Article

A network biology approach to aging in yeast

The Howard Hughes Medical Institute, Bioinformatics Program, Center for Advanced Biotechnology and Department of Biomedical Engineering. Boston University, 44 Cummington Street, Boston, MA 02215, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 02/2009; 106(4):1145-50. DOI: 10.1073/pnas.0812551106
Source: PubMed
ABSTRACT
In this study, a reverse-engineering strategy was used to infer and analyze the structure and function of an aging and glucose repressed gene regulatory network in the budding yeast Saccharomyces 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 model correctly identified the known interactions of key regulators 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 experiments, revealing a more complex network architecture than previously 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 transcriptional 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.

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A network biology approach to aging in yeast
David R. Lorenz, Charles R. Cantor
1
, and James J. Collins
1
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
model correctly identified the known interactions of key regulators
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
C
haracterizing biomolecular interaction networks can shed
new insight into biological processes, including the complex
mechan isms regulating longevity and aging. Organisms like the
budding yeast Saccharomyces cerevisiae have become valuable
model systems to discover genes modulating longevity and to
identif y their associated interaction networks, many of which are
c onserved in met azoans (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 defin itions of yeast age that have
achieved common ac ceptance (1, 3). RLS and CLS have been
proposed as models for mitotically active and postmitotic meta-
zoan cells, respectively (1, 3).
L ongevity 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
c oncentration in the g rowth 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
c ould further our understanding of the processes that underlie
aging.
The development of methods to characterize biomolecular
net works has been an active area of research (10–18). Recently,
we developed an integrated experiment al/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
f rom the glucose-responsive Snf1 pathway (Fig. 1 and Table S1).
Snf1, the homolog of human AMP kinase, is essential for release
of glucose repression (20). It is the cataly tic subunit of the
heterotrimeric SNF1 complex, which also c ontains the coacti-
vating
subun it Snf4 and 1 of 3 ß subunits (Sip2 in our network)
that influence subcellular localization of the complex (21). Many
genes in the Snf1 net work 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
gluc oneogenesis gene expression, including enz yme SUC2 and
TF CAT8 (7, 9, 21). Hxk2, the predominant gluc ose 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
ex pression (24, 25). Gluc ose 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,TFSIP4, and their downstream target
FBP1 (7, 25). Expression changes in response to calorie restric-
tion (Fig. S1) are thus consistent with the known interactions
between these genes, as illustrated by the condensed representation
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,
sec ond copy over-expressions) to formulate a first-order net-
work model, which prov ides 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 utilit y of this approach in a
euk aryotic organism using the known interactions of the Snf1
gene regulatory network as an initial benchmark.
Results
mRNA Expression Profiling and Network Inference. Expression
changes in response to 2- to 4-fold over-expression of each
net work gene were profiled using strains containing an inte-
grated second c opy of each gene under the control of a doxy-
c ycline-inducible promoter (26). After induction, cultures were
grown overnight in 2% glucose synthetic media to OD
600
0.5,
which maintained cells in log phase at transcriptional steady-
st ate conditions. Real-time quantit ative RT-PCR (qRT-PCR)
was used to assay mRNA expression changes relative to an
isogen ic 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.
1
To whom correspondence may be addressed. E-mail: jcollins@bu.edu or ccantor@
sequenom.com.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0812551106/DCSupplemental.
© 2009 by The National Academy of Sciences of the USA
www.pnas.orgcgidoi10.1073pnas.0812551106 PNAS
January 27, 2009
vol. 106
no. 4
1145–1150
GENETICS
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