RNA

Maps

Reveal

New

RNA

Classes

and

a

Possible

Function

for

Pervasive

Transcription

Philipp

Kapranov

et

al.

Science

-LRB-

-RRB-

DOI

science

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The

title

Science

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AAAS

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from

on

May

REPORTS

cate

that

these

molecules

also

function

in

early

growth

responses

to

sarcomere

dysfunction

Myocardial

fibrosis

is

characteristic

of

advanced

HCM

pathology

and

contributes

to

impaired

cardiac

relaxation

heart

failure

arrhythmias

and

sudden

death

-LRB-

-RRB-

The

increased

expression

of

-LRB-

transforming

growth

factor

-RRB-

Ctgf

-LRB-

connective

tissue

growth

factor

-RRB-

and

Postn

-LRB-

periostin

-RRB-

potent

regulators

of

fibrosis

and

collagen

deposition

-LRB-

-RRB-

in

prehypertrophic

ventricles

indicates

early

activation

of

this

pathway

which

raises

the

possibility

that

fibrosis

is

not

an

advanced

secondary

phenomenon

but

a

primary

contributor

to

myocardial

dysfunction

Impaired

relaxation

is

the

fundamental

physiologic

abnormality

in

HCM

-LRB-

-RRB-

Cardiac

relaxation

and

contraction

reflects

Ca

cycling

between

the

sarcoplasmic

reticulum

and

the

sarcomere

in

cardiomyocytes

Ca

uptake

into

the

sarcoplasmic

reticulum

occurs

via

sarcoplasmic

reticulum

Ca

transport

adenosine

triphosphatase

-LRB-

ATPase

-RRB-

-LRB-

-RRB-

which

is

regulated

by

phospholamban

-LRB-

Pln

-RRB-

and

sarcolipin

-LRB-

Sln

-RRB-

-LRB-

-RRB-

Transcripts

encoding

each

of

these

proteins

were

significantly

decreased

in

prehypertrophic

hearts

-LRB-

fig

-RRB-

which

may

directly

account

for

the

early

impairment

in

cardiac

relaxation

previously

observed

in

this

model

-LRB-

-RRB-

Downregulation

of

-LSB-

adenosine

triphosphate

-LRB-

ATP

-RRB-

binding

cassette

subfamily

C

member

-RRB-

which

encodes

suggested

another

mechanism

for

Ca

imbalance

in

prehypertrophic

hearts

is

the

ATPase-regulatory

subunit

of

the

inwardly

rectifying

cardiac

KATP

channel

which

balances

Ca

homeostasis

with

energetic

demands

-LRB-

-RRB-

mice

develop

arrhythmias

and

myocardial

calcium

overload

-LRB-

-RRB-

Notably

PMAGE

also

revealed

significant

-LRB-

P

-RRB-

differences

in

the

expression

of

genes

encoding

transcription

factors

between

wildtype

and

prehypertrophic

hearts

-LRB-

table

-RRB-

The

biologic

processes

evoked

by

these

molecules

are

likely

to

be

considerable

By

interrogating

the

temporal

and

spatial

expression

of

these

transcription

factors

we

can

potentially

dissect

the

networks

activated

in

this

cardiomyopathy

which

in

turn

should

help

identify

new

molecular

targets

for

therapeutic

intervention

In

summary

PMAGE

profiling

provided

reproducible

large-scale

transcript

identification

with

sequence

accuracy

comparable

to

SAGE

and

greater

sensitivity

for

quantification

of

rare

transcripts

We

estimate

that

sampling

million

tags

provides

comprehensive

assessment

of

most

mRNAs

-LRB-

fig

-RRB-

nevertheless

the

current

PMAGE

platform

has

the

capacity

to

read

more

than

million

tags

per

experiment

Thus

PMAGE

can

be

used

for

very

deep

sampling

of

one

library

or

analyses

of

multiple

libraries

simultaneously

by

adapting

polony

beads

that

contain

unique

sequence

identifiers

PMAGE

offers

several

advantages

over

other

currently

available

transcription

profiling

methods

at

a

potentially

lower

cost

-LRB-

fig

-RRB-

We

anticipate

that

PMAGE

studies

will

help

further

define

mRNA

regulatory

networks

that

orchestrate

critical

cellular

processes

in

healthy

and

diseased

tissues

References

and

Notes

G.

A.

Churchill

Nat

Genet

-LRB-

suppl

-RRB-

-LRB-

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V.

E.

Velculescu

et

al.

Cell

-LRB-

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M.

J.

Holland

J.

Biol

Chem

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S.

Draghici

P.

Khatri

A.

C.

Eklund

Z.

Szallasi

Trends

Genet

-LRB-

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S.

Audic

J.

M.

Claverie

Genome

Res

-LRB-

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Materials

and

methods

are

available

as

supporting

material

on

Science

Online

D.

Dressman

H.

Yan

G.

Traverso

K.

W.

Kinzler

B.

Vogelstein

Proc

Natl.

Acad

Sci

U.S.A.

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J.

Shendure

et

al.

Science

-LRB-

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A.

A.

Geisterfer-Lowrance

et

al.

Science

-LRB-

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S.

Blackshaw

J.

B.

Kim

B.

St.

Croix

K.

Polyak

in

Current

Protocols

in

Molecular

Biology

F.

M.

Asubel

Ed

-LRB-

Greene

Publishing

Associates

and

Wiley-Interscience

New

York

-RRB-

pp

v

-LRB-

loose

leaf

-RRB-

V.

E.

Velculescu

et

al.

Nat

Genet

-LRB-

-RRB-

S.

Dinel

et

al.

Nucleic

Acids

Res

-LRB-

-RRB-

N.

D.

Hastie

J.

O.

Bishop

Cell

-LRB-

-RRB-

J.

G.

Seidman

C.

Seidman

Cell

-LRB-

-RRB-

F.

Chen

et

al.

Cell

-LRB-

-RRB-

C.

H.

Shin

et

al.

Cell

-LRB-

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D.

Srivastava

P.

Cserjesi

E.

N.

Olson

Science

-LRB-

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T.

Maeda

D.

L.

Chapman

A.

F.

Stewart

J.

Biol

Chem

-LRB-

-RRB-

Y.

Albert

et

al.

J.

Cell

Biol

-LRB-

-RRB-

A.

M.

Varnava

P.

M.

Elliott

N.

Mahon

M.

J.

Davies

W.

J.

McKenna

Am

J.

Cardiol

-LRB-

-RRB-

K.

M.

Harris

et

al.

Circulation

-LRB-

-RRB-

P.

J.

Lijnen

V.

V.

Petrov

R.

H.

Fagard

Mol

Genet

Metab

-LRB-

-RRB-

M.

S.

Ahmed

et

al.

J.

Mol

Cell

Cardiol

-LRB-

-RRB-

R.

A.

Norris

et

al.

J.

Cell

Biochem

-LRB-

-RRB-

B.

J.

Maron

JAMA

-LRB-

-RRB-

M.

Asahi

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

D.

Georgakopoulos

et

al.

Nat

Med

-LRB-

-RRB-

M.

Bienengraeber

et

al.

Nat

Genet

-LRB-

-RRB-

L.

V.

Zingman

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

R.

T.

Lee

et

al.

J.

Clin

Invest

-LRB-

-RRB-

We

thank

J.

Shendure

S.

Barr

S.

DePalma

M.

Maida

P.

Teekakirikul

S.

Blackshaw

Z.

Arany

J.

Loscalzo

and

B.

Vogelstein

for

advice

and

technical

assistance

Funded

by

grants

from

NIH

and

Howard

Hughes

Medical

Institute

Materials

and

Methods

Figs

to

Tables

to

References

November

accepted

May

science

RNA

Maps

Reveal

New

RNA

Classes

and

a

Possible

Function

for

Pervasive

Transcription

Philipp

Kapranov

Jill

Cheng

Sujit

Dike

David

A.

Nix

Radharani

Duttagupta

Aarron

T.

Willingham

Peter

F.

Stadler

Jana

Hertel

J

rg

Hackerm

ller

Ivo

L.

Hofacker

Ian

Bell

Evelyn

Cheung

Jorg

Drenkow

Erica

Dumais

Sandeep

Patel

Gregg

Helt

Madhavan

Ganesh

Srinka

Ghosh

Antonio

Piccolboni

Victor

Sementchenko

Hari

Tammana

Thomas

R.

Significant

fractions

of

eukaryotic

genomes

give

rise

to

RNA

much

of

which

is

unannotated

and

has

reduced

protein-coding

potential

The

genomic

origins

and

the

associations

of

human

nuclear

and

cytosolic

polyadenylated

RNAs

longer

than

nucleotides

-LRB-

nt

-RRB-

and

whole-cell

RNAs

less

than

nt

were

investigated

in

this

genome-wide

study

Subcellular

addresses

for

nucleotides

present

in

detected

RNAs

were

assigned

and

their

potential

processing

into

short

RNAs

was

investigated

Taken

together

these

observations

suggest

a

novel

role

for

some

unannotated

RNAs

as

primary

transcripts

for

the

production

of

short

RNAs

Three

potentially

functional

classes

of

RNAs

have

been

identified

two

of

which

are

syntenically

conserved

and

correlate

with

the

expression

state

of

protein-coding

genes

These

data

support

a

highly

interleaved

organization

of

the

human

transcriptome

A

large

fraction

of

the

noncoding

part

of

a

eukaryotic

genome

is

used

to

make

RNA

that

is

sufficiently

stable

in

a

cell

to

be

detected

by

different

technological

approaches

-LRB-

-RRB-

The

biological

significance

of

this

pervasive

transcription

is

unclear

and

controversial

One

possibility

is

that

only

very

short

regions

of

such

unannotated

RNA

are

biologically

relevant

-LRB-

-RRB-

In-depth

characterization

of

RNAs

as

to

their

subcellular

compartmentalization

size

modifications

and

genomic

origins

can

potentially

provide

clues

to

their

functions

This

study

reports

two

general

observations

derived

from

VOL

SCIENCE

the

maps

of

nuclear

and

cytosolic

polyadenylated

-LSB-

poly

-LRB-

A

-RRB-

-RSB-

RNAs

longer

than

nucleotides

-LRB-

nt

-RRB-

-LRB-

long

RNAs

lRNAs

-RRB-

and

whole-cell

RNAs

less

than

nt

-LRB-

short

RNAs

sRNAs

-RRB-

over

the

entire

nonrepetitive

portion

of

the

human

Affymetrix

Laboratory

Affymetrix

Inc.

Central

Expressway

Santa

Clara

CA

USA

of

Leipzig

Department

of

Computer

Science

Leipzig

Germany

Fraunhofer

Institute

for

Cell

Therapy

and

Immunology

Leipzig

Germany

for

Theoretical

Chemistry

University

of

Vienna

Austria

To

whom

correspondence

should

be

addressed

E-mail

JUNE

Downloaded

from

on

May

Supporting

Online

Material

REPORTS

nome

First

the

potential

biological

function

of

an

appreciable

portion

of

long

unannotated

transcripts

is

to

serve

as

precursors

for

sRNAs

Second

these

maps

reveal

three

classes

of

RNAs

that

have

specific

genomic

localization

at

gene

boundaries

Biological

relevance

of

these

classes

of

RNAs

is

supported

by

strong

correlation

with

the

expression

state

of

genes

they

associate

with

as

well

as

their

syntenic

conservation

between

human

and

mouse

The

complexity

of

steady-state

RNA

populations

was

profiled

by

using

tiling

arrays

at

resolution

to

detect

transcribed

regions

in

the

human

genome

-LRB-

-RRB-

Overall

we

found

the

extent

and

general

properties

of

the

lRNA

portion

of

the

human

transcriptome

to

be

similar

to

our

earlier

study

-LRB-

-RRB-

Patterns

of

annotated

and

unannotated

transcription

were

similar

among

cell

lines

and

within

subcellular

compartments

-LRB-

fig

A

to

J

and

table

A

and

B

-RRB-

About

of

detected

poly

-LRB-

A

-RRB-

transcription

-LRB-

nucleus

and

cytosol

-RRB-

did

not

align

with

annotations

-LRB-

fig

-RRB-

-LRB-

-RRB-

Of

the

annotated

exons

were

expressed

in

at

least

one

cell

line

-LRB-

fig

-RRB-

A

total

of

of

the

interrogated

genome

is

covered

by

transcribed

fragments

-LRB-

transfrags

-RRB-

representing

sRNAs

-LRB-

summarized

in

table

and

figs

and

-RRB-

sRNA

transfrags

have

a

nonrandom

association

with

genomic

features

including

EvoFold

structure

predictions

-LRB-

figs

and

and

table

-RRB-

In

addition

a

tendency

for

some

to

map

antisense

to

splice

junctions

was

also

found

sRNAs

were

found

in

intronic

intergenic

and

annotated

regions

-LRB-

fig

-RRB-

Unannotated

sRNAs

were

verified

by

Northern

blots

-LRB-

table

and

fig

-RRB-

and

real-time

reverse

transcription

polymerase

chain

reaction

-LRB-

table

-RRB-

with

an

overall

verification

rate

of

-LRB-

-RRB-

Maps

of

sRNAs

and

lRNAs

from

different

subcellular

compartments

can

be

further

used

to

provide

a

virtual

genealogy

describing

origins

of

particular

classes

of

RNAs

-LRB-

Fig.

and

table

-RRB-

A

total

of

of

all

interrogated

nucleotides

can

be

detected

as

composing

lRNAs

or

sRNAs

in

HeLa

or

cell

lines

One-third

of

this

total

-LRB-

-RRB-

is

exclusively

observed

in

the

nucleus

as

lRNAs

and

overlapping

sRNAs

-LRB-

Fig.

-RRB-

Another

is

exclusively

found

as

cytosolic

lRNAs

and

overlapping

sRNAs

A

total

of

of

the

sequences

detected

are

found

in

both

the

nucleus

and

cytosol

-LRB-

Fig.

-RRB-

Finally

of

the

nucleotides

were

detected

in

sRNA

transfrags

exclusively

The

origin

of

of

all

transcribed

bases

can

be

mapped

back

to

the

nucleus

as

lRNAs

with

found

only

in

cytosol

-LRB-

Fig.

-RRB-

About

of

the

sequences

seem

to

remain

exclusively

in

the

nucleus

the

remainder

is

transported

into

the

cytosol

Furthermore

of

the

exclusively

nuclear

lRNA

sequences

and

of

the

nuclear

sequences

transported

into

the

cytosol

overlap

sRNA

sequences

which

suggests

that

of

the

latter

may

be

processed

from

long

nuclear

transcripts

-LRB-

Fig.

and

table

-RRB-

About

one-fifth

of

sRNAs

transfrags

-LRB-

-RRB-

and

-LRB-

HeLa

-RRB-

were

identified

as

evolutionarily

conserved

The

PhastCons

scores

-LRB-

-RRB-

associated

with

sRNAs

are

significantly

enriched

in

conserved

sequences

-LRB-

P

value

Wilcoxon

nonparametric

test

-RRB-

over

random

-LRB-

fig

A

and

B

-RRB-

which

points

to

the

possible

biological

relevance

of

these

transcripts

-LRB-

Fig.

-RRB-

There

is

also

a

statistically

significant

concordance

-LRB-

P

permutation

test

-RRB-

observed

between

the

locations

of

sRNA

and

nuclear

lRNA

transfrags

A

total

of

-LRB-

-RRB-

and

-LRB-

HeLa

-RRB-

nuclear

lRNA

transfrags

overlap

sRNAs

Conversely

and

of

sRNA

transfrags

overlap

with

nuclear

lRNA

transfrags

Such

an

association

is

potentially

confounded

by

the

elevated

G-C

composition

of

these

regions

-LRB-

-RRB-

To

explore

this

further

we

divided

the

transfrags

obtained

from

nuclear

lRNA

into

those

that

do

and

do

not

overlap

sRNA

transfrags

-LRB-

Fig.

-RRB-

Mean

PhastCons

scores

for

the

lRNA

transfrags

that

do

overlap

with

sRNAs

are

significantly

higher

than

such

of

the

transfrags

that

do

not

-LRB-

Fig.

and

table

-RRB-

For

lRNA

transfrags

overlapping

sRNAs

a

total

of

-LRB-

-RRB-

and

-LRB-

HeLa

-RRB-

exhibit

PhastCons

scores

equivalent

or

higher

than

the

average

score

observed

for

annotations

-LRB-

Fig.

-RRB-

The

conservation

of

the

nuclear

lRNA

transfrags

often

extends

beyond

a

sRNA

transfrag

it

overlaps

-LRB-

Fig.

-RRB-

indicating

that

other

sequences

outside

of

the

overlapping

regions

may

be

important

reminiscent

of

the

extended

conservation

seen

in

the

miRNA

precursors

-LRB-

-RRB-

Taken

together

these

data

suggest

a

possible

product-precursor

relation

between

overlapping

transfrags

derived

from

lRNAs

and

sRNAs

underscored

by

the

enrichment

of

evolutionarily

conserved

sequences

in

genomic

regions

found

transcribed

in

both

lRNAs

and

sRNAs

Conservatively

of

and

of

HeLa

nuclear

lRNA

transfrags

may

be

parts

of

precursors

of

sRNAs

The

full

extent

of

transcription

which

may

serve

as

precursors

of

sRNA

could

however

be

much

larger

because

lRNA

transfrags

that

directly

overlap

sRNAs

are

almost

certainly

connected

to

other

transfrags

in

a

precursor

transcript

Thus

any

given

lRNA

transfrag

can

be

an

order

of

magnitude

smaller

than

the

lRNA

transcript

it

represents

B

A

Fig.

Relations

among

the

transcribed

bases

in

the

nonrepeat

portions

of

the

human

genome

-LRB-

A

-RRB-

Distribution

of

nucleotides

in

transfrags

from

long

nuclear

and

cytosolic

RNAs

and

sRNAs

from

HeLa

and

-LRB-

table

-RRB-

-LRB-

B

-RRB-

Genealogy

of

nucleotides

detected

in

lRNAs

and

sRNAs

based

on

the

association

of

the

array-detected

transcription

shown

in

-LRB-

A

-RRB-

Putative

product-precursor

associations

between

lRNAs

and

sRNAs

are

indicated

by

arrows

SCIENCE

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REPORTS

sRNA

mapping

and

sequence

conservation

analysis

-LRB-

fig

-RRB-

indicate

that

sRNAs

cluster

at

the

and

of

genes

-LRB-

Fig.

-RRB-

We

denote

these

classes

of

sRNAs

promoter-associated

sRNAs

-LRB-

PASRs

-RRB-

and

termini-associated

sRNAs

-LRB-

TASRs

-RRB-

The

occurrence

of

sRNAs

centers

around

or

termini

and

is

statistically

significant

compared

with

G-C-matched

random

regions

-LRB-

Fig.

B

and

C

and

table

-RRB-

Northern

hybridization

analysis

revealed

that

PASRs

and

TASRs

can

vary

in

length

-LRB-

to

nt

-RRB-

with

one

prominent

class

of

PASRs

with

lengths

of

and

nt

-LRB-

Fig.

B

and

C

and

figs

and

and

tables

and

-RRB-

PASRs

were

expressed

at

levels

similar

to

those

of

the

proteincoding

genes

they

overlap

-LRB-

-RRB-

Several

characteristics

of

both

PASRs

and

TASRs

support

the

biological

significance

of

these

sRNAs

As

explained

below

gene

expression

correlates

with

the

density

of

PASRs

and

PASRs

associate

with

other

lRNAs

at

the

boundaries

of

genes

Also

expressed

PASRs

are

syntenic

with

mouse

The

correlation

of

gene

expression

with

the

density

of

PASRs

-LRB-

Fig.

-RRB-

is

similar

to

a

trend

seen

for

antisense

TASRs

-LRB-

fig

A

and

B

-RRB-

Overall

and

of

genes

found

to

be

expressed

in

cytosol

or

nucleus

have

PASR

association

Another

and

of

genes

had

signal

only

in

the

first

exon

in

cytosolic

or

nuclear

RNAs

respectively

Almost

half

of

those

are

observed

to

have

PASRs

-LRB-

fig

C

and

D

-RRB-

Conversely

for

of

silent

genes

-LRB-

of

exons

detected

-RRB-

no

PASRs

were

observed

A

third

class

of

RNAs

is

the

long

transcripts

that

overlap

boundaries

of

protein-coding

genes

but

do

not

include

most

of

the

other

exons

This

is

exemplified

by

genes

that

show

signal

only

in

their

first

exons

-LRB-

Fig.

-RRB-

To

characterize

these

promoter-associated

lRNAs

-LRB-

PALRs

-RRB-

we

performed

and

RACE

analysis

-LRB-

rapid

amplification

of

cDNA

ends

-RRB-

followed

by

hybridization

to

tiling

arrays

-LRB-

fig

-RRB-

These

experiments

revealed

that

transcripts

overlapping

the

promoter

and

the

first

exon

and

intron

regions

ranging

in

length

from

hundreds

of

base

pairs

to

more

than

kb

are

made

and

map

to

the

same

genomic

regions

as

PASRs

We

constructed

sRNA

maps

in

two

syntenic

regions

of

the

human

and

mouse

genomes

-LRB-

cytokine

cluster

and

four

Hox

loci

-RRB-

using

mouse

STO

and

and

human

and

HeLa

cell

lines

-LRB-

-RRB-

Both

species-specific

and

conserved

PASRs

and

TASRs

were

found

with

of

PASR

sequences

and

of

TASR

sequences

mapping

into

syntenically

conserved

regions

-LRB-

Fig.

-RRB-

Genomic

regions

shared

by

the

PASR

-LRB-

-RRB-

at

the

boundaries

of

the

Hox

and

the

TASR

-LRB-

-RRB-

in

the

termini

of

genes

of

both

species

are

illustrated

in

Fig

The

sizes

of

PASRs

and

TASRs

are

similar

for

mouse

and

human

cell

lines

-LRB-

fig

and

table

-RRB-

We

have

found

that

of

detected

transcription

is

present

in

sRNA

sequences

-LRB-

ranging

from

to

nt

in

length

-RRB-

The

distribution

of

these

sRNAs

is

not

uniform

across

the

genome

because

sRNAs

are

more

frequent

among

genes

than

in

intergenic

regions

Further

Fig.

Sequence

conservation

analysis

of

short

and

long

nuclear

RNA

-LRB-

-RRB-

-LRB-

A

-RRB-

Conserved

sRNAs

surrounding

the

first

exon

of

gene

-LRB-

B

-RRB-

Long

nuclear

transfrag

that

overlaps

sRNA

is

more

conserved

than

adjacent

lRNA

transfrag

that

does

not

-LRB-

C

-RRB-

Quantile-quantile

plot

of

PhastCons

scores

of

long

nuclear

transfrags

that

do

-LRB-

x

axis

-RRB-

and

do

not

-LRB-

y

axis

-RRB-

overlap

sRNAs

For

any

given

point

on

the

curve

an

equal

proportion

of

each

quantile

distribution

occurs

at

this

juncture

-LRB-

D

-RRB-

Distribution

of

PhastCons

scores

of

long

nuclear

transfrags

that

overlap

sRNAs

binned

on

the

basis

of

PhastCons

scores

-LRB-

x

axis

-RRB-

versus

percentage

of

transfrags

in

each

bin

-LRB-

y

axis

-RRB-

Highly

conserved

transfrags

-LRB-

scores

-RRB-

are

indicated

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VOL

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May

REPORTS

more

sRNA

transfrags

overlap

a

collection

of

lRNA

transfrags

that

are

significantly

enriched

in

conserved

sequences

Taken

together

these

data

suggest

that

these

lRNA

transfrags

potentially

represent

parts

of

nuclear

primary

transcripts

that

encode

conserved

functional

sRNAs

Several

other

observations

are

also

derived

from

these

mapping

data

First

an

appreciable

fraction

of

protein-coding

genes

have

expression

only

in

the

first

exon

and

intron

This

suggests

that

transcription

may

have

two

different

states

that

are

characterized

by

the

lengths

of

transcripts

made

from

the

transcriptional

start

site

of

gene

locus

Second

the

fate

of

the

transcripts

derived

from

a

particular

detected

transcribed

region

could

be

predicted

on

the

basis

of

their

retention

in

the

nucleus

transport

into

cytosol

or

processing

into

sRNAs

Overall

these

RNA

maps

provide

a

virtual

genealogy

of

RNAs

-LRB-

Fig.

-RRB-

Third

PASRs

often

align

within

the

boundaries

of

some

of

the

PALRs

The

genomic

loci

and

boundaries

of

PASRs

appear

to

be

well

conserved

in

two

human

cell

lines

and

in

some

cases

between

mouse

and

human

cells

this

may

indicate

that

there

could

be

common

processing

signals

used

to

create

them

Fourth

the

ends

of

almost

half

of

human

proteincoding

genes

were

found

to

be

bracketed

by

PASRs

and

TASRs

Given

that

large

regions

-LRB-

i.e.

kb

-RRB-

are

contained

in

the

sequences

covered

by

the

sRNAs

the

functional

roles

of

these

sRNAs

may

involve

broad

domains

consistent

with

involvement

in

chromatin

alterations

Other

recent

studies

also

report

the

presence

of

multiple

transcripts

at

the

boundaries

of

genes

-LRB-

-RRB-

including

unstable

lRNAs

postulated

to

be

involved

in

regulation

of

gene

expression

-LRB-

-RRB-

Thus

these

results

suggest

a

model

of

genome

organization

where

protein-coding

genes

are

at

the

center

of

a

complex

network

of

overlapping

sense

and

antisense

lRNA

transcription

with

interleaved

sRNAs

often

marking

their

boundaries

and

correlating

with

their

expression

state

-LRB-

fig

-RRB-

Our

studies

also

highlight

a

possible

important

biological

function

for

a

portion

of

unannotated

nuclear

transcription

as

possible

precursors

for

sRNAs

Such

interleaved

transcription

produces

a

variety

of

non

protein

coding

sRNA

and

lRNA

species

that

offer

cis

and

trans-regulatory

potential

-LRB-

-RRB-

Fig.

sRNAs

are

enriched

at

boundaries

of

transcripts

-LRB-

A

-RRB-

Smoothed

density

of

sRNAs

and

the

map

of

long

nuclear

RNA

from

are

shown

for

a

region

of

chromosome

-LRB-

B

and

C

-RRB-

Association

of

sRNAs

with

and

boundaries

of

annotated

transcripts

is

enriched

compared

with

a

set

of

random

regions

with

matched

G-C-content

The

fold

enrichment

over

random

is

plotted

as

a

function

of

a

distance

from

the

or

termini

for

sRNAs

on

the

same

-LRB-

sense

-RRB-

or

opposite

-LRB-

antisense

-RRB-

strand

as

the

annotations

Examples

of

Northern

blots

for

PASRs

and

TASRs

are

shown

below

-LRB-

B

-RRB-

PASRs

-LRB-

C

-RRB-

TASRs

-LRB-

D

-RRB-

A

positive

correlation

between

the

density

of

PASRs

and

the

expression

level

of

the

associated

genes

Violin

plots

illustrate

the

frequency

distribution

of

measured

expression

levels

for

bins

of

genes

-LRB-

-RRB-

The

median

and

mean

expression

levels

for

each

bin

are

indicated

by

and

respectively

The

numbers

on

top

indicate

the

number

of

genes

in

each

bin

SCIENCE

VOL

JUNE

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from

on

May

REPORTS

References

and

Notes

J.

Cheng

et

al.

Science

-LRB-

-RRB-

ENCODE-Project-Consortium

in

preparation

P.

Kapranov

et

al.

Science

-LRB-

-RRB-

A.

T.

Willingham

T.

R.

Gingeras

Cell

-LRB-

-RRB-

J.

Ponjavic

C.

P.

Ponting

G.

Lunter

Genome

Res

-LRB-

-RRB-

Data

available

at

http

genome.ucsc.edu

Materials

and

methods

are

available

as

supporting

material

on

Science

Online

U.

Ohler

S.

Yekta

L.

P.

Lim

D.

P.

Bartel

C.

B.

Burge

RNA

-LRB-

-RRB-

P.

Carninci

et

al.

Nat

Genet

-LRB-

-RRB-

C.

A.

Davis

M.

Ares

Jr.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

I.

Martianov

A.

Ramadass

A.

Serra

Barros

N.

Chow

A.

Akoulitchev

Nature

-LRB-

-RRB-

R.

J.

Britten

E.

H.

Davidson

Science

-LRB-

-RRB-

F.

Jacob

J.

Monod

J.

Mol

Biol

-LRB-

-RRB-

J.

S.

Mattick

Curr

Opin

Genet

Dev

-LRB-

-RRB-

We

thank

M.

Mittmann

D.

Le

and

E.

Schell

for

design

of

tiling

arrays

K.

Kole

D.

Barone

and

C.

Chen

for

their

help

with

direct

RNA

labeling

G.

Hannon

K.

Fejes-Toth

D.

Gerhard

and

K.

Nussbacher

for

technical

discussion

and

assistance

in

manuscript

preparation

Mt.

Sinai

Hospital

and

A.

Nagy

R.

Nagy

W.

Abramow-Newerly

J.

Rossant

and

J.

Roder

for

procurement

of

the

cell

line

and

M.

Brown

and

D.

Menke

at

Stanford

for

help

in

preparation

of

mouse

embryo

fibroblasts

This

project

has

been

funded

in

part

with

funds

from

the

National

Cancer

Institute

NIH

under

contract

no

and

from

the

National

Human

Genome

Research

Institute

NIH

under

grant

no

and

by

Affymetrix

Inc.

The

data

discussed

in

this

publication

have

been

deposited

in

National

Center

for

Biotechnology

Information

's

Gene

Expression

Omnibus

-LRB-

GEO

nlm.nih.gov

geo

-RRB-

and

are

accessible

through

GEO

Series

accession

number

Supporting

Online

Material

Materials

and

Methods

Figs

to

Tables

to

References

December

accepted

April

Published

online

May

science

Include

this

information

when

citing

this

paper

A

Common

Allele

on

Chromosome

Associated

with

Coronary

Heart

Disease

Ruth

McPherson

Alexander

Pertsemlidis

Nihan

Kavaslar

Alexandre

Stewart

Robert

Roberts

David

R.

Cox

David

A.

Hinds

Len

A.

Pennacchio

Anne

Tybjaerg-Hansen

Aaron

R.

Folsom

Eric

Boerwinkle

Helen

H.

Hobbs

Jonathan

C.

Coronary

heart

disease

-LRB-

CHD

-RRB-

is

a

major

cause

of

death

in

Western

countries

We

used

genomewide

association

scanning

to

identify

a

interval

on

chromosome

that

was

consistently

associated

with

CHD

in

six

independent

samples

-LRB-

more

than

participants

-RRB-

from

four

Caucasian

populations

This

interval

which

is

located

near

the

and

genes

contains

no

annotated

genes

and

is

not

associated

with

established

CHD

risk

factors

such

as

plasma

lipoproteins

hypertension

or

diabetes

Homozygotes

for

the

risk

allele

make

up

to

of

Caucasians

and

have

a

to

increased

risk

of

CHD

oronary

heart

disease

-LRB-

CHD

-RRB-

is

the

single

greatest

cause

of

death

worldwide

-LRB-

-RRB-

Although

CHD

is

highly

heritable

the

DNA

sequence

variations

that

confer

cardiovascular

risk

remain

largely

unknown

To

identify

sequence

variants

associated

with

CHD

we

undertook

a

genome-wide

association

study

using

single-nucleotide

polymorphisms

Study

-LRB-

OHS

-RRB-

Cases

had

severe

premature

CHD

with

a

documented

onset

before

the

age

of

years

and

culminating

in

coronary

artery

revascularization

-LRB-

table

-RRB-

To

limit

confounding

by

factors

that

strongly

predispose

to

premature

CHD

we

excluded

individuals

with

diabetes

or

plasma

cholesterol

levels

consistent

with

Division

of

Cardiology

University

of

Ottawa

Heart

Institute

Ottawa

Canada

W.

Reynolds

Cardiovascular

Clinical

Research

Center

and

the

Eugene

McDermott

Center

for

Human

Growth

and

Development

University

of

Texas

Southwestern

Medical

Center

Dallas

TX

USA

Sciences

Mountain

View

CA

USA

Division

Lawrence

Berkeley

National

Laboratory

Berkeley

CA

USA

S.

Department

of

Energy

Joint

Genome

Institute

Walnut

Creek

CA

USA

Department

of

Clinical

Biochemistry

Rigshospitalet

Copenhagen

University

Hospital

Copenhagen

Denmark

Division

of

Epidemiology

and

Community

Health

University

of

Minnesota

Minneapolis

MN

USA

Genetics

Center

and

Institute

for

Molecular

Medicine

University

of

Texas

Health

Science

Center

Houston

TX

USA

Howard

Hughes

Medical

Institute

at

the

University

of

Texas

Southwestern

Medical

Center

Dallas

TX

USA

for

Human

Nutrition

at

the

University

of

Texas

Southwestern

Medical

Center

Dallas

TX

USA

C

-LRB-

SNPs

-RRB-

To

minimize

false

positive

associations

without

unduly

sacrificing

statistical

power

we

designed

the

study

to

comprise

three

sequential

case-control

comparisons

performed

at

a

nominal

significance

threshold

of

P

-LRB-

Fig.

-RRB-

For

the

initial

genome-wide

scan

cases

and

controls

were

Caucasian

men

and

women

from

Ottawa

Canada

who

participated

in

the

Ottawa

Heart

VOL

SCIENCE

These

authors

contributed

equally

to

this

work

To

whom

correspondence

should

be

addressed

E-mail

jonathan.cohen@utsouthwestern.edu

-LRB-

J.C.C.

-RRB-

rmcpherson

ottawaheart.ca

-LRB-

R.M.

-RRB-

JUNE

Downloaded

from

on

May

Fig.

-LRB-

A

-RRB-

Distribution

of

syntenically

conserved

sRNAs

The

fractions

of

sRNAs

in

each

class

-LRB-

ordinate

-RRB-

found

in

a

syntenic

location

in

both

species

are

shown

as

percentages

of

the

total

number

of

sRNAs

in

the

class

-LRB-

B

and

C

-RRB-

Characterization

of

syntenically

conserved

PASRs

-LRB-

B

-RRB-

and

TASRs

-LRB-

C

-RRB-

Combined

maps

of

syntenic

sRNAs

from

HeLa

and

human

cell

lines

-LRB-

black

-RRB-

and

and

MEF

mouse

cell

lines

-LRB-

gray

-RRB-

are

shown

Syntenic

PASR

and

TASR

are

shown

on

either

top

-LRB-

-RRB-

or

bottom

-LRB-

-RRB-

strands

Northern

blots

show

and

in

both

species

with

comparable

sizes

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

transcriptome rna expression genome transcription

classification
non coding
ncrna
noncoding rna


Network

Motifs

Simple

Building

Blocks

of

Complex

Networks

R.

Milo

et

al.

Science

-LRB-

-RRB-

DOI

science

The

following

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related

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this

information

is

current

as

of

May

-RRB-

Updated

information

and

services

including

high-resolution

figures

can

be

found

in

the

online

version

of

this

article

at

Supporting

Online

Material

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at

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on

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Web

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This

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by

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of

Science

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article

has

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by

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see

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article

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is

published

weekly

except

the

last

week

in

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for

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Washington

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by

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Association

for

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Advancement

of

Science

all

rights

reserved

The

title

Science

is

a

registered

trademark

of

AAAS

Downloaded

from

on

May

REPORTS

Cl

concentrations

in

the

Sajama

ice

core

and

to

a

number

of

other

pedological

and

geomorphological

features

indicative

of

long-term

dry

climates

-LRB-

-RRB-

This

decline

in

human

activity

around

the

Altiplano

paleolakes

is

seen

in

most

caves

with

early

and

late

occupations

separated

by

largely

sterile

mid-Holocene

sediments

However

a

few

sites

including

the

caves

of

and

show

that

people

did

not

completely

disappear

from

the

area

All

of

the

sites

of

sporadic

occupation

are

located

near

wetlands

in

valleys

near

large

springs

or

where

lakes

turned

into

wetlands

and

subsistence

resources

were

locally

still

available

despite

a

generally

arid

climate

-LRB-

-RRB-

Archaeological

data

from

surrounding

areas

suggest

that

the

Silencio

Arqueologico

applies

best

to

the

most

arid

areas

of

the

central

Andes

where

aridity

thresholds

for

early

societies

were

critical

In

contrast

a

weaker

expression

is

to

be

expected

in

the

more

humid

highlands

of

northern

Chile

-LRB-

north

of

S

such

as

Salar

Huasco

-RRB-

and

Peru

-LRB-

-RRB-

In

northwest

Argentina

the

Silencio

Arqueologico

is

found

in

four

of

the

six

known

caves

-LRB-

-RRB-

-LSB-

see

review

in

-LRB-

-RRB-

-RSB-

It

is

also

found

on

the

coast

of

Peru

in

sites

that

are

associated

with

ephemeral

streams

-LRB-

-RRB-

The

southern

limit

in

Chile

and

northwest

Argentina

has

yet

to

be

explored

T.

Dillehay

Science

-LRB-

-RRB-

D.

J.

Meltzer

et

al.

Am

Antiq

-LRB-

-RRB-

T.

F.

Lynch

C.

M.

Stevenson

Quat

Res

-LRB-

-RRB-

D.

H.

Sandweiss

et

al.

Science

-LRB-

-RRB-

L.

Nunez

M.

Grosjean

I.

Cartajena

in

Interhemispher

ic

Climate

Linkages

V.

Markgraf

Ed

-LRB-

Academic

Press

San

Diego

CA

-RRB-

pp.

M.

A.

Geyh

M.

Grosjean

L.

Nunez

U.

Schotterer

Quat

Res

-LRB-

-RRB-

J.

L.

Betancourt

C.

Latorre

J.

A.

Rech

J.

Quade

K.

Rylander

Science

-LRB-

-RRB-

M.

Grosjean

et

al.

Global

Planet

Change

-LRB-

-RRB-

C.

Latorre

J.

L.

Betancourt

K.

A.

Rylander

J.

Quade

Geol

Soc

Am

Bull

-LRB-

-RRB-

Charcoal

in

layers

containing

triangular

points

has

been

dated

at

San

and

between

and

cal

yr

B.P.

-LRB-

table

and

fig

-RRB-

P.

A.

Baker

et

al.

Science

-LRB-

-RRB-

G.

O.

Seltzer

S.

Cross

P.

Baker

R.

Dunbar

S.

Fritz

Geology

-LRB-

-RRB-

L.

G.

Thompson

et

al.

Science

-LRB-

-RRB-

M.

Grosjean

Science

-LRB-

-RRB-

E.

P.

Tonni

written

communication

M.

T.

Alberdi

written

communication

J.

Fernandez

et

al.

Geoarchaeology

-LRB-

-RRB-

The

histogram

of

middens

is

processed

from

-LRB-

-RRB-

M.

Grosjean

L.

Nunez

I.

Cartajena

B.

Messerli

Quat

Res

-LRB-

-RRB-

The

term

Silencio

Arqueologico

describes

the

mid

Holocene

collapse

of

human

population

at

those

archaeological

sites

of

the

Atacama

Desert

that

are

vulnerable

to

multicentennial

or

millennial-scale

drought

The

term

Silencio

Archaeologico

does

not

conflict

with

the

presence

of

humans

at

sites

that

are

not

susceptible

to

climate

change

such

as

in

spring

and

river

oases

that

drain

large

-LRB-

Pleistocene

-RRB-

aquifers

or

at

sites

where

wetlands

were

created

during

the

arid

middle

Holocene

such

as

and

Laguna

Miscanti

M.

Aldenderfer

Science

-LRB-

-RRB-

A

mid-Holocene

hiatus

is

found

at

Inca

Cueva

Huachichocana

Pintocamayoc

and

Yavi

whereas

occupation

continued

at

the

oases

of

Susques

and

Quebrada

Seca

L.

Nunez

et

al.

Estud

Atacamenos

-LRB-

-RRB-

D.

H.

Sandweiss

K.

A.

Maasch

D.

G.

Anderson

Science

-LRB-

-RRB-

Grants

from

the

National

Geographic

Society

-LRB-

-RRB-

the

Swiss

National

Science

Foundation

-LRB-

-RRB-

and

Fondo

Nacional

de

Desarrollo

Cienti

fico

y

Tecnologico

-LRB-

-RRB-

and

comments

by

J.

P.

Bradbury

B.

Meggers

G.

Seltzer

and

D.

Stanford

are

acknowledged

Supporting

Online

Material

Figs

to

Tables

and

July

accepted

September

Network

Motifs

Simple

Building

Blocks

of

Complex

Networks

R.

Milo

S.

Shen-Orr

S.

Itzkovitz

N.

Kashtan

D.

Chklovskii

U.

Downloaded

from

on

May

Complex

networks

are

studied

across

many

fields

of

science

To

uncover

their

structural

design

principles

we

defined

network

motifs

patterns

of

interconnections

occurring

in

complex

networks

at

numbers

that

are

significantly

higher

than

those

in

randomized

networks

We

found

such

motifs

in

networks

from

biochemistry

neurobiology

ecology

and

engineering

The

motifs

shared

by

ecological

food

webs

were

distinct

from

the

motifs

shared

by

the

genetic

networks

of

Escherichia

coli

and

Saccharomyces

cerevisiae

or

from

those

found

in

the

World

Wide

Web

Similar

motifs

were

found

in

networks

that

perform

information

processing

even

though

they

describe

elements

as

different

as

biomolecules

within

a

cell

and

synaptic

connections

between

neurons

in

Caenorhabditis

elegans

Motifs

may

thus

define

universal

classes

of

networks

This

approach

may

uncover

the

basic

building

blocks

of

most

networks

Many

of

the

complex

networks

that

occur

in

nature

have

been

shown

to

share

global

statistical

features

-LRB-

-RRB-

These

include

the

small

world

property

-LRB-

-RRB-

of

short

paths

between

any

two

nodes

and

highly

clustered

connections

In

addition

in

many

natural

networks

there

are

a

few

nodes

with

many

more

connections

than

the

average

node

has

In

these

types

References

and

Notes

Departments

of

Physics

of

Complex

Systems

and

Molecular

Cell

Biology

Weizmann

Institute

of

Science

Rehovot

Israel

Spring

Harbor

Laboratory

Cold

Spring

Harbor

NY

USA

To

whom

correspondence

should

be

addressed

Email

urialon@weizmann.ac.il

of

networks

termed

scale-free

networks

-LRB-

-RRB-

the

fraction

of

nodes

having

k

edges

p

-LRB-

k

-RRB-

decays

as

a

power

law

p

-LRB-

k

-RRB-

k

-LRB-

where

is

often

between

and

-RRB-

To

go

beyond

these

global

features

would

require

an

understanding

of

the

basic

structural

elements

particular

to

each

class

of

networks

-LRB-

-RRB-

To

do

this

we

developed

an

algorithm

for

detecting

network

motifs

recurring

significant

patterns

of

interconnections

A

detailed

application

to

a

gene

regulation

network

has

been

presented

-LRB-

-RRB-

Related

methods

were

used

to

test

hypotheses

on

social

networks

-LRB-

-RRB-

Here

we

generalize

this

approach

to

virtually

any

type

of

connectivity

graph

and

find

the

striking

appearance

of

Fig.

-LRB-

A

-RRB-

Examples

of

interactions

represented

by

directed

edges

between

nodes

in

some

of

the

networks

used

for

the

present

study

These

networks

go

from

the

scale

of

biomolecules

-LRB-

transcription

factor

protein

X

binds

regulatory

DNA

regions

of

a

gene

to

regulate

the

production

rate

of

protein

Y

-RRB-

through

cells

-LRB-

neuron

X

is

synaptically

connected

to

neuron

Y

-RRB-

to

organisms

-LRB-

X

feeds

on

Y

-RRB-

-LRB-

B

-RRB-

All

types

of

three-node

connected

subgraphs

OCTOBER

VOL

SCIENCE

REPORTS

motifs

in

networks

representing

a

broad

range

of

natural

phenomena

We

started

with

networks

where

the

interactions

between

nodes

are

represented

by

directed

edges

-LRB-

Fig.

-RRB-

Each

network

was

scanned

for

all

possible

n-node

subgraphs

-LRB-

in

the

present

study

n

and

-RRB-

and

the

number

of

occurrences

of

each

subgraph

was

recorded

Each

network

contains

numerous

types

of

nnode

subgraphs

-LRB-

Fig.

-RRB-

To

focus

on

those

that

are

likely

to

be

important

we

compared

the

real

network

to

suitably

randomized

networks

-LRB-

-RRB-

and

only

selected

patterns

appearing

in

the

real

network

at

numbers

significantly

higher

than

those

in

the

randomized

networks

-LRB-

Fig.

-RRB-

For

a

stringent

comparison

we

used

randomized

networks

that

have

the

same

single-node

characteristics

as

does

the

real

network

Each

node

in

the

randomized

networks

has

the

same

number

of

incoming

and

outgoing

edges

as

the

corresponding

node

has

in

the

real

network

The

comparison

to

this

randomized

ensemble

accounts

for

patterns

that

appear

only

because

of

the

single-node

characteristics

of

the

network

-LRB-

e.g.

the

presence

of

nodes

with

a

large

number

of

edges

-RRB-

Furthermore

the

randomized

networks

used

to

calculate

the

significance

of

nnode

subgraphs

were

generated

to

preserve

the

same

number

of

appearances

of

all

-LRB-

n

-RRB-

node

subgraphs

as

in

the

real

network

-LRB-

-RRB-

This

ensures

that

a

high

significance

was

not

assigned

to

a

pattern

only

because

it

has

a

highly

significant

subpattern

The

network

motifs

are

those

patterns

for

which

the

probability

P

of

appearing

in

a

randomized

network

an

equal

or

greater

number

of

times

than

in

the

real

network

is

lower

than

a

cutoff

value

-LRB-

here

P

-RRB-

Patterns

that

are

functionally

important

but

not

statistically

significant

could

exist

which

would

be

missed

by

our

approach

We

applied

the

algorithm

to

several

networks

from

biochemistry

-LRB-

transcriptional

gene

regulation

-RRB-

ecology

-LRB-

food

webs

-RRB-

neurobiology

-LRB-

neuron

connectivity

-RRB-

and

engineering

-LRB-

electronic

circuits

World

Wide

Web

-RRB-

The

network

motifs

found

are

shown

in

Table

Transcription

networks

are

biochemical

networks

responsible

for

regulating

the

expression

of

genes

in

cells

-LRB-

-RRB-

These

are

directed

graphs

in

which

the

nodes

represent

genes

-LRB-

Fig.

-RRB-

Edges

are

directed

from

a

gene

that

encodes

for

a

transcription

factor

protein

to

a

gene

transcriptionally

regulated

by

that

transcription

factor

We

analyzed

the

two

best

characterized

transcriptional

regulation

networks

corresponding

to

organisms

from

different

kingdoms

a

eukaryote

-LRB-

the

yeast

Saccharomyces

cerevisiae

-RRB-

-LRB-

-RRB-

and

a

bacterium

-LRB-

Escherichia

coli

-RRB-

-LRB-

-RRB-

The

two

transcription

networks

show

the

same

motifs

a

three-node

motif

termed

feedforward

loop

-LRB-

-RRB-

and

a

four-node

motif

termed

bi-fan

These

motifs

appear

numerous

times

in

each

network

-LRB-

Table

-RRB-

in

nonhomologous

gene

systems

that

perform

diverse

biological

functions

The

number

of

times

they

appear

is

more

than

standard

deviations

greater

than

their

mean

number

of

appearances

in

randomized

networks

Only

these

subgraphs

of

the

possible

different

three-node

subgraphs

-LRB-

Fig.

-RRB-

and

different

four-node

subgraphs

are

significant

and

are

therefore

considered

network

motifs

Many

other

three

and

four-node

subgraphs

recur

throughout

the

networks

but

at

numbers

that

are

less

than

the

mean

plus

standard

deviations

of

their

appearance

in

randomized

networks

We

next

applied

the

algorithm

to

ecosystem

food

webs

-LRB-

-RRB-

in

which

nodes

represent

groups

of

species

Edges

are

directed

from

a

node

representing

a

predator

to

the

node

representing

its

prey

We

analyzed

data

collected

by

different

groups

at

seven

distinct

ecosystems

-LRB-

-RRB-

including

both

aquatic

and

terrestrial

habitats

Each

of

the

food

webs

displayed

one

or

two

three-node

network

motifs

and

one

to

five

four-node

network

motifs

One

can

define

the

consensus

motifs

as

the

motifs

shared

by

networks

of

a

given

type

Five

of

the

seven

food

webs

shared

one

three-node

motif

and

all

seven

shared

one

four-node

motif

-LRB-

Table

-RRB-

In

contrast

to

the

three-node

motif

-LRB-

termed

three

chain

-RRB-

the

three-node

feedforward

loop

was

underrepresented

in

the

food

webs

This

suggests

that

direct

interactions

between

species

at

a

separation

of

two

layers

-LSB-

as

in

the

case

of

omnivores

-LRB-

-RRB-

-RSB-

are

selected

against

The

biparallel

motif

indicates

that

two

species

that

are

prey

of

the

same

predator

both

tend

to

share

the

same

prey

Both

network

motifs

may

thus

represent

general

tendencies

of

food

webs

-LRB-

-RRB-

We

next

studied

the

neuronal

connectivity

network

of

the

nematode

Caenorhabditis

elegans

-LRB-

-RRB-

Nodes

represent

neurons

-LRB-

or

neuron

Fig.

Schematic

view

of

network

motif

detection

Network

motifs

are

patterns

that

recur

much

more

frequently

-LRB-

A

-RRB-

in

the

real

network

than

-LRB-

B

-RRB-

in

an

ensemble

of

randomized

networks

Each

node

in

the

randomized

networks

has

the

same

number

of

incoming

and

outgoing

edges

as

does

the

corresponding

node

in

the

real

network

Red

dashed

lines

indicate

edges

that

participate

in

the

feedforward

loop

motif

which

occurs

five

times

in

the

real

network

Fig.

Concentration

C

of

the

feedforward

loop

motif

in

real

and

randomized

subnetworks

of

the

E.

coli

transcription

network

-LRB-

-RRB-

C

is

the

number

of

appearances

of

the

motif

divided

by

the

total

number

of

appearances

of

all

connected

three-node

subgraphs

-LRB-

Fig.

-RRB-

Subnetworks

of

size

S

were

generated

by

choosing

a

node

at

random

and

adding

to

it

nodes

connected

by

an

incoming

or

outgoing

edge

until

S

nodes

were

obtained

and

then

including

all

of

the

edges

between

these

S

nodes

present

in

the

full

150 200 250 300

network

Each

of

the

subSubnetwork

size

networks

was

randomized

-LRB-

-RRB-

-LRB-

shown

are

mean

and

SD

of

subnetworks

of

each

size

-RRB-

Concentration

of

Feedforward

loop

Real

Random

SCIENCE

VOL

OCTOBER

Downloaded

from

on

May

REPORTS

classes

-RRB-

and

edges

represent

synaptic

connections

between

the

neurons

We

found

the

feedforward

loop

motif

in

agreement

with

anatomical

observations

of

triangular

connectivity

structures

-LRB-

-RRB-

The

four-node

motifs

include

the

bi-fan

and

the

bi-parallel

-LRB-

Table

-RRB-

Two

of

these

motifs

-LRB-

feedforward

loop

and

bi-fan

-RRB-

were

also

found

in

the

transcriptional

gene

regulation

networks

This

similarity

in

motifs

may

point

to

a

fundamental

similarity

in

the

design

constraints

of

the

two

types

of

networks

Both

networks

function

to

carry

information

from

sensory

components

-LRB-

sensory

neurons/transcription

factors

regulated

by

biochemical

signals

-RRB-

to

effectors

-LRB-

motor

neurons/structural

genes

-RRB-

The

feedforward

loop

motif

common

to

both

types

of

networks

may

play

a

functional

role

in

information

processing

One

possible

function

of

this

circuit

is

to

activate

output

only

if

the

input

signal

is

persistent

and

to

allow

a

rapid

deactivation

when

the

input

goes

off

-LRB-

-RRB-

Indeed

many

of

the

input

nodes

in

the

neural

feedforward

loops

are

sensory

neurons

which

may

require

this

type

of

information

processing

to

reject

transient

input

fluctuations

that

are

inherent

in

a

variable

or

noisy

environment

We

also

studied

several

technological

networks

We

analyzed

the

benchmark

set

of

sequential

logic

electronic

circuits

-LRB-

-RRB-

The

nodes

in

these

circuits

represent

logic

gates

and

flip-flops

These

nodes

are

linked

by

directed

edges

We

found

that

the

motifs

separate

the

circuits

into

classes

that

correspond

to

the

circuit

's

functional

description

In

Table

we

present

two

classes

consisting

of

five

forwardlogic

chips

and

three

digital

fractional

multipliers

The

digital

fractional

multipliers

share

three

motifs

including

three

and

four-node

feedback

loops

The

forward

logic

chips

share

the

feedforward

loop

bi-fan

and

bi-parallel

motifs

which

are

similar

to

the

motifs

found

in

the

genetic

and

neuronal

information-processing

networks

We

found

a

different

set

of

motifs

in

a

network

of

directed

hyperlinks

between

World

Wide

Web

pages

within

a

single

domain

-LRB-

-RRB-

The

World

Wide

Web

motifs

may

reflect

a

design

aimed

at

short

paths

between

related

pages

Application

of

our

approach

to

nondirected

networks

shows

distinct

sets

of

motifs

in

networks

of

protein

interactions

and

Internet

router

connections

-LRB-

-RRB-

of

the

network

motifs

shared

by

the

food

webs

matched

the

motifs

found

in

the

gene

regulation

networks

or

the

World

Wide

Web

Only

one

of

the

food

web

consensus

motifs

also

appeared

in

the

neuronal

network

Different

motif

sets

were

found

in

electronic

circuits

with

different

functions

This

suggests

that

motifs

can

define

broad

classes

of

networks

each

with

specific

types

of

elementary

structures

The

motifs

reflect

the

underlying

processes

that

generated

each

type

of

network

for

example

food

webs

evolve

to

allow

a

flow

of

energy

from

the

bottom

to

the

top

of

food

chains

whereas

gene

regulation

and

neuron

networks

evolve

to

process

information

Information

processing

seems

to

give

rise

to

significantly

different

structures

than

does

energy

flow

We

further

characterized

the

statistical

significance

of

the

motifs

as

a

function

of

network

size

by

considering

pieces

of

various

sizes

-LRB-

subnetworks

-RRB-

of

the

full

network

The

concentration

of

motifs

in

the

subnetworks

is

about

the

same

as

that

in

the

full

network

-LRB-

Fig.

-RRB-

In

contrast

the

concentration

of

the

corresponding

subgraphs

in

the

randomized

versions

of

the

subnetworks

decreases

sharply

with

size

In

analogy

with

statistical

physics

the

number

of

appearances

of

each

motif

in

the

real

networks

Network

Nodes

Edges

Nreal

Nrand

SD

X

Y

Z

Z

score

Feedforward

loop

Nreal

X

Nrand

SD

Y

Z

score

Bi-fan

Nreal

Nrand

SD

Z

score

Gene

regulation

-LRB-

transcription

-RRB-

Z

E.

coli

S.

cerevisiae

Neurons

424 685 519

X

Y

Z

X

Y

Z

NS

NS

X

Feedforward

Y

loop

Z

Electronic

circuits

-LRB-

digital

fractional

multipliers

-RRB-

424 413 612 211

X

285 120 400 140

Threenode

feedback

loop

Feedback

with

two

mutual

dyads

800 1040 1739 2404

X

Feedforward

loop

X

W

Y

Bi-fan

Y

W

X

Z

Biparallel

Z

X

Y

W

7295 1357 382

26 181 397 267

X

W

Biparallel

C.

elegans

Food

webs

Three

chain

Z

Little

Rock

Ythan

St.

Martin

Chesapeake

Coachella

Skipwith

B.

Brook

Electronic

circuits

-LRB-

forward

logic

chips

-RRB-

984 391 205

67 243 189 104

3219 1182 469

80 279 184 181

Y

Bi-fan

Y

X

Z

W

Biparallel

Z

W

Y

1200 800 2550 1050

Bi-fan

480 711 531 209

X

Y

Y

World

Wide

Web

189 399 819

Z

X

Y

Z

Z

X

Y

W

Fully

connected

triad

Z

W

X

335 320 340 200

Fournode

feedback

loop

Uplinked

mutual

dyad

Z

Y

Z

nd.edu

Has

additional

four-node

motif

-LRB-

Z

W

Z

W

W

-RRB-

Nreal

Nrand

Z

Has

additional

four-node

motif

-LRB-

Y

Z

Z

W

-RRB-

Nreal

Nrand

Z

The

three-node

pattern

-LRB-

Y

Z

Z

Y

-RRB-

also

occurs

significantly

more

than

at

random

It

is

not

a

motif

by

the

present

definition

because

it

does

not

appear

with

completely

distinct

sets

of

nodes

more

than

U

times

Has

additional

four-node

motif

-LRB-

Y

Has

two

additional

three-node

motifs

-LRB-

Y

Z

Z

W

X

X

-RRB-

Nreal

Nrand

Z

Z

Y

-RRB-

Nreal

Nrand

Z

and

-LRB-

Y

Z

Z

-RRB-

Nreal

Nrand

Z

OCTOBER

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Table

Network

motifs

found

in

biological

and

technological

networks

The

numbers

of

nodes

and

edges

for

each

network

are

shown

For

each

motif

the

numbers

of

appearances

in

the

real

network

-LRB-

Nreal

-RRB-

and

in

the

randomized

networks

-LRB-

Nrand

SD

all

values

rounded

-RRB-

-LRB-

-RRB-

are

shown

The

P

value

of

all

motifs

is

P

as

determined

by

comparison

to

randomized

networks

-LRB-

in

the

case

of

the

World

Wide

Web

-RRB-

As

a

qualitative

measure

of

statistical

significance

the

Z

score

-LRB-

Nreal

Nrand

-RRB-

SD

is

shown

NS

not

significant

Shown

are

motifs

that

occur

at

least

U

times

with

completely

different

sets

of

nodes

The

networks

are

as

follows

-LRB-

-RRB-

transcription

interactions

between

regulatory

proteins

and

genes

in

the

bacterium

E.

coli

-LRB-

-RRB-

and

the

yeast

S.

cerevisiae

-LRB-

-RRB-

synaptic

connections

between

neurons

in

C.

elegans

including

neurons

connected

by

at

least

five

synapses

-LRB-

-RRB-

trophic

interactions

in

ecological

food

webs

-LRB-

-RRB-

representing

pelagic

and

benthic

species

-LRB-

Little

Rock

Lake

-RRB-

birds

fishes

invertebrates

-LRB-

Ythan

Estuary

-RRB-

primarily

larger

fishes

-LRB-

Chesapeake

Bay

-RRB-

lizards

-LRB-

St.

Martin

Island

-RRB-

primarily

invertebrates

-LRB-

Skipwith

Pond

-RRB-

pelagic

lake

species

-LRB-

Bridge

Brook

Lake

-RRB-

and

diverse

desert

taxa

-LRB-

Coachella

Valley

-RRB-

electronic

sequential

logic

circuits

parsed

from

the

benchmark

set

-LRB-

-RRB-

where

nodes

represent

logic

gates

and

flip-flops

-LRB-

presented

are

all

five

partial

scans

of

forward-logic

chips

and

three

digital

fractional

multipliers

in

the

benchmark

set

-RRB-

and

World

Wide

Web

hyperlinks

between

Web

pages

in

a

single

domain

-LRB-

-RRB-

-LRB-

only

three-node

motifs

are

shown

-RRB-

e

multiplied

by

the

power

of

-LRB-

e.g.

-RRB-

REPORTS

appears

to

be

an

extensive

variable

-LRB-

i.e.

one

that

grows

linearly

with

the

system

size

-RRB-

These

variables

are

nonextensive

in

the

randomized

networks

The

existence

of

such

variables

may

be

a

unifying

property

of

evolved

or

designed

systems

The

decrease

of

the

concentration

C

with

randomized

network

size

S

-LRB-

Fig.

-RRB-

qualitatively

agrees

with

exact

results

-LRB-

-RRB-

on

Erdos-Renyi

random

graphs

-LRB-

random

graphs

that

preserve

only

the

number

of

nodes

and

edges

of

the

real

network

-RRB-

in

which

C

In

general

the

larger

the

network

is

the

more

significant

the

motifs

tend

to

become

This

trend

can

also

be

seen

in

Table

by

comparing

networks

of

different

sizes

The

network

motif

detection

algorithm

appears

to

be

effective

even

for

rather

small

networks

-LRB-

on

the

order

of

edges

-RRB-

This

is

because

three

or

four-node

subgraphs

occur

in

large

numbers

even

in

small

networks

Furthermore

our

approach

is

not

sensitive

to

data

errors

for

example

the

sets

of

significant

network

motifs

do

not

change

in

any

of

the

networks

upon

addition

removal

or

rearrangement

of

of

the

edges

at

random

In

information-processing

networks

the

motifs

may

have

specific

functions

as

elementary

computational

circuits

-LRB-

-RRB-

More

generally

they

may

be

interpreted

as

structures

that

arise

because

of

the

special

constraints

under

which

the

network

has

evolved

-LRB-

-RRB-

It

is

of

value

to

detect

and

understand

network

motifs

in

order

to

gain

insight

into

their

dynamical

behavior

and

to

define

classes

of

networks

and

network

homologies

Our

approach

can

be

readily

generalized

to

any

type

of

network

including

those

with

multiple

colors

of

edges

or

nodes

It

would

be

fascinating

to

see

what

types

of

motifs

occur

in

other

networks

and

to

understand

the

processes

that

yield

given

motifs

during

network

evolution

arrows

for

each

node

The

randomized

networks

used

for

detecting

four-node

motifs

preserve

the

above

characteristics

as

well

as

the

numbers

of

all

three-node

subgraphs

as

in

the

real

network

Algorithms

for

constructing

these

randomized

network

ensembles

are

described

-LRB-

-RRB-

Additional

information

is

available

at

Methods

are

available

as

supporting

material

on

Science

Online

D.

Thieffry

A.

M.

Huerta

E.

Perez-Rueda

J.

ColladoVides

Bioessays

-LRB-

-RRB-

M.

C.

Costanzo

et

al.

Nucleic

Acids

Res

-LRB-

-RRB-

J.

Cohen

F.

Briand

C.

Newman

Community

Food

Webs

Data

and

Theory

-LRB-

Springer

Berlin

-RRB-

R.

Williams

N.

Martinez

Nature

-LRB-

-RRB-

S.

Pimm

J.

Lawton

J.

Cohen

Nature

-LRB-

-RRB-

J.

White

E.

Southgate

J.

Thomson

S.

Brenner

Philos

Trans

R.

Soc

London

Ser

B

-LRB-

-RRB-

F.

Brglez

D.

Bryan

K.

Kozminski

Proc

IEEE

Int

Symp

Circuits

Syst.

-LRB-

-RRB-

In

Erdos-Renyi

randomized

networks

with

a

fixed

connectivity

-LRB-

-RRB-

the

concentration

of

a

subgraph

with

n

nodes

and

k

edges

scales

with

network

size

as

C

Sn

k

-LRB-

thus

C

for

the

feedforward

loop

of

Fig.

where

n

k

-RRB-

The

sole

exception

in

Table

in

which

C

should

not

vanish

at

large

S

is

the

three-chain

pattern

in

food

webs

where

n

and

k

D.

Callaway

J.

Hopcroft

J.

Kleinberg

M.

Newman

S.

Strogatz

Phys

Rev.

E

-LRB-

-RRB-

We

thank

S.

Maslov

and

K.

Sneppen

for

valuable

discussions

We

thank

J.

Collado-Vides

N.

Martinez

R.

Govindan

R.

Durbin

L.

Amaral

R.

Cancho

S.

Maslov

and

K.

Sneppen

for

kindly

providing

data

as

well

as

D.

Alon

E.

Domany

M.

Elowitz

I.

Kanter

O.

Hobart

M.

Naor

D.

Mukamel

A.

Murray

S.

Quake

R.

Raz

M.

Reigl

M.

Surette

K.

Sneppen

P.

Sternberg

E.

Winfree

and

all

members

of

our

lab

for

comments

We

thank

Caltech

and

the

Aspen

Center

for

Physics

for

their

hospitality

during

part

of

this

work

We

acknowledge

support

from

the

Israel

Science

Foundation

the

Human

Frontier

Science

Program

and

the

Minerva

Foundation

Supporting

Online

Material

Methods

Table

May

accepted

September

Progression

of

Vertebrate

Limb

Development

Through

SHH-Mediated

Counteraction

of

Pascal

te

Welscher

Aimee

Zuniga

Sanne

Kuijper

Thijs

Drenth

Hans

J.

Goedemans

Frits

Meijlink

Rolf

Distal

limb

development

and

specification

of

digit

identities

in

tetrapods

are

under

the

control

of

a

mesenchymal

organizer

called

the

polarizing

region

Sonic

Hedgehog

-LRB-

SHH

-RRB-

is

the

morphogenetic

signal

produced

by

the

polarizing

region

in

the

posterior

limb

bud

Ectopic

anterior

SHH

signaling

induces

digit

duplications

and

has

been

suspected

as

a

major

cause

underlying

congenital

malformations

that

result

in

digit

polydactyly

Here

we

report

that

the

polydactyly

of

mice

arises

independently

of

SHH

signaling

Disruption

of

one

or

both

alleles

in

mouse

embryos

lacking

Shh

progressively

restores

limb

distal

development

and

digit

formation

Our

genetic

analysis

indicates

that

SHH

signaling

counteracts

repression

of

key

regulator

genes

cell

survival

and

distal

progression

of

limb

bud

development

The

Hedgehog

-LRB-

Hh

-RRB-

signaling

pathway

controls

many

key

developmental

processes

during

animal

embryogenesis

-LRB-

-RRB-

In

Drosophila

embryos

all

known

functions

of

Hh

signaling

are

mediated

by

the

transcriptional

effector

Cubitus

interruptus

-LRB-

Ci

-RRB-

-LRB-

-RRB-

Several

homologs

of

Hh

and

Ci

have

been

identified

in

higher

vertebrates

In

particular

Sonic

Hedgehog

-LRB-

SHH

-RRB-

and

the

Ci

homolog

are

required

for

vertebrate

limb

development

-LRB-

-RRB-

acts

first

during

the

initiation

of

References

and

Notes

S.

H.

Strogatz

Nature

-LRB-

-RRB-

B.

Bollobas

Random

Graphs

-LRB-

Academic

London

-RRB-

D.

Watts

S.

Strogatz

Nature

-LRB-

-RRB-

A.-L

Barabasi

R.

Albert

Science

-LRB-

-RRB-

M.

Newman

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

H.

Jeong

B.

Tombor

R.

Albert

Z.

N.

Oltvai

A.

L.

Barabasi

Nature

-LRB-

-RRB-

R.

F.

Cancho

C.

Janssen

R.

V.

Sole

Phys

Rev.

E

-LRB-

-RRB-

R.

F.

Cancho

R.

V.

Sole

Proc

R.

Soc

London

Ser

B

-LRB-

-RRB-

L.

Amaral

A.

Scala

M.

Barthelemy

H.

Stanley

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

B.

Huberman

L.

Adamic

Nature

-LRB-

-RRB-

S.

Shen-Orr

R.

Milo

S.

Mangan

U.

Alon

Nature

Genet

-LRB-

-RRB-

P.

Holland

S.

Leinhardt

in

Sociological

Methodology

D.

Heise

Ed

-LRB-

Jossey-Bass

San

Francisco

-RRB-

pp.

S.

Wasserman

K.

Faust

Social

Network

Analysis

-LRB-

Cambridge

Univ.

Press

New

York

-RRB-

N.

Guelzim

S.

Bottani

P.

Bourgine

F.

Kepes

Nature

Genet

-LRB-

-RRB-

M.

Newman

S.

Strogatz

D.

Watts

Phys

Rev.

E

-LRB-

-RRB-

S.

Maslov

K.

Sneppen

Science

-LRB-

-RRB-

The

randomized

networks

used

for

detecting

threenode

motifs

preserve

the

numbers

of

incoming

outgoing

and

double

edges

with

both

incoming

and

outgoing

Department

of

Developmental

Biology

Faculty

of

Biology

Utrecht

University

Padualaan

CH

Utrecht

Netherlands

Laboratorium

Uppsalalaan

CT

Utrecht

Netherlands

To

whom

correspondence

should

be

addressed

Email

R.Zeller@bio.uu.nl

limb

bud

development

and

before

the

activation

of

SHH

signaling

in

posterior

restriction

of

the

basic

helix-loop-helix

transcription

factor

dHAND

dHAND

in

turn

prevents

expression

from

spreading

posteriorly

-LRB-

Fig.

panel

-RRB-

-LRB-

-RRB-

In

addition

restricts

the

SHH-independent

early

expression

of

HoxD

genes

and

Gremlin

to

the

posterior

mesenchyme

-LRB-

-RRB-

Subsequently

dHAND

functions

in

the

activation

of

Shh

expression

-LRB-

-RRB-

Limb

bud

morphogenesis

is

then

controlled

by

reciprocal

interactions

of

two

signaling

centers

-LRB-

Fig.

panel

-RRB-

the

polarizing

region

an

instructive

organizer

located

in

the

posterior

limb

bud

mesenchyme

and

the

apical

ectodermal

ridge

-LRB-

AER

-RRB-

SHH

signaling

by

the

polarizing

region

in

combination

with

bone

morphogenetic

proteins

SCIENCE

VOL

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

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