The

Connectivity

Map

Using

Gene-Expression

Signatures

to

Connect

Small

Molecules

Genes

and

Disease

Justin

Lamb

et

al.

Science

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AAAS

RESEARCH

ARTICLES

The

Connectivity

Map

Using

Gene-Expression

Signatures

to

Connect

Small

Molecules

Genes

and

Disease

Justin

Lamb

Emily

D.

Crawford

David

Peck

Joshua

W.

Modell

Irene

C.

Blat

Matthew

J.

Wrobel

Jim

Lerner

Jean-Philippe

Brunet

Aravind

Subramanian

Kenneth

N.

Ross

Michael

Reich

Haley

Hieronymus

Guo

Wei

Scott

A.

Armstrong

Stephen

J.

Haggarty

Paul

A.

Clemons

Ru

Wei

Steven

A.

Carr

Eric

S.

Lander

Todd

R.

To

pursue

a

systematic

approach

to

the

discovery

of

functional

connections

among

diseases

genetic

perturbation

and

drug

action

we

have

created

the

first

installment

of

a

reference

collection

of

gene-expression

profiles

from

cultured

human

cells

treated

with

bioactive

small

molecules

together

with

pattern-matching

software

to

mine

these

data

We

demonstrate

that

this

Connectivity

Map

resource

can

be

used

to

find

connections

among

small

molecules

sharing

a

mechanism

of

action

chemicals

and

physiological

processes

and

diseases

and

drugs

These

results

indicate

the

feasibility

of

the

approach

and

suggest

the

value

of

a

large-scale

community

Connectivity

Map

project

is

truly

generalizable

systematic

and

biologically

relevant

However

several

potential

pitfalls

must

be

considered

Conceivably

a

large

number

of

parameters

would

need

to

be

optimized

for

each

perturbation

including

cell

type

concentration

and

treatment

duration

Equally

analytical

methods

capable

of

detecting

relevant

signals

in

the

data

might

not

be

generally

applicable

If

so

generation

of

a

useful

Connectivity

Map

would

be

impractical

However

here

we

demonstrate

through

the

recovery

of

known

and

the

discovery

of

new

biological

connections

that

the

Connectivity

Map

concept

is

indeed

viable

Creating

a

First-Generation

Connectivity

Map

Perturbagens

We

studied

distinct

smallmolecule

perturbagens

selected

to

represent

a

broad

range

of

activities

and

including

U.S.

Food

and

Drug

Administration

-LRB-

FDA

-RRB-

approved

drugs

and

nondrug

bioactive

tool

compounds

We

included

multiple

compounds

sharing

molecular

targets

-LRB-

e.g.

histone

deacetylase

inhibitors

-RRB-

to

determine

whether

such

compounds

would

share

a

molecular

signature

Similarly

we

profiled

compounds

with

the

same

clinical

indication

-LRB-

e.g.

antidiabetics

-RRB-

which

allowed

us

to

determine

whether

connections

could

be

established

on

the

basis

of

therapeutic

class

even

though

the

mechanisms

of

action

might

be

distinct

Furthermore

we

chose

some

small

molecules

that

act

proximal

to

gene

expression

-LRB-

e.g.

selective

estrogen

receptor

modulators

-RRB-

and

some

whose

primary

targets

are

much

more

distal

-LRB-

e.g.

immunomodulators

inhibitors

of

signal

transduction

-RRB-

Finally

we

included

some

compounds

whose

targets

are

not

expressed

in

all

cell

types

-LRB-

e.g.

inhibitors

-RRB-

whose

clinical

effects

are

non

cell-autonomous

-LRB-

e.g.

aromatase

inhibitors

-RRB-

or

whose

activities

are

only

discernible

after

chronic

in

vivo

exposure

-LRB-

e.g.

antipsychotics

-RRB-

Cell

lines

Ideally

one

would

generate

profiles

in

a

wide

diversity

of

established

and

primary

cells

but

practicality

limits

us

to

only

a

few

lines

that

can

be

stably

grown

over

long

periods

of

time

For

this

pilot

study

we

generated

most

of

our

data

in

the

breast

cancer

epithelial

cell

line

because

it

has

been

extensively

molecularly

characterized

is

used

as

a

reference

cell

line

in

laboratories

throughout

the

world

and

is

amenable

to

culture

in

microtiter

plates

A

subset

of

perturbagens

were

also

profiled

in

the

prostate

cancer

epithelial

cell

line

and

the

nonepithelial

lines

-LRB-

leukemia

-RRB-

and

-LRB-

melanoma

-RRB-

This

diversity

of

cell

types

provides

an

opportunity

to

assess

the

extent

to

which

results

are

context

dependent

Concentration

and

duration

of

treatment

High-throughput

cell-based

small-molecule

screens

are

often

performed

at

a

single

relatively

high

concentration

of

mM

We

adopted

this

approach

as

well

given

that

the

optimal

concentration

is

not

known

for

many

compounds

of

potential

interest

For

some

compounds

we

used

concentrations

reported

to

be

effective

in

cell

A

fundamental

challenge

that

arises

throughout

biomedicine

is

the

need

to

establish

the

relation

among

diseases

physiological

processes

and

the

action

of

smallmolecule

therapeutics

Our

goal

is

to

provide

a

generic

solution

to

this

problem

by

attempting

to

describe

all

biological

states

physiological

disease

or

induced

with

a

chemical

or

genetic

construct

in

terms

of

genomic

signatures

create

a

large

public

database

of

signatures

of

drugs

and

genes

and

develop

pattern-matching

tools

to

detect

similarities

among

these

signatures

Using

such

a

resource

a

researcher

studying

a

drug

candidate

a

gene

or

a

disease

state

could

compare

its

signature

to

the

database

to

discover

unexpected

connections

much

as

one

can

compare

a

DNA

sequence

to

the

GenBank

database

to

identify

similar

genes

We

will

refer

to

this

resource

as

a

BConnectivity

Map

-LSB-

because

of

its

potential

to

reveal

Bconnections

-LSB-

among

drugs

genes

and

diseases

In

principle

there

are

many

possible

genomic

signatures

that

might

be

used

including

DNA

methylation

patterns

mRNA

levels

and

protein

expression

or

metabolite

profiles

To

be

practical

however

such

signatures

should

be

generated

from

a

small

number

of

cells

at

low

Broad

Institute

of

Massachusetts

Institute

of

Technology

and

Harvard

University

Cambridge

MA

USA

of

Pediatric

Oncology

Dana-Farber

Cancer

Institute

Boston

MA

USA

of

Medicine

Children

's

Hospital

Boston

Boston

MA

USA

for

Human

Genetic

Research

Massachusetts

General

Hospital

Boston

MA

USA

Medical

School

Boston

MA

USA

Institute

for

Biomedical

Research

Cambridge

MA

USA

Hughes

Medical

Institute

Chevy

Chase

MD

USA

To

whom

correspondence

should

be

addressed

E-mail

golub@broad.harvard.edu

justin@broad.mit.edu

Present

address

University

of

California

San

Francisco

CA

USA

cost

in

high

throughput

and

with

sufficiently

high

complexity

to

provide

a

rich

description

At

present

only

mRNA

expression

assayed

on

DNA

microarrays

meets

these

criteria

We

have

therefore

chosen

this

as

the

Buniversal

language

-LSB-

with

which

to

describe

cellular

responses

Gene-expression

profiling

has

historically

been

applied

in

specific

settings

to

elucidate

the

mechanisms

underlying

a

biological

pathway

-LRB-

-RRB-

to

reveal

cryptic

subtypes

of

a

disease

-LRB-

-RRB-

and

to

predict

cancer

prognosis

-LRB-

-RRB-

But

here

we

envisage

its

use

as

the

means

to

catalog

the

biological

responses

to

a

large

number

of

diverse

perturbations

Of

course

this

idea

is

not

entirely

new

A

landmark

study

by

Hughes

et

al.

-LRB-

-RRB-

demonstrated

that

a

compendium

of

geneexpression

data

could

be

used

for

the

functional

annotation

of

small

molecules

and

genes

at

least

in

yeast

Although

that

study

was

encouraging

the

extent

to

which

the

approach

would

be

applicable

to

mammalian

biology

was

not

obvious

More

recently

a

variety

of

commercial

databases

of

expression

profiles

from

rat

tissues

after

systemic

administration

of

known

drugs

have

been

developed

Ee.g.

-LRB-

-RRB-

and

these

appear

to

have

value

for

the

identification

of

potential

toxicities

of

new

chemical

entities

Ee.g.

-LRB-

-RRB-

However

such

in

vivo

studies

suffer

from

serious

practical

limitations

First

the

type

of

perturbagens

that

can

be

studied

is

limited

Only

small

molecules

with

druglike

physicochemical

properties

can

be

effectively

administered

to

live

animals

And

systematic

genetic

perturbation

-LRB-

i.e.

with

RNA

interference

-RRB-

is

not

yet

possible

Second

the

high

cost

of

whole-animal

studies

precludes

contemplating

such

an

approach

at

the

genome

scale

We

hypothesized

that

perturbations

in

mammalian

cell

culture

might

provide

an

approach

that

SCIENCE

VOL

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from

on

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RESEARCH

ARTICLES

culture

or

to

approximate

the

maximum

attainable

plasma

concentrations

after

therapeutic

dosing

We

also

profiled

a

subset

of

compounds

across

a

range

of

concentrations

to

explore

the

sensitivity

of

results

to

dose

As

with

concentration

the

duration

of

compound

treatment

might

also

affect

the

geneexpression

profiles

Profiles

obtained

too

early

might

not

yield

robust

signals

particularly

for

perturbations

that

do

not

directly

modulate

transcription

and

those

obtained

too

late

might

reflect

secondary

and

tertiary

responses

Because

our

goal

was

to

obtain

signatures

related

to

direct

mechanisms

of

action

we

selected

a

relatively

early

time

point

-LRB-

hours

after

compound

addition

with

a

subset

also

profiled

at

hours

for

comparison

-RRB-

Control

perturbations

Every

treatment

instance

was

defined

relative

to

a

control

consisting

of

cells

grown

in

the

same

plate

and

treated

with

vehicle

alone

This

approach

was

taken

to

minimize

the

impact

of

batch-to-batch

biological

and

technical

variation

Most

of

the

perturbagens

were

also

profiled

multiple

times

Overall

data

Our

data

set

was

thus

composed

of

genomewide

mRNA

expression

data

for

distinct

bioactive

small-molecule

perturbagens

and

corresponding

vehicle

controls

applied

to

human

cell

lines

for

short

duration

These

data

were

collected

in

multiple

batches

over

a

period

of

year

by

means

of

Affymetrix

GeneChip

microarrays

A

total

of

geneexpression

profiles

were

produced

representing

individual

instances

-LRB-

i.e.

one

treatment

and

vehicle

pair

-RRB-

Full

details

of

the

data

set

are

provided

as

table

The

data

are

freely

available

for

download

at

Querying

the

Connectivity

Map

The

traditional

method

for

identifying

small

molecules

with

similar

effects

on

the

basis

of

gene-expression

profiles

is

hierarchical

clustering

Indeed

such

a

strategy

was

found

to

be

useful

for

analyzing

data

from

yeast

-LRB-

-RRB-

and

rat

tissues

-LRB-

-RRB-

However

we

saw

three

drawbacks

with

such

an

approach

First

with

mammalian

cell

culture

the

dominant

structure

we

detected

by

hierarchical

clustering

was

related

to

cell

type

and

batch

effects

-LRB-

similarity

among

cells

grown

at

the

same

time

-RRB-

and

this

masked

the

more

subtle

signals

from

short-duration

treatment

with

small

molecules

-LRB-

fig

-RRB-

Second

a

hierarchical

clustering

approach

would

require

that

all

profiles

be

generated

on

the

same

microarray

platform

limiting

future

utility

Third

and

most

important

we

required

an

analytical

method

that

could

detect

multiple

components

within

the

cellular

response

to

a

given

perturbation

For

these

reasons

we

adopted

a

nonparametric

rank-based

pattern-matching

strategy

based

on

the

Kolmogorov-Smirnov

statistic

-LRB-

-RRB-

as

we

described

previously

and

later

formalized

in

Gene

Set

Enrichment

Analysis

-LRB-

GSEA

-RRB-

-LRB-

-RRB-

The

approach

starts

with

a

query

signature

and

assesses

its

similarity

to

each

of

the

reference

expression

profiles

in

the

data

set

A

query

signature

is

any

list

of

genes

whose

expression

is

correlated

with

a

biological

state

of

interest

Examples

could

include

genes

correlated

with

a

subtype

of

disease

-LRB-

e.g.

drug-resistant

versus

drug-sensitive

leukemia

-RRB-

or

regulated

by

a

biological

process

of

interest

-LRB-

e.g.

experimental

activation

of

a

signaling

pathway

-RRB-

Each

gene

in

the

query

signature

carries

a

sign

indicating

whether

it

is

up-regulated

or

down-regulated

Because

the

query

signature

is

unitless

it

is

not

tied

to

any

technology

platform

The

reference

gene-expression

profiles

in

the

Connectivity

Map

data

set

are

also

represented

in

a

nonparametric

fashion

Each

profile

is

compared

to

its

corresponding

intrabatch

vehicle-treated

control

The

genes

on

the

array

are

rank-ordered

according

to

their

differential

expression

relative

to

the

control

each

treatment

instance

thus

gives

rise

to

a

rank-ordered

list

of

genes

The

query

signature

is

then

compared

to

each

rank-ordered

list

to

determine

whether

up-regulated

query

genes

tend

to

appear

near

the

top

of

the

list

and

down-regulated

query

genes

near

the

bottom

-LRB-

positive

connectivity

-RRB-

or

vice

versa

-LRB-

negative

connectivity

-RRB-

yielding

a

connectivity

score

ranging

from

to

A

-LRB-

zero

-RRB-

connectivity

score

is

assigned

where

the

enrichment

scores

for

the

up

and

down

regulated

genes

have

the

same

sign

All

instances

in

the

database

are

then

ranked

according

to

their

connectivity

scores

those

at

the

top

are

most

strongly

correlated

to

the

query

signature

and

those

at

the

bottom

are

most

strongly

anticorrelated

-LRB-

Fig.

-RRB-

-LRB-

For

expression

profiles

derived

from

a

single

technology

platform

we

obtained

similar

results

using

conventional

measures

of

correlation

such

as

the

Pearson

correlation

coefficient

-RRB-

There

is

no

standard

approach

for

estimating

the

statistical

significance

of

the

connections

observed

We

therefore

resorted

to

the

simplest

most

empirical

and

most

transparent

test

we

could

devise

and

note

that

the

power

to

detect

connections

may

be

greater

for

compounds

with

many

replicates

Below

we

have

focused

on

cases

where

the

precise

calculation

of

P-values

is

not

critical

to

support

our

conclusions

because

the

observed

connections

were

so

striking

-LRB-

and

in

several

cases

validated

with

functional

experiments

-RRB-

Connections

Between

Small

Molecules

HDAC

inhibitors

We

first

determined

whether

a

query

signature

derived

from

a

class

of

small

molecules

could

recover

those

same

compounds

in

the

Connectivity

Map

A

recent

report

-LRB-

-RRB-

described

gene-expression

responses

of

-LRB-

bladder

-RRB-

MDA

-LRB-

breast

carcinoma

-RRB-

and

MDA

-LRB-

breast

carcinoma

-RRB-

cells

treated

with

three

histone

deacetylase

-LRB-

HDAC

-RRB-

inhibitors

vorinostat

-LRB-

also

known

as

suberoylanilide

hydroxamic

acid

or

SAHA

-RRB-

and

trichostatin

A

The

authors

of

this

study

defined

a

signature

-LRB-

up-regulated

and

downregulated

genes

Signature

-RRB-

that

was

used

to

query

our

database

Despite

the

differences

in

the

cells

used

to

generate

the

query

signature

and

reference

profiles

the

two

highest-scoring

compounds

in

the

ConCONNECTIONS

Fig.

The

Connectivity

Map

BIOLOGICAL

STATE

REFERENCE

DATABASE

Concept

Gene-expression

proOF

INTEREST

-LRB-

PROFILES

-RRB-

files

derived

from

the

treat

-LRB-

SIGNATURE

-RRB-

ment

of

cultured

human

cells

with

a

large

number

of

perturbagens

populate

a

reference

up

database

Gene-expression

signatures

represent

any

induced

or

organic

cell

state

of

interest

-LRB-

left

-RRB-

Pattern-matching

algoquery

output

rithms

score

each

reference

profile

for

the

direction

and

down

strength

of

enrichment

with

the

query

signature

-LRB-

center

-RRB-

Perturbagens

are

ranked

by

this

connectivity

score

those

at

strong

weak

strong

the

top

-LRB-

positive

-RRB-

and

bottom

positive

positive

negative

-LRB-

negative

-RRB-

are

functionally

connected

with

the

query

state

-LRB-

right

-RRB-

through

the

transitory

feature

of

common

gene-expression

changes

positive

negative

SEPTEMBER

VOL

SCIENCE

Downloaded

from

on

May

RESEARCH

ARTICLES

nectivity

Map

were

vorinostat

and

trichostatin

A

-LRB-

Fig.

-RRB-

More

important

the

Connectivity

Map

also

revealed

strong

connectivity

with

two

structurally

distinct

compounds

valproic

acid

-LRB-

initially

developed

as

an

antiseizure

drug

-RRB-

and

HC

toxin

both

of

which

are

now

known

to

have

HDACinhibitory

activity

but

were

not

used

to

define

the

query

signature

-LRB-

Fig.

A

and

B

-RRB-

These

results

indicate

that

the

Connectivity

Map

would

have

suggested

the

HDAC-inhibitory

activity

of

these

compounds

had

it

not

already

been

known

The

ability

to

detect

these

HDAC

inhibitors

was

not

highly

sensitive

to

the

precise

concentration

of

drug

used

to

generate

the

reference

profiles

Specifically

the

Connectivity

Map

contains

instances

of

valproic

acid

at

six

concentrations

-LRB-

and

mM

and

mM

-RRB-

bracketing

the

commonly

used

HDACinhibitory

level

of

mM

Only

the

two

lowest

concentrations

failed

to

yield

a

positive

connectivity

score

-LRB-

Fig.

-RRB-

The

results

indicate

that

at

least

for

this

example

connectivity

can

be

established

without

elaborate

optimization

of

cell

type

and

compound

concentration

Estrogens

We

next

studied

the

effects

of

estrogen

which

is

known

to

modulate

nuclear

hormone

signaling

The

query

signature

was

taken

from

a

report

by

an

independent

group

-LRB-

-RRB-

in

which

cells

were

treated

with

the

natural

estrogen

receptor

-LRB-

ER

-RRB-

ligand

-LRB-

-RRB-

The

query

signature

consisted

of

genes

-LRB-

up

and

down-regulated

Signature

-RRB-

The

Connectivity

Map

yielded

high

positive

connectivity

scores

for

all

instances

of

in

cells

High

connectivity

scores

were

also

observed

for

genistein

which

is

a

phytoFig

HDAC

Inhibitors

-LRB-

A

-RRB-

HDAC

inhibitors

are

highly

ranked

with

an

external

HDAC

inhibitor

signature

The

barview

is

constructed

from

horizontal

lines

each

representing

an

individual

treatment

instance

ordered

by

their

corresponding

connectivity

scores

with

the

Glaser

et

al.

-LRB-

-RRB-

signature

-LRB-

top

bottom

-RRB-

All

valproic

acid

-LRB-

n

-RRB-

trichostatin

A

-LRB-

n

-RRB-

vorinostat

-LRB-

n

-RRB-

and

HC

toxin

-LRB-

n

-RRB-

instances

in

the

data

set

are

colored

in

black

Colors

applied

to

the

remaining

instances

reflect

the

sign

of

their

scores

-LRB-

green

positive

gray

red

negative

-RRB-

The

rank

name

-LSB-

instance

id

-RSB-

concentration

cell

line

and

connectivity

score

for

each

of

the

selected

HDAC

inhibitor

instances

is

shown

Unabridged

results

from

this

query

are

provided

as

Result

-LRB-

B

-RRB-

Chemical

structures

estrogen

-LRB-

-RRB-

Weaker

connectivity

was

seen

with

consistent

with

its

markedly

lower

affinity

for

ER

than

its

stereoisomer

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

The

Connectivity

Map

also

identified

compounds

with

clear

negative

connectivity

indicating

an

opposite

effect

to

that

of

The

highest

negative

connectivity

scores

came

from

fulvestrant

a

known

anti-estrogenic

drug

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

Tamoxifen

and

raloxifene

also

antiestrogens

scored

negatively

but

to

a

lesser

extent

-LRB-

fig

-RRB-

Together

these

results

indicate

that

both

agonists

and

antagonists

can

be

discovered

directly

from

the

Connectivity

Map

We

used

estrogen

connectivity

to

explore

the

impact

of

physiological

context

Such

context

is

known

to

be

particularly

important

in

the

study

of

ER

activity

where

growing

cells

in

culture

medium

containing

the

estromimetic

phenol

red

and

supplemented

with

complete

serum

-LRB-

which

contains

endogenous

estrogens

-RRB-

often

obscures

estrogen

stimulation

signals

as

measured

with

traditional

read-outs

such

as

reporter

assays

or

gel

shifts

We

therefore

asked

whether

the

estrogen

signature

might

be

more

robust

to

the

particulars

of

culture

medium

composition

Indeed

the

presence

of

phenol

red

and

complete

serum

had

little

effect

on

connectivity

scores

for

even

though

the

query

signature

was

defined

under

estrogen-free

conditions

-LRB-

-RRB-

Indeed

the

connectivity

scores

were

similar

to

those

made

in

phenol

red-free

medium

with

charcoal-stripped

serum

-LRB-

Fig.

-RRB-

However

the

antiestrogen

fulvestrant

received

a

connectivity

score

in

cells

under

estrogen-free

conditions

consistent

with

its

pure

antagonist

mode

perturbagen

vorinostat

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

vorinostat

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

HC

toxin

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

trichostatin

A

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

valproic

acid

-LSB-

-RSB-

dose

M

M

nM

nM

M

M

nM

nM

M

M

nM

nM

nM

mM

mM

mM

nM

mM

mM

mM

M

mM

M

mM

M

mM

mM

nM

mM

M

M

M

M

cell

score

A

rank

89 92 297 388

B

O

O

N

H

N

OH

trichostatin

A

H

N

O

O

N

H

OH

vorinostat

O

OH

valproic

acid

O

H

N

NH

O

O

O

NH

O

O

HC

toxin

N

SCIENCE

VOL

SEPTEMBER

Downloaded

from

on

May

of

action

-LRB-

Fig.

-RRB-

Similarly

no

robust

estrogenic

or

anti-estrogenic

connections

were

recovered

in

treatments

performed

in

or

cells

neither

of

which

expresses

ER

These

results

indicate

that

although

gene-expression

signatures

can

be

highly

sensitive

some

connections

will

not

be

found

if

the

reference

profiles

are

collected

in

cells

that

lack

the

appropriate

physiological

or

molecular

context

Phenothiazines

We

next

considered

small

molecules

that

do

not

directly

regulate

gene

expression

We

studied

the

phenothiazine

antipyschotics

which

as

dopamine

receptor

antagonists

and

calmodulin

inhibitors

are

not

immediately

proximal

to

transcription

Reference

profiles

were

generated

for

five

phenothiazines

-LRB-

chlorpromazine

fluphenazine

prochlorperazine

thioridazine

trifluoperazine

-RRB-

representing

three

structural

subclasses

-LRB-

Fig.

-RRB-

At

least

three

instances

of

each

were

produced

mainly

in

cells

and

at

a

concentration

of

mM

with

the

exception

of

chlorpromazine

which

was

profiled

three

times

at

mM

and

only

once

at

mM

For

these

experiments

query

signatures

were

generated

from

a

subset

of

the

reference

profiles

within

the

Connectivity

Map

data

set

itself

A

query

signature

consisting

of

genes

consistently

regulated

across

one

instance

of

each

of

the

five

phenothiazines

was

first

established

-LRB-

Signature

-RRB-

We

then

used

this

signature

to

assess

the

recovery

of

all

of

the

remaining

phenothiazine

instances

from

our

database

As

anticipated

the

five

instances

used

to

derive

the

signature

received

the

highest

connectivity

scores

More

important

of

the

nonsignature

instances

were

also

highly

ranked

-LRB-

Fig.

-RRB-

The

three

instances

not

RESEARCH

ARTICLES

receiving

high

connectivity

scores

were

the

lowconcentration

chlorpromazine

treatments

these

therefore

served

as

useful

specificity

controls

Similar

results

were

obtained

with

signatures

produced

from

different

phenothiazine

instances

and

with

different

gene-selection

criteria

-LRB-

Signatures

to

fig

-RRB-

These

results

show

that

the

common

activity

of

these

phenothiazine

antipsychotic

compounds

can

be

recovered

by

the

Connectivity

Map

even

when

analyzed

in

nonneural

cells

They

also

demonstrate

that

the

approach

is

not

unduly

sensitive

to

signature-definition

parameters

The

phenothiazine

query

signature

did

not

show

strong

connectivity

with

the

nonphenothiazine

antipsychotics

haloperidol

and

clozapine

-LRB-

Fig.

-RRB-

This

is

not

surprising

because

although

these

antipsychotics

ultimately

target

the

same

neurotransmitter

receptors

the

receptors

themselves

are

not

expressed

in

the

cell

lines

used

Indeed

the

antipsychotics

were

included

in

this

data

set

as

an

extreme

test

of

the

Connectivity

Map

concept

The

analysis

of

the

phenothiazine

query

signature

did

yield

consistently

strong

negative

connectivity

scores

for

arachidonic

acid

-LRB-

Fig.

-RRB-

Arachidonic

acid

is

the

primary

substrate

for

cyclooxygenases

and

lipoxygenases

and

is

thus

a

critical

precursor

for

both

prostaglandin

and

leukotreine

syntheses

The

Connectivity

Map

result

suggests

that

phenothiazines

have

an

activity

that

mimics

ablation

of

the

arachidonic

acid

cascade

and

is

therefore

entirely

consistent

with

the

observation

that

phenothiazines

can

inhibit

prostaglandin

synthesis

-LRB-

-RRB-

Indeed

more

recently

phenothiazine

derivatives

have

been

developed

as

potent

dual

cyclooxygenase/lipoxygenase

inhibitors

that

exhibit

anti-inflammatory

activity

-LRB-

-RRB-

Had

this

activity

of

phenothiazines

not

been

previously

discovered

by

serendipity

it

would

have

been

systematically

revealed

by

the

Connectivity

Map

These

findings

confirm

that

even

perturbagens

not

acting

immediately

proximal

to

transcription

do

give

rise

to

distinguishable

geneexpression

profiles

and

demonstrate

again

that

the

Connectivity

Map

can

reveal

complex

biological

activities

They

also

show

that

the

Connectivity

Map

approach

can

use

both

internal

as

well

as

external

query

signatures

Identification

of

gedunin

as

an

inhibitor

We

next

sought

to

use

the

Connectivity

Map

to

generate

hypotheses

about

the

mechanism

of

action

of

an

uncharacterized

small

molecule

In

a

separate

study

we

performed

a

high-throughput

gene

expression

based

screen

for

small

molecules

capable

of

abrogating

the

gene-expression

signature

of

androgen

receptor

-LRB-

AR

-RRB-

activation

in

prostate

cancer

cells

The

details

of

the

screen

and

its

biochemical

followup

are

described

elsewhere

-LRB-

-RRB-

One

of

the

hits

from

the

screen

was

the

triterpenoid

natural

product

gedunin

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

purified

from

the

Meliacae

family

of

medicinal

plants

The

mechanism

by

which

gedunin

abrogated

AR

activity

was

entirely

unknown

because

this

compound

has

not

been

extensively

characterized

In

an

effort

to

elucidate

its

mechanism

of

action

we

defined

a

signature

for

gedunin

-LRB-

Signature

-RRB-

by

treating

LNCaP

prostate

cancer

cells

for

hours

with

the

compound

and

queried

the

Connectivity

Map

High

connectivity

scores

were

found

for

multiple

instances

of

three

heat

shock

protein

-LRB-

-RRB-

inhibitors

geldanamycin

and

-LRB-

Fig.

-RRB-

As

a

class

these

inhibitors

showed

marked

connectivity

to

the

gedunin

signature

-LRB-

permutation

P-value

G

-RRB-

This

result

suggests

that

gedunin

though

structurally

dissimilar

from

known

inhibitors

-LRB-

Fig.

-RRB-

might

impinge

upon

the

pathway

Because

the

stability

of

AR

is

known

to

be

dependent

upon

activity

we

asked

whether

AR

expression

could

be

diminished

by

gedunin

treatment

Immunoblotting

indicated

that

AR

protein

as

well

as

dose

cell

score

other

proteins

was

nearly

entirely

eliminated

in

gedunin-treated

LNCaP

and

cells

-LRB-

Fig.

-RRB-

consistent

with

gedunin

acting

as

an

inhibitor

of

function

Moreover

mutant

interacting

proteins

such

as

the

BCR-ABL

point

mutant

and

the

internal

tandem

duplication

-LRB-

ITD

-RRB-

mutant

show

increased

sensitivity

to

gedunin-mediated

inhibition

as

is

seen

upon

inhibition

by

geldanamycins

-LRB-

-RRB-

Further

biochemical

studies

demonstrated

that

the

mechanism

of

abrogating

function

was

distinct

from

geldanamycin

and

its

analogs

-LRB-

-RRB-

These

experiments

demonstrate

that

the

Connectivity

Map

can

generate

testable

hypotheses

about

the

target

pathways

of

poorly

characterized

small

molecules

providing

a

potentially

powerful

tool

for

pharmaceutical

development

Connections

with

Disease

States

We

next

sought

to

collect

query

signatures

from

disease

states

and

scan

the

Connectivity

Map

to

identify

small

molecules

that

might

mimic

or

suppress

that

disease

Diet-induced

obesity

We

made

use

of

a

signature

for

the

obese

state

from

a

published

report

-LRB-

-RRB-

of

the

genes

differentially

expressed

in

a

rat

model

of

diet-induced

obesity

-LRB-

Signature

-RRB-

The

conditions

used

in

that

study

differed

sharply

from

those

used

to

build

the

Connectivity

Map

with

respect

to

RNA

source

-LRB-

adipose

tissue

versus

cell

lines

-RRB-

treatment

duration

-LRB-

days

versus

hours

-RRB-

and

species

-LRB-

rat

versus

human

-RRB-

Despite

these

differences

instances

of

three

peroxisome

proliferatoractivated

receptor

gamma

-LRB-

PPARg

-RRB-

agonists

the

thiazolidindiones

-LRB-

TZD

-RRB-

troglitazone

and

rosiglitazone

and

indometacin

-LRB-

-RRB-

received

high

connectivity

scores

-LRB-

fig

-RRB-

Indeed

all

three

compounds

are

potent

inducers

of

adipogenesis

in

vitro

-LRB-

-RRB-

Further

that

TZDs

promote

weight

gain

in

vivo

has

been

widely

observed

as

a

consequence

of

their

clinical

use

dose

M

M

M

nM

M

nM

M

cell

score

A

rank

51 64 148 152

perturbagen

estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

genistein

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

alpha-estradiol

-LSB-

-RSB-

alpha-estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

genistein

-LSB-

-RSB-

genistein

-LSB-

-RSB-

alpha-estradiol

-LSB-

-RSB-

genistein

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

genistein

-LSB-

-RSB-

genistein

-LSB-

-RSB-

alpha-estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

alpha-estradiol

-LSB-

-RSB-

genistein

-LSB-

-RSB-

alpha-estradiol

-LSB-

-RSB-

estradiol

-LSB-

-RSB-

B

rank

171 261 447 450

451 452 453

perturbagen

fulvestrant

-LSB-

-RSB-

fulvestrant

-LSB-

-RSB-

fulvestrant

-LSB-

-RSB-

fulvestrant

-LSB-

-RSB-

fulvestrant

-LSB-

-RSB-

fulvestrant

-LSB-

-RSB-

fulvestrant

-LSB-

-RSB-

nM

nM

M

nM

nM

nM

nM

nM

nM

M

M

nM

M

nM

M

M

nM

nM

nM

nM

M

nM

nM

Fig.

Estrogen

receptor

agonists

and

antagonists

-LRB-

A

-RRB-

Estrogen

receptor

agonists

are

highly

ranked

with

an

external

signature

Barview

-LRB-

as

in

Fig.

-RRB-

with

all

estradiol

-LRB-

n

-RRB-

alpha-estradiol

-LRB-

n

-RRB-

and

genistein

-LRB-

n

-RRB-

instances

shown

-LRB-

B

-RRB-

A

pure

antiestrogen

is

negatively

connected

Barview

with

all

fulvestrant

-LRB-

n

-RRB-

instances

shown

Unabridged

results

from

this

query

are

provided

as

Result

SEPTEMBER

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from

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RESEARCH

ARTICLES

as

oral

antidiabetic

agents

and

is

considered

a

major

drawback

of

their

use

-LRB-

-RRB-

The

Connectivity

Map

would

have

predicted

this

particular

adverse

effect

These

results

must

be

tempered

however

because

they

derive

solely

from

the

only

cell

line

in

our

panel

to

express

PPARg

at

high

levels

-LRB-

-RRB-

and

TZD

and

indometacin

instances

made

in

all

other

cell

lines

yielded

or

negative

scores

Clearly

these

connections

would

not

have

been

made

had

this

particular

cellular

context

not

been

represented

Of

note

the

other

known

PPARg

agonist

in

our

collection

prostaglandin

-LRB-

-RRB-

received

a

score

even

in

although

the

entire

set

of

agonists

-LRB-

including

prostaglandin

-RRB-

still

showed

significant

enrichment

as

a

class

-LRB-

permutation

P-value

-RRB-

Overall

it

is

notable

that

a

signature

derived

from

rat

adipose

tissue

after

many

weeks

of

treatment

can

generate

connections

with

small

molecules

applied

acutely

to

epithelial

cells

in

culture

Alzheimer

's

disease

We

next

explored

query

signatures

for

Alzheimer

's

disease

-LRB-

AD

-RRB-

AD

is

the

most

common

cause

of

dementia

in

the

elderly

but

its

pathogenesis

is

poorly

understood

and

effective

therapies

remain

elusive

We

made

use

of

two

independent

reports

of

the

gene-expression

changes

in

brain

tissue

from

AD

patients

The

first

signature

consisted

of

genes

identified

through

a

comparison

of

hippocampus

from

AD

and

normal

brain

-LRB-

-RRB-

-LRB-

Signature

-RRB-

The

second

derived

from

the

comparison

between

cerebral

cortex

from

AD

brain

and

age-matched

controls

contained

genes

-LRB-

-RRB-

-LRB-

Signature

-RRB-

Although

there

were

no

genes

in

common

between

these

two

query

signatures

both

yielded

statistically

significant

negative

connectivity

with

the

two

independent

instances

of

-LRB-

DAPH

-RRB-

in

the

Connectivity

Map

-LRB-

fig

-RRB-

No

other

compound

in

the

database

shared

this

behavior

DAPH

was

recently

identified

in

a

cell-free

screen

for

small

molecules

that

could

reverse

the

formation

of

fibrils

-LRB-

specifically

decreasing

the

b-sheet

content

of

aggregating

peptide

-RRB-

thought

to

be

responsible

for

the

acHO

N

N

F

A

N

N

N

N

F

N

S

trifluoperazine

N

N

N

S

chlorpromazine

Cl

N

S

S

N

S

F

F

F

F

N

S

prochlorperazine

Cl

thioridazine

fluphenazine

B

rank

21 58 256 332

perturbagen

trifluoperazine

-LSB-

-RSB-

prochlorperazine

-LSB-

-RSB-

fluphenazine

-LSB-

-RSB-

thioridazine

-LSB-

-RSB-

chlorpromazine

-LSB-

-RSB-

thioridazine

-LSB-

-RSB-

trifluoperazine

-LSB-

-RSB-

thioridazine

-LSB-

-RSB-

prochlorperazine

-LSB-

-RSB-

thioridazine

-LSB-

-RSB-

trifluoperazine

-LSB-

-RSB-

fluphenazine

-LSB-

-RSB-

prochlorperazine

-LSB-

-RSB-

fluphenazine

-LSB-

-RSB-

fluphenazine

-LSB-

-RSB-

chlorpromazine

-LSB-

-RSB-

chlorpromazine

-LSB-

-RSB-

chlorpromazine

-LSB-

-RSB-

dose

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

cell

score

C

rank

27 80 127 216

perturbagen

haloperidol

-LSB-

-RSB-

clozapine

-LSB-

-RSB-

haloperidol

-LSB-

-RSB-

haloperidol

-LSB-

-RSB-

clozapine

-LSB-

-RSB-

haloperidol

-LSB-

-RSB-

haloperidol

-LSB-

-RSB-

haloperidol

-LSB-

-RSB-

dose

M

M

M

M

M

M

M

M

cell

score

rank

427 442 451

perturbagen

arachidonic

acid

-LSB-

-RSB-

arachidonic

acid

-LSB-

-RSB-

arachidonic

acid

-LSB-

-RSB-

dose

M

M

M

cell

score

Fig.

Phenothiazine

connections

-LRB-

A

-RRB-

Chemical

structures

Three

structural

subclasses

are

shown

with

a

piperazine

group

in

the

side

chain

-LRB-

fluphenazine

trifluoperazine

prochlorperazine

-RRB-

with

a

piperidine

ring

in

the

side

chain

-LRB-

thioridazine

-RRB-

and

with

an

aliphatic

side

chain

-LRB-

chlorpromazine

-RRB-

-LRB-

B

-RRB-

Recovery

of

phenothiazine

instances

with

an

internal

phenothazine

signature

Barview

-LRB-

as

in

Fig.

-RRB-

with

all

thioridazine

-LRB-

n

-RRB-

chlorpromazine

-LRB-

n

-RRB-

fluphenazine

-LRB-

n

-RRB-

trifluoperazine

-LRB-

n

-RRB-

SCIENCE

and

prochlorperazine

-LRB-

-RRB-

instances

shown

The

instances

used

to

generate

the

signature

are

shaded

-LRB-

C

-RRB-

Ranking

of

nonphenothiazine

antipsychotics

and

arachidonic

acid

instances

with

the

phenothiazine

signature

Barview

showing

all

haloperidol

-LRB-

n

-RRB-

clozapine

-LRB-

n

-RRB-

and

arachidonic

acid

-LRB-

n

-RRB-

instances

Permutation

P-values

are

and

respectively

Unabridged

results

from

this

query

are

provided

as

Result

VOL

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from

on

May

celerated

neuronal

cell

death

in

the

brains

of

AD

patients

-LRB-

-RRB-

Indeed

a

variety

of

new

DAPH

analogs

have

since

been

synthesized

as

potential

treatments

for

AD

-LRB-

-RRB-

Our

observations

strengthen

the

candidacy

of

DAPH

as

a

potential

AD

therapeutic

and

further

illustrate

the

potential

of

the

Connectivity

Map

to

generate

novel

unbiased

hypotheses

concerning

the

pharmacologic

modulation

of

disease

states

Dexamethasone

resistance

in

ALL

As

a

final

example

we

considered

one

of

the

most

vexing

problems

in

cancer

chemotherapy

drug

resistance

Specifically

we

explored

resistance

to

the

glucocorticoid

dexamethasone

in

children

with

acute

lymphoblastic

leukemia

-LRB-

ALL

-RRB-

Dexamethasone

resistance

has

been

observed

both

in

vivo

and

in

primary

leukemia

cells

grown

in

short-term

culture

-LRB-

-RRB-

We

defined

a

geneexpression

signature

of

dexamethasone

sensitivity

-LRB-

Signature

-RRB-

by

comparing

bone-marrow

leukemic

cells

from

patients

exhibiting

either

dexamethasone

sensitivity

or

resistance

in

vitro

The

details

of

this

signature

are

reported

elsewhere

-LRB-

-RRB-

RESEARCH

ARTICLES

When

the

signature

of

dexamethasone

sensitivity

was

used

to

query

the

Connectivity

Map

we

found

strong

connectivity

to

the

mTOR

inhibitor

sirolimus

-LRB-

also

known

as

rapamycin

-RRB-

-LRB-

Fig.

-RRB-

This

result

suggested

that

sirolimus

might

revert

dexamethasone

resistance

Indeed

treatment

of

the

lymphoid

cell

line

with

sirolimus

conferred

dexamethasone

sensitivity

to

this

otherwise

resistant

cell

line

reducing

the

median

inhibitory

concentration

-LRB-

-RRB-

by

a

factor

of

more

than

-LRB-

Fig.

-RRB-

Additional

experiments

indicated

that

this

activity

was

mTOR

dependent

resulting

in

apoptosis

mediated

through

downregulation

of

the

anti-apoptotic

protein

-LRB-

-RRB-

Whatever

the

mechanism

the

result

from

A

Fig.

Sirolimus

reverses

glucocorticoid

resistance

in

acute

sirolimus

-LSB-

-RSB-

nM

lymphoblastic

leukemia

-LRB-

A

-RRB-

sirolimus

-LSB-

-RSB-

nM

Barview

-LRB-

as

in

Fig.

-RRB-

showing

sirolimus

-LSB-

-RSB-

nM

all

sirolimus

instances

sirolimus

-LSB-

-RSB-

nM

nM

sirolimus

-LSB-

-RSB-

Permutation

P-value

for

this

nM

sirolimus

-LSB-

-RSB-

set

of

instances

is

sirolimus

-LSB-

-RSB-

nM

Unabridged

results

from

this

sirolimus

-LSB-

-RSB-

nM

query

are

provided

as

Result

sirolimus

-LSB-

-RSB-

nM

-LRB-

B

-RRB-

The

effect

of

a

sirolimus

-LSB-

-RSB-

nM

bination

of

sirolimus

and

dexamethasone

on

the

viability

of

glucocorticoid-resistant

oid

cells

cells

were

dexamethasone

concentation

-LRB-

M

-RRB-

treated

with

nM

sirolimus

and

various

concentrations

of

dexamethasone

-LRB-

closed

circles

-RRB-

or

dexamethasone

alone

-LRB-

open

circles

-RRB-

for

hours

Cell

viability

was

assessed

by

MTT

reduction

and

expressed

relative

to

untreated

control

cultures

Plot

shows

means

and

standard

deviations

for

triplicate

determinations

from

one

representative

experiment

rank

perturbagen

dose

cell

score

B

SEPTEMBER

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viability

-LRB-

of

control

-RRB-

SCIENCE

Downloaded

from

on

May

Fig.

Gedunin

modulates

the

pathway

-LRB-

A

-RRB-

Chemical

structure

of

gedunin

and

-LRB-

B

-RRB-

Gedunin

is

connected

with

geldanamycin

and

its

analogs

Barview

-LRB-

as

in

Fig.

-RRB-

showing

all

-LRB-

n

-RRB-

geldanamycin

-LRB-

n

-RRB-

and

-LRB-

n

-RRB-

instances

for

the

gedunin

signature

Unabridged

results

from

this

query

are

provided

as

Result

-LRB-

C

-RRB-

Gedunin

lowers

the

levels

of

proteins

including

the

androgen

receptor

-LRB-

AR

-RRB-

in

LNCaP

cells

and

cells

ectopically

expressing

them

Mutant

proteins

-LRB-

BCR-ABL

point

mutant

and

the

internal

tandem

duplication

mutant

-RRB-

show

increased

sensitivity

to

gedunin

treatment

EGFR

epidermal

growth

factor

receptor

RESEARCH

ARTICLES

the

Connectivity

Map

immediately

suggests

that

sirolimus

should

be

tested

in

a

clinical

trial

of

ALL

patients

with

dexamethasone

resistance

Sirolimus

is

already

FDA

approved

as

an

immunosuppressant

and

is

well

tolerated

in

children

and

the

clinical

prognosis

of

dexamethasoneresistant

ALL

is

poor

-LRB-

-RRB-

This

example

demonstrates

that

the

Connectivity

Map

is

one

approach

to

the

rapid

identification

of

new

potential

uses

for

existing

drugs

Discussion

The

value

of

a

Connectivity

Map

depends

on

many

open

questions

How

many

distinct

cellular

pathways

and

states

actually

exist

How

many

cell

types

must

be

studied

to

provide

sufficient

diversity

How

many

perturbagens

-LRB-

small

molecules

inhibitory

RNAs

open

reading

frames

-RRB-

would

need

to

be

characterized

to

provide

substantial

coverage

How

many

concentrations

time

points

and

replicates

would

be

required

to

provide

reliable

data

What

analytical

tools

will

be

needed

to

interpret

the

data

and

determine

precise

estimates

of

statistical

significance

and

false-positive

rates

And

most

important

what

will

be

the

biomedical

value

of

the

data

Only

empirical

evidence

will

resolve

these

issues

Although

only

a

first

step

our

results

are

encouraging

They

show

that

genomic

signatures

can

be

used

to

recognize

drugs

with

common

mechanisms

of

action

-LRB-

HDAC

inhibitors

and

estrogen

receptor

modulators

-RRB-

discover

unknown

mechanisms

of

actions

-LRB-

gedunin

as

an

inhibitor

-RRB-

and

identify

potential

new

therapeutics

-LRB-

the

ability

of

sirolimus

to

overcome

dexamethasone

resistance

in

ALL

-RRB-

Our

findings

also

reveal

that

signatures

are

often

conserved

across

diverse

cell

types

and

settings

-LRB-

the

signature

of

dexamethasone

resistance

was

defined

in

bone-marrow

samples

but

searched

against

profiles

from

the

breast

cancer

line

-RRB-

At

the

same

time

the

results

demonstrate

the

limitations

of

using

only

a

few

cell

lines

-LRB-

the

signature

of

estradiol

was

not

detected

in

cells

that

lack

estrogen

receptors

-RRB-

or

only

a

few

concentrations

-LRB-

chlorpromazine

was

not

recognized

as

a

phenothiazine

at

mM

-RRB-

It

is

also

likely

that

our

methodologies

can

still

be

refined

Indeed

alternative

signature-based

patternmatching

methods

have

been

developed

-LSB-

e.g.

-LRB-

-RRB-

-RSB-

In

addition

the

interpretation

of

results

depends

on

the

ability

to

confidently

call

connections

More

rigorous

methods

for

the

estimation

of

statistical

significance

are

therefore

probably

also

required

especially

as

the

size

of

the

database

grows

But

overall

the

basic

features

of

our

approach

appear

to

work

well

We

have

therefore

created

a

Web-based

tool

-LRB-

mit.edu/cmap

-RRB-

to

allow

researchers

to

perform

their

own

Connectivity

Map

analyses

with

userdefined

signatures

in

real

time

On

the

basis

of

the

results

of

this

pilot

study

we

propose

that

a

sensible

next

step

would

be

the

generation

of

an

expanded

Connectivity

Map

as

a

community

resource

project

in

the

spirit

of

other

genomic

efforts

An

initial

goal

might

be

to

profile

all

FDA-approved

drugs

and

inhibitory

RNAs

targeting

a

large

collection

of

genes

in

perhaps

diverse

cell

lines

Further

goals

would

depend

on

the

utility

of

the

data

Ultimately

it

will

be

interesting

to

explore

whether

it

is

possible

to

create

a

truly

comprehensive

catalog

that

begins

to

saturate

all

possible

cellular

states

In

the

meanwhile

even

an

incomplete

Connectivity

Map

will

likely

accelerate

progress

in

characterizing

new

chemical

entities

finding

new

uses

for

existing

drugs

and

understanding

the

molecular

mechanisms

of

disease

References

and

Notes

J.

L.

DeRisi

V.

R.

Iyer

P.

O.

Brown

Science

-LRB-

-RRB-

J.

Lamb

et

al.

Cell

-LRB-

-RRB-

T.

R.

Golub

et

al.

Science

-LRB-

-RRB-

C.

M.

Perou

et

al.

Nature

-LRB-

-RRB-

S.

L.

Pomeroy

et

al.

Nature

-LRB-

-RRB-

L.

J.

van

t

Veer

et

al.

Nature

-LRB-

-RRB-

T.

R.

Hughes

et

al.

Cell

-LRB-

-RRB-

B.

Ganter

et

al.

J.

Biotechnol

-LRB-

-RRB-

M.

R.

Fielden

et

al.

Toxicol

Pathol

-LRB-

-RRB-

J.

F.

Waring

et

al.

Toxicol

Appl

Pharmacol

-LRB-

-RRB-

M.

Hollander

D.

Wolfe

Nonparametric

Statistical

Methods

-LRB-

Wiley

New

York

ed

-RRB-

pp.

V.

K.

Mootha

et

al.

Nat

Genet

-LRB-

-RRB-

A.

Subramanian

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

K.

B.

Glaser

et

al.

Mol

Cancer

Ther

-LRB-

-RRB-

J.

Frasor

et

al.

Cancer

Res

-LRB-

-RRB-

P.

M.

Martin

K.

B.

Horwitz

D.

S.

Ryan

W.

L.

McGuire

Endocrinology

-LRB-

-RRB-

D.

P.

Edwards

W.

L.

McGuire

Endocrinology

-LRB-

-RRB-

A.

E.

Wakeling

M.

Dukes

J.

Bowler

Cancer

Res

-LRB-

-RRB-

J.

Y.

Vanderhoek

M.

B.

Feinstein

Mol

Pharmacol

-LRB-

-RRB-

B.

L.

Mylari

T.

J.

Carty

P.

F.

Moore

W.

J.

Zembrowski

J.

Med

Chem

-LRB-

-RRB-

H.

Hieronymus

et

al.

Cancer

Cell

in

press

S.

A.

Khalid

H.

Duddeck

M.

Gonzalez-Sierra

J.

Nat

Prod

-LRB-

-RRB-

M.

E.

Gorre

K.

Ellwood-Yen

G.

Chiosis

N.

Rosen

C.

L.

Sawyers

Blood

-LRB-

-RRB-

Q.

Yao

et

al.

Clin

Cancer

Res

-LRB-

-RRB-

I.

P.

Lopez

et

al.

Obes

Res

-LRB-

-RRB-

B.

M.

Forman

et

al.

Cell

-LRB-

-RRB-

J.

M.

Lehmann

et

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J.

Biol

Chem

-LRB-

-RRB-

J.

M.

Lehmann

J.

M.

Lenhard

B.

B.

Oliver

G.

M.

Ringold

S.

A.

Kliewer

J.

Biol

Chem

-LRB-

-RRB-

T.

M.

Larsen

S.

Toubro

A.

Astrup

Int

J.

Obes

-LRB-

-RRB-

T.

Kubota

et

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Cancer

Res

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R.

Hata

et

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Biochem

Biophys

Res

Commun

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R.

Ricciarelli

et

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IUBMB

Life

-LRB-

-RRB-

B.

J.

Blanchard

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

E.

J.

Hennessy

S.

L.

Buchwald

J.

Org

Chem

-LRB-

-RRB-

W.

J.

Tissing

J.

P.

Meijerink

M.

L.

den

Boer

R.

Pieters

Leukemia

-LRB-

-RRB-

G.

Wei

et

al.

Cancer

Cell

in

press

R.

Pieters

et

al.

Lancet

-LRB-

-RRB-

T.

Hongo

S.

Yajima

M.

Sakurai

Y.

Horikoshi

R.

Hanada

Blood

-LRB-

-RRB-

G.

J.

Kaspers

et

al.

Blood

-LRB-

-RRB-

G.

J.

Kaspers

et

al.

Blood

-LRB-

-RRB-

G.

Natsoulis

et

al.

Genome

Res

-LRB-

-RRB-

We

thank

S.

Schreiber

E.

Scolnick

D.

Altshuler

B.

Wagner

B.

Ebert

N.

Tolliday

M.

Brown

B.

Wong

and

members

of

the

Broad

Cancer

and

Chemical

Biology

Programs

This

work

was

supported

in

part

by

grants

from

the

National

Cancer

Institute

Howard

Hughes

Medical

Institute

and

The

Paul

G.

Allen

Family

Foundation

Supporting

Online

Material

Materials

and

Methods

Figs

to

Table

Signatures

to

Results

to

References

July

accepted

September

science

Structure

of

the

Ribosome

Complexed

with

mRNA

and

tRNA

Maria

Selmer

Christine

M.

Dunham

Frank

V.

Murphy

IV

Albert

Weixlbaumer

Sabine

Petry

Ann

C.

Kelley

John

R.

Weir

V.

Ramakrishnan

The

crystal

structure

of

the

bacterial

ribosome

refined

to

angstrom

resolution

reveals

atomic

details

of

its

interactions

with

messenger

RNA

-LRB-

mRNA

-RRB-

and

transfer

RNA

-LRB-

tRNA

-RRB-

A

metal

ion

stabilizes

a

kink

in

the

mRNA

that

demarcates

the

boundary

between

A

and

P

sites

which

is

potentially

important

to

prevent

slippage

of

mRNA

Metal

ions

also

stabilize

the

intersubunit

interface

The

interactions

of

E-site

tRNA

with

the

subunit

have

both

similarities

and

differences

compared

to

those

in

the

archaeal

ribosome

The

structure

also

rationalizes

much

biochemical

and

genetic

data

on

translation

A

major

breakthrough

for

our

mechanistic

understanding

of

translation

was

achieved

some

years

ago

when

highresolution

structures

of

the

and

ribosomal

subunits

were

solved

-LRB-

-RRB-

Progress

has

also

been

made

in

obtaining

structural

data

on

the

whole

ribosome

The

subunit

structures

were

used

to

facilitate

interpretation

of

maps

at

-RRB-

resolution

of

the

whole

ribosome

complexed

SCIENCE

VOL

with

mRNA

and

tRNA

-LRB-

-RRB-

More

recently

the

structure

of

the

Escherichia

coli

ribosome

was

solved

at

-RRB-

resolution

-LRB-

-RRB-

At

the

same

MRC

Laboratory

of

Molecular

Biology

Hills

Road

Cambridge

UK

These

authors

contributed

equally

to

this

work

To

whom

correspondence

should

be

addressed

E-mail

ramak@mrc-lmb.cam.ac.uk

SEPTEMBER

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from

on

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