Identification of genes potentially involved in supporting hematopoietic stem cell activity of stromal cell line MC3T3-G2/PA6

Although coculture of hematopoietic stem cells (HSCs) with stromal cells is a useful system to study hematopoiesis in the niche, little is known regarding the precise cellular and molecular mechanisms of maintaining HSCs through cell–cell interactions. The murine preadipose stromal cell line MC3T3-G2/PA6 (PA6) has been demonstrated to support HSCs in vitro. In this study, microarray analysis was performed on PA6 cells and HSC-nonsupporting PA6 subclone cells to identify genes responsible for supporting HSC activity. Comparison of gene expression profiles revealed that only 144 genes were down-regulated by more than twofold in PA6 subclone cells. Of these down-regulated genes, we selected 11 candidate genes and evaluated for the maintenance of HSC function by overexpressing these genes in PA6 subclone cells. One unknown gene, 1110007F12Rik (also named as Tmem140), which is predicted to encode an integral membrane protein, demonstrated a partial restoration of the defect in HSC-supporting activity.
Introduction
Hematopoietic stem cells (HSCs) are capable of self-renewal and multilineage differentiation and generate all types of blood cells throughout the lifetime. A major challenge for a long time has been the establishment of culture conditions that can facilitate ex vivo expansion of HSCs. To date, a large number of attempts using combinations of multiple cytokines have resulted in limited success, with a several-fold increase in HSC numbers under the optimal culture conditions [1, 2]. A major problem is that culture conditions established thus far have induced HSC proliferation coupled with differentiation, leading to a progressive loss of in vivo long-term repopulating potential [3–8]. Therefore, a better understanding of mechanisms that regulate HSC self-renewal and differentiation is required to achieve ex vivo expansion of HSCs.
In the adult bone marrow, HSCs are thought to reside in a specific microenvironment, referred to as the niche, which is composed of stromal cells that play a vital role in determining stem cell fate. Indeed, several stromal cell lines have been established not only from bone marrow but also from fetal liver and the aorta-gonad-mesonephros region and have been shown to maintain HSCs in vitro [9–16]. Coculture of HSCs with stromal cell lines is a useful system to study hematopoiesis in the niche. Although physical contact between HSCs and stromal cell lines has been demonstrated to be essential, little is known regarding the precise cellular and molecular mechanisms of maintaining HSCs by stromal cell lines.
MC3T3-G2/PA6 (PA6) is a preadipose stromal cell line derived from newborn mouse calvaria that can support long-term hematopoiesis in vitro [17]. In addition, PA6 subclones incapable of supporting the proliferation of HSCs have been isolated [18]. It has also been shown that the hematopoiesis-supporting ability of PA6 cells is not solely conferred by their expression of c-kit ligand, stem cell factor (SCF). In this study, microarray analysis was performed to identify genes responsible for supporting HSC activity of PA6 cells. Eleven genes specifically down-regulated in the subclone cells were selected and characterized by overexpressing the corresponding cDNAs in the subclone cells.
Materials and methods
Mice
C57BL/6 (B6-Ly5.2) mice were purchased from Charles River Laboratories Japan (Yokohama, Japan). C57BL/6 mice congenic for the Ly5 locus (B6-Ly5.1) were obtained from RIKEN BRC. B6-Ly5.1/Ly5.2 F1 mice were obtained from mating pairs of B6-Ly5.1 and B6-Ly5.2 mice. All animal experiments were approved by the Animal Experiment Committee of the RIKEN Tsukuba Institute.
Stromal cell culture and lentiviral transduction
The OP9 stromal cell line was obtained from RIKEN BRC Cell Bank. PA6 and OP9 cells were maintained in Minimum Essential Medium Eagle-α (MEM-α) (Sigma-Aldrich, St Louis, MO) containing 20% fetal bovine serum (FBS) (Sigma-Aldrich) at 37°C in a 5% CO2 atmosphere. FANTOM cDNA clones (kindly provided by Dr. Y. Hayashizaki and Dr. J. Kawai, RIKEN GSC) corresponding to the genes used in this study were subcloned into the lentiviral vector plasmid pCSII-EF-MCS-IRES2-Venus. Recombinant lentiviral vectors were produced as described previously [19]. PA6 subclone cells were transduced with lentiviral vectors expressing cDNAs at a multiplicity of infection of 200 and >90% of transduction efficiency was confirmed by fluorescence-activated cell sorting (FACS) analysis for Venus expression.
Purification of CD34−KSL cells and coculture with stromal cells
CD34−/lowc-Kit+Sca-1+lineage marker− (CD34−KSL) cells were purified as described previously with minor modifications [20]. Briefly, bone marrow cells isolated from 10- to 16-week-old B6-Ly5.2 mice were stained with a lineage marker antibody cocktail consisting of biotinylated anti-Gr-1, anti-Mac-1, anti-B220, anti-IgM, anti-CD4, anti-CD8, and anti-Ter119 antibodies (eBioscience, San Diego, CA). Lineage marker+ cells were depleted using streptavidin-coupled Dynabeads M-280 (Invitrogen, Carlsbad, CA). The remaining cells were stained with fluorescein isothiocyanate (FITC)-conjugated anti-CD34, phycoerythrin (PE)-conjugated anti-Sca-1, and allophycocyanin (APC)-conjugated anti-c-Kit antibodies (all from BD Biosciences, San Jose, CA). The biotinylated antibodies were developed with streptavidin-APC-Cy7 (BD Biosciences). FACS was performed with a FACSVantage SE (BD Biosciences). CD34−KSL cells were sorted into individual wells of a 96-well plate containing stromal cells (1 × 104 cells/well) irradiated with 1.5 Gy and were cocultured in 150 μL of MEM-α containing 20% FBS.
Colony-forming cell assay
After 10 days of coculture, CD34−KSL HSCs were collected and plated in a 12-well plate containing 0.6 mL of methylcellulose medium (MethoCult GF M3434) (StemCell Technologies, Vancouver, Canada) containing 50 ng/mL rmSCF, 10 ng/mL rmIL-3, 10 ng/mL rhIL-6, and 3 units/mL rhEPO. After 12 days of incubation, colonies were recovered, cytospun onto glass slides, and then subjected to Hemacolor (Merck KGaA, Darmstadt, Germany) staining for morphological examination. Colony-forming unit-granulocyte, erythrocyte, monocyte, megakaryocyte (CFU-GEMM), CFU-granulocyte, erythrocyte, monocyte (CFU-GEM), CFU-granulocyte, monocyte (CFU-GM), CFU-granulocyte (CFU-G), CFU-monocyte (CFU-M), and burst forming unit-erythrocyte (BFU-E) were scored using standard scoring criteria. The total colony number was expressed as colony-forming cell (CFC).
Competitive repopulation assay
The competitive repopulation assay was performed by using the congenic Ly5 mouse system as described previously [8]. CD34−KSL HSCs after coculture were mixed with 2 × 105 total bone marrow competitor cells from B6-Ly5.1 mice and transplanted into lethally (9.5 Gy) irradiated B6-Ly5.1 mice. At 12 to 16 weeks after transplantation, peripheral blood cells of the recipient mice were collected by retro-orbital bleeding. After lysis of red blood cells with ammonium chloride buffer, the remaining nucleated cells were stained with FITC-conjugated anti-Ly5.2, PE-conjugated anti-Ly5.1, biotinylated anti-Mac1, and biotinylated anti-Gr1 antibodies, followed by addition of streptavidin-PerCP. The cells were stained simultaneously with APC-conjugated anti-B220 antibody or a mixture of APC-conjugated anti-CD4 and anti-CD8 antibodies. FACS analysis was performed with a FACSCalibur. Donor chimerism was determined as the percentage of Ly5.2+ cells. When the percent chimerism was >1.0% for all myeloid, B-lymphoid, and T-lymphoid lineages, recipient mice were considered to be multilineage reconstituted.
Microarray analysis
Total RNA was isolated from stromal cells using the ISOGEN reagent (Nippon Gene, Tokyo, Japan) and purified with the RNeasy MinElute cleanup kit (Qiagen, Hilden, Germany). Biotin-labeled cRNA was prepared from 1 μg of the purified total RNA with a one-cycle cDNA synthesis kit and 3′-amplification reagents for IVT labeling (Affymetrix, Santa Clara, CA) and was hybridized to an Affymetrix Gene Chip Mouse Genome 430 2.0 array (Affymetrix), which contains approximately 45,000 probe sets for analyzing the expression levels of more than 34,000 mouse genes. After washing and staining with the antibody amplification procedure, the microarrays were scanned with an Affymetrix GeneChip Scanner 3000 7G. All these procedures were carried out according to the manufacturer’s instructions. The expression value (Signal) and detection call (Present (P), Absent (A), or Marginal (M)) for each probe set were calculated using GeneChip Operating Software version 1.4 (Affymetrix). The Signal values were normalized so that their mean in each experiment was 100 in order to adjust for minor differences between the experiments. The change value (Signal Log Ratio) and change call (Increase, Marginal Increase, No Change, Marginal Decrease, or Decrease) for each probe set were calculated by Comparison Analysis of the software. All experiments were performed in duplicate using two independent cell samples. To identify differentially expressed genes, we selected probe sets that showed a change call of Decrease and a Signal Log Ratio value of ≤–1 (more than twofold down-regulation) or a change call of Increase and a Signal Log Ratio value of ≥1 (more than twofold up-regulation) in each of the two independent experiments.
Quantitative real-time polymerase chain reaction
Total RNA was isolated from stromal cells using the ISOGEN reagent, and cDNA was synthesized from 1 μg of total RNA using SuperScript II reverse transcriptase (Invitrogen). Real-time polymerase chain reaction (PCR) amplification with two independent cell samples was carried out using the LightCycler FastStart DNA Master SYBR Green I kit (Roche, Penzberg, Germany) according to the manufacturer’s instructions. The primer sets used in this study are listed in Table 1. Data were normalized GAPDH mRNA levels. Gene-specific amplification was confirmed by determining the melting curves of the PCR products and by a single band of the expected size in agarose gel electrophoresis.
Table 1Primers used in quantitative real-time PCRGenePrimer sequenceProduct size (bp)1110007F12RikForward5′-GCCCTGTGCCTGATGTTCTAC-3′111 Reverse5′-GCCCATGTCCTCCTTCCAC-3′2900064A13RikForward5′-GTTTGACCCTGTCCGAGTCG-3′205 Reverse5′-CGGGAGAACCATCATCATAACC-3′Ccl2Forward5′-TTAAAAACCTGGATCGGAACCAA-3′121 Reverse5′-GCATTAGCTTCAGATTTACGGGT-3′Ccl9Forward5′-TCAGATTGCTGCCTGTCCTAT-3′117 Reverse5′-GAACCCCCTCTTGCTGATAAAG-3′Cxcl5Forward5′-TGCGTTGTGTTTGCTTAACCG-3′107 Reverse5′-AGCTATGACTTCCACCGTAGG-3′IL-1rnForward5′-GCTCATTGCTGGGTACTTACAA-3′132 Reverse5′-CCAGACTTGGCACAAGACAGG-3′IL-6Forward5′-TAGTCCTTCCTACCCCAATTTCC-3′76 Reverse5′-TTGGTCCTTAGCCACTCCTTC-3′
Results and discussion
Subclones of the PA6 cell line are defective in supporting HSCs
Although PA6 subclones 2, 12, and 14 have been isolated in terms of inability to support long-term hematopoiesis in vitro, their HSC-supporting capacity has not been precisely assessed [18]. Therefore, we evaluated the maintenance of HSC function after coculture with PA6 subclones 2 and 12 (hereinafter referred to as S-2 and S-12, respectively) by in vitro CFC and in vivo competitive repopulation assays using CD34−KSL cells as highly purified HSCs [20]. Murine bone marrow-derived OP9 stromal cell line that can support HSCs was also used as a control [13]. As shown in Table 2, the CFC frequency significantly decreased after 10 days of coculture with PA6 S-2 and S-12 cells compared with PA6 cells, and lower levels of engraftment were observed in transplanted mice. These results indicate that PA6 subclone cells have a substantial defect in supporting HSCs even after short-term coculture.
Table 2CFC frequency and competitive repopulation capacityStromal cellsCFC frequencyNo. of reconstituted mice (%)% ChimerismPA610.8 ± 7.516/9 (66.7)13.2 ± 19.9PA6 S-20.60 ± 0.97*4/10 (40.0)4.5 ± 11.2**PA6 S-123.2 ± 3.05*3/8 (37.5)3.4 ± 5.9**OP928.2 ± 6.615/9 (55.6)8.6 ± 11.5A total of 80 CD34−KSL HSCs (10 cells/well) were cocultured with stromal cells for 10 days and were then subjected to the CFC assay. The CFC frequency represents the colony number per ten input CD34−KSL HSCs. For the competitive repopulation assay, 30 CD34−KSL HSCs were cocultured with stromal cells for 10 days and then transplanted into lethally irradiated mice. At 12 weeks after transplantation, peripheral blood cells of the recipient mice were analyzed. Data represent the mean ± SD of two independent experiments*P < 0.01, **P > 0.2 versus PA6, Student’s t-test
Identification of genes that were specifically down-regulated in PA6 subclone cells
We hypothesized that genes responsible for supporting HSCs would be down-regulated in PA6 subclone cells. To identify genes specifically down-regulated in PA6 subclone cells, microarray analysis was performed on PA6 cells, PA6 S-2 and S-12 cells, and OP9 cells using an Affymetrix GeneChip array containing approximately 45,000 probe sets representing over 34,000 mouse genes. As expected, gene expression profiling showed a few differences in gene expression between PA6 cells and PA6 subclone cells. Compared to PA6 cells, only 144 genes were down-regulated by more than twofold in both PA6 subclone cells. Of these down-regulated genes, 41 genes were of unknown function, and 67 of 103 (65%) known genes encoded membrane and extracellular space proteins according to the Gene Ontology cellular component classification. On the other hand, 121 genes were up-regulated by more than twofold and 30 of 67 (45%) known genes encoded membrane and extracellular space proteins. The signal values and detection calls for this analysis are provided in supplemental data. Among the down-regulated genes, eight genes of known/predicted function including chemokine- and cytokine-related genes, which were expected to effect on the HSC-supporting activity, and three genes of unknown function, which were predicted to encode membrane proteins, were selected for further analysis (Table 3).
Table 3List of candidate genes down-regulated in PA6 subclone cells compared with PA6 cellsGene symbolGene titleRefseq IDMicroarrayqPCRvs. PA6vs. OP9vs. PA6vs. OP9PA6 S-2PA6 S-12PA6 S-2PA6 S-12PA6 S-2PA6 S-12PA6 S-2PA6 S-121110007F12RikRIKEN cDNA 1110007F12 geneNM_1979860.300.460.110.190.290.230.160.131200009O22RikRIKEN cDNA 1200009O22 geneNM_0258170.300.212.791.48NDNDNDND2900064A13RikRIKEN cDNA 2900064A13 geneNM_1337490.410.371.231.160.290.330.480.55Ccl2Chemokine (C-C motif) ligand 2NM_0113330.090.130.040.060.160.070.090.04Ccl9Chemokine (C-C motif) ligand 9NM_0113380.140.190.030.040.100.090.040.03Cd53CD53 antigenNM_0076510.230.150.280.14NDNDNDNDCxcl5Chemokine (C-X-C motif) ligand 5NM_0091410.330.230.430.380.200.130.900.60HgfHepatocyte growth factorNM_0104270.410.410.850.88NDNDNDNDIl1rn(IL-1rn)Interleukin 1 receptor antagonistNM_0311670.090.120.260.31<0.030.04<1.251.45Il6(IL-6)Interleukin 6NM_0311680.380.442.072.620.300.162.421.31Ppap2bPhosphatidic acid phosphatase type 2BNM_0805550.220.220.090.09NDNDNDNDRelative expression levels in PA6 subclone cells (S-2 and S-14) compared with PA6 and OP9 cells were calculated using data from microarray and quantitative real-time PCR (qPCR) analyses. Data represent the average of fold changes from two independent experimentsND not determined
Characterization of candidate genes
To determine the requirement of candidate genes for the supportive activity of PA6 cells, PA6 S-2 cells were transduced with lentiviral vectors containing the corresponding cDNAs. In all experiments, the transduction efficiency was more than 90% as determined by FACS analysis for Venus-positive cells. CD34−KSL HSCs were cocultured with PA6 S-2 cells expressing candidate genes for 10 days and then subjected to the CFC assay. As shown in Fig. 1, the CFC frequency increased when cocultured with PA6 S-2 cells overexpressing the candidate genes except Hgf compared with PA6 S-2 cells. Morphological evaluation of the colonies revealed that the number of CFU-GEMM, which is the most primitive colony type observed in the CFC assay, increased except Ccl2 and Ccl9. Of note, although the overexpression of IL-6 increased the number of colonies to the same level as PA6 cells, a large proportion (∼80%) of colonies were CFU-M.
Fig. 1Effect of over expression of candidate genes on the CFC frequency. A total of 80 CD34−KSL HSCs (10 cells/well) were cocultured with PA6 cells, PA6 S-2 cells, or PA6 S-2 cells expressing the indicated genes for 10 days and then subjected to the CFC assay. Individual colonies were scored according to their morphology. Data represent the average of two independent experiments
We next performed the competitive repopulation assay to determine the effect of overexpressing seven candidate genes including Ccl9, IL-6, Ppap2b, and Cxcl5, and three unknown genes. As shown in Table 4, successful multilineage reconstitution was observed in all mice transplanted with HSCs cocultured with PA6 S-2 cells overexpressing 1200009O22Rik or IL-6 as well as with PA6 cells. However, comparison of the chimerism showed no significant differences from that of PA6 S-2 cells, suggesting that the overexpression of neither 1200009O22Rik nor IL-6 completely restored the defective HSC-supporting activity of PA6 S-2 cells. Microarray analysis showed that the expression of IL-6 was approximately twofold up-regulated in PA6 S-2 and S-12 cells when compared with OP9 cells; this finding was confirmed by quantitative real-time PCR analysis (Table 3). It has also been demonstrated that IL-6 alone or in combination with SCF is incapable of maintaining CD34−KSL HSCs [8]. Although IL-6 is involved in various steps of hematopoiesis and has been used for the ex vivo expansion of HSCs [21], IL-6 may not be crucial for the maintenance of HSCs governed by stromal cells.
Table 4Effect of overexpression of candidate genes on the competitive repopulation capacityNo. of reconstituted mice (%)% ChimerismTotalMyeloidB-lymphoidT-lymphoidPA69/9 (100)22.1 ± 25.130.9 ± 30.925.1 ± 26.517.4 ± 23.0PA6 S-24/10 (40)7.3 ± 14.04.0 ± 7.410.8 ± 18.89.2 ± 17.81110007F12Rik7/10 (70)16.2 ± 14.8*21.8 ± 26.318.7 ± 16.915.5 ± 16.71200009O22Rik8/8 (100)8.2 ± 10.214.5 ± 12.29.6 ± 13.48.3 ± 11.32900064A13Rik2/5 (40)3.3 ± 4.610.5 ± 20.63.6 ± 5.41.0 ± 2.2Ccl92/5 (40)11.1 ± 15.014.2 ± 19.512.7 ± 17.611.4 ± 15.7IL-69/9 (100)8.5 ± 13.58.1 ± 7.111.6 ± 16.99.2 ± 16.3Ppap2b8/10 (80)9.7 ± 10.420.5 ± 26.79.2 ± 9.79.6 ± 14.6Cxcl53/5 (60)3.0 ± 4.012.8 ± 23.62.5 ± 3.51.9 ± 1.8Thirty CD34−KSL HSCs were cocultured with PA6 cells, PA6 S-2 cells, or PA6 S-2 cells expressing the indicated genes for 10 days and then transplanted into lethally irradiated mice. At 16 weeks after transplantation, peripheral blood cells of the recipient mice were analyzed. Data represent the mean ± SD*P = 0.18 versus PA6 S-2, Student’s t-test
PA6 S-2 cells overexpressing 2900064A13Rik or CCl9 had no apparent effect on the HSC-supporting activity. In contrast, the overexpression of 1110007F12Rik, Ppap2b, or Cxcl5 resulted in a certain increase in the frequency of reconstituted mice. Although these genes as well as 1200009O22Rik and IL-6 could significantly increase the CFC frequency, the effect on the HSC-supporting activity assessed by the competitive repopulation assay was limited. This may be because 10 days of coculture was not long enough to detect a significant difference in HSC-supporting activity. It is of course well known that many growth factors and cytokines that can increase the CFC frequency have little influence on the maintenance and proliferation of HSCs. Of note, the overexpression of 1110007F12Rik showed a substantial increase in the chimerism, although the difference did not reach statistical significance (Table 4). To confirm the effect of 1110007F12Rik expression on the supportive activity of PA6 cells, we performed the following experiments. First, down-regulation of 1110007F12Rik expression in PA6 S-2 and S-12 cells compared to that in PA6 and OP9 cells was confirmed by quantitative real-time PCR analysis (Table 3). Next, PA6 S-2 cells were transduced with a myc-tagged 1110007F12Rik expression lentiviral vector and its expression was detected by Western blotting and immunofluorescence staining with anti-myc antibody. Coculture with PA6 S-2 cells overexpressing myc-tagged 1110007F12Rik resulted in an increase in the CFC frequency at a level similar to that shown in Fig. 1 (data not shown). The 1110007F12Rik cDNA encodes a predicted protein of 185 amino acids, which is rich in leucine (23%) and has no significant homology with any known protein sequences. 1110007F12Rik is also named as Tmem140 (transmembrane protein 140), which is predicted to be an integral membrane protein gene according to the Gene Ontology classification. Further studies are required to determine whether 1110007F12Rik is involved in the HSC-supporting activity of PA6 cells.
Thus far, a large number of microarray analyses of stromal cell lines derived from a variety of tissue types have been reported. Comparison of gene expression profiles between HSC-supporting and HSC-nonsupporting stromal cell lines has resulted in the identification of a number of differentially expressed genes [22]. In the present study, we expected a few differences in gene expression by comparing HSC-supporting PA6 cells with HSC-nonsupporting PA6 subclone cells and indeed, we could narrow down the candidate genes involved in maintaining HSCs. Although one of the analyzed candidate genes, 1110007F12Rik, demonstrated a partial restoration of the defect in HSC-supporting activity of PA6 S-2 cells, it is possible that other down-regulated genes in PA6 subclone cells may also be required for the maintenance of HSCs. Continuing studies to identify genes responsible for HSC-supporting activity of PA6 cells would facilitate the understanding of the mechanisms that control stem cell fate through interaction with stromal cells.
Electronic supplementary material
Below is the link to the Supplemental data.
Electronic supplementary material (XLS 388 kb)

hematopoietic stem cells
stromal cells
mc3t3-g2/pa6
coculture
microarray


Stromal mast cells in invasive breast cancer are a marker of favourable prognosis: a study of 4,444 cases

Purpose We have previously demonstrated in a pilot study of 348 invasive breast cancers that mast cell (MC) infiltrates within primary breast cancers are associated with a good prognosis. Our aim was to verify this finding in a larger cohort of invasive breast cancer patients and examine the relationship between the presence of MCs and other clinical and pathological features.
Introduction
Mast cells (MCs) are part of the innate immune system and are recruited to and activated in the microenvironment of a developing tumor. MCs originate from multipotential hemopoietic stem cells in the bone marrow, and express the proto-oncogene c-kit, a transmembrane type III tyrosine kinase receptor protein [1]. After leaving the vascular bed they differentiate and acquire functional maturity.
MC infiltrates have been described in a variety of human cancers, including non-small-cell lung cancer [2–4], breast cancer [5], colorectal cancer [6], basal cell carcinoma [7] and pulmonary adenocarcinoma [8]. MCs are attracted to the tumor by tumor-derived chemo attractants where they either degranulate to release potential tumor cytotoxic compounds or become innocent bystanders depending on local tumor conditions [9]. There is controversy about the pro- and anti-tumorigenic effects of MCs in different cancers. The accumulation of MCs has been associated with enhanced growth and invasion of several human cancers [10]. On the other hand, MC infiltration has been associated with good prognosis in breast [11, 12] ovarian [13], lung [14] and colorectal [15] carcinomas.
Murine models have shown that tumor incidence and growth are inversely correlated with the MC density in MC-deficient mice compared to normal [16].
Our previously published pilot report of 348 case series of breast carcinomas showed that the presence of stromal MCs correlated with a good prognosis (P = 0.0036) in invasive breast cancer [11].We present here a large tissue microarray (TMA) study of 4,444 cases of invasive breast carcinomas with clinical outcome data. This study validates the independent prognostic significance of stromal MCs in breast cancer.
Materials and methods
Patient selection
A total of 4,620 archival samples from patients with invasive breast carcinoma referred to the British Columbia Cancer Agency between January 1986 and September 1992 were used for TMA construction. The Clinical Research Ethics Board of the University of British Columbia approved the study. Patients with in-situ disease, metastatic disease at presentation, and male breast cancer were excluded from analysis, thus bringing the final tally to 4,444 cases. This represents 34% of all patients diagnosed with breast cancer in the province of British Columbia during this time period. This large, well characterized cohort is derived from a consecutive series of patients who were referred to the BC Cancer Agency for consultation and had tumor samples sent to a central laboratory at the Vancouver General Hospital for estrogen receptor (ER) status. Consequently, for all of these patients we have available detailed demographic and outcome data, as well as formalin fixed paraffin embedded (FFPE) primary tumor samples for immunohistochemical analysis. Available clinical information includes age, histology, tumor grade, tumor size, lymph node status, type of local and adjuvant systemic therapy, and dates of first recurrence and death. Clinical and pathological variables were determined following well-established criteria. A portion of this cohort of patients was recently used in a population study validating the on-line breast cancer prognostic calculator ADJUVANT! Online [17].
TMA construction
The Vancouver Hospital ER laboratory retained single archival tumor blocks from each case in this patient cohort. The material had been frozen prior to neutral buffered formalin fixation. All the paraffin sections were first stained with H&E and reviewed by a pathologist. Representative tumor areas were circled and matched with the donor blocks. 0.6-mm cores were punched out from the donor blocks and embedded 1 mm apart in 17 recipient blocks using Tissue Microarrayer (Beecher instruments Silver Springs, MD). These tissue arrays have been used to test the new ER SP1 antibody that improves the sensitivity for detecting ER by IHC [18].
Immunohistochemistry (IHC)
Sections from TMA were cut at 4 μm and immunostained with antibody to KIT (CD117), a transmembrane tyrosine kinase acting as a type III receptor for MC growth factor. Slides were also concurrently stained for ER and Her2 using standard immunoperoxidase techniques. The antibodies and antigen retrieval methods are summarized in Table 1.
Table 1Details of antibodies used for immunohistochemistryAntibodyIsotype CompanyAntigen retrievalConcentrationc-kitRabbit polyclonalDakoVentana1:100Her2 SP3Rabbit monoclonalLab visionSteam 30 min, 0.05 M TRIS buffer (pH 10)1:100ER SP1Rabbit monoclonalLab visionCitrate buffer (pH 6)1:250
Immunohistochemical image processing and scoring
The stained slides were digitally scanned with a BLISS automated digital imaging microscope (Bacus Laboratories, Lombard, IL) which consists of a microscope with a scanning stage, video camera and software designed for scanning TMAs. A relational database was constructed using identification information and immunohistochemistry scores for each tissue core in the microarrays. An internet website was then constructed using this database and a WebSlideViewer Java applet provided by the manufacturer to view the microarray images and allow for an image zooming functionality. This website is publicly accessible through http://www.gpecimage.ubc.ca/tma/web/viewer.php.
The slides were scored manually by two independent pathologists, blinded to the clinical outcome, as previously described [11]. Total number of stained MCs in each core was recorded. ER status was assessed using the rabbit monoclonal antibody (SP1) antibody [18]. The fractions of ER positive tumor nuclei were scored as 0 (<1%), 1 (1–25%), 2 (25–75%), and 3 (>75%). Her2 SP3 rabbit monoclonal from LabVision (NeoMarkers) was used to stain the TMA slides and were scored using Hercept test® (Dako Corporation, Carpinteria, CA) scoring system. Final Her2 score was derived using both IHC and Fluorescent in situ hybridization (FISH) assays. Cases with Her2 IHC Herceptest score = 3, were scored as positive. Those cases with Her2 IHC Herceptest score = 2 were re-evaluated using FISH assay, and only those cases with Her2 FISH amplification ratio ≥2.0 were scored as Her2 positive. C-Kit and ER scores were binarized for statistical analysis as follows: CD-117: 0 = no MCs; 1 = any MCs; ER: 0 ≤ 1% nuclei stained; 1 ≥ 1% nuclei stained. We excluded cases for which it was not possible to assign a score to the immunostaining (insufficient invasive tumor in the core, or missing core).
Statistical analysis
The raw scores were entered into an Excel database and each TMA core was assigned a unique core ID number. The spreadsheet was then processed utilizing TMA-Deconvoluter 1.06 software that had been adapted for TMA analysis [19]. A database was created by incorporation of all deconvoluted marker data into the clinicopathologic patient database.
We applied a split-sample validation technique for our statistical analysis. Our 4,444 patient cohort was stratified into eight subgroups (Table 2) based on adjuvant treatment received and then randomized into equal sized training and validation sets. The two groups were balanced with respect to treatment received; but there are also no significant differences in clinical or pathological variables, including age, tumor size and grade, nodal status, and ER status. For this study, and future studies using this TMA, the primary investigator is given access to all clinical, outcome, and TMA data from the training set only. The training set is used to generate and refine hypotheses regarding the biomarker under study. Significant findings are then formally presented at a bimonthly joint scientific group meeting of the Genetic Pathology Evaluation Centre (GPEC, a collaborative group of scientists and pathologists) and Breast Cancer Outcomes Unit (BCOU, a group of oncologists and epidemiologists based at the BC Cancer Agency). Those findings considered to be of clinical and scientific interest are then re-tested on the validation set. A separate researcher who did not participate in the training set analysis performs the re-testing on the validation set. Our statistical approach is intended to minimize false positive results, particularly with subgroup analysis.
Table 2Summary of clinical-pathological characteristics of the 4,444 breast cancer patientsTest setValidation setn% within group% within known valuesn% within group% within known valuesTotal2,2222,222Age at diagnosis (years)Median (range)60 (25–95)60 (23–91)<401707.77.71577.17.140–5445720.620.646821.121.155–6970231.631.680936.436.4≥7089340.240.278835.535.5GenderFemale2,21199.599.52,21099.599.5Male110.50.5100.50.5Nodal statusNegative1,27257.257.41,25656.556.8Positive94342.442.695743.143.2Unknown70.390.4Number of positive nodesMedian (range)2 (1–24)2 (1–28)1–358662.164.361464.266.24–924325.826.722923.924.7≥10828.79.0848.89.1Unknown323.4303.1ER Status at diagnosisNegative47221.221.847221.221.9Positive1,68976.078.21,68775.978.1Unknown612.7632.8Tumor size (cm)Median (range)2.0 (0.1–9.9)2.0 (0.1–9.9)0.1–1.026511.912.128512.813.01.1–2.086438.939.486438.939.52.1–5.094942.743.291541.241.8>5.01175.35.31265.75.8Unknown271.2321.4Tumor grade11255.66.01034.64.9287639.442.084438.040.231,08348.752.01,15351.954.9Unknown1386.21225.5
Statistical analysis was performed using SPSS 14.0. In univariate analysis, breast-cancer specific survival (BCSS) was estimated using Kaplan–Meier (KM) curves, and significant differences determined by Log rank tests. For BCSS, survival time was censored at the date of death if the cause was not breast cancer or at the date of last follow-up if the patient was still alive at the end of the study period. 6 patients with unknown cause of death were excluded from BCSS analysis. Cox proportional hazards models were used to calculate adjusted hazard ratios accounting for covariates. Kendall’s tau-b and the Mann-Whitney tests were used to measure the correlation of c-kit status to pathological variables. All statistical tests were two-sided. The Bonferroni correction for multiple comparisons was applied during validation set analysis, and the alpha value for each comparison is 0.05/n, where n is the total number of comparisons.
X-tile analysis
We also used X-tile software [20] to find the optimal cut-off point for the total number of MCs that will predict prognosis in breast cancer patients. X-tile program split the cohort randomly into a matched training and validation set as a method for selecting optimal cut-points. It than calculated a P value for every possible division of the cohort expression data. A two-dimensional graph with its corresponding survival curves was plotted where each colored pixel was proportional to is χ2 Value. The program automatically calculated the maximum χ2 value which served as a cut-point to separate the number of MCs that predicted prognosis.
Results
Out of 4,620 cases on the TMAs, we selected 4,444 breast cancers that showed invasive tumor in the cores. The clinico-pathologic characteristics of patients included in the study are depicted in Table 2. The total number of stained MCs was recorded as a continuous variable with counts ranging from 0 to 24 MCs per core. They were seen as 4–20 μm round to oval mononuclear cells with granular cytoplasm and single oval nucleus. The cytoplasmic granules were ganglion-, net-, or crystal-shaped (Fig. 1).
Fig. 1TMA core showing stromal mast cells stained with c-kit (CD-117). Magnification, 20×. MCs are seen as brown, granular stained oval, spindle or polygonal cells
Training set results
Survival analysis
A total of 2,222 patients were included in the training set analysis. After excluding cases which had insufficient invasive tumor, missing core or un-interpretable staining pattern, 1,801 cases were carried forward for the analysis. Out of these, MCs were present in 508 (28.2%) cases. The mean survival time of patients with presence of stromal MCs was 15.0 years (95% CI, 14.5–15.5) compared to 13.9 years (95% CI, 13.5–14.2) for those who did not have positively stained MCs in their tumor stroma. KM survival analysis (Fig. 2a) showed that the presence of stromal MCs was a favourable prognostic marker in the entire training set (BCSS @ 18.4 years, Log rank [Mantel Cox], P = 0.001).
Fig. 2KM survival curve for all patients in training set (a) and validation set (b) with presence of stromal mast cells
Correlation with other biomarkers
There was positive correlation between MCs and ER (Kendall’s tau-b [τb], 0.034, P = 0.148), Bcl2 (τb = 0.077, P = 0.002), and Her2 (τb = 0.049, P = 0.052), and negative correlation between MCs and EGFR (τb = −0.029, P = 0.228) and CK5/6 (τb = −0.003, P = 0.906) in the training set analysis (Table 3). As these correlations were either not significant or extremely weak, they were not carried forward to the validation set for further analysis.
Table 3Correlations between mast cells and other biomarkersTraining setMast cellsERKendall’s tau-b0.034Significance (2-tailed)0.148N1,788EGFRKendall’s tau-b−0.029Significance (2-tailed)0.228N1,646Her2Kendall’s tau-b0.049Significance (2-tailed)0.052N1,746CK5/6Kendall’s tau-b−0.003Significance (2-tailed)0.906N1,624Bcl2Kendall’s tau-b0.077Significance (2-tailed)0.002N1,616N—scorable for both markersAll scores binarized as detailed in the text
Nodal status
KM survival analysis showed no statistically significant difference in the survival between tumors with and without MCs in node-negative (BCSS @ 18.1 years, Log rank [Mantel Cox], P = 0.1199) and a significant difference in the node-positive group (BCSS @ 18.3 years, Log rank [Mantel Cox], P = 0.0140). Hence, this result was also not carried forward to the validation set.
Multivariate analysis
Cox proportional hazard model was used to carry out the multivariate analysis and included age, tumor grade, tumor size, nodal status, ER and Her2 as independent predictors of BCSS. All the above variables achieved statistical significance as shown in Table 4(a). Presence of MCs achieved statistical significance (P = 0.041) with a HR = 0.804, 95% CI 0.653–0.991.
Table 4Cox proportional hazard regression analysis showing hazard ratios and P-values in patients with invasive breast carcinomaSignificanceHRBCSS 95% CI for HRLowerUpper(a) Training setMast cells0.0410.8040.6530.991ER0.0180.7770.6310.957Her20.0031.4391.1291.834Size of the lesion    2–5 cm vs. ≤2 cm0.0001.8841.5472.295    >5 cm vs. ≤2 cm0.0011.9311.3302.803Grade*    Grade 1, 2 vs. Grade 3 0.0011.3901.1391.696Nodal status0.0002.3801.9652.882Age    40–49 vs. <400.0000.5670.4200.765    50–65 vs. <400.0130.6890.5130.924    >65 vs. <400.0190.6770.4890.938(b) Validation setMast cells0.1280.8460.6831.049ER0.02890.7930 .6440 .976Her20.00181.4631.1521.859Size of the lesion    2−5 cm vs. ≤2 cm3.07 × 10−41.4391.1811.753    >5 cm vs. ≤2 cm1.66 × 10−62.2581.6183.151Grade*    Grade 1, 2 vs. Grade 3 1.96 × 10−61.6581.3462.042Nodal status7.06 × 10−192.4101.9842.927Age    40–49 vs. <400.4321.1580.8031.671    50–65 vs. <400.6341.0900.7651.552    >65 vs. <400.071.3940.9731.998* Grade 1—well differentiatedGrade 2—moderately differentiatedGrade 3—poorly differentiatedHR = adjusted hazard ratio, CI = confidence interval and BCSS = Breast Cancer Specific Survival
Validation set results
Survival analysis
This group included the remaining 2,222 patients from the whole cohort. The mean age at diagnosis was 60 years and the median follow-up was 12.4 years. The median tumor size was 2.0 cm. 50% of patients had Grade 3 tumors, 43% were node positive, and 76% were ER positive. After excluding cases that had insufficient invasive tumor, missing core or un-interpretable staining pattern, 1,796 cases were carried forward for the analysis. Out of these, MCs were present in 494 (27.5%) cases. KM survival analysis (Fig. 2b) showed that the presence of stromal MCs was a favourable prognostic marker in the validation set (P = 0.006).
Multivariate analysis
Cox proportional hazard model was used to carry out the multivariate analysis and included age, tumor grade, tumor size, nodal status, ER and Her2 as independent predictors of BCSS. All the above variables achieved statistical significance as shown in Table 4(b). Presence of MCs did not achieve statistical significance (P = 0.128) but retained a similar HR = 0.846, 95% CI 0.683–1.049 as in the training set analysis.
X-tile analysis results
We assessed the association between patient outcome and number of positively stained MCs in the stroma of tumors using X-tile software. This software allowed us to define an optimal cut-off point that defined the number of MCs needed to predict good prognosis in the cancer patients. The program divided the population into low and high-risk groups based on the number of MCs detected. It converted continuous data into ordinal classes for statistical analysis. The X-tile plot showed that breast cancer patients with any number of stromal MCs had better prognosis compared to those who have no MC infiltration in their stroma. MCs ranging between 1 and 22 in the tumor stroma were present in 507 (27.61%) patients and were grouped together in the low population group of the training set. The high population group comprised of 1,329 (72.39%) patients in the training set. It reiterated the findings obtained by KM survival analysis that the presence of any number of stromal MCs predicts good prognosis in invasive breast cancer patients.
Discussion
The importance of the reciprocal relationship between tumor and stroma is being increasingly recognized [21], and is the role of stromal inflammatory cells like MCs, macrophages, fibroblasts and T cell subtypes in cancer initiation and progression. cDNA microarray analysis has shown that genes expressed by stromal cells correlate with differences in the biology of the tumors and are prognostic predictors in breast cancer [22].
There is an ongoing debate about possible detrimental or beneficial effects of MC accumulation in the stroma of solid tumors. In-vitro studies have shown that MC inhibits tumor growth [23]. This is in agreement with other studies showing increase in MC count in early stage of non-small cell lung cancer and thereby supporting their anti-tumor role. [3]. Increased islet/stromal MC-ratio and presence of tumor islet MCs were shown to be independent good prognostic indicators in non-small-cell lung cancer [24]. Recent studies in ovarian cancer [13] and colorectal cancer [15] have also confirmed their correlation with prognosis.
MCs secrete factors like heparin, interleukin-8 (IL-8) and vascular endothelial growth factor that promote neovascularization; histamine which suppresses immune response; platelet-derived growth factor, nerve growth factor and stem-cell factors which are mitogenic; and proteases that promote metastasis [9]. On the other hand, MCs can inhibit tumor growth by releasing endogenous peroxidase that is cytotoxic to mammalian tumor cells [25], by natural cytotoxicity [21] and by recruiting neutrophils, eosinophils, lymphocytes and macrophages [26]. MCs can secrete protective substances without degranulation in the presence of tumor-derived blockers like oxidized polyamines and thus be detrimental to the tumor [27]. They can destroy tumor cell surface structures directly and indirectly in a fashion similar to the effect of arginase [28]. MCs can also inhibit tumor growth by secreting beneficial cytokines like IL-1, IL-4, IL-6, and tumor necrosis factor-α that induce apoptosis of endothelial cells [29]; and chondroitin sulphate which inhibits metastases [30]. Cathepsin G secreted by MC activates platelets, lymphocytes and macrophages, and is known to be cytotoxic to some mammalian cells [31]. Its inhibition by a tumor cell product was implicated in the progression of advanced squamous cell tumors [32] suggesting its cytotoxic capability against target cells. MCs secrete chymase, which stimulates apoptosis in different target cells [33] and inhibits angiogenesis by exerting cytotoxic effect on vascular smooth muscles [34]. MC tryptase stimulates inflammation and recruits fibroblasts leading to tumor fibrosis, and thereby limiting tumor growth and metastasis [35]. Tryptase, however, is also implicated in angiogenesis and its effect correlated with a better response to chemotherapy in ovarian cancer. This suggests that MC tryptase-mediated fibrosis and angiogenesis may be responsible for the good prognostic effect seen in advanced ovarian cancer [13].
In this TMA study, we showed that the presence of even one or more MCs in the tumor microenvironment was sufficient to exert a positive prognostic effect. Our finding supports the idea of assessing inflammatory cell infiltrates as prognostic markers in cancer. In our previously published pilot report, we had observed no correlation between the presence of stromal MCs with either B-cells (CD20-positive) or T-cells (CD3-positive) [11], and hence we did not stain for these inflammatory cells in this study. We also did not find a statistically significant prognostic effects of MCs in the node-negative group in the training set analysis. This differs from our earlier published report [11] on a 348 case series which concluded that the presence of MCs was a favourable prognostic factor in the node-negative patients (P = 0.018) but not the node-positive group (P = 0.384). This finding underscores the importance of validating clinically relevant findings on a larger series of patients and using the test-validation approach to arrive at a meaningful conclusion.
A number of studies have elaborated on the role of immune cells in cancer etiopathogenesis. T regulatory (Treg) cells are known to be associated with poor outcome in ovarian cancer [36] and the initial results of a clinical trial aimed at specifically eliminating Treg cells has shown promising results [37]. Lymphoma-associated macrophages have been used to predict the outcome of follicular lymphoma [38] and reactive macrophages are used to predict breast, prostate, ovarian, and cervical cancer outcome [39]. Tumor-infiltrating neutrophils are used to predict outcome in patients with adenocarcinoma of the bronchioloalveolar carcinoma subtype [40] and gastric carcinoma [41]. In a recent report, it was concluded that the type, density, and location of immune cells within the tumor samples are a better predictor of patient survival than the histopathological methods currently used to stage colorectal cancer [42].
In conclusion, we confirm our earlier pilot study findings and confirm that stromal MCs correlate with a good prognosis in a large cohort of 4,444 invasive breast cancer patients with long-term follow-up. It highlights the critical role that the host stromal reaction, in particular the inflammatory cell infiltrate, plays in modulating cancer progression. MCs can be used as markers for risk stratification in invasive breast cancers.

stroma
mast cells
breast cancer
inflammatory infiltrate


The SPINK gene family and celiac disease susceptibility

The gene family of serine protease inhibitors of the Kazal type (SPINK) are functional and positional candidate genes for celiac disease (CD). Our aim was to assess the gut mucosal gene expression and genetic association of SPINK1, -2, -4, and -5 in the Dutch CD population. Gene expression was determined for all four SPINK genes by quantitative reverse-transcription polymerase chain reaction in duodenal biopsy samples from untreated (n = 15) and diet-treated patients (n = 31) and controls (n = 16). Genetic association of the four SPINK genes was tested within a total of 18 haplotype tagging SNPs, one coding SNP, 310 patients, and 180 controls. The SPINK4 study cohort was further expanded to include 479 CD cases and 540 controls. SPINK4 DNA sequence analysis was performed on six members of a multigeneration CD family to detect possible point mutations or deletions. SPINK4 showed differential gene expression, which was at its highest in untreated patients and dropped sharply upon commencement of a gluten-free diet. Genetic association tests for all four SPINK genes were negative, including SPINK4 in the extended case/control cohort. No SPINK4 mutations or deletions were observed in the multigeneration CD family with linkage to chromosome 9p21-13 nor was the coding SNP disease-specific. SPINK4 exhibits CD pathology-related differential gene expression, likely derived from altered goblet cell activity. All of the four SPINK genes tested do not contribute to the genetic risk for CD in the Dutch population.
Introduction
Celiac disease (CD) is a chronic inflammatory condition of the small intestine because of an immunological intolerance for the food protein gluten. Patients have to adhere to a life-long diet devoid of gluten to prevent the detrimental effects of a prolonged nutrient and mineral deficiency (Green and Jabri 2003). Susceptibility for CD is predominantly determined by genetic factors, and the complex inheritance patterns suggest the interaction of multiple genes (van Heel et al. 2005). It is well established that the adaptive immune response to gluten plays a pivotal role in the pathogenesis of CD. Th1 activation of CD4+ T cells follows gluten-peptide presentation by DQ2 or DQ8 molecules expressed on antigen-presenting cells (Sollid 2002). The HLA-DQA and -DQB gene variants coding for these molecules are the major genetic determinants for CD susceptibility (Koning et al. 2005). Recently, the importance of innate immunity in CD pathogenesis was also underscored by the observation of induced IL15 expression and NKT cell chemotaxis through the MICA and NKG2D molecules (Hue et al. 2004; Meresse et al. 2004). However, no genetic contribution of the cognate genes has been demonstrated. The notion of crosstalk between the adaptive and innate immune systems is not limited to CD and gets much attention in studies of the inflammatory process (Hoebe et al. 2004). This raises the question whether some aspects of innate immunity may contribute to the genetic susceptibility for CD. The innate immune system uses a wide array of defense mechanisms against the invasion of pathogens. These encompass the expression of pattern recognition receptors, release of antimicrobial molecules, and preservation of epithelial barrier and tissue integrity by, e.g., serine protease inhibitors (Kimbrell and Beutler 2001).
One branch of the family of serine protease inhibitors is that of the Kazal type (SPINK) that originally consisted of four members in humans (SPINK1, SPINK2, SPINK4, and SPINK5). Recently, as part of a cluster of SPINK genes on chromosome 5q32 that already included SPINK1 and SPINK5, five new SPINK(-like) members were identified that were located more distally: SPINK5L2, SPINK6, SPINK5L3, SPINK7, and SPINK9, respectively (NCBI Map Viewer, build 36.1). However, these new members lack functional annotation and were therefore not included in this study. SPINK family members 1, 2, and 4 have a comparable size and structure coded for by 4 exons with a single Kazal type serine protease inhibitor domain. SPINK5, in contrast, contains 33 exons that encode 15 inhibitory domains. All four SPINK members are thought to be involved in the protection against proteolytic degradation of epithelial and mucosal tissues, although their major site of expression may differ. SPINK1 is expressed in the pancreas and the gastrointestinal tract, and mutations in this gene are reported in various forms of pancreatitis (Pfutzer and Whitcomb 2001). SPINK2 (located on 4q12) is expressed in the testis, epididymis, and seminal vesicle, where its antimicrobial function may be involved in protection of fertility (Rockett et al. 2004). SPINK4 was originally isolated from pig intestine (Agerberth et al. 1989) and is abundantly expressed in human and porcine goblet cells in the crypts of Lieberkühn but was also found in monocytes and in the central nervous system (Metsis et al. 1992; Norberg et al. 2003). SPINK5 is expressed in the thymus, vaginal epithelium, Bartolin’s glands, oral mucosa, tonsils, and the parathyroid glands (Magert et al. 1999). Mutations in SPINK5 are responsible for the Netherton syndrome, a lethal skin disorder characterized by ichthyosis, hair shaft defects, atopy, skin barrier defects, and recurrent bacterial infections (Bitoun et al. 2002). Mouse models of the Netherton syndrome have shown enhanced proteolysis of desmoglein 1 and filaggrin in SPINK5 mutants (Descargues et al. 2005; Hewett et al. 2005). Moreover, SPINK5 has also been associated with asthma and atopic dermatitis (Blumenthal 2005).
Interestingly, both SPINK1 and SPINK5 are located on chromosome 5q32. This region contains the CELIAC2 susceptibility locus that emerged repeatedly in linkage studies (Babron et al. 2003). Despite the fact that this region is rich in candidate cytokine genes and intense mapping efforts were made, no closely associated genes were identified (Ryan et al. 2005). Likewise, SPINK4 is located on chromosome 9p13.3 and resides within a linkage region (9p21-13) where we previously identified a novel CD locus that segregated within a four-generation Dutch family (van Belzen et al. 2004). Taken together, the role of SPINK genes in epithelial and mucosal protection and the important genetic locations of SPINK1, SPINK4, and SPINK5 prompted us to subject the four conventional members of the SPINK family to gene expression and genetic association analyses to ascertain their possible role in CD pathogenesis.
Materials and methods
Patient material
Duodenal biopsy samples were collected by endoscopy as part of a routine CD diagnostic procedure or to monitor the response to a gluten-free diet in previously diagnosed patients. All patients were classified using the Marsh nomenclature according to the UEGW criteria (Report of a working group of the United European Gastroenterology Week in Amsterdam 2001). Two biopsy samples taken in parallel to those used for histological examination were pooled and used for determination of gene expression. In total, 62 individuals were examined with quantitative reverse transcription polymerase chain reaction (qRT-PCR), of which 16 were normal controls, and 46 were CD patients (Fig. 1). The patient group consisted of 15 untreated cases with villous atrophy (MIII) and 31 patients treated with a gluten-free diet who were in various stages of mucosal recovery: MII (crypt hyperplasia; n = 11), MI (lymphocyte infiltration; n = 8), and M0 (complete remission; n = 12). The biopsies of all participating patients were reevaluated by an experienced pathologist, and only CD patients with a proven original Marsh III lesion were included in this study. The genetic association study on all four SPINK genes was initially conducted on a cohort of 310 independent CD patients and 180 independent age- and sex-matched controls, all of which were from Dutch Caucasian decent. In a second stage, exclusively focused on all SPINK4 SNPs, we added 360 controls to a total of 540. In case of three SPINK4 variants with suggestive P values, the power of the study was further enhanced by adding 169 extra CD cases to a total of 479. In parallel, we also examined the SPINK4 gene in a previously described four-generation Dutch CD family (van Belzen et al. 2004). Six family members and a CEPH control were subjected to DNA sequence analysis of the SPINK4 coding regions and splice sites boundaries. Additionally, we performed SPINK4 SNP haplotype analysis in this family. All patients and family members that volunteered for this study signed an informed consent. The study was approved by the Medical Ethics Committee of the University Medical Center Utrecht.
Fig. 1Results of qRT-PCR of SPINK genes in normal controls (NC) and CD patients, either untreated (MIII) or on a gluten-free diet (MII–M0). The Marsh stages refer to the pathological conditions of the mucosa, characterized by atrophy of the villi (MIII); hyperplastic crypts between the villi (MIII–MII); and enhanced lymphocyte infiltration (MIII–MII–MI). Stage M0 indicates complete remission comparable to controls. The genes tested were as follows: SPINK1 (a); SPINK2 (b); SPINK4 (c); and SPINK5 (d). Measurements were made in triplicate, on pools of separately prepared cDNA samples. Expression data were normalized to the normal control pool.(e) Relative expression of all four SPINK genes with respect to SPINK2 in the healthy duodenal mucosa. Note the logarithmic scale here. The GUSB gene was used as an endogenous control in all tests. Errorbars indicate standard deviations
Expression study
The isolation of total RNA from biopsy samples and the analysis of gene expression by real-time qRT-PCR on an ABI Prism 7900HT was performed as described before (Wapenaar et al. 2004). We used the commercially available Assay-on-Demand test for SPINK1 (Hs00162154_m1), SPINK2 (Hs00221653_m1), SPINK4 (Hs00205508_m1), SPINK5 (Hs00199260), and the endogenous control gene GUSB (PDAR 4326320E; Applied Biosystems, Foster City, CA). All samples were tested in triplicate on pooled cDNA samples representing each Marsh class. The results were confirmed with cDNA from individual samples tested in duplicate. Relative levels of gene expression were obtained using the SDS2.1 software (Applied Biosystems).
Genetic association and data analysis
Haplotype tagging SNPs were selected for SPINK1, SPINK2, SPINK4, and SPINK5 based on HapMap (Phase I) data using Haploview (Barrett et al. 2005). For each haploblock containing SNPs in high linkage disequilibrium, one or more representative SNPs were selected that should capture the genetic variation within that block. For the four SPINK genes tested, this resulted in a set of 18 haplotype tagging SNPs and one coding SNP (see Table 1 and Fig. 2). SNP assays were obtained from Applied Biosystems and analyzed on an ABI Prism 7900HT. Hardy–Weinberg Equilibrium (HWE) was evaluated separately in cases and controls for all SNPs tested. Allele frequencies were compared between cases and controls, and P values were obtained by χ2 analysis.
Table 1Allelic distribution of SPINK haplotype tagging SNPs in a Dutch CD case-control cohort Cases (n = 310)Controls (n = 180)  AlleleAllele countsAllele frequency (%)Allele countsAllele frequency (%)Gene nameSNP i.d.Position*Minor/majorMinorMajorMinorMajorMinorMajorMinorMajorχ2P valueSPINK1rs10515593147,178,993A/G11944921797626822.177.90.1660.6834SPINK1rs3777125147,184,010C/G22833640.459.615020042.957.10.5270.4681SPINK1rs4705204147,195,313C/A12542922.677.46027617.982.12.8130.0935SPINK1rs891992147,205,707G/A7748313.886.24730313.486.60.0190.8906SPINK2rs1001563057,520,070A/G21632839.760.313621638.661.40.1020.7489SPINK2rs78154257,528,232G/A22039036.163.912322135.864.20.0090.9237SPINK4rs56335333,206,428G/C12047220.379.76727919.480.60.1120.7375SPINK4rs56351233,206,484G/A505568.391.73631010.489.61.2430.2648SPINK4rs89167133,222,271G/T7054011.588.55230414.685.41.9980.1575SPINK4rs706107**33,230,225A/G11749919818126923.176.92.3580.1246SPINK4rs70610933,230,668A/G11848619.580.58227622.977.11.5490.2133SPINK4rs70611533,243,605G/C7753712.587.55230814.485.60.7160.3975SPINK5rs3756688147,422,972G/A23238037.962.113522137.962.10.0000.9968SPINK5rs4472254147,433,830A/C240376396114221439.960.10.0810.7757SPINK5rs4519913147,452,004G/A27933145.754.316819246.753.30.0790.7792SPINK5rs1422987147,466,552T/C495598.191.9273297.692.40.0700.7917SPINK5rs3815740147,471,386G/A385746.293.8213375.994.10.0470.8291SPINK5rs2052532147,476,500G/A20041432.667.411724132.767.30.0010.9723SPINK5rs3764930147,485,309G/A19941332.567.511724332.567.50.0000.9958*Basepair position according to NCBI build 35.1**Coding SNP, nonsynonymous change (Val7Ile)Fig. 2Genomic organization of the four SPINK genes. The upperhorizontalline indicates exon locations (verticalbars) and SNP positions (numberedasterisks). The SNPs are numbered for each gene consecutively as they appear in Table 1. SPINK4 SNP no. 4 represents the nonsynonymous (Val7Ile) coding SNP rs706107. The arrowpoints indicate the orientation of transcription. The lowerportion of the figure shows the pairwise linkage-disequilibrium structure between indicated SNPs given by D′ statistics based on the European population in the HapMap database (Phase II). Darkerred intensities indicate higher D′ values (numbers indicate D’ value, whereas SNP pairs without number have a D′ = 1)
DNA sequence analysis
DNA sequence analysis was performed on SPINK4 in six members of a four-generation Dutch CD family and one CEPH control (family, 1,331; individual, 2). Of these six family members, four were affected (index 02, 08, 32, and 41) and carried the disease-linked haplotype, and two were nonaffected (index 21 and 31) without this haplotype (Fig. 3). All coding sequences of the SPINK4 gene were PCR-amplified, including the intron–exon boundaries (for primers and protocols, see supplementary data Table 1). PCR products were examined on a 2% agarose gel and purified with a Millipore Vacuum Manifold (Billerica, MA). Samples were prepared with the BigDye terminator cycle sequencing ready kit (Applied Biosystems) according to the manufacturer’s protocol. PCR and sequencing amplification were performed on a GeneAmp PCR system 9700 (Perkin Elmer, Foster City, CA). Sequences were run on an ABI Prism 3730 analyzer (Applied Biosystems). Analysis and sequence alignment was carried out with Sequence Navigator (Applied Biosystems) and Vector NTI (InforMax, Massachusetts) software packages.
Fig. 3Pedigree of the Dutch multigeneration CD family. Only affected descendents are depicted (10 out of 13 siblings in the second generation were affected). The grandparental SPINK4 haplotypes that are boxed and shaded are identical to the grandmaternal at-risk haplotype (noninformative). The SNPs are ordered (top-to-bottom) as they appear in Table 1. Genotype numbers 1, 2, 3, and 4 refer to A, C, G, and T alleles, respectively. Sequence analysis was performed on family members 02, 08, 21, 31, 32, and 41. Family member index numbers are indicated in bold
Results
SPINK gene expression in the CD mucosa
The expression of all four conventional members of the human SPINK family was determined by real-time qRT-PCR on duodenal biopsy-derived cDNA pools from normal controls and CD patients, either untreated or in various stages of remission on a gluten-free diet. The results shown in Fig. 1 indicate that only SPINK4 (Fig. 1c) is differentially expressed, and that its transcriptional activity, which is at its highest in Marsh III (20-fold compared to controls), decreases sharply (fourfold) when patients improve and make a transition to Marsh II. To preclude that the results for SPINK4 might be biased by fortuitous differences in individual expression levels within the generated pools, we also examined the control and case samples each separately. This did not change the observed drop in SPINK4 expression during tissue recovery (see supplementary Fig. 1). Likewise, we performed the same analysis for the other three SPINK genes without affecting the profile already observed in the pools (results not shown). We also examined the relative expression of the four SPINK genes with respect to each other in the normal intestinal mucosa. This showed that both SPINK1 and SPINK4 have the highest expression, which is respectively 480-fold and 240-fold higher compared to SPINK2, whereas SPINK5 is in the same order of magnitude (fivefold) as SPINK2 (Fig. 1e). In conclusion, only the SPINK4 gene appears to be differentially regulated in the intestinal mucosa during recovery from the gluten-evoked CD lesion. This observation prompted us to examine whether SPINK4, or any of the other SPINK genes, could also be causally related to the CD pathogenesis.
Genetic association analysis of SPINK genes
We designed a haplotype tagging SNP strategy to capture all genetic variation in SPINK1, -2, -4, and -5. An overview of these four SPINK genes with their genomic organization, linkage-disequilibrium structure, and the position of the haplotype tagging SNPs used is depicted in Fig. 2. Initially, these haplotype tagging SNPs were tested in 310 CD cases and 180 controls (Table 1) and showed no significant association for any of the haplotype tagging SNPs in the four SPINK genes. Despite the initial negative result, we decided to pursue SPINK4 further because it is expressed in goblet cells (Metsis et al. 1992), displayed a CD pathology-related differential expression in the intestinal mucosa, and mapped within a CD linkage region (van Belzen et al. 2004). Initially, we expanded the control group with 360 samples to a total of 540 for all SPINK4 SNPs tested. As a result, the Val7Ile coding variant rs706107 and its flanking haplotype tagging SNPs rs891671 and rs706109 yielded suggestive but nonsignificant P values of 0.0595, 0.0510, and 0.1122, respectively (data not shown). To increase the power of the study even further, we subsequently added 169 CD cases to a total of 479. The effect on the P values of the three SNPs tested was such that they dropped below the significance threshold (see Table 2). From this, we conclude that the four SPINK genes tested do not contribute to the genetic susceptibility in the Dutch CD population.
Table 2Allelic distribution of three selected SPINK4 SNPs in the extended Dutch CD case-control cohort Cases (n = 479)Controls (n = 540)  AlleleAllele countsAllele frequency (%)Allele countsAllele frequency (%)Gene nameSNP i.d.Position*Minor/majorMinorMajorMinorMajorMinorMajorMinorMajorχ2P valueSPINK4rs89167133,222,271G/T12681413.486.615890414.985.10.8890.3457SPINK4rs706107**33,230,225A/G20573721.878.224281422.977.10.3820.5365SPINK4rs70610933,230,668A/G21272422.677.424281622.977.10.0140.9053*Basepair position according to NCBI build 35.1**Coding SNP, nonsynonymous change (Val7Ile)
SPINK4 sequence analysis in a multigeneration family
We have previously described a four-generation CD family with an extraordinary high incidence of affected individuals (see Fig. 3). The disease segregated with a grandmaternal haplotype on chromosome 9p21-13 (van Belzen et al. 2004), a region that encompasses SPINK4. The apparent dominant inheritance pattern could be caused by a mutation that is rare in the general CD population but present with a high phenotypic penetration in this specific family. To assess if any functional variants of the SPINK4 gene were present in this family, we sequenced all its exons and intron–exon boundaries in six family members. However, we did not observe mutations in any of the samples tested (results not shown). Neither was the exon 1 coding SNP rs706107 specific for affected individuals as all seven individuals tested (including the CEPH control) carried the most frequent GG genotype (Fig. 3). To exclude the possibility of deletions in SPINK4 to be misinterpreted from the sequence data as homozygous genotypes, we also performed segregation analysis of the grandparental SPINK4 haplotypes within the entire family but observed no suspect inheritance pattern (Fig. 3). In conclusion, we have found no evidence that SPINK4 is a candidate gene for the chromosome 9p21-13 CD locus in the Dutch population in general or in the multigeneration Dutch CD family specifically.
Discussion
Chronic inflammatory conditions and autoimmune disorders are typically characterized by a deregulated adaptive and innate immune system. The innate defense consists of multiple components that include physical barriers, antimicrobial molecules, pattern recognition receptors, circulating phagocytes, and the complement system (Hoebe et al. 2004). A breach of the epithelial barrier and loss of microbial containment is often the first of a series of events that trigger or sustain chronic inflammatory diseases (Tlaskalova-Hogenova et al. 2004) as described, e.g., in Crohn’s disease, atopic eczema, asthma, and psoriasis (Schreiber et al. 2005). In CD, the gut–lumen separation is undermined by dietary gluten that evokes a combined innate and adaptive immune response (Londei et al. 2005). It is the joined action of gluten peptides, environmental factors, and genetic determinants that precipitates this enteropathy. The human leukocyte antigen locus is the major genetic contribution to the adaptive Th1 reaction (Koning et al. 2005). Recently, we identified MYO9B as a susceptibility gene in the Dutch population that possibly has an effect on epithelial barrier integrity (Monsuur et al. 2005). Several other studies have underscored the involvement of innate immunity in CD, however, without identification of underlying causative gene variants (Londei et al. 2005). Interestingly, it was also reported that the epithelial glycocalyx and the bacterial composition in the CD gut is distinct (Forsberg et al. 2004; Tjellstrom et al. 2005).
In search of genes that may have a primary contribution to CD pathogenesis, we focused our attention to the SPINK family of serine protease inhibitors that play an important role in tissue preservation through the containment of uncontrolled proteolysis and bacterial growth. In this study, we demonstrated differential gene expression of mucosal SPINK4 in CD. Crypt hyperplasia is a feature of the Marsh III and Marsh II stages of CD, and the concomitant increase in the number of goblet cells may contribute to the increased SPINK4 expression. However, the observed sharp decrease in gene expression sets in during the MIII/MII transition, whereas crypt normalization is observed only later at the MII/MI recovery phase. This suggests that SPINK4 downregulation sets in soon after commencement of the gluten-free diet. This SPINK4 differential expression probably reflects altered goblet cell activity, but its functional significance and regulatory mechanism in CD pathology remains to be established.
The combination of functional relevance and mapping to CD linkage intervals pointed to the SPINK family members as attractive functional and positional candidate genes. We have chosen a robust strategy for genetic association testing based on haplotype tagging SNPs and linkage-disequilibrium structure of the SPINK loci applied to a considerably sized Dutch case-control cohort. With our study design, we had 75% power to confirm association with SPINK1, -2, and -5 (relative risk 2.0; allele frequency 0.1–0.45; 95% confidence interval), whereas this was even 95% (RR 2.0) and 80% (RR 1.6) for SPINK4. These power estimates reflect a Type I error rate of 0.05, which is appropriate for testing a previously reported result. Initial detection of a new genetic association would require much more stringent criteria to assure reproducibility, and power would be correspondingly less.
In parallel, we examined the extended Dutch CD family for variants and deletions in SPINK4. We hypothesized that a specific SPINK4 mutation, although rare in the general population, could have a dramatic impact on mucus composition, bacterial containment, and gluten sensitivity, thereby explaining the apparent dominant and high penetration inheritance pattern in our extended CD family. With both approaches, we were not able to establish a genetic involvement of the SPINK genes tested. However, we cannot completely rule out the possibility of a rare noncoding mutation in SPINK4 (outside the splice donor and acceptor regions) that might specifically segregate in this atypical CD family, characterized by an exceptional high prevalence of affected members.
Despite this negative result in the Dutch CD population, we cannot formally rule out the possibility of genetic contribution of SPINK genes to CD in other European populations like the Italian in whom, unlike the Dutch (van Belzen et al. 2003), chromosome 5q linkage was established (Greco et al. 1998; Percopo et al. 2003). Genuine population heterogeneity has been reported before, e.g., between CARD15/NOD2 and Crohn’s disease (Lesage et al. 2002; Croucher et al. 2003) and between SPINK5 and asthma (Blumenthal 2005; Jongepier et al. 2005). The new SPINK members on chromosome 5q (SPINK5L2, SPINK6, SPINK5L3, SPINK7, and SPINK9) were not part of this study. Currently, no functional annotation is available for these genes that are located near SPINK1 and SPINK5 in a chromosomal region that appears to have been subjected to gene duplication during evolution. Therefore, we cannot exclude their possible involvement in CD or any other inflammatory disorder.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Supplementary Fig. S1
(GIF 14.0 kb)
Supplementary Table 1
Primers and reaction conditions used for SPINK4 DNA sequence analysis. (PDF 80.0 KB)