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@
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