Abstract
Background
Heart failure preceded by hypertrophy is a leading cause of death, and
sex differences in hypertrophy are well known, although the basis for
these sex differences is poorly understood.
Methods and Results
This study used a systems biology approach to investigate mechanisms
underlying sex differences in cardiac hypertrophy. Male and female mice
were treated for 2 and 3 weeks with angiotensin II to induce
hypertrophy. Sex differences in cardiac hypertrophy were apparent after
3 weeks of treatment. RNA sequencing was performed on hearts, and sex
differences in mRNA expression at baseline and following hypertrophy
were observed, as well as within‐sex differences between baseline and
hypertrophy. Sex differences in mRNA were substantial at baseline and
reduced somewhat with hypertrophy, as the mRNA differences induced by
hypertrophy tended to overwhelm the sex differences. We performed an
integrative analysis to identify mRNA networks that were differentially
regulated in the 2 sexes by hypertrophy and obtained a network centered
on PPARα (peroxisome proliferator‐activated receptor α). Mouse
experiments further showed that acute inhibition of PPARα blocked sex
differences in the development of hypertrophy.
Conclusions
The data in this study suggest that PPARα is involved in the
sex‐dimorphic regulation of cardiac hypertrophy.
Keywords: hypertrophy, sex, systems biology
Subject Categories: Animal Models of Human Disease, Metabolism, Basic
Science Research
__________________________________________________________________
Clinical Perspective
What Is New?
* Although sex differences in the development of hypertrophy and
heart failure are known, the mechanisms responsible are poorly
understood.
* In this study, we used bioinformatics and systems biology
approaches to identify mRNAs that were differentially expressed as
a function of sex and hypertrophy and identified a role for PPARα
(peroxisome proliferator‐activated receptor α) in modulating the
sex differences in hypertrophy.
* We find that PPARα is at the center of a network that is
differentially regulated by sex and hypertrophy.
What Are the Clinical Implications?
* Better understanding the sex differences in the development of
heart failure may lead to treatments targeting sex‐specific
patterns of cardiac maladaptation and damage.
* These data could have important implications for drugs used to
treat hypertrophy.
* Understanding what protects women from heart failure could
potentially allow us to offer the same protection to our male
patients.
Introduction
Heart failure (HF) is the leading cause of death in industrialized
nations, and hypertrophy is a strong predictor of HF. Sex differences
in hypertrophy have been observed; premenopausal women exhibit lower
rates of cardiac hypertrophy than their male counterparts.[46]1, [47]2
HF with preserved ejection fraction is also more common in women.[48]3,
[49]4, [50]5 Studies in rodent models demonstrate that when exposed to
a predisposing factor or stimulus, females develop less hypertrophy
than their male cohorts, even when exposed to identical levels of
pathologic insult such as angiotensin II or transaortic
constriction.[51]6, [52]7, [53]8, [54]9, [55]10
Despite evidence that sex‐based differences exist between men and
premenopausal women in HF and other forms of heart disease,[56]1 large
randomized clinical trials have not demonstrated a beneficial effect by
treating postmenopausal women with hormone replacement therapy,[57]11
although a recent update of the Women's Health Initiative examined age
dependence and concluded that there were some beneficial effects of
estrogen in younger women.[58]12 Taken together, these findings
underscore the need for a better understanding of the mechanisms
responsible for the male–female difference in HF.
Cardiac hypertrophy and HF have been associated with significant
changes in the cardiac transcriptome, and altered expression of a
number of mRNA transcripts and proteins have been associated with
hypertrophy and HF.[59]13, [60]14, [61]15, [62]16, [63]17 It is clear,
however, that complex diseases such as hypertrophy typically are caused
not by alterations in a single mRNA or protein but rather by altered
regulation of gene networks.[64]18, [65]19 A systems biology approach
to understand the development of and the sex differences in hypertrophy
is needed. In this study, we used bioinformatics and systems biology
approaches to identify mRNAs that were differentially expressed as a
function of sex and hypertrophy and identified a role for PPARα
(peroxisome proliferator‐activated receptor α) in modulating the sex
differences in hypertrophy.
Methods
Mice
All mice were treated and cared for in accordance with the Guide for
the Care and Use of Laboratory Animals (National Institutes of Health,
revised 2011), and protocols were approved by the National Heart, Lung,
and Blood Institute institutional animal care and use committee. Male
and female C57BL/6 mice (12–14 weeks old, obtained from the Jackson
Laboratory) were given angiotensin II at 1.5 mg/kg per day or saline
(the vehicle) via Alzet minipumps for 2 or 3 weeks. In some studies,
GW6471, a potent inhibitor of PPARα,[66]20 was also given at 4 mg/kg
per day. GW6471 has an IC[50] of 240 nmol/L and has been shown to
function as an antagonist in mice within the range of 2 to 10 mg/kg per
day.[67]21, [68]22 Following treatment, echocardiography was performed
on the mice. Mice were then anesthetized and euthanized. Their heart
weights and tibia lengths were recorded, and harvested hearts were snap
frozen and stored in liquid nitrogen.
2‐Dimensional and M‐Mode Echocardiography
Transthoracic echocardiography was performed using a high‐frequency
linear array ultrasound system (Vevo 2100, VisualSonics) and the MS‐400
transducer (VisualSonics) with a center operating frequency of 30 MHz,
broadband frequency of 18 to 38 MHz, axial resolution of 50 μm, and
footprint of 20×5 mm. M‐mode images of the left ventricle were
collected from the parasternal short‐axis view at the midpapillary
muscles at a 90° clockwise rotation of the imaging probe from the
parasternal long‐axis view. From the M‐mode images, the left ventricle
systolic and diastolic posterior and anterior wall thicknesses and
end‐systolic and ‐diastolic internal left ventricle chamber dimensions
were measured using the leading‐edge method. Left ventricle functional
values of fractional shortening and ejection fraction (EF) were
calculated from the wall thicknesses and chamber dimension measurements
using system software. Mice were lightly anesthetized with isoflurane
delivered via a nose cone. The mice were imaged in the supine position
while placed on a heated platform equipped with ECG leads.
RNA Extraction
0.5 mL TRI reagent was added to heart tissue along with Precellys
(Bertin Technologies) homogenizing beads. Homogenization was carried
out in a Precellys homogenizer (Bertin Technologies) chilled with
liquid nitrogen. The samples were spun twice at 5000 rpm for 30 seconds
for each cycle. RNA was isolated according to the TRI reagent protocol
provided by Life Technologies. The isolated RNA samples were further
cleaned with the miRNeasy Kit, according to the protocol provided by
Qiagen. Samples were then treated with DNase (Ambion) and further
cleaned with another Qiagen miRNeasy column. RNA concentration was
determined by optical density at 260 nm.
RNA Sequencing Library Preparation and Statistical Analysis
The RNA sequencing libraries were constructed using a TruSeq Stranded
Total RNA Sample Preparation Kit (Illumina), according to the
manufacturer's protocols. Briefly, the ribosomal RNA was removed using
Ribo‐Zero (Illumina) rRNA removal beads. The resulting RNA was then
fragmented using divalent cations under elevated temperature. The RNA
fragments were copied into first‐strand cDNA using reverse
transcriptase and random hexamers. After second‐strand synthesis,
double‐stranded cDNAs were ligated with Illumina adaptors. The final
RNA sequencing library was enriched by low‐cycle polymerase chain
reaction and sequenced with paired 50‐bp reads on an Illumina HiSeq
2000.
The raw data in fastq format were aligned to the mouse reference
Ensembl GRCm38 genome using TopHat2[69]23 (tophat/2.0.13,
Bowtie1/2.2.3, and samtools/0.1.19) with default settings except for
the parameter –g 1. For transcript‐level analysis, the raw counts of
the transcripts in the mm10_refSeq.bed, which were produced by the
software RSeQC/2.6, were used as the input for the Bioconductor edgeR
package. TMM (Trimmed mean of M values) algorithms were used to
normalize read counts across all 24 samples. The lowly expressed
transcripts were discarded by requiring a count per million >1 in at
least 3 samples. The euclidean distance metric was used for principal
component analysis. The normalized and log[2] transformed output
count‐per‐million values were compared with a generalized linear model
among 4 conditions. The differentially expressed transcripts were
defined as ≥2‐fold changes with a 10% false discovery rate (FDR).
For gene‐level analysis, read counts were generated for each gene by
HTSeq software[70]24 using the UCSC RefSeq annotation downloaded from
iGenome in the “union” model. The statistical significance of
differentially expressed genes was evaluated using edgeR.[71]25 The sex
difference in hypertrophy was tested with edgeR's interaction
(sex×disease) generalized linear model.
Functional enrichment analysis of differentially expressed transcripts
was carried out using GOstats
([72]https://www.bioconductor.org/packages/release/bioc/html/GOstats.ht
ml), and the results were summarized and visualized using REVIGO[73]26
([74]http://revigo.irb.hr/).
Transcription factor families whose binding sites were overrepresented
in the promoter regions of the genes of interest were identified using
Genomatix ([75]https://www.genomatix.de/).
Coexpression Network Analysis
Coexpression networks shared in all 24 samples were constructed using
the WGCNA package (weighted correlation network analysis,
[76]https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackage
s/WGCNA/).[77]27 WGCNA identifies robust clusters of highly correlated
genes that serve as modules in coexpression networks and allows users
to further examine correlation of network modules with experimental
conditions and external traits. Global coexpression network
construction is computationally intensive; therefore, to improve
efficiency, we included only genes that showed differential expression
at an FDR <10% and |log[2] fold change|>log[2](1.2), in at least 1 of
the 4 comparisons (baseline male and female, male control versus
hypertrophy, female control versus hypertrophy, and male and female
hypertrophy), totaling 4422 genes.
To examine the relevance to experimental conditions (sex and
hypertrophy) of the resultant coexpression network modules, we
constructed four 24‐dimension condition‐specific vectors (for male
control, male hypertrophy, female control, and female hypertrophy,
respectively). In the vectors, each element represents 1 sample, with a
value of 1 if the sample belongs to the condition and 0 otherwise. The
Pearson correlation between eigengenes of network modules and the
condition vectors was then calculated for association estimation.
Protein–Protein Interaction Network Analysis and Subnetwork Identification
Protein–protein interaction annotation was downloaded from STRING
(version 9),[78]28 a database of known and predicted protein–protein
interactions ([79]http://string-db.org/). Only interactions with a
confidence score >700 were kept. Protein–protein interaction
subnetworks with gene expression variations significantly associated
with a factor (sex, hypertrophy, and the interaction between sex and
hypertrophy) were identified using jActiveModulesTopo, a software
package for trait‐relevant subnetwork identification that takes network
topology into consideration.[80]29
Integrated Analysis of mRNA and MicroRNA Data
We developed a 3‐step pipeline that determines regulatory relationships
between microRNA (miRNA) and mRNA by integrating sequence‐based
prediction and experimental condition‐dependent correlation in
expression of miRNA and mRNA. First, TargetScan
([81]http://www.targetscan.org/mmu_61/) was used to predict miRNA–mRNA
regulatory relationships. TargetScan identified likely target mRNAs for
each given miRNA by searching mRNAs for the presence of conserved
sequence sites that matched the miRNA's seed regions. Second,
correlation in expression variations of mRNA and miRNA were integrated
with TargetScan predictions through Lasso (least absolute shrinkage and
selection operator) regression to further narrow the list of candidate
regulatory pairs. An R package developed by Lu et al[82]30 was used to
carry out this step. Briefly, let y[i] denote the expression level of
the ith mRNA and x[k,i] the expression level of its k‐th out of a total
number (n) of candidate targeting miRNA in the simple linear regression
model for y[i] is given by:
[MATH: yi=β0,i+∑<
mrow>k = 1nβk,ixk,i+εi :MATH]
(1)
In equation (1), β represents the regression coefficients, and ε[i] is
a random error term. The lagrangian form of Lasso regression[83]31 of
this model is as follows:
[MATH: ∑l=1Nyi−β0,i−∑<
mrow>k=1nβk,ixk,i2+λ∑k=1n|βk,i| :MATH]
(2)
In equation (2), λ is the lagrangian multiplier, and the regression is
to find coefficients {β[k,i]} that minimize the value of
equation [84](2). Lasso performs the regression with the sum of the
absolute value of the regression coefficients constrained to balance
between improving prediction accuracy and avoiding overfitting.
Third, the predicted miRNA–mRNA pairs were further filtered, and only
those showing differential expression in 1 of the 3 statistical
models—sex with hypertrophy controlled, hypertrophy with sex
controlled, and the interaction between sex and hypertrophy—were
retained. FDRs of 20% and 40% were used as the thresholds for mRNA and
miRNA, respectively.
Results
Sex Differences in Angiotensin II–Induced Hypertrophy in Mice
Although previous studies have reported sex differences in
hypertrophy,[85]6, [86]7, [87]8, [88]32, [89]33, [90]34, [91]35 the
mechanistic basis for this difference is poorly understood. In this
study, we measured global changes in mRNA and used a systems biology
approach[92]18 to determine pathways and gene networks responsible for
these sex differences. We first confirmed previous studies showing sex
differences in hypertrophy. We initially treated male and female mice
with angiotensin II for 3 weeks. As shown in Figure [93]1, after
3 weeks of angiotensin II treatment, male mice exhibited significantly
more hypertrophy than female mice (Figure [94]1A). Furthermore, after
3 weeks of treatment, EF in female mice was not different compared with
the control value (54% versus 56% at baseline). In male mice, however,
EF dropped from the baseline level of 55% to 37% (P<0.05), which was
significantly lower than EF in female mice after 3 weeks of treatment
(Figure [95]1B). We also measured the percentage increase in aortic
velocity (mean and peak) following angiotensin II treatment, and we
observed similar increases in both male and female mice (36% increase
in females and 29% increase in males).
Figure 1.
Figure 1
[96]Open in a new tab
Sex differences in hypertrophy and ejection fraction following 3 weeks
treatment with angiotensin II (AngII). Changes in (A) heart weight to
tibia length and (B) ejection fraction in males and females following 3
weeks of AngII treatment. Data are mean±SEM, n=3 to 4. We performed a
2‐way ANOVA. The heart weight/tibia length data showed significant
differences based on sex and Ang II treatment, but there was not a
significant interaction between sex and hypertrophy. The male
vehicle‐treated hearts were significantly different than male
AngII‐treated hearts, and male and female AngII‐treated hearts were
significantly different. The ejection fraction (EF) data showed a
significant interaction between sex and hypertrophy. The males were
significantly different between vehicle and AngII, and the males and
females showed a significant difference with hypertrophy.
^#Significantly different compared with vehicle treated. *Significantly
different from male Ang II treatment. P<0.05 was considered
significant.
We next used RNA sequencing to examine sex differences in mRNA at
baseline and after 2 weeks of angiotensin II treatment. We measured
mRNA after 2 weeks of treatment because at that time, sex differences
in EF had not occurred (65±9% in females versus 61±9% in males, P=0.53,
n=5–7). Thus, the changes in mRNA that occur at 2 weeks of treatment
are more likely to be a cause rather than an effect of sex differences.
Data were analyzed using pairwise comparisons of the conditions. As
shown in Figure [97]2, principal component analysis revealed not only
clustering differences between baseline and hypertrophy but also
discrete clustering for mRNA of male and female mice at baseline that
persisted following hypertrophy. The full data set of mRNA, with
significant changes, is provided in Table [98]S1. The volcano plots in
Figure [99]3 show differences in expression of mRNA between the groups.
As illustrated in Figure [100]3A, comparing male and female mice at
baseline, 174 transcripts showed a significant sex difference (using an
FDR <10% and log[2] fold change >1). Male mice tended to have more
upregulated than downregulated genes compared with female mice (127
versus 47, respectively). Hypertrophy led to differential expression of
mRNA in both male and female mice (Figure [101]3B through [102]3D). The
mRNA changes that occur with hypertrophy largely overwhelm the baseline
sex difference such that with hypertrophy, fewer mRNAs show a sex
difference (compare Figure [103]3A and [104]3B). These differences are
shown in more detail in the Venn diagram in Figure [105]4A. At an FDR
<10% and |log[2](fold change)| >1, there were 174 mRNAs (141 unique to
baseline +33 overlapping with the list from hypertrophy) that exhibited
a sex difference at baseline; however, following hypertrophy, only 71
mRNAs showed a sex difference, with 33 of these mRNAs showing a sex
difference at both baseline and following hypertrophy. For the 141
transcripts that showed a sex difference only at baseline, 61 did not
display a change with hypertrophy in either sex compared with their
corresponding baselines, 10 displayed a hypertrophy‐induced change in
both sexes, 63 changed only in female hypertrophy, and 7 changed only
in male hypertrophy.
Figure 2.
Figure 2
[106]Open in a new tab
Principal component analysis for mRNA in male (M) and female (F) mice
at baseline and with hypertrophy (Hyp), n=5 to 7 per group. Ctrl
indicates control.
Figure 3.
Figure 3
[107]Open in a new tab
Volcano plots of mRNA differences. We used a filter of log[2] fold
change (FC) ≥1 and a false discovery rate (FDR) of 10% to select the
transcripts with significant differences, which are shown in color. A,
Data for mRNA in control (Ctrl) male vs female mice, with 174
transcripts (in blue) showing significant differences. B, 71 mRNA
transcripts (in green) with significant differences between male and
female hearts treated with angiotensin II (AngII). C, 328 transcripts
(in purple) show significant differences in females with and without
AngII treatment. D, 174 transcripts (in red) exhibit significant
differences in males with and without AngII treatment. mRNA was
measured in hearts from male and female mice treated with vehicle or
AngII for 2 weeks. Hyp indicates hypertrophy.
Figure 4.
Figure 4
[108]Open in a new tab
Sex differences in mRNA with angiotensin II (Ang II) treatment. The
Venn diagram in (A) shows transcripts with significant sex differences
at baseline and with Ang II treatment. Overall, 141 transcripts show
differences only at baseline, 33 show differences at both baseline and
hypertrophy, and 38 show a significant sex difference only with
hypertrophy. B, Transcripts with a significant difference following Ang
II treatment in males vs females. C, Quantitative polymerase chain
reaction analysis showing changes in brain natriuretic peptide (BNP;
mean and SEM) in fold change normalized to control males (n=5–7;
^#Significantly different compared with the vehicle control using 2‐way
ANOVA [P<0.05]).
If we examine differences between baseline and hypertrophy as a
function of sex, we find that 328 mRNAs changed with hypertrophy in
female mice, whereas only 174 mRNAs changed with hypertrophy in male
mice. There are 117 mRNAs that changed in both male and female mice
with hypertrophy; thus, 211 mRNAs are altered only in females with
hypertrophy compared with 57 mRNAs that change only in males (see
Figure [109]4B). Taken together, these data suggest that the process of
hypertrophy overwhelms the sex differences observed at baseline.
To confirm the validity of our model, we confirmed an elevation in
natriuretic peptide B (Figure [110]4C) and skeletal α‐actin 1
(Figure [111]4D) with hypertrophy. Interestingly, although there were
sex differences in the development of hypertrophy and in cardiac
function following angiotensin II treatment, consistent with the RNA
sequencing data, both male and female hearts showed significant
increases in brain natriuretic peptide (Figure [112]4C). We found a
similar discrepancy in a transaortic constriction model of hypertrophy
in which we also observed a sex difference in hypertrophy but found no
sex difference in natriuretic peptide B levels in heart.[113]6 We also
confirmed the sex differences that were found in the transcriptomic
analysis for metallothionein 1 and tissue metalloproteinase inhibitor 4
(see Figure [114]S1).
Coexpression Network Analysis
Coexpression network analysis identifies gene sets showing significant
and robust coregulations under the conditions of interest (hypertrophy
and sex), manifested as modules in coexpression networks. Compared with
the conventional pathway enrichment analysis that examines gene sets
belonging to predefined pathways, it has the advantage of uncovering
condition‐specific gene signatures. In this study, we used WGCNA[115]27
to construct the coexpression network of the 4422 genes showing
significant variations in response to sex, to hypertrophy, or to the
interaction of sex and hypertrophy. Using all 24 samples, 20
coexpressed modules were identified. Figure [116]5 shows the
module–condition correlation. The complete list of 4422 genes and their
module memberships are given in Table [117]S2. Coexpression network
construction using gene lists filtered at other statistical
stringencies or by variance in expression yielded similar module
composition (data not shown).
Figure 5.
Figure 5
[118]Open in a new tab
The 20 coexpression network modules shared by all samples and their
correlation to experimental conditions. Each row corresponds to a
module eigengene and each column to a condition. Each cell contains the
corresponding Pearson correlation of the eigengene's expression and the
condition vector, and in parentheses is the P value of the correlation.
The table is color‐coded by correlation value. Ctrl indicates control;
F, female; hyp, hypertrophy, M, male.
The light yellow module with 25 mRNAs showed the most significant
condition‐specific differences along with the highest correlation and P
value. At baseline, genes in this module were highly expressed in male
but not in female mice. With hypertrophy, the sex difference
diminished, and the genes were moderately expressed in both sexes.
Consequently, this module displayed a sex difference in changes with
hypertrophy. The module contained several mRNAs involved in metabolism
including PPARα, Ccrn4l (carbon catabolite repression 4‐like), Fitm1
(fat storage‐inducing transmembrane domain 1), Acot1 (acyl–coenzyme A
thioesterase 1), and Sik1 (salt inducible kinase 1). Several
transcription factors (in addition to PPARα) and splicing factors were
also contained in this module (Per1 [period circadian clock 1], SRSF3
[serine rich splicing factor 3], and Siah2 [siah E3 ubiquitin protein
ligase 2]).
Interaction Model and Pathway Enrichment Analysis
To dissect the contributions from sex and hypertrophy and their
interactions, we identified genes that showed significant changes in
hypertrophy when sex as a cofactor was controlled, significant
sex‐dependent variation when hypertrophy as a cofactor was controlled,
and significant sex‐dependent differential hypertrophy‐associated
changes. This analysis was done at the gene level to avoid
complications due to multiple splicing variants. At an FDR <40%, this
approach gives 379 genes with significant interactions. The complete
lists of genes with the statistics and pathway enrichment analysis
results are available in Table [119]S3. The pathway results were
further simplified by removing redundancy in the tree‐structured Gene
Ontology terms and visualized using REVIGO (Figure [120]6. Four main
categories were identified: cell‐cycle and growth‐related biosynthesis,
cellular development process, extracellular matrix organization, and
response to organic substances.
Figure 6.
Figure 6
[121]Open in a new tab
Pathways exhibiting alterations with sex and hypertrophy. Listed are
pathways with enriched presence in genes that showed significant,
sex‐dependent differential hypertrophy‐induced changes. This is a
visualization of the results to help present the overall theme of the
enriched pathways, in which the names in gray are representative and
summarize pathway categories, and many redundant pathways are removed.
IP‐10 indicates chemokine (c‐c motif) ligand 10.
Identification of a Gene Subnetwork That Is the Most Relevant to
Sex‐Dependent Difference in Hypertrophy‐Induced Expression Changes
To gain further insight into the mRNA networks that played a role in
the sex‐dependent differences in hypertrophy‐induced gene expression
changes, we selected genes that were significant at an FDR <40% for the
interaction and mapped them to the protein–protein interaction network
constructed using STRING data. This provided a network with 178 genes
and 313 interactions. We used an in‐house software,
jActiveModulesTopo[122]29, [123]36 to identify the subnetworks that
were most relevant to the sex–hypertrophy interaction (ie, connected
sets of genes with high levels of sex–hypertrophy difference), using a
simulated annealing method and setting the search depth at 2, and the
result is given in Figure [124]7, with 17 genes and 22 interactions.
This subnetwork is centered on PPARα, a clear hub with 8 interactions
with other members, whereas the interactions for other genes ranged
from 1 to 4. Note that PPARα is also implicated in the coexpression
network analysis, being a member of a module that exhibited the most
significant sex difference at baseline but not at hypertrophy
(Figure [125]5, light yellow module).
Figure 7.
Figure 7
[126]Open in a new tab
Network interactions. A, The top protein–protein interaction subnetwork
associated with sex‐dependent, hypertrophy‐induced differential changes
in gene expression are illustrated. B, Top microRNA–mRNA network
relevant to sex–hypertrophy interaction.
PPARα has been shown previously to be involved in regulating
hypertrophy.[127]37, [128]38, [129]39, [130]40, [131]41, [132]42 Esrrg
(estrogen‐related receptor γ), a regulator of mitochondrial function
that has been shown to play a role in regulating hypertrophy, is also
found in this subnetwork. Intriguingly, a number of genes involved in
circadian rhythms were also shown to have a sex and disease
interaction; these include Per1, Arntl (aryl hydrocarbon receptor
nuclear translocator‐like protein 1; also known as Bmal [brain and
muscle ARNT]), and Ccrn4ls. Among them, Per1 and Ccrn4l were also
members of the light yellow module shown in Figure [133]5. The
expression levels of the key genes in this subnetwork that showed a sex
bias are given in Figure [134]S2. We also confirmed a sex difference in
PPARα level with quantitative polymerase chain reaction
(Figure [135]S1).
Integrated Analysis of mRNA and miRNA
It is known that mRNAs are regulated in groups or networks by common
transcriptional regulators, such as miRNAs; therefore, we performed an
analysis of miRNAs on sham‐ and angiotensin II–treated male and female
hearts after 2 weeks of treatment, using the same hearts that were used
for mRNA measurement. The volcano plots illustrating miRNA differences
between male and female hearts at baseline, male control and
hypertrophy hearts, female control and hypertrophy hearts, and male and
female hearts with hypertrophy are shown in Figure [136]S3. The full
data set is available in Table [137]S4. Consistent with previous data
in the literature and supporting the validity of the model, we found
increases in miRNAs 15b, 21, 34, 199, 208b, and 214, which have all
been reported previously to change with hypertrophic stimuli.[138]43,
[139]44, [140]45, [141]46, [142]47
We further performed an integrative analysis of mRNA–miRNA interaction.
As described in the Methods, a Lasso regression model was used for
identification of miRNA–mRNA targeting relationships that combine
sequence‐based prediction and experimental condition–dependent
correlation in miRNA/mRNA expression variations. This was designed to
overcome the problem of high false‐positive rates in sequence‐based
predictions. This filtering with dynamic information of biological
context‐specific interactions improves reliability.
In total, TargetScan predicted 72 311 miRNA–mRNA targeting pairs, and
less than one‐third (23 323 pairs) remained after filtering with the
Lasso regression. We then used cytoscape/Partek to generate a network
of miRNA–mRNA targeting pairs most relevant to sex and hypertrophy
interaction, as shown in Figure [143]7B. This analysis demonstrates sex
and hypertrophy regulation of miRNAs 208b and 124. TargetScan shows
miRNA 124 as a regulator of PPARα.
PPARα Inhibition Ameliorates Sex Difference in Hypertrophy
PPARα was found to be at the center of the network of mRNAs that were
significantly different based on sex and hypertrophy (Figure [144]7);
therefore, we examined whether PPARα contributes to sex differences in
cardiac hypertrophy. To test whether PPARα regulates sex differences in
hypertrophy, we examined whether inhibition of PPARα would block the
sex differences observed with hypertrophic stimuli.
Cardiac hypertrophy was induced by treating male and female mice for
3 weeks with angiotensin II with and without GW6471, an inhibitor of
PPARα. Consistent with the data in Figure [145]1, a sex difference in
hypertrophy was observed with angiotensin II treatment (Figure [146]8).
Interestingly, this sex difference in cardiac hypertrophy was blocked
by treatment with GW6471 (Figure [147]8). These results are consistent
with the hypothesis that PPARα contributes to sex differences in
cardiac hypertrophy.
Figure 8.
Figure 8
[148]Open in a new tab
Inhibition of peroxisome proliferator‐activated receptor α (PPARα)
eliminates sex differences in cardiac hypertrophy. Angiotensin II (Ang
II) and the PPARα inhibitor GW6471 (4 mg/kg per day) were administered
for 3 weeks via osmotic minipumps, n=5 to 6. ^#Significantly different
compared with the vehicle group. Values represented as mean±SEM.
Significance was determined by ANOVA followed by a post hoc test.
P<0.05 was considered significant.
Discussion
Using a systems biology approach to explore sex differences in the
cardiac transcriptome, we identified a genetic network surrounding
PPARα that appears to be involved in the sexual dimorphism in cardiac
hypertrophy. Most studies on hypertrophy focus on 1 or 2 mRNA or
protein changes as the cause of hypertrophy, but it is becoming
increasingly apparent that clusters of genes operating in networks play
a role in regulating complex traits such as hypertrophy.[149]18 Because
there are sex differences in the development of hypertrophy, it is
reasonable to expect that sex differences in the regulation of these
networks may contribute to sex differences in hypertrophy. Consistent
with the concept that there are sex differences in the regulation of
metabolic networks, metformin,[150]48 which is used to treat diabetes
mellitus, has different effects on males and females.[151]49
To study sex differences in hypertrophy, we used a well‐established
model of angiotensin II–induced hypertrophy that was previously shown
to be associated with estrogen‐mediated differences in
hypertrophy.[152]50 The mRNA was extracted from the whole heart, thus
sex differences in cell‐type composition could influence the result.
Consistent with previous studies, we found major sex differences in
mRNAs at baseline.[153]51 A number of studies have examined
transcriptome changes in hypertrophy; however, only a few studies have
used a systems approach, and even fewer have looked at sex differences.
Rau et al[154]13 used a systems genetic approach to identify gene
pathways involved in isoproterenol‐mediated hypertrophy and identified
Adamts2 (ADAM metallopeptidase with thrombospondin type 1 motif 2) as a
driver of isoproterenol‐mediated hypertrophy. Interestingly, Adamts2
was found in the turquoise coexpression network module (see
Figure [155]5). Park et al[156]14 used a genomewide approach to
identify pathways involved in hypertrophy; they found an increase in
pathways involved in the immune response, extracellular matrix, and
cell morphology in hypertrophy and a decrease in mitochondria and
energy‐producing pathways. Foster et al[157]15 examined the protein and
mRNA changes in hypertrophy and found data suggesting a metabolic
bottleneck in fatty acid oxidation. Drozdov et al[158]16 performed a
similar coexpression network analysis to compare physiological and
pathological hypertrophy. They reported major differences in network
structure between physiological and pathological hypertrophy. Lai
et al[159]17 performed transcriptomic and metabolic profiling in a
hypertrophy model and an HF model in female mice. They reported that
transcription and posttranscriptional changes in mitochondrial
metabolic pathways in pressure overload induced HF. Sasagawa
et al[160]52 compared 5 models of hypertrophic cardiomyopathy and found
a consistent decrease in GSTK1 (glutathione S‐transferase κ1). Taken
together, the data in the literature suggest that hypertrophy involves
changes in inflammation, fibrosis, metabolism, extracellular matrix,
and ion channels. These data are consistent with our findings.
Only a few studies have examined sex differences in hypertrophy, and in
contrast to our study, which used RNA sequencing, all of these studies
used microarray chips to evaluate transcriptional changes. Heidecker
et al[161]53 studied 29 men and 14 women with idiopathic dilated
cardiomyopathy using the Affymetrix GeneChip. They reported that,
compared with women, men exhibited an increase in 35 and a decrease in
16 transcripts. Many of these differences were on sex chromosomes.
Kararigas et al[162]54 reported sex‐dependent differences in
transcripts in fibrosis and inflammation pathways in human pressure
overload hypertrophy. Fermin et al[163]55 studied dilated
cardiomyopathy in 30 female and 72 male patients and reported 1800
genes showing a sex difference, including genes in ion transport and
G‐protein–coupled receptor pathways. They also noted age dimorphisms in
female but not in male patients. Sex‐ and age‐dependent regulation of
collagen has been reported in humans.[164]56 In young women, collagen
types I and III are lower than in men, but with age, the trend
reverses, and women have higher collagen I and III levels compared with
men. Sex differences in collagen also occur during the development of
hypertrophy. Michel et al reported that chronic isoproterenol
stimulation in spontaneously hypertensive rats leads to an increase in
collagen deposition in males but not in females.[165]57
Using a statistical analysis, we identified mRNA differences associated
with sex and hypertrophy, and using STRING9, we built a gene
interaction network to identify subnetworks that are likely to be
involved in the difference in hypertrophy between males and females. As
shown in Figure [166]7, PPARα, a well‐established factor in
hypertrophy,[167]37, [168]38, [169]39, [170]40, [171]41 is at the
center of this hub. PPARα, a transcription factor that regulates
metabolism, was previously reported to play a role in regulation of
hypertrophy; however, it has not been implicated in sex differences in
hypertrophy. During hypertrophy, the heart's metabolism shifts with an
increase in glycolysis and a decreased reliance on fatty acid
oxidation, consistent with a reversion to a fetal gene program with
hypertrophy. Because PPARα is known to regulate fatty acid oxidation,
it is not surprising that PPARα has been well documented to play a role
in cardiac hypertrophy, although there is some disagreement as to
whether it is beneficial or detrimental.[172]39 Most studies,[173]40,
[174]41 but not all,[175]42 report a decrease in PPARα with
hypertrophy. Following hypertrophy, we found a decrease in PPARα in
males but not in females (see Figure [176]S2). It is possible that some
of the discrepancy in the literature might be explained by this sex
difference. The effects of increased or decreased levels or activity of
PPARα are complex, and depending on the conditions, both can enhance
hypertrophy. Addition of fenofibrate, a PPARα agonist, has been shown
to reduce hypertrophy.[177]55, [178]56 In contrast, treatment with a
different PPARα agonist, WY‐14643 did not reduce cardiac hypertrophy
and resulted in increased contractile dysfunction.[179]41 In addition,
mice with cardiac‐specific transgenic overexpression of PPARα develop
cardiac hypertrophy and a phenotype similar to that seen with diabetic
cardiomyopathy.[180]38
Hypertrophy has also been assessed in mice with a global deletion of
PPARα. Consistent with a role for PPARα in regulating fatty acid
oxidation, PPARα‐knockout mice have a decrease in fatty acid oxidation.
Most studies report no baseline dysfunction in PPARα‐knockout
mice.[181]60 Oka et al[182]42 reported that haplosufficiency of PPARα
attenuated pressure overload–induced hypertrophy. Taken together, the
data suggest that dysregulation of PPARα can modulate the development
of hypertrophy. It appears, however, that there is a “sweet spot” for
PPARα levels or activity; too much or too little PPARα can lead to
hypertrophy. The data in Figure [183]7 suggest that sex differences in
PPARα are involved in sex differences in hypertrophy. This hypothesis
would be consistent with data showing sex differences in the regulation
of PPARα.[184]61, [185]62, [186]63 Of note in PPARα‐knockout mice,
inhibition of carnitine palmitoyltransferase I resulted in death of
100% of the male mice but only 25% of the female mice, suggesting sex
differences in the response to changes in PPARα.[187]64 We reasoned
that if PPARα mediates the sex difference in hypertrophy, elimination
of sex differences in PPARα using an acute 3‐week treatment with an
inhibitor should block the sex differences in the development of
hypertrophy. Because adaptive changes can occur during global loss of
PPARα, we used an inhibitor to acutely inhibit PPARα. In support of a
role of PPARα in mediating sex differences in hypertrophy, we found
that 3 weeks of treatment with a PPARα antagonist eliminated the sex
difference in hypertrophy. These data support the concept that this
PPARα‐centered network is involved in sex differences in hypertrophy;
however, given the established role of PPARα in regulating hypertrophy,
it is not surprising that inhibition of PPARα blocks hypertrophy in
males. Because both inhibition and overexpression of PPARα can increase
hypertrophy, it is difficult to unambiguously test its role in sex
differences.
Although the sex‐ and hypertrophy‐dependent network in Figure [188]7 is
centered on PPARα, other known regulators of cardiac hypertrophy are
also involved, including ESRRG. ESRRG is activated by PGC‐1α (PPARγ
coactivator 1α) and PGC‐1β and has been shown to coordinate with PPARα
to regulate myocardial metabolism and hypertrophy. PPARα is known to be
involved in circadian regulation of metabolism, and BMAL1 (also known
as nocturnin), which is a component of circadian regulation and
regulates metabolism,[189]65 is also involved in this network. PPARα
has been shown to bind to a response element on the BMAL1 promoter; in
turn, BMAL1 is an upstream regulator of PPARα expression.[190]66
Cardiac‐specific deletion of BMAL leads to altered metabolism and
development of cardiomyopathy with aging.[191]67 BMAL1 forms a
heterodimer with CLOCK (clock circadian regulator), which regulates
Per1, shown in Figure [192]7 to be a component of this sex and
hypertrophy regulated network. Interestingly, BMAL1 regulates Ccrn4l,
which is also a member of the network in Figure [193]7. These data are
consistent with an emerging body of literature showing sex differences
in circadian regulation.
These data raise the question of what mediates the differential
regulation of this network. Because networks are typically regulated by
common transcription factors or common epigenetic signals, we
considered whether transcription factors might be involved in the
differential regulation of the network. The transcription factor
binding‐site family of KLF (Kruppel‐like factor) transcription factors
was overrepresented and showed a high Z‐score on promoters of genes
significant for sex–hypertrophy interaction (see Table [194]S5). Some
KLF transcription factors are reported to regulate PPARα,[195]68 and
KLF family members and PPAR exhibit circadian regulation,[196]69,
[197]70, [198]71 making them potential candidates. KLF4 has also been
shown to cooperate with ESRR and PGC‐1 to regulate mitochondrial
function and metabolism.[199]72
We also observed a sex‐ and hypertrophy‐dependent difference in
miR208b. miR208b is contained in an intron of Myh7, and miR208a, which
has close homology with miR208b, is generated from an intron of
Myh6.[200]73 In mice, Myh7 is the cardiac fetal isoform, and after
birth, the heart switches to Myh6. With hypertrophy, there is a switch
from Myh6 back to the fetal isoform Myh7 and a concomitant increase in
miR208b.[201]74, [202]75 Mechanical stress and hypothyroidism result in
a shift from Myh6 to Myh7, and this shift requires miR208a. When
miR208a is deleted, stress or hypothyroidism no longer leads to
upregulation of Myh7. Although an increase in miR208b has been reported
to occur with hypertrophy, there are no reports on sex differences.
PPARα and ESRRG were recently shown to regulate the skeletal muscle
fiber type switch to Myh7.[203]76 ESRRG was shown to activate, whereas
PPARα inhibited miR208b, and miR449 mediated upregulation of
Myh7.[204]76 Whether a similar program is present in the heart is
unclear.
In summary, we find that PPARα is at the center of a network that is
differentially regulated by sex and hypertrophy. We further demonstrate
that acute inhibition of PPARα blocks the sex difference in
hypertrophy. Many of the mRNAs regulated by sex and hypertrophy were
previously shown to be involved in hypertrophy, although sex
differences in their signaling have not been examined. The data in our
study suggest that the network in Figure [205]7 is differentially
regulated in hypertrophy and that this differential regulation leads to
sex differences in hypertrophy. These data could have important
implications for drugs used to treat hypertrophy.
Sources of Funding
All investigators in this study were funded by the intramural program
of the National Heart, Lung, and Blood Institute of National Institutes
of Health.
Disclosures
None.
Supporting information
Table S1. Selected mRNA Transcripts: mRNA Transcripts With >2‐Fold
Changes and 10% False Discovery Rate
[206]Click here for additional data file.^ (494.3KB, xlsx)
Table S2. Coexpression Network of the 4422 Genes Showing Significant
Variations in Response to Sex, Hypertrophy, or the Interaction of Sex
and Hypertrophy (Their Membership Modules are Given in the GO and
Panther Tabs)
[207]Click here for additional data file.^ (2MB, xlsx)
Table S3. Genes That Showed Significant Changes in Hypertrophy When Sex
as a Cofactor Was Controlled, Significant Sex‐Dependent Variation When
Hypertrophy as a Cofactor Was Controlled, and Significant Sex‐Dependent
Differential Hypertrophy‐Associated Changes (Pathway Enrichment
Analysis Results Are in Tab S2)
[208]Click here for additional data file.^ (110.3KB, xlsx)
Table S4. Data Set of MicroRNAs
[209]Click here for additional data file.^ (115KB, xlsx)
Table S5. Overrepresentation of Transcription Factor (TF) Families in
Identified Genes by Interaction Model
Figure S1. Quantitative polymerase chain reaction measurement.
Figure S2. Sex differences in expression changes of genes identified by
network analysis in Figure [210]7.
Figure S3. Volcano plots of microRNAs with sex and hypertrophy.
[211]Click here for additional data file.^ (1.1MB, pdf)
(J Am Heart Assoc. 2017;6:e005838 DOI:
[212]10.1161/JAHA.117.005838.)28862954
References