BIOINFORMATICS

Systems

biology

ORIGINAL

PAPER

Vol

no.

pages

doi

Global

topological

features

of

cancer

proteins

in

the

human

interactome

Pall

F.

Jonsson

and

Paul

A.

Bates

Biomolecular

Modelling

Laboratory

Cancer

Research

UK

London

Research

Institute

Lincoln

's

Inn

Fields

London

UK

Received

on

May

revised

on

July

accepted

on

July

Advance

Access

publication

July

Associate

Editor

Jonathan

Wren

ABSTRACT

Motivation

The

study

of

interactomes

or

networks

of

protein-protein

interactions

is

increasingly

providing

valuable

information

on

biological

systems

Here

we

report

a

study

of

cancer

proteins

in

an

extensive

human

protein-protein

interaction

network

constructed

by

computational

methods

Results

We

show

that

human

proteins

translated

from

known

cancer

genes

exhibit

a

network

topology

that

is

different

from

that

of

proteins

not

documented

as

being

mutated

in

cancer

In

particular

cancer

proteins

show

an

increase

in

the

number

of

proteins

they

interact

with

They

also

appear

to

participate

in

central

hubs

rather

than

peripheral

ones

mirroring

their

greater

centrality

and

participation

in

networks

that

form

the

backbone

of

the

proteome

Moreover

we

show

that

cancer

proteins

contain

a

high

ratio

of

highly

promiscuous

structural

domains

i.e.

domains

with

a

high

propensity

for

mediating

protein

interactions

These

observations

indicate

an

underlying

evolutionary

distinction

between

the

two

groups

of

proteins

reflecting

the

central

roles

of

proteins

whose

mutations

lead

to

cancer

Contact

paul.bates@cancer.org.uk

Supplementary

information

The

interactome

data

are

available

though

the

PIP

-LRB-

Potential

Interactions

of

Proteins

-RRB-

web

server

at

Further

additional

material

is

available

at

bioinformatics

INTRODUCTION

The

availability

of

high-throughput

experimental

data

has

allowed

construction

of

increasingly

comprehensive

and

accurate

proteinprotein

interaction

networks

Initial

network

studies

were

performed

on

yeast

-LRB-

Uetz

et

al.

Ito

et

al.

Ho

et

al.

Gavin

et

al.

-RRB-

but

more

complex

organisms

are

gradually

being

surveyed

-LRB-

Giot

et

al.

Li

et

al.

-RRB-

The

structure

or

topology

of

such

networks

not

only

sheds

light

on

the

complex

cellular

mechanisms

and

processes

but

also

gives

insight

into

evolutionary

aspects

of

the

proteins

involved

-LRB-

Jeong

et

al.

Fraser

et

al.

Bu

et

al.

Wuchty

Calvano

et

al.

-RRB-

Charting

interaction

maps

of

entire

genomes

is

undoubtedly

useful

for

improved

understanding

of

cellular

function

especially

once

they

have

been

integrated

with

a

wider

collection

of

biological

data

It

is

now

possible

to

map

a

number

of

different

attributes

or

data

types

onto

interactomes

Examples

of

such

are

microarray

expression

data

-LRB-

Ideker

et

al.

Sohler

et

al.

de

Lichtenberg

et

al.

-RRB-

gene

ontology

-LRB-

Formstecher

et

al.

-RRB-

structural

information

-LRB-

Dunker

et

al.

-RRB-

and

domain

composition

-LRB-

Riley

et

al.

Wuchty

and

Almaas

-RRB-

Studies

focusing

on

the

human

interactome

have

thus

far

been

limited

due

to

lack

of

reliable

and

comprehensive

experimental

data

To

compensate

for

this

several

computational

methods

have

been

developed

with

the

aim

of

predicting

protein-protein

interactions

One

approach

is

based

on

the

principle

of

orthologous

interactions

described

by

Matthews

et

al.

-LRB-

-RRB-

which

was

subsequently

implemented

-LRB-

Lehner

and

Fraser

Brown

and

Jurisica

-RRB-

and

further

extended

with

the

development

of

a

confidence

scoring

function

for

interactions

-LRB-

Jonsson

et

al.

-RRB-

Based

on

our

previous

approach

we

report

an

extensive

genomewide

study

of

cancer

and

non-cancer

proteins

integrated

into

a

carefully

validated

protein-protein

interaction

network

of

human

proteins

Wachi

et

al.

-LRB-

-RRB-

have

reported

increased

interaction

connectivity

in

differentially

expressed

proteins

in

lung

cancer

tissues

However

a

comprehensive

study

of

the

interaction

attributes

of

all

known

predisposing

human

cancer

genes

has

not

previously

been

attempted

In

this

study

we

examined

the

connectivity

of

proteins

known

to

be

susceptible

to

mutations

leading

to

cancer

-LRB-

Furteal

et

al.

-RRB-

In

addition

to

statistical

analysis

of

the

network

properties

of

cancer

proteins

we

utilized

a

clustering

method

aimed

at

highlighting

proteins

in

centrally

connected

hubs

that

form

the

backbone

of

the

interactome

We

show

that

cancer

proteins

display

a

global

topology

significantly

different

from

non-cancer

proteins

indicating

an

increased

central

role

of

cancer

proteins

within

the

interactome

METHODS

Construction

of

the

human

interactome

To

whom

correspondence

should

be

addressed

Networks

of

interacting

proteins

were

constructed

for

the

entire

human

genome

using

an

orthology-based

method

described

by

Jonsson

et

al.

-LRB-

-RRB-

One

of

the

benefits

of

the

orthologous

approach

is

the

reduced

noise

in

protein

interaction

data

which

allows

predictions

of

interactions

The

Author

-LRB-

s

-RRB-

This

is

an

Open

Access

article

distributed

under

the

terms

of

the

Creative

Commons

Attribution

Non-Commercial

License

-LRB-

uk

-RRB-

which

permits

unrestricted

non-commercial

use

distribution

and

reproduction

in

any

medium

provided

the

original

work

is

properly

cited

P.F.Jonsson

and

P.A.Bates

Cancer

Non-cancer

Connectivity

sensitivity

specificity

Fig.

Curve

of

receiver

operating

characteristics

-LRB-

ROC

-RRB-

at

different

cut-off

points

of

scores

The

area

under

the

curve

is

Table

Sensitivity

specificity

and

PPV

Fig.

The

number

of

interactions

in

which

cancer

proteins

participate

-LRB-

left

-RRB-

compared

with

the

number

of

interactions

of

non-cancer

proteins

-LRB-

right

-RRB-

Cancer

proteins

are

on

average

involved

in

interactions

whereas

non-cancer

proteins

are

involved

in

The

centre

of

the

box

is

the

median

and

the

box

spans

from

first

to

third

quartiles

-LRB-

the

inter-quartile

range

-RRB-

The

whiskers

extend

to

the

furthest

point

within

times

the

inter-quartile

range

Beyond

the

whiskers

all

outliers

are

shown

in

open

circles

up

to

a

distance

of

three

times

the

inter-quartile

range

and

closed

beyond

that

Score

cut-off

Sensitivity

Specificity

PPV

Interactions

51 50 213 113

944 947 034 378

interacting

through

different

homologues

The

complete

interactome

data

are

available

in

the

Supplementary

Material

ROC

curve

analysis

Sensitivity

specificity

and

positive

predictive

value

-LRB-

PPV

-RRB-

as

a

function

of

the

cut-off

score

The

positive

predictive

value

PPV

TP

-LRB-

TP

FP

-RRB-

indicates

the

likelihood

of

true

interaction

given

a

positive

prediction

Also

shown

is

the

total

number

of

interactions

for

each

score

cut-off

that

would

not

have

been

detected

in

a

single

species

-LRB-

Sharan

et

al.

-RRB-

In

short

the

method

identifies

putative

interactions

based

on

homology

to

experimentally

determined

interactions

in

a

range

of

different

species

BLAST

searches

were

run

for

the

human

genome

as

documented

in

version

of

the

Reference

Sequence

Database

-LRB-

Pruitt

et

al.

-RRB-

against

all

proteins

in

the

DIP

-LRB-

Salwinski

et

al.

-RRB-

and

MIPS

Mammalian

Protein-Protein

Interaction

databases

-LRB-

Pagel

et

al.

-RRB-

The

putative

interactions

were

given

confidence

scores

based

on

two

factors

the

level

of

homology

to

proteins

found

experimentally

to

interact

and

the

amount

of

experimental

data

available

The

confidence

score

S

is

given

by

S

N

X

ln

sai

sbi

n

i

Data

were

analysed

by

sensitivity

and

specificity

derived

from

the

receiver

operating

characteristic

-LRB-

ROC

-RRB-

curve

and

area

under

the

curve

The

receiver

operating

characteristic

analysis

builds

on

the

outcome

of

the

prediction

in

particular

on

the

rates

of

true

and

false

positive

identification

of

interaction

-LRB-

TP

and

FP

respectively

-RRB-

True

positive

interactions

were

sourced

from

the

Human

Protein

Reference

Database

Version

-LRB-

Peri

et

al.

-RRB-

Interactions

were

classified

as

false

positive

if

interacting

proteins

had

been

documented

in

incompatible

cellular

compartments

-LRB-

e.g.

plasma

membrane

proteins

interacting

with

nuclear

proteins

-RRB-

as

annotated

in

the

December

release

of

the

Gene

Ontology

-LRB-

GO

-RRB-

Consortium

database

-LRB-

Ashburner

et

al.

-RRB-

Clustering

of

interconnected

proteins

where

sai

and

sbi

are

sequence

similarity

bit

scores

to

proteins

ai

and

bi

respectively

which

have

experimentally

been

shown

to

interact

n

is

the

number

of

experiments

linking

protein

ai

to

protein

bi

and

N

is

the

total

number

of

instances

where

the

same

pair

of

proteins

is

identified

as

The

network

of

interactions

was

analysed

using

clique

percolation

clustering

-LRB-

Adamcsek

et

al.

-RRB-

which

locates

maximal

complete

subgraphs

-LRB-

k-cliques

-RRB-

in

the

networks

and

then

identifies

communities

by

carrying

out

standard

component

analysis

of

the

clique-clique

overlap

In

this

context

the

variable

k

is

defined

as

the

number

of

nodes

in

the

subgraph

and

a

k-clique

community

is

defined

as

the

union

of

all

k-cliques

that

can

be

reached

from

each

other

through

a

series

of

adjacent

k-cliques

where

cliques

sharing

k

nodes

are

defined

as

adjacent

The

resulting

communities

are

allowed

to

exhibit

a

degree

of

overlap

which

is

particularly

useful

as

such

methods

have

been

shown

to

be

more

suitable

for

identifying

central

hubs

in

biological

networks

compared

with

non-overlapping

clustering

algorithms

-LRB-

Wuchty

and

Almaas

-RRB-

The

protein

clustering

data

are

available

in

the

Supplementary

Material

Topology

of

cancer

protein

networks

Table

Connectivity

of

cancer

proteins

Interactions

fObs

fExp

Difference

-LRB-

-RRB-

Table

Connectivity

ratio

of

somatic

and

germline

mutated

cancer

protiens

-LRB-

number

of

proteins

in

parentheses

-RRB-

The

observed

difference

is

statistically

significant

-LRB-

p

-RRB-

Interactions

Sum

Somatic

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Germline

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Difference

-LRB-

-RRB-

Observed

interaction

frequency

distribution

of

cancer

proteins

compared

to

the

expected

value

as

observed

in

the

genome-wide

network

Datasets

Information

on

cancer

genes

was

obtained

from

a

comprehensive

census

of

human

cancer

genes

-LRB-

Futreal

et

al.

-RRB-

The

data

genes

encoding

protein

isoforms

were

mapped

on

to

the

protein-protein

interaction

network

Statistics

relating

to

the

frequency

of

domain-domain

interactions

were

obtained

from

version

of

PFAM

-LRB-

Bateman

et

al.

-RRB-

Proteins

were

classified

into

cellular

processes

according

to

information

from

release

of

the

KEGG

Pathway

Database

-LRB-

Kanehisa

et

al.

-RRB-

and

June

release

of

the

Gene

Ontology

database

RESULTS

AND

DISCUSSION

Network

validation

The

human

interactome

was

built

on

a

network

of

predicted

proteinprotein

interactions

After

constructing

the

human

interactome

we

assessed

the

sensitivity

and

specificity

of

the

predictions

on

which

the

interactome

was

based

Each

interaction

received

a

score

according

to

Equation

-LRB-

-RRB-

and

the

scoring

function

assessed

by

a

ROCcurve

shown

in

Figure

The

area

under

the

curve

is

equal

to

which

illustrates

that

the

derived

scoring

function

is

a

good

indicator

of

prediction

reliability

an

area

of

would

indicate

a

zero

rate

of

false

negative

and

rate

of

true

positive

identification

The

standard

error

for

the

graph

is

which

indicates

a

sufficient

sample

size

The

trade-off

for

high

a

sensitivity

is

a

lower

specificity

which

means

a

higher

fraction

of

false

positives

For

a

score

cut-off

of

we

obtained

sensitivity

of

and

specificity

of

-LRB-

Table

-RRB-

which

is

a

reasonable

balance

between

the

true

positive

fraction

-LRB-

sensitivity

-RRB-

and

the

true

negative

fraction

-LRB-

specificity

-RRB-

The

interactome

consisted

of

binary

interactions

when

a

minimum

score

of

was

implemented

This

compares

to

and

for

cut-off

score

of

and

respectively

Network

properties

of

cancer

proteins

The

construction

of

a

validated

human

protein-protein

interaction

network

allows

an

in-depth

analysis

of

individual

proteins

in

the

context

of

their

surroundings

Here

the

network

topographies

of

human

cancer

proteins

were

examined

with

the

aim

of

uncovering

intrinsic

properties

that

distinguish

proteins

prone

to

cancerous

mutations

from

those

that

are

not

Connectivity

The

number

of

interaction

partners

for

each

protein

in

the

network

was

calculated

using

the

cut-off

score

described

above

Statistics

were

accumulated

for

two

groups

proteins

classified

as

cancer

proteins

and

those

that

were

not

classified

as

being

linked

to

cancer

Cancer

proteins

were

shown

to

have

on

average

twice

as

many

interaction

partners

as

non-cancer

proteins

with

and

interaction

partners

respectively

-LRB-

Fig.

-RRB-

Table

shows

a

more

detailed

breakdown

of

the

interaction

frequency

Cancer

proteins

are

under

represented

in

the

category

of

the

least

connected

proteins

but

show

the

reverse

trend

in

all

other

categories

This

trend

is

highly

statistically

significant

-LRB-

p

-RRB-

As

each

interaction

in

our

predicted

protein-protein

interaction

map

receives

a

confidence

score

it

is

possible

to

test

the

robustness

of

the

above

observation

Increasing

the

cut-off

score

limits

the

amount

of

interaction

data

but

the

confidence

of

each

interaction

is

increased

Cancer

proteins

consistently

showed

twice

as

many

interaction

partners

as

non-cancer

proteins

when

the

score

cut-off

was

increased

indicating

that

the

observation

is

reliable

-LRB-

data

not

shown

-RRB-

The

discovery

of

a

difference

in

the

number

of

interaction

partners

between

cancer

and

non-cancer

proteins

could

suggest

a

differentiation

in

evolutionary

aspects

of

these

two

groups

Indeed

the

evolutionary

rate

and

age

of

genes

or

proteins

has

been

the

subject

of

several

publications

showing

increasing

evidence

for

a

correlation

between

the

age

of

proteins

and

the

number

of

interactions

they

participate

in

-LRB-

Fraser

et

al.

Eisenberg

and

Levanon

Wuchty

-RRB-

This

correlation

has

been

disputed

by

Jordan

et

al.

-LRB-

-RRB-

but

in

a

recent

publication

Saeed

and

Deane

-LRB-

-RRB-

show

that

a

correlation

does

exist

although

it

is

dependent

on

the

completeness

and

quality

of

the

dataset

under

study

These

results

indicate

that

proteins

whose

mutation

results

in

a

detrimental

change

of

function

that

leads

to

cancer

may

generally

be

older

than

the

non-cancer

ones

Jeong

et

al.

-LRB-

-RRB-

showed

that

the

most

highly

connected

proteins

in

yeast

are

also

the

ones

that

are

phenotypically

most

important

and

critical

for

the

survival

of

the

organism

Furthermore

it

has

been

shown

that

toxicity-modulating

proteins

exhibit

a

greater

number

of

interactions

-LRB-

Said

et

al.

-RRB-

It

follows

that

the

increased

connectivity

of

cancer

proteins

shown

here

suggests

that

they

play

a

central

role

in

the

protein

network

Cancers

result

from

the

accumulation

of

inherited

and

somatic

mutations

in

oncogenes

and

tumour

suppressor

genes

With

this

in

mind

it

is

of

interest

to

distinguish

between

somatic

and

germline

mutations

that

result

in

cancer

Germline

mutations

can

be

passed

between

generations

whereas

somatic

mutations

are

not

passed

on

to

offspring

This

study

shows

a

modest

but

statistically

significant

difference

-LRB-

p

-RRB-

in

the

distribution

of

interaction

partners

of

somatically

mutated

versus

germline

mutated

proteins

About

twothirds

of

the

proteins

showing

somatic

mutations

interact

with

a

relatively

low

number

of

proteins

-LRB-

interaction

partners

or

less

-RRB-

whereas

just

over

half

of

germline

mutated

proteins

fell

in

to

the

P.F.Jonsson

and

P.A.Bates

Table

The

most

frequently

observed

protein

domains

PFAM

id

Domain

annotation

Immunoglobulin

domain

Zinc-finger

type

Protein

kinase

domain

PHD-finger

Zinc-finger

type

PWWP

domain

Homeobox

domain

Zinc-finger

type

plus

B-box

EF

hand

EGF-like

domain

C

Terminus

-LRB-

BRCT

-RRB-

domain

Helix-loop-helix

DNA-binding

domain

Paired-box

domain

SET-domain

RNA

recognition

motif

-LRB-

RRM

RBD

or

RNP

-RRB-

TNFR/NGFR

cysteine-rich

region

Bromodomain

Death-domain

Ets-domain

fObs

fExp

n-fold

difference

ppromiscuity

The

most

frequently

observed

domains

in

human

cancer

proteins

listed

in

a

descending

order

Their

frequency

is

compared

against

the

expected

frequency

derived

from

a

network-wide

survey

The

ppromiscuity-value

shows

the

probability

of

observing

a

domain

with

higher

interaction

promiscuity

as

calculated

by

a

Probability

Density

Function

based

on

the

PFAM

domain

population

Domains

preceded

with

show

statistically

higher-than-expected

interaction

promiscuity

-LRB-

ppromiscuity

-RRB-

in

terms

of

the

number

of

different

domains

they

interact

with

Domains

without

any

documented

PFAM

interactions

receive

a

p-value

of

Table

Number

of

communities

and

proteins

at

different

k-values

Table

Multiple

community

membership

distribution

k-value

Communities

Non-cancer

proteins

8870 4245 1918 764

Cancer

proteins

-LRB-

-RRB-

334 234 117

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

k-value

Observed

-LRB-

-RRB-

Expected

-LRB-

-RRB-

Fold

difference

The

number

of

protein

communities

in

the

entire

human

interactome

identified

by

k-clique

analysis

at

different

k-values

The

number

of

non-cancer

and

cancer

proteins

in

the

communities

at

each

k-value

is

listed

on

the

right-hand

side

of

the

table

Percentage

of

cancer

proteins

belonging

to

more

than

one

community

-LRB-

based

on

proteins

identified

by

clustering

as

belonging

to

a

community

-RRB-

Expected

value

was

based

on

noncancer

proteins

same

grouping

-LRB-

Table

-RRB-

This

may

indicate

somewhat

younger

average

evolutionary

age

of

the

somatically

mutated

cancer

proteins

compared

with

germline

reflecting

the

fact

that

evolutionary

selection

pressure

affects

germline

mutated

proteins

Protein

domain

frequencies

Comparing

the

structural

and

domain

composition

of

cancer

proteins

against

domain

propensities

of

the

general

network

reveals

a

functional

diversity

that

differs

from

that

of

proteins

in

general

Table

lists

the

most

frequent

domains

found

in

the

cancer

protein

population

Most

of

those

domains

appear

with

a

higher

frequency

than

expected

Many

of

the

proteins

whose

frequency

is

increased

compared

with

the

expected

values

appear

to

be

of

nuclear

origin

and

have

functionality

that

particularly

focuses

on

DNA

regulation

and

repair

such

as

the

Zinc-finger

PHD-finger

BRCT

and

Paired-box

domains

The

concern

that

the

apparent

increased

connectivity

of

cancer

proteins

is

a

result

of

a

bias

in

the

protein

network

needed

to

be

addressed

As

described

the

construction

of

the

human

interaction

network

builds

on

experimental

data

and

it

could

be

argued

that

cancer

proteins

having

been

under

particular

scrutiny

by

the

scientific

community

have

been

studied

in

greater

detail

which

could

explain

the

higher

number

of

known

interaction

partners

for

cancer

proteins

However

this

appears

not

to

be

the

case

and

is

more

likely

to

be

a

consequence

of

domain

usage

Using

interaction

frequencies

for

domain-domain

interactions

from

the

PFAM

database

-LRB-

Bateman

et

al.

-RRB-

we

found

that

the

propensity

of

highly

promiscuous

domains

in

terms

of

the

variety

of

different

domains

they

interact

Topology

of

cancer

protein

networks

Cell

motility

Cytoskeleton

Immune

system

Leukocyte

migration

Signal

transduction

Jak-ST

AT

Adipocytokine

Cell

communication

Adherens

junction

Signal

transduction

Jak-ST

AT

Adipocytokine

signalling

Signal

transduction

Wnt

signalling

Cell

communication

Adherens

junction

Focal

adhesion

Signal

transduction

Jak-STAT

Adipocytokine

Cell

communication

Adherens

junction

Focal

adhesion

Signal

transduction

Insulin

signalling

MAPK

Cell

communication

Focal

adhesion

Tight

junction

Signal

transduction

Insulin

signalling

Cell

motility

Regulation

of

actin

Carbohydrate

metabolism

Immune

system

T

cell

receptor

signalling

B

cell

receptor

signalling

Toll-like

receptor

signalling

Signal

transduction

Adipocytokine

signalling

MAPK

Cell

growth

death

Apoptosis

Cell

growth

death

Apoptosis

Signal

transduction

Adipocytokine

signalling

TGF-beta

Immune

system

Antigen

processing

Cell

communication

Adherens

junction

Signal

transduction

TGF-beta

Hedgehog

MAPK

Cytokine-cytokine

interaction

Immune

system

T

cell

receptor

signalling

B

cell

receptor

signalling

Toll-like

receptor

signalling

Signal

transduction

Insulin

signalling

Adipocytokine

signalling

MAPK

Cell

growth

death

Apoptosis

Folding

sorting

degredation

Cell

growth

death

Cell

cycle

Signal

transduction

Adipocytokine

signalling

TGF-beta

Neuroactive

ligandreceptor

interaction

Amino

acid

metabolism

Signal

transduction

Jak-STAT

Signal

transduction

Adipocytokine

signalling

MAPK

Cytokine-cytokine

interaction

Cell

growth

death

Apoptosis

Signal

transduction

Notch

Fig.

A

description

of

the

protein

communities

identified

by

k-clique

cluster

analysis

-LRB-

k

-RRB-

Each

community

is

distinctly

coloured

with

cancer

proteins

shown

as

triangles

The

main

functional

classes

of

each

cluster

-LRB-

in

bold

-RRB-

and

individual

pathways

as

defined

in

the

KEGG

database

are

listed

alongside

each

community

Note

that

proteins

can

be

members

of

more

than

one

community

but

the

figure

shows

only

one

community

assignment

for

each

protein

A

detailed

version

of

this

figure

including

gene

names

for

each

protein

is

available

as

supplementary

material

-LRB-

see

Supplementary

Figure

-RRB-

with

was

higher

in

the

cancer

population

with

of

domains

highly

promiscuous

compared

with

of

the

non-cancer

population

-LRB-

the

number

of

unique

domains

N

and

N

respectively

-RRB-

Some

of

these

domains

-LRB-

ppromiscuity

-RRB-

appear

in

the

list

of

the

most

frequently

observed

cancer

domains

-LRB-

Table

-RRB-

and

are

preceded

with

the

symbol

This

is

based

on

a

statistical

analysis

of

interaction

frequencies

of

PFAM

domains

in

general

which

conform

to

a

probability

density

function

if

x

f

x

l

le

lx

if

x

where

x

is

the

domain

interaction

frequency

and

l

is

the

rate

constant

derived

from

the

average

value

of

interactions

per

domain

-LRB-

-RRB-

as

observed

in

the

PFAM

data

There

still

remains

the

argument

that

the

PFAM

domain-domain

interaction

data

could

be

biased

towards

cancer

protein

studies

Since

PFAM

is

derived

from

the

Protein

Data

Bank

-LRB-

PDB

-RRB-

-LRB-

Berman

et

al.

-RRB-

we

checked

for

bias

within

this

primary

structural

database

We

found

that

of

the

PDB

contains

structural

information

on

the

cancer

proteins

studied

here

or

their

close

homologues

-LRB-

E-value

sequence

identity

-RRB-

thereby

indicating

no

obvious

bias

towards

cancer

proteins

P.F.Jonsson

and

P.A.Bates

Cancer

Non-cancer

Community

size

k-value

Fig.

Average

size

distribution

of

protein

communities

that

contain

cancer

proteins

compared

to

those

containing

non-cancer

proteins

Distributions

are

classified

according

to

clustering

k-value

with

cancer

communities

on

the

left

and

non-cancer

on

the

right

The

difference

between

cancer

and

non-cancer

groups

is

statistically

significant

according

to

Wilcoxon

rank

sum

tests

for

k-values

-LRB-

p

-RRB-

and

-LRB-

p

-RRB-

Cluster

analysis

of

the

human

interactome

Clustering

methods

have

previously

been

shown

to

be

useful

in

identifying

protein

interactions

that

take

place

within

the

same

cellular

process

-LRB-

Palla

et

al.

Jonsson

et

al.

-RRB-

This

can

be

attributed

to

the

fact

that

subnetworks

of

proteins

involved

in

a

defined

cellular

process

are

more

heavily

interconnected

by

direct

protein

interactions

than

would

be

expected

by

chance

-LRB-

Jeong

et

al.

Gunsalus

et

al.

-RRB-

The

k-clique

clustering

method

was

applied

repeatedly

at

different

k-cluster

values

-LRB-

Supplementary

Figure

-RRB-

A

low

k-value

yields

a

large

number

of

extensive

communities

of

less

tightly

connected

protein

communities

showing

a

high

degree

of

overlap

whereas

raising

the

k-value

results

in

identification

of

fewer

and

more

distinct

protein

communities

that

have

a

high

degree

of

interconnection

-LRB-

Table

-RRB-

Interestingly

even

though

cluster

sizes

decrease

with

increasing

k-value

the

proportion

of

cancer

genes

identified

in

the

protein

communities

increases

indicating

the

enrichment

of

cancer

proteins

in

the

most

tightly

connected

communities

An

example

of

the

protein

communities

identified

by

this

method

is

shown

in

Figure

The

communities

contain

proteins

involved

in

a

diverse

range

of

protein

functions

and

are

either

self-contained

or

connected

to

other

protein

communities

In

the

latter

case

cancer

proteins

can

be

seen

mediating

interactions

between

different

pathways

such

as

in

the

largest

collection

where

four

communities

are

linked

together

ranging

from

proteins

involved

in

signal

transduction

to

the

immune

system

and

cell

growth

and

death

regulation

Community

size

and

overlap

On

examining

the

protein

communities

some

interesting

differences

in

the

community

size

were

noticed

Cancer

proteins

on

average

belonged

to

more

highly

populated

communities

compared

with

non-cancer

proteins

-LRB-

Fig.

-RRB-

It

may

be

that

cancer

proteins

take

part

in

more

complex

cellular

processes

than

their

non-cancer

counterparts

It

is

also

conceivable

that

the

larger

communities

contain

larger

or

more

complicated

cellular

machinery

where

cancer

proteins

play

a

role

Proteins

identified

as

members

of

more

than

one

protein

community

are

of

particular

interest

In

general

each

protein

community

represents

a

distinct

cellular

process

therefore

proteins

that

have

multiple

community

membership

may

be

participating

in

multiple

processes

and

can

be

considered

to

be

at

the

interface

of

distinct

but

adjacent

cellular

processes

Comparing

the

cancer

protein

population

against

the

non-cancer

population

reveals

that

cancer

proteins

reside

at

community

interfaces

to

a

much

greater

extent

than

their

non-cancer

counterparts

as

shown

in

Table

While

connectivity

gives

an

indication

of

a

protein

's

importance

it

is

possible

to

further

classify

the

topological

role

of

highly

connected

proteins

based

on

their

locality

Wuchty

and

Almaas

-LRB-

-RRB-

used

a

k-core

decomposition

method

to

distinguish

between

highly

connected

domains

in

peripheral

cores

-LRB-

locally

central

-RRB-

and

highly

connected

domains

in

central

cores

-LRB-

globally

central

-RRB-

They

show

that

globally

central

proteins

form

an

evolutionary

backbone

of

the

proteome

present

elevated

evolutionary

conservation

and

are

essential

to

the

organism

The

protein

community

identification

by

the

k-clique

clustering

used

in

this

study

allows

a

similar

distinction

to

be

made

owing

to

the

overlapping

nature

of

the

communities

Proteins

in

overlapping

communities

can

be

classified

as

global

central

cores

and

nonoverlapping

ones

as

local

central

cores

The

above

results

thus

emphasize

the

key

role

of

cancer

proteins

which

is

reflected

in

their

protein-network

topology

This

observation

supports

and

extends

the

findings

of

Wachi

et

al.

-LRB-

-RRB-

who

showed

that

differentially

expressed

proteins

in

squamous

cell

carcinoma

of

the

lung

tend

to

be

global

hubs

Overall

the

above

findings

reveal

topological

distinction

of

cancer

proteins

that

is

primarily

displayed

in

increased

interaction

frequency

compared

with

non-cancer

proteins

In

the

light

of

this

we

investigated

whether

this

is

owing

to

a

pronounced

increase

within

a

few

cellular

processes

or

whether

there

is

a

uniform

distribution

of

raised

connectivity

of

cancer

proteins

in

all

aspects

of

cellular

function

Information

from

the

Gene

Ontology

Consortium

was

used

to

assign

proteins

to

cellular

process

categories

showing

that

cancer

proteins

in

all

cellular

processes

contribute

to

the

increase

although

proteins

in

the

stimulus

response

and

cellular

process

groups

do

so

more

than

others

-LRB-

Supplementary

Table

-RRB-

CONCLUSION

In

summary

we

have

shown

that

human

proteins

involved

in

cancer

exhibit

a

network

topology

different

from

that

of

proteins

not

documented

as

being

mutated

in

cancer

The

observation

is

based

on

the

study

of

a

global

protein-protein

interaction

network

constructed

by

a

homology

based

method

which

we

have

shown

to

be

capable

of

accurately

predicting

protein-protein

interactions

The

most

striking

property

of

cancer

proteins

is

the

increased

frequency

of

interactions

they

participate

in

This

observation

indicates

an

underlying

evolutionary

pressure

to

which

cancer

genes

as

genes

of

central

importance

are

subjected

The

k-clique

clustering

algorithm

allows

us

to

explore

proteinprotein

connectivity

in

a

more

informative

way

than

is

possible

just

Topology

of

cancer

protein

networks

by

looking

at

the

interaction

frequency

of

each

protein

Its

feature

of

overlapping

protein

communities

allows

us

to

distinguish

between

central

and

peripheral

hubs

of

highly

connecting

proteins

revealing

proteins

forming

the

backbone

of

the

proteome

The

fact

that

we

observe

an

enrichment

of

cancer

proteins

in

this

group

indicates

the

central

role

of

these

proteins

The

domain

composition

of

cancer

proteins

may

indicate

why

this

is

the

case

we

have

shown

that

cancer

proteins

contain

a

high

ratio

of

highly

promiscuous

domains

in

terms

of

the

number

of

different

proteins

with

which

they

interact

The

results

presented

here

provide

first

insights

into

the

global

network

properties

of

cancer

proteins

and

can

be

used

to

guide

experiments

towards

regions

of

the

interactome

likely

to

modulate

cellular

processes

involved

in

cancer

Further

studies

however

are

required

to

fully

resolve

the

evolutionary

aspect

of

these

findings

ACKNOWLEDGEMENTS

The

authors

would

like

to

thank

members

of

the

Biomolecular

Modelling

Laboratory

for

helpful

discussions

This

work

was

funded

by

Cancer

Research

UK

Conflict

of

Interest

declared

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Discovering

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signalling

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in

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Bioinformatics

Ito

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et

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-RRB-

A

comprehensive

two-hybrid

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explore

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Proc

Natl

Acad

Sci

USA

Jeong

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et

al.

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Lethality

and

centrality

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protein

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Nature

Jonsson

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-RRB-

Cluster

analysis

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networks

generated

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homology

automatic

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of

important

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communities

involved

in

cancer

metastasis

BMC

Bioinformatics

Jordan

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-RRB-

No

simple

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protein

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only

the

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BMC

Evol

Biol.

Kanehisa

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From

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new

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Res.

Lehner

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A

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Genome

Biol.

Li

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A

Map

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Interactome

Network

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Metazoan

C.

elegans

Science

de

Lichtenberg

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et

al.

-LRB-

-RRB-

Dynamic

complex

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during

the

yeast

cell

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Science

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-RRB-

Identification

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Genome

Res.

Pagel

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The

MIPS

mammalian

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Bioinformatics

Palla

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-RRB-

Uncovering

the

overlapping

community

structure

of

complex

networks

in

nature

and

society

Nature

Peri

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et

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-LRB-

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Development

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reference

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biology

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Genome

Res.

Pruitt

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NCBI

Reference

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RefSeq

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Nucleic

Acids

Res.

Riley

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-RRB-

Inferring

protein

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Genome

Biology

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and

Deane

C.M.

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-RRB-

Protein

protein

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evolutionary

rate

abundance

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BMC

Bioinformatics

Said

M.R.

et

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-LRB-

-RRB-

Global

network

analysis

of

phenotypic

effects

protein

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and

toxicity

modulation

in

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Proc

Natl

Acad

Sci

USA

Salwinski

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The

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Nucleic

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Res.

Sharan

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Conserved

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Proc

Natl

Acad

Sci

USA

Sohler

F.

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New

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Bioinformatics

Uetz

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-LRB-

-RRB-

A

comprehensive

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in

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Nature

Wachi

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-LRB-

-RRB-

Interactome-transcriptome

analysis

reveals

the

high

centrality

of

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expressed

in

lung

cancer

tissues

Bioinformatics

Wuchty

S.

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-RRB-

Evolution

and

topology

in

the

yeast

protein

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network

Genome

Res.

Wuchty

S.

and

Almaas

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-RRB-

Evolutionary

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BMC

Evol

Biol.

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

cancer
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protein interactions


PERSPECTIVES MOLECULAR BIOLOGY A New RNA Dimension to Genome Control Richard W. Carthew icroRNAs -LRB- miRNAs -RRB- and small interfering RNAs -LRB- siRNAs -RRB- are 21 - to 25nucleotide RNA molecules that influence their much bigger relatives , the messenger RNAs -LRB- mRNAs -RRB- . Over the past few years , these small RNA species have captivated the study of gene regulation and modified our notions about how gene expression is controlled . A recent clutch of papers describe for the first time a class of small RNA cousins that are distinct from miRNAs and siRNAs -LRB- 1 6 -RRB- . They promise to yield fascinating new insights into genome control . The genesis of the discovery of a third type of small RNAs is linked to the Argonaute family of proteins . Certain Argonaute proteins such as Ago1 and Ago2 associate with miRNAs and siRNAs to form ribonucleoprotein complexes that associate and repress the expression of target mRNAs . Sometimes , mRNA targets are cleaved by a mechanism that is catalyzed by the particular associated Ago protein . However , a subclade of Argonaute proteins are phylogenetically distinct from the Ago1/Ago2 subclade and do not appear to associate with siRNAs or miRNAs -LRB- 7 -RRB- . Led by its founding member , the Piwi protein of Drosophila melanogaster , this subfamily appears to play an important role in germline development . For example , genetic analysis of Piwi and its mouse orthologs -LRB- Miwi , Mili , Miwi2 -RRB- indicates that they are essential for spermatogenesis -LRB- 7 9 -RRB- . How the Piwi subfamily regulates the germline has remained for the most part elusive . A recent breakthrough regarding this question has emerged from the identification of the small RNA partners that associate with Piwi proteins . In research reported on page 363 of this issue , Lau et al. -LRB- 1 -RRB- partially purified a ribonucleoprotein complex from extracts of rat testis and found testis-specific RNAs of 25 to 31 nucleotides , with a dominant subpopulation of 29 - to 30-nucleotide RNAs . These RNAs are distinct in size from miRNAs and are associated with distinct protein complexes . Lau et al. purified the RNA-protein complex by conventional chromatography and identified the rat homologs to Piwi -LRB- Riwi -RRB- and the human RecQ1 protein as subunits of the complex . On this basis , the RNAs have been named Piwi-interacting RNAs -LRB- piRNAs -RRB- and the complex is called the Piwiinteracting RNA complex -LRB- piRC -RRB- . The discovery of small RNAs , microRNA and siRNA , has revolutionized thinking about the control of gene expression . Now a third new species of small RNA is shown to control gene expression in germline cells . M piRC exhibits adenosine triphosphate dependent DNA helicase activity , which is likely attributable to RecQ1 . Interestingly , the RecQ1 homolog in Neurospora crassa has been implicated in gene silencing -LRB- 10 -RRB- . Lau et al. -LRB- 1 -RRB- also found that piRC will cleave RNA targets in a manner dependent on piRNA complementarity , much like Ago2 cleavage of siRNA targets . This might imply that piRC has some posttranscriptional role in gene silencing . Indeed , piRNAs are associated with polysomes fractionated from mouse testis extract -LRB- 4 -RRB- . However , genetic studies have implicated Piwi proteins in transcriptional gene silencing by altering chromatin conforma - erates piRNAs from both strands , but plus-strand piRNAs are segregated from minus-strand piRNAs into distinct regions that are separated by a gap of a few hundred base pairs -LRB- see the figure -RRB- . The paucity of evidence for overlapping complementary RNAs or potential foldback RNA precursors suggests that piRNAs are not derived from double-stranded RNA precursors . This would suggest a biogenesis mechanism distinct from that of siRNA and miRNA , both of which are derived from dsRNA through enzymatic cleavage by Dicer . piRNAs and piRC complexes are not restricted to rats ; they have been detected in testes DNA RNA polymerase Transcriptional regulation of piRNA clusters mRNA Histone mRNA piRC assembly Mechanism of piRC action : at DNA or histones or RNA ? piRNA biogenesis piRC Piwi RecQ1 piRNA The known and unknown features of piRNAs . Shown is a genomic region that generates a cluster of piRNAs . The left side of the region generates antisense RNA transcripts -LRB- blue -RRB- and the right side generates sense transcripts -LRB- green -RRB- ; a short gap in between likely acts as the promoter for transcription of both sides -LRB- divergent red arrows -RRB- . An RNA polymerase of unknown identity is shown in active transcription . These transcripts are processed into 25 - to 31-nucleotide piRNAs by an unknown mechanism . piRNAs then associate with Piwi and RecQ1 homologs to form piRC complexes . These complexes might regulate the genome at the level of DNA or histones , or at a posttranscriptional level . The events that are under direct control of the piRC mechanism within developing sperm cells are currently unknown . The author is in the Department of Biochemistry , Molecular Biology , and Cell Biology , Northwestern University , Evanston , IL 60208 , USA . E-mail : r-carthew@northwestern.edu tion -LRB- 7 -RRB- . Consistent with the idea that piRC plays a role in transcriptional silencing , RNAs that are longer than 24 nucleotides -LRB- such as piRNAs -RRB- have been associated with this mode of gene silencing in a wide variety of species -LRB- 11 -RRB- . One of the real surprises has been the nature of piRNAs themselves . Deep sequencing of complementary DNAs derived from piRNAs revealed that they correspond to regions of the genome previously thought not to be transcribed -LRB- 1 3 -RRB- . These regions are limited in number to about 100 clusters ranging in size from 1 to 100 kilobases and are distributed across the genome . Very few clusters contain repetitive DNA ; in fact , repetitive DNA is underrepresented . A greater surprise is that piRNAs in a typical cluster exclusively map to either one or the other strand of genomic DNA -LRB- 1 5 -RRB- . A minority of clusters genSCIENCE VOL 313 of other mammals , including mouse and human -LRB- 1 5 -RRB- . The organization of piRNA genomic clusters is conserved in other mammalian species as well . Most clusters in the rat , mouse , and human are homologous or syntenic , even extending to strand specificity . Nonetheless , piRNA sequences are not conserved between species . Sequence heterogeneity is consistent with the idea that the genomic clusters are subject to neutral selection pressure . What testis cells express piRNAs ? In the mouse , Mili is expressed in male germ cells from primordial to pachytene stages , whereas Miwi is expressed from pachytene to spermatid stages -LRB- 8 , 9 -RRB- . Both Mili and Miwi associate with piRNAs , which suggests that piRNAs are produced within the developing male germ cells . Consistent with a germline-specific expression www.sciencemag.org 21 JULY 2006 305 Published by AAAS PERSPECTIVES pattern , piRNAs are not detectable in W V mutant mice , which are missing differentiating germ cells -LRB- 2 , 5 -RRB- , and piRNAs are reduced in Miwi mutants -LRB- 4 -RRB- . piRNAs are detected throughout sperm development but appear to peak in abundance at the round-spermatid stage -LRB- 1 5 -RRB- . The abundance of piRNAs in spermatids is staggering ; about 1 million molecules are estimated per round-spermatid cell -LRB- 3 -RRB- . Although mammalian piRNAs are not associated with repetitive DNA , the situation might be different in Drosophila . On page 320 of this issue , Vagin et al. -LRB- 6 -RRB- describe repeat-associated siRNAs -LRB- rasiRNAs -RRB- in the fly germline as 24 - to 29-nucleotide species that arise primarily from the antisense strand of repetitive sequences such as retrotransposons . These RNAs are associated with Piwi and another member of the Piwi subclade , and mutations in the Piwi class of genes cause derepressed retrotransposon silencing coupled with altered levels of rasiRNA abundance . Interestingly , these effects are not restricted to the male germline but also apply to the female germline . Perhaps rasiRNAs in Drosophila use a molecular mechanism similar to that of mammalian piRNAs to silence portions of the genome . Many questions ensue from these studies . Are testis-specific piRNAs found in species other than mammals ? Does piRC regulate male meiosis by regulating genome organization , or is it a surveillance mechanism to ensure genome integrity during germ cell maturation , including suppression of selfish elements ? Is piRC male-specific , or are other classes of RNAs associated with Piwi in the female germline ? How are piRNAs produced ? Their structures might suggest a ribonuclease III independent origin . Indeed , neither of the two Dicers from Drosophila is essential for rasiRNA biogenesis and repeat DNA silencing -LRB- 6 -RRB- , although it is possible that each is redundant for the other or that a third enzyme , Drosha , carries out processing . Further investigation should reveal how piRCs regulate the genome . References 1 . N. C. Lau et al. , Science 313 , 363 -LRB- 2006 -RRB- ; published online 15 June 2006 -LRB- 10.1126 / science . 1130164 -RRB- . 2 . A. Girard , R. Sachidanandam , G. J. Hannon , M. A. Carmell , Nature 10.1038 / nature04917 -LRB- 4 June 2006 -RRB- . 3 . A. Aravin et al. , Nature 10.1038 / nature 04916 -LRB- 4 June 2006 -RRB- . 4 . S. T. Grivna , E. Beyret , Z. Wang , H. Lin , Genes Dev . 10.1101 / gad .1434406 -LRB- 9 June 2006 -RRB- . 5 . T. Watanabe et al. , Genes Dev . 10.1101 / gad .1425706 -LRB- 9 June 2006 -RRB- . 6 . V. V. Vagin et al. , Science 313 , 320 -LRB- 2006 -RRB- ; published online 29 June 2006 -LRB- 10.1126 / science .1129333 -RRB- . 7 . M. A. Carmell , Z. Xuan , M. Q. Zhang , G. J. Hannon , Genes Dev . 16 , 2733 -LRB- 2002 -RRB- . 8 . W. Deng , H. Lin , Dev . Cell 2 , 819 -LRB- 2002 -RRB- . 9 . S. Kuramochi-Miyagawa et al. , Development 131 , 839 -LRB- 2004 -RRB- . 10 . C. Cogoni , G. Macino , Science 286 , 2342 -LRB- 1999 -RRB- . 11 . M. A. Matzke , J. A. Birchler , Nat . Rev. Genet . 6 , 24 -LRB- 2005 -RRB- . 10.1126 / science .1131186 EVOLUTION Darwin for All Seasons E rs Szathm ry any regard the Darwinian theory of evolution by natural selection as one of the most important and powerful theories of our times , in the good company of the general theory of relativity and quantum theory . What will be Darwin 's legacy in the 21st century ? Will new work be mainly confirmatory , or can we expect new breakthroughs ? What constitutes a Darwinian way of thinking in biology , or more broadly in science ? Is it still timely to think in a genuine Darwinian way , or should we resort only to some basic Darwinian principles ? These questions were discussed by researchers at a recent conference at Trinity College , Cambridge , UK -LRB- 1 -RRB- , which was hosted by the president of the Royal Society , Martin Rees . There was fair agreement among the participants that Darwin 's way of approaching problems remains valid and should be encouraged if possible . A feel for the organism , theoretical ideas guiding and aided by keen observations of meticulous details , excellent knowledge of natural history : These traits were characteristic of Darwin -LRB- as discussed by Randal Keynes -RRB- , and there is little hope for biology in this century if at least some people will not walk in Darwin 's footsteps . It seems mandatory that `` professional generalists , '' when they rarely surface , A recent conference featured discussions of Darwin 's approach to science in the 19th century and how his methods may apply to 21st-century research . ability lies in chemistry , and the origin of replicators and life may be -LRB- one of -RRB- the greatest challenges for that field . We do not know how RNA originated . We do not know how the first cells got organized , and there is no full scenario for the origin of the genetic code either . Chemistry has helped biology enormously : The development of biochemistry and molecular biology , and their contributions to our understanding of some fundamental features of life , have been profound . This may be the era when biology pays back its debt : Many fields within chemistry are more and more adopting evolutionary approaches . The triumph of in vitro genetics in producing catalytic RNA molecules -LRB- ribozymes -RRB- is a success story beyond doubt . An evolutionary approach toward nanotechnology may bear further fruits . Mathematical modeling has contributed considerably to the foundations of modern evolutionary theory . Sometimes even the questions can not be properly formulated without at least some elementary algebra or population dynamics . The problem of the evolutionary maintenance of sex in eukaryotes -LRB- consisting of cells much more complex than bacteria -RRB- is a good case in point . If an asexual female produces , on average , twice as many female progeny as a sexual one , then other things being equal , it is a miracle why the latter -LRB- with the necessary males -RRB- are still around . Many models have been put forward to solve this conundrum ; some people would say far too many . What we need is more data . But relevant data are hard to arrive at . There is the M The author is at the Institute of Biology , E tv s University Budapest , and Collegium Budapest -LRB- Institute for Advanced Study -RRB- , 2 Szenth roms g utca , H-1014 Budapest , Hungary . E-mail : szathmary@colbud.hu should be cultivated and encouraged . Presumably such individuals arise by nature rather than nurture , but a mechanism to identify and support such rare people is badly needed . As Darwin said : `` My mind seems to have become a kind of machine for grinding general laws out of large collections of facts '' -LRB- 2 -RRB- . We should facilitate the emergence of this mindset in able people . At the beginning of the 21st century , we are well equipped with the knowledge of two disciplines that were practically closed books to Darwin : classical and molecular genetics , and mathematical modeling . Units of evolution must multiply , have heredity , and possess variability ; and among the heritable traits , some must affect survival and/or reproduction . If these criteria are met , evolution by natural selection is possible in a population of such entities . There are at least three remarkable features of this short description . First , it is extremely short , but very powerful -LSB- this is why philosopher Daniel Dennett speaks about `` Darwin 's dangerous idea '' -LRB- 3 -RRB- -RSB- . Second , it is not restricted to living organisms , and if the criteria are met , Darwinian evolution may unfold in the realm of chemistry and culture as well . Third , although we have learned a lot since Darwin 's times , Darwin would have presumably agreed with this telegraphic description . There are still enormous challenges ahead of us in areas where a Darwinian way of thinking could turn out to be fruitful . The origin of evolvVOL 313 SCIENCE 306 21 JULY 2006 www.sciencemag.org Published by AAAS

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