Shotgun proteomics and network analysis of neuroblastoma cell lines treated with curcumin

1068 Mol. BioSyst., 2012, 8, 1068–1077 This journal is c The Royal Society of Chemistry 2012

Cite this: Mol. BioSyst., 2012, 8, 1068–1077

Shotgun proteomics and network analysis of neuroblastoma cell lines

treated with curcuminwzSimona D’Aguanno,

abIgea D’Agnano,

cMichele De Canio,

aClaudia Rossi,

de

Sergio Bernardini,aGiorgio Federici

aand Andrea Urbani*

ab

Received 14th December 2011, Accepted 17th January 2012

DOI: 10.1039/c2mb05498a

Curcumin is a natural compound with recognized anti-inflammatory properties, but its anticancer

activity is still object of study. We provided an unsupervised molecular investigation of the

main proteome rearrangements involved in the cellular response to curcumin in a human

neuroblastoma cell line sensitive to cisplatin and its resistant counterpart by a comparative

proteomic approach. Shotgun analysis demonstrated that 66 proteins were differentially expressed

in response to 24 h treatment with 40 mM curcumin in sensitive cells, whereas 32 proteins were

significantly modulated in treated resistant cells. Functional analysis revealed that proteins

involved in cellular assembly and organization, biosynthesis and glycolysis were down-regulated

by curcumin treatment. Proteome changes were associated to cell cycle arrest in the G2/M phase

and accumulation of polyubiquitinated proteins, also confirmed by flow cytometry and

immunoblotting analysis, but not to a significant increment of reactive oxygen species production.

Since the polyubiquitination of proteins influences a wide range of cellular pathways, the

inhibition of the ubiquitin–proteasome system may be the main way through which curcumin

performs its multi-target activity.

Introduction

Neuroblastoma (NB) is the most common extracranial child-

hood tumor. Due to its aggressiveness it is responsible for

approximately 15% of all childhood cancer deaths.1 Current

treatment approaches have not been able to completely over-

come this tumor. The major limitation of the effectiveness of

clinical treatment is represented by the emergence of resistance

to anti-cancer drugs. Thus there is the necessity to evaluate the

efficacy of novel molecules and/or the patients response to

therapy based on the combined administration of more than

one drug. In the last few years an increasing number of natural

compounds have been considered in order to develop anti-

cancer drugs.2,3 The great part of phytochemicals causes

simultaneous alterations in different molecular pathways.

This peculiarity may represent an advantage in comparison

to drugs which focus their action on a single target, since the

molecular basis of most common diseases are so complex to

limit the efficacy of drugs acting in a specific manner. On the

other hand multi-target drugs able to affect different mecha-

nisms may reduce the probability for cancer cells to develop

resistance against chemotherapeutic molecules.4

Curcumin is one of the most studied phytochemicals. It is a

phenolic compound with anti-inflammatory, anti-oxidant and

anti-cancer activities, isolated from the plant Curcuma longa,

used in oriental medicine.5 The pleiotropic effects of curcumin

derive from its capability to influence multiple cellular targets,

such as nuclear factor-kB (NF-kB), COX-2 and kinases

associated with survival signaling (IKK, factor-kB-inducingkinase, NIK, and AKT), cell proliferation (ERK) and cell

cycle regulation.4,6 Studies conducted on a wide range of cell

lines showed that curcumin acts in a cell line- and dose-dependent

manner.4,6 The anti-carcinogenic properties of curcumin were

evaluated in animal models, in which the inhibition of both

tumor initiation and promotion was demonstrated.7,8 The

curcumin effectiveness was also investigated in clinical trials.9

The proteomic platform represents a powerful tool to perform

high-throughput studies allowing the detection of modulated

proteins in response to drug treatment. In particular the

shotgun approach by label-free nanoLiquid Ultra Pressure

Chromatography coupled with fast Q-TOF MS-MS/MS

acquisition (nLC-MSE) allows the qualitative and quantitative

aDepartment of Internal Medicine, University of Rome Tor Vergata,Via Montpellier 1, 00133, Rome, Italy.E-mail: [email protected]; Fax: +39-06-501703332

b Laboratory of Proteomics and Metabonomics, S. LuciaFoundation—IRCCS, Rome, Italy

c CNR-Institute of Cell Biology and Neurobiology, Rome, ItalydCentre of Study on Aging (Ce.S.I.), ‘‘G. d’Annunzio’’University Foundation, Chieti, Italy

eDepartment of Biomedical Science, ‘‘G. d’Annunzio University’’,Chieti–Pescara, Italyw Presented, in part, at the 6th Annual National Conference of theItalian Proteomics Association held in Turin, 21st–24th June 2011.z Electronic supplementary information (ESI) available. See DOI:10.1039/c2mb05498a

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This journal is c The Royal Society of Chemistry 2012 Mol. BioSyst., 2012, 8, 1068–1077 1069

analysis of complex samples by the simultaneous screening of a

large number of proteins, avoiding two-dimensional electro-

phoresis (2-DE) and isotope or tag labelling.10,11 Such an investi-

gative approach is rendered more insightful by the support of

bioinformatics tools, able to highlight the main cellular pathways

altered in the samples under investigation. Due to these features

the proteomic platform may be the most appropriate strategy to

investigate the impact of multi-target agents such as curcumin.

Here we performed a comparative proteomic study by label-

free nLC-MSE of a human NB cell line SH-SY5Y and its

cisplatin resistant counterpart in order to provide an unsuper-

vised molecular investigation of the main proteome rearrange-

ments involved in the cellular response to curcumin exposure.

Results and discussion

Evaluation of curcumin toxicity

In order to establish the concentration of curcumin to be used

in the proteomic analysis we first evaluated curcumin toxicity.

The effect of curcumin treatment was determined by a dose–

response experiment. A human neuroblastoma SH-SY5Y cell

line sensitive to cisplatin (WT) and its resistant counterpart

(DDP) selected in our laboratory as previously described10

were incubated with different concentrations of curcumin for

24 h and cell viability was monitored by 3-(4,5-dimethylthiazol-

2-yl)-2,5-diphenyltetrazolium bromide assay (MTT assay)

(Fig. 1). The calculated EC50 for the resistant cell line was

36.5 � 1.1 mM, while the EC50 for the sensitive one was 21.6 �1.0 mM. A curcumin concentration of 40 mM was considered

the dose able to induce proteome changes in bothWT andDDP

cells. In the subsequent investigations we evaluated using an

integrated proteomic-bioinformatics platform if an identical

concentration of curcumin (40 mM) may modulate the same

classes of proteins in the two cell lines. An identical concen-

tration was used for both cell lines in order to reduce the

number of experimental variables, which should be controlled

in a shotgun experiment, where large datasets are produced.

Shotgun proteomic analysis

In order to highlight the main proteome alterations in the two

NB cell lines in response to curcumin exposure we generated

protein expression profiles of both cell lines by a label-free

shotgun proteomic approach prior to and after 24 h treatment

with 40 mM curcumin. Prior to relative quantitation, experi-

mental reproducibility was evaluated (Fig. S1, ESIz). In each

condition (T0 and T24) for both cell lines (WT and DDP), the

distribution of mass error was under 15 ppm, the retention time

coefficient of variation expressed as percentage (% CV RT) was

under 10% with most of the species under 5%, and the intensity

coefficient of variation expressed as percentage (% CV intensity)

had Gaussian distributions with all values under 4.5%. The

experimental reproducibility was considered sufficient since the

observed values were near to those expected.10,11

A total of 82856 EMRTs and 517 proteins were qualitatively

identified across both conditions in WT dataset, while 62 470

EMRTs and 525 proteins were identified in the DDP experi-

ment. Relative quantitative analysis revealed 7 and 59 proteins

which were significantly up- and down-regulated in curcumin-

treated WT cells with respect to untreated cells, whereas 7 and 25

proteins were significantly up- and down-regulated in DDP

cells exposed to curcumin (Tables 1 and 2, details of protein

identifications are reported in ESIz tables).

Functional analysis of modulated proteins

The significant modulated proteins were classified on the basis

of their molecular functions using the PANTHER Classification

System (http://pantherdb.org) (Fig. 2). The functions indicated

as Antioxidant Activity, Binding, Catalytic Activity, Ion Channel

Activity, Structural Molecular Activity and Translational

Regulator Activity were common to both datasets, while the

functions Enzyme Regulatory Activity and Transporter Activity

were represented only in the WT experiment.

Moreover, to identify the key candidates involved in the

cellular response to curcumin, we performed an unsupervised

bioinformatics analysis using the proteomic datasets of modulated

proteins by Ingenuity Pathway Analysis software (IPA).

Results are summarized in Tables 3–5. For pathways evaluation

we considered networks with score >54. Consequently, the

final network derived from the merge of networks 1 and 2 in

the WT experiment is reported in Fig. 3(A), while the network

with highest score in the DDP experiment is reported in

Fig. 3(B). As expected in both cases the transcription factor

NF-kB was one of the main nodes since it is the most studied

key survival pathway regulated by curcumin,9 playing a

critical role in chronic and acute inflammatory diseases and

various cancers.12,13 It is reported that curcumin inhibition of

NF-kB is mediated by the diminished IkB and p65 phosphoryl-

ation in different human cell lines, such as myeloid leukemia

and embrionic kidney.14,15 Moreover Freudlsperger and

colleagues16 demonstrated that curcumin induced apoptosis

in Lan-5 and SK-N-SH neuroblastoma cells through inhibition

of NF-kB. The other elements of the networks shown in Fig. 3

and 4 were representatives of the significant molecular and

cellular functions in which the modulated proteins were

grouped by IPA (Table 4).

According to our results curcumin influences cellular assembly,

organization and maintenance probably through the modulation

of tubulin, which was down-regulated by the treatment. This

finding was supported by recent evidence indicating that the

Fig. 1 Cell viability. Cell survival measured in SH-SY5Y cell lines

sensitive (WT) and resistant to cisplatin (DDP) following treatment with

curcumin. WT and DDP cells were maintained in media containing

10% FBS for 24 h, followed by incubation with different concentrations

of curcumin for other 24 h. Cell survival was monitored by MTT assay.

Values are means� SD from three experiments. *po 0.05, ***po 0.001.

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Table 1 Significantly modulated proteins in SH-SY5Y cells sensitive to cisplatin (WT) after 40 mM curcumin treatment identified by label-freeLC-MSE

Accessiona Descriptionb Scorec WT T24/T0d WT T0/T24e SD f

P0CG48 Polyubiquitin-C, UBC 1000.29 2.63 �0.96 0.1P08107 Heat shock 70 kDa protein 1A/1B, HSPA1A 2548.45 2.27 �0.82 0.09P17066 Heat shock 70 kDa protein 6, HSPA6 1085.71 1.72 �0.55 0.13P34931 Heat shock 70 kDa protein 1-like, HSPA1L 1132.96 1.67 �0.51 0.11P50914 60S ribosomal protein L14, RPL14 244.22 0.67 0.4 0.29Q92841 Probable ATP-dependent RNA helicase, DDX17 432.12 0.67 0.4 0.21P60842 Eukaryotic initiation factor 4A-I, EIF4A1 573.21 0.67 0.4 0.15P08729 Keratin, type II cytoskeletal 7, KRT7 382.57 0.67 0.4 0.17P17844 Probable ATP-dependent RNA helicase, DDX5 391.96 0.67 0.4 0.29P61978 Heterogeneous nuclear ribonucleoprotein K, HNRNPK 374.52 0.67 0.4 0.16P25398 40S ribosomal protein S12, RPS12 145.68 0.66 0.41 0.32P46781 40S ribosomal protein S9, RPS9 204.2 0.65 0.43 0.28P62269 40S ribosomal protein S18, RPS18 187.35 0.65 0.43 0.21P09923 Intestinal-type alkaline phosphatase, ALPI 368.98 0.65 0.44 0.2Q14240 Eukaryotic initiation factor 4A-II, EIF4A2 385.24 0.64 0.45 0.22P19338 Nucleolin, NCL 237.52 0.64 0.45 0.22P32119 Peroxiredoxin-2, PRDX2 123.92 0.63 0.46 0.19P0CG39 POTE ankyrin domain family member J, POTEJ 600.11 0.63 0.46 0.14A5A3E0 POTE ankyrin domain family member F, POTEF 876.37 0.62 0.48 0.12Q05639 Elongation factor 1-=lpha 2, EEF1A2 737.74 0.62 0.48 0.1P04075 Fructose-bisphosphate aldolase A, ALDOA 640.35 0.61 0.49 0.11P27348 14-3-3 protein theta, YWHAQ 276.87 0.60 0.51 0.15P61981 14-3-3 protein gamma, YWHAG 247.94 0.60 0.51 0.2Q06830 Peroxiredoxin-1, PRDX1 578.53 0.60 0.51 0.12P26641 Elongation factor 1-gamma, EEF1G 456.89 0.60 0.52 0.14Q04917 14-3-3 protein eta, YWHAH 261.8 0.59 0.53 0.19P68104 Elongation factor 1-alpha 1, EEF1A1 1333.68 0.58 0.54 0.06Q5VTE0 Putative elongation factor 1-alpha-like 3, EEF1AL3 1338.84 0.58 0.54 0.07P61247 40S ribosomal protein S3a, RPS3A 272.81 0.58 0.54 0.23P31947 14-3-3 protein sigma, SFN 241.99 0.58 0.55 0.22P14618 Pyruvate kinase isozymes M1/M2, PKM2 961.27 0.58 0.55 0.08P23396 40S ribosomal protein, RPS3 230.33 0.56 0.58 0.16P62258 14-3-3 protein epsilon, YWHAE 281.05 0.56 0.58 0.16P07195 L-lactate dehydrogenase B, LDHB 603.47 0.56 0.59 0.1P30041 Peroxiredoxin-6, PRDX6 372.92 0.56 0.59 0.2P05388 60S acidic ribosomal protein P0, RPLP0 220.42 0.55 0.6 0.22P62826 GTP-binding nuclear protein Ran, RAN 363.4 0.55 0.6 0.15P68366 Tubulin alpha-4A chain, TUBA4A 684.2 0.55 0.6 0.08Q9H4B7 Tubulin beta-1 chain, TUBB1 370.46 0.54 0.61 0.2P62277 40S ribosomal protein S13, RPS13 319.93 0.54 0.61 0.25O43707 Alpha-actinin-4, ACTN4 605.77 0.54 0.62 0.25P13639 Elongation factor 2, EEF2 1511.06 0.53 0.63 0.08P06733 Alpha-enolase, ENO1 1130.42 0.53 0.64 0.07P00338 L-lactate dehydrogenase A chain, LDHA 639.13 0.53 0.64 0.08P63104 14-3-3 protein zeta/delta, YWHAZ 282.6 0.53 0.64 0.15P08865 40S ribosomal protein SA, RPSA 231.75 0.53 0.64 0.24P62937 Peptidyl-prolyl cis–trans isomerase A, PPIA 334.18 0.52 0.66 0.11P37802 Transgelin-2, TAGLN2 264.15 0.50 0.69 0.22P29401 Transketolase, TKT 646.79 0.50 0.7 0.15P00558 Phosphoglycerate kinase 1, PGK1 598.16 0.50 0.7 0.16P04406 Glyceraldehyde-3-phosphate dehydrogenase, GAPDH 1155.01 0.50 0.7 0.09P12814 Alpha-actinin-1, ACTN1 555.11 0.50 0.7 0.31P23528 Cofilin-1, CFL1 231.84 0.49 0.71 0.19Q9BUF5 Tubulin beta-6 chain, TUBB6 512.79 0.49 0.72 0.14P60174 Triosephosphate isomerase, TPI1 458.65 0.47 0.75 0.21P07437 Tubulin beta chain, TUBB 1185.08 0.47 0.76 0.09P00966 Argininosuccinate synthase, ASS1 300.61 0.45 0.79 0.15Q16719 Kynureninase, KYNU 276.31 0.45 0.8 0.36P10599 Thioredoxin, TXN 239.87 WT T24 WT T24 —P07197 Neurofilament medium polypeptide, NEFM 534.22 WT T24 WT T24 —P09601 Heme oxygenase 1, HMOX1 222.68 WT T24 WT T24 —O00410 Importin-5, IPO5 441.06 WT T0 WT T0 —P13804 Electron transfer flavoprotein subunit alpha, ETFA 158.58 WT T0 WT T0 —P62158 Calmodulin, CALM1 218.23 WT T0 WT T0 —P30086 Phosphatidylethanolamine-binding protein 1, PEBP1 206.94 WT T0 WT T0 —P08708 40S ribosomal protein S17, RPS17 135.99 WT T0 WT T0 —

a Accession number according to SwissProt database. b Protein description with relative symbol. c PLGS score obtained for protein identification.d Ratio expressed in decimal scale. e Ratio expressed in loge scale.

f Standard deviation expressed in the loge scale; WT T0 and WT T24 indicate

proteins that were found highly represented in control or treated cells, respectively.

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Table 2 Significantly modulated proteins in SH-SY5Y cells resistant to cisplatin (DDP) after 40 mM curcumin treatment identified by label-freeLC-MSE

Accessiona Descriptionb Scorec DDP T24/T0d DDP T24/T0e SD f

P62937 Peptidyl-prolyl cis–trans isomerase A, PPIA 580.75 0.56 �0.58 0.07P07195 L-lactate dehydrogenase B chain, LDHB 892.76 0.57 �0.56 0.07P00338 L-lactate dehydrogenase A chain, LDHA 1131.53 0.57 �0.56 0.07P13639 Elongation factor 2, EEF2 2061.54 0.58 �0.54 0.07P62277 40S ribosomal protein S13, RPS13 310.42 0.58 �0.54 0.25P46776 60S ribosomal protein L27a,RPL27A 184.63 0.59 �0.53 0.24P62826 GTP-binding nuclear protein Ran, RAN 452.93 0.61 �0.49 0.13P62249 40S ribosomal protein S16, RPS16 226.03 0.61 �0.5 0.2P62258 14-3-3 protein epsilon, YWHAE 254.98 0.62 �0.48 0.14P07437 Tubulin beta chain, TUBB 1693.59 0.63 �0.46 0.06P25398 40S ribosomal protein S12, RPS12 211.13 0.63 �0.47 0.18P05388 60S acidic ribosomal protein P0, RPLP0 353.62 0.64 �0.44 0.19P60174 Triosephosphate isomerase, TPI1 529.22 0.64 �0.44 0.14Q02878 60S ribosomal protein L6, RPL6 340.25 0.64 �0.45 0.18P29401 Transketolase, TKT 732.67 0.64 �0.45 0.14P68366 Tubulin alpha-4A, TUBA4A 1004.33 0.65 �0.43 0.07P04406 Glyceraldehyde-3-phosphate dehydrogenase, GAPDH 1428.46 0.66 �0.41 0.06P26641 Elongation factor 1-gamma, EEF1G 606.59 0.66 �0.42 0.12P46781 40S ribosomal protein S9, RPS9 201.42 0.67 �0.4 0.18Q06830 Peroxiredoxin-1, PRDX1 590.84 0.67 �0.4 0.09P63104 14-3-3 protein zeta/delta, YWHAZ 430.73 0.67 �0.4 0.17P11021 78 kDa glucose-regulated protein, HSPA5 801.27 1.51 0.41 0.08P17066 Heat shock 70 kDa protein 6, HSPA6 428.56 2.61 0.96 0.17P08107 Heat shock 70 kDa protein 1A/1B, HSPA1A 776.73 3.78 1.33 0.08P50990 T-complex protein 1 subunit theta, CCT8 430.84 DDPT0 DDPT0 —P60866 40S ribosomal protein S20, RPS20 125.2 DDPT0 DDPT0 —P18621 60S ribosomal protein L17, RPL17 181.99 DDPT0 DDPT0 —Q02543 60S ribosomal protein L18a, RPL18A 125.51 DDPT0 DDPT0 —Q92598 Heat shock protein 105 kDa, HSPH1 371.91 DDPT24 DDPT24 —P04843 Dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit 1, RPN1 363.99 DDPT24 DDPT24 —Q86Y46 Keratin, type II cytoskeletal 73, KRT73 258.9 DDPT24 DDPT24 —P09601 Heme oxygenase 1, HMOX1 205.01 DDPT24 DDPT24 —

a Accession number according to SwissProt database. b Protein description with relative symbol. c PLGS score obtained for protein identification.d Ratio expressed in decimal scale. e Ratio expressed in loge scale.

f Standard deviation expressed in loge scale; DDP T0 and DDP T24 indicate

proteins that were found highly represented in control or treated cells, respectively.

Fig. 2 Protein classification. The differentially expressed proteins, between the curcumin treated and untreated WT cells (A) and DDP cells (B),

were classified by their molecular functions using the PANTHER Classification System.

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anti-proliferative effect of curcumin involves the perturbation of

microtubule dynamics.17 Moreover curcumin has been found to

bind to purified tubulin, to inhibit tubulin polymerization in vitro,

to depolymerize microtubules in HeLa and MCF-7 cells and to

perturb the microtubule spindle structure.18,19

Furthermore our results provided evidence of glycolysis

inhibition after treatment. In fact glycolysis/gluconeogenesis

is one of the significant canonical pathways reported in

Table 5 and glycolytic enzymes, such as lactate dehydrogenase

(LDH), triosephosphate isomerase (TPI1), glyceraldehyde-3-

phosphate dehydrogenase (GAPDH), were down-regulated in

both WT and DDP treated cells and in addition enolase

(ENO1) and aldolase (ALDOA) were down-regulated in the

WT dataset (Tables 1 and 2). Increased glucose uptake and

high aerobic glycolysis occur in a wide spectrum of human

cancers and it is considered one of the main alterations during

malignant transformation.20 On the basis of these considerations

targeting aerobic glycolysis may be a promising approach

for anticancer treatment. Several glycolysis inhibitors are

actually in preclinical and clinical development, such as lactate

dehydrogenase A inhibitor FX1121 or hexokinase inhibitor

2-deoxyglucose (2DG).22 The transcription of genes involved

in energy production and metabolism is under the control

of the transcription factors c-Myc, hypoxia-inducible factor

1-alpha (HIF1a) and p53.23 Although curcumin is known to

inhibit the oncogenic c-Myc,9 glycolysis inhibition was not

previously reported among the effects of this molecule.

Beside the modulation of glycolysis we reported the down-

regulation of both ribosomal proteins and initiator and

elongation translational factors after exposure to curcumin,

thus indicating a reduced protein synthesis function in treated

cells (Table 4). The observation that relevant cellular processes

such as cellular assembly, glycolysis and protein synthesis were

altered after drug exposure prompted us to evaluate the

impact of curcumin on cell cycle progression.

Validation of pathway analysis and functional classification

IPA analysis pointed out a possible G2/M arrest in treated

cells (Table 5). In order to validate bioinformatics results we

examined cell cycle distribution of both cell lines after 24 h of

treatment with 40 mM curcumin (Fig. 4). The percentage of

cells in the G2/M phase increased after drug exposure, thus

confirming IPA evidence. This result was in accordance with

several studies indicating that curcumin might exert its anti-

cancer effect by modulating cell cycle regulatory machineries,

promoting prevalently G1/S or G2/M arrest, depending on the

cellular models considered.24,25 The modulation of the expres-

sion levels of different 14-3-3 family members recorded in our

proteomic experiments (Table 1 and 2) and highlighted in the

networks reported in Fig. 3 may contribute to cell cycle arrest.

The 14-3-3 proteins are relevant molecular scaffolds, able to

affect many biologically important processes, including cell

cycle regulation, through the modulation of the conformation of

their binding partners.26 Cell cycle progression is known to be

Table 3 Ingenuity pathway analysis results. List of significant networks

Network Associated network functions Score

Wt experiment1 Hematological disease, immunological disease, inflammatory disease 582 Protein synthesis, cancer, gastrointestinal disease 543 Neurological disease, skeletal and muscular disorders, organismal injury and abnormalities 144 Cellular development, cellular growth and proliferation, hair and skin development and function 10DDP experiment1 Genetic disorder, neurological disease, hematological disease 692 Cancer, cell death, cell cycle 19

Table 4 Ingenuity pathway analysis results. List of significant molecular and cellular function

Molecular and Cellular Functions p Value No. of molecules

WT experimentProtein synthesis 6.18 � 10�11 to 3.60 � 10�2 17Carbohydrate metabolism 3.76 � 10�8 to 3.99 � 10�2 10Small molecule biochemistry 7.51 � 10�6 to 4.38 � 10�2 26Cell cycle 2.25 � 10�5 to 4.77 � 10�2 11Cellular assembly and organization 2.25 � 10�5 to 4.77 � 10�2 16DDP experimentProtein synthesis 2.42 � 10�7 to 3.13 � 10�2 8Cell sycle 3.46 � 10�4 to 4.97 � 10�2 4Cellular assembly and organization 3.46 � 10�4 to 4.50 � 10�2 5DNA replication, recombination, and repair 3.46 � 10�4 to 4.50 � 10�2 5Cellular function and maintenance 4.95 � 10�4 to 3.54 � 10�2 6

Table 5 Ingenuity pathway analysis results. List of significant topcanonical pathway

Canonical pathways p Value

WT experiment14-3-3-mediated signaling 4.29 � 10�11

Glycolysis/gluconeogenesis 8.14 � 10�9

Cell cycle: G2/M DNA damage checkpoint regulation 2.19 � 10�8

Myc mediated apoptosis signaling 1.80 � 10�7

DDP experimentGlycolysis/gluconeogenesis 5.00 � 10�5

14-3-3-mediated signaling 8.78 � 10�5

p70S6K signaling 1.86 � 10�3

Cell cycle: G2/M DNA damage checkpoint regulation 3.47 � 10�3

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tightly regulated by the phosphorylation and dephosphory-

lation of the CDK-cyclin complexes, which ensure well deli-

neated transitions between cell cycle phases.25 Correct cell cycle

progression is also regulated by the selective degradation of

proteins mediated by the ubiquitin–proteasome system (UPS),

which plays an essential role in several basic cellular processes

such as proliferation, apoptosis and differentiation.24,27

Interestingly among the proteins up-regulated after curcumin

treatment in WT we found polyubiquitin (UBC), which is also

among the nodes of the corresponding IPA network (Table 1

and Fig. 3A). This finding suggested to investigate the level of

polyubiquitinated proteins in control and treated cells by

western blotting analysis (Fig. 5). The experiment revealed

an increased amount of polyubiquitinated proteins after

curcumin exposure in both cell lines. Our result was in

accordance with recent studies reporting the impairment of

UPS mediated by curcumin.24,28,29 The up-regulation of UBC

was found even in the DDP shotgun experiment but it is not

reported in Table 2 because it did not match the criterion of

reproducibility of three runs (see the Experimental section for

more details about ‘‘exclusion criteria’’). Moreover in the DDP

dataset dolichyl-diphosphooligosaccharide-protein glycosyl-

transferase (ribophorin or Rpn1) was found among the

proteins highly represented after curcumin treatment. Rpn1

was reported to be a component of the 19S regulatory complex

of the 26S proteasome, which may mediate binding of ubiquitin-

like domains to 26S proteasome itself.30 Since there are many

regulatory proteins among the UPS targets, its impairment

might be the key to explain the multiple effects of curcumin.

Fig. 4 Cell cycle. Cells were incubated in the absence or presence of

40 mM curcumin for 24 h and then stained with propidium iodide.

DNA content of the cells was quantified by flow cytometry. A total of

20 000 cells were acquired for each cell line and condition. The

percentage of cells in the three phases of the cell cycle was estimated

using ModFit software. An increased percentage of cells in the G2/M

phase was observed after treatment with a significant p-value according

to Student’s t-test (p o 0.01). Columns are means of three experiments.

Bars = SD. *p o 0.05, **p o 0.01 and ***p o 0.001.

Fig. 3 Pathway analysis. The network derived from the merge of networks 1 and 2 for the WT experiment was reported in Panel A, while the

network with highest score for the DDP experiment was reported in Panel B. Nodes represent proteins: shaded features describe proteins identified

in the present study (red = up-regulated and green = down-regulated after treatment with 40 mM curcumin) whereas un-shaded features describe

additional members of these networks and pathways which were not detected by proteomic analysis. Node shapes indicate function: enzymes

(diamond), transcription regulators (oval), nuclear receptors (rectangle), cytokines (square), transporter (trapezoid), and ‘‘other’’ (circles).

Protein–protein associations are indicated by edges containing single lines, whereas proteins acting upon other proteins (controlling their

expression) are indicated by arrows. Continuous or dotted line indicates, respectively, direct or indirect protein interactions.

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Another relevant aspect pointed out by the molecular

function classification (Fig. 2) and underlined by IPA networks

(Fig. 3) was the modulation of antioxidant proteins such as

peroxiredoxin isoforms (PRDXs), heme oxigenase-1 (HO-1, or

HOMX-1 according to IPA annotation) and heat shock

protein 70 isoform (Hsp70).

The up-regulation of Hsp70 after treatment in both cell lines

found by proteomic analysis and confirmed by immunoblotting

analysis (Fig. 5) was in accordance with findings reporting the

ability of curcumin to induce Hsp70 under various stress

conditions in rat cells31 and more recently also in human

leukemia cells.32 Hsp90, Hsp70 and Hsp27 (HSPs) are generally

known as a family of stress proteins induced in response to a

wide variety of physiological and environmental insults.

In addition to their cytoprotective role, HSPs act also as

molecular chaperones by assisting the folding of nascent or

misfolded proteins and by preventing their aggregation.33

Since it is reported that proteasome inhibition induces hsp70

gene expression,34 the observed up-regulation of Hsp70 in WT

and DDP after curcumin treatment may be associated to the

increment of polyubiquitinated proteins. It is also known that

Hsp70 isoforms may promote cell survival by inhibiting

apoptosis.35 Consistent with this cytoprotective function,

increased expression of Hsp70 isoforms is commonly asso-

ciated with the malignant cell phenotype. Even if there is a

good correlation between HSPs expression and the resistance

of cancer cells to chemotherapy, Hsp70 may not be considered

an indisputable marker of cancer.33 Hsp70 expression may

represent the initial attempt of the cell to escape apoptosis, but

cellular proliferation may be likewise affected after drug

exposure. In a recent paper apoptosis induced by curcumin

in a human schwannoma cell line (HEI-193) has been

described to be associated to Hsp70 up-regulation.36 Although

the increment of Hsp70 level was observed after drug treat-

ment, in both WT and DDP curcumin exposure was able to

inhibit cellular proliferation causing cell cycle arrest in the

G2/M phase. Moreover the observed Hsp70 up-regulation

may not result from acquired chemoresistance to curcumin,

since resistance to anti-cancer molecules is acquired only after

a long period of drug exposure rather than 24 hours, that is the

time of treatment used in our experiments. Furthermore it was

reported that HEI-193 cells over-expressing Hsp70 selected

after long exposure to curcumin (resistant cells to curcumin)

were more sensitive to treatment with specific Hsp70 inhibitors

than HEI-193 cells expressing lower level of Hsp70, thus

suggesting the promising use of curcumin in a combinatory

pharmacological strategy against cancer cells.36

The transcription of PRDX and HOMX-1 enzymes is regu-

lated by the transcription factor NF-E2-related factor 2 (Nrf2)

which is known to be activated by curcumin.9 Western blotting

analysis detected the increment of Nrf2 level, which is more

prominent in WT cells than in DDP cells exposed to curcumin

(Fig. 5). Nevertheless the trend of expression of PRDXs and

HOMX-1 found by the shotgun approach was in the opposite

direction. HOMX-1 was among the highly represented proteins

found in the treated cells, whereas PRDX1 was among the

down-regulated proteins in bothWT and DDP treated cells and

PRDX2 and PRDX6 decreased after treatment in the WT cell

line. Immunoblotting analysis confirmed the down-regulation

of PRDX1 in both cell lines after treatment (Fig. 5). These

apparent conflicting results may be explained considering that

other signal transductions also contribute to regulate PRDXs

expression. In addition to Nrf2 consensus binding, two potential

MYC binding sites and two consensus sites for NF-kB were

described in the Prdx6 promoter, thus explaining the marked

increase of Prdx6 level in response to inhibition of NF-kBobserved in mouse liver cells.37 Moreover it was reported that

the transcriptional induction of Prdx1 gene by LPS in the mouse

macrophage cells involved Src tyrosine kinases, phosphoinositide

3-kinase (PI3K) and c-Jun-NH2 terminal kinase (JNK) and was

regulated via an AP-1 site described in the rat promoter.38

Concerning PRDX2, it is supposed to be activated through

Foxo3a expression in trabecular meshwork cells treated with

nipradilol and timolol.39 Interestingly PRDX1 and PRDX6

may play a role in cancer since they were described to be

over-expressed in human breast carcinoma and associated to

promotion of invasion and metastasis of lung cancer cells.40,41

Consequently curcumin may play its anticancer activities even

through the down-regulation of PRDX1 and PRDX6.

Fig. 5 Immunoblotting analyses for selected differentially expressed

proteins identified by proteomic analysis. (A) Western-blotting analysis

was carried out using 60 mg of whole cell lysate and antibody against

poly-Ubiquitin. (B) Western-blotting analysis was carried out using

60 mg of whole cell lysate and antibodies against Nrf2, Hsp70 and Prdx1.

b-actin was used as loading control. The images shown are representative

of three independent experiments. (C) Bar chart graph showing

densitometric analysis of western blot results after actin normalization.

Measurements were done in triplicate and data are presented as

mean + SD. Statistical analysis was performed applying Student’s

t-test. (Reported data are significant with p o 0.05). WT� and WT+,

untreated and treated cells, respectively; DDP� and DDP+,

untreated and treated cells, respectively.

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Furthermore the modulation of antioxidant enzymes

suggested to investigate the redox state of WT and DDP cells

in the presence and absence of curcumin (Fig. 6). The detec-

tion of reactive oxygen species (ROS) showed a not relevant

production in DDP cells after drug exposure with respect to

basal conditions and control with H2O2. In the case of WT

cells we observed a more evident increase of ROS production

after treatment with respect to untreated cells, which anyway

did not reach the value obtained with H2O2. Curcumin is

known to be an antioxidant agent due to its property of

quenching ROS, although several in vitro studies suggested

that apoptosis mediated by curcumin was associated with

ROS production and/or oxidative stress in transformed cells.4

Jaisin and colleagues42 demonstrated that curcumin reduced

ROS levels induced by 6-hydroxydopamine (6-OHDA) in a

neuroblastoma cell line, whereas other researchers reported

the increased ROS production in the presence of curcumin in

normal human skin fibroblast.43 Production of ROS in

response to curcumin is probably both dose- and cell type-

dependent.4,6 The reduced effect on ROS production after

drug exposure observed in DDP cells with respect to WT cells

may be explained considering the different proteome back-

ground between the cell line resistant to cisplatin and the

sensitive one, already investigated in a previous work.10 The

mechanisms of drug resistance activated in DDP cells may

render them more prompt to quench ROS.

Conclusion

We evaluated the anticancer properties of curcumin in

a human NB cell line sensitive to cisplatin and its resistant

counterpart. High-throughput proteomics and pathway ana-

lysis allowed us to evaluate curcumin targets in these cellular

models. Results revealed that in both cell lines drug exposure

reduced expression of proteins involved in biosynthesis and

glycolytic activity and favoured the accumulation of poly-

ubiquitinated proteins in association to cell cycle arrest in the

G2/M phase. The observed UPS impairment, confirmed by an

independent experiment, may probably be the main respon-

sible player of the pleiotropic effects of curcumin and for its

dose- and cell type-dependent activity. However further

investigations are necessary to understand in which way

curcumin might act as a proteasome inhibitor. In order to

achieve this challenging purpose it will be necessary to carry

out a systematic study of experimental validations based on

biochemical and other functional experiments, which goes

beyond the aim of the present work.

Experimental

Cell culture

The human neuroblastoma (NB) cell line SH-SY5Y was

maintained in DMEM High glucose (GIBCO, Paisley, UK)

containing 10% bovine serum albumin (FBS) (GIBCO), 2 mM

L-glutamine (GIBCO), 1% NEAA (GIBCO), 1% sodium

pyruvate (GIBCO), 10 mM HEPES (GIBCO) and 1% anti-

biotics (100 U ml�1 penicillin/streptomycin) (GIBCO) under

standard conditions (37 1C temperature, 5%CO2 in a humidified

atmosphere). The SH-SY5Y cell line resistant to cisplatin

(SIGMA, Saint Louis, Missouri, USA) was selected in our

laboratory as previously described10 and maintained in

complete medium supplemented with 1 mM cisplatin.

MTT assay

NB cell lines were seeded at 1.5 � 104 cells per well in 96 well

flat-bottom plates and cultured for 24 h in 100 ml of complete

medium. After 24 h media were replaced with fresh media

containing vehicle (DMSO) or curcumin at different concen-

trations and cells were left at 37 1C for 24 h. Then 3-(4,5-

dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT,

SIGMA) 0.5 mg ml�1 was added to wells and cells were

incubated for 4 h. Formazan crystals were dissolved by adding

100 ml of solubilization solution (TOX-1 kit, SIGMA) and

after 1 h absorbance was measured at 570 nm subtracting 690 nm.

Fig. 6 Reactive oxygen species (ROS) levels. (A) Representative images

of cell fluorescence indicating ROS production detected by DCF–DA

reaction prior to and after curcumin treatment. I, WT CTRL; II, WT

treated; III, DDP CTRL; IV, DDP treated. (B) Flow cytometric analysis

of ROS levels in WT and DDP. NB cells were treated with 40 mMcurcumin for 4 h to detect the changes of ROS. 50 mMH2O2 was used as

positive control. *p o 0.05 and **p o 0.01 versus basal.

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Experiments were performed in triplicate. OD values were

normalized versus the starting point value when added drug

is zero and viability is maximum. Statistical analysis was

performed by Student’s t-test. The results were analysed and

the EC50 values were determined with the GraphPad Prismt

analysis software package (Graph-Pad Software, San Diego,

USA) using non-linear regression (sigmoidal dose response,

variable slope).

Proteomics analysis by LC-MSE

Sample preparation, data acquisition and processing were

performed as previously described.10 A total of 0.5 mg of

protein digestion were loaded on the nanoACQUITY UPLC

System (Waters Corp., Milford, MA) coupled to a Q-Tof

Premier mass spectrometer (Waters Corp., Manchester, UK).

Prior to loading, 200 fmol of the digestion of enolase from

Saccharomyces cerevisiae (Waters Corp.) was added to the

sample as the internal standard. Samples were injected onto a

Symmetry C18 5 mm, 180 mm � 20 mm precolumn (Waters

Corp.) for preconcentration and desalting and subsequently

separated using a NanoEaset BEHC18 1.7 mm, 75 mm� 25 cm

nanoscale LC column (Waters Corp.) maintained at 35 1C.

Mobile phase A was water with 0.1% formic acid, and mobile

phase B was 0.1% formic acid in acetonitrile. Peptides were

eluted by a gradient of 3–40% mobile phase B over 150 min at

a flow rate of 250 nl min�1, followed by a gradient of 40–90%

mobile phase B over 5 min and a 15 min rinse with 90%mobile

phase B. The Q-Tof Premier mass spectrometer (Waters

Corp.) was programmed to step between low (4 eV) and high

(15–40eV) collision energies using a scan time of 1.5 s over

50–1990 m/z (expression mode). Samples of each condition

were run at least in triplicate. Continuum LC-MS data were

processed and searched using ProteinLynx GlobalServer v2.3

(PLGS) (Waters Corporation). Protein identifications were

obtained with the embedded ion accounting algorithm of the

software and searching a Uniprot/SWISSProt human database

release 2010_11 (20259 entries) to which sequence of enolase

from Saccharomyces cerevisiae was appended. Parameters for

database search were: automatic tolerance for precursor ions,

automatic tolerance for product ions, minimum 3 fragment

ions matched per peptide, minimum 7 fragment ions matched

per protein, minimum 2 peptide matched per protein, 1 missed

cleavage, carbamidomethylation of cysteine and oxidation of

methionine as modifications. The false positive rate (FPR) of

the identification algorithm is typically 3 to 4% with a

randomized database, appended to the original one, which is

five times the size of the original utilized database.10,11 Identified

proteins displayed in the protein table were normalized against

P00924 entry (enolase from Saccharomyces cerevisiae) while

the most reproducible peptides for retention time and intensity

deriving from digestion of enolase from Saccharomyces cerevisiae

(m/z 807.43,m/z 1159.60,m/z 1288.70,m/z 1755.94,m/z 1840.89)

were used to normalize the EMRTs table, that is the list of

paired peptide exact masses and their retention time. The list

of normalized proteins were screened according to the following

criteria: protein identified in at least 3 out of 3 injections of the

same conditions; proteins with 0 o P o 0.05 or 0.95 o P o 1,

and proteins with a ratio of expression level within the conditions

above 1.5 on a decimal scale. If 0o Po 0.05 the likelihood of

down-regulation is greater than 95%, if 0.95 o P o 1 the

likelihood of up-regulation is greater than 95%. Setting the

threshold of ratio at 1.5 on a decimal scale allowed us to

consider average relative fold change �0.50 on a natural log

scale. This setting is more stringent than �0.30 on a natural

log scale which is typically 2–3 times higher than the estimated

error on the intensity measurements.11

Bioinformatics analysis

Modulated proteins identified by proteomic analysis were

further analysed by the PANTHER Classification System

(http://www.pantherdb.org) and Ingenuity Pathway Analysis

software v.8.8 (IPA). Using PANTHER resource it is possible

to categorize genes by their molecular functions and/or biological

processes on the basis of published papers and by evolutionary

relationships to predict function when experimental evidence

is missing. IPA highlights protein networks or pathways

starting from a continuous updated database of known protein–

protein interactions based on direct (physical) and indirect

(functional) associations. The algorithm gives back a prob-

ability score for each possible network. Scores of 10 or higher

(negative log of the p value) have a high confidence of not

being generated by random chance alone and they were the

only considered in the present work.

Immunoblot analysis

Total protein extracts were separated on 12% SDS-PAGE and

transferred to nitrocellulose membranes (Bio-Rad) using a

SEMI-PHOR semi-dry transfer unit (Amersham Biosciences).

The transferred membranes were blocked with 3% low fat dry

milk in TPBS (0,1% Tween20 in PBS buffer) for 1 h and incu-

bated overnight with primary antibodies : anti-Multiubiquitin

(1 : 1000) (MBL), anti-Nrf2 (1 : 1000) (Santa Cruz), anti-

Hsp70 (1 : 1000) (Santa Cruz), anti-Prdx1 (1 : 5000) (Santa

Cruz), b-actin (1 : 5000) (Sigma), diluted in 1% low fat dry

milk in TPBS. Membranes were then incubated with secondary

antibody conjugated with horseradish peroxidase (Bio-Rad)

for 1 h and detection was done with Enhanced Chemiluminescence

Plus reagent (ECL plus, Amersham Biosciences). Experiments

were performed in triplicate. Densitometric analysis was

performed using ImageJ v1.43d software.

Flow cytometry analysis (FACS)

Cells were plated in 10 cm dishes at a density of 5 � 105 cells

per dishes. After 24 h medium was replaced with medium

containing 40 mM curcumin and cells were left for another

24 h. Then cells were harvested, washed once in PBS and fixed

with 70% ethanol at�20 1C, at a concentration of 1� 106 ml�1.

5 � 105 cells were washed in PBS and resuspended in RNaseA

(75 KU ml�1 final concentration) and propidium iodide

(50 mg ml�1 final concentration) overnight. Samples were

measured with a FACScan cytofluorimeter and 20 000 events

per samples were acquired using CELLQuest BD software.

Cell cycle phase percentages were estimated using ModFit

software. Experiments were repeated in triplicate and statistical

analysis was performed applying Student’s t-test.

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Reactive oxygen species levels

5-6-Chloromethyl-20,70-dichlorofluorescin diacetate (CM-H2DCF-

DA, Molecular Probes, Invitrogen) is a permeable tracer

specific for reactive oxygen species (ROS) assessment. It can

be deacetylated by intracellular esterase to the nonfluorescent

20,70-dichlorofluorescin, which is oxidized by ROS to the

fluorescent compound 20,70-dichloroflorescein. Thus, the

fluorescence intensity of 20,70-dichloroflorescein is propor-

tional to the amount of ROS produced by the cells. Approxi-

mately 1 � 105 cells per well were seeded in a six-well plate.

After 24 h medium was replaced with medium containing

40 mM curcumin and cells were left for another 4 h. After

treatment, the cells were incubated with 10 mM 20,70-dichloro-

fluorescein diacetate at 37 1C for 30 min in the dark. After

incubation, the cells were harvested and washed with PBS

three times. DCF-DA reaction was checked using a Leica

DFC 340FX microscope and cell images were acquired using

Leica Application Suite v3.3.0 software. ROS production was

measured flow cytometry. ROS generation was expressed as

the mean fluorescence intensity. H2O2 was used in a control

experiment. The experiments were done at least in triplicate

and statistical analysis was performed by Student’s t-test.

Author contributions

SD and AU designed the study; SD performed proteomics,

bioinformatics and validation experiments; ID performed flow

cytometry analysis; MD assisted in interpreting and presenting

data; CR assisted in technical set up; SD wrote the paper;

ID, SB, GF, and AU assisted in interpreting the study and

provided feedback on the manuscript.

Acknowledgements

This work has been supported by the ‘‘Rete Nazionale di

Proteomica’’, FIRB RBRN07BMCT Project, Fondazione

Roma 2008.

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Shotgun proteomics and network analysis of neuroblastoma cell lines treated with curcumin

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Transcript of Shotgun proteomics and network analysis of neuroblastoma cell lines treated with curcumin