From Midbody Protein-Protein Interaction Network Construction to Novel Regulators in Cytokinesis Tzu-Chi Chen,†,# Sheng-An Lee,§,# Tse-Ming Hong,|,# Jin-Yuan Shih,⊥,# Jin-Mei Lai,∇ Hsin-Ying Chiou,O Shuenn-Chen Yang,O Chen-Hsiung Chan,§ Cheng-Yan Kao,§ Pan-Chyr Yang,*,|,⊥,O and Chi-Ying F. Huang*,†,‡,§ Institute of Biotechnology in Medicine, Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan, R.O.C., Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, R.O.C., NTU Center for Genomic Medicine, College of Medicine, National Taiwan University, Taipei 100, Taiwan, R.O.C., Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan, R.O.C., Department of Life Science, Fu-Jen Catholic University, Taipei Hsien 242, Taiwan, R.O.C., and Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan, R.O.C. Received April 9, 2009
Midbody, a transient organelle-like structure, is known as central for abscission and is indispensable for termination of cytokinesis. Here, we used the midbody proteome inventories to construct the potential midbody protein-protein interaction (PPI) network. To delineate novel regulators participating in cytokinesis, the z-score, a standard statistic score, rather than hub degree was implemented to prioritize the novel hubs. Of these hubs, KIAA0133, SEPT1, KIAA1377, and CRMP-1 were localized to the midbody, whereas HTR3A and ICAM2 were associated with the cleavage furrow as examined by immunofluorescence. Knockdown of SEPT1 and KIAA1377 resulted in increasing numbers of cytokinesis defect cells, suggesting these newly identified hubs play critical roles in cytokinesis progression. Moreover, ectopic expression of CRMP-1 mutant in which Aurora-A phosphorylation sites have been replaced with Ala results in a cytokinesis defect. This subproteome network construction not only sheds light on the intimate interactions of the midbody proteomes, but also prioritizes novel hubs or protein complexes that may govern the process of cytokinesis. Keywords: protein-protein interactions • midbody • cytokinesis • protein network • z-score
Introduction Cytokinesis, an event common to all eukaryote organisms, is the process by which a dividing cell splits into two and is essential for cell division. Failure of cytokinesis can cause cell death or lead to genome amplification, which is characteristic of many cancers.1 During cell division, the new daughter cells remain connected by the midbody, a transient organelle-like structure, consisting of a compact, dense matrix of proteins that are indispensable for cytokinesis, which assembles just before the final stage of cell division. The mechanisms required for division of the midbody between postmitotic cells are poorly understood. Several proteins and events have been linked to * Additionalcorrespondingauthor:Pan-ChyrYang.E-mail:
[email protected]; Contact Information: Correspondence: Chi-Ying F. Huang, Professor, Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan, Phone: 886-228267904, Fax: 886-228745074, E-mail:
[email protected]. † Institute of Biotechnology in Medicine, National Yang-Ming University. # These authors contributed equally to this work. § Department of Computer Science and Information Engineering, National Taiwan University. | NTU Center for Genomic Medicine, College of Medicine, National Taiwan University. ⊥ Department of Internal Medicine, National Taiwan University Hospital. ∇ Fu-Jen Catholic University. O Institute of Biomedical Sciences, Academia Sinica. ‡ Institute of Clinical Medicine, National Yang-Ming University. 10.1021/pr900325f CCC: $40.75
2009 American Chemical Society
cytokinesis, supporting the idea that completion of cytokinesis involves the precise coordination of independent pathways and protein interaction networks.2 Recently, a proteomic screen identified many components of the mammalian midbody,3 but relatively little is known about how these midbody proteins are functionally organized and their interactions with each other in such a compact environment. Since protein-protein interactions (PPIs) may be the major driving force leading to the formation of cellular structures, it is urgent to understand the protein networks in terms of precise spatial proximity and/or temporal synchronicity. These inventories of subproteome components therefore offer an opportunity to elucidate the protein networks at specified localizations and to uncover novel regulators of cytokinesis. Traditionally, the degree (i.e., number of interactions associated with a protein) is considered the most important feature that determines the essential proteins in a PPI network.4 It has been illustrated using a yeast PPI network that the network is scale-free, and contains some proteins (referred to as hubs) that have more interactions than others and these proteins are considered more important than those with fewer interacting proteins.5 However, analysis of the human interactome has revealed contradictory results,6 which might be due, at least in part, to the fact that the human PPIs are not saturated Journal of Proteome Research 2009, 8, 4943–4953 4943 Published on Web 10/02/2009
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Figure 1. Schematic illustration of in silico approaches to reveal potentially novel midbody proteins. (A) Using 190 midbody proteins as queries (gray nodes) to search for their interacting proteins from previously established protein-protein interaction (PPI) database (POINT) results in 3690 PPIs in the midbody PPI network. Of particular interests are those interactions among queries, which are referred to as query-query PPI (QQ-PPI). A total of 145 PPIs can be identified from the midbody QQ-PPI network. To reduce the complexity, those nodes, which do not belong to queries and contain only 1 PPI (degree ) 1), were removed from the 3690 midbody PPI network. There are 176 queries containing more than 1 PPI (degree g 2) and these queries are referred to as query-also-hubs (QH, labeled with red nodes). In addition, there are 626 nodes (labeled with blue nodes), which have more than 1 PPI and are defined as hubs, could be considered as potential midbody proteins. (B) To prioritize novel midbody proteins, traditional degree sorting and random sampling with z-score sorting were employed to prioritize 626 potential midbody proteins. These two sorting methods yielded two distinct results. The hubs with higher and lower degree were labeled with blue and light blue nodes, respectively. (C) Empirical methods were then used to validate 6 novel proteins with top z-score prioritizations.
yet. This suggests that essential hubs can only be evaluated when all of proteins in a given organism have been studied with equal effort. In fact, many reports have addressed the characteristics and biological functions of PPI networks,7 and the interpretation of these findings differ,8 suggesting target prioritizations from a given network should be examined cautiously. In addition, the technological advances in proteomics have resulted in an enormous amount of PPI information, raising additional challenges in assigning priority to PPIs. Although there are different opinions about PPI sources,6,9,10 in short, the platform to select the high confidence PPIs and vertices from plenty of PPIs with specific spatial proximity and/ or temporal synchronicity is in urgent need of improvement. In this study, the first question we asked was whether the limited numbers of midbody proteins, identified in the recent proteomic screen,3 participate individually in the process of cytokinesis or whether groups of the midbody proteins interact with each other and form a network. The second was how to fill in the missing gaps in the constructed midbody PPI network and identify novel targets participating in the process of cytokinesis. We first used bioinformatics approaches to extract and convert the seemly random and independent midbody proteomes and their corresponding PPIs into a highly interactive biological network of cytokinesis. Instead of using hub degree to sort the proteins, the z-score, a standard statistic score, was used to prioritize novel and poorly characterized 4944
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hubs, where many of the proteins involved in this essential process remain to be identified. Empirically, we uncovered 6 midbody proteins (SEPT1, KIAA1377, CRMP-1, KIAA0133, HTR3A, and ICAM2) as novel targets participating in the process of cytokinesis. Knockdown of the first two proteins by siRNA results in defective cytokinesis, suggesting that the methodology of sorting by z-score might represent an important bioinformatics advance for genome annotation and provide insights into the PPI network of cell division.
Results Construction of the Midbody PPI Network To Reveal Important Targets, “Hub Proteins”. To identify novel targets participating in the process of cytokinesis, we used 160 mammalian midbody proteins3 in this study. After removing the duplicate proteins, there are 158 midbody proteins remaining (Supplementary Table 1). Since the list of midbody proteomes was nowhere near completion and many known midbody proteins were not included in the 158 midbody protein catalog, we further surveyed the literature and included 32 additional known mammalian midbody proteins (for reviews, see refs 11-13) in our list, resulting in a total of 190 midbody proteins (Supplementary Table 1). A schematic overview of this study is illustrated in Figure 1. Using newly updated PPI data sets stored in POINeT (http://poinet.bioinformatics.tw),14 the fun-
Midbody Protein-Protein Interaction Network Construction damental structural details of around half of the midbody query nodes constituted a protein network with a total of 145 PPIs (referred to as “query-query PPIs, QQ-PPIs”, see later in Figure 3; detail in Supplementary Table 2 or demonstration in POINeT). The midbody QQ-PPI network construction reveals that several proteins of the midbody queries could serve as “hubs”, which are defined as “proteins connecting with more than one midbody query node in the midbody network” (Figures 1A and 3). These hubs might be important for the process of cytokinesis. On inspection, most of these 145 PPIs formed a tightly connected interaction network, referred to as the core network, whereas 6 of them were not in the core network and formed binary or trinary complexes (Figure 3). Of 190 human midbody proteins, 183 of them have at least one PPI and are involved in 3690 experimental PPIs (see Methods). To simplify the analysis from the fully extended midbody PPI network, we used the number of degree, which represents the numbers of interacting protein(s), in the midbody PPI network as a threshold. Subsequently, the nodes, which do not belong to the midbody queries and contain only one interacting protein, were removed from the PPI network. These analyses resulted in a total of 802 hubs, including 176 query-also-hubs (QH) and 626 hubs (Figure 1A). Prioritization of Midbody Hubs by Hub Degree and z-Score and Their Functional Implications. Possible approaches to annotate these hubs are to implement the Gene Ontology (GO) data sets and the concept of Interologs (interactions conserved in other organisms).14,15 A systematic analysis of these 802 hubs allowed us to recognize that there are 176 query-also-hubs (QH), 226 GO hubs (GH) and 236 interologs hubs (IH), with a total of 379 intersected hubs (Figure 2A). However, most of the hubs (523 hubs) in the network remain unknown for their functions. Therefore, the challenge is how to select the crucial ones, which might participate in the process of cytokinesis, from 626 hubs. To prioritize 626 hubs, we used two different methodologies to analyze the hubs, namely, traditional degree sorting and random sampling with z-score sorting (Figures 1B and 2B). When the 692 midbody hubs were sorted by degree, GRB2, which interacts with 36 midbody nodes and has the largest number of interacting proteins (407 PPIs), ranked number one, followed by YWHAZ, which associates with 28 midbody nodes and has 391 PPIs. However, neither protein has an established role in cytokinesis or the midbody, raising the possibility that when sorting by degree proteins that have many interacting proteins may have a better chance of being scored as an important hub in a given network than those with less interacting partners. Therefore, we implemented random sampling with z-score analysis to re-evaluate the importance of hubs, with particular interest in poorly annotated proteins (i.e., 523 hubs in Figure 2A). The z-score, a standard statistical score, can be converted to probabilities of observing specified hub degrees in random networks (see Methods). Higher zscores mean that the associations of hubs within a particular network are statistically significant and the probabilities of forming such associations by chance are smaller. Therefore, higher z-scores in this study imply that interactions associated with a particular protein are clustered in the midbody network and are rarely observed in random networks. Sorting hubs by degree and z-score clearly exhibits distinct patterns (Figure 2B). GRB2 and YWHAZ, the top two ranking proteins as sorted by hub degree (Figure 2B, top panel), appear at the bottom of the list when sorted by z-score. Conversely, HTR3A, ranking
research articles number one in uncharacterized midbody protein as sorted by z-score, appears at the bottom of list when sorted by degree. To evaluate the effectiveness of these two methods as a way to prioritize targets, the top 60 uncharacterized hubs (i.e., do not belong to midbody query hubs) sorted by degree were compared with top 60 uncharacterized hubs sorted by z-score (Figure 2C and Table 1; detail in Supplementary Table 3). Careful examination, through manually curated PubMed and GO analyses (by cellular component), of the top 60 hubs sorted by degree revealed that only 7 of them (IKBKG, TP53, PRKCA, SRC, FYN, BRCA1, and WAS) have been reported to be related to cytokinesis, whereas 9 proteins of the top 60 hubs sorted by z-score are known to function in cytokinesis (KTN1, ESPL1, ASPM, SEPT11, BUB1, CEP55, SEPT7, CENPA, and BUB1B). Although this difference in well-known cytokinesis hubs is small, hubs sorted by z-score have significantly fewer proteins without any information related to cytokinesis, referred to as “other”, as compared with those sorted by hub degree. Moreover, hub degree sorting identified only one protein related to the function of Rho proteins out of the top 60 hubs: ARHGDIA, which is well-known in furrow progression.11 In contrast, sorting by z-score identified 7 proteins (DOCK7, PLEKHG2, OPHN1, GEFT, MCF2L, SRGAP1, and ARHGDIG) in the Rhorelated category. In addition, hubs sorted by z-score included more membrane proteins, which may be important targets in membrane trafficking,16 than those sorted by degree. The detailed comparison lists and PubMed IDs are in Table 1, which shows the summary of the major features of the 60 hubs sorted by these two methods. Together, these comparisons suggest that the implementation of z-score to prioritize hubs could, at least in part, enhance the prioritization of poorly characterized proteins and might be better at identifying novel molecules participating in cytokinesis than sorting by hub degree. Moreover, hubs sorting by z-score in this study is reminiscent of “date hubs”,5 which interact with different partners at different times (e.g., cytokinesis) or locations (e.g., midbody). It has been demonstrated that, if date hubs are destroyed in network, it could result in a vast majority of effects in biology and topology of PPI network. These findings may provide a foundation on which research can be based to study potential new cytokinesis players through a systematic bioinformatics approach in the postgenomic era. Localization of Candidate Midbody Proteins during Cytokinesis. To validate the effectiveness of z-score analysis, 6 proteins from z-score sorting (HTR3A, KIAA0133, ICAM2, SEPT1, KIAA1377, and FLOT2) and 4 proteins from degree sorting (YWHAZ, ACTB, EGFR, and MYC), with no previous report relating them to cytokinesis, were randomly selected for immunofluorescence analysis. The results showed that all 4 proteins from degree sorting were not localized at the midbody (Table 1), whereas only FLOT2 from z-score sorting did not localize at the midbody. We therefore continued to analyze the remaining 5 proteins identified from z-score sorting. The interactions of these 5 proteins and the midbody QQ-PPI network are illustrated in Figure 3. HTR3A and KIAA0133 ranked high when sorted by z-score; ICAM2 and SEPT1 belonged to the top 60 hubs as sorted by z-score (Figure 2B, bottom panel). Although KIAA1377 did not rank in the top 60 hubs, the interactors of KIAA1377 in the midbody PPI network (i.e., PBK and VIM) are known to be vital regulators in the progression of cytokinesis17,18 (Figure 3). Immunofluorescence analysis of HeLa cells transiently expressing epitope-tagged constructs of the genes of interest indicated that KIAA0133, Journal of Proteome Research • Vol. 8, No. 11, 2009 4945
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Figure 2. The analysis of hubs in the midbody protein-protein interaction network. (A) The annotation of hubs. The X-axis of each column, containing 802 hubs, is rearranged using cluster analysis and only 47% of them can be classified into GO hubs (GH, labeled in yellow), interologs hubs (IH, labeled in green), and query-also-hubs (QH, labeled in red). In summary, 379 hubs can be annotated by these bioinformatics analyses and can be considered as characterized proteins. In contrast, 523 hubs do not have information linking to the midbody and are considered as uncharacterized hubs. (B) Distributions of midbody hubs sorted by degree and z-score. A total of 802 hubs were sorted by degree, ranging from 86 to 2 PPIs (top panel), or by z-score (bottom panel). GRB2 and YWHAZ (labeled by *), which do not belong to queries, serve as top two ranking proteins as sorted by hub degree. Several poorly characterized hubs (e.g., HTR3A) with top z-score prioritizations were selected for empirical verification in this study. (C) Sorting hubs by degree and z-score exhibits great diversity. The top 60 midbody hubs, sorted either by degree or z-score, can be divided into several functional categories. (Right) sorting by z-score: 9 proteins have well-known function in cytokinesis (labeled in red), whereas 16 of them are not known to be related to cytokinesis (labeled in purple). The rest of them includes 7 Rho related proteins (labeled in green), 23 membrane proteins (labeled in blue), and 5 actin-related proteins (labeled in yellow). (Left) sorting by degree: 7 proteins have well-known function in cytokinesis (labeled in red). Only 1 protein (ARHGDIA) is a Rho related protein, and 36 proteins have not been linked to midbody and cytokinesis (Table 1).
SEPT1, KIAA1377 were localized to the midbody (Figure 4A, upper panel), whereas HTR3A and ICAM2 were associated with the cleavage furrow (Figure 4A, lower panel). HTR3A and ICAM2 are membrane proteins, which might have the potential to participate in membrane trafficking during cytokinesis. Even though HTR3A does not interact with the core midbody QQPPI network (Figure 3), it might interact with the other yet to be identified proteins to link to the core QQ-PPI network. Interestingly, KIAA1377, an uncharacterized protein, was not in the top 60 hubs identified by z-score but specifically colocalized with the midbody matrix, the region without tubulin association. This localization pattern is similar to many well-known regulators of cytokinesis, for example, MKLP119 and 4946
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RACGAP1,20 raising the possibility that KIAA1377 might play roles in the process of cytokinesis. Validation of Novel Interactions and Regulators in the Midbody PPI Network. HTR3A, KIAA0133 and ICAM2 were identified in low-throughput PPIs, whereas SEPT1-SEPT621 and the KIAA1377 interaction set (Figure 3, red dotted line) were identified in high-throughput PPIs. To validate the PPIs from high-throughput data, yeast two-hybrid analysis was used to confirm the SEPT1-SEPT6 interactions (data not shown). Furthermore, coimmunoprecipitation showed that SEPT1 interacted with SEPT6 in both unsynchronized interphase and synchronized mitotic cells (Figure 4B), and the interactions between SEPT1 and Aurora-B (data not shown) were consistent
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Midbody Protein-Protein Interaction Network Construction a
Table 1. Prioritization of Poorly Characterized Hubs with Degree and z-Score Sorting degree
ranking
gene symbol
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
GRB2 YWHAZc IKBKE TRAF6 HLA-B MAP3K3 ACTBc YWHAG RIPK3 IKBKG MCC EGFRc MYCc TP53 CASP3 EIF1B CDH1 PRKCA VHL SRC ACTA1 DISC1 EPB41 TNFRSF1A CFTR PRKAB1 FYN SMAD3 GH1 EIF6 APP TNFRSF1B NFKB2 TGFBR1 YWHAB CCNB1 ARHGDIA CASP7 GRIN1 NME2 FLNA RASA1 TNIK DYNLL1 PPP1CA CASP8 BRCA1 MTNR1B WAS TJP1 NFKBIE PRIM1 YWHAQ PIK3R1 CTNNB1 SFN AR CSNK2A1 SMAD2 TRAF2
hub POINT total gene ID degree degree
2885 7534 9641 7189 3106 4215 60 7532 11035 8517 4163 1956 4609 7157 836 10289 999 5578 7428 6714 58 27185 2035 7132 1080 5564 2534 4088 2688 3692 351 7133 4791 7046 7529 891 396 840 2902 4831 2316 5921 23043 8655 5499 841 672 4544 7454 7082 4794 5557 10971 5295 1499 2810 367 1457 4087 7186
36 28 24 24 20 18 17 16 15 15 14 14 13 12 11 11 10 10 10 10 10 9 9 9 9 9 9 9 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6
407 391 328 369 273 173 187 309 88 155 217 261 322 315 139 153 80 181 208 217 103 113 128 128 135 153 161 193 76 101 105 107 149 160 165 35 36 51 55 59 75 69 90 81 82 88 178 18 40 50 49 74 121 137 164 148 167 180 175 183
Z-score survey with Pubmed and GO (Pubmed ID)b
gene symbol
other other other other membrane other other other other cytokinesis (16148947) other membrane other cytokinesis (16222300) other other membrane cytokinesis (11763196) membrane cytokinesis (16326390) actin other other other membrane other cytokinesis (8567733) other other other membrane membrane other membrane other other Rho (16943322) other membrane other actin other other actin other other cytokinesis (16258266) membrane cytokinesis (17724125) membrane other other other other other other other membrane other membrane
HTR3Ad KIAA0133d PPP1R14B ARPC5 DOCK7 PLEKHG2 OPHN1 MALL LCT PLEKHM2 TOR1AIP1 MYT1 FLOT2c GEFT ARPC4 KTN1 ESPL1 MCF2L ASPM CASC3 DIS3L2 CD163 SEC24D SEPT11 PLP1 NRAP ACP6 ABCC2 RPRM ACTR3B SRGAP1 SLC2A1 PKMYT1 STRN BUB1 HMG20B ALS2CR11 TMED2 DYNC1I1 XPO6 BUB1B FBXO5 SLC27A6 ICAM2d TAGLN APOB MTNR1B NGB COPE CEP55 CCNB1 SEPT7 DEF6 RPGR FAM113A DNAJC1 EPN2 ARHGDIG SEPT1d CENPA
hub POINT total gene ID degree degree z-score
3359 9816 26472 10092 85440 64857 4983 7851 3938 23207 26092 4661 2319 115557 10093 3895 9700 23263 259266 22794 129563 9332 9871 55752 5354 4892 51205 1244 56475 57180 57522 6513 9088 6801 699 10362 151254 10959 1780 23214 701 26271 28965 3384 6876 338 4544 58157 11316 55165 891 989 50619 6103 64773 64215 22905 398 1731 1058
2 2 2 5 2 2 2 2 2 2 2 2 3 2 5 3 2 2 2 2 2 2 2 2 2 2 3 2 2 5 2 3 2 3 4 3 2 3 3 3 4 2 2 2 2 4 6 2 2 2 7 3 2 3 2 2 2 2 2 2
2 2 2 9 3 3 3 3 3 3 3 3 6 3 13 7 4 4 4 4 4 4 5 5 5 5 9 5 5 19 5 10 6 10 15 11 6 11 11 11 16 6 6 6 6 17 18 7 7 7 35 7 7 13 7 7 7 7 4 7
13.8 13.6 13.1 12.7 11.0 10.8 10.8 10.7 10.6 10.5 10.4 10.3 10.2 10.2 9.7 9.0 8.9 8.8 8.6 8.6 8.6 8.4 7.8 7.6 7.6 7.6 7.5 7.5 7.4 7.4 7.3 7.2 7.0 7.0 6.9 6.8 6.8 6.7 6.7 6.7 6.7 6.6 6.6 6.5 6.5 6.5 6.4 6.4 6.3 6.2 6.1 6.1 6.0 6.0 5.9 5.9 5.9 5.9 5.8 5.8
survey with Pubmed and GO (Pubmed ID) b
membrane membrane other actin Rho (18426980) Rho (18045877) Rho (12932438) membrane membrane membrane membrane other membrane Rho (12547822) actin cytokinesis (8769096) cytokinesis (11509732) Rho (14701795) cytokinesis (17534152) membrane other membrane other cytokinesis (16767699) membrane actin membrane membrane membrane actin Rho (11672528) membrane membrane membrane cytokinesis (16046481) other other membrane other other cytokinesis other other membrane actin membrane membrane other membrane cytokinesis (16790497) other cytokinesis (17935997) membrane other other membrane other Rho (9787082) other cytokinesis (11756469)
a The details of information were listed in Supplementary Table 3. b The “other” means the group of proteins, which are difficult to categorize them into any other group related to cytokinesis. c Prioritized hubs are not localized to midbody in our test. d Prioritized hubs are localized to midbody or cleavage furrow in our test.
with the previous report.21 Although septin family participated in cytoskeleton events and is known to maintain cell polarity
and involved in membrane trafficking,22 SEPT1 is an uncharacterized protein. Because Aurora-B is a crucial regulator in Journal of Proteome Research • Vol. 8, No. 11, 2009 4947
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Figure 3. An architectural map of the midbody through a query-query PPI network. The midbody interaction network involves 145 query-query PPIs (blue lines). The PPIs of five prioritized targets (the pink nodes) and their corresponding interacting queries in the midbody network are illustrated by red dotted lines. CRMP-1 (the green node) interacts with 3 midbody query proteins (the purple nodes), which associate with a total of 12 midbody query proteins (the yellow nodes). Shaded circles around groups of proteins are known complexes participating in the process of cytokinesis, including chromosomal passenger complex (CPC, light pink), centrospindlin complex (light green), and Arp2/3 complex (orchid).
cytokinesis, it is likely that SEPT1, like other septin members such as SEPT2 and SEPT9, may also be a regulator in cytokinesis. To test our speculation, we knocked down expression of SEPT1 in lung cancer CL1-5 cells. SEPT1 siRNA significantly increased the number of cytokinesis defective cells in CL1-5 cells (Figure 4C), demonstrating a novel function of a septin family member during cytokinesis. To dissect the signaling pathway of SEPT1 in cytokinesis, we examined one of the essential pathways for cytokinesis, the MRLC pathway. However, there is no significant difference in the amount of MRLC and phospho-MRLC in SEPT1 overexpressing HeLa cells (data not shown), suggesting that SEPT1 did not modulate cytokinesis through the MRLC pathway, at least in the HeLa cell we analyzed. The other poorly uncharacterized hub, KIAA1377, which is localized in the midbody during cytokinesis (Figure 4A), interacted with four proteins, VIM, PBK, NSF, DNM1, in the midbody PPI network (Figure 3). Because VIM and PBK play a critical role in cytokinesis,17,18 we speculated that KIAA1377 might be a novel regulator of the midbody or cytokinesis. Knockdown of KIAA1377 by siRNA in CL1-5 cells resulted in an increase in cytokinesis aberrant cells (Figure 4C), similar to the effect of PBK knockdown in cells.17,23 By analysis of the midbody QQ-PPI network, the other hub, CRMP-1, has drawn our attention (Figure 3, the green node). Even if CRMP-1 interacts with only three midbody query proteins (PFN, VIM, and ROCK1) (Figure 3, the purple nodes), these three interaction proteins of CRMP-1 associate with 12 midbody query proteins (Figure 3, the yellow nodes), implicat4948
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ing that CRMP-1 may play a role in midbody QQ-PPI network. In addition, we have previously shown that CRMP-1 is a suppressor gene of lung cancer invasion.24 Notably, many tumor suppressors are also localized at midbody (i.e., BRCA225 and CYLD26). Subcellular localization studies have indicated that CRMP-1 appears first in the cytoplasm and/or nucleus in interphase. CRMP-1 is strongly associated with the mitotic spindle and the centrosomes during metaphase, but concentrates in the midbody during cytokinesis.27 Together, these examples suggest that the essentialness of hub prioritization and/or protein complex may uncover novel regulators in the midbody network that faithfully monitor the completion of cytokinesis. Aurora-A Partially Colocalizes with CRMP-1 and Phosphorylates CRMP-1 at Thr101 and Thr102. There is compelling evidence that protein phosphorylation is a regulatory factor for numerous mitosis-related molecules. Our initial mapping of the midbody network for cytokinesis did not take post-translational modifications (e.g., phosphorylation) into account, thus, greatly underestimating the complexity of the midbody PPI network. In fact, previous studies have shown that diverse CRMP forms are phosphorylated in vivo, including CRMP-1.28 This suggests that the functions of CRMPs may be regulated by phosphorylation. Moreover, the unique localization of CRMP-1 in mitosis is reminiscent of several mitotic kinases. Among the mitotic kinases tested, Aurora-A (reviewed in refs 29, 30) was of particular interest. Aurora-A is localized to the centrosome in prophase, subsequently spreading to the mitotic spindles and centrosomes, where it remains until the
Midbody Protein-Protein Interaction Network Construction
Figure 4. Validation of the prioritized targets in z-score sorting. (A) Five putative midbody hubs localized in the midbody or cleavage furrow during cytokinesis. Indirect immunofluorescence of HeLa cells transiently expressing various HA-tagged, Myc-tagged or EGFP-tagged constructs. Cells were fixed and then stained with anti-HA antibody (green), anti-Myc antibody (green), anti-R-tubulin antibody (red), Rhodamine-phalloidin for F-actin (red), and DAPI (blue). Scale bar, 10 µm. (B) Identification of SEPT1-SEPT6 interactions. HEK293T cells cotransfected with HA-tagged SEPT1 and Flag-tagged SEPT6 were treated with or without nocodazole for 16 h, and released for 1.5 h. Lysates were subjected to immunoprecipitation with anti-HA or anti-Flag antibody and immunoblotted with antiFlag or anti-HA antibodies, respectively. (C) Knockdown of SEPT1 or KIAA1377 results in an increase in cytokinesis defective cells. CL1-5 cells, transiently transfected with siRNAs of SEPT1 or KIAA1377 for 48 h, were subjected to nocodazole treatment for 16 h. Cells were released for 2 h, and then subjected to immunofluorescence analyses. The percentages of cytokinesis defective cells with vehicle control and siRNAs treatment were calculated. Around 1500 cells were counted in each treatment.
research articles end of mitosis. A low level of GFP-tagged Aurora-A has also been reported at the spindle midzone and the midbody late in mitosis. Evidence also suggests that Aurora-A participates in the regulation of cytokinesis. In metaphase cells microinjected with an antibody against Aurora-A, cytokinesis is reversed instead of being completed, and this leads to cytokinesis failure, resulting in the formation of binucleated cells.31 Moreover, overexpression of either the wild-type or catalytically inactive Aurora-A also results in the accumulation of cells with aberrant cytokinesis, including broad cytoplasmic connections and lagging chromosomes.32 The dynamic localization pattern and biochemical properties of Aurora-A prompted us to explore the possibility that Aurora-A might regulate cytokinesis through CRMP-1, despite the fact that Aurora-A is not very close to CRMP-1 in the midbody QQ-PPI network (Figure 3). Immunofluoresence analysis of CL1-0 lung cancer cells demonstrated that endogenous CRMP-1 partially colocalized with Aurora-A to the centrosomes during metaphase (Figure 5A-b), in the interchromosomal spindles during anaphase (Figure 5A-c), and in the midbody during cytokinesis (Figure 5A-d). In an in vitro kinase assay, CRMP-1 served as the substrate of Aurora-A, but not Aurora-B (data no shown). To investigate the kinase relay signals, it is essential to identify the phosphorylation site(s) for a given substrate to allow for studies that use phosphorylation site mutants. To determine the in vitro Aurora-A phosphorylation sites on CRMP-1, recombinant CRMP-1 was incubated with Aurora-A in an in vitro kinase reaction and subjected to SDS-PAGE analysis. The CRMP-1 phosphorylation band was carefully excised and subjected to LC MS/MS analysis as described previously.33 Two phosphorylation sites (Thr101 and Thr102) were identified with ∼70% amino-acid sequence coverage (data not shown). To provide additional support for the assignment of the two phosphorylated residues, we replaced each phosphorylation site in CRMP-1 with an alanine either individually or together (designated CRMP-1-T101A, CRMP-1T102A and CRMP-1-T101A/T102A, respectively). These mutants were assayed for their ability to serve as an Aurora-A substrate in an in vitro kinase reaction, and evaluated for their effect on cytokinesis. First, recombinant CRMP-1-WT and the mutants were purified and tested for γ-32P incorporation in an in vitro kinase reaction with Aurora-A. The results showed that the single site mutants as well as the double mutant of CRMP-1 incorporated significantly less γ-32P than CRMP-1-WT (Figure 5B). This suggests that Aurora-A phosphorylates CRMP-1 at both Thr101 and Thr102. Aurora-A Regulates CRMP-1 To Complete Cytokinesis by Phosphorylation. To address whether Aurora-A might regulate CRMP-1’s role in cytokinesis through phosphorylation of CRMP-1, EGFP-tagged CRMP-1-T101A/T102A was expressed in CL1-0 cells. Ectopic expression of a low level of EGFP-CRMP1-T101A/T102A in CL1-0 cells delayed their exit from mitosis as examined by time-lapse visualization (Supplementary timelapse video). Stable transfectants expressing either HA-tagged CRMP-1-WT or CRMP-1-T101A/T102A were established in CL1-0 cells (Figure 5C). Overexpression of CRMP-1-T101A/ T102A resulted in an approximately 2.5-fold increase in cells with a defect of cytokinesis compared with CRMP-1-WT or the parental control (data not shown). Further observation showed that cells overexpressing CRMP-1-T101A/T102A also exhibited a defective morphology during cytokinesis (Figure 5D). The intracellular bridge, instead of breaking off at one point, became longer and thinner (Figure 5D-d). In addition, there Journal of Proteome Research • Vol. 8, No. 11, 2009 4949
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Figure 5. CRMP-1 and Aurora-A are partly cocalized in mitosis, particularly in the midbody, and CRMP-1 is a substrate of Aurora-A. (A) Indirect immunofluorescence of endogenous CRMP-1 and Aurora-A in lung adenocarcinoma (CL1-0) cells was detected with antiCRMP-1 monoclonal antibody (green), anti-Aurora-A monoclonal antibody (red), and DAPI (blue). (a) In interphase, CRMP-1 was distributed in the cytoplasm and Aurora-A was at the centrosomes. During metaphase (b) and anaphase (c), CRMP-1 was associated with the mitotic spindle and partially colocalized with Aurora-A at the centrosomes. (d) CRMP-1 and Aurora-A also colocalized to the midbody at cytokinesis. Scale bar, 10 µm. (B) Purified recombinant CRMP-1-WT and its phosphorylation site mutants (CRMP-1-T101A, CRMP-1-T102A, and CRMP-1-T101A/T102A) were incubated with Aurora-A in the presence of [γ-32P]-ATP to test the ability of Aurora-A to phosphorylate these various forms of CRMP-1. Reaction mixtures were analyzed by SDS-PAGE followed by autoradiography. Analysis of the autoradiography data demonstrated that Aurora-A could efficiently phosphorylate CRMP-1-WT, whereas mutants of CRMP-1 had lower Aurora-A-mediated γ-32P incorporation. An arrow indicates autophosphorylation of Aurora-A. (C) Western blot analysis of CL1-0 cells stably expressing CRMP-1-WT or CRMP-1-T101A/T102A protein tagged with an HA epitope. (D) Ectopic expression of CRMP1-AA leads to cytokinesis defects. Cells were fixed and permeabilized in methanol and subjected to indirect immunofluorescence. (a) CL1-0; (b) CRMP-1-WT; and (c and d) CRMP-1-T101A/T102A stable transfectants were stained with R-tubulin antibody (green) and DAPI (blue). Cells undergoing cytokinesis are shown. Cells expressing CRMP-1-AA result in delay in the cytokinesis process. (E) CL1-0 cells were transiently transfected with EGFP-tagged CRMP-1-WT or CRMP-1-T101A/T102A. After transfection, cells were treated with 50 ng/mL Nocodazol for 18 h to synchronize the cells in prometaphase, resulting in about 75% synchronization. Cells were released from prometaphase by washing with PBS and then harvested at 2, 4, 6 h followed by immunostaining analysis. Cells were stained with anti-R-tubulin antibody to indicate the midbody structure. Cells that connected by a midbody structure were counted as cytokinesis cells. Solid bar: WT. Blank bar: T101A/T102A.
were also cells with a thicker midbody in the CRMP-1-T101A/ T102A clones (Figure 5D-c) than in the CRMP-1-WT clones (Figure 5D-b) or parental cells (Figure 5D-a) during cytokinesis. When CRMP-1-WT and CRMP-1-T101A/T102A clones were synchronized at mitosis by nocodazole treatment, followed by release from the nocodazole block, the CRMP-1-T101/T102A clones exhibited a delayed exit from mitosis (Figure 5E). Alternatively, we attempted to knock down CRMP-1 by siRNA. Despite using 36 different siRNA or shRNA constructs, the best knockdown effect was ∼50% (data not shown). As a result, depletion of CRMP-1 by shRNA in CL1-0 cells only caused a 1.5-fold increase in cytokinesis aberrant cells (data not shown). Together, ectopic expression of a CRMP-1 phosphorylationsite mutant resulted in cytokinesis defects, suggesting that 4950
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CRMP-1 is likely regulated by Aurora-A-elicited phosphorylation to monitor the completion of cytokinesis.
Discussion In this study, we aim to address how to prioritize novel hubs from a given network. To easily score the phenotypic changes upon perturbation of the selected targets, we chose cytokinesis, where many key regulators remain to be identified, as our observation time point in order to narrow down the complicated spatial and temporal issues in the PPI network. Using manual curation of the involvement of more than 100 proteins in cytokinesis and empirical analysis of novel hubs, we conclude that sorting targets by z-score is better than hub
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Midbody Protein-Protein Interaction Network Construction degree to prioritize novel hubs with less PPI information. From the constructed midbody PPI network, we not only reveal the missing interactions of SEPT1/SEPT6 and underlying regulators of cytokinesis, but also identify six novel midbody hubs that subsequently uncover three new and/or vital players, that is, SEPT1, KIAA1377 and CRMP-1, participating in progression of cytokinesis. Moreover, ectopic expression of CRMP-1-T101A/ T102A mutant, in which Aurora-A phosphorylation sites have been replaced, leads to cytokinesis defects, suggesting AuroraA/CRMP-1 may work together to regulate cytokinesis. Several proteins and events are specifically required during three sequential stages of cytokinesis: furrow ingression (stage 1), contractile ring disassembly (stage 2), membrane remodeling and midbody abscission (stage 3).13 During contractile ring disassembly, Aurora-B is a major regulator that phosphorylates RACGAP1, one of the components of the spindlin complex, to complete cytokinesis.34 Septins is one class of regulators that facilitate the formation and stabilization of intercellular bridge.35 The complex of SEPT2/SEPT7/SEPT6/SEPT9 participates in furrow ingression (stage 1) and membrane trafficking (stage 3) during cytokinesis. In this study, SEPT1 was identified to interact with SEPT6 and Aurora-B in the midbody PPI network, and knockdown of SEPT1 increased the number of cytokinesis aberrant cells (Figure 4C), implying that the SEPT1 might be a crucial hub, bridging the critical regulators (i.e., SEPT2/SEPT6/SEPT7/SEPT9 complex and Aurora-B) in each stage of cytokinesis, to lead to the completion of cytokinesis. Although it is not clear at which stage of cytokinesis SEPT1 exerts its effect or by which pathway (i.e., MRLC), the RNA interference assay showed the crucial role of SEPT1 in cytokinesis and midbody bridge. Because the kinase activity of Aurora-B is unique to all stages of mitosis, it would be of interest to address whether SEPT1 might regulate the kinase activity of Aurora-B and how the SEPT1/Aurora-B complex might modulate cytokinesis. Aurora-A is reported to associate with NM23-H1 (a metastasis suppressor)36 and phosphorylate tumor suppressors such as p53,37,38 BRCA1,39 and Lats2,40 and is therefore able to regulate their activities. For instance, Aurora-A phosphorylates p53, leading to ablation of p53’s DNA binding activity.37,38 The findings that both CRMP-1 and Aurora-A share similar subcellular localization in mitosis, particularly in the midbody, raises the possibility that Aurora-A-elicited CRMP-1 phosphorylation might, at least in part, regulate cells to exit mitosis. Failure of cell division will result in cell death or polynucleated cells, and cells expressing the CRMP-1-T101A/T102A mutant exhibit a polynucleated phenotype (data not shown), which might be the result of the cytokinesis defect. However, it should be noted that overexpression of the CRMP-1-WT or CRMP-1T101A/T102A mutant does not affect centrosome number, spindle structure or the localization of Aurora-A to the spindle (data not shown). The addition of an invasion suppressor, CRMP-1, to the handful of known Aurora-A substrates has further revealed a role for Aurora-A in the regulation of cytokinesis, supporting the notion that Aurora-A participates in diverse mitotic events. Similarly, a rapidly expanding body of knowledge indicates that the functions of CRMPs are not solely limited to the signaling transduction of the Sema3A guidance cue or axon extension inhibitor. They are probably also involved in multiple cellular and molecular events ranging across apoptosis, proliferation, cell migration, and differentiation. Our study has also uncovered a novel function for CRMP-1 as a participant in cytokinesis.
In this study, a combination of bioinformatics and empirical approaches significantly enhances the predictive power in the postgenomic era, as illustrated by 6 validated midbody hubs. Moreover, this in silico PPI data set processing overcomes the long-term uncertainty of using high-throughput PPI data sets, as well as fills in the potential missing link in this cytokinesis network. Using the random sampling and z-score sorting, we acquire the balance between the advantages and disadvantages of large-scaled and small-scaled data of PPIs to broaden the horizons of the PPI network to a network of biological significance. Taken altogether, from midbody PPI network construction to target prioritization, this study highlights a promising approach to address disease-related networks and potential new therapeutic targets from such diseasomes.
Methods Protein-protein Interaction Data Set Stored on POINT. The newly updated version of POINT (http://point. bioinformatics.tw)41,42 integrates several publicly accessible PPI data sets, including DIP, MINT, BIND, HPRD, MIPS, CYGD, and BioGRID. Supplementary Table 4 shows the distribution profiles of the POINT collections from various organisms. The data set includes a total of 237 475 PPIs, of which 53 367 are human PPIs. These human PPIs involve 10 629 proteins; however, more than one-third of them have more than one PPIs. Interestingly, 97 human proteins have enormous numbers of interactions (more than 100 human interaction partners), suggesting that the human PPIs are far from saturation. This, therefore, calls for alternative bioinformatics approaches to reveal potential human PPIs. Moreover, we employed the interologs concept to predict additional human PPIs in order to fill in the potential missing interactions; this also allowed us to estimate the reliability of the collected PPIs.14 The 183 Midbody Proteins Constitute a Highly Interactive Protein Network. Recently, a proteomic screen identified 160 human midbody candidate proteins.3 After removal of the redundant proteins, there are 158 midbody proteins remaining (an updated annotation list appears in Supplementary Table 1). We further surveyed the literature and included 32 additional known mammalian midbody proteins (for reviews, see refs 11-13) in our list, resulting in a total of 190 midbody proteins (Supplementary Table 1). Of these 190 midbody proteins, only 183 proteins have PPI information and contain 3690 human experimental PPIs in POINT. Moreover, to convert the seemly random and independent PPI data sets into, at least in part, a biologically meaningful midbody network, we have developed a new web tool, POINeT (http://poinet.bioinformatics.tw)14 to annotate potential new players in a given network. Details of all available midbody PPIs are available on POINeT. Statistically Ranking the Hubs in Protein-Protein Interaction Network. To assess the relevance of proteins in the midbody network, we calculated the z-scores of the degrees (number of interactions) for these proteins. The degree of the protein in the random network was measured using a bootstrap method, which involved the random sampling of the full human protein network (53 367 PPIs with 10 629 proteins). The sampling process with vertices equal to the midbody network (3690 vertices) was repeated 10 000 times for each protein. We did not restrain the number of edges to give the unbiased random PPI network. This sampling process resulted in a distribution of degrees with 10 000 data points. The mean and variance of the distribution were calculated, and z-scores were obtained using the following equation: Journal of Proteome Research • Vol. 8, No. 11, 2009 4951
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d-µ σ
where d is the degree of the protein in midbody network, µ is the mean value of degrees in a random network, σ is the standard deviation and z is the z-score of the protein. We used equivalent to a 90% confidence level of 190 queries as a cutoff value to obtain the top 171 queries. High z-score proteins, the top 60 unknown proteins, were obtained from ranking the 802 hubs excluding the 171 queries. Applying this statistical ranking, we selected the hubs with statistical significance in the midbody. Preparation of Recombinant Proteins, in Vitro Kinase Reaction and Phosphorylation Site Determination. Human CRMP-1 recombinant protein was prepared using a bacterial expression system. CRMP-1-WT and its mutant forms were subcloned into the pET32a expression vector (Novagen) and expressed as His-tagged fusion proteins. The proteins were partially purified by nickel agarose (Qiagen) as described in the manufacturer’s manual. The purified recombinant CRMP-1 proteins were incubated in kinase reaction buffer (25 mM Tris [pH 7.4], 10 mM MgCl2, 10% glycerol, 1 mM DTT, 1 mM NaF, 20 µM ATP, and 10 µCi [γ-32P] ATP) with 1 µg of purified recombinant GST-Aurora-A or GST-Aurora-B at 30 °C for 30 min as described previously.43 The kinase reaction mixtures were resuspended in SDS sample buffer and separated on 12.5% or 8% SDS-polyacrylamide gels. Proteins were transferred to PVDF membranes and detected using autoradiography. For phosphorylation site determination, recombinant CRMP-1 proteins were incubated with Aurora-A in kinase buffer containing cold ATP. After reaction for 30 min, samples were subjected to SDS-PAGE and stained with Coommassie blue. CRMP-1 proteins were excised from the gel and phosphorylation sites were determined by LC MS/MS analysis as previously described.33 Cell Cultures, Transfection, Cell Cycle Synchronization and Antibodies. CL1-0 and CL1-5 human lung adenocarcinoma cell line was established and grown as reported earlier.44 Transient transfection of HEK293T and HeLa cells was performed with Lipofectamine (Invitrogen) according to the manufacturer’s instructions. We transfected siRNA duplexes (Ambion, Inc.) targeting the following sequences for RNA mediated inhibition: SEPT1, UCGAGCCAGCCGCAGUAAGdTdT; KIAA1377 #1, AAACUAAGCAAGGUAAGCCdTdT; KIAA1377 #2, CUGUUAUGAGAAGAAAACGdTdT. To generate functional shRNAs, 36 different siRNAs and shRNAs were screened. Two of them, pSUPER-CRMP-1-#16 and pSUPER-CRMP-1-#20, had a better knockdown effect than the other shRNA screened (data not shown). Briefly, pSUPER vector45 was digested with BglII and HindIII, and annealed oligonucleotides (pSUPER-CRMP-1-#16, 5′gatccccTATGCAACTCCCGCTCCTTttcaagagaAAGGAGCGGGAGTTGCATAtttttggaaa3′; pSUPER-CRMP-1-#20, 5′gatccccCATCAACGTCAACAAGGGCttcaagagaGCCCTTGTTGACGTTGATGtttttggaaa3′ [where uppercase letters indicate siRNA sequences]) were ligated into the vector. CL1-0 and HEK293T cells were synchronized at mitosis by 50 ng/mL nocodazole treatment for 16 h, whereas HeLa cells were treated with 80 ng/mL. Cell-free lysates were prepared as described earlier.43 The lysates were analyzed by SDS-PAGE and immnoblotted with appropriate antibodies. The following antibodies were from commercial sources: mouse anti-Flag tagged (Sigma-Aldrich), mouse anti-HA tagged (Upstate), anti-Flag conjugated FITC (SigmaAldrich), mouse anti-FLOT2 (Abnova), rabbit anti-FLOT2 (GeneTex), rabbit anti-YWHAZ (GeneTex), rabbit anti-ACTB (GeneTex), mouse anti-EGFR (Abcam), mouse anti-MYC 4952
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(Abnova), rabbit anti-Aurora-A (BD), mouse anti-R-tubulin (Sigma-Aldrich), and anti-R-tubulin FITC conjugated (SigmaAldrich); Alexa-labeled secondary antibodies (Invitrogen), Alexa Fluor 568 phalloidin (Invitrogen). Monoclonal antibody against CRMP-1 was described previously.27 Immunoprecipitation. HEK293T cell pellets collected were lysed in the NP-40 lysis buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1% NP-40, 0.25% sodium deoxycholate, 1 mM EGTA, 1 mM phenylmethylsulfonyl fluoride, and 10 µg/mL each of leupeptin, aprotinin, and chymostatin. Lysates were centrifuged and incubated with protein A beads coupled with preimmune rabbit IgG at 4 °C for 1 h. The precleared lysates were then incubated with anti-Flag M2 or anti-HA agarose affinity gel (Sigma-Aldrich) at 4 °C overnight. Beads were recovered by centrifugation, washed three times with the TBS buffer, resuspended in SDS sample buffer, heated at 95 °C, analyzed by SDSPAGE, and immunoblotted with appropriate antibodies. Immunofluorescence. CL1-0 and HeLa cells were seeded onto coverslips. After reaching 75% confluence, the cells were fixed for 5 min in PBS containing 3.7% formaldehyde. After fixation, the cells were permeabilized for 5 min in PBS containing 0.5% Triton X-100, blocked in 5% normal goat serum for 30 min, and then incubated with primary antibodies for 1 h at room temperature. The coverslips were then washed three times with PBS containing 0.1% Triton X-100 and incubated with secondary antibodies and 0.5 µg/mL DAPI (Sigma) for 1 h at room temperature. Immunofluorescent cell images were acquired using an Olympus Fluoview FV1000 Confocal laser scanning microscope (Olympus). Images were processed with Adobe Photoshop software (Adobe Systems).
Acknowledgment. We are grateful to An-Chi Tien, YueLi Juang and Yi-Ren Hong for critical reading of the manuscript and valuable suggestions. We thank Dr. Chris Connolly (University of Dundee, U.K.) for the kind gift of a HTR3A construct. This research was supported by grants from Department of Health (DOH95-TD-G-111-013), Program for Promoting Academic Excellence of Universities (National Yang Ming University), and NSC97-3112-B-010-025-CC1 to C. F. Huang and NSC (Program for Interdisciplinary Research Project: NSC95-2627-B-400002 to C. Huang, NSC95-2627-B-030-001 to J.-M. Lai, and NSC952627-B-002-011 to C.-Y. Kao). Supporting Information Available: Supplementary time-lapse video, EGFP-CRMP-1-T101A/T102A in CL1-0 cells delays their exit from mitosis. Supplementary Table 1, annotation of 190 midbody proteins. Supplementary Table 2, proteinprotein interaction pairs in the midbody QQ-PPI network. Supplementary Table 3, details of top 60 uncharacterized hubs with degree and z-score sorting. Supplementary Table 4, protein-protein interaction data sources. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Fujiwara, T.; Bandi, M.; Nitta, M.; Ivanova, E. V.; Bronson, R. T.; Pellman, D. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 2005, 437 (7061), 1043–7. (2) Eggert, U. S.; Mitchison, T. J.; Field, C. M. Animal cytokinesis: from parts list to mechanisms. Annu. Rev. Biochem. 2006, 75, 543–66. (3) Skop, A. R.; Liu, H.; Yates, J., III; Meyer, B. J.; Heald, R. Dissection of the mammalian midbody proteome reveals conserved cytokinesis mechanisms. Science 2004, 305 (5680), 61–6. (4) Jeong, H.; Mason, S. P.; Barabasi, A. L.; Oltvai, Z. N. Lethality and centrality in protein networks. Nature 2001, 411 (6833), 41–2. (5) Han, J. D.; Bertin, N.; Hao, T.; Goldberg, D. S.; Berriz, G. F.; Zhang, L. V.; Dupuy, D.; Walhout, A. J.; Cusick, M. E.; Roth, F. P.; Vidal,
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