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Equipe Institut National de la Santé et de la Recherche Médicale "Génétique Cardiovasculaire" CIC 9501, Faculté de Pharmacie, Université Henri Poincaré–Nancy I, Nancy, France
(Received June 21, 2007; Accepted October 11, 2007)
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Abstract |
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, aryl hydrocarbon receptor (AHR), T-cell factor 7, constitutive androstane receptor, and aryl hydrocarbon receptor nuclear translocator (ARNT) were expressed in the majority of the subjects. Glucocorticoid receptor, peroxisome proliferator-activated receptor (PPAR)-
, and LXRβ were expressed only in some individuals. PPAR mRNA was found in one subject only, and farnesoid X-activated receptor was not expressed. In addition, we found significant correlations between the expression of AHR, ARNT, and CYP1A1 and between PXR and P450 involved in leukotriene metabolism (CYP2C, CYP4F2, CYP4A11, CYP2J2, and CYP11B2). We describe here for the first time the presence of the majority of TF and DME in PBMC of healthy subjects without previous induction. The expression of these genes in lymphocytes could be a useful tool for further studying the physiological and pathological variations of DME and TF related to environment, to drug intake, and to cardiovascular metabolic cycles.
). DME are important in the follow-up of cardiovascular drugs because many drugs are metabolized by them. These enzymes are also involved in the metabolism of natural substrates (such as leukotrienes, steroids, and bile acids) that are in turn implicated in several cardiovascular-related pathways, including inflammation, lipid metabolism, and blood pressure regulation. In addition, many environmental factors (tobacco, polycyclic hydrocarbons and dioxins, alcohol, and nutrients) modulate their patterns of expression, and expression of several of these genes is under control of transcription factors (TF), such as the pregnane X receptor (PXR), the constitutive androstane receptor (CAR), the glucocorticoid receptor (GR), or the aryl hydrocarbon receptor (AHR). Finally, some polymorphisms in genes coding for these DME are well known to influence the level of expression with clinical and pharmacological relevance. Some DME, including glutathione S-transferases (GST), N-acetyltransferases, and sulfotransferases (ST), are soluble enzymes measurable as phenotypes in the plasma. However, the majority is mainly localized in the endoplasmic reticulum and is rarely excreted or found in the plasma. That is one of the reasons we looked for the expression of DME in an easily accessible type of cells: the peripheral blood mononuclear cells (PBMC). The second reason is the involvement of lymphocytes in cardiovascular events, i.e., through inflammation. Lymphocytes could be a useful target and tool for investigating relationships between DME, inflammation, and other metabolic pathways related to cardiovascular physiopathology. In addition, the mechanisms of inflammation and immune defenses are regulated by the same TF.
During the past 10 years, a large number of P450s have been studied in PBMC of healthy subjects, including CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2J2, CYP3A3, CYP3A4, CYP3A5, CYP3A7, CYP4A11, CYP4B1, and CYP4F. Epoxide hydrolase (EH), GST, N-acetyltransferase, and ST also have been described (Raucy et al., 1997; Baron et al., 1998; Dassi et al., 1998; Spencer et al., 1999; Stärkel et al., 1999; Takeda et al., 1999; Boucher et al., 2000; Krovat et al., 2000; Nakamoto et al., 2000; Nguyen et al., 2000; Smart and Daly, 2000; Finnström et al., 2001, 2002; Hannon-Fletcher et al., 2001; Asghar et al., 2002; Carcillo et al., 2003; Gashaw et al., 2003; Landi et al., 2003, 2005; Lin et al., 2003; Toide et al., 2003; Tuominen et al., 2003; Furukawa et al., 2004; Lampe et al., 2004; Yamamoto et al., 2004; Haas et al., 2005; Liangpunsakul et al., 2005; Miura et al., 2006). However, study of the expression of both DME and TF is rarely conducted at the same time. The existing work in the field examined preferentially CYP1A1, AHR, and the aryl hydrocarbon receptor nuclear translocator (ARNT) (Smart and Daly, 2000; Landi et al., 2003; Lin et al., 2003; Yamamoto et al., 2004).
The global objectives of this investigation are to propose useful biomarkers easily measurable without any activation on a large scale, i.e., during clinical trials. The main objective consisted in investigating patterns of expression of DME together with their related TF in PBMC, a type of cells easily accessible and closely related to inflammation. Before studying these genes in pathological states or in patients undergoing treatment, looking for them in healthy subjects is an obligatory step. Therefore, we studied the simultaneous expression of an important number of DME and TF RNA in lymphocytes (without previous induction) of 20 supposedly healthy subjects. Finally, we have reviewed articles that described expression of DME and TF in PBMC of healthy subjects, and we discussed the biological variation factors found in these articles.
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Materials and Methods |
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). The investigation conformed to the principles outlined in the Declaration of Helsinki. Individuals were of French origin and exempt of any acute or chronic diseases. Some of them had slight cardiovascular risk (i.e., obesity and hypertension). Specific exclusion criteria were medication except contraceptives, current smoking, and heavy alcohol consumption. Characteristics are shown in Table 1.
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PBMC Collection. The PBMC bank was constituted according to a well validated protocol, with a high recovery in lymphocytes (97%). Briefly, fresh whole blood (10 ml) was collected from 8 to 10 AM by standardized venipuncture in EDTA tubes (Vacutainer, BD Biosciences, Franklin Lakes, NJ) during a period of 5 months (November to March). PBMC were isolated by centrifugation on a density gradient of Ficoll (Ficoll-Paque PLUS; GE Healthcare, Orsay, France). Percentages of lymphocytes, monocytes, and polynuclear cells were determined in some samples by microscopic observation after May-Grünwald-Giemsa staining. PBMC were stored at -80°C until RNA extraction. RNA quality and stability were carefully tested (Marteau et al., 2005).
Microarray Design. An in-house microarray was designed as previously described (Visvikis-Siest et al., 2007). Briefly, we selected numerous genes including 16 DME and 13 TF. In addition, we included a nonhuman RNA (Arabidopsis thaliana) to test unspecificity. Oligonucleotides were selected from the MWG database, and the chip was manufactured by MWG Biotech (Ebersberg, Germany). RNA was extracted by an automated isolation procedure (MagNA Pure LC instrument; Roche Applied Science, Meylan, France). Concentration and quality were determined by the spectrophotometer Nano-Drop ND-1000 (Labtech International, Paris, France). RNA was amplified using the amino allyl MessageAmp II aRNA Amplification Kit (Ambion, Austin, TX) and a T7(dT)24 primer. The double-strand cDNA obtained was transcribed in amplified RNA using 5-(3-aminoallyl)-UTP (Ambion). The RNA yield ranged from 1.3 to 8 µg. RNA samples were labeled with fluorochrome Cy3, and a reference RNA (Universal Human Reference RNA, Stratagene, La Jolla, CA) was labeled with fluorochrome Cy5. The labeled RNA sample and the corresponding quantity of labeled reference RNA were prehybridized on each slide in 5% bovine serum albumin. Cy3- and Cy5-labeled RNA were cohybridized to the microarray at 50°C for one night. Slides were scanned using an Axon GenePix 4000B scanner and GenePix version 6 software (Molecular Devices, Sunnyvale, CA).
Data Normalization and Analysis. Normalization and analysis were assessed using Genespring version 6.1 software (Silicon Genetics, Agilent Technologies, Palo Alto, CA). The ratio of Cy3 intensity on Cy3 background noise was calculated for each spot. Then, to evaluate the expression or the nonexpression, we used a Student's t test (p < 0.01). If the mean of ratios for a given RNA (2 spots/slide) is not significantly different from the mean of ratios for A. thaliana RNA (8 spots/slide), the gene was considered nondetectable. Spearman's correlation coefficients were calculated to look for potential relationship between gene expression, age, and body mass index (BMI). Mann-Whitney test was used to identify significant difference between men and women for the gene expression.
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). Validation of the results obtained with this chip was performed by quantitative real-time polymerase chain reaction (PCR) on four genes expressed in a significant number of individuals: intercellular adhesion molecule 1, tumor necrosis factor-, L selectin, and interleukin 6. |
Results |
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The variability is important (coefficients of variation shown in Fig. 1). We obtained no difference depending on gender, age, or BMI. We observed no variation resulting from the time of the day or the day or month of collection (data not shown).
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, AHR, ARNT, vitamin D receptor (VDR), TF-7, and myocyte enhancer factor 2 were expressed in the majority of subjects. Peroxisome proliferator-activated receptor (PPAR)-, LXRβ, and GR were expressed in some subjects, PPAR in one individual, and farnesoid X-activated receptor was not detected. The variability is also high (coefficients of variation shown in Fig. 2). No trends of age, sex, and BMI were observed that could have explained part of this variability (data not shown).
Association of TF and DME. We found results of interest as concern P450 involved in inflammation and blood pressure regulation. There is a significant correlation between the expression of PXR and the CYP2C (p < 0.0001; Fig. 3). The CYP2C were also correlated with LXR (p < 0.001) and LXRβ (p < 0.07 except CYP2C9) and with CAR (p 
0.001) but not with GR (data not shown).
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Finally, we found also a significant correlation between ARNT and AHR (p < 0.0001), ARNT and CYP1A1 (p < 0.006), and a trend between AHR and CYP1A1 (p = 0.022; Fig. 5).
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Discussion |
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We measured simultaneously 11 P450 and 3 GST mRNA in lymphocytes without any previous induction or cell culture. Most of the genes are expressed in each individual, contrary to what was observed in other studies (Asghar et al., 2002; Haas et al., 2005). GSTM1 is only expressed in four subjects. This is not surprising because the complete gene is only present in 50% of Caucasians.
We report here for the first time the expression of CYP2C18, CYP2J2, and CYP4F2 in lymphocytes of healthy subjects. In addition, we also found CYP2A6 in contrast to previous studies (Koskela et al., 1999; Krovat et al., 2000). As for the lack of expression of CYP3A4 and CYP3A5 in our subjects, these findings are not in agreement with others previously reported (Nakamoto et al., 2000; Finnström et al., 2001; Gashaw et al., 2003). Krovat et al. (2000), who were not able to detect CYP3A5 and whose detection of CYP3A4 was near the detection limit of the assay, proposed as an explanation the preferential localization of CYP3A in B cells, which constitute only a small part of total lymphocytes.
TF and P450 Expression in PBMC of Healthy Subjects. To our knowledge, this is the first time that such an important number of DME and TF are simultaneously studied. We have found a large panel of TF in PBMC. Only a few authors have been interested in measuring TF expression in PBMC of healthy subjects (Table 2). In addition, study of the expression of both P450 and TF is rarely conducted at the same time. AHR pathway is associated with induction of CYP1A1, CYP1A2, and CYP1B1 (Gueguen et al., 2006). Four authors have looked for the TF regulating CYP1A1 expression: Lin et al. (2003) (AHR), Smart and Daly (2000), Landi et al. (2003) (AHR, ARNT), and Yamamoto et al. (2004) (AHR, ARNT, and AHR repressor). Expression of retinoic acid receptor and retinoid X receptor (RXR) has been described in healthy subject PBMC but only qualitatively (Szabova et al., 2003). No one has looked in healthy lymphocytes for the three other transcriptional activation mechanisms involved in P450 regulation: CAR-RXR, PXR-RXR, and PPAR-RXR. It is generally admitted that PXR regulates CYP2B6, CYP2C8, CYP2C9, CYP3A4, CYP3A7, GST, ST, UGT1A1, and ABCB1 mainly in the liver (Gueguen et al., 2006), whereas CAR modulates CYP2B6, CYP2C9, and CYP2C19.
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P450s are often regulated simultaneously by two or more mechanisms (Miao et al., 2004), including VDR and GR (Pascussi et al., 2003). The different TF influence mutually their relative expression. CAR and PXR are regulated, at least in part, by the GR, and a signal transduction cascade GR-[CAR-PXR]-P450 exists at least for CYP3A4 and CYP2C9 (Dvorak et al., 2003) and maybe CYP2C19 (Chen et al., 2003). In addition, VDR probably regulates CYP3A4, CYP2C9, and CYP2B6 (Drocourt et al., 2002). Only limited information is available on the regulation of CYP3A5 expression, but it appears to be inducible via the GR, PXR, and CAR as for CYP3A4 (Daly, 2006) and CYP2C9 (Kirchheiner et al., 2004). CYP2C8 expression is regulated by CAR, PXR, GR, and hepatic nuclear factor 4 in the liver (Ferguson et al., 2005). Finally, constitutive hepatic expression of CYP2A6 is governed by interplay between the TF hepatic nuclear factor-, CCAAT/enhancer-binding protein-, CCAAT/enhancer-binding protein-β, and octamer TF 1 (Pitarque et al., 2005).
It is also possible to describe activation through the antioxidant response element, which could cross-talk with the xenobiotic response elements (Miao et al., 2004). CAR could in addition be regulated by activation through phosphorylation, which permits its translocation in the nucleus (Sueyoshi and Negishi, 2001). Finally, regulation could also be mediated through mRNA or protein stabilization (CYP2E1). These mechanisms are mainly studied in the liver (Sueyoshi and Negishi, 2001; Pascussi et al., 2003; Miao et al., 2004; Handschin and Meyer, 2005).
Choice of the PBMC as a Tool for Measuring DME and TF. As previously mentioned, a large number of P450s have been described in PBMC of healthy subjects (Table 2). Gene's expression in lymphocytes is not always representative of expression in other tissues. However, considering that they are involved in cardiovascular-related diseases, mainly through the inflammation pathway, lymphocyte expression could be used to evaluate modification of expression observed with this system. In addition, gene's expression in lymphocytes remains a good biomarker to evaluate P450 and TF phenotypes and thus to monitor, for example, exposure to and risk associated with xenobiotics. We would like to mention here the work of Wibaut-Berlaimont et al. (2005). Using an Affymetrix (Santa Clara, CA) chip, they described the significant regulation of 240 genes (among 12,650 genes) in PBMC of dyslipidemic patients after atorvastatin treatment. Unfortunately, neither P450 nor TF that we studied here appears among the regulated genes.
PBMC are easily accessible cells. They could be a great tool to investigate biomarkers in large population studies if only a small quantity of blood is taken from the patient (we recommend 5–10 ml). Quantities of blood taken are often too high (Table 2), the main reason being the need of enough material for measuring protein levels of DME in microsomes after ultracentrifugation (Raucy et al., 1997; Baron et al., 1998; Hannon-Fletcher et al., 2001) or after lymphocytes cultures (Spencer et al., 1999; Smart and Daly, 2000; Landi et al., 2003; Lin et al., 2003).
We consider that the choice of PBMC is a good one because P450s are largely represented in lymphocytes (Raucy et al., 1997). In PBMC we found essentially T lymphocytes, which are the richest cells in P450 content. In addition, monocytes, even if they contaminate PBMC, have a low P450 content. Our experimental conditions were well defined for PBMC preparation, and the lymphocyte purity obtained was very high (97%) compared with the 70 to 90% described in articles cited in Table 2. Total white blood cells (WBC) were used without any problem by Finnström et al. (2001) and buffy coat with all the WBC by Furukawa et al. (2004). The heterogeneity of the cell population is no longer an argument. It is not more heterogeneous than a liver extract that is also a mixture of more than four different cell types. In comparison with the liver, the concentration of P450 in PBMC is 20 to 2000 times lower, thus creating some limitations. Some authors have used cultures and inducers to increase the P450 levels, but such a strategy is not applicable to studies in large populations or to new drug trials. We show here that it is possible to measure the majority of them without induction. However, we observed a high variability in expression of these DME. This, as well as the very low level for CYP3A4, could be attributed to expression in a limited number of cells (Gashaw et al., 2003). We are looking essentially with our PBMC preparation to T lymphocytes with a low content of B (<5%) and monocytes (<5%). The difficulties that should be more deeply studied are linked to the low levels of expression and to the nonsystematic expression in all the subjects.
The work of Whitney et al. (2003), which also uses microarray expression to study interindividual and temporal variations in healthy subjects, stimulated interest of studying WBC and lymphocytes for health surveillance. However, they did not investigate P450 expression (Whitney et al., 2003). The majority of the published data on P450 mRNA content in PBMC has been obtained using real-time PCR (Table 2), except for two who used microarray technology (Nguyen et al., 2000; Lampe et al., 2004).
Biological Variations. Determination of reference values and biological variations in healthy individuals is an obligatory step in the development of any candidate biomarker before its application in diagnostic or pharmacogenetic studies. In laboratory medicine, biological variation data that are two times higher than the analytical variations could be retained. A biomarker with high variability is of great interest in health screening and prepathological state studies.
The main factors affecting biological variability of clinical chemistry constituents are age, gender, biological rhythms, BMI, alcohol, tobacco, diet, drug intake, and genetic variations. We tested the possible contribution of some factors, but the biological variation observed could not by explained by them. We should study these factors on a greater set of healthy subjects. Finally, the mRNA profiling approaches by microarray technologies should be confirmed by real-time reverse transcription-PCR.
We show here that the majority of DME and TF are expressed in lymphocytes of 20 healthy subjects. This is of importance not only for pharmacokinetic studies in drug clinical trials but also because it gives the perspective of further studying cardiovascular-related pathways such as inflammation, blood pressure regulation and lipid metabolism by using PBMC. These TF are involved in cholesterol (LXR), triglycerides (PPAR), bile acids (VDR, farnesoid X-activated receptor, LXR), steroids (CAR, PXR), and bilirubin (CAR, PXR) (Handschin and Meyer, 2005) metabolism. TF also interact with many other cardiovascular-related pathways, including the cytokines for PPAR (Jones et al., 2002; Trifilieff et al., 2003). To conclude, a biological system approach could be defined for better understanding on the relation of each TF with every P450 or other constituent candidate marker measured in PBMC.
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Acknowledgments |
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Footnotes |
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Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.
ABBREVIATIONS: DME, drug-metabolizing enzyme(s); P450, cytochrome P450; TF, transcription factor(s); PXR, pregnane X receptor(s); CAR, constitutive androstane receptor(s); GR, glucocorticoid receptor(s); AHR, aryl hydrocarbon receptor(s); GST, glutathione S-transferase(s); ST, sulfotransferase(s); PBMC, peripheral blood mononuclear cell(s); EH, epoxide hydrolase(s); ARNT, aryl hydrocarbon receptor nuclear translocator(s); BMI, body mass index; PCR, polymerase chain reaction; LXR, liver X receptor(s); VDR, vitamin D receptor(s); PPAR, peroxisome proliferator-activated receptor(s); RXR, retinoid X receptor(s); WBC, white blood cell(s).
Address correspondence to: Gérard Siest, Faculté de Pharmacie, Equipe Inserm "Génétique Cardiovasculaire" CIC 9501, 30 rue Lionnois, 54000 Nancy, France. E-mail: gerard.siest{at}pharma.uhp-nancy.fr
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