Peripheral blood mononuclear cells as a source to ...

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ScienceDirect Journal of Nutritional Biochemistry 26 (2015) 398 – 407

Peripheral blood mononuclear cells as a source to detect markers of homeostatic alterations caused by the intake of diets with an unbalanced macronutrient composition☆,☆☆ Rubén Díaz-Rúa a , Jaap Keijer b , Antoni Caimari c , Evert M. van Schothorst b , Andreu Palou a,⁎, Paula Oliver a a

Laboratory of Molecular Biology, Nutrition and Biotechnology, Universitat de les Illes Balears and CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERobn), Palma de Mallorca, Spain b Human and Animal Physiology, Wageningen University, Wageningen, the Netherlands c Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, CEICS, Reus, Spain

Received 5 June 2014; received in revised form 4 November 2014; accepted 20 November 2014

Abstract Peripheral blood mononuclear cells (PBMCs) are accessible in humans, and their gene expression pattern was shown to reflect overall physiological response of the body to a specific stimulus, such as diet. We aimed to study the impact of sustained intake (4 months) of diets with an unbalanced macronutrient proportion (rich in fat or protein) administered isocalorically to a balanced control diet, as physiological stressors on PBMC whole-genome gene expression in rats, to better understand the effects of these diets on metabolism and health and to identify biomarkers of nutritional imbalance. Dietary macronutrient composition (mainly increased protein content) altered PBMC gene expression, with genes involved in immune response being the most affected. Intake of a high-fat (HF) diet decreased the expression of genes related to antigen recognition/presentation, whereas the high-protein (HP) diet increased the expression of these genes and of genes involved in cytokine signaling and immune system maturation/activation. Key energy homeostasis genes (mainly related to lipid metabolism) were also affected, reflecting an adaptive response to the diets. Moreover, HF diet feeding impaired expression of genes involved in redox balance regulation. Finally, we identified a common gene expression signature of 7 genes whose expression changed in the same direction in response to the intake of both diets. These genes, individually or together, constitute a potential risk marker of diet macronutrient imbalance. In conclusion, we newly show that gene expression analysis in PBMCs allows for detection of diet-induced physiological deviations that distinguish from a diet with a proper and equilibrated macronutrient composition. © 2015 Elsevier Inc. All rights reserved. Keywords: Peripheral blood mononuclear cells; High-fat diet; High-protein diet; Transcriptomics; Nutritional markers; Unbalanced diet gene signature

1. Introduction Dietary macronutrients should be ingested in defined proportions, dependent on species requirements, to maintain a good health status ☆

Financial support: CIBER de Fisiopatología de la Obesidad y Nutrición is an initiative of the ISCIII. This work was supported by the Spanish government (Ministerio de Educación y Ciencia, BIOBESMARKERS—AGL2009-11277) and by the EU FP7 project BIOCLAIMS (FP7-244995). The groups of A.P. and J.K. are members of the European Research Network of Excellence NuGO (The European Nutrigenomics Organization, EU Contract: FOOD-CT-2004-506360 NUGO). Nutrigenomics group of A.P. has been awarded as “Group of Excellence” of CAIB and supported by “Direcció General d'Universitats, Recerca i Transferència del Coneixement” of Regional Government (CAIB) and FEDER funds (EU). R.D.R. is a recipient of a fellowship from the Spanish government. ☆☆ Conflict of interest: The authors declare that they have no conflict of interest. ⁎ Corresponding author at: Laboratory of Molecular Biology, Nutrition and Biotechnology, Universitat de les Illes Balears. Cra. Valldemossa Km 7.5. E-07122, Palma de Mallorca, Spain. Tel.: +34 971173170; fax: +34 971173426. E-mail address: [email protected] (A. Palou). http://dx.doi.org/10.1016/j.jnutbio.2014.11.013 0955-2863/© 2015 Elsevier Inc. All rights reserved.

[1,2]. Diets that do not accomplish recommended macronutrient intakes, that is, diets with an unbalanced proportion of macronutrients or unbalanced diets (e.g., rich in fat or protein), have been related to several metabolic disorders both in humans and animal models and may challenge the homeostatic response of the body. An increase in dietary fat content generally produces an increase in body weight and adiposity, increased caloric intake and alterations related to metabolic syndrome (e.g., insulin resistance, hypertriglyceridemia and hypercholesterolemia) [3,4]. On the other hand, diets rich in proteins produce a decrease in body weight and an improvement in metabolic parameters related to excessive adiposity (e.g., decrease in blood lipids and improvement of insulin sensitivity) [5,6]. However, sustained intake of high-protein (HP) diets has also been associated with complications such as insulin resistance, renal disease and bone resorption [7–9]. Nowadays there is an increase in the consumption of unbalanced diets, as those in which the percentage of carbohydrates is decreased in favor of increasing the fat or protein content over the recommend amounts [high-fat (HF) or HP diets, respectively]. According to Dietary Reference Intakes, in humans 20%–35% of total energy should be derived

R. Díaz-Rúa et al. / Journal of Nutritional Biochemistry 26 (2015) 398–407

from fats [1]. An increased proportion of fat (over 35% kcal) can be the result of increased intake of fat-rich foods as is seen in Western society [10], but it can also be due to the reduction of carbohydrate intake in weight loss diets [11]. HF diets are especially problematic when total calories are controlled, as they are not necessarily related to body weight gain. In animal models, isocaloric intake of an HF diet does not affect body weight but is related to increased adiposity and complications that are associated with metabolic syndrome [12,13]. Also in humans, metabolically obese, normal-weight individuals present an increased metabolic risk [14,15]. For proteins, an average diet should have a desirable protein intake of around 15% of total energy [16]. However, the use of HP diets and very-HP diets (with ≥20% or ≥30% of total energy, respectively) has become popular in recent years for weight loss purposes [16]. This could be problematic, as there is scientific evidence indicating potential harmful effects, especially in nonhealthy individuals [17], like those who usually follow these diets. Although nutritional studies can be performed directly in humans, these studies are subject to restrictions. Rodents provide a useful model that has been traditionally used to study the effects of dietary disequilibrium [3,18]; they allow obtaining metabolic information not attainable with noninvasive sampling and also performing highly controlled long-term nutritional studies which would be difficult, or even impossible, to develop in humans. Different unbalanced diets for rodents are commercially available or can be customized; their use can help to gain insight in the potential impact of intake diets that strongly deviate from the recommended macronutrient proportion in humans. Whole-genome gene expression of peripheral blood mononuclear cells (PBMCs) can be used to understand global metabolic effects of unbalanced diets. PBMCs are a subset of blood cells primarily consisting of lymphocytes and monocytes and are of interest because they are readily obtained and, apart from their role in immunology, express a large proportion (approximately 80%) of the genes encoded by the human genome [19]. Moreover, these cells reflect gene expression responses to changes in environmental conditions of other tissues that are more difficult to obtain, and thus, PBMCs are considered as sentinels that can be used as a surrogate tissue to perform gene expression analysis [19]. The use of PBMCs for transcriptome analyses is useful for clinical purposes, as they can reflect gene signatures related to diseases or drug response [20–23]. In addition, in recent years, their relevance for nutritional studies has been demonstrated [24–27]. PBMCs are known to reflect responses to nutrients and diets which occur in different tissues with an important role in the control of energy metabolism [24–26,28]. Based on this information, we hypothesize that gene expression changes in these cells could reflect metabolic effects (adaptive or harmful) of unbalanced diets. Thus, we aimed to study, using rat as animal model, the impact of long-term intake of diets with an unbalanced macronutrient composition, rich in either fat or protein, relative to a balanced control diet, on whole-genome PBMCs gene expression to better understand the effects of these diets on metabolism and health. We were particularly interested in identifying a common gene signature for diets with disproportioned macronutrient composition. To our knowledge, this is the first time in which the effects of an HP diet on PBMCs expression are studied. 2. Materials and methods 2.1. Animals We performed PBMCs gene expression analysis in a set of Wistar rats (Charles River Laboratories España, SA, Barcelona, Spain) single-housed at 22°C with a light/ dark cycle of 12 h from a previous study that reported on body weight, adiposity, serum parameters and gene expression in key energy homeostasis tissues [29]. Male rats at the age of 2 months were divided into three groups for a 4-month dietary intervention: a control group (n=7), an HF diet group (n=7) and an HP diet group (n=6). Control animals were fed a normolipidic diet (D12450B, Research Diets) containing 70% of energy (kcal) from carbohydrates, 10% from fats and 20% from proteins. The HF diet

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(D12492, Research Diets) contained 20% of energy from carbohydrates, 60% from fats (37% saturated and 63% unsaturated fat of which 46% was monounsaturated and 17% polyunsaturated) and 20% from proteins. The HP diet (Research Diets) contained 45% of energy from carbohydrates, 10% from fats and 45% from proteins (mainly casein). Diets were purchased from Brogaarden (Gentofte, Denmark). Detailed composition of the diets is presented in Supplementary Table 1. During the intervention, the different diets were administered isocalorically to the experimental groups, relative to the control group; the HF and the HP groups received an amount of kcal equal to the average amount consumed by the control group the day before. We proceeded as follows: animals in the control group had free access to food and their food intake was recorded daily in order to calculate their exact energy (kcal) consumption. The amount of food (grams) administered to the HF and the HP groups was calculated to contain the same kcal as those ingested by the control group the day before. The energy density of the diets used for calculation was as follows: control: 3.85; HF: 5.24 and HP: 3.85 kcal/g, respectively. When present, residual food in each cage was weighed, discarded and replaced with fresh diet every 24 h. Food intake of all groups was recorded daily to calculate the daily caloric intake and cumulative caloric intake throughout the experiment; body weight was recorded three times a week. One week prior to sacrifice, animals were submitted to nocturnal 14-h fast to collect serum in fasted conditions to determine the HOMA-IR index. At the end of the experiment, animals were sacrificed in the fed state by decapitation. Anesthesia was not used to avoid interferences. After sacrifice, different white adipose tissue depots — epididymal, inguinal, mesenteric and retroperitoneal — and kidney were rapidly removed and weighed. Other key tissues in energy homeostasis control were collected as well. Truncal blood was collected from the neck, stored at room temperature for 1 h and centrifuged at 1000g for 10 min at 4°C to collect serum.

2.2. Isolation of PBMCs For PBMCs isolation, and prior to the sacrifice of the animals, peripheral blood samples (1.5–2.5 ml) were collected from the safena vein, using heparin in NaCl (0.9%) as anticoagulant. Immediately after blood collection, PBMCs were isolated by Ficoll gradient separation, according to the instructions indicated by manufacturer (GE Healthcare Bio Sciences, Barcelona, Spain), with some modifications. Briefly, the anticoagulant-treated blood was diluted with an equal volume of balanced salt solution, which was prepared by mixing two stock solutions (1/10); solution A (5.5 mM anhydrous D-glucose, 5 mM CaCl22H2O, 0.98 mM MgCl26H2O, 5.4 mM KCl, 145 mM Tris) and solution B (140 mM NaCl). Afterward, the blood was layered carefully over Ficoll without intermixing (1.5 ml of Ficoll for 2 ml of blood mixed with balanced salt solution) in a centrifuge tub and centrifuged at 900g for 40 min at 20°C. PBMCs, together with platelets, were harvested from the interface between Ficoll and sample layers. This material was then centrifuged in balanced salt solution at 400g for 10 min at 20°C to wash PBMCs and to remove platelets.

2.3. Total RNA isolation Total RNA from PBMCs samples was extracted using Tripure Reagent (Roche Diagnostics, Barcelona, Spain) and purified with Quiagen RNesay Mini Kit spin columns (Izasa SA, Barcelona, Spain). RNA yield was quantified on a Nanodrop ND 1000 spectrophotometer (NanoDrop Technologies, Wilmintog, DE, USA), and its integrity was measured on an Agilent 2100 Bioanalyzer with RNA 6000 Nano chips (Agilent Technologies, South Queensferry, United Kingdom).

2.4. Microarray processing For microarray analysis, a total of 18 PBMCs RNA samples from the control (n=6), HF (n=7) and HP (n=5) groups were used. Two other samples, one from the control and the other from the HP group, were excluded because of a low RNA amount. Samples were randomized before individual hybridization, and the arrays were hybridized simultaneously. For microarray hybridization, 0.20 μg RNA of each sample was reverse transcribed using the Agilent Low RNA Input Fluorescent Linear Amplification Kit (Agilent), according to the manufacturer's protocol (all materials and reagents are from Agilent Technologies, Palo Alto, CA, USA, unless stated otherwise). One half of the cDNA sample (10 μl) was used for linear RNA amplification and labeling with Cy5, and the other half was used for labeling with Cy3. All reactions were performed using half of the amounts indicated by the manufacturer [30]. In vitro transcription and labeling were carried out at 40°C for 2 h. The labeled cRNA samples were purified using Qiagen RNA easy mini-spin columns (Qiagen, Venlo, the Netherlands). Dye incorporation and cRNA concentration was measured using the “microarray measurement mode” of the Nanodrop spectrophotometer. Cy3 labeled cRNA samples were pooled on an equimolar basis. Each sample containing 825 ng of cRNA labeled with Cy5 and 825 ng of Cy3 pool was hybridized on 4×44K G4131F rat whole-genome Agilent microarrays (Agilent Technologies) for 17 h at 65°C in hybridization chambers in an oven rotating at 10 rpm (Agilent Technologies). After hybridization, the arrays were subsequently washed according to manufacturer's protocol (Agilent Technologies).

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2.5. Microarray data analysis Arrays were scanned with an Agilent Microarray Scanner (Agilent Technologies). Spot intensities were quantified using Feature extraction 10.5.1.1 (Agilent Technologies). Median density values and background values of each spot were extracted for both the experimental samples (Cy5) and the reference samples (Cy3). Subsequently, quality control was performed for each microarray using LimmaGUI package in R from Bioconductor 2.1. All arrays passed the quality control based on MA plot and signal intensity distribution [31]. Thereby, the data set contained 18 arrays in total. Data were imported into GeneMaths XT 2.12 (Applied Maths, Sint-Martens-Latem, Belgium) for background correction and normalization. Locally weighted linear regression (lowess) analysis was chosen as a normalization method, which enables intensity-dependent effects in the log2 (ratio) values to be removed [32]. Target signals with an average intensity lower than twofold above average background were discarded from further normalization to increase accuracy of the data. Then, the values were converted to log2 values, and the target samples (Cy5) intensities were normalized against the intensities of reference samples (Cy3), as described previously [33]. The data were deposited in NCBIs Gene Expression Omnibus and are accessible through GEO Series accession number GSE54897. Statistical differences between the HF or the HP group vs. the control group were assessed by Student's t test in GeneMaths XT; the generated P values were used to obtain insight into significantly affected genes. Fold change calculations were performed in Microsoft Excel; fold change equals HF group/control or HP/control ratio in the case of increase or equals −1/ratio in the case of decrease. For analysis of the microarray data, a threshold of Pb.05 and absolute fold change ≥1.2 was selected. Correction for multiple testing was not applied, as these corrections are often too strict to identify the small effects which are usually observed in nutritional studies [34]. Subsequently, a statistically generated list of genes was distributed into different metabolic pathways according to the use of Metacore program. In addition, affected genes were manually analyzed for biological information, using available databases [Rat Genome Database (RGD), Genecards, KEGG, NCBI, Reactome, UniProt, USCN, WikiPathways, PubMed), focusing on key biological domains, such as molecular function and biological process. Some of these processes overlapped; thus, they were bundled and renamed. All the unique genes were assigned to a biological process according to their function. We manually supplemented the significantly enriched biological processes with nonassigned genes from the selected gene set using biological databases (Biocarta, SOURCE, GenMAPP, KEGG) and scientific literature. In some cases, we annotated probes manually in order to have the best annotated list of probes. 2.6. Real-time RT-polymerase chain reaction (RT-PCR) analysis To validate mRNA levels in PBMCs samples used for microarray analysis, real-time RT-PCR was used. The following genes were analyzed: Copg2, Cpt1a, Dhfr, Ethe1, Eno1, Eepd1, Glycam, Hsd17b13, Pfkbp2, Slc2a4/Glut4 and Tmcc2. These genes were selected because they were among the top 10 of most affected genes, they had differential expression with a low P value, or they were involved in energy homeostasis or immune response. Additionally, Acox1 gene expression was analyzed in liver and retroperitoneal adipose tissue, to establish a correlation with gene expression changes observed in PBMCs. Fifty nanograms (for PBMCs) or 250 ng (for liver and adipose tissue) of total RNA in a final volume of 5 μl was denatured at 65°C for 10 min and then reverse transcribed to cDNA using murine leukemia virus reverse transcriptase (Applied Biosystem, Madrid, Spain) at 20°C for 15 min, 42°C for 30 min, with a final step of 5 min at 95°C in an Applied Biosystems 2720 Thermal Cycler (Applied Biosystem). PCRs were performed from diluted (1:5 for PBMCs and 1:20 for liver and adipose tissue) cDNA template, forward and reverse primers (in a final concentration of 0.4 μM each), and Power SYBER Green PCR Master Mix (Applied Biosystems) in a total volume of 11 μl, with the following profile: 10 min at 95°C, followed by a total of 40 temperature cycles (15 s at 95°C and 1 min at 60°C). Primer sequences are provided in Supplementary Table 2. All primers were obtained from Sigma Genosys (Sigma Aldrich Química SA, Madrid, Spain). In order to verify the purity of the reaction products, a melting curve was produced after each run according to the manufacturer's instructions. The threshold cycle (Ct) was calculated by the instrument's software (StepOne Software v2.0), and the relative expression of each mRNA was calculated as a percentage of control rats, using the 2−ΔΔCt method [35]. Data from PBMCs samples were normalized against Integrin, used as a reference gene, which was chosen because our microarray data showed equal and high expression for all microarrays in the different experimental groups. For liver and adipose tissue, data were normalized against the reference gene guanosine diphosphate dissociation inhibitor 1 (Gdi1), which has been previously identified as a stable reference gene in these tissues [25]. 2.7. Statistical analysis Data of body weight, adiposity, serum parameters and the confirmatory results of the microarray data are expressed as the mean±SEM. Differences between groups were analyzed using Student's t test or one-way analysis of variance (ANOVA) and least significant difference post hoc comparisons. The test used for each comparison is specified in the footnote of the specific tables. All analyses were performed with SPSS

for windows (version 15.0; SPSS, Chicago, IL, USA). Threshold of significance was defined at Pb.05. The statistical analysis of the microarray data has been previously indicated in the microarray data analysis section.

3. Results 3.1. Body weight, adiposity and serum parameters Data of body weight, adiposity and serum parameters of the experimental model used have been previously reported [29]. Table 1 shows the most relevant parameters. In brief, compared to the rats on a control diet, 6-month-old animals fed an isocaloric HF diet for 4 months did not increase their body weight, but displayed increased adiposity and liver-fat deposition, as well as signs of insulin resistance, such as increased circulating glucose levels and increased HOMA-IR index. On the other hand, rats fed an HP diet showed a lower cumulative food intake and body weight in comparison to control rats and lower serum cholesterol levels and were apparently healthy. HP-fed animals displayed increased circulating insulin, but this was not related to insulin resistance or alterations in glycemia. In addition, HP diet induced an increase in kidney size, which has been related to several renal complications [36], although serum creatinine (indicator of kidney function) was not affected. Serum TNF-alpha levels, measured as a marker of inflammation, were increased in both HF and HP groups. 3.2. Effects of HF or HP diets on PBMCs gene expression: differential gene expression Our microarray analysis results showed that PBMCs gene expression was affected by the macronutrient composition of the diet. In total, about 52% of all probes were considered to be expressed (21,530 of 41,012 probes). Of these, 261 and 580 probes changed as a result of the intake of the HF and the HP diets, respectively, in comparison to the control group (Student's t test, Pb.05). In order to be more restrictive, we considered those probes significantly affected by the HF or the HP diets (Student's t test, Pb.05) with an absolute fold change ≥1.2. In this way, in PBMCs of the HF-fed group, a total of 113 probes had an altered expression in comparison to control animals; in the HP-fed group, the number of differentially expressed probes was higher, namely, 337. Duplicates with the highest P values were removed, resulting in 92 unique genes differentially expressed in the HF group (72 known and 20 unknown) and 309 in the HP group (219 known and 90 unknown). Of the known genes, 47 (65%) were down-regulated and 25 (35%) up-regulated in the HF group, and 79 (36%) were down-regulated and 140 (64%) up-regulated in the HP group. 3.3. Pathway analysis Pathway analysis was performed taking all probes that were affected by HF of HP diet feeding in consideration, 261 and 580 probes, respectively (Student's t test, Pb.05). Genes affected by the HF diet were mainly related to signal transduction, cell communication and cell proliferation, whereas HP diet affected genes related to cell cycle, signal transduction and immune response. For a more accurate analysis, known genes that changed with a P value lower than .05 and with an absolute fold change ≥1.2 were manually assigned to different biological processes. The processes that were affected in PBMCs in response to both unbalanced diets (HF and HP) are represented in Fig. 1. These results were similar to those obtained using Metacore analysis of the full data set. The most significantly affected processes for both diets were as follows: cell communication and signal transduction, gene expression, immune response and cell cycle (mainly genes involved in cell proliferation). Genes involved in energy metabolism were also affected by the intake


of the HF and the HP diets. Other affected processes were membrane trafficking and vesicle transport, blood and iron metabolism and nitrogen metabolism. A small subset of genes could not be classified into these processes and were involved in other biological processes or had an unknown role. The genes involved in immune response and energy metabolism affected by the diets were further subclassified for a refined interpretation of the results (Tables 2 and 3). The HF diet feeding affected expression of 10 immune responserelated genes, 9 of them were down-regulated. HP diet feeding had a higher impact than the HF diet, and mostly in the other direction, as it affected the expression of 30 genes related to immune response and, contrary to what happened with the HF diet, most of them (22) were up-regulated. These genes encode proteins involved in antigen recognition, cytokine signaling and immune system activation and maturation. HF and HP diets also altered the expression of energy metabolism-related genes, including key genes in lipid and carbohydrate metabolism. A detailed analysis revealed that HF diet feeding affected the expression of three genes with a role in the regulation of oxidative balance and hypoxia. These genes are present in Table 4 and are further commented in the discussion section.

3.4. Common genes affected by both HF and HP diets We identified a set of genes that were significantly affected by both diets studied (HF and HP) under the previously defined statistical conditions. A total of 7 common genes were identified (Table 5). Interestingly, gene expression of these genes was altered in the same direction by both unbalanced diets. Among these common affected genes were two that are involved in cell cycle (Cenpn and Dhfr), one in energy metabolism (Acox1), two in signal transduction (S1pr4 and Narg1), one in vesicle transport (Copg2) and one in DNA repair (Eepd1).

Table 1 Body weight, adiposity and circulating parameters in animals of the HF and HP groups.

Body weight (g) Adiposity index (%) Accumulated caloric intake (kcal) Body fat content (% of fat) Lipid content in liver (mg/g tissue) Glucose (mg/dl) Feeding glucose (mg/dl) Fasting Insulin (μg/L) Feeding insulin (μg/l) Fasting HOMA-IR Cholesterol (mg/dl) Creatinine (mg/dl) TNF-alpha (pg/ml)

Control

HF group

HP group

500 ± 13a 8.43±0.70a 10227±182a 17.8±1.6a 25.5±4.3a

495±10a 11.1±0.9b ⁎ 9789±203a 22.8±2.1b ⁎ 48.9±6.7b ⁎

455±12b ⁎ 8.31±1.07a 8971±495b ⁎ 18.3±1.7a 31.4±3.6a

97.1±4.4a 80.5±4.3a

107±2.4b 86.4±1.4a

91.5±3.0a 87.8±4.0a

1.23±0.22a 0.36±0.08a 2.17±0.49a 1.54±0.21a 0.81±0.09a,b 2.64±0.55a

0.88±0.21a 0.82±0.08a,b 4.18±0.49b ⁎ 1.75±0.32a 1.12±0.21a 5.90±0.75b ⁎

2.12±0.27b ⁎ 0.37±0.16a 1.85±0.65a 0.92±0.16b ⁎ 0.55±0.07b 13.3±5.7b ⁎

The adiposity index was computed as the sum of epididymal, inguinal, mesenteric and retroperitoneal white adipose tissue depot weights and expressed as a percentage of total body weight. The percentage of body fat was measured using an EchoMRIRMI. Lipid content in the liver was measured using the Folch method. Glucose was measured in the fed state using and Accu-Chek glucometer (Roche Diagnostics). Insulin and TNF-alpha levels were measured by ELISA kits (from DGR Instruments and R&D Systems Europe, respectively) in serum of fed animals, and total cholesterol and creatinine using commercial enzymatic colorimetric kits (both from BioSystems). Serum parameters were measured in the fed state; additionally, glucose and insulin levels were measured after a nocturnal 14-fast to calculate HOMA-IR, which was computed using the formula of Matthews et al. [78]. Results represent mean±S.E.M. (n=7 in control and HF groups and n=6 in HP group). Values not sharing a common letter (a, b, c) are significantly different (one-way ANOVA, Pb.05). ⁎ Versus control group (Student's t test, Pb.05).

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3.5. Top 10 up- and down-regulated genes The top 10 genes up-regulated and down-regulated by HF and HP diets are presented as supplementary information (Supplementary Tables 3 and 4, respectively). For the HF diet, these genes were involved, in this order, in gene expression, immune response, signal transduction, cell cycle and energy metabolism. For the HP diet, the top 10 regulated genes were involved in: immune response, cell cycle, energy metabolism, gene expression, signal transduction and nitrogen metabolism. 3.6. Confirmation of array results by real-time RT-PCR Real-time RT-PCR analysis was performed on RNA from PBMCs samples of the different experimental groups to substantiate the microarray data. A total of 11 genes were verified (Table 6): 5 genes differentially expressed in the HF group (Cpt1a, Ethe1, Eno1, Hsd17b13 and Tmcc2), 3 in the HP group (Glycam, Pfkfb2 and Slc27a2) and 3 genes which were differentially expressed in both the HF and HP groups (Copg2, Dhfr and Eepd1). Real-time RT-PCR analysis confirmed the microarray data; the studied genes were significantly affected by the different diets (Student's t test, Pb.05) and followed the same pattern observed in the microarray analysis, with only two exceptions (Tmcc2 and Copg2), which changed in the same direction as indicated by the microarray, but did not reach statistical significance. 3.7. Tissue-specific analysis To better understand some of the gene expression changes occurring in PBMCs in response to the intake of unbalanced diets, it is important to make a comparison with tissue gene expression. Expression of energy homeostatic genes in key tissues of the same set of animals has been previously reported [29]. Generally, gene expression in PBMCs reflected regulatory behavior observed in the energy homeostatic tissues analyzed. Briefly, changes at gene expression level evidenced an adaptation to lower dietary carbohydrate content present in the studied diets, and to increased fat or protein content. Both diets decreased gene expression of glycolytic genes in liver, Pklr in the HF and Pfk in the HP group. Regarding lipid metabolism genes, in general terms, HF feeding resulted in decreased expression of genes involved in fatty acid synthesis (Acc1, Fasn and Srebp1) in liver, adipose tissue and muscle, and increased expression of genes involved in fatty acid oxidation, Cpt1 in liver and adipose tissue and Atgl in adipose tissue. HP diet intake also decreased expression of fatty acid synthesis genes in muscle (Srebp1), white adipose tissue (Fasn) and brown adipose tissue (Fasn and Acc1) and increased expression of the lipolytic gene Atgl in adipose tissue. In addition to the above-mentioned genes, we have analyzed gene expression of Acox1 in liver and adipose tissue. Acox1 is involved in fatty acid beta-oxidation and is one of the common genes overexpressed by the two unbalanced diets. As observed in PBMCs, Acox1 expression increased both in liver and retroperitoneal adipose tissue in HF and HP-fed animals (for liver, HF: 1.41±0.14 and HP: 1.45±0.09 vs. 1.00±0.06 in controls; for retroperitoneal adipose tissue, HF: 1.42±0.12 and HP: 1.38±0.16 vs. 1.00±0.09 in controls, Student's t test, Pb.05 in all the cases). Moreover, decreased PBMCs expression in the HF group of the glycolytic gene Eno1, which was among the top 10 down-regulated, and of Ethe1, potentially linked to antioxidant protection, was also evident in the liver of the same animals as determined in a microarray gene expression analysis (GEO accession number GSE57858). For both Eno1 and Ethe1, a significant decrease was seen in the liver of HF vs. controls: fold change −1.45 (P=.002, Student's t test) and −1.82 (P=.002, Student's t test), respectively.

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Down-regulated Up-regulated

a) HF vs control group Cell communication/ signal transduction

5

8

Gene expression

4

7

Immune response

1

9

Cell cycle

2

8

Energy metabolism

4

3

Membrane trafficking/ vesicle transport

2

2 0

2

4

6

8

10

12

14

Number of genes per biological process

b) HP vs control group Cell comunication/ signal transduction

19 7

Gene expression

31

Immune response

8

Cell cycle

8

Membrane trafficking/ vesicle transport

22 16

10

11

7

Energy metabolism Blood/iron metabolism Nitrogen metabolism

22

6

6

2

1 3 0

5

10

15

20

25

30

35

40

45

Number of genes per biological process Fig. 1. Classification into biological processes of the genes affected by the intake of an HF or an HP diet in comparison to animals fed a control diet (Student's t test, Pb.05 and absolute fold change ≥1.2).

4. Discussion Diets with an unbalanced macronutrient proportion have been related to metabolic alterations which can affect health in the long-term [37]. To gain insight into the effects of unbalanced diets, diets rich in fats or proteins, on metabolism, we studied wholegenome gene expression in PBMCs. While effects were higher for the HP than the HF diet, in both cases, affected genes were mainly related to immune response.

4.1. Immune response PBMCs constitute an important part of the peripheral immune system; for this reason, it seems logical that the intake of diets with an

unbalanced macronutrient proportion had an important effect on expression of immune response-related genes in these cells. Isocaloric intake of an HF diet affected the expression of 10 genes involved in immune response, 9 of which were down-regulated. The affected genes were mainly related to antigen recognition/presentation. This down-regulation could be reflecting immune impairment due to the increased fat intake, as has been previously reported [38,39]. The effect on immune system is dependent not only on the content but also on the type of fat [28,38,40,41]. It has been demonstrated that immunosuppressive effects on both innate and adaptive immunity are higher for unsaturated fat, mainly polyunsaturated fatty acids (PUFAs) [41–43], while consumption of saturated fat results in a proinflammatory gene expression profile in PBMCs [40]. Our HF diet had a higher proportion of unsaturated fat compared to saturated fat, 63% vs. 37%, which could explain the decreased expression of genes involved in


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Table 2 Detailed classification of genes involved in immune response differentially expressed in PBMCs of the HF (a) and HP (b) groups. Immune response (a) HF group Antigen recognition/presentation

Immune system maturation/activation

Others (b) HP group Antigen recognition/presentation

Cytokine signaling

Immune system maturation/activation

Others

Gene name

Gene symbol

Sequence ID

Ratio

P value

T-cell receptor alpha chain for RT1L haplotype T-cell receptor V-alpha J-alpha T-cell receptor beta-chain T-cell receptor beta-2 chain C region TAP binding protein Cathepsin W Immunoglobulin lambda-like polypeptide 1-like Lymphocyte-activation gene 3 Sequestosome 1 WD repeat domain 34

Tra RT1L haplotype Tcr V-alpha J-alpha Trb Trbc2 Tapbp Ctsw Igll1 Lag3 Sqstm1 Wdr34

L11022 XM_224059 M23889 BC084707 NM_033098 NM_001024242 ENSRNOT00000029851 NM_212513 NM_175843 NM_001005542

−1.44 −1.35 −1.27 −1.24 −1.20 −1.26 2.13 −1.27 −1.30 −1.35

.0322 .0424 .0490 .0479 .0324 .0466 .0129 .0439 .0268 .0289

CD8b molecule Histocompatibility 2, K1, K region (predicted) Inner membrane protein, mitochondrial Ly6-B antigen gene (similar) Lymphocyte antigen 6 complex, locus G6C Proline-rich coiled-coil 2A RT1 class I, A3 RT1 class I, locus CE12 RT1 class II, locus Bb RT1-B beta chain, partial cds T-cell receptor, partial cds T-cell receptor alpha chain for RT1L haplotype Chemokine (C-C motif) ligand 11 Lysyl-tRNA synthetase Interferon, alpha 4 Interferon regulatory factor 7 Similar to nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1 (predicted) Cd7 molecule Clec10a C-type lectin domain family 10, member A Glycosylation dependent cell adhesion molecule 1 Inositol polyphosphate-5-phosphatase D Melanoma associated antigen (mutated) 1 Lectin, galactoside-binding, soluble, 3 binding protein Ly-49 stimulatory receptor 3 CDC42 small effector 1 Drebrin-like, transcript variant 2, Glycoprotein V (platelet) Progesterone immunomodulatory binding factor 1 Proteasome (prosome, macropain) subunit, beta type 8 (large multifunctional peptidase 7) Tissue specific transplantation antigen P35B

Cd8b H2-k1 Immt Ly6-A Ly6g6c Prrc2a/Bat2 RT1-A3 RT1-CE12 RT1-Bb RT1-B Tcr Tra RT1L haplotype Ccl11 Kars Ifna4 Irf7 Nfat

NM_031539 XM_006523674 BQ190284 LOC300024 NM_001001969 BF420514 NM_001008830 NM_001008835 NM_001004084 U65217 L20999 L11025 NM_019205 BQ211327 ENSRNOT00000046207 BF284803 XM_225713

+1.20 −1.43 +1.24 +1.34 −1.83 +1.22 −1.82 −1.70 −8.24 +1.60 +1.34 +1.29 +1.66 +1.22 +1.54 +1.27 +2.12

.0207 .0219 .0082 .0008 .0188 .0146 .0210 .0286 .0493 .0281 .0073 .0037 .0295 .0394 .0378 .0233 .0068

Cd7 Clec10a Glycam1 Inpp5d Mum1 Lgals3bp Ly49s3 Cdc42se1 Dbnl Gp5 Pibf1 Psmb8

BI290548 NM_022393 NM_012794 AI501398 BF282863 AA866448 NM_153726 BM387413 NM_031352 NM_012795 RGD1305077 AI178629

+1.27 −1.26 +1.59 +1.20 +1.26 +1.31 +3.22 +1.37 −1.36 −1.89 +1.21 +1.20

.0297 .0413 .0017 .0243 .0313 .0457 .0297 .0061 .0099 .0483 .0074 .0396

Tsta3

BQ204779

+1.21

.0472

The expression of all genes changed significantly when comparing the HF or HP group vs. the control group (Student't t test, Pb.05 and absolute fold change ≥1.2 in microarray analysis). Ratio: HF or HP group/control group. + indicates up-regulation and − indicates down-regulation. When the gene symbol is underlined, this means that the gene was among the top 10 up- or down-regulated genes.

immune response. In fact, dietary PUFAs have been shown to depress antigen presentation [44], which correlates with the observed decrease in T-cell receptor gene expression. A sustained decrease in the expression of genes involved in antigen presentation as a result of long-term intake of an HF diet could contribute to a greater risk of infections [45]. HF diet did not affect cytokine gene expression in PBMCs. Nevertheless, HF diet induced an increase in serum TNF-alpha levels, which may be due to increased adiposity in the HF-fed animals [46]. The effect on immune response was especially evident in animals fed an HP diet which, contrary to what was observed for the HF diet, produced an up-regulation of immune response genes in PBMCs. Long-term increased protein intake affected the expression of 30 genes involved in antigen recognition/presentation, cytokine signaling and immune system maturation/activation, with 22 of these genes being up-regulated. The increase in cytokine signaling in PBMCs is in accordance to increased TNF-alpha serum levels in the animals. This supports the notion that increased protein intake has been related to an increase in plasma levels of acute inflammatory cytokines [47]. A chronic immune-enhancement may underlie problems like metabolic syndrome [48–50], and our data indeed suggest that care should to be

taken when using HP diets. The protein source in our diet was casein, an animal protein that is a major constituent of milk. Interestingly, peptides derived from bovine casein digests have been reported to produce an enhancement of immune system [51–53]. It is therefore of interest to include different protein sources in future studies to assess to which extent the observed increased immune response may be dependent on the protein composition of the diet. 4.2. Energy homeostasis Unbalanced diets also affected the expression of genes related to energy homeostasis in PBMCs. In animals fed an HF diet, we observed an increased expression of three key genes involved in fatty acid beta-oxidation, Acox1, Acsl1 and Cpt1a. This diet also affected the expression of the glycolytic gene Eno1, which was among the top 10 down-regulated genes in the HF group. These changes reflect an increased use of fatty acids as an energy source and a preservation of glucose as an adaptation to the increased fat content in the diet. Decreased expression of Eno1 observed in PBMCs was also seen in the liver. Moreover, increased expression of fatty acid oxidation genes

404


Table 3 Detailed classification of genes involved in energy metabolism differentially expressed in PBMCs of the HF (a) and HP (b) groups. Energy metabolism (a) HF group Carbohydrate metabolism Lipid metabolism

Others (b) HP group Carbohydrate metabolism

Lipid metabolism

Others

Gene name

Gene symbol

Sequence ID

Ratio

P value

Enolase 1 (alpha) Fructose-1,6- biphosphatase 1 Acyl-CoA oxidase 1, palmitoyl Acyl-CoA synthetase long-chain family member 1 Acyl-Coenzyme A binding domain containing 4 Carnitine palmitoyltransferase 1a, liver Sortilin-related receptor

Eno1 Fbp1 Acox1 a Acsl1 Acbd4 Cpt1a Sorl1/Lr11

NM_012554 NM_012558 AA924697 NM_012820 NM_001012013 NM_031559 CF108748

−1.52 −1.22 +1.24 +1.24 −1.20 +1.23 +1.20

.0062 .0486 .0100 .0417 .0051 .0304 .0310

Glyceraldehyde-3-phosphate dehydrogenase (phosphorylating) (similar) Hexokinase 1 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2, transcript variant 3 Solute carrier family 2 (facilitated glucose transporter), member 4 Acyl-CoA oxidase 1, palmitoyl Dehydrogenase/reductase (SDR family) member 3 Farnesyl diphosphate synthase Malonyl-CoA decarboxylase Patatin-like phospholipase domain containing 7 Preadipocyte factor 1 Protein tyrosine phosphatase-like A domain containing 2 (predicted) Sideroflexin 5 Solute carrier family 16 (monocarboxylic acid transporters), member 1

Gapdh Hk1 Pfkfb2 Slc2a4/Glut4 Acox1 a Dhrs3 Fdps Mlycd Pnpla7 Pref-1/Dlk1 Ptplad2 Sfxn5 Slc16a1

XM_346024 NM_012734 NM_080477 NM_012751 AA924697 NM_001037199 AY321335 NM_053477 BE107890 RRU25680 ENSRNOT00000033651 NM_153298 NM_012716

−1.33 −1.41 +1.22 −2.13 +1.37 −1.31 −1.28 −1.40 +1.35 +2.12 −1.39 +1.28 +1.27

.0363 .0328 .0044 .0192 .0046 .0328 .0426 .0116 .0101 .0172 .0214 .0137 .0169

The expression of all genes changed significantly when comparing HF or the HP group vs. the control group (Student't t test, Pb.05 and absolute fold change ≥1.2 in microarray analysis). Ratio: HF or HP group/control group. + indicates up-regulation and − indicates down-regulation. When the gene symbol is underlined, this means that the gene was among the top 10 up- or down-regulated genes. a Indicates that the gene was differentially expressed in both the HF and HP groups

was also observed in liver and adipose tissue of the same HF-fed animals [29]. These data furthermore confirm the increased expression of lipolytic genes in PBMCs that was seen upon ad libitum hyperlipidic cafeteria diet administration to rats [24]. In addition, we found increased gene expression of Lr11, also referred as Sorl1, which belongs to the LDL receptor gene family. LR11 protein is highly expressed in the atheromatous plaques, particularly in vascular cells [54], and moreover, it has been demonstrated that it is expressed on the cell surface of monocytes in human PBMCs [55]. LR11 is a membrane protein with a large extracellular part, known as soluble LR11 (sLR11), which is released to circulation by proteolytic shedding and its increase in serum is considered a biomarker of pathologies related to increased adiposity as atherosclerosis [56]. Interestingly, increased levels of sLR11 in the cerebrospinal fluid are also a biomarker of Alzheimer disease [57], another pathology related to increased adiposity [58]. The intake of the HP diet also altered the expression of energy homeostatic-genes in PBMCs. The expression of two lipolytic genes was increased: Acox1, involved in fatty acid beta-oxidation, and Pnpla7, encoding for an enzyme with lipolytic acyl hydrolase activity that catalyses the cleavage of fatty acids from membrane lipids. Meanwhile, HP diet decreased the expression of lipogenic genes: Mlycd, functionally involved in fatty acid biosynthesis; Ptplad2, involved in very long-chain fatty acids synthesis; and Fdps encoding for farnesyl diphosphate synthase which produces a key intermediate in cholesterol and sterol biosynthesis. We have previously reported that PBMCs gene expression reflects nutritional regulation of

cholesterol metabolism which occurs in the liver [25]; thus, decreased expression of Fdps could be related to the lower serum cholesterol levels observed in our HP-fed animals. Down-regulation of lipogenic genes, which was also observed in adipose tissue of these HP-fed animals [29], probably reflects adaptation to a decreased carbohydrate content in the diet and thus a down-regulation of de novo lipogenesis from glucose [59–61], while lipid oxidation would be enhanced to produce energy. This is further confirmed by decreased expression of two key glycolytic genes, Gapdh and Hk1, and of Glut4, which facilitates cellular glucose uptake. A decreased activity of key glycolytic enzymes and a decrease in GLUT4 content have been previously reported as an adaptation to an HP diet in other tissues, such as brown adipose tissue [62]. In addition, two other genes involved in lipid metabolism/adipogenesis were affected in PBMCs as a result of the intake of an HP diet, Dhrs3 and Pref-1. Dhrs3 encodes a protein associated with lipid droplet accumulation [63] and was down-regulated in PBMCs of the HP group, which is in accordance with the decreased expression of lipogenic genes. Pref-1, also known as Dlk1, encodes a protein that inhibits adipogenesis at transcriptional level [64] and was one of the top 10 up-regulated genes by HP diet. It has been described that mice overexpressing Pref-1 are resistant to diet-induced obesity, but are severely insulin resistant due to decreased insulin-stimulated glucose uptake [65]. It should be noted that due to the lower cumulative food intake of the HP group in comparison to the control and HF groups, it cannot be discarded that some of the observed changes could be related to decreased energy intake.

Table 4 Genes involved in regulation of oxidative balance affected by HF diet feeding in PBMCs. Gene name

Gene symbol

A kinase (PRKA) anchor protein 12 Akap12 Ethylmalonic encephalopathy 1 Ethe1 Methionine sulfoxide reductase A Msra

Sequence ID

Ratio

P value Function

NM_057103 +1.30 0.0122 NM_001106234 −1.27 0.0090 NM_053307 −1.21 0.0321

Binding to the regulatory subunit of protein kinase A; increased in hypoxia in endothelial cells Antioxidant protection and vasorelaxation Antioxidant protection

The expression of all genes changed significantly when comparing the HF or HP group vs. the control group (Student't t test, Pb.05 and absolute fold change ≥1.2 in microarray analysis). Ratio: HF group/control group. + indicates up-regulation and − indicates down-regulation. When the gene symbol is underlined, this means that the gene was among the top 10 up- or down-regulated genes.


405

Table 5 Genes affected by both unbalanced diets, HF and HP, in PBMCs. Metabolic pathway

Gene name

Gene symbol

Sequence ID

Ratio

Function

HF

HP

Cell cycle

Centromere protein N

Cenpn

NM_001008366

−1.36

−1.35

Cell cycle

Dihydrofolate reductase

Dhfr

NM_130400

+1.20

+1.28

Energy metabolism

Acyl-CoA oxidase 1, palmitoyl

Acox1

EAA924697

+1.24

+1.37

Signal transduction

Sphingosine-1-phosphate receptor 4

S1pr4/S1p4

ENSRNOT00000007117

−1.37

−1.41

Signal transduction

NMDA receptor regulated 1-like, transcript variant 1 (predicted) Coatomer protein complex, subunit gamma 2 Endonuclease/exonuclease/phosphatase family domain containing 1

Narg1

XM_001073036

+1.21

+1.21

Copg2

NM_001106929

+1.23

+1.31

Cytoskeleton organization (centromere assembly) Proliferation (synthesis of nucleic acid precursors) Lipid metabolism (key enzyme of the beta-oxidation pathway) G-protein-coupled receptor (specifically expressed in lymphoid tissue) Glutamate receptor (important role in synaptic and memory regulation) Necessary for vesicle traffic

Eepd1

NM_001014088

−1.31

−1.45

DNA repair

Vesicle transport Others

Genes whose expression changed significantly in response to both an HF and HP diet (Student't t test, Pb.05 and absolute fold change ≥1.2 in microarray analysis). Ratio: HF or HP group/control group. + indicates up-regulation and − indicates down-regulation. When the gene symbol is underlined, this means that the gene was among the top 10 regulated genes in the HF group.

for protein kinase A anchoring protein 12 (also known as gravin), which is responsive to hypoxia [71], a condition associated with increased levels of reactive oxygen species. Ethe1 codes for a dioxygenase located in the mitochondria involved in hydrogen sulphide detoxification [72]. Although hydrogen sulphide has toxic properties, its basal endogenous concentration has been found to have a cardioprotective effect mainly due to its antioxidant and vasorelaxation properties [73]. A decrease in hepatic ETHE1 protein levels of HF-fed mice has been observed [74]; we accordingly observed decreased Ethe1 mRNA expression in PBMCs as well as in the liver, which could reflect greater cardiovascular risk or other complications related to increased oxidation in animals fed a hyperlipidic diet.

4.3. Genes potentially linked to oxidative stress Apart from inflammation, oxidative stress plays a major role in metabolic syndrome [66]. Leukocytes (main PBMCs constituents) release reactive oxygen species, and this has been related to pathologies as hypertension, diabetes and cardiovascular disease [67,68]. On the other hand, long-term HF diet feeding as well as increased adiposity are known to increase oxidative stress [69]. Detailed inspection of differentially expressed genes revealed that isocaloric HF diet intake altered the expression of genes related to oxidative stress in PBMCs. Intake of the HF diet produced a decreased expression in PBMCs of Msra, encoding methionine sulfoxide reductase A. Methionine sulfoxide reductases reduce oxidized methione residues, protecting proteins from oxidation. Their activity may be linked to insulin resistance since mice lacking Msra are prone to the development of HF diet-induced insulin resistance and present a reduced insulin response [70]. Further indication for increased oxidative stress comes from the observed induction of Akap12, coding

4.4. Genes affected by both unbalanced diets One of our objectives was to identify a pool of genes, a PBMCs gene signature, which could be used as common marker for unbalanced

Table 6 Real-time RT-PCR confirmation of microarray data. Gene name

(a) Genes differentially expressed in PBMCs of the HF or HP groups HF group Carnitine palmitoyltransferase 1a, liver Ethylmalonic encephalopathy 1 Enolase 1 (alpha) Hydroxysteroid (17-beta) dehydrogenase 13 Transmembrane and coiled-coil domain family 2 (predicted) HP group Glycosylation dependent cell adhesion molecule 1 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 Solute carrier family 2 (facilitated glucose transporter), member 4

Gene symbol

Sequence ID

Cpt1a Ethe1 Eno1 Hsd17b13 Tmcc2 Glycam Pfkfb2 Slc2a4/Glut4

NM_031559 NM_001106234 NM_012554 NM_001009684 RGD1311960 NM_012794 NM_080477 NM_012751

Microarray

RT-PCR

Ratio

P

Ratio

P

+1.23 −1.27 −1.52 +1.30 −2.19 +1.59 +1.22 −2.13

.0304 .0090 .0062 .0001 .0033 .0017 .0044 .0192

+1.50 −1.30 −1.34 +1.34 −1.80 +2.00 +1.37 −2.01

.0189 .0148 .0157 .0094 .2581 .0006 .0326 .0103

(b) Common genes differentialy expressed in PBMCs of the HF and HP groups Gene name

Coatomer protein complex, subunit gamma 2 Dihydrofolate reductase Endonuclease/exonuclease/phosphatase family domain containing 1

Gene symbol

Sequence ID

Copg2 Dhfr Eepd1

NM_001106929 NM_130400 NM_001014088

Microarray

RT-PCR

HF (ratio)

P

HP (ratio)

P

HF (ratio)

P

HP (ratio)

P

+1.23 +1.20 −1.31

.0303 .0170 .0268

+1.31 +1.28 −1.45

.0000 .0038 .0092

+1.01 +1.37 −1.82

.9148 .0412 .0027

+1.23 +1.37 −1.81

.0434 .0140 .0031

Ratio: HF or HP group/control group. + indicates up-regulation and − indicates down-regulation. P values of microarray and real-time RT–PCR data are given (Student's t test).

406


diets, independent of the physiological endpoints, which could be different for the different unbalanced diets. The expression of seven genes involved in different processes (cell cycle, energy metabolism, signal transduction, vesicle transport and DNA repair) was commonly altered in PBMCs in response to the intake of the HF and HP unbalanced diets. Moreover, the direction of expression was the same for both diets. One of these genes was Acox1, involved in fatty acid beta-oxidation, being overexpressed in the HF and HP groups. As commented above, the increased mRNA expression of lipolytic genes in PBMCs, including Acox1, is also seen in key metabolic tissues such as liver or adipose tissue and is related to the decreased carbohydrate content in the unbalanced diets. Another common affected gene was S1pr4/S1p4, which encodes for a G-protein-coupled receptor that binds the lipid signaling molecule sphingosine 1-phosphate. Unlike other receptors of the same family, S1P4 expression is restricted to lymphoid tissue [75], and although little is known on its function, this expression pattern suggests a potential involvement of this receptor in immune system regulation. S1pr4 was between the top 10 overexpressed genes in the HF group and was also overexpressed in the HP-fed animals. Expression analysis of Acox1, S1pr4, alone or in combination with the other common affected genes in PBMCs, could be useful to detect metabolic deviations of a diet with a proper and balanced macronutrient composition. The use of unbalanced diets is increasing in specific groups of population; for that reason, it would be interesting to have a gene signature available from an easily obtainable biological material, such as blood cells, to identify deviations from a healthy diet. To better understand the possible clinical relevance of our findings, it is interesting to compare the unbalanced diets used in our study with those consumed by humans. As rats and humans have different macronutrient requirements, we cannot perform a direct comparison based on %kcal provided by macronutrients, but it can be based on the excess amount of fat or protein ingested over the requirements of a balanced control diet. The HF diet used in our experiment was particularly rich in fat as HF-fed animals ingested 5.8 times more fat than controls. Consumption of diets with particular high contents of fat is also encountered in society, one example being the ketogenic diet, with 90% of total energy from fat (3.2 times more than recommended) [11]. The HP-fed animals ingested 2.3 times higher amount of protein than the amount ingested by control rats fed a balanced diet; this overconsumption is similar to the extent of overconsumption observed for humans following HP weight loss diets, such as the Zone diet, in which 30% kcal is in the form of protein [76]. 4.5. Conclusion Diet macronutrient composition affects PBMCs gene expression. One of the most affected processes was immune response. Sustained intake of an HF diet produced a down-regulation, while intake of an HP diet produced an up-regulation of immune-response-related genes in PBMCs. Our results in PBMCs confirm that proper functioning of the immune system depends on balanced and adequate nutrition. This is relevant as immune system alterations are in the basis of different pathologies [38,77]. Moreover, PBMCs gene expression reflects the expected nutritional adaptive changes to a decreased carbohydrate content in the diet due to an increased proportion of one of the other two macronutrients (fat or protein) that is also seen in other tissues. More studies in relevant tissues are needed to understand if PBMCs gene expression can reflect harmful metabolic effects as increased oxidative or cardiovascular risk as result of the intake of an HF diet. Importantly, we have identified a pool of genes whose expression in PBMCs is affected by the intake of diets rich in fats or proteins and could be used as a common risk gene signature for unbalanced diets with disproportioned macronutrient composition. Thus, changes in PBMCs gene expression can provide useful information

to detect metabolic deviations and health consequences of diets with an unbalanced macronutrient composition. These results encourage future studies for the identification of biomarkers in PBMCs to detect deviations of healthy homeostatic equilibrium due to the intake of unbalanced diets. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jnutbio.2014.11.013.

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