential cloning experiments. The arrays were pre-hybridized in 1% BSA, 5XSSC and 0.1% SDS at 42°C for 1–2 hours, washed in milli-Q water, and dried immediately before the probe was applied. Total RNA was reverse-transcribed in the presence of Cy3- or Cy5-conjugated dUTP (PerkinElmer, MA) and purified as described previously . In all studies except the one with T3-treated mice, RNA samples originating from livers of individual animals were labeled and each tester sample was hybridized against a control sample. In the T3-study, a pool of tester samples (from 6 different T3-treated animals) was hybridized against a pool of control samples due to limited availability of RNA. Dye-swaps were used in all studies to reduce systematic errors . The final volume was adjusted to 25 μl with hybridization buffer consisting of 3.4XSSC, 0.3% SDS, 20 μg mouse Cot1 DNA (Invitrogen, CA), 20 μg polyA RNA, and 20 μg yeast tRNA. After heating at 98°C for 2 min and cooling to room temperature, the probe was added to the array and covered with a plastic cover slip (Grace Bio-Labs, OR). Hybridization took place at 65°C for 15–18 hours. The array was then washed and scanned with a GMS 418 scanner (Affymetrix, CA).
Data processing and analysis
Data processing was performed essentially as described previously . The software GenePix Pro (Axon Instruments, CA) was used to quantify the fluorescence intensity of each spot and the surrounding background. Automatic and manual flagging were used to localize absent or very weak spots (less than 2 times above background), which were excluded from analysis. The signal from each spot was calculated as the average intensity minus the average local background. We used a normalization method that takes into account and corrects for intensity-dependent artifacts in the measurements, the locally weighted linear regression (Lowess) method in the SMA package (Statistics for Microarray Analysis, available at http://www.stat.berkeley.edu/users/terry/Group/software.html) . SMA is an add-on library written in the statistical language R.
We next used EndoGED to extract all expressed hepatic genes from the included experiments concerning hormone treatment of rodents. In total, 6096 transcripts were expressed in one or more hybridizations. Hierarchical clustering using the TIGR Multiexperiment Viewer (MeV) software (available at http://www.tigr.org) was performed to explore and compare the different hormone treatments in the generated gene expression matrix. The euclidean distance was used as distance metric. We first compared the usage of ratios from all hybridizations with the usage of just the median log2 ratio for each set of replicated measurements (experiment group). These strategies gave similar results in the clustering, grouping the replicated measurements closely together. The median rather than the mean ratio was used to minimize the influence of outliers. Therefore, we calculated the median log2 ratio for each transcript within each experiment group, and used this ratio for further analysis. Genes that were detected in only half of the replicated measurements, or less, were excluded since we did not consider these measurements reliable. Included in the hierarchical clustering were only genes that had four or more median expression ratios from the six experiment groups (2518 genes). Using the same dataset with median ratios from each experiment group, we also calculated the correlation coefficient between the different experiment groups.
A statistical evaluation of differentially expressed genes was performed using the SAM (Significance Analysis for Microarrays) statistical technique . This was done for each experiment group separately. A 5% false discovery rate (FDR) was used as cutoff. A gene expression matrix containing only genes affected by GH in vivo in a statistically significant manner was extracted from EndoGED. The T3-regulated genes in this list were identified by applying a log2 ratio cutoff of ± 1 (2-fold regulation) in each of the T3 hybridizations. We next used the web-based tool eGOn (developed at the Norwegian University of Science and Technology, available at http://nova2.idi.ntnu.no/egon/) to functionally classify the transcripts. With the two-sided one-sample binomial test implemented in eGOn, we compared the list of differentially expressed genes to all genes expressed in the T3 and GH experiments. The same software was also used to classify all T3-regulated genes (at least 2-fold up-regulation), and to compare them to all genes expressed in the experiments regarding T3. For the comparisons between gender differentiated genes and the effects of GH and estrogen, a mean ratio cutoff (log2 ratio treated/untreated >0.58, corresponding to at least 1.5-fold difference) was applied on top of the SAM statistical criteria.
Brief description of EndoGED
The EndoGED system includes a Lab Information Management System (LIMS) to manage array fabrication, and modules to collect, store and process gene expression data concerning the actions of hormones. The system offers a flexible solution to integrate external analysis tools, including possibilities to store transformed expression values (e.g. normalized ratios) and associated parameters derived from statistical evaluation. The systems run on Microsoft operating system and have a client-server architecture implemented in SQL-Server as the database engine and Borland Delphi for the program modules. In the database design, we have taken into consideration the latest MIAME recommendations from the MGED Society regarding microarray data description . We have implemented a detailed description of biomaterials and treatments used in the experiments, carefully considering what information would be significant to the endocrine researcher. The exploration tools have been implemented to allow easy retrieval of relevant data through a multilevel search engine. Furthermore, we have made part of our collected data available through Internet for easy access to researchers worldwide http://www.cmm.ki.se/EndoGED. The database structure and software are available for free to academic and other nonprofit researchers.
The expression of some genes from the array experiments were verified using quantitative real-time PCR. The Dynamo kit (Finnzymes Oy, Finland) containing SYBR Green was used for quantification. The primers and applied annealing temperatures are listed in Table 2. The expression of all genes was normalized to glyceraldehyde-3-P dehydrogenase (GAPDH), which was always measured in parallel to the other genes.
We undertook a promoter analysis of a group of genes that were all up-regulated more than 2-fold two hours after T3 treatment in hypothyroid mice. We sought to find out if there was a statistically significant overrepresentation of SREBP and TRE binding sites in the T3-regulated group of genes compared to a control group, unaffected by the treatment. We used pair wise cross-species comparison (phylogenetic footprinting) as described by Lenhard et al  to identify putative transcription factor binding sites in the regions upstream of the transcriptional start site (TSS). Comprehensive reviews covering the field of transcriptional regulation bioinformatics are available [41, 42].
As the study concerned mice, we used mouse orthologs to the rat genes printed on the arrays. To locate mouse orthologs for the rat sequences, we aligned the rat sequences to the mouse genome (NCBI build 30) using BLAT  and then searched for mouse cDNA sequences aligning to the same loci as the rat sequences and indicating similar gene structures. Human cDNA sequences orthologous to the mouse cDNA sequences were identified using the GeneLynx database [44, 45]. Guided by genomic mappings of the cDNA sequences from the UCSC Genome Browser Database , we retrieved human (NCBI build 33) and mouse gene and promoter sequences. Since cDNA sequences are often truncated, we used consensus exon-intron structures derived from overlapping and similar cDNA mappings. We aimed to retrieve sequence from -5000 to +100 relative to TSSs. However, to ensure that the corresponding human and mouse genomic regions were extracted, we extended this region if, in an alignment of the human and mouse gene sequences, the TSSs could not be align. In addition, to avoid inclusion of other nearby genes in the retrieved sequences, we truncated the sequences at the border of any multi-exon cDNA mapping upstream of a TSS. If such a mapping was encountered within 100 bps of a TSS, no sequence was retrieved. Genes which could not confidently be mapped onto either mouse or human genomes were excluded from the analysis.
We analyzed 30 genes rapidly up-regulated by T3 (indicated with a * in Table 6) and, as a control group, 300 expressed but unaffected genes that were randomly selected from the array. We used a 50 bp sliding window and 70% sequence conservation for the alignment windows. The binding model was constructed by merging two matrix models (M00220 and M00221) from the TRANSFAC database . The score cutoff for this model was set to 75%.
Whole cell protein extracts were prepared from frozen liver tissue by homogenization in RIPA buffer. Protein extracts were resolved by SDS-PAGE and transferred to PVDF membranes with a Trans-Blot SD semi-dry transfer cell (Hoefer, Pharmacia Biotech, Sweden). The filters were blocked for 1 h at RT in TTBS (20 mM Tris-HCl, 150 mM NaCl, Tween 0.1%, pH 7.4) containing 5% skim milk powder. Membranes were incubated overnight at 4°C with an anti-SREBP1 or antiβ-actin, as a loading control (SantaCruz Biotechnology, USA). After three 10 min washes in TTBS, binding of primary antibody was visualized using horseradish peroxidase-conjugated secondary antibodies, and the immunolabeling was detected by an enhanced chemoluminescence (ECL) method according to the manufacturer's instructions (Pierce Chemical Company, USA).
We are grateful to Dr. Vennstrom for providing tissues from T3 treated mice. This work was supported by grants from the Swedish Medical Research Council, Wallenberg Consortium North, the Swedish Society of Medical Research, the Fredrik and Ingrid Thuring Foundation, the Tore Nilsson Foundation for Medical Research, the Magnus Bergvall Foundation, the Swedish Medical Association, the Loo and Hans Osterman Foundation and the Åke Wiberg Foundation, the MECD (PM98-033 to L.F.), the Ministerio de Sanidad y Consumo (FIS 1/1000 to L.F.), and the MCYT (SAF2003-02117 to L.F.). L. H-H is recipient of predoctoral fellowship from the Ministerio Educación, Cultura y Deportes.
- Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O'Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES: Initial sequencing and comparative analysis of the mouse genome. Nature. 2002, 420 (6915): 520-562. 10.1038/nature01262.View ArticlePubMedGoogle Scholar
- Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la Bastide M, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, Szustakowki J, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S, Chen YJ: Initial sequencing and analysis of the human genome. Nature. 2001, 409 (6822): 860-921. 10.1038/35057062.View ArticlePubMedGoogle Scholar
- Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S, Scott G, Steffen D, Worley KC, Burch PE, Okwuonu G, Hines S, Lewis L, DeRamo C, Delgado O, Dugan-Rocha S, Miner G, Morgan M, Hawes A, Gill R, Celera, Holt RA, Adams MD, Amanatides PG, Baden-Tillson H, Barnstead M, Chin S, Evans CA, Ferriera S, Fosler C, Glodek A, Gu Z, Jennings D, Kraft CL, Nguyen T, Pfannkoch CM, Sitter C, Sutton GG, Venter JC, Woodage T, Smith D, Lee HM, Gustafson E, Cahill P, Kana A, Doucette-Stamm L, Weinstock K, Fechtel K, Weiss RB, Dunn DM, Green ED, Blakesley RW, Bouffard GG, De Jong PJ, Osoegawa K, Zhu B, Marra M, Schein J, Bosdet I, Fjell C, Jones S, Krzywinski M, Mathewson C, Siddiqui A, Wye N, McPherson J, Zhao S, Fraser CM, Shetty J, Shatsman S, Geer K, Chen Y, Abramzon S, Nierman WC, Havlak PH, Chen R, Durbin KJ, Egan A, Ren Y, Song XZ, Li B, Liu Y, Qin X, Cawley S, Cooney AJ, D'Souza LM, Martin K, Wu JQ, Gonzalez-Garay ML, Jackson AR, Kalafus KJ, McLeod MP, Milosavljevic A, Virk D, Volkov A, Wheeler DA, Zhang Z, Bailey JA, Eichler EE, Tuzun E, Birney E, Mongin E, Ureta-Vidal A, Woodwark C, Zdobnov E, Bork P, Suyama M, Torrents D, Alexandersson M, Trask BJ, Young JM, Huang H, Wang H, Xing H, Daniels S, Gietzen D, Schmidt J, Stevens K, Vitt U, Wingrove J, Camara F, Mar Alba M, Abril JF, Guigo R, Smit A, Dubchak I, Rubin EM, Couronne O, Poliakov A, Hubner N, Ganten D, Goesele C, Hummel O, Kreitler T, Lee YA, Monti J, Schulz H, Zimdahl H, Himmelbauer H, Lehrach H, Jacob HJ, Bromberg S, Gullings-Handley J, Jensen-Seaman MI, Kwitek AE, Lazar J, Pasko D, Tonellato PJ, Twigger S, Ponting CP, Duarte JM, Rice S, Goodstadt L, Beatson SA, Emes RD, Winter EE, Webber C, Brandt P, Nyakatura G, Adetobi M, Chiaromonte F, Elnitski L, Eswara P, Hardison RC, Hou M, Kolbe D, Makova K, Miller W, Nekrutenko A, Riemer C, Schwartz S, Taylor J, Yang S, Zhang Y, Lindpaintner K, Andrews TD, Caccamo M, Clamp M, Clarke L, Curwen V, Durbin R, Eyras E, Searle SM, Cooper GM, Batzoglou S, Brudno M, Sidow A, Stone EA, Payseur BA, Bourque G, Lopez-Otin C, Puente XS, Chakrabarti K, Chatterji S, Dewey C, Pachter L, Bray N, Yap VB, Caspi A, Tesler G, Pevzner PA, Haussler D, Roskin KM, Baertsch R, Clawson H, Furey TS, Hinrichs AS, Karolchik D, Kent WJ, Rosenbloom KR, Trumbower H, Weirauch M, Cooper DN, Stenson PD, Ma B, Brent M, Arumugam M, Shteynberg D, Copley RR, Taylor MS, Riethman H, Mudunuri U, Peterson J, Guyer M, Felsenfeld A, Old S, Mockrin S, Collins F: Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature. 2004, 428 (6982): 493-521. 10.1038/nature02426.View ArticlePubMedGoogle Scholar
- Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN: A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000, 24 (3): 236-244. 10.1038/73439.View ArticlePubMedGoogle Scholar
- Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, Van de Rijn M, Waltham M, Pergamenschikov A, Lee JC, Lashkari D, Shalon D, Myers TG, Weinstein JN, Botstein D, Brown PO: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000, 24 (3): 227-235. 10.1038/73432.View ArticlePubMedGoogle Scholar
- Willson TM, Moore JT: Genomics versus orphan nuclear receptors--a half-time report. Mol Endocrinol. 2002, 16 (6): 1135-1144. 10.1210/me.16.6.1135.PubMedGoogle Scholar
- Flores-Morales A, Stahlberg N, Tollet-Egnell P, Lundeberg J, Malek RL, Quackenbush J, Lee NH, Norstedt G: Microarray analysis of the in vivo effects of hypophysectomy and growth hormone treatment on gene expression in the rat. Endocrinology. 2001, 142 (7): 3163-3176. 10.1210/en.142.7.3163.PubMedGoogle Scholar
- Stahlberg N, Rico-Bautista E, Fisher RM, Wu X, Cheung L, Flores-Morales A, Tybring G, Norstedt G, Tollet-Egnell P: Female-predominant expression of fatty acid translocase/CD36 in rat and human liver. Epub Endocrinology Dec 18;. 2003Google Scholar
- Tollet-Egnell P, Flores-Morales A, Stahlberg N, Malek RL, Lee N, Norstedt G: Gene expression profile of the aging process in rat liver: normalizing effects of growth hormone replacement. Mol Endocrinol. 2001, 15 (2): 308-318. 10.1210/me.15.2.308.View ArticlePubMedGoogle Scholar
- Flores-Morales A, Gullberg H, Fernandez L, Stahlberg N, Lee NH, Vennstrom B, Norstedt G: Patterns of liver gene expression governed by TRbeta. Mol Endocrinol. 2002, 16 (6): 1257-1268. 10.1210/me.16.6.1257.PubMedGoogle Scholar
- Tellgren A, Wood TJ, Flores-Morales A, Torndal UB, Eriksson L, Norstedt G: Differentially expressed transcripts in neoplastic hepatic nodules and neonatal rat liver studied by cDNA microarray analysis. Int J Cancer. 2003, 104 (2): 131-138. 10.1002/ijc.10946.View ArticlePubMedGoogle Scholar
- Pang ST, Dillner K, Wu X, Pousette A, Norstedt G, Flores-Morales A: Gene expression profiling of androgen deficiency predicts a pathway of prostate apoptosis that involves genes related to oxidative stress. Endocrinology. 2002, 143 (12): 4897-4906. 10.1210/en.2002-220327.View ArticlePubMedGoogle Scholar
- Dillner K, Kindblom J, Flores-Morales A, Pang ST, Tornell J, Wennbo H, Norstedt G: Molecular characterization of prostate hyperplasia in prolactin-transgenic mice by using cDNA representational difference analysis. Prostate. 2002, 52 (2): 139-149. 10.1002/pros.10102.View ArticlePubMedGoogle Scholar
- Dillner K, Kindblom J, Flores-Morales A, Shao R, Tornell J, Norstedt G, Wennbo H: Gene expression analysis of prostate hyperplasia in mice overexpressing the prolactin gene specifically in the prostate. Endocrinology. 2003, 144 (11): 4955-66. Epub 2003 Aug 7.. 10.1210/en.2003-0415.View ArticlePubMedGoogle Scholar
- Tollet-Egnell P, Parini P, Stahlberg N, Lonnstedt I, Lee NH, Rudling M, Flores-Morales A, Norstedt G: Growth hormone-mediated alteration of fuel metabolism in the aged rat as determined from transcript profiles. Physiol Genomics. 2004, 16 (2): 261-7. Epub 2003 Nov 11.. 10.1152/physiolgenomics.00093.2002.View ArticlePubMedGoogle Scholar
- Tollet P, Enberg B, Mode A: Growth hormone (GH) regulation of cytochrome P-450IIC12, insulin-like growth factor-I (IGF-I), and GH receptor messenger RNA expression in primary rat hepatocytes: a hormonal interplay with insulin, IGF-I, and thyroid hormone. Mol Endocrinol. 1990, 4 (12): 1934-1942.View ArticlePubMedGoogle Scholar
- Horton JD, Goldstein JL, Brown MS: SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver. J Clin Invest. 2002, 109 (9): 1125-1131. 10.1172/JCI200215593.PubMed CentralView ArticlePubMedGoogle Scholar
- Sakai J, Duncan EA, Rawson RB, Hua X, Brown MS, Goldstein JL: Sterol-regulated release of SREBP-2 from cell membranes requires two sequential cleavages, one within a transmembrane segment. Cell. 1996, 85 (7): 1037-1046. 10.1016/S0092-8674(00)81304-5.View ArticlePubMedGoogle Scholar
- Shin DJ, Osborne TF: Thyroid hormone regulation and cholesterol metabolism are connected through Sterol Regulatory Element-Binding Protein-2 (SREBP-2). J Biol Chem. 2003, 278 (36): 34114-8. Epub 2003 Jun 26.. 10.1074/jbc.M305417200.View ArticlePubMedGoogle Scholar
- Mode A, Norstedt G: Effects of gonadal steroid hormones on the hypothalamo-pituitary-liver axis in the control of sex differences in hepatic steroid metabolism in the rat. J Endocrinol. 1982, 95 (2): 181-187.View ArticlePubMedGoogle Scholar
- Rudling M, Norstedt G, Olivecrona H, Reihner E, Gustafsson JA, Angelin B: Importance of growth hormone for the induction of hepatic low density lipoprotein receptors. Proc Natl Acad Sci U S A. 1992, 89 (15): 6983-6987.PubMed CentralView ArticlePubMedGoogle Scholar
- Mode A, Tollet P, Strom A, Legraverend C, Liddle C, Gustafsson JA: Growth hormone regulation of hepatic cytochrome P450 expression in the rat. Adv Enzyme Regul. 1992, 32: 255-263. 10.1016/0065-2571(92)90021-Q.View ArticlePubMedGoogle Scholar
- Ahluwalia A, Clodfelter KH, Waxman DJ: Sexual dimorphism of rat liver gene expression: regulatory role of growth hormone revealed by deoxyribonucleic Acid microarray analysis. Mol Endocrinol. 2004, 18 (3): 747-60. Epub 2003 Dec 18.. 10.1210/me.2003-0138.View ArticlePubMedGoogle Scholar
- Lahuna O, Fernandez L, Karlsson H, Maiter D, Lemaigre FP, Rousseau GG, Gustafsson J, Mode A: Expression of hepatocyte nuclear factor 6 in rat liver is sex-dependent and regulated by growth hormone. Proc Natl Acad Sci U S A. 1997, 94 (23): 12309-12313. 10.1073/pnas.94.23.12309.PubMed CentralView ArticlePubMedGoogle Scholar
- Choi HK, Waxman DJ: Plasma growth hormone pulse activation of hepatic JAK-STAT5 signaling: developmental regulation and role in male-specific liver gene expression. Endocrinology. 2000, 141 (9): 3245-3255. 10.1210/en.141.9.3245.View ArticlePubMedGoogle Scholar
- Kindblom JM, Gothe S, Forrest D, Tornell J, Vennstrom B, Ohlsson C: GH substitution reverses the growth phenotype but not the defective ossification in thyroid hormone receptor alpha 1-/-beta-/- mice. J Endocrinol. 2001, 171 (1): 15-22. 10.1677/joe.0.1710015.View ArticlePubMedGoogle Scholar
- Kuhn ER, Verheyen G, Chiasson RB, Huts C, Huybrechts L, Van den Steen P, Decuypere E: Growth hormone stimulates the peripheral conversion of thyroxine into triiodothyronine by increasing the liver 5'-monodeiodinase activity in the fasted and normal fed chicken. Horm Metab Res. 1987, 19 (7): 304-308.View ArticlePubMedGoogle Scholar
- Frick F, Linden D, Ameen C, Eden S, Mode A, Oscarsson J: Interaction between growth hormone and insulin in the regulation of lipoprotein metabolism in the rat. Am J Physiol Endocrinol Metab. 2002, 283 (5): E1023-31.View ArticlePubMedGoogle Scholar
- Jump DB, Thelen AP, Mater MK: Functional interaction between sterol regulatory element-binding protein-1c, nuclear factor Y, and 3,5,3'-triiodothyronine nuclear receptors. J Biol Chem. 2001, 276 (37): 34419-34427. 10.1074/jbc.M105471200.View ArticlePubMedGoogle Scholar
- Yin L, Zhang Y, Hillgartner FB: Sterol regulatory element-binding protein-1 interacts with the nuclear thyroid hormone receptor to enhance acetyl-CoA carboxylase-alpha transcription in hepatocytes. J Biol Chem. 2002, 277 (22): 19554-65. Epub 2002 Mar 20.. 10.1074/jbc.M111771200.View ArticlePubMedGoogle Scholar
- Baumann CT, Maruvada P, Hager GL, Yen PM: Nuclear cytoplasmic shuttling by thyroid hormone receptors. multiple protein interactions are required for nuclear retention. J Biol Chem. 2001, 276 (14): 11237-45. Epub 2001 Jan 4.. 10.1074/jbc.M011112200.View ArticlePubMedGoogle Scholar
- Bassett JH, Harvey CB, Williams GR: Mechanisms of thyroid hormone receptor-specific nuclear and extra nuclear actions. Mol Cell Endocrinol. 2003, 213 (1): 1-11. 10.1016/j.mce.2003.10.033.View ArticlePubMedGoogle Scholar
- Adams CM, Goldstein JL, Brown MS: Cholesterol-induced conformational change in SCAP enhanced by Insig proteins and mimicked by cationic amphiphiles. Proc Natl Acad Sci U S A. 2003, 100 (19): 10647-52. Epub 2003 Sep 8.. 10.1073/pnas.1534833100.PubMed CentralView ArticlePubMedGoogle Scholar
- Chehin RN, Isse BG, Rintoul MR, Farias RN: Differential transmembrane diffusion of triiodothyronine and thyroxine in liposomes: regulation by lipid composition. J Membr Biol. 1999, 167 (3): 251-256. 10.1007/s002329900489.View ArticlePubMedGoogle Scholar
- Sundqvist A, Ericsson J: Transcription-dependent degradation controls the stability of the SREBP family of transcription factors. Proc Natl Acad Sci U S A. 2003, 100 (24): 13833-8. Epub 2003 Nov 13.. 10.1073/pnas.2335135100.PubMed CentralView ArticlePubMedGoogle Scholar
- Quackenbush J: Microarray data normalization and transformation. Nat Genet. 2002, 32 (Suppl): 496-501. 10.1038/ng1032.View ArticlePubMedGoogle Scholar
- Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34 (2): 374-378.PubMedGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001, 98 (9): 5116-21. Epub 2001 Apr 17.. 10.1073/pnas.091062498.PubMed CentralView ArticlePubMedGoogle Scholar
- Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001, 29 (4): 365-371. 10.1038/ng1201-365.View ArticlePubMedGoogle Scholar
- Lenhard B, Sandelin A, Mendoza L, Engstrom P, Jareborg N, Wasserman WW: Identification of conserved regulatory elements by comparative genome analysis. J Biol. 2003, 2 (2): 13. Epub 2003 May 22.-10.1186/1475-4924-2-13.View ArticleGoogle Scholar
- Stormo GD: DNA binding sites: representation and discovery. Bioinformatics. 2000, 16 (1): 16-23. 10.1093/bioinformatics/16.1.16.View ArticlePubMedGoogle Scholar
- Wasserman WW, Sandelin A: Applied bioinformatics for the identification of regulatory elements. Nature Reviews Genetics. 2004, 5 (4): 276-287. 10.1038/nrg1315.View ArticlePubMedGoogle Scholar
- Kent WJ: BLAT--the BLAST-like alignment tool. Genome Res. 2002, 12 (4): 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMed CentralView ArticlePubMedGoogle Scholar
- Lenhard B, Wahlestedt C, Wasserman WW, Hayes WS: GeneLynx mouse: integrated portal to the mouse genomeGeneLynx: a gene-centric portal to the human genome. Genome Res. 2003, 13 (6B): 1501-1504. 10.1101/gr.951403.PubMed CentralView ArticlePubMedGoogle Scholar
- Lenhard B, Hayes WS, Wasserman WW: GeneLynx: a gene-centric portal to the human genome. Genome Res. 2001, 11 (12): 2151-2157. 10.1101/gr.199801.PubMed CentralView ArticlePubMedGoogle Scholar
- Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu YT, Roskin KM, Schwartz M, Sugnet CW, Thomas DJ, Weber RJ, Haussler D, Kent WJ: The UCSC Genome Browser Database. Nucleic Acids Res. 2003, 31 (1): 51-54. 10.1093/nar/gkg129.PubMed CentralView ArticlePubMedGoogle Scholar
- Matys V, Fricke E, Geffers R, Gossling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV, Kloos DU, Land S, Lewicki-Potapov B, Michael H, Munch R, Reuter I, Rotert S, Saxel H, Scheer M, Thiele S, Wingender E: TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 2003, 31 (1): 374-378. 10.1093/nar/gkg108.PubMed CentralView ArticlePubMedGoogle Scholar
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