|
|
||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1 Division of Cell and Molecular Biology, Department of Biology, Boston University, Massachusetts; and Merck and Company, Incorporated
2 Departments of Biometrics Research, West Point, Pennsylvania
3 Molecular Profiling, West Point, Pennsylvania
4 Molecular Endocrinology, West Point, Pennsylvania
| ABSTRACT |
|---|
|
|
|---|
signal transducer and activator of transcription-5a; knockout; liver sexual dimorphism; growth hormone action; strain-dependent gene expression
| INTRODUCTION |
|---|
|
|
|---|
3.5 h are followed by a GH-free interval lasting
2 h, whereas in females, GH is present in plasma in a more continuous manner (15). Mice also show sexually dimorphic GH secretory patterns, with females characterized by more frequent GH pulses and a distinctly shorter GH-free interpulse interval than males (20). These sex-dependent plasma GH profiles control liver gene expression at the level of transcription, as demonstrated for several GH-regulated genes that encode liver cytochrome P450 (Cyp) enzymes active in the metabolism of steroids, drugs, and environmental chemicals (34). GH signaling is initiated by the binding of GH to its cell surface receptor, which activates Janus kinase (JAK)2, a GH receptor-associated tyrosine kinase. JAK2, in turn, phosphorylates GH receptor on multiple cytoplasmic domain tyrosine residues, several of which serve as docking sites for signal transducer and activator of transcription (STAT) transcription factors (6, 12), including STAT5a and STAT5b (7, 9, 25). Each STAT is phosphorylated on a single tyrosine residue, which leads to STAT homo- and heterodimer formation, translocation to the nucleus, binding to STAT5 DNA response elements, and stimulation of gene transcription. Both STAT5a and STAT5b are directly activated in male rat liver in response to each incoming plasma GH pulse (4), whereas in livers of female rats, the persistence of plasma GH stimulation leads to partial desensitization of the STAT5 signaling pathway and substantially lower nuclear STAT5b protein than the peak levels seen in males (3, 4, 35). On the basis of these and other findings, STAT5b has been proposed to serve as a mediator of the sex-dependent effects that GH has on liver gene expression (32). This proposal is supported by the characterization of STAT5b-deficient male mice, which display a reduced body growth rate at puberty and a loss of sex-specific liver expression of Cyps and other genes (11, 28, 30). The effects of STAT5b loss on liver gene expression, but not the effects on body growth rates, are recapitulated in a liver-specific STAT5a-STAT5b double-knockout model. Microarray analysis has further demonstrated that the impact of STAT5b on sex-dependent liver gene expression is global, with 90% of male-predominant genes downregulated and 61% of female-predominant genes upregulated in livers of STAT5b-deficient male mice (5). Studies of STAT5a-deficient mice (19) raise the possibility that STAT5a may be required for expression of certain female-specific mouse Cyp proteins (24); however, this has not been investigated in a systematic way.
Sexual dimorphism of liver gene expression is extensive, affecting at least 1,500 genes (5, 38), including many members of the Cyp gene superfamily (34). These sex differences may be impacted by genetic factors that are manifest as strain differences in sex-dependent gene expression. For example, certain Cyp2b steroid 16
-hydroxylase enzymes show strain-dependent differences that affect sex specificity (23). Moreover, Cyp2a4 is subject to strain-specific regulation associated with a recessive mutation that derepresses its expression in males (2). Strain-dependent expression also characterizes sex-specific genes regulated by regulators of sex limitation (Rsl), transcriptional repressors that are deficient in certain mouse strains and modulate the expression of Slp, Mup, and select other male-specific GH-regulated genes (17, 29). Mutations in Rsl1 and Rsl2 serve as examples of strain-specific alleles in transcriptional regulators that impact the sex specificity of GH-responsive genes in the liver (16, 33). It is uncertain whether these or other strain-dependent factors have a widespread effect on the sex specificity of hepatic gene expression.
The present study uses microarray technology to assess the impact of STAT5a deficiency on the sex specificity of liver gene expression. In addition, global assessment of sex specificity was investigated in three different mixed or outbred mouse strains to identify a robust set of strain-independent sex-specific genes, as well as sets of genes whose sex dependence apparently varies with the genetic background. Our findings reveal that Stat5a disruption affects the expression of a discrete subset of sex-dependent genes in female mouse liver, in sharp contrast to Stat5b disruption, which primarily abrogates sex-specific gene expression in male liver.
| MATERIALS AND METHODS |
|---|
|
|
|---|
RNA isolation.
Total RNA was isolated from
0.1 g of frozen mouse liver using Trizol reagent (Invitrogen Life Technologies, Carlsbad, CA) according to the manufacturer's protocol. Liver RNAs prepared from 12 individual 129J x Black Swiss mice (30 µg of RNA per liver, dissolved in diethyl pyrocarbonate-treated water) were used in the present study: three WT males, three STAT5a KO males, three WT females, and three STAT5a KO females. Ten individual ICR mouse liver RNAs (5 WT female livers and 5 WT male livers), kindly provided by Dr. Ekaterina Laz of this laboratory, were used to prepare two pooled liver RNA samples (n = 2 livers and n = 3 livers, respectively). Two of the individual livers for each sex were also used to prepare single liver RNA samples.
Quantitative PCR analysis.
Liver RNA samples were converted to cDNA and used in quantitative PCR (qPCR) assays for individual genes from groups 13 and 14 (see Table 3) using methods detailed elsewhere (11). Amplification of a single specific product during qPCR cycling was verified by examination of dissociation curves of each amplicon. Relative RNA levels were determined after normalization to the 18S RNA content of each sample. Statistical analysis was carried out by Student's t-test using GraphPad Prism software version 4 (San Diego, CA). P values <0.05 were considered significant. qPCR primer design was carried out using Primer Express software (Applied Biosystems), and all primers were verified with respect to their specificity for the target transcript by BLAT [basic local alignment search tool (BLAST)-like alignment tool] analysis of the mouse genome (February 2006 assembly) at http://genome.ucsc.edu/cgi-bin/hgBlat. Primer sequences are shown in Supplemental Table S1.
Microarray analysis.
Global expression analysis was determined using the 23,574-feature mouse Rosetta/Merck Mouse TOE 75k Array 1 [Gene Expression Omnibus (GEO) Platform: GPL 3562; Agilent Technology, Palo Alto, CA], with each feature corresponding to a single 60-mer oligonucleotide antisense probe. Each probe is herein referred to as representing a distinct gene, although the actual number of genes analyzed is likely to be smaller than this number because of the presence of nonannotated sequences, some of which may duplicate results for other genes represented on the chip. Liver RNA samples (n = 3 independent biological replicates for each of 4 sex-genotype combinations) were used in four separate competitive hybridization experiments in a loop design: male (M) WT vs. female (F) WT (M-WT:F-WT), male WT vs. male STAT5a KO (M-WT:M-KO), female WT vs. female STAT5a KO (F-WT:F-KO), and male STAT5a KO vs. female STAT5a KO (M-KO:F-KO). Sample labeling, hybridization, and array scanning were performed as described (13). Briefly, total RNA was reverse transcribed using an oligo(dT) primer, followed by second-strand cDNA synthesis. Labeled cRNA was generated in a two-step process by derivatization of the transcribed products with either Cy3 or Cy5 dyes. For each microarray hybridization, a mixture of equal amounts of a Cy3-labeled and Cy5-labeled sample was used. Each sample pair was hybridized on two separate microarrays, with fluor reversal. All four comparison groups consisted of three biological replicates, giving a total of 24 microarrays for the STAT5a data set. Four fluor-reversed pairs, corresponding to the two pooled RNA and two single liver RNA samples, were hybridized for the WT male and WT female comparison of the ICR mice.
Microarrays were scanned, and individual feature intensities were preprocessed in a series of steps, consisting of background subtraction, normalization to mean intensities of the Cy3 and Cy5 channels, and detrending to fit a linear relationship between channels (8). Normalized intensities from fluor-reversed pairs of arrays were used to derive expression ratios using the Rosetta error model (8, 36). Probes with intensities resulting from oversaturation were excluded from downstream statistical analysis. The microarray platform and analytical methods used were highly sensitive, and were able to reliably detect gene expression fluorescent signal intensities over a >600-fold dynamic range (0.085–53.2 intensity units), with 18,204 of the 23,574 probes deemed to be reliably expressed in the mouse liver samples analyzed (signal intensities >5 times the whole chip background value for either channel). A similar result was obtained, based on the minimum signal intensities, that reliably allowed for discrimination of sex-specific genes. Thus, for each 129J x Black/Swiss M-WT:F-WT fluorescent reverse pair, the two average intensity values for each gene were compared by t-test to determine whether the two values were significantly different at P < 0.0001. For each channel, the lowest signal intensity at which gene expression was reliably demonstrable (the cut-off intensity value) is given by the minimum value of the greater of the two intensity values for those genes that met the threshold for significant differential expression. A total of 16,876 genes with intensities above the cut-off intensity value for either channel were thus found to be expressed in WT mouse livers of either sex.
Expression ratios obtained in this study are included in Supplemental Table S2. The data are also available for query or download from the GEO website at the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/geo) as GEO series GSE7169 and GSE7170. GenBank accession numbers and associated gene names, gene descriptions, and Unigene numbers based on Unigene build 161 were obtained for unassigned features using the BLAT analysis tool located on the University of California Santa Cruz Genome Browser website. Assignments were based on the best matches (identity of
56 of 60 nucleotides) to the 60-nucleotide probe sequences of each probe. Probes that returned multiple accession numbers were verified to make sure that all accession numbers represented the same gene. Probe sequences are available on request.
Statistical analysis.
Moderated t-statistics, using standard errors moderated by a simple Bayesian model, were generated using the linear models for microarray data (LIMMA) package as part of the Bioconductor project within the R statistical program (26). This is the same P value used in our prior microarray study on the effect of STAT5b in mouse liver gene expression (5). A filter (P < 0.05) was applied to the P values to determine the statistical significance of each gene's differential expression for each of the four microarray comparisons (M-WT:F-WT, M-WT:M-KO, F-KO:F-WT, F-KO:M-KO). A fold-change filter of 1.5-fold was combined with the above P value filter to reduce the false discovery rate (FDR) to <6%, as follows. Of the 23,574 features represented on the array, 4,597 met the 1.5-fold expression filter for at least one of the four microarray comparisons. The number of genes expected to meet the combined threshold (P < 0.05 and >1.5-fold change in expression) by chance (type I errors) is 0.05 x 4,597, or 230 genes. The actual number of genes meeting the combined threshold was 3,905 genes (see RESULTS), corresponding to an FDR of 230/3,905, or 5.9%. Multiple testing correction methods, such as Bonferroni or Holm step-down, were not applied because these options depend heavily on the independence of each gene's expression and thus filter out many bona fide regulated genes (e.g., genes validated by qPCR) to avoid all type I errors; these methods are thus too restrictive in their effort to avoid false positives, as noted elsewhere (1). Fisher's exact test (http://www.exactoid.com/fisher/) was used to compare the effects of STAT5a deficiency on gene expression in males vs. females.
To compare sex specificity across strains, microarrays comparing WT male vs. WT female gene expression in three distinct genetic backgrounds were averaged (n = 10 arrays) to determine the robustness of sex specificity across all three strains and experiments. These three data sets were based on analysis using the same microarray platform for WT males and WT females from the present STAT5a KO study, from the prior study of STAT5b KO mice (5), and from a set of ICR mouse livers, as described above. A differential expression filter (mean ratio >1.5) was applied to the gene expression values deemed statistically significant by the P < 0.05 filter. Analysis of variance (ANOVA) with a Benjamini and Hochberg FDR of 0.001 was used, as implemented in GeneSpring GX 7.3.1 software (Agilent Technologies, Santa Clara, CA), to determine strain specificity for the genes that met the threshold criteria for only one of the three independent data sets for M-WT:F-WT comparison.
A system of binary and decimal flags was applied for clustering genes based on expression ratios obtained in all four microarrays, as previously described in our STAT5b KO microarray study (5). For the purpose of this clustering, threshold ratios for differential expression were reduced to values of >1.25 and <0.8 for the three arrays that involved STAT5a KO liver RNAs, with retention of the P < 0.05 threshold for statistical significance. Average ratios meeting these threshold and significance criteria contributed to the binary- and decimal-based flag. Thus genes with a M-WT:F-WT microarray ratio meeting the criteria were assigned a binary flag value of 1, while genes meeting the criteria for the M-WT:M-KO, F-KO:F-WT, and F-KO:M-KO microarrays were, respectively, assigned binary flag values of 2, 4, and 8. Genes not meeting these criteria were assigned flag values of 0. The sum of these binary-based flag values defines the whole number portion of the flag and was used as a simple method to identify which of the four microarrays met our criteria for inclusion for any given gene of interest, regardless of the direction (up or down) of the regulation. The flag value was then extended using decimal values of 0.1, 0.01, 0.001, and 0.0001 or 0.2, 0.02, 0.002, and 0.0002 for each of the four microarrays to indicate the direction of regulation between the two conditions on the microarray. Thus average ratios for the M-WT:F-WT microarray >1.5 were assigned a decimal value of 0.2 to indicate upregulation, while average ratios <0.66 were assigned a value of 0.1 to indicate downregulation. The three other microarray ratios were similarly flagged based on threshold values of >1.25 and <0.8, as indicated above, by advancing to a new decimal position for each microarray (i.e., the M-WT:M-KO flag is in the hundredths position, and so on). For each gene, the resulting binary sum describes which microarray ratios met the selection criteria, and the four-digit decimal value describes the direction of regulation (total flagging sum).
Gene Ontology term enrichment analysis.
Gene Ontology (GO) molecular function annotations were analyzed for term enrichment using the GO Browser within GeneSpring GX software. Because the GO term assigned to each gene represents a subclass of its parent class within the ontology, each gene was also assigned all parent GO class terms. For each of the GO categories present among each list of genes with GO annotations, the number of genes assigned that term was calculated along with a count of all genes on the microarray assigned that term. The P value for each category was calculated based on the total number of genes on the microarray within the category and the number of genes in the group being tested within the category. The P value represents the likelihood that at least as many genes would occur if a list of equal size were selected by chance from the total gene count.
| RESULTS |
|---|
|
|
|---|
Overview of sex specificity and impact of STAT5a deficiency.
Gene expression differences between WT male and WT female liver were found for 2,482 of the 3,905 genes of interest (64%), indicating that these genes are sexually dimorphic in WT liver. These genes are colored green (M-WT > F-WT) or red (M-WT < F-WT) in Fig. 1, lane 1, where they are displayed at the far ends of a false-color heat map containing all 3,905 genes sorted by average M-WT:F-WT ratio. Expression of 1,045 of the 2,482 genes was male predominant in WT mouse liver (M-WT:F-WT >1.5; Fig. 1, lane 1, top yellow box), while 1,437 genes were female predominant (M-WT:F-WT <0.667; Fig. 1, lane 1, bottom yellow box).
|
|
|
|
|
0.0004) among the 194 genes and encompass 68 unique genes. Of note, groups 13A and 14A both include multiple members of the Serpina proteinase inhibitor family in the listing of top genes (Tables 4 and 5).
|
|
|
|
4% of the 2,482 sex-specific genes in the present STAT5a study (Table 3). Six of the seven largest groups of coexpressed genes identified in the present study are made up of sex-specific genes that did not respond to the loss of STAT5a in either males or females (groups 4A, 4B, 5A, 5B, 7A, and 7B). Together, these six groups comprise 76% of the genes of interest in the STAT5a study vs. only 23% of the corresponding set of genes in the STAT5b study. Major differences in the impact of STAT5a vs. STAT5b deficiency are thus apparent.
Comparison of sex specificity among three mouse strains.
M-WT:F-WT expression ratios, obtained in the present analysis of 129J x Black Swiss mouse livers, were compared with the corresponding ratios determined using the same microarray platform for two sets of male and female mouse livers from different genetic backgrounds: 129J x BALB/c mice [genetic background used in the STAT5b KO mouse study (5)] and ICR mice, an outbred strain. Normalized M-WT:F-WT expression ratios for a total of 10 microarrays (3 from 129J x Black Swiss mice, 3 from 129J x BALB/c mice, and 4 from ICR mice) were combined to obtain a single data set, of which 1,028 genes (635 male predominant, 393 female predominant) met the threshold of >1.5-fold differential expression at P < 0.001 (Supplemental Table S4). These 1,028 genes include 109 of the 275 of the genes (40%) whose sex specificity decreased in female liver in the absence of STAT5a (c.f., Table 2). Furthermore, 523 of the 1,028 genes (370 male predominant, 153 female predominant) met the threshold of >1.5-fold differential expression and P < 0.05 in all three independent data sets, indicating robust sex-specific expression across strains and experiments (Supplemental Fig. S2 and Supplemental Table S5).
Further examination of the three microarray data sets revealed genes that met the criteria for sex specificity in only one of the data sets (Supplemental Fig. S2). ANOVA analysis identified 54 genes showing sex specificity in 129J x BALB/c mice but not in the other two strains at P < 0.001 (Supplemental Table S6). Similarly, 49 genes were identified as sex specific in ICR mice only, while 551 genes were sex specific in 129J x Black Swiss mice only. Top candidates for these strain-dependent, sex-specific genes are presented in Supplemental Table S6.
| DISCUSSION |
|---|
|
|
|---|
Earlier studies suggested a role for STAT5a in the expression of a female-specific Cyp2b family member, based on the loss of a female-specific Cyp2b protein in STAT5a-deficient female mouse liver (24). In that case, however, the Cyp2b protein was also decreased in STAT5b-deficient females, a pattern of regulation distinct from that of the major group of STAT5a-dependent female genes described here. The role of STAT5a as a feminizing factor in female mouse liver (positive regulation of female genes and negative regulation of male genes) is analogous to that of STAT5b in male liver, where STAT5b serves as a masculinizing factor (positive regulation of male genes and negative regulation of female genes). STAT5a but not STAT5b also plays the major role in another female-specific function, mammary gland differentiation and lactogenesis in response to prolactin stimulation (19).
The present study includes an analysis of sex-specific mouse liver genes across three mixed or outbred mouse strains. Combined analysis of the data from all three strains (10 microarrays) taken as a single data set identified 1,028 genes showing sex-dependent expression at P < 0.001 across all three studies (Supplemental Table S4). These include liver-expressed Y-linked genes such as Ddx3y, Eif2s3y, and Jarid1d as well as 26 Cyp genes and 10 Sult (sulfotransferase) genes. A subset comprised of 523 genes met the threshold for sex specificity in all three mouse strain backgrounds and thus corresponds to a robust core of sex-specific hepatic genes. Forty-seven genes annotated as transcriptional regulators by GO were included among the robust sex-specific genes, including cut-like 2 (Cutl2), SRY-box containing gene 15 (Sox15), and myogenic factors 5 and 6 (Myf5, Myf6), a subset of which could potentially mediate the sex-specific actions of GH-activated STAT5a or STAT5b, as discussed elsewhere (34). Cutl2, in particular, has recently been characterized as a highly female-specific, continuous GH-regulated nuclear factor in both mouse and rat liver (F > M
100) that could contribute to the establishment or enforcement of liver sex specificity (18).
Also identified here are genes whose liver sex specificity was apparent in only one of the three mouse genetic backgrounds investigated. Some of these genes may be subject to strain-dependent genetic regulation. Genes exhibiting significant discrepancies in expression across the three strain backgrounds are listed in Supplemental Table S6. Only a subset of these genes is likely to exhibit true strain-dependent sex specificity. Technical factors inherent in microarray analysis, as well as the fact that individual livers, rather than pools of livers, comprised the biological triplicates for one of the microarray studies, could contribute to the apparent strain-dependent loss of sex specificity for some of these genes. Nevertheless, these findings raise the possibility that the strain dependence of mouse liver sex specificity may be more extensive than was previously recognized. Earlier studies identified Rsl alleles that conferred strain-specific regulation of certain sex-specific genes, notably Slp, Cyp2d9, and several Mup family members (17). In other studies, strain-specific regulation of the female-predominant Cyp2a4 was associated with a recessive mutation at the GH-dependent repression (Gdr) locus that abolishes repression of Cyp2a4 expression in males (2). Further investigation using larger numbers of arrays representing several pure outbred and inbred mouse strains, including strains containing the Rsl and Gdr mutations, will be necessary to isolate effects due to strain differences from those due to individual variability that is strain independent, as well as from technical limitations of microarray technology.
It is apparent that STAT5a plays a unique role in sexually dimorphic gene expression in female mouse liver. Presumably, these actions of STAT5a are GH regulated, insofar as the vast majority of liver sexual dimorphism, including at least some of the STAT5a-dependent genes presently identified, is dictated by plasma GH profiles (34). It is not known, however, whether the unique actions of STAT5a in female liver reported here reflect a direct action of STAT5a on the genes in groups 13 and 14 (Table 3), or whether the requirement for STAT5a is indirect. It is also unclear why STAT5b is unable to compensate for the loss of STAT5a, given its 10- to 20-fold higher expression in liver (24) and the very close similarities in biochemical and gene regulatory activities of these two closely related (>90% identical) STAT5 proteins (7, 14). Conceivably, the unique actions of STAT5a could reflect a requirement for tetrameric STAT5a complexes for trans-activation via paired STAT5 response elements, insofar as STAT5a, but not STAT5b, readily forms tetrameric STAT-DNA complexes (27). Formation of DNA-bound STAT5a tetramers could be less efficient in male than in female liver owing to competition for DNA binding by the much more abundant dimeric STAT5b complexes that form in response to each incoming plasma GH pulse (3, 4). Further study will be required to investigate these and other possible mechanisms.
STAT5b has been proposed to serve as an upstream transcriptional regulator of a much larger downstream transcriptional network that leads to activation of many of the male-predominant genes and repression of the many female-predominant genes whose hepatic expression is dependent on STAT5b (34). Conceivably, STAT5a could play such a role, albeit on a much smaller scale, in female liver. Of note, 11 of the sex-specific genes dependent on STAT5a in female liver were annotated as transcriptional regulators by GO; these include inhibitor of DNA binding 2 (Id2) and interferon regulatory factor 2 (Irf2). The apparent STAT5a-dependent repression of male-predominant genes in female liver might not be a direct function of STAT5a. For example, an inhibitory nuclear factor, such as Id2, could serve as a direct target of STAT5a and contribute to STAT5a inhibition of select male-specific genes in female liver. Interestingly, Id2 and STAT5a show cooperative activity in development and maturation of mouse mammary tissue, where the loss of Id2 decreased active tyrosine-phosphorylated STAT5a levels by 40% (22). A similar pathway, involving STAT5a and other transcriptional regulators, could be responsible for the limited number of genes that exhibit STAT5a dependence in female liver. Further studies will be required to elucidate the role of STAT5a in female gene expression in liver and other tissues, including mammary gland, where STAT5a is the major STAT5 form.
| GRANTS |
|---|
|
|
|---|
| FOOTNOTES |
|---|
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).
| REFERENCES |
|---|
|
|
|---|
This article has been cited by other articles:
![]() |
S. Surya, J. F. Horowitz, N. Goldenberg, A. Sakharova, M. Harber, A. S. Cornford, K. Symons, and A. L. Barkan The Pattern of Growth Hormone Delivery to Peripheral Tissues Determines Insulin-Like Growth Factor-1 and Lipolytic Responses in Obese Subjects J. Clin. Endocrinol. Metab., August 1, 2009; 94(8): 2828 - 2834. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. J. Waxman and M. G. Holloway Sex Differences in the Expression of Hepatic Drug Metabolizing Enzymes Mol. Pharmacol., August 1, 2009; 76(2): 215 - 228. [Abstract] [Full Text] [PDF] |
||||
![]() |
C. J. Krebs, S. Khan, J. W. MacDonald, M. Sorenson, and D. M. Robins Regulator of sex-limitation KRAB zinc finger proteins modulate sex-dependent and -independent liver metabolism Physiol Genomics, June 10, 2009; 38(1): 16 - 28. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. van Nas, D. GuhaThakurta, S. S. Wang, N. Yehya, S. Horvath, B. Zhang, L. Ingram-Drake, G. Chaudhuri, E. E. Schadt, T. A. Drake, et al. Elucidating the Role of Gonadal Hormones in Sexually Dimorphic Gene Coexpression Networks Endocrinology, March 1, 2009; 150(3): 1235 - 1249. [Abstract] [Full Text] [PDF] |
||||
![]() |
V. Wauthier and D. J. Waxman Sex-Specific Early Growth Hormone Response Genes in Rat Liver Mol. Endocrinol., August 1, 2008; 22(8): 1962 - 1974. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. Hosui and L. Hennighausen Genomic dissection of the cytokine-controlled STAT5 signaling network in liver Physiol Genomics, July 1, 2008; 34(2): 135 - 143. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. G. Holloway, G. D. Miles, A. A. Dombkowski, and D. J. Waxman Liver-Specific Hepatocyte Nuclear Factor-4{alpha} Deficiency: Greater Impact on Gene Expression in Male than in Female Mouse Liver Mol. Endocrinol., May 1, 2008; 22(5): 1274 - 1286. [Abstract] [Full Text] [PDF] |
||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |