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(INTERACTOMICS

LIFE SCIENCE TECHNOLOGIES

The human genome has been called the “blueprint of life,” but it’s really more of

a parts list. Cellular architecture is better defi ned by its complexes, the molecular

machines that actually make a cell, a cell. French researchers fi rst coined the

term “interactome” in 1999; the fi rst protein-protein interactome data appeared

in 2000. Today the fi eld—like the 11-year-old it is—is maturing rapidly. Interactome

research has racked up more than 560 publications, and databases now house

interactions numbering in the hundreds of thousands. Still, as international

efforts to map the human protein-interaction network get under way, it’s clear

interactomics has a long way to go. By Jeffrey M. Perkel

There are some 213,000 protein-protein interactions

logged in the IntAct database, 169,000 in the BioGRID

database. Represented graphically as starbursts of pro-

tein “nodes” linked by interaction “edges,” these inter-

actions are collected for one important reason: Proteins,

like humans, are social animals. From DNA replication to protein

degradation, the work of the cell is accomplished mostly by macro-

molecular complexes—a fact that researchers, awash in genome se-

quence data but bereft of functional annotation, can exploit to gain

insight into what proteins do.

“Knowing about the sociology of your protein is an integral part

of today’s discovery process,” says Giulio Superti-Furga, scientifi c

director of the Center for Molecular Medicine at the Austrian

Academy of Sciences.

It’s like profi ling a protein by cataloging its Facebook friends. Func-

tional annotation by molecular association is becoming standard

practice, and today a genome without an interactome is effectively

unfi nished. Large-scale efforts are under way to fi ll the gaps. The Ca-

nadian government, in collaboration with other partners, has awarded

nearly $23 million “for the creation of a national technology platform

aimed at mapping the human interactome,” including $9.16 million

in June 2009 from the Canada Foundation for Innovation (CFI).

A subset of these researchers, with colleagues in the United States

and Europe, now seeks to take the project international through a

nascent effort called the International Interactome Initiative, or I3.

“After the sequencing of the human genome, the next step for

many people is to fi nd how these proteins and other molecules

interact and combine together to sustain life,” says Benoit Cou-

lombe of the Clinical Research Institute of Montreal, who is the

principal investigator on the CFI grant and a member of the I3 Steer-

ing Committee.

Technologies are already in place, but no one lab can do it alone;

the interactome, like the genome, must be deciphered collabora-

tively. Yet if the human genome is any guide, the time may not be

long until a fi rst, albeit “drafty,” human interactome is unveiled, says

Marc Vidal, director of the Center for Cancer Systems Biology at the

Dana-Farber Cancer Institute and professor of genetics at Harvard

Medical School.

Protein-Protein Interaction TechnologiesToward a Human Interactome

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AAAS/Science Business Office Feature

continued »

UPCOMING FEATURES

Proteomics 2: Biomarkers—September 10

Genomics 2: Structural Genomics—October 29

Flow Cytometry—November 5

“After the sequencing of the human genome, the next step for many people is to fi nd how these proteins

and other molecules interact and combine together to sustain life.”

“We are now 21 years after Fields and Song [published the pivotal

yeast two-hybrid method of interaction detection],” Vidal says. “It

took 25 years between Sanger and Maxim and Gilbert to get a good

version of the human genome sequence. So let’s say we are half a

decade away from that, give or take.”

A DYNAMIC PROBLEMVidal’s enthusiasm notwithstanding, even a drafty interactome will

require massive effort.

A contraction of interaction and genome, the term interactome

refl ects the fact that its practitioners apply whole-genome sensibili-

ties to their work. But that’s where the similarities end. Whereas ge-

nomes are discrete entities of defi ned size, interactomes are mov-

ing targets. Like Sisyphus with his boulder, researchers could labor

forever and never “fi nish” the interactome.

First, there’s an issue of scale. The human genome encodes about

20,000 genes, producing some 200 million possible total pairwise

protein combinations. In 2009, Vidal calculated that the human in-

teractome, “excluding splice variant complexity,” contains between

74,000 and 200,000 binary interactions. Of these, he says, research-

ers have mapped “perhaps 10,000” high-quality interactions. “So we

are probably one order of magnitude if not 50-fold away from where

we need to go.”

464 www.sciencemag.org/products

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Yet the problem certainly is bigger than that, if for no other reason

than that these interactions need to be detected multiple times to

be of high confi dence. And in any event, the interaction landscape

is fl uid.

“These are very dynamic things,” says Tony Pawson, distinguished

scientist at the Samuel Lunenfeld Research Institute (SLRI) in

Toronto. “Many of the interactions that control cellular behavior are

dependent on posttranslational modifi cations induced by growth

factors or other signals, and those interactions change continuously.

And the expression of proteins and their interactions vary from cell

to cell.”

For every cell type, stressor, stimulus, nutritional condition—basi-

cally, for any variable one can imagine, including time—there exists a

unique interactome, like a frame in a movie. Even without dramatic

cellular changes, the interactome is a living, breathing thing. “The

interactome is basically in fl ux all the time,” says SLRI senior inves-

tigator Jeff Wrana.

Michael Rout of Rockefeller University and John Aitchison of the

Institute for Systems Biology have devised one approach to deal

with that problem. The two researchers, both PIs with the National

Center for Dynamic Interactome Research (NCDIR), capture pro-

teomic motion via interactome dynamics—effectively, collecting

multiple interaction network movie frames over time.

“We are big believers that the most dangerous thing you can

do in science is to get put off because you can’t solve a problem,”

Aitchison says.

Focusing on such problems as viral infection, nuclear transport,

and peroxisome biogenesis, NCDIR researchers collect interactomic

“stills” under different biological conditions and then strive compu-

tationally to infer what’s different.

“We start with two states and then try to bring down the time

resolution to see how those interactions change with time,” Aitchi-

son says.

TWO APPROACHES TO THE INTERACTOMETo collect their data, Rout and Aitchison use affi nity purifi cation cou-

pled to mass spectrometry (AP/MS), in which endogenous multipro-

tein complexes are purifi ed and analyzed en masse; Vidal prefers the

yeast two-hybrid assay (Y2H), in which two proteins are coupled to

the two halves of a transcription factor and expressed in yeast. If the

two proteins—bait and prey—interact in the nucleus, they reconsti-

tute a functioning DNA-binding protein, activating a reporter gene

that signals the molecules’ intracellular pas de deux.

Easily automated and amenable to high throughput analysis, both

approaches have been applied in genome-scale studies. In one re-

cent example, Vidal’s team probed 100 million pairwise protein com-

binations in the Caenorhabditis elegans proteome using Y2H, from

(INTERACTOMICS

LIFE SCIENCE TECHNOLOGIES AAAS/Science Business Office Feature

which they derived 1,816 interactions—about 1 percent of the pre-

dicted nematode network of 116,000 interactions.

Yet Y2H and AP/MS represent two complementary—some would

say competing—strategies for interactome mapping, and most agree

both are critical to a fully populated interactome map. “You are asking

different questions with the [two] approaches,” says Superti-Furga,

who uses AP/MS to probe cell signaling pathways. On the one hand

are what Vidal calls “binary interactome mappers,” researchers who

use techniques (such as Y2H, luminescence-based mammalian in-

teractome [LUMIER], and the protein complementation assay [PCA])

to directly probe pairwise interaction potential—that is, can protein

X interact with protein Y. On the other hand are “co-complex mem-

bership guys,” scientists (including most members of the Canadian

national platform) who use AP/MS to ask, when purifying protein X,

what else comes along for the ride (whether directly or not)?

Not surprisingly, the two workfl ows produce different and often

only partly overlapping results. For instance, AP/MS can detect

indirect interactions in complexes mediated by a third (bridging)

protein—contacts that cannot be seen via traditional binary meth-

ods. Yet co-complex-based approaches can distinguish neither pro-

teins that physically interact directly from those that do not, nor

mutually exclusive forms of the same complex, complicating the

resulting networks.

PROBING INTERACTOME LOGICComplicated as they are, protein-protein interactions represent only

part of the interactome; a complete network map also

( Interactomics, like genomics, requires serious automation. Pipetting,

tracking, and logging all those pairwise combinations or AP/MS trials

is not for the faint of heart.

For those who lack the stomach (or the infrastructure), fi rms like

Hybrigenics (www.hybrigenics.com) and Dualsystems Biotech

(www.dualsystems.com) offer yeast two-hybrid–based screening

services.

Alternatively, researchers can do the work in-house. Vectors,

strains, and reagents for both omics-level strategies are widely avail-

able and include the Invitrogen ProQuest Two-Hybrid System with

Gateway Technology from Life Technologies (www.invitrogen.com)

and the InterPlay Adenoviral TAP System from Agilent Technologies

(www.agilent.com).

Lower throughput methods have also been commercialized. One

new offering: Life Technologies’ TaqMan Protein Assays (www.ap-

pliedbiosystems.com/taqman4antibodies). Combining TaqMan-based

real-time PCR assays with Olink Biosciences’ (www.olink.com)

proximity ligation assay technology, the assay uses two antibodies,

each directed to one member of an interacting pair and coupled to

a unique DNA sequence. If the proteins physically interact in vitro,

the binding of their cognate antibodies brings the oligonucleotide

tails close enough for ligation to occur, the result of which may be

detected via real-time PCR.

Whichever method you choose to tackle the interactome, be pre-

pared for data overload. According to Anne-Claude Gingras, an inves-

tigator at the Samuel Lunenfeld Research Institute, data tracking “is

a tremendous problem” in AP/MS studies, and in interactomics in

general. A good laboratory information management system (LIMS),

she says, “is actually really key.”

“The interactome is

basically in fl ux all the time.“

COMMERCIAL INTERACTOME TOOLS

continued »

465www.sciencemag.org/products

(contains protein–nucleic acid, protein–small molecule, and genetic

interactions, too.

In 2009 Bernhard Palsson, Galletti Professor of Bioengineering at

the University of California, San Diego (UCSD), and his group com-

bined genome-scale protein-DNA interaction (ChIP-chip) data, ex-

pression profi ling, 5'-end sequencing, and mass spectrometry data

to detail what he calls “the metastructure of a genome”—basically,

a multilayer atlas correlating gene expression with RNA polymerase

binding and transcription start sites.

Using this dataset, collected using E. coli, Palsson’s team deter-

mined that the classical model of bacterial operons is incomplete.

“It turns out that segments of DNA can generate multiple different

transcripts,” he says. “For instance, the threonine operon in E. coli

was once thought of as one operon, but we show that at least fi ve

different transcripts can come off that region of genome.”

By analyzing such datasets and comparing them across multiple

species, researchers can interrogate the evolutionary plasticity

of regulatory networks. According to Trey Ideker, chief of medical

genetics at UCSD, who has studied protein-protein network

conservation across evolutionary time, while protein-protein

interaction networks are largely static, transcriptional networks are

relatively fl uid. “Where those transcriptional complexes bind and

what genes they control seems to change a lot from species to

species,” he says.

Also relatively plastic, Ideker says, are genetic interactions, which

probe the programming logic of interaction networks.

Genetic interaction screens combine genetic mutations to

infer redundancies and identify relationships between biological

processes. Basically, when mutant A and mutant B are joined in a

cell, if the resulting phenotype is synergistic rather than additive, an

interaction is inferred.

“You can’t just assay which proteins physically bump up against

one another; one has to know about how these proteins function

relative to one another,” says Ideker.

Vidal has devised a different approach to sussing out logical

relationships. Recognizing that many proteins have multiple partners,

and thus that probing interaction function by deleting those proteins

altogether likely will have unintended consequences, his team has

begun developing what he calls “edgetic” mutants, that is, mutants

that affect only a single protein-protein interaction “edge” in the vast

interactome network.

“We call them edgetic because we want to push the notion of

‘genetics of edges,’” explains Vidal, who in 2009 used such mutants

to probe the biology of the apoptotic protein CED-9 in C. elegans.

“We want to go after genetic perturbations that affect one edge at

a time, leaving all the others wild-type and then asking in vivo, what

is the consequence?”

QUALITY ASSESSMENTSAs with all genomic-scale datasets, such information is merely a

jumping-off point, a wellspring of new hypotheses. Yet many re-

searchers question the quality of interactions derived from high

throughput assays. To assuage that concern, researchers have de-

vised confi dence metrics, similar to the Q20 scores assigned to se-

quenced nucleotides.

Alexey Nesvizhskii, a computational scientist at the University of

Michigan, together with Anne-Claude Gingras and Mike Tyers, two

SLRI investigators who study kinase/phosphatase interaction net-

works, has developed one such metric for individual protein-protein DOI: 10.1126/science.opms.p1000046

Jeffrey M. Perkel is a freelance writer based in Pocatello, Idaho.

interactions detected by AP/MS, which they call SAINT (signifi cance

analysis of interactome).

SAINT, Gingras explains, essentially computes an interaction’s P

value based on the number of peptides detected for a given protein,

how often the protein is detected across different purifi cation condi-

tions, the length of the protein, and other variables.

“We are actually able to give you a number saying, for this par-

ticular bait-prey [pair], you get a score of 0.9, which we expect gives

you a probability of 90 percent that it is real and not just a spurious

detection,” she says.

With Nesvizhskii and Tyers, Gingras has used SAINT to qualify in-

teractions in a yeast kinase-phosphatase interaction network com-

prising some 1,800 interactions, and now is working with interaction

databases like BioGRID (thebiogrid.org) and IntAct (www.ebi.ac.uk/

intact/) to incorporate those scores into their record architecture.

Vidal and his colleagues have also developed a confi dence score.

They tested reference sets of 92 well-characterized, positive-control

interacting pairs and 92 random negative-control pairs in each of fi ve

binary approaches: Y2H, LUMIER, PCA, MAPPIT (mammalian pro-

tein-protein interaction trap), and NAPPA (nucleic acid programmable

protein array). By counting how often a given interaction appears in

each of the fi ve methods, the score refl ects the likelihood that the

interaction is “true.”

Yet the team’s analysis also highlighted a concern with existing

interaction methodologies: As the team profi led and tweaked the

various assays, they found that under conditions that maximized de-

tection of the positive controls while minimizing detection of the

negative controls, “none of the methods were perfect,” Vidal says.

In fact, each technique detected only 20 percent to 30 percent of the

positive control interactions.

But there is a silver lining: Now that the team knows how to maxi-

mize positive-control detection while minimizing negative-control

detection, confi dence in future experiments should rise accordingly.

Given the magnitude of the problem, these experiments should

keep Vidal and his colleagues busy for some time to come.

“When can we declare victory? I don’t know if we ever will,” Vidal

says. “It depends on how we defi ne it.”

INTERACTOMICS

LIFE SCIENCE TECHNOLOGIES AAAS/Science Business Office Feature

Austrian Academy of Sciences

www.oeaw.ac.at/english

Canada Foundation

for Innovation

www.innovation.ca/en

Clinical Research Institute

of Montreal

www.ircm.qc.ca/en

Dana Farber Cancer Institute

www.dana-farber.org

Harvard Medical School

www.hms.harvard.edu

Institute for Systems Biology

www.systemsbiology.org

National Center for Dynamic

Interactomics Research

www.ncdir.org

Rockefeller University

www.rockefeller.edu

Samuel Lunenfeld

Research Institute

www.lunenfeld.ca

University of California,

San Diego

www.ucsd.edu

University of Michigan

www.umich.edu

FEATURED PARTICIPANTS

466 www.sciencemag.org/products

Newly offered instrumentation, apparatus, and laboratory materials of interest to researchers in all disciplines in academic, industrial, and governmental organizations are

featured in this space. Emphasis is given to purpose, chief characteristics, and availability of products and materials. Endorsement by Science or AAAS of any products or

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FUNCTIONAL PROTEIN EXPRESSION KIT

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vides a simple and precise way to profi le the human miRNA

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(NEW PRODUCTS: INTERACTOMICS

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MOLECULAR INTERACTION ANALYSIS

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action analyses, the Biacore 4000 is capable of analyzing up to 4,800

interactions in 24 hours. Dedicated software packages are available to

support small-molecule drug discovery and antibody screening and char-

acterization. In combination with the LMW Extension Package software,

the Biacore 4000 delivers the sensitivity, throughput, and high-quality

data required for fragment screening, lead selection, and optimization.

The Antibody Extension Package provides dedicated software tools for

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identifi cation of promising candidates.

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Electronically submit your new product description or product literature information! Go to www.sciencemag.org/products/newproducts.dtl for more information.

Visualization in all its

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The National Science

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American Association for the

Advancement of Science, invite

you to participate in this year’s

Challenge. The competition

recognizes scientists, engineers,

visualization specialists, and

artists who produce innovative

work in visual communication.

Winning entries will be

published in Science and Science

Online, and will be displayed on

the NSF website.

Award Categories

• Photography

• I l lustrat ions

• Informational Posters

and Graphics

• Interac tive Games

• Non- Interac tive Media

I N T E R N A T I O N A L S C I E N C E & E N G I N E E R I N G

V I S U A L I Z A T I O N C H A L L E N G E

COMPETITION DEADLINE

APPROACHING

’

ENTRY DEADLINE: SEPTEMBER 15, 2010

C E I:

..//

Optimized for TaqMan®

Array Micro Fluidic Cardsand reagents.

FOR RESEARCH, FORENSIC OR PATERNITY USE ONLY. NOT INTENDED FOR ANY ANIMAL OR HUMAN THERAPEUTIC OR DIAGNOSTIC USE. The trademarks mentioned herein are the property of Life Technologies Corporation or their respective owners.

TaqMan is a registered trademark of Roche Molecular Systems, Inc. © 2010 Life Technologies Corporation.

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