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  MP Rs

in Bergmann glia

re

necessary for

preventing surplus CFs from producing syn-

apses onto single Purkhje cells.

Conversion of Ca2+-permeableAMIJARs

into Ca2+-impermeable receptors in Berg-

mann glia elicited morphological changes in

fine glial processes wrapping Purkinje cell

synapses, prolonged the kinetics of glutama-

tergic synaptictransmission,and caused mul-

tiple innervation of Purkinje cells by CFs.

Thus, the morphology of glial processes and

the synaptic activities would be interdepen-

dent. The Ca2+-permeableAMPARs in glial

cells probably play key roles in such in-

teractions between glia and glutamatergic

synapses.

References and otes

1. P. H. Seeburg, Trends Neumsci. 16,359 (1993).

2. M. Hollmann, 5. Heinemann, Annu. Rev. Neumsci. 17,

31 (1994).

3. C Steinhauser,

V.

Gallo, TrendsNeurosci. 19,339 (1996).

4. M. Hollm ann, M. Hart ley, 5 Heinemann,Science252,

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Burnashev, H. Monyer, P. H. Seeburg, B Sakmann,

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mann, Science 256, 1563 (1992).

8.

N.

Bumashev e t al., Science 256, 1566 (1992).

9. For the m ethods used, see the su pplementary infor-

mation on Science 'Online at www.sciencemag.org/

cgi/content/fulV292/5518/926/DCl

10. K A. Yamada, C M. Tang, j Neurosci. 13, 3904 (1993).

11. K. M. Partin, M.

W.

Fleck, M. L Mayer,j Neurosci. 16,

6634 (1996).

Integrated Genomic and

Proteomic Analyses o f a

Systematically Perturbed

Metabolic Network

Trey Ideker, *2* Vesteinn Thor~son, ,~effreyA. Ranish, s2

Rowan Christmas, Jeremy~uhler?Jimmy K Eng,

Roger Bumgarner: David

R

Goodlett, Ruedi Aebersold, n2

Leroy Hood s2

We demonstrate an integrated approach t o build, test, and refine a model of

a cellular pathway, in which perturbations t o critical pathway components are

analyzed using DNA microarrays, quantitative proteomics, and databases of

known physical interactions. Using this approach, w e identify 997 messenger

RNAs responding to 20 systematic perturbations of the yeast galactose-utili-

zation pathway, provide evidence tha t approximately 15of 289 detected pro-

teins are regulated posttranscriptionally, and identify explicit physical inter-

actions governing the cellular response t o each perturbation. W e refine the

model through further iterations of perturbation and global measurements,

suggesting hypotheses about the regulation of galactose utilization and phys-

ical interactions between this and a variety of other metabolic pathways.

For organisms with fully sequenced ge-

nomes, DNA micromays are an extremely

powerful technology for measuring the

mRNA expression responses of practically

every gene

1 .

Technologiesfor globally and

quantitatively measuring protein expression

are also becoming feasible 2 ) , and develop-

ments such as the two-hybrid system are

enablingconstructionof a map of interactions

among proteins

3).

Although such large-

scale data have proven invaluable for distin-

guishing cell types and biological states, new

'The Insti tute for Systems Biology, 4225 Roosevelt

Way NE, Suite 200, Seattle, W A 98105, USA. Depart-

ments of 'Molecular Biotechnology, 3Computer Sci-

12. 1 A. Dzubay, C. E Jahr,

j.

Neumsci. 19, 5265 (1999).

13. M. lino, S Ozawa, K. Tsuzuki, j Physiol. 424, 151

(1990).

14. 5. Ozawa, M. lino, K. Tsuzuki, j Neurophysiol. 66 2

(1991).

15. T Isa, 5. Itazawa, M. lino, K. Tsuzuki, 5. Ozawa,

j Physiol. 491, 719 (1996).

16. 1. Grosche et al., Natu re Neurosci. 2, 139 (1999).

17. Quantitative evaluation of the morphological chang-

es is available in the supplemen tary informat ion on

Science Online at

www sc iencemag org/cgilcontent/

fuIV292/5518/926/DCl.

18.

F

A. Chaudhry et al., Neu ron 15, 711 (1995).

19. K. Yamada e t al., j. Comp. Neuml. 418, 106 (2000).

20. B. Barbour, B U. Keller,

I.

Llano, A. Marty, Neuron 12,

1331 (1994).

21. C. Auger, D. Attw ell, Neuron 28 547 (2000).

22. 1. Mariani, 1.-P. Changeux,j. Neurosci.

1

696 (1981).

8 January 2001; accepted 20 March 2001

approaches are needed which, by integrating

these diverse data types and assimilating

them into biological models, can predict cel-

lular behaviors that can be tested experimen-

tally. We propose and apply one such strate-

gy

here, consisting of four distinct steps:

i) Define all of the genes in the genome

and the subset of genes, proteins, and other

small molecules constituting the pathway of

interest.If possible, define

n

initial model of

the molecular interactionsgoverningpathway

function, drawn from previous genetic and

biochemical research.

ii) Perturb each pathway component

through a series of genetic e.g., gene deletions

or overexpressions) or environmental e.g.,

changes in growth conditions or temperature)

manipulations. Detect and quantifl the corre-

sponding global cellular response to each per-

turbation

with

technologies for large-scale

mRN and protein-expression measurement.

iii) Integrate the observed mRNA and

protein responses with the current, pathway-

specificmodel and with the g lobal network of

protein-protein, protein-DNA, and other

known physical interactions.

iv) Formulate new hypotheses to explain

GALACTOSEMETABOLIC FLOW

I

;

PROTEIN

METABOLISM

**am,

 

PSS

GAL --O ~e hb ra neransport

: ? Transcript. activa tion

COKE

REGULATORY ? ..  - ..

...

.

.

.

 

+ Proteinactivation

MECHANISM

-4

Protein inhibition

encel and 4Microbio W, University of Washington,

Fig. 1.Model of galactose utilization. Yeast metabolize galactosethrough a series of steps involving

Seattle, WA 98195, USA.

the GAL2 transporter and enzymes produced by GAL1 GAL7 GAL10 and GALS. These genes are

*TO whom correspondence should be addressed a t

transcriptionally regulated by a mechanism consisting primarily of GAL4 CAL80 and GAL3. GAL6

The lnsti tute for Systems Biology. E-mail: tideker@ produces another regulatory factor thought to repress the

GAL

enzymes in a manner similar to

systemsbiology.org

CAL80. Dotted interactions denote model refinements supported by this study.

www.sciencemag.org

SCIENCE

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observations not predicted by the model. De-

sign additional perturbation experiments to

test these, and iteratively repeat steps (ii),

(iii), and (iv).

As proof-of-principle, we now implement

this integrated approach to explore the pro-

cess of galactose utilization (GAL) in the

yeast Saccharomyces cerevisiae. The GAL

pathway is a classic example of a genetic

regulatory switch, in which enzymes required

specifically for transport and catabolism of

galactose are expressed only when galactose

is present and repressing sugars such as glu-

cose are absent. Extensive biochemical stud-

ies (4 ) and saturating mutant screens (5) have

defined the genes, gene products, and meta-

bolic substrates required for function of this

process and have elucidated the key molecu-

wt +gal +gal: 

vs wt

al;

vs. wt

A

PREDICTED

CHANGE

increase

decrease-

C r 1 3)

44)

3 47)

4

5

95)

8(2s)

7(lSa)

lo(=)

Q

1

(09)

12 (881

13

(53)

14 78)

1s 36)

16 33

T W hex-)

119 ge 117120126141248

87 msoolm i i 6 1 3 3 i ~ 1 2 9 i ~ 1 2 5 ~ 2 s 1 2 8 2 0 ~ )

Fig. 2. Perruruation matrix. Microarrays were used to measure the mRNA expression profiles of

yeast growing under each o 20 perturbations to the GAL pathway. A) Each spot represents the

change in expression of a GAL gene due to a particular perturbation listed above each column);

medium gray i.e., the same level as figure background) represents no change, whereas darker

or

lighter shades represent increased or reduced expression, respectively. The left half o the matrix

shows expression changes for each deletion strain

as

compared to wt, with both strains grown in the

presence of galactose; the right half shows the same differential comparison, but with both strains

grown in the absence of galactose

35).

The wt+gal versus wt-gal perturbation far left) isolates the

effects of growth with and without galactose, whereas the wt+gal versus wt+gal perturbation  second

from left) serves as a negative control. B) Expression profiles as in A), with significant changes A

45) circled in magenta. Also superimposedare the qualitative changes +I- that we expect using the

Fig. 1model [see Step iv)]. C) Average expression profiles for genes in each of 16 clusters. Clusters

contained genes involved in a variety of metabolic processes, as well as genes of unknown function

w e b able

1

 20)]; particular BiologicalProcesses [Gene Ontology Database. March 2001 36)] occurring

at

higher-than-expected frequencies within each cluster are annotated at right.

D)

Cellular doubling

time in each of the 20 conditions, measured before harvest.

lar interactions that lead to pathway activa-

tion or inhibition. Thus, by combining this

prior work with the complete sequence of the

yeast genome, step (i) above has in large part

already been accomplished. In steps (ii)

through (iv) that follow, we report the first

large-scale comparison of mRNA and protein

responses, describe an ongoing attempt to

systematically explain these responses using

existing databases of regulatory and other

physical interactions, and explore a number

of refinements to the GAL model suggested

by these integrative studies.

Step (i): As shown in Fig. 1,

galactose

utilization consists of a biochemical pathway

that converts galactose into glucose-6-phos-

phate and a regulatory mechanism that con-

trols whether the pathway is on or off. This

process has been reviewed extensively (4,

6 )

and involves at least three types of proteins.

A transporter gene (GAL2) encodes a per-

mease that transports galactose into the cell;

several other hexose transporters (HXTs)

may also have this ability

(7).

A group of

enzymatic genes encodes the proteins re-

quired for conversion of intracellular galac-

tose, including galactokinase (CALI), uri-

dylyltransferase (GAL7), epimerase (GALIO),

and phosphoglucomutase (GALSIPGMZ). The

regulatory genes GAL3, CALI, and GAL80 ex-

ert

tight transcriptional control over the trans-

porter, the enzymes, and to a certain extent,

each other. GALAp is a DNA-binding factor

that can strongly activate transcription, but in

the absence of galactose, G k 8 0 p binds

GAL4p and inhibits its activity. When galac-

tose is present in the cell, it causes GAL3p to

associate with GAL80p. This association caus-

es GAL80p to release its repression of GAL4p,

so that the transporter and enzymes are ex-

pressed at a

high

level.

Although these genes and interactions

form the core of the GAL pathway, the com-

plete regulatory mechanism is more complex

(8-11) and involves genes whose roles in

galactose utilization are not entirely clear (12,

13). For instance, the gene GAL6 (LAP3)

functions predominantly in a drug-resistance

pathway, but can suppress transcription of the

GAL transporter and enzymes under certain

conditions and may itself be transcriptionally

controlled by GAL4 (14).

Step (ii): Guided by the current model, we

applied

20

initial perturbations to the GAL

pathway. Wild-type

(wt)

and nine genetically

altered yeast strains were examined (15),

each with a complete deletion of one of the

nine GAL genes: transport (ga12A), enzymat-

ic (gallA, ga156, ga17A, or gallOA), or reg-

ulatory (ga13A, gal4A, gal6A, or gal80A).

These strains were perturbed environmentally

by growth in the presence (+gal) or absence

(-gal) of

2

galactose, with 2 raffinose

provided in both media (16).

W e examined global changes in mRNA

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R E P O R T S

expression resulting from each perturbation,

with DNA microarrays of approximately

6200 nuclear yeast genes as described (1 7).

In each experiment, fluorescently labeled

cDNA from a perturbed strain was hybridized

against labeled cDNA from a reference strain

( i t , grown in +gal media). To obtain robust

estimates of fluorescent intensity, four repli-

cate hybridizations were performed for each

perturbation. Using a statistical method based

on maximum-likelihood estimation (18), we

identified 997 genes whose mRNA levels

differed significantly from reference under

one or more perturbations. This set was then

divided into 16 clusters using self-organizing

maps (19), where each cluster contained

genes with similar expression responses over

all perturbations. Figure 2 displays a matrix

su arizing the effects of perturbation on

mRNA expression of the GAL genes and

gene clusters [completedata provided in Web

table 1 (20)l.

Are the observed changes in rnRNA expres-

sion also reflected at the level of protein abun-

dance? To address this question, we examined

differences in protein abundance between

wt+gal and wt-gal conditions using isotope-

coded afiinity tag (ICAT) reagents and tandem

mass spectrometry (MSMS) (21). Equal

amounts of protein extracts h m wt+gal and

wt-gal cultures were labeled with isotopically

heavy and normal ICAT reagents, respectively,

then

combined and digested

with

trypsin. The

resulting peptide mixture was hctionated by

multidimensional chromatography and

ana-

lyzed by MSMS. Computational analysis of

the tandem mass spectra was used

to

identify

the proteins h m which specific peptides orig-

inated and

to

indicate relative abundances of the

heavy and normal ICAT isotopes of each of

these peptides.

We obtained protein-abundanceratios for

a total of 289 proteins [Web table 1 (20)],

including all of the GAL enzymes and the

transporter. Figure 3 shows protein-abun-

dance ratios versus the corresponding

mRNA-expression ratios obtained with DNA

microarrays: as a whole, protein-abundance

ratios were moderately correlated with their

mRNA counterparts r 0.61, P 1.3

X

Although approximately 30 proteins

displayed clear changes in abundance be-

tween the wt+gal and wt-gal conditions

(Ilog,, ratio1 > 0.25), mRNA levels for 15 of

thesedid not change significantly in response

to any perturbation, suggesting that these pro-

teins may be regulated posttranscriptionally.

In

addition, many ribosomal-protein genes

increased three- to fivefold in mRNA but not

in protein abundance in response to galactose

addition. These results underscore the impor-

tance of integrated mRNA- and protein-ex-

pression measurements for understandig bi-

ological systems.

Step (iii): Can we attribute the observed

mRNA and protein changes to underlying

regulatory interactions in the cell? Although

we already have a model of interactions

among the GAL genes, it does not address

changes in expression observed for the hun-

dreds of other genes appearing in Figs. 2 and

3. To supplement this model, we assembled a

catalog of previously observed physical inter-

actions in yeast by combining a published list

of 2709 protein-protein interactions (3) with

317 protein+DNA interactions recorded in

the transcription-factor databases (22). A to-

tal

of 348 genes associated with interactions

in this catalog were affected in mRNA or

protein expression by at least one perturba-

tion or involved in two or more interactions

with affected genes. Figure 4A displays these

genes graphically, along with their 362 asso-

ciated interactions, as a physical-interaction

network.

Genes linked by physical interactions in

the network tend to have more strongly cor-

related expression profiles than genes chosen

at random (P 0.001). We believe that these

correlations identify network interactions that

are likely to have transmitted a change in

expression from one gene (or protein) to an-

other over our 20 perturbations. Most

straightforwardly, a protein+DNA interac-

tion may be responsible for directly transmit-

ting an expression change from a transcrip-

tion factor to a highly correlated target

gene (e.g., Mcml+Farl and Migl+Fbpl;

mRNA expression profile correlations are

r ~ c m ~ f i r ~0 82

and

r ~ i g ~ ~ b p ~0'63)'

Alternatively, genes A and B may be under

control of a common transcription factor

C+(A,B): coexpressionof A and B provides

evidence that C transmits these changes, re-

gardless of whether C itself changes detect-

ably in expression. This is the case for the

GAL enzymes regulated by Gal4 (Fig. 4B),

amino acid synthesis genes regulated by

Gcn4 (Fig. 4C), and a class of gluconeogenic

genes controlled by Sip4 (Sip4+Fbpl, Pckl,

Icll). Finally, we may scan the network for

indirect effects, such as a change in A trans-

mitted to B through a protein-protein interac-

tion with a signaling protein (e.g., Gcr2-

Gcrl+Tpil; r r TpiI -0.86). Many oth-

er physically interacting, strongly correlated

genes are listed in Web table 2 (20); each of

these associates an observed change in gene

expression with the regulatory interaction(s)

likely to have caused it.

Ultimately, we wish to determine paths

through the network connecting perturbed

GAL genes to every other affected gene. This

is not always possible, because many of the

required interactions linking galactose utili-

zation to other metabolic processes

are

still

unknown. However, analysis of our expres-

sion data suggests that Gal4p directly regu-

lates genes in several of these processes

through novel protein+DNA interactions.

To identify putative interactions, we looked

for the well-characterized Gal4p-binding site

(23) upstream of genes in expression clusters

1, 2, and 3, which together contained all

seven genes with established Gal4p-binding

sites. Of the 87 remaining genes in these three

clusters, nine had Gal4p-binding sites not

previously identified [Web table 3 (20)], a

significantly greater proportion than were

mRNA

expressionratio loglo)

Fig. 3. Scatter plot of protein expression versus mRNA expression ratios. Ratios of wt+gal to

wt-gal protein expression, measured for each of 289 genes using the ICAT technique, are plotted

against the corresponding mRNA expression ratios measured by microarray. Many genes with

elevated mRNA or protein expression in wt+gal were metabolic A) or ribosomal e), hereas

genes involved in respiration V) lmost always had reduced expression levels. Names of genes that

were indistinguishable in both mRNA and protein due to high sequence similarity) are separated

by a slash.

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found

in

clusters

4

through 16 (10.3 versus prote in metabolism

as

wel l as several genes o f

prote in responses

to

galactose induction) (24).

2.8 ; P

<

0.002). This set o f nine contained un kno wn fun ction (e.g.,

I1MR318C

a gene

As shown

in

Fig. 1, we suggest that Gal4p ma y

genes involve d

in

glycogen accumulation

and

shown

in

Fig. 3 to have strong mRNA nd regulate these genes by dire t binding.

LC

Fig. 4.

Integrated physical-interaction network. Nodes represent genes, a gal4A+gal perturbation are superimposed on the network, with GAL4

yellow arrow directed from one node o another signifies hat the protein

colored red and the gray scale intensity of other nodes representing

encoded by the first gene can influence the transcription of the second changes in mRNA as in Fig. 2 (node diameter also scales wi th the

by DNA binding (protein+DNA), and a blue line between two nodes

magnitude o f change). Regions corresponding o

(B)

galactose utilization

signifies hat the correspondingproteins can physically interact (protein-

and

(C)

amino acid synthesis are detailed a t right. Graphical layout and

protein). Highly interconnected groups of genes tend t o have common

network display were performed automatically using software based on

biological function and are labeled accordingly.

A)

Effects of the the LEDA toolbox (37). An enlarged version o f (A) is provided in

20).

Fig.

5. Tree com paring gene-expression changes resulting

from different perturbations to the GAL pathway. We used

the Neighbor and Drawtree programs 38) to construct a

hierarchical-clustering tree (39) based on Euclidean dis-

tance between perturbation profiles, where each profile

consists of log,, mRNA expression ratios over the set of

997

significantly a ffected genes. The closer two perturba-

tions are to each other through th e branches of the tree,

the more similar th eir observed changes in gene expression.

Leaves of the tree are labeled with the relevant genetic

perturbation (wild-type or gene deletion) followed by the

environmental perturbation +I- gal). Twenty ini tia l per-

turbations (solid branches) and three follow-up perturba-

tions are sho wn (dotte d branches). As i n Fig. 2, profiles for

all genetic perturbations are relative to tha t of the w ild

type, with both strains grown in identical media (+gal or

-gal).

wt gal

vs

wt-gal

gal vs wt gal

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R E P O R T S

Step (iv): Lastly, how do the observed re-

sponses of GAL genes compare to their predict-

ed behavior? Figure 2B shows the qualitative

changes

+

and

-)

in mRNA expression that we

predicted based on the model shown in Fig. 1

and from current knowledge of galactose utili-

zation as summarized in Step (i).

In

general,

these were in good agreement with the observed

changes. For example, growth of wild-type cells

in + gal versus -gal media significantly induced

GALl, GAL2, GAL7, GALlO, and GAL80 as

expected, while deleting the positive regulators

GAL3 and GAL4 led to a significant expression

decrease in many of these genes. In -gal media,

deletion of the repressor GAL80 caused a dra-

matic increase in GAL-enzyme expression; in

+gal, this deletion had little or no effect on

these genes, presumably because they were al-

ready htghly expressed.

A num ber of observations were not predict-

ed by the model and are listed in Web table 4

(20); in many cases, these suggest new regula-

tory phenomena that may be tested by hypoth-

esis-driven approaches. For example, in the

presence of galactose, gal7 and gall0 deletions

unexpectedly reduced the expression levels of

other GAL enzymes. Because the metabolite

Gal-1-P is h o w n to accumulate in cells lacking

functional Gal7 and is detrimental in large quan-

tities (25), one hypothesis is that the observed

expression-level changes are dependent on

build-up of Gal-1-P or one of its metabolic

derivatives. Under this model, the cell would

limit metabolite accumulation by first sensing

toxic levels through an unknown mechanism,

then triggering a decrease in GAL-enzyme ex-

pression (Fig. 1). Alternative scenarios are also

possible, such as a model in which GALlO

influences the expression of GAL7 and GALl

through transcriptional interference within the

GALI-10-7 locus

9).

To test the hypothesis that the effects of

ga17A and gall0A are dependent on increased

levels of Gal-1-P or a derivative molecule,

we examined the expression profile of a

gallA gall0 A double deletion growing in +gal

conditions (relative to the wtfgal reference).

We predicted that in thts strain, the absence of

GALl activity would prevent build-up of Gal-

l-P and the changes in GAL gene expression

would not occur. Conversely, if the expression

changes d not depend on Gal-1-P (e.g., are

caused by chromosomal interactions at the

GAL l-10-7 locus), they would also be likely to

occur in the gallAgall0A strain. Consistent

with our initial hypothesis, GAL-enzyme ex-

pression was not significantly affected by this

perturbation, and as shown in Fig. 5, the expres-

sion profile of gallAgall0A over all affected

genes was more similar to the profile of

ga l lA

+

gal than to that of gall0A+gal or any

other perturbation.

Another unanticipated observation was the

slow growth of the ga180A mutant in -gal con-

ditions (Fig. 2D), the large number of gene

clusters affected by this perturbation (Fig. 2C,

compare rightmost column to the other eight

columns in the -gal set), and the corresponding

large distance between the gal80A-gal expres-

sion profile and every other profile in Fig. 5.

Since this perturbation leads to constitutive ex-

pression of the GAL enzymes and transporter,

we w ished to determine whether the widespread

expression changes were dependent on these

genes. Accordingly, we measured the expres-

sion profile of a gal4Agal80A-gal double dele-

tion, in which the GA L enzymes and transporter

are not expressed. Both the doubling time (144

min) and overall expression profile of this strain

(Fig. 5) were more similar to those of gal4A

(129 min) than ga180A

205

rnin), suggesting

that the effects of the gal80A perturbation are

indeed mediated by other GAL genes. To

r

ther determine which GAL genes were impor-

tant for the effect, we measured the expression

profile of a gal2Agal80A-gal perturbation, in

which the GAL transporter was absent. This

profile was more similar to that of gal2A

than gal80A, providing evidence that the

transporter is necessary to produce the slow

growth and expression changes seen for the

gal80A perturbation.

We expect that more directed experimental

approaches (i.e., biochemistry, genetics, cell bi-

ology) will be required to test these ideas and

further deepen our understanding of galactose

utilization and its interacting networks. Even so,

global and integrated analyses are extremely

p o w e h l for suggesting new hypotheses, espe-

cially with regard to the regulation of a pathway

and its interconnections with other pathways. As

technologies for cellular perturbation and global

measurement mature, these approaches will

soon become feasible in higher eukaryotes.

References and Notes

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3. B. Schwikowski. P. Uetz, S. Fields, Natur e Biotechnol.

18, 1257 (2000). [www nature comlnbtlweb_extras/

supp-infolnbt1200-125711

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(1995).

5. H. C. Douglas, D. C. Hawthorne, Genetics 49, 837

(1964).

6. R. J Reece, Cell Mol. Life Sci. 57, 1161 (2000).

7. R. Wiec zorke et dl., FEES Lett. 464 123 (1999).

8. M. John ston, J. 5. Flick, T. Pexton, Mol. Cell. Biol. 14,

3834 (1994).

9.

1

H. Creger, N.J. Proud foot, EMBO

J.

17,4 771 (1 998).

10. C. Peng, j. E. Hopper. Mol. Cell. Biol. 20, 5140 (2000).

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gani, Int. Biochem. Cell Biol. 32. 215 (2000).

13. L. Fu, A. M ise ta, D. Hunt on, R. B. Marchase , D. M.

Bedwell, iol. Chem. 275, 5431 (2000).

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30350 (1997).

15. Strains were derived from th e wild-typ e haploid

MATa strain BY4741 (ATCC #201388. MATa his3 Al

leu2AO metlSA0 ura3AO). The mutants gallA,

ga13A, galSA, gal7A, and gallOA were c onstruc ted by

complete replacement of the corresponding genes

with kanR using the loxP-kanR-loxP cassette (26),

while galZA, ga/4A, gal6A, and gal80A were obtained

from the Saccharomyces Genome Deletion Project

(27) and were constructed analogously. Strains

galZAgal80A and gal4Aga180A were obtained by

mating and sporulation of the single-deletion mu-

tants; gallAgallOA (#R4146) was a generous gift

f rom C. Roberts of Rosetta Inpharmatics.

16. Yeast were inoculated in 100 m l of either CAL-

inducing "+gal" media (1% yeast extract, 2% pep-

tone, 2% raffinose, 2% galactose) or noninducing

"-gal" media (1% yeast extract, 2% peptone, 2%

raffinose). Cultures were grown ov ernigh t at 30°C t o

a densi ty o f 1 t o 2 OD was hed i n 5 m l H,O, and

snap-frozen.

17. T. Ideker, V. Thorsson, A. Siegel, L Hood,]. Compu-

tational Biol. 7, 805 (2001). [www.systemsbiology.

orgNERAandSAM/l

18. A likeliho od statistic was comp uted for each gene

to determine whether its mRNA expression level

differed between the tw o cell populations compared

by a microarray (17); genes having 45 were

selected as differentially expressed. This value was

approximately the maxim um obtained in control ex-

periments in which the tw o mRNA populations were

derived from identical strains and growth conditions

@+gal).

19. The 997 affected genes were clustered based on

Euclidean distanc e betw een the ir log,, expre ssion

ratios over all perturbation conditions, using a 4 row

by 4 column self-organizing map (SOM) implement-

ed by the Genecluster application (28), with 100

epochs.

20. Supplementary material is available at www.science-

mag.org/cgi/content/fulV292/5518/929/DCl

21. Cells were grown a nd harvested as for mRNA measu re-

ment, with proteins extracted according to Futcher (29).

Extracts were desalted (Biorad lODC columns) in 50 mM

tris 8.3, 1 mM EDTA, and 0.05% SDS. The ICAT method

(2) was applied to 300 p g of p rotein from each extract.

with the following modifications. After trypsin digestion,

peptides were fractionated on a 2.1 mm by 200 mm

polySULFOETHYL A co lumn (PolyLC) by runnin g a salt

gradient from 0 to 25% Buffer B (5 mM KHPO pH 3.0,

350 m M KCI. 25% CH,CN) over 30 min. followed b y 25

to 100% Buffer B over 20 min a t 0.2 mumin. Labeled

peptides were isolated rom each cation-exchange-chro-

matography raction by monomeric avidin (Pierce) affin-

ity chrom atography. Then. 10 to 80% of the peptide

mixture was analyzed by capillary-liquid-chromatogra-

phy MS/MS.

22. Protein-protein interactions, as reported in (3).

were der ived predominant ly through two-hybrid

assays and interactions culled from the literature.

Protein-DNA interact ions were obtained w ith per-

mission from TRANSFAC (30) and SCPD (31) and

represent all interactions in these databases as of

July 2000. A more comprehensive analysis of the

physical- interact ion network wi l l be provided in a

future publicat ion.

23. A nu cleotide weight m atrix model of the binding site

(TRANSFAC site matrix

M00049) was used to identify

potential bind ing sites in the promoter regions of genes

in the 997-gene set. Nu cleotide sequences of up to 800

bp upstream of translation start sites, terminating at the

nearest upstream ORFs, were scored against the w eight

matrix using Matlnspector (32) with core similarity 0.7

and matrix similarity 0.8.

24. While this work was in review, one of these genes

(PCL10) was identified as a target of Cal4p by direct

DNA-binding assay (33). Also, YMR318C was impli-

cated in a previous binding-site prediction study (34).

25.

K.

Lai, L.

J

Elsas, Bioch em. Biophys. Res. C omm un.

271, 392 (2000).

26. U. Culdener, S. Heck. T. Fielder, J Beinhauer, j. H.

Hegemann , Nucle ic Acids Res. 24, 251 9 (1996).

27. E A. Winzeler et al., Science 285, 901 (1999).

28. P. Tamayo e t al., Proc. Natl. Acad. Sci. U.S.A. 96,

2907 (1999).

29. B. Futcher, C. I. Latter, P. Monardo, C. S McLaughlin,

J

I. Carrels, Mol. Cell Biol. 19. 7357 (1999).

30. E Wingender e t dl., N ucleic Acids Res. 28, 316

(2000).

[http://transfac.gbf.delTRANSFAC/] 

31.

J

Zhu, M. Q Zhang. Bioinformatics 15. 607 (1999).

[http://cgsigma.cshl.org/jian/] 

32.

K.

Quan dt, K. Frech, H. Karas, E Wingender, T. Werner,

Nucle ic Acids Res. 23. 487 8 (19 95).

33. B. Ren et al., Science 290, 2 306 (200 0).

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34. F P Rot h.

J

D. Hughes.P W. Estep,  M. Church, 36. M. Ashburner et al., Nature Genet. 25 25 (2000).

interactions;A. Siegel,K. Y. Yeung, E. Cho,Q. Zong,

NatureBiotechnol.16. 939(1998).

[www.geneontology.org/]

and E. Yi fora ssistancewit hglo balan alysis;T.Young.

35. InFig.2,deletionstrainsgrownin -ga l aredisplayed

37. K. Mehlho rn,S Naeher,The LEDA Platformof Combina-

D. Morris,K. Dimitrov,

M.

Johnston,and I.Sadowski

relativetowt-gal conditionsbysubtractingthelog,, torialandGeometricComputing(CambridgeUniv.Press,

for inform ative discussions;andJ.Hopper,J.Aitch i-

expressionratioofwt-gal versusreferencefromthe

Cambridge.1999).[www.algorithmic solutions.com/]

son,andL.Kruglyakforcriticalreadingofthemanu-

log,, expressionratio of the deletionstrain versus

38.

J

Felsenstein,Cladistics

5

164(1989).

script.ThisworkwasfundedbygrantsfromNIH(T.I.,

reference.Aspredictedandconfirmedexperimentally

39. M.B.Eisen.  T.Spellman,P.  Brown.D.Bots tein.Proc.

J.A.R.. R.A.) and the Sloan Foundation/U.S. Depart-

(T.Ideker,V.Thorsson,datanotshown),thisopera-

Natl.Acad.Sci.U.S.A.

95,

14863(1998).

mentof Energy(V.T.).

t ion results inapproximately a f i - f o l d increase in

40. WethankG vandenEnghforideasonvisualdisplay;

error.

B. Schwikow skiandP. Uetzforearlyaccesstoprote in 5October2000;accepted5April2001

V ita l Involvement of a Na tural

counter infections through a nonspecific re-

sponse to inflammatory cytokines that induce

Killer Ce ll Activation Receptor

their production of interferon-y 2). Yet, NK

cells appear to respond specifically against

certain pathogens. For example, in humans,

in Resistance to V ira l Infection

selective NK cell deficiency is associated

with recurrent systemic infections, especially

MichaelG Brown,'*? Ayotunde Dokun,',**

with herpesviruses such as cytomegalovirus

Jonathan W. Heusel,' Hamish R. C. Smith,' Diana L Beckman,'

3). This is closely paralleled by the suscep-

Erika A. ~lattenberger,'Chad

E.

Dubbelde,' Laurie

R.

Stone,'

tibility of NK cell-depleted mice to murine

Wayne M. Yokoyamal$

cytomegalovirus (MCMV) but not to lym-

Anthony A. S~ a lz o ,~

phocytic choriomeningitis virus (4 ) . Al-

though the mechanisms underlying this sus-

Natural k i l ler (NK)cel lsare lymphocytes that canbedist inguishedf romTand

ceptibility are incompletely understood. NK

Bce l ls th rough the i r invo lvement in inna te immun i ty and the i r lack o f rear - cell receptors that activate tumor cytotoxicity

rangedant igenreceptors. Al thoughNKcel lsandtheir receptorswere in i t ia l ly

may play important roles in innate defense

charac te r ized in te rmso f tumor k i l l i ng in v i t ro , we have de te rm ined tha t the

against specific infections 5).

NK cel lact iva tion receptor,Ly-49H,iscri t ica llyinvolve dinresistancet o mu rine

The critical involvement of NK cell acti-

cy tomega lov irus in v ivo. Ly-49H requi res an immunorecep to r t y ros ine-based

vation receptors in defense against pathogens

a c t i v a t i o n m o t i f ( I ~ ~M ) - c o n t a i n i n g

is highlighted by the expression of virus-

ransmembrane mo lecu le fo r express ion

andsignal t ransduct ion. Thus, NK cel lsusereceptors funct ion al ly resembl ing

encoded proteins that interfere with natural

ITAM-coup led Tand

B

cel lant ige nreceptorsto provi devi ta ln nat ehos tdefense.

killing (6 ) . In many cases. these proteins

enhance the function of inhibitory major his-

Natural killer (NK) cells were first identified

cytes can be distinguished from T and

B

cells tocompa tibility complex

(MHC)

class I-spe-

because of their natural ability to kill tu-

because they do not express rearranged anti- cific NK cell receptors that potentially inter-

mors in vitro, an ability that is now known to

gen receptors and are not directly involved in

fere with signals from activation receptors,

occur through activation receptors that trigger

acquired immu nity. However, NK cells par- such as Ly-49D and Ly-49H , that are coupled

the release of perforin-containing cytolytic

ticipate in early innate host defense against to inimunorecep tor tyrosine-based activa-

granules [reviewed in

I)].

These lympho- pathogens and are generally thought to

tion motif (ITAMbontaining transmembrane

%

Fig 1.

BXD-8mice possessthe NKCB6haplotype anddis-

play MCMVsusceptibility that is not complemented by

O

DBN2.  A ) Geneticand physicalmapsof NKC-linkedloci

 

on mouse chromosome 6. A

genetic l inkage map and

schematicdiagramoftheBXD -8chimericchromosome6is

representedatleft,basedontheMouseGenomeInformat-

0

icsdatabase(14,25).Chromosomal regionsderivedfrom

the C57BU6(solidbar)or DBN2 (openbars)inbredpro-

genitorstrainsare indicated,alongwi th genetic position

(distance is indicated in centimorgans from the centro-

mere)of C57BU6 alleles(solidarrowheads)and DBN 2

(open arrowheads) alleles for the recombination break-

points.Sequence-taggedsite markersthat residecloset o

thecentromereandtelomerearealsoshown.Thusfar,in

BXD-8,all loci reportedt o residebetween D6Mit86and

D6Rik59(36tested loci) andbetween lva2andXmmv54

6

DBAI2 BXD 8

F1

(10loci)containDBN2alleles.C57BU6allelesaccountfor

allBXD-8loci reportedt o residebetween D6Rik61and

D6Mit198,exceptforthe Cmv l locus (9,25).A physical

linkagemapoftheNKCisdepictedinthecenter,with selectedlocithat havebeenusefult o distinguishalleles(79).

A physicalmapoftheLy49geneclusterisexpandedatright(26,27).BXD-8andC57BU6NKCallelesareidentically

sizedforallNKClocishownatcenterandforD6Wum4,D6Mit370,Ly49g,andLy49a(L22).SurroundingLy49harethe

Ly49kandLy49npseudogenes(28).

 0

MCMVreplica tioninF, hybridoffspringfromD BN 2andBXD-8.Threedaysafterinfection [wi thMCMV Smithstrain,

2

x

l o 4 plaque-fo rmingunits(PFUs)],organviraltiterswereassessedintissuehomogenatescollectedfromC57BU6,DBN2,BXD-8,and(DBN 2

X

BXD-8)

F, hybridm ice(fivem icepergroup),asindicated.Spleentitersareshown here;livertitersareavailableonline(8).Eachpo intrepresentsthe averagetiter

determinedforan individualmouse.Inthespleensof tw oC57BU6-infected mice,viralreplication was belowthe levelofdetectionbythis assayandis

indicatedwith asterisks.Meanviraltitersfo reachgrouparedepictedas horizonta lbars.Formice with titers belowthe levelof detectiono fthe assay,the

min imum numberofdetectablePFUs(lo 2) wasassumedt o determinethemean.Thisassumptionoverestimatesthemeanforthegrouphavingtitersbelow

detectablelevels.

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