ORIGINAL RESEARCH ARTICLE

Journal of

Expression Variability and Function of the RET Gene in Adult Peripheral Blood Mononuclear Cells

Cellular Physiology

MARTA RUSMINI,1 PAOLA GRISERI,1 IVANA MATERA,1 ELENA PONTARINI,2 ROBERTO RAVAZZOLO,1,3 DOMENICO MAVILIO,2,4 AND ISABELLA CECCHERINI1* 1

U.O.C. Genetica Medica, Istituto Giannina Gaslini, Genova, Italy

2

Unit of Clinical and Experimental Immunology, Humanitas Clinical and Research Center, Rozzano, Milan, Italy

3

Dipartimento di Neuroscienze, Oftalmologia, Genetica e Materno Infantile (DINOGMI), Università di Genova, Genova, Italy

4

Department of Medical Biotechnologies and Translational Medicine, University of Milan, Milan, Italy

RET is a gene playing a key role during embryogenesis and in particular during the enteric nervous system development. High levels of RET gene expression are maintained in different human tissues also in adulthood, although their physiological role remains unclear. In particular, collected evidences of a RET contribution in the development and maintenance of the immune system prompted us to investigate its levels of surface expression on peripheral blood mononuclear cells (PBMCs) from adult healthy donors. Despite variability among samples, RET expression was conserved at similar levels in the different immune cell subsets, with higher correlations in similar lymphocyte populations (i.e. CD4þ and CD8þ T cells). Conversely, no correlation was found between the amount of RET receptor, the expression of its putative ligands and co-receptors and the genotypes at the RET locus. Moreover, we investigated the RET-associated inflammatory pathways in PBMCs from healthy donors both in resting conditions and upon glial cell derived neurotrophic factor (GDNF) and GPI-linked co-receptors alpha 1 (GFRa1) mediated RET activation. RET mRNA levels positively correlated with the transcript amount of interleukin-8 (IL-8), a cytokine produced by monocytes and macrophages, though we could not demonstrate its direct effect on RET expression by in vitro experiments on THP1 human monocytic cells. These results imply that RET expression might be influenced by either cis- and/or trans-factors, which together would account for its high variability within the general population, and suggest a putative functional role of the RET gene in modulating immune cell responses during inflammation and carcinogenesis. J. Cell. Physiol. 229: 2027–2037, 2014. © 2014 Wiley Periodicals, Inc.

RET (REarranged during Transfection) is a gene located on chromosome 10 (Pasini et al., 1995) that codes for a glycoprotein belonging to the tyrosine kinase (TK) receptor family involved in many cellular processes such as differentiation, proliferation, migration, and cell survival (Kodama et al., 2005). RET activation is secondary to the formation of a multi-protein complex, including one of four soluble ligands, namely glial cell derived neurotrophic factor (GDNF), neurturin (NRTN), artemin (ARTN), or persephin (PSPN) and one of four GPI-linked co-receptors (GFRa1–4) (Airaksinen et al., 1999), that leads to RET dimerization and autophosphorylation of tyrosine residues in the intracellular TK-domain (Richardson et al., 2006). Germline mutations of the RET gene are responsible for two different disorders: Hirschsprung’s disease (HSCR) and multiple endocrine neoplasia type 2 (MEN2), that are due to loss-of-function and gain-of-function mutations, respectively. HSCR disease is a rare congenital anomaly of the enteric nervous system (ENS) and it is characterized by the absence of enteric ganglia in variable lengths of the distal intestinal tract (Badner et al., 1990; Parisi and Kapur, 2000), while MEN2 is a dominant inherited cancer syndrome that affects neuroendocrine organs and is characterized by a RET activation which is constitutive and independent of ligands binding (Arighi et al., 2005). Many polymorphisms and haplotypes of the RET gene are known to be involved both in HSCR and thyroid cancer. A haplotype, that includes two variants at 5 and 1 bp from the transcription starting site and a single nucleotide polymorphism (SNP) in exon 2, has been found associated with HSCR phenotype (Sancandi et al., 2003; Fernandez et al., 2005; Garcia-Barcelo et al., 2005) and © 2 0 1 4 W I L E Y P E R I O D I C A L S , I N C .

with a reduced level of RET gene expression (Griseri et al., 2005). Moreover, other SNPs at exon 11 and exon 14 have widely been considered in association studies involving RET-

Marta Rusmini, Paola Griseri, Domenico Mavilio and Isabella Ceccherini contributed equally to this work. Conflict of interest statement: The authors have neither actual nor potential conflict of interest to disclose. Contract grant sponsor: Ministero della Salute; Contract grant number: GR-2008-1135082. Contract grant sponsor: Fondazione Umberto Veronesi. Contract grant sponsor: Istituto Superiore di Sanità (ERare-HSCR Consortium grant, 2007 call). Contract grant sponsor: Associazione Italiana per la Ricerca sul Cancro (AIRC); Contract grant number: IG-2012-13217. Contract grant sponsor: Ministero della Salute: “Cinque per mille” and Ricerca Corrente to Gaslini Institute. *Correspondence to: Isabella Ceccherini, U.O.C. Genetica Medica, Istituto Giannina Gaslini, Via G. Gaslini 5, 16148 Genova, Italy. E-mail: [email protected] Manuscript Received: 9 July 2013 Manuscript Accepted: 25 April 2014 Accepted manuscript online in Wiley Online Library (wileyonlinelibrary.com): 29 April 2014. DOI: 10.1002/jcp.24660

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related pathologies: the variant in exon 11 (rs1799939), the only non-synonymous RET SNP, has turned out to potentially affect the protein phosphorylation pattern (Lantieri et al., 2012) while the exon 14 SNP (rs1800862) is a tag for a protective haplotype already characterized at the 30 UTR of the gene (Griseri et al., 2002). RET is a key regulator in the ENS development (Marcos and Pachnis, 1996; Arighi et al., 2005), spermatogenesis (Jain et al., 2004), and kidney organogenesis (Ivanchuk et al., 1997). On the other hand, very little is known about RET functions in adulthood. Besides ganglionic gut (Miao et al., 2010), RET has been reported to be constitutively expressed in kidney, ganglia and nerves (Martucciello et al., 1995; Widenfalk et al., 1999; Flavin et al., 2012), bladder, cervix, esophagus, lung, lymph nodes, placenta, salivary glands, skin, stomach, thymus and seminal vesicle of testis (Yang et al., 2006), substantia nigra pars compacta and brain (Quartu et al., 2007; Alladi et al., 2010), ocular and nasopharyngeal tissue (Qi et al., 2008), benign prostatic secretory epithelium (Dawson et al., 1998), human ovaries, particularly in both oocytes and granulose cells (Farhi et al., 2010). There are also evidences about the expression of RET in healthy and tumor-transformed pancreatic tissue (Kikuchi, 2004) and mammary gland (Yang et al., 2006; Boulay et al., 2008). RET expression was also investigated in circulating immune cells and it has been reported to be present on monocytes, polymorphonucleated, B and CD34þ stem cells (Visser et al., 1996; Gattei et al., 1997), although RET phenotypic distribution on peripheral blood mononuclear cells has been controversial for many years (Vargas-Leal et al., 2005; Veiga-Fernandes et al., 2007). Only recently, two studies conducted both in healthy individuals and in HSCR patients characterized the RET expression in PBMCs (Matera et al., 2013; Rusmini et al., 2013). In this regard, experimental evidences clearly demonstrated the RET involvement in the development and maintenance of the immune system. Indeed, RET knockout mice lack intestinal Peyer’s patches, one of the most important secondary lymphoid organs associated with the gut mucosa (VeigaFernandes et al., 2007). Moreover, RET receptor, with its signaling partners GFRa1 and GFRa2, has turned out to be dispensable for the development of T lymphocytes (Almeida et al., 2012). In RET rearranged forms (RET/PTC1 and RET/PTC3 oncogenes) a transcriptional pro-inflammatory program is activated (Borrello et al., 2005) with expression of high levels of pro-inflammatory cytokines and other proteins involved in the immune response (Melillo et al., 2005; Neely et al., 2011). Therefore, while RET activation, triggered by its specific ligands, results in a proinflammatory response, impairment of RET function might lead to a specific immunological phenotype. Following this hypothesis, we have recently shown that the immune system is involved in the physiopathology of HSCR disease by modulating inflammatory programs that are either dependent on or independent of RET signaling (Rusmini et al., 2013). Taking into account the existence of RET-dependent inflammatory programs and a high inter-individual variability of RET expression in blood cells, the present study aims to characterize the molecular mechanisms underlying the heterogeneous expression of RET on circulating immune cells from healthy donors. Moreover, we also analyze the relationship between RET expression and members of its signaling complex, and between RET haplotypes and inflammatory mediators produced by PBMCs. Materials And Methods Samples

Peripheral blood was collected from 76 healthy donors, that signed consent forms in accordance with clinical protocols JOURNAL OF CELLULAR PHYSIOLOGY

approved by the IRB of Desio Hospital (Milan, Italy). After dilution with DPBS (BioWhittaker Lonza, Inc., Walkersville, MD) and stratification onto Ficoll-Paque (GE Healthcare, Piscataway, NJ) density gradient, samples were centrifuged at 1500 rpm for 30 min. Peripheral blood mononuclear cells (PBMC) were collected and washed twice in DPBS, and platelets were separated from cells by ten minutes centrifugation at 900 rpm without brake. Monocytic leukemia cell line THP1 was grown in RPMI-1640 medium (Euroclone, Pero, Milan, I) supplemented with 10% fetal bovine serum (FBS, New Zealand), 1% L-glutamine (100), 100 U/ml penicillin and 100 mg/ml streptomycin and maintained at 37 °C with 5% CO2 in a humidified incubator. Flow cytometry

PBMCs thus obtained were washed with washing buffer (HBSS, BioWhittaker Lonza, plus 2% FBS) and Fc block was achieved using IgG from human serum (Sigma–Aldrich, Steinheim, Germany) at 10 ng/ml. Cells were then stained with appropriate FITC- and PerCp (or PeCy5)- labeled antibodies and washed twice with washing buffer. Fixation and permeabilization were performed by Citofix/Citoperm kit (BD-Pharmigen, San Diego, CA) according to the manufacturer’s instructions. Samples were then incubated with phycoerythrin-labeled RET antibody (R&D Systems, Minneapolis, MN) or the specific phycoerythrin-labeled isotype anti-IgG1. The acquisition was carried out by Facs Calibur (BDPharmigen, San Diego, CA) and analysis performed by FlowJo software (Tree Star Inc., San Carlos, CA). The following antibodies were used to verify the purification of cells: antihCD56 PeCy5, anti-hCD20 FITC, anti-hCD3 PeCy5, and antihCD14 FITC (BD-Pharmigen). Cell treatments

PBMCs obtained from 10 healthy donors were resuspended in RPMI-1640 and splitted into two vials at 10 E7cells/ml each. Cells in one vial were freshly stimulated with human GDNF and human GFRa1 at the final concentration of 100 ng/ml and 1 mg/ ml respectively (R&D Systems) and incubated at 37°C in 5% CO2 for 5 h. Cells were then pelleted and washed with DPBS and total RNA was extracted with RNeasy plus mini kit (QIAGEN, GmbH Hilden, Germany). RNA samples thus obtained were quantified by NanoDrop (Thermo Scientific, Rockford). For each sample, cells in the second vial were used to extract DNA by standard techniques. THP1 cells were treated with IL8 (R&D Systems) at final concentration of 10 ng/ ml for 5 h at 37°C. To test the effect of estrogens, THP1 were cultured in dishes and treated with 17b-estradiol (Sigma–Aldrich) at 1 nM and 10 nM for 24 h at 37°C and 5% CO2. Cells were then collected and RNA was extracted as described above. RNA isolation and real time PCR

Total RNA from cells was isolated by a commercial RNA purification kit (RNeasy Mini kit, Qiagen, GmbH, Germany) according to the manufacturer’s protocol. One ug of total RNA was reverse transcribed with iScript cDNA Syntesis kit (Bio-Rad Laboratories, Hercules) according to the manufacture’s protocol. Real time quantitative PCR was performed using inventoried Assays-on-DemandTM provided by Applied Biosystem. Hs01120027_m1 was used to detect RET gene and Hs99999905_m1 was used to detect the reference gene GAPDH. To study the inflammatory panel, we used Assays-onDemandTM for CCL20 (Hs00171125_m1), CCR6 (Hs00171121_m1), IL8 (Hs00174103_m1), TNF (Hs00174128_m1), CXCL1 (Hs00236937_m1), IL1b

RET GENE EXPRESSION IS MODULATED IN PBMCs

(Hs00174097_m1), CCL2 (Hs00234140_m1), and CCL7 (Hs00171147_m1). To study ligands and coreceptors of RET we used Assays-on-DemandTM for GDNF (Hs00181185_m1), NRTN (Hs00177922_m1), ARTN (Hs00365083_m1), PSPN (Hs00358822_g1), GFRa1 (Hs00237133_m1), GFRa2 (Hs00176393_m1), GFRa3 (Hs00181751_m1), and GFRa4 (Hs00360831_g1). The expression of IL-8 receptor, CXCR2, was studied with the assay Hs00174304_m1. PCR reactions were performed using the iQTM5 Real Time PCR (Bio-Rad Laboratories). The expression of mRNA was evaluated using the relative Ct method (DDCt) and real time PCR amplification was performed in triplicate and repeated at least twice. Genomic analysis of the RET gene

Genomic DNA was extracted from peripheral blood mononuclear cells (PBMCs) by a standard technique. DNA samples were subjected to screening of selected regions of the RET gene by means of direct sequencing of the corresponding amplification products. In particular, we focused on selected amplimers of exon 2, 11, and 14 (Rusmini et al., 2013) and introns 4, 15, and 20. These latter reactions were set up according to the standard protocol (Rusmini et al., 2013) by using primers Intron 4 Forward (Int4F 50 -GCCGTGGTGGAGTTCAAG-30 ) and Intron 4 Reverse (Int4R 50 -TCAGGTCAAGGTCACACACAGG-30 ), Intron 15 Forward (Int15F 50 CTCATGCTGCGGAACTAACA-30 ) and Intron 15 Reverse (Int15R 50 -GCAGGGTGCTTTTAGCATCT-30 ), Intron 20 Forward (Int20F 50 -GCCCTGATGACCTGTCCTT-30 ) and Intron 20 reverse (Int20R 50 -GCTGCTGAGACTTCCCAAA-30 ) and the following amplification conditions: 30 cycles at 95°C for 4500 , 60°C for 4500 , and 72 for 9000 . All polymerase chain reaction (PCR) amplifications were performed on ABI 2700 thermal cycler and PCR products were purified by ExoSAP-IT (GE Healthcare) and directly sequenced using Big Dye v1.1 and a ABI3130 automated sequencer (Applied Biosystems, Foster City, CA). Cells separation

PBMCs from 16 different healthy donors were purified by negative selection to obtain enriched cultures of B cells, T cells, NK cells, and monocytes (Stem Cell Technologies, Vancouver, Canada), using RoboSep automated cell separator (StemCell Technologies, Vancouver, Canada) according to the manufacturer’s protocols (Hudspeth et al., 2012). Cells were stained with anti-CD3 (FITC), anti-CD19 (PerCP), anti-CD14 (FITC), and anti-CD56 (PerCP) and verified, by flow cytometry, that monocytes, T, B, and NK cells were constantly 85%. Cells were splitted, resuspended in RPMI-1640 and cultured with the following stimuli and maintained in culture for 4 days: Macrophage-Colony Stimulating Factor (M-CSF) at a final concentration of 100 ng/ml, 200 IU/ml of interleukin-2 (IL-2) for NK cells, 100 IU/ml of IL-2 for T cells, 100 ng/ml of CD40 ligand (CD40L), and 2 mg/ml and anti-human immunoglobulins for B cells. We harvested immune cells at time 0 and after one, two, and four days of culture to extract RNA, as described above. The RNA quality was assessed by gel electrophoresis and 1 mg of RNA was reverse transcribed as described above.

(New Zealand) and 100 ng/ml M-CSF. Two different cell types were generated: freshly isolated monocytes (from now on named Mo), and resting fully differentiated macrophages (from now on named M). Statistical analysis

RET MFIs values obtained by flow cytometry were analyzed by non parametric Kruskal-Wallis and Mann-Whitney tests to compare levels of RET protein expressed on immune cell subsets. The correlation between cytokines and RET expression in ten healthy donors (þ/GDNF and GFRa1) was performed by Pearson correlation. The same test was used to correlate the amount of ligands and co-receptors with each other and with RET in all the subclasses of PBMCs. The correlation was significant (P < 0.05) for r > 0.6. GraphPad Software was used to detect any possible outlier value. The association between allelic distribution and RET expression, before and after activation, was evaluated by Fisher’s test by comparing values lower and higher the median of the values in the whole group (GraphPad Software, San Diego, CA). Results RET expression in adult tissues and PBMCs

A careful analysis of both literature and in silico data, allowed us to collect all the information already available regarding the expression of the RET gene in human adult tissues. In particular, we integrated the EST (Expression Sequence Tag) profile of the human RET gene (http://www.ncbi.nlm.nih.gov/UniGene/ ESTProfileViewer.cgi?uglist=Hs.350321) with literature data (Supporting information Table S1). We found that there are tissues that either do not show any RET expression in adult individuals (Supporting information Table S1, group 1), express RET though in the absence of any EST signal (Supporting information Table S1, group 2), display a EST profile consistent with the literature (Supporting information Table S1, group 3) or, in the case of one tissue, have a EST profile but no literature data (Supporting information Table S1, group 4). Noteworthy, in line with what reported in the literature, the EST profile of the blood shows a positive RET signal (Visser et al., 1996), though without distinguishing amongst the different immune cell types. To deepen into the expression of RET in PBMCs, we studied the expression of both RET mRNA, by quantitative PCR, and

Macrophage differentiation

Human monocytes were obtained from three additional and unrelated healthy blood donor buffy coats as described above. Cells were stained with anti-CD14 (FITC) to verify by flow cytometry that the percentage of purified monocytes was more than 85%. Macrophages were obtained by culturing monocytes for 7 days in RPMI 1640 (Euroclone) supplemented with 10% FBS JOURNAL OF CELLULAR PHYSIOLOGY

Fig. 1. RET expression in PBMCs samples. Bar graph showing RET mRNA levels in two samples of peripheral blood mononuclear cells (PBMC1 and PBMC2). Values (2DDCt) are normalized against the sample with the highest expression (PBMC1) and are reported as expression fold increase.

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RUSMINI ET AL. RET protein, by flow cytometry. As shown in Figure 1, RET shows different mRNA levels in the PBMCs of two unrelated healthy donors (namely PBMC1 and PBMC2), a finding consistent with the amount of RET receptors we detected, as already reported (Rusmini et al., 2013), on the surface of the same PBMCs (data not shown). Previous observations regarding the high variability of RET receptor expressed on immune circulating cells are therefore confirmed (Matera et al., 2013; Rusmini et al., 2013). In addition, we also failed to explain the variable RET expression, as assessed by measuring mRNA and protein levels, in terms of both sex and age differences among donors’ PBMCs (data not shown). We then compared the surface levels of RET receptor between lymphocytes and monocytes and, although no statistically significant difference could be detected between these two blood cell compartments (P ¼ 0.33), monocytes

presented a wider range of expression values compared to lymphocytes (Fig. 2A, left panel). Within the lymphocyte gate, we also analyzed the surface levels of RET protein on total T cells as well as on CD4þ and CD8þ T cell subsets and on B and NK cells (Fig. 2B, left panel). Monocytes and B cells showed the highest levels of RET expression and, among lymphocytes, the RET transcript resulted significantly higher in B cells than T cells (P < 0.0001) and NK cells (P ¼ 0.0028). The comparison between CD4þ and CD8þ cells showed that T helper lymphocytes express significantly higher levels of RET, compared to the T cytotoxic ones (Mann-Whitney test, P ¼ 0.01). These differences between subclasses of lymphocytes were confirmed after applying another nonparametric test, the Kruskal-Wallis test, which allowed to detect different RET expression between T, B, and NK cells (P ¼ 0.0002).

Fig. 2. Expression of RET receptor on immune cells form healthy individuals. (A) On the left: summary graph of dot plots showing normalized mean fluorescent intensity (MFI) values of RET receptor expressed on lymphocytes and monocytes from 50 healthy individuals with medians (horizontal black bars). On the right: regression analysis of the RET expression MFI values in total lymphocytes (x-axis) versus monocytes (y-axis) (r2 ¼ 0.45). (B) On the left: summary graph of dot plots with medians (horizontal black bars) showing normalized MFIs of RET receptor on lymphocytes sub-classes: T cells, CD4þ T cells, CD8þ T cells, NK cells, and B cells. Mann-Whitney test shows a statistically significant difference between B cells and both NK cells (P ¼ 0.0028) and T cells (P < 0.0001) and between CD4þ and CD8þ T cells (P ¼ 0.01). On the right: regression analysis of the RET expression MFI values in T cell (x-axis) versus B cells (y-axis) (r2 ¼ 0.52).

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RET GENE EXPRESSION IS MODULATED IN PBMCs

In order to verify whether individual patterns of RET expression among the sub-cellular classes are correlated, we performed pairwise regression analyses among these cell types finding that RET levels showed higher correlation when similar subgroups were compared (CD4þ and CD8þ cells: r2 ¼ 0.85; NK and T cells; r2 ¼ 0.6; B and T lymphocytes: r2 ¼ 0.5). In contrast, correlations decreased when distant cell subgroups were compared such as lymphocytes and monocytes (r2 ¼ 0.4), NK cells and B cells (r2 ¼ 0.3), as exemplified in the right panels of Figure 2A and B for the regression analysis between lymphocytes vs monocytes and between T cells and B cells, respectively, and in Supporting information Figure S1 for the other comparisons. These results confirm that RET expression is highly variable among individuals and is also dependent on the cellular subtype. Levels of expression of RET ligands and co-receptors

To understand the mechanisms underlying the regulation of RET receptor expression in PBMCs, we focused on the mRNA transcription of other members of RET signaling pathway, which participate to the assembly of the biological active complex. In particular, we first assessed the expression of ligands and co-receptors on freshly purified cells from four controls. Only NRTN, GFRa2, ARTN, GFRa3, and RET could be detected by quantitative PCR in the immune subclasses analyzed (Fig. 3). Interestingly, monocytes do not seem to express NRTN, and show very low levels of ARTN and GFRa3, while they express normal amount of GFRa2. As relative expression of RET ligands and coreceptors may be crucial in determining different levels of RET expression/

activation, immune cell subsets obtained from the four controls were purified and cultured for a total of 24, 48, and 96 h with appropriate stimuli (see Materials and Methods). Similar to resting cells, stimulated monocytes and lymphocytes showed higher levels of NRTN and GFRa2 transcripts compared to ARTN and GFRa3 ones, while RET confirmed a variable expression of mRNA in all the donors tested. In order to assess a possible correlation between mRNA levels of ligands, coreceptors and RET, data thus collected were compared through the pairwise Pearson test (Table 1). RET expression significantly correlates with GFRa2 levels in both T lymphocytes (r ¼ 0.59) and in monocytes (r ¼ 0.93). No other correlation was found between RET mRNA levels and its ligands or co-receptors. It is to notice that other correlations between molecules of the RET signaling pathway were detected: GFRa2 is associated to its natural ligand NRTN both in T cells and in NK cells (r ¼ 0.66 and 0.60, respectively) and GFRa3 is associated to GFRa2 and NRTN in B cells (r ¼ 0.74 and 0.88). Based on these findings, we conclude that RET and GFRa2 mRNA levels seem to be co-regulated in monocytes. Genotype-phenotype RET correlations

To deepen into the variability of RET expression we investigated RET mRNA levels in the PBMCs of additional 10 individuals and we correlated them with the genotype at the RET locus in the same individuals. To this end, we treated PBMCs with or without GDNF and GFRa1 for 5 h to induce activation of RET signaling pathway and we quantified RET mRNA levels before and after treatment. First of all, we confirmed also at mRNA level the high variability of RET

Fig. 3. Quantitative PCR on immune subsets. Histograms of RET ligands and co-receptors mRNA expression levels on purified monocytes and T, B, and NK cells. Images from one representative experiment of four independent samples for each cell subset are reported. The relative expression is reported in each case on the y-axis as 2DCt.

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TABLE 1. Pearson test analysis NRTN T cells

B cells

NK cells

Monocytes

RET NRTN GFRa2 ARTN RET NRTN GFRa2 ARTN RET NRTN GFRa2 ARTN RET NRTN GFRa2 ARTN

GFRa2

ARTN

0.1232

0.5959 0.6594

0.2889 0.3406 0.4835

0.3144

0.2034 0.5375

0.3675 0.0653 0.1100

0.4273

0.2160 0.6001

0.1160 0.1622 0.5278

0.1633

0.9290 0.2118

–0.1947 0.2001 0.2347

GFRa3 0.0944 0.4754 0.3886 0.0507 0.2695 0.8846 0.7421 0.0850 0.1333 0.1196 0.1886 0.4431 0.0758 0.0506 0.1498 0.4014

Results of the Pearson test comparing the mRNA expression of RET, ligands and coreceptors obtained by qPCR on cDNA from four independent cultures of circulating immune cell subsets. Data collected after 24, 48, and 96 h of culture were pooled together for the analysis. Statistically significant results are reported in bold.

expression on freshly purified PBMCs as detected by qPCR (Fig. 4A). We then observed that GDNF treatment has variable effects on RET expression, that turned out to be down- or upregulated independently of the basal level of RET transcripts (Fig. 4B). The genotype of each of the ten subjects under study was then assessed at the following known RET variant loci: SNP of exon 2 (rs1800858), tag marker of the RET predisposing haplotype associated with reduced expression of the RET gene (Griseri et al., 2005), SNP of exon 11 (rs1799939), whose variant allele has been reported to be associated with a strong response to GDNF (Sawai et al., 2005), and SNP of exon 14 (rs1800862), demonstrated to increase RET mRNA stability (Griseri et al., 2007). No correlation could be assessed between genotypes at the above polymorphic markers, gender and the amount of RET detected with and without GDNF treatment (Supporting information Table S2). To determine any possible transcriptional effect of RET genotypes, we subdivided individuals according to their genotypes, namely wild-type (wt) homozygotes, heterozygotes and variant homozygotes, and performed pairwise Student’s t tests, without detecting any significant difference among the RET expression levels of the three genotypes at each locus.

Moreover, we did not observe any difference in the distribution of the wt and variant alleles in individuals showing RET expression values lower or higher than the median for the whole group. In the attempt to find new variants located in regulatory regions which may be involved in variable RET expression, we carried out a bioinformatic analysis through the UCSC browser, which led us to identify candidate regulatory regions in intron 5, 15, and 20, already classified as putative enhancers (http://genome.ucsc.edu/ENCODE) (Fig. 5). After direct sequencing of these regions in ten controls we identified three additional SNPs (rs2435352, rs2742235, and rs2435355) which were found, again, not to correlate with RET expression (Supporting information Table S2). RET-associated inflammatory patterns

Once confirmed that the expression of RET is highly variable between individuals and it is also independent from RET genotypes, we sought to verify whether different levels of RET mRNA could be associated with the modulation of specific inflammatory genes. To this end, and on the basis of both our previous studies and literature data (Borrello et al., 2005; Rusmini et al., 2013), an “inflammatory panel” was designed to assess levels of seven selected pro-inflammatory cytokines and one cytokine-receptor in PBMCs either in the presence or in the absence of GDNF þ GFRa1 treatment (Fig. 6A). The results showed an high degree of heterogeneity, since there are cytokines and chemokines, such as CCL20, TNF-a, CXCL1, CCL2, and CCL7 with variable expression in PBMCs of all donors tested. In contrast, CCR6 transcript resulted to be always present at low level, while the constitutive amounts of IL-8 and IL1-b mRNA were high. We then measured the mRNA levels of the same molecules on PBMCs stimulated with GDFN and GFRa1 and we reported the transcript fold changes, in term of 2DDCt (Fig. 6B). In order to understand the correlations between RET transcripts and the mRNA levels of the abovementioned cytokines and chemokines/chemokine receptor, a Pearson test was performed after excluding outlier values statistically identified (GraphPad Software). Correlation values with r > 0.6 were considered significant. As reported in the right part of Figure 6C, CCL20 is correlated with TNF, IL-8, CCL7, CCR6, CXCL1, and IL1-b in the absence of GDFN and GFRa1 treatment. We also found that IL1-b correlates with all the inflammatory molecules analyzed with the only exception

Fig. 4. RET mRNA expression with and without GDNF treatment. (A) RET mRNA expression evaluated on immune cells from ten unrelated controls. Data are reported as 2DCt . (B) Fold induction of RET mRNA after GDNF and GFRa1 treatment for 5 h. Data were obtained with DDCt method and represented as 2DDCt.

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RET GENE EXPRESSION IS MODULATED IN PBMCs

Fig. 5. RET gene representation from exon 2 to exon 20. Representation of the RET gene, from exon 2 to the 30 UTR, as obtained from the UCSC Genome Browser (http://genome.ucsc.edu/ENCODE). Known polymorphisms at the exons 2 (SNP2), 11 (SNP11), and 14 (SNP14) are reported at the top, while candidate regulatory regions (intron 4, intron 15, and intron 20) suggested by bioinformatic analysis are represented at the bottom.

Fig. 6. GDNF effect on the amount of RET and inflammatory molecules in ten healthy donors. (A) Transcript levels of molecules belonging to the inflammatory panel: the expression of CCL20, CCR6, IL-8, TNF, CXCL1, IL1B, CCL2, and CCL7 is reported as 2DCt. (B) Fold induction, upon GDNF treatment, of the expression of the above mentioned molecules. (C) The expression of these inflammatory molecules, analyzed by quantitative PCR, has been detected before and after GDNF and GFRa1 treatment and thus compared by Pearson correlation. Significant correlations are reported on gray boxes. Results from not treated () and treated (þ) cells are reported on the right and on the left of the diagonal line, respectively.

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of CCL2. Furthermore, CCR6 correlates with TNF-a, CXCL1 with IL-8, and TNF-a with CCL2. Interestingly, RET transcript levels show a direct and statistically significant correlation only with IL-8 mRNA (r ¼ 0.8). Five out of seven significant correlations were found to be conserved also in PBMCs incubated with GDNF and GFRa1, thus indicating that these correlations do not depend on RET engagement. Only two statistical associations were induced by RET triggering, namely transcript levels of IL-8 associated with TNF-a and transcript levels of CCL2 associated with CCL7 (Fig. 6C). RET and IL-8

Since RET levels have turned out to correlate with IL-8, that is one of the key cytokines produced by both monocytes and macrophages (Remick, 2005), we measured the expression of RET in the THP1 cell line, a monocytic leukemia-derived lineage

whose biology recapitulates the monocyte features (Tsuchiya et al., 1980). We found that, similarly to freshly purified PBMCs, THP1 cells express levels of RET mRNA and surface receptor (Supporting information Fig. S2), thus demonstrating that THP1 cells can be used as immortalized cell model to study RET expression. Since THP1 cells express also detectable amount of mRNA for the IL-8 receptor CXCR2 (Supporting information Fig. S3), we proceeded to treat this cell line with either IL-8 or GDNF and GFRa1 and we then analyzed the levels of mRNA of RET and IL-8 (Fig. 7A). While the treatment with both stimuli had no effect on the amounts of RET transcripts, mRNA levels of IL-8 was significantly increased after incubation of THP1 with IL-8 and GDNF plus GFRa1 (P < 0.05 and 0.001 for GDNF and IL8 treatment, respectively), in line with previous reports (Iwahashi et al., 2002; Borrello et al., 2005). As both these genes are under the control of estrogens (Freund et al., 2003; Stine et al., 2011), we sought to see whether IL8 and Ret can be modulated also by additional factors, such as steroid hormones. To this end, we treated THP1 cells with two concentrations of estradiol (1 and 10 nM) for 24 h and observed a comparable increase of RET and IL8 expression levels (Fig. 7B). To investigate whether the RET gene could be also modulated by state of differentiation of monocytes, we analyzed RET transcript levels in fresh monocytes (Mo) and fully differentiated macrophages (M). As shown in Figure 8, macrophages M express higher levels of RET receptor compared to monocytes Mo, although the fold increase is highly variable. Discussion

The molecular mechanisms underlying RET expression in different cell types are a main issue for the full comprehension of RET-related pathologies such as HSCR and thyroid cancer. To date, most of these studies have been performed either in animal models, during development, or in tumor immortalized cell lines. In the latest years, the RET gene has shown a peculiar pattern of expression in normal tissues: eye, pancreas, mammary gland, prostate, and blood. In this work, we sought to characterize the mechanisms and events at the basis of physiological RET expression in freshly isolated PBMCs from healthy individuals in the context of the already

Fig. 7. RET and IL8 induction in THP1 cell line following GDNF and interleukin-8 treatment. (A) RET (left panel) and IL-8 (right panel) transcript levels are reported in term of 2DDCt and compared between before and after the following stimuli: GDNF and GFRa1 (þG) and interleukin-8 (þIL8). Values represent the mean of three independent experiments and are normalized against untreated THP1 cells (nt). The Student t-test confirmed a significant difference of the fold increase in the IL8 expression in THP1 treated with GDNF (P < 0.05) and in THP1 treated with IL8 (P < 0.001) compared to untreated cells. (B) RET and IL-8 expression (dark and light gray, respectively) in THP1 cells with and without b-estradiol treatment. Cells were maintained in culture with 1 and 10 nM of the estrogen for 24 h and gene expression level is represented as fold induction with respect to untreated cells. RET expression did significantly increase upon incubation with 10 nM b-estradiol (Student t-test; P ¼ 0.01). Values obtained with the highest concentration represent the mean of four independent qPCR reactions from two independent treatments. Values obtained upon 1 nM-treatment represent two independent quantitative PCR reactions from a single cellular treatment.

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Fig. 8. RET expression on monocytes (Mo) and macrophages (M). The expression of RET was evaluated by qPCR in monocytes (Mo) and macrophages (M) obtained from three unrelated healthy donors. The bar graphs show the amount of RET mRNA in all the xaxis conditions in term of 2DCt.

RET GENE EXPRESSION IS MODULATED IN PBMCs

reported interactions between RET and inflammatory programs (Borrello et al., 2005; Melillo et al., 2005; Rusmini et al., 2013). Here, we show that RET expression is very variable among individuals, both at mRNA and protein levels, thus confirming our previous findings on HSCR patients (Rusmini et al., 2013). In addition, we have found that, despite differences among donors, RET expression is conserved at similar levels in the different immune cell subsets, with higher correlations in similar lymphocyte populations (i.e. CD4þ and CD8þ T cells). These results imply that RET expression might be influenced by either genetic background (cis-factors) or environmental factors (trans-factors), which together would account for a wider variability within the general population. In fact, we found that RET gene is clearly more expressed in monocytes and B lymphocytes than in other cell types, thus suggesting a specific role of the RET receptor in the adaptive and innate immune responses mediated by these cells. Our analyses of the expression pattern of single members of the RET pathway showed that freshly purified PBMCs express higher levels of NRTN/GFRa2 than ARTN/GFRa3 mRNA. Indeed, NRTN and GFRa2 are in a tight relationship during RET activation and represent the main signaling complex in blood cells (VargasLeal et al., 2005). This observation is in contrast with what it happens during development, where the GDNF/GFRa1 axis is the main signaling pathway for RET. Interestingly, fresh monocytes expressing both RET and GFRa2, did not show any NRTN expression, the physiological ligand of RET. This is consistent with the existence of a paracrine loop which would lead RET expressing monocytes to migrate toward physiological source of GDNF or NRTN like, for instance, an injured nerve (Mills et al., 2007; Hara et al., 2012). Moreover, RET is modulated according to the differentiation state of monocytes, as resulted by the analysis of RET transcript levels in fresh monocytes (Mo) and in fully differentiated macrophages (M), these latter turned out to express higher RET levels. The biological significance of this observation of ours will need further studies. With respect to the high variability of RET expression at both mRNA and protein levels, we have unexpectedly found that GDNF treatment, previously reported to regulate RET expression both at transcript and protein levels (Heanue and Pachnis, 2006; Pierchala et al., 2006), is able to induce different or even opposite effects in PBMCs of healthy donors. Indeed, the engagement of RET signaling complex by GDNF is associated with an extreme variability of RET mRNA levels, that change independently of constitutive amounts of RET transcripts. In addition, the treatment with GDNF does have an effect on total mRNA levels of several cytokines, as we previously demonstrated in HSCR patients (Rusmini et al., 2013). Overall, we observed a high degree of heterogeneity in the response to GDNF. There are cytokines and chemokines, such as CCL20, TNF-a, CXCL1, CCL2, and CCL7 with variable expression in PBMCs of all donors tested. In contrast, the CCR6 transcript resulted to be always present at low level, while the constitutive amounts of IL-8 and IL1-b mRNA were quite high (Rusmini et al., 2013). This data confirms that RET plays an active role in the immune response and its activation likely mediates a pro-inflammatory program whose details need to be disclosed yet. Moreover, we found that RET expression in cells either treated or not with GDNF does not correlate with any particular RET genotype, including the SNP tagging the predisposing RET haplotype (SNP2). The absence of undescribed variants lying in putative regulatory regions has also confirmed recent findings which suggest different tissue and time specific regulations of RET in adult and embryo (Matera et al., 2013). Similar negative results were also JOURNAL OF CELLULAR PHYSIOLOGY

obtained when we attempted to draw a correlation between RET expression and gender. In regard to trans acting factors possibly involved in the control of RET expression, we have interestingly found that RET mRNA levels are correlated to IL-8 mRNA transcripts in freshly purified PBMCs. Interleukin-8 is a proinflammatory CXC chemokine also known as CXCL8, secreted by monocytes and macrophages as a protein of 72 amino acids. Its biological effects are mediated through its binding to the cellsurface G protein-couple receptor CXCR2 (Murphy and Tiffany, 2009) and include the activation of AKT (Knall et al., 1997) and the regulation of the activity of the mitogen-activated protein kinase (MAPK) signaling cascade (Knall et al., 1996). Our finding is in agreement with a previous observation of a high level of IL-8 production in SK-N-MC neuroectodermal tumor cells stably transfected with the human RET gene and incubated with GDNF (Iwahashi et al., 2002). IL-8 was also present at high levels in SK-N-MC cell line expressing RETMEN2A mutant proteins, as well as in TT medullary thyroid carcinoma cells (Iwahashi et al., 2002) and PTC-1 papillary thyroid carcinoma cells (Iwahashi et al., 2002; Borrello et al., 2005). In particular, PTC-1 cells are characterized by the presence of activated forms of RET, a circumstance which suggests that RET activation mediates IL-8 production in these cells. The positive correlation between RET and IL-8 in the absence of any GDNF treatment can also be due to a direct effect of IL-8 on RET expression or to regulatory factors which mediate and modulate the expression of both genes. To test this hypothesis and to avoid the high variability of PBMCs, we analyzed the effect of IL-8 treatment on RET expression in THP1, a monocytic leukemia cell line. After 6 h, we did not observe any change in the levels of RET mRNA, but we found an increase of IL-8 mRNA transcripts. Though gene expression regulation induced by inflammatory stimuli is expected to be a very fast process, we cannot exclude that a prolonged IL-8 treatment would have given different results. Present data seem therefore to exclude a direct effect of IL-8 on RET expression and, more likely, both RET and IL-8 could share a similar response to different stimuli. In this light, it is to notice that the expression of both these genes is under the control of steroid hormones like estrogens (Freund et al., 2003; Stine et al., 2011). The effects of sex steroids on IL-8 expression seem to be complex and the existing data are conflicting. Indeed, while estrogens seem to have an inhibitory effect on IL8 production in epithelial cells, immune cells are stimulated to produce IL-8 upon estrogen treatment (Freund et al., 2003). The discovery that RET is an estrogen-regulated gene was achieved by investigating gene enhancers and by performing functional assays in breast cancer cell lines (Stine et al., 2011; Wang et al., 2012). Based on this evidence, as well as on the similar response to b-estradiol treatments we have observed in THP1 cells, a correlation between IL-8 and RET mRNA, likely due to a common estrogen-mediated regulatory mechanism, may be hypothesized. We can alternatively speculate that, as both these genes show NF-kB recognition sequences in their promoters and/or enhancers (http://genome.ucsc.edu/ENCODE), NF-kB may be a key upstream regulatory molecule inducing expression of both RET and IL-8 genes, which, in turn, would act in response to inflammatory stimuli. Consistent with this view, RET expression seems to be modulated during monocytes/ macrophages differentiation, being more expressed during the latest stage of cell maturation. Unfortunately, the high variability among individuals prevented us from deepening into the physiological role played by RET in the immune system. To gain more insights into such a question, and particularly to find out whether reported genomic variation at different loci could account for expression traits of RET receptor in immune cells, we looked at eQTL (expression Quantitative Trait Loci) data

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(www.genenetwork.org). Analysis of data reported for lymphoblastoid cells in CEPH families highlighted a strong correlation between RET and the IL-8 receptor, CXCR2 (IL8RA) (P ¼ 7.08E-04), thus confirming the relationship our data suggest between RET and IL-8. Furthermore, a correlation between the RET receptor and IL-22 (P < 1E-16) was also evident looking at the same data analysis. This latter cytokine is a member of IL-10 family, is produced by a wide variety of cells both of the adaptive and the innate immune systems (Cella et al., 2009; Cupedo et al., 2009; Martin et al., 2009) and has cell targets only non-hematopoietic stromal cells such as epithelial cells, keratinocytes, and hepatocytes. In particular, IL-22 is believed to strengthen the epithelial barrier and to play a key role in modulating tissue homeostasis, repair, and wound healing (Sonnenberg et al., 2011). Indeed, literature data demonstrate a direct link between IL-22 and IL-8 since glucocorticoid agents, such as dexamethasone, suppress the synthesis and the release of IL-22 and of IL-8 (Ziesche et al., 2009). Together with our findings, these evidence suggest that modulation of RET expression might be associated not only with IL-8 but also with other cytokines, such as IL-22 in different subsets of immune cells. Morever, since RET is principally involved in the development of the enteric nervous system and its ligand GDNF is mainly produced by glial cells in the gut and considering that, among other functions, IL-22 induces the production of IL-6 and IL-8 by intestinal epithelial cells (Andoh et al., 2005; Brand et al., 2006), it is conceivable to hypothesize a direct role of RET and its associated cytokine patterns in the pathogenesis of enterocolitis often associated with HSCR. In summary, the present study highlights the active role of RET in modulating immune responses and confirm the direct interactions between RET pathway and immune system inflammatory programs. Taking into account the contribution of the immune microenvironment and in particular the role of macrophages in tumor development, we think that our findings can also be useful in the study of RET-related cancers. Acknowledgements

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