Mapping the Spatial Proteome of Metastatic Cells in Colorectal Cancer

Mapping the spatial proteome of metastatic cells in colorectal cancer

Marta Mendes‡¶, Alberto Peláez-García‡¥, María López-Lucendo‡, Ruben A. Bartolomé‡, Eva Calviño‡, Rodrigo Barderas‡§**, J. Ignacio Casal‡**

‡Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas (CIB-CSIC), Madrid, Spain §Instituto de Salud Carlos III. Majadahonda. Spain ¶Present address: Technische Universität Berlin, Germany ¥Present address: Department of Pathology, Hospital Universitario La Paz, IdiPAZ, Madrid, Spain

Running title: Mapping the metastatic proteome

Keywords: Spatial proteome, cell fractionation, SILAC, metastasis, colorectal cancer

** To whom correspondence should be addressed: Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas (CIB-CSIC), Ramiro de Maeztu, 9, 28040 Madrid, Spain. Tel.: +34 918373112; E-mail: [email protected]. Instituto de Salud Carlos III, Majadahonda, Spain Carretera de Majadahonda - Pozuelo, Km. 2.200, 28220 Majadahonda, Madrid, Spain. Tel.: +34 913944155; E-mail: [email protected]

ABSTRACT Colorectal cancer (CRC) is the second deadliest cancer worldwide. Here, we aimed to study metastasis mechanisms using spatial proteomics in the KM12 cell model. Cells were SILAC-labeled and fractionated into five subcellular fractions corresponding to: cytoplasm, plasma, mitochondria and ER/golgi membranes, nuclear, chromatin-bound and cytoskeletal proteins and analyzed with high resolution mass spectrometry. We provide localization data of 4863 quantified proteins in the different subcellular fractions. A total of 1318 proteins with at least 1.5-fold change were deregulated in highly metastatic KM12SM cells respect to KM12C cells. The protein network organization, protein complexes and functional pathways associated to CRC metastasis was revealed with spatial resolution. Although 92% of the differentially expressed proteins showed the same deregulation in all subcellular compartments, a subset of 117 proteins (8%) showed opposite changes in different subcellular localizations. The chaperonin CCT, the Eif2 and Eif3 initiation of translation and the oxidative phosphorylation complexes together with an important number of guanine nucleotide- binding proteins, were deregulated in abundance and localization within the metastatic cells. Particularly relevant was the relationship of deregulated protein complexes with exosome secretion. The knowledge of the spatial proteome alterations at subcellular level contributes to clarify the molecular mechanisms underlying colorectal cancer metastasis and to identify potential targets of therapeutic intervention.

Significance of the study

Metastasis is responsible for 90% of the cancer-associated deaths. To understand underlying cellular mechanisms that favor metastasis is essential to discover new biomarkers and therapeutic strategies. Here, we have performed a multidimensional quantitative proteomic analysis to identify not only those differences in protein abundance between metastatic and non-metastatic cells, but also differences in subcellular location. An exhaustive analysis of the quantified proteome with different bioinformatics tools enabled the characterization of large macromolecular complexes and pathways alterations in metastatic cells. In addition, we have identified a number of protein complexes that showed disparities in the expression levels between different locations in metastatic versus non metastatic cells. These alterations play a significant role in cellular processes such as adhesion, migration and vesicle trafficking. We hypothesized that vesicle trafficking alterations must be related with the increase in exosome secretion characteristic of the metastatic cells. Finally, some potential targets of therapeutic intervention have been identified.

1. INTRODUCTION

Current proteomic strategies use a monodimensional bottom-up workflow where quantitative data are averaged between different cellular pools. Despite its relevance, the single dimension analysis of the proteome misses very relevant functional information. Protein properties, including abundance levels, protein-protein interactions, post-translational modifications (PTMs), subcellular localization patterns and protein synthesis and degradation rates, are all highly dynamic and can change rapidly between different compartments during the course of different biological processes, such as cell proliferation, cell migration, differentiation and metastasis [1]. Spatial proteomics, which studies organelle proteomics and the dynamics of subcellular distribution of proteins in response to a variety of factors, is a rapidly growing field [2-5]. Changes in the spatiotemporal subcellular distribution of proteins have been detected in many cell types in response to environmental stress, cell-cycle signals, growth factors, or hormone exposure [1, 6-9]. Indeed, changes in subcellular localization may affect their cellular functions of proteins and their capacity to interact with other proteins. Protein alterations in localization are critical in disease [10], as proteins at different subcellular localizations possess different functional roles [2, 11, 12]. It is well established that loss- or gain-of-function of proteins in many diseases could be attributed to unexpected protein subcellular localizations [13, 14].

Despite recent advances in diagnosis and therapy, colorectal cancer (CRC) remains as the second deadliest cancer in developed countries [15]. The identification of metastasis-associated proteins and altered pathways underlying CRC metastasis would contribute to discover new targets and to improve patient survival. Metastasis is a multistage process that implies adaptation of cells to distant microenvironments beyond the primary tumor [16, 17]. For this study, we used the isogenic KM12 cell model

4 consisting of KM12SM and KM12C cells [18]. KM12SM CRC cell line with high metastatic capacity was isolated from liver metastases in nude mice after five cycles of intrasplenic injection of the poorly metastatic KM12C cell line. In previous reports, we analyzed alterations in protein expression in the secretome and the plasma membrane of this cell system by antibody microarrays and quantitative proteomics, respectively [19-

21].

Here, KM12 cells were metabolically labelled using SILAC. SILAC has been successfully used in spatial proteomics for the analysis of proteome turnover and for the identification of changes in proteome localization as a consequence of DNA damage [9,

22, 23]. Subcellular fractionation of the KM12 contributed to clarify the molecular mechanisms underlying CRC metastasis. Protein abundance and localization were measured in parallel for the five separate subcellular fractions, providing a draft map of the spatially deregulated protein complexes and networks in CRC metastasis.

2. Materials and Methods

2.1 Cell culture, labeling and subcellular fractionation

Human colon cancer cells KM12C and KM12SM were provided by Dr Fidler’s laboratory (MD Anderson Cancer Center, TX, USA) [18, 24]. DMEM (Dulbecco’s modified Eagle’s medium) supplemented with L-lysine and L-arginine (light medium)

13 13 or [ C6]-L-lysine and [ C6]-L-arginine (heavy medium) was purchased from Dundee

Cell Products. Both cell lines were labeled with heavy and light medium to perform label swapping SILAC experiments as biological and technical replicates, as previously described [20, 25]. After 8 cell doublings to ensure >98% incorporation of labeled amino acids, cells were resuspended with 4 mM EDTA PBS and washed with ice-cold

PBS prior to fractionation with the Subcellular Protein Fractionation Kit for Cultured

Cells (Pierce). Cells were divided into five subcellular fractions corresponding to cytoplasm (CEB), plasma, mitochondria and ER/golgi membranes (MEB), nuclear soluble (NEB), nuclear chromatin-bound (NEB-CBP) and cytoskeletal proteins (PEB).

2.2 SDS-PAGE and protein digestion

Protein concentration after subcellular fractionation was quantified using the 2D Quant

Kit (GE Healthcare). Then, 25 μg of each subcellular fraction was separated by SDS

PAGE. Gels were stained with Colloidal Blue Staining Kit (Invitrogen). Lanes containing forward and reverse SILAC experiments for each fraction were cut into 20 slices that were further cut in small pieces prior to in-gel digestion with trypsin.

Peptides were extracted using 100% ACN and 0.5% TFA. Extracted peptides were dried and purified using a C18 resin Zip Tip (Millipore). Finally, samples were reconstituted in 5 μl 0.1% formic acid/2% ACN before LC-MS/MS.

2.3 LC-MS/MS and data analyses

Peptides were trapped in a C18-A1 ASY-Column 2-cm precolumn (Thermo Scientific) and eluted to a C18 reverse phase column (15 cm x 75 μm, 3 μm particle

Nanoseparations), where they were separated using a 150 min gradient from 0 to 35% buffer B (Buffer A: 0.1% formic acid/2% ACN; Buffer B: 0.1% formic acid in ACN) at a flow rate of 250 nl/min. A NanoEasy HPLC (Proxeon) was coupled to an LTQ

Orbitrap Velos (Thermo Scientific) equipped with a nano-electrospray ion source

(Proxeon). The LTQ Orbitrap Velos was operated in positive mode and data-dependent manner. Full scan MS spectra (m/z 400-1200) were acquired using a target value of 106 and a resolution of 60000 at m/z 400. The top 15 ions were selected for fragmentation in the linear ion trap using a target value of 104 and a normalized collision energy of 35%.

Precursor ion charge state screening and monoisotopic precursor selection were enabled and singly charged ions and unassigned charge states were rejected. Dynamic exclusion was enabled and set up to 30s and one repeat count.

MS data were analyzed and quantified using Proteome Discoverer (PD, version

1.3.0.339, Thermo Scientific). Proteins were identified by interrogating the human

SwissProt Database (SwissProt_57.15.fasta) with Mascot (version 2.3, MatrixScience).

Carbamidomethylation of cysteines was set as a fixed modification, while oxidation of

13 13 methionine, N-terminal acetylation and [ C6]-Arg, [ C6]-Lys were set as variable modifications. Precursor and fragment mass tolerance were set to 10 ppm and 0.8 Da, respectively, and a maximum of two missed cleavages were allowed. Identified peptides were filtered using Percolator algorithm with a q-value threshold of 0.01 and a Mascot significant threshold of 0.05. The mass spectrometry data have been deposited in the

ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD006656.

Only those proteins quantified with at least 2 unique peptides in both, forward and reverse, experiments were considered in the analysis. Protein ratios with variability greater than 20% were discarded from the analysis. All experiments were corrected by normalizing data against the 5% trimmed mean of the data set to center the log2 ratio distribution to zero [20]. As we previously described for KM12 cells, arginine to proline conversion did not affect quantification accuracy [20]. A 1.5-fold change cut-off for deregulated proteins was determined to be significant using a permutation-based test

[25, 26].

2.4 Western blot analysis

Ten μg of each fraction were separated by 10% SDS-PAGE and directly transferred to

Hybond-C nitrocellulose membranes (GE Healthcare) using mini trans-blot equipment

(Bio-Rad). Membranes were blocked and incubated with optimized dilutions of monoclonal and polyclonal primary antibodies (Table S1) followed by either HRP anti- mouse IgG (Pierce) or HRP anti-rabbit IgG (Sigma-Aldrich) at a 1:5000 dilution.

Proteins were visualized with ECL SuperSignal West Pico (Pierce). The abundance of the proteins in western blot assays was determined by densitometry using Quantity One

1D Analysis Software v4.6 (Bio-Rad Laboratories).

2.5 Confocal microscopy

KM12SM cells were cultured on glass coverslips pre-treated with Matrigel for 24 h.

They were fixed in a fresh solution of 4% paraformaldehyde in PBS for 15 min and then permeabilized with Triton X-100 1% in PBS for 10 min. After two washes with PBS, cells were incubated with the indicated primary antibodies for 1 h at 1:50/1:100 dilutions (Table S1), followed by incubation with Alexa-488 labelled secondary

8 antibodies for 35 min, phalloidin coupled to Alexa 568 and 4,6-diamidino-2- phenylindole (DAPI). Samples were mounted with Mounting Fluorescence Medium

(Dako, Copenhagen, Denmark). Images used for protein localization were acquired at room temperature using a Leica TCS-SP5-AOBS microscope equipped with a 63×oil- immersion objective.

2.6 Bioinformatics and statistical analysis

Subcellular classification and significant GO terms were annotated by PANTHER, as determined by Fisher’s exact test (p value ≤ 0.05) [27]. To identify CRC metastasis specific alterations, bioinformatic analysis was performed using DAVID, KEGG,

STRING and IPA (Qiagen Bioinformatics) to identify altered protein networks and pathways [28-32]. For protein-protein interaction analysis we used STRING. A circular layout algorithm was used to minimize overlapping edges and facilitate the visualization of the interaction between the nodes [33]. Significant subnetworks were shown as inner circles within the global networks. Cytoscape was used for network representation and visualization [34]. Unsupervised cluster analysis was performed with MeV [35].

3 RESULTS

3.1 Subcellular fractionation and differential protein expression in metastatic

KM12 cells

Forward and reverse SILAC-labeled KM12 cells were biochemically fractionated into five subcellular fractions –CEB, MEB, NEB, NEB-CBP and PEB- using a commercial kit based on the use of successive detergents and differential centrifugation (Fig. 1A).

To confirm a correct fractionation, we investigated the quality of the different subcellular fractions using proteins of known subcellular localization (Rho-GDI, EGFR,

SP1, Histone 3 and VIM) by western blot (Fig. 1B). After MS analysis, these control proteins showed the highest intensity at the same subcellular compartment than western blot. However, the detection of control proteins at low intensity in adjacent subcellular fractions confirmed some minor cross-contamination during cell partitioning (Fig. 1C).

We identified a total of 6420 proteins in the five subcellular fractions, corresponding to 4863 unique quantified proteins. A detailed description of identified and quantified peptides and proteins in all the subcellular fractions is shown in Fig. 2A (Tables S2,

S3). From the total of 4863 proteins quantified, we observed the presence of 1453 proteins in only one compartment, 1590 proteins in two compartments, 1146 in three fractions, 559 in four, and 115 proteins ubiquitously distributed in the five compartments (Fig. 2B). As another test to confirm a correct cellular fractionation, we carried out a GO classification of the identified proteins in each of the 5 subcellular fractions (Fig. 2C). In all fractions, the quantified proteins were correctly classified within the three first GO classification hits of each fraction, except for PEB proteins. In

PEB, only 234 out of the 1583 quantified proteins were classified as cytoskeletal proteins. PEB fraction contained cytoskeletal proteins, but also included insoluble high molecular weight proteins.

Regarding global protein alterations in metastasis, we found more down- than up- regulated proteins in the subcellular fractions of KM12SM highly metastatic cells.

Using a 1.5-fold change, determined as significant by a permutation-based statistical test, we observed a total of 1318 altered proteins in the different subcellular compartments (Fig. 2D, Table S4). Collectively, western blot, MS and bioinformatics analyses results suggested an overall correct subcellular fractionation and the wide distribution of the proteins in multiple subcellular locations.

3.2 Protein clusters, pathways and network analysis of differentially expressed proteins in CRC metastatic cells

To characterize the metastatic behavior, we carried out a global in silico analysis of the differentially-expressed proteins in order to visualize protein-protein interactions and the most significantly altered protein clusters and macromolecular complexes using a circular layout algorithm with Cytoscape (Fig. 3, Table S5). Despite the high number of nodes and connections among the quantified proteins, the graphic representation highlighted significant networks and complexes. In the cytoplasmic compartment

(CEB), GO annotation and network analysis showed a highly significant enrichment of

“Translation initiation” and “Chaperonin-containing T (CCT) complex” among the up- regulated proteins (Fig. 3A), being EiF2 signaling one of the most significant canonical pathways affected (p: 6.67x10-8). Regarding down-regulation, we observed significant alterations in “Vesicle-mediated transport” and “Small GTPase mediated signal transduction” (Fig. 3B).

In the membrane fraction (MEB), the SNARE complex, the “Ubiquinol cytochrome c reductase complex core” and the “mitochondrial proton-transporting ATP synthase” complexes showed the highest enrichment among the up-regulated proteins (Fig. 3A).

Among the down-regulated proteins, the “Proteasome complex” was highly enriched, as well as the “eukaryotic translation initiation factor”, the “CCT complex” and

“ribosomal proteins” (Fig. 3B).

In the nuclear compartment (NEB), whereas no significant networks or pathways were up-regulated (Fig. 3A), the “Arp2/3 complex”, the “Proteasome complex” and the

“MCM-complex” were down-regulated together with mitochondrial ribosomal proteins

(Fig. 3B). In the chromatin-binding fraction (NEB-CBP), we noticed an unexpected enrichment of proteins implicated in the “Adherens Junction” and the “Tight Junction” pathway for the up-regulated proteins (Fig. 3A). For down-regulated proteins, the

“replication fork” and “DNA replication” were highly enriched (Fig. 3B).

Finally, in the cytoskeletal compartment (PEB), the “COPI vesicle coat” and the

“AP-type membrane coat adaptor complex” were highly prominent (Fig. 3A). In contrast, no significant networks and pathways were enriched within the 121 down- regulated proteins (Fig. 3B). Interestingly, some protein complexes exhibited different alterations in abundance depending on the cellular localization.

3.3 Mapping multidimensional protein-complex alterations in CRC metastatic cells

Although 91% of the 1318 unique deregulated proteins showed similar alterations in the same or different subcellular compartments, 117 proteins (8.9%) showed an opposite quantification in different subcellular localizations (Fig. 4, Table S6). Among protein complexes showing similar alterations in different subcellular compartments, the Arp2/3 complex, the replication fork, the MCM complex and the proteasome complex were the most down-regulated complexes. On the other hand, the most up-regulated complexes were those related to vesicle membrane trafficking, including the SNARE complex and

12 proteins of the AP-type membrane coat adaptor complex and COPI vesicle coat (Fig.

4A, B).

Regarding the subset of 117 proteins showing opposite changes in expression (Fig.

4C), three main complexes were significantly altered in different subcellular compartments: the cytosolic chaperonin CCT/TRiC complex (CCT-complex), the mitochondrial proton-transporting ATP synthase, and the translation initiation –Eif2 and Eif3- complexes (Fig. 4C).

3.4 Clustering and validation of spatially-deregulated proteins and their interactome

To further investigate the distribution of the spatially deregulated proteins, the 117 proteins were subjected to unsupervised clustering according to their differential expression in the subcellular fractions (Fig. 5A) and protein-protein interaction analysis using STRING (Fig. 5B). STRING provided a vision of the “core” proteins of the complex and other interacting proteins. In general, the core proteins of the complex had similar expression patterns in different locations, with some exceptions like CCT2 or

EIF4A1, which showed additional high expression associated to the cytoskeleton fraction (PEB). While CCT2 has been associated to tubulin, the cytoskeletal location would be novel for EIF4A1. Other interacting non-core proteins of the complex may present different trends in expression in different localizations (Fig. 5B). The 11 proteins related to cell adhesion, including CTNNB1, CTNNA1, TJP2/ZO2, MARCKS and FERMT1, showed more variability in localization and significant differences among them. These changes in location during cancer progression were well known for

α- and β-catenin, but they were novel for the other proteins. A potential explanation is that the network core proteins travel collectively as a complex to different locations,

13 whereas the “non-core” interacting proteins may vary according to the organelle or subcellular fraction.

To verify the MS dataset and the in silico cluster analysis, we investigated protein expression of some representative complexes by western blot and confocal microscopy. By western blot, TCP1 and CCT2 from the CCT-complex showed similar alterations to MS ratios as well as G3BP1, GNB2 and Rab35 of the guanine-nucleotide binding proteins. A similar situation was observed for the adhesion proteins TLN1 and

ZO1 proteins, which showed opposite expression in different compartments by MS and western blot (Fig. 5C). Cytoplasmic protein RhoGDi was included as loading control.

Visualization by confocal microscopy confirmed MS results at the subcellular level

(Fig. 6). An increased cytoplasmic expression of CCT2 and G3BP1 was observed in the metastatic KM12SM cells, with increased association to the cytoskeleton for CCT2 and some nuclear location for G3BP1. For RAB35, we noticed a predominant membrane location in metastatic cells, with reduced cytoplasmic staining. GTPase RAB35 plays an important role in endocytic recycling. In agreement with MS data, RAB35 was up- regulated in MEB and down-regulated in other compartments in KM12SM cells, suggesting an implication of RAB35 in the formation of vesicles in highly metastatic cells. In summary, metastatic cells present changes in the spatio-temporal distribution of altered protein abundances, which would be impossible to detect without subcellular fractionation. These proteins might possess different functions in different localizations which might be relevant for CRC metastasis.

4. Discussion

Here, we have performed a draft map of the spatial proteome in metastatic colorectal cancer cells. Subcellular fractionation enabled the characterization and quantification of

4863 proteins in different subcellular compartments. The deregulated proteome and their spatial distribution between the cytoplasm, membrane, nucleus, chromatin and cytoskeletal fractions were used to study global alterations in cancer metastatic cells. In total, 1318 proteins were deregulated in metastatic cells and 8.9% of deregulated proteins showed a multidimensional alteration. Spatial alterations were confirmed by western blot and confocal microscopy. To our knowledge, this study provides one of the widest quantitative analysis of multidimensional proteome alterations in CRC metastasis.

Most of the proteins and protein complexes showed similar expression trends in the different cellular compartments. The most up-regulated protein alterations in metastatic cells involved the SNARE complex, the AP-type membrane coat adaptor complex and the COPI vesicle coat related with the transport of vesicles. SNARE and other components of the fusion machinery seem to be involved in exosome secretion [36]. A higher exosome release has been associated with tumor progression and metastasis [37].

Previously, we reported that 88 out of 155 deregulated proteins found in the secretome of KM12 cells were exosome-related [20]. Therefore, alterations in the membrane fusion machinery of the highly metastatic cells would explain the increased vesicular trafficking and exosome release. In neuroinflammation and neurodegeneration processes [38, 39], it has been suggested an alternative use of exosomes as a compensatory mechanism to get rid of unwanted or unfolded proteins. Intracellular traffic of membrane vesicles is also responsible for the maintenance and regulation of the components of the plasma membrane [40]. Alterations in the accurate delivery of

15 proteins to the cell surface can lead to the loss of cellular polarity, one of the earliest stages of carcinogenesis [41, 42], invasion and metastasis [43]. Thus, vesicle trafficking is central for understanding carcinogenesis and for developing novel therapeutic strategies. Proteins from the SNARE complex, like syntaxins, might be used as novel targets in cancer [44].

The most down-regulated processes in metastatic cells were those related to the proteasome complex, the ARP2/3 complex, the replication fork and the MCM complex.

The ubiquitin-proteasome system is the major mechanism regulating protein turnover, and thus, cells are highly vulnerable to its malfunction [45]. We observed that 10 proteins of the 19S proteasome regulatory complex were down-regulated, including all six ATPase and four non-ATPase subunits. Interestingly, reduced expression of individual subunits of the proteasome's 19S regulatory complex increases cell survival and drug-resistance of myeloma cells [46]. Our findings would support an increase in the survival and proteasome drug-resistance of highly metastatic CRC cells.

The ARP2/3 complex participates in the nucleation of actin and in the process of actin polymerization and depolymerization [47, 48]. ARP2/3 complex dysfunction has been associated with the increased capability of metastatic cells to migrate away from primary tumors and invade healthy tissues [49]. Although usually present in the cytoskeleton and cytosol, some studies have demonstrated that the ARP2/3 complex is present in the nucleus, interacting with the NonO/PSF/N-WASP/RNA POL II complex to regulate transcription and chromatin organization [50, 51]. The observed down regulation of the ARP2/3 complex in the nuclear compartment might be related with a lower presence of actin in the nucleus due to higher cytoplasmic requirements [51].

Interestingly, 117 proteins showed multidimensional dysregulation in expression and localization. Many of these proteins were forming large macromolecular complexes.

For example, we found spatially-dependent alterations in the MS ratios of 7 out of 8 proteins of the chaperonin CCT-complex, changing the MS ratio from 2 in CEB to 0.5 in MEB. The CCT/TRiC complex seems to play a key role in cell cycle progression and tumor development [52]. An analysis of the interaction network of the CCT chaperonins revealed a high relevance in the folding of many proteins, including actin and tubulin

[53]. Indeed, CCT targets can be divided in ATP-dependent or independent. ARP2/3 proteins were ATP independent, whereas mitochondrial and other membrane proteins required ATP for the interaction with CCT [53]. This ATP-dependence might explain the differential expression of the CCT complex between the CEB fraction and the MEB fraction, enriched in cytosolic and mitochondrial and ribosomal proteins, respectively.

Sixteen proteins of the mitochondrial proton-transporting ATP synthase complexes,

ABCB7, ATP5B, ATP5C1, ATP5D, ATP5F1, ATP5J2, ATP5O, COX6C, COX7A2, and DLST, associated to the oxidative phosphorylation (OxPhos) were mostly up- regulated in MEB compartment (mitochondrial localization). However, ATP5A1,

ATP5H, UQCRC1, UQCRC2, and UQCRFS1, including three proteins of the

Cytochrome b-c1 mitochondrial complex, were reduced in the cytoskeleton compartment (PEB). These data -apart from indicating a mitochondrial dysfunction (as also suggested by pathway analysis)- suggest that proteins belonging to the TCA and

OxPhos processes might have dual cell localizations with different functions in these secondary localizations (nucleus and cytoskeleton), as reported in MCF7 breast cancer cells [54].

Proteins from the heterotrimeric-G protein complex (GNA11, GNB1, GNB2, and

GNAI3), GTPases (RAB35, SRP54, and RHOG) or GTPase-activating proteins

(ARFGAP2 and ARHGAP18) were up-regulated in the MEB fraction (plasma, mitochondria and ER/golgi membranes). However, they were down-regulated in the

CEB (cytoplasm) compartment in metastatic cells. Proteins of this complex are localized to different organelles and they cooperate with the SNARE proteins for vesicle docking and the fusion machinery. RAB GTPases are probably sequestered from the cytosol and moved to the membrane compartment, where they control the SNARE complex formation and are involved in exosome secretion [36]. Thus, it is consequent that RAB GTPases (i.e. RAB35) are upregulated in the MEB compartment, as well as the SNARE proteins, and down-regulated in the CEB compartment. In particular,

RAB35 mediates slow endocytic recycling through recycling endosome to the basolateral plasma membrane [55], and has been involved also in the late stages of cytokinesis [56]. RAB35 and RAB27A/B allowed the docking of vesicular bodies to the plasma membrane [57]. Therefore, RAB35 might constitute a new potential therapeutic target in colon cancer metastasis, as has been suggested for other GTPases and GTPase- activating proteins in melanoma or lung cancer [58].

Dysregulation of cell junction adhesion proteins has been heavily implicated in the epithelial-mesenchymal transition that leads to metastasis [59]. For instance, metastatic breast cancer cells express relatively little ZO-1 protein [60]. In this group of proteins, there was not a homogeneous distribution pattern. Whereas our results confirm the translocation of β-catenin to the nucleus (NEB-CBP) associated to cancer progression,

α-catenin showed increased association to actin cytoskeleton (PEB). TJP2, aka ZO2, a tight junction protein was detected at the nucleus and the cytoskeleton. This interesting finding supports a multifunctional role for this protein in chromatin organization and transcriptional control in cancer metastatic cells [61]. We also confirm the recent nuclear assignment of MARCKS and its potential function in regulation of gene expression [62], indicating another dual localization for this adhesion protein.

In summary, we quantified deregulated proteins and mapped its localization in metastatic colorectal cancer cells. Our data highlight the importance of protein localization to distinguish proteins and complexes that behave differently in different organelles and locations in order to identify underlying mechanisms of CRC metastasis.

Subcellular alterations of proteins are a characteristic of metastatic cells that may be vital for cancer progression and metastasis, including exosome secretion. In addition, this multidimensional information has provided a new panel of potential subcellular biomarkers and targets for intervention in cancer metastasis.

Conflict of interest

The authors declare no conflict of interest

Acknowledgements

This research was supported by grants BIO2012–31023 and BIO2015-66489-R from the MINECO, grant PRB2 (IPT13/0001-ISCIII-SGEFI/FEDER) and Foundation Ramón

Areces to JIC. Work at RB’s lab was supported by a grant from the MINECO

(SAF2014-53209-R).

5. References [1] Larance, M., Lamond, A. I., Multidimensional proteomics for cell biology. Nat Rev Mol Cell Biol 2015, 16, 269-280. [2] Henke, R. M., Dastidar, R. G., Shah, A., Cadinu, D., et al., Hypoxia elicits broad and systematic changes in protein subcellular localization. Am J Physiol Cell Physiol 2011, 301, C913-928. [3] Chu, H. Y., Hopper, A. K., Genome-wide investigation of the role of the tRNA nuclear-cytoplasmic trafficking pathway in regulation of the yeast Saccharomyces cerevisiae transcriptome and proteome. Mol Cell Biol 2013, 33, 4241-4254. [4] Fabre, B., Lambour, T., Delobel, J., Amalric, F., et al., Subcellular distribution and dynamics of active proteasome complexes unraveled by a workflow combining in vivo complex cross-linking and quantitative proteomics. Mol Cell Proteomics 2013, 12, 687- 699. [5] Lamond, A. I., Uhlen, M., Horning, S., Makarov, A., et al., Advancing cell biology through proteomics in space and time (PROSPECTS). Mol Cell Proteomics 2012, 11, O112 017731. [6] Kimura, M., Kose, S., Okumura, N., Imai, K., et al., Identification of cargo proteins specific for the nucleocytoplasmic transport carrier transportin by combination of an in vitro transport system and stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics. Mol Cell Proteomics 2013, 12, 145-157. [7] Sorokin, A. V., Kim, E. R., Ovchinnikov, L. P., Nucleocytoplasmic transport of proteins. Biochemistry (Mosc) 2007, 72, 1439-1457. [8] Baqader, N. O., Radulovic, M., Crawford, M., Stoeber, K., Godovac-Zimmermann, J., Nuclear cytoplasmic trafficking of proteins is a major response of human fibroblasts to oxidative stress. J Proteome Res 2014, 13, 4398-4423. [9] Boisvert, F. M., Lam, Y. W., Lamont, D., Lamond, A. I., A quantitative proteomics analysis of subcellular proteome localization and changes induced by DNA damage. Mol Cell Proteomics 2010, 9, 457-470. [10] Park, S., Yang, J. S., Shin, Y. E., Park, J., et al., Protein localization as a principal feature of the etiology and comorbidity of genetic diseases. Mol Syst Biol 2011, 7, 494. [11] Sirover, M. A., Subcellular dynamics of multifunctional protein regulation: mechanisms of GAPDH intracellular translocation. J Cell Biochem 2012, 113, 2193- 2200.

[12] Tristan, C., Shahani, N., Sedlak, T. W., Sawa, A., The diverse functions of GAPDH: views from different subcellular compartments. Cell Signal 2011, 23, 317- 323. [13] Laurila, K., Vihinen, M., Prediction of disease-related mutations affecting protein localization. BMC Genomics 2009, 10, 122. [14] Kau, T. R., Way, J. C., Silver, P. A., Nuclear transport and cancer: From mechanism to intervention. Nature Reviews Cancer 2004, 4, 106-117. [15] Siegel, R., Desantis, C., Jemal, A., Colorectal cancer statistics, 2014. CA Cancer J Clin 2014, 64, 104-117. [16] Valastyan, S., Weinberg, R. A., Tumor metastasis: molecular insights and evolving paradigms. Cell 2011, 147, 275-292. [17] Fidler, I. J., The pathogenesis of cancer metastasis: the 'seed and soil' hypothesis revisited. Nat Rev Cancer 2003, 3, 453-458. [18] Morikawa, K., Walker, S. M., Jessup, J. M., Fidler, I. J., In vivo selection of highly metastatic cells from surgical specimens of different primary human colon carcinomas implanted into nude mice. Cancer Res 1988, 48, 1943-1948. [19] Barderas, R., Bartolome, R. A., Fernandez-Acenero, M. J., Torres, S., Casal, J. I., High expression of IL-13 receptor alpha2 in colorectal cancer is associated with invasion, liver metastasis, and poor prognosis. Cancer Res 2012, 72, 2780-2790. [20] Barderas, R., Mendes, M., Torres, S., Bartolome, R. A., et al., In-depth characterization of the secretome of colorectal cancer metastatic cells identifies key proteins in cell adhesion, migration, and invasion. Mol Cell Proteomics 2013, 12, 1602- 1620. [21] Luque-Garcia, J. L., Martinez-Torrecuadrada, J. L., Epifano, C., Canamero, M., et al., Differential protein expression on the cell surface of colorectal cancer cells associated to tumor metastasis. Proteomics 2010, 10, 940-952. [22] Boisvert, F. M., Ahmad, Y., Gierlinski, M., Charriere, F., et al., A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol Cell Proteomics 2012, 11, M111 011429. [23] Christoforou, A., Mulvey, C. M., Breckels, L. M., Geladaki, A., et al., A draft map of the mouse pluripotent stem cell spatial proteome. Nat Commun 2016, 7, 8992. [24] Morikawa, K., Walker, S. M., Nakajima, M., Pathak, S., et al., Influence of organ environment on the growth, selection, and metastasis of human colon carcinoma cells in nude mice. Cancer Res 1988, 48, 6863-6871.

[25] Pelaez-Garcia, A., Barderas, R., Batlle, R., Vinas-Castells, R., et al., A proteomic analysis reveals that Snail regulates the expression of the nuclear orphan receptor Nuclear Receptor Subfamily 2 Group F Member 6 (Nr2f6) and interleukin 17 (IL-17) to inhibit adipocyte differentiation. Mol Cell Proteomics 2015, 14, 303-315. [26] Chen, D., Shah, A., Nguyen, H., Loo, D., et al., Online quantitative proteomics p- value calculator for permutation-based statistical testing of peptide ratios. J Proteome Res 2014, 13, 4184-4191. [27] Mi, H., Muruganujan, A., Casagrande, J. T., Thomas, P. D., Large-scale gene function analysis with the PANTHER classification system. Nat Protoc 2013, 8, 1551- 1566. [28] Medina, I., Carbonell, J., Pulido, L., Madeira, S. C., et al., Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res 2010, 38, W210-213. [29] Huang da, W., Sherman, B. T., Lempicki, R. A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009, 4, 44-57. [30] Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013, 41, D808-815. [31] Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., et al., Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 2014, 42, D199-205. [32] Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., et al., The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010, 38, W214-220. [33] Agapito, G., Guzzi, P. H., Cannataro, M., Visualization of protein interaction networks: problems and solutions. BMC Bioinformatics 2013, 14 Suppl 1, S1. [34] Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., et al., Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007, 2, 2366-2382. [35] Babel, I., Barderas, R., Diaz-Uriarte, R., Martinez-Torrecuadrada, J. L., et al., Identification of tumor-associated autoantigens for the diagnosis of colorectal cancer in serum using high density protein microarrays. Mol Cell Proteomics 2009, 8, 2382-2395.

[36] Colombo, M., Raposo, G., Thery, C., Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol 2014, 30, 255-289. [37] Ginestra, A., Miceli, D., Dolo, V., Romano, F. M., Vittorelli, M. L., Membrane vesicles in ovarian cancer fluids: A new potential marker. Anticancer Research 1999, 19, 3439-3445. [38] Brites, D., Fernandes, A., Neuroinflammation and Depression: Microglia Activation, Extracellular Microvesicles and microRNA Dysregulation. Front Cell Neurosci 2015, 9, 476. [39] Joshi, P., Benussi, L., Furlan, R., Ghidoni, R., Verderio, C., Extracellular vesicles in Alzheimer's disease: friends or foes? Focus on abeta-vesicle interaction. Int J Mol Sci 2015, 16, 4800-4813. [40] Mellman, I., Nelson, W. J., Coordinated protein sorting, targeting and distribution in polarized cells. Nat Rev Mol Cell Biol 2008, 9, 833-845. [41] Royer, C., Lu, X., Epithelial cell polarity: a major gatekeeper against cancer? Cell Death Differ 2011, 18, 1470-1477. [42] McCaffrey, L. M., Macara, I. G., Epithelial organization, cell polarity and tumorigenesis. Trends Cell Biol 2011, 21, 727-735. [43] Goldenring, J. R., A central role for vesicle trafficking in epithelial neoplasia: intracellular highways to carcinogenesis. Nat Rev Cancer 2013, 13, 813-820. [44] Riggs, K. A., Hasan, N., Humphrey, D., Raleigh, C., et al., Regulation of integrin endocytic recycling and chemotactic cell migration by syntaxin 6 and VAMP3 interaction. J Cell Sci 2012, 125, 3827-3839. [45] Petrocca, F., Altschuler, G., Tan, S. M., Mendillo, M. L., et al., A Genome-wide siRNA Screen Identifies Proteasome Addiction as a Vulnerability of Basal-like Triple- Negative Breast Cancer Cells. Cancer Cell 2013, 24, 182-196. [46] Acosta-Alvear, D., Cho, M. Y., Wild, T., Buchholz, T. J., et al., Paradoxical resistance of multiple myeloma to proteasome inhibitors by decreased levels of 19S proteasomal subunits. Elife 2015, 4, e08153. [47] Goley, E. D., Welch, M. D., The ARP2/3 complex: an actin nucleator comes of age. Nat Rev Mol Cell Biol 2006, 7, 713-726. [48] Korobova, F., Svitkina, T., Arp2/3 complex is important for filopodia formation, growth cone motility, and neuritogenesis in neuronal cells. Molecular Biology of the Cell 2008, 19, 1561-1574.

[49] Wang, W., Goswami, S., Sahai, E., Wyckoff, J. B., et al., Tumor cells caught in the act of invading: their strategy for enhanced cell motility. Trends Cell Biol 2005, 15, 138-145. [50] Yoo, Y., Wu, X., Guan, J. L., A novel role of the actin-nucleating Arp2/3 complex in the regulation of RNA polymerase II-dependent transcription. J Biol Chem 2007, 282, 7616-7623. [51] Gieni, R. S., Hendzel, M. J., Actin dynamics and functions in the interphase nucleus: moving toward an understanding of nuclear polymeric actin. Biochem Cell Biol 2009, 87, 283-306. [52] Boudiaf-Benmammar, C., Cresteil, T., Melki, R., The cytosolic chaperonin CCT/TRiC and cancer cell proliferation. PLoS One 2013, 8, e60895. [53] Dekker, C., Stirling, P. C., McCormack, E. A., Filmore, H., et al., The interaction network of the chaperonin CCT. EMBO J 2008, 27, 1827-1839. [54] Qattan, A. T., Radulovic, M., Crawford, M., Godovac-Zimmermann, J., Spatial distribution of cellular function: the partitioning of proteins between mitochondria and the nucleus in MCF7 breast cancer cells. J Proteome Res 2012, 11, 6080-6101. [55] Stenmark, H., Rab GTPases as coordinators of vesicle traffic. Nat Rev Mol Cell Biol 2009, 10, 513-525. [56] Kouranti, I., Sachse, M., Arouche, N., Goud, B., Echard, A., Rab35 regulates an endocytic recycling pathway essential for the terminal steps of cytokinesis. Curr Biol 2006, 16, 1719-1725. [57] Hsu, C., Morohashi, Y., Yoshimura, S., Manrique-Hoyos, N., et al., Regulation of exosome secretion by Rab35 and its GTPase-activating proteins TBC1D10A-C. J Cell Biol 2010, 189, 223-232. [58] Gysin, S., Salt, M., Young, A., McCormick, F., Therapeutic strategies for targeting ras proteins. Genes Cancer 2011, 2, 359-372. [59] Martin, T. A., Jiang, W. G., Loss of tight junction barrier function and its role in cancer metastasis. Biochim Biophys Acta 2009, 1788, 872-891. [60] Hoevel, T., Macek, R., Mundigl, O., Swisshelm, K., Kubbies, M., Expression and targeting of the tight junction protein CLDN1 in CLDN1-negative human breast tumor cells. J Cell Physiol 2002, 191, 60-68. [61] Traweger, A., Lehner, C., Farkas, A., Krizbai, I. A., et al., Nuclear Zonula occludens-2 alters gene expression and junctional stability in epithelial and endothelial cells. Differentiation 2008, 76, 99-106.

[62] Rohrbach, T. D., Shah, N., Jackson, W. P., Feeney, E. V., et al., The Effector Domain of MARCKS Is a Nuclear Localization Signal that Regulates Cellular PIP2 Levels and Nuclear PIP2 Localization. PLoS One 2015, 10, e0140870.

Legend to Figures

Figure 1. Subcellular fractionation and differential protein expression in KM12 cells. (A) For metabolic labeling, KM12C or KM12SM were grown and maintained in light and heavy-labeled SILAC DMEM medium supplemented with 10% dialyzed FBS.

Label swapping (forward and reverse) SILAC experiments were biochemically fractionated into five subcellular fractions corresponding to: cytoplasm (CEB), plasma, mitochondria and ER/golgi membranes (MEB), nuclear soluble (NEB), nuclear chromatin-bound (NEB-CBP) and cytoskeletal (PEB) proteins. Protein extracts were separated by SDS-PAGE. Mass spectra were acquired on an LTQ-Orbitrap Velos mass spectrometer. (B) Verification of the correct subcellular fractionation by western blot using antibodies against proteins of known subcellular localization. (C) The abundance of the proteins determined by MS was compared with western blot results. The relative average quantification of the forward and reverse peak areas obtained with Proteome

Discoverer is depicted for each protein in each subcellular fraction. Values were normalized using the top area value in one of the 5 subcellular fractions as 1. The rest of the values were calculated accordingly.

Figure 2. Analysis of the identified, quantified and deregulated proteins in KM12 cells. (A) Summary of the identified and quantified peptides and proteins in both

SILAC experiments for the five subcellular fractions. (B) Venn diagram of the proteins quantified in CEB, MEB, NEB, NEB-CBP and PEB. (C) GO analysis for the subcellular classification of the proteins in each compartment was made with

PANTHER. The top 5 subcellular GO classifications in each fraction are shown with the calculated correlation value. (D) Venn diagram of deregulated proteins showing a fold-change ≥1.5.

Figure 3. Deregulated protein networks, macromolecular complexes, and pathways in CRC metastatic cells. STRING, KEGG and IPA bioinformatics suites were used for in silico analysis. (A) up-regulated and (B) down-regulated protein- protein interactions and networks of interest in KM12 cells with different metastatic properties. STRING was used to determine protein-protein interactions of the deregulated proteins. Then, data were exported to Cytoscape for the representation of deregulated alterations in all five subcellular fractions. Dashed lines correspond to the protein-protein interactions of deregulated proteins identified by STRING. Red: up- regulated proteins, green: down-regulated proteins in KM12SM cells. Light to dark

(green and red) colors and spot size are proportional to the fold-change variation observed for each protein in each subcellular fraction. Cytoscape was used for representation of deregulated alterations in all five subcellular fractions according to a circular layout algorithm.

Figure 4. Summary of subcellular alterations in protein networks and macromolecular complexes in CRC metastasis. (A) In total, we observed 1318 deregulated proteins, with ~60% of the proteins showing downregulation, and 117 proteins an opposite regulation in different compartments. (B) Among the down- regulated proteins, we mainly observed downregulation in the replication fork and in the proteasome, ARP2/3, and MCM complexes. Among the up-regulated proteins, we mainly observed upregulation in the SNARE complex and the COPI vesicle coat and

AP-type membrane coat adaptor complex. (C) Proteins with opposite changes in expression in different subcellular compartments. Methods for data visualization were similar to the figure 3.

Figure 5. Hierarchical clustering and validation of proteins showing multidimensional alterations. (A) Unsupervised cluster analysis of the complete set of

117 proteins altered in expression. Green, down-regulation, and red, over-expression in

KM12SM highly metastatic cells. Color scale is related to the fold-change observed for each protein in each subcellular fraction. (B) The 117 proteins were analyzed by

STRING and IPA to find protein-protein interacting partners, and protein complexes.

Then, they were separately analyzed by hierarchical clustering, and visualized through protein clusters by using STRING. In the cluster, green showed down-regulation, and red, over-expression in KM12SM cells for the indicated proteins. For a better visualization, fold-change values (rows) were normalized and the color scale set up between 0.16 (green) and 1.3 (red). Complex core proteins are encircled by a red line.

KM12SM were subjected to western blot analysis and quantified by densitometry. Rho

GDi was used as a control.

Figure 6. Confocal microscopy analysis of differential protein localization between

KM12C and KM12SM cells. KM12C and KM12SM cell were cultured on glass coverslips pretreated with Matrigel for 24 h and subjected to confocal microscopy analysis using antibodies for CCT2, G3BP1 and RAB35 (green). Cells were counterstained with the nuclear probe DAPI (blue) and the actin cytoskeleton probe

Phalloidin (red). Representative images show a differential staining distribution in the different cellular compartments for the three proteins between the metastatic and non- metastatic cells.

A KM12C Forward R0K0 R6K6

Experiment KM12SM

•SDS-PAGE R0K0 R6K6 •In-gel trypsin digestion KM12C

•Sample Clean-Up Intensity Intensity Reverse R6K6 Subcellular Fractionation Experiment KM12SM nLC-MS/MS m/z R0K0 B C

Rho-Gdi (Cytoplasmic)

EGFR (Membrane) SP1 (Nuclear soluble)

Histone 3 (Chromatin bound)

Vimentin (Cytoskeletal)

Figure 1 Identified Quantified A Subcellular fraction Peptides Proteins Peptides Proteins CEB 19334 2416 16519 1828 MEB 25779 4086 24178 2914 NEB 25364 3836 23435 3030 NEB-CBP 16667 1853 14689 1483 PEB 17275 2117 15306 1583

CEB CEB B D 354 153

9 53 4 6 1 6 23 PEB 0 4 PEB 5 45 355 23 94 0 26 MEB 4 260 MEB 0 148 484 62 261 417 2 5

75 115 76 26 1 5 141 0 2 1 216 172 12 2 529 8 109 394 222 5 130 8 1 9 79 44 16 2 312 80 32 65 275 208 NEB−CBP NEB−CBP NEB NEB Quantified proteins 1.5-fold deregulated proteins C Quantified Proteins 1828 R=0.769 Cytoplasm (GO:0005737) 1581 Cytosol (GO:0005829) 1004 CEB Membrane-bounded vesicle (GO:0031988) 887 Extracellular exosome (GO:0070062) 814 Extracellular vesicle (GO:1903561) 814 Extracellular organelle (GO:0043230) 814 p values < 8.43E-217

Quantified Proteins 2914 Intracellular (GO:0005622) 2674 Intracellular part (GO:0044424) 2660 MEB Organelle (GO:0043226) 2583 Membrane-bounded organelle (GO:0043227) 2530 Organelle part (GO:0044422) 2009 Intracellular organelle part (GO:0044446) 1998 p values < 1.59E-234

Quantified Proteins 3030 Intracellular (GO:0005622) 2851 Organelle (GO:0043226) 2761 NEB Membrane-enclosed lumen (GO:0031974) 1519 Organelle lumen (GO:0043233) 1519 Intracellular organelle lumen (GO:0070013) 1519 p values < 6.67E-209 Nuclear part (GO:0044428) 1299

Quantified Proteins 1483 Nucleus (GO:0005634) 1096 Membrane-enclosed lumen (GO:0031974) 972 NEB- Organelle lumen (GO:0043233) 972 CBP Nuclear part (GO:0044428) 918 Nuclear lumen (GO:0031981) 871 p values < 1.35E-244 Nucleoplasm (GO:0005654) 753

Quantified Proteins 1583 organelle (GO:0043226) 1453 membrane-bounded organelle… 1422 intracellular organelle (GO:0043229) 1420 PEB intracellular membrane-bounded… 1366 organelle part (GO:0044422) 1269 p values < 1.29E-172 intracellular organelle part (GO:0044446) 1265 0 1000 2000 3000 Number of proteins Figure 2 A Up-regulated pathways and protein complexes in CRC metastasis

CEB MEB NEB NEB-CBP PEB

NUDT2 SEC24D EPRS ANXA5 CAPRIN1 RARS CPT2TMED5EHHADHDLSTHMGCS2 QRSL1 PET112L SDHA AFG3L2TMED10 SACM1L ALDH2 C11orf73 GATM HCCS FAAH ZFAND1 HK1 STOML2 SYNCRIPCAPRIN1 LMNADLAT CS CSNK1A1LGALS3 ALDH1B1 PHB2 RSF1 CCNK LGALS4 NEK9 SUCLA2 TMED2 G3BP1 MAP2K3 FLNB APOOLOC653884 TOR1AIP2 ZNF326 NIPBL HIP1R CACYBP IPO8 S100P TMX1 RALA ACAA2 RPL5 DIMT1L MAP2K1 ERAP1 ILF2 TJP2 HMGCL PAK2 B2M HNRNPA0 PABPC1 RRS1 CTNNA1 RPL22L1 ATP5A1 GTF2F2 GSN SUCLG2 ABCB7 RRAGA TOR1A ATP1A1 Translation PRDX3 ATP5C1 ACAA2 C1orf77 UPF3B EHHADH SNX6 ALDH5A1 ATP5L ATP5H ISCA1 ACADVL ME2 CAPZA1 SAR1A RDH11 SPCS2 NDUFV1 ALDH6A1 SNX2 initiation ATP5I ATP5B PURA CAPZB CNBP CYP51A1 SEC11A COQ9 CPT1A HK1TRIP11 CCT5 FAM120A KPNB1 DHX9 ARAF ACAD8 ATP5J2 ATP5S SDHB SRP54 SND1 GTF2F1 EFHD2 CCT4 C14orf1 UQCRFS1 ACSL4 RPL10A Adherens FUS CCT3 UQCRB UQCRC1 USMG5 MRPS35 UPF1 CABC1SLC16A1 GOLGA5GLUD1 VAPA AP-type membrane NAA38 HTATIP2 EHD1 TWF1 ATP5D BRP44L COX7A2 UQCRQ BNIP1 RPF2 junctions GTF2F1 COG1 EIF5B DBT SEP15 ATP5F1 CS BZW2 ATP5O NME6 coat adaptor complex ANXA7 EIF2S2 ACADSB GOT2 C11orf59 CXADR CCT8 COX7A2L MRPS36 ALDH6A1 TOMM70A STX5 CALM1 CLTA COG6 CCT6A TCP1 EIF4A1 ACAT1 SCP2 FLOT2 EIF2B2 RRAGC CD97 AP1G1 AP3M1 NDUFB1 NDUFB5 Mitochondrial ATP6V1D MRPS28 C1QBP HSPA1A CLTC FSCN1 EIF3A RPS29 EIF2B1 TJP2 FLOT1 SCFD1 STX6 TSR1 COX6C NDUFS2 STX4 STX18 GET4 APAF1 UQCRC2 TIMM9 PNPT1 EHHADH PECI SPTAN1 AP3S1 AP2M1 CCT2 CCT7 EIF2S1 NSF proton- TOMM70A FLNB SLK EIF3H EIF3C NDUFAF2 RAB14 IMPA1 DHX29 SEC22B MRPS33 DHCR24 LSM6 transporting SUPV3L1 TJP1 TUBG1 PRKAG1 Ubiquinol STX3 ACOX1 CS CTNNB1 AP1B1 AP1M2 COPA SLC25A46 EIF3F OXCT1 NAPG TMEM85 EIF3L SUPV3L1 ACSL4 FXR2 ANP32E VTI1B ATP synthase EXOSC5 HSD17B4 COPB2 COASY EIF3E OAT DERL2 MDN1 cytochrome STX5 SLC27A2 PRKAA1 ARCN1 NAA15 VAPA CCT complex TMED7 STX10 STX18 MRE11A AP2M1 SLC27A2 NAA10 complexes PAK1IP1 GTF2F2 C reductase UFSP2 LRSAM1 COPB1 NDUFAF3 TXNRD2 AP2B1 AP2A2 MECP2 CCT2 PUS7 SOD2 CISD1 DDRGK1 TUFM CTPS2 complex core RAB5B PICALM RAB17 GPN1 GLRX5 SNARE RAB21 PI4K2A CDK5RAP3 SPCS3 MARCKS KIAA0174 SRP68 GNAI3 GNA11 PLS1 ERLIN2 LYRM7 EIF4A1 complex NLE1 NUPL2 NECAP2 COPI vesicle BZW1 RAP1B GNG12 DERL1 SCARB1 DDX6 AIMP3 SND1 DDX6 SRP72 GNB2 RHOG VPS28 TXNDC12 MRPS14 EXOSC9 G3BP1 ARFGAP2 coat VARS CACYBP RAB7A GNB1 SPCS2 VTA1 NSDHL RHOF DIABLO NIP7 KPNA4 MYH9 VCL CDC23 BPNT1 ME2 IVD SSBP1 CASK TLN1 FAM50A ABCE1 GLUD1 SPTBN1 ACSL4 KIF11 G3BP1 ELOVL1 EPB41NDUFS1ACADVL ATP6V1H ETHE1 SCFD1CLTC PTPN23 GTPase RTN3 SEC31ATSG101 PCK2 REEP5 ZMPSTE24 SLC25A20 TMEM14C activity SLC25A11 SSBP1 BCAP31 CYB5A SLC27A2 PHB PCCB SIGMAR1 OSBPL8 HSD17B7ARL8B MRPS21ABCD3

B Down-regulated pathways and protein complexes in CRC metastasis

CEB MEB NEB MCM complex NEB-CBP PEB YLPM1

MCM3 MCM7 TOP2ACCT5COX4I1COX5A DRAP1CCT8 ATP5J2ATP6V1C1 RARSSARSTCEB1RANACLYACO2ACTR3CTTNWDR61 CDC42 CCT2 AIPFKBP1A STIP1MSH2UBE2V2 VCL ANXA1 PPP3R1PPP1CA WDR77PRMT5 TLN1 BZW1 PFKL ETFA PPP5CCPNE1 ATIC SNX12 PRPF40A MIF ASNSADSS ATP5F1 SART1 LARP7DDX27 CTNNA1 AHSA1FKBP3 USP7 ANXA7 PRPF6 Eukaryotic RBM8A FAM50A ATP6V1E1 DCAF13 TLN2 EEF2 DYNC1I2 RANGAP1 IARS THOC4 SMS HSPA4 ATP5B UTP20 STX4 PDCD5 IDH1 DTYMK FLII PGD COPS6 ILF3 ITGA2 FKBP4 BBS1 VCP MCM4 MCM6 WDR46 ETF1 LSSMYL12AS100A6 C14orf156 PAAF1 NUDC TPT1 MVK FH MATR3 NT5E TARS ARPC2 RFC1 PGM1 SRP68 DDX52 SRP14 STRAP PDIA6 RPL6 Arp2/3 PA2G4 SLC25A10 CHD1L ELAVL1 initiation factor VCL RPL3 FKBP4 STXBP3 PPA1 ANXA1 ATP5B PHF6 MYL6 SH3GL1 RPL34 PPA2 DDX18 CSE1L UBE2I RAN MTHFD2 CTPS CAPZA2 NMD3 RPL18A PLCB4 NMT1 PPP1CB EIF2S2 CAPZA1 WDR18 EIF3C BID ETFA RPA1 SNAP23 ATP6V1E1 ACLY MCM5 MCM2 ZC3H14 EIF3B RUVBL1 COX7A2 complex SMC4 MYL6 RPA2 CARS CLIC1 ADRM1 EIF2S1 TRAP1 EMG1 AHCY MCM2 PDF HSD17B10 TSNAX ATP5D RFC3 RPL35A GAK TIA1 COPS3 EIF3L AASDHPPT DTYMK MRPL22 DDX42 MRPS23 PSAT1 RPL18 MRPS33 CDK5 RIF1 EIF5 AIMP3 HSPA4 MRPL24 RPL36 PFAS RPL13A ATP5O SRP19 MRPS35 PRPF4B CASP8 RPL11 CDC37 PGK1 LRPPRC MRPS11 PTCD3 ACTR2 ECHS1 GDI1 RPL37A CSE1L SF3B3 EIF3I HAT1 ARHGAP18 MRPL43 CTTN RCN2 DDX6 PRPF4 SHMT1 EIF4A1 RPL9 USMG5 PSMG1 FHL2 GRPEL1 LARS MRPS9 ARPC1B ARPC3 PFDN1 ATP5C1 RALY CENPB RPL18A MDH1 SIRT5 TBCA RANBP1 MRPL9 RPL13A Arc40 EIF3E ACP1 TBP HMGB1 HNRNPC C14orf166 CNBP ADRBK1 MAPK1 RPL22 SNRNP70 FDPS RPL8 MRPS18A RPL37A TAF4 SRP9 EIF1AX HSPA5 AIMP2 CFL1 SF3A1 ARPC5 RPL7A SRP72 IMPDH2 MRPL28 EIF5 COX6C GMDS METAP1 EIF5A PGM1 TRMT112 DLG1 UGDH ARPC2 ARPC5L RPL5 G6PD MRPL2 USO1 RBM8A DCTPP1 EIF4A2 RPA3 RPL7A YARS CAND1 DERA ACYP1 MRPL3 PKM2 RPL10 AKR7A2 MRPL4 DDX18 ALDH4A1 RFC4 RPA3 PDIA6 ACO1 NASP HSP90B1 GTPase EIF3J MCM6 SSB HNRNPABACTR3 BLMH EIF3F DARS USP14 CHDH NT5C2 RBKS RPL35A COQ6 MRPL3 ARPC2MTA1 OGDH AIMP1 RAN ICT1 HNRNPH3 GPI AHSA1 EIF4A3 NSFL1C RBBP4 DCTN2 RPL27 COQ5 ACTR3 ARC20 CRYZ MRPL1 EIF3D SRP54 ETF1 MRPS23 GAPDH ZNF326 DTYMK activity SOD1 COPS7A EIF3H HGS SLC25A12 ACAT2 EIF3A RPL26L1 ARHGEF1 EIF3K VBP1 MRPL1 ARPC5 MCM3 ZFR HMGCS1 NACA PGK1 COPS4 ALDH7A1 RPA2 PCNA EEF1A1 RCC2 AK1 PARK7 MTHFD1 RPL36AL DYNC1LI2 PPP6R3 NAPRT1 RPS12 IMPDH2 HNRNPA1 NNT PRPS2 MRPL17 BCCIP MRPL46 RPS23 RPS17 RPL30 PAICS MAP2K1 HSPD1 PGD LDHB DYNLRB1 TMPO COPS2 DAK BCCIP RPL32 ATAD3A GGH HNRNPUL2 TIPRL NAMPT ENO1 METAP2 MRPS27 FLNA MRPL46 GNA11 UBE2M RPL34 DYNC1H1 GNB1 CTSD RPS19 RPS21 RPSA HNRPDL DYNC1I2 RPL28RPL27A CECR5 TOP2A GNAI2 PAIP1 CLIP2 Ribosomal ATP6V1G1 RACGAP1 MRPS14 MAPRE1 EPHX1 MRPL44 TRIP6 TBRG4 RPA1 MCM3 EEF1B2 COQ5 MARCKS GNAI1 SKP1 CNDP2 CAMK2D ANXA4 SEC24C TALDO1 EEF1B2 MOBKL1B PARP1 ANXA11 MRPS22 BCKDHB GNAQ SMC2 RPS2 RPS3A UCHL5 GPD1L COPS4 MRPL15 BID INPP1 DNAJC9 SYAP1 proteins PSMG1 ARHGEF1 ITGB4 MRPL44 NSUN2 DDI2 PSMC3 XRCC6 GNAI3 BRCC3 UBXN1 WDR1 AKR1C3 CBX1 HSPBP1 MRPS15 PRKCSH PSMC6PSMC3 NAP1L4 PPP2R1A GGH TNPO1 SPR SEC24D VTA1 RPS13 RPS27A MRPL21 PSMC1 LACTB2 GNB2 DTD1 YARS TMPO MRPL37 CHMP7 PPIH NAMPT HRSP12 LIN7C PPIA PSMC2 PSMD3 PPP5C TAF2E, MRPL19 SCRN1 FARSB MRPL32 RALB QDPR COPG2 CAP1 RPS20 RPS18 NAP1L4 ST13 RAD23B MRPS7 G3BP2 PAFAH1B1 MSH2 SATB1 RCC2 PDIA4 RPS6 TP53I3 MRPL23 Replication ATP5C1 MRPL47 AK1 CCT7 BCS1L LSM12 GPS1 RAC1 ADSL PSMD10 PSMD4 ARF3 MRPL11 CCT2 CNN3 MAPK1 MRPS15 HGS TRAP1 NME1 GLRX3 HSPD1 D2HGDH PSMC2 MRPS23 SNX5 UGGT1 ATP5A1 RAB1A TCP1 SETD3 RPL7 MRPL38 GDI2 STOML2 ANXA7 TP53RK fork SNRPD3 PURA TBCB SNX1 UBXN1 TOP2A ATP5O TIMM23 PSMC5 PSMC4 SNX2 RCC2 MRPS16 P4HB ARF6 ACTR2 CCT8 OXSR1 MRPL24 ATXN10 VPS25 ATP5H MCM5 UGP2 MRPS18B ADRM1 SLC25A5 USP9X MSH6 PIN1 CHD1L SUMO1 ATP5F1 RAB13 CAPG PSMD8PSMC1 CCT4 RNH1 SNX5 MRPS34 MRPS2 PEBP1 NCKAP1 PLCB3 RPL28 MRPL53 PPIA UTP20 UQCRC2 SLC25A13 GNAS G3BP1 UTP20 CCT5 OXSR1 CTBP1 MRPS26 PDHB RHOG CAPZA2 ROCK2 MRPL18 KIAA1967 UBL5 RAB35 TXNDC12 CCT6A SLK MRPL37 PCBP1 DNAJA2 NUDT5 CUL3 MRPL46 GHITM CUL3 PDLIM1 LONP1 MYH9 ARHGAP18 EF1G SLC25A19 TUBG1 DCTN3 LRPPRC CYFIP1 GAK NAA50 NAA50 GTPBP1 SPTLC1 DCXR BOLA2 HDAC6 CD9 UBA6 NT5C3 EIF2B2 CYC1 GLRX3 SEPHS1 Proteasome PMPCA ARL3 MRPS34 WDR1 Proteasome SCO2 RPL10A MEMO1 MRPS5 TSG101 CSDE1 SLC25A11 ATP2B1 KPNA1 UQCRFS1UQCRC1 VPS37B COASY IDH1 GLTSCR2 C19orf62 CCT complex PMVK PCNA OLA1 STOML2 MVK ADAM10 GLOD4 PDCD4 RPL4 EIF4A2 WIBG RAB1A BZW2 IDH3A HTRA2 SEPT6 TRMT112 PAK2 RAB1ASDHA HUWE1GRPEL1 NIPSNAP1 TSTA3 KPNA2 complex MAPRE1 complex HARS RUVBL2 C12orf10 MSH3 CTNNB1 GALM DYNC1LI1 MCM7 ANP32B WASF2 PPP3CA GALK2 ABCB7 CYB5R3 CDC37 SRI MAP2K3 ARF6 FERMT1 ARFIP2 PPP4R2 EFHD2 DCTN5 TFG USO1 TRNT1 PPA2 PCBP2 DCTN3 CAPG TJP2 PAFAH1B1 SETD3 ATP5J2 GSS KIAA1967 SHMT1 ITGAV ADPGK GPKOW BCKDHA HSP90B1 PSMG1 ARHGAP1 GSS FERMT1 CLASP2 NSUN2 ACADS NAP1L1 METTL1 DNM1L DSTN CFL1 ATP6V1B2 TKT SNX2 CAPNS1 ATP6V1G1 ILKAP SLKCAB39 CAPZA1 UBE2K RBM34DPYSL2SARSDLST HMBS C9orf64 PRPS1DBNL CHORDC1 DCPS CARM1DNM1L SNX6PTPN11 C22orf28DNAJA1 HUWE1THUMPD3SMN1 CSDE1PRKRA AASDHPPTPDCD4CHMP4BCIAPIN1PAK1ARF3GDI2GET4OTUB1TAGLN2 Mitochondrial Ribosomal proteins A

B Down-regulated Up-regulated in all compartments in all compartments

NIPBL AFG3L2SDHBHK1C1orf163NIP7RPL22L1NDUFS1DHX37FLOT2RALARAB14OXCT1HMGCLNAA38 AGR2 RSF1 TXNRD2GLRX5ISCA1 LSM6RAB5BCPT1A GDAP1LMNAPRDX3 ACADVLPICALMATPIF1 FXR2RTN3 COX7A2LUQCRQ PCK2PUS7 HCCSFECH AGR3 GOLGA5 CAPN1GNG12 SACM1LNDUFB5 HSPA1ATMED2 LOC653884ATP5I TOMM70AUFSP2 NDUFAF3KIAA0368 OSBPL8HSD17B4 ACSL4HTATIP2 SPCS2 ME2 BAG2 CARS2 CXADR TRIP11 SOD2 EHHADH DHCR24 HMGCS2 PHB2 PYGL TUFM ENO2 CCT3 KPNB1 VKORC1L1 EPS15 RNASET2 SEC31A PSMB8 SAR1A CD97 COQ9 ARAF GOT2 RAP1B CTH ABCD3 CLTC APAF1 HMOX2 GTF2F1 CASK GTF2F2 AK4 RAB21 CABC1 RPS6KA1 C1orf57 MRPS36 HNRNPA0 NADSYN1 DHX9 GATM PHB ALDH2 NDUFS2 ARL8B TBC1D15 ALDH1B1 ACSL1 PABPC1 ACOX1 UPF1 SPCS3 PDS5B TUBB2A ATP5L BNIP1 TMED10 EPCAM PCCB NSDHL SLC27A2 STX3 SEC22B RAB17 MRPS21 RAB7A ACADSB ACAA2 ACAT1 ALDH6A1 CPT2 STX5 NSF NDUFV1 PI4K2A CS SUCLA2 PITPNA C1orf77 SCP2 ILF2 SNARE PSME2 IPO8 VAPA STX10 UBE2L6 GLUD1 CAPRIN1 ATP6V1H RTCD1 ATP5S NLE1 ELOVL1 complex UPF3B CACYBP RPS29 ANP32E STX18 VTI1B TP53 Arp2/3 DDX31 KPNA4 TSR1 C1QBP MRE11A SCFD1 NAPG C14orf1 FSCN1 SIGMAR1 GSN STX6 FTH1 Complex FLOT1 ABCE1 FLNB EPRS Proteasome B2M SYNCRIP COMMD7 RRS1 CSNK1A1 RPF2 UQCRB EIF2B1 ETHE1 KIAA0174 Complex TMED5 ZFAND1 EXOSC9 IMPA1 Replication TTC19 THYN1 C11orf73 CISD1 TPP2 ARFIP1 PSMD5 DARS2 COL4A3BP AP1G1 ALDH5A1 AP2B1 Fork EIF4G2 AP-type FAAH DIMT1L AP3M1 TBK1 NME6 XPO4 POLD3 AP2A2 LGALS4 CLTA KPNA6 DRG2 membrane coat APOO NDUFB1 AP1M2 KIAA0776 TIMM9 LRSAM1 NECAP2 COPB1 TAOK3 PNPT1 PSPH SUPV3L1 adaptor complex PDCD10 RRAGC ARCN1 UBE2Z C11orf59 SFN NUPL2 PRKAG1 HIP1R AP1B1 FAM120A TARS2 and COPI vesicle LYRM7 QRSL1 COPB2 LGALS3 PET112L ALDH3A1 CYP51A1 AP2M1 RAB27A BCAP31 COPA PYCR1 DDRGK1 BPNT1 CDK5RAP3 coat BLVRA SSBP1 MYO1C DNAJC10 EPS8 DERL1 TNPO2 OAT CAPZB DIABLO MECP2 UBE2H KIFC1 CUL5 DERL2 ACOT9 KLC4 CKMT1A TXNDC5 SUCLG2 SLC25A22 COG6 DHX29 PECI REEP5 CALM1 ERAP1 TBC1D4 VARS DIS3 ACAD8 MCM COMMD3 RDH11 IGF2BP2 HSD17B7 KIF11 NEK9 AK3 CHP ACOT13 CDC23 TMEM14C NAA15 FUS NDUFAF2 SPTAN1 PLS1 Complex TJP1 OSBP SERPINB1 EHD1 NUDT2 PHGDH EXOSC5 CUL2 SEC11A DPH1 CCNK SEP15 XPO1 LGTN COG1 HSD17B12 CTPS2PRKAA1 PDP1NSUN4 EIF5BSPTBN1 TMEM85IVD RAB2APABPC4 ERLIN2ANXA5 Opposite regulation in OAS3MRRF HMGB2EPB41 L2HGDH YTHDF2 TUBGCP3TOR1A TOR1AIP2AKR1A1 DBTVPS28PTPN23 MRPS28PAK1IP1VILL S100PCAPN2GOLGB1 ATP6V1DCYB5R1CYB5A different compartments MYO1DTMX1MDN1NAA10RRAGASND1STK39KIF5BSCARB1SLC25A20LGMNDLATNARS2WRB

VTA1 PPCS SEC24D EIF4A1 TLN1 SRP72 C SRP54 CAP1 SRP68 BZW2 ARHGAP18 SNX2 RHOG ATP5B ATP6V1C1 SNX6 COX7A2 Mitochondrial proton- CAPNS1 USMG5 ANXA7 STOML2 UQCRC1 transporting ATP synthase G3BP1 UQCRFS1 GNA11 complexes ATP5D PAK2 STX4 COX6C MYH9 CNBP ATP5F1 MARCKS COASY ATP5O

ATP5A1 CCT7 TNPO1 CCT4 COMMD10 UQCRC2 ATP5J2 CCT8 RPL5 ATP5H AP3S1 ATP5C1 CCT2 BZW1 TMED7Translation TCP1 CCT6A RARS CCT5 SLC25A11initiation EIF3L CAPG EIF3A SDHA EIF2B2 CCT CAPZA1 RPL10A EIF3C EIF3H TJP2 KIAA0664 complex EIF2S2 CTNNA1 USP9X EIF2S1 EIF3F CTNNB1 DLST EIF3E VCL MRPS14 AIMP3 MAP2K1 GNAI3 GNB1 GNB2 FERMT1 MRPS33 Figure 4 MRPS35TSG101 DDX6 A B C CCT-Complex CCT-complex, stress fiber proteins SUBCELLULAR FRACTIONATION and tubulin TOTAL EXTRACT KM12C KM12SM

kDa CCT2 57 1 0.91 1.00 1.00 1.00 0 0 1.39 0.72 1.56 0 0 KM12C KM12SM

Eif2 and Eif3 complex kDa TCP1 60 1 1.05 1.00 1.00 1.00 1.00 0 1.35 0.54 2.12 1.23 1.00

Adherens junctions SUBCELLULAR FRACTIONATION TOTAL EXTRACT KM12C KM12SM

Oxidative Phosphorylation kDa 250 Talin1

0 0 1.00 1.00 1.00 1.00 1.00 0.42 2.21 0.67 0.75 1.35

ZO-1 195

1 1.52 0 0 1.00 1.00 1.00 0 0 0 4.03 0

Guanine-nucleotide binding proteins Guanine-nucleotide binding proteins SUBCELLULAR FRACTIONATION TOTAL EXTRACT KM12C KM12SM

kDa G3BP1 68

1 3.35 1.00 0 1.00 1.00 0 1.08 1.00 2.51 3.67 0

Adherens junctions GNB2 36

1 0.98 1.00 0 1.00 1.00 1.00 0.10 1.00 1.47 0.82 0.93

RAB35 17

1 1 0 1.00 1.00 0 1.00 0 1.25 0.82 0 0

Rho GDi 28

1 0.77 1.00 1.00 1.00 0 0 1.05 0.95 1.00 0 0 Figure 5

CCT2

KM12C KM12SM

DAPI Phalloidin CCT2 Merged

G3BP1

KM12C KM12SM

DAPI Phalloidin G3BP1 Merged

RAB35

KM12C KM12SM

DAPI Phalloidin RAB35 Merged

Figure 6