Detection of Proteome Changes in Human Colon Cancer Induced by

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Detection of proteome changes in human colon cancer induced by cell surface binding of growth-inhibitory human galectin-4 using quantitative SILAC-based proteomics Malwina Michalak, Uwe Warnken, Sabine André, Martina Schnölzer, Hans-Joachim Gabius, and Juergen Kopitz J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00473 • Publication Date (Web): 01 Nov 2016 Downloaded from http://pubs.acs.org on November 3, 2016

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Journal of Proteome Research

Detection of proteome changes in human colon cancer induced by cell surface binding of growthinhibitory human galectin-4 using quantitative SILAC-based proteomics 1,2

Malwina Michalak, 3Uwe Warnken, 4Sabine André, 3Martina Schnoelzer, 4Hans-Joachim Gabius, 1,2

Juergen Kopitz*

1

Department of Applied Tumor Biology, Institute of Pathology, Medical School of the Ruprecht-KarlsUniversity, 69120 Heidelberg, Germany

2

Cancer Early Detection, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

3

Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

4

Institute of Physiological Chemistry, Faculty of Veterinary Medicine, Ludwig-Maximilians-University Munich, Veterinärstr. 13, 80539 Munich, Germany

*Correspondence to: Juergen Kopitz, PhD Department of Applied Tumor Biology Institute of Pathology University Hospital Heidelberg Im Neuenheimer Feld 224 69221 Heidelberg Germany Tel.: (+49) 6221-564227 Fax: (+49) 6221-565981 E-mail: [email protected]

Running Title: Galectin-4-induced proteome changes in CRC cells

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ABSTRACT Endogenous lectins have the capacity to translate glycan-encoded information on the cell surface into effects on cell growth. As test case to examine changes in protein presence associated with tumor growth inhibition, we applied SILAC-based proteomics on human colon carcinoma cells treated with galectin-4 (Gal-4). The five tested lines LS 180, Vaco 432, Colo 205, CX 1 and HCT 116 responded with differentiation and reduced proliferation to Gal-4 binding. In proteomic analysis (mass spectral data deposited with PRIDE - PXD003489), 2654 proteins were quantified: 190 downand 115 up-regulated (>2-fold). 1D annotation analysis of the results indicated down-regulation of DNA replication-associated processes, while protein presence for secretory and transport functions appeared increased. Strongest induction was found for CALB2 (calretinin; ~24-fold), TGM2 (proteinglutamine γ-glutamyltransferase 2; ~11-fold), S100A3 (~10-fold) and GSN (gelsolin; 9.5-fold), most pronounced decreases were seen for CDKN2A (tumor suppressor ARF; ~6-fold), EPCAM (epithelial cell adhesion molecule; ~6-fold), UBE2C (ubiquitin-conjugating enzyme E2 C; ~5-fold), KIF2C (kinesinlike protein KIF2C; -fold) and LMNB1 (lamin-B1; ~5-fold). Presence of the common proliferation marker Ki-67 was diminished about 4-fold. By tracing significant alterations of protein expression likely relevant for the observed phenotypic effects the capacity of a galectin to affect the proteome of human colon cancer cells at multiple sites is revealed. Keywords: adhesion, cancer, lectin, malignancy, proliferation, SILAC


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INTRODUCTION A phenotypic hallmark of malignancy is the alteration of cell surface glycosylation1-6. Technically, sugar receptors (lectins) from plants or fungi are commonly used to map such changes7-10, and glycoproteomics is then employed to define the nature of the proteins subject to aberrant glycosylation, e. g. in colon cancer11-14. The emerging realization that glycans presented by distinct proteins or sphingolipids are active counterreceptors for tissue lectins gives these tumor-associated shifts in the glycome a new functional dimension. Explicitly, carbohydrate epitopes can engage in a functional pairing with endogenous lectins and hereby regulate pivotal physiological processes such as adhesion or growth15,16. To give an instructive example from cancer biology the tumor suppressor p16INK4a reestablishes susceptibility to anoikis induction in carcinoma cells via a genetic reprogramming that leads to the functional pairing between glycans of the α5β 1-integrin (fibronectin receptor) and a cross-linking lectin (galectin-1) that triggers caspase-8 activation17. A decrease in α2,6-sialylation, rendering N-glycans reactive to this lectin in this concerted action, is crucial for the resulting effector activity of the suppressor18, illustrating the clinically relevant impact of a galectin on tumor cells. Intriguingly, the same galectin causes p27-dependent cell cycle arrest in carcinoma cells19-21. This case thus directs interest to the members of the galectin family and to their potential to serve as negative growth regulator in malignancy. In this respect, our attention was attracted by reports that a decrease in expression of galectin-4 (Gal-4) is not only an early event in colon carcinogenesis but establishes this galectin’s status of a suppressor candidate22-26. Gal-4 is constituted of two carbohydrate recognition domains covalently connected by a linker, making it suited for bridging two different types of glycans27-29. In its role in glycoprotein sorting and routing, it interacts with sulfatide and N-acetyllactosamine termini of Nglycans, carcinoembryonic antigen and the L1 glycoprotein among its targets30-33. Engineering its expression in colon cancer (HT29) cells (overexpression or silencing) indicated a connection to Wnt and to NF-κB/STAT3 signaling24,26. As to its site of action, nuclear Gal-4 in oligodendrocytes is able to activate the promoter of myelin basic protein mediated by p2734, while presence in leading edge of 3 ACS Paragon Plus Environment


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lamellopodia of colon cancer (T84) cells suggests a role in adhesion and/or migration35. As consequence, besides intracellular activities, an impact of Gal-4 binding to the cell surface, ‘reading’ glycan signals of colon cancer cells and initiating their ‘translation’ into bioeffects is timely to be investigated. Gal-4 as the other galectins can efficiently be secreted, e. g. from colon cancer (HT-29) cells, and then likely exert activities via trans-bridging or cis-interactions eliciting signaling32, 34-38. Explicitly, an influence on the cell proteome by a galectin has so far not been analysed quantitatively. In order to address this issue and perform such a pilot study on the proteomic level we selected five colon cancer lines, which display striking changes in morphology and growth behavior representative of the in vivo situation, and examined their response to Gal-4 treatment. The detection of growth inhibition and morphological indications for differentiation revealed cell surface binding of Gal-4 as a molecular switch. Owing to its reliability and robustness in quantitative proteomics39,40 SILAC was then applied to define any Gal-4-dependent changes of the level of proteins in a proof-of-principle manner.

EXPERIMENTAL PROCEDURES Galectin-4 Human Gal-4 was obtained by recombinant production, chromatographically purified, checked by one- and two-dimensional gel electrophoresis for purity and controlled for activity by haemagglutination as well as solid-phase and cell binding assays, stringently ensuring its carbohydrate-specific activity41-43. In addition, to completely ascertain the correct primary structure of the recombinant protein we performed peptide mass fingerprinting and ISD sequencing (Figures S1-S4, Table S1, Supplementary methods).

Cell culture


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Five human colorectal cancer cell lines (LS 180, Vaco 432, Colo 205, CX 1 and HCT 116) were maintained in DMEM supplemented with 10% fetal bovine serum (FBS), 1 mM glutamine, 100 U/ml penicillin, 100 µg/ml streptomycin (Life Technologies, Karlsruhe, Germany) and cultured at 37 °C in a 5 % CO2 atmosphere.

Proliferation assay Cell proliferation was assessed using a colorimetric tetrazolium (MTS) assay (CellTiter 96® Aqueous Non-Radioactive Cell Proliferation Assay; Promega, Mannheim, Germany). Cells were plated onto the surface of wells of 96-well cell culture plates at a density of 5000 cells/well (HCT 116) or 10 000 cells/well (LS 180, Vaco 432, Colo 205, CX 1). After 24 h incubation, the medium was removed and the cells were exposed to Gal-4 for additional 72 h, before the number of cells per well was determined according to the manufacturer’s instructions. Comparison between Gal-4-treated and control cells was performed by the two-sample t-test using SigmaPlot (11.0).

SILAC labeling and incubation with Gal-4 To generate SILAC conditions, DMEM medium lacking arginine and lysine was supplemented with 10% dialyzed FBS, 1 mM glutamine, 0.798 mM arginine and 0.398 mM lysine in either light (L[12C6,14N4] arginine (R0)& L-[12C6,14N2] lysine (K0)) or heavy (L-[13C6, 15N4] arginine (R10)& L-[13C6, 15N2] lysine (K8)) forms (Silantes, Munich, Germany). To avoid arginine-to-proline conversion the medium was additionally supplemented with L-proline (Sigma-Aldrich, Munich, Germany) to a final concentration of 200 µg/ml44. Two cell populations of LS 180 were cultured separately in ‘light’ or ‘heavy’ medium at 37 °C in a 5% CO2 atmosphere. After eight days of incorporation equal numbers of ‘heavy’ and ‘light’ labeled LS 180 cells were seeded (8 – 10x105 cells/flask) and incubated for 24 h to attach to the surface of the flasks. ‘Heavy’ labeled cells were then treated for 72 h with Gal-4, whereas control cell populations were exposed to Gal-4-free medium. The experiment was performed in triplicate. 5 ACS Paragon Plus Environment


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Cell lysis and determination of protein concentration After detaching with 0.05% trypsin-EDTA (Life Technologies), centrifugation and two washing steps with PBS, the cell pellets were stored at -80 °C for further processing. For protein extraction, cells were suspended in RIPA buffer with 50 mM Tris-HCl (pH 7.5) containing 150 mM NaCl, 1% Triton X100, 0.5% sodium deoxycholate, 0.1% SDS supplemented with 1% DTT and fresh protease (cOmplete Mini; Roche, Basel, Switzerland), and treated with benzonase (125 U; Merck Millipore, Darmstadt, Germany) on an orbital shaker (at 300 rpm) on ice for 1 h. After centrifugation at 13 000 rpm for 30 min at 4 °C, protein concentration of the extracts was measured by using 2D Quant Kit reagents (GE Healthcare, Freiburg, Germany) according to the manufacturer’s instructions.

Protein fractionation and tryptic digestion Protein lysates from both culture conditions were mixed in 1:1 ratio based on their protein concentration. 25 µg of mixed extract protein were separated by SDS-PAGE using NuPAGE Novex 412% Bis-Tris (Invitrogen, Karlsruhe, Germany) following the manufacturer’s instructions. Proteins were visualized by using the sensitive Coomassie staining protocol, as described45, and each stained lane was sliced into 27 sections. Tryptic digestion and extraction were performed, as previously described45 with adaption to the volume of the gel slices. Briefly, after incubation with 100 μl water at 37 °C for five min, water was removed (washing step), and gel pieces were shrunk by dehydration with 100 μl of a water/acetonitrile mixture (50:50, v/v) at 37 °C for five min in a Thermomixer (shaking at 600 rpm). Again, the solution was removed, and the proteins were reduced with 100 μl of a solution containing 10 mM DTT in 40 mM NH4HCO3 for 1 h at 56 °C at 600 rpm. The solution was removed, and gel pieces were incubated with 100 μl water for five min at 37 °C at 600 rpm. After removing this solution from the gel plugs, proteins were then alkylated with 100 μl of a solution containing 55 mM iodoacetamide in 40 mM NH4HCO3 for 30 min at 25 °C at 600 rpm in the dark, followed by three alternating washing steps each with 100 μl of water and a mixture of 6 ACS Paragon Plus Environment

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water/acetonitrile (50:50, v/v) for eight min at 37 °C at 600 rpm. Gel pieces were next dehydrated with 100 μl acetonitrile for 1 min at RT, dried for 15 min and subsequently rehydrated with porcine trypsin (sequencing grade; Promega) with a minimal volume sufficient to cover the gel pieces after rehydration (100 ng trypsin in 40 mM NH4HCO3). Samples were incubated overnight at 37 °C, then centrifuged, and supernatants were collected, while gel pieces were subjected to four further extraction steps. Gel pieces were sonicated for five min in a mixture of acetonitrile/0.1% aqueous TFA 50:50 (v/v). Following centrifugation the supernatant was collected, and gel pieces were sonicated for five min in acetonitrile. After collecting the supernatant, gel pieces were sonicated for five min in 0.1% TFA followed by another extraction step with acetonitrile. The combined extracts were vacuum dried in a speed-vac at 30 °C for two h. Peptides were redissolved in five µl of a solution of 2.5% hexafluoroisopropanol/0.1% TFA by sonication for five min and subsequently analysed by nanoLC ESI-MS/MS.

NanoLC ESI-MS/MS Mixtures of tryptic peptides were separated using a nanoAcquity ultra-high-performance UPLC system. A C18 trap column (180 μm ×20 mm, particle size of five µm; Waters GmbH, Eschborn, Germany) was used. Liquid chromatography separation was performed on a BEH130 C18 maincolumn (100 μ m × 100 mm, particle size of 1.7 µm; Waters) at a flow rate of 0.4 μl/min. Peptides from each gel slice were separated by a one h gradient consisting of 1% acetonitrile, 0.1% formic acid in water (A) and 0.1% formic acid in 99.9% acetonitrile (B) set as follows: from 0 to 4% B in one min, from 4 to 40% B in 39 min, from 40 to 60% B in five min, from 60 to 85% B in 0.1 min, 6 min at 85% B, from 85 to 0% B in 0.1 min, and nine min at 0% B. The nanoUPLC system was coupled online to a LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Data were acquired using XCalibur (version 2.0.7; Thermo Fisher Scientific) by scan cycles of one FTMS scan with a resolution of 60,000 at m/z 400 and a range from 300 to 2000 m/z in parallel with six MS/MS scans in the ion trap of the most abundant precursor ions of charge state 2 or higher and with the intensity 7 ACS Paragon Plus Environment


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threshold of 500. Mass window for precursor ion selection was set to 2 Da. Normalized collision energy was 35 % and the dynamic exclusion was enabled with the following parameters: repeat count – 2, repeat duration - 4 s and precursor exclusion duration – 15 s.

Protein identification and quantification The MS files were processed with the MaxQuant software version 1.5.3.847 and searched with Andromeda search engine48 against the human SwissProt database (download: 2016.04.01, 20199 entries). Enzyme specificity was set to that of trypsin, allowing for cleavage N-terminally to proline residues and up to two missed cleavage sites. The minimum peptide length of seven amino acids was required. Carbamidomethylation (C) was set as fixed modification, whereas oxidation (M), deamidation (NQ) and protein N-terminal acetylation were considered as variable modifications. No labeling or double SILAC labeling was defined with a maximum of three labeled amino acids. Mass tolerances were set for precursor and fragmented ions as follows: MS first search – 20 ppm, MS main search – 6 ppm and MS/MS – 0.5 Da. The false discovery rates (FDRs) at the protein and peptide levels were set to 1%. SILAC-based quantification was based on unique and razor peptides only, and a minimum of two ratio counts was required. Peptide ratios were calculated and normalized for each arginine- and/or lysine-containing peptide as described47. Matches to the reverse database, proteins only identified by site in modified peptides and common contaminant (KRT1, KRT10, and KRT82) hits were removed from MaxQuant output. Exclusively proteins identified with at least two unique peptides and quantified in at least two (out of three) biological replicates were considered for the further analysis.

Statistical analysis 1D annotation enrichment analysis to identify the regulated Gene Ontology Biological Processes (GOBP) was performed in Perseus (Version 1.5.2.4) on the mean values of quantified ratios. For each GOBP category, it was tested whether the corresponding mean value of ratios had a preference to be 8 ACS Paragon Plus Environment

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systematically larger or smaller than the global distribution of all protein ratios (indicated by the calculated position score). Multiple hypothesis testing was controlled by using a Benjamini-Hochberg FDR threshold of 2%. For GOBP categories that passed the test, the median ratio of the proteins belonging to the category was calculated (for details, please see49). Global interaction network of regulated proteins was predicted in STRING v10 database50 (available at www.string-db.org). Each protein-protein interaction (PPI) has a combined score (edge score), which represents the reliability of the interaction between proteins. PPIs with a combined score (0: lowest confidence; 1: highest confidence) larger than 0.7 were used for network visualization. In addition, enrichment analysis of regulated proteins was also performed in STRING v10 for Gene Ontology Biological Processes (GOBP) and Cellular Compartments (GOCC). Visible clusters on PPI map were assigned to enriched ontologies. In parallel, regulated proteins were additionally subjected to the pathway enrichment analysis (overrepresentation test) using Reactome Pathway Database including IntAct interactors51,52 (available at www.reactome.org).

Western blotting Protein extracts (20-30 µg) were processed by SDS-PAGE using NuPAGE Novex 4-12% Bis-Tris (Invitrogen) as described above or on 4-20% polyacrylamide gradient gels (RunBlue SDS-PAGE precast gels; Expedeon, Harston, UK). Proteins were then transferred onto a nitrocellulose membrane (0.45 μm; Invitrogen) for one h at 100 V. Subsequently, the membranes were blocked in TBS containing 0.1% (v/v) Tween-20 (TBST) and 5% (w/v) milk for four h at room temperature or overnight at 4 °C. Primary antibodies in blocking buffer were added to the membrane for one h at room temperature or were left overnight at 4 °C. The following primary antibodies were used: anti-cofilin (clone D3F9, PN 5175, Lot 2; 1:2000; Cell Signaling, Leiden, Netherlands), anti-gelsolin (PN SAB140589, Lot 12157; 1:1000; Sigma-Aldrich); anti-laminB1 (clone D9V6H, PN 13435, Lot 1; 1:1000; Cell Signaling); anti-β1integrin (PN 4706, Lot 3; 1:2000; Cell Signaling). After three washing steps in TBST, the membrane was incubated with a solution containing horseradish peroxidase (HRP)-labeled secondary antibody 9 ACS Paragon Plus Environment


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in blocking buffer: anti-rabbit (1:2500; Promega, Mannheim, Germany), anti-mouse (1:5000; GE Healthcare) for at least 1 h at room temperature. The membranes were then again washed three times, and reactive proteins were visualized with enhanced chemiluminescence (ECL) using a ChemiDoc™ MP System (Bio-Rad, Munich, Germany). Actin detection, using a mouse anti-actin monoclonal antibody (clone C4, PN 69100MP, Lot Q2623; 1:1000, 30 min at room temperature; MP Biomedicals, Heidelberg, Germany), served as loading control. Considering the recent discussions on the reliability and reproducibility of antibody-based analyses in biomedical research, we only applied commercial antibodies that were rigorously validated in Western blotting by manufacturers and also by work of other groups as indicated in the information provided by manufacturers. In our experience, antibody preparations meeting these criteria resulted in readily assessable blots in line with data of our SILAC-based quantitative proteomics.

RESULTS In order to test whether binding of Gal-4 to the cell surface of colon carcinoma cells influences their growth behavior and differentiation state, the lectin was added to the culture medium of five colon carcinoma cell lines (LS 180, Vaco 432, Colo 205, CX 1 and HCT 116). In all cases, a growth-inhibitory effect was seen (Figure 1). However, gradual differences in the cells` sensitivity to Gal-4 treatment and the course of response were noticed. For the CX 1 cell line a significant growth inhibition was already observed after 24 h of treatment, while it took 48h for Vaco 432, LS 180 and HCT 116 to respond. For Colo 205, a moderate effect on proliferation was noticed after 72 h. In addition to its negative effect on cell growth, Gal-4 induced changes in the cells` morphology (Figure 2). Again, Vaco 432, LS 180 and CX 1 were the most sensitive cell lines in this respect, indicating that growth inhibition is accompanied by differentiation of these cells. No signs of apoptosis like membrane blebbing or formation of apoptotic bodies were observed. Based on these results we selected the LS 180 cell line for detailed proteomic analysis of the Gal-4-induced changes by the SILAC approach for MS-based quantitative proteomics. 10 ACS Paragon Plus Environment

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The experiments were conducted in three biological replicates. The Gal-4-treated cells were labeled with “heavy medium” (containing L-[13C6, 15N4] arginine (R10)& L-[13C6, 15N2] lysine), while the untreated controls were grown in “light medium” (containing L-[12C6,14N4] arginine (R0)& L-[12C6,14N2] lysine). To reach the level of exhaustive labeling the cells were grown in these media for eight days (> five cell population doubling times), before they were seeded for the experiments. Correlation analysis for the biological replicates is given in Figure 3, proving good reproducibility of the measurements. 2654 individual proteins were identified (unique peptides > 2) and quantified (in at least two replicates) after tryptic digestion of the protein extracts by UPLC coupled to LTQ Orbitrap XL MS. The raw data of MS proteomics are deposited to the ProteomeXchange Consortium via the PRIDE52 partner repository with the dataset identifier PXD003489 (Reviewer account details: Username: [email protected] Password: Tv0Q7KZu). For 2349 proteins, the „heavyto-light ratio” (H/L ratio) was in the range of 0.5 – 2, which was considered as unchanged in their expression („non-regulated”) by Gal-4. Among the 305 proteins with altered expression (two or more fold change in protein expression in at least two replicates, thus termed „regulated proteins”), 190 were down- and 115 were up-regulated in the Gal-4-treated cells (Figure 4). The top 25 up-regulated proteins are given in Table 1, representing proteins, whose presence was increased 4 – 22-fold. Intriguingly, galectin-1 was among this list. The top 25 down-regulated proteins are shown in Table 2, covering the proteins, whose presence was decreased about 3 – 6-fold by Gal-4. The list of all detected proteins together with the H/L-ratios is compiled in Table S2. 1D Annotation enrichment analysis to identify the regulated Gene Ontology Biological Processes (GOBP) performed in Perseus (1.5.2.4) is outlined in Tables 3A and 3B. Among the remarkable results, reduction of proteins associated with DNA replication-associated processes and increase in proteins for secretory and transport functions were seen. In addition, interaction network among regulated proteins was mapped and visualized using STRING database (Figure S5). Four clear groupings were visible showing enrichment in proteins involved in chromosome organization and mitotic cell division, focal adhesion, RNA degradation as well as L-serine biosynthesis. Further analysis using Reactome 11 ACS Paragon Plus Environment


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Pathway Database revealed that protein alterations upon Gal-4 treatment can be associated with DNA metabolism and mitotic cell cycling (Table S3). In order to obtain independent evidence to corroborate the proteomics data we selected one example from the top up-regulated proteins (gelsolin), one from the list of the top down-regulated proteins (laminB1), one moderately regulated protein (β1-integrin), and one from the proteins with no significant change of signal intensity (cofilin) upon Gal-4 treatment for validation by Western blotting. The results in this assay for the LS 180 cells correlate to the data obtained by SILAC-based proteomics (Figure 5A, B). Answering the question whether comparable effects were triggered in the four tested lines by Gal-4, these experiments were also carried out on samples of the other four lines: data similar to those seen with LS 180 cells were obtained for the Vaco 432, Colo 205 and HCT 116 cell lines, whereas the CX 1 cell line did not respond to Gal-4 treatment in a comparable manner (Figure 5B).

DISCUSSION The concept of the sugar code interprets glycans as a biochemical platform for encoding information on the cell surface54,55. According to this concept, these biochemical signals can be decoded by cognate lectins56, among them members of the galectin family57. Cell surface binding and ensuing clustering of glycans by a cross-linking lectin is then one route to elicit signaling that may eventually lead to alterations on the level of the proteome. Here, we present respective data by a quantitative proteomic analysis for Gal-4, a negative growth regulator of colon cancer cells.

Colorectal carcinomas inherently being heterogeneous by their nature in diverse features, e. g. in responses to growth factors58, the non-uniform response profile of the five lines to Gal-4 is not bothersome. Reassuringly, despite the differential degree of the cells´ reactivity to Gal-4 treatment, all tested cell lines presented growth inhibition and change in cell morphology towards 12 ACS Paragon Plus Environment

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differentiation. Considering both tested parameters, i.e. growth inhibition and change in morphology, the cells of LS 180 line were most susceptible to Gal-4. This cell line was therefore selected as a model system for the proteomic analysis.

Gal-4 treatment altered levels of presence of 11.4 % of the quantitated proteins, indicating a profound impact of the lectin`s binding to the surface of the cells on their proteome. 1D annotation enrichment results of those proteins, which are subject to highest extents of regulation, discloses a striking decrease in the representation of replication-associated pathways, hereby intimating an effector route for the Gal-4-induced reduction of cell proliferation. Fittingly, prediction of global interaction network and enrichment analysis of Gal-4-regulated proteins by protein-ontology analyses point to a growth-controlling action of the galectin. Because normally differentiated colon epithelium is characteristically active in absorptive and secretory mechanisms, the increase in such functions, as seen in the annotation enrichment results for up-regulated processes after Gal-4 treatment, can be linked to the Gal-4-induced shift of the cell`s phenotype towards a more differentiated state, as observed morphologically.

From the list of top up-regulated proteins, gelsolin (GSN) deserves special attention. One of the most fundamental characteristics of transformed cells is the aberrant organization of the cytoskeleton. Although mechanisms underlying transformation and the associated disruptions of the cytosceleton are not yet clearly defined, actin-binding proteins are considered to play a pivotal role in actin filament assembly and remodeling. A particularly high-abundance actin-binding protein is gelsolin, a Ca2+-dependent actin filament-severing and -capping protein59. Remarkably, decreased expression has been observed in many cancer cells60, including colorectal cancer cells61. A critical role of gelsolin as a tumor suppressor was assumed on the basis of transfection experiments. The hereby induced overexpression in human bladder cells reduced tumorigenicity and colony-forming ability62. In a mouse model, overexpression of the protein caused reversion of the transformed phenotype63. 13 ACS Paragon Plus Environment


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Besides gelsolin, two other Ca2+-binding proteins are strikingly regulated by Gal-4. Calretinin (CALB2),which is more than 20-fold up-regulated upon Gal-4 treatment, is a multifunctional protein that acts as a major Ca2+-binding (storage) protein in the lumen of the endoplasmic reticulum. It is also found in the nucleus, suggesting that it may have a role in transcriptional regulation. In colon carcinoma cells, calretinin is involved in apoptosis induction through the intrinsic mitochondrial pathway64. As calretinin, S100-A3 is also an EF-hand calcium-binding protein. It belongs to the S100 protein family which is composed of 21 members. The biology of this protein family is complex and multifactorial. Emerging evidence suggests that they actively contribute to tumorigenic processes such as cell proliferation, metastasis, angiogenesis and immune evasion. Up- as well as downregulation has been described for individual family members and different tumor entities65. Except for one report on a growth-stimulatory effect of S100A3 in prostate cancer cells66, no further information is currently available on details of functions of this specific family member in cancer. TGM2 is the top up regulated enzyme. In a recent report, it was shown to contribute to a TP53induced autophagy program to prevent oncogenic transformation67. Due to its known effector activity on growth, the 4-fold increase of galectin-1 is noteworthy. As part of a network with potentially additive functionality68-70, such a response may contribute to growth inhibition and differentiation. In fact, acquiring sensitivity to this aspect of galectin-1 functionality is a biochemical hallmark of neuroblastoma (SK-N-MC) cells upon differentiation71 and destines activated effector T cells for apoptosis/anergy via galectin-1-dependent Ca2+-influx using TRPC5 channels72,73. That this family member can engage the same signaling route to neuritogenesis74 and the NF-κB route of transcriptional regulation to activate a pro-degradative/-inflammatory gene signature in osteoarthritic chondrocytes75,76 underscores that the response profile to galectin binding is specific for the cell type and its actual context.

Lamin B1 was selected as candidate for verification by Western blotting of the SILAC results, because lamins are of general interest for tumor biology. They are major components of the nuclear lamina, 14 ACS Paragon Plus Environment

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changes in their expression have been reported in a variety of cancer types, frequently correlating with tumorigenic potential and malignant transformation77 ,78. Likewise, EPCAM, about 6-fold downregulated, is a known tumor promoter, discovered as one of the first tumor-specific antigens overexpressed in epithelial cancer79. Decrease of EPCAM expression has been suggested as an inverse indicator of tumor aggressiveness and poor prognosis in CRC80. For two other frontrunners in the list of down-regulated proteins, i.e. UBE2C and KIF2C, too, an association with tumor promotion is known. UBE2C participates in cell cycle progression and checkpoint control by targeted degradation of short-lived proteins including mitotic spindle integrity. Thus, cells overexpressing UBE2C ignore the mitotic spindle checkpoint signals and lose genomic stability81. KIF2C (also known as mitotic centromere-associated kinesin; MCAK) is the best-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule dynamics. MCAK regulates microtubule dynamics as a potent depolymerizer of microtubules by removing tubulin subunits from the polymer end, thereby playing pivotal roles in spindle formation and chromosome movement. Up-regulation of its activity in cancer cells has been linked to increased malignancy, invasiveness, metastasis and drug resistance, most probably due to increased chromosomal instability and remodeling of the microtubule cytoskeleton in cancer cells. Due to KIF2C´s strong tumor-promoting potential its pharmacological suppression has already been suggested as a strategy of tumor therapy82. In terms of proliferation, the about 4-fold down-regulation in the expression of MKI67 (Ki-67 antigen), representing a popular proliferation marker widely used as a prognostic and predictive indicator for the assessment of biopsies from patients with cancer83, is a strong indicator of Gal-4’s impact on proliferation. Not immediately fitting the overall scheme is the occurrence of CDKN2A, a known suppressor, in this list. This counterintuitive finding cannot be explained without obtaining further data and can be considered as incentive for future study.



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CONCLUSIONS Altogether, the examples taken from the list of proteomic changes induced by cell surface binding of Gal-4 in a colorectal tumor cell line illustrate a series of reasonable candidates that can underlie observed shifts in growth behavior/morphology, documenting the power of SILAC-based proteomics. In addition to intracellular roles, mediated e. g. by interacting with components of Wnt signaling (βcatenin, APC, axin)24 or keeping the NF-κB-dependent pro-tumoral expression profile at bay26, our data establish a new functional dimension via cell surface binding. Given the uncertainty of protein levels when performing transcriptomics, this experimental approach provides definitive quantitative information on protein presence. To now gain insight into routes of signaling, e. g. relevant to manifest the inhibition, it can be extended to SILAC-based phosphoproteomics84. Considering the pathophysiological context of presence of galectins in a network, whose description has recently been completed for a model organsim85-87, it is now also a challenge to examine other galectins, alone and in their natural combination.

ASSOCIATED CONTENT Supporting information The Supporting information is available free of charge on the ACS Publications website. Supporting Methods: MALDI-TOF MS Supporting Figures Figure S1. Characterization of rhGal-4 by MALDI-TOF. Figure S2. Peptide mass fingerprinting of rhGal-4 by MALDI-TOF-MS using chymotrypsin as protease. Figure S3. Peptide mass fingerprinting of rhGal-4 by MALDI-TOF-MS using trypsin as protease. Figure S4. Sequencing of the N- and C-terminus of rhGal-4 by reISD and linSD. Figure S5. Interaction network of regulated proteins in LS 180 cells upon Gal-4 treatment. 16 ACS Paragon Plus Environment

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Supporting Tables Table S1 Calculated and experimental masses of c-ions observed in the reISD and linISD spectra for rhGal-4 with SA and sDHB. Table S2. Detailed information for each identified and quantified protein., Table S3. Pathway enrichment analysis of regulated proteins in LS 180 upon Gal-4 treatment.

ACKNOWLEDGEMENTS This work was generously supported by funding from the European Union’s Seventh Framework Program FP7/2007-2013 under REA grant agreement no. 317297 (“GLYCOPHARM”).

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Table 1: Top 25 up-regulated proteins Gene name

Protein name

Ratio H/L*

CALB2

Calretinin

23.84

TGM2

Protein-glutamine gamma-glutamyltransferase 2

11.36

S100A3

Protein S100-A3

10.35

GSN

Gelsolin

9.50

CTSB

Cathepsin B;Cathepsin B light chain;Cathepsin B heavy chain

8.84

ANXA1

Annexin A1

8.36

MVP

Major vault protein

7.99

SRXN1

Sulfiredoxin-1

7.41

CLIC3

Chloride intracellular channel protein 3

6.29

HSPA2

Heat shock-related 70 kDa protein 2

5.97

SLC2A1

Solute carrier family 2, facilitated glucose transporter member 1

5.96

CRABP2

Cellular retinoic acid-binding protein 2

5.89

FAT1

Protocadherin Fat 1;Protocadherin Fat 1, nuclear form

5.60

LIMA1

LIM domain and actin-binding protein 1

5.00

CCDC186

Coiled-coil domain-containing protein 186

4.89

CBR1

Carbonyl reductase [NADPH] 1

4.56

ZFAND5

AN1-type zinc finger protein 5

4.41

ACSL4

Long-chain-fatty-acid--CoA ligase 4

4.35

PLCG2

1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2

4.34

SH3BGRL3

SH3 domain-binding glutamic acid-rich-like protein 3

4.29

ITGA2

Integrin alpha-2

4.24

SERPINB9

Serpin B9

4.20

LGALS1

Galectin-1

4.18

GGCX

Vitamin K-dependent gamma-carboxylase

4.16

GLRX

Glutaredoxin-1

4.03

*mean of ratios from 3 biological replicates


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Table 2: Top 25 down-regulated proteins Gene name

Protein name

Ratio H/L*

CDKN2A

Tumor suppressor ARF

0.16

EPCAM

Epithelial cell adhesion molecule

0.16

UBE2C

Ubiquitin-conjugating enzyme E2 C

0.18

KIF2C

Kinesin-like protein KIF2C

0.19

LMNB1

Lamin-B1

0.20

PBRM1

Protein polybromo-1

0.20

TNS3

Tensin-3

0.21

MAD2L1

Mitotic spindle assembly checkpoint protein MAD2A

0.21

CLDN1

Claudin-1

0.23

C9orf114

Putative methyltransferase C9orf114

0.23

MKI67

Antigen KI-67

0.25

CDK6

Cyclin-dependent kinase 6

0.25

POLD3

DNA polymerase delta subunit 3

0.26

TCEAL4

Transcription elongation factor A protein-like 4

0.27

EPS8L3

Epidermal growth factor receptor kinase substrate 8-like protein 3

0.27

RCC2

Protein RCC2

0.27

Guanine nucleotide-binding protein G(s) subunit alpha isoforms GNAS

SYK

short;Guanine nucleotide-binding protein G(s) subunit alpha isoforms XLas

0.27

Tyrosine-protein kinase SYK

0.27

Chromosome-associated kinesin KIF4A;Chromosome-associated KIF4A;KIF4B kinesin KIF4B

0.27

MCM7

DNA replication licensing factor MCM7

0.28

SMC4

Structural maintenance of chromosomes protein 4

0.28

GTF2I

General transcription factor II-I

0.28

UHRF1

E3 ubiquitin-protein ligase UHRF1

0.28

SERPINH1

Serpin H1

0.29

ITGB4

Integrin beta-4

0.29

*mean of ratios from 3 biological replicates



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Table 3A: 1D annotation enrichment results. Score-based top 10 up-regulated Gene Ontology Biological Processes (GOBP) with median ratio of the proteins belonging to the category. Median GOBP name

Size

Score*

Benj. Hoch. FDR ratio H/L**

phagosome maturation

9

0.841

0.000438

1.73

long-chain fatty acid metabolic process

6

0.788

0.017329

1.75

ferric iron transport

12

0.773

0.000153

1.70

transferrin transport

12

0.773

0.000152

1.70

ATP hydrolysis coupled proton transport

9

0.772

0.001769

1.63

9

0.772

0.001762

1.63

vesicle docking

12

0.750

0.000274

1.55

vesicle docking involved in exocytosis

9

0.737

0.003562

1.50

iron ion transport

14

0.690

0.000302

1.65

fatty acid transport

8

0.687

0.016396

1.65

energy coupled proton transport, against electrochemical gradient

*A position score value near 1 indicates that the protein category is strongly concentrated at the high end of the overall numerical distribution. **Median ratio of the proteins belonging to the category.


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Table 3B: 1D annotation enrichment results for score-based top 10 down-regulated Gene Ontology Median GOBP name

Size

Score*

Benj. Hoch. FDR ratio H/L**

DNA-dependent DNA replication initiation

10

-0.938

1.78E-05

0.32

5

-0.897

0.011619

0.45

U4 snRNA 3'-end processing

5

-0.897

0.01158

0.45

DNA strand elongation

22

-0.888

9.76E-11

0.39

22

-0.888

9.56E-11

0.39

5

-0.867

0.016652

0.44

16

-0.850

4.27E-07

0.47

nucleotide-excision repair, DNA gap filling

12

-0.820

4.62E-05

0.50

mismatch repair

11

-0.813

0.000135

0.52

sister chromatid segregation

7

-0.780

0.008291

0.44

polyadenylation-dependent snoRNA 3'end processing

DNA strand elongation involved in DNA replication mitotic sister chromatid segregation telomere maintenance via semiconservative replication

*A position score value near -1 means that the values are all at the low end of the overall distribution. **Median ratio of the proteins belonging to the category.



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Legends to Figures Figure 1 Effect of Gal-4 on cell proliferation. Proliferation assay was conducted as described in `Experimental Procedures´ in the presence and absence of galectin-4 (50 µg/ml) and data obtained at the indicated time points. Optical densities (OD) represent relative cell numbers in the MTS assay. Results are the means of eight independent series (+/-S.D.) (*p

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