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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,

    Neur on 8, 189 (1992).

    6. K. Keinanene t al., Science 249, 556 (1990).

    7. T. Miiller, T Moller, T Berger, 1. Schnitzer,H. Ketten-

    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

    VOL

    292 4

    MAY

    2001

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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.

    ,.sciencernag.org SCIENCE

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    R E P O R T S

    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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    4. D. Lohr, P. Venkov, j. Zlatanova, FASEB 1 9, 777

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

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

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    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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    J

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

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    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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