Download Elucidating the Role of Gonadal Hormones in Sexually

GENERAL
ENDOCRINOLOGY
Elucidating the Role of Gonadal Hormones in Sexually
Dimorphic Gene Coexpression Networks
Atila van Nas,* Debraj GuhaThakurta,* Susanna S. Wang, Nadir Yehya, Steve Horvath,
Bin Zhang, Leslie Ingram-Drake, Gautam Chaudhuri, Eric E. Schadt, Thomas A. Drake,
Arthur P. Arnold,* and Aldons J. Lusis*
Departments of Human Genetics (A.v.N., S.S.W., N.Y., S.H., L.I.-D., A.J.L.), Microbiology, Immunology, and Molecular
Genetics (A.J.L.), Pathology and Laboratory Medicine (T.A.D.), Medicine (A.J.L.), and Obstetrics and Gynecology and
Molecular and Medical Pharmacology (G.C.) and Molecular Biology Institute (A.J.L.), David Geffen School of Medicine;
Department of Biostatistics (S.H.), School of Public Health; and Department of Physiological Science and Laboratory of
Neuroendocrinology of the Brain Research Institute (A.P.A.), University of California, Los Angeles, California 90095;
and Department of Genetics (D.G., B.Z., E.E.S.), Rosetta Inpharmatics, a wholly owned subsidiary of Merck & Co. Inc.,
Seattle, Washington 98109
We previously used high-density expression arrays to interrogate a genetic cross between strains
C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes.
We now report analyses of the molecular basis of these sex differences and of the effects of sex on
gene expression networks. We analyzed liver gene expression of hormone-treated gonadectomized mice as well as XX male and XY female mice. Differences in gene expression resulted in large
part from acute effects of gonadal hormones acting in adulthood, and the effects of sex chromosomes, apart from hormones, were modest. We also determined whether there are sex differences
in the organization of gene expression networks in adipose, liver, skeletal muscle, and brain tissue.
Although coexpression networks of highly correlated genes were largely conserved between sexes,
some exhibited striking sex dependence. We observed strong body fat and lipid correlations with
sex-specific modules in adipose and liver as well as a sexually dimorphic network enriched for genes
affected by gonadal hormones. Finally, our analyses identified chromosomal loci regulating sexually
dimorphic networks. This study indicates that gonadal hormones play a strong role in sex differences
in gene expression. In addition, it results in the identification of sex-specific gene coexpression networks
related to genetic and metabolic traits. (Endocrinology 150: 1235–1249, 2009)
ex differences are observed in most complex genetic disorders,
including atherosclerosis, obesity, diabetes, and cancer (1).
Males are more susceptible to cardiovascular disease, as are females
to higher fat depots and insulin resistance; therefore, sex differences
may have a substantial impact on the prevention, diagnosis, and
treatment of disease (2– 4). Sexual differentiation is manifested
through various biological factors, stemming from the organizational and activational effects of gonadal hormones to the direct
effects of sex-specific genes on the sex chromosomes, such as the
testis determining factor Sry (5–7). For example, organizational sex
differences in brain, liver, and other tissues are induced during de-
S
velopment when the testes secrete testosterone, which acts to permanently masculinize the structure and function of tissues.
Later in life, differential exposure of testicular or ovarian
hormone secretions results in the reversible, activational sexspecific acute effects of gonadal hormones that disappear after
gonadectomy in adults. The organizational and activational effects of testosterone may be mediated by two primary metabolites of testosterone: nonaromatized metabolites such as dihydrotestosterone (DHT), which bind to androgen receptors, and
aromatized metabolites such as estradiol (E2), which bind to
estrogen receptors. The sex chromosome effects stem from the
ISSN Print 0013-7227 ISSN Online 1945-7170
Printed in U.S.A.
Copyright © 2009 by The Endocrine Society
doi: 10.1210/en.2008-0563 Received April 21, 2008. Accepted October 22, 2008.
First Published Online October 30, 2008
* A.N., D.G., A.P.A., and A.J.L. contributed equally to this work.
Abbreviations: DHT, Dihydrotestosterone; E2, estradiol; eQTL, expression quantitative trait
loci; FCG, four-core genotype; FDR, false discovery rate; pc1, first principal component.
For editorial see page 1075
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direct action of the Y-chromosome genes or by the differential
dose of X-chromosome genes, resulting from differences in the
genomic dose of the gene or the sex difference in the parental
genomic imprint of the X-chromosome genes (8).
Sex hormones such as androgens and estrogens cause brain
sex differentiation in mammals and affect tissues such as adipose,
liver, and skeletal muscle (5, 9). For instance, the regulation of
body fat distribution is influenced by changes in gonadal hormones, including the adipose-derived hormone leptin (10). Regional sex differences may be required for adipose metabolism
that supports the reproduction process, which is dependent on
the location of the fat depot (11).
In the liver, metabolism of steroid hormones and drugs has
been shown to be sexually differentiated, mainly due to gonadal
hormones causing differences in GH. Studies have indicated that
sex differences in hepatic steroid metabolism may support a
pregnant state when the liver is exposed to high continuous levels
of steroid hormones (12). Thus, tissue-specific sex differences
may underlie sex-specific roles in reproduction, whereas changes
in gonadal hormones contribute to the development of obesity
and other related metabolic disorders (10).
The profound effect of sex on metabolic traits has also been
observed in chromosomal linkage to these complex biological
systems; for example, quantitative trait loci (QTL) have often
been observed in one sex but not the other (13–17). These sexgene interactions imply an underlying genetic network invoked
by sex-specific regulation influencing gene expression (13). Indeed, sex differences in the expression of thousands of genes
across several tissues has recently been documented; however,
the molecular mechanisms driving this large-scale sexual dimorphism of gene expression and whether or not these sex differences are also reflected in their gene interactions remain to be
elucidated (18).
In a previous study, the molecular mechanisms that lead to sex
differences in the liver tissue were determined by analyzing the
effects of the GH-activated transcription factor signal transducer
and activator of transcription-5b (STAT5b) on sex-specific gene
expression (19). Here, we investigated the activational effects of
gonadal hormones and the effect of sex-chromosomes on liver
gene expression. We determined the contribution of gonadal
hormones and sex chromosomes in driving sexually dimorphic
gene expression by comparing gene expression of liver tissue
from gonadally intact mice and gonadectomized (GDX) mice
that were treated with DHT, E2, or placebo. We also studied
GDX four-core genotype (FCG) mice, in which the sex chromosomes are independent of gonadal sex (8). The genes whose expression was affected by hormone treatment were significantly
enriched for the sex-specific differentially expressed genes from
the previous study by Yang et al. (18). Relative to the gonadal
hormones, the sex chromosomes had a minor influence on gene
expression changes in the adult mice.
Previous studies of sex differences in gene expression focused
mainly on differences in the level of transcript abundance (15).
In this study, we asked whether the organization of gene expression networks differ between the sexes. By integrating genetics
and gene expression, we sought to examine the genetic architecture of the complex biological systems between male and female.
Gene coexpression networks, where nodes and edges reflect gene
expression correlation, have been successfully applied to gene
expression profiles in many species by investigating the functional relationship of gene coexpression networks in the context
of complex biological traits (20 –27). We constructed gene coexpression networks in adipose, brain, liver, and muscle of male
and female mice from a segregating population of a cross between mouse strains C3H/HeJ and C57BL/6J on a hyperlipidemic apolipoprotein E-null background (BXH 䡠 ApoE⫺/⫺). We
identified sexually dimorphic modules of these networks corresponding to highly correlated gene expression, in adipose, brain,
liver, and muscle as well as a sex-specific network enriched for
genes affected by gonadal hormones. In addition, we detected
sex-specific modules in adipose and liver correlated with biological traits and the chromosomal regions regulating these
networks.
To our knowledge, this is the first microarray-based global
study that begins to address how gene network interactions differ
between the sexes and how these modules correlate with sexually
dimorphic traits as well as sex-specific patterns of genetic linkage. Integrating genomic, gene expression, and biological trait
data has allowed us to detect genes and genetic networks influenced by sex that may, in part, explain the differential susceptibility toward diseases between males and females.
Materials and Methods
Mice inbred strains C57BL/6J ApoEⴚ/ⴚ (B6 䡠 ApoEⴚ/ⴚ) and
C3H/HeJ ApoEⴚ/ⴚ (C3H 䡠 ApoEⴚ/ⴚ) F2 intercross data set
The genotype and gene expression data from adipose, brain, liver,
and muscle of the F2 mouse population used in this study have been
previously described (17, 18). The F2 mouse population consisting of
334 mice (169 female, 165 male) was generated by intercrossing F1 mice
of parental strains B6 䡠 ApoE⫺/⫺ and C3H 䡠 ApoE⫺/⫺. The knockout of
the ApoE allele results in hypercholesterolemia, making the mice susceptible to atherosclerosis when they are fed a high-fat diet. Mice were
fed chow diet containing 4% fat until 8 wk of age, when they were placed
on a Western diet containing 42% fat and 0.15% cholesterol for 16 wk.
At 24 wk of age, mice were fasted for 4 h in the morning, then anesthetized with isoflurane for retroorbital sinus blood collection, and then
euthanized for collection of adipose, brain, liver, and muscle. The mice
were genotyped at 1032 single-nucleotide polymorphisms uniformly distributed over the mouse genome at an average density of 1.5 cM.
Blood sample assays
Plasma glucose, free fatty acids, triglycerides, high-density lipoprotein cholesterol, unesterified and total cholesterol were measured as previously described (17, 28). Very-low-density lipoprotein and low-density
lipoprotein cholesterol levels were calculated by subtracting high-density
lipoprotein cholesterol from total cholesterol levels. Plasma glucose concentrations were measured using a glucose kit (315–100; Sigma Chemical Co., St. Louis, MO). Plasma leptin, adiponectin, and monocyte chemotactic protein-1 levels were measured using mouse ELISA kits
(MOB00, MRP300, and MJE00; R&D Systems, Minneapolis, MN).
Gene expression analysis
The RNA and microarray processing were as previously described
(29). The custom inkjet microarrays (Agilent Technologies, Palo Alto,
CA) contained 2186 control probes and 23,574 noncontrol oligonucleotides extracted from mouse Unigene clusters and combined with RefSeq
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sequences and RIKEN full-length clones. The number of distinct transcripts and gene models represented on the array were 23,574 and
21,859, respectively. Gene models were created based on clustering of
transcripts on the genome. The 18,833 gene models were annotated with
a LocusLink identifier, and each gene model was assigned to only one
LocusLink identifier. Total RNA was extracted from homogenized mouse
tissues with Trizol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s protocol. cDNA labeled with either Cy3 or Cy5 was hybridized
to at least two microarray slides with fluor reversal and subsequently
scanned using a laser confocal scanner. Gene expression changes between
two samples were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average
intensity over multiple channels, and fit to an error model to determine
significance (type I error). Gene expression is reported as the ratio of the
mean log10 intensity (ml-ratio) relative to the pool derived from 150 mice
randomly selected from the F2 population. Full gene and probe information
of the array is shown on supplemental Table 2S (published as supplemental
data on The Endocrine Society’s Journals Online web site at http://endo.endojournals.org). The microarray data from this study have been deposited
to GEO under accession nos. GSE13264 and GSE13265.
Gonadectomy and hormone treatment of mice
Mice of strain C57BL6/J were purchased from The Jackson Laboratory (Bar Harbor, ME) and were placed on a chow diet. The animals
were GDX at 8 wk of life, implanted with hormone pellets at 12 wk, and
killed at 14 wk. Male and female mice of the hormone-treated groups
received sc implants of either 0.5-mg E2 pellet (plasma yield of 300
pg/ml) or a 5-mg DHT pellet (plasma yield of 1–2 ng/ml), designed to
release over 21 d (blood levels reported by manufacturer, Innovative
Research of America, Sarasota, FL; 17B-E2, catalog item no. E-121, 0.5
mg/pellet; 5␣-DHT, catalog item no. A-16). Control mice were treated
with the placebo pellet (Innovative Research of America; placebo, catalog item no. C-111) (30). Plasma, whole brain, heart, kidney, skeletal
muscle (hamstring), visceral fat, and liver were harvested from the animals. Liver mRNA from five mice was profiled from each group.
Four core genotype (FCG) mice
In mice of the FCG, the Y chromosome is deleted for the testisdetermining gene Sry, producing the Y⫺ chromosome (31). The Sry
transgene is inserted onto an autosome, so that testis determination is
independent of the complement of sex chromosomes. XY⫺Sry gonadal
males are bred with XX gonadal females, producing progeny with four
different genotypes: two types of gonadal males (XXSry and XY⫺Sry)
and two types of gonadal females (XX and XY⫺). The FCG model allows
comparison of gonadal males and females independent of their sex chromosome complement, or XX and XY⫺ independent of their gonadal
type. The FCG mice for this study were C57BL/6J, produced by backcrossing the Y⫺ chromosome and Sry transgene from MF1 to C57BL/6J
for 10 –11 generations. The Y⫺ chromosome derives from strain 129.
C57BL6/J mice (n ⫽ 5) from each of the above groups were fed
normal chow diet. At 8 wk, the mice were GDX (to eliminate the effect
of the gonadal hormones) and killed at 12 wk. Liver mRNA of each of
these groups (n ⫽ 5 per group) were profiled.
Analysis of differential expression
Gene expression levels of pairs of treatment groups were compared using
a one-way ANOVA. To control for false discovery, we used the Q-value
software and an estimated 10% false discovery rate (FDR) threshold for all
ANOVA results. Genes that passed an FDR of 10% were considered as
being differentially expressed between the two groups compared (32, 33).
Power calculations to detect gene expression
differences between males and females
For each gene that showed differential expression between males and
females in the F2 population of the BXH 䡠 ApoE⫺/⫺ cross, we wanted to
compute the number of animals that would be required to identify dif-
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ferential expression in an independent study. The formula for computing
n, the number of mice needed to observe differential expression of a gene
between any two groups is given by n ⫽ (Z␣/2 ⫹ Z␤) ⫻ (␴g1⫹ ␴g2)2/␶2,
where ␣ and ␤ are the probabilities associated with type I and type II
errors, g1 and g2 refer to the two groups (in this case, male and female
F2 samples in the BXH 䡠 ApoE⫺/⫺ cross), ␴ refers to SD of gene expression
in each group, and ␶ refers to the difference in the means of gene expression values between the two groups. For each of the genes that was
differentially expressed between the males and females of the F2 samples
in the BXH 䡠 ApoE⫺/⫺ cross with ANOVA P value of ⱕ 0.01, we computed n, taking ␣ to be 0.05 and ␤ to be 0.2, where power ⫽ 1 ⫺ ␤ ⫽ 0.8.
Of the 12,789 genes differentially expressed between males and females
of the BXH 䡠 ApoE⫺/⫺ F2 animals, n was found to be ⱕ5 (study sample
size) for 70 genes, indicating that our current study was underpowered
to detect many of the sexually dimorphic gene expression patterns observed in the earlier cross where the sample sizes were much larger (34).
Construction of weighted gene coexpression networks
and modules
Four thousand probe sets were selected for network analysis based on
high variance across the BXH 䡠 ApoE⫺/⫺ F2 mouse population. Gene
expression data from mice with complete genotype data and at least 95%
complete phenotype and array data were used.
We used the general framework of weighted gene coexpression network
analysis presented in the study by Zhang and Horvath (20). The absolute
value of the Pearson correlation coefficient was calculated for all pairwise
comparisons of gene expression values across all microarray samples. The
Pearson correlation matrix was transformed into an adjacency matrix A, a
matrix of connection strengths by using a power function (␤). Microarray
data can be noisy, and the number of samples is often small, so we weight
the Pearson correlations by taking their absolute value and raising them to
the power ␤. Thus, the connection strength (adjacency) a(i,j) between gene
expressions xi and xj is defined as a(i,j) ⫽ [cor(xi,xj)]␤.
The network connectivity ki of the ith gene expression profile xi is the
sum of the connection strengths with all other genes in the network. The
network connectivity ki represents a measure of how correlated the ith
gene is with all the other genes in the network.
The resulting weighted network represents an improvement over unweighted networks based on dichotomizing the correlation matrix because 1) the continuous nature of the gene coexpression information is
preserved and 2) the results of weighted network analyses are highly
robust with respect to the choice of the parameter ␤, whereas unweighted
networks display sensitivity to the choice of the cutoff.
To determine the power ␤ used in the definition of the network adjacency matrix, we made use of the fact that gene expression networks,
like virtually all types of biological networks, have been found to exhibit
an approximate scale-free topology (20 –27). To choose a particular
power ␤, we used the scale-free topology criterion described in Zhang
and Horvath (20), which led us to the following choices for the power ␤:
for female and male in adipose, 6 and 6; brain, 5 and 5; liver, 3.3 and 4;
and muscle, 11 and 8, respectively.
The next step in network construction is to identify modules, which
were defined as groups of genes with high topological overlap (20 –27).
The use of topological overlap serves as a filter to exclude spurious or
isolated connections during network construction. Using the topological
overlap dissimilarity measure (1 ⫺ topological overlap) in average linkage hierarchical clustering, the modules were defined as branches of the
resulting dendrogram.
Analysis of coexpression network correlation to
metabolic and genetic traits
To determine coexpression networks trait correlations, the module
eigengene was correlated to metabolic traits, and the module gene significance was determined. The module eigengene is the first principal
component (pc1) of gene expression of each module and explained most
of the variance in gene expression of each module in this study (25, 26).
The analysis was performed on the same set of genes that clustered to-
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gether in both males and females. Gene significance was calculated as the
average absolute value of the correlation between gene expression profile
of each module and clinical trait.
Two modules in adipose and liver tissue were correlated with metabolic traits. The pc1 of the adipose blue module explained 44% of the
gene expression variance in females and 51% in males, and the pc1 of the
liver green module explained 39% of the variance in females and 47%
in males. The threshold of significance of correlation between the pc1 of
the module and metabolic trait was 0.30 at a P value of 0.05 for any
module, given n ⫽ 135 after correcting for multiple comparisons.
Expression QTL (eQTL) were determined by using the transcript
abundance of each gene as a quantitative trait and correlating them to
genetic markers to map gene eQTL (25).
mechanisms underlying the profound sexual differences in gene
expression observed in our previous study (18). We analyzed the
liver gene expression of GDX mice treated with E2, DHT, or
placebo and the FCG mice to evaluate the effects of the sex
chromosomes in the absence of gonads (Table 1).
First, we determined the degree of overlap between the sexspecific differentially expressed genes of this study and our previous study of the BXH 䡠 ApoE⫺/⫺ F2 mice cross (18). We detected 2597 genes differentially expressed between gonadally
intact males and females compared with our previous study
where we observed 17,834 differentially expressed genes between males and females in the liver tissue of the F2
BXH 䡠 ApoE⫺/⫺ F2 mice cross (supplemental Table 1S) (18).
The difference in gene number is most likely due to the difference in statistical power of the two studies. In this study, we
used a sample size of five in each treatment group, in contrast to
the sample size of 165 males and 169 females in our previous
report. Given the mean and SD of gene expression and sample size
from males and females in each study, we calculated that the
current study would be powered to detect sex differences in 2834
genes. Indeed, the current study detected 91% of that number, or
2597 genes, which overlapped significantly with the 17,384 differentially expressed genes of the BXH 䡠 ApoE⫺/⫺ F2 animals
(P ⫽ 4 ⫻ 10⫺136) (supplemental Table 1S). The strong overlap
of differentially expressed genes between the two studies indicates that the sexual dimorphism in liver gene expression of the
F2 mice lacking ApoE in the previous study and in the parental
mice with a functional ApoE is comparable.
Coexpression network t-statistic scores
We used the software Significance Analysis of Microarrays to identify
differentially expressed genes (35). The modified t test score was chosen
such that the resulting gene list had a median 5% FDR. Specifically, we
used the following thresholds for determining differentially expressed
genes between males and females: brain, 1.16; liver, 0.76; adipose, 0.58;
and muscle, 0.96.
Gene enrichment analysis using DAVID software
Each network module was analyzed separately for pathway enrichment by the use of the Expression Analysis Systemic Explorer (EASE)
software, which was downloaded from the Database for Annotation,
Visualization, and Integrated Discovery (DAVID) website (36, 37). Fisher’s exact test was used to determine whether enrichment in pathway
genes within the module is significant; we report Fisher exact P values
and the multiple-testing corrected P values (Bonferroni).
Differential network analysis
Differential expression and connectivity was constructed using the
method described in Fuller et al. (26). Briefly, the whole-network connectivity in networks 1 and 2 is denoted by for the ith gene, k1(i) and k2(i),
respectively. To compare the connectivity measures of each network, we
divide the connectivity of each gene by the maximum network connectivity: K1(i) ⫽ k1(i)/max k1 and K2(i) ⫽ k2(i)/max k2.
Next we define a measure of differential connectivity as Diff K(i) ⫽
K1(i) ⫺ K2(i), but other measures of differential connectivity could also
be considered. A permutation test was performed to determine statistical
significance of observing genes with k ⬎ 0.2 differential connectivity by
permuting the sex assignment and repeating the permutation process
1000 times (number of permutations ⫽ 1000).
Gene coexpression network software
The statistical analysis was carried out with the R software (http://
www.R-project.org). The statistical code for generating the weighted gene
coexpression network, which also serves as a tutorial, can be found at http://
www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ (20).
Visualization of network module
The method by Oldham et al. (23) was used to identify which pairs
of genes are connected within the network structure. The network structure of approximately 66 genes found on supplemental Table 8S was
determined by the pairwise topological overlap value and depicted by
VisANT integrative visual analysis tool software (38).
Effects of gonadectomy on sexually dimorphic gene
expression
To study the hormonal effects, we analyzed the liver gene
expression of the GDX mice treated with DHT, E2, and placebo.
Between the GDX males and females treated with placebo, we
detected 12 differentially expressed genes (supplemental Table
1S). This gene number was considerably fewer than the 2597
genes that were differentially expressed between gonadally intact
males and females, indicating that the activational effects of gonadal secretions account for most of the sex differences found in
gonadally intact mice, and very few expression differences remain in the livers of adult mice when gonadal hormones are
absent. The few genes whose expression levels are different between the sexes after gonadectomy may be due to the long-lasting
TABLE 1. Mice treatment groups to determine the molecular
basis of sexually dimorphic gene expression
Mice
Treatment
Intact female
Intact male
GDX female or male
None
None
Placebo
E2
DHT
Sex chromosomes
XX
XY (Sry gene removed from Y chromosome)
XY
XX (Sry gene on autosome)
Results
Molecular mechanisms of sexually dimorphic gene
expression
We investigated the role of gonadal hormones and sex chromosome complement, XX vs. XY, to determine the molecular
FCG Mice
GDX Female
GDX Male
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developmental effects of the hormones or the sex chromosomes.
Moreover, the near absence of sex differences after gonadectomy
does not rule out finding differences due to organizational or sex
chromosome effects when those variables are manipulated
directly.
In general, genes that were differentially expressed between
gonadally intact males and females, between GDX and gonadally intact mice, or between GDX mice treated with placebo or
gonadal hormones overlapped significantly with the genes that
were sexually dimorphic in the BXH 䡠 ApoE⫺/⫺ F2 animals (supplemental Table 1S). The strong overlap between the studies
reflect the significant role the gonadal hormones play in driving
the sexual differences in liver gene expression in the adult, gonadally intact, mice.
Effects of gonadal hormones on sexually dimorphic
gene expression in males
To further investigate the effects of gonadal hormones on
gene expression in males, we identified genes that were differentially expressed between GDX males treated with placebo and
gonadal hormones. Specifically, by comparing the expression
changes in GDX males brought about by treatment of either
DHT or E2 relative to placebo, we examined whether these treatments reverse the gene expression changes brought about by
gonadectomy. There were 4297 genes differentially expressed
between intact males and GDX males treated with placebo, 1248
genes differentially expressed between intact males and GDX
males treated with DHT, and 1825 genes differentially expressed
between intact males and GDX males treated with E2. There was
strong overlap of these sets of differentially expressed genes with
the sexually dimorphic genes from the BXH 䡠 ApoE⫺/⫺ F2 mice
(P ⫽ 10⫺23 to 10⫺173) (supplemental Table 1S).
There were 592 genes differentially expressed between GDX
males treated with placebo and GDX males treated with DHT,
whereas 64 differentially expressed genes were detected between
GDX males treated with placebo and GDX males treated with
E2. Of the 4297 genes that are differentially expressed between
intact males and GDX males with placebo, 359 overlap with this
set of 633 genes that are differentially expressed in GDX males
upon DHT or E2 treatment (P ⫽ 10⫺96).
Effects of gonadal hormones on sexually dimorphic
gene expression in females
We identified genes that were differentially expressed between GDX females treated with placebo and gonadal hormones. There were no significantly differentially expressed genes
detected between intact females and GDX females with placebo
at a 10% FDR. Although this observation is surprising, this
could be due to the heterogeneity of gene expression patterns in
the intact females as a result of cycling estrogen and progesterone
levels. Based on an expression hierarchical clustering of all the
animals profiled in this study, the intact females appear more
heterogeneous relative to intact males (data not shown). Alternatively, the level of estradiol in the intact females may have been
relatively low.
There were 30 differentially expressed genes detected between intact females and GDX females treated with E2, and 1858
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were differentially expressed between intact females and GDX
females treated with DHT. We observed nine genes that were
differentially expressed between GDX females treated with placebo vs. E2, whereas 1392 differentially expressed genes between
GDX females treated with placebo vs. DHT, which overlapped
significantly with the dimorphic genes in the BXH 䡠 ApoE⫺/⫺ F2
animals (P ⫽ 4.4 ⫻ 10⫺27).
Comparison of the effects of DHT and E2 on sexually
dimorphic gene expression
Gene expression differences between GDX mice treated with
placebo and GDX mice treated with gonadal hormones suggest
that DHT may have a larger influence on liver gene expression
compared with E2 in driving sexually dimorphic gene expression. Analysis of GDX males treated with placebo vs. DHT
shows that 592 genes differed in expression, whereas only 64
were differentially expressed between GDX males with placebo
vs. E2 (supplemental Table 1S). A similar difference was found
in GDX females where nine genes were differentially expressed
between GDX females treated with placebo vs. E2 and 1392
genes with DHT. However, in this study, we have compared one
dose of DHT and E2, which might not have achieved comparable
physiological levels of hormone.
Functional significance of hormone regulated sexually
dimorphic genes in liver
We determined the general functions of the sexually dimorphic genes of the male and female mice treated with or without
gonadal hormones using the pathway enrichment analysis of
Gene Ontology (GO) and Panther Pathways. Most of the comparisons yielded differentially expressed genes enriched for biological pathways involved in lipid and steroid metabolism (P ⬍
0.01) (supplemental Table 1S) (39).
Effects of sex chromosome complement on sexually
dimorphic gene expression
To further study the effect of sex chromosomes on gene expression, we examined mice of the FCG, in which the testisdetermining Sry gene is genetically moved from the Y chromosome to an autosome by deletion and transgenic insertion. The
model produces four groups of gonadally intact mice (XX and
XY males; XX and XY females), allowing for comparisons of the
independent and interacting roles of gonadal hormones and sex
chromosome complement (Table 1) (8).
To eliminate the activational effects of the gonadal hormones
on gene expression, the FCG mice were GDX at 8 wk. We compared male XY to male XX and female XX to female XY group,
where male and female are defined by gonadal sex at birth. We
detected fewer than 10 genes that were differentially expressed in
XX vs. XY in both the male and female groups.
These results indicate that few genes are regulated by the
complement of sex chromosomes when gonadal hormones are
absent. In addition, the sex differences in liver gene expression
found in gonadally intact adult mice are mostly explained by
genes that show regulation by gonadal hormones rather than by
the genes that are regulated by sex chromosome complement in
GDX mice.
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Even though the FCG animals were GDX before profiling,
differences in gene expression were observed between males and
females with the same sex chromosome status. We observed 552
genes that were differentially expressed between males and females with the XX genotype, and eight genes differed in expression between males and females with the XY genotype. Because
the mice had no gonads, the sex differences reflect either direct
effects of Sry on the liver or the enduring organizational effects
of gonadal secretions before the time of gonadectomy (12). Interestingly, of the 552 differentially expressed genes between XX
males and females, 142 overlapped with the 1880 differentially
expressed genes between GDX mice treated with placebo vs.
gonadal hormones (P ⫽ 1.2 ⫻ 10⫺33). The overlap suggests some
of the differences between males and females with same sexchromosome status are due to the enduring effects of gonadal
secretions. The direct effects of Sry on the liver are not tested
independently in the FCG model because XX and XY mice of the
same gonadal sex do not differ in the presence of Sry.
female muscle was also found in male muscle (Fig. 1). To further
investigate the degree of network preservation between male and
female, a percent score was calculated to determine the number
of genes clustered within the modules that were retained between
male and female (supplemental Fig. 1S). Modules were clustered
based on degree within the network, and the percent overlap of
the male and female module is shown by increasing color intensity depicting higher percent preservation.
Consistent with the results of the overall gene coexpression
network of each tissue, the gene content of network modules was
largely preserved between males and females, although the degree of preservation varied across tissues. The network preservation as depicted in the pattern of Fig. 1S is reflected in the large
number of dark blue boxes depicting modules in which a high
percentage of genes in a module of one sex is found also in a
corresponding module of the other sex. Here, high preservation
is defined as more than 50% overlap of genes in the female
module that clustered with the same genes in the male module,
and vice versa. In brain and muscle, 88 –94% of the modules
were highly preserved between males and females. The adipose
and liver tissues were the least preserved of all the tissues. Approximately 61– 69% of the adipose and 53– 84% of the liver
modules, respectively, was preserved between males and females
(Table 3S).
The threshold of significance of the gene overlap of modules
between males and females was based on an enrichment analysis
using the Fisher’s exact test (Table 3S). Specifically, we determined for each module which module of one sex was enriched in
genes found in a module in the opposite sex. The majority of the
modules in all the tissues significantly corresponded to a module
in the opposite sex. Significant network preservation of a Bonferroni-corrected P ⬍ 10⫺50 threshold was observed for 50% of
the modules in adipose, 61% in brain, 41% in liver, and 31% in
muscle. Although it appears the muscle tissue was not significantly preserved, most of the genes in the male and female network fell into four large modules ranging from 312-1538 genes,
with a P ⬍ 3.61 ⫻ 10⫺86 to P ⬍ 3.15 ⫻ 10⫺316, compared with
the liver tissue where modules were evenly disbursed among 39 –
909 genes. These results show that the overall gene coexpression
network between males and females are highly preserved across
tissues, indicating that the same modules of genes that are highly
connected in one sex are also highly connected to each other in
the other sex. However, the degree of preservation varied between tissues, being greatest in the brain and muscle tissue and
least in adipose and liver (Fig. 1 and supplemental Fig. 1S).
Gene coexpression networks in males and females
We next explored the large-scale organization of gene expression to characterize the gene network properties that are preserved between the two sexes or are sex specific. In addition, we
investigated the sex-specific gene networks in relation to metabolic and genetic traits as well as determined whether these networks are influenced by the same molecular mechanisms that
affect sexually dimorphic gene expression, detailed above,
namely the gonadal hormones.
We constructed gene coexpression networks to detect the architectural differences underlying gene expression between male
and female mice. To generate the networks, we used microarray
data from the F2 intercross BXH 䡠 ApoE⫺/⫺ (17, 18). The genetic
perturbations regulating gene expression in the cross allowed us
to identify genes with common patterns of expression, termed
modules, in which the network nodes represent gene transcripts
and the edges are the degree of connectivity of transcript abundance among F2 mice. For each gene, the connectivity is defined
as the sum of connection strengths (k), determined by the pairwise correlations of gene expression levels across microarray
samples, with the other network genes. In coexpression networks, the connectivity measures how correlated a gene is with
all other genes; thus, a network module consists of a cluster of
highly correlated, connected genes (40 – 44). For each tissue, we
selected 4000 genes for the network analysis exhibiting the highest variance of expression across the mouse population. Male
and female networks of adipose, brain, liver, and muscle tissue
were constructed in parallel.
A plot of the topological overlap matrix depicting the gene
coexpression networks between male and female within adipose,
brain, liver, and muscle tissue is shown in Fig. 1. The heat map
of the topological overlap matrix plot reflects the degree of gene
expression correlation within each module identified by color.
The module color of the genes in the female network was assigned to the genes in the corresponding male network to visualize the overall high network preservation between males and
females. In general, modules found in one sex were also present
in the other sex. For example, the large red module found in
Network connectivity between males and females
In addition to comparing general network properties between
male and female tissues, we also asked whether the gene connectivity within the network is preserved. For each tissue, female
gene connectivity was correlated with male gene connectivity.
The relationship between connectivity (k) in the female network
and that in the male network is depicted in Fig. 2S and Table 4S;
genes in the male network are assigned female module colors.
Overall correlation (Pearson correlation coefficient) of gene connectivity within each tissue was as follows: adipose tissue r ⫽
0.67, liver r ⫽ 0.56, brain r ⫽ 0.92, and muscle tissue r ⫽ 0.96
Endocrinology, March 2009, 150(3):1235–1249
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1241
tive to adipose and liver. To assess the extent of preservation of
individual modules, we also determined the correlation of gene connectivity of the preserved modules in all the tissues. All the preserved
modules had high significant correlation (P ⬍ 5.7 ⫻ 10⫺3) of gene
connectivity with the exception of one module (red) in the adipose
network and one module (white) in the muscle tissue (Table 5S).
Network hub genes between males and females
To determine whether the most connected genes (network
hubs) are preserved, we compared male and female genes with
the highest connectivity index (k). A percent score was used to
reflect the degree of preservation between male and female hubs.
The percent overlap of male and female hub genes across all
modules of the top 10 and 25% most connected genes are depicted in supplemental Fig. 3S. In adipose tissue, 45 or 57% of
the hub genes (10% most connected and 25% most connected,
respectively) were common to male and female. In liver tissue,
there was a 45 or 51% preservation of hub genes in both male
and female. In muscle, 61 or 66% of the genes were preserved,
and in brain, 56 or 70% of the hub genes overlapped between
male and female. These results indicate that the hub genes are
highly preserved in all the tissues.
FIG. 1. Topographical overlap matrix plot of genetic coexpression network in
males and females. The x-axis and y-axis are modules of genes in the entire
network and are identified as blocks of colors at the top or left, marking the
rows and columns. The plot represents the degree of correlation of gene
expression of each gene with all the other genes as indicated by the color bar
where increasing color intensity depicts higher correlation (r). Thus, each row or
column of this plot represents the connectivity strength of a single gene within
the network, with increasing color intensity depicting higher connectivity (higher
correlation with other gene’s expression pattern across animals). To visualize the
degree of network preservation between males and females, the genes in the male
modules were identified as the color of the genes in the female network. Modules
were composed of genes with high topological overlap (similar patterns of correlated
expression with other genes). The group of genes with low connectivity is colored
beige. The plots show that genes within modules have high correlation of expression
across mice, which appear as box-like clusters of high heat (high correlation). A,
Adipose tissue; B, brain tissue; C, liver tissue; D, muscle tissue.
(P ⬍ 2.2 ⫻ 10⫺16 for all tissues). The greater correlation coefficients in brain and muscle suggest greater similarity between
the sexes of gene network interactions in these two tissues rela-
Differential network analysis between male and female
To explore how sex bias in gene expression relates to gene
connectivity, the differences of gene expression (differential expression) and gene connectivity (differential connectivity) were
integrated. To measure differential gene expression between
male and female mice, we used the Student’s t test statistic. The
difference in gene connectivity (k) between male and female mice
was used as a measure of differential connectivity, and a threshold of significance was established. Differential connectivity, defined as the connectivity of each gene in the female network
subtracted from the connectivity of the corresponding gene in
the male network allowed us to determine which gene is more
connected in one sex compared with the other. A permutation
test was performed to determine statistical significance of observing genes with k ⬎ 0.2 differential connectivity and Student’s t test (q value FDR 5%) was used to determine the
statistical significance of differential gene expression (26).
In Fig. 2, the differential connectivity (x-axis) and differential
expression (y-axis) are shown for all tissues, along with the differential connectivity P values for each sector. The data points
are individual genes that are colored according to their assigned
module. The left sectors (1, 7, and 8) indicate higher connectivity
in male than female, and the right sectors (3–5) indicate higher
connectivity in female, whereas the top sectors (1–3) show genes
with higher gene expression in males than females, and the lower
sectors (5–7) show great gene expression in females. Thus, the
upper left sector (1) reflects male bias for both gene expression
and gene connectivity, whereas the lower right sector (5) indicates female bias for both those characteristics.
In adipose tissue, the blue module (sectors 1–7) was enriched
for genes with both male and female-biased gene expression, but
with high connectivity in males. The green module (sector 3) was
primarily enriched for female-biased gene expression and higher
female gene connectivity but also contained male-biased gene
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van Nas et al.
Sexually Dimorphic Coexpression Networks
Endocrinology, March 2009, 150(3):1235–1249
Network correlation with
metabolic traits between males
and females
We examined how gene networks differ between males and females in relation to biological traits
using a similar method described in
the study by Ghazalpour et al. (25)
using this same dataset. We used the
principal component (pc) of gene
expression of each module and correlated each of the metabolic traits
measured. Briefly, we defined the
module eigengene as the first principal component of the module expressions. The module eigengene can
be considered the most representative
gene expression in a module, because
it captures most of the variance explained. In addition, for each clinical
trait, we defined a measure of gene
significance (gs) as the absolute value
of the correlation between gene expression profile of each module and
the clinical trait. Module significance
was determined as the average gene
expression correlation for all genes in
a given module. This measure is
highly related to the correlation beFIG. 2. Differential expression and differential connectivity profiles between males and females. The difference
between male and female connectivity was calculated by subtracting female connectivity (k) from male connectivity
tween module eigengene and the trait.
(k), and threshold for significance was determined by a permutation test (P values) (vertical lines). Differential gene
We detected modules of the adiexpression was measured by Student’s t test, threshold for significance based on 5% FDR (q value) (horizontal lines).
pose and liver correlated to metabolic
Differential connectivity is depicted on the x-axis, where male differential connectivity is on the left (negative) and
female differential connectivity is on the right (positive). Differential expression is depicted on the y-axis, where male
traits but no trait correlations with
differential expression is on the top (positive) and female differential expression is on the bottom (negative). A,
modules of the brain or muscle tissue
Adipose tissue; B, brain tissue; C, liver tissue; D, muscle tissue.
were observed (Fig. 3 and supplemental Table 6S). Although the study by
Ghazalpour et al. (25) also detected a
expression. In liver tissue, there was primarily one module, cyan
liver module correlated with metabolic traits, the analysis was per(sector 1), which was distinctly different from other modules
formed only in the female network with a different set of genes and
because of its high male bias in gene expression and higher conprimarily focused on the network analyses of one module. We obnectivity in males. The blue, red, and yellow liver modules (secserved a sexually dimorphic module in the adipose and liver network
tors 3–5) showed higher female gene connectivity as well as high
correlated to metabolic traits. The adipose blue module was strongly
female and male-biased gene expression. Brain tissue consisted of
correlated with traits related to body fat, lipid, and atherosclerosis in
one module (red) spanning sectors 1, 7, and 8, which reflected
both males and females. In adipose, the pc of the blue module was
both female and male biased gene expression but primarily
highly correlated to body weight in both males and females (r ⫽ 0.63–
higher male gene connectivity. Enrichment for female gene con0.73) as well as total cholesterol, aortic lesions, and the monocyte chenectivity was observed in the pink and green modules in brain
motactic protein-1 (MCP-1), a marker for inflammation (r ⫽ 0.31–
(sectors 3–5). Lastly, in muscle tissue, modules with high differ0.37). Correlations with the adipose blue module and abdominal fat,
ential connectivity were not observed for either sex.
insulin, and leptin were also observed but were more correlated in
Integrating the differential gene expression with connectivity
females (r ⫽ 0.51– 0.57) than in males (r ⫽ 0.34 – 0.4). The liver
allowed us to determine whether gene transcript abundance afgreen module was correlated to body fat, glucose, and triglycerides
fects the organization of gene expression. Despite the large-scale
in females but not in males. Abdominal and total fat was strongly
differences in gene expression between males and females, gene
correlatedwiththefemalelivernetwork(r⫽0.5–0.57)comparedwith
connectivity was still largely preserved. Nevertheless, the distrithe male network (r ⫽ 0.1–0.14); glucose and triglycerides was more
butions of genes sorted by differential expression and conneccorrelated with the female network (r ⫽ 0.34–0.39) than the males
tivity showed striking tissue-specific patterns and allowed for the
network (r ⫽ 0.09–0.25). However, the liver green module was highly
identification of modules with sex-specific patterns.
Endocrinology, March 2009, 150(3):1235–1249
endo.endojournals.org
1243
in the male network, eQTL hotspots were observed on chromosomes 8 and 11 (Fig. 4A). A similar result was found when the LOD
score of the principal component of the same modules was determined (supplemental Fig. 4S); the female blue adipose module had
LOD higher than 6 for chromosome 1, and the same module in
males showed LOD higher than 4 for chromosome 8. In the liver
tissue, the cyan module of the male network showed enrichment for
eQTL on the X chromosome but no strong eQTL were found for the
equivalent module in the female network (Fig. 4B).
In brain and muscle tissue, strong sex-specific eQTL hotspots
were not observed. The sex differences in the QTL detected of the
adipose and liver module provide evidence for genetic locations
that perturb networks in a sex-specific manner.
Networks enriched for genes driven by sex hormones
In addition to determining whether sexually dimorphic coexpression network modules may be involved in certain biological pathways, we examined modules for enrichment of genes
whose expression was differentially affected by gonadal hormones. Differentially expressed genes between the GDX mice
treated with placebo compared with GDX mice treated with E2
and DHT (P ⬍ 0.01) were screened for module enrichment in the
liver tissue. Genes belonging to the cyan module of the liver tissue
with the highest connectivity in male (shown in Fig. 2, sector 1)
were largely the same set of genes significantly affected by both
the presence of the testes and DHT (P ⫽ 5.6 ⫻ 10⫺28) (Tables 2
and 3). Similar network enrichment was also observed in the
GDX female mice treated with DHT. Genes affected by the presence of ovaries and E2 were not strongly enriched for any modules, which was not surprising because highly connected female
modules were not observed in the liver tissue.
FIG. 3. Network correlation with metabolic traits between male and female. We
measured the correlation of each module with metabolic traits representing body fat,
lipid, and atherosclerosis. The principal component of gene expression (pc) of each
module was calculated and correlated to each of the metabolic traits. A, Adipose
blue module; B, liver green module.
correlated to weight and leptin (r ⫽ 0.4–0.65) as well as total cholesterol and insulin (r ⫽ 0.31–0.41) in both males and females.
Genetic regulation of male and female networks
In previous studies, sex differences in gene eQTL have been observed reflecting sex-specific gene regulation (13–17). To examine
the chromosomal loci regulating the networks between male and
female mice , we screened the sex-specific modules for eQTL (Fig.
4, A and B, and supplemental Fig. 4S). The sex-biased modules
consisted of genes that were highly connected in one sex and not the
other as detected by the differential network connectivity analysis
(Fig. 2). The transcript abundance of each gene in the sex-biased
module was then used to detect its corresponding QTL. We also
calculated the principal component of the transcript abundance of
the sex-biased module to determine how the module relates to the
QTL as a whole.
In adipose tissue, the blue module of the female network consisted of striking eQTL hotspots on chromosome 1 and 7, whereas
Discussion
Males and females are affected by different patterns of disease
manifestation and progression, but how these differences are
biologically determined remains to be elucidated. In our recent
study, we identified thousands of genes differentially expressed
across tissues under the control of sex-specific QTL hotspots
(18). In this report, we investigated sexual dimorphism of gene
expression at the molecular level and explored gene coexpression
networks between males and females. To our knowledge, this is
the first study to ask whether sexual dimorphisms can be detected
at the level of global gene expression networks, rather than simply at the level of expression of individual genes. These analyses
provide new perspectives on sex-specific organization of gene
networks and their regulation and relationship to clinical traits.
The major conclusions that emerged from our study are: 1)
in the liver tissue, many differences in gene expression between males and females can be attributed to the acute effects
of gonadal hormones acting in adulthood; 2) relatively few sex
differences in liver are explained by direct effects of the sex
chromosome genes; 3) although the difference in gene expression typically does not influence the overall pattern of gene
connectivity in the two sexes, in some cases, they result in
1244
van Nas et al.
Sexually Dimorphic Coexpression Networks
Endocrinology, March 2009, 150(3):1235–1249
expression by investigating the effects of gonadal hormones and sex chromosomes on
gene expression. We observed 2597 genes
differentially expressed between gonadally
intact males and females in liver tissue that
were highly enriched for the sexually dimorphic genes observed in the liver tissue of the
BXH 䡠 ApoE⫺/⫺ F2 mice cross (P ⫽ 4.1 ⫻
10⫺136) (18). In addition, the genes that
were differentially expressed between the intact and the GDX mice, treated with or without gonadal hormones, overlapped significantly with the genes that were sexually
dimorphic in the BXH 䡠 ApoE⫺/⫺ F2 animals, indicating that gonadal hormones play
a strong role in sexual dimorphism of gene
expression. The gonadal hormone effects
were observed to be larger with the treatment of DHT in either sex, and thus the hormone effects are likely mediated by androgen receptors. The number of genes that
were differentially expressed between GDX
mice treated with placebo vs. DHT were
much higher than the number of genes that
were differentially expressed between GDX
FIG. 4. eQTL of the blue module in the adipose network and the cyan module in the liver network. The
mice treated with placebo vs. E2. The genes
gene transcript abundance was used as a trait to detect QTL and the number of eQTL with a LOD of 3 or
higher are plotted. The red curve indicates the eQTL count in the female network, and the blue curve
affected by DHT were primarily enriched for
indicates the eQTL count male network. The adipose blue module consisted of 1090 genes and the cyan
genes involved in pathways for lipid and steconsisted of 82 genes. A, Adipose blue module; B, liver cyan module.
roid metabolism (supplemental Table 1S).
In addition to studying the effects of godramatic effects on modules in coexpression networks, parnadal hormones on sexually dimorphic gene expression, we meaticularly in liver and adipose and to a lesser extent in muscle
sured the role of the sex chromosomes on gene expression. We
and brain; and 4) certain sex-specific modules are clearly asdetected eight differentially expressed genes between XX and XY
sociated with clinical traits as well as chromosomal loci regmales, and no genes were differentially expressed between XX
ulating these networks; 5) We have identified at least one
and XY females. These results suggest that compared with the
module in liver (cyan) that shows male-specific expression and
gonadal hormones, the sex chromosomes play a modest role in
connectivity and is largely composed of genes that respond
driving differential gene expression in liver between adult males
directly to androgens in adulthood.
We explored the molecular basis of sexually dimorphic gene
and females. Because the sex chromosome effects were measured
TABLE 2. Sexually dimorphic network enriched for genes affected by gonadal hormones
Experimental variable
Experiment
Castrated male placebo vs. castrated male DHT
Castrated male placebo vs. castrated male E2
Castrated male placebo vs. ovariectomized
female placebo
Intact female vs. intact male
Ovariectomized female placebo vs.
ovariectomized female DHT
Ovariectomized female placebo vs.
ovariectomized female E2
Intact male vs. castrated male placebo
Intact female vs. ovariectomized female placebo
Intact male vs. castrated male DHT
Intact female vs. ovariectomized female E2
Testis
Ovaries
E2
DHT
X
X
X
X
X
1.71 ⫻ 10⫺26
NS
NS
X
X
1
1
3.12 ⫻ 10⫺34
1.86 ⫻ 10⫺13
2.81 ⫻ 10⫺33
1.67 ⫻ 10⫺12
NS
NS
NS
1
NS
1
1
5.64 ⫻ 10⫺28
NS
2.47 ⫻ 10⫺16
NS
5.07 ⫻ 10⫺27
NS
2.23 ⫻ 10⫺15
NS
X
X
1.91 ⫻ 10
NS
NS
Bonferroni P value
⫺27
1
NS
NS
X
X
EASE score
X
X
X
Sector
The genes in a male sex-specific liver module of the BXH 䡠 ApoE⫺/⫺ F2 cross were screened for liver genes differentially expressed between mice with or without
gonadal hormones (P ⬍ 0.01). Degree of enrichment was measured by the Fisher exact test within the EASE software. NS, Not significant.
Endocrinology, March 2009, 150(3):1235–1249
endo.endojournals.org
1245
TABLE 3. Differentially connected genes of the liver ‘cyan’ module enriched for differentially expressed genes between
gonadectomized males treated with placebo vs. DHT
Gene
symbol
Locus
link ID
Differential Sex differential Hormone differential
Fold
connectivity
expression
expression
change
(k)
(t score)
(P value)
Chromosome
Official gene name
Ankrd13b 268445
Bmf
171543
C8b
110382
Chad
12643
Chst11
58250
11
2
4
11
10
Csf3
Cyp26b1
Egfr
12985
232174
13649
11
6
11
Es31
F11
Gab3
Gpx1
Hydin
Ighg
Kcne2
382053
109821
210710
14775
244653
16061
246133
8
8
X
9
8
12
16
Krt15
Lgtn
Ly9
Mars
Mllt6
Mug1
Myh2
Ppme1
16665
16865
17085
216443
246198
17836
17882
72590
11
1
1
10
11
6
11
7
98710
19363
20174
20714
1
12
7
12
Serpinb3c 381286
1
Ankyrin repeat domain 13b
Bcl2 modifying factor
Complement component 8
Chondroadherin
Carbohydrate
sulfotransferase 11
Colony stimulating factor 3
Cytochrome P450
Epidermal growth factor
receptor
Esterase 31
Coagulation factor XI
Growth factor receptor
Glutathione peroxidase 1
Hydrocephalus inducing
Immunoglobulin heavy chain
Potassium voltage-gated
channel
Keratin 15
Ligatin
Lymphocyte antigen 9
Methionine-tRNA synthetase
Myeloid/lymphoid
Murinoglobulin 1
Myosin
Protein phosphatase
methylesterase 1
RAB interacting factor
RAD51-like 1 (S. cerevisiae)
RuvB-like protein 2
Serine (or cysteine) peptidase
inhibitor
Serine (or cysteine) peptidase
inhibitor
Solute carrier family 22
Solute carrier organic anion
transporter
SET and MYND domain containing 5
Sperm associated antigen 16
Speedy homolog B
(Drosophila)
splA/ryanodine receptor/
SOCS box
Sulfotransferase family 5A
Sushi domain containing 4
Transmembrane channel-like
Vomeronasal 1 receptor
Wnt inhibitory factor 1
Zona pellucida glycoprotein 3
Rabif
Rad51l1
Ruvbl2
Serpina3k
Slc22a16
Slco1a1
Smyd5
Spag16
Spdyb
Spsb1
Sult5a1
Susd4
Tmc3
V1ra2
Wif1
Zp3
70840
28248
10
6
232187
66722
74673
6
1
5
74646
4
57429
96935
233424
22297
24117
22788
8
1
7
6
10
5
1.28
1.45
1.19
1.14
1.12
⫺0.72
⫺0.62
⫺0.31
⫺0.78
⫺0.58
6.26
6.02
3.80
4.90
3.95
7.20 ⫻ 10⫺4
1.14 ⫻ 10⫺3
4.78 ⫻ 10⫺4
4.84 ⫻ 10⫺4
2.16 ⫻ 10⫺3
1.08
1.26
1.40
⫺0.67
⫺0.55
⫺0.30
4.50
5.51
5.89
2.54 ⫻ 10⫺3
1.10 ⫻ 10⫺3
1.72 ⫻ 10⫺4
1.17
0.95
1.18
1.06
1.16
1.23
1.08
⫺0.24
⫺0.15
⫺0.74
⫺0.19
⫺0.57
⫺0.76
⫺0.45
6.02
⫺2.55
5.75
2.98
5.71
5.89
3.91
1.18 ⫻ 10⫺3
2.11 ⫻ 10⫺3
2.13 ⫻ 10⫺4
2.19 ⫻ 10⫺3
2.14 ⫻ 10⫺3
5.21 ⫻ 10⫺4
8.86 ⫻ 10⫺4
1.19
1.24
1.10
1.27
1.10
1.09
1.36
1.35
⫺0.69
⫺0.53
⫺0.58
⫺0.73
⫺0.61
⫺0.24
⫺0.23
⫺0.72
4.84
5.36
4.49
6.00
5.44
3.54
4.80
5.30
7.25 ⫻ 10⫺4
9.75 ⫻ 10⫺4
7.19 ⫻ 10⫺4
5.20 ⫻ 10⫺4
1.59 ⫻ 10⫺3
1.33 ⫻ 10⫺3
1.77 ⫻ 10⫺3
2.25 ⫻ 10⫺3
1.38
0.88
1.17
1.27
⫺0.71
⫺0.56
⫺0.76
⫺0.43
5.39
⫺3.48
5.58
5.47
2.70 ⫻ 10⫺3
6.49 ⫻ 10⫺4
1.64 ⫻ 10⫺3
1.28 ⫻ 10⫺3
1.16
⫺0.82
4.80
6.04 ⫻ 10⫺4
1.15
1.63
⫺0.67
⫺0.51
4.83
6.12
2.84 ⫻ 10⫺3
1.27 ⫻ 10⫺3
1.16
1.17
1.11
⫺0.52
⫺0.80
⫺0.63
5.37
5.15
4.82
1.10 ⫻ 10⫺3
2.73 ⫻ 10⫺3
2.27 ⫻ 10⫺3
1.20
⫺0.72
4.61
2.26 ⫻ 10⫺3
1.14
1.65
1.15
1.10
1.09
1.17
⫺0.42
⫺0.19
⫺0.76
⫺0.23
⫺0.66
⫺0.82
3.65
6.32
5.76
4.04
4.29
5.39
4.57 ⫻ 10⫺4
3.93 ⫻ 10⫺4
1.29 ⫻ 10⫺3
2.92 ⫻ 10⫺3
7.90 ⫻ 10⫺4
2.58 ⫻ 10⫺4
Approximately 16% of the cyan module consisted of genes that were highly differentially connected and expressed in males (P ⬍ 0.05), and 79% of these genes were
enriched for differentially expressed genes affected by DHT. Fold change indicates the difference of the transcript abundance between GDX males treated with DHT,
and the hormone differential expression is the P value between the treatment groups. Differential connectivity (k) is the difference between male and female
connectivity. Sex differential expression (t score) reflects the difference between the means of the transcript abundance between males and females. Degree of
enrichment was measured by the Fisher exact test within the EASE software.
in GDX mice, it is conceivable that more sex chromosome effects
would occur in mice that have higher levels of gonadal hormones.
For example, some effects of X or Y genes might occur only when
androgens or estrogens are present. This analysis provides some
evidence for interactions between sex chromosome complement
and gonadal sex, suggesting that some hormone effects differ in
XX and XY mice.
Next, we investigated the organization of gene expression
between males and females. This analysis allowed us to identify
genes as well as gene coexpression networks that are highly con-
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Sexually Dimorphic Coexpression Networks
Endocrinology, March 2009, 150(3):1235–1249
nected in one sex but not the other, providing evidence for a
sex-specific functional relationship. Sexually dimorphic gene coexpression networks were identified by the degree of differential
gene connectivity between males and females and subsequently
characterized in relation to genetic and physiological traits. Although sexually dimorphic modules were observed in all tissues,
the coexpression networks of adipose and liver tissue were highly
enriched for modules with striking sex differences compared
with brain and muscle. These results are not unexpected, because
both adipose and liver are endocrine-responsive organs that interact with sex hormones. For example, Cyp19a1, the aromatase
involved in estrogen regulation in both adipose and liver tissue,
was identified as a hub in the adipose network in females but not
in males (9). The high degree of network preservation of brain
tissue was somewhat unexpected, because it is a well-documented sexually dimorphic organ (6). Sexual dimorphism has
been reported in specific brain regions, such as the hypothalamus
(45). Because the brain is very heterogeneous with respect to gene
expression, examination of the entire brain may have resulted in
a loss of power to identify sex-specific expression patterns.
Among all tissue, the muscle network showed the greatest preservation of module composition between the sexes, suggesting
similar overall function in male and female muscle. These observations indicate that the gene clustering of modules of these
coexpression networks are highly preserved between males and
females but that the degree of preservation is tissue-specific (Fig.
1 and supplemental Fig. 1S).
We determined whether the preservation observed in the
overall tissue network extended to gene connectivity and the hub
genes. We observed a high number of sexually dimorphic hub
genes in adipose and liver compared with brain and muscle tissue. Approximately 50% of the hub genes in adipose and liver
were identified as a hub gene in one sex but not the other compared with 34% of the hub genes in brain and 30% in muscle
(supplemental Fig. 3S).
Our analyses based on network connectivity and differential
expression allowed us to detect three sex-specific gene coexpression modules in adipose (male, blue; female, blue and green), five
sex-specific modules in liver (male, green and cyan; female, blue,
red, and yellow), three sex-specific modules in brain (male, red;
female, pink and green) but no sex-specific modules in muscle
(Fig. 2). Some modules had high sexually dimorphic gene connectivity and high differential expression, but the majority
showed sex-biased differential expression but not differential
connectivity (females, k ⬍ 0.2; males, k ⬎ ⫺0.2; Fig. 2). These
results indicate that gene coexpression networks are preserved
between males and females despite a higher or lower abundance
of gene transcript between the sexes. Therefore, a significant sex
difference in gene expression does not necessarily lead to a difference in the organization of the gene expression network between males and females.
Although the majority of gene coexpression networks consisted of the same genes that clustered together in both males and
females, modules with highly differentially connected genes were
detected. The preserved modules consisting of genes that are
more connected in one sex compared with the other imply that
the network connectivity is sexually dimorphic. To illustrate this
observation, we used the VisANT visual analysis tool to depict
the network structure of a module made up of correlated genes
in both males and females but are sex-specifically differentially
connected (Fig. 5). This module consists of the same group of
correlated genes in both the male and female liver network (Fig.
2, sector 1, and supplemental Table 8S); however, the male module is more connected than the female module as reflected in the
number of genes with at least 40 connections in the male network
compared with the female network consisting primarily of genes
with fewer than 40 connections.
We characterized these sexually dimorphic gene coexpression
networks further by measuring the correlation of genetic and
metabolic traits in males and females (Fig. 3 and supplemental
Table 6S). The adipose blue module was significantly correlated
with metabolic traits such as body weight, leptin, and insulin (r ⬎
FIG. 5. Visualization of sex-specific differentially connected genes of the liver cyan module (Fig. 2, sector 1). These genes were also enriched for differentially expressed
genes affected by DHT. Genes with 40 or more connections are identified by the larger nodes as seen primarily in the male network, and genes with fewer than 40
connections are identified by the smaller nodes as seen primarily in the female network.
Endocrinology, March 2009, 150(3):1235–1249
0.4) (Fig. 3) and genetic loci in both sexes (Fig. 4A). These results
are not surprising because the blue module is highly enriched for
genes known to be involved in metabolism and obesity, including
leptin, lipin, USF1, and PPAR-␣, as well as genes involved the
inflammatory response, biopolymer metabolism, catalytic and
GTPase regulator activity (Table 4) (46).
The QTL detected for this adipose module in males and females were on different chromosomes (Fig. 4A). These QTL results indicate that the blue module, which is made up of highly
differentially connected genes and is strongly correlated to metabolic traits, is also regulated by genes on different chromosomes
in the male and female adipose network. These QTL sex differ-
endo.endojournals.org
ence results are further evidence that by integrating sex as a
covariate when evaluating QTL may increase the power to detect
genes underlying complex genetic traits (13–17).
Our analysis also uncovered a sex-specific gene network in
the liver tissue. The cyan module showed strong differential expression and connectivity in males (Fig. 2, sector 1, and Fig. 5)
and was highly enriched for genes affected by gonadal hormones.
The module was not significantly correlated with any metabolic
traits nor did it have any module QTLs (data not shown), but it
did consist of eQTLs on the X chromosome in males (Fig. 4B).
These results reiterate the findings from the sex chromosome
complement analysis in our study, where genes on the sex chro-
TABLE 4. Functional significance of sexually dimorphic network modules using pathway enrichment of DAVID software
Module, sex specificity
Adipose tissue
Blue, male
Green, female
Brain tissue
Green, female
Pink, female
Red, male
Liver tissue
Blue, female
Cyan, male
Green, male
Red, female
Yellow, female
Gene enrichment category
P value
Bonferroni P value
Lysosome
Catalytic activity
Phosphorylation
GTPase regulator activity
Hemopoiesis
Immune response
Cell communication
Sensory perception of chemical stimulus
Olfactory receptor activity
Signal transduction
Neurophysiological process
1.26 ⫻ 10⫺6
1.55 ⫻ 10⫺6
1.49 ⫻ 10⫺5
2.04 ⫻ 10⫺5
5.87 ⫻ 10⫺5
7.96 ⫻ 10⫺5
1.54 ⫻ 10⫺6
4.89 ⫻ 10⫺6
1.02 ⫻ 10⫺5
1.32 ⫻ 10⫺5
1.82 ⫻ 10⫺5
9.40 ⫻ 10⫺4
2.00 ⫻ 10⫺3
1.10 ⫻ 10⫺2
2.60 ⫻ 10⫺2
1.30 ⫻ 10⫺1
1.70 ⫻ 10⫺1
3.50 ⫻ 10⫺3
1.10 ⫻ 10⫺2
1.30 ⫻ 10⫺2
3.00 ⫻ 10⫺2
4.10 ⫻ 10⫺2
RRM
Cell organization and biogenesis
Intracellular organelle
Intracellular organelle
Organelle
KRAB
Zinc finger, C2H2-subtype
Regulation of cellular metabolism
Nucleus
Transcription, DNA-dependent
1.83 ⫻ 10⫺4
5.99 ⫻ 10⫺4
7.13 ⫻ 10⫺4
1.26 ⫻ 10⫺3
1.30 ⫻ 10⫺3
1.11 ⫻ 10⫺18
6.70 ⫻ 10⫺12
2.14 ⫻ 10⫺7
2.21 ⫻ 10⫺6
6.03 ⫻ 10⫺7
7.30 ⫻ 10⫺2
7.20 ⫻ 10⫺1
2.30 ⫻ 10⫺1
3.70 ⫻ 10⫺1
3.80 ⫻ 10⫺1
4.60 ⫻ 10⫺16
1.30 ⫻ 10⫺8
4.50 ⫻ 10⫺4
8.00 ⫻ 10⫺4
1.30 ⫻ 10⫺3
Membrane
Transmembrane
Signal
Immune response
Endoplasmic reticulum
Heme
Serine esterase
Serine esterase activity
Carboxylesterase activity
Cytochrome P450
Transport
Cell adhesion
Extracellular matrix
Signal
Glycoprotein
Metal ion binding
Ion binding
Organelle
Intracellular
Nuclear protein
Kinase
Alcohol metabolism
Monosaccharide biosynthesis
7.65 ⫻ 10⫺18
1.86 ⫻ 10⫺15
3.13 ⫻ 10⫺15
1.18 ⫻ 10⫺14
4.90 ⫻ 10⫺4
1.40 ⫻ 10⫺3
2.00 ⫻ 10⫺3
1.85 ⫻ 10⫺3
1.85 ⫻ 10⫺3
1.61 ⫻ 10⫺3
1.75 ⫻ 10⫺3
2.11 ⫻ 10⫺8
1.44 ⫻ 10⫺7
3.85 ⫻ 10⫺6
1.86 ⫻ 10⫺5
7.61 ⫻ 10⫺5
7.61 ⫻ 10⫺5
1.51 ⫻ 10⫺5
1.64 ⫻ 10⫺5
2.06 ⫻ 10⫺5
5.88 ⫻ 10⫺5
8.09 ⫻ 10⫺6
3.40 ⫻ 10⫺5
8.70 ⫻ 10⫺15
2.20 ⫻ 10⫺12
3.50 ⫻ 10⫺12
4.30 ⫻ 10⫺11
3.10 ⫻ 10⫺1
6.60 ⫻ 10⫺1
7.80 ⫻ 10⫺1
9.00 ⫻ 10⫺1
9.00 ⫻ 10⫺1
9.60 ⫻ 10⫺1
9.80 ⫻ 10⫺1
4.80 ⫻ 10⫺5
1.10 ⫻ 10⫺4
2.90 ⫻ 10⫺3
1.40 ⫻ 10⫺2
9.10 ⫻ 10⫺2
9.10 ⫻ 10⫺2
9.20 ⫻ 10⫺3
1.00 ⫻ 10⫺2
2.30 ⫻ 10⫺2
6.50 ⫻ 10⫺2
2.90 ⫻ 10⫺2
1.20 ⫻ 10⫺1
RRM, RNA recognition motif; KRAB, Krüppel-associated box.
1247
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van Nas et al.
Sexually Dimorphic Coexpression Networks
mosome could influence or be influenced by gonadal secretions.
In addition, genetic variation on the X chromosome may cause
differences in the expression of its genes in cis (genetic variation
in one locus that drives differences in expression of genes on or
near that locus), which subsequently act in trans (genetic variation in one locus that drives differences in expression of genes
distant from that locus) to affect the autosomal genes of this
module. The regulation by the X chromosome in males but not
females may reflect the hemizygous exposure of X alleles in
males, so that variation in the effects of X alleles is more pronounced in males than in females.
The cyan module was enriched for genes involved in carboxyl
and serine esterase activity and endoplasmic reticulum, which included genes involved in spermatogenesis, such as the activator of
CREM (Fhl5), zona pellucida glycoprotein 3 (Zp3), sperm-associated antigen 16 (Spag16), and Y-box protein 2 (Ybx2) as well as
hormone-related genes such as esterase 31 (Es31), hydroxysteroid
dehydrogenase-5 (Hsd3b5), and cytochrome P450 (Cyp26b1 and
Cyp7b1), which are known to be regulated by the sexually differentiated GH (Table 3) (19). Although most of the genes in this
network have not been well annotated, we speculate that the genes
may be regulated by a common transcription factor influenced by
gonadal hormones. Thus, the cyan module in liver is an androgensensitive group of genes that show male bias in expression and
network connectivity, which are driven more in males than females
by genetic variation on the sex chromosomes. The X chromosome
is expected under some conditions to be enriched for genes that play
a role in male-specific functions (47).
In conclusion, using a constellation of global network and
linkage analysis tools, we identified sexually dimorphic gene
networks in both adipose and liver tissue associated with sexspecific metabolic traits and regulated by different genetic loci
between males and females. Investigation of sexually dimorphic gene coexpression networks as they relate to biological
traits allowed us to obtain a better understanding of the interactions that may play a significant role in sex-biased disease
(48, 49). Genes whose expression and interactions are affected
as a response to the gonadal hormones and are highly correlated to biological traits related to atherosclerosis, diabetes,
and obesity may help uncover key sex-specific metabolic therapeutic targets.
Acknowledgments
We thank Anatole Ghazalpour, Sudheer Doss, Jason Aten, Michael Oldham, Charles Farber, Ai Li, Esther Melamed, and Wei Zhao for discussions.
Address all correspondence and requests for reprints to: Aldons J.
Lusis, University of California, Los Angeles, Med-Cardio/Microbio, Box
951679, 3730 MRL, Los Angeles, California 90095-1679. E-mail:
[email protected].
This work was supported by National Institutes of Health (NIH)
Grants HL28481 (A.J.L.), DIC 071673 (A.J.L.), NS045966 (A.P.A.),
NS043196 (A.P.A.), and 5R01DK72206 (T.D.). A.V. was supported by
NIH Training Grant 5T32HD07228.
Disclosure Statement: The authors have nothing to disclose.
Endocrinology, March 2009, 150(3):1235–1249
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