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Division of Cell and Molecular Biology, Department of Biology, Boston University, Boston, Massachusetts 02215
Address all correspondence and requests for reprints to: David J. Waxman, Department of Biology, Boston University, 5 Cummington Street, Boston, Massachusetts 02215. E-mail: djw{at}bu.edu.
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ABSTRACT |
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INTRODUCTION |
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Using the power of microarray technology, we have undertaken a global analysis of the role of GH in the sexual dimorphism of rat liver gene expression. GH is secreted by the pituitary gland in a sex-dependent manner in both rodents (11) and humans (12). In male rats, GH is released into circulation approximately every 3.5 h, giving a repeated, pulsatile plasma hormone pattern (13) that contrasts with the more frequent, nearly continuous profile of plasma GH exposure that occurs in adult females. The resultant sex differences in plasma GH profiles are particularly striking in rodents, where they serve as a major determinant of the observed sex differences in body growth rates and the expression in liver of several cytochrome P450 (CYP) enzymes that metabolize steroids, drugs, and environmental chemicals of importance to endocrinology, pharmacology, and toxicology (14, 15). Well-studied examples of sex-specific, plasma GH pattern-regulated liver genes include the male-specific androgen 2
- and 16-hydroxylase CYP2C11, which is strongly induced at puberty in male but not female rat liver, and the steroid sulfate 15ß-hydroxylase CYP2C12, which is exclusively expressed in adult female rat liver (16, 17). The sexual dimorphism of liver with respect to expression of these and other liver CYPs is regulated by GH at the level of transcription initiation (18, 19). Sex-dependent expression and GH regulation also characterizes other families of genes involved in steroid or foreign compound metabolism, such as the sulfotransferases (20) and class and class µ glutathione-S-transferases (21).
DNA microarrays have been employed to determine the expression profiles of key liver-specific genes in rodents as modulated by insulin signaling (22) and small molecules such as leptin (23). Microarrays have also been used to investigate the impact of hormonal insufficiency (hypophysectomy), GH replacement therapy, and GH overexpression on liver gene expression (24, 25, 26, 27, 28). The impact of aging on gene expression and the normalizing effects of GH treatment have also been investigated (29). In one study, the effects of hypophysectomy and GH treatment on the gene expression patterns in heart, liver, and kidney were evaluated using cDNA microarrays containing 3000 different rat genes (24). cDNA microarrays have also been used to identify genes that are responsive to GH in the GH-deficient dwarf rat model (25). In another study, subtractive hybridization was used to identify 173 potential GH target genes; however, only 41 of those genes could be confirmed in subsequent cDNA array experiments (26). Of note, these studies of GH-regulated gene expression employed cDNA arrays, whose ability to discriminate between closely related DNA sequences may be limited by the cross-hybridization that invariably occurs when a single set of hybridization conditions is applied to the thousands of genes represented on the array. These ambiguities can be avoided by using microarrays constructed with oligonucleotides that are chosen to be gene specific and matched with respect to length, GC content, and melting temperature.
The present study was undertaken to investigate the role of GH in the sexual dimorphism of rat liver gene expression. Using two commercial oligonucleotide microarrays, we have identified 86 genes that show sexual dimorphism in their patterns of expression in rat liver. The large majority of these genes (72 of 86) were also found to be subject to GH regulation, demonstrating that GH is a major determinant of sexually dimorphic liver gene expression.
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RESULTS |
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Fold increases or decreases in gene expression compared with the untreated male group were scored as male:female and male:male + GH ratios. Genes found to be reproducibly expressed in either a sex-dependent manner (male:female 
1.5 or 
0.66) or a GH-regulated manner (male:male + GH 1.5 or 0.66) are listed in Tables 1–4
. These regulated genes varied approximately 900-fold in their relative levels of expression after background correction, with the urinary pheromone-binding protein -2u globulin being the most highly expressed gene in male liver (averaged normalized fluorescence intensity = 45,540), and heme-oxygenase 3 being the least expressed (average normalized fluorescence intensity = 51) (c.f. Table 1, right column). The log distributions of the male:female and male:male + GH ratios were compared with 0 on a per-gene basis to examine their statistical significance. Genes identified as sex-dependent and/or GH-regulated and whose distributions surpassed the significance level of P < 0.05 are shown in bold in the tables. A summary of the numbers of genes identified in each category of regulation is presented in Table 5.
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), whereas five of the 49 male-predominant genes were apparently not responsive to GH treatment (Tables 3A and 5). Four other genes were down-regulated by continuous GH treatment but did not display a detectable sex-difference in expression (Tables 4B and 5).
Thirty-seven genes were found to be expressed in a female-predominant fashion. Of these, 27 genes (73%) were both female predominant and GH regulated, as demonstrated by their up-regulation in continuous GH-treated male liver as compared with untreated male liver (Table 2). Three other genes were induced in male rat liver by GH treatment, but in contrast to the other continuous GH-inducible genes, these three genes were not expressed at a significantly higher level in female compared with male liver (Table 4A). Nine other genes were expressed in a female-predominant fashion but were not induced in male liver by continuous GH treatment (Table 2B), whereas one female-predominant gene was suppressed after GH treatment (Tables 3C and 5).
The data presented in Table 5, which plots sex specificity vs. GH regulation, lie heavily on the diagonal, indicating that GH is an important determinant of the sex dependence of liver gene expression. Analysis of these data showed the distribution to be highly significant (P << 0.001 by Pearson’s 
2 analysis), supporting the conclusion that GH is a major regulatory determinant for both the male-predominant and the female-predominant genes. Furthermore, analysis of the quantitative relationship between sex specificity and GH regulation revealed a linear correlation (r = 0.814) when male:female and the corresponding male:male + GH expression ratios were plotted for a group of 278 genes selected on the basis of their highly reproducible ratios between the different liver pairs (P < 0.1 for n = 4 or n = 8) (Fig. 1). A similar correlation was obtained by analysis of male:female and male:male + GH ratio distributions for approximately all 6000 genes represented on the arrays (data not shown).
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and 2 are presented in Table 6, A and B. In all 10 cases, the overall pattern of expression revealed by QPCR was in good agreement with the results of the microarray experiments. Moreover, for seven of the 10 genes, male:female and/or male:male + GH expression ratios determined by QPCR were substantially higher (Table 1 genes) or substantially lower (Table 2 genes) than the corresponding ratios determined by microarray analysis (Table 6, A and B). In the case of one of the genes, CYP4A2, the higher expression ratio obtained by QPCR reflects the specificity of the PCR primers, in so far as the oligonucleotide used to detect CYP4A2 on the microarray cross-hybridizes with CYP4A3, whose expression in liver is not sex dependent or hormonally regulated (30). However, in the case of the other CYP genes, the microarray primers are gene specific (Fig. 2 and data not shown). Expression ratios determined by microarray analysis may therefore understate the true extent of differential regulation of those genes that are highly regulated, as quantitated by QPCR, in agreement with another report (31).
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) exhibited higher expression in female and in GH-treated male liver than in untreated male liver by microarray analysis, but did not make the male:female and male:male +GH threshold ratio of 0.66 for inclusion in Table 2. In all five cases, QPCR verified the overall pattern of expression seen in the microarray analysis. Indeed, QPCR indicated a substantially higher degree of regulation in several cases: most notably, activated leukocyte cell adhesion molecule. QPCR also verified the unique pattern of expression seen for two other genes (Table 6D): hydroxysteroid-17ß dehydrogenase type 2, which was female predominant but suppressed by continuous GH treatment (Table 3C), and growth response protein CL-6, which was sex independent and induced by GH but did not meet the criteria for inclusion in Table 4A. |
DISCUSSION |
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and Table 5). The majority of female-predominant genes identified were also shown to be responsive to the effects of GH (27 of 37 genes), as demonstrated by their up-regulation in male rat liver after continuous GH treatment. However, nine other female-dominant genes did not apparently exhibit this GH response, suggesting that their female-predominant pattern of expression reflects other endocrine regulatory factors. In addition, three genes were expressed at similar levels in male and female liver, yet were induced by continuous GH treatment, and four genes that were not sexually dimorphic were suppressed by GH treatment. These observations indicate that the actions of GH are not restricted to sexually dimorphic liver gene expression and may include regulation of genes that are not gender specific.
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1B-glycoprotein (34), prolactin receptor (35), and several hydroxysteroid sulfotransferases (20). Male-predominant genes listed in Table 1 and shown by traditional methods to be subject to GH regulation include CYP2C11 (16, 17), CYP2C13 (36), CYP3A2 (37), CYP3A18 (38), CYP4A2 (30), corticosteroid 11ß-dehydrogenase (39), carbonic anhydrase 3 (40), and sulfotransferases ST1E2 and 1C1 (20, 41). One of the male-predominant genes, CYP2C22 (42), was confirmed to be male predominant, but unexpectedly, was not found to be suppressed by continuous GH treatment (Table 3A). Thus, there may be multiple mechanisms for regulation of male-predominant liver gene expression. Earlier studies identified two distinct classes of GH-regulated, male-specific genes (15). Members of one class, represented by CYP2C11, are obligatorily dependent on pulsatile GH for expression, whereas members of the second class, which includes CYP2A2, CYP3A2, and CYP4A2, do not exhibit this GH dependence, as indicated by their full expression in hypophysectomized male rat liver and by their up-regulation to near male levels after hypophysectomy in female rat liver (16). However, both classes of male-specific genes are down-regulated by continuous GH treatment, a response that is distinct from that exhibited by CYP2C22 in the present study. Additional studies are required to further characterize the male-predominant, GH-regulated genes presently identified to determine which of these classes they belong to, and to elucidate the mechanisms that underlie their distinct responses to GH ablation by hypophysectomy.
cDNA-based microarrays were used in several earlier studies to identify target genes for GH action in the paradigm of liver gene expression (24, 25, 26, 27). Although cDNA and oligonucleotide-based microarrays exhibit similar sensitivities for detecting changes in gene expression (31, 43), cDNA microarrays can be limited by their ability to discriminate between closely related DNA sequences because of the cross-hybridization that invariably occurs when a single arbitrary set of hybridization conditions is applied uniformly across the thousands of genes represented on the cDNA array (c.f. Refs. 44 and 45). The present study was carried out using microarrays constructed from 50-nucleotide-long oligonucleotides chosen to maximize gene specificity and matched with respect to length, GC content, melting temperature and designed to be free from secondary structure and self-annealing tendencies. These oligonucleotides were generally effective in distinguishing between closely related cDNAs within complex gene families and subfamilies, as exemplified by CYPs belonging to subfamily CYP2C. This gene subfamily includes seven closely related rat genes (75–86% nucleotide identity), all of which are expressed in liver, and six of which (CYPs 2C7, 2C11, 2C12, 2C13, 2C22, and 2C23) were represented on the arrays used in the present study. The percent identity between any given 50-nucleotide-long CYP2C oligomer on the array and nontarget CYP2C transcripts was generally between 40% and 70%, and in all cases was devoid of complementary sequences greater than 15 contiguous bases (Fig. 2 and data not shown). Cross-hybridization is unlikely under these conditions (43). As noted above, several rat CYP2C genes are GH regulated and sexually dimorphic in their expression, with CYPs 2C11, 2C13, and 2C22 being male dominant and CYPs 2C7 and 2C12 female dominant. These patterns of expression were verified in the present study, supporting the specificity of the oligonucleotide probes spotted on the arrays.
The two commercial microarrays used in this study encompassed a total of 5889 rat genes, enabling us to identify both known genes and novel genes that display sexual dimorphism and/or regulation by GH, including six sequences not represented in GenBank. Unlike a previous study of GH-regulated liver genes (24), the present study specifically sought to identify liver-expressed genes that are both sex dependent and GH regulated. Many of the sex-dependent, GH-regulated genes that we identified are involved either in steroid synthesis or in the metabolism of steroids and foreign compounds. Others encode serum proteins, structural proteins, receptors, membrane trafficking proteins, and transporters. Several of the genes identified code for signaling molecules such as nuclear factors and phosphatases, some of which may potentially help mediate the effects of GH on sex-dependent liver gene expression. GH-regulated signaling molecules that are more highly expressed in female than male liver include the H-ras-like tumor suppressor H-rev 107, fibroblast growth factor 5 and several hormone, cytokine, and G protein-coupled receptors (Table 2). Potential signaling molecules found to be GH-regulated and more highly expressed in males include phosphodiesterase I/nucleotide pyrophosphatase 2, the protein kinase phosphorylase kinase 
-2, muscarinic acetylcholine receptor M2, and the cell growth regulator and gap junction component connexin 30 (Table 1). Further investigation will be required to ascertain whether these changes in mRNA expression are accompanied by corresponding changes in protein levels, and to elucidate the precise role that these proteins play in liver physiology and in the sexual dimorphism of liver function.
Many of the genes presently identified displayed moderate regulation by GH (i.e. 1.5- to 3-fold differences in expression in response to changes in GH status), as compared with the more than 100-fold differences in gene expression seen with the classic sexually dimorphic, GH pattern-regulated genes CYP2C11 and CYP2C12. These striking quantitative differences in the extent of sexual dimorphism and GH regulation were verified by QPCR (Table 6). This finding suggests that intrinsic signals distinct from GH, both inductive and repressive, contribute to the observed sexually dimorphic pattern of liver gene expression. QPCR also verified the expression patterns for several genes identified in the microarray studies that were not previously known to be sexually dimorphic and GH-regulated (transcobalamin I precursor, phosphodiesterase I/nucleotide pyrophosphatase 2, acyl-coenzyme A (CoA) synthase 5, and H-ras-like tumor suppressor H-rev 107). Several other genes that were found by microarray analysis to be more highly expressed in female than in male liver, but did not meet our threshold ratio criteria for inclusion in Table 2, were also analyzed by QPCR and shown to be female predominant and induced by continuous GH treatment (Table 6C). The absence of these five genes from Table 2 serves as an example of false negatives, which occur frequently in microarray analysis. False positives are also an unavoidable feature of microarray analysis. However, this possibility may be minimized by the requirement that the genes listed in Tables 1–4 show a consistent pattern of regulation in at least three of four independent pairs of male:female livers and a corresponding number of male:GH-treated male livers, and by the fact that a majority of the genes listed in Tables 1–4 gave expression ratios that were tightly clustered in the four pairs of livers analyzed, and thus highly significant (table entries shown in bold). Nevertheless, it will be important to validate the patterns of gene expression profiling obtained in the present microarray studies through the use of alternative, more traditional methods, such as the QPCR analyses presented in Table 6.
Whereas the microarray analysis reported here is by no means comprehensive, it has led to the identification of several distinct groups (classes) of liver-expressed genes, each of which is unique with respect to its responsiveness to sex-dependent hormonal factors, of which GH is seen to be the major, but apparently not the sole determinant (Table 5). Conceivably, genes within each of the classes represented by Tables 1 and 2 are likely to share common regulatory mechanisms that enable them to respond to continuous GH treatment by gene up-regulation (Table 2) or down-regulation (Table 1). As GH-regulated, sexual dimorphic liver gene expression is primarily, if not exclusively, controlled at the level of transcription initiation (18, 19), future studies designed to elucidate common promoter region sequences and upstream regulatory elements may provide further insight into the mechanisms that underlie the sexual dimorphism of hepatic gene expression.
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MATERIALS AND METHODS |
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RNA Isolation
Total liver RNA was isolated from frozen liver samples using Trizol reagent (Invitrogen Life Technologies, Carlsbad, CA). Poly(A) RNA was then isolated using a commercial kit (Ambion, Austin, TX). RNA samples having an A260/A280 ratio of at least 1.8 were deemed suitable for use in subsequent analyses. Total liver RNA and Poly(A) RNA samples were treated with deoxyribonuclease I (Ambion, Austin, TX) as described previously (46) and their integrity was checked on Northern blots probed for CYP2C6 mRNA (47), which is expressed at high levels in both male and female rat liver.
Oligonucleotide Microarray Experiments
The expression of sex-dependent and GH-regulated liver mRNAs was investigated using two commercial DNA microarrays, Pan Rat Liver Oligonucleotide array and 5K Rat Oligonucleotide array (MWG Biotech, Inc., High Point, NC). These arrays are respectively comprised of oligonucleotide sequences 50 nucleotides in length selected to represent unique (gene specific) sequences, generally derived from the coding sequence or 3'-untranslated region of each mRNA. The Pan Rat Liver and 5K Rat arrays used respectively represent 1353 unique liver-expressed genes (1408 features, including replicates; Pan Rat Liver array) and 5535 unique rat genes (5760 features, including replicates; 5K Rat array). Together, these microarrays represent 5889 unique rat genes, of which 999 genes are represented on both arrays. Poly(A) RNA purified from individual livers was labeled with Cy3-deoxyuridine triphosphate (dUTP) or Cy5-dUTP (PerkinElmer Life Sciences Inc., Boston, MA) in a reverse transcription reaction using 1 µg Poly(A) RNA and Superscript II (ribonuclease H mutant) enzyme (Invitrogen Life Technologies, Carlsbad, CA) at 39 C for 2 h. Unincorporated deoxynucleotide triphosphates were removed using the Qiaquick PCR-purification kit (QIAGEN, Valencia, CA). Cy5-labeled male liver cDNA was mixed with Cy3-labeled female or Cy3-labeled GH-treated male liver cDNA followed by cohybridization of the mixed cDNA population to the microarray for 18 h at 42 C. Arrays were washed according to the manufacturer’s protocol. RNA from each of four individual sets of male, female and GH-treated male liver was converted to cDNA and labeled in duplicate. One series of eight hybridizations (four hybridizations with male + female liver cDNA, and four hybridizations with male + GH-treated male liver cDNA; n = 4 livers/treatment group) was carried out with the Pan Rat Liver arrays, and a second series containing the same eight hybridizations (n = 4 livers/group) was carried out with the 5K Rat arrays. Dye swapping experiments using a single pair of male and female RNA samples, respectively labeled with Cy3- and Cy5-dUTP, were performed to verify the independence of the microarray results from the nature of the cyanine dye label.
Data Acquisition and Analysis
Fluorescent cDNA bound to each microarray was detected with a GenePix 4000B array scanner (Axon Instruments, Foster City, CA) using a fixed laser power intensity and the minimum photomultiplier tube setting necessary to avoid bleaching while allowing for detection of features with faint fluorescent intensities. The manufacturer’s GenePix 3.0 software package was used to locate individual spots, quantitate the Cy3- and Cy5-fluorescence intensity at each spot, and determine background signal intensities. Data from spots that were determined to result from hybridization anomalies or microarray errors (e.g. dust particles, fibers, or spotting anomalies including bleeding around the actual spotting area) were excluded from the analysis. The data were imported into GeneSpring 6.0 (Silicon Genetics, Redwood City, CA). Fluorescence intensity values were calculated by subtracting the local median background value from the median foreground value for each spot. The use of median values instead of mean values reduced the effect of outliers generated by imperfections in GenePix’s automatic spot detection protocol. Average background values were determined to be 40 ± 8 (Cy5 channel) and 51 ± 13 (Cy3 channel), mean ± SD. Lowess normalization, using 20% of the data for smoothing, was applied to the Cy3 channel for each microarray spot on each array using GeneSpring software. Lowess-normalized Cy3 values below 10 were set to a baseline value of 10. The ratio of the normalized channels (Cy5/Cy3) as reported by GeneSpring was used for expression analysis. All genes included in our analysis and listed in Tables 1–4 exhibited expression differences of 1.5-fold or higher (i.e. at least 50% greater expression in the Cy5 sample than in the Cy3 sample) or 0.66-fold or lower that were reproducible in microarray slides representing at least three of four male:female or male:male+ GH liver pairs, or at least two of three liver pairs for those genes where only three of the four microarrays gave data of suitable technical quality. The threshold values of 1.5 and 0.66 were chosen so as to not exclude genes whose expression is known to be sex dependent and/or GH regulated by other, independent studies, but whose microarray expression ratios did not satisfy a 2.0-threshold value. Genes with reproducible expression ratios between 0.8 and 1.2 were not considered to be sex specific (male:female comparisons) or GH regulated (male:male + GH comparisons). For those genes that are represented on both microarrays, the mean ratio and SE values were calculated based on n = 8 replicates. Expression ratios for all other genes were calculated based on n = 4 unless one of the replicates returned no data for that spot. Average fluorescence intensity values reported in Tables 1–4 are normalized values and were corrected for background fluorescent intensity. Expression ratios for the approximately 6000 genes examined on the 16 microarray slides used in this study are available as published as supplemental data on The Endocrine Society’s Journals Online web site at http://mend.endojournals.org. The data are also available for query or download from the Gene Expression Omnibus (GEO) web site at NCBI (http://www.ncbi.nlm.nih.gov/geo).
Statistical Analysis
A one-sample t test (GeneSpring 6.0 software) was applied to the distribution of natural logs of the ratios for each gene. The t test implemented by the GeneSpring software package calculates the P value for the distribution of natural logs as compared with 0. A filter (P < 0.05) was applied to the P values to determine the statistical significance of each gene’s differential expression for each DNA array type (Pan Rat Liver and 5K Rat array). Expression ratios that met the P < 0.05 criterion are shown in bold in Tables 1–4. Multiple testing correction methods, such as Bonferroni or Holm step-down, were not applied to the P values because these options, available in the GeneSpring 6.0 software package, depend heavily on the independence of each gene’s expression and thus filter out many bona fide regulated genes to avoid all type I errors; they are thus too restrictive in their effort to avoid false positives (48).
A Pearson’s 2 test was applied using MATLAB 6.1.0.450 (The MathWorks, Inc., Natick, MA) on a table of the gene counts for each observed relation between sex dominance and GH regulation (Table 5) to validate the correlation between the two experimental paradigms (sex specificity and GH regulation). The numbers for these relationships were set into a 3 x 3 table and the test was applied to determine whether the observed distribution differs significantly from the expected normal distribution. Any element in the table less than five was increased by pseudo-counts to five to fit the assumptions of the 2 test (i.e. no observation should be less than 5). The value of the non-GH-responsive, non-sex-specific genes was set at an underestimated value of 2475, to take into account potential experimental error. A 2 value of 2993, corresponding to P << 0.001, was calculated. The same high level of significance was found when a second calculation for the 2 test was made underestimating the value of the non-GH-responsive, non-sex-specific genes as 20, to reduce the bias on the expected distribution due to the large difference between 2475 and the other table entries. An equally high level of significance was found when a 2 test for a 2 x 2 table of only up-regulated and down-regulated genes for each condition was calculated.
Real-Time PCR Studies
The expression profiles of select genes represented on the microarrays were examined by a reverse transcription real-time QPCR method using an ABI Prism 7700HT instrument (Applied Biosystems, Foster City, CA) and SYBR green detection. cDNA was prepared from individual livers, and each cDNA was assayed in triplicate PCRs. Briefly, 1 µg of total liver RNA was treated with deoxyribonuclease I and reverse transcribed with oligo deoxythymidine20-VN primers (V = any nucleotide but thymidine) using the GeneAmp RNA-PCR kit (Applied Biosystems) in a reaction volume of 20 µl as recommended by the manufacturer and described earlier (46). cDNA samples were used at a dilution of 1:100 in QPCR assays using the SYBR Green mix kit (Applied Biosystems, Foster City, CA). PCRs (4–5 µl total volume per well) were carried out in triplicate in individual wells of a 384-well plate. With each cycle of PCR, more SYBR Green dye molecules bind to the newly synthesized double-stranded DNA amplicon, resulting in an increase in fluorescence in real-time. Gene-specific primers were designed using Primer Express software (Applied Biosystems). Primer sequences used for the 17 genes shown in Table 6 are published as supplemental data on The Endocrine Society’s Journals Online web site at http://mend.endojournals.org. Primer specificity and the extent of possible cross-hybridization with other genes in GenBank was evaluated by basic local alignment and search tool analysis (BLAST). Primer concentrations of 300–600 nM were generally found to be optimal. Assays were performed using liver cDNA prepared from n = 4–8 individual male, female and continuous GH-treated male livers, all separate from the livers used in the microarray analysis. QPCR amplification of 18S rRNA was carried out in parallel to normalize the input cDNA for each PCR. Products of mock reverse transcription reactions for each of the liver RNA samples were included as negative controls to verify the absence of amplification signal attributable to contamination by genomic DNA.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Abbreviations: CoA, Coenzyme A; CYP, cytochrome P450; dUTP, deoxyuridine triphosphate; QPCR, quantitative PCR.
Received for publication April 15, 2003. Accepted for publication December 10, 2003.
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 K. E. Govoni, S. K. Lee, R. B. Chadwick, H. Yu, Y. Kasukawa, D. J. Baylink, and S. Mohan Whole genome microarray analysis of growth hormone-induced gene expression in bone: T-box3, a novel transcription factor, regulates osteoblast proliferation Am J Physiol Endocrinol Metab, July 1, 2006; 291(1): E128 - E136. [Abstract] [Full Text] [PDF]
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K. H. Clodfelter, M. G. Holloway, P. Hodor, S.-H. Park, W. J. Ray, and D. J. Waxman Sex-Dependent Liver Gene Expression Is Extensive and Largely Dependent upon Signal Transducer and Activator of Transcription 5b (STAT5b): STAT5b-Dependent Activation of Male Genes and Repression of Female Genes Revealed by Microarray Analysis Mol. Endocrinol., June 1, 2006; 20(6): 1333 - 1351. [Abstract] [Full Text] [PDF]
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M. G. Holloway, E. V. Laz, and D. J. Waxman Codependence of Growth Hormone-Responsive, Sexually Dimorphic Hepatic Gene Expression on Signal Transducer and Activator of Transcription 5b and Hepatic Nuclear Factor 4{alpha} Mol. Endocrinol., March 1, 2006; 20(3): 647 - 660. [Abstract] [Full Text] [PDF]
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 C. Cheung, A.-M. Yu, C.-S. Chen, K. W. Krausz, L. G. Byrd, L. Feigenbaum, R. J. Edwards, D. J. Waxman, and F. J. Gonzalez Growth Hormone Determines Sexual Dimorphism of Hepatic Cytochrome P450 3A4 Expression in Transgenic Mice J. Pharmacol. Exp. Ther., March 1, 2006; 316(3): 1328 - 1334. [Abstract] [Full Text] [PDF]
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T. Landsman and D. J. Waxman Role of the Cytokine-induced SH2 Domain-containing Protein CIS in Growth Hormone Receptor Internalization J. Biol. Chem., November 11, 2005; 280(45): 37471 - 37480. [Abstract] [Full Text] [PDF]
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M. F. Paine, S. S. Ludington, M.-L. Chen, P. W. Stewart, S.-M. Huang, and P. B. Watkins DO MEN AND WOMEN DIFFER IN PROXIMAL SMALL INTESTINAL CYP3A OR P-GLYCOPROTEIN EXPRESSION? Drug Metab. Dispos., March 1, 2005; 33(3): 426 - 433. [Abstract] [Full Text] [PDF]
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E. Rico-Bautista, C. J. Greenhalgh, P. Tollet-Egnell, D. J. Hilton, W. S. Alexander, G. Norstedt, and A. Flores-Morales Suppressor of Cytokine Signaling-2 Deficiency Induces Molecular and Metabolic Changes that Partially Overlap with Growth Hormone-Dependent Effects Mol. Endocrinol., March 1, 2005; 19(3): 781 - 793. [Abstract] [Full Text] [PDF]
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C. A. Wiwi and D. J. Waxman Role of Hepatocyte Nuclear Factors in Transcriptional Regulation of Male-specific CYP2A2 J. Biol. Chem., February 4, 2005; 280(5): 3259 - 3268. [Abstract] [Full Text] [PDF]
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 S Eleswarapu and H Jiang Growth hormone regulates the expression of hepatocyte nuclear factor-3 gamma and other liver-enriched transcription factors in the bovine liver J. Endocrinol., January 1, 2005; 184(1): 95 - 105. [Abstract] [Full Text] [PDF]
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P. Kotokorpi, C. Gardmo, C. S. Nystrom, and A. Mode Activation of the Glucocorticoid Receptor or Liver X Receptors Interferes with Growth Hormone-Induced akr1b7 Gene Expression in Rat Hepatocytes Endocrinology, December 1, 2004; 145(12): 5704 - 5713. [Abstract] [Full Text] [PDF]
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 E. V. Laz, C. A. Wiwi, and D. J. Waxman Sexual Dimorphism of Rat Liver Nuclear Proteins: Regulatory Role Of Growth Hormone Mol. Cell. Proteomics, December 1, 2004; 3(12): 1170 - 1180. [Abstract] [Full Text] [PDF]
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 F. R. Simon, J. Fortune, M. Iwahashi, I. Qadri, and E. Sutherland Multihormonal regulation of hepatic sinusoidal Ntcp gene expression Am J Physiol Gastrointest Liver Physiol, October 1, 2004; 287(4): G782 - G794. [Abstract] [Full Text] [PDF]
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