P-MAPA and IL-12 Differentially Regulate Proteins Associated with Ovarian Cancer Progression: A Proteomic Study.

To investigate the potential role of immunotherapies in the cellular and molecular mechanisms associated with ovarian cancer (OC), we applied a comparative proteomic toll using protein identification combined with mass spectrometry. Herein, the effects of the protein aggregate magnesium-ammonium phospholinoleate-palmitoleate anhydride, known as P-MAPA, and the human recombinant interleukin-12 (hrIL-12) were tested alone or in combination in human SKOV-3 cells. The doses and period were defined based on a previous study, which showed that 25 μg/mL P-MAPA and 1 ng/mL IL-12 are sufficient to reduce cell metabolism after 48 h. Indeed, among 2,881 proteins modulated by the treatments, 532 of them were strictly concordant and common. P-MAPA therapy upregulated proteins involved in tight junction, focal adhesion, ribosome constitution, GTP hydrolysis, semaphorin interactions, and expression of SLIT and ROBO, whereas it downregulated ERBB4 signaling, toll-like receptor signaling, regulation of NOTCH 4, and the ubiquitin proteasome pathway. In addition, IL-12 therapy led to upregulation of leukocyte migration, tight junction, and cell signaling, while cell communication, cell metabolism, and Wnt signaling were significantly downregulated in OC cells. A clear majority of proteins that were overexpressed by the combination of P-MAPA with IL-12 are involved in tight junction, focal adhesion, DNA methylation, metabolism of RNA, and ribosomal function; only a small number of downregulated proteins were involved in cell signaling, energy and mitochondrial processes, cell oxidation and senescence, and Wnt signaling. These findings suggest that P-MAPA and IL-12 efficiently regulated important proteins associated with OC progression; these altered proteins may represent potential targets for OC treatment in addition to its immunoadjuvant effects.

Introduction

Ovarian cancer (OC) represents the most lethal of gynecological malignancies being often diagnosed at a late stage.[1,2] The majority of patients experience difficulties over the course of treatment, with a 5 years survival rate of 35% when diagnosed late.[3] Because no evident symptom is present at an early-stage OC, the disease can rapidly progress to an incurable form.[4−11] Importantly, patients are generally responsive to the treatments; however, through several mechanisms, they develop resistance to chemotherapy and OC grows wildly.[12] New promising strategies to overcome chemoresistance and increase chemosensitivity have been developed for OC treatment, including immunotherapies.

We evaluated the effect of two immunotherapeutic agents from the proteomic standpoint. The immunomodulatory agent termed protein aggregate magnesium-ammonium phospholinoleate-palmitoleate anhydride (P-MAPA) is a natural biopolymer extracted from the Aspergillus oryzae. Experimentally, P-MAPA exhibits a number of antitumor responses in different in vivo models of cancer,[11,13,14] and specifically in OC, P-MAPA potentiated the effects of cisplatin, thereby enhancing TLR signaling in immune cells and attenuating tumor growth.[11] Another tested agent was interleukin-12 (IL-12), a cytokine secreted by antigen-presenting cells (APCs), which is involved in the differentiation of naïve T cells into a polarized Th1 immune response.[15,16] Treatment with IL-12 promotes tumor regression and reduces the potential of tumor outgrowth in patients with recurrent OC;[16,17] although IL-12 therapy results in an OC refractory state, the major challenge is related to its high cellular toxicity.[18] Although these immunotherapies have demonstrated to be quite effective in the treatment of OC with regard to the tumor microenvironment, their exact role on OC cells in terms of protein synthesis and secretion is far from being understood; certainly, there must be other particular functions in addition to those related to the immune system.

Identifying successfully signaling pathways that coordinate tumor cell metabolism, protein–protein interactions, secretion of molecules and factors, and activity of specific enzymes is needed to discover new chemical agents and further modify the OC aggressiveness. Thus, proteomic analysis using mass spectrometry (MS) may provide a wide body of information considering proper candidates for OC development and treatment.[10,19−21] Large or small molecules are definitely recognized to be involved in intra- or intercellular processes, in which one molecule can possibly be sharing different signaling mechanisms in the same tumor cell.

Although there are important studies reporting the use of mass spectrometry in the diagnosis of cancers, including the monitoring strategies for OC,[10,22,23] none has shown the concurrent role of immunotherapies in predicting clinical outcomes from the “omics” standpoint. To further strengthen this issue, we studied the effects of immunotherapeutic agents, P-MAPA and IL-12, directly on human ovarian cancer SKOV-3 cells through proteomic profiling.

Results and Discussion

Influence of Low-Dose Administration of P-MAPA and IL-12 in SKOV-3 Cells’ Viability

The global behavior of OC cells in response to different conditions of treatment is crucial for designing future strategies. We previously reported an immunostimulatory effect of P-MAPA in an in vivo OC model,[11] while IL-12 is a well-known inductor of polarized Th1 immune response.[15,16] Despite the consistent effects on the tumor microenvironment, the direct effect of these compounds in cancer cells is uncertain, and understanding how they act in OC in a safe dose regimen may be greatly beneficial for patients. Although several proteomic approaches in animal models with OC have recently been tested, little is known about the effects of P-MAPA and IL-12 on this disease. The effects of P-MAPA and IL-12 were first tested using three different doses and four exposure times (Figure A). After an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, cell viability was reduced by P-MAPA at a dose of 25 μg/mL (∼ 25% reduction), by IL-12 at a dose of 1 ng/mL (∼ 25% reduction), and by the association of P-MAPA with IL-12 (∼ 30% reduction) after 48 h (Figure B) (see details in Experimental Section, Section 4.3). We then performed a global proteomic analysis in OC cells treated with P-MAPA, IL-12, and P-MAPA associated with IL-12, reporting an extensive catalogue of differentially expressed proteins that are affected by these immunotherapies. The majority of proteins described herein are involved in key cellular processes and signaling pathways, such as metabolic processes; catalytic functions; structural activity; protein, DNA, and RNA binding; and transcription/translation regulatory activity. The variety of biological functions impacted by these agents makes them interesting candidates as potential alternatives to OC treatment.

Figure 1

Defining the treatments with P-MAPA and IL-12. (A) Cell viability (%) after exposure to three different doses of P-MAPA (25, 50, and 100 μg/mL) in four different periods (0, 24, 48, and 72 h). (B) Cell viability (%) after exposure to three different doses of IL-12 (0.5, 1, and 2 ng/mL) in four different periods (0, 24, 48, and 72 h). Colored line of the selected dose is highlighted. (C) Cell viability (%) was assessed by MTT (1 × 103 cells) after exposure to standardized doses of treatments (25 μg/mL P-MAPA, 1 ng/mL IL-12, or both) at 48 h. The results are expressed as the mean ± standard deviation (SD). *p < 0.05 vs control.

Label-Free Proteomic Analysis of SKOV-3 Cells Treated with P-MAPA and IL-12

Samples were fractionated with reverse-phase columns to separate protein mixtures, and label-free analysis was performed on peptides by their charges and hydrophobicity. This analysis identified a total of 896 proteins among control, P-MAPA, IL-12, and P-MAPA + IL-12 groups. We reported a total of 636 molecules in the control, 719 in the P-MAPA group, 709 in the IL-12 group, and 740 proteins in the P-MAPA + IL-12 group, while 532 were commonly found in all tested groups (Figure ). General intersections showing the proteins that are present in one or more groups were represented and varied with the treatment. Figure shows the interaction between up- and downregulated proteins with the highest confidence (0.900) after P-MAPA therapy compared with the control. We found a total of 770 proteins in these groups, while 585 were coexpressed. In the P-MAPA group, there were 50 downregulated and 21 upregulated proteins (Table ) compared with the control, considering the differential expression by at least ±1.5-fold change. The great majority of upregulated molecules are proteins related to protein localization to the membrane, epithelial cell differentiation, SRP-dependent cotranslational protein targeting the membrane, and translational initiation. Otherwise, the downregulated proteins are involved in protein peptidyl-prolyl isomerization, intracellular transport, error-free and -prone translesion synthesis, and the TRIF-dependent toll-like receptor signaling pathway.

Figure 2

Cartoon displaying the intersection of proteins between the experimental groups; 532 proteins coexisted among the control, P-MAPA, IL-12, and P-MAPA + IL-12 groups. CP, control; I, IL-12; P, P-MAPA; P + I, P-MAPA + IL-12.

Figure 3

Mapping of the protein association network in P-MAPA-treated SKOV-3 cells. Source of the protein–protein interaction is based on cellular processing and its related molecular systems. Thicker lines show the highest confidence score (0.900) for upregulated (A) and downregulated (B) molecules of a functional association in response to P-MAPA therapy in SKOV-3 cells. (C) Volcano plot indicates large magnitude fold-changes (y-axis) and statistical significance (p-value, x-axis); this plot indicates log 2 fold change vs −log 2 false discovery rate (FDR)-corrected p-value. Differentially expressed proteins are shown in the upper and lower right areas. See legends in Table .

Table 1

Differentially Expressed Proteins in the P-MAPA Group versus the Control Group

protein ID	description	% coverage	protein score	fold change
P62979	ubiquitin-40S ribosomal protein S27a	21.8	15.5	2.90
P62987	ubiquitin-60S ribosomal protein L40	26.6	15.5	2.90
P0CG47	polyubiquitin-B	14.9	15.5	2.90
P28066	proteasome subunit α type-5	12.0	8.5	2.58
P0CG48	polyubiquitin-C	14.9	15.5	2.35
P49773	histidine triad nucleotide-binding protein 1	7.1	2.9	2.34
Q13268	SDR family member 2, mitochondrial	3.6	4.6	2.05
Q9UQ80	proliferation-associated protein 2G4	5.1	5.6	1.89
B2RPK0	putative high mobility group protein B1-like 1	10.9	7.6	1.78
P52895	aldo-keto reductase family 1 member C2	10.2	8.8	1.62
Q6DRA6	putative histone H2B type 2-D	10.4	12.9	1.60
Q6DN03	putative histone H2B type 2-C	8.8	12.9	1.60
P52565	Rho GDP-dissociation inhibitor 1	8.3	7.3	1.54
P13010	X-ray repair cross-complementing protein 5	3.1	4.4	1.54
P00441	superoxide dismutase [Cu–Zn]	10.4	3.0	1.52
P0DN37	peptidyl-prolyl cis–trans isomerase A-like 4G	7.9	4.4	1.51
A0A0B4J2A2	peptidyl-prolyl cis–trans isomerase A-like 4C	7.9	4.4	1.51
A0A075B759	peptidyl-prolyl cis–trans isomerase A-like 4E	7.9	4.4	1.51
F5H284	peptidyl-prolyl cis–trans isomerase A-like 4D	7.9	4.4	1.51
P0DN26	peptidyl-prolyl cis–trans isomerase A-like 4F	7.9	4.4	1.51
Q9Y536	peptidyl-prolyl cis–trans isomerase A-like 4A	7.9	4.4	1.51
P63241	eukaryotic translation initiation factor 5A-1	18.2	6.6	–1.50
P46779	60S ribosomal protein L28	25.6	10.1	–1.51
P00558	phosphoglycerate kinase 1	18.2	24.0	–1.51
P80723	brain acid soluble protein 1	37.9	19.0	–1.53
P15880	40S ribosomal protein S2	13.7	15.3	–1.53
P83881	60S ribosomal protein L36a	8.5	3.8	–1.59
Q969Q0	60S ribosomal protein L36a-like	8.5	3.8	–1.59
O14950	myosin regulatory light chain 12B	11.6	4.6	–1.59
P24844	myosin regulatory light polypeptide 9	11.6	4.6	–1.59
P19105	myosin regulatory light chain 12A	11.7	4.6	–1.59
P27797	calreticulin	20.4	39.1	–1.60
P04626	receptor tyrosine-protein kinase erbB-2	4.3	9.4	–1.63
Q99460	26S proteasome non-ATPase regulatory subunit 1	1.0	3.1	–1.63
P31948	stress-induced-phosphoprotein 1	10.3	15.8	–1.64
P08670	vimentin	24.5	46.7	–1.64
O75369	filamin-B	6.8	41.9	–1.65
P30101	protein disulfide-isomerase A3	19.2	36.4	–1.66
Q9Y490	Talin-1	6.9	49.0	–1.67
P61978	heterogeneous nuclear ribonucleoprotein K	23.5	44.7	–1.67
P30050	60S ribosomal protein L12	24.2	18.5	–1.68
P08727	keratin, type I cytoskeletal 19	33.5	51.3	–1.71
P0DME0	protein SETSIP	4.3	7.9	–1.72
Q15149	plectin	3.5	37.4	–1.72
P02042	hemoglobin subunit delta	12.9	8.9	–1.75
P68871	hemoglobin subunit β	12.9	8.9	–1.75
Q04695	keratin, type I cytoskeletal 17	21.5	54.7	–1.76
P35241	radixin	10.6	23.9	–1.78
P02538	keratin, type II cytoskeletal 6A	21.6	41.7	–1.80
P60900	proteasome subunit α type-6	9.4	11.1	–1.82
P22392	nucleoside diphosphate kinase B	36.2	25.8	–1.85
Q8NC51	plasminogen activator inhibitor 1 RNA-binding protein	12.5	17.6	–1.86
Q02543	60S ribosomal protein L18a	11.9	6.0	–1.87
Q32P51	heterogeneous nuclear ribonucleoprotein A1-like 2	10.0	10.3	–1.87
P48668	keratin, type II cytoskeletal 6C	21.6	44.2	–1.87
P35579	myosin-9	9.7	49.0	–1.91
P62633	cellular nucleic acid-binding protein	11.9	5.8	–1.94
P62491	ras-related protein Rab-11A	14.8	6.7	–1.94
Q15907	ras-related protein Rab-11B	14.7	6.7	–1.94
P04632	calpain small subunit 1	9.3	5.3	–1.95
Q14240	eukaryotic initiation factor 4A-II	9.1	13.9	–1.95
P05556	integrin β-1	7.6	15.6	–1.96
P04843	dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 1	4.5	6.7	–1.97
O60361	putative nucleoside diphosphate kinase	31.4	18.4	–2.05
P23246	splicing factor, proline- and glutamine-rich	5.9	12.4	–2.06
P78371	T-complex protein 1 subunit β	11.0	15.6	–2.08
P09651	heterogeneous nuclear ribonucleoprotein A1	15.9	18.9	–2.10
P19338	nucleolin	6.2	13.9	–2.19
Q99623	prohibitin-2	4.0	5.0	–2.24
Q15185	prostaglandin E synthase 3	14.4	7.2	–2.28
Q07020	60S ribosomal protein L18	11.7	8.2	–2.52

Figure shows the up- and downregulated protein networks with clusters of strong confidence (highest confidence: 0.900) between the proteins produced by the SKOV-3 cells after IL-12 treatment versus the control group. A total of 762 proteins were identified, and 583 of them were coexpressed in the two groups. Based on differential expression by at least a factor of ±1.5 in the IL-12 group relative to the corresponding control group, we reported 85 downregulated proteins (Table ), which are involved in different biological processes and molecular function: cytoskeleton organization, cellular component organization or biogenesis, structural molecule activity, protein-containing complex binding, and actin filament binding. Conversely, from the 30 upregulated proteins, we found some of them involved in a number of cellular events, such as intracellular transport, chromosome and organelle organization, chromatin silencing, structural molecule activity, and protein heterodimerization.

Figure 4

Mapping of the protein association network in IL-12-treated SKOV-3 cells. Source of the protein–protein interaction is based on cellular processing and its related molecular systems. Thicker lines show the highest confidence score (0.900) for upregulated (A) and downregulated (B) molecules of a functional association in response to IL-12 therapy in SKOV-3 cells. (C) Volcano plot indicates large magnitude fold-changes (y-axis) and statistical significance (p-value, x-axis); this plot indicates log 2 fold change vs −log 2 FDR-corrected p-value. Differentially expressed proteins are shown in the upper and lower right areas. See legends in Table .

Table 2

Differentially Expressed Proteins in the IL-12 Group versus the Control Group

protein ID	description	% coverage	protein score	fold change
O95678	keratin, type II cytoskeletal 75	4.9	9.2	4.84
Q9Y281	cofilin-2	16.9	7.7	2.41
P07858	cathepsin B	6.2	4.7	2.03
Q8N0Y7	probable phosphoglycerate mutase 4	3.9	3.9	2.01
P62979	ubiquitin-40S ribosomal protein S27a	21.8	15.5	1.94
P62987	ubiquitin-60S ribosomal protein L40	26.6	15.5	1.94
P0CG48	polyubiquitin-C	5.0	15.5	1.94
P0CG47	polyubiquitin-B	14.9	15.5	1.94
P49207	60S ribosomal protein L34	15.4	7.8	1.94
P16520	guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit β-3	2.9	3.7	1.91
P84098	60S ribosomal protein L19	4.6	3.0	1.90
P02538	keratin, type II cytoskeletal 6A	6.6	15.8	1.89
P69905	hemoglobin subunit α	15.5	6.1	1.88
P29622	kallistatin	1.9	5.7	1.84
P13647	keratin, type II cytoskeletal 5	7.8	21.7	1.81
P48668	keratin, type II cytoskeletal 6C	6.6	15.8	1.72
P28066	proteasome subunit α type-5	12.0	8.5	1.65
B2RPK0	putative high mobility group protein B1-like 1	10.9	7.6	1.61
P62937	peptidyl-prolyl cis–trans isomerase A	18.8	18.9	1.58
P0C0S5	histone H2A.Z	12.5	17.8	1.57
Q71UI9	histone H2A.V	12.5	17.8	1.57
Q7L7L0	histone H2A type 3	12.5	17.8	1.57
P20671	histone H2A type 1-D	12.5	17.8	1.57
P04908	histone H2A type 1-B/E	12.5	17.8	1.57
P16104	histone H2AX	12.5	17.8	1.57
P06753	tropomyosin α-3 chain	10.9	15.6	1.57
P61586	transforming protein RhoA	10.9	7.8	1.55
P04083	annexin A1	17.3	32.9	1.52
P04843	dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 1	1.7	3.8	1.52
P09429	high mobility group protein B1	17.2	16.2	1.51
O94925	glutaminase kidney isoform, mitochondrial	7.9	19.7	–1.51
P04080	cystatin-B	39.8	8.6	–1.53
P04439	HLA class I histocompatibility antigen, A-3 α chain	9.0	8.5	–1.55
P16188	HLA class I histocompatibility antigen, A-30 α chain	9.0	8.5	–1.55
P13746	HLA class I histocompatibility antigen, A-11 α chain	9.0	8.5	–1.55
P30512	HLA class I histocompatibility antigen, A-29 α chain	9.0	8.5	–1.55
P16189	HLA class I histocompatibility antigen, A-31 α chain	9.0	8.5	–1.55
P30453	HLA class I histocompatibility antigen, A-34 α chain	9.0	8.5	–1.55
P30459	HLA class I histocompatibility antigen, A-74 α chain	9.0	8.5	–1.55
P10316	HLA class I histocompatibility antigen, A-69 α chain	9.0	8.5	–1.55
P05534	HLA class I histocompatibility antigen, A-24 α chain	9.0	8.5	–1.55
Q09160	HLA class I histocompatibility antigen, A-80 α chain	9.0	8.5	–1.55
P30450	HLA class I histocompatibility antigen, A-26 α chain	9.0	8.5	–1.55
P10314	HLA class I histocompatibility antigen, A-32 α chain	9.0	8.5	–1.55
P16190	HLA class I histocompatibility antigen, A-33 α chain	9.0	8.5	–1.55
P01891	HLA class I histocompatibility antigen, A-68 α chain	9.0	8.5	–1.55
P30456	HLA class I histocompatibility antigen, A-43 α chain	9.0	8.5	–1.55
P30447	HLA class I histocompatibility antigen, A-23 α chain	9.0	8.5	–1.55
P01892	HLA class I histocompatibility antigen, A-2 α chain	9.0	8.5	–1.55
P30457	HLA class I histocompatibility antigen, A-66 α chain	9.0	8.5	–1.55
P18462	HLA class I histocompatibility antigen, A-25 α chain	9.0	8.5	–1.55
P35080	profilin-2	10.0	8.8	–1.55
P60763	ras-related C3 botulinum toxin substrate 3	15.6	7.2	–1.55
P14866	heterogeneous nuclear ribonucleoprotein L	4.4	10.1	–1.55
P60900	proteasome subunit α type-6	9.4	7.3	–1.57
P62879	guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit β-2	9.4	6.5	–1.57
P06454	prothymosin α	23.4	23.0	–1.57
P0DME0	protein SETSIP	6.3	5.9	–1.57
P30050	60S ribosomal protein L12	24.2	17.1	–1.57
P19012	keratin, type I cytoskeletal 15	7.5	27.6	–1.58
Q9NQC3	reticulon-4	2.3	9.9	–1.58
P22392	nucleoside diphosphate kinase B	31.6	18.1	–1.58
P04626	receptor tyrosine-protein kinase erbB-2	1.9	8.5	–1.59
Q7Z406	myosin-14	1.8	16.5	–1.60
P35749	myosin-11	1.8	16.5	–1.60
P18206	vinculin	12.8	50.7	–1.60
P12956	X-ray repair cross-complementing protein 6	3.9	4.6	–1.61
P05783	keratin, type I cytoskeletal 18	10.0	17.8	–1.61
P07237	protein disulfide-isomerase	12.8	12.8	–1.64
O43707	α-actinin-4	15.2	49.8	–1.64
O75369	filamin-B	5.4	37.0	–1.66
P25786	proteasome subunit α type-1	15.2	10.2	–1.67
P05141	ADP/ATP translocase 2	14.4	16.7	–1.69
P12814	α-actinin-1	9.9	43.6	–1.70
P83881	60S ribosomal protein L36a	8.5	3.5	–1.70
Q969Q0	60S ribosomal protein L36a-like	8.5	3.5	–1.70
P13646	keratin, type I cytoskeletal 13	10.3	24.1	–1.72
P35241	radixin	9.4	21.8	–1.74
Q92945	far upstream element-binding protein 2	4.8	7.3	–1.75
P40121	macrophage-capping protein	6.3	4.2	–1.77
Q9H4B7	tubulin β-1 chain	6.2	11.5	–1.78
P52907	F-actin-capping protein subunit α-1	8.0	5.5	–1.81
P60842	eukaryotic initiation factor 4A-I	10.6	18.3	–1.81
Q9Y490	talin-1	8.2	52.2	–1.82
P08727	keratin, type I cytoskeletal 19	26.3	46.6	–1.84
P15880	40S ribosomal protein S2	16.0	19.5	–1.84
P0DP25	calmodulin-3	6.0	8.1	–1.86
P0DP24	calmodulin-2	6.0	8.1	–1.86
P0DP23	calmodulin-1	6.0	8.1	–1.86
P30101	protein disulfide-isomerase A3	21.2	31.3	–1.89
Q13509	tubulin β-3 chain	13.8	15.8	–1.89
Q14240	eukaryotic initiation factor 4A-II	5.4	11.6	–1.93
O60361	putative nucleoside diphosphate kinase	31.4	18.1	–1.94
Q8IVF2	protein AHNAK2	1.0	12.8	–1.95
P23396	40S ribosomal protein S3	21.4	19.5	–1.97
P80723	brain acid soluble protein 1	45.8	24.8	–2.00
P19338	nucleolin	8.3	13.7	–2.02
P35579	myosin-9	8.3	52.2	–2.03
P63000	ras-related C3 botulinum toxin substrate 1	22.9	10.1	–2.03
Q15149	plectin	4.0	47.9	–2.04
P61313	60S ribosomal protein L15	4.4	5.4	–2.05
P28070	proteasome subunit β type-4	3.8	4.0	–2.07
Q99623	prohibitin-2	4.0	3.9	–2.07
Q86V81	THO complex subunit 4	4.3	4.8	–2.07
P78371	T-complex protein 1 subunit β	9.2	16.0	–2.24
P62633	cellular nucleic acid-binding protein	11.9	7.7	–2.30
Q15185	prostaglandin E synthase 3	11.3	5.7	–2.36
Q8NFI4	putative protein FAM10A5	5.2	5.2	–2.44
P50502	Hsc70-interacting protein	5.2	5.2	–2.44
Q8IZP2	putative protein FAM10A4	7.9	5.2	–2.44
P52209	6-phosphogluconate dehydrogenase, decarboxylating	6.6	15.4	–2.51
P24844	myosin regulatory light polypeptide 9	17.4	8.2	–2.66
O14950	myosin regulatory light chain 12B	17.4	8.2	–2.66
P19105	myosin regulatory light chain 12A	17.5	8.2	–2.66
P05556	integrin β-1	6.3	19.3	–2.78
Q9BUF5	tubulin β-6 chain	12.3	14.4	–3.01

The combinatory treatment of P-MAPA and IL-12 versus the control group showed the up- and downregulated protein network with a very strong confidence relationship (0.900) (Figure ). We found a total of 817 proteins, with 559 of them being differentially expressed by at least a factor of ±1.5 in the P-MAPA + IL-12 group relative to the corresponding untreated group. Among the 31 upregulated proteins, most of them were involved in different biological processes and molecular functions including canonical glycolysis, doxorubicin and daunorubicin metabolic processes, trans-1,2-dihydrobenzene-1,2-diol dehydrogenase activity, ketosteroid monooxygenase activity, androsterone dehydrogenase activity, phenanthrene 9,10-monooxygenase activity, and aldo-keto reductase (NADP) activity (Table ). We found 76 downregulated proteins, with proteins involved in translational initiation, SRP-dependent cotranslational protein targeting the membrane, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay, mRNA catabolic process, cellular component biogenesis, nucleosomal DNA binding, structural molecule activity, protein-containing complex binding, cytoskeletal protein binding, and structural constituent of ribosome.

Figure 5

Mapping of the protein association network in P-MAPA + IL-12-treated SKOV-3 cells. Source of the protein–protein interaction is based on cellular processing and its related molecular systems. Thicker lines show the highest confidence score (0.900) for upregulated (A) and downregulated (B) molecules of a functional association in response to P-MAPA + IL-12 therapy in SKOV-3 cells. (C) Volcano plot indicates large magnitude fold-changes (y-axis) and statistical significance (p-value, x-axis); this plot indicates log 2 fold change vs −log 2 FDR-corrected p-value. Differentially expressed proteins are shown in the upper and lower right areas. See legends in Table .

Table 3

Differentially Expressed Proteins in the P-MAPA + IL-12 Group versus the Control Group

protein ID	description	% coverage	protein score	fold change
P62987	ubiquitin-60S ribosomal protein L40	26.6	15.5	2.60
P62979	ubiquitin-40S ribosomal protein S27a	21.8	15.5	2.60
P0CG47	polyubiquitin-B	14.9	15.5	2.60
Q9Y281	cofilin-2	16.9	7.7	2.46
O95678	keratin, type II cytoskeletal 75	4.9	9.2	2.10
Q04828	aldo-keto reductase family 1 member C1	8.1	8.8	2.06
P42330	aldo-keto reductase family 1 member C3	7.7	7.6	2.06
P52895	aldo-keto reductase family 1 member C2	8.1	8.8	2.06
P0CG48	polyubiquitin-C	5.0	15.5	2.05
P28066	proteasome subunit α type-5	12.0	7.7	1.98
P48509	CD151 antigen	6.7	8.3	1.95
P52565	Rho GDP-dissociation inhibitor 1	8.3	6.3	1.86
P81605	dermcidin	10.0	5.3	1.83
P15259	phosphoglycerate mutase 2	7.9	5.5	1.80
P18669	phosphoglycerate mutase 1	7.9	5.5	1.80
P09429	high mobility group protein B1	13.5	16.2	1.78
P09104	γ-enolase	10.4	18.8	1.78
P13929	β-enolase	10.4	18.8	1.78
P26447	protein S100-A4	26.7	7.6	1.77
P17516	aldo-keto reductase family 1 member C4	2.8	5.6	1.76
P46782	40S ribosomal protein S5	8.3	7.3	1.75
P05062	fructose-bisphosphate aldolase B	3.9	9.1	1.67
P62937	peptidyl-prolyl cis–trans isomerase A	18.8	18.9	1.67
B2RPK0	putative high mobility group protein B1-like 1	7.1	7.6	1.65
P29622	kallistatin	1.9	5.7	1.61
Q71UI9	histone H2A.V	12.5	17.8	1.58
P0C0S5	histone H2A.Z	12.5	17.8	1.58
P16104	histone H2AX	16.1	17.8	1.51
P20671	histone H2A type 1-D	17.7	17.8	1.51
Q7L7L0	histone H2A type 3	17.7	17.8	1.51
P04908	histone H2A type 1-B/E	17.7	17.8	1.51
P60763	ras-related C3 botulinum toxin substrate 3	10.4	5.6	–1.52
P02533	keratin, type I cytoskeletal 14	27.1	47.6	–1.52
Q9H4B7	tubulin β-1 chain	6.2	11.5	–1.53
Q71DI3	histone H3.2	14.0	7.1	–1.53
Q16695	histone H3.1t	14.0	7.1	–1.53
P68431	histone H3.1	14.0	7.1	–1.53
O94925	glutaminase kidney isoform, mitochondrial	7.9	21.8	–1.53
O43707	α-actinin-4	15.4	46.5	–1.53
P19338	nucleolin	4.7	9.7	–1.54
P62873	guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit β-1	9.4	11.9	–1.54
P60953	cell division control protein 42 homolog	11.5	7.8	–1.54
O60361	putative nucleoside diphosphate kinase	31.4	13.5	–1.55
Q9HAV0	guanine nucleotide-binding protein subunit β-4	5.9	7.8	–1.55
P30050	60S ribosomal protein L12	24.2	15.6	–1.56
P06454	prothymosin α	23.4	23.5	–1.56
P40121	macrophage-capping protein	6.3	4.0	–1.57
Q9UQ80	proliferation-associated protein 2G4	12.2	12.0	–1.58
P52907	F-actin-capping protein subunit α-1	8.0	5.3	–1.58
P12814	α-actinin-1	10.3	40.8	–1.61
Q00610	clathrin heavy chain 1	4.8	21.3	–1.62
P60842	eukaryotic initiation factor 4A-I	10.6	15.9	–1.63
P30101	protein disulfide-isomerase A3	19.4	30.0	–1.64
O75369	filamin-B	5.5	34.6	–1.65
P12235	ADP/ATP translocase 1	11.1	16.5	–1.66
P12236	ADP/ATP translocase 3	11.1	16.5	–1.66
P61978	heterogeneous nuclear ribonucleoprotein K	17.3	30.1	–1.66
Q01130	serine-/arginine-rich splicing factor 2	10.0	7.4	–1.67
Q99460	26S proteasome non-ATPase regulatory subunit 1	1.0	3.3	–1.68
P63000	ras-related C3 botulinum toxin substrate 1	10.4	7.9	–1.69
Q15185	prostaglandin E synthase 3	19.4	5.5	–1.69
P62879	guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit β-2	6.5	7.8	–1.71
P62241	40S ribosomal protein S8	13.0	9.8	–1.75
P05141	ADP/ATP translocase 2	14.4	20.8	–1.77
P35241	radixin	10.3	25.2	–1.77
Q14240	eukaryotic initiation factor 4A-II	5.4	11.3	–1.78
Q9Y266	nuclear migration protein nudC	8.8	9.5	–1.78
P50914	60S ribosomal protein L14	10.7	11.4	–1.84
Q13509	tubulin β-3 chain	13.8	14.8	–1.85
P0DP24	calmodulin-2	6.0	6.6	–1.86
P0DP25	calmodulin-3	6.0	6.6	–1.86
P0DP23	calmodulin-1	6.0	6.6	–1.86
Q9NQC3	reticulon-4	2.3	10.2	–1.87
P08727	keratin, type I cytoskeletal 19	33.3	54.1	–1.88
P14866	heterogeneous nuclear ribonucleoprotein L	4.4	10.8	–1.91
P35637	RNA-binding protein FUS	3.0	7.3	–1.94
P05783	keratin, type I cytoskeletal 18	11.9	22.4	–1.97
P61313	60S ribosomal protein L15	4.4	6.6	–2.00
Q86V81	THO complex subunit 4	4.3	5.1	–2.02
P23396	40S ribosomal protein S3	21.0	18.2	–2.03
P25788	proteasome subunit α type-3	11.0	6.8	–2.05
Q92945	far upstream element-binding protein 2	4.8	9.1	–2.05
P60900	proteasome subunit α type-6	12.6	11.0	–2.06
P80723	brain acid soluble protein 1	45.8	20.6	–2.10
Q8IVF2	protein AHNAK2	1.0	15.5	–2.14
P35749	myosin-11	2.2	22.4	–2.16
Q7Z406	myosin-14	2.2	22.4	–2.16
P35580	myosin-10	2.6	22.2	–2.30
Q99623	prohibitin-2	7.0	5.7	–2.34
O14950	myosin regulatory light chain 12B	6.4	6.9	–2.34
P24844	myosin regulatory light polypeptide 9	6.4	6.9	–2.34
P19105	myosin regulatory light chain 12A	6.4	6.9	–2.34
P52209	6-phosphogluconate dehydrogenase, decarboxylating	6.6	13.4	–2.35
Q8NFI4	putative protein FAM10A5	5.2	5.3	–2.38
P50502	Hsc70-interacting protein	5.2	5.3	–2.38
Q8IZP2	putative protein FAM10A4	7.9	5.3	–2.38
P15880	40S ribosomal protein S2	16.0	26.8	–2.50
P35579	myosin-9	9.7	62.2	–2.51
P08670	vimentin	31.3	88.0	–2.52
Q15149	plectin	6.4	71.7	–2.61
P83881	60S ribosomal protein L36a	16.0	7.8	–2.66
Q969Q0	60S ribosomal protein L36a-like	16.0	7.8	–2.66
P26373	60S ribosomal protein L13	14.2	17.2	–2.87
P78371	T-complex protein 1 subunit β	11.0	19.5	–3.08
Q9BUF5	tubulin β-6 chain	9.6	15.1	–3.12
Q02543	60S ribosomal protein L18a	16.5	14.2	–3.27
Q07020	60S ribosomal protein L18	30.9	20.8	–4.29

P-MAPA and IL-12 Differentially Alter Cellular Function-Related Proteins in SKOV-3 Cells

To provide another view of which categories the differentially expressed proteins belong to, gene ontology (GO) annotation was carried out. We used PANTHER classification to demonstrate the molecular function, cellular component, biological processes, and protein class whereby specific treatment was able to alter. The result was a large and complex molecule network for each treatment. The main protein functions that differentially varied after treatments with P-MAPA, IL-12, and P-MAPA + IL-12 are summarized in Figures –8, respectively. The proteins differentially regulated by P-MAPA, IL-12, and P-MAPA + IL-12 treatments were the most closely related to molecular functions such as binding, catalytic activity, and structural molecule activity. With regard to biological processes, the majority of these proteins were involved in a metabolic process, biological regulation, and cellular component organization or biogenesis, being represented mostly by chaperones, cytoskeletal proteins, enzyme modulators, and hydrolases. Importantly, modulation of key processes that allow cancer cells to have a metabolic advantage and increased capacity to regulate cell binding with other tissues might be an important feature of all treatments.

Figure 6

Pie charts of the proteins that were differentially altered in SKOV-3 cells following P-MAPA treatment. PANTHER classification indicates functionally distinct proteins according to their molecular functions, biological processes, cellular components, and class.

Figure 8

Pie charts of the proteins that were differentially altered in SKOV-3 cells following P-MAPA + IL-12 treatment. PANTHER classification indicates functionally distinct proteins according to their molecular functions, biological processes, cellular components, and class.

Pie charts of the proteins that were differentially altered in SKOV-3 cells following IL-12 treatment. PANTHER classification indicates functionally distinct proteins according to their molecular functions, biological processes, cellular components, and class.

We observed increased expressions of hybrid ubiquitin-ribosomal protein L40 (RPL40) and ubiquitin-ribosomal protein s27a (RPS27a) after the treatment with P-MAPA (2.9-fold increase), IL-12 (1.94-fold increase), and P-MAPA + IL-12 (2.6-fold increase). It has long been documented that ubiquitin targets proteins for proteasome degradation or nondegradation signaling.[24] Anticancer drugs, such as 5-fluorouracil, trichostatin A, and paclitaxel, often lead to overexpression of ubiquitin, which causes increased susceptibility to apoptotic cell death in a number of tumor cell lines.[25] In this process, the aggregation of ubiquitylated proteins in the nucleus is an important event, and this transport can be facilitated by ubiquitin binding to either RPS27a or RPL40.[25] In colorectal cancer cells, upregulation of the UBA52 gene, which encodes the ubiquitin-RPL40 hybrid protein, is related to cell-cycle arrest and apoptosis induction via activation of the RPL40-MDM2-p53 pathway, resulting in increased expression of the tumor-suppressor p53.[26] Additionally, all treatments were able to upregulate polyubiquitin-B and -C, in which the chains are highly related with caspase-8 activation mediated by tumor necrosis factor receptor-associated factor 2 (TRAF2) and, consequently, sensibilization of TNF-related apoptosis-inducing ligand (TRAIL)-induced apoptosis.[27] Since chemoresistance and tumor aggressiveness are common challenges in the treatment of OC, the combined use of traditional chemotherapeutics and adjuvant components capable of modulating ubiquitination and promoting cell death might improve the therapy efficacy.

Among proteins that were downregulated by P-MAPA, IL-12, and P-MAPA + IL-12 treatments were myosin-9 (1.9-, 2.03-, and 2.5-fold decrease vs the control group, respectively), T-complex protein 1 (2.08-, 2.24-, and 3.08-fold decrease), and prohibitin-2 (2.24-, 2.07-, and 2.34-fold decrease). Myosin-9 is a heavy chain of myosin IIA and is involved in cytokinesis, maintenance of cell shape, and cell motility.[28] It has been suggested as a possible target for anti-invasive treatment in the breast cancer cell line MCF-7[29] and in gastric cancer.[30] Its overexpression has been related to both decreased overall survival and disease-free survival in esophageal squamous cell carcinoma (ESCC)[30] and also in lung adenocarcinoma.[31] T-complex 1 protein is involved in a number of biological processes, particularly in cytoskeletal organization and cell-cycle progression,[32] and many studies found these proteins to be strictly related to tumor progression.[33,34] Particularly, in ESCC, a T-complex protein 1 subunit expression increased cell migration and invasion via regulation of α-actin and β-tubulin, and its silencing has been suggested as a possible treatment alternative.[35] Notably, prohibitins have been shown to be involved in cancer cell growth and survival, resistance to chemotherapy, immune response, and metastasis through a number of mechanisms, including p53-related transcriptional regulation, TGF-β signaling pathway modulation, and Ras/ERK pathway activation.[36,37] In ovarian epithelial tumors, prohibitin-2 is thought to induce cancer cell growth and increase the susceptibility of malignant transformation,[38] while prohibitin-1 promotes survival of cancer cells, acting as an antiapoptotic factor.[39] Furthermore, cytoplasmic expression of prohibitin-2 is involved in hormone-related growth of breast cancer cells because it upregulates the estrogen-signaling pathway.[40] Prohibitin-1 and prohibitin-2 are overexpressed in colorectal cancer, and serum concentration of these proteins, also elevated in colorectal cancer patients, was suggested as a potential biomarker for this disease.[41] Prohibitin-2 clustering with glucose-related protein 75 (GPR75)/mortalin is related to increased cell proliferation and tumorigenesis in metastatic cells.[42] To determine whether P-MAPA and IL-12 are capable of slowing down the tumor cell motility (as they were already proved to reduce migratory/proliferation capacity of cells), novel approaches based on timing and tumor features (subtype, grading, and staging) should be taken into consideration during OC treatment.

Among proteins that were simultaneously and significantly altered after treatments with IL-12 or the association P-MAPA + IL-12, we observed upregulation of cofilin-2 (2.41- and 2.46-fold increase vs the control group, respectively). Cofilin-2, which is known as the actin depolymerization factor, is a member of the cofilins family, being crucial modulators of actin dynamics.[43] The role of human cofilin-2 (protein and gene) is controversial in cancer. Important data suggest that cofilin-2 is downregulated in pancreatic tumor tissues[44] and that the ability of actin depolymerization by cofilin is associated with mitochondrial damage, cytochrome c release, and apoptosis induction in cancer cells.[45] Additionally, cofilin-2 translocation to mitochondria and interaction with Drp1 constitute key events for mitochondrial fission and apoptosis induced by erucin in breast cancer cells.[46] On the other hand, studies have suggested that cofilin-2 expression may play a role in malignant progression, modulating cancer cell proliferation, invasion, and metastasis.[47−49] Since we previously reported a reduction in cell viability after exposure to all treatments, we believe that increased cofilin-2 levels might hamper the ovarian tumor growth by disrupting the cell metabolism, thus inducing cell death and diminishing metabolic activity.

The most positive effects of treatments with IL-12 and P-MAPA + IL-12 seem to be associated with downregulated proteins: 6-phosphogluconate dehydrogenase (6PGD) (2.51- and 2.35-fold decrease vs the control group, respectively), Hsc70-interacting protein (2.44- and 2.38-fold decrease), plectin (2.04- and 2.61-fold decrease), and myosin light chains (2.66- and 2.34-fold decrease). 6PGD is an enzyme of the oxidative pentose phosphate pathway, which is involved in anabolic biosynthesis, glycolysis, and redox homeostasis in cancer cells, thus providing metabolic advantages to cellular survival and proliferation.[50,51] The activity of this enzyme is upregulated in a number of cancers and has been implicated in chemoresistance of ovarian and other tumors.[52] Inhibition of 6PGD selectively targeted breast cancer cells, sparing normal breast cells[53] and increasing the efficacy of chemotherapy in cervical cancer through AMPK-independent inhibition of RhoA and Rac1 activities.[54] In addition, suppression of 6PGD sensitized cisplatin-resistant ovarian cancer cells to cisplatin treatment via the AMPK-dependent pathway, thus restoring its therapeutic efficacy.[52] In the highly aggressive anaplastic thyroid carcinoma, 6PGD inhibition is associated with resensitization to doxorubicin treatment by decreasing levels of NADPH, NADH, and enzymatic activity of sirtuin-1.[55] Briefly, the disruption of cancer cell metabolism provided by downregulation of 6PGD might be a highly positive effect of treatment with IL-12 and association of P-MAPA and IL-12, including chemosensitivity restoration.

Modulating the tumor ability for signaling and interacting with normal cells is an important aim of alternative therapy. In this context, treatment of OC cells with IL-12 and P-MAPA + IL-12 significantly reduced the levels of the carboxyl terminus Hsc70-interacting protein (CHIP). Notably, CHIP seems to have dual functions in cancer, with some studies reporting its role as a tumor suppressor and others as an oncogene.[56] Decreased CHIP expression in ER-positive breast cancer and pancreatic cancer tissues is related with poor prognostic and short survival of patients.[56,57] Additionally, CHIP levels are inversely correlated with tumor malignancy in gastric cancer.[58] Conversely, CHIP promotes the downregulation of Profilin-1 in breast cancer cells, thus possibly playing a role in cell migration and metastasis.[59] A number of studies have been implicating CHIP in the modulation of the apoptosis-inducing factor (AIF), tumor-suppressor p53, and interferon regulatory factor 1 (IRF-1).[60−62] In esophageal squamous cell carcinoma, high CHIP expression is correlated with increased number of metastatic lymph nodes.[63] Xu and colleagues[64] reported CHIP activation of MAPK and AKT signaling activities and upregulation of E-cadherin, which led to epithelial–mesenchymal transition and enhanced migration and invasion potential of cells.

Cellular dynamics is a fundamental characteristic for cellular proliferation and, consequently, for tumor growth. Decreased levels of myosin regulatory light chain (MLC) and plectin after IL-12 and the combination of P-MAPA and IL-12 may be able to impair cancer cell dynamics, directly by avoiding cell proliferation and reducing their aggressiveness. The MLC phosphorylation seems to be an important marker for cancer cell functions, including cell growth, adhesion, and migration.[65,66] Leiomyosarcomas with high proliferative activity present increased MLC phosphorylation,[67,68] which is majorly measured by MLC kinase (MLCK) expression. In fact, the blockage of MLCK activities by a pharmacological inhibitor decreases breast cancer cell growth.[69,70] The expression of phosphomimetic MLC is able to increase the proliferative rate calculated by Ki-67 expression.[69] Another important protein to control cellular dynamics is plectin, playing key roles in cytoskeleton anchoring, signal transduction, and even in apoptosis induction.[71−73] Katada and colleagues[74] observed that plectin expression is significantly higher in tumor tissues compared with nontumor tissues in head and neck squamous cell carcinoma (HNSCC). Overexpression of this protein has also been reported in colorectal cancer, pancreatic cancer, and prostatic cancer.[75−77] Additionally, its upregulation is directly associated with poor prognosis and increased frequency of recurrence. Plectin also activates ERK 1/2 kinases to promote migration and invasion of HNSCC cells.[74] The depletion of plectin is able to impair cellular proliferation and migration in the MCF-7 breast cancer cell line[78] and in PC3 prostate cancer cells.[79]

Finally, treatment with P-MAPA and the association of P-MAPA and IL-12 significantly reduced the levels of the key protein vimentin (1.64- and 2.52-fold decrease vs the control group, respectively). Vimentin is an intermediate filament found in various mesenchymal cells in a wide variety of tissues, responsible for the maintenance of cell and tissue integrity.[80] In platinum-resistant ovarian cancer cell lineages, the reduction of vimentin levels by miRNA let-7g overexpression resulted in reduced epithelial-to-mesenchymal transition (EMT), decreased their migratory potential, and restored sensitivity to platinum-based chemotherapy.[81] High expression of vimentin has also been implicated in poor prognosis in a number of other cancer types, such as breast cancer, prostate cancer, colorectal cancer, and nonsmall-cell lung cancer.[82−85] Its upregulation is further associated with decreased disease-free survival and increased lymph node metastasis in colorectal cancer.[85] Vimentin is a key factor for epithelial plasticity and cytoplasm architecture maintenance during the epithelial–mesenchymal transition, and its overexpression confers elevated cancer cell motility, directional migration, and metastasis.[82,86,87] In the MDA-MB231 breast cancer cell line, the depletion of vimentin promoted reorganization of the cytoskeleton and decreased cell proliferation and motility.[87] We believe that the association of P-MAPA with IL-12 is important to disassemble part of the cytoskeleton that regulates tumor cell aggressiveness.

In summary, our present findings reveal important mechanisms by which immunotherapy with P-MAPA and IL-12 may impact OC progression and aggressiveness. The wide variety of targets exhibited herein shows that these treatments are able to attack OC cell malignant functions through different regulatory mechanisms. Future studies associating these therapies with OC standard chemotherapy (e.g., taxanes and platinum derivatives) could also bring an important approach regarding the use of these compounds as adjuvant therapeutics.

Conclusions

We described a number of important changes in several protein signatures that are modulated by P-MAPA and IL-12 therapies in ovarian cancer SKOV-3 cells. While P-MAPA and IL-12 alone were efficient to upregulate structural proteins (e.g., related to tight junctions), they downregulated molecules involved in cell signaling that are associated with cancer progression. Combinatory therapy with P-MAPA and IL-12 was the most efficient to distinctively regulate proteins involved in metabolic processes, such as energy and mitochondrial processes, cell oxidation, and senescence; inhibition of these activities may render cancer cells more vulnerable to structural instability, apoptosis, and metabolical dysfunctions. This approach provides reliable insights into the cellular regulation associated with OC progression and, alternatively, suggests the therapeutic use of P-MAPA and IL-12 as a complementary strategy for OC treatment.

Experimental Section

Cell Line and Culture

Human OC cell line, SKOV-3, was purchased from the American Type Culture Collection (ATCC, Rockville, MD). During the experiment, SKOV-3 cells were incubated with RPMI 1640 (Life Technologies, Grand Island, NY) supplemented with 10% fetal bovine serum and 1% anti-anti solution (100 mg/mL penicillin G, and 100 μg/mL streptomycin (Merck, Darmstadt, Germany)). Cells were maintained at 37 °C in a humidified atmosphere of 5% CO2, and the culture medium was changed every 2–3 days.

P-MAPA and IL-12 Treatments

To determine the better dose–response effect, three different doses of P-MAPA (25, 50, and 100 μg/mL) were used in accordance with Fávaro.[13] Initially, 5 mg of P-MAPA was diluted in 1 mL of saline to achieve a stock solution of 5 mg/mL; this solution was then diluted in the cell culture medium to obtain the proper concentrations. For the treatment with recombinant (rh)IL-12, doses of 0.5, 1, and 2 ng/mL were used in the culture medium in accordance the method of Su and colleagues.[88] To combine P-MAPA with IL-12, the most representative dose and incubation time were chosen after performing the MTT assay. The saline solution was used as a solvent vehicle control and administered in the same procedures for treatments.

MTT Assay

SKOV-3 cells were seeded in a plate at a density of 1 × 103 cells/well. Cellular activity or toxicity was first evaluated using the three concentrations of both treatments, P-MAPA and IL-12, after 0, 24, 48, and 72 h exposure. All analyses were performed in three technical and biological replicates. MTT solution was added to the wells for 4 h, and the crystals were diluted with dimethyl sulfoxide (DMSO) under agitation. The concentration was determined by an Epoch microplate reader (BioTek Instruments, Highland Park, PO) at 540 nm, with the reference curve fixed at 650 nm. The percentage (%) of crystal formation was calculated based on the control group as the reference. After the testing assay, we determined the doses of 25 μg/mL P-MAPA and 1 ng/mL IL-12 and set the period of treatment at 48 h. Since the study proposed to globally identify proteins involved in potential signaling pathways that are modulated by the treatments, we defined doses and periods capable of decreasing 10–30% cell viability; higher doses could bias the results because the increased cell death might result in loss of important proteins.

Protein Quantification

After treatments, the proteins were extracted from SKOV-3 cells in biological triplicate from each experiment and were quantified in triplicate using a colorimetric method described by Bradford (BioRad Protein Assay Kit; Cod. 500-0001). Bovine serum albumin (0.1% BSA) was used as the reference protein. After quantification, individual samples were transferred to Eppendorf LoBind tubes and diluted with 0.9% (m/v) saline solution until reaching the concentration of 50 μg/40 μL for each sample.

In-Solution Trypsin Digestion

All samples were subjected to the protocol for trypsin digestion. First, 50 μg of lyophilized proteins was diluted in 50 mM ammonium bicarbonate, and 25 μL of RapiGest SF surfactant (code 186001861, Waters Corporation) was added to the samples for 60 min at 37 °C. Then, samples were reduced and alkylated with 10 mM dithiothreitol (DTT) and 45 mM iodoacetamide (IAA) at room temperature (RT) for 20 min. Enzymatic digestion was performed using trypsin (1:100; enzyme/sample) solubilized in 50 mM ammonium bicarbonate buffer (pH 7.8) for 18 h. Hydrolysis was stopped by adding 1% (v/v) formic acid to the samples for 1 h. Samples were incubated for 90 min at RT and then centrifuged at 14 000g for 30 min at 6 °C. Supernatants were removed into a new tube and subjected to desalting columns Peptide Cleanup C18 Spin (code 5188-2750 Agilent Technologies). After peptides were dried in vacuum (SpeedVac; Thermo Scientific), they were dissolved in 3% acetonitrile (ACN) with 0.1% formic acid solution before analysis.

Peptide Sequencing for Mass Spectrometry

Label-free analysis was performed using a liquid nanocromatograph (Ultimate 3000 LC Dionex, Germering, Germany) coupled to a quadrupole-orbitrap model mass spectrometer Q-Exactive (ThermoFisher Scientific, Bremen, Germany). The chromatograph was equipped with a binary system of pumps and an automatic sample applicator. The mobile phase consisted of 0.1% (v/v) formic acid in water LCMS (solvent A) and 0.1% (v/v) formic acid in 80% (v/v) ACN (solvent B). The peptides were loaded in a precolumn C18, 30 μm × 5 mm (code 164649, ThermoFisher Scientific), and desalting was carried out in an isocratic gradient of 4% B for 3 min at a flow rate of 300 nL/min. Then, peptides were fractioned using the analytical column Reprosil-Pur C18-AQ, 3 μm, 120 Å, 105 mm (code 1PCH7515-105H354-NV, PICOCHIP) using a linear gradient of 4–55% B for 30 min, 55–90% B for 1 min, maintained at 90% B for 5 min, and recalibrated at 4% B for 20 min (flow rate of 300 nL/min). Positive ionization was obtained in a Nanospray ion source (PICOCHIP) with the DDA method. Mass spectra were acquired in the mass range of m/z 200–2000, resolution of 70.000, and 100 ms for injection time. The fragmentation chamber was conditioned with collision energy between 29 and 35%, with resolution of 17.500, 50 ms of injection time, 4.0 m/z of MS/MS isolation window, and dynamic exclusion of 10 s. All samples were quantified in biological and technical triplicate. Spectrometry data were acquired using Thermo Xcalibur software (version 4.0.27.19, ThermoFisher Scientific Inc.).

Data Analysis

For proteomic analyses, the raw data.RAW was subjected to software PatternLab (version 4.0.0.84; Carvalho and colleagues)[89] to identify and determine which proteins were differentially expressed. The parameters used were the Swiss-Prot database (Homo sapiens taxonomy), trypsin as the proteolytic enzyme, permission of two lost cleavages, fixed modifications of cysteine carbamidomethylation and methionine oxidation as variable modification, and tolerance errors of MS 40 ppm and MS/MS 0.0200 ppm. The false discovery rate (FDR) was set at ≤1%. We used only proteins obtained in all three runs, and the spectral counts for each protein were normalized by the weighted average of replicates of each sample. Missing data were analyzed by multiple imputation according to Royston,[90] and the standard errors were computed according to the “Rubin rules”.[91] Only proteins that showed statistical significance at p < 0.05 and a protein ratio less than 1.5-fold change or greater than 1.5-fold change were used. Results were compared using Student’s t test to set the differences between the groups (p < 0.05). Additional analyses of cellular components, molecular function, and biological processes were determined through Protein Annotation Through Evolutionary Relationship (PANTHER) (http://pantherdb.org/) classification, and network interactions between proteins were obtained using STRING software (http://string-db.org/) under the basic parameters of cutoff score of 0.900 (highest confidence), evidence as network edges, and PPI enrichment p-value of <1.0 × 10–16.