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Introduction

The endocannabinoid system consists of the cannabinoid type 1 (CB1) and cannabinoid type 2 (CB2) receptors, their endogenous ligands anandamide and 2-arachidonoyl glycerol and the enzymes involved in their biosynthesis and metabolism (Di Marzo, Bifulco & De Petrocellis, 2004). CB1 is involved in the regulation of food intake, energy balance and metabolism of glucose and lipids (Di Marzo & Matias, 2005). In clinical studies, CB1 receptor blockage by the selective CB1 antagonist rimonabant (SR141716A) induced weight loss and improvement in serum lipid, glucose and insulin levels by targeting central and peripheral CB1 receptors (Van Gaal et al., 2008). However, some patients experienced depression, and this was considered an unacceptable side effect for treating obesity/metabolic syndrome. Hence, the drug was withdrawn from clinical use but there is remaining interest in some of its many potential medical applications (Cooper & Regnell, 2014; Zhou et al., 2012) including treatment of various malignancies. It is therefore of interest to investigate possible adverse effects on blood cells in patients treated with rimonabant.

The endocannabinoid system is regulating various aspects of lymphocyte proliferation, maturation and immune response (Klein, 2005; Muppidi et al., 2011; Pandey et al., 2009; Pereira et al., 2009; Sido, Nagarkatti & Nagarkatti, 2014). Targeting the endocannabinoid system may therefore be a possible new treatment option in various lymphoproliferative disorders. CB1 receptors are expressed on cells of the immune system, but generally at lower levels than CB2 (Bouaboula et al., 1993; Galiegue et al., 1995). We and others have found that CB1 and CB2 are highly expressed on neoplastic lymphocytes in malignant lymphoma (Gustafsson et al., 2008; Islam et al., 2003; McKallip et al., 2002; Wasik et al., 2014). Targeting of CB1 and CB2 with endogenous or synthetic agonists reduced cell proliferation in vitro and in vivo and induced programmed cell death selectively in tumor cells of mantle cell lymphoma (Flygare et al., 2005; Gustafsson et al., 2006; Gustafsson et al., 2008; Wasik et al., 2011). Similarly, CB2 agonists induced cell death in T cell lymphoblastic leukemia (McKallip et al., 2002). Also the CB1 antagonist rimonabant impaired proliferation and induced cell death in ex vivo isolated mantle cell lymphoma cells, alone, or in combination with anandamide (Flygare et al., 2005). Others have reported antiproliferative effects of rimonabant on in vitro activated PBMC but not on freshly isolated, non-activated PBMC (Gallotta et al., 2010; Malfitano et al., 2008). These results show that CB1 blockade may have immunomodulatory and antiproliferative effects on malignant lymphoma and on activated normal lymphocytes in vitro but seems to spare resting lymphocytes. Very little is published on the effects of rimonabant on human PBMC in vivo, and the aim of this study was to investigate how treatment with rimonabant affected blood leukocytes. We therefore collected blood cells from obese patients treated with rimonabant and analyzed blood leukocytes by flow cytometry before and during treatment. To investigate which genes were differentially expressed in PBMC during rimonabant treatment, we used oligonucleotide arrays to compare gene expression profiles in PBMC before and at 4 weeks of treatment. This pilot study shows that rimonabant treatment induces expression of genes involved in immune responses but have only marginal effects on leukocyte subset frequencies in blood.

Materials and Methods

Patients and study design

Rimonabant was prescribed to eight patients, admitted to the Overweight Study Unit at the Department of Medicine, Karolinska University Hospital. Rimonabant was administered according to the manufacturers guidelines. All patients had a BMI >35 kg/m2, were treated on clinical indications (metabolic and mechanical disability) and without mental disturbances. They were not included in any other study. The clinical characteristics of these patients are presented in Table 1. Blood samples were collected before treatment and at the first clinical control, when the patients had received rimonabant, 20 mg daily, for 4 weeks. All patients gave their informed consent and the study was performed in accordance with the Declaration of Helsinki and approved by the Regional Ethical Committee in Stockholm.

Table 1:
Clinical parameters of included subjects and percentages of blood cells as analyzed by flow cytometry before and after 4 weeks of rimonabant treatment.
Total T cells were defined as CD3+, CD4+ T cells as CD3+CD4+, CD8+ T cells as CD3+CD8+ and B cells as CD19+. The CD3−CD4+ cell population consists of monocytes and dendritic cells. The CD3−, CD16+ and/or CD56+ contain NK cells and monocytes with CD16 expression. There was a significant increase in CD3−, CD16+ and/or CD56+ cells after treatment (p = 0.049, paired t-test), all other changes were non significant.
Patient Age, sex Weight change (kg) Leukocyte subsets as analyzed by flow cytometry*
Total T cells CD4+ T cells CD8+ T cells B cells CD3–CD4+ cells CD3−, CD16+ and/or CD56+
Before After Before After Before After Before After Before After Before After
1 50, F nd 75 73 61 59 14 13 15 12 7.2 8 9 14
2 41, F −6.3 58 61 37 34 21 26 32 25 7 6.6 8 12
3 55, F nd 72 72 54 53 19 20 16 12 5.7 8 11 15
4 56, M −3.1 70 72 48 51 21 20 15 17 5.9 4.6 14 10
5 47, F −2.8 75 73 51 49 23 24 11 12 4.9 8 13 15
6 44, F 0.0 69 70 51 50 17 19 20 18 3.9 4.6 8 12
7 69, F −2.0 81 81 37 35 45 46 8 8 3.3 3.4 9 11
8 58, M −3.0 85 87 52 53 32 40 4 3 2.1 5.7 7 9
Median 52.5 2.9 73.5 72.5 51 50.5 21 22 15 12 5.3 6.15 9 12
(range) (41–69) (0–6.3) (58–85) (61–87) (37–61) (34–59) (14–45) (13–46) (4–32) (3–25) (2.1–7.2) (3.4–8) (8–14) (9–15)
DOI: 10.7717/peerj.1056/table-1

Notes:

Values are percentage of cells in mononuclear gate.
Table 2:
Genes differentially expressed in PBMC after treatment with rimonabant (ratio >1.5 between rimonabant treated and controls, p-value <0.001, false discovery rate <0.1).
Probe set ID Gene symbol Fold change p-value Gene name
7953892 KLRF1 2.50 0.00046 Killer cell lectin-like receptor subfamily F, member 1
7983910 AQP9 2.25 0.00033 Aquaporin 9
8031207 LILRA2 2.10 0.00059 Leukocyte immunoglobulin-like receptor, subfamily A
8078008 LSM3 2.00 0.00068 LSM3 homolog
7981290 WARS 1.99 0.00028 Tryptophanyl-tRNA synthetase
8149330 CTSB 1.90 0.00089 Cathepsin B
8127534 C6orf150 1.87 0.00035
7919243 CD160 1.79 0.00096 CD160 molecule
8130732 BRP44L 1.74 0.00073 Brain protein 44-like
8110318 PRELID1 1.72 0.00084 PRELI domain containing 1
8003953 PSMB6 1.69 0.00013 Proteasome subunit, beta type, 6
8015545 RAB5C 1.68 0.00085 RAB5C, member RAS oncogene family
8133690 MDH2 1.68 0.00081 Malate dehydrogenase 2
8178676 NEU1 1.67 0.00052 Sialidase 1
7973110 RNASE2 1.65 0.00028 Ribonuclease, RNase A family, 2
026541 FAM32A 1.65 0.00032 Family with sequence similarity 32, member A
8004247 C17orf49 1.64 0.00062
8088820 RYBP 1.63 0.00076 RING1 and YY1 binding protein
8058373 WDR12 1.62 0.00077 WD repeat domain 12
8071119 BCL2L13 1.61 0.00092 BCL2-like 13 (apoptosis facilitator)
8174103 GK 1.61 0.00086 Glycerol kinase
8016099 EFTUD2 1.60 0.00079 Elongation factor Tu GTP binding domain containing 2
7914563 YARS 1.60 0.00086 Tyrosyl-tRNA synthetase
979085 PYGL 1.59 0.00092 Phosphorylase, glycogen
8004237 RNASEK 1.59 0.00049 Ribonuclease, RNase K
7947681 ARHGAP1 1.58 0.00025 Rho GTPase activating protein 1
7959153 COX6A1 1.57 0.00096 Cytochrome c oxidase subunit VIa polypeptide 1
8017437 FTSJ3 1.56 0.00055 FtsJ homolog 3
8049180 EIF4E2 1.55 0.00096 Eukaryotic translation initiation factor 4E family member
8146934 LY96 1.54 0.00075 Lymphocyte antigen 96
7900922 ATP6V0B 1.54 0.00064 ATPase, H+ transporting
8037913 NAPA 1.53 0.00037 N-ethylmaleimide-sensitive factor attachment protein, alpha
8037037 ATP5SL 1.52 0.00031 ATP5S-like
8016708 LRRC59 1.51 0.00019 Leucine rich repeat containing 59
8163383 SUSD1 1.51 0.00062 Sushi domain containing 1
7990151 PKM2 1.51 0.00093 Pyruvate kinase, muscle
7978123 PSME2 1.51 0.00065 Proteasome activator subunit 2
8075564 RFPL2 −1.51 0.00020 Ret finger protein-like 2
7900878 ARTN −1.51 0.00041 Artemin
8141228 TMEM130 −1.51 0.00075 Transmembrane protein 130
8037298 CD177 −1.58 4.0e−005 CD177 molecule
8069142 KRTAP10-4 −1.59 0.00023 Keratin associated protein 10-4
8070771 KRTAP10-1 −1.60 0.00054 Keratin associated protein 10-1
8172713 LOC347549 −1.61 0.00061 Hypothetical LOC347549
8075200 RHBDD3 −1.63 0.00067 Rhomboid domain containing 3
8167575 GAGE12B −1.66 0.00028 G antigen 12B
8010901 DOC2B −1.82 0.00072 Double C2-like domains, beta
DOI: 10.7717/peerj.1056/table-2

Flow cytometry

The phenotypes of cells in the blood were analyzed by flow cytometry according to standard procedures at the Hematopathology Unit, Dept. of Pathology, Karolinska University Hospital, using 6 color flow cytometry to detect T, B, NK cells and CD3− CD4+ cells (monocytes and dendritic cells). Flow cytometry was performed on a CANTO 1 flow cytometer (BD, Becton-Dickinson, Europe).

For data acquisition and analysis, a CANTO 1 flow cytometer (BD, Becton Dickinson, Europe) was used with Cell Quest software (Becton Dickinson, Franklin Lakes, New Jersey, USA). All samples were analyzed by setting appropriate side and forward scatter gates to identify the mononuclear cell population, using CD45 and forward and side scatter for gate setting. Consistency of analysis parameters was ascertained by calibrating the flow cytometer with calibrating beads and FacsComp software, both from Becton Dickinson. The results are reported as percentage of gated cells positive for each antibody. The following fluorochrome conjugated antibodies, all from BD, were used: CD4 PE, CD3 PerCP-Cy5.5, CD19 PE-Cy7, CD8 APC and CD45 APC-H7. We also used BD Multitest 6-Color TBNK Reagent containing CD3 FITC clone SK7, CD16 PE clone B73, CD56 PE clone NCAM 16.2, CD45 PerCP-Cy5.5 clone 2D1, CD4 PE-Cy7 clone SK3, CD19 APC clone SJ25C1 and CD8 APC-Cy7, clone SK1. The gating strategy is shown in Fig. S1.

RNA isolation and oligonucleotide array hybridization

Blood mononuclear cells were isolated by Ficoll separation (Ficoll-Paque PLUS, GE Healthcare, Little Chalfont, UK). From the cell-pellet total RNA was prepared using Qiagen midi plus kit (Qiagen GmbH, Hilden Germany) as recommended by the manufacturer and was quality controlled on an Agilent Bioanalyzer (Agilent Technologies, Inc. Palo Alto, California, USA). Six pretreatment samples and seven samples obtained after rimonabant treatment passed the quality control. The cRNA synthesis for microarray experiments and the hybridizations were carried out using Affymetrix Human Gene 1.0 ST Array (Affymetrix, Inc., Santa Clara, California, USA) according to standard Affymetrix protocols at the core facility for Bioinformatics and Expression Analysis, Department of Biosciences and Nutrition, Karolinska Institutet.

Gene expression data analysis

We used tools provided in the Partek Genomic Suite 6.5 software (Partek Inc., St. Louis, Missouri, USA). Normalization was done by Robust Multiarray Analysis (RMA) followed by 1-way Analysis Of Variance (ANOVA) comparing the patient group before and after treatment. Significantly changed genes and exons were selected with an unadjusted p-value of <0.001, a False Discovery Rate (FDR) <0.1 and a fold-change equal or greater than >1.5 for up regulated genes and equal or less than <−1.5 for down regulated genes. Gene functional annotations were performed by using the free software DAVID v6.7 (Database for Annotation, Visualization and Integrated Discovery) (Huang da, Sherman & Lempicki, 2009). The gene expression data are deposited at the GEO repository under the number GSE68055.

Statistical analysis

Leukocyte subsets (as measured by flow cytometry) in blood before and after rimonabant treatment were analyzed using a paired t-test.

Results

Analysis of PBMC by flow cytometry before and during treatment with rimonabant

Blood levels of mononuclear cells on eight obese patients were analyzed by flow cytometry before and during treatment with rimonabant. There were no significant changes in the relative frequencies of total CD3+ T cells, CD4+ T cells, CD8+ T cells, B cells or CD3–CD4+ cells (monocytes and dendritic cells) in the patients during the treatment period (Table 1, graphically presented in Fig. 1) There was however a trend towards an increase in percentage of CD3−, CD16+/and or CD56+ cells (before treatment median 9% range 7–14%; after treatment median 12% range 9–15% p = 0.049) (Table 1 and Fig. 1).

Percentage of peripheral blood mononuclear cells (PBMC) before and during treatment with rimonabant.

Figure 1: Percentage of peripheral blood mononuclear cells (PBMC) before and during treatment with rimonabant.

PBMC were analyzed by flow cytometry before start of therapy and 4 weeks later and results are given as percentage of mononuclear cells in blood. Each data point represents results from one patient. In cases with no change in the frequency of a certain cell type the data point would fall on the line. The only statistically significant change was for CD3−, CD16+ and/or CD56+ cells (p = 0.049). For the other subsets the p-values were as follows: CD3+ p = 0.47; CD3 + CD4 + p = 0.25; CD3 + CD8 + p = 0.11; CD19+ p = 0.13; CD3–CD4+ p = 0.11. The mononuclear gate was defined by CD45 in combination with side and forward scatter. Within this gate the frequencies of CD3+ T cells, CD19+ B cells, CD3−, CD56+ and/or CD16+ cells (NK cells and subpopulation of CD16+ monocytes) and CD3−, CD4+ cells (monocytes and dendritic cells) were analyzed.

Whole-transcript gene expression analysis demonstrates significant changes in genes belonging to innate immune system pathways

Treatment with rimonabant might influence the expression of genes in patient leukocytes. To explore possible differences in gene expression profiles, whole-transcript expression analysis of PBMC before and during treatment was done, using Affymetrix Human Gene 1.0 ST Arrays. 47 probe sets were significantly differently expressed during treatment with a fold change of at least 1, 5, 37 probe sets showed increased expression and 10 decreased expression, respectively (Table 2). Several of the genes with significantly increased expression after rimonabant treatment are known components of the innate immune system (as exemplified by KLRF1, LILRA2, CTSB, CD160, CD177, and LY96). KLRF1 (also named NKp80) encodes a lectin-type of receptor that is expressed on nearly all NK cells and stimulates their cytotoxicity and cytokine release (Kuttruff et al., 2009). LILRA2 is the gene for an immune receptor that is expressed on monocytes, B cells, NK cells and dendritic cells and affects antigen presentation and innate immune responses (Lu et al., 2012). CTSB encodes cathepsin B, a protein that can be expressed in several immune cells including monocytes and that is involved in cell migration and immune modulation (Staun-Ram & Miller, 2011). CD160 is an essential NK cell receptor and is involved in regulation of cytokine production (reviewed in Le Bouteiller et al., 2011). CD177 is a GPI linked cell surface molecule that regulates activation and migration of neutrophil granulocytes (Stroncek, 2007). LY96 (also named MD-2) is associating with toll-like receptor 4 and is involved in signaling by LPS (Mancek-Keber & Jerala, 2015). A few genes promoting increased apoptosis were also upregulated (BCL-like 13, an apoptosis facilitator (Jensen et al., 2014; Kataoka et al., 2001), RING1- and YY1-binding protein, a regulator of MDM2 (Chen et al., 2009)).

It has previously been shown that chronic marijuana users have increased expression of CB1 in peripheral blood mononuclear cells (Nong et al., 2002). We therefore specifically analyzed the expression of genes belonging to the endocannabinoid system in our patient cohort. Rimonabant treatment did neither affect the expression of CB1 (mean and standard deviation of CB1 expression values before and after treatment were 12.5 ± 3.24 and 10.79 ± 2.43, respectively, corresponding to a fold change of −1.1) nor of CB2 or the enzymes involved in the degradation and/or synthesis of endocannabinoids (fatty acid amide hydrolase, FAAH, and N-acyl phosphatidylethanolamine phospholipase D, NAPEPLD) either when analyzed by gene expression analysis or by RT-PCR (data not shown).

Discussion

In this study we investigated the possible effects on PBMC of treatment with the CB1 antagonist rimonabant in patients taking the drug for obesity. We found that the distribution of leukocyte subsets remained rather constant, as analyzed by flow cytometry before treatment and after 4 weeks of treatment with rimonabant with the exception of CD3−, CD16+ and/or CD56+ cells that increased after treatment. This subset includes NK cells (CD3−, CD56+ and/or CD16+) and also subsets of monocytes (CD3–CD16+). There were no significant changes in expression levels of cannabinoid receptors or enzymes involved in synthesis and metabolism of endocannabinoids. However gene expression analysis suggested that genes involved in metabolism, cell death and the innate immune system were up regulated during treatment.

Rimonabant is the first selective CB1 antagonist registered for clinical use and was clinically developed for treatment of obesity and the metabolic syndrome. Beside the effect on food intake, anti-proliferative actions on normal and malignant cells have been reported. Cannabinoid receptors are often more highly expressed on malignant cells than on their normal counterparts and cancer cells are usually more sensitive to the action of cannabinoids than normal cells (reviewed in Flygare & Sander, 2008; Sido, Nagarkatti & Nagarkatti, 2014; Wasik, Christensson & Sander, 2011). Rimonabant has been reported to induce growth inhibition or apoptosis on several malignancies including breast, thyroid and colon cancer (Bifulco et al., 2004; De Petrocellis et al., 1998; Santoro et al., 2009; Sarnataro et al., 2006). We have previously demonstrated that mantle cell lymphoma and other B cell lymphomas have higher expression of CB1 and CB2 than normal lymphocytes (Gustafsson et al., 2008; Islam et al., 2003; Wasik et al., 2014). Cannabinoid receptor agonists, at 1–10 µM levels, reduced proliferation and induced programmed cell death in mantle cell lymphoma in vitro and in a xenotransplant model (Flygare et al., 2005; Gustafsson et al., 2006; Gustafsson et al., 2008; Schatz et al., 1997; Wasik et al., 2011). Interestingly, similar concentrations of rimonabant induced cell death in ex vivo isolated mantle cell lymphoma cells (Flygare et al., 2005). While these studies suggest that targeting of CB1 may be of use in cancer therapy, concern may be raised since anti-proliferative effects have been reported in PBMC (Malfitano et al., 2008). In these studies, rimonabant inhibited mitogen induced cell proliferation in vitro via G1/S phase arrest without induction of cell death (Malfitano et al., 2008). In contrast, Gallotta et al. (2010) reported that freshly isolated PBMC are highly resistant to the cytotoxic and cytostatic effects of rimonabant compared to leukemia-derived cell lines. It is possible that the different sensitivity to CB1 antagonism in freshly isolated, compared to mitogen activated, PBMC may reflect differences in expression levels of CB1. Resting leukocytes express very low levels of CB1 (Bouaboula et al., 1993; Galiegue et al., 1995; Kaminski et al., 1992) while receptor levels may increase upon activation by mitogens, cytokines or exposure to CB1 agonists (Borner et al., 2007; Nong et al., 2002; Schatz et al., 1997). We did not detect any significant differences in expression levels of CB1 or other components of the endocannabinoid system during rimonabant treatment for 4 weeks. Furthermore, our studies on ex vivo isolated PBMC from rimonabant treated patients demonstrated very minor changes in frequencies of T cells, B cells, CD3−, CD16+ and/or CD56+ cells or CD3–CD4+ cells or on total lymphocyte counts, in line with the results of Gallotta et al. (2010).

Global gene expression analysis demonstrated significant changes in genes coding for components of the innate immune system. The study design does not make it possible to discriminate if the differences in gene expression can be ascribed to certain subsets of leukocytes or if it is a general process, seen in all PBMC. However, many of the genes that were more highly expressed after treatment with rimonabant are expressed in NK cells (such as KLRF1 and CD160) and monocytes, which imply that the treatment is associated with the activation of certain inflammatory and immunological functions of the innate immune system. Interestingly, rimonabant has been shown to directly activate human and mouse macrophages and thereby inhibit the development of the intracellular pathogen Brucella suis (Gross et al., 2000). Furthermore, studies on lipopolysaccharide activated human macrophages showed that CB1 receptor blockade by rimonabant suppressed production of inflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) and matrix metalloproteinase-9 (Sugamura et al., 2009).

Conclusions

In conclusion our results show that rimonabant treatment induces expression of genes involved in immune responses but have only marginal effects on leukocyte subset frequencies in blood. This is in marked contrast to previous studies in which rimonabant induced cell death in malignant B lymphocytes that express high levels of CB1 (Flygare et al., 2005). The relatively small effects on normal leukocytes suggest that CB1 targeting may be further investigated as a therapeutic approach in lymphoma treatment, enabling selective effects of tumor cells.

Supplemental Information

Gating strategy for CD3−, CD4+cells

The gating strategy is shown in the dendrogram. First a mononuclear cell gate was set. Within this gate subpopulations of CD3+ T cells, CD19+ B cells and CD3−, CD4+ cells were identified by additional gating.

DOI: 10.7717/peerj.1056/supp-1