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Gene expression profiles in HEK-293 cells with low or high store-operated calcium entry: can regulatory as well as regulated genes be identified?

Department of Neurobiology, Pharmacology and Physiology, The University of Chicago, Chicago, Illinois

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
Gene expression profiles were generated using cDNA microarray technology for clones of human embryonic kidney (HEK)-293 cells selected to have either high or low levels of store-operated Ca²⁺ entry (SOCE). For five high clones, three low clones, and control HEK-293 cells, duplicate Affymetrix U133A human gene arrays were run after extraction of total RNA from cells growing in the presence of serum. Of the 22,000 genes represented on the microarray, 58 genes had readings at least twofold higher, while 32 genes had readings at least twofold lower, in all five high SOCE clones compared with control HEK-293 cells. In the low SOCE clones, 92 genes had readings at least twofold higher, while 58 genes had readings at least twofold lower, than in HEK-293 cells. Microarray results were confirmed for 18 selected genes by real-time RT-PCR analysis; for six of those genes, predicted changes in the low SOCE clone were confirmed by an alternative method, monitoring mRNA levels in HEK-293 with SOCE decreased by expression of small interfering (si)RNA to canonical transient receptor potential protein-1. Genes regulated by SOCE are involved in signal transduction, transcription, apoptosis, metabolism, and membrane transport. These data provide insight into the physiological role of SOCE. In addition, a potential regulator of SOCE, insulin receptor substrate (IRS)-2, has been identified. A reduction of IRS-2 levels by siRNA methods in two high clones dramatically reduced SOCE, whereas overexpression of IRS-2 in a low SOCE clone elevated SOCE.

cDNA microarray; fluorescence-activated cell sorting analysis; thapsigargin; insulin receptor substrate-2; small interfering RNA; real-time PCR; canonical transient receptor potential protein-1

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
MANY PLASMA MEMBRANE RECEPTORS utilize Ca²⁺ as a second messenger to initiate downstream physiological processes (4, 5, 8, 54). Activation of these receptors generally results in a biphasic Ca²⁺ response involving an initial release of internal Ca²⁺ stores, followed by Ca²⁺ entry through receptor-operated or capacitative Ca²⁺ entry channels (43), also called store-operated channels (SOCs). Although progress has been made in identifying proteins that assemble to form SOCs, little is known about the downstream physiological consequences of Ca²⁺ entry via these channels. Recent studies have indicated that activation of store-operated Ca²⁺ entry (SOCE), via depletion of stores with inhibitors of sarco(endo)plasmic reticulum Ca²⁺-ATPase pumps, can lead to the regulation of a handful of specifically monitored genes such as Nur77 (33), c-fos and grp78 (23), and pip92 (10); however, these studies do not provide an insight into how widespread the involvement of SOCE is in regulating mRNA levels. Several recent investigations have utilized cDNA microarrays to broaden the range of mRNA expression that can be monitored. One such study in T lymphocytes utilized cDNA microarrays (representing 7,396 cDNA clones) to investigate the gene regulation in response to ionomycin and phorbol ester treatment of control cells, or cells thought to have a defect in SOCE (18). Although this study greatly advanced our understanding of the role of SOCE in regulating gene expression, there are several reasons that additional studies are required. First, a significantly larger number of genes are represented on more recent cDNA microarrays, allowing a fuller description of the expression profile. Second, the lymphocyte is a specialized cell in which induced gene expression is heavily directed toward producing cytokines to regulate the population of immune cells, and therefore the expression profile is possibly quite different from the gene expression profile elicited in other types of cells. Third, while ionomycin and phorbol ester approximate a physiological stimulus in lymphocytes, an ionomycin stimulus in most other cell types fully depletes the endoplasmic reticulum (ER) stores to the point of blocking ER protein processing, resulting in the activation of the ER stress response and ultimately activation of apoptosis. The peripheral effects of fully depleting internal Ca²⁺ stores can be seen in a gene expression profiling study of RBL-2H3 mast cells stimulated with 2,5-di(tert-butyl)-1,4-hydroquinone (DTBHQ), another inhibitor of the ER Ca²⁺-ATPase, where DTBHQ upregulated many more stress-inducible genes than cross-linking of IgE receptors, a more physiological stimulus (38). Therefore, the gene expression profiles produced by thapsigargin and ionomycin may vary significantly from those produced by activation of SOCE by receptor agonists. Another recent study utilized cDNA arrays (Atlas 1.2 mouse array from Clontech, which represents 1,200 genes) to demonstrate that inhibition of serum-stimulated 3T3 cells with SKF-96365, an inhibitor of SOCs, leads to the upregulation, or downregulation, of 29 genes (27). However, the interpretation of these SKF-96365 experiments is complicated by the lack of specificity of this reagent. Recent papers report SKF-96365 inhibition of Na⁺ channels (26), K⁺ channels (49), maitotoxin-induced Ca²⁺ entry (14, 51), and facilitation of nicotinic receptor desensitization (25) in addition to the more widely recognized inhibition of SOCs. One recent study used SKF-96365 to distinguish between receptor-operated channels (ROCs) and SOCs, but as an inhibitor of ROCs, not SOCs (36). Given the limitations of the previous investigations into the role of SOCE in regulating mRNA levels, we decided to take an alternative approach to investigate this question.

Previously, we reported that a clonal variation of SOCE exists in the human embryonic kidney (HEK)-293 cell population (2). In the current study, we took advantage of that clonal variation and selected HEK-293 cell clones that varied in their levels of SOCE. We used these clonal populations to investigate the effect of low or high levels of SOCE on the gene expression profile for cells maintained in their normal growth environment. The mRNA expression profiles for the various clones were evaluated by utilizing Affymetrix cDNA microarrays. Comparisons of mRNA profiles of five clones high in SOCE, the parent HEK-293 cell population, and three clones low in SOCE provide valuable information about which genes are regulated by changes in SOCE for cells in their normal growth environment. In addition, this study provides important information on potential upstream regulators of SOCE, as evidenced by our results demonstrating that reduction of the elevated insulin receptor substrate (IRS)-2 levels in two high SOCE clones by small interfering (si)RNA methods, or overexpression of IRS-2 in a low SOCE clone, alters their levels of SOCE.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
Materials
Fura-2 free acid, fura-2 AM, indo-1, and Pluronic F-127 were purchased from Molecular Probes; thapsigargin was purchased from LC Laboratory. Cyclopiazonic acid (CPA) was purchased from Calbiochem. Hanks’ balanced salt solution (HBSS); Ca²⁺-free, Mg²⁺-free, and HCO₃^–-free HBSS; DMEM; penicillin-streptomycin, L-glutamine, and trypsin-EDTA were purchased from Life Technologies (GIBCO BRL). Chelex-100 came from Bio-Rad.

Cell Culture
HEK-293 cells were cultured in DMEM supplemented with 10% FBS, 50 U/ml penicillin, 50 µg/ml streptomycin, and 2 mM glutamine. Cells were grown in an incubator at 37°C with humidified 5% CO₂ and 95% air.

Cell Sorting and Clone Selection
HEK-293 cells were grown to confluency on 15-cm plates, and then the cells were loaded with Fluo-3 and Fura Red. In the absence of Ca²⁺, cells were stimulated with 10 µM CPA for 10 min to deplete intracellular Ca²⁺ stores. Cells were then removed from the dish by rinsing in EDTA medium, transferred to a 50-ml sterile centrifuge tube, and washed with nominally Ca²⁺-free HBSS. After sufficient time for the return of cytosolic Ca²⁺ to basal levels, intracellular Ca²⁺ concentration ([Ca²⁺]_i) was measured by fluorescence-activated cell sorting (FACS) before and after the addition of 1.8 mM Ca²⁺. The sorted population of cells with high SOCE (or low SOCE) was plated on six-well plates and grown until individual cell clones were visible. Cell clones were then selected and expanded into clonal cell lines. Thirty-one high SOCE clonal cell lines and twenty-four low SOCE clonal cell lines were generated and tested to confirm that they were high or low in SOCE by monitoring thapsigargin-stimulated Ba²⁺ entry. Five high SOCE clones and three low SOCE clones were selected for use on the basis of their level of SOCE and low Ba²⁺ leak influx before thapsigargin stimulation.

Ca²⁺ Imaging
[Ca²⁺]_i was measured in cells loaded with the fluorescent indicator fura-2. Cells were plated onto 25-mm coverslips 1 day before the experiment. On the next morning, cells were washed twice with HEPES-buffered HBSS, loaded for 30 min with 5 µM fura-2 AM in HBSS supplemented with 1 mg/ml BSA + 0.025% Pluronic F-127, and then unloaded in HBSS for another 30 min. The coverslips were mounted as the bottom of a chamber, and cells in the chamber were perfused via an eight-channel syringe system. A suction pipette maintained a constant volume of solution (0.5 ml) in the chamber. An InCyt IM2 dual-wavelength fluorescence imaging system (Intracellular Imaging, Cincinnati, OH) was used to measure [Ca²⁺]_i during the experiment, as previously described (57). In short, Ba²⁺ influx was measured before (to determine leak flux) and after (to determine total flux) store depletion by thapsigargin. SOCE was defined as the difference between total and leak Ba²⁺ influxes. Nominally Ca²⁺-free HBSS was prepared by stirring Ca²⁺-free, Mg²⁺-free, and HCO₃^–-free HBSS with Chelex-100 beads. After the Chelex-100 beads were filtered out, MgCl₂ was added to a final concentration of 1 mM.

Total RNA Isolation
Total RNA was isolated from HEK-293 cells and clones by use of the RNeasy Mini Kit (Qiagen) and treated with DNase I (Invitrogen). The RNA sample was additionally purified by ethanol precipitation, and its concentration was determined by measuring absorbance at 260 nm.

cDNA Microarray Analysis
The five high SOCE clones, three low SOCE clones, and HEK-293 cells (to serve as the general control population) were plated onto 15-cm plates 1 day before RNA purification. Two separate experiments were run. In the first experiment, microarrays were run on duplicate samples from five high clones (H1, H15, H24, H36, and H39), one low clone (L3), and the HEK control cells. In the second experiment, microarrays were run on duplicate samples from two low clones (L28 and L29), one high clone (H36), and the HEK control cells. Cells were taken from their growth medium, and total RNA was immediately purified. The quality of the RNA was evaluated by agarose gel electrophoresis. A quantity in excess of 20 µg of RNA from each clone (duplicate sample) was submitted to our Functional Genomics Core Facility for microarray analysis. To confirm the integrity of the RNA, samples were applied on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA), and the purity and the concentration were determined with a GeneSpec III (Miraibio). The target preparation protocol followed the Affymetrix GeneChip Expression Analysis Manual (Santa Clara, CA). Briefly, 10 µg of total RNA were used to synthesize double-stranded cDNA using the Superscript Choice System (Life Technologies). First-strand cDNA synthesis was primed with a T7-(dT24) oligonucleotide. From the phase-log gel-purified cDNA, biotin-labeled antisense cRNA was synthesized with BioArray High Yield RNA Transcript Labeling Kit (Enzo Diagnostics, Farmingdale, NY). After precipitation with 4 M lithium chloride, 12 µg of fragmented cRNA were hybridized to human 133A arrays for 16 h at 45°C and 60 rpm in an Affymetrix Hybridization Oven 640. The arrays were washed and stained with streptavidin phycoerythrin in Affymetrix Fluidics Station 400, using the Affymetrix GeneChip protocol, and then scanned using the Affymetrix Agilent GeneArray Scanner. The acquisition and initial quantification of array images were performed using the Affymetrix Microarray Suite Version 5.0 (MAS 5.0) with the default analytic parameters. Complete microarray expression data are available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (GEO submissions GSE1309, GSM21234–21235, GSM21258–21264, GSM21378–21382, GSM21385, GSM21391, GSM21393–21396, GSM21400–21401) at http://www.ncbi.nlm.nih.gov/geo. Subsequent data analyses were performed by our laboratory.

The Functional Genomics Core provided us with two types of data output, each contained in a separate Excel file. The first file contained normalized fluorescence values and an absolute call for presence or absence of a transcript (see Supplemental Table S1; available at the Physiological Genomics web site)¹ The calls were present (P), absent (A), or marginal (M) to reflect whether the particular gene was expressed on the basis of a complex algorithm that weighs the number of matched vs. mismatched probes that are positive for a particular gene. Supplemental Table S2 contained comparative data; for each gene, a ratio of the normalized fluorescence value for each experimental case (each high or low clone) vs. the normalized fluorescence value for its HEK cell control was calculated. In the absolute data set, all genes with fluorescence values less than 200, both in the control HEK samples and in the high or low clones, were identified; this list of genes was matched against the Excel file containing the comparative data, and those genes were eliminated from further consideration. From this modified comparative file, a new Excel file was generated that contained the comparative data for only the five high SOCE clones. With duplicate values for each clone being individually compared with the duplicate values for the control (HEK cells), four comparative ratios for each clone were generated, each accompanied by an identifier for increase (I), decrease (D), or no significant change (NC). A Countif routine was run within Excel to total the number of I or D identifiers present within a given row (for a given gene). Thus, if the Countif routine gave a value of 20 Is for a given row, this would mean that this gene increased in all four comparisons for each of the five high clones. The file was then sorted based on the value within the "Countif column," and all genes containing a "Countif value" above 16 were selected and pasted into a new file. A Countif value of 16 almost always meant that the gene had increased for all four comparisons in four of the five clones. This data set was then sorted based on the ratio values in one of the high clone columns. Any gene with a value above 1 or below –1 (values were based on a log₂ scale, so a ratio >1 meant the level had increased at least 2-fold) for at least 16 of the 20 comparisons was selected and pasted into a new file. Each of these genes was then checked against the data for the low clone (L3) run in this experiment, and genes were maintained on the list only if their low clone levels either did not change dramatically or changed in the opposite direction seen in the high clones.

For the low clones, we report the genes from the second experiment (see Supplemental Tables S3 and S4) that change in both the L28 and L29 clones compared with the HEK control cells, but either do not dramatically change or change in the opposite direction in the high clone (H36) run in this experiment.

PCR Primers
PCR primers for the genes chosen for real-time RT-PCR were designed based on published sequences in GenBank.

RT-PCR
First-strand cDNA was prepared from 1 µg of total RNA, using SuperScript III RNase H^– RT (Invitrogen) and 1 µg of oligo(dT). The mRNA samples were denatured at 65°C for 5 min. Reverse transcription was performed at 50°C for 55 min and was stopped by heating samples at 75°C for 10 min.

Quantitative Real-Time RT-PCR
Real-time PCR was performed on the ABI Prism 7700 Sequence Detection System using SYBR7 Green PCR Core Reagents (Applied Biosystems) and cDNA synthesized as described above. PCR was performed using the kit protocol in a 25-µl reaction volume. The integrity of the RT-PCR products was confirmed by melting-curve analysis. Melting curves for each primer pair showed one specific signal. The amount of PCR products in parental HEK-293 cells or in clones H36 and L28 was calculated in reference to the individual calibration curves based on cDNA obtained from parental HEK-293 cells.

Western Blotting
Cells were grown on 10-cm dishes under the conditions described above. Cells were lysed in modified radioimmunoprecipitation (RIPA) buffer (10 mM Tris·HCl, pH 7.5, 500 mM NaCl, 0.1% SDS, 1% NP-40, 1% Na-deoxycholate, 2 mM EDTA, 2 mM Na₂VO₄, 2 mM Na₄P₂O₇, 2 mM NaF). The lysates were clarified by centrifugation, and protein concentration was measured by a bicinchoninic acid (BCA) kit (Pierce). Total protein extract (50 µg) was applied on 8% SDS-PAGE (16 cm x 16 cm gels) and run overnight. The proteins were transferred onto an Immobilon-P membrane (Millipore), and the uniformity of protein transfer for all the lanes was evaluated by reversibly staining with BLOT-Fast-Stain (Geno Technology). After 1 h of blocking, membranes were treated with monoclonal anti-IRS-2 antibodies (Upstate) at a dilution of 1:1,000. Membranes were washed 4x 10 min with Tris-buffered saline containing 0.1% Tween 20 (TBS-T), incubated for 30 min at room temperature with secondary anti-mouse antibody (1:10,000 in TBS-T), washed under the same conditions, and developed with SuperSignal West Pico Chemiluminescent Substrate (Pierce) for a suitable time so as not to saturate the film. The films were digitized on a flatbed scanner, and the relative spot intensities were determined in Photoshop 6.0. The images were inverted, the bands were outlined, and the average gray level and number of pixels in the spot were obtained from the histogram function. The product of the average gray level value and the number of pixels was used to represent the integrated signal in the band. Each Western blot was repeated at least three times using different cell lysates.

siRNA Constructs
For human IRS-2 (GenBank no. AF073310), potential siRNA target sites (19 nucleotides in length) were chosen. The location of the selected IRS-2 gene sequence is 573–591. The potential target sites were compared with the human genome database by using BLAST (http://www.ncbi.nlm.nih.gov/BLAST), and any target sequences with homology to other coding sequences were eliminated from consideration. Hairpin siRNA template oligonucleotide design was done by entering siRNA target sequences into the web-based insert design tool at the following address: http://www.ambion.com/techlib/misc/psilencer_converter.html. Then, two complementary oligonucleotides (forward 5'-GATCCCGCCTCAACAACAACAACAACTTCAAGAGAGTTGTTGTTGTTGTTGAGGTTTTTTGGAAA-3' and reverse 5'-AGCTTTTCCAAAAAACCTCAACAACAACAACAACTCTCTTGAAGTTGTTGTTGTTGTTGAGGCGG-3') were synthesized, annealed, and ligated into the linearized pSilencer 3.1 H1 neo vector (Ambion). All procedures were performed as directed by the manufacturer’s instruction manual (Ambion). The inserts were sequenced to confirm that there were no unwanted mutations.

Transfection
Cells were grown in 75-cm² flasks to 60% confluency and transfected by use of PerFectin Transfection reagent (Gene Therapy Systems). For transient transfection experiments (IRS-2 overexpression), cells were used 48 h after transfection. For stable transfection experiments (siRNA expression), cells were transfected and later treated with 400 µg/ml G418. Cells that survived after 2 wk were collected, and this population of cells was used for future experiments.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
Selection of HEK-293 Cell Clones That Have Low or High Levels of SOCE
In a previous study, we reported that HEK-293 cells plated at clonal density demonstrate considerable clone-to-clone variation of SOCE (2). This clonal variation was utilized to select cell populations that had either high or low levels of SOCE. To screen for clones on a high throughput basis, HEK-293 cells were grown on 15-cm plates, loaded with Fluo-3 and Fura Red, and removed from their plates in EDTA-containing medium. They were washed by centrifugation and resuspended in Ca²⁺-free HBSS. An aliquot of these cells was aspirated into the FACS machine, and a baseline for intracellular Ca²⁺ was established. The aspiration of the sample was interrupted to add Ca²⁺, and then the aspiration was resumed. The data in Fig. 1A show that there is no significant change in [Ca²⁺]_i when Ca²⁺ is added back to control cells. Another aliquot of cells was incubated for 10 min in 10 µM CPA in Ca²⁺-free HBSS, and the cells were removed from the dish. The data in Fig. 1B illustrate that, after depletion of internal Ca²⁺ stores, the addition of external Ca²⁺ resulted in a dramatic elevation of [Ca²⁺]_i. A series of similar experiments was run, and the cells were sorted on the basis of the sorting profile shown in Fig. 1C. Cells in the low end of the profile, and in other runs the cells in the high end of the profile, were collected in sterile centrifuge tubes. This provided us with two populations of cells, one enriched for cells with low levels of CPA-stimulated Ca²⁺ entry and one enriched for cells with high levels of CPA-stimulated Ca²⁺ entry.

View larger version (26K): [in this window] [in a new window]   Fig. 1. Fluorescence-activated cell sorting (FACS) of human embryonic kidney (HEK)-293 cells based on Ca2+ entry levels. HEK-293 cells were grown to confluency on 15-cm plates. Cells were loaded with Fluo-3 and Fura Red, removed from the dish by rinsing in EDTA medium, transferred to a 50-ml tube, and washed in Ca2+-free HBSS. Cells were aspirated by FACS and excited at 488 nM, and the emission was monitored at 530 and 585 nM. The ratio of 530-to-585 nM emission intensity (FL1/FL2) was recorded as a measure of the Ca2+ level, the aspiration was interrupted to add 1.8 mM Ca2+, and the aspiration was resumed (A). In a parallel experiment, cells were stimulated with 10 µM cyclopiazonic acid (CPA) in Ca2+-free HBSS for 10 min before their removal from the dish. The cells were then treated as described above (B). Ca2+ entry into CPA-treated cells was initiated, and then the cells were sorted by FACS. A typical sorting profile is shown to illustrate the fractions collected for either low store-operated Ca2+ entry (SOCE) cells or high SOCE cells (C).

Of the 31 potentially high clones rescreened by image analysis, 13 clones appeared to be authentic high SOCE clones. Of the 24 potentially low clones rescreened by image analysis, 5 clones appeared to be authentic low SOCE clones. On the basis of their low basal Ba²⁺ leaks, we selected five high clones and three low clones to utilize in cDNA microarray experiments (Fig. 2, top). Ba²⁺ uptake rates were as follows: HEK-293 = 0.00061 ± 0.00005 min^–1 (n = 20); H1 = 0.00098 ± 0.00012 min^–1 (n = 18); H24 = 0.00106 ± 0.00009 min^–1 (n = 19); H39 = 0.00097 ± 0.00011 min^–1 (n = 10); H15 = 0.00095 ± 0.00010 min^–1 (n = 11); H36 = 0.00107 ± 0.00011 min^–1 (n = 16); L3 = 0.00039 ± 0.00002 min^–1 (n = 7); L28 = 0.00034 ± 0.00003 min^–1 (n = 10); L29 = 0.00034 ± 0.00004 min^–1 (n = 9). To assure that the variation in SOCE between clones was not simply due to variations in membrane potential, similar experiments were performed in a high-potassium medium to depolarize the membrane potential. As seen in Fig. 2, bottom, although the magnitude of the SOCE was reduced in all clones, membrane depolarization did not normalize the differences in SOCE between the various clones and the parent HEK-293 cell population. An analysis of the Ca²⁺ transients (area under the curve) in response to thapsigargin and the Ba²⁺ leak fluxes indicated that there were no statistically significant differences in these parameters between the clones and the parent HEK-293 cells (data not shown). This indicates that the clonal variations in thapsigargin-stimulated Ba²⁺ entry were not the result of clonal variations in cell volumes or cytoplasmic Ca²⁺ buffering capacities. Changes in cell volume or buffering capacity would be expected to alter Ba²⁺ leak fluxes as well as thapsigargin-stimulated Ba²⁺ flux. Likewise, clonal variations in cytoplasmic Ca²⁺ buffering capacity would be expected to produce clonal variations in the Ca²⁺ transients in response to thapsigargin.

View larger version (24K): [in this window] [in a new window]   Fig. 2. SOCE in selected HEK cell clones. Subpopulations of HEK-293 cells selected by FACS were plated at clonal density, and macroscopic clones were selected and expanded to produce monoclonal lines. Top: high or low levels of SOCE were confirmed in each clone by monitoring thapsigargin-stimulated Ba2+ entry. In the absence of Ca2+, 2 mM Ba2+ was added to Ca2+-free HBSS to measure the rate of Ba2+ leak. After switching to Ca2+-free medium, Ca2+ stores were released by adding 1 µM thapsigargin. After Ca2+ returned to basal levels, 2 mM Ba2+ was added. SOCE was determined by subtracting initial Ba2+ leak influx from the thapsigargin-stimulated Ba2+ influx. Compared with HEK control cells, 5 clones (H1, H15, H24, H36, H39) have significantly (P < 0.01) higher SOCE levels and 3 clones (L3, L28, L29) have significantly (P < 0.0005) lower SOCE levels. B: SOCE was measured in a high K+ medium (133 mM K+, with K+ above 5.4 mM being added as a replacement for Na+). Compared with the HEK control, all 5 high SOCE clones had significantly higher SOCE levels (P < 0.05) and all 3 low SOCE clones had significantly lower SOCE levels (P < 0.05). The no. of coverslips tested for SOCE is shown in parenthesis, with each determination being the average response of 800 cells.

Because we wanted to investigate the effect of low or high SOCE levels on gene expression under normal physiological conditions, we harvested logarithmically growing cells directly from their growth medium (DMEM + 10% FBS) for the subsequent RNA purification. Previous studies have reported that various growth factors stimulate SOCE (32, 34, 59), suggesting that an increase or decrease in SOCE should be reflected in a change in Ca²⁺ entry for cells growing in serum. The data in Fig. 3 confirm that serum-stimulated Ca²⁺ entry is higher in the five high SOCE clones than in HEK-293 cells and is lower in the low SOCE clones (all statistically significant, P < 0.03).

View larger version (18K): [in this window] [in a new window]   Fig. 3. Serum-stimulated Ca2+ entry in the high and low SOCE clones. Cells from different clones were plated on coverslips 24 h before the experiment, and serum-stimulated Ba2+ entry was monitored in each clone. In the absence of Ca2+, 2 mM Ba2+ was added to Ca2+-free HBSS to measure the rate of Ba2+ leak. After switching to Ca2+-free medium, Ca2+ stores were released by adding 0.5% FBS. After Ca2+ returned to basal levels, 2 mM Ba2+ was added in Ca2+-free medium containing 0.5% FBS. The serum-stimulated Ba2+ entry was determined by subtracting initial Ba2+ leak influx from the Ba2+ influx measured after serum treatment. Compared with the HEK control, all 5 high SOCE clones had significantly higher serum-stimulated Ba2+ entry levels (P < 0.03) and all 3 low SOCE clones had significantly lower serum-stimulated Ba2+ entry levels (P < 0.03). The no. of coverslips tested is shown in parenthesis, with each determination being the average response of 800 cells.

View larger version (47K): [in this window] [in a new window]   Fig. 4. Microarray data for HEK clones with high or low SOCE. Representative scatter plots are shown for the microarray experiments comparing gene expression profiles of HEK cells, high SOCE clones, and low SOCE clones. Green data points represent those genes for which the absolute fluorescence measurements fell below the threshold of 200. Red points represent the genes for which the fluorescence was above 200 for both measurements. A: gene-by-gene comparison of the fluorescence values obtained from the 2 duplicate chips run for the H1 clone. This comparison is shown to indicate the quality of the technical replication of the data. B: gene-by-gene comparison of the fluorescence values obtained from 1 chip run for the H1 clone (high SOCE) and 1 chip run for the HEK control. C: gene-by-gene comparison of the fluorescence values obtained from 1 chip run for the L3 clone (low SOCE) and 1 chip run for the HEK control. D: gene-by-gene comparison of the fluorescence values obtained from 1 chip run for the H24 clone (high SOCE) and 1 chip run for the L3 clone (low SOCE).

In Fig. 4C, we have plotted the data for one replicate of the L3 clone vs. the data for one replicate of the parent HEK-293 cells. This plot also looks quite different from the plot demonstrating the quality of technical replication. There are a larger number of genes with a signal above 100 that fall in the region of the plot that indicates a greater than twofold change in gene expression. These results suggest that there may be a significant number of genes that increase in cells that have low levels of SOCE.

Finally, in Fig. 4D, we have plotted one replicate of the L3 clone vs. one replicate of the H24 clone. Compared with Fig. 4B, there are many more genes within the region representing changes in level of gene expression between 2-fold and 10-fold. This suggests that there may be a number of genes that increase in cells having high SOCE levels and also decrease in cells having low levels of SOCE.

Changes in Gene Expression in Clones with High Levels of SOCE
We report all genes that change their expression level by at least twofold in at least four of the five high clones and that, in the low clone, do not undergo a change in the same direction. We chose to report the four-of-five category of genes because it was felt that two classes of interesting genes might be present in this category. Some genes would be in this category because changes were above threshold in four of five genes and slightly below threshold in the fifth clone. For example, if, for a particular gene, the ratios (on the log₂ scale) were 1.1, 1.2, 1.2, 1.1, and 0.9 for the five high clones, we would consider it to be an interesting gene. We were also interested in the much smaller subclass of genes where the change was dramatic in four of the five clones but did not change, or changed in the opposite direction, in the fifth clone. Our prediction was that genes regulated downstream of SOCE would change comparably in five of five high clones. We also theorized that some genes that are elevated might be responsible for the elevated SOCE. Three obvious theories to explain the elevated SOCE would be an increase in channel proteins, an increase in a positive channel regulator, or a decrease in a negative channel regulator; it is not necessary for all high clones to have the same underlying mechanism for the elevated SOCE. We hypothesized that we possibly could identify an elevated positive regulator as being the underlying mechanism responsible for the elevated SOCE in some of the high clones. Thus the lack of response of a gene in one of five clones would suggest that it is not Ca²⁺ regulated and is not the underlying cause for elevated SOCE in that particular clone, but could be the cause for the elevated SOCE in the other four clones. This is only one of a number of potential scenarios one can use to explain elevated SOCE levels in the high clones, but it serves to explain our rationale for wanting to examine genes that respond in four of five clones. Thus we have reported genes that changed above the twofold threshold in at least four of five high clones.

Identifying genes that changed expression levels by at least twofold in at least four of the five high clones involved two different sorts of the comparative data file. We initially ran a Countif routine to determine the number of I (significant increase) parameters on each row (each row representing all of the 20 comparisons for a single gene). For each gene with 16 or more Is, we determined that they had all presents (P) in the absolute file and that they achieved a value of 200. Thus genes that started with a value below 200 in HEK cells but increased above 200 in the high clones were counted. Genes that increased dramatically in the low clone for this experiment were excluded. The genes that fit these criteria are listed in Table 1.

View larger version (16K): [in this window] [in a new window]   Fig. 5. Quantitative (Q)-PCR confirmation for selected genes whose expression levels increase in high SOCE clones. Expression levels measured in the microarray assay for dihydrolipoamide branched-chain transacylase (DBT), ATP1B8, and fibroblast growth factor (FGF)-13 in control, low SOCE clones, and high SOCE clones are shown at left. Microarray results for these 3 genes were confirmed by Q-PCR, and the data are shown at right. As indicated, the increases in gene expression in the high SOCE clones predicted by the microarray assay for DBT, ATP1B8, and FGF-13 were confirmed by the real-time PCR assay. In some cases, the Q-PCR assay demonstrated more dramatic changes than were predicted by the microarray assay (e.g., the decrease in ATP1B8 in the low SOCE clones).

View larger version (19K): [in this window] [in a new window]   Fig. 6. Q-PCR confirmation for selected genes whose expression levels decreased in the high SOCE clones and increased in the low SOCE clones. Expression levels measured in the microarray assay for glutaminyl peptide cyclotransferase (QPCT), G protein-coupled receptor (GPR)50, and 14-3-3 epsilon in control, low SOCE clones, and high SOCE clones are shown at left. Microarray results for these 3 genes were confirmed by Q-PCR, and the data are shown at right. As indicated, the microarray and Q-PCR showed qualitative agreement, but in some instances the Q-PCR assay demonstrated more dramatic changes than the microarray assay predicted.

View larger version (14K): [in this window] [in a new window]   Fig. 7. Q-PCR confirmation for selected genes whose expression levels decreased in low SOCE clones. Expression levels measured in the microarray assay for bone morphogenetic protein (BMP)-2, lymphotoxin ß-receptor (LTBR), and ras-related C3 botulinum toxin substrate 3 (RAC3) in control, low SOCE clones, and high SOCE clones are shown at left. Microarray results for these 3 genes were confirmed by Q-PCR, and the data are shown at right. As indicated, the decreases in gene expression in the low SOCE clones predicted by the microarray assay for BMP-2, LTBR, and RAC3 were confirmed by the real-time PCR assay, as was the increased LTBR expression in the high SOCE clones.

View larger version (18K): [in this window] [in a new window]   Fig. 8. Q-PCR confirmation for selected genes whose expression levels increased in low SOCE clones. Expression levels measured in the microarray assay for diaphanous protein homolog 2 (DIAPH2), C-type lectin, and ribosomal protein, large, P1 (RPLP1) in control, low SOCE clones, and high SOCE clones are shown at left. Microarray results for these 3 genes were confirmed by Q-PCR, and the data are shown at right. As indicated, the increases in gene expression in the low SOCE clones predicted by the microarray assay for DIAPH2, C-type lectin, and RPLP1 were confirmed by real-time PCR assay.

View larger version (16K): [in this window] [in a new window]   Fig. 9. Comparison of expression levels for select genes in low SOCE clones vs. cells in which SOCE is suppressed by expression of small interfering (si)RNA to TRPC1. Expression levels for MN1, KOX15, CCNT2, Rab3-GAP, Proto-LBC, and MAGEA1 genes were compared by the real-time RT-PCR method in 3 cell lines: HEK-293 cells, L29 cells, and siTRPC1 cells (HEK-293 cells stably expressing siRNA to TRPC1). A: expression levels of 2 genes (MN1 and KOX15) are confirmed to be downregulated in L29 cells, as predicted by the microarray data (Table 4). They are also downregulated when SOCE is suppressed by siRNA to TRPC1. B: expression levels of 4 genes (CCNT2, Rab3-GAP, proto-LBC, and MAGEA1) are confirmed to be upregulated in L29 cells, as predicted by the microarray results (Table 3). They are also upregulated when SOCE is suppressed by siRNA to TRPC1.

View larger version (21K): [in this window] [in a new window]   Fig. 10. Elevated endogenous insulin receptor substrate (IRS)-2 protein expression in high SOCE clone. Cells (HEK control, L3 clone, and H36 clone) were grown on dishes as described in MATERIALS AND METHODS. A: Western blot analysis. Cells were lysed in modified radioimmunoprecipitation (RIPA) buffer, and Western blots were performed using monoclonal anti-IRS-2 antibody (1:1,000 dilution) and anti-mouse antibody (1:10,000 dilution) as the secondary antibody. Western blots were repeated at least 3 times, using different cell lysates. B: microarray analysis. Data represent average fold changes in IRS-2 gene expression calculated for HEK control, L3, and H36 cells.

View larger version (16K): [in this window] [in a new window]   Fig. 11. Effect of suppressing IRS-2 protein levels on SOCE in H36 cells. H36 cells were stably transfected with pSilencer siIRS-2 as described in MATERIALS AND METHODS. A: representative Western blots for H36 cells expressing siIRS-2 compared with HEK control cells and H36 cells. In these experiments, samples of 50 µg of total protein extract were applied to individual lanes. B: protein expression in H36 cells stably transfected with siIRS-2 compared with H36 cells. Data from 3 independent experiments were averaged. Western blots were quantitated based on the relative spot intensities and are plotted as percent control. *Significantly different from H36 control (P = 0.005). C: SOCE in H36 cells stably transfected with siIRS-2 compared with H36 cells. SOCE was determined as described in Fig. 2. As indicated, SOCE was inhibited by 60% in siIRS-2-transfected cells compared with their controls. *Significantly different from H36 control (P = 0.005). The no. of coverslips tested for SOCE is shown in parenthesis, with each determination being the average response of 800 cells.

View larger version (24K): [in this window] [in a new window]   Fig. 12. Overexpression of IRS-2 on SOCE. L29 cells (low SOCE) were transiently cotransfected with pcDNA3.1EYFP vector and pSMVhis-IRS2 as described in MATERIALS AND METHODS. L29 cells transiently cotransfected with pcDNA3.1EYFP and pSMVhis were used as a control (Mock). Cells were circled based on their EYFP fluorescence when excited at 500 nM. SOCE was determined as described in Fig. 2. Cells overexpressing IRS-2 had significantly higher SOCE levels than did their controls (*P < 0.0001). The no. of cells assayed is shown in parenthesis.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION GRANTS REFERENCES

The types of genes affected by changes in SOCE cover a wide range on the functional spectrum, including signal transduction molecules, transcription factors, regulators of apoptosis, metabolic enzymes, cytoskeletal elements, and membrane transporters. Although it is obviously impossible to discuss all of the implications that can be drawn from a complete review of Tables 1–4, it is worthwhile to discuss several important points.

Changes in the level of SOCE have a significant impact on several genes that are related to cell cycle or cell proliferation.
There is extensive literature describing changes in Ca²⁺ levels during proliferation and at specific points within the cell cycle, as well as reports that imposed changes in intracellular Ca²⁺ levels can block the cell cycle at specific points. (4, 54) Thus the genes listed below could serve as an important focus for future investigations to clarify the role of Ca²⁺ in regulating cell cycle and proliferation.

1) In HEK cell clones with high levels of SOCE, the G2 and S phase expressed-1 (GTSE-1) gene expression is upregulated. Murine GTSE-1 was cloned as a p53-inducible gene (11), and it, as well as its human homolog, have been demonstrated to be cell cycle regulated (37). The product of GTSE-1, which localizes to microtubules, has been reported to delay G2/M progression and to negatively regulate p53 function and p53-dependent apoptosis (11). Given the previously described changes in Ca²⁺ within the cell cycle and the reported effects of Ca²⁺ on G2/M progression, it will be important to determine whether GTSE-1 plays any role in cell cycle regulation attributed to changes in cytosolic Ca²⁺.

2) Cyclin T2 is another example of a cell cycle- or proliferation-related gene that is regulated by SOCE (upregulated in the low clones). This protein is found complexed to cyclin-dependent kinase (CDK)9, which is known to phosphorylate the protein product of the retinoblastoma gene (15). Although the kinase activity of CDK9 does not appear to be cell cycle dependent, there is recent evidence that it can be involved in controlling cell growth and/or cell viability (13). Other studies point to a role for CDK9 in differentiation, and, given the role of Ca²⁺ in differentiation, it would be important to investigate possible Ca²⁺-mediated changes in cyclin T2 in differentiating cells (15).

3) MAD1L1, the human homolog of the yeast mitotic checkpoint gene MAD1 (16, 28), is also regulated by SOCE. MAD1L1 mRNA was observed to decrease in cells with high SOCE. Although the precise function of MAD1L1 in the mitotic spindle checkpoint remains unknown, this again would be an interesting protein to explore in terms of the role of Ca²⁺ in regulating cell cycle.

4) In the high clones, a decrease in the expression of a cdk inhibitor p21 binding protein (TOK-1) was observed. A p21(Cip1/Waf1/Sdi1) protein is thought to negatively regulate the cell cycle by inhibiting kinase activity of a variety of cyclin-dependent kinases. TOK-1 is expressed at the G₁/S boundary of the cell cycle and is thus thought to be a new type of CDK2 modulator (42). An investigation of whether TOK-1 plays a role in the Ca²⁺-mediated regulation of cell cycle is warranted.

Changes in the level of SOCE have a significant impact on several genes that are related to various disease states.
1) The most striking observation in this regard is the large number of melanoma-associated genes the expression levels of which are increased when SOCE is decreased. In the low SOCE clones, an increase in gene expression was observed for melanoma antigen family members MAGEA1, MAGEA2A, MAGEC1, and MAGE6. The melanoma antigen genes were initially identified in melanomas and were subsequently found to have an expression pattern almost exclusively confined to tumors (39). Given that distribution pattern and our finding that four family members are upregulated in cells with low SOCE, it would be important to investigate whether changes, especially decreases, in SOCE levels occur in melanomas or other types of tumors.

2) The tumor protein D52 is another cancer-associated gene the expression of which is changed in cell clones with modified SOCE. Its expression level is decreased in the high SOCE clones. This gene was discovered in a differential screening of a breast carcinoma cDNA library; subsequent studies showed that this gene is overexpressed in 40% of breast carcinomas (9).

3) The dyskeratosis congenita 1 gene is downregulated in clones with high SOCE. The X-linked form of this disease, which results in skin and bone marrow failure, is due to mutations in the dyskerin gene. The dyskerin protein is a component of small nucleolar ribonuclear protein particles as well as the telomerase complex (35). The gene or genes involved in the recessive forms of the disease still remain unknown, so the regulation of dyskerin gene expression by SOCE should be of interest to those who study this particular disease.

4) Finally, the gene DSHP [Src homology (SH)2 domain protein-1A; Duncan’s disease] is upregulated in clones with low SOCE. DSHP encodes a single SH2 domain protein that is mutated in some patients with X-linked lymphoproliferative syndrome. Because DSHP is upregulated late in the immune response, this form of immunodeficiency differs from most others, where the mutations occur in a signaling molecule that is hard wired into the signaling complex (47). Thus it will be of great interest to determine whether changes in levels of SOCE can play a role in Duncan’s disease.

Changes in the level of SOCE alter the expression levels of several hormones, cytokines, and growth factors in HEK-293 cells.
1) In the cells with low levels of SOCE, a decreased expression of BMP-2, a member of the transforming growth factor (TGF)-ß superfamily of polypeptide signaling molecules, was observed. Although BMPs were first discovered for their osteogenic effects (56), they were later found to be expressed in a wide range of vertebrate embryonic structures (24) and to be involved in dorsal-ventral axis specifications (21). Mice having null mutations in BMP-2 die early in embryogenesis (62). In early reports, BMP-2 expression was found to be controlled by both retinoic acid and cAMP (44), but in recent studies in limb bud mesechymal cells, BMP-2 gene expression was increased by ionomycin and suppressed by the calcineurin inhibitor cyclosporine A (53). These results, coupled to our observation of a reduction of BMP-2 expression in low SOCE clones, suggest that further study into the role of store-operated channels in regulating BMP-2 would be important.

2) Another member of the TGF-ß superfamily found to be decreased in the clones with low SOCE is TGF-ß1. The TGF-ß compounds have three well-characterized biological activities. They inhibit growth in most cells except for chondrocytes and osteoblasts, in which they stimulate growth. They have an immunosuppressive effect by inhibiting T and B lymphocytes. They also stimulate the deposition of collagens, fibronectin, and proteoglycans (7). Additional studies will be required to determine the importance of the downregulation of TGF-ß1 levels in HEK cells that have low levels of SOCE.

3) The level of gene expression for FGF-13 was also found to be decreased in clones with low levels of SOCE. FGF-13 is a member of the large family of FGFs that were originally found to stimulate growth (20). They are now known to regulate differentiation and a number of other physiological functions in a wide variety of cells. They play an important physiological role in development, maintenance of tissues, and wound repair and have been postulated to play a pathophysiological role in arthritis, tumor proliferation, and arteriosclerosis. Because little is known about the regulation of gene expression for this recently discovered FGF family member (22), our observation that SOCE plays a role in regulating FGF-13 mRNA levels is an important contribution to this area.

Changes in the level of SOCE in HEK cells had an effect on a number of genes related to apoptosis.
Given the rich, but confusing, literature on the role of Ca²⁺ in apoptosis, there should be considerable interest in apoptotic genes regulated by SOCE.

1) The expression of Fas-associated death domain (FADD)-like apoptosis regulator (FLAME-1) was observed to increase in clones with high SOCE. FLAME-1, which contains FADD death effector domain homology regions, can be recruited to the Fas receptor complex, where it inhibits Fas/TNF receptor (TNFR)-induced apoptosis, possibly by acting as a dominant-negative inhibitor (52).

2) The programmed cell death 4 (PDCD4) gene was observed to decrease in clones having high levels of SOCE. This gene was first discovered as one that is upregulated after initiation of apoptosis in a number of different cell types, and recent evidence suggests that PDCD4 may function as a tumor suppressor gene (30). A recent paper (19) described the upregulation of PDCD4 in HEK-293 cells that were transfected with the fas ligand gene. However, with other apoptotic signals, PDCD4 can be unaffected or even downregulated (40, 41), indicating that we do not fully understand the role of this molecule in apoptosis.

3) The apoptosis-inducing factor (AIF, PDCD8) gene was also observed to decrease in clones with high levels of SOCE. AIF is expressed in both normal cells and a variety of cancer cells. The mature protein is confined to the mitochondrial intermembrane space, but in response to apoptosis-inducing conditions, it is released to the cytosol to act by a caspase-independent process to promote nuclear chromatin condensation and DNA fragmentation (12).

4) The Fas apoptotic inhibitory molecule (FAIM) is also downregulated in cells with high levels of SOCE. This gene was cloned by differential display comparing Fas-resistant and Fas-sensitive primary murine B lymphocytes. FAIM is evolutionarily conserved and expressed in a wide range of tissues, suggesting that its gene product plays a key physiological role. It will be interesting to investigate why this apoptotic inhibitory molecule is downregulated along with the cell death genes (listed above) when SOCE is increased.

A number of enzymes involved in metabolism are represented in the list of genes in Tables 1–4.
These include enzymes involved in carbohydrate metabolism such as galactokinase 1, glycerol kinase, phosphoglycerate kinase, solute carrier family 2, phosphofructokinase, dehydrogenase/reductase SDR family member, and HEP27; enzymes involved in amino acid metabolism such as serine hydroxymethyltransferase, phosphoserine aminotransferase, pyrroline-5-carboxylate synthetase, phosphoribosyl pyrophosphate amidotransferase, and glutamate decarboxylase 1; and enzymes involved in lipid metabolism such as very-long-chain acyl-CoA synthetase homolog 2, hydroxyacyl-CoA dehydrogenase, 3,2-trans-enoyl-CoA isomerase, dehydrocholesterol reductase, steroid sulfatase, and androgen-regulated short-chain dehydrogenase/reductase.

Changes in the level of SOCE had a dramatic effect on a number of signaling molecules.
These include protein kinases, protein phosphatases, and transcription factors. We will only discuss a subset of these, some of which have been demonstrated in other studies to be regulated by SOCE and some that are of interest to investigate as potential regulators of SOCE.

A number of signaling genes whose levels of expression were altered in our studies were also found to be Ca²⁺ regulated in other studies linking gene expression to SOCE (18, 27). These include calcineurin, cAMP-dependent protein kinase catalytic-ß, FLAME-1, c-myc, frizzled homolog 7, Sine oculis homeobox homolog 2 (SIX2), and TGF-ß1. The number of genes the expression of which is modified in the low SOCE clones (150 genes either increasing or decreasing) is not out of line with the number of genes proposed to respond to a decrease in SOCE in the T lymphocyte study (18) mentioned earlier (111 genes either increasing or decreasing), especially when one considers that the cDNA microarray used for our study had significantly more genes represented. Although there are a few genes in common between our study and the lymphocyte study, for the most part, SOCE appears to regulate different populations of genes in the two cell types, a possibility that we considered at the outset of the investigation (see INTRODUCTION). Compared with the data from the fibroblast study (27), the 150 genes regulated by a decrease in SOCE are much higher than the 29 genes seen to change. However, the microarray used in that study only represented 1,200 cDNA clones compared with the 22,000 cDNA clones represented on the Affymetrix microarray.

1) Our initial hypothesis was that some of the signaling genes that are high or low in the clones with high or low SOCE might be setting the levels of SOCE rather than responding to alterations in SOCE. This hypothesis was further supported by the observation that there were not significant changes in levels of TRPC genes in the high or low clones. Our results showing that expression of IRS-2 is high in four of five high clones and that expression of siRNA specific for IRS-2 inhibits SOCE in H36 cells, together with the observation that overexpression of IRS-2 in L29 cells elevates SOCE, support the hypothesis that IRS-2 may be involved in the SOCE signaling pathway. Because IRS-2 is clearly not causative for the high SOCE levels in the H1 clone, it is worthwhile to discuss what other signaling molecules listed in Tables 1–4 should be investigated as potential regulators of SOCE. Previous studies based on microinjection of GTPS (6, 17), microinjection of Clostridium C3 transferase, or overexpression of wild-type rho (60) suggested that small-molecular-weight G proteins might be involved in regulating SOCE. Thus it will be important to further investigate the following genes on our lists: Rab3 GTPase-activating protein, ras-related C3 botulinum toxin substrate 3 (RAC3), rho GDP dissociation inhibitor (GDI)-, and Rab1.

2) Previous data from our laboratory (1, 31) and from several other laboratories (45, 46, 55) suggest that there is a tyrosine phosphorylation step involved in regulating SOCE. Thus it will be important to investigate those molecules on our list that mediate changes in protein tyrosine phosphorylation levels or that interact with tyrosine phosphorylated proteins. These include the following: protein tyrosine kinase 9, H-Ryk receptor tyrosine kinase, protein tyrosine phosphatase receptor type F, CAK tyrosine protein kinase, protein tyrosine phosphatase nonreceptor type 4 (PTPN4), and SH2 domain protein 1A (DSHP).

It is possible that some of the genes upregulated in high SOCE may serve as negative regulators of SOCE, thereby preventing cells from achieving even higher levels of SOCE. Alternatively, it is possible that some of the genes upregulated in the low clones may serve to prevent the SOCE from falling to lower levels.

In summary, the selection of HEK-293 cell clones with high or low levels of SOCE, and the suppression of SOCE levels by the expression of siRNA specific for TRPC1, has enabled us to begin asking important questions concerning the physiological role of SOCE. The gene expr