A

map

of

human

protein

interactions

derived

from

co-expression

of

human

mRNAs

and

their

orthologs

The

human

protein

interaction

network

will

offer

global

insights

into

the

molecular

organization

of

cells

and

provide

a

framework

for

modeling

human

disease

but

the

network

's

large

scale

demands

new

approaches

We

report

a

set

of

physical

associations

among

human

proteins

inferred

from

indirect

evidence

the

comparison

of

human

mRNA

co-expression

patterns

with

those

of

orthologous

genes

in

five

other

eukaryotes

which

we

demonstrate

identifies

proteins

in

the

same

physical

complexes

To

evaluate

the

accuracy

of

the

predicted

physical

associations

we

apply

quantitative

mass

spectrometry

shotgun

proteomics

to

measure

elution

profiles

of

human

proteins

during

native

biochemical

fractionation

demonstrating

systematically

that

putative

interaction

partners

tend

to

co-sediment

We

further

validate

uncharacterized

proteins

implicated

by

the

associations

in

ribosome

biogenesis

including

associated

with

Williams

Beuren

syndrome

This

meta-analysis

therefore

exploits

non-protein-based

data

but

successfully

predicts

associations

including

novel

human

physical

protein

associations

with

measured

accuracies

of

comparable

to

direct

large-scale

interaction

assays

The

new

associations

derivation

from

conserved

in

vivo

phenomena

argues

strongly

for

their

biological

relevance

Introduction

Although

considerable

progress

has

been

made

in

mapping

the

protein

interaction

network

of

yeast

-LRB-

Ito

et

al

Uetz

et

al

Ho

et

al

Gavin

et

al

Krogan

et

al

-RRB-

only

minimal

progress

has

been

made

on

the

interaction

networks

of

higher

eukaryotes

due

primarily

to

their

scale

for

the

human

proteins

we

expect

a

network

of

roughly

interactions

-LRB-

Hart

et

al

-RRB-

Among

the

few

methods

scaleable

to

this

size

the

yeast

two-hybrid

assay

has

proven

the

most

successful

with

maps

of

interactions

in

fly

-LRB-

Giot

et

al

-RRB-

in

worm

-LRB-

Li

et

al

-RRB-

and

more

recently

assays

of

and

human

protein

interactions

-LRB-

Rual

et

al

Stelzl

et

al

-RRB-

Direct

mapping

of

protein

complexes

by

mass

spectrometry

has

also

contributed

another

interactions

-LRB-

Ewing

et

al

-RRB-

After

including

previously

known

human

protein

interactions

-LRB-

Bader

et

al

Lehner

and

Fraser

Peri

et

al

Joshi-Tope

et

al

Ramani

et

al

-RRB-

the

human

protein

interaction

map

is

currently

perhaps

complete

-LRB-

Hart

et

al

-RRB-

It

is

therefore

important

to

identify

and

employ

methods

for

discovering

interacting

proteins

without

exhaustive

experimental

measurement

of

all

pairs

of

proteins

under

each

relevant

condition

or

assay

Proteins

are

evolved

to

interact

under

specific

conditions

in

the

cell

with

the

cell

correspondingly

optimized

to

facilitate

these

events

e.g.

by

expressing

mRNAs

before

proteins

are

required

coordinating

the

expression

of

interacting

partners

directing

proteins

to

appropriate

locations

for

their

interactions

and

so

on

In

this

way

in

vivo

protein

interactions

are

accompanied

by

corollary

events

that

can

be

used

to

identify

biologically

relevant

physical

interaction

partners

We

took

advantage

of

two

such

corollary

data

types

the

tendency

for

interacting

proteins

to

have

correlated

mRNA

expression

patterns

and

the

evolutionary

conservation

of

such

patterns

to

identify

new

human

protein

interactions

It

is

well

established

that

genes

whose

mRNA

expression

patterns

are

correlated

across

many

diverse

conditions

can

often

be

inferred

to

work

together

i.e.

to

be

functionally

coupled

-LRB-

Eisen

et

al

Marcotte

et

al

Stuart

et

al

Lee

et

al

Segal

et

al

-RRB-

Analyses

of

co-expression

patterns

of

orthologous

genes

have

shown

that

the

conserved

correlation

in

expression

can

also

be

used

to

transfer

functional

information

across

species

-LRB-

Teichmann

and

Babu

Stuart

et

al

van

Noort

et

al

Bergmann

et

al

Snel

et

al

-RRB-

Transcriptional

co-expression

patterns

have

proved

useful

for

inferring

physical

protein

interactions

-LRB-

e.g.

Deane

et

al

Jansen

et

al

-RRB-

with

strongly

co-expressed

mRNAs

more

likely

to

indicate

long-lived

interactions

-LRB-

Ge

et

al

Jansen

et

al

Simonis

et

al

-RRB-

In

general

we

do

not

expect

transcriptional

data

to

distinguish

between

direct

protein

binding

and

membership

in

the

same

protein

complex

and

we

term

all

such

cases

physically

associated

proteins

To

exploit

these

trends

we

applied

a

supervised

algorithm

to

discover

physical

associations

among

human

proteins

based

upon

the

co-expression

of

their

mRNAs

and

that

of

their

orthologs

in

five

organisms

-LRB-

the

mustard

plant

Arabidopsis

thaliana

the

mouse

Mus

musculus

the

fly

Drosophila

melanogaster

the

nematode

Caenorhabditis

elegans

and

the

yeast

Saccharomyces

cerevisiae

-RRB-

By

this

approach

we

mapped

predicted

human

protein

physical

associations

of

which

are

new

to

this

analysis

Results

Predicting

physically

associated

proteins

from

patterns

of

conserved

co-expression

Figure

illustrates

the

overall

method

We

first

identified

orthologs

for

human

genes

in

five

other

organisms

using

the

InParanoid

algorithm

-LRB-

Remm

et

al

-RRB-

We

then

compared

the

correlation

in

mRNA

expression

of

each

pair

of

human

genes

with

the

correlations

in

expression

of

each

of

their

corresponding

ortholog

pairs

from

five

organisms

in

all

calculating

mRNA

expression

correlations

for

human

gene

pairs

on

the

basis

of

DNA

microarrays

After

removing

gene

pairs

likely

to

cross-hybridize

on

the

microarrays

-LRB-

see

Materials

and

methods

-RRB-

and

filtering

pairs

with

nonsignificant

correlations

we

employed

a

supervised

algorithm

on

these

data

to

identify

those

patterns

of

conserved

co-expression

-LRB-

CCE

-RRB-

diagnostic

of

physical

protein

associations

based

upon

the

correlations

observed

for

known

protein

interactions

versus

random

protein

pairs

By

searching

for

additional

gene

pairs

exhibiting

these

patterns

we

identified

new

associations

Figure

plots

the

derivation

of

the

relationship

between

CCE

and

the

tendency

to

be

in

the

same

physical

complex

relying

in

this

case

on

the

comparison

of

human

and

C.

elegans

mRNA

expression

data

Briefly

the

distribution

of

mRNA

co-expression

relationships

was

measured

for

gene

pairs

whose

corresponding

proteins

are

known

to

physically

associate

-LRB-

Ramani

et

al

-RRB-

serving

as

positive

training

examples

-LRB-

Figure

-RRB-

these

pairs

represent

the

subset

of

known

human

protein

associations

in

the

training

set

that

also

occur

in

the

human

worm

co-expression

data

sets

Likewise

the

distribution

was

measured

for

gene

pairs

that

are

in

the

physical

interaction

training

set

but

are

not

known

to

physically

associate

serving

as

a

negative

training

set

-LRB-

Figure

-RRB-

Therefore

the

log

ratio

of

these

two

plots

corrected

by

prior

expectation

represents

the

log

likelihood

for

protein

pairs

to

physically

associate

given

any

particular

pattern

of

co-expression

conservation

-LRB-

Figure

-RRB-

where

P

-LRB-

I

D

-RRB-

and

P

-LRB-

I

D

-RRB-

are

the

frequencies

of

positive

-LRB-

I

-RRB-

and

negative

-LRB-

I

-RRB-

training

associations

observed

in

the

data

set

-LRB-

D

-RRB-

respectively

while

P

-LRB-

I

-RRB-

and

P

-LRB-

I

-RRB-

represent

the

overall

frequencies

of

positive

and

negative

training

associations

respectively

This

score

indicates

how

likely

two

proteins

are

to

physically

associate

given

their

specific

mRNA

co-expression

conservation

in

these

data

The

training

set

includes

both

direct

interactions

and

protein

pairs

belonging

to

the

same

complex

we

therefore

consider

this

approach

to

support

the

more

general

case

i.e.

proteins

belonging

to

the

same

complex

whether

or

not

directly

interacting

Note

that

the

highest

scores

do

not

necessarily

occur

in

the

extreme

top

right

corner

of

Figure

lower

counts

of

both

positive

and

negative

examples

in

the

extreme

corner

as

well

as

filtration

of

highly

correlated

gene

pairs

where

they

may

suffer

from

DNA

microarray

cross-hybridization

-LRB-

see

Materials

and

methods

-RRB-

results

in

the

highest

scores

occurring

at

correlation

coefficients

less

than

one

We

similarly

analyzed

co-expression

patterns

of

human

gene

pairs

with

orthologs

from

four

other

organisms

-LRB-

A.

thaliana

M.

musculus

D.

melanogaster

and

S.

cerevisiae

-RRB-

analyzing

DNA

microarray

experiments

in

all

From

each

analysis

-LRB-

Figure

and

Supplementary

Figure

-RRB-

strongly

co-expressing

human

genes

with

co-expressing

orthologs

are

generally

likely

to

encode

physically

associated

proteins

The

highest

likelihoods

of

associating

occur

when

the

mRNA

expression

patterns

of

both

human

gene

pairs

and

their

orthologs

are

positively

correlated

with

odds

of

associating

approaching

for

C.

elegans

for

A.

thaliana

for

M.

musculus

for

D.

melanogaster

and

for

S.

cerevisiae

These

learned

relationships

between

mRNA

expression

profiles

and

physical

associations

were

then

applied

to

protein

pairs

not

in

the

training

set

thereby

assigning

a

likelihood

of

physically

associating

to

each

untested

protein

pair

Each

human

gene

pair

discovered

has

at

least

one

log

likelihood

score

to

a

maximum

of

five

from

which

the

highest

score

was

identified

pairs

were

ranked

based

on

this

score

then

evaluated

as

a

function

of

their

rank

Validation

of

predicted

physical

protein

associations

using

known

interactions

As

this

assay

relies

upon

indirect

evidence

it

is

critical

that

putative

physical

associations

discovered

by

this

approach

be

carefully

evaluated

We

devised

six

tests

for

the

enrichment

of

true

physical

associations

including

direct

experimental

assay

of

physical

association

and

of

four

proteins

functions

suggested

by

the

associations

First

and

most

critically

to

verify

the

accuracy

of

the

co-expression-derived

associations

we

measured

their

likelihood

to

physically

associate

using

an

independent

test

set

of

known

physical

associations

including

both

direct

interactions

and

complexes

-LRB-

Ramani

et

al

-RRB-

Figure

shows

that

the

CCE

associations

are

highly

enriched

for

true

physical

associations

varying

from

a

likelihood

ratio

of

to

as

high

as

of

correctly

capturing

true

physical

associations

Importantly

the

CCE

pairs

score

times

higher

than

randomized

pairs

of

the

same

proteins

as

well

as

associations

derived

in

the

same

manner

but

using

only

human

-LRB-

not

ortholog

-RRB-

DNA

microarray

data

-LRB-

Figure

-RRB-

Therefore

the

data

from

orthologs

enriched

the

signal

for

human

physical

protein

associations

considerably

beyond

the

human

data

alone

Second

we

examined

the

functional

relationships

between

the

putative

interaction

partners

For

this

test

we

compared

the

Gene

Ontology

-LRB-

GO

-RRB-

and

KEGG

pathway

database

annotations

of

interacting

partners

using

a

log

likelihood

framework

-LRB-

Lee

et

al

Ramani

et

al

-RRB-

and

testing

the

performance

of

the

mapped

associations

with

that

of

literature

physical

interactions

-LRB-

Figure

-RRB-

-LRB-

Bader

et

al

Peri

et

al

Joshi-Tope

et

al

Ramani

et

al

-RRB-

Literature

associations

score

in

the

range

of

log

likelihood

ratio

-LRB-

LLR

-RRB-

indicating

high

consistency

with

GO/KEGG

annotation

As

expected

randomized

interactions

score

near

zero

and

interactions

derived

from

human-only

co-expression

data

score

lower

-LRB-

LLR

-RRB-

The

CCE

associations

are

comparable

to

the

literature

associations

Using

interactions

transferred

from

other

organisms

-LRB-

orthology

core

set

-LRB-

Lehner

and

Fraser

-RRB-

LLR

-RRB-

to

define

a

threshold

of

minimum

acceptable

quality

and

choosing

associations

-LRB-

in

bins

of

-RRB-

exceeding

this

threshold

we

obtain

associations

from

the

present

analysis

and

all

subsequent

tests

were

performed

on

this

set

These

associations

have

a

minimum

likelihood

of

-LRB-

-RRB-

of

belonging

to

the

same

GO/KEGG

pathways

For

consistency

subsequent

tests

include

comparisons

to

the

top-scoring

associations

derived

from

human-only

mRNA

co-expression

as

well

as

to

networks

generated

from

the

same

proteins

found

in

the

CCE

associations

but

connected

by

random

interactions

-LRB-

N

random

networks

-RRB-

Validation

of

predicted

physical

protein

associations

by

mass

spectrometry

We

next

used

quantitative

mass

spectrometry

to

test

for

physical

associations

between

CCE

partners

-LRB-

Figure

-RRB-

Performing

purifications

under

native

conditions

known

to

keep

protein

complexes

intact

-LRB-

Dignam

et

al

-RRB-

HeLa

cells

were

lysed

the

cytoplasmic

and

nuclear/mitochondrial

fractions

were

separated

and

their

respective

contents

were

fractionated

biochemically

on

two

sucrose

density

gradients

Proteins

were

quantified

in

each

fraction

by

mass

spectrometry

In

all

proteins

were

quantified

across

cytoplasmic

and

nuclear/mitochondrial

sucrose

density

gradient

fractions

-LRB-

Figure

-RRB-

As

proteins

in

the

same

complex

should

generally

co-sediment

we

expected

physically

associated

proteins

to

often

have

correlated

elution

profiles

Analysis

of

known

protein

complexes

verified

that

components

of

a

complex

tended

to

co-elute

-LRB-

Figure

additional

controls

in

Supplementary

Figures

-RRB-

For

example

components

of

the

chaperone

complex

show

strongly

correlated

elution

profiles

as

do

core

components

of

RNA

polymerase

II

the

latter

profiles

are

distinct

from

the

former

Likewise

components

of

the

NADH

dehydrogenase

complex

show

strongly

correlated

elution

profiles

eluting

entirely

in

the

nuclear/mitochondrial

fraction

-LRB-

Figure

-RRB-

As

an

example

of

the

utility

of

this

approach

the

protein

initially

identified

as

a

regulator

of

cell

death

induced

by

interferon-beta

and

retinoic

acid

was

later

identified

to

be

a

subunit

of

the

NADH

dehydrogenase

complex

-LRB-

Fearnley

et

al

-RRB-

this

association

is

clearly

evident

in

the

co-elution

of

with

other

components

of

this

complex

More

systematically

positive

control

human

protein

interaction

partners

known

from

literature

-LRB-

Joshi-Tope

et

al

-RRB-

show

highly

correlated

elution

profiles

-LRB-

Figure

-RRB-

unlike

negative

control

random

pairs

-LRB-

see

histograms

in

Figure

-RRB-

For

cases

in

which

both

interaction

partners

were

observed

in

the

mass

spectrometry

experiment

of

the

positive

control

pairs

exhibited

Pearson

correlation

coefficients

indicating

a

false-negative

rate

for

identifying

physical

associations

using

the

mass

spectrometry-based

elution

profiles

of

at

this

correlation

threshold

-LRB-

if

considering

correlation

coefficients

-RRB-

This

agrees

with

the

expectation

that

not

all

interacting

proteins

will

co-sediment

with

a

probable

bias

toward

stable

complexes

Similarly

for

proteins

in

the

positive

control

set

protein

pairs

with

the

most

correlated

elution

profiles

showed

probability

of

being

in

the

same

physical

complex

-LRB-

Figure

-RRB-

Thus

correlated

elution

across

these

fractions

is

a

strong

indicator

of

direct

physical

association

As

this

assay

is

not

used

independently

for

discovery

but

is

confirmatory

in

nature

the

false-negative

and

true-positive

rates

are

sufficient

for

evaluating

CCE

associations

Although

individual

associations

could

be

validated

in

this

manner

by

instead

examining

the

aggregate

distribution

of

elution

profile

correlation

coefficients

we

could

directly

estimate

the

error

rate

of

the

CCE

associations

We

calculated

histograms

of

Pearson

correlation

coefficients

from

pairwise

comparisons

of

elution

profiles

for

CCE

protein

pairs

for

protein

pairs

known

from

literature

-LRB-

Joshi-Tope

et

al

-RRB-

to

be

in

the

same

complexes

and

from

random

pairs

of

proteins

-LRB-

Figure

-RRB-

We

then

fit

the

CCE

histogram

as

a

linear

mixture

of

the

positive

and

negative

control

histograms

the

proportions

that

give

the

best

fit

thereby

provided

an

estimate

of

the

relative

proportions

of

true

and

false

associations

in

the

CCE

set

From

this

analysis

we

estimate

that

of

the

CCE

associations

correspond

to

true

physical

associations

-LRB-

Figure

inset

-RRB-

Comparing

the

CCE

associations

and

the

shotgun

proteomics

elution

profiles

reveals

many

interesting

associations

For

example

known

complexes

are

correctly

recovered

as

for

the

DNA

replication

licensing

factors

and

or

for

components

of

the

proteasome

Figure

shows

the

example

of

the

proteins

prohibitin

and

known

to

form

a

large

complex

on

the

mitochondrial

membrane

that

acts

to

suppress

apoptosis

but

which

also

shuttles

to

the

nucleus

in

an

estrogen-receptor-dependent

manner

and

acts

to

repress

transcription

-LRB-

Kasashima

et

al

-RRB-

New

associations

are

also

revealed

we

observed

a

predicted

physical

association

between

and

with

the

retinoblastoma-binding

protein

-LRB-

-RRB-

is

known

to

participate

in

several

chromosome

replication

and

chromatin

remodeling

complexes

among

them

the

chromatin

assembly

factor

and

a

DNA

replication-dependent

chromatin

assembly

complex

-LRB-

Verreault

et

al

-RRB-

The

CCE

associations

supported

by

mass

spectrometry

suggest

direct

physical

association

of

with

the

replication

initiation

complex

as

well

Figure

illustrates

two

other

such

examples

first

we

predict

the

ras

oncogene-related

small

GTPase

an

essential

component

of

receptor-mediated

endocytosis

-LRB-

Bucci

et

al

-RRB-

to

associate

with

the

clathrin

assembly

lymphoid

myeloid

leukemia

gene

-LRB-

CALM

-RRB-

a

protein

that

helps

recruit

clathrin

to

endocytic

vesicles

The

CALM

gene

is

a

recurring

site

for

chromosomal

translocations

in

acute

myeloid

and

mixed

lineage

leukemias

-LRB-

Wechsler

et

al

-RRB-

Physical

association

with

highlights

a

possible

functional

connection

between

these

two

endocytic

components

and

is

interesting

in

light

of

the

leukemogenesis

potential

of

chromosomal

translocations

involving

CALM

Likewise

we

predict

the

A-kinase

anchor

protein

to

be

associated

with

the

splicing

factor

is

primarily

involved

in

anchoring

protein

kinases

phosphatases

and

a

phosphodiesterase

to

specific

cellular

locations

but

also

contains

KH

and

Tudor

domains

motifs

for

single-strand

RNA

binding

-LRB-

Trendelenburg

et

al

-RRB-

which

help

target

to

well-defined

nuclear

foci

in

an

RNA-dependent

manner

-LRB-

Rogne

et

al

-RRB-

The

association

with

which

among

other

functions

is

involved

in

both

constitutive

and

alternative

splicing

and

can

be

specifically

localized

with

other

RNA

processing

proteins

to

nuclear

stress

bodies

-LRB-

Denegri

et

al

-RRB-

suggests

that

may

also

have

a

role

in

these

processes

or

in

mRNA

localization

perhaps

integrating

RNA

processing

with

signaling

Quantitative

estimates

of

interaction

accuracy

We

further

validated

predicted

physical

associations

by

additional

approaches

For

each

of

these

tests

we

defined

a

standard

curve

relating

a

quantitative

property

of

a

protein

pair

with

the

tendency

of

the

pair

to

physically

associate

constructing

the

curve

from

control

mixtures

of

literature

-LRB-

positive

-RRB-

and

randomized

-LRB-

negative

-RRB-

physically

associating

proteins

-LRB-

Figure

-RRB-

A

set

of

all

true-positive

physical

associations

typical

of

those

used

to

construct

the

curve

scores

high

on

these

tests

-LRB-

for

each

standard

curve

-RRB-

the

addition

of

random

interactions

degrades

the

performance

The

relationship

between

each

test

's

performance

and

inclusion

of

false

positives

is

unambiguous

and

well

behaved

as

judged

by

the

quality

of

the

standard

curves

agreement

between

the

tests

and

performance

curves

from

mixtures

of

known

accuracy

-LRB-

Supplementary

Figure

-RRB-

It

is

important

to

note

that

there

are

possible

sources

of

bias

for

each

test

however

taken

as

a

whole

the

tests

are

strong

indicators

for

the

enrichment

of

true

physical

associations

First

true

human

physical

protein

associations

should

be

enriched

for

physical

interactions

among

orthologous

proteins

in

model

organisms

We

generated

control

association

sets

with

known

error

rates

by

randomly

selecting

sets

of

interactions

with

varying

proportions

of

true-positive

and

true-negative

associations

ranging

from

true

positive

-LRB-

all

interactions

chosen

from

the

true-negative

set

-RRB-

to

true

positive

-LRB-

all

interactions

chosen

from

the

literature

set

-RRB-

repeating

the

randomization

times

We

measured

the

overlap

of

each

control

set

with

a

benchmark

set

of

human

protein

pairs

whose

worm

yeast

or

fly

orthologs

have

been

observed

to

interact

by

yeast

two-hybrid

-LRB-

Ito

et

al

Uetz

et

al

Giot

et

al

Li

et

al

-RRB-

or

affinity

purification/mass

spectrometry

assays

-LRB-

Ho

et

al

Gavin

et

al

Krogan

et

al

-RRB-

Figure

shows

the

resulting

standard

curve

that

relates

enrichment

for

orthologous

interactions

to

percentage

of

true

physical

associations

On

the

basis

of

standard

curve

we

estimated

that

of

the

CCE

pairs

correspond

to

true

physical

associations

somewhat

lower

than

the

value

by

mass

spectrometry

co-sedimentation

but

considerably

higher

than

both

randomized

pairs

and

pairs

derived

from

only

human

co-expression

data

As

physically

associated

proteins

often

share

similar

functional

annotation

-LRB-

von

Mering

et

al

-RRB-

we

also

created

a

standard

curve

based

upon

the

agreement

of

interaction

partners

functional

annotation

relating

agreement

of

SwissProt

keywords

-LRB-

Wu

et

al

-RRB-

to

the

percentage

of

true

physical

associations

-LRB-

Figure

-RRB-

For

each

control

set

we

measured

the

average

keyword

overlap

across

SwissProt

keywords

between

the

interaction

partners

Keyword

overlap

varied

from

for

the

true-negative

set

to

for

the

true-positive

set

From

this

curve

and

measurements

of

the

keyword

overlap

by

the

interactions

we

estimate

of

the

CCE

set

represent

physical

associations

Measurements

of

the

percentage

overlap

of

GO

biological

process

and

KEGG

pathway

annotations

result

in

comparable

values

-LRB-

-RRB-

Finally

legitimately

associated

proteins

should

be

closer

in

a

gene

network

-LRB-

i.e.

separated

by

fewer

interactions

-RRB-

than

random

pairs

For

each

putative

physical

association

we

calculated

the

distance

between

the

genes

yeast

orthologs

in

a

functional

gene

network

-LRB-

Lee

et

al

-RRB-

We

compared

the

distribution

of

path

lengths

to

distributions

from

positive

and

negative

control

sets

Figure

shows

that

the

interactions

from

CCE

have

a

path

length

distribution

more

similar

to

the

positive

control

set

than

the

negative

set

indicating

strong

enrichment

for

true-positive

associations

among

the

interactions

We

fit

the

distribution

of

path

lengths

from

the

CCE

associations

as

a

linear

combination

of

the

positive

and

negative

control

distributions

-LRB-

as

in

Deane

et

al

-RRB-

The

proportions

from

the

best

fit

-LRB-

Figure

-RRB-

provide

an

estimate

of

the

percentage

of

true

physical

associations

This

approach

estimates

the

CCE

set

at

true

physical

associations

Table

I

summarizes

the

measurements

of

physical

association

along

with

comparisons

to

the

randomized

and

human-only

co-expression

control

sets

Estimates

vary

only

minimally

with

changes

in

parameters

-LRB-

e.g.

using

percentage

keyword

overlap

for

Figure

versus

Jaccard

coefficient

-RRB-

or

choice

of

control

sets

-LRB-

e.g.

employing

alternative

literature

positive

control

sets

for

Figures

or

see

Supplementary

Table

-RRB-

Although

individual

tests

may

show

some

bias

we

expect

these

biases

to

average

out

across

the

five

tests

in

fact

the

estimates

are

similar

across

the

five

tests

These

measures

demonstrate

that

CCE

associations

are

on

average

reasonably

accurate

-LRB-

true

physical

associations

-RRB-

and

biologically

relevant

are

comparable

in

accuracy

to

direct

large-scale

experimental

assays

-LRB-

Rual

et

al

Stelzl

et

al

-RRB-

and

are

significantly

more

enriched

for

physical

associations

than

random

controls

To

summarize

benchmark

support

for

individual

CCE

associations

we

calculated

a

Binary

Interaction

Overlap

Score

-LRB-

BIOS

-RRB-

-LRB-

Stelzl

et

al

-RRB-

for

each

association

-LRB-

Supplementary

Figure

-RRB-

By

this

measure

-LRB-

-RRB-

of

the

associations

have

at

least

one

line

of

additional

evidence

supporting

them

Scores

are

reported

in

the

supporting

data

file

Detailed

evaluation

of

ribosome

biogenesis

proteins

To

experimentally

evaluate

the

quality

of

hypotheses

arising

from

the

CCE

associations

and

given

a

statistical

enrichment

for

proteins

of

ribosome

biogenesis

-LRB-

see

below

-RRB-

we

analyzed

proteins

predominantly

linked

to

proteins

of

ribosome

biogenesis

a

pathway

involving

several

hundred

proteins

yet

still

incomplete

-LRB-

Granneman

and

Baserga

-RRB-

We

chose

four

proteins

with

yeast

orthologs

for

direct

validation

-LRB-

i

-RRB-

named

for

Williams

Beuren

syndrome

chromosome

region

shares

high

sequence

similarity

with

duplicate

genes

and

This

gene

is

deleted

in

Williams

Beuren

syndrome

a

multi-system

developmental

disorder

caused

by

deletion

of

genes

at

the

locus

-LRB-

Doll

and

Grzeschik

-RRB-

encodes

a

conserved

family

protein

domain

and

is

also

a

member

of

the

protein

family

other

members

of

which

are

tRNA

and

rRNA

involved

in

translation

ribosomal

structure

and

biogenesis

the

yeast

ortholog

of

is

also

uncharacterized

-LRB-

ii

-RRB-

BCCIP

or

and

interacting

protein

is

an

evolutionarily

conserved

nuclear

protein

with

multiple

protein

interaction

domains

This

protein

may

be

an

important

cofactor

for

in

tumor

suppression

-LRB-

Lu

et

al

-RRB-

and

a

modulator

of

kinase

activity

via

-LRB-

Meng

et

al

-RRB-

The

yeast

ortholog

of

this

protein

-LRB-

-RRB-

is

an

essential

nuclear

protein

involved

in

nuclear

export

of

lipid

kinase

-LRB-

Audhya

and

Emr

-RRB-

-LRB-

iii

-RRB-

EPRS

is

predicted

by

sequence

to

be

a

multi-functional

aminoacyl-tRNA

synthetase

Its

yeast

ortholog

is

essential

with

sequence

similarity

to

proline-tRNA

ligase

but

otherwise

uncharacterized

-LRB-

iv

-RRB-

LYAR

is

a

nucleolar

zinc-finger-containing

protein

-LRB-

Su

et

al

-RRB-

whose

yeast

ortholog

is

nucleolar

-LRB-

Huh

et

al

-RRB-

but

uncharacterized

We

tested

the

ribosomal

processing

phenotypes

of

yeast

strains

with

tetracycline-controlled

downregulatable

alleles

of

the

genes

-LRB-

Mnaimneh

et

al

-RRB-

Two

of

the

strains

-LRB-

and

-RRB-

show

clear

ribosomal

processing

defects

upon

downregulation

of

the

genes

-LRB-

Figure

and

C

-RRB-

From

polysome

profiles

-LRB-

corresponding

to

human

protein

BCCIP

-RRB-

appears

to

participate

in

ribosomal

subunit

biogenesis

loss

of

the

protein

results

in

the

reduction

in

the

peak

relative

to

the

peak

-LRB-

corresponding

to

human

protein

EPRS

-RRB-

appears

to

participate

in

biogenesis

resulting

in

a

decreased

ratio

when

depleted

We

also

tested

each

of

the

four

proteins

for

co-sedimentation

with

the

or

monoribosomes

which

would

provide

additional

support

for

the

proteins

participation

in

ribosome

processing

From

crude

cell

lysates

of

yeast

strains

expressing

TAP-tagged

versions

of

each

protein

-LRB-

Ghaemmaghami

et

al

-RRB-

we

size-fractionated

ribosomal

subunits

free

ribosomes

and

polysomes

using

sucrose

gradients

Three

of

the

proteins

-LRB-

and

-RRB-

showed

clear

association

with

and

subunits

-LRB-

Figure

-RRB-

with

and

associated

with

both

and

and

showing

preferential

co-sedimentation

with

the

subunits

Mass

spectrometry

of

untagged

analyzing

yeast

lysate

with

the

approach

of

Figure

-LRB-

data

not

shown

-RRB-

indicates

that

also

co-sediments

with

ribosomal

subunits

-LRB-

Z

Li

and

EM

Marcotte

unpublished

data

-RRB-

The

co-sedimentation

is

less

definitive

than

the

controls

however

the

polysome

profile

of

the

TAP-tagged

strain

indicates

that

the

TAP

tag

itself

disrupts

activity

-LRB-

Figure

-RRB-

causing

a

ribosomal

biogenesis

defect

and

definitively

implicating

the

protein

in

this

process

The

human

BCCIP

protein

was

also

found

by

mass

spectrometry

to

co-sediment

with

free

cytosolic

and

ribosomal

subunits

-LRB-

Supplementary

Figure

-RRB-

raising

the

possibility

of

a

role

in

ribosome

recycling

or

nuclear

export

All

four

genes

assayed

could

therefore

be

implicated

in

ribosomal

biogenesis

Discussion

Characteristics

of

the

newly

mapped

associations

We

have

described

the

prediction

of

human

protein

physical

associations

from

indirect

transcriptional

evidence

as

well

as

measurement

of

overall

error

rates

and

validation

of

specific

associations

We

further

examined

the

associations

for

novelty

functional

bias

and

evidence

for

stable

protein

complexes

First

we

compared

the

predicted

interaction

set

directly

to

the

existing

human

protein

interaction

data

sets

Roughly

of

the

CCE

associations

can

be

directly

verified

from

previously

known

interactions

while

are

new

Our

analysis

bears

some

relation

to

one

reported

by

Stuart

et

al

which

analyzed

CCE

although

not

for

the

purpose

of

discovering

physical

interactions

However

we

obtain

a

largely

non-overlapping

set

of

associations

sharing

only

of

associations

-LRB-

Supplementary

Table

-RRB-

Differences

arise

primarily

because

we

are

explicitly

learning

physical

associations

using

a

supervised

training

framework

other

differences

include

the

choice

of

expression

data

the

methods

for

defining

orthologs

the

criterion

used

to

define

co-expression

-LRB-

we

set

a

statistical

significance

threshold

on

the

correlation

coefficient

Stuart

et

al

use

correlation

coefficients

-RRB-

and

our

removal

of

potential

cross-hybridization

artifacts

all

of

which

contribute

to

producing

largely

distinct

sets

of

associations

Only

three

CCE

interactions

are

shared

with

large-scale

yeast

two-hybrid

analyses

of

human

proteins

-LRB-

Rual

et

al

Stelzl

et

al

-RRB-

with

affinity

purification/mass

spectrometry

analysis

-LRB-

Ewing

et

al

-RRB-

with

a

previous

computational

analysis

-LRB-

Rhodes

et

al

-RRB-

and

with

interactions

inferred

from

other

organisms

in

the

OPHID

database

-LRB-

Brown

and

Jurisica

-RRB-

These

comparisons

are

summarized

in

Supplementary

Table

In

all

of

the

associations

predicted

in

this

analysis

were

not

identified

in

previous

high-throughput

human

protein

interaction

screens

Besides

simply

being

novel

associations

of

the

interaction

partners

-LRB-

of

annotated

interaction

partners

-RRB-

share

neither

KEGG

nor

GO

annotations

While

this

extent

of

annotation

sharing

across

the

set

of

partners

is

sufficient

to

imply

high

confidence

associations

-LRB-

Table

I

-RRB-

these

results

indicate

that

the

inferred

associations

extend

beyond

trivial

identification

of

new

associations

among

proteins

already

known

to

be

in

the

same

pathways

We

examined

the

functions

of

the

proteins

in

the

set

of

associations

-LRB-

Supplementary

Figure

-RRB-

using

for

this

purpose

the

proteins

KOG

annotations

-LRB-

Tatusov

et

al

-RRB-

We

find

the

dominant

class

of

proteins

to

be

those

for

whom

only

general

function

is

known

-LRB-

proteins

-RRB-

followed

by

proteins

participating

in

post-translational

modifications

protein

turnover

and

chaperones

in

signal

transduction

in

translation

ribosomal

structure

and

biogenesis

in

transcription

of

RNA

processing

and

modification

and

of

unknown

function

Therefore

the

proteins

are

not

dominated

by

a

single

structure

-LRB-

e.g.

the

ribosome

-RRB-

but

are

generally

informative

for

major

cellular

systems

and

uncharacterized

proteins

Nonetheless

some

specific

functional

biases

occur

among

the

CCE

proteins

-LRB-

Supplementary

Figures

and

-RRB-

mostly

notable

a

statistical

enrichment

for

proteins

of

membrane-bound

organelles

-LRB-

e.g.

nucleus

mitochondria

nucleolus

etc.

-RRB-

presumably

reflecting

evolutionary

conservation

of

these

proteins

and

their

regulation

favoring

detection

by

the

CCE

method

Likewise

proteins

of

RNA

metabolism

are

enriched

especially

ribosome

biogenesis

with

accompanying

enrichment

for

nucleotide-binding

protein

domains

The

overall

trends

among

the

proteins

can

be

seen

in

a

plot

of

the

CCE

associations

clustered

by

the

spectrum

of

associations

per

protein

-LRB-

Figure

-RRB-

which

shows

that

although

several

major

clusters

exist

many

associations

are

binary

protein

pairs

that

are

not

otherwise

seen

to

exist

in

larger

assemblies

and

thus

lie

far

from

the

diagonal

of

the

clustergram

This

clustering

along

with

the

associations

overlapping

other

data

sets

provides

some

insight

into

the

nature

of

the

CCE

associations

Both

direct

interactions

especially

among

members

of

larger

complexes

as

well

as

co-complex

physical

associations

are

observed

For

example

interactions

are

observed

between

alpha

and

beta-tubulin

which

assemble

into

a

heterodimer

SNRPE

and

SNRPF

known

to

bind

directly

in

the

core

complex

of

spliceosomal

and

snRNPs

-LRB-

Camasses

et

al

-RRB-

and

the

and

subunits

of

pyruvate

dehydrogenase

which

interact

directly

A

comparison

of

the

CCE

associations

with

experimentally

determined

protein

interactions

among

components

of

the

proteasome

as

determined

from

the

X-ray

crystal

structure

-LRB-

Groll

et

al

-RRB-

reveals

four

interactions

between

proteins

that

directly

contact

each

other

in

the

proteasome

-LRB-

and

-RRB-

and

nine

interactions

between

proteins

that

assemble

into

the

same

physical

complex

but

do

not

directly

contact

each

other

-LRB-

and

-RRB-

Therefore

as

expected

both

direct

binding

and

co-complex

interactions

can

be

found

among

the

associations

In

fact

the

CCE

pairs

are

strongly

statistically

enriched

for

co-complex

associations

typical

of

affinity

purification/mass

spectrometry

interaction

assays

If

we

consider

only

the

subset

of

CCE

pairs

-LRB-

out

of

the

-RRB-

in

which

both

proteins

have

yeast

orthologs

of

these

can

be

verified

by

the

MIPS

database

as

belonging

to

the

same

yeast

complex

-LRB-

using

the

hand-curated

set

of

protein

complexes

Guldener

et

al

-RRB-

This

value

is

standard

deviations

-LRB-

P

-RRB-

above

the

mean

of

random

trials

randomizing

the

interactions

and

repeating

the

comparison

gives

a

mean

of

confirmed

interactions

for

N

random

trials

Similar

enrichment

can

be

seen

by

comparison

with

yeast

protein

complexes

defined

by

affinity

purification/mass

spectrometry

-LRB-

Gavin

et

al

Krogan

et

al

-RRB-

of

the

CCE

pairs

can

be

confirmed

standard

deviations

-LRB-

P

-RRB-

above

the

mean

of

random

trials

-LRB-

N

-RRB-

Likewise

of

the

CCE

pairs

can

be

confirmed

by

comparison

to

the

full-matrix

form

of

these

data

-LRB-

i.e.

considering

both

bait

prey

and

prey

prey

interactions

-RRB-

standard

deviations

-LRB-

P

-RRB-

above

the

mean

of

random

trials

-LRB-

N

-RRB-

Finally

we

looked

for

organismal

bias

among

the

CCE

pairs

examining

which

model

organism

contributed

the

top

LLR

score

for

each

interaction

-LRB-

Supplementary

Table

-RRB-

The

most

associations

were

contributed

from

the

comparison

of

human

and

C.

elegans

expression

accounting

for

of

the

associations

and

the

least

-LRB-

-RRB-

from

mouse

The

low

number

contributed

by

comparison

to

mouse

may

suggest

the

importance

of

employing

more

distant

orthologs

especially

to

non-mammalian

animals

in

identifying

interactions

by

this

approach

but

more

probably

stems

from

characteristics

of

the

data

employed

such

as

the

smaller

number

of

mouse

microarray

experiments

analyzed

-LRB-

Supplementary

Table

-RRB-

One

interesting

aspect

of

the

CCE

assay

is

that

it

intrinsically

samples

all

pairs

of

genes

that

are

measured

on

the

DNA

microarrays

This

has

the

effect

of

increasing

the

numbers

of

proteins

for

which

interactions

are

observed

and

thereby

decreasing

the

number

of

interactions

per

protein

-LRB-

interactions

for

proteins

interactions

per

protein

somewhat

lower

than

the

interactions

per

protein

observed

in

other

data

sets

-LRB-

Ramani

et

al

-RRB-

-RRB-

Limitations

false

positives

and

potential

improvements

Given

the

derivation

of

CCE

pairs

from

transcriptional

evidence

there

are

important

features

and

limitations

to

note

First

strong

co-expression

tends

to

coincide

with

stable

rather

than

transient

physical

association

-LRB-

Jansen

et

al

-RRB-

and

we

expect

CCE

pairs

to

reflect

this

trend

with

a

correspondingly

higher

false-negative

rate

for

transient

interactions

Second

based

on

our

measured

error

rates

there

are

still

appreciable

false-positive

associations

although

the

false-positive

and

negative

error

rates

are

comparable

to

the

only

direct

experimental

approaches

yeast

two-hybrid

assays

and

mass

spectrometry

of

cloned

epitope-tagged

human

proteins

that

have

been

applied

to

map

physical

associations

on

this

scale

However

CCE

false

positives

have

unusual

properties

As

the

CCE

pairs

were

the

highest

scoring

-LRB-

top

-RRB-

of

million

tested

gene

pairs

the

association

partners

are

strongly

co-regulated

in

an

evolutionarily

conserved

manner

and

thus

are

highly

likely

to

function

together

even

if

not

physically

associated

Finally

algorithmic

improvements

such

as

better

orthology

assignment

and

alternative

supervised

learning

frameworks

and

application

to

additional

DNA

microarray

data

e.g.

tissue

and

cell-type-specific

data

to

learn

tissue

and

cell-type-specific

associations

are

certain

to

reveal

new

associations

when

applied

in

the

general

framework

we

have

described

Thus

we

expect

new

CCE

associations

can

be

identified

by

modifications

to

this

method

Similarly

the

mass

spectrometry

data

used

to

test

the

CCE

associations

have

some

important

features

and

limitations

Primarily

co-sedimentation

alone

is

not

proof

of

physical

association

it

is

possible

for

unrelated

complexes

to

co-sediment

as

reflected

in

the

measured

true-positive

and

false-negative

rates

for

associations

inferred

solely

from

these

data

These

sedimentation-derived

associations

should

thus

not

be

viewed

as

standalone

However

as

a

benchmark

applied

in

the

manner

we

present

-LRB-

e.g.

analyzed

in

aggregate

form

-RRB-

or

when

considered

in

combination

with

other

data

such

as

incorporated

into

the

BIOS

scores

of

the

CCE

associations

we

find

the

mass

spectrometry

data

to

be

extremely

valuable

We

suggest

that

benchmarks

of

this

sort

could

be

of

great

utility

for

evaluating

physical

complexes

determined

by

other

methods

and

could

be

generally

adopted

for

measuring

assay

accuracy

Conclusions

The

scale

of

the

human

interactome

appears

to

be

beyond

any

individual

technique

a

combination

of

complementary

approaches

will

be

needed

to

map

the

complete

human

protein

protein

interaction

network

Although

current

methods

for

mapping

interactions

focus

largely

on

direct

experimental

observations

sufficient

functional

genomics

data

exist

that

physical

protein

associations

can

also

be

indirectly

identified

from

these

data

We

demonstrate

that

these

approaches

can

be

comparable

in

scale

and

quality

both

in

terms

of

false-positive

and

false-negative

rates

to

the

current

largest

scale

experimental

screens

Finally

as

CCE-based

physical

protein

association

mapping

is

based

on

conserved

in

vivo

phenomena

this

approach

is

likely

to

specifically

discover

associations

relevant

to

in

vivo

biology

Materials

and

methods

Mapping

of

orthologs

Orthologs

were

obtained

from

the

InParanoid

database

-LRB-

Remm

et

al

-RRB-

as

SwissProt

identifiers

for

human

proteins

and

their

orthologs

from

five

other

organisms

-LRB-

A.

thaliana

C.

elegans

D.

melanogaster

M.

musculus

and

S.

cerevisiae

-RRB-

Using

ID-Serve

-LRB-

-RRB-

and

organism-specific

databases

the

SwissProt

identifier

for

each

gene

was

mapped

to

alternate

identifiers

LocusLink

identifiers

-LRB-

human

-RRB-

common

names

-LRB-

M.

musculus

-RRB-

WormBase

identifiers

-LRB-

C.

elegans

-RRB-

Locus

codes

-LRB-

A.

thaliana

-RRB-

Flybase

gene

identifiers

-LRB-

D.

melanogaster

-RRB-

and

standardized

gene

names

-LRB-

S.

cerevisiae

-RRB-

Supplementary

Table

lists

the

numbers

of

orthologous

genes

analyzed

mRNA

expression

data

All

mRNA

expression

data

-LRB-

Supplementary

Table

-RRB-

were

obtained

from

the

Stanford

Microarray

Database

-LRB-

Ball

et

al

-RRB-

It

has

previously

been

shown

that

extraction

of

co-expression

relationships

is

improved

by

restricting

comparisons

to

similar

conditions

and

experiments

-LRB-

Lee

et

al

Segal

et

al

-RRB-

We

therefore

divided

the

available

human

DNA

microarray

experiments

into

categories

of

experiments

as

assigned

by

the

Stanford

Microarray

Database

and

restricted

comparisons

to

experiments

in

the

same

category

Expression

data

for

other

organisms

were

treated

as

single

categories

Each

of

the

microarray

expression

vectors

was

mean

centered

-LRB-

row

and

column

-RRB-

and

normalized

before

carrying

out

correlation

analysis

Calculation

of

co-expression

For

each

pair

of

human

genes

as

well

as

for

their

corresponding

orthologs

the

Pearson

correlation

coefficient

was

computed

between

the

mRNA

expression

vectors

For

each

gene

pair

this

gives

measurements

of

correlation

corresponding

to

the

categories

of

human

expression

data

sets

and

up

to

for

the

correlation

between

the

orthologs

in

the

other

organisms

Paralogs

-LRB-

as

defined

by

InParanoid

-RRB-

were

excluded

from

being

compared

to

each

other

as

they

tend

to

have

similar

expression

profiles

and

thus

high

correlation

which

we

empirically

observe

to

substantially

increase

the

false-positive

rate

The

significance

of

each

correlation

was

computed

based

on

t-test

statistics

as

where

r

is

the

minimum

significant

correlation

for

n

values

in

the

two

expression

vectors

being

compared

and

t

is

the

t-test

value

at

a

probability

of

P

from

a

t-test

table

Only

statistically

significant

correlation

coefficients

were

retained

thereby

accounting

for

variability

in

the

sparseness

of

expression

vectors

For

example

using

expression

vectors

of

experiments

with

only

data

points

available

for

both

genes

being

compared

the

absolute

value

of

correlation

must

be

for

the

comparison

to

be

statistically

significant

at

P

Removal

of

cross-hybridization

artifacts

Cross-hybridization

occurs

when

an

mRNA

probe

binds

to

a

non-cognate

spot

on

the

microarray

instead

of

its

perfect

complement

spot

This

creates

both

false

positives

-LRB-

due

to

additional

signal

at

incorrect

positions

on

the

array

-RRB-

and

false

negatives

-LRB-

due

to

reduced

signal

in

correct

positions

-RRB-

Although

cross-hybridization

is

well

established

in

spotted

cDNA-based

DNA

microarray

experiments

-LRB-

Kane

et

al

Murray

et

al

Xu

et

al

-RRB-

there

are

no

universal

standards

for

filtering

such

effects

In

this

analysis

we

expected

that

cross-hybridizing

gene

pairs

would

appear

to

have

similar

expression

patterns

and

therefore

contribute

false

positives

to

our

analysis

To

filter

out

these

potentially

spurious

interactions

arising

from

cross-hybridization

we

established

a

threshold

for

excluding

cross-hybridization

based

upon

analysis

of

the

hybridization

of

four

yeast

genes

-LRB-

and

-RRB-

to

their

homologs

on

a

yeast

DNA

microarray

The

four

genes

were

chosen

such

that

BLAST-based

comparisons

of

the

genes

DNA

sequences

to

other

genes

in

the

yeast

genome

yielded

hits

with

percent

identities

to

the

query

sequence

in

the

range

of

and

BLAST

E-values

The

four

query

genes

were

amplified

using

standard

PCR

techniques

and

primers

to

flanking

DNA

labeled

with

mixed

with

reference

DNA

-LRB-

Carlson

-RRB-

and

hybridized

to

a

yeast

cDNA

microarray

containing

spots

comprising

all

the

yeast

genes

and

intergenic

regions

-LRB-

Carlson

Hahn

et

al

Kim

and

Iyer

-RRB-

Standard

microarray

analysis

was

carried

out

to

quantify

hybridization

strength

as

the

mean

of

ratios

of

fluorescence

intensities

across

spots

By

plotting

hybridization

strength

against

the

DNA

sequence

identity

of

the

genes

-LRB-

Supplementary

Figure

-RRB-

we

identified

an

operational

threshold

of

BLAST

E-value

and

DNA

sequence

identity

within

the

aligned

regions

Gene

pairs

that

exceed

this

threshold

-LRB-

with

either

the

human

or

model

organism

gene

pair

DNA

sequences

-RRB-

were

likely

to

cross-hybridize

and

were

excluded

from

further

analysis

This

filter

removes

protein

pairs

from

the

plant

human

analysis

from

the

worm

human

from

the

fly

human

from

the

mouse

human

and

from

the

yeast

human

analysis

This

filtration

preferentially

removes

many

false-positive

interactions

as

the

average

expression

correlation

of

the

filtered

pairs

was

significantly

higher

than

for

the

remaining

pairs

-LRB-

e.g.

the

average

expression

correlation

in

the

human

plant

analysis

was

while

the

average

for

the

filtered

pairs

was

-RRB-

with

the

maximum

expression

correlation

among

the

removed

pairs

equal

to

for

all

comparisons

Training

to

extract

physical

protein

associations

We

used

the

human

protein

interactions

from

Ramani

et

al

-LRB-

-RRB-

as

the

physical

association

benchmark

The

associations

were

randomly

separated

into

testing

and

training

data

sets

-LRB-

and

associations

respectively

-RRB-

For

each

of

the

five

human

gene

pair/ortholog

gene

pair

sets

the

maximum

expression

correlation

of

the

human

genes

from

the

data

sets

was

plotted

along

the

x

axis

and

the

correlation

of

the

orthologous

genes

plotted

along

the

y

axis

-LRB-

as

in

Figure

-RRB-

The

fraction

of

gene

pairs

that

showed

a

particular

expression

pattern

was

measured

in

bins

of

units

Two-dimensional

histograms

were

calculated

for

interacting

proteins

and

for

non-interacting

proteins

in

the

training

set

The

logarithm

of

the

ratio

of

the

histograms

at

a

given

position

in

the

plot

corrected

by

the

background

likelihood

of

physical

associations

in

the

training

set

gives

the

log

likelihood

estimate

of

physical

association

conditioned

on

the

degree

of

co-expression

of

the

human

genes

and

their

orthologs

in

that

organism

To

minimize

possible

errors

due

to

orthology

assignments

we

further

considered

only

counts

in

the

upper

right-hand

quadrant

of

each

analysis

corresponding

to

gene

pairs

for

which

the

human

and

other

organismal

experiments

describe

similar

co-expression

trends

Protein

pairs

outside

of

the

training

set

were

then

assigned

log

likelihood

scores

according

to

their

expression

patterns

in

these

data

sets

Similar

analyses

were

performed

for

associations

derived

from

comparison

of

human

expression

data

with

each

of

the

four

other

organism-specific

data

sets

associating

the

maximum

score

from

these

five

analyses

as

each

protein

pairs

estimated

likelihood

of

associating

physically

-LRB-

The

maximum

score

outperformed

the

naïve

Bayes

sum

of

scores

suggesting

that

the

five

scores

are

not

independent

-RRB-

The

top-scoring

associations

are

listed

in

Supplementary

information

The

human-only

co-expression

control

set

was

generated

by

considering

only

the

human

DNA

microarray

data

ignoring

contributions

from

other

organisms

and

lifting

the

requirement

for

each

member

of

a

gene

pair

to

have

orthologs

in

the

same

second

organism

Putative

associations

were

identified

as

for

the

CCE

case

but

instead

using

the

log

likelihood

framework

to

relate

the

correlation

coefficients

across

only

the

human

DNA

microarray

experiments

to

the

likelihood

of

physically

associating

All

other

calculations

were

performed

identically

to

the

CCE

case

including

calculation

of

correlation

coefficients

significance

testing

of

correlations

calculation

of

likelihood

values

selection

of

priors

and

filtration

for

cross-hybridization

Testing

for

enrichment

of

known

physical

associations

We

measured

enrichment

for

known

physical

associations

using

the

independent

test

set

of

physical

associations

and

the

same

LLR

framework

used

to

initially

derive

the

CCE

associations

The

associations

formed

the

positive

test

set

the

negative

test

set

was

defined

as

all

pairs

of

proteins

chosen

from

the

associations

set

omitting

the

associations

themselves

The

prior

odds

ratio

of

interacting

-LRB-

P

-LRB-

I

-RRB-

P

-LRB-

I

-RRB-

-RRB-

equaled

the

ratio

of

positive

to

negative

test

set

examples

-LRB-

-RRB-

For

each

query

association

network

being

tested

-LRB-

or

for

a

given

bin

of

associations

selected

from

a

rank-ordered

list

-RRB-

we

measured

the

fraction

of

query

set

associations

shared

with

the

positive

test

set

-LRB-

P

-LRB-

I

D

-RRB-

-RRB-

as

well

as

the

fraction

shared

with

the

negative

test

set

-LRB-

P

-LRB-

I

D

-RRB-

-RRB-

The

posterior

odds

ratio

was

calculated

as

P

-LRB-

I

D

-RRB-

P

-LRB-

I

D

-RRB-

and

the

LLR

calculated

as

indicated

in

the

main

text

equal

to

the

posterior

odds

ratio

divided

by

the

prior

odds

ratio

For

the

purposes

of

Figure

the

log

likelihood

was

calculated

in

a

cumulative

manner

-LRB-

i.e.

aggregating

successive

bins

of

associations

for

analysis

-RRB-

Testing

for

functional

similarity

We

measured

functional

similarity

of

interacting

protein

pairs

by

using

the

gene

annotation

information

obtained

from

GO

-LRB-

Ashburner

et

al

-RRB-

process

level

annotation

and

KEGG

pathway

annotation

-LRB-

Kanehisa

et

al

-RRB-

These

databases

provide

specific

pathway

and

biological

process

annotations

for

human

genes

assigning

them

into

KEGG

pathways

-LRB-

at

the

lowest

level

of

KEGG

-RRB-

and

GO

pathways

-LRB-

at

level

of

the

GO

biological

process

annotation

-RRB-

Interactions

were

first

rank-ordered

by

confidence

scores

For

each

successive

bin

of

interactions

the

functional

similarity

was

calculated

in

a

cumulative

manner

by

counting

the

number

of

pairs

in

that

bin

or

previous

bins

that

shared

a

functional

annotation

dividing

this

by

the

number

of

pairs

that

did

not

share

functional

annotation

and

correcting

by

the

prior

probability

of

annotated

pairs

sharing

annotation

-LRB-

-RRB-

Construction

of

standard

curves

for

estimating

percentages

of

physical

associations

Standard

curves

were

constructed

as

described

in

the

main

text

Positive

control

sets

for

Figure

and

C

were

selected

from

the

hand-curated

protein

complex

assignments

of

Reactome

-LRB-

Joshi-Tope

et

al

-RRB-

For

the

analysis

of

Figure

we

restricted

the

analysis

to

the

portion

of

each

data

set

for

which

both

interacting

proteins

have

orthologs

among

the

yeast

worm

or

fly

proteins

sampled

by

the

benchmark

assays

-LRB-

i.e.

considering

only

the

subspace

of

interactions

spanned

by

the

assay

bait

prey

pairs

-RRB-

For

the

standard

curves

of

both

Figure

and

C

the

derived

percentages

of

physical

associations

do

not

strongly

depend

upon

the

sizes

of

the

data

sets

or

control

sets

only

upon

their

tendencies

to

share

orthologous

interactions

or

functional

annotations

-LRB-

data

not

shown

-RRB-

Ranges

of

accuracies

were

derived

directly

from

the

standard

curve

-LRB-

i.e.

as

empirically

measured

from

replicate

analysis

of

control

mixtures

of

true

and

false-positive

interactions

-RRB-

For

the

linear

mixture

model

of

Figure

positive

control

associations

were

taken

from

Joshi-Tope

et

al

-LRB-

-RRB-

only

considering

genes

with

yeast

orthologs

and

negative

control

associations

taken

as

pairs

of

human

genes

from

the

positive

control

set

that

have

yeast

orthologs

but

do

not

have

recorded

interactions

To

minimize

possible

circularity

we

removed

all

functional

linkages

from

the

yeast

network

that

were

derived

only

from

mRNA

co-expression

data

The

variances

associated

with

accuracy

estimates

in

Figure

were

derived

from

replicate

analyses

of

mixtures

of

known

proportions

of

true

and

false-positive

interactions

-LRB-

Supplementary

Figure

-RRB-

Binary

interaction

overlap

score

To

further

assign

confidence

to

each

association

we

have

adopted

the

BIOS

of

Stelzl

et

al

-LRB-

-RRB-

based

upon

the

benchmark

sets

-LRB-

Table

I

-RRB-

we

assign

each

association

if

the

protein

pair

is

observed

in

the

physical

interaction

benchmark

for

sharing

GO/KEGG

keywords

for

sharing

SwissProt

keywords

for

sharing

KOG

annotation

for

being

observed

in

the

orthologous

interaction

benchmark

for

having

a

correlation

coefficient

in

the

mass

spectrometry

elution

profile

experiments

and

for

having

yeast

orthologs

that

are

either

directly

connected

or

one

link

separated

in

the

yeast

functional

network

benchmark

-LRB-

with

expression-only

and

orthology-derived

links

omitted

-RRB-

Each

association

is

thus

scored

from

to

based

on

additional

support

for

that

association

the

BIOS

scores

generally

correlate

with

the

LLR

scores

-LRB-

Supplementary

Figure

-RRB-

and

are

reported

in

the

supporting

data

file

Human

cell

culture

and

mass

spectrometry

HeLa

cells

were

cultured

in

Dulbecco

's

modified

Eagle

's

medium

supplemented

with

fetal

bovine

serum

at

°C

with

At

about

confluency

cells

were

treated

with

μg/ml

emetine

for

min

and

harvested

by

scraping

Cells

were

centrifuged

at

g

for

min

washed

three

times

with

cold

PBS

buffer

and

resuspended

in

five

packed

cell

volumes

of

cold

lysis

buffer

-LRB-

mM

Tris

pH

mM

KCl

mM

-RRB-

After

swelling

on

ice

for

min

cells

were

centrifuged

at

g

for

min

and

resuspended

in

one

packed

cell

volume

cold

lysis

buffer

supplemented

with

protease

inhibitor

cocktail

-LRB-

Roche

-RRB-

and

μg/ml

emetine

After

lysing

the

cells

with

a

dounce

homogenizer

nuclei

were

collected

by

centrifuging

at

g

for

min

The

supernatant

was

centrifuged

at

g

for

min

to

obtain

the

cytosolic

fraction

Nuclei

were

suspended

in

lysis

buffer

and

lysed

by

sonication

collecting

the

clarified

supernatant

after

centrifugation

at

g

for

min

The

cytosolic

and

the

nuclear

fractions

were

each

loaded

onto

continuous

sucrose

gradients

in

lysis

buffer

After

a

spin

at

r.p.m.

in

a

Beckman

rotor

the

sucrose

gradient

was

fractionated

using

an

ISCO

gradient

fractionation

system

Proteins

from

each

fraction

were

precipitated

with

cold

trichloroacetic

acid

-LRB-

TCA

-RRB-

and

washed

with

cold

acetone

The

protein

pellets

were

suspended

in

mM

pH

Tris

buffer

and

digested

with

sequencing

grade

trypsin

-LRB-

Sigma

-RRB-

For

each

fraction

tryptic

peptides

were

loaded

onto

a

reverse-phase

column

and

washed

with

water

acetonitrile

and

formic

acid

Peptides

were

eluted

with

a

gradient

from

to

acetonitrile

and

analyzed

online

with

a

nanoelectrospray

ionization

-LRB-

nl/min

flow

rate

-RRB-

LTQ-Orbitrap

hybrid

mass

spectrometer

-LRB-

Thermo

Electron

-RRB-

using

data-dependent

precursor

ion

selection

Each

parent

ion

mass

spectrum

-LRB-

MS

-RRB-

was

analyzed

at

high

resolution

-LRB-

-RRB-

with

the

Orbitrap

the

top

seven

MS

peaks

were

fragmented

by

helium

collision-induced

dissociation

at

eV

analyzing

the

resulting

MS/MS

spectra

with

the

LTQ

Approximately

MS/MS

spectra

were

collected

per

fraction

Spectra

were

searched

against

the

set

of

NCBI

human

protein

sequences

using

TurboSequest

-LRB-

Bioworks

v.

Thermo

Electron

-RRB-

Proteins

from

each

fraction

were

identified

at

a

false

detection

rate

using

Peptide/ProteinProphet

-LRB-

Keller

et

al

Nesvizhskii

et

al

-RRB-

The

spectral

count

-LRB-

number

of

total

observations

of

MS/MS

spectra

from

a

given

protein

in

a

given

fraction

-RRB-

was

used

as

an

estimate

of

protein

abundance

-LRB-

Liu

et

al

-RRB-

dividing

the

spectral

count

of

a

protein

-LRB-

-RRB-

by

the

sum

of

spectral

counts

for

all

proteins

identified

in

that

fraction

Protein

elution

profiles

are

provided

as

Supplementary

information

Yeast

media

and

strains

All

yeast

strains

were

cultured

in

YPD

-LRB-

yeast

extract

peptone

and

dextrose

-RRB-

at

°C

Tetracycline

promoter-controlled

essential

gene

haploid

MATa

strains

-LRB-

Mnaimneh

et

al

-RRB-

and

TAP-tagged

haploid

MATa

strains

-LRB-

Ghaemmaghami

et

al

-RRB-

were

obtained

from

Open

Biosystems

Polysome

profile

analysis

All

yeast

strains

were

cultured

to

For

tetracycline

promoter-controlled

alleles

overnight

cultures

were

diluted

to

μg/ml

doxycycline

-LRB-

Fisher

Scientific

-RRB-

was

added

into

the

media

and

cells

were

grown

to

Cycloheximide

-LRB-

μg/ml

-RRB-

-LRB-

Sigma

-RRB-

was

added

to

each

culture

Cultures

were

immediately

cooled

with

ice

and

all

subsequent

steps

were

performed

on

ice

or

at

°C

Each

cell

pellet

was

washed

once

with

lysis

buffer

-LRB-

mM

Tris

pH

mM

KCl

mM

μg/ml

cycloheximide

mM

β-mercaptoethanol

μg/ml

leupeptin

μg/ml

aprotinin

μg/ml

bestatin

and

μg/ml

pepstatin

A

-RRB-

without

protease

inhibitors

-LRB-

MP

Biomedicals

Inc.

-RRB-

The

cells

were

pelleted

resuspended

in

one

volume

lysis

buffer

and

lysed

with

glass

beads

Crude

lysates

were

centrifuged

at

g

for

min

Fifteen

units

of

each

supernatant

were

loaded

onto

continuous

ml

sucrose

gradients

in

polysome

lysis

buffer

without

protease

inhibitors

as

in

Baim

et

al

-LRB-

-RRB-

After

a

spin

at

r.p.m.

in

a

Beckman

rotor

the

sucrose

gradient

was

fractionated

and

absorbance

at

nm

was

measured

For

TAP-tagged

strains

fractions

were

collected

and

proteins

were

precipitated

with

cold

TCA

and

washed

with

cold

acetone

Immunoblotting

Precipitated

proteins

were

resuspended

in

μl

Laemmli

buffer

and

μl

of

each

sample

was

deposited

onto

nitrocellulose

membrane

TAP-tagged

proteins

were

detected

with

PAP

antibody

-LRB-

Rockland

Immunochemicals

Inc.

-RRB-

and

chemiluminescence

-LRB-

ECL

Amersham

Biosciences

-RRB-

Supplementary

Material

Supplementary

Figures

Supplementary

Tables

Supplementary

Information

Supplementary

Information

interactions
mass spectrometry
networks
proteomics


An approach to classifying sequence tags sampled from Plasmodium falciparum var genes Plasmodium falciparum erythrocyte membrane protein 1 -LRB- PfEMP1 -RRB- appears to play an key role as both a virulence factor and as a target of naturally acquired immunity -LSB- 1,2 -RSB- . This large family of molecules is encoded by the highly polymorphic superfamily of var genes of which there are 60 variants in every genome -LSB- 3 -RSB- . A rapidly growing collection of var sequences is now available from clinical isolates around the world -LSB- 4 -- 11 -RSB- . Despite immense diversity both in terms of overall organization and primary sequence , the majority of var genes contain a DBL1α region -LSB- 3 -RSB- . The existence of short islands of homology within this region has enabled the design of primers that can be used to sample sequence from most var genes to create DBL1α sequence tags -LSB- 5 -RSB- . A standard approach to classification of these sequence tags would enable direct comparisons to be made between different studies . However , the extreme diversity of var genes and the fact that they undergo intra-genic recombination -LSB- 4,12,13 -RSB- , makes this difficult . Despite the high diversity there does appear to be underlying simplicity to the var genes that supports the use of information present in DBL1α sequence tags in making comparisons between the expression levels in different isolates . Analysis of the fully sequenced genome of a single P. falciparum isolate 3D7 suggests that the genomic location of the 60 var genes promotes genetic structuring and the maintenance of genetically distinct sequence types -LSB- 14 -- 16 -RSB- . In addition , structural features of the genes within the single genome of 3D7 closely mirrors the range of structural features among collections of DBL1α sequence tags from clinical parasite isolates -LSB- 9 -RSB- . We previously used a small number of key sequence features in an algorithm to classify the DBL1α sequence tags from a single geographical location in Kenya into six groups -LSB- 9 -RSB- -LRB- see Fig. 1A and below -RRB- . This var tag grouping system , though it is based on portion of the DBL1α domain -LRB- see Supplementary information -RRB- , corresponded well with whole var gene classification based on the whole genome sequence of the parasite line 3d7 -LSB- 9 -RSB- . This grouping system appears to be biologically meaningful . Expression of group 2 sequences was strongly associated with the parasite rosetting phenotype in Kilifi whereas expression of group 1 sequences was negatively associated with the repertoire of antibodies to infected erythrocyte surface antigens carried by the patient at the time of disease -LSB- 9 -RSB- . Thus DBL1α sequence tags appear to contain useful information about the genes to which they belong that is currently not directly accessible in field studies of clinical parasite isolates . We have developed a rapid approach to performing the classification using text string analysis functions in Microsoft Excel and Perl -LRB- see Supplementary files -RRB- . This classifies sequence tags directly without the need for prior alignment and can be performed on many sequences simultaneously . The approach is summarized in Fig. 1A . The classification is based around a count of the number of cysteine residues within the tag region and a set of sequence motifs at four positions of limited variability -LRB- PoLV 1 -- 4 -RRB- whose positions within the sequence are fixed in relation to four anchor points -LRB- a -- d , marked with arrows in Fig. 1A -RRB- . Thus PoLV1 and PoLV4 are fixed in relation to the 5 ′ and 3 ′ ends of the sequence , respectively -LRB- anchor points a and d -RRB- . PoLV2 and PoLV3 are fixed in relation to a `` WW '' motif -LRB- anchor point b -RRB- . The definition of the groups defined by these features is summarized in the box in Fig. 1 . Henceforth we will refer to these groupings as cyteine/PoLV groups . This text string analysis approach was tested on the original set of sequences from Kilifi , Kenya -LSB- 9 -RSB- and sequences from 9 other studies -LRB- see Fig. 1B -- E -RRB- . The sequences were pre-screened to ensure that they contained a 5 ′ DIGDI and 3 ′ PQFLR consensus sequences . Overall 99.6 % of sequences could be classified using this approach . This included 100 % of sequences from Malawi -LRB- J. Montgomery unpublished -RRB- , Papua New Guinea -LSB- 7,17 -RSB- , Mali -LSB- 10 -RSB- , Solomon Islands -LSB- 7 -RSB- , and The Philippines -LSB- 7 -RSB- together with 100 % of sequences from one dataset from Brazil -LSB- 6 -RSB- . A dataset from Venezuela -LRB- 52 non-identical sequences -LSB- 8 -RSB- -RRB- carried two sequences that could not be classified . A dataset from Brazil -LRB- 137 non-identical sequences , -LSB- 18 -RSB- -RRB- carried one sequence that could not be classified . The original dataset from Kilifi -LRB- 878 non-identical sequences -LSB- 9 -RSB- -RRB- carried two sequences that could not be classified . All five of these sequences lacked WW or VW motifs required as anchor points within the sequence . Part of the rationale for this grouping system came from a search for PoLV motifs that were associated with sequences with distinct length distributions -LSB- 9 -RSB- . Two motifs were identified which were independently associated with short sequences . These are MFK * at PoLV1 and * REY at PoLV2 -LRB- an asterisk here denotes any amino acid -RRB- . We hypothesised that if sequences of different length recombine with each other they will generate a wide range of sequences of different lengths whereas genetically isolated sequences , i.e. , those that are not recombining with one another are able to maintain distinct distributions in their length . If these groupings are genuine the sequences classified into different groups should have similar lengths in different settings . As shown in Fig. 1B -- E , broadly similar distributions of sequence length are observed within the six different groups between three different continents , suggesting that sequences generated in these different studies shared the same set of structural features . Specifically , MFK * -LRB- carried at PoLV1 in group 1 -RRB- and * REY -LRB- carried at PoLV2 in groups 2 and 5 -RRB- are associated with short sequences in each geographical region . No examples of sequences with both MFK * and * REY motifs were found , suggesting that these motifs are mutually exclusive . In addition , though * REY motifs were found in sequences with 2 or 4 cysteine residues -LRB- cys2 or cys4 -RRB- , with the exception of a single cys4 -LRB- group 4 -RRB- sequence from the Philippines , MFK * motifs were found exclusively in cys2 -LRB- group 1 -RRB- sequences . Further support for the cysteine/PoLV groupings comes from recent publications . Trimnell et al. found a good correspondence between cysteine/PoLV groupings of cys2 sequences and groups defined phylogenetically within a globally sampled subset of var genes with a specific upstream control region , upsA -LSB- 11 -RSB- . Also evident from sequences reported in that study is the fact that DBL1 from two other globally sampled subsets of var genes can be easily distinguished from DBL1 domains from other vars using unique PoLV motifs . var2csa vars have a unique PoLV2 motif `` EVIT '' , whereas Type3 vars have a unique PoLV4 motif `` PPVV '' -LRB- data not shown -RRB- . Kraemer et al. have recently performed an analysis and re-classification of whole var genes from 3D7 , HB3 and IT4 -LSB- 19 -RSB- . Fig. 2A and B summarizes the relationship between the cysteine/PoLV groupings and whole var gene classification . With the exception of group 6 sequences which were not found in HB3 var genes all sequence groups were represented . In all three genomes cysteine/PoLV group 1 sequences are exclusively found in group A var gene and long genes with > 5 domains whereas cysteine/PoLV group 5 are found only in non-group A genes and those with 4 -- 5 domains . Cys2 sequence tags -LRB- groups 1 -- 3 -RRB- were never found in group C var genes . Kyriacou et al. used a phylogenetic approach to compare DBL1α sequence tags from Mali -LSB- 10 -RSB- . Visual inspection of the layout of these sequences reveals three main groups and a minor group . There was good correspondence between these groups and the cysteine/PoLV groupings -LRB- Fig. 2C -LSB- 10 -RSB- -RRB- . This study showed that cys2 sequence tags were more frequent among parasite isolated from children with cerebral malaria than those from children with hyperparasitaemia . However , division of the sequences into cysteine/PoLV groups suggests that the frequency of group 2 sequences is similar in parasites from these two groups of children -LRB- Fig. 2D -LSB- 10 -RSB- -RRB- . At a higher level of resolution , the distinct sequence identifier -LRB- DSID -RRB- -LRB- see Fig. 1A -RRB- is a potentially useful method of further classifying sequence tags . This consists of a string of sequence features in the form `` PoLV1-PoLV2-PoLV3-number of cysteines-PoLV4-sequence tag length '' . The DSID captures more of the overall sequence diversity than the previously described `` sequence signature '' -LSB- 9 -RSB- whilst remaining robust to minor changes introduced by sequencing or PCR errors . Among the 1595 non-identical sequences identified in all the studies described here , there were 1111 DSIDs . Fig. 1F -- G illustrates the potential usefulness of this approach to classification . In Fig. 1F , 44 `` common '' sequences that were shared between more than one study were selected . Fishers exact test was used to determine whether these common sequences were shared between two studies more or less than would be expected by chance -LRB- + or − symbols , respectively -RRB- . Fig. 1G is the same except that the analysis was done at the level of 157 `` common '' DSIDs that were shared between more than one study . In contrast to Fig. 1F , there was a highly significant similarity between var genes from South American isolates in support a recent study of Amazonian isolates -LSB- 18 -RSB- . In contrast to the low overlap between DSIDs from Kilifi and from South America -LRB- Fig. 1G -RRB- there is considerable overlap in the constituent PoLV motifs themselves -LRB- see Supplementary information -RRB- . This illustrates the potential for recombination to generate diversity from a limited number of sequence blocks -LSB- 4,12,13 -RSB- . Since the cysteine/PoLV system of classification is based on commonly occurring sequence features it is hoped that it will useful for initial analysis and annotation , comparison of different geographical regions over time and identification of unusual sequences .

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

malaria
pfemp1
var
plasmodium falciparum

pfemp1, plasmodium falciparum erythrocyte membrane protein 1