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Homeostatic levels of SRC-2 and SRC-3 promote early human adipogenesis

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Abstract and Figures

The related coactivators SRC-2 and SRC-3 interact with peroxisome proliferator activated receptor γ (PPARγ) to coordinate transcriptional circuits to promote adipogenesis. To identify potential coactivator redundancy during human adipogenesis at single cell resolution, we used high content analysis to quantify links between PPARγ, SRC-2, SRC-3, and lipogenesis. Because we detected robust increases and significant cell-cell heterogeneity in PPARγ and lipogenesis, without changes in SRC-2 or SRC-3, we hypothesized that permissive coregulator levels comprise a necessary adipogenic equilibrium. We probed this equilibrium by down-regulating SRC-2 and SRC-3 while simultaneously quantifying PPARγ. Individual or joint knockdown equally inhibits lipid accumulation by preventing lipogenic gene engagement, without affecting PPARγ protein levels. Supporting dominant, pro-adipogenic roles for SRC-2 and SRC-3, SRC-1 knockdown does not affect adipogenesis. SRC-2 and SRC-3 knockdown increases the proportion of cells in a PPARγ(hi)/lipid(lo) state while increasing phospho-PPARγ-S114, an inhibitor of PPARγ transcriptional activity and adipogenesis. Together, we demonstrate that SRC-2 and SRC-3 concomitantly promote human adipocyte differentiation by attenuating phospho-PPARγ-S114 and modulating PPARγ cellular heterogeneity.
Cell-to-cell relationships between SRC-2, SRC-3, and PPARγ. (A and B) SRC-2 (A) and SRC-3 (B) were monitored along with lipids at the single cell level at the indicated time points. (C) Cell–cell variability, measured as the CV (σ/µ), of the indicated properties at 96 h. (D and E) SRC-2 (D) and SRC-3 (E) normalized median intensities were monitored as a function of time in PPARγlo and PPARγhi cell populations by immunofluorescence. For each experiment and time point, individual cell measurements of PPARγ and SRC were normalized to the median intensity at 96 h. PPARγ normalization set the threshold for binary subdivision into PPARγlo and PPARγhi populations arbitrarily equal to 1. This threshold was then applied to each time point, and normalized SRC levels were calculated (*, P < 0.05 for PPARγlo vs. PPARγhi). (F) Shown are immunofluorescence images of SRC/PPARγ/lipid (L) after 96 h of adipocyte differentiation from one representative experiment with (G) contour mapping of SRC/PPARγ/lipid relationships. Density plots show normalized lipid expression as a function of normalized SRC and PPARγ levels (n ≥ 1,900 cells). (H) Cells were divided into four quadrants based on their median PPARγ and lipid levels, followed by calculation of the normalized median SRC-2 or SRC-3 intensity inside each population. In all experiments, SRC levels were normalized to the median intensity at 96 h (n = 6 independent experiments; *, P < 0.05). Error bars indicate SEM. Bars, 50 µm.
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J. Cell Biol. Vol. 192 No. 1 55–67 JCB 55
Correspondence to Michael A. Mancini:
Abbreviations used in this paper: C/EBP, CAAT/enhancer binding protein-;
CMBl, CellMask blue; CV, coefficient of variation; HCA, high-content analysis;
HTM, high throughput microscopy; IBMX, 3-isobutyl-1-methylxanthine; PPAR,
peroxisome proliferator activated receptor ; qPCR, quantitative RT-PCR; seFRET,
sensitized emission fluorescence resonance energy transfer; SRC, steroid recep-
tor coactivator.
The dominant cellular basis of obesity is increased fat cell size
during the adipocyte differentiation process. The process is
marked by accretion of triglycerides within intracellular lipid
droplets (Farmer, 2006). Adipogenesis is tightly regulated by
peroxisome proliferator activated receptor (PPAR), a mem-
ber of the ligand-activated nuclear receptor superfamily of
transcription factors. Mechanistically, exogenous (thiazolidine-
diones) or endogenous (eicosanoids) ligands activate PPAR by
stabilizing the active conformation of the ligand-binding domain
(Nolte et al., 1998) to induce or repress a wide array of differ-
entiation-dependent and adipose-specic genes. PPAR mRNA
and protein expression are robustly induced in a feed-forward
loop with CAAT/enhancer binding protein- (C/EBP) during
adipogenesis (Wu et al., 1999; Rosen et al., 2002). The process
is initially stimulated by several up-stream transcription factors:
C/EBP, C/EBP (Yeh et al., 1995; Wu et al., 1996; Zuo et al.,
2006), and coregulators, including the p160 class of steroid re-
ceptor coactivators (SRCs; Louet and O’Malley, 2007).
A critical step required for adipogenesis is the down-
regulation of kinase signaling pathways targeting PPAR to
permit its full transcriptional activity (Hu et al., 1996; Adams
et al., 1997; Camp and Tafuri, 1997). Specically, the pro-
adipogenic function of PPAR is decreased by mitogen-activated
protein kinase (MAPK) phosphorylation in the N-terminal A/B
region (mouse S112/human S114), which concomitantly re-
duces thiazolidinedione afnity for PPAR (Shao et al., 1998).
Overexpression of a nonphosphorylatable form of PPAR pro-
motes insulin sensitization and elevated adipogenesis in 3T3L1
(Hu et al., 1996; Shao et al., 1998). Additionally, mouse em-
bryonic broblasts expressing a serine-to-alanine substitution at
codon 112 (Rangwala et al., 2003) exhibit a similar effect. PPAR
phosphorylation at S112/S114 also decreases interactions with
The related coactivators SRC-2 and SRC-3 interact
with peroxisome proliferator activated receptor
(PPAR) to coordinate transcriptional circuits to pro-
mote adipogenesis. To identify potential coactivator re-
dundancy during human adipogenesis at single cell
resolution, we used high content analysis to quantify
links between PPAR, SRC-2, SRC-3, and lipogenesis.
Because we detected robust increases and significant
cell–cell heterogeneity in PPAR and lipogenesis, with-
out changes in SRC-2 or SRC-3, we hypothesized that
permissive coregulator levels comprise a necessary adi-
pogenic equilibrium. We probed this equilibrium by
down-regulating SRC-2 and SRC-3 while simultaneously
quantifying PPAR. Individual or joint knockdown
equally inhibits lipid accumulation by preventing lipo-
genic gene engagement, without affecting PPAR protein
levels. Supporting dominant, pro-adipogenic roles for
SRC-2 and SRC-3, SRC-1 knockdown does not affect
adipogenesis. SRC-2 and SRC-3 knockdown increases
the proportion of cells in a PPARhi/lipidlo state
while increasing phospho-PPAR–S114, an inhibitor
of PPAR transcriptional activity and adipogenesis.
Together, we demonstrate that SRC-2 and SRC-3 con-
comitantly promote human adipocyte differentiation by
attenuating phospho-PPAR–S114 and modulating PPAR
cellular heterogeneity.
Homeostatic levels of SRC-2 and SRC-3 promote
early human adipogenesis
Sean M. Hartig,1 Bin He,1 Weiwen Long,1 Benjamin M. Buehrer,2 and Michael A. Mancini1
1Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030
2Zen-Bio, Inc., Research Triangle Park, NC 27709
© 2011 Hartig et al. This article is distributed under the terms of an Attribution–
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lication date (see After six months it is available under a
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as described at
JCB • VOLUME 192 • NUMBER 1 • 2011 56
to SRC-3/ mice, which provides evidence of a dominant pro-
adipogenic role for SRC-3.
Although the similarities observed among SRC-2 and
SRC-3 knockout mice indicate a functional overlap and domi-
nant pro-adipogenic roles, no data exists on the contributions of
cell–cell variability between SRC-2, SRC-3, and PPAR that
collectively and/or redundantly promote human adipogenesis.
Accordingly, the purpose of this study was to focus upon SRC-2
and SRC-3 and dissect the early interplay between these co-
activators and PPAR that converts a human preadipocyte into a
mature fat cell. Here, we developed and used a high-throughput
microscopy-based, high-content analysis (HCA) approach to
quantify the effects of SRC loss of function on the cell-to-cell
population dynamics of PPAR. Our results emphasize the
novel regulatory role of steady-state levels of SRC-2 and SRC-3
in human adipogenesis, specically by promoting lipid accu-
mulation under both high and low PPAR phenotypes marked
by attenuation of PPAR phosphorylation at S114.
Human adipocyte differentiation occurs
independently of static SRC mRNA profiles
SRCs have been shown to be critical elements of the murine
adipogenic gene program (Picard et al., 2002; Louet et al., 2006;
SRCs (Shao et al., 1998), resulting in a potential negative
cooperative effect on PPAR-regulated, adipocentric genes.
The p160 family of SRCs (SRC-1, SRC-2, and SRC-3) de-
ned the rst class of coregulators (CoR) that enhance nuclear
receptor transactivation in a ligand-dependent manner, bridging
NRs to other components of the basal transcriptional machinery
and integrating both genomic and nongenomic signals (Oñate
et al., 1995; Anzick et al., 1997; Hong et al., 1997). Depending
on the ligand context, biochemical assays have shown that each
of the SRCs potentiate the transcriptional activity of PPAR
through direct interactions (McInerney et al., 1998; Kodera
et al., 2000; Rocchi et al., 2001; Louet et al., 2006). However,
SRC-2 and SRC-3 share the highest degree of sequence ho-
mology and promote adipogenesis in knockout mouse models.
SRC-2/ mice are protected from obesity because of enhanced
energy expenditure, decreased white adipocyte differentiation,
and increased thermogenic activity of brown adipose (Picard
et al., 2002). SRC-3 ablation leads to lean mice with increased
energy expenditure and decreased adipogenesis (Louet et al.,
2006; Coste et al., 2008). However, upon high-fat diet feed-
ing, SRC-1/ knockout mice are slightly prone to obesity be-
cause of both a reduced capacity for fatty acid oxidation and
decreased energy expenditure (Picard et al., 2002). Moreover,
double knockout of SRC-1 and SRC-3 results in a lean pheno-
type and increased metabolic rate (Wang et al., 2006), similar
Figure 1. Expression of SRC-2, SRC-3, PPAR, and adipocentric genes during human adipocyte differentiation. Subcutaneous human preadipocytes were
differentiated for 4 d, and total mRNA was isolated at each indicated interval. The mRNA levels of SRC-1, SRC-2, and SRC-3 (A); and PPAR2, C/EBP,
SREBP1c, ADFP, and FASN (B) were determined by qPCR. -actin served as an internal control. RNA was collected from at least two independent isolations
and error bars represent SEM. Asterisks indicate levels of induction statistically different from 0 h (P < 0.05).
57p160 coactivators promote adipocyte heterogeneity • Hartig et al.
After 7 d, 88.1% of cells were positive for lipids, which sug-
gests that the preadipocyte cell population responds robustly
to chemical induction of adipogenesis in a unidirectional man-
ner (Fig. S1 A). Consistent with the qPCR results (Fig. 1 A),
SRC-1 was signicantly increased (50%), whereas modest,
statistically insignicant changes in SRC-2 (15%) and SRC-3
(22%) were detected. Using additional software with built-in
lipid droplet analysis tools (McDonough et al., 2009), we
detected increases in the number of lipid droplets/nuclei
(+250%; Fig. 2 D), total lipid droplet area (+120%; Fig. 2 E),
and lipid content/cell (12-fold induction; Fig. 2 F). Dose
response experiments using rosiglitazone (BRL49653) or a
natural PPAR ligand (15-deoxy-12,14-prostaglandin J2;
15dPGJ2) showed that both ligands increase lipogenesis
(Fig. S1 B), PPAR (Fig. S1 C), and SRC-1 levels (Fig. S1 D).
Increasing concentrations of 15dPGJ2 and BRL49653 did not
signicantly alter the protein expression of SRC-2 (Fig. S1 E)
and SRC-3 (Fig. S1 F).
Cell-to-cell measurements of lipids as a
function of PPAR represent population
heterogeneity during human adipogenesis
At the population level, the time-dependent induction of
PPAR was positively correlated with the accumulation of
lipids, which is consistent with previously established deter-
ministic paradigms of PPAR-mediated adipogenesis (Rosen
et al., 1999). However, when the differentiating fraction of
preadipocytes was quantitatively examined at the single cell
level, a large degree of population heterogeneity was observed,
which is in agreement with recent studies on the 3T3L1 dif-
ferentiation program (Loo et al., 2009). These subpopulation
changes were apparent as early as 24 h after MIX treatment,
and more visible at 96 h (Fig. 3 A). This heterogeneity is
illustrated in Fig. 3 B by plotting, on a cell-by-cell basis,
Coste et al., 2008). Given these results, we used quantitative
RT-PCR (qPCR) to measure mRNA expression during the
first 4 d of human adipocyte differentiation with rosigli-
tazone, dexamethasone, 3-isobutyl-1-methylxanthine (IBMX),
and insulin (MIX). Surprisingly, we found that although
SRC-1 mRNA levels were increased twofold, SRC-2 and
SRC-3 were not changed during the rst 4 d of differentiation
(Fig. 1 A). We also measured mRNA levels of several tran-
scription factors that stimulate differentiation and markers
of lipogenesis (Fig. 1 B). The mRNA levels of C/EBP (40-
fold), PPAR (10-fold), SREBP1c (10-fold), FASN (20-fold),
and ADFP (threefold) were up-regulated in response to 96 h
of treatment with MIX. These initial experiments suggested
that human adipocytes maintain constant SRC-2 and SRC-3
mRNA levels while up-regulating SRC-1 transcripts during
early differentiation.
HCA of human adipocyte cell populations
validates mRNA results for SRCs
and PPAR
To further understand the dynamics of SRC-1, SRC-2, SRC-3,
and PPAR during differentiation, we validated the mRNA
proling (Fig. 1) with protein and lipid measurements at the
single cell level using HCA. Subcutaneous preadipocytes were
differentiated for 4 d, then xed and labeled for DNA (DAPI),
SRC-1, SRC-2, SRC-3, or PPAR. Lipid droplets were labeled
with a uorescent neutral lipid dye to mark differentiating
cells. We next quantied changes in these properties for each
cell (≥1,000 cells/condition/experiment), by automated cell
and nucleus identication (Fig. 2 A) with both in-house algo-
rithms and commercially available software. As indicated
in Fig. 2 B, mean PPAR protein levels increased monotoni-
cally (2.8-fold) over the rst 4 d, which correlates with an ap-
proximately eightfold induction of lipid accumulation (Fig. 2 C).
Figure 2. Development of an image-based
analysis platform to study PPAR and co-
activator expression in human adipocytes.
(A) Shown are representative grayscale images
of adipocytes differentiated for 96 h, immuno-
labeled with antibodies to SRC-2 or SRC-3,
and then stained with DAPI, CMBl, and Lipid-
TOX (Lipid). Binary nuclear and cellular masks
were generated by a combination of watershed
and threshold image transformations (Pipeline
Pilot; Accelrys). Nuclear masks are indicated
in green; whole cell masks are shown in red.
(B–F) Nuclear and cellular masks were used to
extract pixel-based measurements that describe
nuclear PPAR, SRC-1, SRC-2, SRC-3 levels
(B) and lipid accumulation (C) during a 96-h
differentiation period. Additional software
(CyteSeer; Vala Sciences) was used to calcu-
late the number of lipid droplets/nuclei (D),
lipid droplet area (E), and fold induction of
lipid (F). Experiments shown are the mean of
11 independent experiments. Values are the
mean fold induction. Error bars indicate SEM
(*, P < 0.05 compared with 0 h). Bar, 50 µm.
JCB • VOLUME 192 • NUMBER 1 • 2011 58
SRC-2 and SRC-3 levels are correlated
with PPAR-dependent lipogenesis
Biochemical studies and mouse models have indicated that pro-
adipogenic transcriptional activity of PPAR is maintained by
functional interactions with the coactivators SRC-2 and SRC-3
(McInerney et al., 1998; Rocchi et al., 2001; Louet et al., 2006).
Therefore, we sought to understand the single cell relationship
between these SRCs and PPAR. First, we analyzed the level
of heterogeneity that existed for both SRC-2 and SRC-3 in
individual cells during the rst 96 h of human adipocyte differen-
tiation. Analysis of the cell–cell heterogeneity in both SRC-2
(Fig. 4 A) and SRC-3 (Fig. 4 B) levels indicated a <10-fold
lower range of expression levels compared with PPAR (>100-
fold). This tighter range of SRC-2 or SRC-3 levels was consis-
tent with (96 h) and without (0 h) differentiation, whereas lipid
content increased independent of single cell SRC-2 and SRC-3
expression. We further represented this contrast in variability
by calculating the coefcient of variation (CV = /) after
96 h of differentiation, where a higher CV indicates wider sys-
tem heterogeneity. In agreement with scatter plot representations
of PPAR (Fig. 3 B), SRC-2 (Fig. 4 A), and SRC-3 (Fig. 4 B),
CVs for PPAR and lipid were signicantly greater than those
calculated for SRC-2 and SRC-3.
Next, we simultaneously detected SRC/PPAR (Fig. 4 F)
to determine if there were cells with SRC-2 or SRC-3 levels
that correlated with PPAR and/or lipids. Individual cell
measurements of PPAR and SRC were normalized to their
respective median intensities at 96 h. PPAR normalization
cellular lipid content as a function of nuclear PPAR protein
levels. At 96 h after induction by MIX, 100-fold variation
in PPAR and a range of three orders of magnitude in lipid
content were observed. This was an undetectable response at
the median protein (immunouorescence; Fig. 2) and mRNA
levels (qPCR; Fig. 1). To explore this heterogeneity across
experiments, we subdivided the cell populations. For each
experiment and time point, individual cell measurements of
PPAR and lipid were normalized to the median intensity at
96 h, which set the threshold for quadrant subdivision for
both properties arbitrarily equal to 1. This threshold was then
applied to each time point. Subsequently, this created four
subpopulation quadrants (Fig. 3 C): high PPAR/high lipid
(PPARhi /lipidhi), high PPAR/low lipid (PPARhi/lipidlo),
low PPAR/low lipid (PPARlo/lipidlo), and low PPAR/high
lipid (PPARlo/lipidhi). Temporal analysis of these quadrants
showed that the PPARhi/lipidhi population increased from 2%
after 24 h of differentiation to 33% at 96 h. More interestingly,
however, was the up-regulation of the PPARhi/lipidlo popula-
tion at 24 h (21% change) followed by a decrease (12%) at
96 h. Based on these results, the nonessential role of SRC-1
(Fig. S2), and the central functions of SRC-2 and SRC-3 in
adipogenesis (Picard et al., 2002; Louet et al., 2006; Wang
et al., 2006; Coste et al., 2008), we proposed that variation in
PPAR (Fig. 3 C) might be dictated by factors, specically
SRC-2 and SRC-3, whose overall expression level was not
regulated by differentiation but nonetheless were important
for the early human adipogenic phenotype.
Figure 3. Population dynamics of PPAR protein
expression as a function of lipid content during
the early phases of human adipocyte differen-
tiation. (A) Representative images of adipocytes
during differentiation were immunolabeled for
PPAR, and lipids were labeled and imaged by
high throughput microscopy (HTM). Bar, 50 µm.
(B) Cell-to-cell variation in PPAR and lipids dur-
ing the first 96 h of differentiation was monitored.
One representative experiment is shown. Individ-
ual cell measurements of PPAR and lipid were
normalized to the median intensity at 96 h, which
set a threshold (dotted lines) that was applied
to each time point to create the PPARx/lipidy
populations. An example cell is shown from each
quadrant. Bar, 20 µm. (C) Pie charts are shown
that indicate the change in population distribution
over this time period after median threshold appli-
cation (n = 5 independent experiments).
59p160 coactivators promote adipocyte heterogeneity • Hartig et al.
and lipids exhibited a less pronounced relationship. Speci-
cally, SRC-2 and SRC-3 intensities within cell subpopulations
dened by PPAR and lipid levels in Fig. 3 showed that the
PPARhi/lipidhi cells had signicantly higher levels of SRC-2
or SRC-3 than the other quadrants (Fig. 4 H). Interestingly,
this population also had signicantly higher correlation co-
efcients between SRC-2/PPAR and SRC-3/PPAR. In con-
trast, PPARhi /lipidlo cells exhibited the lowest SRC/PPAR
correlation (Table I).
The correlations between SRCs and PPAR established
a quantitative relationship between lipid, PPAR, and SRC
levels in human adipocytes, but did not indicate the nature
of the interaction between these proteins. To visualize the
interactions between PPAR/SRC-2 and PPAR/SRC-3 occur-
ring in response to differentiation cues, we used sensitized
emission uorescence resonance energy transfer (seFRET).
set the threshold for binary subdivision into PPARlo and
PPARhi populations arbitrarily equal to 1. This threshold was
then applied to each time point, and normalized SRC levels
were calculated. As shown in Fig. 4 D, SRC-2 levels were
higher in the PPARhi population across all time points. SRC-3
(Fig. 4 E) showed a similar pattern at 24 h and 96 h only. The
SRC-2 and SRC-3 levels in the PPARlo and PPARhi were
not affected by differentiation, which suggested that these
populations might represent, in terms of SRC and PPAR,
similar biological states. Single cell analysis of the correla-
tion (Pearson’s r [Pr]) between SRC and PPAR levels at 96 h
showed signicant, positive correlation between both SRC-2
and PPAR (Pr = 0.39, n = 6) and SRC-3 and PPAR (Pr =
0.33, n = 6). Contour mapping (Fig. 4 G) of lipid intensity,
as a function of SRC and PPAR, showed that high SRC-2
and PPAR correlated with increased lipids. SRC-3, PPAR,
Figure 4. Cell-to-cell relationships between SRC-2, SRC-3, and PPAR. (A and B) SRC-2 (A) and SRC-3 (B) were monitored along with lipids at the single
cell level at the indicated time points. (C) Cell–cell variability, measured as the CV (/µ), of the indicated properties at 96 h. (D and E) SRC-2 (D) and SRC-3
(E) normalized median intensities were monitored as a function of time in PPARlo and PPARhi cell populations by immunofluorescence. For each experiment
and time point, individual cell measurements of PPAR and SRC were normalized to the median intensity at 96 h. PPAR normalization set the threshold
for binary subdivision into PPARlo and PPARhi populations arbitrarily equal to 1. This threshold was then applied to each time point, and normalized SRC
levels were calculated (*, P < 0.05 for PPARlo vs. PPARhi). (F) Shown are immunofluorescence images of SRC/PPAR/lipid (L) after 96 h of adipocyte
differentiation from one representative experiment with (G) contour mapping of SRC/PPAR/lipid relationships. Density plots show normalized lipid expres-
sion as a function of normalized SRC and PPAR levels (n ≥ 1,900 cells). (H) Cells were divided into four quadrants based on their median PPAR and lipid
levels, followed by calculation of the normalized median SRC-2 or SRC-3 intensity inside each population. In all experiments, SRC levels were normalized
to the median intensity at 96 h (n = 6 independent experiments; *, P < 0.05). Error bars indicate SEM. Bars, 50 µm.
JCB • VOLUME 192 • NUMBER 1 • 2011 60
SRC-2 and SRC-3 are essential for the
adipocentric phenotype
The combination of qPCR, HCA, and FRET results led us to
hypothesize that static, permissive SRC-2 and SRC-3 levels,
occurring in the entire population contribute to an equilib-
rium condition that controls human adipogenesis. To perturb
the postulated SRC-2 and SRC-3 equilibrium, we performed
siRNA-based knockdowns (individually or in tandem) while
simultaneously using antibodies to detect cell-to-cell changes in
individual SRC protein levels. This approach uniquely allows
cell-by-cell monitoring of target knockdown and any effect on
differentiation. After a 48-h siRNA knockdown, preadipocytes
were induced to differentiate for up to 96 h, and the extent of
lipid accumulation and SRC levels was quantied by HCA.
Shown in Fig. 6 A, single knockdown of SRC-2 or SRC-3 re-
sulted in decreased lipid accumulation. In line with previous
observations (Louet et al., 2006), SRC-1 siRNA had no effect
on lipogenesis or adipocentric gene expression (Fig. S2). More-
over, there was no apparent synergistic or additive inhibition
of adipogenesis with dual SRC-2/SRC-3 siRNA knockdown.
Additionally, siRNA targeting SRC-2 or SRC-3 did not alter
the expression of the other CoR detected at the immunouores-
cence level (Fig. S3 A) and quantied by HCA (Fig. 6 B). qPCR
analyses of SRC levels in the median population levels for
single or dual siRNA (Figs. 6 C and S3, B–F) showed consistent
knockdown for both messages, validating our protein (HCA)
measurements 6 d after transfection.
CFP PPAR2/YFP SRCs were cotransfected for 48 h and
subsequently treated with MIX or vehicle (DMSO) for 2 h.
A strong FRET signal, chiey localized in a heterotypic pat-
tern within the nucleus, was observed when CFP-PPAR/
YFP–SRC-2 or CFP-PPAR2/YFP–SRC-3 were coexpressed
in HeLa cells (Fig. 5 A). FRET was measured both treatment
conditions implying differentiation-independent interactions
between SRCs and PPAR. Although vehicle treatment ex-
hibited a high basal level of FRET, statistically signicant
increases (>1.5-fold change) in FRET were detected in the
presence of MIX (Fig. 5 B). Cells coexpressing YFP-SRC/
ECFP-NLS or CFP-PPAR2/EYFP-NLS were used as nega-
tive controls and exhibited signicantly less FRET (Fig. 5 C)
than PPAR–SRC fusion pairs.
Figure 5. SRC-2 and SRC-3 interact with
PPAR in a differentiation-independent manner.
(A) seFRET was used to evaluate the inter-
actions between CFP-PPAR2/YFP SRC-2 or
CFP-PPAR2/YFP SRC-3 after exposure to
either vehicle (DMSO) or MIX for 2 h in wild-
type HeLa cells. Representative images are
shown from one experiment for a single channel
(CFP or YFP) with the calculated FRET image.
Bars, 10 µm. (B) For each cell, the net FRET
between CFP-PPAR2 and YFP-SRC was de-
termined using the softWoRx user interface.
FRET was measured within nucleoplasmic re-
gions of interest only. (C) Control plasmids,
ECFP or EYFP fused to a NLS sequence, were
coexpressed, and FRET was determined. On
average, CFP-PPAR2/YFP-SRC FRET signals
were 8–20× greater than those measured in
vector control experiments (n 22 cells mea-
sured over three independent experiments;
*, P < 0.05 compared with vehicle treatment).
FRET signals were scaled between minimum
and maximum signals (0–1,200 pixels), and
intensity was colored as shown. Error bars
indicate SEM.
Table I. Pearson’s product moment correlation coefficients
were calculated for PPAR/SRC-2 and PPAR/SRC-3 inside of
PPARx/lipidy subpopulations
Phenotype Lipidlo Lipidhi
PPARlo 0.21 ± 0.05 0.25 ± 0.10
PPARhi 0.16 ± 0.06 0.43 ± 0.10
PPARlo 0.17 ± 0.10 0.13 ± 0.06
PPARhi 0.09 ± 0.08 0.33 ± 0.13
Data are presented as the mean ± SEM. n = 6.
61p160 coactivators promote adipocyte heterogeneity • Hartig et al.
down-regulation of SRC-2, SRC-3, or SRC-2/SRC-3 resulted in
decreases in central adipogenic transcription factors (Fig. 6 H):
PPAR (>29%) and C/EBP (>40%). Further, reductions in lipo-
genic gene expression were also measured (Fig. 6 H): ADFP
(>57%), SREBP1c (>59%), and FASN (>64%). To probe the
action of SRCs in gain-of-function experiments, SRC-1, SRC-2,
and SRC-3 were overexpressed by lentiviral infection. Impor-
tantly, we ignored expression level artifacts that present them-
selves as protein aggregates (Stenoien et al., 2000, 2002; Feige
et al., 2005). Although single or double SRC-2/SRC-3 siRNA
decreased lipogenesis, moderate (1.5–2.5 times endogenous
protein) overexpression of SRC-1, SRC-2, and SRC-3 showed
modest increases in lipogenesis (<1.6-fold compared with
FLAG control) after 96 h of differentiation (Fig. S5). Each of
these results suggested that equilibrium levels of only SRC-2
and SRC-3 are needed for human adipocyte differentiation and
lipogenic gene regulation.
Quantication over a large span of experiments (n = 7
independent replications, >1,000 cells/condition) showed that
a >60% reduction in either or both SRC-2 and SRC-3 led to
a 40% decrease (Fig. 6 D) in the number of lipid droplets/
nuclei without altering lipid droplet size (Fig. 6 E). We next
determined the effect of SRC-2/SRC-3 on the rate of lipo-
genesis in the presence of a synthetic (BRL49653/rosiglitazone)
or the natural PPAR agonist, 15dPGJ2 (Forman et al., 1995;
Kliewer et al., 1995). In these loss-of-function experiments,
SRC-2/SRC-3 single or joint knockdown slowed lipogenesis
at 96 h by at least 50% (compared with scrambled siRNA con-
trol) without signicantly affecting the induction of PPAR.
Additionally, the effect was observed under differentiation with
rosiglitazone (Fig. 6 F) or 15dPGJ2 (Fig. 6 G). This result sug-
gests that SRC-2 and SRC-3 are critical components of the basal
adipogenic machinery, in agreement with in vivo data (Picard
et al., 2002; Louet et al., 2006; Coste et al., 2008). By qPCR,
Figure 6. Single or double siRNA knockdown of SRC-2 and SRC-3 disrupts adipocyte differentiation without affecting PPAR protein induction. (A) Pre-
adipocytes were reverse-transfected with scrambled (scR) or siRNA to SRC-2, SRC-3, or both SRC-2/SRC-3 for 48 h followed by induction of differentia-
tion, and imaging. Bar, 50 µm. (B) HCA detection of p160 levels after siRNA transfection. (C) qPCR was used to validate measurements of SRC-2 or SRC-3
knockdown by HCA. (D and E) Lipid droplet count (D) and lipid droplet area (E) were determined after siRNA knockdown and 4 d of differentiation (n = 7
independent experiments). (F and G) The effects on the rate of lipogenesis and PPAR induction were determined by differentiation of preadipocytes for the
indicated time points in the presence of either 3 µM BRL49653 (rosiglitazone; F) or 30 µM 15dPGJ2 (G) after SRC siRNA transfection (n = 3 independent
experiments). (H) Heat map summary of the downstream effects of siRNA to SRC-2/SRC-3 on lipid accumulation markers as measured by qPCR: PPAR2,
C/EBP, ADFP, FASN, and SREBP1c. RNA was isolated from two independent experiments. Asterisks indicate measured variables statistically different from
the nontargeting siRNA control at the 95% confidence level (*, P < 0.05). Error bars indicate SEM.
JCB • VOLUME 192 • NUMBER 1 • 2011 62
suggested: (a) up-regulation of phospho-PPAR levels coincided
with (b) reduction of lipid accumulation and (c) enrichment of
cells in a PPARhi/lipidlo state. Simultaneous immunouores-
cence detection of phospho-PPAR and total PPAR in our
subpopulation analyses also showed that, with respect to the
nontargeting control, the mean single cell intensity ratio of
phospho-PPAR to PPAR (ph-PPAR/PPAR) was highest in
the lipidlo populations (Fig. 7 E). This nding was consistent
with our hypothesis that the observed defect in lipogenesis was
caused by a specic subpopulation up-regulation of phospho-
PPAR. Collectively, data from Figs. 6 and 7 suggest that per-
missive levels of the coactivators SRC-2 and SRC-3 attenuate
phospho-PPAR to promote a full adipogenic response.
The contribution of transcription factors and the associated regula-
tory machinery to the development of functional heterogeneity
among white fat depots remains largely undiscovered, especially
in the context of human adipogenesis. Recent cell culture studies
(Le and Cheng, 2009; Loo et al., 2009) have indicated that concur-
rent physiological and molecular states may exist in differentiating
3T3L1 preadipocytes largely as a response to systemic or growth
factor stimulation. Additionally, PAR titrated knock-in transgenic
mice (Tsai et al., 2009) demonstrated that selective reduction of
PPAR only affected the accumulation of perigonadal fat, without
decreasing retroperitoneal, inguinal, mesenteric, or subcapsular
adipose mass, an indication that PPAR functional heterogeneity
at the whole animal level can exist without compromising general-
ized adipogenesis. Our single cell–oriented data are consistent and
expand upon these ndings, showing a wide cell-to-cell variability
(Fig. 3) in the early human adipocyte differentiation cascade.
During this period, cells exhibit and support continuous PPAR
states with and without lipids, even while robust activation of pro-
adipogenic genes occurs at the population level (Fig. 1).
Synthetic and natural ligands bind PPAR in a relatively
large, promiscuous ligand-binding pocket that alters receptor
conformation to assemble active transcriptional machinery
(Nolte et al., 1998). Distinct from the hormonal up-regulation of
PPAR and its effects upon its downstream targets, bulk mRNA
(Fig. 1) and protein (Fig. 2) levels of pro-adipogenic coregulators
SRC-2 and SRC-3 remain quite constant during the rst 96 h of
differentiation independent of natural (15dPGJ2) and synthetic
(rosiglitazone) ligands (Fig. S2). Further examination of the
cell–cell correlations between SRC-2, SRC-3, and PPAR re-
vealed that (a) PPARhi/lipidhi cells also exhibited the highest
SRC levels and a correlation between PPAR/SRC, whereas
(b) all other PPAR/lipid populations showed little or no correla-
tion between PPAR and SRC (Table I). In addition to correlations
between SRCs, PPAR, and lipids, we have also shown a ligand-
independent direct (FRET) interaction between SRC-2/PPAR
and SRC-3/PPAR. Recent data suggests that the N-terminal
A/B domain of PPAR can act as a docking site for coregula-
tors, in the absence of a ligand, to maintain a basal level of
constitutive transcriptional activity, but it can also direct and
enhance target gene specicity of the receptor (Gelman et al.,
1999; Feige et al., 2005; Molnár et al., 2005; Tudor et al., 2007).
SRC-2 and SRC-3 promote adipocyte
heterogeneity and attenuate
PPAR phosphorylation
Although our experiments revealed a central function of SRC-2
and SRC-3 in promoting human adipogenesis both phenotypi-
cally (HCA) and transcriptionally (qPCR), follow-up experi-
ments showed that when PPAR mRNA was reduced by 29%
(Fig. 6 H), PPAR protein levels and induction were not signi-
cantly altered (Fig. 6, F and G). We then analyzed the cell–cell
variability of PPAR and lipids after SRC siRNA knockdown
(Fig. 7 A). For each experiment and transfection, individual cell
measurements of PPAR and lipid were normalized to the me-
dian intensity of the scrambled control, which set the threshold
for quadrant subdivision for both properties arbitrarily equal to 1.
This threshold was then applied to each siRNA transfection to
create the PPARx/lipidy populations. As validation of the
PPAR/lipid gating, PPAR siRNA inhibits lipid accumula-
tion (Fig. S4 C) and shifts the cells to a predominantly (73%)
PPARlo/lipidlo state by decreasing PPARhi fractions (Fig. S4 D).
Although the total PPAR was largely unchanged at the whole
population (Fig. 6 F) and subpopulation level (Fig. 7 B), siRNA
to SRC-2 and/or SRC-3 caused shifts in each PPAR/lipid sub-
population. SRC siRNA increased the proportion of cells in
a PPARlo/lipidlo state by >4% while decreasing the PPARhi/
lipidhi percentage >6%. Concurrent with these changes in sub-
population distributions, more signicant effects were detected
on the PPARlo/lipidhi (13% to 15%) and the PPARhi/lipidlo
lipid populations (+15% to +19%). The changes in population
variation indicate a role for SRC-2 and SRC-3 in controlling
cell heterogeneity that promotes lipogenesis over a wide con-
tinuum of PPAR expression. Additionally, up-regulation of the
PPARhi/lipidlo population, along with decreases in downstream
PPAR-dependent (ADFP) and adipocentric/lipogenic genes
(Fig. 6 H), led us to hypothesize that the loss of the coactivators
resulted in higher levels of PPAR phosphorylation at S114,
leading to delayed/reduced adipogenesis.
MAPK–ERK phosphorylation of PPAR at S112/S114
diminishes its ligand afnity, transcriptional activity, adipogenic
capacity, and interactions with SRCs (Hu et al., 1996; Adams
et al., 1997; Shao et al., 1998; Rangwala et al., 2003). We pos-
tulated that our PPARhi/lipidlo populations might represent
higher levels of phospho-PPAR S114 and that the presence
of SRC-2 and/or SRC-3 minimizes this proportion of cells to
promote adipocyte differentiation. To test this hypothesis, we
knocked down SRC-2 and/or SRC-3 with siRNA and evaluated
the levels of phospho-PPAR S114 at 0, 24, and 96 h after dif-
ferentiation. Upon immunolabeling with a specic antibody to
phospho-PPAR S114 and total PPAR (Fig. 7 C), higher levels
of phospho-PPAR were present when SRC-2 and/or SRC-3
levels were reduced by siRNA. Further quantitative analysis in-
dicated increases in phospho-PPAR (Fig. 7 D) at each time
point for individual knockdowns of SRC-2 (2.89-fold, 96 h) or
SRC-3 (2.6-fold, 96 h), or when SRC-2 and SRC-3 (2.75-fold,
96 h) are both knocked down, respectively. Contrasting the
effect of SRC siRNA, scrambled siRNA (scR) reduces phospho-
PPAR S114 by 32% over the 96-h differentiation period.
As shown in Fig. 7 A and Fig. 7 D, several correlated ndings are
p160 coactivators promote adipocyte heterogeneity • Hartig et al.
Figure 7. SRC-2/SRC-3 single or double knockdown alters PPAR heterogeneity and phosphorylation status. (A) PPAR was immunolabeled and imaged
by HTM with DAPI/CMBl and lipid counterstains under conditions of SRC-2, SRC-3, or SRC-2/SRC-3 siRNA. The effects of SRC-2/SRC-3 siRNA on sub-
population distributions (*, P < 0.05, n = 3) were tabulated. For each experiment and transfection, individual cell measurements of PPAR and lipid were
normalized to the median intensity of the scrambled control, setting the threshold for quadrant subdivision for both properties arbitrarily equal to 1. This
threshold was then applied to each siRNA transfection to create the PPARx/lipidy populations. (B) The normalized median PPAR level was determined
in each subpopulation for scrambled (scR) and SRC siRNA conditions. (C) Human preadipocytes were reverse-transfected with siRNA to SRC-2, SRC-3, or
both coactivators and treated with MIX for up to 96 h. Subsequent to the perturbations, cells were immunolabeled with antibodies to phosphoPPAR-S114
and total PPAR, followed by HTM imaging. Bar, 50 µm. (D) After imaging, phosphoPPAR-S114 was quantified for 0, 24, and 96 h of differentiation
(*, P < 0.05; n = 3). (E) The single cell intensity ratios of phosphoPPAR-S114 to PPAR were determined for the lipidlo and lipidhi populations at 96 h
(*, P < 0.05; n = 3). Error bars indicate SEM.
JCB • VOLUME 192 • NUMBER 1 • 2011 64
implications for targeting the PPAR–SRC interaction surface
as strategies for new therapeutics to prevent the onset of obesity
associated with the treatment of type 2 diabetes.
Materials and methods
Primary cell culture and differentiation
Cryopreserved, subcutaneous primary human preadipocytes from normal
female donors with a mean body mass index of 27.51 were provided by
Zen-Bio Inc. Cells were maintained at 5% CO2/37°C in DME/F12 (Media-
tech, Inc.) with 10% FBS (Gemini Bio-Products), 100 U/ml penicillin, and
100 µg/ml streptomycin (growth media). Medium was replaced during
routine maintenance every 2 d. Cells were received at passage 2, and ex-
periments were performed before cells reached passage 10. Experiments
were performed using pooled human preadipocytes from five individual
female donors (Lot SL0033).
Unless otherwise indicated, all components were purchased from
Sigma-Aldrich. After seeding to the appropriate experimental format
(coverslips, 96- or 384-well plate format), cells were differentiated using
growth media supplemented with 100 nM human insulin, 0.250 mM IBMX,
500 nM dexamethasone, and either rosiglitazone (BRL49653; Cayman
Chemical Company) or 15dPGJ2 (Cayman Chemical Company). Unless
otherwise indicated, differentiation was performed with IBMX, dexametha-
sone, human insulin, and 3 µM rosiglitazone.
The following antibodies were purchased from commercial sources and
used for immunofluorescence: mouse monoclonal AIB1/SRC-3 (BD), mouse
monoclonal TIF2/SRC-2 (BD), rabbit polyclonal phospho-PPAR S112/
S114 (Abcam), rabbit monoclonal PPAR (Cell Signaling Technology),
mouse monoclonal PPAR (clone E-8; Santa Cruz Biotechnology, Inc.),
SRC-1 (BD), and mouse monoclonal FLAG-M2 (Sigma-Aldrich). Rabbit poly-
clonal antibody to SRC-3 was provided by B. O’Malley (Baylor College
of Medicine, Houston, TX).
For fluorescence detection of antibodies and neutral lipid content in multi-
well plates, the following protocol was performed on the BioMek NX (Beck-
man Coulter). The well plate systems used were: 96-well and 384-well
(Sensoplate Plus; Greiner). Aspirations and plate washes were performed
with an ELx405 (BioTek). After differentiation, media was aspirated, and
4% paraformaldehyde (ultrapure; Electron Microscopy Sciences) in PBS
was immediately added for 30 min at room temperature. Plates were then
quenched with 100 mM ammonium chloride. After quenching, plates were
washed three times with TBS. Fixed adipocytes were permeabilized with
0.1% Triton X-100 in TBS for 10 min and washed three times with TBS.
A 2% BSA in TBS/0.01% saponin (antibody diluent) blocking solution
was added for 30 min at room temperature followed by three TBS washes.
Antibodies were then diluted at a 1:200 concentration in antibody dilu-
ent and incubated overnight at 4°C. Subsequently, plates were washed
with TBS and incubated with secondary antibodies for 1 h at room tem-
perature. Alexa Fluor 647–-conjugated anti–mouse and Alexa Fluor 568–
conjugated anti–rabbit secondary antibodies (Invitrogen) were used.
Cells were again washed three times and incubated with 1 µg/ml Cell-
Mask blue (CMBl; Invitrogen), 1:1,000 LipidTOX green (Invitrogen), and
10 µg/ml DAPI in PBS for 45 min at room temperature. Dyes were then
aspirated and PBS/0.01% azide was added. Plates were then sealed and
imaged immediately.
seFRET imaging
CFP/YFP FRET experiments were performed using PPAR2-ECFP (provided
by F. Schaufele, University of California, San Francisco, CA), EYFP-SRC-2
(R. Michalides, Netherlands Cancer Institute, Amsterdam, Netherlands)
and EYFP-SRC-3 (Amazit et al., 2007) expressed in HeLa cells grown on
standard 12-mm glass coverslips. Constructs were cotransfected using Lipo-
fectamine 2000 (Invitrogen). Media was removed and replaced with fresh
DME/F12 with 5% FBS 24 h after transfection. After a further 24 h, cells
were treated for 2 h with either DMSO or differentiation cocktail (IBMX,
human insulin, dexamethasone, and rosiglitazone), both prepared in
growth media (DME/F12, 5% FBS). Treatment was followed with these
steps: fixation 4% PFA (30 min), quench 100 mM NH4Cl (10 min), and
mount with SlowFade Gold (Invitrogen). After fixation, cells were washed
with PBS++ three times, while all other wash steps were performed with
Pipes/Hepes/EGTA/MgCl2 (PEM) buffer, prepared at a final pH of 6.8.
In contrast to the SRC-1/SRC-3 double knockout mouse
(Wang et al., 2006), the SRC-2/SRC-3 and SRC-1/SRC-2
mutant mice are lethal (Mark et al., 2004; Xu et al., 2009), mak-
ing our experiments the rst to analyze compensation between
SRC-2 and SRC-3 during human adipogenesis. Although over-
expression suggested that SRC-1, SRC-2, and SRC-3 modestly
increase adipogenesis, siRNA established that the endogenous
levels of SRC-2 and SRC-3 are essential for differentiation.
To determine if a functional overlap exists specically between
SRC-2 and SRC-3 during differentiation of cultured human
adipocytes, we used a dual siRNA approach. Strikingly, we found
that single or double knockdown of SRC-2 and SRC-3 inhibited
adipogenesis to the same extent for both natural (15dPGJ2) and
synthetic PPAR ligands with comparable PPAR induction.
Consistent with pro-adipogenic SRCs being dominant in adipo-
cyte development (Wang et al., 2006), SRC-1 siRNA had no
effect on differentiation. This result suggests that SRC-2 and
SRC-3 are fundamental components of the basal, preligand
adipogenic machinery driven by PPAR. The data at both the
phenotypic (Fig. 6, A–G) and gene regulatory level (Fig. 6 H) did
not exhibit any apparent compensation by the nontargeted SRCs
in single siRNA treatments. In support of this nding, it has
been proposed that SRC-2 and SRC-3 preferentially pair and
interact to promote gene transcription (Zhang et al., 2004).
Upon single or double SRC-2/SRC-3 siRNA treatment,
PPAR protein levels were unchanged; interestingly, there was
an enrichment of cells in a PPARhi/lipidlo state, with decreases
in the PPARhi/lipidhi and PPARlo/lipidhi proportions (Fig. 7 A).
Coincident with decreased expression of PPAR downstream
genes, the increased PPARhi/lipidlo population reected an in-
crease in the amount of phospho-PPAR S114 (Fig. 7 D). When
the levels of coactivator are reduced, the signal transduction
environment elevates phospho-PPAR S112/S114 status, low-
ers ligand (thiazolidinedione/eicosanoid) afnity, and increases
interactions with the corepressors SMRT (Shao et al., 1998) or
PER2 (Grimaldi et al., 2010) to reduce transcriptional activity
(Lavinsky et al., 1998). Collectively, these results imply that
SRC-2 and SRC-3, together, collaborate to promote adipocyte
differentiation through potential multimerization (McKenna
et al., 1998) and/or dimerization via protein dimerization/
interaction domains (Lodrini et al., 2008). To add to this model,
we combined quantitative analysis of SRC/PPAR/lipid kinet-
ics, correlations, FRET data, and loss-of-function experiments
at single cell resolution. These important single cell char-
acteristics suggest a novel mechanism of action. Specically,
permissive, homeostatic levels of SRC-2 and SRC-3 can inter-
act with the PPAR in the absence of MIX to regulate PPAR
heterogeneity, reduce inhibitory PPAR phosphorylation, and
promote adipogenesis.
Our quantitative, cell-by-cell approach has identied a
unique interplay between SRC-2, SRC-3, and PPAR that pro-
motes adipogenesis. Small molecule inhibitors that block SRC
recruitment or disrupt the predifferentiation complex between
SRCs and PPAR might maintain the positive (insulin sensiti-
zation) while reducing negative (weight gain) effects of thia-
zolidinediones (Reginato et al., 1998; Rocchi et al., 2001;
Michalik et al., 2006). Collectively, the data presented here have
65p160 coactivators promote adipocyte heterogeneity • Hartig et al.
TE2000-U) and a triple band filter set (Chroma 82000; Chroma Technol-
ogy Corp.). A progressive scan camera (COHU) functioned as the focusing
camera. The imaging camera (Hamamatsu Photonics) was set to capture
8-bit images at 2 × 2 binning (672 × 512 pixels, 0.684 × 0.684 µm2/pixel)
with five images captured per field (DAPI, CMBl, LipidTOX, Alexa Fluor
568, and Alexa Fluor 647 secondary antibodies). All high-throughput
microscopy experiments were performed with an S Fluor 20×/0.75 NA
objective lens (Nikon). In general, 12–16 images were captured per well
for image analysis. All imaging was performed at room temperature.
Image analysis
Images were analyzed using custom algorithms developed with the Pipeline
Pilot (v7.5) software platform (Accelrys) in a similar workflow as described
previously (Szafran et al., 2008, 2009) and summarized in the following
steps. After background subtraction, nuclear and cell masks were gener-
ated using a combination of nonlinear least squares and watershed-from-
markers image manipulations of the DAPI images. Specifically, a nonlinear
least squares threshold was applied to a DAPI image to create a binary
image. This image was subsequently eroded and distance transformed
to generate a marker image identifying the approximate center for each
nuclei. This marker image in combination with the original DAPI image was
used in a watershed-from-markers operation to define the full nuclear mask
for each nucleus. A final morphological open operation was used to gener-
ate the final nuclear masks. Then, cellular masks were created by applying
watershed segmentation on the CellMask images using nuclei regions as
seeds. To prevent cell body oversegmentation, cell regions were trimmed
so their boundaries did not exceed an empirically determined maximal
distance from the nucleus. All events with whole cell masks bordering the
edge of the image were additionally eliminated from analysis. Both whole
cell and nucleus segmentation generate regions under which single cell
intensity features were extracted. Cell populations were filtered to discard
events with cell aggregates, mitotic cells, apoptotic cells, cellular debris, or
poor segmentation. Applied gates were based upon nuclear area, nuclear
circularity, and cell size/nucleus ratio. In general, these filters removed
10% of the population of segmented cells. An additional image analysis
platform, CyteSeer (Vala Sciences), was also used to support the Pipe-
line Pilot-driven algorithms. Measurements extracted using lipid droplet
analysis intrinsic to CyteSeer software (McDonough et al., 2009) included
lipid droplet count, total integrated intensity of the lipid mask on the lipid
image, and lipid droplet area. Post-analysis measurements were exported
to spreadsheet software (Excel; Microsoft) for further analysis.
Statistical analyses
Data presented were acquired from a minimum of two (qPCR) or three
(HCA) independent experiments performed on multiple days, unless other-
wise indicated. Analysis of variance (ANOVA) was first used to compare
the effects of time or siRNA treatment. If significant differences were identi-
fied, then data were compared with Tukey’s HSD post-hoc tests. All tests
were performed at the 95% confidence interval using JMP-IN 7 (SAS).
Online supplemental material
Fig. S1 shows the extension of the human adipogenesis assay to later
time points and dose response experiments for BRL49653 and 15dPGJ2
with detection of SRC-1, SRC-2, SRC-3, PPAR, and lipid content. Fig. S2
shows the effect of SRC-1 siRNA on human adipogenesis. Fig. S3 dis-
plays immunofluorescent detection of SRC-2 and SRC-3 under single
or double knockdown conditions. Fig. S3 also redisplays mRNA data
represented in Fig. 6 H. Fig. S4 shows the validation of subpopulation
analyses using PPAR siRNA. Fig. S5 describes SRC gain-of-function ex-
periments. Online supplemental material is available at
The authors thank Drs. N. McKenna and Z.D. Sharp for critically reviewing the
manuscript; I.P. Uray for qPCR assay design; J.Y. Newberg, A.T. Szafran,
M.G. Mancini, L. Vergara, and J. Broughman for technical resource support;
and P. McDonough and J.H. Price (Vala Sciences) for longstanding support in
automated cytometry. Benjamin Buehrer is an employee of Zen-Bio, Inc.
This work was funded by National Institutes of Health (NIH) grant
5R01DK055622, the Hankamer Foundation, and pilot grant and equipment sup-
port from the John S. Dunn Gulf Coast Consortium for Chemical Genomics (to
M.A. Mancini). Additional funding was provided by NIH 1F32DK85979 (to
S.M. Hartig), 5T32HD007165 (to B.W. O’Malley), 5K01DK081446 (to B. He),
and 5R01CA090464 (to E. Chang). Imaging resources were supported by
Specialized Cooperative Centers Program in Reproduction U54 HD-007495
(to B.W. O’Malley), P30 DK-56338 (to M.K. Estes), P30 CA-125123 (to C.K.
Osborne), and the Dan L. Duncan Cancer Center of Baylor College of Medicine.
FRET imaging was performed as described previously (Trinkle-
Mulcahy et al., 2001; Chusainow et al., 2006) with the DeltaVision Core
Image Restoration Microscope (Applied Precision). Z stacks were imaged at
0.2 µm separation and a frame size of 1,024 × 1,024 pixels at 1 × 1 bin-
ning with a microscope (IX71; Olympus) using a 60×, 1.42 NA Plan Achro-
mat objective (Olympus) and a charge-coupled device camera (CoolSnap
HQ2; Photometrics). Filter sets were as follows, with a dichroic to split CFP
and YFP: excitation 430 nm/emission 470 nm (CFP), excitation 500 nm/
emission 535 nm (YFP), and excitation 430 nm/emission 535 nm (FRET).
Z series stacks were deconvolved with the DeltaVision constrained iterative
algorithm. After deconvolution (softWoRx; Applied Precision), FRET calcula-
tions were performed using the Applied Precision FRET user interface. FRET
measurements on individual nuclei were acquired on maximum intensity pro-
jections of the derived FRET image. Spectral bleed-through was corrected for
by acquiring specimens containing only CFP-PPAR2 and YFP-p160. Stan-
dard values for and coefficients were 0.6 (CFP) and 0.12 (YFP) acquired
from single donor/acceptor plasmid expression experiments.
siRNA transfection
siRNA to SRC-2 and SRC-3 oligomers was provided by E. Lader (QIAGEN,
Germantown, MD). Before transfection, optical quality 384-well plates (Senso-
Plate Plus; Greiner) were coated with 20 µl of FBS (Gemini Bio-Products)
overnight at 37°C. Cells were reverse-transfected with siRNA or mismatch
control at a final concentration of 10 nM using HiPerFect transfection reagent
(QIAGEN). siRNA and transfection reagents were mixed in OptiMEM I reduced
serum media (Invitrogen) and allowed to complex at room temperature for
20 min. Preadipocytes, diluted to a final density of 5,000 cells/well, were
then added to the HiPerFect–siRNA complexes followed by immediate seed-
ing to plates. The total volume of cells and transfection reagent was 30 µl/
well. Final volume of each condition upon seeding to the 20 µl FBS coat was
50 µl/well. After reverse transfection, cells were incubated for 48 h at 5%
CO2/37°C before induction of differentiation for up to 96 h.
Production of lentiviral particles
SRC-1, SRC-2, and SRC-3 cDNAs were cloned into the lentiviral expres-
sion vector pCDH–CMV-MCS-EF1-Puro (System Biosciences) by XbaI–NheI
digestion. Pseudolentiviruses were produced in 293TN cells by cotrans-
fecting lentiviral expression constructs and the pPACK packaging plasmid
mix (System Biosciences). Pseudoviral particles were harvested 48 h after
transfection and were concentrated using a PEG-it virus precipitation solu-
tion kit (System Biosciences).
RNA extraction and qPCR analysis
Total RNA was extracted from cells using TRIzol reagent (Invitrogen) ac-
cording to the manufacturer’s instructions. To measure the relative mRNA
levels of SRC-2, SRC-3, PPAR, and adipocentric genes (ADFP, SREBP1c,
FASN, and C/EBP), quantitative real-time RT-PCR was performed using
the Taqman RT-PCR one-step master mix in conjunction with an ABI 7500
real-time PCR system (Applied Biosystems). Each sample was tested in du-
plicate in three independent experiments. -actin was used as the invariant
control. For C/EBP, relative mRNA was evaluated using the TaqMan
Gene Expression Assay (Assay ID Hs00269972_s1). The following primer
and probe sets were used to detect SREBP1c, FASN, SRC-2, SRC-3,
ward), 5-CGCTCCTCCATCAATGACA-3 (reverse), and Roche Universal
ProbeLibrary probe No. 77 (probe); FASN: 5-CGGAGTGAATCTGGG-
TTGAT-3 (forward), 5-CAGGCACACACGATGGAC-3 (reverse), and
Roche Universal Probe Library probe No. 11 (probe); SRC-2: 5-GGAC-
GGAGAAG-3 (forward), 5-TCCGACTCCCCAAGACTGT-3 (reverse),
(reverse), and 5-TCTCATATCCGAGGGCCAAGGCTTC-3 (probe); SRC-1:
TACA-3 (probe).
Imaging and microscopy
For high-speed image acquisition and subsequent analysis, cells were
imaged using the Cell Laboratory IC-100 Image Cytometer (IC-100; Beck-
man Coulter) controlled by CytoShop 2.0 (Beckman Coulter). The IC-100
platform was equipped with an inverted microscope (Nikon; Eclipse
JCB • VOLUME 192 • NUMBER 1 • 2011 66
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... Interestingly, TBT also affected the levels of its two main target NRs, PPARG and RXRA, but in opposite directions, with PPARG being increased, while RXRA was decreased in a 26S proteasome-dependent manner. Moreover, at the single cell level, RXRA levels did not correlate with lipid content, similar to our previous results in adipocytes where coregulator proteins did not correlate with NR levels and lipid content [19]. In conclusion, we validated that TBT acts as an obesogen in human liver cells through modulation of lipogenic gene expression and PPARG/RXRA levels. ...
... Immunofluorescence experiments were completed as previously described [19,20]. Briefly, cells were fixed in 4% formaldehyde in PBS, quenched with 0.1 M ammonium chloride for 10 min, and permeabilized with 0.5% Triton X-100 for 30 min. ...
... Mimicking the classical adipogenesis assays in the 3T3-L1 cell model, we treated with TBT chloride for 14 days, and measured neutral lipid content by LipidTox staining, automated imaging and high content image analysis (HCA), as previously described [19,26]. The treatment doses were chosen based on TBT concentration found in human samples [10], and to avoid cytotoxicity. ...
Full-text available
A subset of environmental chemicals acts as “obesogens” as they increase adipose mass and lipid content in livers of treated rodents. One of the most studied class of obesogens are the tin-containing chemicals that have as a central moiety tributyltin (TBT), which bind and activate two nuclear hormone receptors, Peroxisome Proliferator Activated Receptor Gamma (PPARG) and Retinoid X Receptor Alpha (RXRA), at nanomolar concentrations. Here, we have tested whether TBT chloride at such concentrations may affect the neutral lipid level in two cell line models of human liver. Indeed, using high content image analysis (HCA), TBT significantly increased neutral lipid content in a time- and concentration-dependent manner. Consistent with the observed increased lipid accumulation, RNA fluorescence in situ hybridization (RNA FISH) and RT-qPCR experiments revealed that TBT enhanced the steady-state mRNA levels of two key genes for de novo lipogenesis, the transcription factor SREBF1 and its downstream enzymatic target, FASN. Importantly, pre-treatment of cells with 2-deoxy-D-glucose reduced TBT-mediated lipid accumulation, thereby suggesting a role for active glycolysis during the process of lipid accumulation. As other RXRA binding ligands can promote RXRA protein turnover via the 26S proteasome, TBT was tested for such an effect in the two liver cell lines. We found that TBT, in a time- and dose-dependent manner, significantly reduced steady-state RXRA levels in a proteasome-dependent manner. While TBT promotes both RXRA protein turnover and lipid accumulation, we found no correlation between these two events at the single cell level, thereby suggesting an additional mechanism may be involved in TBT promotion of lipid accumulation, such as glycolysis.
... mechanism. Furthermore, it has been reported that SRC can directly affect transcription of PPARg (36,37), SRC expression increased in TFDR group indirectly confirmed the importance of PPARg in GIOP. ...
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Background Glucocorticoid-induced osteoporosis (GIOP) is a common form of secondary osteoporosis caused by the protracted or a large dosage of glucocorticoids (GCs). Total flavonoids of Drynariae rhizoma (TFDR) have been widely used in treating postmenopausal osteoporosis (POP). However, their therapeutic effects and potential mechanism against GIOP have not been fully elucidated.Methods Ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESIQ-TOF-MS) experiments were performed for qualitative analysis. We performed hematoxylin-eosin (HE) staining and microcomputed tomography (micro-CT) analysis to detect the changes in bone microstructure. The changes in biochemical parameters in the serum samples were determined by performing an enzyme-linked immunosorbent assay (ELISA). The prediction results of network pharmacology were verified via quantitative real-time polymerase chain reaction (qRT-PCR) to elucidate the potential mechanism of TFDR against GIOP.ResultsA total of 191 ingredients were identified in vitro and 48 ingredients in vivo. In the in-vivo experiment, the levels of the serum total cholesterol (TC), the serum triglyceride (TG), Leptin (LEP), osteocalcin (OC), osteoprotegerin (OPG), bone morphogenetic protein-2 (BMP-2), propeptide of type I procollagen (PINP), tartrate-resistant acid phosphatase (TRACP) and type-I collagen carboxy-terminal peptide (CTX-1) in the TFDR group significantly changed compared with those in the GIOP group. Moreover, the TFDR group showed an improvement in bone mineral density and bone microstructure. Based on the results of network pharmacology analysis, 67 core targets were selected to construct the network and perform PPI analysis as well as biological enrichment analysis. Five of the targets with high “degree value” had differential gene expression between groups using qRT-PCR.ConclusionTFDR, which may play a crucial role between adipose metabolism and bone metabolism, may be a novel remedy for the prevention and clinical treatment of GIOP.
... This activation of Wnt signaling results in enhanced osteogenesis [99]. Notably, some HATs function in a histone-independent way, regulating differentiation-related gene expression in MSCs and thereby fate commitment of MSCs through direct interaction with other non-histone proteins [100][101][102][103]. Several pan-HDAC inhibitors have also been reported to upregulate the osteogenic potential of MSCs [104][105][106], indicating the significant roles of HDACs in osteogenic differentiation. ...
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Mesenchymal stem cells (MSCs) are considered the most promising seed cells for regenerative medicine because of their considerable therapeutic properties and accessibility. Fine-tuning of cell biological processes, including differentiation and senescence, is essential for achievement of the expected regenerative efficacy. Researchers have recently made great advances in understanding the spatiotemporal gene expression dynamics that occur during osteogenic, adipogenic and chondrogenic differentiation of MSCs and the intrinsic and environmental factors that affect these processes. In this context, histone modifications have been intensively studied in recent years and have already been indicated to play significant and universal roles in MSC fate determination and differentiation. In this review, we summarize recent discoveries regarding the effects of histone modifications on MSC biology. Moreover, we also provide our insights and perspectives for future applications.
... Numerous genomic studies have shown that the majority of the long noncoding RNAs in mammalian genomes are lincRNAs [17,42]. Due to the similarity between pigs and humans in physiology process, organ development, disease research, and so on, they are widely used as important animal models, but the lincRNAs identified in pigs are far less perfect than that in humans and mice [13]. ...
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Muscle growth and fat deposition are the two important biological processes in the development of pigs which are closely related to the pig production performance. Long intergenic noncoding RNAs (lincRNAs), with lack of coding potential and the length of at least 200nt, have been extensively studied to play important roles in many biological processes. However, the importance and molecular regulation mechanism of lincRNAs in the process of muscle growth and fat deposition in pigs are still to be further studied comprehensively. In our study, we used the data, including liver, abdominal fat, and longissimus dorsi muscle of 240 days’ age of two F2 full-sib female individuals from the white Duroc and Erhualian crossbreed, to identify 581 putative lincRNAs associated with pig muscle growth and fat deposition. The 581 putative lincRNAs shared many common features with other mammalian lincRNAs, such as fewer exons, lower expression levels, and shorter transcript lengths. Cross-tissue comparisons showed that many transcripts were tissue-specific and were involved in the important biological processes in their corresponding tissues. Gene ontology and pathway analysis revealed that many potential target genes (PTGs) of putative lincRNAs were involved in pig muscle growth and fat deposition-related processes, including muscle cell proliferation, lipid metabolism, and fatty acid degradation. In Quantitative Trait Locus (QTLs) analysis, some PTGs were screened from putative lincRNAs, MRPL12 is associated with muscle growth, GCGR and SLC25A10 were associated with fat deposition, and PPP3CA, DPYD, and FGGY were related not only to muscle growth but also to fat deposition. Therefore, it implied that these lincRNAs might participate in the biological processes related to muscle growth or fat deposition through homeostatic regulation of PTGs, but the detailed molecular regulatory mechanisms still needed to be further explored. This study lays the molecular foundation for the in-depth study of the role of lincRNAs in the pig muscle growth and fat deposition and further provides the new molecular markers for understanding the complex biological mechanisms of pig muscle growth and fat deposition.
... It was found that the mouse model of NCOA3 knockout showed a decrease in lipid fat deposition and an increase in energy consumption (25)(26)(27). Hartig et al (28), reported that NCOA3 can combine with peroxisome proliferator activated receptor gamma (PPARγ) and lead to a decrease in PPARγ-s114 phosphorylation. Furthermore, it promoted the nuclear receptor PPARγ transcription activity and thus promoted the deposition of fat. ...
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The present study investigated the key genes, which cause switch from adipogenic to osteogenic differentiation of human mesenchymal stem cells (hMSCs). The transcriptomic profile of hMSCs samples were collected from Array Express database. Differential expression network was constructed by calculating the Pearson's correlation coefficient and ranked according to their topological features. The top 5% genes with degree ≥2 were selected as ego genes. Following the KEGG pathway enrichment analysis and the relevant miRNAs prediction, the miRNA-mRNA-pathway networks were constructed by combining the miRNA-mRNA pairs and mRNA-pathway pairs together. In total, we obtained 84, 119, 94 and 97 ego-genes in B, BI, BT and BTI groups, and DLGAP5, DLGAP5, NUSAP1 and NDC80 were the ego-genes with the highest z-score of each group, respectively. Beginning from each ego-gene, we identified 2 significant ego-modules with gene size ≥4 in group BI, and the ego-genes were PBK and NCOA3, respectively. Through KEGG pathway analysis, we found that most of the pathways enriched by ego-genes were associated with gene replication and repair, and cell proliferation. According to the miRNA prediction results, we found that some of the predicted miRNAs have been validated to be the regulatory miRNAs of these corresponding mRNAs. Finally we constructed a miRNA-mRNA-pathway network by integrating the miRNA-mRNA and mRNA-pathway pairs together. The constructed network gives us a more comprehensive understanding of the mechanism of osteogenic differentiation of hMSCs.
... Besides, SRC-3 induces PGC1α acetylation and consequently inhibits its activity in brown fat (60). SRC-3 also cooperates with SRC-2 to attenuate PPARγ phosphorylation at S114, which in turn increase PPARγ transcriptional activity and adipogenesis (61). Collectively, these data summarize the crucial roles of SRC family members that cooperate or antagonize with each other to regulate the interaction of PPARγ/PGC1α complex and its transactivation activity on the thermogenic network. ...
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Peroxisome proliferator-activated receptor γ (PPARγ), a ligand-dependent transcription factor highly expressed in adipocytes, is a master regulator of adipogenesis and lipid storage, a central player in thermogenesis and an active modulator of lipid metabolism and insulin sensitivity. As a nuclear receptor governing numerous target genes, its specific signaling transduction relies on elegant transcriptional and post-translational regulations. Notably, in response to different metabolic stimuli, PPARγ recruits various cofactors and forms distinct transcriptional complexes that change dynamically in components and epigenetic modification to ensure specific signal transduction. Clinically, PPARγ activation via its full agonists, thiazolidinediones, has been shown to improve insulin sensitivity and induce browning of white fat, while undesirably induce weight gain, visceral obesity and other adverse effects. Thus, deciphering the combinatorial interactions between PPARγ and its transcriptional partners and their preferential regulatory network in the processes of development, function and senescence of adipocytes would provide us the molecular basis for developing novel partial agonists that promote benefits of PPARγ signaling without detrimental side effects. In this review, we discuss the dynamic components and precise regulatory mechanisms of the PPARγ-cofactors complexes in adipocytes, as well as perspectives in treating metabolic diseases via specific PPARγ signaling.
MicroRNA-30a (miR-30a) impacts adipocyte function, and its expression in white adipose tissue (WAT) correlates with insulin sensitivity in obesity. Bioinformatic analysis demonstrates miR-30a expression contributes to 2% of all miRNA expression in human tissues. However, molecular mechanisms of miR-30a function in fat cells remain unclear. Here, we expanded our understanding of how miR-30a expression contributes to anti-diabetic peroxisome proliferator-activated receptor gamma (PPARγ) agonist activity and metabolic functions in adipocytes. We found WAT isolated from diabetic patients show reduced miR-30a levels and diminished expression of the canonical PPARγ target genes ADIPOQ and FABP4 relative to lean counterparts. In human adipocytes, miR-30a required PPARγ for maximal expression and the PPARγ agonist rosiglitazone robustly induced miR-30a, but not other miR-30 family members. Transcriptional activity studies in human adipocytes also revealed ectopic expression of miR-30a enhanced the activity of rosiglitazone coupled with higher expression of fatty acid and glucose metabolism markers. Diabetic mice that overexpress ectopic miR-30a in subcutaneous WAT display durable reductions in serum glucose and insulin levels for over 30 days. In agreement with our in vitro findings, RNA-Seq coupled with Gene Set Enrichment Analysis (GSEA) suggested miR-30a enabled activation of the beige fat program in vivo as evidenced by enhanced mitochondrial biogenesis and induction of UCP1 expression. Metabolomic and gene expression profiling established the long-term effects of ectopic miR-30a expression enable accelerated glucose metabolism coupled with subcutaneous WAT hyperplasia. Together, we establish a putative role of miR-30a in mediating PPARγ activity and advancing metabolic programs of white to beige fat conversion.
Benzophenone-3 (BP-3) and benzopenone-8 (BP-8) are commonly used ultraviolet (UV) filter ingredients in diverse sunscreen products. Recently, the obesogenic activity of avobenzone, a long wave UV A filter, was elucidated in the adipogenesis model of human bone marrow mesenchymal stem cells (hBM-MSCs). In this study, the obesogenic potentials of BP-3 and BP-8 were investigated because of their chemical similarity to avobenzone. During the adipogenesis in hBM-MSCs, BP-3 and BP-8 (EC50, 25.05 and 43.20 μM, respectively) potently promoted adiponectin secretion than avobenzone (EC50, 72.69 μM). In target identification, both BP-3 and BP-8 directly bound to peroxisome proliferator-activated receptor γ (PPARγ), which was associated with the recruitment of steroid receptor coactivator-2 (SRC-2). BP-3 functioned as a PPARγ full agonist whereas BP-8 was a PPARγ partial agonist. In addition, BP-3 and BP-8 significantly increased the gene transcription of PPARα, PPARγ, and major lipid metabolism-associated enzymes in human epidermal keratinocytes, a major target site of UV filters in human skin. This study suggests that BP-3 and BP-8 are obesogenic environmental chemicals similar to phthalates, bisphenols, and organotins.
Endocrine disrupting chemicals interact with transcription factors essential for adipocyte differentiation. Exposure to endocrine disrupting chemicals corresponds with elevated risks of obesity, but the effects of these compounds on human cells remain largely undefined. Widespread use of Bisphenol AF (BPAF) as a BPA alternative in the plastics industry presents unknown health risks. To this end, we discovered BPAF interferes with the metabolic function of mature human adipocytes. Although four-day exposures to BPAF accelerated adipocyte differentiation, we observed no effect on mature fat cell marker genes. Additional gene and protein expression analysis showed BPAF treatment during human adipocyte differentiation failed to suppress the pro-inflammatory transcription factor STAT1. Microscopy and respirometry experiments demonstrated BPAF impaired mitochondrial function and structure. To test the hypothesis that BPAF fosters vulnerabilities to STAT1 activation, we treated mature adipocytes previously exposed to BPAF with interferon gamma (IFNg). BPAF increased IFNg activation of STAT1 and exposed mitochondrial vulnerabilities that disrupt adipocyte lipid and carbohydrate metabolism. Collectively, our data establish BPAF activates inflammatory signaling pathways that degrade metabolic activity in human adipocytes. These findings suggest how the BPA alternative BPAF contributes to metabolic changes that correspond with obesity.
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Adipocyte differentiation is an important component of obesity and other metabolic diseases. This process is strongly inhibited by many mitogens and oncogenes. Several growth factors that inhibit fat cell differentiation caused mitogen-activated protein (MAP) kinase-mediated phosphorylation of the dominant adipogenic transcription factor peroxisome proliferator-activated receptor γ (PPARγ) and reduction of its transcriptional activity. Expression of PPARγ with a nonphosphorylatable mutation at this site (serine-112) yielded cells with increased sensitivity to ligand-induced adipogenesis and resistance to inhibition of differentiation by mitogens. These results indicate that covalent modification of PPARγ by serum and growth factors is a major regulator of the balance between cell growth and differentiation in the adipose cell lineage.
Prostaglandins (PGs) of the J2 series form in vivo and exert effects on a variety of biological processes. While most PGs mediate their effects through G protein-coupled receptors, the mechanism of action for the J2 series of PGs remains unclear. Here, we report that PGJ2 and its derivatives are efficacious activators of peroxisome proliferator-activated receptors α and γ (PPARγ and PPARγ, respectively), orphan nuclear receptors implicated in lipid homeostasis and adipocyte differentiation. The PGJ2 metabolite 15-deoxy-Δ12,14-PGJ2 binds directly to PPARγ and promotes efficient differentiation of C3H10T1/2 fibroblasts to adipocytes. These data provide strong evidence that a fatty acid metabolite can function as an adipogenic agent through direct interactions with PPARγ and, furthermore, suggest a novel mechanism of action for PGs of the J2 series.
Regulation of adipose cell mass is a critical homeostatic process in higher vertebrates. The conversion of fibroblasts into cells of the adipose lineage is induced by expression of the orphan nuclear receptor PPARyγ. This suggests that an endogenous PPARγ ligand may be an important regulator of adipogenesis. By assaying arachidonate metabolites for their capacity to activate PPAR response elements, we have identified 15-deoxy-Δ12,14-prostaglandin J2 as both a PPARγ ligand and an inducer of adipogenesis. Similarly, the thiazolidinedione class of antidiabetic drugs also bind to PPARγ and act as potent regulators of adipocyte development. Thus, adipogenic prostanoids and antidiabetic thiazolidinediones initiate key transcriptional events through a common nuclear receptor signaling pathway. These findings suggest a pivotal role for PPARγ and its endogenous ligand in adipocyte development and glucose homeostasis and as a target for intervention in metabolic disorders.
Mice deficient in C/EBPα have defective development of adipose tissue, but the precise role of C/EBPα has not been defined. Fibroblasts from C/EBPα(−/−) mice undergo adipose differentiation through expression and activation of PPARγ, though several clear defects are apparent. C/EBPα-deficient adipocytes accumulate less lipid, and they do not induce endogenous PPARγ, indicating that cross-regulation between C/EBPα and PPARγ is important in maintaining the differentiated state. The cells also show a complete absence of insulin-stimulated glucose transport, secondary to reduced gene expression and tyrosine phosphorylation for the insulin receptor and IRS-1. These results define multiple roles for C/EBPα in adipogenesis and show that cross-regulation between PPARγ and C/EBPα is a key component of the transcriptional control of this cell lineage.
The process of adipogenesis is known to involve the interplay of several transcription factors. Activation of one of these factors, the nuclear hormone receptor PPARγ, is known to promote fat cell differentiation in vitro. Whether PPARγ is required for this process in vivo has remained an open question because a viable loss-of-function model for PPARγ has been lacking. We demonstrate here that mice chimeric for wild-type and PPARγ null cells show little or no contribution of null cells to adipose tissue, whereas most other organs examined do not require PPARγ for proper development. In vitro, the differentiation of ES cells into fat is shown to be dependent on PPARγ gene dosage. These data provide direct evidence that PPARγ is essential for the formation of fat.
FMOC-L-Leucine (F-L-Leu) is a chemically distinct PPARγ ligand. Two molecules of F-L-Leu bind to the ligand binding domain of a single PPARγ molecule, making its mode of receptor interaction distinct from that of other nuclear receptor ligands. F-L-Leu induces a particular allosteric configuration of PPARγ, resulting in differential cofactor recruitment and translating in distinct pharmacological properties. F-L-Leu activates PPARγ with a lower potency, but a similar maximal efficacy, than rosiglitazone. The particular PPARγ configuration induced by F-L-Leu leads to a modified pattern of target gene activation. F-L-Leu improves insulin sensitivity in normal, dietinduced glucose-intolerant, and in diabetic db/db mice, yet it has a lower adipogenic activity. These biological effects suggest that F-L-Leu is a selective PPARγ modulator that activates some (insulin sensitization), but not all (adipogenesis), PPARγ-signaling pathways.
Several lines of evidence indicate that the nuclear receptor corepressor (N-CoR) complex imposes ligand dependence on transcriptional activation by the retinoic acid receptor and mediates the inhibitory effects of estrogen receptor antagonists, such as tamoxifen, suppressing a constitutive N-terminal, Creb-binding protein/coactivator complex-dependent activation domain. Functional interactions between specific receptors and N-CoR or SMRT corepressor complexes are regulated, positively or negatively, by diverse signal transduction pathways. Decreased levels of N-CoR correlate with the acquisition of tamoxifen resistance in a mouse model system for human breast cancer. Our data suggest that N-CoR- and SMRT-containing complexes act as rate-limiting components in the actions of specific nuclear receptors, and that their actions are regulated by multiple signal transduction pathways.
Accumulating evidence highlights intriguing interplays between circadian and metabolic pathways. We show that PER2 directly and specifically represses PPARγ, a nuclear receptor critical in adipogenesis, insulin sensitivity, and inflammatory response. PER2-deficient mice display altered lipid metabolism with drastic reduction of total triacylglycerol and nonesterified fatty acids. PER2 exerts its inhibitory function by blocking PPARγ recruitment to target promoters and thereby transcriptional activation. Whole-genome microarray profiling demonstrates that PER2 dictates the specificity of PPARγ transcriptional activity. Indeed, lack of PER2 results in enhanced adipocyte differentiation of cultured fibroblasts. PER2 targets S112 in PPARγ, a residue whose mutation has been associated with altered lipid metabolism. Lipidomic profiling demonstrates that PER2 is necessary for normal lipid metabolism in white adipocyte tissue. Our findings support a scenario in which PER2 controls the proadipogenic activity of PPARγ by operating as its natural modulator, thereby revealing potential avenues of pharmacological and therapeutic intervention.