A network biology approach to aging in yeast
David R. Lorenz, Charles R. Cantor1, and James J. Collins1
The Howard Hughes Medical Institute, Bioinformatics Program, Center for BioDynamics, Center for Advanced Biotechnology and Department of Biomedical
Engineering. Boston University, 44 Cummington Street, Boston, MA 02215
Contributed by Charles R. Cantor, December 9, 2008 (sent for review November 21, 2008)
In this study, a reverse-engineering strategy was used to infer and
analyze the structure and function of an aging and glucose re-
pressed gene regulatory network in the budding yeast Saccharo-
myces cerevisiae. The method uses transcriptional perturbations to
model the functional interactions between genes as a system of
first-order ordinary differential equations. The resulting network
in a 10-gene network from the Snf1 signaling pathway, which is
required for expression of glucose-repressed genes upon calorie
restriction. The majority of interactions predicted by the network
model were confirmed using promoter-reporter gene fusions in
gene-deletion mutants and chromatin immunoprecipitation exper-
iments, revealing a more complex network architecture than pre-
viously appreciated. The reverse-engineered network model also
predicted an unexpected role for transcriptional regulation of the
SNF1 gene by hexose kinase enzyme/transcriptional repressor
Hxk2, Mediator subunit Med8, and transcriptional repressor Mig1.
These interactions were validated experimentally and used to
design new experiments demonstrating Snf1 and its transcrip-
tional regulators Hxk2 and Mig1 as modulators of chronological
lifespan. This work demonstrates the value of using network
inference methods to identify and characterize the regulators of
complex phenotypes, such as aging.
chronological aging ? gene networks ? Snf1 pathway ? systems biology
mechanisms regulating longevity and aging. Organisms like the
budding yeast Saccharomyces cerevisiae have become valuable
model systems to discover genes modulating longevity and to
identify their associated interaction networks, many of which are
conserved in metazoans (1, 2). Replicative lifespan (RLS), the
number of daughter cells an individual mother can produce
before senescence, and chronological lifespan (CLS), the length
of time cells from stationary phase cultures can remain viable in
a quiescent state, are two definitions of yeast age that have
achieved common acceptance (1, 3). RLS and CLS have been
proposed as models for mitotically active and postmitotic meta-
zoan cells, respectively (1, 3).
Longevity extension in response to calorie restriction has been
observed in organisms ranging from yeasts to mammals (1, 2, 4)
and can be observed in S. cerevisiae by limiting the glucose
concentration in the growth medium (2, 5). Consequently, many
longevity genes have been identified by their role in relevant
cellular processes, such as glucose signaling (5–9). Identifying
these genes and growth conditions is a first step in understanding
the mechanisms linking aging and calorie restriction. Defining
the network of regulatory interactions between these genes
could further our understanding of the processes that underlie
The development of methods to characterize biomolecular
networks has been an active area of research (10–18). Recently,
we developed an integrated experimental/computational re-
verse-engineering strategy, network identification by regression
(NIR) (19), for the elucidation of gene regulatory networks. In
the present study, we applied this method to 10 genes selected
from the glucose-responsive Snf1 pathway (Fig. 1 and Table S1).
Snf1, the homolog of human AMP kinase, is essential for release
haracterizing biomolecular interaction networks can shed
new insight into biological processes, including the complex
of glucose repression (20). It is the catalytic subunit of the
heterotrimeric SNF1 complex, which also contains the coacti-
vating ? subunit Snf4 and 1 of 3 ß subunits (Sip2 in our network)
that influence subcellular localization of the complex (21). Many
genes in the Snf1 network are known to affect RLS when
perturbed, including Snf1, Sip2, Snf4 (22), Mig1 (23), and Hxk2
(5, 8). In a glucose-rich environment, the transcription factor
(TF) Mig1 represses alternative carbon source metabolism and
gluconeogenesis gene expression, including enzyme SUC2 and
TF CAT8 (7, 9, 21). Hxk2, the predominant glucose kinase in the
first step of glycolysis, shows glucose-dependent nuclear local-
ization and associates with DNA-binding factors Mig1 and
Mediator subunit Med8 at the promoter of SUC2 to repress its
expression (24, 25). Glucose exhaustion activates SNF1, which
translocates from the cytoplasm to the nucleus and deactivates
Mig1 by phosphorylation. This triggers the translocation of Mig1
and Hxk2 to the cytoplasm, resulting in increased expression of
SUC2, as well as CAT8, TF SIP4, and their downstream target
FBP1 (7, 25). Expression changes in response to calorie restric-
tion (Fig. S1) are thus consistent with the known interactions
of the Snf1 gene regulatory network architecture shown in Fig. 1.
The NIR method uses mRNA expression changes that arise in
response to network gene perturbations (applied here as small,
second copy over-expressions) to formulate a first-order net-
work model, which provides a quantitative, directed, and unsu-
pervised description of functional transcriptional interactions.
We previously applied the NIR method to a nine-gene subnet-
work of the SOS DNA damage response pathway in Escherichia
coli (19). Here, we investigate the utility of this approach in a
eukaryotic organism using the known interactions of the Snf1
gene regulatory network as an initial benchmark.
mRNA Expression Profiling and Network Inference. Expression
changes in response to ?2- to 4-fold over-expression of each
network gene were profiled using strains containing an inte-
grated second copy of each gene under the control of a doxy-
cycline-inducible promoter (26). After induction, cultures were
grown overnight in 2% glucose synthetic media to OD600? 0.5,
which maintained cells in log phase at transcriptional steady-
state conditions. Real-time quantitative RT-PCR (qRT-PCR)
was used to assay mRNA expression changes relative to an
isogenic control strain expressing GFP (Tables S2–S4). GFP
mRNA levels in control cultures were measured as an indicator
of perturbation size. Expression changes in response to these
perturbations were nearly all less than 2-fold relative to control
Author contributions: D.R.L., C.R.C., and J.J.C. designed research; D.R.L. performed re-
search; D.R.L. and J.J.C. contributed new reagents/analytic tools; D.R.L. analyzed data; and
D.R.L., C.R.C., and J.J.C. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
1To whom correspondence may be addressed. E-mail: firstname.lastname@example.org or ccantor@
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2009 by The National Academy of Sciences of the USA
January 27, 2009 ?
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no. 4 ?
(Fig. 2A), and included responses from regulators normally
active in low-glucose conditions, such as Snf1 (21), Cat8, and
Sip4 (7). Thus, exogenous cis-regulatory elements in the doxy-
cycline-inducible promoter construct (26) allowed forced ex-
pression of genes that are normally glucose repressed. Notable
among these was the autotranscriptional activation of SIP4 (7):
a 2-fold level of SIP4 overexpression from the second copy of the
gene resulted in a 26-fold overexpression of total SIP4 mRNA.
To infer the Snf1 gene regulatory network, expression changes
less than the propagated standard error were first filtered from
the dataset. This significance threshold was chosen based on the
12 replicate RT-PCR reactions for each gene in each perturba-
tion and control strain and the small magnitude of most expres-
sion changes. The optimal model recovered from this analysis
(Fig. 2B and Tables S5–S9) correctly identified Hxk2, Med8, and
Snf1 as the major transcriptional regulators in this network, as
evidenced by the proportion of predicted targets and compar-
ative magnitude of regression coefficients (indicative of inter-
active strength) for these regulators. Snf1 and Hxk2 are well-
is considered a general TF required for RNA polymerase II
transcription (28). In contrast, network genes with no previous
known roles as transcriptional regulators, such as enzymes Fbp1
and Suc2, were predicted to have distinctly fewer low-strength
regulatory influences or had regression coefficients statistically
not different from zero.
We compared evidence from the literature (summarized in
Table S10) with our network model to determine the sensitivity
(the percentage of known interactions the NIR model success-
fully identified) and precision (the percentage of predicted
interactions that are consistent with known interactions) of the
NIR algorithm, which were found to be 65% and 22%, respec-
tively. Correctly identified known interactions between Snf1 and
its targets contributed largely to the sensitivity measure. How-
ever, the NIR model also predicted many interactions with no
previous literature evidence. The highest proportion of these
were observed as input predictions for SNF1 complex subunits
SNF1, SNF4, and SIP2, whose regulatory roles have been
well-characterized (21) but whose transcriptional regulation has
not been previously detailed. We therefore performed addi-
tional experiments to test and validate these predicted interac-
tions, as described below.
Experimental Verification of Network Model Predictions. In pre-
dicted functional interactions, a putative regulator affects the
expression of its target through one or more intermediaries.
These were tested using deletion strains for each network gene
(except MED8, whose deletion mutant is inviable), transformed
with plasmid shuttle vectors containing the promoter region
from each potential target cloned in-frame with the lacZ re-
porter gene. ß-galactosidase (ß-gal) activity for each possible
target promoter-lacZ plasmid/regulator gene deletion pair was
compared to activity from the same plasmid construct in the
isogenic wild-type strain. These experiments were performed
with cultures grown in both 2% and 0.05% glucose, as some NIR
model predictions were observed for regulators previously char-
acterized to be active either during calorie restriction or in both
Fig. 3A summarizes experimental results for statistically sig-
nificant (P ? 0.05) ratios of ß-gal activity in gene deletion strains
relative to control strains (see also Tables S11–S14). The func-
tional importance of Hxk2 and Snf1 as key regulators, based on
the number of targets and the relative magnitude of expression
changes, was confirmed by these data. Fbp1 and Suc2 deletions
caused relatively few low-magnitude changes in target-gene
promoter activity, also consistent with the NIR model. Only one
network regulator (Snf4) caused significant changes in target
negatives). These results may be because of Snf4’s role as
regulatory influences previously described in the literature (see Table S1) by
regulator proteins (source of arrows) on the expression of target genes
(arrowheads) in glucose repressing (2% glucose, red edges) vs. low glucose
growth conditions (including nonfermentable carbon sources) (blue edges).
Solid lines denote known physical TF binding to regulatory sequences of
expression is regulated through possible intermediaries. Arrows denote acti-
vation, bars denote repression, and boxes denote physical associations estab-
lished but not completely characterized. Nontranscriptional regulatory inter-
actions are included where relevant: ‘‘PO4’’ denotes phosphorylation and
‘‘loc’’ denotes regulation through subcellular localization. Shapes of nodes
indicate known functional attributes of proteins: rectangle, kinase or kinase-
associated protein; circle, TF; diamond, enzyme; hexagon, dual enzyme/TF.
Known interactions in the Snf1 network. Diagram of transcriptional
systematic perturbations. Color intensities represent the magnitude of mRNA
expression changes for each gene (Rows) in response to 2- to 4-fold over-
expression of each other network gene (Columns), relative to an isogenic
quantitative RT-PCR, normalized by internal standard genes ACT1 and
RDN18–1. Only significant expression changes (greater than the propagated
standard error) were used for network model inference and are displayed
here. (B) NIR-predicted transcriptional interactions in the Snf1 gene network.
This matrix is a quantitative model of predicted regulatory influences. Color
intensities denote the relative strength of regulators (Column vectors) upon
mRNA expression of predicted target genes (Row vectors).
(A) mRNA expression profiling of network genes in response to
www.pnas.org?cgi?doi?10.1073?pnas.0812551106Lorenz et al.
coactivator of Snf1 (see Fig. 1), so that over-expression of Snf4
does not lead to appreciable changes in activity of Snf1, while
deletion of Snf4 does.
In vivo TF-promoter binding for the repressor Mig1 (10) and
the gluconeogenic activators Cat8 (29) and Sip4 (10) has been
observed by chromatin immunoprecipitation/DNA microarrays
(ChIP-chip) and other experimental methods (see Table S1).
Hxk2 and Med8 were previously identified with Mig1 as physical
repressors of SUC2 expression (24, 25), and Med8 as a repressor
of HXK2, binding cis-regulatory sequences within its coding
region (24). We used ChIP with detection by real-time quanti-
tative PCR (ChIP-qPCR) to test for Hxk2 and Med8 binding to
the promoters [?350 base pairs (bp) before the ATG start
codon] and downstream regions (?300 bp after the start codon)
of all network genes for cells grown in both 2% and 0.05%
glucose. These experiments enabled us to test the NIR network
model predictions for the essential Med8 protein, and to ascertain
whether Hxk2 acts as a TF for network genes other than SUC2.
ChIP-qPCR results revealed that Med8 and Hxk2 bind in
varying combinations to regulatory regions of all network genes
in a statistically significant manner (Fig. 3B, Tables S15–S18). A
P value of ?0.10 was used reflecting the lower sample numbers,
diminished template quality, and consequent measurement
noise associated with the ChIP assay. Med8 results were con-
sistent with the NIR network model (see Fig. 2B) and expecta-
tions that the Mediator complex acts as a general RNA Pol II
transcription factor (28). Interestingly, both promoter and down-
stream chromatin fragments of the same gene immunoprecipi-
tated with Med8 in many instances, which may be indicative of
DNA secondary structures spanning both regions. Evidence for
this type of Mediator regulation has been reported for the HXK2
gene (30), the GAL1 gene (31), and in genome-wide ChIP-chip
studies of Mediator in the fission yeast Schizosaccharomyces
pombe (31). Dual binding was not observed in reactions for the
control actin gene ACT1 (see Tables S15–S18), ruling out
artifacts of inefficient chromatin shearing. Data for Hxk2 were
consistent with evidence for binding to the SUC2 gene (25). We
also saw enrichment of Hxk2-bound chromatin from the CAT8
and SNF1 promoters and downstream regions, and from the
enrichment of Hxk2-bound chromatin was observed in cells
grown in 0.05% glucose, consistent with the cytoplasmic local-
ization of Hxk2 in the absence of glucose (25).
Compared to our experimental data (in both 2% and 0.05%
glucose culture growth) and literature evidence (Table S19), the
NIR network model showed 62% sensitivity and 69% precision.
False-positive interactions contributing to the precision result
may be a result of posttranscriptional influences predicted in the
NIR model that are not identified by measuring promoter-lacZ
fusion expression. This type of regulation has been reported for
other glucose-repressed genes (7, 32). Of the interactions pre-
dicted by NIR, 24% had the opposite sign; that is, a repressing
influence was predicted when an activating influence was indi-
cated by experiments, or vice versa. The majority (9 of 16) of
these were TF Hxk2 and Mig1 interactions. As an additional
means of assessing performance, we performed a permutation
test with models generated from 10,000 iterations of NIR using
randomized expression data. The true NIR model had higher
measures of sensitivity and precision than 88% and 98% of the
randomized models, respectively (Fig. S2). Thus, our results show
that the NIR algorithm infers the Snf1 gene regulatory network
with a high degree of sensitivity, precision and significance.
Regulation of SNF1 Gene Expression and its Effect on Chronological
Lifespan. The NIR network model predicted that Med8, Hxk2,
Snf1, and Mig1 have the largest effects on SNF1 gene expression.
ChIP-qPCR data further confirmed Med8 and Hxk2 as direct
regulators of SNF1, with higher affinity to downstream than
upstream regions for both regulators. In previous studies, SNF1
gene expression was insensitive to growth on a nonfermentable
carbon source (33) and increased only slightly during the diauxic
shift (6, 34). However, we observed consistent reductions in
log-phase growth rate in response to modest levels of SNF1
overexpression during perturbation expression profiling (a dou-
bling time of 1.70 h?1vs. 1.46 h?1for the GFP overexpression
control strain; P ? 0.01), suggesting that small changes in SNF1
mRNA levels are physiologically important. Hxk2 functions as
both a glycolytic enzyme and a transcriptional regulator; con-
sequently, a clear definition of its role in glucose signaling has
remained elusive (9, 24, 27). Because Snf1 is required for release
from glucose repression, and Hxk2 was confirmed as a direct
regulator of SNF1, we carried out additional experiments to
clarify its role in SNF1 transcriptional control (see hypothesized
schematic in Fig. 4A).
We first tested if putative Med8 and Mig1 cis-regulatory
elements in the SNF1 coding region affected expression of
SNF1-reporter gene constructs. Motifs with close similarity to
the known Med8 binding sequence (24) were identified at ? 247,
?305, and ? 377 nucleotides (nts) past the ATG start codon.
Inclusion of the ? 305 motif significantly decreased ß-gal activity
compared to a SNF1-lacZ fusion construct truncated at ? 285
nts (Fig. 4B). HXK2 deletion also derepressed ß-gal activity in
constructs containing the ? 305 motif, suggesting Hxk2 acts as
a corepressor in this region. A putative Mig1 motif (7) at ? 336
nts in similar constructs did not cause consistent, significant
tions. Color intensities represent the absolute magnitude of ß-gal activity in
deletion strains (Columns) expressing lacZ target-promoter fusions (Rows)
relative to the same construct in the isogenic wild-type strain (BY4742). The
essential Med8 protein (asterisk) could not be tested with this method. For
clarity of presentation, absolute values of statistically significant (P ? 0.05)
expression ratios are displayed. (B) In vivo ChIP-qPCR experimental results for
regulators Hxk2 and Med8. Upstream denotes detection of Hxk2 or Med8
binding (Columns) to promoter DNA for each network gene (Rows); down-
stream indicates binding detected within the target coding region. Color
intensities represent the ratio of enrichment of target DNA captured by
immunoprecipitated Hxk2 or Med8 relative to control (isogenic wild-type
strain BY4742), determined by real-time qPCR. Only statistically significant
(P ? 0.10) expression changes are displayed.
(A) Experimental confirmation of NIR-predicted functional interac-
Lorenz et al.
January 27, 2009 ?
vol. 106 ?
no. 4 ?
differences in ß-gal activity in mig1? vs. wild-type strains (data
not shown). However, SNF1 expression measured by qRT-PCR
exhibited a small but significant increase in a hxk2?mig1?
double-deletion mutant in 2% glucose growth, an effect that was
greater than the deletion of either HXK2 or MIG1 individually
(Fig. 4C). This increase in SNF1 expression in the hxk2?mig1?
strain was of a comparable magnitude to that reported previ-
ously in the diauxic shift (34), and in response to calorie
restriction (see Fig. S1), which is consistent with the transloca-
tion of Hxk2 and Mig1 from the nucleus to the cytoplasm in
response to glucose depletion (25). Additionally, a log-phase
growth defect was observed in the hxk2?mig1? strain relative to
its wild-type parent (doubling time 1.79 h?1vs. 1.55 h?1for
BY4742; P ? 0.01), similar to that seen in response to SNF1
overexpression. These results, together with our ChIP-qPCR
data, support a model for direct transcriptional regulation of
SNF1 by Hxk2 and Med8 and indirect regulation by Mig1 (see
Fig. 4A), revealing a mechanism by which Hxk2 affects glucose-
regulated gene expression.
We next applied knowledge of the interaction network gov-
erning SNF1 expression to examine its regulators’ influence on
CLS. Increased Snf1 activity, through deletion of repressor
subunit SIP2 or forced overexpression of the SNF1 gene, has
been previously shown to decrease RLS (22). We tested whether
Snf1 similarly affects CLS using overexpression strains from
perturbation experiments grown in synthetic CLS media (3)
containing 2% glucose. CLS was determined from CFUs from
aliquots after cultures reached stationary phase. SNF1 over-
expression caused a marked decrease in CLS compared to the
GFP-expressing control strain (Fig. 4D), with statistically sig-
nificant differences (P ? 0.05) in CFU counts at several time
points, despite a high level of variance among biological repli-
cates. On the basis of the model of interactions regulating SNF1
(see Fig. 4A), we expected to observe a decrease in CLS in the
hxk2?mig1? strain relative to its wild-type parent strain, and this
was indeed the case (Fig. 4E). No significant changes in CLS
were observed in the single hxk2? or mig1? mutant strains.
Detailed characterization of the interaction network thus en-
abled the identification of CLS mediators Hxk2 and Mig1 acting
synergistically; these influences were not detectable by pertur-
bation of the regulators individually.
In this study we show that the NIR reverse-engineering strategy
(19) can be successfully applied to infer gene regulatory net-
works in eukaryotic organisms. We assessed the performance of
the method by comparing the NIR-inferred Snf1 network model
to interactions previously described in the literature, which
suggested that many interactions with no previous literature
evidence were predicted by the model. The majority of these
interactions were validated in experiments employing promoter-
lacZ fusion constructs in gene deletion strains and ChIP-qPCR
assays for physical targets of the Med8 and Hxk2 TFs. The NIR
model showed good measures for sensitivity and precision, as
compared with confirmation experiments and known literature
interactions, and revealed a greater degree of complexity be-
tween regulators in the network than previously appreciated.
An equally important assessment of a network-identification
method is the utility of the inferred model to suggest biologically
meaningful, testable hypotheses for phenotype regulation. We
by Hxk2, Med8, and Mig1, and confirmed Hxk2 and Med8 as
direct regulators and Mig1 as an indirect regulator of SNF1
expression in 2% glucose growth. Hxk2 and Med8 were also
found to repress CAT8, a major activator of gluconeogenic genes
(7, 29). These results suggest a glucose-responsive signaling mech-
anism for Hxk2 worthy of further study, given the previously
reported challenges in clarifying its downstream targets (9, 24, 27).
We also showed that SNF1 up-regulation reduces CLS, and
knowledge of the network architecture governing SNF1 expres-
experiments that SNF1 gene expression is repressed by Hxk2, Med8, and Mig1 in 2% glucose (red arrows), in a manner analogous to the previously detailed
containing SNF1 promoters and variable lengths of the SNF1 coding region to include (SNF1?330) or exclude (SNF1?285), a sequence similar to the Med8
consensus motif fused in-frame to the lacZ reporter gene were transformed into hxk2? and isogenic wild-type strain BY4742. ß-gal activity was measured in SC
P ? 0.05 relative to SNF1 ? 330/BY4742. (C) Double deletion of HXK2 and MIG1 derepresses SNF1 expression synergistically. SNF1 and SUC2 (positive control)
in SC media ? 2% glucose. Error bars denote propagated standard error. (D and E) Effects of SNF1 expression on chronological longevity. Percent survival of
poststationary phase cultures was determined from CFUs of batch cultures grown in SC media ? 2% glucose. SNF1 and GFP overexpression strains (D) were the
same as those used in perturbation experiments, constructed in the W303-derived strain BMA64–1A. Error bars denote standard deviation; data points with
asterisks (*) signify statistically significant differences (P ? 0.05) in CFUs relative to control at the same time point.
Characterization of SNF1 transcriptional regulation and its effects on chronological lifespan. (A) Schematic of hypothesis tested in subsequent
www.pnas.org?cgi?doi?10.1073?pnas.0812551106 Lorenz et al.
sion led to the identification of Hxk2 and Mig1 as synergistic but
not individual modulators of CLS. Because the HXK2 and MIG1
single-deletion mutants caused no change in CLS, it is unlikely
that these two modulators of CLS would be identified without
knowledge of the gene regulatory network architecture. The
sequence, function, and regulatory interactions of Hxk2, Med8,
and Snf1/AMP kinase are highly conserved among eukaryotes
(21, 24, 28), and therefore, these results may have implications
for understanding the role of AMP kinase in regulating meta-
zoan organism lifespan (1).
Materials and Methods
Strains and Culture Growth. Strains for network inference were constructed in
the W303-dervied parent BMA64–1A (MATa ura3–1 ade2–1 leu2–3, 112
his3–11, 15 trp1? can1–100) using a tetracycline-inducible expression system
(both were obtained from the EUROSCARF repository). Plasmid pCM252 (26)
from this set was modified for chromosomal integration at the his3 auxotro-
this vector, which was transformed into BMA64–1A. For ß-gal assay strains,
when noted) were PCR cloned in-frame with the lacZ gene in YEp356R and
or ATCC), and transformed into Saccharomyces Gene Deletion Project strains
and isogenic parent BY4742 (MAT? his3?1 leu2?0 lys2?0 ura3?0) (Invitro-
gen). ChIP strains were constructed in BY4742 by tagging the 3? termini of
HXK2 and MED8 genes with the 13-Myc epitope (35). The hxk2?mig1? strain
was constructed by replacement of HXK2 with the LEU2 gene amplified from
pRS305 (ATCC) in the mig1? deletion strain. See SI Materials and Methods for
Cells were cultured at 30 °C with shaking at 300 RPM in the appropriate
selective synthetic complete (SC) dropout media (Sigma), except for ChIP
experiments performed in YPD (36). For network inference experiments,
saturated overnight (o/n) cultures diluted ?1/400 in fresh media were grown
8 h to OD600? 0.2 to 0.5, then diluted again in media containing doxycycline
and grown 14 h to OD600? 0.5 for RNA extraction. Perturbation and GFP
strains were cultured concurrently in quadruplicate for each experiment.
Doxycycline concentration was varied from 0.8 to 3 ?g/ml to induce over-
expression from 2- to 4-fold, based upon estimated basal expression of the
glucose media and grown 4 to 5 h to OD600? 0.8–1.0.
Network Inference Approach. The NIR system-identification method (19) mod-
els the regulatory interactions between transcripts as a system of ordinary
differential equations describing the rate of accumulation of each network
species as a weighted sum of the quantity of other species in the network:
i, xjis the level of transcript j, and piis the level of an external perturbation of
transcript i, applied here as additional copies of transcript i. At steady-state
experimental conditions, where the quantities of transcripts remain constant
over time (dxi/dt ? 0), Eq. 1 reduces to:
The coefficients aijare then learned using an iterative algorithm employing
gene fitting the expression data with the minimal least-squares error. The
matrix A of coefficients aijrepresents the functional effects of gene j (regu-
activating (aij? 0), repressing (aij? 0) or null (aij? 0) influence. For this study,
the algorithm was modified to (i) consider values of k inputs from 3 to 8
the maximum value for each row in which the significance of the regression
model (P value of the F test) is ?0.05, and (ii) perform a t test on individual
from zero. See SI Materials and Methods for details.
qRT-PCR mRNA Expression Profiling. RNA was extracted using an acid phenol
method, then treated with DNA-Free RNase-free DNase (Ambion). Reverse
transcription of normalized total RNA and qPCR were performed using Taq-
Man and SYBR Green reagents (Applied Biosystems) according to the manu-
facturer’s instructions. See SI Materials and Methods for details.
ß-Galactosidase Assays. All experiments used 3 to 5 cultures grown from fresh
transformations of promoter-lacZ fusion plasmids into the gene-deletion
strain of interest and control strain BY4742. Control and experimental strains
with the Yeast ?-Galactosidase Assay Kit (Pierce Biotechnology), according to
the manufacturer’s instructions.
Med8 and isogenic wild-type strain BY4742 were grown and processed in
parallel. ChIP was performed as previously described (10), with minor alter-
ations detailed in SI Materials and Methods. Following final DNA extraction
and purification, qPCR was used to detect significant enrichment (P ? 0.10) of
network gene promoter and coding region DNA in immunoprecipitates from
tagged strains compared to wild-type (See SI Materials and Methods).
Chronological Lifespan Assays. CLS experiments were performed in standard
auxotrophic nutrients (Uracil, Adenine, Leu, His and Trp for BMA64–1A;
Uracil, Leu, His and Lys for BY4742) (3). Triplicate CLS cultures were grown
from 60-?l saturated o/n cultures inoculated into 6.0 ml of the appropriate SC
media maintained at 30 °C, 300 RPM in 14-ml tubes. SNF1 and GFP over-
serially diluted to 1,000 to 3,000 cells/ml. Of these, 100 ?l were spread on YPD
plates, and colonies were counted manually after 48 h growth at 30 °C. The
starting number of CFUs (‘‘Day 0’’ in Fig. 4) was sampled at 72 h growth, after
which no appreciable OD600changes were observed.
Numerics. NIR algorithm computations and data analysis were performed
using MATLAB v7.4 (The Mathworks). Statistical analyses and outlier deter-
mination for data are detailed in SI Materials and Methods. All P values were
calculated using Student’s t test (unpaired, heteroscedastic), unless otherwise
ACKNOWLEDGMENTS.WethankGa ´borBala ´zsi,WilliamJ.Blake,andMichael
J. Thompson for helpful discussions with experimental design and analysis,
by the Ellison Medical Foundation, the National Institutes of Health through
OD003644, and the Howard Hughes Medical Institute.
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