Comparative proteomic profiling of Hodgkin lymphoma cell lines

Abstract

Classical Hodgkin lymphoma (cHL) is a malignancy with complex pathogenesis. The hallmark of the disease is the presence of large mononucleated Hodgkin and bi- or multinucleated Reed/Sternberg (H/RS) cells. The origin of HRS cells in cHL is controversial as these cells show the coexpression of markers of several lineages. Using a proteomic approach, we compared the protein expression profile of cHL models of T- and B-cell derivation to find proteins differentially expressed in these cell lines. A total of 67 proteins were found differentially expressed between the two cell lines including metabolic proteins and proteins involved in the regulation of the cytoskeleton and/or cell migration, which were further validated by western blotting. Additionally, the expression of selected B- and T-cell antigens was also assessed by flow cytometry to reveal significant differences in the expression of different surface markers. Bioinformatics analysis was then applied to our dataset to find enriched pathways and networks, and to identify possible key regulators. In the present study, a proteomic approach was used to compare the protein expression profiles of two cHL cell lines. The identified proteins and/or networks, many of which not previously related to cHL, may be important to better define the pathogenesis of the disease, to identify novel diagnostic markers, and to design new therapeutic strategies.

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1. Introduction

Classical Hodgkin's lymphoma (cHL) is a lymphoid tumor with high incidence and complex pathogenesis.¹ More than 90% of cases belong to the classical form of the disease, while the remaining represent nodular lymphocyte predominant HL (NLPHL).¹ The diagnostic lesion of Hodgkin's disease is characterised by the disruption of the normal lymph node structure and the presence of a minority of malignant Hodgkin's and Reed–Sternberg (H/RS) cells localised in a mixed cellular background of non-neoplastic cell populations that comprise T and B cells, eosinophils, neutrophils, plasma cells, histiocytes, fibroblasts, and stromal cells.²

The pattern of somatic mutations observed in the rearranged immunoglobulin (Ig) loci suggests that H/RS cells derive from germinal center B cells.³ Nevertheless, the lack of common B cell surface markers and of Ig gene transcripts leads to the conclusions that H/RS cells are dedifferentiated B cells with decreased mRNA levels for nearly all established B-lineage-specific genes.^4,5 This specific transcriptional program is a consequence of the over-expression of a network of transcriptional factors including the helix-loop-helix proteins ABF-1 and Id2, which together with the transmembrane receptor Notch1, antagonizes the function of the B cell-transcription factors E2A and EBF.^6,7 Although most studies support a B-cell derivation of H/RS cells, other studies that associate cHL with a T-cell genotype were reported.^8–10 These phenotypic examinations revealed that H/RS cells express different B- and T-cell markers supporting their B or T cell origin.

To date, these differences have been limited to classical surface markers, for this reason we applied a proteomic and a flow cytometry approach to identify a set of proteins differentially expressed between two Hodgkin's lymphoma cell models of the B- and T-cell origin. A protein list was then defined that integrated experimental data from proteomics and flow cytometry and subjected to bioinformatics analysis to determine pathways and networks over-represented in our dataset. Results pointed out to a modulation of proteasome, cytoskeletal signaling, metabolism, and haematological functions.

2. Experimental

2.1 Cell culture and chemicals

Classical HL cell lines were purchased from German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany). L-1236 and L-428 cell lines were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 100 U mL⁻¹ penicillin and 100 μg mL⁻¹ streptomycin. HDLM-2 cell line was maintained in RPMI-1640 complete medium supplemented with 20% FBS. Both cell lines were grown at 37 °C in an atmosphere of 5% CO₂.

The following reagents were obtained from GE Healthcare: IPGs pH 3–10 NL, IPG buffer 3–10, dry strip cover fluid and Hybond ECL membrane and ECL western blotting detection reagents. Primary antibodies used in this work are listed in Table S1 (ESI†). All other reagents were from standard commercial sources and were of the highest grade available.

2.2 Flow cytometry analysis

For surface marker staining, 10⁶ cells were first incubated with monoclonal antibodies in phosphate-buffered saline (PBS) containing 1% bovine serum albumin and 0.1% sodium azide for 30 minutes at 4 °C. The cells were then washed twice with PBS and analyzed on a FACSCanto Flow Cytometer (BD Biosciences, San Jose, CA) equipped with 488 nm (blue) and 633 (red) lasers. The antibodies used in this study are given in Table S1 (ESI†).

Appropriate isotype control was used for each sample to assess background fluorescence intensity.¹¹ 30 000 events were recorded for each sample. The acquisition and analysis gates were based on forward (FSC) and side scatter (SSC) properties of cells. FSC and SSC were set in a linear scale. Flow cytometry data were analyzed using FACSDiva Software v 6.1.3 (BD Biosciences, San Jose, CA) and FlowJo software v10.0.7 (TreeStar, Ashland, OR). Fluorescence data were evaluated in terms of mean fluorescence intensity (MFI) related to isotype control.

2.3 2-DE and image analysis

Cell pellets were resuspended in lysis buffer containing 7 M Urea, 2 M thiourea, 4% CHAPS, protease inhibitor cocktail, 1 mM sodium orthovanadate, 10 mM sodium fluoride and sonicated in ice for three rounds of 10 s, and processed as previously described.¹² Briefly, protein samples (80 μg) were diluted up to 250 μL with rehydration buffer (7 M urea, 2 thiourea, 4% CHAPS, 65 mM DTT and 0.5% v/v IPG buffer) and applied to IPG strips (13 cm, pH 3–10 NL). IEF in the second dimension was performed using IPGphor IEF and Hoefer SE 600 Ruby electrophoresis (GE Healthcare). The IPG-strips were loaded and run on a 12% SDS-PAGE gel at a constant current of 20 mA per gel. Gels were fixed overnight in a fixing solution and stained according to the protocol of Chevallet and collaborators.¹³ Gels were scanned using an Image Master scanner at 300 dpi and analysed using Image Master software 5.0. Spot detection and matching were carried out by the software tools and corrected manually when necessary. The analysis was performed by comparing the volume% (vol%) of each spot, expressed as the percentage of the spot volume over the total volume of all spots in the gel. Significant differences in protein levels were determined by Student's t-test with a set value of p < 0.05. Experiments were performed three times independently.

2.4 Mass spectrometry analysis

Protein identification by nHPLC ESI-Trap analysis. Protein spots were manually picked from 2D-gels, destained with H₂O₂ and subjected to trypsin digestion followed by identification using nLC-MS/MS as described.^12–14 The nano-HPLC used was a Proxeon Easy-nLC (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a NS-AC-10 analytical column, 5 μM, C18, 375 μM OD × 75 μM ID × 10 cm length, protected by a NS-MP-10 guard column, 5 μM, C18, 375 μM OD × 100 μM ID × 2 cm length (Nano Separations, Nieuwkoop, The Netherlands). The gradient was (A: 0.1% formic acid in water; B: 0.1% formic acid in acetonitrile) 5% to 35% B from 0 to 10 min, 35% to 100% B from 10 to 12 min, 100% B from 12 to 20 min, with a flow of 0.3 μL min⁻¹. An HCT ESI trap mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany), equipped with an ESI nanosprayer, was used to record peptide over the mass range of 100 to 1800 m/z, at a speed of 8100 m/z per s; MS/MS spectra were automatically obtained from the 3 precursor ions with highest intensity over the mass range of 100 to 2800 m/z, at a speed of 23 000 m/z per s.

Protein identification by MALDI-TOF/TOF. Spots of interest were dehydrated with 50 μL of acetonitrile for 10 min and subjected to in-gel digestion as described.¹² Briefly, dried gel pieces were rehydrated in 15 μL (20 μg mL⁻¹) of trypsin proteomics grade solution (Sigma) and digested overnight at 37 °C. Peptides were concentrated by using C18 Zip-Tips (Millipore), eluted with 2 μL of CHCA matrix (66 μL TFA, 0.1%; 33 μL ACN), spotted on the MTP AnchorChipTM 384 BC plate (Bruker Daltonics) and allowed to dry at room temperature. Peptides were analyzed by peptide mass fingerprinting (PMF) and MS/MS analysis with a MALDI-TOF/TOF Ultraflextreme (Bruker Daltonics) in positive ion reflector mode (m/z range 500–4000), operating at 1 Khz frequency and controlled using the FlexControl 3.4 software. External calibration was performed using the Peptide Standard Calibration II (Bruker Daltonics). Spectra were processed using the software FlexAnalysis (version 3.4, Bruker Daltonics) and precursor ions with a signal to noise ratio greater than 10 were selected for subsequent MS/MS analysis.

MS data were exported by the software BioTools (version 3.2, Bruker Daltonics) and subjected to database search by using Matrix Science (http://www.matrixscience.com). Peptide masses were compared with those present in the SwissProt human protein database (release 2014_03 of 19-Mar-2014 of UniProtKB/TrEMBL, 54247468 sequence entries). The searching parameters were set as follows: peptide tolerance, 0.05 Da; fragment mass tolerance, 0.25 Da, enzyme, trypsin; missed cleavage, one; instrument, MALDI-TOF/TOF. Peptide tolerance was set to ±1.2 Da, the MS/MS tolerance was set to 0.6 Da, and searching peptide charge of 1+, 2+ and 3+ for ESI-Trap data. Moreover, carbamidomethyl (C) and oxidation (M) were chosen as fixed and variable modifications, respectively.

2.5 Data analysis and bioinformatics

Data were analysed using the statistical software GraphPad (version 4.0). The two-tailed t-test was used to test statistical significance. Differences were considered statistically significant for p-values < 0.05.

The bioinformatics interpretation of experimental data was performed based on two main groups of tools: enrichment analysis and protein–protein interaction analysis. The signaling hubs and connectivity networks were obtained using Ingenuity Pathway analysis (IPA, Ingenuity Systems, http://www.ingenuity.com) considering direct and indirect relationships. IPA determines biologically enriched pathways and networks based on direct and indirect relationships derived from the published literature. IPA was also used to identify upstream regulators that are activated or inhibited in our dataset. The p-value is calculated using Fisher's exact test. Pathway analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software (http://david.abcc.ncifcrf.gov/) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) bioinformatics resource. Protein–protein interaction analysis was performed using the software NetworkAnalyst (http://www.networkanalyst.ca/NetworkAnalyst/), using the InnateDB database as described.¹⁵

2.6 Western blot analysis

Proteins were extracted in radioimmunoprecipitation assay (RIPA) buffer (Cell Signaling) and quantified by the Bradford protein assay (BIORAD). Samples were separated by 10% SDS-PAGE and transferred to the Hybond ECL nitrocellulose membrane. The membranes were blocked overnight in Blotto A (Santa Cruz) at 4 °C, and subsequently probed by the appropriately diluted primary antibodies for 2 h at room temperature as described.¹²

3. Results and discussion

In this paper, we performed a 2-DE MS/MS analysis of whole proteins isolated from two Hodgkin's cell lines L-1236 (B-cell lineage) and HDLM-2 (T-cell lineage), in an attempt to characterize significant proteomic differences.

The experimental workflow is reported in Fig. S1 (ESI†). Whole proteins were separated by 2-DE and analysed for the identification of differentially expressed proteins, which were identified by MS/MS and validated by western blot. Moreover, a set of B-cell, plasma cell, T-cell, monocytes, myeloid and hematopoietic stem cell surface markers was analysed by flow cytometry to determine the expression of typical as well as unique antigens in these cells. These results were combined with those of 2-DE and analysed by bioinformatics tools.

As shown in Fig. 1, seven markers showed statistically significant differences between the two cell lines. L-1236 showed a higher expression of the B cell marker CD32, of the lymphocyte homing receptor CD44 and its variant isoform CD44v6, of the marker CD99, and of CD58, which is involved in the interaction with the immune system. Conversely, the expression of T cell markers CD25 and CD123, associated with the generation of the RS phenotype, T-cell activation and proliferation, and cHL diagnosis, was found to be higher in HDLM-2.^16–18

Fig. 1 Flow cytometry analysis of cell-surface markers in cHL cell lines. (a) The bar charts show the expression in terms of mean fluorescence intensity (MFI) ± SD related to isotype control. Significant differences in unpaired t tests are indicated (*p < 0.05, **p < 0.005, ***p < 0.0005). (b and c) Representative histograms showing the expression of CD25 and CD32 antigens in HDLM-2 (light blue histogram plot) and L-1236 (red histogram plot). (d) Background signal was assessed by staining with a matched-isotype control in HDLM-2 (light blue histogram plot) and L-1236 (red histogram plot).

In Fig. 2, representative 2-DE gels of HDLM-2 and L-1236 cells are shown. After 2-DE software analysis, 85 spots were shown to be differentially expressed proteins and 67 of them were identified by MS/MS. The list of these proteins is reported in Table 1. Several proteins previously reported to be expressed or up-regulated in H/RS cells were identified, including fascin, which were found to be up-regulated in L-1236 cells. Fascin is an actin cross-linking protein, involved in cell motility in several cell types.¹⁹ The protein was found to be up-regulated in L-1236 cells with respect to germinal center (GC) B cells, and proposed as a marker to distinguish cHL from non-Hodgkin lymphoma.^20,21

Fig. 2 Proteomic analysis of Hodgkin's lymphoma cell lines. (left) Representative 2-DE gel map of HDLM-2 and L-1236 cell proteins. A total of 80 μg of proteins were separated by 2-DE using a 13 cm IPG strip pH 3–10 NL and 12% SDS-PAGE. Proteins were visualized by silver staining. Spot numbers indicate differentially expressed proteins. (right) Western blotting analysis was performed with peroredoxin-1, -2, -6 (PRDX1, PRDX2, PRDX6), lactate dehydrogenase-B (LDH-B), and monocarboxylate transporter 1 (MCT-1) antibodies on 25 μg of total proteins from HDLM-2, L-1236, and L-428 cells. Heat shock protein 70 (HSP70) and β-actin were used as loading control.

Table 1 Differentially expressed proteins identified by 2-DE and mass spectrometry analysis

Spot number	Swiss Prot accession no.	Protein name	Gene name	Mascot score		Sequence coverage MS	Sequence coverage MS/MS	Peptides	Fold change HDLM-2/L-1236	p-Value	Instrument
				PMF	MS/MS
Spot numbers match those reported in the representative 2-DE images shown in Fig. 1 (panel A and B). Accession number in Swiss-Prot/UniprotKB (http://www.uniprot.org/). Fold change (HDLM-2 cells vs. L-1236) was calculated by dividing the average of %V^HDLM-2 by the average of %V^L-1248 cells of three independent experiments. n.a., not applicable, expressed only in L-1236 cells. p-value: **<0.01; ***<0.001.
1	P23528	Cofilin (COF1)	CFL1	89	122	42%	15%	K.AVLFCLSEDKK.N K.HELQANCYEEVKDR.C	−1.5	**	MALDI-TOF/TOF
2	P62937	Peptidyl-prolyl cis–trans isomerase A (PPIA)	PPIA	64	107	40%	13%	R.VSFELFADK.V R.IIPGFMCQGGDFTR.H R.IIPGFMCQGGDFTR.H + oxidation (M)	2.1	***	MALDI-TOF/TOF
3	P23284	Peptidyl-prolyl cis–trans isomerase B (PPIB)	PPIB	—	82	—	48%	K.FEDENFILK.H K.KITIADCGQLE.- K.EGMNIVEAM̲ER.F + oxidation (M) K.EGM̲NIVEAM̲ER.F + 2 oxidation (M) K.VKEGM̲NIVEAM̲ER.F + 2 oxidation (M) R.IIPGFM̲CQGGDFTR.H + oxidation (M) K.SIYGEKFEDENFILK.H M.VNPTVFFDIAVDGEPLGR.V M.VNPTVFFDIAVDGEPLGR.V	2.9	***	ESI-Trap
4	P22392	Nucleoside diphosphate kinase B (NDKB)	NME2	60	216	31%	31%	R.GFDCIQVGR.N K.DRPFFPGLVK.Y R.TFIAIKPGVR.G R.VMLGETNPADSKPGTIR.G + oxidation (M)	3.3	***	MALDI-TOF/TOF
6	P15531	Nucleoside diphosphate kinase A (NDKA)	NME1	97	119	42%	14%	K.DRPFFAGLVK.Y R.TFIAIKPDGVQR.G	3.2	***	MALDI-TOF/TOF
7	P61088	Ubiquitin-conjugating enzyme E2 N (UBE2N)	UBE2N	57	143	34%	19%	K.WSPALQIR.T K.DKWSPALQIR.T R.YFHVVIAPQDSPFEGGTFK.L	2	***	MALDI-TOF/TOF
8	Q01469	Fatty acid-binding protein, epidermal (FABP5)	FABP5	79	184	53%	32%	K.ELGVGIALR.K K.TQTVCNFTDGALVQHQEWDGK.E K.LVVECVM̲NNVTCTR.I + oxidation (M)	1.9	***	MALDI-TOF/TOF
10	P31949	Protein S100-A11 (S10AB)	S100A11	63	29	34%	12%	K.YAGKDGYNTLSK.T	7.2	***	MALDI-TOF/TOF
11	P09382	Galectin-1 (LEG1)	LGALS1	73	263	51%	30%	K.DSNNLCLHFNPR.F R.FNAHGDANTIVCNSK.D R.LNLEAINYM̲AADGDFK.I + oxidation (M)	2.1	***	MALDI-TOF/TOF
12	P32119	Peroredoxin-2 (PRDX2)	PRDX2	69	51	23%	5%	R.QITVNDLPVGR.S	5.4	***	MALDI-TOF/TOF
13	P07741	Adenine phosphoribosyltransferase (APT)	APRT	60	147	28%	21%	R.IDYIAGLDSR.G R.SFPDFPTPGVVFR.D K.AELEIQKDALEPGQR.V	7.5	***	MALDI-TOF/TOF
14	Q06830	Peroxiredoxin-1 (PRDX1)	PRDX1	—	47	—	29%	R.TIAQDYGVLK.A K.ATAVM̲PDGQFK.D + oxidation (M) R.LVQAFQFTDK.H R.QITVNDLPVGR.S	2.6	***	MALDI-TOF/TOF
16	P30041	Peroxiredoxin-6 (PRDX6)	PRDX6	76	113	20%	12%	R.NFDEILR.V R.VVFVFGPDK.K K.LSILYPATTGR.N	11.1	***	MALDI-TOF/TOF
17	P61106	Ras-related protein Rab-14 (RAB14)	RAB14	59	107	31%	13%	K.IYQNIQDGSLDLNAAESGVQHKPSAPQGGR.L	2.3	***	MALDI-TOF/TOF
18	Q99497	Protein DJ-1 (PARK7)	PARK7	63	42	37%	7%	K.GAEEM̲ETVIPVDVM̲R.R + 2 oxidation (M)	2.7	***	MALDI-TOF/TOF
19	P49720	Proteasome subunit beta type-3 (PSB3)	PSMB3	61	130	22	14.6	R.FGPYYTEPVIAGLDPK.T R.FGIQAQM̲VTTDFQK.I + oxidation (M)	1.8	**	MALDI-TOF/TOF
20	P30041	Peroxiredoxin-6 (PRDX6)	PRDX6	75	115	23%	13%	K.LPFPIIDDR.N R.VVFVFGPDKK.L K.LSILYPATTGR.N	2.1	***	MALDI-TOF/TOF
21	P09211	Glutathione S-transferase P (GSTP1)	GSTP1	136	214	57%	20%	M.PPYTVVYFPVR.G K.EEVVTVETWQEGSLK.A K.FQDGDLTLYQSNTILR.H	2.2	***	MALDI-TOF/TOF
24	Q9UL46	Proteasome activator complex subunit 2 (PSME2)	PSME2	107	138	39%	14%	R.KQVEVFR.Q K.TKVEAFQTTISK.Y R.ALVHERDEAAYGELR.A	1.5	**	MALDI-TOF/TOF
25	P35232	Prohibitin (PHB)	PHB	75	153	30%	12%	K.DLQNVNITLR.I R.ILFRPVVASQLPR.I R.IFTSIGEDYDER.V	1.5	**	MALDI-TOF/TOF
26	P07195	L-Lactate dehydrogenase B chain (LDH-B)	LDHB	125	127	33%	11%	K.IVVVTAGVR.G R.VIGSGCNLDSAR.F K.GEMM̲DLQHGSLFLQTPK.I + oxidation (M)	−1.4	**	MALDI-TOF/TOF
27	P17987	T-complex protein 1 subunit alpha (TCPA)	TCP1	96	214	22%	6%	K.YFVEAGAMAVR.R K.IHPTSVISGYR.L R.YINENLIVNTDELGR.D	1.5	***	MALDI-TOF/TOF
28	Q16222	UDP-N-acetylhexosamine pyrophosphorylase (UAP1)	UAP1	90	112	13%	4%	K.TLFQIQAER.I R.KTLFQIQAER.I K.VAVLLLAGGQGTR.L	2.8	***	MALDI-TOF/TOF
29	O00299	Chloride intracellular channel protein 1 (CLIC1)	CLIC1	149	276	52%	18%	K.IEEFLEAVLCPPR.Y R.GFTIPEAFR.G R.EEFASTCPDDEEIELAYEQVAK.A	1.7	**	MALDI-TOF/TOF
30	P62826	GTP-binding nuclear protein Ran (RAN)	RAN	74	99	25%	8%	K.YVATLGVEVHPLVFHTNR.G K.KYVATLGVEVHPLVFHTNR.G	−1.8	***	MALDI-TOF/TOF
31	P04083	Annexin A1 (ANXA1)	ANXA1	109	399	41%	15%	K.GTDVNVFNTILTTR.S K.QAWFIENEEQEYVQTVK.S K.GGPGSAVSPYPTFNPSSDVAALHK.A	2.5	***	MALDI-TOF/TOF
32	P40925	Malate dehydrogenase, cytoplasmic (MDHC)	MDH1	58	173	18%	6%	K.GEFVTTVQQR.G K.FVEGLPINDFSR.E	−1.7	**	MALDI-TOF/TOF
33	P49721	Proteasome subunit beta type 2 (PSB2)	PSMB2	59	212	26%	17%	R.YYTPTISR.E K.NGIHDLDNISFPK.Q R.NGYELSPTAAANFTR.R	1.8	***	MALDI-TOF/TOF
34	P28062	Proteasome subunit beta type-8 (PSB8)	PSMB8	66	42	18%	9%	K.FQHGVIAAVDSR.A K.KGPGLYYVDEHGTR.L	2.5	***	MALDI-TOF/TOF
36	P15311	Ezrin (EZRI)	EZR	111	134	25%	4%	K.IGFPWSEIR.N R.IQVWHAEHR.G K.KAPDFVFYAPR.L	1.9	***	MALDI-TOF/TOF
37	P26038	Moesin (MOES)	MSN	—	166	—	10%	K.ESPLLFK.F K.APDFVFYAPR.L R.EVWFFGLQYQDTK.G K.KTQEQLALEMAELTAR.I + oxidation (M) K.IAQDLEMYGVNYFSIK.N + oxidation (M)	2.4	***	ESI-Trap
38	P43246	DNA mismatch repair protein Msh2 (MSH2)	MSH2	67	194	11%	4%	K.LDSSAQFGYYFR.V R.FFQGM̲PEKPTTTVR.L + oxidation (M) R.GDFYTAHGEDALLAAR.E	−2.5	***	MALDI-TOF/TOF
39	P42704	Leucine-rich PPR motif-containing protein, mitochondrial (LPPRC)	LRPPRC	109	37	15%	1%	K.SNTLPISLQSIR.S K.VIEPQYFGLAYLFR.K	−1.9	**	MALDI-TOF/TOF
40	P26038	Moesin (MOES)	MSN	139	42	21%	4%	K.IGFPWSEIR.N K.APDFVFYAPR.L R.IQVWHEEHR.G	−3.9	***	MALDI-TOF/TOF
41	P26641	Elongation factor 1-gamma (EF1G)	EEF1G	166	166	25	8%	K.ALIAAQYSGAQVR.V K.DGWSLWYSEYR.F R.VLSAPPHFHFGQTNR.T	1.5	**	MALDI-TOF/TOF
42	Q16658	Fascin (FSCN1)	FSCN1	220	249	41%	7%	R.FLIVAHDDGR.W K.NASCYFDIEWR.D R.LVARPEPATGYTLEFR.S	−8.9	***	MALDI-TOF/TOF
44	P09960	Leukotriene A-4 hydrolase (LKHA4)	LTA4H	66	42	17%	5%	R.TLTGTAALTVQSQEDNLR.S R.MQEVYNFAINNSEIR.F + oxidation (M)	3.1	***	MALDI-TOF/TOF
45	Q9Y230	RuvB-like 2 (RUVB2)	RUVBL2	83	67	21%	7%	R.GTSYQSPHGIPIDLLDR.L K.EVVHTVSLHEIDVINSR.T	−3.7	***	MALDI-TOF/TOF
46	Q16658	Fascin (FSCN1)	FSCN1	86	99	33%	8%	R.FLIVAHDDGR.W K.LINRPIIVFR.G K.KNGQLAASVETAGDSELFLM̲K.L + oxidation (M)	−9	***	MALDI-TOF/TOF
48	Q7KZF4	Staphylococcal nucleare domain- containing protein (SND1)	SND1	110	202	18%	3%	K.FVDGEWYR.A K.DTPDEPWAFPAR.E R.NDIASHPPVEGSYAPR.R	−2.1	***	MALDI-TOF/TOF
49	P34897	Serine hydroxymethyltransferase, mitochondrial (GLYM)	SHMT2	61	142	27%	9%	R.LIIAGTSAYAR.L K.TGLIDYNQLALTAR.L R.GYSLVSGGTDNHLVLVDLRPK.G	−2.5	***	MALDI-TOF/TOF
50	Q16658	Fascin (FSCN1)	FSCN1	150	171	42%	6%	R.FLIVAHDDGR.W K.LINRPIIVFR.G K.NASCYFDIEWR.D	−1.8	**	MALDI-TOF/TOF
51	P02775	Platelet basic protein (CXCL7)	PPBP	86	239	38%	29%	K.ICLDPDAPR.I K.GTHCNQVEVIATLK.D K.GKEESLDSDLYAELR.C	2.9	***	MALDI-TOF/TOF
53	P07355	Annexin A2 (ANXA2)	ANXA2	67	82	21%	7%	K.WISIM̲TER.S + oxidation (M) K.AYTNFDAER.D R.QDIAFAYQR.R	7.8	***	MALDI-TOF/TOF
55	O15067	Phosphoribosylformylglycinamide synthetase (PUR4)	PFAS	109	45	15%	2%	K.FGEPVLAGFAR.S R.SLGLQLPDGQR.R K.GWAAAVTFHPR.A	−3.4	***	MALDI-TOF/TOF
56	P14625	Endoplasmin (ENPL)	HSP90B1	78	80	14%	3%	K.IYFMAGSSR.K K.FAFQAEVNR.M K.SILFVPTSAPR.G	1.5	**	MALDI-TOF/TOF
57	P13796	Plastin-2 (PLSL)	LCP1	100	81	18%	2%	R.WANYHLENAGCNK.I	1.7	**	MALDI-TOF/TOF
58	P30740	Leukocyte elastase inhibitor	SERPINB1	115	48	27%	6%	R.HNSSGSILFLGR.F K.TFHFNTVEEVHSR.F	1.7	**	MALDI-TOF/TOF
59	P49368	T-complex protein 1 subunit gamma (TCPG)	CCT3	87	145	12%	6%	R.NLQDAMQVCR.N R.TLIQNCGASTIR.L K.AM̲TGVEQWPYR.A + oxidation (M)	1.4	**	MALDI-TOF/TOF
60	P13639	Elongation factor 2 (EF2)	EEF2	160	109	25%	3%	M.VNFTVDQIR.A K.YEWDVAEAR.K K.TGTITTFEHAHNMR.V	1.4	**	MALDI-TOF/TOF
61	P13639	Elongation factor 2 (EF2)	EEF2	93	58	18%	2%	M.VNFTVDQIR.A K.YEWDVAEAR.K	−4.8	***	MALDI-TOF/TOF
62	P19338	Nucleolin (NUCL)	NCL	61	97	13%	2%	K.GLSEDTTEETLKESFDGSVR.A	n.a	n.a	MALDI-TOF/TOF
63	P33993	DNA replication licensing factor MCM7 (MCM7)	MCM7	106	118	20%	2%	R.FELYFQGPSSNKPR.V R.RFELYFQGPSSNKPR.V	−1.5	**	MALDI-TOF/TOF
67	P14866	Heterogeneus nuclear ribonucleoprotein L (HNRNPL)	HNRNPL	92	86	11%	5%	R.RMGPPVGGHR.R R.YYGGGSEGGRAPK.R R.NNRFSTPEQAAK.N	−1.7	**	MALDI-TOF/TOF
68	P11586	C-1-tetrahydrofolate synthase, cytoplasmic (C1TC)	MTHFD1	105	71	17%	2%	K.VVGDVAYDEAKER.A R.LDIDPETITWQR.V	−1.9	**	MALDI-TOF/TOF
71	P35998	26S proteasome regulatory subunit 7 (PRS7)	PSMC2	61	67	20%	9%	R.KIEFSLPDLEGR.T K.QTLQSEQPLVAR.C K.ACLIFFDEIDAIGGAR.F	1.7	**	MALDI-TOF/TOF
72	Q92499	ATP-dependent RNA helicase DDX1 (DDX1)	DDX1	150	98	28%	5%	K.MDQAIIFCR.T K.HYYEVSCHDQGLCR.V R.DQLSVLENGVDIVVGTPGR.L	−2.1	***	MALDI-TOF/TOF
74	P26038	Moesin (MOES)	MSN	140	176	28%	7%	R.IQVWHEEHR.G K.TQEQLALEMAELTAR.I K.FYPEDVSEELIQDITQR.L	−1.9	**	MALDI-TOF/TOF
76	P11498	Pyruvate carboxylase, mitochondrial (PYC)	PC	139	128	17%	3%	R.FIGPSPEVVR.K K.VVEIAPAAHLDPQLR.T R.VVHSYEELEENYTR.A	−2.4	***	MALDI-TOF/TOF
78	P63241	Eukaryotic translation initiation factor 5A-1 (IF5A1)	EIF5A	62	45	35%	7%	R.EDLRLPEGDLGK.E	−2	***	MALDI-TOF/TOF
79	Q8WVY7	Ubiquitin-like domain-containing CTD phosphatase 1 (UBCP1)	UBLCP1	63	29	18%	4%	K.LLVLDVDYTLFDHR	2.6	***	MALDI-TOF/TOF
80	P61160	Actin-related protein 2 (ARP2)	ACTR2	61	151	20%	10%	K.HLWDYTFGPEK.L R.GYAFNHSADFETVR.M K.HIVLSGGSTMYPGLPSR.L + oxidation (M)	1.6	**	MALDI-TOF/TOF
81	P55263	Adenosine kinase (ADK)	ADK	135	146	29%	9%	R.IVIFTQGR.D K.VAQWMIQQPHK.A + oxidation (M) K.FKVEYHAGGSTQNSIK.V	2.1	***	MALDI-TOF/TOF
82	Q9BV57	1,2-Dihydroxy-3-keto-5-methylthiopentene dioxygenase (MTND)	ADI1	75	124	38%	13%	R.YILDGSGYFDVR.D K.GDM̲VTLPAGIYHR.F + oxidation (M)	2.1	***	MALDI-TOF/TOF
83	P23526	Adenosylhomocysteinase (SAHH)	AHCY	—	84	—	10%	K.VPAINVNDSVTK.S K.ALDIAENEM̲PGLMR.M + oxidation (M) K.ALDIAENEM̲PGLM̲R.M + 2 oxidation (M) R.ATDVM̲IAGKVAVVAGYGDVGK.G + oxidation (M)	1.8	**	ESI-Trap
84	P07237	Protein disulfide-isomerase (PDIA1)	P4HB	130	142	27%	7%	K.FFPASADR.T K.VDATEESDLAQQYGVR.G K.ILFIFIDSDHTDNQR.I	1.6	**	MALDI-TOF/TOF
85	P08865	40S ribosomal protein SA (RSSA)	RPSA	149	207	53%	14%	K.FAAATGATPIAGR.F R.AIVAIENPADVSVISSR.N R.EHPWEVMPDLYFYR.D	2.9	***	MALDI-TOF/TOF

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2-DE analysis of cHL cells also revealed a significant difference in the expression of three members of peroxiredoxins (PRDXs) family. This is a group of proteins (PRDX1-6) that catalyse the reduction of molecules causing oxidative stress such as reactive oxygen species. Schneider and collaborators demonstrated that an elevated PRDX2 methylation was present in cHL cell lines L-428, KMH2, L-1236, and UHO1, but not HDLM-2.²² This is consistent with our data that showed an up-regulation of PRDX2 in HDLM-2. Moreover, our results extend these differences to other members of PRDX family, PRDX1 and PRDX6, which are differentially expressed in cHL cell lines (Table 1 and Fig. 2). As shown in Fig. 2, western blot analysis confirmed that all B-cell lineage cells (L-1236, and L-428) exhibited lower protein levels compared to HDLM-2.

Altered metabolism is a hallmark of cancer. High glycolytic activity was observed in cHL cells, and increased glycolysis and lactate export occur in cancer-associated stroma.²³ Stroma cells can then metabolize lactate and shuttle this energy source to cancer cells. As demonstrated by 2-DE and validated by western blot, cHL cells L-1236 showed a higher expression of lactate dehydrogenase-B (LDH-B), an enzyme that mediates the metabolism of lactate through the conversion of pyruvate into lactate. Moreover, a significant increase in the levels of the lactate transporter MCT1 was observed in L-1236 and L-428 compared to HDLM-2 (Fig. 2) further supporting the role of lactate in cHL cells of B-cell lineage.

The data obtained from proteomics and flow cytometry were then combined and analysed in silico using different bioinformatics tools. To identify the KEGG pathway statistically over-represented in our dataset, a pathway analysis was performed using the software DAVID. After p-value and Benjamini correction, two pathways were identified as the most significantly enriched, including the proteasome pathway (p-value < 4.6 × 10⁻⁴; Benjamini 2.3 × 10⁻²), which includes five members (Fig. S2, ESI†) whose expression is increased in HDLM-2, and the hematopoietic cell lineage pathway (p-value 5.1 × 10⁻⁴; Benjamini 1.3 × 10⁻²), which includes six members (Fig. S3, ESI†) some of these differentially expressed as described (Fig. 1).

The proteasome system plays an important role in regulating several biological processes including cell cycle, and antigen presentation.²⁴ Proteasome targeting emerged as a therapeutic option for the treatment of different tumor types including relapsed or refractory mantle cell lymphoma.²⁵

Experimental data were analyzed using IPA. We first examined the most relevant canonical pathways; this analysis confirms the role of proteasome in addition to pathways related to metabolism and Rho signalling that were enriched in our dataset (Table 2). Top pathways were related to protein ubiquitination (7.52 × 10⁻⁵), folate polyglutamylation (1.80 × 10⁻⁴), RhoGDI signaling (5.00 × 10⁻⁴), folate transformations I (6.40 × 10⁻⁴), and RhoA signaling (1.18 × 10⁻³).

Table 2 Top Canonical pathways identified by IPA

Name	p-Value	Overlap
NOTE: Fisher's test was used to calculate a p-value determining the statistical association between genes in the dataset and those in the canonical pathway (p<0.05). Overlap, a ratio of the number of genes in our dataset that maps to the pathway divided by the total number of genes in that pathway.
Protein ubiquitination pathway	7.52 × 10^−5	7/241 (0.029)
Folate polyglutamylation	1.80 × 10^−4	2/5 (0.4)
RhoGDI signaling	5.00 × 10^−4	5/153 (0.033)
Folate transformations I	6.40 × 10^−4	2/9 (0.222)
RhoA signaling	1.18 × 10^−3	4/108 (0.037)

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A total of three significant networks were identified by IPA (Fig. 3 and Table 3): (i) cellular growth and proliferation, cellular development, hematological system development and function, (ii) cellular development, cellular growth and proliferation, hematological system development and function, and (iii) hematological system development and function, tissue morphology, and cellular development. These results reveal the role of input proteins in a particular biological context and help to identify functional interactions with additional molecules in an enriched network. For example, in the cellular representation of the cellular growth and proliferation, cellular development, hematological system development and function, IPA identified interactions between our input proteins (shown in green and red) and other molecules participating in possible functional interactions, and highlights the role of IL2RA (CD25) as the main hub of this network (Fig. 3). As described in Table 3, two networks were enriched for the same biological functions related to cellular development, cellular growth and proliferation, hematological system development and function. However, as described in Table 3 and as shown in Fig. 3, proteins associated with these two networks are different. This means that in our models this network can be modulated by the action of a different set of proteins. In fact, as shown in Fig. 3, main hubs are represented by IL2RA and IFNG, for the first network and CD28, IL15, and LCK for the second one.

Fig. 3 Graphical representation of the top score networks identified by IPA. The network is displayed graphically as nodes and edges. The node color intensity indicates the expression of genes: red up-regulated, green down-regulated in HDLM2 vs. L-1236. Lines indicate the interactions between proteins, in which solid lines show the direct interaction and broken lines show indirect interaction. Lines with arrows represent direct interactions between proteins, while no-arrow lines represent binding only.

Table 3 Top networks identified by IPA

Top diseases and functions	Score	Molecules in network
NOTE: The score is a numerical value used to rank networks according to how relevant they are to the genes in the input dataset. This score may not be an indication of the quality or significance of the network. Score network represents −log10 of the p-value that is calculated by Fisher's exact test right-tailed.
Cellular growth and proliferation, cellular development, hematological system development and function	19	ADK, AHCY, APRT, BCL2, CCND1, CCND2, CCT3, CR2,CTNNB1, ETS2, FLI1, GRAP2, IL27, IL2RA, LDHB, MTHFD1, MYB, MYBL1, MYC, NCL, NOTCH1, PARK7, PARP1, PFAS, PRDX6, REL, RUVBL2, SHMT2, SIN3B, SPI1, STAT5B, TCP1, TP53, VIM, WT1
Cellular development, cellular growth and proliferation, hematological system development and function	19	ANXA1, BCL11B, BHLHE40, CD44, CEBPA, EZR, GJA1, GZMA, HAVCR1, HMGA1, IFNG, IL13, IL27RA, IL2RA, IL3RA, LGALS1, LTA4H, mir150, MSN, NFATC3, NFKBIA, NME2, PRDX2, PSME2, SERPINB1, SHH, SND1, STAT6, THY1, TNFRSF21, TNFSF15, TNFSF18, UBE2N, ULBP1, XCL1
Cellular development, cellular growth and proliferation, hematological system development and function	18	ANXA1, CD2, CD28, CD46, CD58, CD80, CFL1, CRK, CYLD, DOK1, ETS1, FCGR2C, FYB, HNRNPL, ICOS, IL15, IL15RA, IL2RB, IL7R, IRF3, LAT, LCK, LCP1, MAF, NME1, PPIA, PPIB, PRDX1, PRKCQ, PSMA5, PSMB8, PSMC2, RAPGEF1, S100A11, STAT5B
Hematological system development and function, tissue morphology, cellular development	18	ANXA2, APP, BCL11B, CD80, CD40LG, CLIC1, CREB1, CXCR4, EEF2, FABP5, FSCN1, GSTP1, HDAC1, HSP90B1, ICOS, IL3, MAP3K8, MCM7, MMP1, MSH2, NFATC3, NFKB1, PHB, PIM1, PSMB3, RAG2, RAN, RPSA, S100A10, SMARCA4, STAT3, TNF, TNFAIP6, TNFRSF1B, TNFSF15

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In all these networks, central hubs with high connectivity were identified including NF-κB, STAT, Bcl-2, IFNG, NOTCH1, and Myc, which are important regulators of cHL growth and survival.^26–28 A constitutive activation of phospho-STAT3 Tyr705 was detected in cHL cell lines, with high levels in HDLM-2 compared to L-1236 and L-428, and H/RS cells from 27 of 31 (87%) classical cHL cases.²⁷ A functional role for canonical and non-canonical NF-κB signaling was also described in cHL.²⁸

The molecular mechanisms behind the regulation of our entire dataset can be under control of a group of potential regulators. We used IPA to define a possible list of molecules with the role of regulation. Results are shown in Fig. 4 and Table 4. Among the identified proteins, IL1B has the most significant z-score. This cytokine may be involved in the regulation of cancer invasion and B-cell activation but its role in cHL needs to be further evaluated.

Fig. 4 IPA top upstream regulators. Upstream regulator analysis identifies the cascade of upstream transcriptional regulators that can explain the observed gene expression changes in our dataset.

Table 4 Predicted upstream regulators of the protein dataset, as determined by IPA software

Upstream regulator	Activation z-score	p-Value of overlap	Molecule	Target molecules in dataset
NOTE: IPA upstream regulator analysis was used to identify upstream transcriptional regulators that can explain the observed protein expression changes in the lymphoma dataset. For each potential transcriptional regulator two statistical measures were calculated: p-value overlap and an activation z-score. The p-value overlap identifies likely upstream regulators based on significant overlap between dataset genes and known targets regulated by a regulator. The activation z-score is used to predict probable activation states of upstream regulators based on comparison with a model that assigns random regulation directions. In the table are reported upstream regulators with a calculated z score. IL1B has a statistical significant z score (z score ≥2 or ≤2).
IL1B	2.000	2.56 × 10^−3	Cytokine	IL2RA, PSMB8, PSME2, RAN
IL5	1.953	1.52 × 10^−2	Cytokine	ANXA2, HSP90B1, IL2RA, IL3RA
IFNG	1.814	2.23 × 10^−2	Cytokine	CD44, IL2RA, IL3RA, PRDX2, PSMB8, PSME2
CD40LG	1.664	3.03 × 10^−4	Cytokine	APRT, FSCN1, IL2RA, IL3RA, MSH2, PSMB8, PSME2
IL4	0.640	1.06 × 10^−5	Cytokine	ANXA2, APRT, CCT3, CD44, IL2RA, IL3RA, LGALS1, MSH2, NCL, PHB, PSMB8
IL2	0.362	2.23 × 10^−2	Cytokine	CD44, HSP90B1, IL2RA, NME1, PHB
WT1	−0.391	2.06 × 10^−5	Transcription regulator	AHCY, APRT, HSP90B1, NME1, PPIB
CD28	−0.692	6.10 × 10^−3	Transmembrane receptor	ANXA1, IL2RA, LCP1, NCL, PHB

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To elucidate the possible interactome of our dataset, a network of protein–protein interaction (PPI) was generated using the software NetworkAnalyst. Fig. S4 (ESI†) shows the network constructed from our dataset, six important nodes were identified and are listed in Table S2 (ESI†). This list includes: Nucleolin, Ezrin, 40S Ribosomal protein SA, 14-3-3 protein zeta/delta, Protein disulfide-isomerase, and Elongation factor 2. Previous studies already described the role of some of these proteins in the regulation of cellular processes involved in cancer progression²⁹ and their up-regulation in cHL and lymphoma models.^30,31 If we consider the main hubs identified by NetworkAnalyst, we can observe the presence of Ezrin, which further underscores the relevance of proteins involved in cytoskeletal organization/signaling to differentiate the two cell models. Ezrin together with the members of the Rho pathway, which was identified by IPA as the enriched canonical pathway of our dataset, bridge the plasma membrane with the actin cytoskeleton to regulate actin polymerization, cell motility, and gene expression (Fig. 5). Fig. 5 shows a graphical representation of this pathway, which includes members of Ezrin–Moesin–Radixin (ERM) protein family, several transmembrane proteins, members of the cofilin pathway, and other signalling molecules. Up-regulation of Cofilin, Rock, and Ezrin was observed in HDLM-2 cells, while a higher expression level of Src was observed in L-1236 and L-428 (Fig. 5 inbox).

Fig. 5 IPA graphic representation of the RhoGDI pathway. Colored spots represent proteins of our dataset, white symbols depict neighbouring genes in this analysis. Box highlights proteins that were further evaluated by western blot analysis. Expression was normalized to HSP70 and used as internal control.

4. Conclusion

The ability to discriminate H/RS cells of B- and T-cell origin may represent an important requirement for the diagnosis and therapy of cHL. Although gene expression profiling analysis seems to support a common H/RS gene signature for B and T derived cHL cells,³⁰ results of proteomics and flow cytometry analysis revealed that Hodgkin's cell lines are characterised by a different protein expression signature. MS-based proteomic approaches have been used in recent years in the search for potential cHL markers.^32,33 Ma and colleagues analysed by LC-MS/MS the secretome of H/RS cells to identify a set of markers elevated in the plasma of cHL patients compared to healthy controls.³² Wallentine and collaborators compared the subcellular proteome of two cHL cell lines to identify possible candidates for the diagnosis and monitoring of the disease.³³

In the present work, our dataset confirms known proteins previously discovered by other studies, supporting the reliability of our approach, and describes novel ones. In particular, we highlight the possible role of proteasome, peroxiredoxins, and metabolism related proteins to differentiate cHL models. Moreover, the combination of flow cytometry limited one of the main problems associated with 2-DE analysis that is the isolation and the characterization of surface proteins. This uncovered the expression of membrane markers differentially expressed in HDLM-2 and L-1236 cells. Network analysis revealed how these markers are interconnected with the proteomic dataset and which molecules are potential regulators of these proteins, including hubs involved in transcriptional regulation or cytokine-mediated immune response.

In conclusion, the present work suggests that B- and T-cell derived H/RS cells have a different proteomic profile. The latter point could be considered as the basis for large screening in cHL samples.

Conflict of interest

The authors declare that there are no conflicts of interest.

Author contributions

D. Vergara, P. Simeone, S. De Matteis, and S. Carloni performed the biochemical and flow cytometry assays; P. Simeone and D. Vergara performed the bioinformatics analysis; P. Lanuti, M. Marchisio, and R. Napolitano analysed and interpreted the flow cytometry data; D. Vergara and T. Rizzello performed 2D-PAGE and MS/MS assays; D. Vergara, P. Simeone, and S. De Matteis planned the study and co-wrote the paper; M. Maffia, S. Miscia, and C. Agostinelli directed the research activities and supervised the writing of the manuscript. All authors took part in discussion of the findings obtained and in the approval of the final version of the manuscript.

Acknowledgements

We acknowledge Dr Antonio Danieli for technical assistance. We are grateful to the managers of ASL_LE, Dr Giovanni Gorgoni, Dr Antonio Sanguedolce, and Dr Vito Gigante for their support to the Lab of Clinical Proteomics. This work was supported by the PON project 254/Ric. “Implementation of human and environment health research center” cod. PONa3_00334, PONa3_00334 “Research Center for Environment and Human health”, PRIN 2010FPTBSH “NANO Molecular tEchnologies for Drug delivery – NANOMED”.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5mb00654f

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