Integrated

Genomic

and

Proteomic

Analyses

of

a

Systematically

Perturbed

Metabolic

Network

Trey

Ideker

et

al.

Science

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AAAS

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from

on

May

REPORTS

AMPARs

in

Bergmann

glia

are

necessary

for

preventing

surplus

CFs

from

producing

synapses

onto

single

Purkinje

cells

Conversion

of

permeable

AMPARs

into

impermeable

receptors

in

Bergmann

glia

elicited

morphological

changes

in

fine

glial

processes

wrapping

Purkinje

cell

synapses

prolonged

the

kinetics

of

glutamatergic

synaptic

transmission

and

caused

multiple

innervation

of

Purkinje

cells

by

CFs

Thus

the

morphology

of

glial

processes

and

the

synaptic

activities

would

be

interdependent

The

permeable

AMPARs

in

glial

cells

probably

play

key

roles

in

such

interactions

between

glia

and

glutamatergic

synapses

P.

H.

Seeburg

Trends

Neurosci

-LRB-

-RRB-

M.

Hollmann

S.

Heinemann

Annu

Rev.

Neurosci

-LRB-

-RRB-

C.

Steinhauser

V.

Gallo

Trends

Neurosci

-LRB-

-RRB-

M.

Hollmann

M.

Hartley

S.

Heinemann

Science

-LRB-

-RRB-

N.

Burnashev

H.

Monyer

P.

H.

Seeburg

B.

Sakmann

Neuron

-LRB-

-RRB-

K.

Keinanen

et

al.

Science

-LRB-

-RRB-

T.

Muller

T.

Moller

T.

Berger

J.

Schnitzer

H.

Ketten

mann

Science

-LRB-

-RRB-

N.

Burnashev

et

al.

Science

-LRB-

-RRB-

For

the

methods

used

see

the

supplementary

information

on

Science

Online

at

cgi/content/full

K.

A.

Yamada

C.

M.

Tang

J.

Neurosci

-LRB-

-RRB-

K.

M.

Partin

M.

W.

Fleck

M.

L.

Mayer

J.

Neurosci

-LRB-

-RRB-

References

and

Notes

J.

A.

Dzubay

C.

E.

Jahr

J.

Neurosci

-LRB-

-RRB-

M.

Iino

S.

Ozawa

K.

Tsuzuki

J.

Physiol

-LRB-

-RRB-

S.

Ozawa

M.

Iino

K.

Tsuzuki

J.

Neurophysiol

-LRB-

-RRB-

T.

Isa

S.

Itazawa

M.

Iino

K.

Tsuzuki

S.

Ozawa

J.

Physiol

-LRB-

-RRB-

J.

Grosche

et

al.

Nature

Neurosci

-LRB-

-RRB-

Quantitative

evaluation

of

the

morphological

changes

is

available

in

the

supplementary

information

on

Science

Online

at

F.

A.

Chaudhry

et

al.

Neuron

-LRB-

-RRB-

K.

Yamada

et

al.

J.

Comp

Neurol

-LRB-

-RRB-

B.

Barbour

B.

U.

Keller

I.

Llano

A.

Marty

Neuron

-LRB-

-RRB-

C.

Auger

D.

Attwell

Neuron

-LRB-

-RRB-

J.

Mariani

J.-P

Changeux

J.

Neurosci

-LRB-

-RRB-

January

accepted

March

Trey

Ideker

Vesteinn

Thorsson

Jeffrey

A.

Ranish

Rowan

Christmas

Jeremy

Buhler

Jimmy

K.

Eng

Roger

Bumgarner

David

R.

Goodlett

Ruedi

Aebersold

Leroy

We

demonstrate

an

integrated

approach

to

build

test

and

refine

a

model

of

a

cellular

pathway

in

which

perturbations

to

critical

pathway

components

are

analyzed

using

DNA

microarrays

quantitative

proteomics

and

databases

of

known

physical

interactions

Using

this

approach

we

identify

messenger

RNAs

responding

to

systematic

perturbations

of

the

yeast

galactose-utilization

pathway

provide

evidence

that

approximately

of

detected

proteins

are

regulated

posttranscriptionally

and

identify

explicit

physical

interactions

governing

the

cellular

response

to

each

perturbation

We

refine

the

model

through

further

iterations

of

perturbation

and

global

measurements

suggesting

hypotheses

about

the

regulation

of

galactose

utilization

and

physical

interactions

between

this

and

a

variety

of

other

metabolic

pathways

For

organisms

with

fully

sequenced

genomes

DNA

microarrays

are

an

extremely

powerful

technology

for

measuring

the

mRNA

expression

responses

of

practically

every

gene

-LRB-

-RRB-

Technologies

for

globally

and

quantitatively

measuring

protein

expression

are

also

becoming

feasible

-LRB-

-RRB-

and

developments

such

as

the

two-hybrid

system

are

enabling

construction

of

a

map

of

interactions

among

proteins

-LRB-

-RRB-

Although

such

largescale

data

have

proven

invaluable

for

distinguishing

cell

types

and

biological

states

new

The

Institute

for

Systems

Biology

Roosevelt

Way

NE

Suite

Seattle

WA

USA

Departments

of

Biotechnology

Science

and

University

of

Washington

Seattle

WA

USA

To

whom

correspondence

should

be

addressed

at

The

Institute

for

Systems

Biology

E-mail

tideker

systemsbiology.org

Fig.

Model

of

galactose

utilization

Yeast

metabolize

galactose

through

a

series

of

steps

involving

the

transporter

and

enzymes

produced

by

and

These

genes

are

transcriptionally

regulated

by

a

mechanism

consisting

primarily

of

and

produces

another

regulatory

factor

thought

to

repress

the

GAL

enzymes

in

a

manner

similar

to

Dotted

interactions

denote

model

refinements

supported

by

this

study

SCIENCE

VOL

MAY

Downloaded

from

on

May

Integrated

Genomic

and

Proteomic

Analyses

of

a

Systematically

Perturbed

Metabolic

Network

approaches

are

needed

which

by

integrating

these

diverse

data

types

and

assimilating

them

into

biological

models

can

predict

cellular

behaviors

that

can

be

tested

experimentally

We

propose

and

apply

one

such

strategy

here

consisting

of

four

distinct

steps

-LRB-

i

-RRB-

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

an

initial

model

of

the

molecular

interactions

governing

pathway

function

drawn

from

previous

genetic

and

biochemical

research

-LRB-

ii

-RRB-

Perturb

each

pathway

component

through

a

series

of

genetic

-LRB-

e.g.

gene

deletions

or

overexpressions

-RRB-

or

environmental

-LRB-

e.g.

changes

in

growth

conditions

or

temperature

-RRB-

manipulations

Detect

and

quantify

the

corresponding

global

cellular

response

to

each

perturbation

with

technologies

for

large-scale

mRNA

and

protein-expression

measurement

-LRB-

iii

-RRB-

Integrate

the

observed

mRNA

and

protein

responses

with

the

current

pathwayspecific

model

and

with

the

global

network

of

protein-protein

protein-DNA

and

other

known

physical

interactions

-LRB-

iv

-RRB-

Formulate

new

hypotheses

to

explain

REPORTS

observations

not

predicted

by

the

model

Design

additional

perturbation

experiments

to

test

these

and

iteratively

repeat

steps

-LRB-

ii

-RRB-

-LRB-

iii

-RRB-

and

-LRB-

iv

-RRB-

As

proof-of-principle

we

now

implement

this

integrated

approach

to

explore

the

process

of

galactose

utilization

-LRB-

GAL

-RRB-

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

glucose

are

absent

Extensive

biochemical

studies

-LRB-

-RRB-

and

saturating

mutant

screens

-LRB-

-RRB-

have

defined

the

genes

gene

products

and

metabolic

substrates

required

for

function

of

this

process

and

have

elucidated

the

key

molecular

interactions

that

lead

to

pathway

activation

or

inhibition

Thus

by

combining

this

prior

work

with

the

complete

sequence

of

the

yeast

genome

step

-LRB-

i

-RRB-

above

has

in

large

part

already

been

accomplished

In

steps

-LRB-

ii

-RRB-

through

-LRB-

iv

-RRB-

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

-LRB-

i

-RRB-

As

shown

in

Fig.

galactose

utilization

consists

of

a

biochemical

pathway

that

converts

galactose

into

and

a

regulatory

mechanism

that

controls

whether

the

pathway

is

on

or

off

This

process

has

been

reviewed

extensively

-LRB-

-RRB-

and

involves

at

least

three

types

of

proteins

A

transporter

gene

-LRB-

-RRB-

encodes

a

permease

that

transports

galactose

into

the

cell

several

other

hexose

transporters

-LRB-

HXTs

-RRB-

may

also

have

this

ability

-LRB-

-RRB-

A

group

of

enzymatic

genes

encodes

the

proteins

required

for

conversion

of

intracellular

galactose

including

galactokinase

-LRB-

-RRB-

uridylyltransferase

-LRB-

-RRB-

epimerase

-LRB-

-RRB-

and

phosphoglucomutase

-LRB-

-RRB-

The

regulatory

genes

and

exert

tight

transcriptional

control

over

the

transporter

the

enzymes

and

to

a

certain

extent

each

other

is

a

DNA-binding

factor

that

can

strongly

activate

transcription

but

in

the

absence

of

galactose

binds

and

inhibits

its

activity

When

galactose

is

present

in

the

cell

it

causes

to

associate

with

This

association

causes

to

release

its

repression

of

so

that

the

transporter

and

enzymes

are

expressed

at

a

high

level

Although

these

genes

and

interactions

form

the

core

of

the

GAL

pathway

the

complete

regulatory

mechanism

is

more

complex

-LRB-

-RRB-

and

involves

genes

whose

roles

in

galactose

utilization

are

not

entirely

clear

-LRB-

-RRB-

For

instance

the

gene

-LRB-

-RRB-

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

-LRB-

-RRB-

Step

-LRB-

ii

-RRB-

Guided

by

the

current

model

we

applied

initial

perturbations

to

the

GAL

pathway

Wild-type

-LRB-

wt

-RRB-

and

nine

genetically

altered

yeast

strains

were

examined

-LRB-

-RRB-

each

with

a

complete

deletion

of

one

of

the

nine

GAL

genes

transport

-LRB-

-RRB-

enzymatic

-LRB-

or

-RRB-

or

regulatory

-LRB-

or

-RRB-

These

strains

were

perturbed

environmentally

by

growth

in

the

presence

-LRB-

gal

-RRB-

or

absence

-LRB-

gal

-RRB-

of

galactose

with

raffinose

provided

in

both

media

-LRB-

-RRB-

We

examined

global

changes

in

mRNA

Fig.

Perturbation

matrix

Microarrays

were

used

to

measure

the

mRNA

expression

profiles

of

yeast

growing

under

each

of

perturbations

to

the

GAL

pathway

-LRB-

A

-RRB-

Each

spot

represents

the

change

in

expression

of

a

GAL

gene

due

to

a

particular

perturbation

-LRB-

listed

above

each

column

-RRB-

medium

gray

-LRB-

i.e.

the

same

level

as

figure

background

-RRB-

represents

no

change

whereas

darker

or

lighter

shades

represent

increased

or

reduced

expression

respectively

The

left

half

of

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

-LRB-

-RRB-

The

wt

gal

versus

wt

gal

perturbation

-LRB-

far

left

-RRB-

isolates

the

effects

of

growth

with

and

without

galactose

whereas

the

wt

gal

versus

wt

gal

perturbation

-LRB-

second

from

left

-RRB-

serves

as

a

negative

control

-LRB-

B

-RRB-

Expression

profiles

as

in

-LRB-

A

-RRB-

with

significant

changes

-LRB-

-RRB-

circled

in

magenta

Also

superimposed

are

the

qualitative

changes

-LRB-

-RRB-

that

we

expect

using

the

Fig.

model

-LSB-

see

Step

-LRB-

iv

-RRB-

-RSB-

-LRB-

C

-RRB-

Average

expression

profiles

for

genes

in

each

of

clusters

Clusters

contained

genes

involved

in

a

variety

of

metabolic

processes

as

well

as

genes

of

unknown

function

-LSB-

Web

table

-LRB-

-RRB-

-RSB-

particular

Biological

Processes

-LSB-

Gene

Ontology

Database

March

-LRB-

-RRB-

-RSB-

occurring

at

higher-than-expected

frequencies

within

each

cluster

are

annotated

at

right

-LRB-

D

-RRB-

Cellular

doubling

time

in

each

of

the

conditions

measured

before

harvest

MAY

VOL

SCIENCE

Downloaded

from

on

May

REPORTS

expression

resulting

from

each

perturbation

with

DNA

microarrays

of

approximately

nuclear

yeast

genes

as

described

-LRB-

-RRB-

In

each

experiment

fluorescently

labeled

cDNA

from

a

perturbed

strain

was

hybridized

against

labeled

cDNA

from

a

reference

strain

-LRB-

wt

grown

in

gal

media

-RRB-

To

obtain

robust

estimates

of

fluorescent

intensity

four

replicate

hybridizations

were

performed

for

each

perturbation

Using

a

statistical

method

based

on

maximum-likelihood

estimation

-LRB-

-RRB-

we

identified

genes

whose

mRNA

levels

differed

significantly

from

reference

under

one

or

more

perturbations

This

set

was

then

divided

into

clusters

using

self-organizing

maps

-LRB-

-RRB-

where

each

cluster

contained

genes

with

similar

expression

responses

over

all

perturbations

Figure

displays

a

matrix

summarizing

the

effects

of

perturbation

on

mRNA

expression

of

the

GAL

genes

and

gene

clusters

-LSB-

complete

data

provided

in

Web

table

-LRB-

-RRB-

-RSB-

Are

the

observed

changes

in

mRNA

expression

also

reflected

at

the

level

of

protein

abundance

To

address

this

question

we

examined

differences

in

protein

abundance

between

wt

gal

and

wt

gal

conditions

using

isotopecoded

affinity

tag

-LRB-

ICAT

-RRB-

reagents

and

tandem

mass

spectrometry

-LRB-

MS/MS

-RRB-

-LRB-

-RRB-

Equal

amounts

of

protein

extracts

from

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

fractionated

by

multidimensional

chromatography

and

analyzed

by

MS/MS

Computational

analysis

of

the

tandem

mass

spectra

was

used

to

identify

the

proteins

from

which

specific

peptides

originated

and

to

indicate

relative

abundances

of

the

heavy

and

normal

ICAT

isotopes

of

each

of

these

peptides

We

obtained

protein-abundance

ratios

for

a

total

of

proteins

-LSB-

Web

table

-LRB-

-RRB-

-RSB-

including

all

of

the

GAL

enzymes

and

the

transporter

Figure

shows

protein-abundance

ratios

versus

the

corresponding

mRNA-expression

ratios

obtained

with

DNA

microarrays

as

a

whole

protein-abundance

ratios

were

moderately

correlated

with

their

mRNA

counterparts

-LRB-

r

P

-RRB-

Although

approximately

proteins

displayed

clear

changes

in

abundance

between

the

wt

gal

and

wt

gal

conditions

-RRB-

mRNA

levels

for

of

-LRB-

ratio

these

did

not

change

significantly

in

response

to

any

perturbation

suggesting

that

these

proteins

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

importance

of

integrated

mRNA

and

protein-expression

measurements

for

understanding

biological

systems

Step

-LRB-

iii

-RRB-

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

hundreds

of

other

genes

appearing

in

Figs.

and

To

supplement

this

model

we

assembled

a

catalog

of

previously

observed

physical

interactions

in

yeast

by

combining

a

published

list

of

protein-protein

interactions

-LRB-

-RRB-

with

DNA

interactions

recorded

in

the

transcription-factor

databases

-LRB-

-RRB-

A

total

of

genes

associated

with

interactions

in

this

catalog

were

affected

in

mRNA

or

protein

expression

by

at

least

one

perturbation

or

involved

in

two

or

more

interactions

with

affected

genes

Figure

displays

these

genes

graphically

along

with

their

associated

interactions

as

a

physical-interaction

network

Genes

linked

by

physical

interactions

in

the

network

tend

to

have

more

strongly

correlated

expression

profiles

than

genes

chosen

at

random

-LRB-

P

-RRB-

We

believe

that

these

correlations

identify

network

interactions

that

are

likely

to

have

transmitted

a

change

in

expression

from

one

gene

-LRB-

or

protein

-RRB-

to

another

over

our

perturbations

Most

straightforwardly

a

DNA

interaction

may

be

responsible

for

directly

transmitting

an

expression

change

from

a

transcription

factor

to

a

highly

correlated

target

gene

-LRB-

e.g.

and

mRNA

expression

profile

correlations

are

and

-RRB-

Alternatively

genes

A

and

B

may

be

under

control

of

a

common

transcription

factor

-LRB-

A

B

-RRB-

coexpression

of

A

and

B

provides

evidence

that

C

transmits

these

changes

regardless

of

whether

C

itself

changes

detectably

in

expression

This

is

the

case

for

the

GAL

enzymes

regulated

by

-LRB-

Fig.

-RRB-

amino

acid

synthesis

genes

regulated

by

-LRB-

Fig.

-RRB-

and

a

class

of

gluconeogenic

genes

controlled

by

-LRB-

-RRB-

Finally

we

may

scan

the

network

for

indirect

effects

such

as

a

change

in

A

transmitted

to

B

through

a

protein-protein

interaction

with

a

signaling

protein

-LRB-

e.g.

-RRB-

Many

other

physically

interacting

strongly

correlated

genes

are

listed

in

Web

table

-LRB-

-RRB-

each

of

these

associates

an

observed

change

in

gene

expression

with

the

regulatory

interaction

-LRB-

s

-RRB-

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

utilization

to

other

metabolic

processes

are

still

unknown

However

analysis

of

our

expression

data

suggests

that

directly

regulates

genes

in

several

of

these

processes

through

novel

DNA

interactions

To

identify

putative

interactions

we

looked

for

the

well-characterized

site

-LRB-

-RRB-

upstream

of

genes

in

expression

clusters

and

which

together

contained

all

seven

genes

with

established

sites

Of

the

remaining

genes

in

these

three

clusters

nine

had

sites

not

previously

identified

-LSB-

Web

table

-LRB-

-RRB-

-RSB-

a

significantly

greater

proportion

than

were

Fig.

Scatter

plot

of

protein

expression

versus

mRNA

expression

ratios

Ratios

of

wt

gal

to

wt

gal

protein

expression

measured

for

each

of

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

-LRB-

OE

-RRB-

or

ribosomal

-LRB-

-RCB-

-RRB-

whereas

genes

involved

in

respiration

-LRB-

-RRB-

almost

always

had

reduced

expression

levels

Names

of

genes

that

were

indistinguishable

in

both

mRNA

and

protein

-LRB-

due

to

high

sequence

similarity

-RRB-

are

separated

by

a

slash

SCIENCE

VOL

MAY

Downloaded

from

on

May

REPORTS

found

in

clusters

through

-LRB-

versus

P

-RRB-

This

set

of

nine

contained

genes

involved

in

glycogen

accumulation

and

protein

metabolism

as

well

as

several

genes

of

unknown

function

-LRB-

e.g.

a

gene

shown

in

Fig.

to

have

strong

mRNA

and

protein

responses

to

galactose

induction

-RRB-

-LRB-

-RRB-

As

shown

in

Fig.

we

suggest

that

may

regulate

these

genes

by

direct

binding

Fig.

Integrated

physical-interaction

network

Nodes

represent

genes

a

yellow

arrow

directed

from

one

node

to

another

signifies

that

the

protein

encoded

by

the

first

gene

can

influence

the

transcription

of

the

second

by

DNA

binding

-LRB-

DNA

-RRB-

and

a

blue

line

between

two

nodes

signifies

that

the

corresponding

proteins

can

physically

interact

-LRB-

proteinprotein

-RRB-

Highly

interconnected

groups

of

genes

tend

to

have

common

biological

function

and

are

labeled

accordingly

-LRB-

A

-RRB-

Effects

of

the

Fig.

Tree

comparing

gene-expression

changes

resulting

from

different

perturbations

to

the

GAL

pathway

We

used

the

Neighbor

and

Drawtree

programs

-LRB-

-RRB-

to

construct

a

hierarchical-clustering

tree

-LRB-

-RRB-

based

on

Euclidean

distance

between

perturbation

profiles

where

each

profile

consists

of

mRNA

expression

ratios

over

the

set

of

significantly

affected

genes

The

closer

two

perturbations

are

to

each

other

through

the

branches

of

the

tree

the

more

similar

their

observed

changes

in

gene

expression

Leaves

of

the

tree

are

labeled

with

the

relevant

genetic

perturbation

-LRB-

wild-type

or

gene

deletion

-RRB-

followed

by

the

environmental

perturbation

-LRB-

gal

-RRB-

Twenty

initial

perturbations

-LRB-

solid

branches

-RRB-

and

three

follow-up

perturbations

are

shown

-LRB-

dotted

branches

-RRB-

As

in

Fig.

profiles

for

all

genetic

perturbations

are

relative

to

that

of

the

wild

type

with

both

strains

grown

in

identical

media

-LRB-

gal

or

gal

-RRB-

gal

perturbation

are

superimposed

on

the

network

with

colored

red

and

the

gray

scale

intensity

of

other

nodes

representing

changes

in

mRNA

as

in

Fig.

-LRB-

node

diameter

also

scales

with

the

magnitude

of

change

-RRB-

Regions

corresponding

to

-LRB-

B

-RRB-

galactose

utilization

and

-LRB-

C

-RRB-

amino

acid

synthesis

are

detailed

at

right

Graphical

layout

and

network

display

were

performed

automatically

using

software

based

on

the

LEDA

toolbox

-LRB-

-RRB-

An

enlarged

version

of

-LRB-

A

-RRB-

is

provided

in

-LRB-

-RRB-

MAY

VOL

SCIENCE

Downloaded

from

on

May

REPORTS

Step

-LRB-

iv

-RRB-

Lastly

how

do

the

observed

responses

of

GAL

genes

compare

to

their

predicted

behavior

Figure

shows

the

qualitative

changes

-LRB-

and

-RRB-

in

mRNA

expression

that

we

predicted

based

on

the

model

shown

in

Fig.

and

from

current

knowledge

of

galactose

utilization

as

summarized

in

Step

-LRB-

i

-RRB-

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

and

as

expected

while

deleting

the

positive

regulators

and

led

to

a

significant

expression

decrease

in

many

of

these

genes

In

gal

media

deletion

of

the

repressor

caused

a

dramatic

increase

in

GAL-enzyme

expression

in

gal

this

deletion

had

little

or

no

effect

on

these

genes

presumably

because

they

were

already

highly

expressed

A

number

of

observations

were

not

predicted

by

the

model

and

are

listed

in

Web

table

-LRB-

-RRB-

in

many

cases

these

suggest

new

regulatory

phenomena

that

may

be

tested

by

hypothesis-driven

approaches

For

example

in

the

presence

of

galactose

and

deletions

unexpectedly

reduced

the

expression

levels

of

other

GAL

enzymes

Because

the

metabolite

is

known

to

accumulate

in

cells

lacking

functional

and

is

detrimental

in

large

quantities

-LRB-

-RRB-

one

hypothesis

is

that

the

observed

expression-level

changes

are

dependent

on

build-up

of

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

expression

-LRB-

Fig.

-RRB-

Alternative

scenarios

are

also

possible

such

as

a

model

in

which

influences

the

expression

of

and

through

transcriptional

interference

within

the

locus

-LRB-

-RRB-

To

test

the

hypothesis

that

the

effects

of

and

are

dependent

on

increased

levels

of

or

a

derivative

molecule

we

examined

the

expression

profile

of

a

double

deletion

growing

in

gal

conditions

-LRB-

relative

to

the

wt

gal

reference

-RRB-

We

predicted

that

in

this

strain

the

absence

of

activity

would

prevent

build-up

of

and

the

changes

in

GAL

gene

expression

would

not

occur

Conversely

if

the

expression

changes

did

not

depend

on

-LRB-

e.g.

are

caused

by

chromosomal

interactions

at

the

locus

-RRB-

they

would

also

be

likely

to

occur

in

the

strain

Consistent

with

our

initial

hypothesis

GAL-enzyme

expression

was

not

significantly

affected

by

this

perturbation

and

as

shown

in

Fig.

the

expression

profile

of

over

all

affected

genes

was

more

similar

to

the

profile

of

gal

than

to

that

of

gal

or

any

other

perturbation

Another

unanticipated

observation

was

the

slow

growth

of

the

mutant

in

gal

conditions

-LRB-

Fig.

-RRB-

the

large

number

of

gene

clusters

affected

by

this

perturbation

-LRB-

Fig.

compare

rightmost

column

to

the

other

eight

columns

in

the

gal

set

-RRB-

and

the

corresponding

large

distance

between

the

gal

expression

profile

and

every

other

profile

in

Fig.

Since

this

perturbation

leads

to

constitutive

expression

of

the

GAL

enzymes

and

transporter

we

wished

to

determine

whether

the

widespread

expression

changes

were

dependent

on

these

genes

Accordingly

we

measured

the

expression

profile

of

a

gal

double

deletion

in

which

the

GAL

enzymes

and

transporter

are

not

expressed

Both

the

doubling

time

-LRB-

min

-RRB-

and

overall

expression

profile

of

this

strain

-LRB-

Fig.

-RRB-

were

more

similar

to

those

of

-LRB-

min

-RRB-

than

-LRB-

min

-RRB-

suggesting

that

the

effects

of

the

perturbation

are

indeed

mediated

by

other

GAL

genes

To

further

determine

which

GAL

genes

were

important

for

the

effect

we

measured

the

expression

profile

of

a

gal

perturbation

in

which

the

GAL

transporter

was

absent

This

profile

was

more

similar

to

that

of

than

providing

evidence

that

the

transporter

is

necessary

to

produce

the

slow

growth

and

expression

changes

seen

for

the

perturbation

We

expect

that

more

directed

experimental

approaches

-LRB-

i.e.

biochemistry

genetics

cell

biology

-RRB-

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

powerful

for

suggesting

new

hypotheses

especially

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

E.

S.

Lander

Nature

Genet

-LRB-

-RRB-

S.

P.

Gygi

et

al.

Nature

Biotechnol

-LRB-

-RRB-

B.

Schwikowski

P.

Uetz

S.

Fields

Nature

Biotechnol

-LRB-

-RRB-

-LSB-

-RSB-

D.

Lohr

P.

Venkov

J.

Zlatanova

FASEB

J.

-LRB-

-RRB-

H.

C.

Douglas

D.

C.

Hawthorne

Genetics

-LRB-

-RRB-

R.

J.

Reece

Cell

Mol

Life

Sci

-LRB-

-RRB-

R.

Wieczorke

et

al.

FEBS

Lett

-LRB-

-RRB-

M.

Johnston

J.

S.

Flick

T.

Pexton

Mol

Cell

Biol

-LRB-

-RRB-

I.

H.

Greger

N.

J.

Proudfoot

EMBO

J.

-LRB-

-RRB-

G.

Peng

J.

E.

Hopper

Mol

Cell

Biol

-LRB-

-RRB-

J.

R.

Rohde

J.

Trinh

I.

Sadowski

Mol

Cell

Biol

-LRB-

-RRB-

S.

Rudoni

I.

Mauri

M.

Ceriani

P.

Coccetti

E.

Martegani

Int

J.

Biochem

Cell

Biol

-LRB-

-RRB-

L.

Fu

A.

Miseta

D.

Hunton

R.

B.

Marchase

D.

M.

Bedwell

J.

Biol

Chem

-LRB-

-RRB-

W.

Zheng

H.

E.

Xu

S.

A.

Johnston

J.

Biol

Chem

-LRB-

-RRB-

Strains

were

derived

from

the

wild-type

haploid

MATa

strain

-LRB-

ATCC

MATa

-RRB-

The

mutants

and

were

constructed

by

complete

replacement

of

the

corresponding

genes

with

kanR

using

the

loxP-kanR-loxP

cassette

-LRB-

-RRB-

while

and

were

obtained

from

the

Saccharomyces

Genome

Deletion

Project

-LRB-

-RRB-

and

were

constructed

analogously

Strains

and

were

obtained

by

mating

and

sporulation

of

the

single-deletion

mutants

-LRB-

-RRB-

was

a

generous

gift

from

C.

Roberts

of

Rosetta

Inpharmatics

Yeast

were

inoculated

in

ml

of

either

GALinducing

gal

media

-LRB-

yeast

extract

peptone

raffinose

galactose

-RRB-

or

noninducing

gal

media

-LRB-

yeast

extract

peptone

raffinose

-RRB-

Cultures

were

grown

overnight

at

C

to

a

density

of

to

washed

in

ml

and

snap-frozen

T.

Ideker

V.

Thorsson

A.

Siegel

L.

Hood

J.

Computational

Biol

-LRB-

-RRB-

-LSB-

org/VERAandSAM

-RSB-

A

likelihood

statistic

was

computed

for

each

gene

to

determine

whether

its

mRNA

expression

level

differed

between

the

two

cell

populations

compared

by

a

microarray

-LRB-

-RRB-

genes

having

were

selected

as

differentially

expressed

This

value

was

approximately

the

maximum

obtained

in

control

experiments

in

which

the

two

mRNA

populations

were

derived

from

identical

strains

and

growth

conditions

-LRB-

wt

gal

-RRB-

The

affected

genes

were

clustered

based

on

Euclidean

distance

between

their

expression

ratios

over

all

perturbation

conditions

using

a

row

by

column

self-organizing

map

-LRB-

SOM

-RRB-

implemented

by

the

GeneCluster

application

-LRB-

-RRB-

with

epochs

Supplementary

material

is

available

at

Cells

were

grown

and

harvested

as

for

mRNA

measurement

with

proteins

extracted

according

to

Futcher

-LRB-

-RRB-

Extracts

were

desalted

-LRB-

Biorad

columns

-RRB-

in

mM

tris

mM

EDTA

and

SDS

The

ICAT

method

-LRB-

-RRB-

was

applied

to

g

of

protein

from

each

extract

with

the

following

modifications

After

trypsin

digestion

peptides

were

fractionated

on

a

mm

by

mm

polySULFOETHYL

A

column

-LRB-

PolyLC

-RRB-

by

running

a

salt

gradient

from

to

Buffer

B

-LRB-

mM

pH

mM

KCl

-RRB-

over

min

followed

by

to

Buffer

B

over

min

at

ml/min

Labeled

peptides

were

isolated

from

each

cation-exchange-chromatography

fraction

by

monomeric

avidin

-LRB-

Pierce

-RRB-

affinity

chromatography

Then

to

of

the

peptide

mixture

was

analyzed

by

capillary-liquid-chromatography

MS/MS

Protein-protein

interactions

as

reported

in

-LRB-

-RRB-

were

derived

predominantly

through

two-hybrid

assays

and

interactions

culled

from

the

literature

Protein-DNA

interactions

were

obtained

with

permission

from

TRANSFAC

-LRB-

-RRB-

and

SCPD

-LRB-

-RRB-

and

represent

all

interactions

in

these

databases

as

of

July

A

more

comprehensive

analysis

of

the

physical-interaction

network

will

be

provided

in

a

future

publication

A

nucleotide

weight

matrix

model

of

the

binding

site

-LRB-

TRANSFAC

site

matrix

-RRB-

was

used

to

identify

potential

binding

sites

in

the

promoter

regions

of

genes

in

the

set

Nucleotide

sequences

of

up

to

bp

upstream

of

translation

start

sites

terminating

at

the

nearest

upstream

ORFs

were

scored

against

the

weight

matrix

using

MatInspector

-LRB-

-RRB-

with

core

similarity

and

matrix

similarity

While

this

work

was

in

review

one

of

these

genes

-LRB-

-RRB-

was

identified

as

a

target

of

by

direct

DNA-binding

assay

-LRB-

-RRB-

Also

was

implicated

in

a

previous

binding-site

prediction

study

-LRB-

-RRB-

K.

Lai

L.

J.

Elsas

Biochem

Biophys

Res

Commun

-LRB-

-RRB-

U.

Guldener

S.

Heck

T.

Fielder

J.

Beinhauer

J.

H.

Hegemann

Nucleic

Acids

Res

-LRB-

-RRB-

E.

A.

Winzeler

et

al.

Science

-LRB-

-RRB-

P.

Tamayo

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

B.

Futcher

G.

I.

Latter

P.

Monardo

C.

S.

McLaughlin

J.

I.

Garrels

Mol

Cell

Biol

-LRB-

-RRB-

E.

Wingender

et

al.

Nucleic

Acids

Res

-LRB-

-RRB-

-LSB-

-RSB-

J.

Zhu

M.

Q.

Zhang

Bioinformatics

-LRB-

-RRB-

-LSB-

-RSB-

K.

Quandt

K.

Frech

H.

Karas

E.

Wingender

T.

Werner

Nucleic

Acids

Res

-LRB-

-RRB-

B.

Ren

et

al.

Science

-LRB-

-RRB-

References

and

Notes

SCIENCE

VOL

MAY

Downloaded

from

on

May

REPORTS

F.

P.

Roth

J.

D.

Hughes

P.

W.

Estep

G.

M.

Church

Nature

Biotechnol

-LRB-

-RRB-

In

Fig.

deletion

strains

grown

in

gal

are

displayed

relative

to

wt

gal

conditions

by

subtracting

the

expression

ratio

of

wt

gal

versus

reference

from

the

expression

ratio

of

the

deletion

strain

versus

reference

As

predicted

and

confirmed

experimentally

-LRB-

T.

Ideker

V.

Thorsson

data

not

shown

-RRB-

this

operation

results

in

approximately

a

increase

in

error

M.

Ashburner

et

al.

Nature

Genet

-LRB-

-RRB-

-LSB-

-RSB-

K.

Mehlhorn

S.

Naeher

The

LEDA

Platform

of

Combinatorial

and

Geometric

Computing

-LRB-

Cambridge

Univ.

Press

Cambridge

-RRB-

-LSB-

-RSB-

J.

Felsenstein

Cladistics

-LRB-

-RRB-

M.

B.

Eisen

P.

T.

Spellman

P.

O.

Brown

D.

Botstein

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

We

thank

G.

van

den

Engh

for

ideas

on

visual

display

B.

Schwikowski

and

P.

Uetz

for

early

access

to

protein

interactions

A.

Siegel

K.

Y.

Yeung

E.

Cho

Q.

Zong

and

E.

Yi

for

assistance

with

global

analysis

T.

Young

D.

Morris

K.

Dimitrov

M.

Johnston

and

I.

Sadowski

for

informative

discussions

and

J.

Hopper

J.

Aitchison

and

L.

Kruglyak

for

critical

reading

of

the

manuscript

This

work

was

funded

by

grants

from

NIH

-LRB-

T.I.

J.A.R.

R.A.

-RRB-

and

the

Sloan

Foundation/U

S.

Department

of

Energy

-LRB-

V.T.

-RRB-

October

accepted

April

Vital

Involvement

of

a

Natural

Killer

Cell

Activation

Receptor

in

Resistance

to

Viral

Infection

Michael

G.

Brown

Ayotunde

O.

Dokun

Jonathan

W.

Heusel

Hamish

R.

C.

Smith

Diana

L.

Beckman

Erika

A.

Blattenberger

Chad

E.

Dubbelde

Laurie

R.

Stone

Anthony

A.

Scalzo

Wayne

M.

Natural

killer

-LRB-

NK

-RRB-

cells

are

lymphocytes

that

can

be

distinguished

from

T

and

B

cells

through

their

involvement

in

innate

immunity

and

their

lack

of

rearranged

antigen

receptors

Although

NK

cells

and

their

receptors

were

initially

characterized

in

terms

of

tumor

killing

in

vitro

we

have

determined

that

the

NK

cell

activation

receptor

is

critically

involved

in

resistance

to

murine

cytomegalovirus

in

vivo

requires

an

immunoreceptor

tyrosine-based

activation

motif

-LRB-

ITAM

-RRB-

containing

transmembrane

molecule

for

expression

and

signal

transduction

Thus

NK

cells

use

receptors

functionally

resembling

ITAM-coupled

T

and

B

cell

antigen

receptors

to

provide

vital

innate

host

defense

Natural

killer

-LRB-

NK

-RRB-

cells

were

first

identified

because

of

their

natural

ability

to

kill

tumors

in

vitro

an

ability

that

is

now

known

to

occur

through

activation

receptors

that

trigger

the

release

of

perforin-containing

cytolytic

granules

-LSB-

reviewed

in

-LRB-

-RRB-

-RSB-

These

lymphocytes

can

be

distinguished

from

T

and

B

cells

because

they

do

not

express

rearranged

antigen

receptors

and

are

not

directly

involved

in

acquired

immunity

However

NK

cells

participate

in

early

innate

host

defense

against

pathogens

and

are

generally

thought

to

Fig

mice

possess

the

haplotype

and

display

MCMV

susceptibility

that

is

not

complemented

by

-LRB-

A

-RRB-

Genetic

and

physical

maps

of

NKC-linked

loci

on

mouse

chromosome

A

genetic

linkage

map

and

schematic

diagram

of

the

chimeric

chromosome

is

represented

at

left

based

on

the

Mouse

Genome

Informatics

database

-LRB-

-RRB-

Chromosomal

regions

derived

from

the

-LRB-

solid

bar

-RRB-

or

-LRB-

open

bars

-RRB-

inbred

progenitor

strains

are

indicated

along

with

genetic

position

-LRB-

distance

is

indicated

in

centimorgans

from

the

centromere

-RRB-

of

alleles

-LRB-

solid

arrowheads

-RRB-

and

-LRB-

open

arrowheads

-RRB-

alleles

for

the

recombination

breakpoints

Sequence-tagged

site

markers

that

reside

close

to

the

centromere

and

telomere

are

also

shown

Thus

far

in

all

loci

reported

to

reside

between

and

-LRB-

tested

loci

-RRB-

and

between

and

-LRB-

loci

-RRB-

contain

alleles

alleles

account

for

all

loci

reported

to

reside

between

and

except

for

the

locus

-LRB-

-RRB-

A

physical

linkage

map

of

the

NKC

is

depicted

in

the

center

with

selected

loci

that

have

been

useful

to

distinguish

alleles

-LRB-

-RRB-

A

physical

map

of

the

gene

cluster

is

expanded

at

right

-LRB-

-RRB-

and

NKC

alleles

are

identically

sized

for

all

NKC

loci

shown

at

center

and

for

and

-LRB-

-RRB-

Surrounding

are

the

and

pseudogenes

-LRB-

-RRB-

-LRB-

B

-RRB-

MCMV

replication

in

hybrid

offspring

from

and

Three

days

after

infection

-LSB-

with

MCMV

Smith

strain

plaque-forming

units

-LRB-

PFUs

-RRB-

-RSB-

organ

viral

titers

were

assessed

in

tissue

homogenates

collected

from

and

-LRB-

-RRB-

hybrid

mice

-LRB-

five

mice

per

group

-RRB-

as

indicated

Spleen

titers

are

shown

here

liver

titers

are

available

online

-LRB-

-RRB-

Each

point

represents

the

average

titer

determined

for

an

individual

mouse

In

the

spleens

of

two

mice

viral

replication

was

below

the

level

of

detection

by

this

assay

and

is

indicated

with

asterisks

Mean

viral

titers

for

each

group

are

depicted

as

horizontal

bars

For

mice

with

titers

below

the

level

of

detection

of

the

assay

the

minimum

number

of

detectable

PFUs

-LRB-

-RRB-

was

assumed

to

determine

the

mean

This

assumption

overestimates

the

mean

for

the

group

having

titers

below

detectable

levels

MAY

VOL

SCIENCE

Downloaded

from

on

May

counter

infections

through

a

nonspecific

response

to

inflammatory

cytokines

that

induce

their

production

of

interferon

-LRB-

-RRB-

Yet

NK

cells

appear

to

respond

specifically

against

certain

pathogens

For

example

in

humans

selective

NK

cell

deficiency

is

associated

with

recurrent

systemic

infections

especially

with

herpesviruses

such

as

cytomegalovirus

-LRB-

-RRB-

This

is

closely

paralleled

by

the

susceptibility

of

NK

cell

depleted

mice

to

murine

cytomegalovirus

-LRB-

MCMV

-RRB-

but

not

to

lymphocytic

choriomeningitis

virus

-LRB-

-RRB-

Although

the

mechanisms

underlying

this

susceptibility

are

incompletely

understood

NK

cell

receptors

that

activate

tumor

cytotoxicity

may

play

important

roles

in

innate

defense

against

specific

infections

-LRB-

-RRB-

The

critical

involvement

of

NK

cell

activation

receptors

in

defense

against

pathogens

is

highlighted

by

the

expression

of

virusencoded

proteins

that

interfere

with

natural

killing

-LRB-

-RRB-

In

many

cases

these

proteins

enhance

the

function

of

inhibitory

major

histocompatibility

complex

-LRB-

MHC

-RRB-

class

I

specific

NK

cell

receptors

that

potentially

interfere

with

signals

from

activation

receptors

such

as

and

that

are

coupled

to

immunoreceptor

tyrosine-based

activation

motif

-LRB-

ITAM

-RRB-

containing

transmembrane

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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