- Open Access
Sex-specific mouse liver gene expression: genome-wide analysis of developmental changes from pre-pubertal period to young adulthood
© Conforto and Waxman; licensee BioMed Central Ltd. 2012
Received: 3 January 2012
Accepted: 4 April 2012
Published: 4 April 2012
Early liver development and the transcriptional transitions during hepatogenesis are well characterized. However, gene expression changes during the late postnatal/pre-pubertal to young adulthood period are less well understood, especially with regards to sex-specific gene expression.
Microarray analysis of male and female mouse liver was carried out at 3, 4, and 8 wk of age to elucidate developmental changes in gene expression from the late postnatal/pre-pubertal period to young adulthood.
A large number of sex-biased and sex-independent genes showed significant changes during this developmental period. Notably, sex-independent genes involved in cell cycle, chromosome condensation, and DNA replication were down regulated from 3 wk to 8 wk, while genes associated with metal ion binding, ion transport and kinase activity were up regulated. A majority of genes showing sex differential expression in adult liver did not display sex differences prior to puberty, at which time extensive changes in sex-specific gene expression were seen, primarily in males. Thus, in male liver, 76% of male-specific genes were up regulated and 47% of female-specific genes were down regulated from 3 to 8 wk of age, whereas in female liver 67% of sex-specific genes showed no significant change in expression. In both sexes, genes up regulated from 3 to 8 wk were significantly enriched (p < E-76) in the set of genes positively regulated by the liver transcription factor HNF4α, as determined in a liver-specific HNF4α knockout mouse model, while genes down regulated during this developmental period showed significant enrichment (p < E-65) for negative regulation by HNF4α. Significant enrichment of the developmentally regulated genes in the set of genes subject to positive and negative regulation by pituitary hormone was also observed. Five sex-specific transcriptional regulators showed sex-specific expression at 4 wk (male-specific Ihh; female-specific Cdx4, Cux2, Tox, and Trim24) and may contribute to the developmental changes that lead to global acquisition of liver sex-specificity by 8 wk of age.
Overall, the observed changes in gene expression during postnatal liver development reflect the deceleration of liver growth and the induction of specialized liver functions, with widespread changes in sex-specific gene expression primarily occurring in male liver.
The liver performs a variety of physiological functions including glycogen storage, cholesterol catabolism to bile acids, and drug metabolism [1, 2]. Liver development begins around embryonic day 9 (E9) in the mouse and the transcriptional transitions during hepatogenesis are well characterized . Changes in gene expression during the postnatal/pre-pubertal period are less well understood , especially with regards to sex-biased gene expression. Over 1,000 genes are known to be differentially expressed between male and female liver and are regulated primarily by pituitary patterns of growth hormone secretion [4–6], which are sex-dependent and subject to regulation by estrogen and testosterone .
During early mouse postnatal development, rapid growth of the body and somatic organs occurs and then slows down as the animal ages. From 2 to 4 wk of age liver mass increase is predominantly due to polyploidization and to a lesser extent, hypertrophy , while from 4 to 8 wk there is a decrease in polyploidzation and the increase in liver mass is primarily due to hyperplasia. β-catenin, a gene associated with cell proliferation, is induced at postnatal day 5 and highly expressed until postnatal day 20 . Little growth of the liver is observed after 8 wk, at which time growth-promoting genes active at 1 wk, such as Igf2, Mest, Peg3, are repressed as they are in other tissues showing decelerated growth, including kidney, lung, and heart . There is a reciprocal relationship between the decrease in Igf2 expression and the increase of Igf1 expression during postnatal mouse liver development . The developmental decreases in somatic growth and in the expression of Igf2, Mest, Peg3 appear to be regulated by the size and not the age of the animal per se, as seen in studies where the anti-thyroid drug propylthiouracil is used to inhibit body growth .
Large changes in gene expression occur in early postnatal mouse liver as the liver's primary function shifts from hematopoiesis to specialized liver functions . RNA polymerase activity steadily increases and plateaus at 30 days of age , while RNA synthesis peaks at 60 days and decreases at later ages. Innate immune system genes are activated from E18.5 to postnatal day 3, while liver function genes, such as those involved in lipid and fatty acid metabolism, are activated at postnatal day 7 . In rat liver, changes in expression of genes associated with metabolism occur around the time of weaning . In particular, gluconeogenesis and ketogenic enzyme activity decrease, while glucokinase and lipogenic activity increase . A decrease in respiration rates occurs in mouse liver mitochondria right after weaning, but the levels later recover in adults . These changes in metabolic enzyme activity may be due to a rise in thyroid hormone and/or glucocorticoid levels .
Genes encoding cytochromes P450 and other enzymes of drug and steroid metabolism show marked changes in expression during postnatal liver development. In male and female mouse liver, Cyp1a2, Cyp2d33, Cyp2f2, Cyp3a13, and Cyp3a25 are expressed at low levels at postnatal day 10 or 15 and then increase until day 20, after which the expression is largely maintained . A similar pattern of expression was observed for CYP1A2 and CYP2E1 in male rat liver . Many Cyps and other genes active in drug metabolism show sex-differences in expression beginning at puberty [6, 15, 17–19]. Genes such as Cyp2b9, Cyp3a41, and Cyp3a44, are expressed at high levels in both male and female mouse liver prior to puberty, but are then selectively repressed in male liver, resulting in female-biased expression at adulthood [18–20]. In the rat, the male-specific CYP2C11 and the female-specific CYP2C12 are both expressed at low levels in both male and female liver until puberty, at which time CYP2C11 is up regulated in male liver and CYP2C12 is up regulated in female [21, 22]. Sult genes involved in hydroxysteroid sulfate conjugation display sex-specific expression after puberty in mouse liver [17, 23]. Six of seven Sult2a genes show high pre-pubertal expression in both male and female liver, however in adult mouse liver, three Sult2a genes display female-specific expression while three others decrease in expression in both male and female liver . The seventh Sult2a gene is not detectable prior to puberty and is expressed at adulthood in a sex-independent manner .
Presently, we investigate on a global scale the effects of age and sex on gene expression in mouse liver. We compare gene expression profiles in the pre-pubertal period (3 wk and 4 wk of age) to the post-pubertal/young adult stage (8 wk old) and find that changes in sex-specific gene expression primarily occur in male liver. We also show that genes that show changes during this period of development are enriched in the set of genes whose expression is dependent on the liver transcription factor hepatocyte nuclear factor 4α (HNF4α) , as determined using a liver-specific mouse HNF4α knockout model . Finally, we identify other transcription factors that show significant changes in expression during postnatal development and may contribute to the observed changes in liver gene transcription during this developmental period.
Animal treatments, liver RNA isolation, and quantitative PCR (qPCR)
Surrogate mothers with 7 day old male and female crl:CD1 mouse pups were purchased from Charles River Labs (Wilmington, MA). CD1 mice were chosen for this study due to the extensive earlier mechanistic studies of sex-specific liver gene expression carried out in this strain; these include global analysis of DNase I hypersensitivity, responsiveness to hypophysectomy and growth hormone treatment, and the identification of binding sites for the growth hormone-regulated transcription factors STAT5 and BCL6 [26–28]. Male and female mice were killed at 3, 4, or 8 wk of age (n = 10-12 mice/sex/age group) and livers were snap frozen in liquid nitrogen and stored at -80°C. Total RNA was isolated from frozen individual livers using TRIzol reagent (Invitrogen, Carlsbad, CA). RNA samples were converted to cDNA using a high-capacity cDNA reverse transcription kit (Applied Biosystems, Foster City, CA). Triplicate 5-μl real-time PCR mixtures, each containing Power SYBR green PCR master mix (Applied Biosystems), 312 nM each qPCR primer, and 0.5-1.5 μl DNA template were loaded onto a 384-well plate and run through 40 cycles on an ABS 7900HT sequence detection system (Applied Biosystems). Data were graphed as relative values, normalized to the 18S rRNA content of each sample. Statistical analyses were carried out by 2-way ANOVA using PRISM software version 4 (GraphPad, Inc., San Diego, CA). qPCR primers are listed in Additional file 1. RNA integrity (minimum RIN number 8.0) was verified using an Agilent Bioanalyzer 2100 instrument (Agilent Technology, Palo Alto CA).
Seven independent competitive hybridization microarray experiments were carried out: 1) Male 3 wk vs. Female 3 wk; 2) Male 4 wk vs. Female 4 wk; 3) Male 8 wk vs. Female 8 wk; 4) Male 3 wk vs. Male 8 wk; 5) Male 4 wk vs. Male 8 wk; 6) Female 3 wk vs. Female 8 wk; and 7) Female 4 wk vs. Female 8 wk. Two independent pairs of pooled liver RNA randomized samples were used for each microarray comparison; these biological replicates were analyzed as dye swaps to correct for dye bias, giving a total of 14 microarrays. To minimize the impact of individual mouse to mouse variability on the microarray data, each biological replicate was comprised of a randomized pool of liver RNA prepared from n = 5-6 mice. Thus, for array comparison 1, Male 3 wk liver pool A was labeled with Alexa 555 dye and Female 3 wk liver pool A was labeled with Alexa 647 dye (biological replicate one), and Male 3 wk liver pool B was labeled with Alexa 647 dye and Female 3 wk liver pool B was labeled with Alexa 555 dye (biological replicate two); array comparison 1 is thus based on comparisons of 10-12 individual male mice and 10-12 individual female mice at each age. Hybridization of fluorescent labeled RNA to 39,429-feature Agilent microarrays was carried out for each pair of independent biological replicates, giving a total of 14 arrays across the 7 array comparisons. SurePrint G3 4×44 K Mouse Gene Expression microarrays (catalog no. G4846A-026655; Agilent Technology; Gene expression omnibus (GEO) platforms GPL10333 and GPL11202) were used for these studies.
Linear and LOWESS normalization were performed for each microarray using Agilent Feature Extraction software. The Feature Extraction analysis also calculates the variation of pixel intensity for each feature (spot) on the array. These error measurements were input to the Rosetta error model, which was used for subsequent analysis of statistical significance of differential gene expression. The Rosetta error model provides a gene-specific estimate of error by incorporating two elements: a technology-specific estimate of error and an error estimate derived from replicate arrays . The technology-specific component utilizes an intensity-dependent model of error derived from numerous self-self hybridizations. In this study, two arrays, based on independent pools of biological replicates, were used for each comparison of interest. By including the technology-specific estimate, the Rosetta error model is able to avoid false positives that occur from under-estimation of error when a small number of replicate arrays are available, thus resulting in an increase in statistical power equivalent to that which would be obtained with at least one additional replicate. For two-color microarrays a log-ratio error estimate is derived in the Rosetta error model from the individual error estimates of each sample (color) used in the co-hybridization. Then, for each feature an average log ratio and associated p-value are obtained from replicate measurements (arrays) using the Rosetta error model error-weighted averaging method. In this approach, the average ratio is calculated by weighting the ratio from each sample inversely proportional to the variance of that sample. This produces an averaged ratio with the smallest possible error. Validation with spike-in experiments has demonstrated that the Rosetta error model has superior accuracy in detecting and quantifying relative gene expression when compared to other statistical methods commonly used in microarray analysis . Direct experimental validation is provided in the present study, where qPCR analysis of n = 9-10 individuals per group gave results indistinguishable from those obtained by microarray analysis (see below).
The statistical significance of differential expression of each gene was determined by application of a filter (p < 0.0001) to the Rosetta p-values. All together, 7,915 probes met this p-value threshold in one or more of the 7 array comparisons, after removal of redundant probes (see below) and after removal of 142 probes that did not pass Agilent's "well above background" condition, which requires a probe's raw signal to be greater than 99% of the background population signal. The number of microarray probes expected to meet the significance threshold of p < 0.0001 by chance is 0.0001 × 39,429 probes or 4 probes. The actual number of probes that met this p-value ranged from 104 (Male 3 wk vs. Female 3 wk arrays), to 3,784 (Male 3 wk vs. Male 8 wk arrays) across the seven array comparisons, corresponding to a false discovery rate ranging from 4/104 (3.85%) to 4/3,784 (0.11%). Further, a |fold-change| filter of > 1.5-fold was combined with the p < 0.0001 filter to limit consideration to genes showing expression ratios > 1.5 (up regulated genes) or < 0.667 (down regulated genes). When two or more probes assigned the same gene name gave the same pattern of regulation across the seven microarray comparisons, they were deemed to be redundant probes (as indicated by assignment to the same total flag sum (TFS) group; see Additional file 2 and below), and only the probe with the lowest set of p-values was retained. Probes associated with the same gene name but different TFS groups were retained. After removing redundant probes, 5,715 probes (genes) met the above specified p-value, expression ratio, and well above background filters. 1,212 of the 5,715 genes showed significant sex-differences in expression at 8 wk, and on that basis were defined as adult sex-specific (adult sex-biased) genes. All microarray data files are available at the GEO web site  as GEO series GSE34782.
A system of binary and decimal flags (TFS)  was used to classify the 5,715 regulated genes based on expression ratios and p-values across the seven microarray comparisons (Additional file 2). Hierarchical clustering and heat map generation were carried out separately for the 1,212 adult sex-specific genes and 4,503 sex-independent genes using Cluster  and Java TreeView . STEM clustering [35, 36] of the adult sex-specific genes, and separately, STEM clustering of the adult sex-independent genes, was used to identify common patterns present within each gene set. STEM clustering was carried out using log2 gene expression ratios from our microarray study, with each of the 7 microarrays serving as a separate data point for STEM clustering. The maximum number of model STEM profiles considered was set to either 30 or 50 and the maximum unit change in model profiles between time points was set equal to 2 [35, 36]. The STEM profiles are derived from predefined patterns based on the maximum number of profiles and the maximum distance between two data points, which is selected by the user. A greedy approximation algorithm generates a set of patterns that maximizes the minimum distance between any two patterns so that the set of patterns is distinctive but also representative of all the possible patterns [35, 36]. A permutation-based test was used to quantify the expected number of genes that would be assigned to each predefined pattern if the data were randomly generated. If the number of genes assigned to a given predefined pattern is significantly greater than the predicted number of genes, then the profile with the genes assigned to that predefined pattern is assigned a significant p-value.
The DAVID annotation tool [37, 38] was used to analyze the genes in each STEM cluster to identify enrichment clusters deemed significant (minimum enrichment score of 1.3, which is equivalent to a p-value of 0.05). Potential transcriptional regulators were identified by searching the Gene Ontology (GO) descriptors of the 5,715 regulated genes for the terms "DNA binding" or "transcription". The initial list of genes was filtered using the following more stringent criteria: microarray signal intensity ≥ 25 at 8 wk of age, expression ratio > 2-fold in the arrays in which the gene exhibited a p-value < 0.0001, and in the case of adult sex-biased transcriptional regulators, expression ratio > 2 (or < 0.5) for the male 8 wk vs. female 8 wk comparison. The list of adult sex-specific transcriptional regulators were further narrowed down by focusing on genes whose expression was altered by conditions that influence the expression of adult sex-specific genes, such as hypophysectomy , STAT5b deficiency , and HNF4α deficiency [24, 39].
Comparison of microarray data sets and enrichment score calculations
Gene sets identified by microarray analysis of hypophysectomized mouse liver  or HNF4α-deficient mouse liver  were compared to the sets of genes that showed significant changes in expression from 3 wk to 8 wk in the present study. For these analyses, a more stringent threshold was used to define sex-independent genes, to exclude genes showing a weak sex-bias in expression as well as genes giving very low microarray signal intensities. Thus, the stringent criteria used to identify sex-independent genes was male:female (or female:male) expression ratio < 1.2, p-value > 0.01, and Agilent microarray signal intensity ≥ 25. Enrichment scores calculated for comparisons between microarray studies run on different array platforms were based on to the genes represented of both microarray platforms (Agilent mouse microarray G4122F-014868 used previously [24, 27] vs. Agilent mouse microarray G4846A-026655 for the present study). A background gene set comprised of all genes common to both platforms was used when calculating enrichment scores for male-specific, female-specific, and all genes that are either up or down regulated from 3 wk to 8 wk. A background gene set comprised of all sex-independent genes was used as the background when calculating enrichment scores for sex-independent genes that are either up or down regulated from 3 wk to 8 wk. Enrichment p-values were calculated using two tail Fisher Exact Test, with a p-value < 0.0001 deemed significant.
Overall patterns of developmental change in sex-independent mouse liver genes
Gene count distribution and onset of sex specificity
Sex specificity (defined at 8 wk)
Onset at 3 wk
Onset at 4 wk
Onset at 8 wk
No sex specificity
Number of genes
23 (M), 56 (F)***
59 (M), 86 (F)***
Developmental regulation of sex-biased genes
Approximately 1,000 genes showed a change in expression from 3 or 4 wk to 8 wk in male liver as compared to female liver (Additional file 3A). The largest number of genes showed postnatal developmental changes in either male liver only (2,348 genes) or in both male and female liver in a common manner (1,926 genes), as compared to changes in female liver only (981 genes) (Additional file 3C). In particular, many more adult sex-biased genes showed post-pubertal developmental changes in male liver as compared to female liver (Figure 1B, arrays 4 and 5 vs. arrays 6 and 7; Figure 2B, C). Thus, 488 (83%) male-specific genes showed a developmental change in male liver vs. 248 (42%) showed such a change in female liver. Similarly, 358 (57%) of female-specific genes showed a developmental change in male liver vs. 208 (33%) showed such a change in female liver (Additional file 3C). Moreover, 516 (43%) of the adult sex-biased genes displayed a developmental change in male liver only, while 126 (10%) showed a change in expression in female liver only (Additional file 3C). This greater frequency of developmental changes in male liver was seen for both male-specific and female-specific genes: 273 of 587 male-specific genes showed a developmental change in male liver only, while only 33 were changed in female only. Similarly, 243 of 625 female-specific genes were changed in expression in male liver only, while only 93 showed a female-specific change (Additional file 3C).
In male liver, almost all male-biased genes showing a developmental change were up regulated, while almost half of female-biased genes were down regulated after puberty. In contrast, in female liver, the majority of sex-biased genes showed no significant change in expression over the same period (Figure 2B, C). Fewer developmental changes in sex-independent genes were also seen in female liver (Figure 2D). Differences in the direction of regulation between male and female liver were noted for some genes. Examples include genes up regulated in male liver after 3-4 wk but unchanged in female liver, and genes down regulated in male liver but unchanged or slightly up regulated in female liver (Figure 1B, groups E and F, respectively).
Clustering by predefined patterns (STEM analysis)
The largest cluster of adult sex-specific genes (276 male-specific genes; STEM profile 8), displayed strong up regulation from 3 and 4 wk to 8 wk in male liver, and weaker up regulation in female liver (Figure 3C). This profile contained three significant enrichment clusters, consisting of genes involved in pheromone binding and microsomal cytochrome P450/monooxygenase activity (Additional file 5B). The largest female-specific gene cluster (231 genes; STEM profile 11) was characterized by down regulation in male liver from 3 and 4 wk to 8 wk, without a change in female liver (Figure 3D). This profile contained 9 significant enrichment clusters, including cytochrome P450/monooxygenase activity, acyl CoA thioesterase, peroxisome, flavin monooxygenase, sulfotransferase, and lipid biosynthesis (Additional file 5C).
HNF4α-dependence of developmentally responsive genes
Genes showing developmental changes are enriched for genes altered by liver-specific deletion of HNF4α
A. Background: All genes (22,831)
Genes up regulated in male HNF4α- deficient liver (2,619 genes)
Genes down regulated in male HNF4α- deficient liver (2,712 genes)
# of overlapping genes
Fisher Test p -value
# of overlapping genes
Fisher Test p - value
All genes up regulated in male liver 3-8 wk (1,550 genes)
All genes down regulated in male liver 3-8 wk (1,791 genes)
B. Background: All genes (22,831)
Genes up regulated in female HNF4α- deficient liver (2,227 genes)
Genes down regulated in female HNF4α- deficient liver (2,255 genes)
# of overlapping genes
Fisher Test p-value
# of overlapping genes
Fisher Test p- value
All genes up regulated in female liver 3-8 wk (1,087 genes)
All genes down regulated in female liver 3-8 wk (1,360 genes)
Pituitary dependence of developmentally responsive genes
Genes showing developmental changes are enriched for genes altered by hypophysectomy
A. Background: All genes (22,635)
Genes up regulated in male hypophysectomized liver (2,250 genes)
Genes down regulated in male hypophysectomized liver (2,202 genes)
# of overlapping genes
Fisher Test p -value
# of overlapping genes
Fisher Test p -value
All genes up regulated in male liver 3-8 wk (1,542 genes)
All genes down regulated in male liver 3-8 wk (1,773 genes)
B. Background: All genes (22,635)
Genes up regulated in female hypophysectomized liver (1,521 genes)
Genes down regulated in female hypophysectomized liver (1,535 genes)
# of overlapping genes
Fisher Test p-value
# of overlapping genes
Fisher Test p-value
All genes up regulated in female liver 3-8 wk (1,070 genes)
All genes down regulated in female liver 3-8 wk (1,364 genes)
Developmental changes in expression of transcriptional regulators
We sought to identify transcription factors that could potentially contribute to the age dependent changes in gene expression described above. 323 of the 4,503 adult sex-independent genes were identified as potential transcriptional regulators by their Gene Ontology (GO) descriptors. 37 of these genes showed at least a 2-fold change in expression on all arrays that had a significant p-value (p < 0.0001) (Additional file 7A). DAVID analysis of the 37 genes identified 12 significant clusters, 5 of which were closely associated with transcription. The other 7 clusters consisted of genes that contain a basic motif, or are involved in circadian rhythm, chromosomal organization, DNA replication, DNA repair or metal ion binding (Additional file 7B).
Sex-specific transcriptional regulators
Onset of sex specificity
Developmental change in M liver (3, 4 wk to 8 wk)
Developmental change in F liver (3, 4 wk to 8 wk)
Response to HNF4α-knockout
down in M
down in M
up in M
up in M
up in M up in F
up in M up in F
up in M up in F
up in M up in F
Response to STAT5-knockout
down in M
up in M
up in M
up in M
Response to Hypox
down in M
down in M
down in F
up in M, down in F
up in M
up in M
up in M
down in F
Response to continuous GH
down in M at ≥ 4 day
up in M at ≥ 4 day
up in M at ≥ 2 day
up in M at ≥ 10 hr
The present study of genome-wide transcriptional profiles in mouse liver was conducted to identify developmental changes that occur from 3 wk (weaning) to 4 wk (just prior to puberty) to 8 wk of age (young adulthood). During this period of development the liver is completing its final stages of growth and liver function is changing from hematopoiesis to regulation of metabolism and other biological processes, including bile secretion, xenobiotic metabolism, and cholesterol homeostasis [3, 8, 47]. Genes involved in growth, cell cycle, and DNA replication were found to be down regulated after 3 wk and 4 wk, while genes associated with specialized liver functions such as drug metabolism and inflammatory response were up regulated. The latter findings are similar to another study where down regulation of genes associated with mitosis, DNA replication, RNA splicing, and transcription was seen at postnatal days 7, 14, 21 and 126 compared to the mean expression level determined at 14 different time points, beginning in embryonic development . Additionally, extensive changes in the expression of adult sex-specific genes were observed, especially in male liver, where the majority of male-specific genes were up regulated and nearly half of female-specific genes were down regulated. Developmental changes in adult sex-independent genes were also more extensive in male liver compared to female liver.
Summary of the proposed role of HNF4α and pituitary hormone in developmental changes in liver gene expression
Male liver (3 wk to 8 wk)
Female liver (3 wk to 8 wk)
Adult male-specific genes
Pituitary hormone (+)
Adult female-specific genes
Pituitary hormone (+)
Pituitary hormone (-)
Adult sex-independent genes
Pituitary hormone (-)
Pituitary hormone (+, -)
Comparison of the genes undergoing developmental changes to the set of genes whose expression changes in mouse liver following hypophysectomy  revealed differences in the regulation by pituitary hormone between male and female liver (Table 5). In male liver, pituitary hormone positively regulates male-specific genes that are up regulated from 3 wk to 8 wk, while negatively regulating female-specific genes that are down-regulated during the same time period. In contrast, in female liver, pituitary hormone positively regulated female-specific genes up regulated during female development but did not show significant enrichment for effects on male-specific genes. These differences in pituitary hormone regulation of sex-specific genes in male vs. female liver could be explained by the sex differences in pituitary GH secretion patterns, which are known to regulate many sex-dependent genes in the liver . We also observed pituitary hormone regulation of the developmentally regulated stringent sex-independent genes, with negative regulation by pituitary hormone apparent in male liver, and both positive and negative regulation apparent in female liver (Table 5). The latter finding could be explained by increased secretion of a negative regulatory factor after 3 wk of age, or by decreased secretion of a positive regulatory factor. One such candidate factor is corticosterone, whose adrenal production is stimulated by adrenocorticotropic hormone (ACTH) produced by the anterior pituitary gland, and has ~3 times higher plasma concentrations in 20 day old mice compared to adult male mice .
We sought to identify transcriptional regulators that undergo developmental changes in mouse liver, as these could serve as regulators of the developmental changes in RNA transcripts described here. Seven developmentally regulated adult sex-independent transcriptional regulators (Asf1b, Hells, Hmgb2, Padi4, Ppard, Prim2, Top2a) are associated with chromosomal organization and were down regulated from 3-4 wk to 8 wk. One or more of these transcription factors could be associated with the down regulation of cell cycle and mitosis that occurs in liver from the postnatal period to puberty . Seven other adult sex-independent transcriptional regulators identified here (Arntl, Cry1, Dbp, Nr1d1, Per2, Per3, Tef) help establish circadian rhythms. Many genes are expressed in a circadian manner in the liver, most notably genes active in drug metabolism and bile acid synthesis, including sex-specific genes [50–52]. A related gene, Per1, changes in expression at postnatal day 22 in rat liver . Another study found that clock-associated genes become rhythmic by postnatal day 30 .
Prior studies of sex-specific hepatic gene expression have primarily focused on the adult period. Presently, excluding Y-chromosome genes, we found only 13 adult sex-specific genes that displayed sex-specificity at 3 wk and retained their sex specificity at 4 wk and 8 wk of age. By 4 wk, an additional 104 genes showed female-specific expression and an additional 54 genes displayed male-specific expression. Five of the 158 genes that displayed their adult sex-specificity at 4 wk of age encode transcriptional regulators (Cdx4, Cux2, Ihh, Tox, Trim24); these genes could contribute to the developmental changes leading to global acquisition of liver sex-specificity by 8 wk. Consistent with our finding in mouse liver, a microarray study of gene expression in postnatal rat liver (ages ranging from 2 wk to 104 wk) reported very few sex-specific genes at 2 wk and 5 wk. Moreover, there was a large increase in the number of sex-specific genes, including many genes associated with drug metabolism, by 8 wk .
The major increase in sex-specific gene expression between 4 wk and 8 wk of age shown here for mouse liver can in part be explained by the developmental changes in growth hormone (GH) secretion during this developmental period. GH has an established role in regulating sex-specific gene expression in mouse liver [4–6], and the sex-specific patterns of pituitary GH secretion are imprinted during the neonatal period but are not manifested until puberty [6, 42, 56]. CYP3A4 shows female-biased expression in human liver  and displays a similar postnatal development expression pattern in mouse liver when it is introduced as a transgene [20, 58]. This suggests that the genomic sequences that dictate the observed pattern of developmental repression in male liver are conserved between mouse and human. In the present study, the change from sex-independent expression at 3-4 wk to sex-specific expression at 8 wk was closely associated with the up regulation of male-specific genes and the down regulation of female-specific genes in male liver. Conversely, in female liver the most frequent change was one that occurred in both male and female liver. Since GH is known to be the major hormonal regulator of these sex-specific genes, the developmental patterns that we observed suggest that the male-specific GH pattern could either be turning on a transcriptional activator or turning off a transcriptional repressor to up regulate male-specific gene expression. The male-specific GH pattern could also down regulate female-specific genes in male liver by either turning on a transcriptional repressor or by turning off a transcriptional activator. STAT5b and HNF4α are essential transcriptional regulators of sex-specific liver gene expression, and sex-specific genes are enriched for genes that are affected by deletion of STAT5b or HNF4α [24, 32, 39, 46]. However, activation of STAT5 alone is not sufficient to induce male-specific gene expression in pre-pubertal rats , indicating that other developmentally regulated factors, such as the 9 sex-specific transcription factors identified in this study, may be required to achieve sex-specific gene expression. Five of the 9 factors displayed developmental changes in male liver only (Table 4), and could contribute to the selective up regulation of male-specific genes and/or down regulation of female-specific genes seen in male liver but not female liver. The three male-specific transcription factor genes of interest are transcriptional activators. Y-box protein 2 (Ybx2) is an RNA-binding protein in germ cells but also has the ability to bind to and stimulate transcription of the mouse protamine-2 promoter . Indian hedgehog (Ihh) plays a role in endodermal differentiation and can activate gene transcription by binding to Patched receptors Ptc1 and Ptc2 . Finally, Kruppel-like factor 17 (Klf17) is a member of the small protein zinc finger family and can activate transcription from CACCC-box elements .
The six female-specific transcriptional regulators identified here are either known transcriptional repressors or their function is unknown. Cut-like homeobox 2 (Cux2) is a member of the cut/homeodomain family of transcription factors and can act as a transcriptional repressor . Caudal type homeobox 4 (Cdx4) is a homeodomain transcription factor that may play a role in hematopoiesis . Thymus high-mobility group box protein (Tox) is a member of the sequence independent high mobility group (HMG) box family and a regulator of differentiation of developing T-cells . Tripartite motif-containing 24 (Trim24) contains a zinc binding motif, a coiled-coiled region, and a RING domain, which has been shown to act as an E3-ubiquitin ligase and target tumor suppressor p53 for degradation . Juxtaposed with another zinc finger protein 1 (Jazf1) contains three zinc finger motifs and is of unknown function but is associated with lipid metabolism, diabetes mellitus, and prostate cancer . Finally, inhibitor of DNA binding 1 (Id1) functions as a negative regulator of basic helix-loop-helix (bHLH) transcription factors and is trans-activated by JAK/STAT5 signaling in erythroid cells .
Three of the female-specific genes (Cux2, Tox, Trim24) were previously characterized as potential regulators of sex-specific gene expression . Cux2 expression is highly female-specific in both mouse and rat liver . Binding sites for Cux1/Cux2 are statistically overrepresented at or near STAT5b-dependent male-specific genes, suggesting that Cux2 could be acting as a repressor of male-specific gene expression in female liver . Further characterization of Cux2 and the other sex-specific transcriptional regulators is required to ascertain their contributions to sex-specific liver gene expression.
Overall, the observed changes in liver gene expression from the pre-pubertal period to young adulthood reflect the deceleration of liver growth and the induction of specialized liver functions. The number of sex-biased genes expressed during this period also increased dramatically at this time. Widespread changes in both sex-independent and sex-biased genes were observed, and primarily occurred in male liver. This male bias in these gene expression changes may be due to differences in pituitary hormone secretion and/or regulation by HNF4α.
This work was supported by National Institutes of Health (NIH) Grant DK33765 (to D.J.W.). Microarray analysis and data analysis using Rosetta Resolver software were carried out at the Functional Genomics and Bioinformatics Facility at Wayne State University under the direction of Dr. Alan Dombkowski. The authors thank Dr. Dombkowski for many useful discussions.
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