Download High-resolution dynamics of the transcriptional response to nutrition

Physiol Genomics 29: 24 –34, 2007.
First published November 7, 2006; doi:10.1152/physiolgenomics.00061.2006.
High-resolution dynamics of the transcriptional response to nutrition in
Drosophila: a key role for dFOXO
Boris Gershman,1 Oscar Puig,2 Lilian Hang,1 Robert M. Peitzsch,3 Marc Tatar,1 and Robert S. Garofalo3
1
Division of Biology and Medicine, Brown University, Providence, Rhode Island; 2Institute of Biotechnology, University
of Helsinki, Helsinki, Finland; and 3Pfizer Global Research and Development, Groton Labs, Groton, Connecticut
Submitted 6 April 2006; accepted in final form 3 November 2006
metabolism; mitochondria; peroxisome proliferator-␥ coactivator-1;
microarray; gene expression; insulin signaling; nutrient sensing; target
of rapamycin pathway
their nutrient intake to provide
sufficient energy to support locomotion, biosynthetic processes, and reproduction while limiting oxidative damage due
to excess metabolic activity. This complex balance is achieved
via multiple nutrient sensing systems that control food intake,
storage, and utilization. Two major nutrient sensing systems,
the insulin and target of rapamycin (TOR) pathways, are highly
conserved between Drosophila and mammals (16, 43). In
addition, as reported in studies carried out 30 yr ago, Drosophila produce both hyperglycemic and hypoglycemic hormones
that respectively regulate mobilization and storage of carbohydrate (41, 59). The source of the hypoglycemic hormone has
recently been identified as a set of neurosecretory cells that
produce insulin-like peptides (dILPs) (57). Ablation of the
ADULT ORGANISMS REGULATE
Article published online before print. See web site for date of publication
(http://physiolgenomics.physiology.org).
Addresses for reprint requests and other correspondence: M. Tatar, Box
G-W, Div. of Biology and Medicine, Brown Univ., Providence, RI 02912
(e-mail: [email protected]); Robert Garofalo, MS 8220-3082, Eastern
Point Rd, Groton, CT 06340 (e-mail: [email protected]).
24
dILP-secreting cells raised circulating sugar levels and caused
growth inhibition (57) similar to that induced by mutations of
the insulin-like receptor gene, inr, and of the gene encoding the
insulin receptor substrate homolog, chico (3, 5, 7). The TOR
pathway also regulates growth in Drosophila melanogaster, as
it does in mammals (37, 72). Furthermore, mutations that lead
to decreased function of either the insulin or dTOR nutrientsensing pathways in Drosophila extend lifespan (9, 25, 60).
Mutations that decrease activity of the nutrient sensing system
may mimic, at least in part, the effects of decreased nutrient
uptake or dietary restriction (DR), which extends lifespan in
species as diverse as worms, flies, and mammals (4). Overall,
the insulin and TOR nutrient-sensing systems are likely to play
a fundamental and conserved role in coordinating complex
aspects of the adult phenotype - growth, reproduction, and
lifespan - in response to nutrients.
Regulation of these complex responses will require harmonized changes in the expression of many genes. How this
control is affected is largely unclear. Unanswered questions
include: what transcriptional changes are under the control of
the nutrient-sensing systems, what is the temporal and hierarchical relationship of these changes, what magnitude of change
is biologically relevant, and are genes within a functional
metabolic network coregulated at the transcriptional level?
Recent work with D. melanogaster has begun to examine these
questions in the context of transcriptional profiles (4, 48, 73).
Zinke et al. (73) identified numerous genes whose expression
was altered when rapidly growing second instars were starved
or fed only sugar. Striking changes in genes involved in fat
metabolism, protein translation, and several putative transcription factors were detected. These initial data, however, were
limited to a few time points and did not resolve differences
with less than fourfold change. Pletcher et al. (48) examined
global changes in transcript abundance with aging and as a
function of nutrient concentration in the adult food medium.
They identified 2,079 genes whose transcript abundance significantly varied across diet, most of which changed by ⬍1.5fold. Although this study made repeated measures throughout
the adult lifespan, the first was made 3 days after flies were
transferred to restricted diet. Therefore, the design would not
detect transcriptional changes immediately associated with the
induction of the nutrient response. Remarkably, the physiology
of D. melanogaster is extremely responsive to diet, as evidenced by the full adjustment of age-specific mortality and
reproduction within 2–3 days of shifting adults between relatively rich and diluted diets (8, 17, 34). Thus, the transcriptional response to changes in nutrients might occur over a short
time scale.
More detailed knowledge of the transcriptional response to
nutrients is critical to our understanding of metabolic disease
1094-8341/07 $8.00 Copyright © 2007 the American Physiological Society
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Gershman B, Puig O, Hang L, Peitzsch RM, Tatar M,
Garofalo RS. High-resolution dynamics of the transcriptional
response to nutrition in Drosophila: a key role for dFOXO. Physiol
Genomics 28: 24–34, 2007. First published November 7, 2006;
doi:10.1152/physiolgenomics.00061.2006.—A high-resolution time
series of transcript abundance was generated to describe global expression dynamics in response to nutrition in Drosophila. Nonparametric change-point statistics revealed that within 7 h of feeding upon
yeast, transcript levels changed significantly for ⬃3,500 genes or 20%
of the Drosophila genome. Differences as small as 15% were highly
significant, and 80% of the changes were ⬍1.5-fold. Notably, transcript changes reflected rapid downregulation of the nutrient-sensing
insulin and target of rapamycin pathways, shifting of fuel metabolism
from lipid to glucose oxidation, and increased purine synthesis,
TCA-biosynthetic functions and mitochondria biogenesis. To investigate how nutrition coordinates these transcriptional changes, feeding-induced expression changes were compared with those induced by
the insulin-regulated transcription factor dFOXO in Drosophila S2
cells. Remarkably, 28% (995) of the nutrient-responsive genes were
regulated by activated dFOXO, including genes of mitochondrial
biogenesis and a novel homolog of mammalian peroxisome proliferator-␥ coactivator-1 (PGC-1), a transcriptional coactivator implicated
in controlling mitochondrial gene expression in mammals. These data
implicate dFOXO as a major coordinator of the transcriptional response to nutrients downstream of insulin and suggest that mitochondria biogenesis is linked to insulin signaling via dFOXO-mediated
repression of a PGC-1 homolog.
dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
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vator, peroxisome proliferator-␥ coactivator-1 (PGC-1), as a
dFOXO target gene, suggesting that mitochondria biogenesis is
linked to insulin signaling via dFOXO-mediated repression of
a PGC-1 homolog.
METHODS
Drosophila culture. Following Tu and Tatar (67), D. melanogaster
(yw) were reared synchronously to third instars on standard cornmealsugar-yeast medium at 25°C and transferred at 76 h old to sugar-agarcornmeal medium without yeast. Virgin females were collected within
8 h of eclosion and placed on sugar-agar-cornmeal medium without
dietary yeast. At day 4, females were redistributed in groups of 30 into
vials with yeast-free medium or with standard medium supplemented
with live-yeast. Two females were collected at each of 12 sample
times from 15 vials within each treatment group (NY, control; Y,
refed); samples were collected every 60 min. To provide replicated
estimates of expression profiles prior to the study (baseline samples),
four samples of 30 females were collected just before we initiated the
time series collections.
Message isolation and hybridization. Samples were frozen in liquid
nitrogen at each collection time point and stored at ⫺80°C. RNA was
extracted from all samples simultaneously by lysis in a FastPrep
FP120 multitube homogenizer (Q BioGen). Flies were added to 1 ml
of Trizol (Invitrogen) and 10 ␮g of glycogen in iced FastRNA tubes
(cat. no. 6040-600) and spun twice for 8 s at speed setting “6.5”; tubes
were iced between spins. RNA was extracted by phase separation and
precipitation following Trizol protocols. At the Brown University
Genomics Core Facility, RNA quality was verified on agarose gels
and reverse transcribed. The cDNA was amplified by in vitro transcription, producing 5 ␮g of total product. Products were hybridized
to Affymetrix GeneChip Drosophila_1 Genome Arrays in sets of four,
each included a pair of time matched Y and NY treatments sampled
from one early and one late hour. The raw scan data from each chip
were exported to DNA-Chip Analyzer (http://www.dchip.org) and
normalized across chips by the invariant difference selection algorithm with gene expression given by the model-based expression
index. Two samples from the control group produced insufficient
RNA (4 and 8 h); values to calculate expression ratios in the controls
of these time points were interpolated from data of control samples at
adjacent hours.
Change-point statistics. The cumulative sum statistic (CUSUM)
estimates the time of a change point in sequentially ordered data (6).
The CUSUM function is the cumulative difference between the
average among all values and each value taken in time order. A
change point is estimated at the time with the largest absolute change
of this cumulative function that includes a sign change. A complementary statistic for the change point is based on the mean square
error (MSE) statistic, which places the change point as the time that
minimizes the sum of the squares of the errors about the means on
both sides of the point. Following the bootstrap methods proposed by
Taylor (62), GeneTrace uses CUSUM to calculate the change point,
and for each gene it estimates the probability the change point
estimated both by CUSUM and by MSE would be drawn from a
sample distribution of change points derived from random permutations of the observed time series. Because CUSUM and MSE statistics
are insensitive to the order of time points on either side of a mean
shift, the bootstrapped data represent a homogeneous sample of all
potential permuted time series from the observed data. For each gene
time series, GeneTrace returns an estimated change point (CUSUM
estimator) and the two-tailed probability of this statistic from a sample
of 10,000 random permutations across the 16 ordered expression
ratios. The GeneTrace computational platform is available at http://
www.brown.edu/Research/Tatar_Lab/GeneTrace/; information on its
operation is in Supplemental Methods. (The online version of this
article contains supplemental material; there are eight supplemental
tables, one supplemental figure, and a supplemental methods section
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pathogenesis, especially in light of recent evidence that decreased expression of genes of mitochondrial oxidative metabolism is associated with insulin resistance and diabetes (38,
45). Furthermore, the links between nutrient-sensing systems
and regulation of oxidative phosphorylation genes are unclear,
and disease-associated decreases are small, in the range of
20%, requiring sensitive methods of detecting global transcriptional changes.
To understand how genes and gene networks respond to
nutrition requires analysis of transcript dynamics across the
time scale of the physiological response. To date, time series
analysis of transcription dynamics has been limited to manipulations with yeast or cells in culture. These experimental
designs synchronize a population of cells, impose an acute
manipulation (e.g., drug or gene-induction treatment), and
sample at short intervals across the phase of the biological
change while the cells remain synchronized (e.g., 53). Here we
apply key features of this design to study the transcript dynamics in whole adult D. melanogaster as they undergo an acute
physiological response to refeeding: synchronize the physiological state of all animals, impose an abrupt manipulation,
sample from controls and treated groups at each time point, and
sample at short intervals relative to the time frame of the
physiological response. Finally, we apply a computational,
nonparametric change-point analysis to detect when genes
undergo meaningful change and to estimate the magnitude and
likelihood of these changes.
We characterized the transcriptional response in adult females to refeeding on dietary protein. To synchronize their
physiological state at eclosion, we removed yeast from medium of third instar larvae. This produces an eclosion cohort
where insulin-like peptide staining in the brain insulin-producing cells (IPC) is uniformly compressed in all individuals and
oogenesis is fully repressed in all females (67). When synchronized yeast-deprived females are fed yeast, within 12 h the IPC
expand and secrete insulin-containing vesicles, and vitellogenesis is activated. Accordingly, our analysis describes the transcriptional dynamics of metabolic and physiological networks
across the initial 12 h of this physiological response. Furthermore, we compare sets of transcripts modulated in response to
refeeding to candidate targets of dFOXO, the D. melanogaster
ortholog of Caenorhabditis elegans daf-16 and mammalian
FOXO1 and FOXO3a (22, 26, 49). dFOXO activity is modulated in D. melanogaster by insulin signaling through an
insulin-like receptor that controls phosphatidylinositol-3 kinase/dAkt (22, 26, 49). Since yeast restriction impedes secretion of insulin-like peptides in adult flies (67), we expect
dFOXO to be activated in yeast-deprived females and to be
relatively inactivated when adults are refed. To identify potential nutrient-mediated dFOXO transcription targets in the adult
fly we compared the transcriptional responses upon yeast
refeeding to the message profiles from Drosophila S2 cells that
expressed a constitutively active dFOXO-A3 construct (49).
Remarkably, dFOXO target genes represent 28% of the total
nutrient-responsive genes, implicating dFOXO as a major
factor in organizing the transcriptional response to nutrients.
Our data suggest that dFOXO plays a central role in coordinating changes in nutrient sensing, protein synthesis and
growth, fatty acid oxidation and storage, and genes of mitochondrial biogenesis. In addition, we have identified a putative
Drosophila homolog of the mammalian transcriptional coacti-
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dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
associated with this paper.) Microarray datasets have been deposited
in GEO with accession number GSE562.
RESULTS AND DISCUSSION
Fig. 1. Experimental protocol and the global behavior of gene expression upon
refeeding. A: flies were raised on standard diet until 3rd instar, when they were
switched to a diet lacking yeast. Four days after eclosion, a group was refed
yeast (Y-treatment) and the control continued on diet lacking yeast (NYcontrol). Sample hours indicate times postrefeeding when samples where taken
from Y and NY groups for isolation of mRNA to be hybridized to microarrays.
B: number of genes exhibiting change points at each hour after refeeding.
Filled bars, increased expression; open bars, decreased expression. C: distribution of fold-changes at each hour after refeeding.
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High-resolution time series of transcript abundance. Variance among individuals within treatment groups can reduce the
power to detect change in transcript abundance. To minimize
this source of noise, we imposed diet restriction in third instars
(Fig. 1A). This procedure synchronizes the physiological state
among emerging adults and induces a uniform pattern of
suppressed insulin secretion (67). Newly eclosed virgin females were maintained on yeast-free medium until 4 days old
and then transferred either to medium with yeast (Y-treatment)
or to control medium without yeast (NY-control). Baseline
samples were collected prior to refeeding (arbitrarily designated as hours ⫺3, ⫺2, ⫺1, and 0) and every 60 min for the
next 12 h after the transfer (hours 1–12). RNA was isolated
from both Y-treatment and NY-control as described in METHODS
and hybridized to Affymetrix GeneChips. The relative message
abundance at each hour was determined as the ratio of the
Y-treatment relative to its time-matched NY-control. Message
abundance in the baseline samples was calculated relative to
the mean among baseline samples.
These data produce a time-ordered sequence of expression
ratios. To characterize the response at each gene we determined if and when its expression ratio in the time series
presented a change point, a nonrandom change in the arithmetic mean of the series. In the analytical platform GeneTrace,
change points were estimated by the nonparametric CUSUM,
and the two-tailed probability of each was numerically calculated from permuted data. See METHODS and supplemental
online material.
GeneTrace identified 3,519 genes with significant change
point when the significance threshold for each gene was ␣ ⫽
0.005 (Table S1). Based on 14,000 tests at this threshold we
expect 70 false-positive inferences. Overall, the distribution of
observed change points (Fig. 1B) differed significantly from
expectation if observed change points were sampled uniformly
from among the 12 h (Kolmogorov-Smirnov test, Dobs ⫽ 0.46,
P ⬍ 0.001). Likewise, the observed mean among change points
(x៮ ⫽ 5.09 h) occurred earlier than expected from either a
uniform or normal distribution (P ⬍ 0.01). In the first hour
following refeeding, transcript ratios increased for 40 genes
and decreased for 18 genes. Additional first change points
accumulated until 7 h postrefeeding, after which time relatively
few changes were detected (Fig. 1B). The decrease in observed
change points occurs while individuals remained physiologically synchronous since the coefficient of variation among
expression ratios did not increase in late relative to early hours
(data not shown). While the rarity of change points at later
times is not likely to arise from loss of synchrony in the
sample, we cannot rule out that fewer significant change points
at hours 8 –12 may be accounted for by reduced power at these
time points. At the boundaries it is more difficult to detect
change points because on one side of a putative change point
there are a smaller number of observations available to precisely estimate the mean expression ratio. Our detection of
change points in the early hours of the time series is less likely
to be an underestimate because we have buffered this boundary
with four samples prior to refeeding. Decay in power to detect
change points is more likely in the late hour time samples. This
loss of power at the boundaries, however, does not invalidate
any changes that were found. Importantly, of the changes we
do detect at any hour, most were very small (Fig. 1C); only
19% of the 3,519 ratios were ⬎1.5-fold. These data suggest
that the physiological response to nutrient uptake involves
many subtle changes in message abundance that alter systems
of transcripts on a global scale.
Categorical response: gene ontology. Many of the genes
with significant change points are annotated as gene ontology
biological processes involving cell growth and maintenance,
macromolecular and nucleic acid-related metabolism, signal
transduction, and organogenesis (Table 1). Among 1,148 biological process terms annotated for this array, 128 categories
were significantly overrepresented in the response to nutrient
supply (Table S2). These include many aspects of carbohydrate, fatty acid, and RNA metabolism, DNA replication, RNA
processing, translation, and protein catabolism. Terms associ-
dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
Table 1. Distribution of nutrient responding messages
categorized across Gene Ontology biological
processes (level 4)
Ontology Term
Percent
GO:0008151
GO:0043170
GO:0006139
GO:0007165
GO:0009058
GO:0009887
GO:0009056
GO:0019222
GO:0006082
GO:0006629
GO:0019953
GO:0006793
GO:0050877
GO:0009605
GO:0009607
GO:0050879
GO:0009308
GO:0006519
GO:0007267
GO:0006928
GO:0046698
GO:0006950
GO:0008219
GO:0000902
GO:0007155
GO:0006731
GO:0006066
GO:0006725
GO:0046483
GO:0009792
GO:0009798
GO:0006091
GO:0009719
GO:0007269
GO:0045165
GO:0009795
GO:0006118
GO:0002009
GO:0016271
GO:0043067
GO:0008360
GO:0009952
GO:0046530
GO:0019725
GO:0009966
GO:0006955
GO:0006800
GO:0009953
cell growth and/or maintenance
macromolecule metabolism
Nucleo-base/-side/-otide & nucleic acid metabolism
signal transduction
biosynthesis
organogenesis
catabolism
regulation of metabolism
organic acid metabolism
lipid metabolism
sexual reproduction
phosphorus metabolism
neurophysiological process
response to external stimulus
response to biotic stimulus
organismal movement
amine metabolism
amino acid and derivative metabolism
cell-cell signaling
cell motility
metamorphosis (sensu Insecta)
response to stress
cell death
cellular morphogenesis
cell adhesion
coenzyme and prosthetic group metabolism
alcohol metabolism
aromatic compound metabolism
heterocycle metabolism
embryonic development (sensu Animalia)
axis specification
energy pathways
response to endogenous stimulus
neurotransmitter secretion
cell fate commitment
embryonic morphogenesis
electron transport
morphogenesis of an epithelium
tissue death
regulation of programmed cell death
regulation of cell shape
anterior/posterior pattern formation
photoreceptor cell differentiation
cell homeostasis
regulation of signal transduction
immune response
oxygen and reactive oxygen species metabolism
dorsal/ventral pattern formation
39.53
38.96
26.91
14.13
13.97
11.43
11.01
8.94
7.74
7.53
7.48
7.38
6.44
6.18
6.03
5.92
5.4
5.25
4.99
4.68
4.52
4.26
3.84
3.48
3.32
3.17
2.91
2.6
2.44
2.34
2.13
2.13
2.03
1.97
1.92
1.82
1.82
1.66
1.61
1.56
1.56
1.51
1.4
1.14
1.14
1.09
1.09
1.04
Percent is relative to genes with level 4 Gene Ontology (GO) annotation;
only the categories representing ⬎1.0% are shown.
ated with phototransduction and rhabdomere development
were unexpectedly common; this may reflect previously unrecognized stages of photoreceptor maturation that are completed in adults once they feed on a protein-rich diet. Also
notable were genes of purine metabolism. Landis et al. (29)
reported a coordinated increase in 11 messages for enzymes of
the purine metabolic pathway in adult Drosophila as a function
of age and in response to oxidative challenge. mRNAs for nine
of these genes were significantly increased when females ate
dietary yeast, while message for glutamine synthases and
CG3011 decreased (Table S3). Since well-fed females produce
many eggs, the transcriptional upregulation of purine metaPhysiol Genomics • VOL
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bolic enzymes may serve to support the biosynthesis of nucleotides required to provision embryos with maternal mRNA.
Transcriptional response in nutrient sensing and metabolism. Overall these data reveal a global response to nutrition
that involves rapid change in functional sets of transcripts
whose products mediate metabolic physiology. Below, we
elaborate a few of the many responsive metabolic networks:
nutrient sensing via insulin and TOR, fuel metabolism, mitochondria biogenesis, and the control of histone deacetylases.
Each illustrates the potential for discovery with these data and
reinforces how metabolic physiology is globally adjusted
through small yet coordinated change at the level of mRNA.
Nutrient sensing via insulin and TOR. Insulin/IGF signaling
in Drosophila regulates a variety of biological processes,
including growth, reproduction, and aging (16, 43, 60). It
functions as a systemic nutrient signaling system through
circulating dILP (encoded by dilp1–7) (5, 21). TOR signaling
also provides nutrient sensing (19) but in an exclusively cell
autonomous manner. Gain-of-function mutations in either the
insulin or the TOR pathway stimulate cell growth, whereas
hypomorphic mutations decrease growth (16) and, in some
cases, extend lifespan (9, 25, 60). While these regulatory
actions of insulin/IGF and TOR are understood in terms of
signal transduction cascades, our data indicate an additional
level of control: nutrients coordinately regulate the expression
of components of the nutrient sensing systems themselves.
Transcripts encoding proteins of the insulin/IGF and TOR
pathways decreased within 3 h of refeeding (Fig. 2, A and C),
suggesting they were upregulated in the absence of yeast.
mRNAs encoding the InR, the insulin receptor substrate-1
homolog chico, and phosphatidylinositol-dependent kinase-1,
PK61C, synchronously declined by 30 – 40% (Fig. 2A). These
dynamics contrast with the stasis of the dilp loci; there was no
change observed in transcripts for insulin ligands over this time
course (Fig. 2B). It is possible that changes in mRNAs encoding the dILPs, produced in a small set of neurosecretory cells,
would be obscured in the whole organism RNA samples
analyzed here. However, these patterns are consistent with
observations in yeast-deprived adults; dILP peptides are
present but compact within the pars intercerebralis neurons of
the brain (67). These peptides are rapidly processed and secreted along the medial secretory neuron when adults are refed.
Thus, the response to nutrition in D. melanogaster involves
immediate secretion of stored dILPs possibly with delayed
transcription of the ligand.
The InR controls the activity of the transcription factor
FOXO through its phosphorylation of FOXO by the downstream kinase Akt (1). While dfoxo and akt1 expression were
unchanged in refed flies (Fig. 2D), mRNAs for InR and the
translational repressor, 4eBP, were reduced (Fig. 2, A and C).
InR and 4eBP, a component of the dTOR pathway, were
previously defined as dFOXO-induced genes in S2 cells expressing a constitutively nuclear dFOXO mutant, dFOXO-A3
(49). Here we integrate into our analysis additional, previously
unpublished data from these dFOXO-A3 cells. There is extensive agreement between these independent data sets (Table
S4), indicating that many of the genes responsive to refeeding
in D. melanogaster are potential transcriptional targets of
dFOXO. We found that 995 genes that significantly changed
upon refeeding in adults were inversely regulated following
induction of dFOXO-A3. These include several components of
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Ontology ID
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dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
the insulin signal transduction cascade (e.g., INR, PK61C),
which reinforces the hypothesis that cells can autonomously
regulate their own insulin sensitivity in response to the systemic level of insulin peptides (49). Naturally, detailed molecular analyses are needed to verify inferences for specific genes
and to discriminate direct from indirect action of dFOXO. An
important problem relevant to metabolic diseases is to understand the specific way this within-cell feedback network moves
from the repressed state back to insulin sensitivity.
Key components of the TOR pathway also respond transcriptionally to nutrients (Fig. 2C). Amino acid uptake stimulates TOR to phosphorylate 4e-BP, p70 S6 kinase (S6K), and
other targets (19). In refed flies, 4eBP mRNA was dramatically
downregulated (Fig. 2C). Thus, 4e-BP activity is rapidly regulated at both the posttranslational and transcriptional levels.
The early and dramatic downregulation of 4eBP transcripts
(Fig. 2C) suggests this gene functions as a key gatekeeper in
the activation of translation in response to nutrient resupply
(63, 64). Similar regulation was observed in protein-starved
larvae (73). While refeeding may have stimulated translation
by repression of 4eBP, it appeared to simultaneously reduce
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ing the G protein Rheb, an upstream activator of dTOR,
decreased by 36%, and mRNA for S6K decreased by 16% (Fig.
2C). In addition, a homolog of the binding protein required for
repression of TOR by rapamycin, encoded by fkbp12, was
upregulated (Fig. 2E). Additional members of the FK506
binding protein family, including fkbp39, fkbp59, and fkbp13,
increased in parallel with fkbp12 (Fig. 2E). Depending on the
interactions with endogenous substrates, which are unknown,
the transcription changes in the FK506 proteins along with
those in Rheb and S6K may desensitize the dTOR pathway to
nutrient stimuli upon refeeding, much as we propose for insulin
signaling within cells. Notably, some pathway components
were not regulated acutely at the transcriptional level by
nutrients, including dTOR itself and Tsc1 (Fig. 2D).
In contrast to the decline of 4e-BP mRNA, components
required to initiate translation of 5⬘-cap mRNA are coordinately induced, including the initiation factors eIF4E and
eIF4B, the release factors eRF1 and eRF3, and the scaffolding
protein eIF4G (Fig. 2F). Notably, eIF4G competes with 4eBP
for binding sites on eIF4E (19). Transcripts of eIF4E and
eIF4G increase with strikingly similar kinetics, suggesting they
share regulatory control. Of 35 general translation factors
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Fig. 2. Changes in expression of insulin-like receptor
gene (InR) and target of rapamycin (TOR) pathway
genes after refeeding. Expression ratio of the indicated
genes in refed relative to control flies during the 12 h
following restoration of yeast (hour 0). Hours ⫺3 to 0
are baseline replicates. A: Drosophila homologs of the
insulin receptor (Inr), IRS-1 (chico), and PDK-1
(PK61C); B: Drosophila insulin-like peptide (dilp)
genes (dilp5 was not found on the microarray); C: TOR
pathway genes; D: Tor and Inr pathway genes that do
not change with refeeding; E: Drosophila homologs of
fk506 binding proteins required for inhibition of TOR
by rapamycin; F: translation elongation initiation and
release factors.
dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
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identified in the Drosophila genome (30), 25 were upregulated
in refed flies (Table S5). Since the loss of 4e-BP message was
rapid relative to the increase in these initiation factors, we
suggest that translational inhibition is immediately released
upon refeeding but the addition of capacity to initiate translation occurs only gradually.
Fuel metabolism and biosynthesis. When nutrients are resupplied after a fast, mammals shift fuel metabolism from fatty
acid oxidation to fatty acid biosynthesis and glucose oxidation
(12, 52). The mechanisms of this transition are typically
understood in terms of allosteric control by metabolic substrates and through large transcriptional changes of key enzymes. Here we find that the metabolic response to nutrient
resupply in D. melanogaster involves small changes in many
genes that function together in metabolic pathways. Message
for proteins involved in fatty acid oxidation was reduced
35– 41% within 4 h of refeeding (Fig. 3A). These include
mitochondrial carnitine palmitoyltransferase-1 and long chain
acyl-CoA synthetase, which were inversely regulated in
starved larvae (73). Concurrently, message declined for the
subunits of AMP-activated kinase and pyruvate dehydrogenase
kinase (PDHK), enzymes that are typically active during a low
energy state (Fig. 3A). Expression of glycolytic enzymes
6-phosphofructokinase and hexokinase was downregulated
(⫺20 and ⫺34%, respectively), while mRNA level increased
for enzymes of the TCA cycle such as isocitrate dehydrogenase
and citrate synthase (Fig. 3B), consistent with a shift from
glycolytic to oxidative glucose metabolism. Three subunits of
phosphorylase kinase were downregulated following refeeding
(Fig. 3B), suggesting decreased sensitivity to glycogenolytic
signals. Transcripts of fatty acid synthetic enzymes were elevated (Fig. 3A), including citrate lyase, glucose 6-phosphate
dehydrogenase, acetyl-CoA carboxylase, and the subunits of
fatty acid synthase, ␤-ketoacyl-ACP synthase, and ACP-Smalonyltransferase. Expression of serine-palmitoyl transferase,
which catalyzes the first step in ceramide and sphingolipid
biosynthesis, was also elevated (Fig. 3A). Together, these
changes would decrease fatty acid oxidation, increase fatty
acid biosynthesis, and promote glucose oxidation by the TCA
cycle (Fig. 3C).
The TCA cycle also serves a biosynthetic function in a
process described as cataplerosis where four- and five-chain
carbon skeletons are removed from the TCA cycle to be
converted to glucose, fatty acids, or nonessential amino acids
(44). Cataplerosis makes use of many enzymes, including
phosphoenolpyruvate carboxykinase (PEPCK), glutamate dehydrogenase, aspartate aminotransferase, and citrate lyase (44).
The D. melanogaster homolog (CG17725) of human PEPCK
encoded by PCK-1 did not vary with nutrition. However,
mRNA encoding CG10924, the ortholog of human PCK-2
increased. PCK-2, unlike the gluconeogenic enzyme PCK-1,
functions in both mitochondria and cytosol (36) and participates in TCA-mediated biosynthesis (44). Remarkably,
mRNAs for each of the cataplerotic enzymes mentioned above
increased along with CG10924 (Table S6). Cataplerosis is
balanced by anaplerosis, the addition of intermediates to the
TCA cycle. The major anaplerotic enzyme is pyruvate carboxylase, and mRNA encoding this enzyme was upregulated 52%
upon refeeding (Table S6). Overall, resupply of nutrients
elevated coordinated sets of transcripts that support multiple
functions of TCA-associated metabolism.
29
Fig. 3. Changes in expression of genes involved in energy storage and
utilization after refeeding. Expression ratio of the indicated genes in refed
relative to control flies during the 12 h following restoration of yeast (hour 0).
Hours ⫺3 to 0 are baseline replicates. A: genes involved in glucose and fatty
acid oxidation, fatty acid biosynthesis, and energy sensing (AMPK subunits);
B: genes involved in glycolysis, TCA cycle, and regulation of glycogenolysis
(phosphorylase kinase subunits); C: summary of changes in metabolic genes
indicated in A and B. AMPK, AMP-activated protein kinase; PDHK, pyruvate
dehydrogenase kinase; Mito CPT1, mitochondrial carnitine-palmitoyltransferase-1; LC-Acyl-CoA synthetase, long chain acyl-CoA synthetase; ACC,
acetyl-CoA carboxylase; G6PDH, glucose 6-phosphate dehydrogenase; SPT,
serine-palmitoyl transferase; 6-PFK, 6-phosphofructokinase; Phos K, phosphorylase kinase; ACP, acyl carrier protein.
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30
dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
The temporal coordination of such messages suggests they
share some transcriptional control. Indeed, data from dFOXO-A3
cells suggest that dFOXO coordinates the balance between
oxidation of glucose and fatty acids and between fatty acid
oxidation and biosynthesis. mRNAs encoding enzymes of fatty
acid oxidation and fatty acid biosynthesis were inversely regulated by refeeding and dFOXO (Table S4). In addition, PDHK
was reduced in refed flies, and this gene is regulated by
FOXO1 in mammalian tissue (15, 27). How dFOXO might
influence PEPCK in D. melanogaster is ambiguous. Consistent
with the inhibition of Pck1 by insulin in mammals, the message
for D. melanogaster CG17725 increased in dFOXO-A3 cells
and in starved larvae (73) and decreased in KC167 cells treated
with insulin (22). In refed adults, however, message for
CG17725 remained static while transcripts for CG10924
(Pck2) increased. Furthermore, there was no change in mRNA
for CG10924 or other cataplerosis-associated genes or TCA
cycle enzymes in dFOXO-A3 cells. This suggests that TCAassociated carbon metabolism, at least in D. melanogaster, is
nutrient responsive but may not be regulated tightly by insulinIGF via dFOXO.
Mitochondria metabolism and biogenesis. Given the change
in mRNA encoding proteins involved in intermediary metabolism upon refeeding, we expected to find transcriptional
changes in genes underlying mitochondrial function. Indeed,
many genes whose products directly participate in mitochondrial substrate transport and ATP synthesis were coordinately
upregulated (Table S7). These included at least 13 genes
encoding proteins with functions in electron transport and
thirteen substrate carriers (Table S7). Indeed, of 232 genes
assigned Gene Ontology (GO) functions associated with mitochondria, 54% changed with refeeding, and 91% of these were
increased.
Most strikingly, there was a marked and rapid increase in
transcripts encoding genes with roles in mitochondrial biogenesis, such as membrane preprotein translocases, chaperones,
mitochondrial DNA binding proteins, and nuclear encoded
mitochondrial ribosome proteins (Tables S7, S8). Of 60 nuclear encoded mitochondrial ribosomal genes on the array,
expression of 54 was increased an average of 44% at 5–7 h
after refeeding (Table S8). The induction of mitochondrial
ribosomal proteins contrasts with the relative stasis of tranPhysiol Genomics • VOL
29 •
Fig. 5. Domains of CG9809 in common with human peroxisome proliferator-␥ coactivator (hPGC)-1␣ and PGC-1␤, and percent homology of the human
sequences with that of Drosophila CG9809. The acidic amino-terminal,
Arg/Ser-Rich (RS) and RNA-recognition motif (RRM) domains are indicated.
Also noted is the sequence F(D/E)XLL common to the carboxy-terminal
domains of all PGC-1 family members.
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Fig. 4. Changes in expression of histone deacetylase genes and CG9809
(PGC-1␣-like transcript) with refeeding. Expression ratio of the indicated
genes in refed relative to control flies during the 12 h following restoration of
yeast (hour 0). Hours ⫺3 to 0 are baseline replicates.
scripts encoding proteins of the cytoplasmic ribosome. Of the
39 cytoplasmic ribosome genes represented on the array,
mRNA for seven were moderately increased and two were
reduced. An expansion of mitochondria within cells may reflect de novo adipogenesis in adult fat body when females first
feed on yeast, similar to what is observed in mammalian
3T3-L1 cells that increase mitochondrial density as they differentiate into adipocytes (69). The observed increase in mitochondrial gene expression may also reflect activity in skeletal
muscle cells, given that nutrients have been shown to regulate
expression of mitochondrial oxidative phosphorylation genes
in mammalian muscle (42).
How nutrients regulate mitochondrial capacity is poorly
understood. Here we see that many nutrient-induced transcripts
encoding proteins of mitochondria biogenesis were broadly
repressed in cells expressing dFOXO-A3 (Tables S7, S8).
Of the 54 mRNAs for mitochondrial ribosome proteins
that increased upon refeeding, 42 were downregulated by
dFOXO-A3 (Table S8). Likewise, of the 25 upregulated genes
with functions in mitochondrial biogenesis, 14 were repressed
in dFOXO-A3 cells (Table S7). In contrast, only six of the 53
nutrient-regulated, nuclear-encoded genes for mitochondrial
function were inversely regulated by dFOXO-A3. Therefore,
insulin, presumably via dFOXO, has a strong and selective
impact on genes associated with mitochondrial biogenesis.
One candidate to mediate mitochondrial biogenesis is
PGC-1. PGC-1␣ of mammals stimulates mitochondrial biogenesis in muscle, brown adipose tissue, and adipogenic 3T3-L1
cells (50, 33, 70). Interestingly, we find a single ortholog of a
PGC-like peptide in D. melanogaster (CG9809) whose mRNA
increases upon refeeding (⫹49% at 7 h, Fig. 4) and declines in
dFOXO-A3 cells (down 1.4-fold, Table S4). CG9809 encodes
a predicted protein of 1,088 amino acids that exhibits 68 or
52% homology with mammalian PGC-1␣ or PGC-1␤, respectively, in their COOH-terminal RNA-binding motif (Fig. 5).
Other domains in common with its mammalian homologs
include an arginine-serine-rich domain located NH2-terminal
to the RNA-binding motif, an acidic NH2-terminal domain and
leucine-rich motifs, although the canonical LXXLL nuclear
receptor binding motif is not present. However, the LXXLL
motif is not absolutely required for coactivator binding (51),
and CG9809 does contain a variant of this motif near its COOH
terminus, FXXLL, which has been reported to function in
nuclear receptor binding (20). Notably, this sequence is conserved in all mammalian PGC-1 family members (Fig. S1).
dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
Physiol Genomics • VOL
29 •
NCOR/SMRT corepressor complexes. The expression of two
additional messages are strongly correlated to rpd3 (Fig. 4),
and, like rpd3, both are repressed by FOXO-A3 (Table S4):
CG9809, the candidate ortholog of PGC-1, and CG3994, the
ortholog of the yeast zinc transporter ZRT1 that is required for
Rpd3p to suppress transcription of SIR2 (2). The temporal
concordance among rpd-3, CG9809, and CG3994 is consistent
with coregulation by dFOXO. Furthermore, it suggests a coordinated alteration in the composition of NHR coactivators
and SMTRER corepressors in D. melanogaster in response to
nutrition.
Finally, as with each of the metabolic networks featured in
this discussion, the components of the acetylation system
respond to nutrients through a coordinated control of transcripts where many changes are as small as 25%, subtle
differences, we assert, that are statistically reliable and biologically important.
Conclusion. Control of the metabolic response to nutrition is
complex and multifaceted. Although a role for transcription is
well recognized, it is thought to primarily involve regulation of
key, rate-limiting steps. Our fine-grained, genome-wide analysis reveals a different perspective: in response to nutrient
intake extensive networks of metabolic function are coordinated through transcription. These changes, moreover, are
extremely subtle yet biologically profound. While a large
change in the activity of one element within a metabolic
pathway can have little effect on flux (23), small coordinated
changes in transcript levels throughout the network can alter
the global metabolic state (13, 58). Coordination of messages
on this scale requires control through common signals. We find
that many of the genes that vary in response to refeeding are
also influenced by dFOXO expressed within S2 cells. This
suggests that upon feeding, release of insulin ligands induces
an acute yet widespread response via modulation of dFOXO
activity. It remains to be determined whether dFOXO affects
the ⬎900 candidate target genes (Table S4) directly or indirectly and to understand the relative contribution of induction
vs. message stability and degradation in determining how
transcript abundance changes with nutrient uptake (e.g., Ref.
32). To assess the potential for dFOXO to act as the direct
Fig. 6. Proposed role of dFOXO in integrating nutrient levels, via insulin
signaling, with expression of genes involved in nutrient sensing, energy
utilization and storage, mitochondrial biogenesis, growth, and lifespan. Arrows
symbolize activation, bars symbolize inhibition.
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Beside a parallel based on sequence, orthologous targets of
PGC-1 were elevated in refed flies and were reduced by
dFOXO-A3 in S2 cells, including mitochondrial transcription
factor A (Table S7; Ref. 70). Consistent with data presented
here, insulin was reported to upregulate PGC-1␣ and PGC-1␤
mRNA levels in human muscle (33), although the mechanism
was unclear. Our data suggest that insulin signaling may be a
conserved regulator of normal mitochondrial biogenesis via
FOXO-mediated control of PGC-1-like cofactors. Importantly,
this suggests a mechanistic basis for the prevalent mitochondrial dysfunction in humans with insulin resistance and diabetes (38, 45, 46).
Histone deacetylases and cofactors of nuclear hormone
complexes. Histone deacetylases mediate the dietary response
to stress and of aging. Overexpression of the NAD-dependent
deacetylase sir2 extends lifespan in C. elegans and D. melanogaster (54, 65). Mutation of the zinc-dependent histone
deacetylase rpd3 extends D. melanogaster lifespan as does DR,
and both conditions increase sir2 message (55). Here we find
that refeeding simultaneously reduced transcripts of sir2, increased those of rpd3, and coordinately modulated mRNAs for
other potential acetylation factors of nuclear hormone receptors
(diskette, HDAC6, HDAC3) (Fig. 4). Furthermore, message
for several of these genes, including sir2, rpd3, and HDAC3,
are inversely regulated in dFOXO-A3 cells (Table S4). This is
consistent with the reported regulation of SirT1 expression by
insulin (10) and Foxo3a (40) in mammalian cells. In light of
the recent reports that mammalian SIRT enhances the transcriptional activity of FOXO (11, 14, 68), upregulation of Sir2
transcription by dFOXO suggests a conserved feed-forward
mechanism.
The control of acetylation factor expression provides an
additional way for nutrients to regulate metabolism at the level
of transcription. Nuclear hormone receptors (NHRs) interact
with complex sets of transcription factors, cofactors and partner NHRs. The activity of each complex is influenced by
ligand binding, but also by modifications including protein
acetylation (35, 56). For example, SIRT, the mammalian homolog of sir2, represses adipocyte differentiation via an interaction with the adipogenic NHR PPAR␥ that appears to be
dependent on the NCOR/SMRT corepressor (47, 71). Conversely, lipolysis is promoted by SIRT activity (47). Thus,
activation of SIRT in adipocytes under low nutrient conditions
would repress storage and promote release of energy stores.
Our data now suggest that Sir2 modulation of energy storage is
directly linked to the nutrient-sensing pathway via insulinregulated dFOXO activity. The histone deacetylase rpd3 also
appears to be a dFOXO target, but its message increases
dramatically in refed D. melanogaster, while message for sir2
declines (Fig. 4), consistent with repression in the low nutrient
condition when dFOXO is activated. Rpd3 may also interact
with the NCOR/SMRT corepressor, via Sin3A, a scaffold
protein of deacetylation complexes in yeast and mammalian
cells (18, 28). mSin3A and dSin3A have been shown to
associate with the fly functional homolog of the mammalian
NCOR/SMRT corepressor called SMTRER (SMRT-related
protein) (66). Interestingly, mRNA encoding the fly HDAC3
homolog increases somewhat later (Fig. 4), and it is also a
component of the NCOR/SMRT corepressor complex (31).
Thus, nutrient-mediated regulation of HDACs via dFOXO may
modulate gene expression by altering the components of
31
32
dFOXO AND THE TRANSCRIPTIONAL RESPONSE TO NUTRIENTS
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ACKNOWLEDGMENTS
Present address for O. Puig: Merck & Co., Inc., Dept. of Molecular
Profiling—Guided Solutions, Rahway, NJ 07065.
GRANTS
This work was supported by National Institute on Aging Grant AG-16632,
The Ellison Medical Foundation, and The American Federation of Aging
Research. Additional funding was from Pfizer Global Research and Development.
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Four sequences (TANANAAT, AAATNTTA, TTAANATT,
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