ARTICLE

Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions

J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1

In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases.

1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 5 Department Structural and Molecular Biology, University College London, London WC1E 6BT, UK. 6 Van Andel Institute, Grand Rapids, Michigan 49503, USA. * These authors contributed equally to this work. w Present addresses: Department of Physiology, Anatomy and Genetics, University of Oxford (B.S.); The Brigham Regenerative Medicine Center, Harvard Medical School (P.T.); King’s College London (Z.I.); University of Edinburgh Western General Hospital (W.A.). Correspondence and requests for materials should be addressed to V.M.M.B. (email: [email protected]).

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ulti-cellularity and adaptation to distinct environments Results underlie the evolutionary success of metazoans1,2. RNAi screen. In spite of extensive progress in image analysis MA key aspect of multi-cellularity is cell–cell adhesion, and the availability of increasingly sophisticated tools10,17,19,20, which ultimately allowed specialization of different tissues to automated segmentation and quantification of junctional pro- cooperate and perform distinct tasks. E-cadherin, the first teins in a confluent epithelial sheet have been challenging21–23. described member of the cadherin super-family, is essential for In particular, algorithms have not yet been established to segment embryonic survival3 and a paradigm for cell–cell adhesion F-actin at junctions and thin bundles (Fig. 1a,b), parameters with regulation. Dynamic cell–cell contacts are required for tissue well-established biological relevance for epithelial junctions. integrity and morphogenesis, including cell division, epithelial To quantitatively monitor the effectiveness of cell–cell adhesion sheet folding, lumen formation and geometric cell shape in assembly, we developed a workflow (Supplementary Fig. 1, different organisms4. Conversely, dysfunction of E-cadherin Fig. 1c–g) to quantify the levels of E-cadherin at junctions, adhesion leads to the inability of cells to attach strongly to each junctional actin and peripheral thin bundles (Fig. 1a,b). other, sustain stress or maintain epithelial cell shape, thereby A parameter for quality control of cell confluence was also compromising tissue function in different diseases5,6. validated to automatically eliminate images containing gaps in the Formation of E-cadherin-mediated adhesion is a complex, monolayer (likely to result in false-negative data, Supplementary multi-layered process, involving engagement of receptors in Fig. 2). neighbouring cells, reinforcement of attachment and reorganiza- E-cadherin levels at junctions were detected by thresholding to tion of the underlying cortical cytoskeleton. Reinforcement of minimize the contribution of the pool of receptors found in the cadherin adhesion is achieved by intracellular trafficking cytoplasm (Fig. 1c). By comparing images containing different (receptor delivery and turnover at the cell surface) and an levels of cadherin at junctions, the percentage of pixels found indirect association with actin filaments3,4, which drives tension in the thresholded image (% area) reliably detected partial alongside contacts7. It is unclear how these different cellular disruption of E-cadherin levels and provided good dynamic range processes are coordinated at the molecular level or how clustered between negative and positive controls (Fig. 1c,d). This suggested receptors are kept in place to reinforce junctions5,8. that the segmentation successfully detected partial phenotypes It is likely that the epithelial actin organization is maintained obtained by RNAi. The parameters junctional actin (Jun-A) by integration of distinct subsets of actin remodelling and cytoplasmic actin (Cyt-A) were successfully segmented properties. Distinct actin proteins act to polymerize, cap, (Supplementary Fig. 1) and quantified with good dynamic range sever, branch, cross-link and bundle filaments, and contribute to show disruption of the cytoskeleton as partial and distinct to the reorganization of actin cytoskeleton as distinct cellular phenotypes (Fig. 1c–g). Thus, three parameters were validated to structures. In epithelia, polymerization and contraction have specifically detect quantitative changes in E-cadherin and well-established roles in cadherin adhesion and morphogenetic cytoskeletal organization. The low dimensionality provided by processes5,6. Yet, how additional cytoskeletal proteins and actin parameters E-cad Jun-A and Cyt-A was considered against the remodelling functions collaborate during junction biogenesis is inclusion of general biophysical features such as cell area, unclear. perimeter and so on. These biophysical features are not relevant Phenotypic analysis of RNAi screens9 have been instrumental for junction biogenesis per se nor add mechanistically to junction in deciphering novel regulators of actin cytoskeleton10, cell regulation; rather they may generate noise in subsequent shape11, cell motility12,13, or integrin-dependent adhesion phenotypic analysis. and mechanosensing14,15 among others. Yet, while studies on Confluent normal keratinocytes grown in the absence of invertebrate cell adhesion16, epithelial scattering17, or interactome cell–cell contacts were treated with the Actinome library10 of junctional proteins18 have been very informative, global (primary screen targeting 327 actin-binding proteins) understanding of cytoskeletal proteins required for junction (Supplementary Fig. 3a), junctions were induced for 30 min and formation has not been previously addressed. Nor is it clear how images processed (Fig. 2a,b). The data set obtained did not follow their function is integrated towards maintaining stable cadherin Gaussian or log-Gaussian distribution (Supplementary Fig. 3b). adhesion. In spite of the importance that junction dynamics have To normalize between plates, Z-scores were calculated based on for many processes, large scale studies to investigate mechanisms controls in each plate (see methods)24. and regulators of cell–cell contacts have been delayed by the Strong effects of depletion of a given protein could result from absence of quantitative tools specific for epithelial structures and a general perturbation of the actin cytoskeleton upon RNAi of junctional markers. Here we set out to optimize methodology core actin regulators. However, for the majority of samples we relevant for epithelial structures to identify new regulators and think that this is unlikely. First, overall total levels of F-actin in pathways required for junction stabilization. each sample did not correlate with the Z-scores obtained for We hypothesize that a similar phenotype upon depletion of E-cad or Jun-A (total F-actin intensity; Supplementary Fig. 3c,d), specific cytoskeletal proteins may indicate cooperation to rather there was a stronger correlation with Cyt-A parameter organize/maintain specific actin structures and stabilize cadherin (higher R-values). Second, E-cad, Jun-A or Cyt-A values did not receptors at junctions. Using optimized phenotypic clustering correlate with cell number (nuclei count) or the presence of small and enrichment analysis, we identify distinct functional profiles gaps in the monolayer (% area total F-actin; Supplementary and actin-binding proteins that selectively perturb E-cadherin Fig. 4). Finally, a strong correlation between E-cad and Jun-A and junctional actin levels at newly formed contacts. Our Z-scores (Fig. 2c) was consistent with their functional computational analyses (i) associate a selective cytoskeletal interdependence during cadherin assembly. In contrast, Cyt-A protein network with specific biological output, (ii) allow Z-scores values varied independently of either E-cad or Jun-A interrogation of novel signalling pathways relevant for epithelial (Fig. 2c). Thus, the parameters measured behave as predicted function and (iii) identify two new pathways that modulate from our current knowledge of regulation of E-cadherin E-cadherin adhesion. Further dissecting the functional hierarchy adhesion. Taken together, our results suggest that Z-score of cytoskeletal proteins in the context of cadherin adhesion variations by depletion of a specific cytoskeletal protein reflect a will provide insights to overcome junction destabilization bona fide effect on E-cadherin adhesion and/or actin remodelling. that occurs in tumour progression and a variety of epithelial Based on Z-scores values, a subset of candidate proteins (156 pathologies. proteins; Supplementary Table 1) was selected (Fig. 2b). Similar

2 NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 ARTICLE

Z-score correlations as the primary screen were observed for this VASP, ENAH, ABI, CYFP1), ERM proteins (RDX, EZR) and subset (Fig. 2e,f; Supplementary Fig. 3c,d), indicating that others (TRIOBP, MYOIV, FILA, FILB, EPB1L5 and so on.). This candidate proteins were a representative pool of the original analysis supports and validates the methodology deployed here data set. Overall, individual candidate proteins displayed to identify both known and unknown junction regulators. In phenotypes affecting mostly one or two parameters; Fig. 2g,h). addition, some proteins have been shown to regulate epithelial Importantly, around 30 candidate proteins have been previously differentiation, morphogenesis or participate in diseases shown to regulate cadherin adhesion (Supplementary Table 2), (Supplementary Table 3). Clearly, the role of these proteins among these there are regulators of actin (WAS, WASF1/2, may be multi-factorial and, with a few exceptions, a direct

abE-cadherin Junctional actin Time Lateral view Top view α- catenin E-cadherin

0 min β- catenin

+ Ca2+ + Ca2+

Ca2+ 30 min

60 min

Actin Thin bundles

cdJunctions E-cadherin Control Absent Punctated Reduced +cytoplasmic 6 4

% Area 2 * **

E-cadherin * 0 30 0 5 PMA Arf6 Ca2+: +–

+ std Ca2+ (min) + std Ca2+ (30 min) Control Reduced

Punctated Cytoplasmic

efJunctional actin

30

20 * ** % Area 10 *** 0 Ca2+: +++– Control Bleb. Y. F-actin Junctional g Cytoplasmic actin

30

20 * F-actin F-actin Total

% Area ***

Cytoplasmic 10 ***

30 0 Bleb. Y27632 Ca2+: +++– + std Ca2+ (min) + std Ca2+ (30 min) Control Bleb. Y.

Figure 1 | Automated analysis for quantification of epithelia-specific parameters. (a) Cell morphology changes during cell cell adhesion. Diagrams are shown as lateral view (left), top view (middle) or representation of homophilic binding of E-cadherin (right). Addition of calcium ions induces assembly of E-cadherin puncta at the interface between neighbouring cells. F-actin re-organization is shown as appearance of junctional actin (co-localizes with E-cadherin) and circumferential thin bundles that compact towards junctions. (b) Immunofluorescence image showing E-cadherin localization at newly formed junctions (30 min; green), junctional actin and adjacent thin bundles (red). (c–g) Validation of automated image analysis (Supplementary Fig. 1). Positive and negative controls are cells induced to form junctions with standard calcium medium (30 min std.Ca2 þ ) or maintained without cell cell contacts (0 min std.Ca2 þ ), respectively. (c) Keratinocytes were treated to generate images with E-cadherin staining as punctate (5 min std.Ca2 þ ), reduced (PMA treatment) or increased cytoplasmic staining (active Arf6 expression). (d) Quantification of E-cad parameter as % thresholded area. (e)To validate image segmentation of F-actin pools, keratinocytes were pre-treated with blebbistatin (Bleb.) or Y27632 and junctions induced for 30 min in the presence of the drugs. (f,g) Images were quantified to generate the parameter Jun-A (F-actin pool co-localizing with E-cadherin at junctions, (f) or Cyt-A (that is, F-actin pool outside junctions and nucleus, (g). Data from three independent replicates; error bars represent standard error of the means. Scale bars, 25 mm. Statistical analysis t-test paired two samples, assuming equal variance; *Po0.02; **Po0.003; ***Po0.0004.

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a E-cad Z-score values Jun-A Z-score values Cyt-A Z-score values Negative Control Positive Negative Control Positive Negative Control Positive

b E-cadherin Junctional actin Cytoplasmic actin 4 4 8

4 0 0 0 -score -score -score Z Z –4 –4 Z –4

–8 –8 –8 siRNA sample siRNA sample siRNA sample

c Primary screen: R-value d R-value

0.9 6 6 0.9 4 4 0.7 4 2 P<0.001 0.7 2 2 -score) -score) -score) Z

Z 0.5 0 0 Z 0 0.5 –2 –2 –2 0.3 Cyt-A ( Jun-A ( Cyt-A ( 0.3 –4 –4 P<0.001 –4 –6 0.1 –6 (P=0.43) 0.1 –6 –4 –2 0 2 4 –6 –4 –2 0 2 4 –4 –2 0 2 4 6 –0.1 Cyt-A E-cad (Z-score) Jun-A (Z-score) Jun-A Cyt-A

e Candidate proteins: R-value f R-value

0.9 0.9 4 6 6 4 4 2 0.7 0.7 2 P<0.001 2 -score) -score) -score) Z Z 0 Z 0 0.5 0 0.5 –2 –2 –2 0.3 Cyt-A (

Cyt-A ( 0.3 Jun-A ( –4 –4 P<0.05 –4 –6 0.1 –6 (P=0.17) 0.1 6 –6 –4 –2 0 2 4 –6 –4 –2 0 2 4 –0.1 –4 –2 0 2 4 6 Cyt-A E-cad (Z-score) Jun-A Cyt-A Jun-A (Z-score)

g h E-cadherin Single Cyt-A Jun-A + Cyt-A Double Jun-A E-cad + Cyt-A Triple E-cad E-cad + Jun-A 28

100 26%

80 34% 46 45% 40% 60 4 20 40 24% 24 42% 6% Junctional Candidate proteins (%) 20 Cytoplasmic 70% actin 16 18 actin 13%

Figure 2 | Global analysis of RNAi screen. (a) Representative processed images of the parameters E-cad, Jun-A and Cyt-A from controls and selected RNAi samples with positive or negative Z-scores. (b) Distribution of Z-scores of the primary RNAi screen data set (Supplementary Fig. 3a). Grey line marks cutoff Z-scores ( þ / 1.65). (c–f) Correlation of the Z-scores of different parameters obtained in the primary screen (c,d) or the candidate proteins data sets (e,f) and their respective R-values. Partial interdependence of the parameters E-cad and Jun-A is observed, but not Cyt-A. Black lines represent the linear regression models and curved red lines show their 95% confidence interval. (g) Distribution of candidate proteins according to their phenotype showing interference with a single (single) or multiple parameters (double, triple) following depletion of a single protein. Values show the relative percentage in each subgroup. (h) Venn diagram showing the number of proteins and their corresponding phenotypes (single or overlapping phenotypes). Scale bar, 50 mm. Data represent median Z-score values of three independent replicates for each RNAi oligo. The number of data points (thereafter N ¼ ) for b–d, 327 or e–h, 156.

4 NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 ARTICLE regulation of cadherin adhesion in such pathologies sever/depolymerize F-actin accumulated in Clusters 3 and 5 (Supplementary Table 3) has not yet been formally demonstrated. (Fig. 3e). Nevertheless, our data suggest that the new function on junctions Taken together, the above results on our data set indicate that identified herein could potentially contribute to defective the optimized clustering analysis is highly meaningful from the morphogenesis or diseases in which candidate proteins have point of view of the interaction landscape, functional enrichment been implicated. and biological phenotype on epithelial junctions. The systematic methodology optimization shown here can thus be a powerful Phenotypic clustering of candidate proteins. While identifying approach to generate a framework and facilitate pathway novel junction regulators per se is exciting, a major conceptual discovery among the candidate proteins. advance is to elucidate how specific proteins with similar junctional phenotypes cooperate to stabilize cell–cell adhesion. Functional enrichment analysis. While distinct clusters can be Such analysis would accelerate pathway inference and obtained by phenotypic analysis28, the challenge is to find identification of regulatory mechanisms of the epithelial cytos- meaningful intra- and inter-clusters relationships that correlate keleton. A phenotypic similarity analysis25 of candidate proteins imaging data with biological functions. We hypothesized (Supplementary Table 1) was optimized and validated statistically that enrichment of distinct actin remodelling events in each (Silhouette plots, Supplementary Fig. 5) and by functional cluster would allow predictions of cytoskeletal functions that enrichment (Supplementary Fig. 6a–d; see methods). Using differentially contribute to the formation of stable cadherin these criteria, Spectral clustering26 using Mahalanobis distance contacts. Overall, the identification of important actin was the best strategy for phenotypic clustering for our data set. remodelling for junction stabilization provides insights on the Following further optimization (Supplementary Fig. 6e), the relationship between specific cytoskeletal processes and the extent clusters projected in three-dimensional space showed partial of E-cadherin adhesion perturbation. For example, the stronger overlap for clusters 1–3 (Fig. 3a) and very few data points were reduction of E-cadherin levels at junctions in cluster 5–6 wrongly classified in their respective clusters (5 out of highlights the overlooked contribution of actin filament 156 samples with negative Silhouette values, Fig. 3b). To validate turnover/stabilization for E-cadherin levels at cell–cell contacts. the optimized clusters, three additional approaches demonstrated Such insights are highlighted by the hierarchical clustering of their biological significance. First, proteins that participate phenotypic groups (based on Z-scores and function, Fig. 4a). in the same pathway/cellular function are likely to share Specific actin remodelling functions were enriched among binding partners and thus are found together in protein– clusters and classified in three main groups: control of adhesion protein interaction (PPI) networks (see methods)27. Based on and filament organization (Clusters 2, 3), intracellular trafficking neighbour interacting partners of individual candidate proteins, (Clusters 1–3) or filament length (Clusters 1, 3–5). Clusters 1–3 randomized clusters containing the same number of proteins of contained the majority of proteins known to regulate cadherin each phenotypic cluster were produced (Fig. 3c). The random adhesion (68%) and intracellular trafficking (85%, Fig. 4a). clusters generated with the same composition as the phenotypic Considering the functional enrichment and the spectrum clusters (red arrow) were found outside the random background of E-cadherin disruption, milder phenotypes on E-cadherin of the density distribution curve (peak, green line, Fig. 3c). This adhesion correlated well with depletion of trafficking proteins result suggests that the overall partition of the data set into nine (Clusters 1, 2) and stronger phenotypes correlated with clusters was highly significant. Thus, the proteins in the de-regulation of filament length (Clusters 3, 5). experimental clusters tend to be closer together in the protein The hierarchical distribution of enriched functions would interaction space than would be expected by chance. suggest a potential integration among the subsets of proteins in Second, specific cellular phenotypes were well segregated each cluster. In other words, can partners be found that interact among the different clusters (Fig. 3d). A spectrum of disruption with and modulate a specific enriched actin function to control a of E-cadherin adhesion was observed across phenotypic clusters, distinct junctional phenotype (that is, mild to severe defects in from mild (that is, Cluster 2) to severe perturbation (that is, different clusters)? A high degree of PPIs for a specific functional Cluster 5). In addition, depletion of proteins found in Cluster 1 property is observed either within a cluster (for example, Fig. 4b) mostly reduced F-actin at junctions (Jun-A), while a decrease in or spanning different clusters; for example, trafficking (Fig. 4c, E-cadherin levels was predominant in Cluster 2. Clearly, in spite cluster 3), actin polymerization (Fig. 4d, common proteins in of the correlation between E-cad and Jun-A parameters (R-value clusters 1 and 5) or capping (Fig. 4e, clusters 3 and 5). These 0.65; Fig. 2c,d), depletion of specific proteins interfered results suggest that the actin functions of proteins found in differentially with the levels of cadherin receptors or F-actin at distinct clusters may be coordinated to modulate a particular junctions. cellular event during cadherin adhesion. The implication is that, Third, as a specific phenotype profile is found in each cluster in our model system, distinct nodes of a pathway that stabilize (for example, strong interference with cadherin levels), an junctions may also be found in separate phenotypic clusters. enrichment of actin remodelling functions that are relevant to that phenotypic profile is predicted. However, there was no significant enrichment of annotated functions using GO Novel regulators of E-cadherin adhesion. To test the above classification, in contrast to previous RNAi screens10,13–15. Using predictions, we selected candidate proteins for validation with manual curation of the literature, all published actin remodelling four individual oligos (see methods; Supplementary Fig. 3a) and functions of each candidate protein were attributed in 13 plotted the median Z-scores from three experiments (Fig. 5a). functional groups (that is, polymerization, bundling and Out of 49 proteins, 31 samples showed similar E-cad phenotypes so on.). For each cluster (Fig. 3e), functional enrichment is with at least two independent oligos (thereafter referred as shown in two ways: (i) as the percentage of all proteins allocated validated; Fig. 5b). Samples with ambiguous Z-scores (that is, to a specific functional group (grey bars; that is, all capping two positive and two negative) were not considered and require proteins) and (ii) how enriched a particular remodelling function further investigation. Overall, a number of new and known is among all proteins in a given cluster (yellow bars). Indeed, regulators of cadherin adhesion were validated. a striking concentration of specific functions was observed in From this subset of cadherin regulators, we selected proteins Cluster 2 (unconventional myosins), while most proteins that to: (i) dissect their specific role and (ii) test the prediction that

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abc 1

5 N=10,000 P<0.002 Cluster 1 (35) 2 15 Cluster 2 (34) 0

Cluster 3 (31) Cyt-A 3 10 Cluster 4 (18) Experimental −5 Cluster 5 (18) Density clusters 4 Cluster 6 (8) Number of clusters 5 Cluster 7 (5) −5 5 Jun-A 6 Cluster 8 (4) −6 0 0 −4 7 Cluster 9 (3) −2 8 0.1 0.2 0.3 0.4 0 2 9 E-cad 0 0.2 0.4 0.6 0.8 1 Score CK-max Silhouette value

d Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Hit map ABI2 ACTRT1 AFAP ACTL7B TWF1 ACTG2 TBCB CIT ARPC5 SPTBN5 Z-score ACTL6B CNN3 CNN2 CAPZA1 SVIL ACTR1B DCTN1 EPB41L1 CAPZB SYNE1 AZI1 EPB41L5 MAP4K3 CEACAM1 TGFB1I1 6 ADD2 FLNA NAV2 CEACAM6 TLR5 ARPC5L HIP1 MINK CGN TMOD2 ASPM FLNB TAGLN3 COBL TMOD3 CALD1 FLNC MYH1 CORO2A TMSB4X CAP2 FSCN3 MYH13 COTL1 TNNI1 CEACAM5 GARNL3 PARVA 4 CTNNA2 TNNT2 CORO1A GAS2 PLEC1 CYFIP1 TRIOBP PTK2B CTNNA3 TPM3 DIAPH2 IQGAP3 PXK DMD UNC13A EHBP1L1 KIAA1000 PXN ENAH VAV3 EEF1A1 KLHL2 RDX EPB41 EZR 2 ELA1 LRCH1 SEC23A EPRS VILL MAP4K1 MICAL3 SEC23B EPS8 VPS52 MYH3 MTSS1 SI NACSIN WAS MYO5B VAV2 SEC24B E-cad Jun-A Cyt-A E-cad Jun-A Cyt-A PARVB MYH7 SMTN PFN4 MYH7B SPRED1 0 PLS1 MYH8 SPTBN2 Cluster 6 Cluster 7 PTPN21 MYLIP SPTBN4 RAPSN MYO18B TAGLN2 LMOD1 ARPC2 SEC24A MYO1D TMOD1 MYH14 FRMD1 SEC24C MYO5C TNNI2 TLN2 GMFB –2 SMC1A MYO6 TNNI3 TMSB10 GSN TNNT3 SH3RF1 VCL TPM2 TRIM45 LIMD1 SPTBN1 VIL1 TRIP10 TPM4 SYNE2 WASF1 E-cad Jun-A Cyt-A USP6 TRIM3 TMSB4Y VTI1B E-cad Jun-A Cyt-A VASP TPM1 –4 WASF2 VPS39 E-cad Jun-A Cyt-A WIPF1 E-cad Jun-A Cyt-A E-cad Jun-A Cyt-A Cluster 8 Cluster 9 LMOD2 ACTN3 MYH4 ACTN4 –6 MYO5A AVIL TLN1 E-cad Jun-A Cyt-A E-cad Jun-A Cyt-A e Cluster 1 (35) Cluster 2 (34) Cluster 3 (31) % Proteins within 80 80 80 Functional group 60 60 60 Cluster

40 40 40

% Proteins Proteins % % Proteins Proteins % 20 20 Proteins % 20

0 0 0 3 4 1 2 5 6 7 8 9 1 2 4 5 7 9 3 6 8 1 5 6 2 3 4 7 8 9 10 11 12 13 11 13 12 10 12 11 13 10 Functional groups: Cluster 4 (18) Cluster 5 (18) Cluster 6 (8) 1 Unknown 80 80 80 2 Microtubules 60 60 60 3 Actin poly. 4 Sever. depoly. 40 40 40

5 Capping

% Proteins Proteins % % Proteins Proteins % % Proteins Proteins % 20 20 20 6 Crosslinking 7 Contractility 0 0 0 4 8 4 8 9 9 3 3 1 5 6 1 5 6 2 7 2 7 4 1 6 8 3 5 7 9 2 10 10 11 11 12 13 12 13 10 11 12 13 8 Unc. myosins Cluster 7 (5) Cluster 8 (4) Cluster 9 (3) 9 Bundling 80 80 80 10 Trafficking 11 Signalling 60 60 60 12 Cadherin adh.

40 40 40 13 ECM adh.

% Proteins Proteins % % Proteins Proteins % 20 20 Proteins % 20

0 0 0 1 4 5 6 9 2 3 7 8 4 6 5 1 7 8 9 2 3 1 2 4 6 5 7 8 9 3 12 10 11 13 10 12 11 13 12 13 10 11 Functional groups Functional groups Functional groups

Figure 3 | Phenotypic clustering. Using spectral clustering with Mahalanobis distance (Supplementary Figs 5 and 6), candidate proteins were grouped into nine separate clusters. (a) Distribution of clusters in three-dimensional space according to Z-scores for the parameters E-cad, Jun-A or Cyt-A. The number of proteins classified in each cluster is shown in parenthesis. (b) Silhouette plot shows a silhouette value ( 1to þ 1) for each point in the data set measuring how similar it is to points within its own cluster; positive values mean correct classification in its cluster. (c) Adequacy of experimental clusters based on their protein interaction landscape. Around 10,000 random clusters were plotted according to their Commuter time Kernel maximal score (CK-max) to produce the background curve of random clusters (green line). Red arrow points to where experimental clusters are found. (d) Heat map of the optimized clusters based on their corresponding Z-scores for E-cad, Jun-A and Cyt-A. Name of protein depleted is shown on the right of each panel. Data in a–d represent median Z-score values of three independent replicates for each RNAi oligo. (e) Functional enrichment. Following manual curation of the literature, each of the candidate proteins was attributed one or more functions on actin remodelling (functional group; x axis). Enrichment of the 13 functions in each cluster is shown in two ways: the distribution of actin functions relative to all functions found in the candidate protein data set (grey bars, that is, all proteins that cap, sever and so on.) or in the subset of functions found among proteins in the cluster (yellow bars). N ¼ 156.

6 NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 ARTICLE

a Max Min Colour key histogram Z-score heatmap E-cad: 10 Z-score Jun-A: phenotype Cyt-A: 0 Value 0204060 Cluster number: 2 3 5 1 4 6 31.25 25.00 6.25 12.50 12.50 6.25 CCrosslinkingrosslinking 29.03 19.35 6.45 19.35 9.68 3.23 Cadherin adhesion Adhesion and filament 25.00 12.50 12.50 16.67 12.50 8.33 Bundling organization 26.92 34.62 11.54 11.54 0.00 11.54 Contraction/stabiliz.Contraction/stabiliz. 26.09 34.78 8.70 8.70 13.04 4.35 ECM adhesion 33.33 24.24 6.06 27.27 6.06 0.00 Trafficking 18.18 25.00 15.91 31.82 2.27 2.27 Signalling Intracellular 26.32 10.53 0.00 42.11 10.53 5.26 Microtubules trafficking Colour key: 75.00 0.00 0.00 12.50 0.00 0.00 Unconv. myosins Cluster 1 0.00 25.00 31.25 6.25 25.00 6.25 Capping Cluster 2 10.34 10.34 20.69 27.59 13.79 6.90 Polymerisation Filament length Cluster 3 0.00 25.00 50.00 0.00 0.00 0.00 Severing/depolym. Cluster 4 Cluster 5 Cluster 6 Cluster 7 SPRED1

bcTSC22D1 TESK1 OSBP PRMT3 CDKN1A FAF1 EIF1B PPP1R7 OSGEP USP11PINX1 NARS PSMG1 NAGK TTC19 H2AFX HIST1H4A CTNNA1 MDC1 Cluster 2 Cluster 3 FAM118B PLOD2 EIF4B RCC1 HIRIP3 unconventional myosins trafficking SAMHD1 CUL2 NEDD8 DNAJB11 USP1 XPO7 VLDLR GFPT1 GARS TULP3 TROVE2 HSPH1 HSPA4 CUL1 CIAO1 MYO18B CDK2 MSH2 MTAP LDLR TNKS2 CALM3 ACTBL2 ATG5 FTSJ1 TTC1 ATP6V1B1 NEDD4L PPP1CA TAGLN2 ZYX CDC5L ATP6V1A PKP4RAP2A MAP1BACTA2 YWHAB DPYSL3 ILK ELAVL1 TRAF6 COPS5 TERF2 PARP2 KIT HSPA4L CAND1 CRK PXK PSMD6 SIRT7 DAB1 ACTR6 MAP1LC3B ACTB SUMO2 MYLIP LRP8 APP USP11 NEFM DUS3L CARS FN1 ATF2 IKBKE PELO UBASH3B CASKIN1 SIRT7 SEC16A YWHAZ TSG101 EHD2 TSC22D1 GALNT1 SNW1 KIAA0101 TMED10 POLR2A VHL DAB2 GIPC1 UBC FBXO25 SHC1 TUBA1BMAP1LC3AGABARAPL2 MDC1 USP25 MCC SORBS1 TMED3 CUL3GABARAPL1 TMED7 MYO1D PPP1CA LMTK2 MINK1 MAPK13 KDELR1 ECT2 HSP90AB1 PAXIP1 MIA3 GABARAP ACTB SP1 EPB41 UBC PSMC5 APC CNPY2 GRB2 POT1 SH3RF1 BIN1 MYL12B ELAVL1 PXN SEC23A VCAM1 TMED9 TMED2 AP2B1 HLA-B UBE2D4 RNF139 CUL1 ESR2 APC SEC23B PPP4C UBE2D1 ACTA1 UBE2D3 ABI1 ITSN2 FYCO1 AP2A1 SREBF2 MYC GRB2 CDK16 SYK UBE2D2 PIK3R1 CDK18 CAPZA1 ESR1 MAP4K3 SREBF1 CLTC UBE2N GRIA1 MYO5C DLG4 HLA-A LRRK2 UBB NDRG1 SEC24C PREB HLA-C MAP3K1 MAP3K3 MYO6 NCK1 MDM2 CAMK2A EPB41L1 SDCBP2 DCTN1 CALCOCO2 SPTBN2KIFAP3 CANX HLA-C GRIA3 IKBKE MYH15 MBTPS1 RNF11 CDC42 SEC24B SCAP B2M RIPK3 CAMK2B GRIA4 PLCG1 RIPK2 CHUK CALM2 SEC24A DLG1 SH3GL2 HLA-B IKBKG PAK1 CD74 AKAP5 LCP2 SEC24D SEC13 HLA-DRB1 HLA-B CACNG8 HLA-DRB5 SEC31A CAMK2G TNFRSF1A PAK3 CAMK2D HLA-C OPTN HLA-C TAX1BP1 CACNG3 PRKCQ HLA-DRB3 HLA-E TNFRSF1B found 3 SAR1B HLA-A CYLD CACNG4 HLA-C CACNG2 HLA-A HLA-B HLA-DPB1 HLA-B HLA-DRB1HLA-DRB1 HLA-DQA2 HLA-DQB1 HLA-GHLA-B HLA-DQB2 HLA-F HLA-DQA1 HLA-DRB1 HLA-DPA1 found 3

deClusters 1 and 5 Clusters 3 and 5 actin polymerization capping LMOD1 MAGED2 ARRB2 LRRK2 CAP1 ITGA1 SORBS1

ARPC2 MAP1LC3B CALD1 SORBS3 MYL9 CALM2 ACTR3 HSP90AA1 TNFSF11 SMAD3 ACTR2 ACTG2 TNNI3 ARPC4 ITGB5 MYH3 CDC5L MYLK MYH11 CDC42 CDK2 EEF1A1 ACTN2 MYL3 PLCG1 TNNC2 ARPC3 FYN TMOD1 CTTN ACTB NR4A1 MYL6B TPM1 TLN1 ACTA2 TNNT2 CDKN1A PXN ACTC1 MYBPC1 EGFR MYL6 TPM3 MYL2 ACTC1 YWHAG ELAVL1 CUL3 DES NCKIPSD MYL12B TNNI2 NCK1 HCK PPP4C ACTA1 TCAP RAC1 MYBPC2 GRB2 UBC ANXA7 MYL1 TNNT1 MYBPC3 HCLS1 KIAA0101 VCL

CRKL TPM1 UBC MYH6 TK1 TTN TNNT3 NEB BTK SUMO2 TRIP6 MYL4 SMN1 CTTN ESR1 WAS DMD TNNC1 ACTA1 ITSN1 APLP1 VIM SRGAP2 TMSB4X TGFB1I1 MYH8 TNNI1 FN1 CDK2 WIPF1 APBB1 VCL PSTPIP1 BRCA1 KIAA0101 ELAVL1 PRPF40A

PACSIN2 TCERG1 CLTC ACTN1 ITSN2 GAS7 SORBS3 YWHAE PACSIN3 YWHAG PACSIN1

WBP4 HSPA5 LPP

Figure 4 | Protein–protein interaction networks show connectivity within and among different phenotypic clusters. (a) Hierarchical clustering of actin functions and phenotypic clusters. The enrichment of actin functions in specific clusters (as in Fig. 3e) is shown as a heat map. Protein functions are shown on the right and cluster number and phenotype are shown on the top. Analysis shows similarities among clusters containing specific profiles of functions along the grouping of functions controlling filament organization (top group on the right), intracellular trafficking (middle group on the right) or filament length (bottom group on the right). (b–e) Protein–protein interaction networks of unconventional myosins enriched in Cluster 2 (b), intracellular trafficking proteins in Cluster 3 (c), actin polymerization proteins found in Clusters 1 and 5 networks (d) or capping proteins present in Clusters 3 and 5 interaction maps (e). Clusters are colour-coded as shown in the colour key diagram. Grey circles represent proteins found in available PPI datasets.

NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications 7 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542

a Cutoff Oligo 1 Oligo 2 Oligo 3 Oligo 4 4 2 0 –2 -score E-cad Z –4 –6 4 2 0 –2 -score Jun-A –4 Z –6 –8 6 4 2 -score Cyt-A

Z 0 –2 PXK GSN SVIL ABI1 HIP1 TLR5 FLNB FLNA VAV2 USP6 CAP2 ADD3 TPM1 TPM3 GAS2 CNN3 TWF1 MYH3 RDXN ASPM GMFB TNNI3 LIMD1 MYLIP PTK2B TNNT1 FSCN1 FSCN2 UNC13 FSCN3 LRCH2 SYNE2 PARVB TRIP10 LMOD1 FRMD1 LMOD2 RAPSN TMOD1 MYO5A MYO1D TRIM45 DIAPH2 EEF1A1 PTPN21 SPTBN1 TAGLN2 TMSB10 MAP4K4 SPRED1 CORO1A EPB41L1 EHBP1L1 CEACAM5

b Z-scores: Negative Within cutoff Positive Control LMOD1 TRIM45 MYO5A MYO1D GSN TMSB10 scr. # 1 # 2 # 1# 4# 2 # 4 # 2 # 4 # 1# 3 # 1 # 3 E-cad Jun-A Cyt-A

Control CNN3 DIAPH2 FLNB STPNB2 TNNT1 TPM3 scr. # 2 # 4 # 1# 2# 1# 2 # 1 # 3 # 1 # 2 # 2 # 3 E-cad Jun-A Cyt-A

Figure 5 | Validation of candidate proteins. The RNAi phenotype was re-tested in a selection of candidate proteins using four separate siRNA oligos for each protein. (a) Graphs showing the parameters E-Cad (top graph), Jun-A (middle graph) and Cyt-A (bottom graph). Median Z-score values were obtained from three independent replicates for each siRNA oligo. Proteins with two or more oligos with Z-scores outside cutoff were identified as validated proteins. (b) Representative processed images are shown of cells treated with controls (scrambled, scr.) or two selected siRNA oligos against proteins identified as validated proteins. Although only E-cad and Jun-A were used for selection, Cyt-A images are also shown for completeness. Coloured squares within images shows Z-scores below (blue), within (white) or above (pink) the cutoff. Scale bar, 50 mm. pathway finding is facilitated by the integration of phenotypic translation is not the main determinant of the observed and protein network analyses. We chose to follow-up phenotype. The predicted outcome of higher cadherin levels at EEF1A because of its unusual phenotype, a mild increase in the junctions as obtained with EEF1A siRNA is that cell–cell contacts levels of E-cadherin at junctions. EEF1A is an actin bundling are stronger. However, aggregates of cells depleted of EEF1A protein also known to regulate messenger RNA (mRNA) were more easily disrupted by mechanical stress (Fig. 6d; translation at specific intracellular compartments29. Its function Supplementary Fig. 7a), indicating that junctions were not as on regulation of cell–cell contacts is not known. Upon depletion stable as in control cells. In spite of more E-cadherin receptors at of EEF1A, a small but significant increase in the levels of junctions, similar levels of detergent insolubility were observed in E-cadherin at junctions was observed (Fig. 6a,b). EEF1A-depleted and control cells (Fig. 6e,f). Taken together, our Following EEF1A short interfering RNA (siRNA), no changes data strongly suggest that increased levels of E-cadherin at in total protein levels of E-cadherin and associated catenins contact sites do not necessarily translate into more stable (Fig. 6c) were seen, suggesting that dysregulation of their junctions.

8 NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 ARTICLE

To investigate this paradox closer, we evaluated the impact of green fluorescent protein (GFP)-actin at junctions recovered reduced EEF1A levels on junctional actin. Forcibly clustering fluorescence levels similar to controls (Fig. 6h,i), but fluorescence E-cadherin receptors on cells depleted of EEF1A revealed a small, recovery was significantly faster (Fig. 6j). Thus, following EEF1A but significant increase in F-actin recruitment around latex beads depletion, F-actin is more efficiently recruited to cadherin (Fig. 6g). We then tested whether F-actin immobilized at cadherin receptors and yet, it does not mediate stronger adhesive contacts. clusters have similar dynamic properties in EEF1A siRNA-treated Rather, the F-actin pool at junctions is more dynamic and and control cells. After photo-bleaching of EEF1A-depleted cells, mobile, with deleterious consequences for the stabilization of

a b E-cadherin c EEF1A Con d Disaggregate 2.0 Control EEF1A #1 *** Oligo: #1 #2 Scr 1.0 1.5 EEF1A 50 ** 1.0 E-cad 0.5 0.5 100

α-cat Relative size Junctional levels 0.0 100 0.0 siRNA: Con EEF1A Plako 70 Oligo: Scr #1 #2 siRNA: Con EEF1A e siRNA: Control EEF1A fg Control EEF1A #1 Oligo: Scrambled #1 #2 Insoluble E-cad 1.5

1.0 Total F-actin 100 0.5 80 * Junctional levels E-cadherin 0.0 60 Oligo: Scr #1 #2 40 % beads TX100 siRNA: Con EEF1A insoluble 20 0 siRNA: Con EEF1A

h i Recovery 2.0 –40 s 0 s 8 s 16 s 24 s 32 s 40 s 80 s120 s 160 s 1.5

1.0

0.5 Control Relative levels 0.0 siRNA: Control EEF1A

j Half-time 80 @ 60

EEF1A 40

t 1/2 (sec) 20

0 siRNA: Control EEF1A

k Lysates Pull-down l m ΔGBD Diaph2 ΔGBD ΔDAD mEBS Con EEF1A Con EEF1A 140 DIAPH2 Con EEF1A Con EEF1A

PD: Con EEF1A PD: VAV2 100 140 Flag 140 GFP FILB 260 140 140 Lys. Lys. GST- 100 70 70 70 EEF1A EEF1A EEF1A 50 50 50

40 40 40 35 35 35

GST 25 GST 25 GST 25

NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications 9 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542

E-cadherin at adhesive sites and weaker cell–cell adhesion stable cadherin adhesion during junction assembly, their (Fig. 6d). predicted binding implies that these two proteins could cooperate We next addressed whether signalling pathways that regulate functionally during junction assembly. GST-TRIP10 pulled down junctions could be inferred from network and phenotypic endogenous VAV2 from keratinocyte lysates, but not EEF1A similarity analyses. Available data sets of PPIs did not predict (Fig. 7e). In addition, endogenous TRIP10 and VAV2 were binding between EEF1A and other candidate proteins present in co-immunoprecipitated using antibodies against either protein different clusters (not shown). An EEF1A-binding sequence (EBS (Fig. 7f,g). VAV2 binding was mapped to the C-terminal domain domain) has been previously identified in Bni1p, the Rho1p of TRIP10 (Fig. 7h), which lacks the FCH (Fes and CIP4 effector30 in yeast. A similar domain is predicted in the mouse homology) domain; disrupted F-BAR domain) but contains the Diaph1 (aka mDia1 protein) and other DIAPH family HR1 (protein kinase C-related kinase homologous region 1) and members30, but binding has not yet been formally shown SH3 domain. The interaction between TRIP10 and VAV2 is of (Supplementary Fig. 7b). We found that EEF1A (cluster 1) functional significance, as during cell–cell contact assembly, co-precipitated with endogenous DIAPH2 (cluster 1), but not TRIP10 was progressively recruited to junctions and the VAV2 or FILB (cluster 2; Fig. 6k), thereby confirming our recruitment was dependent on VAV2 (Fig. 7i,j). previous results on intra-cluster protein networks (Fig. 4b). VAV2 associated with E-cadherin cytoplasmic tail in the Overexpressed wild-type mouse Diaph2 (that is, mDia3 and absence of cell–cell contacts and this interaction was maintained DIAPH2 orthologue) interacted with GST-EEF1A (Fig. 6l). during junction assembly (Fig. 7k). VAV2 was able to interact GST-EEF1A also bound to diaph3 (mDia2 and DIAPH3 with p120CTN strongly36, but also with a-catenin and b-catenin orthologue) in an activated status, lacking the GTPase to a lesser extent (Fig. 7l). E-cadherin tail unable to interact with binding domain (D GBD, Fig. 6m). Mutations on p120CTN (E-cad AAA) co-precipitated with VAV2, suggesting the EBS domain abolished this interaction (Fig. 6m). It that there are p120CTN-independent interaction of VAV2 with remains to be addressed whether EEF1A interaction is also cadherin complexes in epithelia (Fig. 7l, Supplementary Fig. 7e; important for the function of other DIAPH family members at ref. 37). Endogenous TRIP10 bound to p120CTN and b-catenin, junctions31. but not E-cadherin tail (Fig. 7l), indicating that TRIP10 did not Other validated proteins identified in the screen are TRIP10 stably associate with cadherin tail under the experimental and VAV2. TRIP10 is a F-Bar-containing protein that partici- conditions used. Thus, VAV2 and TRIP10 interact functionally pates in cadherin trafficking in Drosophila, Caenorhabditis and biochemically in the stabilization of E-cadherin complexes, elegans and mammalian tumour cell lines treated with growth potentially in cooperation with p120CTN. factors32–34. In contrast to EEF1A, reduced levels of TRIP10 (Cluster 1) led to a small, but significant decrease of E-cadherin at Discussion newly formed junctions (Fig. 7a). This occurred without overall Expanding the repertoire of cytoskeletal proteins that support changes in the levels of cadherin or catenins (Supplementary cadherin adhesion is clearly important16,18, and extensive Fig. 7c). Consistent with its role in DE-cadherin transport, progress has been made towards this goal4,6. Deciphering how TRIP10 appeared to modulate de novo cadherin recruitment to the functions of cytoskeletal proteins are coordinated in epithelial junctions (Fig. 7b). In control keratinocytes, there was a 20% cells is a major step forward to investigate dynamic processes increase in surface levels of E-cadherin upon induction of cell–cell such as cadherin receptor clustering and movement along the contacts. TRIP10 depletion led to significantly reduced lateral domain8, junction elongation/shrinkage, epithelial sheet E-cadherin surface levels after 30 min of calcium switch, but no folding and tubulogenesis4,5. Here we address the challenge to changes in total cadherin levels (Fig. 7b). Our data suggest that in infer pathways among actin-binding proteins likely to modulate the absence of TRIP10, there is no further recruitment of cell–cell adhesion. We identify: (i) cytoskeletal proteins as intracellular pools or stabilization of cadherin at junctions. new regulators of cadherin adhesion, (ii) subsets of proteins E-cadherin could be internalized by different mechanisms35, but functionally linked during junction assembly and (iii) meaningful fails to redistribute to cell surface and junctions. combinatorial profiles of cytoskeletal functions required for Depletion of the Rho GTPase GEF VAV2 also reduced stabilization of cell–cell contacts. Our data builds on from E-cadherin levels at junctions (Fig. 7c) without interfering with important previous studies on DE-cadherin adhesion and cadherin and catenin levels (Supplementary Fig. 7d). An inter- junctional protein networks16,18,38 to highlight core regulators cluster network between TRIP10 (cluster 1) and VAV2 (cluster 2) of cadherin cell–cell contacts. or VAV3 (cluster 5) was predicted in databases, but previously Using a semi-automated quantification of epithelia-specific unknown (Fig. 7d). As both TRIP10 and VAV2 are necessary for structures, candidate proteins were selected as potential junction

Figure 6 | EEF1A depletion increases E-cadherin levels at junctions without strengthening cell adhesion. Keratinocytes grown in low-calcium medium were depleted of EFF1A before junctions were either induced for 30 min (a–c,e,f) or aggregation assays (d) and actin recruitment (g) performed. (a) E-cadherin staining images were collected and shown as representative inverted images. (b) E-cadherin levels at junctions were quantified as thresholded levels (% area) and normalized to controls (non-targeting oligos). (c) Total protein levels of cadherin and catenins following RNAi treatment. (d) Cells were allowed to aggregate in suspension for 2 h, followed by mechanical trituration (see Supplementary Fig. 7a). Disaggregate sizes were measured and expressed relative to controls. (e,f) Before fixation, keratinocytes were pre-extracted with TX-100-containing buffer and the insoluble pool of E-cadherin at junctions was quantified (f). (g) F-actin recruitment to clustered E-cadherin receptors around latex beads was assessed by phalloidin staining. (h–j) Dynamics of junctional actin in mature junctions was assessed. Following bleaching of selected areas at junctions, fluorescence recovery was recorded (h). Quantification of final fluorescence recovery levels (i) and the half-time required to reach the plateau in each sample (j) are shown. (k) Pull downs using GST or GST-EEF1A were performed and co-precipitated endogenous DIAPH2, VAV2 or FILB detected. (l,m) Similar experiments as in k were performed following expression of flag-Diaph2 wild-type (mouse mDia3, l) or activated Diaph3 (mouse mDia2, DGBD) and mutated at the predicted EEF1A-binding site (DGBDDDADmEBS; m) Molecular weight markers are shown on the right of each panel. Blots and immunofluorescence images are representative of independent replicates. Fusion proteins are detected with Amido Black staining. Scale bars, 20 mm(a,e); 10 mm(g); 2 mm(h). Error bars represent standard error of the means of independent replicates (d (oligo1), f,g, N ¼ 3; b,d (oligo2) N ¼ 2; i,j, control N ¼ 8, EEF1A N ¼ 13). Statistical analyses were performed with two-way Anova (b,f) or Student t-test (d,g,i,j). @ P ¼ 0.01; *P ¼ 0.007; **P ¼ 0.0002; ***P ¼ 0.002.

10 NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 ARTICLE regulators and a subset (49 proteins) further validated with cell–cell adhesion. Among those, a novel regulator of E-cadherin individual siRNA oligos. The phenotype of the majority of the adhesion is identified as EEF1A, whose depletion enhances levels proteins depleted in the screen is a reduction in the levels of of E-cadherin receptors at junctions and F-actin recruitment. E-cadherin or F-actin at junctions. However, an unusual EEF1A is an actin binding and bundling protein that has a phenotype is also observed: an increase in the localization of number of other cellular roles, including its canonical function to E-cadherin at contact sites, which is predicted to strengthen interact with amino acid-loaded transfer RNAs and their deliver

abControl TRIP10 c Control TRIP10 Min 0 5 15 30 0 5 15 30 Control VAV2 E-cad 100

TRIP10 70 E-cadherin E-cadherin GAPDH E-cadherin E-cadherin 35 1.5 1.5 Con TRIP10 1.0 120 1.0 * 80 * ** 0.5 @ 0.5 40 Junctional levels Junctional levels 0.0 0.0 siRNA: Con TRIP10 siRNA: Con VAV2

Surface E-cad (%) 0 0102030 Time (min) d TINF2 POT1 TERF1 e f AKAP9 PD PD Lysate IP HTT Cluster 1 Cluster 2 PRKACA

Cluster 5 IgG VAV2 IgG VAV2 Lysate GST TRIP10 TRIP10 Lysate GST 100 UBC RHOA TRIP10 LYN 50 RHOB 140 RHOG 100 RHOC TRIP10 VAV2 WB: VAV2 EEF1A 100 SRC RAC1 RAC2 RHOT2 SOS1 ARHGEF12 RHOH GST- 100 VAV1 g VAV2 Lysate IP RHOBTB1 RHOF VAV3 TRIP10 70 TRIO RAC3 ARHGAP4 RHOD 50 ABR ECT2 T10 T10 IgG IgG CDC42 ARHGAP17 RHOT1 ITSN1 40 KALRN VAV2 100 RHOQ RHOV 35 RHOBTB2 100 RHOJ GST TRIP10 RHOU 70 Amido black

h TRIP10 i 0 min 15 min 30 min 60 min

Expr.: Empty 1–545 1–284 95–545 100 70

40 VAV2 IP Flag-TRIP10 25 VAV2 100 100 70

40 E-cadherin TRIP10 Lysates Flag-TRIP10 25 VAV2 100

j k l E-cad E-cad Pull down -cat -cat Control VAV2 -cat -cat GST E-cad tail GST WT AAA p120 α β GST WT AAA p120 α β Min: 0 0 5 15 30 100 PD 100 100 100

TRIP10 PD Lys. 100 TRIP10 Lys. WB: VAV2 TRIP10 1.5 100

1.0 100 E-cad 50 100 *** 0.5 50 50 GST 25

Amido black Junctional levels 0.0 Amido black siRNA: Con VAV2 25 25

NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications 11 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 to ribosomes29. Strikingly, with reduced EEF1A levels, higher Discrete interaction networks within each cluster may be concentration of junctional E-cadherin does not correlate with organized in different pathways with similar output (that is, level stronger adhesive function, as assessed by resistance to of cadherin disruption), but controlling distinct cellular processes mechanical stress and receptor detergent insolubility. This (that is, trafficking or actin bundling). Yet, the progressive unexpected finding challenges the notion that the strength of perturbation of E-cadherin adhesion found in different pheno- cell–cell adhesion correlates with the amount of receptors at typic clusters (that is, strong to mild perturbations) suggests an junctions. The reasons for this paradox merit further studies, but alternative distribution of interacting proteins and pathway it might involve: (i) interference of untethered E-cadherin with components that regulate junctions. Indeed, a high level of stabilized E-cadherin complexes8, (ii) inappropriate remodelling interconnectivity of known junction regulators and interacting of actin filaments at cell–cell adhesive sites or (iii) insufficient proteins is observed among different clusters: TRIP10 and WAS incorporation into clusters at junctions. Indeed, junctional actin (Clusters 1 and 5, respectively); or VCL (Cluster 3), TLN1 in EEF1A-depleted cells shows altered dynamics, with faster (Cluster 8) and VASP (Cluster 6). These results are compelling to recovery following photo-bleaching compared with control suggest that distinct nodes of a signalling pathway that regulate keratinocytes. cell–cell contacts may be found in separate phenotypic clusters. Strong correlations between the actin remodelling function of a To validate the existence of pathways across different clusters, particular protein and its impact on cadherin adhesion are a novel functional partnership is identified that stabilizes identified here. Robust junctional defects are observed by cadherin contacts: TRIP10 and VAV2. Both proteins participate depletion of proteins that cap, sever or depolymerize F-actin. In in junction disruption by distinct stimuli32, but their functions addition to the known significance of actin polymerization4,6, our in cell–cell contact assembly/homeostasis have not been data highlight the importance of controlling filament length to previously shown. TRIP10 participates in EGF-dependent stabilize E-cadherin contacts and the unappreciated role of other E-cadherin internalization32 and Cip4, its Drosophila actin functions39. In contrast to the strong defects observed by orthologue, modulates DE-cadherin endocytosis34. In contrast, perturbing filament length, stability and contraction, depletion of junction assembly requires mostly transport of intracellular pools intracellular trafficking proteins leads to milder perturbation of of E-cadherin to the surface, recyclling and their stabilization at contacts. The latter is consistent with the diversity of cellular junctions: TRIP10 appears to participate in these processes. events that control E-cadherin turnover and delivery to VAV2 is an activator (exchange factor) of Rho GTPases42 and the junctions35. latter have important roles in different steps of junction assembly, From the identification of unknown cadherin regulators, the maturation and disruption. The partnership of VAV2 and challenge is to identify how regulators are integrated in TRIP10 identified here is functionally and clinically relevant, discrete pathways. We surmise that the phenotypic clustering of as both have important functions in epithelial differentiation our data set facilitates the enrichment of distinct pathways and morphogenesis43–46, and participate in oncogenic pathways that share the same extent of disruption of E-cadherin or leading to cell invasion32,47–50. junctional actin. The novelty of our approach is to couple In spite of belonging to different phenotypic clusters, TRIP10 functional properties with standard phenotypic clustering (Cluster 1) and VAV2 (Cluster 2) interact functionally: (i) they analysis to guide and validate pathway enrichment. Pathway are found as a new protein complex; (ii) depletion of either identification has been previously achieved using double-knock- protein reduces junctional E-cadherin and (iii) VAV2 is necessary down screens or by predictions that pathways segregate within a for TRIP10 localization at cell–cell contacts. These results indicate single phenotypic cluster to regulate cell morphology or GTPase that VAV2 appears to be upstream of TRIP10 in junction regulation19,28,40. Consistent with the above predictions, we find a regulation. VAV2 has been shown to interact specifically with the statistically significant concentration of interacting partners cytoplasmic pools of p120CTN and b-catenin36,51,52. In contrast, within all phenotypic clusters as compared with the overall in keratinocytes VAV2 interacts with E-cadherin tail at steady protein interaction landscape. An example is a novel interaction state and during junction assembly. There is a large pool of VAV2 between EEF1A and DIAPH2 (both found in cluster1). DIAPH2 associated with p120CTN and a smaller pool that interacts with (aka mDia3) is a formin family member31, whose function is not E-cadherin complexes (even in the absence of p120CTN binding), well characterized. A binding site for EEF1A is found in DIAPH2, a-catenin and b-catenin. Mechanistically, VAV2 stabilization of consistent with the EBS domain found in the yeast homologue newly formed junctions may require a transient positioning of Bni1p30,41. Our finding that endogenous DIAPH2 interacts with TRIP10 at cell–cell contacts to mediate E-cadherin trafficking EEF1A directly or indirectly may suggest a scaffolding or and/or to remodel actin filaments. Indeed, VAV2 associates with regulatory role of signalling by DIAPH proteins in junction TRIP10 C terminus, which includes the binding region of the regulation. small GTPase Cdc42 (refs 53,54). Thus, the VAV2-dependent

Figure 7 | TRIP10 and VAV2 forms a functional partnership to regulate junctions. (a) Keratinocytes were depleted of TRIP10, junctions were induced for 30 min and samples were stained for E-cadherin. Graph shows the quantification of thresholded levels of E-cadherin at junctions (% area) normalized to controls. (b) E-cadherin surface levels were measured in control and TRIP10 siRNA cells during induction of cell–cell contacts. Western blots show total TRIP10, E-cadherin and GAPDH protein levels. (c) Cells with reduced levels of VAV2 were processed as described in a.(d) Predicted binding between TRIP10 and VAV proteins from protein–protein interaction databases. Colour code of cluster numbers is shown. (e) GST or GST-TRIP10 was incubated with keratinocyte lysates and co-precipitated proteins were probed for endogenous VAV2 or EEF1A. (f,g) Lysates were immunoprecipitated with anti-VAV2 (f) or anti-TRIP10 (g) antibodies and probed to detect endogenous TRIP10 or VAV2. (h) Cells were transfected with flag-TRIP10 wild-type and truncation mutants. Endogenous VAV2 was precipitated and flag-TRIP10 detected. (i) Keratinocytes were double-labelled with anti-E-cadherin and anti-TRIP10 during junction assembly. Arrows show TRIP10 localization at junctions. (j) Cells with reduced levels of VAV2 were induced to form junctions for 30 min and stained with anti-TRIP10 antibody. Graph shows quantification of TRIP10 levels at junctions (% area) after VAV2 RNAi. (k) GST-E-cadherin cytoplasmic tail was used to pull down endogenous VAV2 during a time course of junction induction. (l) Pull downs with GST-catenins, E-cadherin cytoplasmic tail wild-type (WT) or mutated to prevent p120 binding (AAA) were performed and probed to detect endogenous VAV2 or TRIP10. Amido black staining (e,k,l) denotes GST-fusion proteins. Images and blots are representative of at least three independent replicates. Statistics were two-way Anova (a,c,j) or Student t-test (b). *P ¼ 0.02; **P ¼ 0.0004; ***P ¼ 0.00002; @ P ¼ 0.004. Error bars represent s.e.m. Scale bars, 20 mm.

12 NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 ARTICLE localization of TRIP10 at new junctions may coordinate TRIP10 10 min and stained as described56. Non-screen images were acquired on a Zeiss binding to GTPases activated locally. The novel interplay between inverted 510 LSM laser-scanning confocal (Carl Zeiss) using a 63 /1.4 Plan Apochromat objective or with an Olympus Provis AX70 microscope coupled to a GTPase signalling and TRIP10 (via the GEF VAV2) or EEF1A SPOT RT monochrome camera using Simple PCI software (Hamamatsu, Japan). (via the GTPase effector DIAPH2) highlight the power of our Aggregation assays were imaged using a phase contrast Olympus CKX41 analyses to infer pathways. Future studies will define further microscope and a Colour View IIIu camera linked to Sort Imaging System mechanisms of how TRIP10 and EEF1A functions are software. Fluorescence recovery was performed on an inverted confocal microscope (LSM-510; Carl Zeiss) using ZEN software. coordinated with GTPase-dependent pathways to modulate cadherin adhesion. Our data demonstrate that coordination of both known and Image processing. In-house segmentation tools were developed (Supplementary unknown junction regulators can be derived by functional Fig. 1) to generate three experimental parameters representative of E-cadherin annotation, interaction profiling and phenotypic similarity (E-cad), junctional actin (that is, co-localizing with E-cadherin and named Jun-A) or cytoplasmic actin (that is, excluding junction and nucleus area; Cyt-A). Analysis analysis. The framework identified here provides an exciting was designed using MBF-ImageJ (http://imagej.nih.gov/ij/) and Metamorph 6.2 platform to identify new molecules that modulate junctions and (Molecular Devices). E-cadherin image was thresholded to minimize contribution to integrate small GTPase signalling with trafficking and actin of cytoplasmic staining (E-cad parameter); positive value for the E-cadherin filament remodelling. Our study presents a powerful strategy parameter may reflect increased cadherin levels at junctions and/or cytoplasm. Thresholded E-cadherin image was used as a mask to segment junctional actin pool to dissect hierarchical relationships that control cadherin (Jun-A) from the corresponding image stained with phalloidin (total F-actin). stabilization with significant implication for research in epithelial Cytoplasmic actin (Cyt-A) was obtained by subtraction of E-cadherin mask and morphogenesis, homeostasis and diseases. nucleus image from the total F-actin image. As a confluent monolayer was used in all experiments, no significant change in cell shape in two-dimensional space is observed (that is, see ref. 62), that would affect E-cadherin mask and thus E-cad Methods and Jun-A parameters. These parameters assess effectiveness of junction assembly Cell culture. Normal human keratinocytes from neonatal foreskin (strain Sf, by an intensity-based approach; their caveat is the inability to identify changes in passages 3 to 5) were cultured on a mitomycin C (Sigma)-treated monolayer of 3T3 the shape, length or continuity of E-cadherin staining. 55 fibroblasts at 37 °C and 5% CO2 in standard medium as described . Unless To validate the new segmentation tool and ensure the dynamic range of the otherwise stated, keratinocytes were seeded in standard calcium medium measurements, images were obtained with different levels of disruption of the actin (containing 1.8 mM CaCl2), transferred to low calcium medium (0.1 mM CaCl2 cytoskeleton or E-cadherin at junctions and different readouts assessed. Algorithms and fetal calf serum depleted from divalent cations) and grown until confluence56. were validated to demonstrate no correlation with cell number. Optimized For siRNA screen keratinocytes were seeded at 2.2 103 per well on 96-well plates algorithms were incorporated into a custom-made automated program to analyse as above and grown in low calcium medium and, when a confluent monolayer is images obtained in the screen. formed, treated with different siRNA oligonucleotides. Induction of cell–cell To quantify the amount of specific proteins at junctions, E-cadherin images contacts (calcium switch) was done for 30 min by addition of 1.8 mM CaCl2 or by were individually thresholded to minimize contribution of cytoplasmic staining transferring cells to standard calcium medium (RNAi screen). and maximize junction coverage. The thresholded binary E-cadherin image was Treatment with different drugs was performed in confluent cultures in low dilated and used as a mask to segment the junctional TRIP10 pool from the calcium medium by pre-incubation for 30 min with PMA (4b,9a,12b,13a,20- corresponding TRIP10 image. A global threshold across all experimental pentahydroxytiglia-1,6-dien-3-one 12-tetradecanoate 13-acetate, Sigma), 5 mM conditions was applied to the segmented TRIP10 images to measure the TRIP10 Y27632 (Sigma) or for 60 min with 50 mM blebbistatin (Merck). Junctions were junction coverage and calculate % thresholded area. Graphs were made using Excel induced for 30 min in the presence of the drugs. or GraphPrism and figures were assembled using Illustrator and Photoshop.

Transfections and constructs used. Mammalian expression vectors were RNAi screen The custom-made Actinome library targeting 327 known or transfected using TransIT (Mirus Bio LCC), JetPRIME (Polyplus transfection) or . predicted actin-binding proteins10 (Thermo Scientific Dharmacon) was made up of Viromer reagent (Lipocalyx). constructs were expressed overnight. SMARTpools (pool of four different siRNA oligos targeting each mRNA) and an Constructs used were: activated Arf6 (pCS2-HA-Arf6Q67L) (ref. 57), accompanying validation library (four single oligos per mRNA). The primary pCDNA3.1.GFP-b-actin (gift from M. Bailly, University College London), screen was carried out as three independent replicates: keratinocytes were pGEX2T-TRIP10, pRK5-Flag-TRIP10 1–545, pRK5-Flag-TRIP10 1–284, transfected with 100 nM of SMARTpool siRNA using RNAifect (Qiagen). As pRK5-Flag-TRIP10 95–545 (ref. 58) (all gifts from P.Aspenstrom, Karolinska controls, a pool of scrambled oligos (siCONTROL siRNA, a RISC-free siRNA) was Institute) and pGEX 6P-E-cadherin tail (mouse)59. For Dia constructs used. siTOX and INCENP (Dharmacon) were used as positive markers for the current nomenclature (http://www.ncbi.nlm.nih.gov/gene) was used transfection, and each plate was visually checked for cell death (siTOX) or increase throughout. Full length mouse Diaph2 (pCMV-3Xflag-1A-mDia3) is based on a in cell size (INCENP) before analysis. In addition to these transfection controls on synthetic construct (accession number BC160217). Mouse Diaph3 (mDia2, each plate, there were multiple wells as negative controls (cells untransfected and accession number AF094519) was truncated at the N terminus (aa 1–254) to not induced to assemble junctions) and positive controls (cells untransfected with partially delete the GTPase-binding domain (GBD; pEF -EGFP-mDia2 DGBD), m junctions induced). After 72 h incubation, cell–cell contacts were induced with and at the C terminus (from aa 1041) to delete the DRF auto-inhibitory fresh standard calcium medium (calcium switch) for 30 min before fixation. Our domain (DAD; pEF -EGFP-mDia2 DGBDDDAD)60. In addition, m previous studies show that junctions assembled for 30 min in keratinocytes are Diaph3 was mutated on the predicted EBS to introduce glycines (Y713G, sufficient to form polarized cuboidal cells and reorganize the actin cytoskeleton as E714G, K715G and R717G) and generate DGBDDDADmEBS that is unable to observed in other simple epithelial cells62,63. Plates were prepared for interact with EEF1A. immunofluorescence using the standard protocol (above) and mounted in 50 ml Mouse EEF1A was subcloned into pGEX2T between BamHI and EcoRI Mowiol and 1% 1,4-diazabicyclo[2.2.2]octane. restriction sites using PCR. Point mutations to E-cadherin cytoplasmic tail were Screen images were acquired on a GE Healthcare InCell Analyser 3000 line made to obtain triple alanine mutations starting at amino acid 764 (E-cad AAA) to scanning confocal system using a 40 /0.8 objective and converted to 16 bit TIFF prevent binding to p120CTN (ref. 37). GST-fusion proteins were produced using format using InCell exporter and images were renamed for documentation and standard protocols and stored in aliquots at 80 °C. batch processing using Irfanview (http://www.irfanview.com). Quality control was carried out visually to eliminate any image in which cells were not 90% confluent Immunofluorescence and microscopy. Antibodies used for fluorescence and or in which there were problems with acquisition (that is, bright/dead cells that western blots were against E-cadherin (HECD-1; 1:1,000–1:3,000), tubulin (Tub2.1, would skew the quantification). Sigma; 1:1,000), VAV2 (EP1067Y, Abcam; 1:2,000–1:20,000), TRIP10 (F-10, St Cruz; 1:200–1:10,000). Catenin p120CTN (98/pp120, BD Biosciences; 1:500), a- catenin (1:1,000) and plakoglobin (1:1,000; ref. 61), EEF1A (D10A5, Cell Signalling, RNAi screen analysis. The semi-automated system used selected controls, 1:100–1:200), DIAPH2 (NBP1-85217 Novus Biologicals and ab102841, Abcam, segments the different experimental parameters from the raw images. The data set 1:250), FILB (A301-726A, Bethyl Laboratories, 1:10,000) and GAPDH (6C5, obtained was tested for Gaussian and log-Gaussian distribution (see below). Abcam; 1:100,000). Secondary antibodies were bought from Jackson Z-scores in each experiment were calculated and normalized to controls in each ImmunoResearch. F-actin was labelled using AlexaFluor 488 phalloidin plate24 due to the high likelihood of obtaining phenotypes with a targeted library (Invitrogen; 1:1,000–1:3,000) or Phalloidin–Atto 565 (Sigma; 1:1,000). DNA was and the non-parametric distribution of the dataset. Final Z-score was computed as labelled using DAPI (Sigma, 1:3,000). Uncropped western blots can be found in the median of the three independent experiments performed during the primary or Supplementary Figs 8 and 9. validation screens. A cutoff was decided based on standard deviation ( þ / 1.65 Unless otherwise stated, keratinocytes were fixed in 3% paraformaldehyde for STD) to select samples with E-cad, Jun-A or Cyt-A Z-scores above or below the 10 min. Cells were permeabilized with 0.1% Triton and blocked with 10% FCS for cutoff as candidate proteins. A total of 156 candidate proteins were selected for

NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications 13 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542 further validation using four independent siRNA oligos, and from this subset, similarity above random must arise from more functionally similar genes being 49 proteins were further investigated. placed in the same cluster as compared with ‘random’. The main strength of this Validation screen was performed in triplicate and processed essentially as approach is that the analyses only included genes from the screen and with described for the primary screen. However, the same cutoff could not be used in functional annotation in the Kernels. Thus, functional similarity above random will the validation screen because the amplitude of the responses (relative to positive not be due to inappropriate choice of a background set of genes. and negative control values) was lower. This may reflect the biological variability of Briefly, around 10,000 randomized clusters were formed by partitioning the primary cells and the fact that single oligos were used rather than a pool of four candidate proteins found in the RNAi screen into clusters of the same size but with oligos in the primary screen (predicted to deplete proteins more efficiently). random, distinct composition of proteins. This was done in order to preserve the Samples with two or more Z-scores outside cutoff in the same direction (either number and size of the clusters to be the same as the optimized experimental positive or negative) were considered validated. A few samples with ambiguous clusters obtained above. The network of PPIs of the candidate proteins was 27,65,66 status (two positive Z-scores and two negative Z-scores) were selected for converted into a Kernel using a Commuter time Kernel (CK) to measure the additional experiments to confirm their role. We were interested in defining homogeneity of the clusters. For a given randomized cluster, the Commuter time junction regulators with a milder rather than very strong phenotype (that is, very (CK) score was calculated as following: for each protein in each cluster, the highest high or very low Z-scores), as this may suggest a non-specific disruption of the kernel similarity score to any other protein in the same cluster is obtained. CK actin cytoskeleton by depletion of a core actin regulator. Milder phenotypes are a scores for all proteins in the cluster and for all clusters were summed to get the bone fide representation of the additive and cooperative actions of proteins towards global score (CK-max score). The density estimate of the CK-max scores was then junction stabilization. Further characterization of hits certified that levels of plotted using the density function in R for all the randomly generated clusters. proteins found in cadherin complexes and Rho GTPases were not altered upon depletion of proteins analysed in more detail. Statistical analysis. Graphs were created using Statistica 9, Microsoft Excell or MATLAB (The MathWorks). Statistical analysis was performed using SPSS Functional analysis. For the sake of consistency, the notation of gene names is Statistica (Wolfram Research Institute), PRISM or Excel. Inclusion or exclusion of kept throughout the text and figures. The three approaches were taken to evaluate data points for the different correlations is described in the respective figure functional enrichment of specific functions in the 156 candidate proteins. First, legends. Test of normality used was Shapiro-Wilk W, in which the closer the value GO terms were obtained and the frequency of a specific attribute plotted. However, is to 1, less likely a normal distribution is (Supplementary Fig. 3). Log-Gaussian there was no significant enrichment of annotated functions using GO classification distribution was also tested, as this distribution is frequently found in biological (data not shown). Second, specific actin remodelling functions were manually systems: raw data values were log-transformed and corrected for skewness before curated from databases and literature so that each candidate protein was ascribed applying the Shapiro-Wilk W test. The values obtained for the latter analyses one or more actin functions that they regulate. All reported activities for a given (Wo0.967 and Po3.4 10 11) indicate that the data do not follow a log- protein were computed in the 13 functional classes by manual curation of the Gaussian distribution. Correlation analysis was done using Pearson correlation literature. Graphs were prepared to show the distribution of 13 distinct actin tests, followed by two-sided F-test to define significance (P value). For quantifi- functions within a cluster in two ways: as a percentage of functions allocated to the cation of VAV2 and TRIP10 phenotypes, two-way Anova was done to test for cluster or as a percentage of all proteins with a particular function. Enrichment was significant changes between experiments and experimental conditions, followed by considered when representation of a class of actin function in a cluster was Z25% MatLab function ‘anovan’ to obtain P values. The samples meet the assumptions of of total number of proteins classified in class. the test (normal distribution, equal variance, independence) and variance is not Finally, publicly available PPI data for the candidate proteins data set were significantly different between different groups. downloaded from IntAct, MINT, BIOGRID, DIP, HPRD and Reactome (level 1 and 2 interactions; total of 68,549 interactions). Each protein was assigned to an Ensembl62 gene using Ensembl62’s external references from Biomart. PPI maps Data availability. The authors declare that all data supporting the findings of using the data sets obtained were produced using Cytoscape. Global interaction this study are available within the article and its Supplementary Information network was obtained for all candidate proteins and within each cluster. To files or from the corresponding author upon reasonable request. Access to the facilitate pathway finding, we focused on addressing sub-networks that may software for large-scale image analysis developed here can be obtained in the link regulate a particular actin remodelling property enriched in specific clusters. http://www.imperial.ac.uk/bioinformatics-data-science-group/resources/software/ rnaiscreener/ Phenotypic clustering. As the data set do not follow a Gaussian distribution (see Statistic section), we optimized the methodology to classify the candidate proteins into distinct groups with functional relevance for the regulation of E-cadherin and References F-actin at junctions. Each of 156 data points in the candidate protein data set is a 1. Dickinson, D. J., Nelson, W. J. & Weis, W. I. An epithelial tissue in three-dimensional vector that contains the Z-scores for E-cad, Jun-A and Cyt-A. Dictyostelium challenges the traditional origin of metazoan multicellularity. Using MATLAB (The MathWorks), we evaluated three different methods26,64 BioEssays 34, 833–840 (2012). (K-means, hierarchical or spectral clustering) and two different distances 2. Oda, H. & Takeichi, M. Evolution: structural and functional diversity of (Euclidean and Mahalonobis). The Mahalanobis distance26 between any two data cadherin at the adherens junction. J. Cell Biol. 193, 1137–1146 (2011). points X and Y is computed here by dM(X,Y), which is transformed into a similarity 3. Green, K. J., Getsios, S., Troyanovsky, S. & Godsel, L. M. Intercellular junction 2 2 measure via s(X, Y) ¼ exp( (dM(X, Y)) /(2s )), where we set s ¼ 1. For spectral assembly, dynamics, and homeostasis. Cold Spring Harb. Perspect. Biol. 2, clustering, we used the mutual k-nearest neighbour graph with 30 neighbours and a000125 (2010). computed the normalized graph Laplacian. For optimization of the parameters, 4. Niessen, C. M., Leckband, D. & Yap, A. S. Tissue organization by cadherin we used a modified version of MatLab GUI (Graph Demo), which can be assessed adhesion molecules: dynamic molecular and cellular mechanisms of in http://www.ml.uni-saarland.de/code/GraphDemo/GraphDemo.htm. morphogenetic regulation. Physiol. Rev. 91, 691–731 (2011). We tested permutations of the best method (Hierarchical or Spectral), distance 5. Papusheva, E. & Heisenberg, C. P. Spatial organization of adhesion: (Euclidian or Mahalanobis) and number of clusters using the silhouette plot force-dependent regulation and function in tissue morphogenesis. EMBO J. 29, 25 method , which provides a graphical representation to assess clustering validity. 2753–2768 (2010). Each data point is given a silhouette value that ranges from 1to þ 1. A positive 6. Ratheesh, A. & Yap, A. S. A bigger picture: classical cadherins and the dynamic value provides evidence that a point is correctly classified in its cluster, while a actin cytoskeleton. Nat. Rev. Mol. Cell Biol. 13, 673–679 (2012). negative value indicates that the point might be better assigned to a different 7. Huveneers, S. & de Rooij, J. Mechanosensitive systems at the cadherin-F-actin cluster. Initially, k ¼ 2, ...., 12 clusters were evaluated selecting the plot with least interface. J. Cell Sci. 126, 403–413 (2013). misclassifications. 8. Hong, S., Troyanovsky, R. B. & Troyanovsky, S. M. Binding to F-actin guides To further refine the best clustering output, we assumed that biological cadherin cluster assembly, stability, and movement. J. Cell Biol. 201, 131–143 significance would be translated as enrichment of phenotypes and protein functions in specific clusters (Supplementary Fig. 6). Spectral clusters obtained (2013). using Euclidian or Mahalanobis distances were re-tested the pattern of segregation 9. Mohr, S., Bakal, C. & Perrimon, N. Genomic screening with RNAi: results and of the different parameters (E-cad, Jun-A or Cyt-A) and the enrichment of specific challenges. 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Wallar for mDia2 mutagenesis; M.A. Miranda for statistical advice J. 41. Bodman, J. A., Yang, Y., Logan, M. R. & Eitzen, G. Yeast translation elongation Baum and M. Zerial, Y. Kalaidzidis, B. Baum and G. Scita for helpful discussions. factor-1A binds vacuole-localized Rho1p to facilitate membrane integrity We are grateful to members of the lab, J. McCormack, N. Welsh for comments on through F-actin remodeling. J. Biol. Chem. 290, 4705–4716 (2015). the manuscript. 42. Cook, D. R., Rossman, K. L. & Der, C. J. Rho guanine nucleotide exchange factors: regulators of Rho GTPase activity in development and disease. Oncogene 33, 4021–4035 (2014). Author contributions 43. Duan, L. et al. Distinct roles for Rho versus Rac/Cdc42 GTPases downstream of L.P. and N.M. performed the RNAi screen quantification, phenotypic and functional Vav2 in regulating mammary epithelial acinar architecture. J. Biol. Chem. 285, analysis and helped with writing; T.P. analysed the validation screen; J.C.E. and 1555–1568 (2010). S.B. designed and performed phenotypic and biochemical, hit validation and helped

NATURE COMMUNICATIONS | 7:13542 | DOI: 10.1038/ncomms13542 | www.nature.com/naturecommunications 15 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13542

with writing; C.T. designed the semi-automated quantification system; J.L. helped with How to cite this article: Erasmus, J. C. et al. Defining functional interactions during analyses of PPIs; I.Z. performed cloning and mutagenesis; A.W. designed and optimized biogenesis of epithelial junctions. Nat. Commun. 7, 13542 doi: 10.1038/ncomms13542 quantification algorithms and performed the primary RNAi screen; A.R. designed and (2016). analysed phenotypic and functional interactions; V.M.M.B. designed experiments, ana- lysed data and wrote the manuscript. Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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J.C. Erasmus, S. Bruche, L. Pizarro, N. Maimari, T. Poggioli, C. Tomlinson, J. Lees, I. Zalivina, A. Wheeler, A. Alberts, A. Russo & V.M.M. Braga

Nature Communications 7:13542 doi: 10.1038/ncomms13542 (2016); Published 6 Dec 2016; Updated 5 Jan 2017

The original version of this Article contained an error in the spelling of the author Tommaso Poggioli, which was incorrectly given as Tommaso Pogglioli. This has now been corrected in both the PDF and HTML versions of the Article.

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