Validating the Interactome

CONNECT THE DOTS

Molecular cartographers who choose to take on the interactome should be patient, long-lived, or both, at least if they adhere to Marc Vidal's definition. The interactome, says the Dana-Farber Cancer Institute researcher, is a map of "all interactions that take place in an organism between all proteins, in all cells, all tissues, at all ages, and in response to all possible environmental conditions the organism sees in the wild."

©2003 AAAS

Full interactome maps are dizzying, yet by zooming in, researchers gain insights into individual pathways. This network showcases a Drosophila splicing complex associated with sex determination. (reprinted with permission, Science, 302:1727–36, 2003.)

All that mapping won't happen soon, considering the hundreds of thousands of proteins in each cell, and their many possible variants. Yet since 2000, scientists using yeast two-hybrid (Y2H) and affinity purification/mass spectrometry (AP/MS)-based approaches have made some headway, mapping networks in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, and humans, as well as in the pathogenic bacteria Helicobacter pylori and Rickettsia sibirica.

The studies are remarkable for their scale. But their real power, says Frank Holstege, who heads the genomics laboratory at University Medical Center, Utrecht University, The Netherlands, lies in what they reveal about the underlying biology. "We're not interested in the interactions per se," he says. "We're interested in what the interactions tell us about the functions of proteins."

By that measure, these maps have in some sense fulfilled their purpose, offering peeks, through guilt by association, into unknown proteins' functions. Takashi Ito of the Human Genome Center, University of Tokyo, and colleagues used their yeast interactome map to place a previously uncharacterized protein, Ydr016c, in the pathway for spindle pole body formation, based upon its association with known members of that process.² Independent investigators later verified that assessment.³

Drug developers, too, are seeing benefits. CuraGen, a drug development company in New Haven, Conn., published Y2H-based maps of S. cerevisiae ⁴ and Drosophila ⁵ and is doing some human interactome work in-house. Its investigators use interaction data to prioritize potential drug targets, says John Chant, head of genomics, proteomics, and biomarkers. These networks, he notes, can help a company weed out "promiscuous" proteins that dabble in several processes.

And proteins often do that: Just as in human society, proteins sometimes participate in multiple complexes, notes Giulio Superti-Furga of Cellzome, a drug development firm in Heidelberg. "I have a group at work, I have a family at home, I have friends with whom I play soccer. It's the same me, but I do have a different behavior and a different function in these different settings."

Drugs targeting these so-called sticky proteins have the potential to produce troubling side effects. Pharmaceutical companies, therefore, are served better by focusing on those molecules that appear limited to whichever process the company is targeting, Chant says.

QUESTIONING QUALITY

Yet lingering doubts about the validity of interactome information remain. All agree that though data-rich, these studies produce lower quality results than would a scientist who devotes years to a single protein. "High-throughput means you do things fast," says Holstege. And if that happens, "You're going to make mistakes." The information, says Jeremy Gunawardena, director of Harvard's Virtual Cell Program, is "data rich and analysis poor."

Joel Bader, assistant professor of biomedical engineering, Johns Hopkins University, pegs the accuracy of high-throughput data at somewhere between 25% and 50%. "It's something people have known all along," he says. "The challenge has been to identify the good part from the bad part."

All of which makes it difficult for academic and pharma researchers alike to put too much stock in the data. Ultimately, the maps will have value, Gunawardena says. But for now, he advises would-be users, "Nullius in verba." The Royal Society of London's motto literally means, "nothing by words." But it implies, "question everything."

Y2H data especially is open to questioning, because the technique essentially amounts to a genetic trick: It relies on artificial fusion constructs, often mere fragments of the full-length protein, that are introduced into cells, forced into the nucleus, and overexpressed. False-positive results are inevitable.

But the AP/MS approach has problems too, notes Vidal, who uses Y2H in his work on C. elegans.⁶ The network of interactions these studies produce is only as good as the purification quality, he says. And, the approach cannot determine whether the various co-eluting proteins all exist in a single complex or in several discrete assemblages; for that, scientists need Y2H.

Indeed, Y2H and AP/MS answer different questions, says Daniel Figeys, senior vice president of systems biology and lead profiling at Toronto-based MDS Proteomics, which uses AP/MS. Y2H experiments ask, 'does protein A interact with protein B' whereas AP/MS asks more broadly, 'which proteins associate with protein A?' And though interactome-focused companies tend to favor one or the other, the approaches actually complement each other, says Sudhir Sahasrabudhe, chief scientific officer of Salt Lake City-based Prolexys Pharmaceutics, a company that uses both methods.

QUALITY CONTROL

Researchers increasingly are trying to factor these concerns into their work. One approach verifies putative interactions by crosschecking other interactomics datasets. But this method seldom strikes paydirt, as experimental conditions can skew the results. A recent analysis of yeast interaction datasets by Bader and colleagues notes, "Only 387 interactions are common to the 6,395 Y2H and 41,775 Co-IP interactions from high-throughput data."⁷

Others look for previously published interactions, or interactions in orthologous proteins in other species, what Vidal calls "interologs." A group led by Trey Ideker, assistant professor of bioengineering at University of California, San Diego, recently described a software tool called PATHBLAST that does exactly that.⁸ "What you're looking for basically is conservation based on proteins in different species that have not only similar sequences but also similar wiring in their protein-interaction networks," Ideker explains.

Some investigators try to attach a quality metric or statistical measure to their data. One simple approach is to score interactions based on the number of times they are observed. Others delve more deeply into mathematics to sift through the data. But Figeys cautions that statistical approaches can be problematic if good quality-control procedures were not in place during data collection. "It's basically garbage in, garbage out," he says.

© 2002 Elsevier Science Ltd.

The interactome provides a natural scaffold to overlay genome-level datasets. Here, a germline interactome map was combined with a transcriptome map comprising 553 microarray experiments. (Reprinted with permission, Curr Biol, 12:1952–8, 2002.)

Providing some perspective, Cantley says the question of quality was raised 20 years ago when sequencing firms began dumping cDNA fragments into nucleotide databases. People warned against using these fragments because they had mistakes and were unverified and misleading, says Cantley. But instead, the fragments proved invaluable: "There may be errors, but they can be teased out by looking at enough data."

'METAGENOMICS'

Some researchers have turned to a new, metascale functional genomics approach for data validation. Basically, the thinking goes, if two proteins interact, they should colocalize in the cell, their genes should be coexpressed, and when those genes are knocked out, the observed phenotypes should be similar. Explains Holstege, "You need to combine completely different sources of functional genomic data and see if you can come up with additional evidence that a particular interaction is correct."

Vidal says the goal is to create an atlas of superimposable biological maps.⁹ The best scaffold on which to build that atlas, says Superti-Furga, is the interactome, which he compares to a corporate organizational chart. "You don't want to assemble them on a linear chromosome," he says; that would be like trying to understand a person's corporate role by the company's alphabetized directory.

In yeast, at least, the atlas is slowly coming together. Saccharomyces researchers have, in the last few years, charted not only the interactome, but also networks of protein localization, protein-DNA interactions, and genetic interactions. A complete set of mutations is available, as is mRNA and protein abundance information, which are key ingredients if these data are ever to be useful in making mathematical models of a cell.

In metazoans, which feature introns, more genes, and inconsistent gene predictions, among other barriers, work is proceeding more slowly. Thanks to genome-wide Y2H and RNAi resources, for instance, Vidal's lab recently published an analysis of the nematode TGF-beta signaling network that merged physical interaction and double-genetic perturbation studies.¹⁰

Vidal and others say these overlays are merely suggestive, not absolute. That two interacting proteins are coexpressed is a positive sign, for instance, but it does not definitively say the two actually interact in vivo. Conversely, the absence of coexpression does not immediately indicate that the interaction is false. Correlations, says Holstege, only increase confidence. "It doesn't say with absolute certainly whether or not the interaction is true or false."

DATA INTEGRATION

With so much disparate data available, the challenge for researchers is to integrate that information into a single interaction network and then to squeeze some useful biology out of it. That's where visualization tools such as Cytoscape come in.

Jointly developed by the Institute for Systems Biology, UC-San Diego, and the Memorial Sloan-Kettering Cancer Center in New York, Cytoscape incorporates interaction data with other large-scale genomic information to build networks of cellular processes and pathways. But via the Systems Biology Markup Language (SBML), it also can communicate with other SBML-enabled programs to test mathematical simulations of cellular events, for example.

Scientists debate whether such simulations can ever be detailed sufficiently enough to accurately predict cellular behavior. Roger Brent, president of the Molecular Sciences Institute in Berkeley, Calif., says it should eventually do that, at least at the pathway level. His institute's Alpha Project is an attempt to model quantitatively and predict the behavior of the yeast pheromone response.

But Ideker, a Cytoscape principal investigator, remains skeptical. "There's no way the data's anywhere near good enough, for one, and it doesn't have anywhere near the kind of quantitative and dynamic data associated with it to do that kind of modeling." Technologies are on the horizon that could overcome these barriers, but Ideker asks, "Who is to say if these will pan out?" Interactome maps display purely binary data – either an interaction exists or it does not – without regards to its timing, location, strength, direction, or consequence. So if proteins A and B form a complex, does A activate or repress B, or vice versa?

©2003 AAAS

This detail of the Drosophila interactome map highlights transcription regulation by the co-repressors CtBP (C-terminal binding protein) and Groucho. Note that the two circuits are largely distinct, and mostly involve separate protein classes. (reprinted with permission, Science, 302:1727–36, 2003.)

This, says Vidal, will be the next iteration of interactome mapping, and his team is developing approaches to address this question. For now, even academic researchers uninterested in protein-protein interaction networks could gain from these studies, says Superti-Furga, because the data are so rich. "The beautiful thing about the [cellular] wiring plan is it is not a point of arrival," he says. "It is the starting point for generating a lot of opportunities for additional experiments."

But it's also a work in progress. "We will keep generating them," says Vidal of interactome maps. "They are far from perfect, they are moving targets.... Interactions are working in so many different ways, at different times, and that makes them hard to characterize. But that's why we need maps in the first place."

Jeffrey M. Perkel can be contacted at jperkel@the-scientist.com.