Cell Reports
Resource
Complementary RNA and Protein Profiling
Identifies Iron as a Key Regulator
of Mitochondrial Biogenesis
Jarred W. Rensvold,1 Shao-En Ong,2 Athavi Jeevananthan,1 Steven A. Carr,3 Vamsi K. Mootha,3,4 and David J. Pagliarini1,*
1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
2Department of Pharmacology, University of Washington, Seattle, WA 98195, USA
3Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
4Departments of Systems Biology and Medicine, Harvard Medical School, Boston, MA 02446, USA
*Correspondence: pagliarini@wisc.edu
http://dx.doi.org/10.1016/j.celrep.2012.11.029
SUMMARY
Mitochondria are centers of metabolism and signaling
whose content and function must adapt to
changing cellular environments. The biological signals
that initiate mitochondrial restructuring and the
cellular processes that drive this adaptive response
are largely obscure. To better define these systems,
we performed matched quantitative genomic and
proteomic analyses of mouse muscle cells as they
performed mitochondrial biogenesis. We find that
proteins involved in cellular iron homeostasis are
highly coordinated with this process and that depletion
of cellular iron results in a rapid, dose-dependent
decrease of select mitochondrial protein levels and
oxidative capacity. We further show that this process
is universal across a broad range of cell types and
fully reversed when iron is reintroduced. Collectively,
our work reveals that cellular iron is a key regulator of
mitochondrial biogenesis, and provides quantitative
data sets that can be leveraged to explore posttranscriptional
and posttranslational processes that are
essential for mitochondrial adaptation.
INTRODUCTION
Mitochondria are ubiquitous organelles that are essential for
cellular energy generation and a range of key metabolic pathways.
The production of mitochondria—termed mitochondrial
biogenesis—is a complex process involving the orchestrated
transcription, translation, and import of more than 1,000 proteins
encoded by two genomes (Mick et al., 2011; Pagliarini et al.,
2008; Scarpulla, 2008; Schmidt et al., 2010). Moreover, these
organelles vary considerably in composition across tissues
(Mootha et al., 2003a; Pagliarini et al., 2008) and remodel to
meet cellular needs (Baltzer et al., 2010; Hock and Kralli,
2009), indicating that the mitochondrial biogenesis program is
customizable and responsive to environmental conditions.
Defects in this process are associated with a range of human
disorders, including mitochondrial encephalomyopathy with
ragged red fibers (MERRF), type 2 diabetes, and various cancers
(Calvo and Mootha, 2010; DiMauro and Schon, 2003; Lowell and
Shulman, 2005; Wallace, 2005).
During the past two decades, major progress has been made
in deciphering the transcriptional networks that drive mitochondrial
biogenesis. Of particular importance was the identification
of peroxisome proliferator-activated receptor g, coactivator 1
a (PGC-1a) (Puigserver et al., 1998). PGC-1a and related coactivators
PGC-1b (Kressler et al., 2002; Lin et al., 2002) and PRC
(PGC-1 related coactivator) (Andersson and Scarpulla, 2001)
coordinate and activate the various transcription factors
required for mitochondrial biogenesis (Scarpulla, 2008). Additionally,
PGC-1a is activated by exercise, adaptive thermogenesis,
changes in cellular redox state, and the availability of
nutrients and growth factors (Hock and Kralli, 2009), helping to
explain how mitochondrial content is responsive to changing
cellular conditions.
Despite significant advancements in our understanding of
PGC-1a and its corresponding transcription factors, important
aspects of the cellular control of mitochondrial content remain
unclear. These include posttranscriptional processes that
control mitochondrial gene expression, mechanisms of active
mitochondrial degradation and clearance, and extramitochondrial
processes that help coordinate communication between
mitochondria and the nucleus (Goldenthal and Marı´n-Garcı´a,
2004). Posttranscriptional control mechanisms, such as upstream
open reading frames (uORFs) (Calvo et al., 2009), ironresponsive
elements (IREs) (Eisenstein and Ross, 2003), and
microRNAs (Li et al., 2012), are already known to affect the
expression of select mitochondrial genes, and the global discordance
between cellular mRNA and protein levels suggests
that these mechanisms are likely more widespread (Mootha
et al., 2003a). Additionally, macroautophagy (i.e., mitophagy) is
emerging as an important mechanism for eliminating damaged
mitochondria (Youle and Narendra, 2011). However, identifying
additional genes subject to posttranscriptional regulation, and
spotlighting cellular processes that help synchronize the mitochondrial
biogenesis program, would benefit from matched,
cell-wide quantitative data of protein and mRNA abundance,
which has largely been lacking.
Here, to produce such a resource, we performed parallel
quantitative SILAC (stable isotope labeling by amino acids in
Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors 237
cell culture)-based proteomics (Ong and Mann, 2006) and microarray
analyses of PGC-1a-induced mitochondrial biogenesis in
mouse muscle C2C12 cells. In doing so, we find that key proteins
involved in maintaining cellular iron homeostasis are correlated
with mitochondrial biogenesis. We further reveal that depriving
various cell types of iron through chelation or active transport
leads to a rapid and dose-dependent attenuation of mitochondrial
transcript and protein levels that is fully reversible within
3–4 days. Together, our work demonstrates that iron deprivation
results in an active and coordinated downregulation of mitochondrial
gene expression, suggesting that the bioavailability
of iron is a key parameter for establishing a set point of cellular
mitochondrial activity. Because iron deficiency anemia is the
most common nutritional disorder worldwide (McLean et al.,
2009), this work has broad implications for understanding mitochondrial
dysfunction in human health and disease. Additionally,
our data serve as a resource for investigating genes subject to
posttranscriptional regulation and for identifying additional auxiliary
pathways that might be important for calibrating or modulating
the mitochondrial biogenesis program.
RESULTS AND DISCUSSION
Complementary RNA and Protein Profiling
of Mitochondrial Biogenesis
We sought to better define the mitochondrial biogenesis
program in C2C12 mouse myotubes by performing complementary
RNA and protein profiling. To maximize the transcriptional
activation of mitochondrial genes, we overexpressed PGC-
1a—the predominant transcriptional coactivator that drives
mitochondrial biogenesis—more than 200-fold using an adenovirus-mediated
delivery system. We chose C2C12 cells as
a model because overexpression of PGC-1a in this cell line is
sufficient to cause an approximate doubling of mitochondrial
mass in 3 days (Wu et al., 1999). This approach allows us to
assess the relative contribution of posttranscriptional mechanisms
in regulating mitochondrial gene expression and provides
a more complete assessment of the cell-wide proteomic
changes that accompany PGC-1a-induced mitochondrial
production.
Following PGC-1a overexpression, we tracked changes in
cellular mRNA and protein levels using microarrays and quantitative
SILAC proteomics (Ong and Mann, 2006), respectively
(Figure 1A). Consistent with previous studies, our microarray
analyses showed that PGC-1a causes a robust increase in the
transcript abundance of nuclear-encoded mitochondrial genes,
especially those involved in oxidative phosphorylation (OxPhos)
(Figure 1B). Our SILAC data revealed similar results for protein
levels: of 442 mitochondrial proteins quantified using at least
two unique peptides, 263 significantly increased in abundance
(Figure 1C). As expected, PGC-1a-induced mRNA and protein
fold changes were largely consistent in the direction of change
(Figure S1A). However, the protein and mRNA abundances
differed by as much as 9-fold for nuclear-encoded mitochondrial
genes, and for 97 of these genes, mRNA abundance was
increased, whereas the corresponding protein abundance was
decreased (Table S1; Figure S1B). This mRNA:protein discordance
reveals that regulation of protein stability or translation
is likely to be important for titrating the expression level of select
genes during PGC-1a-induced mitochondrial biogenesis. More
broadly, these observations reveal the utility of our resource for
spotlighting mitochondrial proteins whose expression may be
subject to multiple levels of regulation.
Iron Chelation Causes a Pervasive Dampening
of Mitochondrial Protein and Transcript Levels
Our experimental approach also enabled us to investigate
peripheral genes and pathways that may be important for the
mitochondrial biogenesis program. Interestingly, we noted that
proteins involved in regulating cellular iron levels were among
the most highly up- and downregulated proteins in our SILAC
analyses. The transferrin receptor, which is responsible for
transporting transferrin-bound iron into cells, increased more
than 4-fold with PGC-1a overexpression (Figure 1C). Reciprocally,
the level of ferritin heavy chain, part of the ferritin complex
that sequesters cellular iron, was decreased (Figure 1C). These
results suggest that iron might be essential for the mitochondrial
biogenesis program. Increased cellular iron availability during
mitochondrial biogenesis might simply be necessary to accommodate
the mitochondrial proteins that contain iron as
a cofactor or may occur in anticipation of increased output
from the mitochondrial iron-sulfur cluster biogenesis pathway
(Lill, 2009; Richardson et al., 2010). However, the magnitude
of these changes prompted us to explore whether cellular iron
levels might impact the mitochondrial biogenesis program
holistically.
To test whether loss of cellular iron is sufficient to induce a restructuring
of cellular mitochondrial content, we modified the
experimental approach outlined in Figure 1A. Here, in lieu of
PGC-1a overexpression, we treated the cells with deferoxamine
(DFO), a clinically used cell-permeable iron chelator (Chaston
and Richardson, 2003). Strikingly, the microarray results revealed
that DFO treatment strongly diminished the abundance
of mitochondrial transcripts (Figure 1D). Overall, the magnitude
of the DFO effect was as robust as the powerful PGC-1a effect
and was also most prominent for genes encoding proteins
involved in OxPhos (Figures 1E and S1C). Once again, our
mRNA and protein measurements were largely consistent in
direction and magnitude (Figure S1D); however, there were
notable differences in these measurements among the OxPhos
complexes. Complexes I and II, each of which contain multiple
iron-sulfur clusters, were most strongly affected, with protein
levels more decreased than the corresponding mRNA levels
(Figure 1F). Conversely, transcripts encoding complex V subunits
were decreased, whereas their corresponding protein
levels were increased or unchanged (Figure 1F). A recent,
large-scale study of protein dynamics found that mitochondrial
proteins, including complex I and complex V, have largely similar
turnover rates under normal conditions (Price et al., 2010), suggesting
that the differences in complex I and complex V subunit
levels following iron deprivation likely involve posttranscriptional
regulation.
To assess whether cellular iron levels affect PGC-1a-induced
mitochondrial biogenesis, we repeated the experimental
approach a third time with PGC-1a adenovirus added to cells
simultaneously with DFO. Here, the presence of DFO had a
238 Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors
pervasive dampening effect on the induction of mitochondrial
transcripts by PGC-1a (Figures S1E and S1F). Notably, for
many of the same OxPhos subunits highlighted in Figure 1F,
transcript expression was increased under these conditions,
whereas the corresponding protein levels were decreased
(Figures 1G and S1G). This again suggests that for select genes,
iron chelation may lead to an active, posttranscriptional reduction
of expression, as opposed to merely thwarting the effects
of PGC-1a.
Our large-scale gene expression and proteomic data
show that iron chelation has an approximately equal and
opposite effect to that of PGC-1a, a powerful inducer of mitochondrial
biogenesis. To validate these results, we performed
Figure 1. Complementary RNA and Protein
Profiling of PGC-1a-Induced Mitochondrial
Biogenesis
(A) Experimental workflow for proteomic and microarray
analysis of differentiated C2C12 mouse
myotubes overexpressing PGC-1a and/or treated
with 100 mM DFO.
(B) Comparison of mRNA expression in GFPtreated
cells and PGC-1a-treated cells (AU, arbitrary
units).
(C) Changes in protein expression during PGC-1a
overexpression. Proteins are ordered from least to
greatest fold change.
(D) Comparison of mRNA expression in GFPtreated
cells and DFO-treated cells.
(E) Comparison of PGC-1a and DFO-induced
changes in mRNA expression (59% of mitochondrial
genes versus 18% of all genes are in the
lower-right quadrant; p = 9.1 3 10À222
, c2
contingency test).
(F) Comparison of OxPhos mRNA and protein
expression during DFO treatment.
(G) Mitochondrial mRNA and protein expression
that show discordance during PGC-1a+DFO
treatment.
See also Figure S1 and Table S1.
immunoblots and real-time quantitative
PCR (qPCR) on the same samples
used for our proteomics and microarray
analyses. Consistent with our SILAC
data, we found that OxPhos proteins
were significantly decreased by the
DFO treatment (Figure 2A). These results
also validated that this effect was
predominantly limited to mitochondrial
proteins because representative endoplasmic
reticulum, nuclear, and cytoplasmic
markers were unaffected by
this treatment (Figure 2A). For qPCR
measurements, we chose two nuclearencoded
OxPhos genes (Ndufb5, which
is part of iron-containing complex I;
and Atp5a1, a subunit of iron-free
complex V) and one mtDNA-encoded
gene (COX1, which was not represented
on the microarray or captured by mass spectrometry). All three
genes were significantly upregulated with PGC-1a overexpression
and diminished by DFO treatment (Figure 2B). These PCR
results reveal that the iron chelation effect is not limited to
nuclear-encoded mitochondrial genes or to genes encoding
iron-dependent mitochondrial proteins. Furthermore, our microarray
and SILAC data demonstrate that the expression of
genes encoding non-mitochondrial iron-dependent proteins is
not diminished by the iron chelation treatment (Figure S1H).
Collectively, our data show that acute iron deprivation has
a specific and dose-dependent dampening effect on mitochondrial
protein expression that exceeds a mere cell-wide loss of
iron-dependent proteins and processes.
Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors 239
Iron Chelation Causes a Rapid, Universal, and DoseDependent
Decrease in Mitochondrial Protein
Abundance
The results above suggest that cells might possess the ability to
calibrate their mitochondrial protein levels to the concentration
of available iron. To further test this hypothesis, we grew myotubes
in the presence of increasing concentrations of DFO.
The results show that even 20 mM DFO has a marked effect on
cytochrome c and complex I levels and that the effect is directly
proportional to the concentration of DFO (Figure 2C). To begin to
assess the universality of the iron deprivation effect, we treated
myotubes alongside their undifferentiated myoblast counterparts.
Surprisingly, the response in myoblasts was even more
robust: after just 24 hr of treatment with DFO, the 8 kDa-complex
I subunit was nearly completely lost, whereas the level of this
protein continued to gradually vanish from myotubes up through
72 hr (Figure S2A). This effect was also evident in a panel of cell
lines differing in species and tissue of origin (Figure 2D), suggesting
that iron availability might be a universal gauge that cells use
to calibrate mitochondrial activity.
To further define the effect of DFO on OxPhos protein abundance
in undifferentiated myoblasts, we examined individual
subunits from each OxPhos complex using standard SDSPAGE
and fully assembled, native OxPhos complexes using
blue native PAGE (BN-PAGE). SDS-PAGE revealed that DFO
causes a decrease in the abundance of complex I, complex II,
and complex IV subunits (Figure 2E). Consistent with our proteomic
analysis of differentiated myotubes (Figure 1F), BN-PAGE
revealed that DFO most strongly affects the abundance of
complex I and complex II (Figure 2F). Moreover, iron chelation
also seems to affect supercomplex formation (Figure 2F). A distinguishing
feature of complexes I and II among the OxPhos
machinery is that they each possess multiple iron-sulfur cluster
centers. As such, although many mitochondrial proteins are
affected by iron deprivation, this suggests that iron-sulfur clusters
might be particularly important for the cellular response to
this perturbation.
Our data demonstrate that iron deprivation causes a pervasive,
but not complete, reduction in the expression of mitochondrial
proteins. This result is consistent with a calibrated remodeling
of the mitochondrial proteome, as opposed to total mitochondrial
turnover. To further test this, we measured changes in mitochondrial
mass from C2C12 myoblasts using MitoTracker and
nonyl acridine orange (NAO) following treatment with 100 mM
DFO. Consistent with a similar recent study by Yoon et al.
(2006), our results revealed either no change or a slight increase
Figure 2. Effect of Iron Deprivation on Nuclear and mtDNA-Encoded
Gene Expression
(A) Level of the indicated proteins from samples used in the proteomic
analyses as assessed by immunoblotting.
(B) Abundance of the indicated transcripts from samples used in the microarray
analyses as detected by real-time qPCR. Data are displayed as mean ±
SD of triplicate measurements (*p < 0.05, ANOVA with Tukey’s test).
(C) Level of the indicated proteins in C2C12 myotubes after treatment with
a range of DFO concentrations for 3 days as assessed by immunoblotting.
(D) Level of the indicated proteins after DFO treatment for 24 hr in the indicated
cell lines as assessed by immunoblotting.
(E) Level of the indicated proteins after DFO treatment for 24 hr in C2C12
myoblasts as assessed by SDS-PAGE and immunoblotting.
(F) Level of the indicated proteins after DFO treatment for 24 hr in myoblasts as
assessed by BN-PAGE and immunoblotting (SC, respiratory supercomplex;
NS, nonspecific band).
(G) Mitochondrial mass in myoblasts after DFO treatment for 24 hr as assessed
by plate reader-based quantitation of MitoTracker Green FM or NAO fluorescence/cell
number. Data are displayed as mean ± SD of triplicate measurements
(NS signifies p R 0.05, Student’s t test).
See also Figure S2.
240 Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors
in mitochondrial mass after DFO treatment (Figure 2G). Additionally,
fluorescence microscopy with MitoTracker staining revealed
no obvious changes in mitochondrial mass or morphology after
administration of DFO (Figure S2B). Together with the observation
that select mitochondrial proteins are not diminished with
DFO treatment, including VDAC (voltage-dependent anion
channel) (Figures 2A and 2C), these results strongly suggest
that the iron chelation effect doesnot involve complete mitochondrial
turnover, as would be expected for a mitophagy process.
Iron-Dependent Mitochondrial Restructuring Is Distinct
from Prominent Regulators of Mitochondrial Biogenesis
Through large-scale and targeted measurements of mRNA and
protein abundance, we have found that iron chelation causes a
powerful downregulation of mitochondrial protein expression.
DFO is a well-characterized and clinically used iron chelator
(Chaston and Richardson, 2003); nonetheless, to ensure that
our observed effects are not an off-target effect of this drug, we
deprived cells of iron through two additional mechanisms. First,
we depleted cellular iron stores by overexpressing the iron
exporter ferroportin (Nemeth et al., 2004) in human embryonic
kidney (HEK) 293cells (oneof thelinestested inFigure2D).Similar
to DFO, ferroportin expression resulted in a loss of cytochrome c,
the complex IV COXIV subunit, and the complex I 8 kDa subunit
(Figure 3A). Both treatments also led to a decrease in ferritin, indicating
a reduction in cellular iron levels (Figure S3A). Second, we
demonstrated that a comparable effect was achieved with 2,20
dipyridyl
(DP), a structurally and functionally distinct iron chelator
(Figure 3H). Moreover, because the iron chelation effect is robust
in the postmitotic myotubes used in our microarray and proteomics
experiments, it is likely independent of the known effect
that DFO has on cell proliferation (Yu et al., 2007).
We next sought to determine whether established regulators
of mitochondrial biogenesis could explain this response. First,
because of the largely reciprocal effects of DFO and PGC-1a
treatments (Figure 1E), we hypothesized that DFO might simply
cause a decrease in the expression of PGC-1a or its associated
transcription factors. From a detailed time course, we found that
the abundance of mitochondrial transcripts (Figures 3B and 3C)
and proteins (Figure S3B) begins to decrease around 12 hr and
reaches a minimum approximately 24 hr after DFO treatment.
As a control, we measured the transcript level of Tfrc, whose
expression is regulated by iron levels (Figure S3C). We found
that DFO had little effect on the transcript levels of Esrra, Nrf1,
Figure 3. The Mitochondrial Response to Iron Deprivation Is Independent
of PGC-1a, PGC-1b, and HIF-1a
(A–I) Immunoblotting was used to assess protein abundance, and real-time
qPCR was used to assess mRNA abundance. Data are displayed as mean ±
SD of triplicate measurements (*p < 0.05, Student’s t test). (A) Protein levels in
HEK293 cells after 100 mM DFO treatment or ferroportin (Fpn-GFP) overexpression
for 48 hr. Abundance of (B) Ndufs8, (C) Uqcrc1, and (D) Ppargc1a in
C2C12 myoblasts following DFO treatment for the indicated times. (E) Level of
the indicated proteins in wild-type or PGC-1aÀ/À
brown preadipocytes after
100 mM DFO treatment for 24 hr. (F) Abundance of Ppargc1b in myoblasts
following DFO treatment for the indicated times. (G) Level of the indicated
proteins in wild-type or PGC-1bf/f/MLC-Cre
soleus muscle primary satellite cells
after 100 mM DFO treatment for 24 hr.
(H) Level of the indicated proteins after 100 mM DFO, 100 mM DP, or 500 mM
DMOG treatment for 24 hr in myoblasts.
(I) Level of the indicated proteins in wild-type or HIF-1aÀ/À
MEFs after 100 mM
DFO treatment for 24 hr.
See also Figure S3.
Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors 241
or Gabpa (the primary PGC-1a-associated transcription factors)
(Figure S3C) and caused a marked increase in the levels of
Ppargc1a mRNA (Figure 3D). Furthermore, PGC-1aÀ/À
cells (Uldry
et al., 2006) responded comparably to wild-type cells when
deprived of iron (Figure 3E), together indicating that iron deprivation
does not affect mitochondrial gene expression by opposing
the function of this coactivator. Interestingly, the levels of
PGC-1b mRNA (Ppargc1b) (Figure 3F) and protein (Figure S3B)
were decreased following DFO treatment. However, similar to
PGC-1aÀ/À
cells, depletion of PGC-1b expression in primary
satellite cells isolated from soleus muscle of PGC-1bf/f/MLC-Cre
mice (Zechner et al., 2010) did not cause diminished levels of
mitochondrial markers, nor did it alter the cellular response to
DFO treatment (Figure 3G). Together, these results suggest
that the observed effects of iron deprivation are independent
of PGC-1a and PGC-1b.
Iron chelation is also known to activate the hypoxia response
by inhibiting the iron-dependent hydroxylases that constitutively
target the transcription factor HIF-1a (hypoxia-inducible factor 1
a) for degradation (Schofield and Ratcliffe, 2004), and HIF-1a
activation has recently been shown to initiate a loss of mitochondrial
mass in renal clear cell carcinoma cells (Zhang et al., 2007).
To determine if the loss of mitochondrial proteins observed here
is also due to HIF-1a stabilization, we treated C2C12 myoblasts
with the 2-oxoglutarate analog dimethyloxalylglycine (DMOG).
The same HIF-1a hydroxylases that require iron also require
2-oxoglutarate as a cofactor and can therefore be inhibited by
2-oxoglutarate analogs even when iron levels are normal (Jaakkola
et al., 2001). As seen in Figure 3H, C2C12 cells treated
with the iron chelators DFO or DP responded comparably in their
ability to stabilize HIF-1a. Cells treated with DMOG also stabilized
HIF-1a but did not affect the OxPhos proteins, suggesting that
HIF-1a activation is not sufficient for the iron chelation response.
To test whether HIF-1a is necessary for this response, we treated
wild-type and HIF-1aÀ/À
cells with DFO for 24 hr. These cells
showed the same response as all other cell types tested, regardless
of the HIF-1a genotype (Figure 3I). Coupled with the fact that
HIF-2a is not expressed in this cell line (data not shown), these
experiments reveal that the widespread loss of mitochondrial
proteins following iron chelation is a distinct, HIF-independent
process. Overall, our data reveal that mere deprivation of iron
elicits an effect comparable in magnitude to, but independent
of, the most well-established drivers of the mitochondrial biogenesis
program.
The Mitochondrial Response to Iron Deprivation
Is Reversible
As noted above, depletion of cellular iron has various known side
effects on cellular functions, including inhibition of cell division,
initiation of apoptosis, and induction of autophagy (De Domenico
et al., 2009; Yu et al., 2007). Therefore, we next sought to determine
whether our observed mitochondrial effect represents
a true acute metabolic adaptation or merely irreversible cellular
damage. To do so, we treated C2C12 mouse myoblasts with
or without DFO and, after 24 hr, passaged the cells into fresh,
DFO-free media (Figure 4A). We continued to passage the cells
every 24 hr and took samples at each time point (Figure 4A).
Once again, 24 hr of DFO treatment resulted in the selective
loss of mitochondrial markers (Figures 4B and S4A). However,
72–96 hr following the removal of DFO, protein (Figure 4B) and
transcript (Figure S4A) levels were comparable to those found
in their untreated counterparts. To determine whether the
recovery of mitochondrial proteins also represents a recovery
of mitochondrial function, we profiled mitochondrial respiration
using a Seahorse XF Analyzer (Figure 4C). As shown in Figure 4D
(and Figure S4B), iron chelation led to a strong decrease in basal
oxygen consumption rate (OCR) and spare respiratory capacity
(Figure 4E). Because DFO is known to suppress cell proliferation
(Yu et al., 2007), we normalized the basal OCR of untreated and
DFO-treated cells to cell number. Remarkably, the basal OCR of
DFO-treated cells recovered within 48 hr following their passage
into DFO-free media (Figure 4D). Interestingly, after this recovery
of basal OCR, the DFO-treated cells continued to recover their
spare respiratory capacity, which reached untreated levels by
day 5 (Figure 4E). Coupling efficiency remained unchanged in
the iron-depleted cells, indicating that DFO likely does not
damage the integrity of the mitochondrial inner membrane
(Figure 4F). Altogether, our results reveal that iron deprivation
initiates a reversible and adaptive cellular response that involves
remodeling of the mitochondrial proteome and a reduction in
mitochondrial respiratory function.
Conclusions
Mitochondria are vital metabolic organelles that must continually
adapt to changing external environments and cellular needs. To
better define this adaptive process, we performed extensive,
matched microarray and quantitative proteomic analyses of
mouse muscle cells under a variety of conditions. In doing so,
we created a robust resource that can be mined to discover
proteins whose expression levels are affected by posttranscriptional
regulation and auxiliary cellular processes that both sense
the need for and drive mitochondrial restructuring. We leveraged
this resource to find that proteins involved in cellular iron homeostasis
are coordinated with mitochondrial biogenesis and have
shown that depriving cells of iron through a variety of mechanisms
results in a rapid downregulation of mitochondrial protein
levels and oxidative capacity. We further demonstrated that this
drastic effect occurs in a wide range of cell types and that it is
fully reversible within 2–3 days following reintroduction of iron.
Last, we have shown that this process is independent of the
well-established PGC-1a-, PGC-1b-, and HIF-1a-driven mitochondrial
biogenesis programs. Because iron deficiency is the
world’s number one nutritional deficiency, affecting approximately
25% of the population (McLean et al., 2009), our work
could have significant implications for human health and
disease. Our extensive, freely available mRNA and protein
profiling data sets will serve as a rich resource for further
exploring the cellular response to acute metabolic stress and
the roles of transcriptional and posttranscriptional processes
important for mitochondrial biogenesis.
EXPERIMENTAL PROCEDURES
Cell Culture
Brown preadipocytes were maintained in high-glucose DMEM with 20% FBS
and 1 3 PS (Invitrogen) at 37
C and 5% CO2. Primary soleus muscle satellite
cells were maintained on rat tail collagen (Invitrogen)-coated plates in Ham’s
242 Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors
F-10 media (as detailed in the Extended Experimental Procedures). All other
cell lines were maintained in high-glucose DMEM with 10% FBS and 1 3
PS. Deferoxamine mesylate salt (DFO), DMOG, and DP were obtained from
Sigma-Aldrich. For ferroportin (Fpn-GFP) expression, HEK293FT cells were
transfected with 1 mg Fpn-GFP (Nemeth et al., 2004) using Lipofectamine
LTX (Invitrogen). Myotube differentiation, metabolic labeling, and adenoviral
infection were performed using standard procedures (as detailed in the
Extended Experimental Procedures).
Microarray and Mass Spectrometry
RNA purification, cRNA preparation, and hybridization to Affymetrix 430 2.0
arrays were performed according to established methods (Mootha et al.,
2003b). Unique Entrez Gene identifiers from the mapped data were selected
and sorted by the average GFP probe intensity from the two metabolic-labeling
experiments. Data corresponding to these brightest probes that gave present
call (P) across all samples were used for analysis (8,313 probes total).
Mass spectrometry was performed using standard procedures (as detailed
in the Extended Experimental Procedures). For comparison of mRNA and
protein abundance, all data were mapped to Entrez Gene identifiers. Gene
products were identified as mitochondrial using the MitoCarta inventory (Pagliarini
et al., 2008). OxPhos and iron-containing proteins were manually
curated from Gene Ontology annotations.
Real-Time qPCR
Total RNA was purified using an RNeasy Kit (QIAGEN). First-strand cDNA was
synthesized from 500 ng RNA using SuperScript III (Invitrogen). Real-time
qPCR was performed using predesigned TaqMan Assays or SYBR greenbased
detection (ABI) with Actb (microarray samples) or Rplp0 as the endogenous
control (see the Extended Experimental Procedures for primer
sequences).
Immunoblotting and ELISA
For immunoblot analysis, 15 mg of cleared whole-cell lysate, as determined by
BCA assay (Pierce), was separated on a 4%–12% Bis-Tris Mini Gel (Invitrogen),
transferred to PVDF, and probed with primary antibodies (listed in Table
S2). For BN-PAGE, 12 mg of mitochondrial protein, prepared as previously
described by Pello et al. (2008), was separated on a 3%–12% NativePAGE
Bis-Tris Mini-Gel (Invitrogen), transferred to PVDF, and probed with an
OxPhos Blue Native Antibody Cocktail (MitoSciences). For ferritin measurement,
15–35 mg of cleared whole-cell lysate was analyzed by ELISA (Ramco
Laboratories) and normalized to total protein concentration, as determined
by BCA assay.
Analysis of Mitochondrial Mass
For determination of mitochondrial mass, C2C12 myoblasts were seeded at
4,500 cells/well in 96-well microplates and incubated for 24 hr. Following
DFO treatment, mitochondria were stained with 100 nM MitoTracker Green
FM or NAO (Invitrogen) for 30 min. The cells were then washed 23 with
PBS, and fluorescence was measured using a BioTek Synergy 2 Microplate
Reader with a 485/20 excitation, 528/20 emission filter set. Fluorescence
was normalized to cell number (as described in the Extended Experimental
Procedures).
Oxygen Consumption Measurements
OCR measurements were performed using a Seahorse Biosciences XF96
Extracellular Flux Analyzer as previously described by Nicholls et al. (2010).
Briefly, C2C12 myoblasts were seeded at 12,000 cells/well in XF96 microplates
(Seahorse Biosciences). After a 24 hr incubation, the growth media
Figure 4. Mitochondrial Gene Expression and Respiratory Function
during Iron Deprivation and Recovery
(A) Experimental workflow for the analysis of mitochondrial gene expression
and respiration during DFO response and recovery.
(B) Level of the indicated proteins in C2C12 myoblasts treated with 100 mM
DFO for 24 hr then passaged every 24 hr in DFO-free media as assessed by
immunoblotting.
(C) Mitochondrial respiratory profile of untreated myoblasts at day 1 of
analysis. Data are displayed as mean ± SD of 14–16 replicates.
(D–F) Basal OCR (pmol/min)/cell number (D), spare respiratory capacity (E), and
coupling efficiency (F) of myoblasts treated with 100 mM DFO for 24 hr then
passagedevery24hrinDFO-freemedia.Dataaredisplayed asmean± SDof14–
16 replicates (*p < 0.05, Student’s t testin D,ANOVA with Tukey’s testin E and F).
See also Figure S4.
Cell Reports 3, 237–245, January 31, 2013 ª2013 The Authors 243
were exchanged for XF Assay Medium (Seahorse Biosciences) supplemented
with 25 mM glucose (Sigma-Aldrich). OCR measurements were 5 min periods
following 3 min mix periods. Myoblasts were treated by sequential addition of
1 mg/ml oligomycin (Sigma-Aldrich), 300 nM FCCP (Sigma-Aldrich), and 2 mM
rotenone (MP Biomedicals). Spare respiratory capacity and coupling efficiency
were calculated using Seahorse Bioscience instructions (see Figure S4B for
details). Basal OCR was normalized to cell number (as described in the
Extended Experimental Procedures).
Statistics
p Values were calculated by Student’s two-tailed t test, one-way ANOVA with
Tukey’s post hoc analysis, Spearman’s rank correlation test, or c2
contingency
test as indicated in the figure legends.
ACCESSION NUMBERS
The Gene Expression Omnibus accession number for the microarray data reported
in this paper is GSE42299. The processed microarray and quantitative
proteomic data are also available for download from http://www.pagliarinilab.
org/datasets.
SUPPLEMENTAL INFORMATION
Supplemental Information includes Extended Experimental Procedures, four
figures, and two tables and can be found with this article online at http://dx.
doi.org/10.1016/j.celrep.2012.11.029.
LICENSING INFORMATION
This is an open-access article distributed under the terms of the Creative
Commons Attribution-NonCommercial-No Derivative Works License, which
permits non-commercial use, distribution, and reproduction in any medium,
provided the original author and source are credited.
ACKNOWLEDGMENTS
We would like to thank the members of the Eisenstein, Kaplan, V.K.M., and
D.J.P. laboratories for helpful discussions and assistance regarding this
project. We specifically thank Sarah Calvo and Dan Arlow of the V.K.M.
lab for assistance with microarray analyses, Jerry Kaplan and Ivana De
Domenico (University of Utah) for providing the ferroportin expression
vector, Bruce Spiegelman (Harvard Medical School) for providing the
PGC-1aÀ/À
cells, Daniel Kelly (Sanford-Burnham) for providing the PGC-
1bf/f/MLC-Cre
cells, Randall Johnson and Alex Weidemann (UCSD) for
providing the HIF-1aÀ/À
cells, Eric Shoubridge (McGill) for providing the
MCH58 cells, and Kelly Werner of the D.J.P. lab for critical reading of the
manuscript. This work was supported by a Searle Scholars Award,
a Shaw Scientist Award, and USDA Hatch Award WIS01671 (to D.J.P.),
NIH R01GM077465 (to V.K.M.), and NIH Molecular Biosciences Training
Grant 5T32GM007215-37 (to J.W.R.).
Received: May 29, 2012
Revised: October 18, 2012
Accepted: November 25, 2012
Published: January 10, 2013
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