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Proteomic Analysis of Peripheral Blood Mononuclear Cells (PBMC) after a High-Fat, High-Carbohydrate Meal With Orange Juice Daniela F. Seixas Chaves, Paulo C. Carvalho, Elisa Brasili, Marcelo Macedo Rogero, Neuza Mariko Aymoto Hassimotto, Jolene K Diedrich, James J Moresco, John R. Yates, and Franco Maria Lajolo J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00476 • Publication Date (Web): 19 Sep 2017 Downloaded from http://pubs.acs.org on September 20, 2017

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Journal of Proteome Research

Proteomic Analysis of Peripheral Blood Mononuclear Cells (PBMC) after a High-Fat, High-Carbohydrate Meal with Orange Juice 1,2

3

1,2

2,4

Daniela F. S. Chaves , Paulo C. Carvalho , Elisa Brasili , Marcelo M. Rogero , Neuza A. 1,2 5 5 5 1,2 Hassimotto , Jolene K. Diedrich , James J. Moresco , John R. YatesIII , Franco M. Lajolo

1

Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São

Paulo, São Paulo Brazil 2

Food Research Center (FoRC), CEPID-FAPESP (Research Innovation and Dissemination Centers São Paulo

Research Foundation), São Paulo, Brazil 3

Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz – Paraná, Brazil

4

Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil

5

The Scripps Research Institute – Department of Chemical Physiology, San Diego, CA 92121

Corresponding author: [email protected]

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Abstract Oxidative stress and inflammation play a role in the physiopathology of insulin resistance, diabetes and cardiovascular disease. A single high-fat, high-carbohydrate (HFHC) meal induces an increase in inflammatory and oxidative stress markers in peripheral blood mononuclear cells (PBMC). Previous studies have shown that orange juice is able to prevent this response by inhibiting toll like receptors (TLR) expression and endotoxemia. Our goal was to study the proteome response in PBMC after the consumption of a HFHC meal consumed with water, orange juice or an isocaloric beverage (water with glucose). Twelve healthy individuals completed the protocol in a cross-over design and blood samples were obtained before and 1, 3, and 5 h after consumption. Proteomic profile, glucose, insulin, lipid and cytokines levels were investigated. The glycemic and insulinemic response was higher when the meal was consumed with glucose while there was no difference in the response between water and orange juice. Proteome analysis in PBMC was carried out using TMT ten-plex. A total of 3,813 proteins, originating from 15,662 peptides were identified. Three proteins showed significantly altered expression in the three treatments: apolipoprotein A-II, ceruloplasmin and hemopexin. When the HFHC meal was consumed with water there was an increase in some inflammatory pathways such as the Fc-gamma receptor dependent phagocytosis and the complement cascade, but the immune system as a whole was not significantly altered. However, when the meal was consumed with glucose, the immune system was up regulated. Among the pathways induced after 3 h were those of the adaptive immune system and cytokine signaling. Five hours after the meal, pathways of the complement cascade and classical antibody mediated complement activation were up regulated. When the meal was consumed with orange juice there was an up regulation of proteins involved in signal transduction, DNA replication and cell cycle. The promyelocytic leukemia protein (PML) showed a 28.2-fold increase. This protein was down regulated when the meal was consumed with water. Regarding the immune system, several of the pathways induced by glucose were down regulated when the meal was consumed with orange juice: proteins involved with the adaptive immune system and cytokine signaling. Therefore, we have shown that orange juice can not only suppress diet induced inflammation, but also regulate the expression of proteins such as PML, that may play a key role in the regulation of metabolism.

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Introduction Chronic consumption of high-fat, high-calorie diets may induce a pro-inflammatory state, which contributes to the development of obesity, type 2 diabetes, and cardiovascular disease (CVD)1. Although plasma levels of inflammatory biomarkers (such as cytokines and highsensitivity C-reactive protein) have been correlated to CVD in epidemiological studies a subclinical state of the disease may be identified earlier if assessed in the peripheral blood mononuclear cells (PBMC). These cells play a key role in atherosclerosis, a complex process in which LDL oxidation and oxidative stress contribute to the expression of several transcription factors, cellular adhesion molecules, and genes that regulate lipid metabolism, cell proliferation, and apoptosis2,3,4,5,6,7. Acute macronutrient intake may induce an oxidative and inflammatory response, even in healthy individuals. A 75-g glucose challenge induced an increase in superoxide generation by leukocytes and in p47phox expression8, and an isocaloric consumption of fat also resulted in increased oxidative stress9. Also, isocaloric meals with different macronutrient/micronutrient composition may have a different effect on the inflammatory response. Aljada et al.10 showed that a high fat breakfast (900 kcal; 51 g fat, 81 g carbohydrate, 32 g protein) induced the activation of NF-κB and increased superoxide radical generation by PBMC in healthy subjects; however, a 900kcal breakfast rich in fruit and fiber did not cause oxidative stress or inflammation11. Furthermore, a high-fat, high-carbohydrate (HFHC) meal consumed with water or water with glucose (75 g) induced an increase in biomarkers of oxidative stress and inflammation in PBMC. Conversely, the same meal, when consumed with orange juice (300 kcal), did not induce such changes, showing the potential anti-inflammatory effect of this beverage12. The proteome profile of PBMCs has already been studied13,14, but proteome analysis of PBMCs has been used in very few studies as a tool to study protein expression changes in response to dietary interventions. One study conducted with men showed that a 1-week intervention with flaxseed affected expression levels of 16 proteins15. Recently, gene expression patterns of PBMC were studied after a 4-month exposure to a high fat (HF) or high protein (HP) diet16 and the authors suggested that changes in PBMC gene expression may be a valid indicator of unbalanced macronutrient composition.



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The aim of this work was to study the acute change in the proteome of PBMC after the consumption of a HFHC meal consumed with water, orange juice, or isocaloric control (water with glucose). SUBJECTS AND METHODS Twelve healthy men (n = 5) and women (n = 7) were recruited for a cross-over study (body mass index 20-25 kg/m2; age range 25-45 years). All subjects received a standard meal a day before the study. The meal consisted of a wrap sandwich with cheese, turkey breast, lettuce and carrots, 200 mL of industrialized coconut water, a cereal bar, and an apple (473kcal, 22% fat, 64% carbohydrates and 14% protein) prepared at the lab. Participants were instructed to fast overnight for at least 10 h prior to a blood draw and were allowed to ingest water ad libitum. In the morning, they consumed a HFHC breakfast consisting of a croissant with butter and cheese plus a chocolate covered wafer (1,037 kcal, 59% fat, 30% carbohydrates, and 7% protein). Each subject consumed the meal in a cross-over design with either 500 ml of water, 100% orange juice (commercial, pasteurized from the same batch) or an isocaloric beverage (water with 57.5 g of glucose) by at least one-week washout period. The orange juice (500 ml) contained 319.75 ± 5.21 mg total phenolics, 25.7 ± 5.14 mg hesperidin, 4.65 ± 0.08 mg narirutin and 1.81 ± 0.16 mg didymin and 57.5 ± 1.5 g of sugar Blood samples were collected before (0h) and at 1h, 3h, and 5h after the breakfast. The experimental protocol was approved by the Ethics Committee of University of São Paulo (CAAE 40338314.6.0000.0067) and the subjects were informed about the project, meal content, and signed an informed consent. The present study was registered at clinicaltrials.gov as NCT02587507. Analysis of Orange Juice Flavonoids For the analysis of flavonoids, 5 mL of homogenized orange juice was centrifuged (11,000 g, 10 min, 4 ºC) and the supernatant was removed. The pellet fraction was centrifuged again (11,000 g, 10 min, 4 ºC) and the tubes were left inverted for 15 min for complete removal of the supernatant. A 1 mL aliquot of the supernatant was filtered through a 0.45 µm PVDF filter (Millipore, Tokyo, Japan) and 15 µL were directly injected in the HPLC. The pellet fraction was then extracted with 10 mL of dimethyl sulfoxide for 18 h. After centrifugation, the supernatant was filtered through a 0.45 µm PVDF filter (Millipore, Tokyo, Japan) and 7 µL were injected in the HPLC. The analysis was performed using an Agilent HPLC-MS-MS-DAD system (Agilent Technologies, Waldbronn, Germany) equipped with a binary pump G1312A, an autosampler G1313 A, a photodiode array detector G1315B, controlled by Agilent software v. A.08.03, and a 3 ACS Paragon Plus Environment

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degasser G1322A. Separations were achieved on a LiChroCART column (Merck, Darmstadt, Germany; ODS-18, 0.4 cm; 5 µm particle size). The mobile phase was water/formic acid (95.5:0.5 v/v) (solvent A) and acetonitrile (solvent B). The flow rate was 1 mL/min and a linear gradient starting with 85% A from 0 to 2 min, 85% A at 7 min, 77.5% A at 15 min, 60% A at 25 min, 30%A at 30 min and 85% A at 35 min. Flavonoids were identified by their UV spectrum, molecular mass, daughter ions and fragmentation pattern. Quantification was performed at a UV detection wavelength of 290nm using calibration curves obtained with the following commercial external standards: hesperidin (hesperetin 7-O-rutinoside, Extrasynthese), narirutin (hesperetin 7-O-neohesperidoside,

Extrasynthese)

and

didymin

(isosakuranetin

7-O-rutinoside,

Extrasynthese). The regression coefficient was above 0.99 for all standards. Soluble Sugar Content Sugar content was determined according to the method of Shiga et al.17 Samples were extracted three times with 80% ethanol (v/v) at 80 °C for 30 min under stirring. After centrifugation, the supernatants were evaporated under vacuum. The residues were reconstituted with water, filtered through 0.22 µm membrane filters, and analyzed by LC coupled to a pulse amperometric detector (PAD) using a one-Dionex DX500 system (Dionex, Thermo Scientific, Waltham, MA, USA), equipped with a CarboPac PA1 column (4 mm × 250 mm) (Dionex). The mobile phase consisted of 18 mM NaOH, at a flow rate of 1 mL/min for 25 min. The quantification was based on an external calibration using sucrose, glucose, and fructose. Measurement of plasma glucose, insulin and serum lipid concentrations Capillary glucose concentration was measured using the Roche ACCU-CHEK instrument and Advantage 11 Test Strips (Roche Diagnostics, Sydney, Australia). Insulin was analyzed by a commercial enzyme-linked immunosorbent assay kit (Mercodia Human Insulin) following the manufacturer´s instructions. Concentrations of total serum cholesterol, LDL cholesterol, HDL cholesterol and triglycerides were assessed by commercial enzymatic kit (Labtest®, São Paulo, Brazil). Serum cytokines The cytokines IL-4 and IL-6 from serum were assayed by flow cytometry, using a BD Cytometric Bead Array (CBA) human cytokine kitTM. The samples and standards were analyzed in a BD FACSCanto IITM flow cytometer (300 events per analyte), with the BD



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CellQuestTM acquisition software and FCAP ArrayTM software for quantitative analysis of cytokines. Peripheral Blood Mononuclear Cells Isolation For PBMC isolation, blood samples were collected in sodium-EDTA tubes (FicollPaqueTM, GE Healthcare). A total of 3–5 mL of blood was layered over 3.5 mL and centrifuged (400g, 20min, 18ºC) to separate the cells. The mononuclear cell layer was then transferred to a sterile centrifuge tube and washed twice with 6 volumes of PBS buffer (400g, 15min, 18ºC). The cell pellet was then resuspended in RIPA buffer with protease inhibitor cocktail (SigmaAldrich). Trypsin Digestion and TMT-labeling One hundred micrograms of protein were precipitated overnight in cold acetone (1:6) at 20 ºC. The samples were then dissolved in 45 µL of TEAB 200 mM pH 8.0 and 5 µL of 2% SDS, and ultrapure water was added to complete 100 µL. The reduction was performed with 10 µL TCEP 200 mM for 60 min at 55 °C, followed by 5 µL of 375 mM iodoacetamide for 30 min in the dark at RT. The samples were precipitated in cold acetone (1:6) at −20 °C overnight and dissolved in 45 µL of TEAB 200 mM pH 8.0, 2.5 µL of SDS 2%, and ultrapure water. Trypsin digestion was carried out overnight at 37 °C with 2.5 µg of enzyme. Ten-plex TMT labeling (Thermo Scientiﬁc) was performed according to the manufacturer’s instructions to isobarically label primary amino groups and thus allow us to simultaneously compare ten samples. LC-MS Analysis The TMT labeled samples were analyzed on a Fusion Orbitrap tribrid mass spectrometer (Thermo). Samples were injected directly onto a 30cm, 75um ID column packed with BEH 1.7um C18 resin (Waters). Samples were separated at a flow rate of 200nl/min on a nLC 1000 (Thermo). Buffer A and B were 0.1% formic acid in water and acetonitrile, respectively. A gradient of 5-25%B over 180min, an increase to 35%B over 120min, an increase to 90%B over another 45min and held at 90%B for a final 15min of washing was used for 360min total run time. Column was re-equilibrated with 20ul of buffer A prior to the injection of sample. Peptides were eluted directly from the tip of the column and nanosprayed directly into the mass spectrometer by application of 2.5kV voltage at the back of the column. The Orbitrap Fusion was operated in a data dependent mode. Full MS1 scans were collected in the Orbitrap at 120K resolution. The cycle time was set to 3 secs, and within this 3 sec the most abundant ions 5 ACS Paragon Plus Environment

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per scan were selected for CID MS/MS in the ion trap. MS3 analysis with multinotch isolation was utilized for detection of TMT reporter ions at 60k resolution. Monoisotopic precursor selection was enabled and dynamic exclusion was used with exclusion duration of 10 sec. Statistical Methods Peptide Spectrum Matching – PSM The data analysis was performed with the PatternLab for proteomics 4.0 software18 that is freely available at http://www.patternlabforproteomics.org. The sequences from Homo sapiens were downloaded from the UniProt consortium and then a target-decoy database was generated to include a reversed version of each sequence plus those from 127 common mass spectrometry contaminants. The Comet19 search engine (v. 2015.02), integrated into the PatternLab, was used to select the most likely candidates for the comparison between tandem mass spectra obtained experimentally and the theoretical spectra generated in silico. The search was limited to fully and semi-tryptic peptide candidates with up to two missed cleavages, considered carbamidomethylation of cysteine and the TMT modifications at the N-terminus and lysine as fixed modifications, and the TMT modification at tyrosine as a variable. The search engine accepted up to 40 ppm of the precursor mass tolerance, peaks from tandem mass spectra were binned at 0.02 m/z and the XCorr was used as the similarity metric. Validation of PSMs The PSMs were validated by the Search Engine Processor (SEPro)20. Briefly, the identifications were grouped into four distinct groups according to the precursor charge state and the tryptic status. For each group, the values of XCorr, DeltaCN, DeltaPPM, and Peaks Matched were used to create a Bayesian discriminator and the identifications were reported in an increasing order of the discriminating values. This procedure was independently performed on each data subset, establishing a cutoff score to accept a false-discovery rate (FDR) of 1% at the peptide level based on the number of labeled decoys. Additionally, the minimum sequence length of six amino acid residues was required and the results were post processed to only accept PSMs with less than 5 ppm and 1.90 as the minimum value of XCorr. One-hit-wonders with an XCorr of less than 2.5 were discarded. These filters led to a 0.6% FDR in the search results.

Relative Quantification of Peptides / Proteins 6 ACS Paragon Plus Environment


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Chimera spectra, generated by two or more co-fragmenting peptides, are common in shotgun proteomics of complex samples. Chimera spectra produce unreliable TMT quantitation signals and therefore should be accounted for. Here, data were acquired in a MS3 based MultiNotch approach that can be seamlessly handled by PatternLab for proteomics18. PatternLab’s isobaric Analyzer module was then employed to normalize the labeled ion signals, this procedure was performed independently for each LC-MS/MS analysis and TMT marker. Briefly, the sum of signals from each labeled ion was obtained, then for each spectrum, the normalized value was generated by dividing the signal from each marker ion from the value of the respective sum, from all spectra, of that marker. Reporter ion contamination was also accounted for by PatternLab according to the software correction factors provided in the TMT kit. Our proteomic comparison considered only proteins identified with two or more unique peptides, a paired t-test equal or less than 0.05; and an absolute peptide log fold change cutoff greater than 0.30. Our TMT analysis was done as described in PatternLab’s bioinformatics protocol18. Clusters listing proteins with similar abundancy profiles were generating using PatternLab’s TrendQuest module. Briefly, to achieve this, a consensus, time-based expression profile vector was generated for each protein by averaging the normalized TMT reporter ions decurrent from each protein’s mapped peptides; such “expression vector” contains 10 arguments. In what followed, PatternLab’s TrendQuest module (1) then uses the k-means++ algorithm (2) to generate 5 clusters; the metric used was the Euclidian distance. Proteins with expression profiles having Euclidian distances greater than 0.05 from the cluster’s consensus were excluded. Statistical analyses of biochemical parameters were conducted with SigmaPlot software (Version 12.0, Systat Software, Inc., CA, USA). Data are representative of three technical replicates and are expressed as mean ± SE.

Two-factor repeated-measures analysis of

variance (RM-ANOVA) analysis, followed by Tukey’s post hoc test, was used for all multiple comparisons between different groups.


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RESULTS Effect of the HFHC Meal and Drink Combinations on Glucose, Insulin and Lipid Concentrations of the Subjects Twelve individuals, both men and women were included in a cross-over study (body mass index 20-25 kg/m2; age range 25-45 years). When a HFHC meal was ingested with orange juice, glucose, or water, a significant increase in glucose concentrations with glucose and orange juice (P < 0.05; Figure 1A) but not with water was observed. However, glucose level was significantly higher in glucose + HFHC meal group compared to orange juice + HFHC meal group. Insulin concentrations increased significantly after the HFHC meal with orange juice, glucose and water (P < 0.05; Figure 1B). The increase in insulin concentration was significantly higher when glucose or orange juice was taken with the meal compared with when water was taken with the meal (P < 0.05, two-way RM-ANOVA). There was a significant increase in total cholesterol and LDL-cholesterol levels at 5h from baseline when the HFHC meal was ingested with glucose. Interesting, HDL-cholesterol increased when the HFHC meal was ingested with orange juice. In particular, an HDL increase of 10.3% was observed after orange juice intake. Triglycerides statistically increased at 1h, 3h and 5h from baseline when the HFHC meal was ingested with water (Supplementary Figure 1). Cytokines response There were no significant differences between experimental conditions for any of the parameters at baseline. At 5 h, there was a significant difference for IL-6, which was lower after the meal consumed with orange juice (p < 0.05, Figure 2). There was no significant difference for IL-4.

Proteome analysis in PBMC The number of identified proteins in PBMC was 3,813 proteins (2,230 according to maximum parsimony21, originating from 15,662 peptides (FDR 0.15%) and from 122,728 mass spectra (0.06% FDR). PatternLab (v.4.0.0.48) was used to assess changes in protein expression using the TrendQuest algorithm20. The Reactome Pathway Database22 was used to identify pathways that were up or down regulated under different conditions (Supplementary



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table 1). The complete tables with the proteins that were differentially expressed are available as supplementary material (Supplementary table 2). Commonly induced proteins Three proteins were induced when the meal was consumed with water, orange juice or glucose: apolipoprotein A-II, ceruloplasmin and hemopexin. The expression level of these proteins was increased at 3 h after the meal and remained high at 5 h. Pathways induced when the HFHC meal was consumed with water The up regulated proteins followed 2 different patterns of protein expression: a. proteins that were induced early (3 h) and the expression level remained high at 5 h; b. proteins that were induced later, at 5 h (Supplementary Figures 2A and 2B). As an early response to the HFHC meal consumed with water (3h), there was an increase in some protein pathways of the immune system: the Fc-gamma receptor (FCGR) dependent phagocytosis and the complement cascade. Although these pathways were activated, the immune system as a whole was not significantly induced (p = 0,395). Among the proteins that were induced at 5 h were proteins involved in the cell cycle: clearance of nuclear envelope membranes from chromatin, initiation of nuclear envelope reformation, nuclear envelope reassembly and nuclear envelope breakdown. Pathways induced when the HFHC meal was consumed with glucose The up regulated proteins followed 2 different patterns of protein expression: a. proteins that were induced at 3 h and the expression level continued increasing at 5 h; b. proteins that were up regulated at 3 h but the expression level went back to basal at 5 h (Supplementary Figures 2C and 2D). When the meal was consumed with glucose, the immune system was up regulated. The pathways that were significantly induced 3 h after the meal (and remained induced at 5 h) were those of the adaptive immune system (Major histocompatibility complex (MHC) class I molecules mediated antigen processing and presentation) and cytokine signaling (interferon signaling: interferon gamma and alpha/beta). Five hours after the meal, pathways of the complement cascade and classical antibody mediated complement activation were up regulated significantly. As can be seen in Supplementary Figure 2C, the expression levels of these proteins started increasing steadily right after the meal and at 5 h had not yet reached a plateau. 9 ACS Paragon Plus Environment

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Three proteins: 60S ribosomal protein L31, nuclear receptor-binding protein and E3 ubiquitinprotein ligase were significantly induced 3 h after the meal but at 5 h the expression levels were back to basal levels (Supplementary Figure 2D). Pathways induced when the HFHC meal was consumed with orange juice The up regulated proteins followed 2 different patterns that were similar to those observed when the HFHC meal was consumed with water: a. proteins that were induced early (3 h) and the expression level remained high at 5 h; b. proteins that were induced later, at 5 h (Supplementary Figures 2E and 2F). The up regulated pathways were those involved in signal transduction, DNA replication and cell cycle. None of the immune system pathways was significantly up regulated. Two proteins involved in protein degradation (26S protease regulatory subunity 7 and 26S proteasome non-ATPase regulatory subunit 11) were induced at 3 h (and remained high at 5 h) and a protein possibly involved in signal transduction (serine-protein kinase ATM) was induced only 5 h after the meal). The protein that had the highest increase in expression levels was de promyelocitic leukemia protein (PML), with a 28.2-fold increase. Pathways inhibited when the HFHC meal was consumed with water When the HFHC meal was consumed with water, a few proteins involved in signal transduction were down regulated. However, due to the small number of proteins, none of the analyzed pathways reached statistical significance. The PML protein which was up regulated with orange juice was down regulated 24-fold when the meal was consumed with water. Pathways inhibited when the HFHC meal was consumed with glucose Similarly to what was observed with water, only a few proteins were down regulated when the meal was consumed with glucose. The greatest reduction in expression levels was the elongation factor 1-delta (27-fold), involved in protein synthesis. Pathways inhibited when the HFHC meal was consumed with orange juice When the meal was consumed with orange juice, there was a down regulation of proteins involved with the adaptive immune system and cytokine signaling. Interestingly, many of the pathways that were induced when the HFHC meal was consumed with glucose were down regulated when the same meal was consumed with orange juice.



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DISCUSSION We studied the proteomic response to a single HFHC meal in PBMC by using a model to induce acute inflammation in healthy individuals. Only 3 proteins were induced when the meal was consumed with water, orange juice or glucose: apolipoprotein A-II, ceruloplasmin and hemopexin. The expression level of these proteins was increased 3 h after the meal and remained high at 5 h. Apolipoprotein A-II is present on the surface of HDL and its expression levels are associated with an increase in triglyceride levels in VLDL. Interestingly, the increase in chylomicrons (as seen by an increase in TG levels, Supplementary Figure 1) was paralleled by an increase in the expression of proteins involved in lipid metabolism in PBMC, which suggests that what happens in the enterocyte after a high fat meal is mirrored in PBMC by a yet undescribed mechanism. Hemopexin is an acute phase protein synthesized by hepatocytes in response to the pro inflammatory interleukins IL-5, IL-1β and TNF-α. It controls the plasma levels of iron and modulates the lymphocyte’s response to IFN-y. A study done with mice showed that 8 weeks of high fat diets increased the expression level of hemopexin23. This finding suggests PBMC may be a valid tool to assess the early response to a high fat meal which may reflect proteins that are modulated in other tissues when such a diet is sustained for longer periods of time.

The

third protein, ceruloplasmin, is a copper transporter glycoprotein synthesized by the liver and it´s induced under pro inflammatory conditions. Although the IL-6 response was similar when the HFHC meal was consumed with either water or glucose, our data showed that when the HFCH meal was consumed with water, some of the immune system pathways were activated in PBMC, but not the immune system as a whole. On the other hand, when the meal was consumed with glucose, the inflammatory response was higher, and the immune system was induced significantly. This suggests that an increase in blood glucose may be the main trigger to induce oxidative stress and inflammation, acting via NFKB as shown by the previous work24. The glycemic curve, however, is not the sole indicator of the inflammatory response. When the meal was consumed with water or orange juice, the glycemic and insulinemic response at 1h was lower than when the meal was consumed with glucose, although the orange juice added 57.5 g of sugar to the meal. The flavonoids contained in orange juice have been shown to inhibit intestinal glucose uptake, which may explain the lower than expected glucose/insulin response25. 11 ACS Paragon Plus Environment

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Orange juice does not cause oxidative and inflammatory stress, and flavonoids in orange juice suppress ROS generation. This fact is related to the anti-inflammatory effect of orange juice, since ROS generation can trigger NF kappaB pathway and increase the inflammatory response25. Concerning the anti-inflammatory effect of orange juice, this food induced the increase in expression levels of promyelocitic leukemia protein (PML), with a 28.2 fold increase. Interestingly, this protein was down regulated 24 fold when the meal was consumed with water and the expression levels did not change when the meal was consumed with glucose.

We

could not find an explanation for this in the literature but it should be investigated whether this response is regulated by insulin, since the treatment with glucose induced the highest insulin response of all groups. The PML protein is a tumor suppressor acting as the organizer of nuclear matrix-associated structures named nuclear bodies. This protein is involved in cell death, senescence or antiviral defence underlines the multiple functions of PML due to its ability to interact with various partners either in the cytoplasm or in the nucleus. More recently, a growing body of evidence also supports PML as a key regulator of cytokine signalling. These findings shed light on unsuspected biological functions of PML such as immune response, inflammation and cytokine-induced apoptosis26. Our results show that the consumption of a HFHC meal with water induces some proteins involved in inflammation, but not the immune system as a whole. When the same meal is consumed with glucose, however, there is an up regulation of the immune system. Also, several of these pathways are down regulated when the meal is consumed with orange juice, showing a strong anti-inflammatory effect. The PML protein, however, responded differently: it was significantly down regulated when the meal was consumed with water and up regulated with orange juice. Our results suggest that PML may play a protective role in diet induced inflammation. The PML can reduce the inflammatory response through their interaction with inflammasome, which are large cytoplasmic multiprotein complexes that act to regulate the activation of caspase-1, the enzyme that processes the pro-forms of the cytokines IL-1β and IL18 into their active forms. PML interacts with the apoptosis-associated speck-like protein to limit inflammasome activation. The interaction between PML and ASC is involved in the retention of ASC in the nucleus. Thus, PML is a negative regulator of ASC, attenuating inflammasome activation by retaining ASC in the nucleus26.



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This study presents some limitations that deserve further discussion. First, we have investigated the short-term response to a HFHC meal after OJ consumption. A recent study27 showed that consumption of 500 mL/d of orange juice as part of a reduced-calorie diet resulted in a decrease in insulin levels (18%, p < 0.05), HOMA-IR (33%, p < 0.04), total cholesterol (24%, P < 0.0001) and high-sensitivity C-reactive protein (33%, p < 0.001) when compared to a reduced-calorie control group without orange juice. Therefore, this study shows that orange juice consumption has anti-inflammatory effects that are independent of weight loss, since both groups lost a similar amount of weight and body fat. However, to the best or our knowledge, the effects of a long term consumption of a HFHC meal with or without OJ has not been explored yet. In our study, blood samples were collected before (0h) and at 1h, 3h, and 5h after the breakfast. This time points are associated with the acute increase in reactive oxygen species (ROS) generation by leukocytes and an increase in plasma thiobarbituric acid–reacting substance (TBARS) and inflammatory biomarkers levels induced by glucose (75 g) intake8,10,28. It should be noted that earlier blood samples could have shown different patterns of protein expression. Unfortunately, we did not collect earlier blood samples. Further studies are needed to investigate the molecular mechanisms underlying the differential expression of proteins also considering the RNAs analysis. The proteome data is available in the PRIDE database. Project accession PXD007636. Username: [email protected]

Password: x2HNvMC5

ACKNOWLEDGEMENTS This work was funded by FAPESP, grant 2014/22864-0. References 1. Hotamisligil, G.S. Inflammation and metabolic disorders. Nature 2006, 444, 860–867. 2. Blake, G.J.; Ridker, P.M. Inflammatory bio-markers and cardiovascular risk prediction. J Intern Med. 2002, 252, 283–94. 3. Ferri, N.; Paoletti, R.; Corsini, A. Biomarkers for atherosclerosis: patho-physiological role and pharmacological modulation. Curr Opin Lipidol. 2006, 17, 495–501. 4. Khuseyinova, N.; Koenig, W. Biomarkers of outcome from cardiovascular disease. Curr Opin Crit Care. 2006, 12, 412–9.


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Figure 1. Mean (± SEM) change in plasma glucose (A) and insulin (B) concentrations after intake of a highfat, high-carbohydrate meal and a drink of orange juice (OJ + HFHC meal), glucose (Glu + HFHC meal), or water (W + HFHC meal) in normal subjects. a,b,c P < 0.05 [two way repeated-measures ANOVA (RMANOVA)] compared with baseline values after OJ+ HFHC meal, Glu+ HFHC meal or W+ HFHC meal, respectively; *,+, # P < 0.05 for comparison between OJ + HFHC meal vs Glu + HFHC meal , OJ + HFHC meal vs W + HFHC meal, or Glu + HFHC meal vs W + HFHC meal treatments, respectively. n = 13. 254x190mm (96 x 96 DPI)


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Figure 2. Mean ± SEM for IL-6 (A) response after the HFHC meal consumed with water, glucose or orange juice. * p < 0.05. 254x190mm (96 x 96 DPI)


Proteomic Analysis of Peripheral Blood ... - ACS Publications

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