Networking at the Second Interactome Meeting

Meeting Report

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and analyses were presented. Andrew Networking at the second Emili (University of Toronto, Canada), and others compared the two TAP–TAG Interactome Meeting datasets of the yeast interactome that were August 30th–September 3rd, 2006, published earlier this year [1,2]. Interest- Wellcome Trust Sanger Institute, Hinxton, UK ingly, the overlap between the two data- sets is surprisingly low, suggesting that, Albertha JM Walhout even in yeast, the organism in which interactome mapping was pioneered University of Massachusetts Medical School, Program in Gene Function and Expression and Program in Molecular Medicine, 364 Plantation Street, LRB 605, Worcester, MA 01605, USA; Tel.: +1 508 856 4364; many years ago, the interactome map is Fax: +1 508 856 5460; [email protected] far from complete. Considering the fact that yeast two-hybrid datasets are also far Expert Rev. Proteomics 3(5), 477–479 (2006) from complete, the development and In late August and early September of this from a significant level of false-positive application of other methods for the sys- year, the second Cold Spring Harbor Inter- predictions. To address these issues, tematic identification of protein–protein actome Networks meeting was held at the Kourosh Salehi-Ashtiani (CCSB, USA) interactions are desired. Wellcome Trust Sanger Institute (Hinxton, described the use of high-throughput UK). Twice as many people participated in rapid amplification of 5´ complementary False negatives & false positives the meeting than in 2005 and many DNA ends (5´ RACE) in Caenorhabditis These are two terms that were heard many nationalities were represented, which high- elegans by using a primer complementary times throughout the meeting. They are lights the rapid growth of the field. The to the splice leader sequence to obtain very important, as it is essential to know meeting was organized, for the second more precise information regarding tran- the sensitivity and specificity of each inter- time, by Anne-Claude Gavin (EMBL, scription and translation start sites. This actome dataset, but these issues also need UK), Ewan Birney (EBI, UK) and Marc method proved highly successful and, by to be resolved to encourage researchers out- Vidal (Center for Cancer Systems Biology using gene-specific primers, will also be side the field to use interactome data for [CCSB], Dana–Farber Cancer Institute, tremendously useful to obtain better gene their own research. Ultimately, it will be MA, USA). The meeting featured an models in other organisms. Stan Fields important to validate each interaction in appealing combination of ‘wet’ and ‘dry’ (University of Washington, USA) can be interactome networks by an independent- talks, or of experimental biology and com- considered as the founding father of inter- interaction detection method. In this light, putation. The focus was primarily on pro- actome mapping. He surprised the audi- the presentation by Katerina Pardali (Ulf tein–protein interactomes, although several ence by discussing an exciting tool for Landegren’s laboratory, Uppsala University, other data types, such as protein–DNA intron detection in yeast, rather than dis- Sweden) provided an exciting possibility. and protein–small-molecule, and genetic cussing protein–protein interaction dis- Katerina discussed a new methodology interactions were also discussed. covery. He took advantage of the observa- named proximity-ligation in situ assay Before one can embark on a systematic tion that yeast that lack the debranching (P-lisa) that is based on proximity ligation and large-scale protein–protein interaction enzyme are viable and accumulate intron of two DNA fragments, each of which is mapping project, one must define protein- lariats. Fields described the use of whole- linked to an antibody that recognizes an coding genes precisely and clone the corre- genome microarrays in yeast to accurately interacting protein. The method uses roll- sponding open-reading frames into vectors. detect known, and to identify several ing circle DNA replication to generate a The meeting started with a presentation by novel, introns. This information is of great ‘blob’ of single-stranded DNA covering the Daniela Gerhard (Mammalian Gene Col- use to improve gene predictions and two interacting proteins. This DNA can be lection Consortium, USA) who discussed should also provide an important tool for detected by using a fluorescently labeled the creation and availability of cDNA col- similar studies in higher eukaryotes. probe combined with microscopy. Using lections for a variety of important model In contrast to last year, when the two this method, one can visualize individual organisms and humans. Such cDNA col- yeast, two-hybrid-based human inter- protein–protein interactions in single cells. lections can be used to create ORFeome actome drafts were presented, no large resources for use in high-throughput inter- new protein–protein interaction datasets Motifs & domains actome mapping projects. Many genes are were presented this year. Rather, the focus Several presentations discussed how subject to alternative splicing and may be was on analyzing the available datasets, interactomes can be used to derive infor- under the control of alternative promoters. both in human and in model organisms, mation regarding the domains within In addition, gene-prediction tools suffer such as yeast. Many types of comparisons proteins that serve to interact with

10.1586/14789450.3.5.477 © 2006 Future Drugs Ltd ISSN 1478-9450 477 Walhout domains or motifs in other proteins. homozygous profiling (HOP). Whereas C. elegans promoters and transcription Hanah Margalit (Hebrew University of HIP is a powerful tool to identify a drug factors. The advantage of this method is Jerusalem, Israel) used a computational target directly and to study essential genes, that it is condition independent. Using approach to find domain–domain pairs HOP can be used to identify genes that two networks, one of which was pub- within interactome networks and com- buffer or offer resistance to a compound. lished earlier this year, we identified sets bined the results with structural infor- Guri described the use of these methods to of global and specific transcriptional regu- mation from the ipfam and 3D structure test 1000 different compounds and 500 lators [3]. In addition, we found two tran- databases. She found that domain pairs different conditions. The data obtained scription factor modules that shed light are used repeatedly within organisms and can be clustered to identify genes that on the function of transcription-factor throughout evolution, thus providing a behave similarly, and can thus be used to families, their expression and the target mechanistic basis for interologs or con- draw a cofitness interactome network. As genes they interact with. In the future, it served protein–protein interactions. Vic- the results obtained predict different func- will be important to extend pro- tor Neduva (Robert Russell’s group, tions than coexpression studies, the data tein–DNA interaction studies to higher EMBL, Germany) focused on linear pro- are highly complementary with other eukaryotes and to integrate the data with tein motifs rather than globular genome-wide datasets. data from other interactome mapping domains. Such motifs have a different Several presentations discussed pro- efforts, most notably to pinpoint conservation pattern to domains, they tein–DNA interactomes. Protein–DNA transcription factor dimers. are less well conserved, more disordered interactions play an important role in Several efforts are underway to identify and can ‘move around in proteins’ organism development and, thus, it is such dimers. Harukazu Suzuki from The (i.e., they occur in different locations in crucial to fully map all interactions Riken Genome Sciences Center in Japan homologous proteins). Experimental between transcription factors and cis-reg- used a mammalian two-hybrid system to efforts are also underway to define the ulatory DNA elements that reside in reg- identify systematically human and minimal protein regions that are neces- ulatory DNA elements, such as enhanc- murine transcription factor dimers. In sary for interactions. Mike Boxem (Marc ers and promoters. Martha Bulyk total, he found more than 4000 inter- Vidal’s group) presented a poster describ- (Harvard Medical School) presented a actions, which were filtered according to ing the use of the yeast two-hybrid sys- transcription factor-centered strategy in an interaction generality strategy that is tem to delineate interaction domains sys- which transcription factor-binding sites based on integration with other data tematically. This strategy will be very are uncovered by protein-binding micro- types. By identifying the tissues in which important to identify interactions arrays: a method she developed. Her each transcription factor is expressed, it between known domains, but also has a approach will be invaluable to determine should become apparent where each great potential to uncover novel protein the specificities and affinities of trans- dimer functions within the organism and domains and motifs. cription factors that are not amenable to to uncover more localized protein–DNA in vivo transcription factor-centered interaction networks. Other types of interactome approaches, such as chromatin immuno- Although the meeting focused primarily precipitations. Trey Ideker (University of Discovering the function of the on protein–protein interactomes, a con- California, CA, USA) discussed the use interactome: edgetics siderable number of speakers focused on of chromatin immunoprecipitations in To understand the function of each inter- other types of interactomes. Chris Bakal combination with transcription factor action, or edge, in interactome networks, (Norbert Perrimon’s group, Harvard deletion-specific changes in gene expres- it will be important to specifically remove Medical School, NJ, USA) described the sion to elucidate the mechanisms of the such edges, while leaving other edges use of phenotypic profiling to gain insight DNA damage response in yeast. He involving the protein pair intact. Matija into the signaling networks that evolve found many protein–DNA interactions Dreze (Marc Vidal’s group) presented an around Rho-like small GTPases in Dro- involving 30 transcription factors known updated reverse two-hybrid method sophila. He presented an impressive array to be involved in DNA damage, and vali- based on the strategy that was first devel- of cellular phenotypes obtained by auto- dated many of these interactions using oped by Marc Vidal [4]. He showed how mated imaging that can be recognized expression profiling in transcription fac- this method can be used efficiently to computationally and clustered to derive tor deletion mutants. By doing so, he obtain mutations in interacting proteins functional hypotheses. Many known found that the factors with the highest that affect a single interaction, but that genes were confirmed using this approach degree in the resulting protein–DNA leaves other interactions with the same and it will be exciting to see new insights interaction network (i.e., that bind the protein intact. He described how inter- that will come out of the study. largest number of promoters) were most action interfaces can be determined using Guri Giaever (University of Toronto, sensitive to DNA damage. this method, which will also provide an Canada) presented two types of genetic In my laboratory, we use a gene-cen- alternative, or complement, to the identi- interactome mapping in yeast, called tered yeast one-hybrid approach to iden- fication of protein interaction domains, haploinsufficiency profiling (HIP) and tify protein–DNA interactions between as discussed previously.

478 Expert Rev. Proteomics 3(5), (2006) Networking at the second Interactome Meeting

Fabulous keynotes one needs to move away from this and should be integrated with spatiotemporal Two fabulous keynote lectures provided toward genome-scale production if one protein expression and localization infor- an inspiration to the many young scien- truly aims to map the various inter- mation to derive networks that control the tists that participated. Richard Gibbs actomes comprehensively. Barbara Wold development and physiology of different (Baylor College of Medicine, TX, USA) also discussed the issue of experimental subsystems within an organism, such as heads one of the primary genome noise (or false positives). She discussed tissues and organs, and to gain insight into sequencing centers in the USA. He the two groups of scientists that make use subcellular networks that partition within started his presentation by drawing a of genome and interactome data: the individual cells. For comprehensive inter- comparison between genome sequenc- noise intolerant who wish to accurately actome mapping, it is also important to ing and collecting butterflies. However, annotate each protein with an ‘Ansel consider splice variants and post-transla- he quickly pointed out that this may Adams resolution’; and those who are tional modifications of proteins and how seem boring and not informative to more forgiving of noise who investigate these affect protein–protein interactions. many people, but that this is pretty much system properties in a more impressionist There is no doubt that the exciting in the eye of the beholder. He pointed fashion. In this regard, the presentation progress in this rapidly evolving field will out that Darwin started by collecting by Olga Troyanskaya (Princeton Univer- be presented in the next few years at Cold observations, for instance, regarding his sity, NJ, USA), who develops statistical Spring Harbor Interactome Meetings. famous finches, but that these observa- methods to interrogate large data sets, tions help to gain insight into the mecha- was particularly noteworthy, since it will References nisms of evolution. Both Richard Gibbs help researchers to navigate through vari- 1 Gavin AC, Aloy P, Grandi P et al. Proteome and the second keynote speaker, Barbara ous datasets confidently. Barbara Wold survey reveals modularity of the yeast cell Wold (California Institute of Technol- went on to explain that, whereas data machinery. Nature 440, 631–636 (2006). ogy, CA, USA) discussed different paral- quality and noise are important issues, 2 Krogan NJ, Cagney G, Yu H et al. Global lels between interactome mapping and they are not new to large-scale biology. landscape of protein complexes in the yeast genome sequencing; they emphasized the Rather, they are very familiar to all exper- Saccharomyces cerevisiae. Nature 440, need for continuing technology develop- imentalists; for instance, she pointed out 637–643 (2006). ment to increase the throughput and that many experiments, such as northern 3 Deplancke B, Mukhopadhyay A, Ao W drive down the unit cost. In addition, blots, end up in the trash bin since they et al. A gene-centered C. elegans protein–DNA interaction network. Cell both pointed out that the interactome do not confirm an observation made in 125, 1193–1205 (2006). community needs a systematic set of other instances. She stressed that it is cru- 4 Vidal M, Brachmann R, Fattaey A, goals and gold standards. This is impor- cial to know how noisy the data really are Harlow E, Boeke JD. Reverse two-hybrid tant as the task of interactome mapping and urged the audience to communicate and one-hybrid systems to detect is even more complex than that of with small-scale biologists to explain dissocation of protein–protein and genome sequencing. It is, as Barbara these issues. DNA–protein interactions. Proc. Natl Wold discussed, much more difficult to Acad. Sci. USA 93, 10315–10320 (1996). define completeness, particularly in Concluding remarks metazoan organisms that are composed Many speakers philosophized about the Affiliation of many cell and tissue types. Thus, the future of interactome research. For • Albertha JM Walhout process needs to be deconstructed to instance, it is well appreciated that inter- University of Massachusetts Medical School, make the task more straightforward. In actome networks, as studied today, are Program in Gene Function and Expression and Program in Molecular Medicine, addition, both argued that, whereas incomplete and represent static models 364 Plantation Street, LRB 605, Worcester, small-scale interactome-mapping projects of physiology. Hence, they should be MA 01605, USA that evolve around a biological process or integrated with interaction dynamics and Tel.: +1 508 856 4364 module are interesting and important, affinities. In addition, these networks Fax: +1 508 856 5460 [email protected]

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