Transcriptional

Regulatory

Networks

in

Saccharomyces

cerevisiae

Tong

Ihn

Lee

et

al.

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ARTICLES

transport

-LRB-

-RRB-

The

model

was

also

used

for

tracer

forecasts

during

the

MINOS

campaign

E.

Roeckner

et

al.

The

atmospheric

general

circulation

model

Model

description

and

simulation

of

present-day

climate

MPI-Report

-LRB-

Max

Planck

Institute

for

Meteorology

Hamburg

Germany

-RRB-

E.

Roeckner

L.

Bengtsson

J.

Feichter

J.

Lelieveld

H.

Rodhe

J.

Clim

-LRB-

-RRB-

N.

A.

Rayner

E.

B.

Horton

D.

E.

Parker

C.

K.

Folland

R.

B.

Hackett

Version

of

the

Global

Sea-Ice

and

Sea

Surface

Temperature

Data

Set

Climate

Research

Technical

Note

-LRB-

Hadley

Centre

Bracknell

UK

-RRB-

available

at

S.

Nicholson

S.

Some

B.

Kone

J.

Clim

-LRB-

-RRB-

D.

P.

Rowell

C.

K.

Folland

K.

Maskell

M.

L.

Ward

Q.

J.

Meteorol

Soc

-LRB-

-RRB-

A.

Avila

F.

Roda

Atmos

Environ

-LRB-

-RRB-

The

Programme

for

Monitoring

and

Evaluation

of

the

Long-Range

Transmission

of

Air

Pollutants

in

Europe

-LRB-

EMEP

-RRB-

at

offers

estimates

of

emissions

variable

over

time

-LRB-

since

-RRB-

of

European

anthropogenic

trace

species

D.

Rosenfeld

Science

-LRB-

-RRB-

L.

D.

Rotstayn

U.

Lohmann

J.

Clim.

-LRB-

-RRB-

A.

S.

Board

et

al.

J.

Geophys

Res

-LRB-

-RRB-

A.

D.

Clarke

et

al.

J.

Geophys

Res

-LRB-

-RRB-

H.

Singh

et

al.

Nature

-LRB-

-RRB-

J.

Sciare

E.

Baboukas

N.

Mihalopoulos

J.

Atmos

Chem

-LRB-

-RRB-

S.

Mylona

Tellus

-LRB-

-RRB-

J.

van

Aardenne

F.

J.

Dentener

J.

G.

J.

Olivier

C.

G.

M.

Klein

Goldewijk

J.

Lelieveld

Global

Biogeochem

Cycles

-LRB-

-RRB-

The

emission

estimates

are

inherently

uncertain

The

decrease

after

is

nevertheless

consistent

with

sulfate

concentration

and

deposition

data

-LRB-

-RRB-

Funding

from

the

NSF

contributed

to

this

work

The

King

Air

flights

were

supported

by

the

Israeli

Space

Agency

as

part

of

the

Mediterranean

Israeli

Dust

Experiment

-LRB-

MEIDEX

-RRB-

June

accepted

September

Tong

Ihn

Lee

Nicola

J.

Rinaldi

Francois

Robert

Duncan

T.

Odom

Ziv

Bar-Joseph

Georg

K.

Gerber

Nancy

M.

Hannett

Christopher

T.

Harbison

Craig

M.

Thompson

Itamar

Simon

Julia

Zeitlinger

Ezra

G.

Jennings

Heather

L.

Murray

D.

Benjamin

Gordon

Bing

Ren

John

J.

Wyrick

Jean-Bosco

Tagne

Thomas

L.

Volkert

Ernest

Fraenkel

David

K.

Gifford

Richard

A.

We

have

determined

how

most

of

the

transcriptional

regulators

encoded

in

the

eukaryote

Saccharomyces

cerevisiae

associate

with

genes

across

the

genome

in

living

cells

Just

as

maps

of

metabolic

networks

describe

the

potential

pathways

that

may

be

used

by

a

cell

to

accomplish

metabolic

processes

this

network

of

regulator-gene

interactions

describes

potential

pathways

yeast

cells

can

use

to

regulate

global

gene

expression

programs

We

use

this

information

to

identify

network

motifs

the

simplest

units

of

network

architecture

and

demonstrate

that

an

automated

process

can

use

motifs

to

assemble

a

transcriptional

regulatory

network

structure

Our

results

reveal

that

eukaryotic

cellular

functions

are

highly

connected

through

networks

of

transcriptional

regulators

that

regulate

other

transcriptional

regulators

Genome

sequences

specify

the

gene

expression

programs

that

produce

living

cells

but

how

cells

control

global

gene

expression

programs

is

far

from

understood

Each

cell

is

the

product

of

specific

gene

expression

programs

involving

regulated

transcription

of

thousands

of

genes

These

transcriptional

programs

are

modified

as

Whitehead

Institute

for

Biomedical

Research

Nine

Cambridge

Center

Cambridge

MA

USA

of

Biology

Massachusetts

Institute

of

Technology

Cambridge

MA

USA

Laboratory

of

Computer

Science

Technology

Square

Cambridge

MA

USA

These

authors

contributed

equally

to

this

work

Present

address

Akceli

Inc.

Hampshire

Street

Cambridge

MA

USA

Present

address

Ludwig

Institute

for

Cancer

Research

Gilman

Drive

La

Jolla

CA

USA

Present

address

California

Institute

of

Technology

Pasadena

CA

USA

To

whom

correspondence

should

be

addressed

Email

young@wi.mit.edu

cells

progress

through

the

cell

cycle

in

response

to

changes

in

environment

and

during

organismal

development

-LRB-

-RRB-

Gene

expression

programs

depend

on

recognition

of

specific

promoter

sequences

by

transcriptional

regulatory

proteins

-LRB-

-RRB-

Because

these

regulatory

proteins

recruit

and

regulate

chromatin-modifying

complexes

and

components

of

the

transcription

apparatus

knowledge

of

the

sites

bound

by

all

the

transcriptional

regulators

encoded

in

a

genome

can

provide

the

information

necessary

to

nucleate

models

for

transcriptional

regulatory

networks

With

the

availability

of

complete

genome

sequences

and

development

of

a

method

for

genome-wide

binding

analysis

-LRB-

also

known

as

genome-wide

location

analysis

-RRB-

investigators

can

identify

the

set

of

target

genes

bound

in

vivo

by

each

of

the

transcriptional

regulators

that

are

encoded

in

a

cell

's

genome

This

approach

has

been

used

to

identify

the

SCIENCE

VOL

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Transcriptional

Regulatory

Networks

in

Saccharomyces

cerevisiae

genomic

sites

bound

by

nearly

a

dozen

regulators

of

transcription

-LRB-

-RRB-

and

several

regulators

of

DNA

synthesis

-LRB-

-RRB-

in

yeast

Experimental

design

We

used

genomewide

location

analysis

to

investigate

how

yeast

transcriptional

regulators

bind

to

promoter

sequences

across

the

genome

-LRB-

Fig.

-RRB-

All

transcription

factors

listed

in

the

Yeast

Proteome

Database

-LRB-

-RRB-

and

reported

to

have

DNA

binding

and

transcriptional

activity

were

selected

for

study

Yeast

strains

were

constructed

so

that

each

of

the

transcription

factors

contained

a

myc

epitope

tag

To

increase

the

likelihood

that

tagged

factors

were

expressed

at

physiologic

levels

we

introduced

epitope

tag

coding

sequences

into

the

genomic

sequences

encoding

the

COOH

terminus

of

each

regulator

as

described

in

-LRB-

-RRB-

We

confirmed

appropriate

insertion

of

the

tag

and

expression

of

the

tagged

protein

by

polymerase

chain

reaction

and

immunoblot

analysis

Introduction

of

an

epitope

tag

might

be

expected

to

affect

the

function

of

some

transcriptional

regulators

for

of

the

factors

we

were

not

able

to

obtain

viable

tagged

cells

despite

three

attempts

to

tag

each

regulator

Not

all

the

transcriptional

regulators

were

expected

to

be

expressed

at

detectable

levels

when

yeast

cells

were

grown

in

rich

medium

but

immunoblot

analysis

showed

that

of

the

tagged

regulator

proteins

could

be

detected

under

these

conditions

We

performed

a

genome-wide

location

analysis

experiment

-LRB-

-RRB-

for

each

of

the

yeast

strains

that

expressed

epitope-tagged

regulators

-LRB-

-RRB-

Each

tagged

strain

was

grown

in

three

independent

cultures

in

rich

medium

-LRB-

yeast

extract

peptone

dextrose

-RRB-

Genome-wide

location

data

were

subjected

to

quality

control

filters

and

normalized

and

the

ratio

of

immunoprecipitated

to

control

DNA

was

determined

for

each

array

spot

We

calculated

a

confidence

value

-LRB-

P

value

-RRB-

for

each

spot

from

each

array

by

using

an

error

model

-LRB-

-RRB-

The

data

for

each

of

the

three

samples

in

an

experiment

were

combined

by

a

weighted

average

method

-LRB-

-RRB-

each

ratio

was

weighted

by

P

value

and

then

averaged

Final

P

values

for

these

combined

ratios

were

then

calculated

-LRB-

-RRB-

Given

the

properties

of

the

biological

sys

RESEARCH

ARTICLES

Fig.

Systematic

genome-wide

location

analysis

for

yeast

transcription

regulators

-LRB-

A

-RRB-

Methodology

Yeast

transcriptional

regulators

were

tagged

by

introducing

the

coding

sequence

for

a

c-myc

epitope

tag

into

the

normal

genomic

locus

for

each

regulator

Of

the

yeast

strains

constructed

in

this

fashion

contained

a

single

epitope-tagged

regulator

whose

expression

could

be

detected

in

rich

growth

conditions

Chromatin

immunoprecipitation

-LRB-

ChIP

-RRB-

was

performed

on

each

of

these

strains

Promoter

regions

enriched

through

the

ChIP

procedure

were

identified

by

hybridization

to

microarrays

containing

a

genome-wide

set

of

yeast

promoter

regions

-LRB-

B

-RRB-

Effect

of

P

value

threshold

The

sum

of

all

regulator-promoter

region

interactions

is

displayed

as

a

function

of

varying

P

value

thresholds

applied

to

the

entire

location

data

set

for

the

regulators

More

stringent

P

values

reduce

the

number

of

interactions

reported

but

decrease

the

likelihood

of

false-positive

results

tem

studied

here

-LRB-

cell

populations

DNA

binding

factors

capable

of

binding

to

both

specific

and

nonspecific

sequences

-RRB-

and

the

expectation

of

noise

in

microarray-based

data

it

was

important

to

use

error

models

to

obtain

a

probabilistic

assessment

of

regulator

location

data

The

total

number

of

protein-DNA

interactions

in

the

location

analysis

data

set

using

a

range

of

P

value

thresholds

is

shown

in

Fig.

We

selected

specific

P

value

thresholds

to

facilitate

discussion

of

a

subset

of

the

data

at

a

high

confidence

level

but

note

that

this

artificially

imposes

a

bound

or

not

bound

binary

decision

for

each

protein-DNA

interaction

We

generally

describe

results

obtained

at

a

P

value

threshold

of

because

our

analysis

indicates

that

this

threshold

maximizes

inclusion

of

legitimate

regulator-DNA

interactions

and

minimizes

false

positives

Various

experimental

and

analytical

methods

indicate

that

the

frequency

of

false

positives

in

the

genome-wide

location

data

at

the

threshold

is

to

-LRB-

-RRB-

For

example

conventional

gene-specific

chromatin

immunoprecipitation

experiments

have

confirmed

of

binding

interactions

-LRB-

involving

different

regulators

-RRB-

that

were

identified

by

location

analysis

data

at

a

threshold

P

value

of

Use

of

a

high-confidence

threshold

should

underestimate

the

regulatorDNA

interactions

that

actually

occur

in

these

cells

We

estimate

that

about

one-third

of

the

actual

regulator-DNA

interactions

in

cells

are

not

reported

at

the

threshold

-LRB-

-RRB-

Regulator

density

We

observed

nearly

interactions

between

regulators

and

promoter

regions

at

a

P

value

threshold

of

The

promoter

regions

of

of

yeast

genes

-LRB-

-RRB-

were

bound

by

one

or

more

of

the

transcriptional

regulators

in

yeast

cells

grown

in

rich

medium

Many

yeast

promoters

were

bound

by

multiple

transcriptional

regulators

-LRB-

Fig.

-RRB-

a

feature

previously

associated

with

gene

regulation

in

higher

eukaryotes

-LRB-

-RRB-

suggesting

that

yeast

genes

are

also

frequently

regulated

through

combinations

of

regulators

More

than

one-third

of

the

pro-

Fig.

Genome-wide

distribution

of

transcriptional

regulators

-LRB-

A

-RRB-

Plot

of

the

number

of

regulators

bound

per

promoter

region

The

distribution

for

the

actual

location

data

-LRB-

red

circles

-RRB-

is

shown

alongside

the

distribution

expected

from

the

same

set

of

P

values

randomly

assigned

among

regulators

and

intergenic

regions

-LRB-

white

circles

-RRB-

At

a

P

value

threshold

of

significantly

more

intergenic

regions

bind

four

or

more

regulators

than

expected

by

chance

-LRB-

B

-RRB-

Distribution

of

the

number

of

promoter

regions

bound

per

regulator

moter

regions

that

are

bound

by

regulators

were

bound

by

two

or

more

regulators

-LRB-

P

value

threshold

-RRB-

and

relative

to

the

expected

distribution

from

randomized

data

a

disproportionately

high

number

of

promoter

regions

were

bound

by

four

or

more

regulators

Because

of

the

stringency

of

the

P

value

threshold

we

expect

that

this

represents

an

underestimate

of

regulator

density

The

number

of

different

promoter

regions

bound

by

each

regulator

in

cells

grown

in

rich

medium

ranged

from

to

-LRB-

P

value

threshold

-RRB-

with

an

average

of

promoter

regions

per

regulator

-LRB-

Fig.

-RRB-

The

regulator

bound

the

largest

number

-LRB-

-RRB-

of

promoter

regions

Regulators

that

should

be

active

under

growth

conditions

other

than

yeast

extract

peptone

and

dextrose

were

typically

found

as

expected

to

bind

the

smallest

number

of

promoter

regions

For

example

which

activates

transcription

of

thiamine

biosynthesis

genes

under

conditions

of

thiamine

starvation

-LRB-

-RRB-

was

among

the

regulators

that

bound

the

smallest

number

-LRB-

-RRB-

of

promoters

Identification

of

a

set

of

promoter

regions

that

are

bound

by

specific

regulators

allowed

us

to

predict

sequence

motifs

that

are

bound

by

these

regulators

-LRB-

-RRB-

Network

motifs

The

simplest

units

of

commonly

used

transcriptional

regulatory

network

architecture

or

network

motifs

provide

specific

regulatory

capacities

such

as

positive

and

negative

feedback

loops

We

used

the

genome-wide

location

data

to

identify

six

regulatory

network

motifs

autoregulation

multicomponent

loops

feedforward

loops

single-input

multi-input

and

regulator

chain

-LRB-

Fig.

-RRB-

These

motifs

suggest

models

for

regulatory

mechanisms

that

can

be

tested

Descriptions

of

the

algorithms

used

to

identify

motifs

and

a

complete

compilation

of

motifs

can

be

obtained

in

-LRB-

-RRB-

An

autoregulation

motif

consists

of

a

regulator

that

binds

to

the

promoter

region

of

its

own

gene

We

identified

autoregulation

motifs

with

genome-wide

location

data

for

the

regulators

-LRB-

P

value

threshold

-RRB-

which

suggests

that

about

of

yeast

genes

encod

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RESEARCH

ARTICLES

master

regulator

at

the

top

of

the

loop

might

be

amplified

at

the

ultimate

target

gene

because

of

the

combined

action

of

the

master

regulator

and

a

second

regulator

that

is

under

the

control

of

the

master

regulator

Single-input

motifs

contain

a

single

regulator

that

binds

a

set

of

genes

under

a

specific

condition

Single-input

motifs

are

potentially

useful

for

coordinating

a

discrete

unit

of

biological

function

such

as

a

set

of

genes

that

code

for

the

subunits

of

a

biosynthetic

apparatus

or

enzymes

of

a

metabolic

pathway

For

example

several

genes

of

the

leucine

biosynthetic

pathway

are

controlled

by

the

transcriptional

regulator

-LRB-

Fig.

-RRB-

Multi-input

motifs

consist

of

a

set

of

regulators

that

bind

together

to

a

set

of

genes

We

found

combinations

of

two

or

more

regulators

that

could

bind

to

a

common

set

of

promoter

regions

This

motif

offers

the

potential

for

coordinating

gene

expression

across

a

wide

variety

of

growth

conditions

For

example

each

of

the

regulators

bound

to

a

set

of

genes

can

be

responsible

for

regulating

those

genes

in

response

to

a

unique

input

In

this

manner

two

different

regulators

responding

to

two

different

inputs

would

allow

coordinate

expression

of

the

set

of

genes

under

these

two

different

conditions

Regulator

chain

motifs

consist

of

chains

of

three

or

more

regulators

in

which

one

regulator

binds

the

promoter

for

a

second

regulator

the

second

binds

the

promoter

for

a

third

regulator

and

so

forth

-LRB-

Fig.

-RRB-

This

network

motif

is

observed

frequently

in

the

location

data

for

yeast

regulators

we

found

regulator

chain

motifs

which

varied

in

size

from

to

regulators

The

chain

represents

the

simplest

circuit

logic

for

ordering

transcriptional

events

in

a

temporal

sequence

The

most

straightforward

form

of

this

appears

in

the

regulatory

circuit

of

the

cell

cycle

where

regulators

functioning

at

one

stage

of

the

cell

cycle

regulate

the

expression

of

factors

required

for

entry

into

the

next

stage

of

the

cell

cycle

-LRB-

-RRB-

The

regulatory

motifs

described

above

suggest

models

for

gene

regulatory

mechanisms

whose

predictions

can

be

tested

with

experimental

data

One

regulatory

motif

that

caught

our

attention

involved

ribosomal

protein

genes

ribosomes

are

important

protein

biosynthetic

machines

but

transcriptional

regulation

of

ribosomal

protein

genes

is

not

well

understood

a

protein

whose

function

was

not

previously

known

forms

a

single-input

regulatory

motif

consisting

of

essentially

all

ribosomal

protein

genes

but

little

else

No

other

regulator

studied

here

exhibited

this

behavior

This

predicts

that

loss

of

function

should

have

a

profound

effect

on

ribosome

biosynthesis

if

no

other

regulators

are

capable

of

taking

its

place

Indeed

a

mutation

in

causes

severe

defects

in

ribosome

biosynthesis

-LRB-

-RRB-

an

observation

that

was

difficult

to

interpret

previously

in

the

Fig.

Examples

of

network

motifs

in

the

yeast

regulatory

network

Regulators

are

represented

by

blue

circles

gene

promoters

are

represented

by

red

rectangles

Binding

of

a

regulator

to

a

promoter

is

indicated

by

a

solid

arrow

Genes

encoding

regulators

are

linked

to

their

respective

regulators

by

dashed

arrows

For

example

in

the

autoregulation

motif

the

protein

binds

to

the

gene

which

is

transcribed

and

translated

into

protein

These

network

motifs

were

uncovered

by

searching

binding

data

with

various

algorithms

For

details

on

the

algorithms

used

and

a

full

list

of

motifs

found

see

-LRB-

-RRB-

ing

regulators

are

autoregulated

This

percentage

does

not

change

substantially

at

less

stringent

P

value

thresholds

In

contrast

studies

of

Escherichia

coli

genetic

regulatory

networks

indicate

that

most

-LRB-

to

-RRB-

prokaryotic

genes

encoding

transcriptional

regulators

are

autoregulated

-LRB-

-RRB-

Autoregulation

is

thought

to

provide

several

selective

growth

advantages

including

reduced

response

time

to

environmental

stimuli

decreased

biosynthetic

cost

of

regulation

and

increased

stability

of

gene

expression

-LRB-

-RRB-

For

example

upon

exposure

to

mating

pheromone

the

concentrations

of

the

pheromone-responsive

transcriptional

regulator

rapidly

increase

because

binds

to

and

up-regulates

its

own

gene

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

The

consequent

increase

in

protein

leads

to

the

binding

of

other

genes

required

for

the

mating

process

-LRB-

-RRB-

A

multicomponent

loop

motif

consists

of

a

regulatory

circuit

whose

closure

involves

two

or

more

factors

-LRB-

Fig.

-RRB-

We

observed

three

multicomponent

loop

motifs

in

the

location

data

for

regulators

-LRB-

P

value

threshold

-RRB-

The

closed-loop

structure

provides

the

capacity

for

feedback

control

and

offers

the

potential

to

produce

bistable

systems

that

can

switch

between

two

alternative

states

-LRB-

-RRB-

The

multicomponent

loop

motif

has

yet

to

be

identified

in

bacterial

genetic

networks

-LRB-

-RRB-

Feedforward

loop

motifs

contain

a

regulator

that

controls

a

second

regulator

and

have

the

additional

feature

that

both

regulators

bind

a

common

target

gene

-LRB-

Fig.

-RRB-

The

regulator

location

data

reveal

that

feedforward

loop

architecture

has

been

highly

favored

during

the

evolution

of

transcriptional

regulatory

networks

in

yeast

We

found

that

regulators

are

involved

in

feedforward

loops

potentially

controlling

genes

in

the

yeast

network

-LRB-

about

of

genes

that

are

bound

in

the

genomewide

location

data

set

-RRB-

In

principle

a

feedforward

loop

can

provide

several

features

to

a

regulatory

circuit

The

feedforward

loop

may

act

as

a

switch

that

is

designed

to

be

sensitive

to

sustained

rather

than

transient

inputs

-LRB-

-RRB-

Feedforward

loops

have

the

potential

to

provide

temporal

control

of

a

process

because

expression

of

the

ultimate

target

gene

may

depend

on

the

accumulation

of

adequate

levels

of

the

master

and

secondary

regulators

Feedforward

loops

may

provide

a

form

of

multistep

ultrasensitivity

-LRB-

-RRB-

as

small

changes

in

the

level

or

activity

of

the

SCIENCE

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RESEARCH

ARTICLES

absence

of

the

genome-wide

location

data

Many

ribosomal

protein

genes

are

also

components

of

a

multi-input

motif

involving

and

additional

regulators

-LRB-

Fig.

-RRB-

which

suggests

that

expression

of

these

genes

may

be

coordinated

by

multiple

regulators

under

various

growth

conditions

This

model

and

others

suggested

by

regulatory

motifs

can

be

addressed

with

future

experiments

Assembling

motifs

into

network

structures

We

assume

that

regulatory

network

motifs

form

building

blocks

that

can

be

combined

into

larger

network

structures

An

algorithm

was

developed

that

explores

all

the

genome-wide

location

data

together

with

the

expression

data

from

over

expression

experiments

to

identify

groups

of

genes

that

are

both

coordinately

bound

and

coordinately

expressed

In

brief

the

algorithm

begins

by

defining

a

set

of

genes

G

that

are

bound

by

a

set

of

regulators

S

with

a

P

value

threshold

of

We

find

a

large

subset

of

genes

in

G

that

are

similarly

expressed

over

the

entire

set

of

expression

data

and

we

use

those

genes

to

establish

a

core

expression

profile

Genes

are

then

dropped

from

G

if

their

expression

profile

is

significantly

different

from

this

core

profile

The

remainder

of

the

genome

is

scanned

for

genes

with

expression

profiles

that

are

similar

to

the

core

profile

Genes

with

a

significant

match

in

expression

profiles

are

then

examined

to

see

if

the

set

of

regulators

S

are

bound

At

this

step

the

probability

of

a

gene

being

bound

by

the

set

of

regulators

is

used

instead

of

the

individual

probabilities

of

that

gene

being

bound

by

each

of

the

individual

regulators

Because

we

are

assaying

the

combined

probability

of

the

set

of

regulators

being

bound

and

are

relying

on

similarity

of

expression

patterns

we

can

relax

the

P

value

for

individual

binding

events

and

thus

recapture

information

that

is

lost

because

of

the

use

of

an

arbitrary

P

value

threshold

The

process

is

repeated

until

all

combinations

of

genes

bound

by

regulators

have

been

considered

Additional

details

of

the

algorithm

are

available

upon

request

The

resulting

sets

of

regulators

and

genes

are

essentially

multi-input

motifs

refined

for

common

expression

-LRB-

MIM-CE

-RRB-

We

expect

these

to

be

robust

examples

of

coordinate

binding

and

expression

and

therefore

useful

for

nucleating

network

models

We

used

the

refined

motifs

to

construct

a

network

structure

for

the

yeast

cell

cycle

by

an

automatic

process

that

requires

no

prior

knowledge

of

the

regulators

that

control

transcription

during

the

cell

cycle

We

selected

the

cell

cycle

regulatory

network

because

of

the

importance

of

this

biological

process

the

availability

of

extensive

genome-wide

expression

data

for

the

cell

cycle

-LRB-

-RRB-

and

the

extensive

literature

that

can

be

used

to

explore

features

of

a

network

model

Our

goal

was

to

determine

whether

the

computational

approach

would

construct

the

regulatory

Fig.

Model

for

the

yeast

cell

cycle

transcriptional

regulatory

network

A

transcriptional

regulatory

network

for

the

yeast

cell

cycle

was

derived

from

a

combination

of

binding

and

expression

data

-LRB-

see

text

-RRB-

Yeast

cell

morphologies

are

depicted

during

the

various

stages

of

the

cell

cycle

Each

blue

box

represents

a

set

of

genes

bound

by

a

common

set

of

regulators

and

coexpressed

throughout

the

cell

cycle

Text

inside

each

blue

box

identifies

the

common

set

of

regulators

that

bind

to

the

set

of

genes

represented

by

the

box

Each

box

is

positioned

in

the

cell

cycle

according

to

the

time

of

peak

expression

levels

for

the

genes

represented

by

the

box

Regulators

represented

by

ovals

are

connected

to

the

sets

of

genes

they

regulate

by

solid

lines

The

arc

associated

with

each

regulator

effectively

defines

the

period

of

activity

for

the

regulator

Dashed

lines

indicate

that

a

gene

in

the

box

encodes

a

regulator

found

in

the

outer

rings

Fig.

-LRB-

Right

-RRB-

Network

of

transcriptional

regulators

binding

to

genes

encoding

other

transcriptional

regulators

All

transcriptional

regulators

that

were

subjected

to

location

analysis

in

rich

medium

are

displayed

in

a

circle

and

segregated

into

functional

categories

on

the

basis

of

the

primary

functions

of

their

target

genes

as

indicated

by

the

color

key

Lines

with

arrows

depict

binding

of

a

regulator

-LRB-

P

value

threshold

-RRB-

to

the

gene

encoding

another

regulator

Circles

with

arrows

depict

binding

of

a

regulator

to

the

promoter

region

of

its

own

gene

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CN

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GA

GA

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RL

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A

HA

HS

M

AC

DNA/RNA/Protein

Biosynthesis

STP

STP

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SF

L

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RT

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M

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SN

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Environmental

Response

Metabolism

STP

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RESEARCH

ARTICLES

STP

STP

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Cell

Cycle

Developmental

Processes

DNA/RNA/Protein

Biosynthesis

Environmental

Response

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RESEARCH

ARTICLES

logic

of

the

cell

cycle

from

the

location

and

expression

data

without

previous

knowledge

of

the

regulators

involved

We

reasoned

that

MIM-CEs

that

are

significantly

enriched

in

genes

whose

expression

oscillates

through

the

cell

cycle

-LRB-

-RRB-

would

identify

the

regulators

that

control

these

genes

We

identified

regulators

with

this

approach

To

construct

the

cell

cycle

network

we

generated

a

new

set

of

MIM-CEs

by

using

only

the

regulators

and

the

cell

cycle

expression

data

-LRB-

-RRB-

To

produce

a

cell

cycle

transcriptional

regulatory

network

model

we

aligned

the

MIM-CEs

around

the

cell

cycle

on

the

basis

of

peak

expression

of

the

genes

in

the

group

by

means

of

an

algorithm

described

in

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

Three

features

of

the

resulting

network

model

are

notable

First

the

computational

approach

correctly

assigned

all

the

regulators

to

stages

of

the

cell

cycle

where

they

were

shown

to

function

in

previous

studies

-LRB-

-RRB-

Second

two

regulators

that

have

been

implicated

in

cell

cycle

control

but

whose

functions

were

ill-defined

-LRB-

-RRB-

could

be

assigned

within

the

network

on

the

basis

of

direct

binding

data

Third

and

most

important

reconstruction

of

the

regulatory

architecture

was

automatic

and

required

no

prior

knowledge

of

the

regulators

that

control

transcription

during

the

cell

cycle

This

approach

should

represent

a

general

method

for

constructing

other

regulatory

networks

Coordination

of

cellular

processes

Transcriptional

regulators

were

often

bound

to

genes

encoding

other

transcriptional

regulators

-LRB-

Fig.

-RRB-

For

example

there

were

many

instances

in

which

transcriptional

regulators

within

a

functional

category

-LRB-

for

example

cell

cycle

-RRB-

bound

to

genes

encoding

regulators

within

the

same

category

We

have

noted

that

cell

cycle

regulators

bound

to

other

cell

cycle

regulators

-LRB-

-RRB-

and

this

phenomenon

was

also

apparent

among

transcriptional

regulators

that

fall

into

the

metabolism

and

environmental

response

categories

For

example

the

metabolic

regulator

bound

to

promoters

for

and

genes

that

encode

transcriptional

regulators

for

amino

acid

and

other

metabolic

functions

The

stress

response

activator

bound

to

the

gene

encoding

the

repressor

and

vice

versa

which

suggests

positive

and

negative

feedback

loops

We

also

found

that

multiple

transcriptional

regulators

within

each

category

were

able

to

bind

to

genes

encoding

regulators

that

are

responsible

for

control

of

other

cellular

processes

For

example

the

cell

cycle

activators

bind

to

genes

for

transcriptional

regulators

that

play

key

roles

in

metabolism

-LRB-

and

-RRB-

environmental

responses

-LRB-

and

-RRB-

development

-LRB-

and

-RRB-

and

DNA

RNA

and

protein

biosynthesis

-LRB-

-RRB-

These

observations

are

likely

to

explain

in

part

how

cells

coordinate

transcriptional

regulation

of

the

cell

cycle

with

other

cellular

processes

These

connections

are

generally

consistent

with

previous

experimental

information

about

the

relationships

between

cellular

processes

For

example

the

developmental

regulator

has

been

shown

to

regulate

genes

involved

in

pseudohyphal

growth

during

certain

nutrient

stress

conditions

we

found

that

also

binds

to

genes

that

are

key

to

regulation

of

general

stress

responses

-LRB-

and

-RRB-

and

metabolism

-LRB-

-RRB-

These

observations

have

several

important

implications

The

control

of

most

if

not

all

cellular

processes

is

characterized

by

networks

of

transcriptional

regulators

that

regulate

other

regulators

It

is

also

evident

that

the

effects

of

transcriptional

regulator

mutations

on

global

gene

expression

as

measured

by

expression

profiling

-LRB-

-RRB-

are

as

likely

to

reflect

the

effects

of

the

network

of

regulators

as

they

are

to

identify

the

direct

targets

of

a

single

regulator

Significance

of

regulatory

network

information

This

study

identified

network

motifs

that

provide

specific

regulatory

capacities

for

yeast

revealing

the

regulatory

strategies

that

were

selected

during

evolution

for

this

eukaryote

These

motifs

can

be

used

as

building

blocks

to

construct

large

network

structures

through

an

automated

approach

that

combines

genome-wide

location

and

expression

data

in

the

absence

of

prior

knowledge

of

regulator

functions

The

network

of

transcriptional

regulators

that

control

other

transcriptional

regulators

is

highly

connected

suggesting

that

the

network

substructures

for

cellular

functions

such

as

cell

cycle

and

development

are

themselves

coordinated

at

a

transcriptional

level

It

is

possible

to

envision

mapping

the

regulatory

networks

that

control

gene

expression

programs

in

considerable

depth

in

yeast

and

in

other

living

cells

More

complete

understanding

of

transcriptional

regulatory

networks

in

yeast

will

require

knowledge

of

regulator

binding

sites

under

various

growth

conditions

-LRB-

-RRB-

and

experimental

testing

of

models

that

emerge

from

computational

analysis

of

regulator

binding

gene

expression

and

other

information

The

approach

described

here

can

also

be

used

to

discover

transcriptional

regulatory

networks

in

higher

eukaryotes

Knowledge

of

these

networks

will

be

important

for

understanding

human

health

and

designing

new

strategies

to

combat

disease

J.

L.

DeRisi

V.

R.

Iyer

P.

O.

Brown

Science

-LRB-

-RRB-

R.

J.

Cho

et

al.

Mol

Cell

-LRB-

-RRB-

P.

T.

Spellman

et

al.

Mol

Biol

Cell

-LRB-

-RRB-

A.

P.

Gasch

et

al.

Mol

Biol

Cell

-LRB-

-RRB-

H.

C.

Causton

et

al.

Mol

Biol

Cell

-LRB-

-RRB-

T.

I.

Lee

R.

A.

Young

Annu

Rev.

Genet

-LRB-

-RRB-

C.

W.

Garvie

C.

Wolberger

Mol

Cell

-LRB-

-RRB-

G.

Orphanides

D.

Reinberg

Cell

-LRB-

-RRB-

M.

Ptashne

A.

Gann

Genes

and

Signals

-LRB-

Cold

Spring

Harbor

Laboratory

Press

Cold

Spring

Harbor

NY

-RRB-

B.

Ren

et

al.

Science

-LRB-

-RRB-

V.

R.

Iyer

et

al.

Nature

-LRB-

-RRB-

J.

D.

Lieb

X.

Liu

D.

Botstein

P.

O.

Brown

Nature

Genet

-LRB-

-RRB-

I.

Simon

et

al.

Cell

-LRB-

-RRB-

J.

J.

Wyrick

et

al.

Science

-LRB-

-RRB-

M.

C.

Costanzo

et

al.

Nucleic

Acids

Res

-LRB-

-RRB-

M.

Knop

et

al.

Yeast

-LRB-

-RRB-

See

supporting

data

on

Science

Online

Additional

information

is

available

at

the

authors

Web

site

network

T.

R.

Hughes

et

al.

Cell

-LRB-

-RRB-

B.

Lemon

R.

Tjian

Genes

Dev

-LRB-

-RRB-

M.

Merika

D.

Thanos

Curr

Opin

Genet

Dev

-LRB-

-RRB-

Y.

Kawasaki

K.

Nosaka

Y.

Kaneko

H.

Nishimura

A.

Iwashima

J.

Bacteriol

-LRB-

-RRB-

H.

Nishimura

Y.

Kawasaki

Y.

Kaneko

K.

Nosaka

A.

Iwashima

FEBS

Lett

-LRB-

-RRB-

D.

Thieffry

A.

M.

Huerta

E.

Perez-Rueda

J.

ColladoVides

Bioessays

-LRB-

-RRB-

S.

S.

Shen-Orr

R.

Milo

S.

Mangan

U.

Alon

Nature

Genet

-LRB-

-RRB-

H.

H.

McAdams

A.

Arkin

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

A.

Becskei

L.

Serrano

Nature

-LRB-

-RRB-

N.

Guelzim

S.

Bottani

P.

Bourgine

F.

Kepes

Nature

Genet

-LRB-

-RRB-

J.

W.

Dolan

S.

Fields

Genes

Dev

-LRB-

-RRB-

J.

E.

Ferrell

Curr

Opin

Cell

Biol

-LRB-

-RRB-

A.

Goldbeter

D.

E.

Koshland

Jr.

J.

Biol

Chem

-LRB-

-RRB-

S.

Hermann-Le

Denmat

M.

Werner

A.

Sentenac

P.

Thuriaux

Mol

Cell

Biol

-LRB-

-RRB-

Z.

Bar-Joseph

G.

Gerber

D.

K.

Gifford

T.

S.

Jaakkola

I.

Simon

Sixth

Annual

International

Conference

on

Research

in

Computational

Molecular

Biology

Washington

DC

to

April

-LRB-

ACM

Press

New

York

-RRB-

pp.

I.

Simon

et

al.

Cell

-LRB-

-RRB-

and

references

therein

B.

A.

Morgan

N.

Bouquin

G.

F.

Merrill

L.

H.

Johnston

EMBO

J.

-LRB-

-RRB-

N.

Bouquin

A.

L.

Johnson

B.

A.

Morgan

L.

H.

Johnston

Mol

Biol

Cell

-LRB-

-RRB-

Y.

Ho

M.

Costanzo

L.

Moore

R.

Kobayashi

B.

J.

Andrews

Mol

Cell

Biol

-LRB-

-RRB-

S.

Chu

et

al.

Science

-LRB-

-RRB-

S.

A.

Jelinsky

L.

D.

Samson

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

H.

D.

Madhani

T.

Galitski

E.

S.

Lander

G.

R.

Fink

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

M.

C.

Lopez

H.

V.

Baker

J.

Bacteriol

-LRB-

-RRB-

T.

J.

Lyons

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

C.

J.

Roberts

et

al.

Science

-LRB-

-RRB-

A.

F.

Shamji

F.

G.

Kuruvilla

S.

L.

Schreiber

Curr

Biol

-LRB-

-RRB-

K.

J.

Travers

et

al.

Cell

-LRB-

-RRB-

C.

B.

Epstein

et

al.

Mol

Biol

Cell

-LRB-

-RRB-

K.

Natarajan

et

al.

Mol

Cell

Biol

-LRB-

-RRB-

F.

Devaux

E.

Carvajal

S.

Moye-Rowley

C.

Jacq

FEBS

Lett

-LRB-

-RRB-

We

thank

J.

Terragni

D.

Pokholok

J.

Lange

and

E.

Kanin

for

experimental

support

J.

Liang

for

assistance

with

the

supporting

Web

site

and

R.

DeShaies

T.

Ideker

and

T.

Jaakkola

for

helpful

discussions

Supported

by

fellowships

from

the

National

Cancer

Institute

of

Canada

-LRB-

F.R.

-RRB-

the

Human

Frontier

Science

Program

-LRB-

J.Z.

-RRB-

the

Sloan/U

S.

Department

of

Energy

Program

for

Computational

Molecular

Biology

-LRB-

D.T.O.

-RRB-

the

Program

in

Mathematics

and

Molecular

Biology

at

Florida

State

University

and

Burroughs

Wellcome

Fund

Interfaces

Program

-LRB-

Z.B.-J

-RRB-

and

the

Helen

Hay

Whitney

Foundation

-LRB-

B.R.

-RRB-

Supported

by

grants

from

NIH

and

Corning

Inc.

Supporting

Online

Material

Materials

and

Methods

Figs

and

Tables

to

June

accepted

August

References

and

Notes

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B O O O O O O O O O O O O O O O O B O O O O O O B 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 B 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 B 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 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 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 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 B 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 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 B 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 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 B 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 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 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 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 B O O O O O O O O O O B 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 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 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 B 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B 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 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 B O O O O O O O O O O O O O O O O O O B 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 B O O O O O O B 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 B O O O O O O O O O O B 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 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 B 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 B O O O O B O O B 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 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 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 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 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 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B O O O O O B 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 B 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 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 B 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 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 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 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