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The Journal of Pharmacology and Experimental Therapeutics logoLink to The Journal of Pharmacology and Experimental Therapeutics
. 2017 May;361(2):219–228. doi: 10.1124/jpet.116.237255

Label-Free Dynamic Mass Redistribution Reveals Low-Density, Prosurvival α1B-Adrenergic Receptors in Human SW480 Colon Carcinoma Cells

Dorathy-Ann Harris 1, Ji-Min Park 1, Kyung-Soon Lee 1, Cong Xu 1, Nephi Stella 1, Chris Hague 1,✉
PMCID: PMC5399639  PMID: 28196836

Abstract

Small molecules that target the adrenergic family of G protein–coupled receptors (GPCRs) show promising therapeutic efficacy for the treatment of various cancers. In this study, we report that human colon cancer cell line SW480 expresses low-density functional α1B-adrenergic receptors (ARs) as revealed by label-free dynamic mass redistribution (DMR) signaling technology and confirmed by quantitative reverse-transcriptase polymerase chain reaction analysis. Remarkably, although endogenous α1B-ARs are not detectable via either [3H]-prazosin–binding analysis or phosphoinositol hydrolysis assays, their activation leads to robust DMR and enhanced cell viability. We provide pharmacological evidence that stimulation of α1B-ARs enhances SW480 cell viability without affecting proliferation, whereas stimulating β-ARs diminishes both viability and proliferation of SW480 cells. Our study illustrates the power of label-free DMR technology for identifying and characterizing low-density GPCRs in cells and suggests that drugs targeting both α1B- and β-ARs may represent valuable small-molecule therapeutics for the treatment of colon cancer.

Introduction

Catecholamines play a key role in regulating multiple physiologic responses, with the overarching goal of enhancing organism survival. However, in certain instances, prolonged catecholamine exposure produces detrimental effects on target organs that lead to cardiovascular disease (e.g., congestive heart failure, hypertension, cardiac arrhythmias, and renal failure) and/or psychiatric disorders (e.g., post-traumatic stress disorder and hyperactivity disorders). Although the molecular mechanisms that mediate these detrimental effects remain to be fully understood, it is well appreciated that chronic adrenergic receptor (AR) activation ultimately leads to deleterious alterations in cell morphology (Lefkowitz et al., 2000; O’Connell et al., 2006; Catapano and Manji, 2007).

Recent evidence suggests that catecholamine signaling may also play a role in cancer pathogenesis. Specifically, chronic β-AR activation drives primary tumor growth and metastasis by increasing the production of angiogenetic factors, enhancing invasion of cancer cells through the stimulation of matrix metalloproteinase–dependent migration, and/or affecting cancer cell viability and resistance to apoptosis (reviewed in Cole and Sood, 2012). As such, nonselective β-AR antagonists improve clinical outcomes of human cancer patients, and single-nucleotide polymorphisms in the β-ARs (or its signaling effectors) influence disease outcome (reviewed in Perez-Sayans et al., 2010). These studies suggest that, among the molecular mechanisms involved in catecholamine signaling, β-ARs represent a promising target to treat cancer.

The effect of chronic activation of α1-ARs on cancer pathogenesis and prognosis remains poorly understood. Quinazoline-based α1-AR antagonists doxazosin and terazosin can induce tumor cell apoptosis (Anglin et al., 2002; Partin et al., 2003), although it remains unclear whether these effects occur through α1-AR and/or non–α1-AR–dependent mechanisms (reviewed in Kyprianou et al., 2009). This question is particularly relevant when considering the three known subtypes of α1-AR (α1A, α1B, α1D) and their differential coupling in various cell types. For example, in murine models, chronic activation of α1A-ARs reduces cancer incidence and prolongs life span, whereas chronic α1B-AR activation typically produces deleterious effects on multiple organ systems (Wang et al., 2000; Zuscik et al., 2000; Papay et al., 2002; Doze et al., 2009; Collette et al., 2014). As such, understanding the effects of chronic α1-AR activation in specific cancer subtypes is essential.

Our laboratory studies macromolecular complexes formed by distinct α1-AR subtypes and how complex architecture influences α1-AR signaling and physiologic function. We recently showed that α1-AR subtypes associate with selective intracellular molecular scaffolds that dictate their pharmacodynamic and signaling characteristics (Lyssand et al., 2008, 2010; Camp et al., 2015). Interestingly, proteomic screening indicates cell-type–specific α1-AR:PDZ-protein complexes are formed in human SW480 colon cancer cells, suggesting α1-ARs may couple to noncanonical, G protein–independent signal transduction mechanisms in this cell line (Camp et al., 2015). Thus, we used label-free dynamic mass redistribution (DMR) assays to pharmacologically identify endogenous α1-AR subtypes functionally expressed in SW480 colon carcinoma cells, thereby facilitating their examination as potential antineoplastic drug targets.

Materials and Methods

Reagents and Plasmids.

Human α1B-AR cDNAs were subcloned in pSNAPf (New England Biolabs, Ipswich, MA) using In-Fusion HD cloning technology (Clontech, Mountain View, CA), as described in Kountz et al. (2016). The 5-methylurapidil (U101), BMY 7378 HCl (B134), clonidine HCl (C7897), (±) cyclazosin HCl (C247), dopamine HCl (H8502), doxazosin mesylate (D9815), histamine (H7125), (-)-isoproterenol HCl (I6504), (R)-(-)-niguldipine HCl (N162), phenylephrine hydrochloride (P6126), prazosin HCl (P7791), phentolamine HCl (P7547), phenoxybenzamine HCl (B019), (±)-propranolol HCl (P0884), serotonin HCl (H9523), tamsulosin HCl (T1330), and terazosin HCl (T4680) were purchased from Sigma-Aldrich (St. Louis, MO). Rauwolscine HCL was from Tocris (Bristol, UK). The [7-methoxy-3H]-prazosin and myo-[2-3H(N)]-inositol were from Perkin-Elmer. SNAP-782 substrate (S9142S) was from New England Biolabs (Corning, NY). Epic 384-well glass-bottomed biosensor plates were from Corning.

Cell Culture and Transfections.

Human SW480 colon carcinoma cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 2 mM L-glutamine. For SNAP–α1B-AR studies, cells were transfected with 1 mg/ml polyethylenimine and indicated concentrations of cDNA constructs, and then assayed 48 hours post-transfection.

Label-Free Dynamic Mass Redistribution Assays.

Label-free DMR assays were performed using a method derived from previously documented studies (Fang et al., 2006, 2007). SW480 cells (passage number 3–10) were seeded at ∼500 k/well in Corning Epic sensor microplates and cultured for 24 hours in DMEM plus fetal bovine serum. On the day of the experiment, cells were washed three times with Hanks’ balanced salt solution (HBSS) buffer and transferred to the Corning Epic BT reader, which was permanently housed in a Thermo cell culture incubator at 37°C with 5% CO2, as this magnified the amplitude of recorded DMR responses. Cells were incubated with antagonists dissolved in HBSS for at least 1 hour prior to adding agonist to ensure that equilibrium between antagonist and receptor had been reached, during which baseline DMR measurements were recorded. All compounds were added using a Sorenson Biosciences 96-well Benchtop Pipettor. Agonist DMR responses were recorded for 1 hour. Raw data were exported to Microsoft Excel using Epic Analyzer Software and then imported into GraphPad Prism software to calculate agonist (potency, intrinsic activity) and antagonist (affinity) properties. Agonist concentration–response curves were fit using variable slope nonlinear regression to determine potency (EC50) and Hill slope. The y-axis values on raw DMR data and agonist DMR concentration–response curves represent the shift in light wavelength in picometers (pm) as a result of DMR. Schild Plot analyses were performed when appropriate using a method derived from that first described in Arunlakshana and Schild (1959). Schild data were calculated for each experimental n (each having four replicates), and final Schild plots were calculated as the mean ± S.E.M. of each experimental value. Schild plots were fit using linear regression analysis. Nonlinear regression analysis was also used to determine whether antagonist Schild slopes were significantly different than 1. Apparent affinity (pKB) was calculated using the equation pKB = log (DR-1) − log [B]. Control experiments were performed, demonstrating antagonists used in this study did not stimulate significant DMR responses (see Supplemental Fig. 1).

Quantitative Reverse-Transcriptase Polymerase Chain Reaction Assays.

Total RNA was isolated from SW480 cells using NucleoSpin RNA isolation kit, according to manufacturer’s instructions (Macherey-Nagel). One-step quantitative reverse-transcriptase polymerase chain reactions (qRT-PCR) were carried out in a final volume of 20 μL that included the following: 2 μL template RNA (25 ng final concentration), 0.8 μL forward and reverse primer (10 μM final concentration), 0.4 μL ROX reference dye, 10 µL 2× one-step SYBR RT-PCR buffer, and 5.2 μL RNase-free H2O using the One Step SYBR PrimeScript RT-PCR kit (Clontech). Primers used for qRT-PCR analysis were as follows: α1A forward, 5′-TGCCAGATCAACGAGGAGC-3′, and reverse, 5′-GGCGTTTTTCCGATGGATGC-3′; α1B forward, 5′-CTTTCACGAGGACACCCTTAGC-3′, and reverse, 5′-GCCCAACGTCTTAGCTGCTT-3′; α1D forward, 5′-CTCCAGCCTGTCGCACAAG-3′, and reverse, 5′-TGTAGTCGGCCAATTCGTAGG-3′; β2 forward, 5′-TGGTGTGGATTGTGTCAGGC-3′, and reverse, 5′-GGCTTGGTTCGTGAAGAAGTC-3′; and adenine phosphoribosyltransferase (APRT) forward, 5′-GGCCGCATCGACTACATCG-3′, and reverse, 5′-CTCAGCCTTCCCGTACTCC-3′.

qRT-PCR reactions were performed using the following protocol: 42°C for 5 minutes; 95°C for 5 seconds; and 60°C for 20 seconds. Melt curve data were collected from 60°C to 95°C at a ramping rate of 0.2°C per second. qRT-PCRs were performed in quadruplicate on a Stratagene Mx3000 Real Time PCR system. Relative expression values of each genes of interest were normalized to the expression value of housekeeping gene APRT.

Radioligand-Binding Assays.

Cell membranes were prepared from cultured SW480 cells via scraping and polytron grinding three times for 30 seconds. Lysates were resuspended in ice-cold phosphate-buffered saline. Protein concentration was determined with an Eppendorf D30 BioPhotometer. A total of 100 μL phosphate-buffered saline containing 100 μg resuspended cell lysates was incubated with 100 μL varying concentrations of [3H]-prazosin for 30 minutes at 37°C with gentle shaking, in the absence (representing total [3H]-prazosin bound) or presence of 100 μM nonselective α-AR antagonist phentolamine (representing nonspecific [3H]-prazosin bound). Samples were then subjected to Brandel vacuum filtration (Brandel, Gaithersburg, MD). Filters were incubated with 5 mL liquid scintillation fluid and counted with a Tri-Carb 2200 CA liquid scintillation analyzer (Packard Instrument, Rockville, MD). Data were converted from cpm to fmol/mg protein (http://www.graphpad.com/quickcalcs/radcalcform/), and specific bound was calculated as total bound − nonspecific bound. Specific bound data were fit with one-site specific binding saturation analysis using GraphPad Prism 6 software. Data are expressed as mean ± S.E.M.

Phosphoinositol Hydrolysis Assays.

SW480 cells were prelabeled with 1 mCi/mL [3H]-myo-inositol. After 48 hours, cells were stimulated with 100 μM phenylephrine for 1 hour in HBSS buffer plus 10 mM LiCl. Total inositol phosphates were purified via Dowex ion exchange chromatography using the method described in Lyssand et al. (2008). Data were analyzed with GraphPad Prism 6 software and expressed as mean ± S.E.M.

SNAP Protein Gels.

SW480 cells were transfected with empty SNAP vector or SNAP-tagged α1B-AR cDNA using the previously described polyethylenimine transfection method. Forty-eight hours after transfection, cells were lysed with 50 mM Tris-HCl, 150 mM NaCl, 1% Nonidet P-40, and 0.1% Tween 20 buffer. A total of 0.5 μM BG-782 substrate and 1 mM dithiothreitol was added to lysates, and samples were incubated for 30 minutes at 37°C in the dark. Approximately 10 μg cell lysates was run on 8–10% gels using SDS-PAGE. Gels were imaged using the LI-COR Odyssey Scanner. Protein band size was determined by comparison with PageRuler Prestained NIR Protein Ladder (Thermo #26635).

Cell Proliferation and Viability.

Cells grown in DMEM supplemented with 10% fetal bovine serum were harvested using trypsin and seeded in DMEM supplemented with 1% fetal bovine serum in 96-well plates (5000 cells/well). Twenty-four hours later, cells were treated by adding drugs to the cell culture media, as previously described (Cherry et al., 2016). Three days later, cell proliferation was measured using 5-bromo-2′-deoxy-uridine cell proliferation enzyme-linked immunosorbent assay (colorimetric) kit (Roche, Indianapolis, IN) and cell viability using water soluble tetrazolium salt-1 (1:20; Roche, Pleasanton, CA) following manufacturer’s protocols.

Results

One study has examined AR expression and function in SW480 cells (Masur et al., 2001). Specifically, using flow cytometry and primary antibodies targeting AR subtypes, this study suggested that SW480 cells express α2 and β-ARs, and little to no α1-ARs. To confirm this finding, we subjected SW480 cells to label-free DMR assays, which measures agonist-stimulated changes in cell shape as a functional output. This highly quantitative technology detects deflections in the wavelength of polarized light reflected through glass-bottom microsensor plates (Camp et al., 2016). Up to 1 pm changes in wavelength are reliably detected, thereby permitting detection of minute changes in cell shape stimulated by endogenous ARs. Our first set of experiments examined SW480 DMR responses stimulated by AR subtype-selective agonists in real-time for 60 minutes. Raw DMR tracings are displayed in Fig. 1, A–E, which were then used to construct agonist concentration curves at t = 60 minutes (Fig. 1F) to calculate agonist potencies and intrinsic activities (Table 1). As shown in Fig. 1A, the endogenous catecholamine norepinephrine (NE) stimulated robust, positive DMR responses in a concentration-dependent manner, presumably through the activation of β- and α2-ARs previously demonstrated to be expressed by this cell type (Masur et al., 2001). Accordingly, the β-AR–selective agonist isoproterenol (Fig. 1B) and the α2-AR partial agonist clonidine (Fig. 1C) stimulated weaker DMR responses than those observed with NE. Clonidine was the most potent adrenergic agonist (Table 1). 5-HT (Fig. 1E) and histamine (data not shown) produced no significant DMR responses, suggesting members of these essential G protein–coupled receptor (GPCR) families are not endogenously expressed by SW480 cells.

Fig. 1.

Fig. 1.

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Agonist-stimulated DMR responses in human SW480 colon carcinoma cells. Raw label-free DMR responses were measured for the nonselective AR agonist norepinephrine (A), the β-AR–selective agonist isoproterenol (B), the α2-AR–selective agonist clonidine (C), the α1-AR–selective agonist phenylephrine (D), and 5-hydroxytryptamine/5-HT (E). (F) Data were used to construct concentration–response curves to calculate agonist potencies and intrinsic activities for stimulating DMR responses at t = 60 minutes (listed in Table 1). Data are mean ± S.E.M. from three to four independent experiments performed with four replicates.

TABLE 1.

Pharmacological properties of GPCR agonists in SW480 cells

Agonist-stimulated DMR concentration–response curves were constructed for responses measured at endogenous receptors expressed in SW480 cells. Log molar agonist potency (as stated prior to pEC50) were calculated using the time at which peak DMR response was observed. Intrinsic activities (IA) were calculated by setting norepinephrine maximal DMR responses equal to 1 and then normalizing other observed agonist maximal DMR values to this value. All data were analyzed with GraphPad Prism using nonlinear regression curve analysis and are expressed as mean ± S.E.M. of three to four independent experiments performed with four replicates.

Agonist pEC50 EC50 (μM) Max (pm) IA
Norepinephrine −5.96 ± 0.04 1.08 354.74 ± 4.72 1
Isoproterenol −4.27 ± 0.13 54.4 151.35 ± 7.66 0.43
Clonidine −6.52 ± 0.12 0.3 144.47 ± 13.2 0.41
Phenylephrine −5.40 ± 0.03 13.9 238.21 ± 4.52 0.67
5-HT ND ND ND ND
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IA, intrinsic activity; Max, maximum response observed in pm; ND, not determined.

Unexpectedly, the α1-AR–selective agonist phenylephrine stimulated positive DMR responses with higher intrinsic activity (0.68) than both clonidine and isoproterenol, suggesting that functional α1-ARs are present in SW480 cells, in direct contrast to the findings of previous studies (Masur et al., 2001).

Because DMR assays are thought to measure the summation of all cell signaling events, the possibility exists that the observed phenylephrine effects are a result of non–α1-AR signaling mechanisms. Phenylephrine has been reported to activate β-ARs in human vascular beds (Torp et al., 2001), rabbit left ventricle (Wagner et al., 1974), and guinea pig ventricles (Chess-Williams et al., 1990), depending on the relative density of α- versus β-ARs expressed. Thus, we assayed the efficacy of AR subtype-selective antagonists to block phenylephrine-stimulated DMR responses. If the observed phenylephrine DMR events are occurring via direct α1-AR stimulation, we expect α1-AR antagonists to inhibit phenylephrine responses at concentrations in the range of their reported nanomolar affinities. Saturating concentrations of the β-AR antagonist propranolol produced no significant changes in the phenylephrine DMR concentration–response curve (Fig. 2A), nor did the α2-AR antagonist rauwolscine (Fig. 2B), suggesting that phenylephrine DMR responses are not a result of β- or α2-AR receptor activation. By sharp contrast, pretreating cells for 1 hour with 100 nM α1/α2-AR irreversible antagonist phenoxybenzamine abolished phenylephrine DMR responses (Fig. 2C). Likewise, the competitive α1/α2-AR antagonist phentolamine produced progressive rightward shifts in phenylephrine concentration–response curve with successive half-log molar increments (Fig. 2D). Phenylephrine potencies in the absence and presence of phentolamine were determined and used to calculate phentolamine affinity using Schild regression analysis (Arunlakshana and Schild, 1959). Phentolamine inhibited phenylephrine DMR responses with affinity constant (pA2) = −6.93, or 117 nM (Fig. 2E; Table 2), which is within the range of previously reported values for binding the α1B-AR subtype (Morrow and Creese, 1986; Hong et al., 2005; Bavadekar et al., 2008).

Fig. 2.

Fig. 2.

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Phenylephrine-stimulated SW480 DMR responses are blocked by α-AR antagonists. Label-free DMR responses were measured for the α1-AR–selective agonist phenylephrine in the absence and presence of (A) the β-AR antagonist propranolol, (B) the α2-AR antagonist rauwolscine, (C) the irreversible α1/α2-AR antagonist phenoxybenzamine, (D) or the competitive α1/α2-AR antagonist phentolamine. For each condition, concentration–response curves were constructed using DMR values obtained at t = 60 minutes, and used to calculate agonist potency. (E) Schild regression analysis of phentolamine affinity from data in (D). Data are mean ± S.E.M. from three independent experiments performed with four replicates.

TABLE 2.

Pharmacological values used for α1-AR subtype–selective antagonist Schild regression analyses

Phenylephrine-stimulated concentration–response curves were calculated for stimulating DMR responses in SW480 cells. Log molar agonist potencies (pEC50) were calculated using the time at which peak DMR response was observed in the absence and presence of various α-AR antagonists (concentration of antagonist used shown in brackets for each phenylephrine pEC50 value). Agonist potencies were subsequently used to calculate antagonist affinity and slope via Schild regression analysis. All data were analyzed with GraphPad Prism and are expressed as mean ± S.E.M. of two to four independent experiments performed with four replicates.

Antagonist Phenylephrine pEC50 ± S.E.M.
 Schild Analysis
Control Dose 1 Dose 2 Dose 3 Dose 4 pA2 Slope
Phentolamine −5.12 ± 0.03 −4.52 ± 0.03 (300 nM) −4.19 ± 0.04 (1 μM) −3.54 ± 0.06 (3 μM) −3.11 ± 0.1 (10 μM) −6.93 ± 0.51 1.03 ± 0.08
Doxazosin −5.56 ± 0.05 −5.24 ± 0.05 (10 nM) −5.23 ± 0.03 (30 nM) −4.86 ± 0.04 (100 nM) −4.07 ± 0.08 (300 nM) −7.76 ± 0.27 1.18 ± 0.34
Terazosin −5.28 ± 0.05 −4.98 ± 0.11 (100 nM) −4.52 ± 0.08 (300 nM) −4.06 ± 0.14 (1 μM) — −7.81 ± 0.25 0.93 ± 0.29
Prazosin −5.70 ± 0.06 −5.29 ± 0.05 (100 nM) −4.69 ± 0.05 (300 nM) −4.38 ± 0.07^^ (1 μM) — −7.47 ± 0.32 1.07 ± 0.21
Tamsulosin −5.43 ± 0.06 −4.87 ± 0.07 (300 nM) −4.54 ± 0.07 (1 μM) −4.38 ± 0.1 (3 μM) −4.05 ± 0.1 (10 μM) ND ND
5-Methylurapidil −5.45 ± 0.1 −5.17 ± 0.14 (300 nM) −4.84 ± 0.14 (1 μM) −4.89 ± 0.25 (3 μM) −4.21 ± 0.23 (10 μM) ND ND
Niguldipine −5.65 ± 0.08 −5.08 ± 0.11 (3 μM) −3.82 ± 0.15 (10 μM) — — ND ND
Cyclazosin −5.42 ± 0.11 −4.86 ± 0.12 (10 nM) −4.62 ± 0.09 (30 nM) −4.01 ± 0.11 (100 nM) — −8.37 ± 0.19 0.97 ± 0.21
BMY7378 −5.14 ± 0.03 −4.57 ± 0.05(300 nM) −4.13 ± 0.03 (1 μM) −3.57 ± 0.07 (3 μM) −3.21 ± 0.1 (10 μM) −6.87 ± 0.47 1.06 ± 0.01
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ND, not determined.

^^

Indicates agonist concentration–response curve Hill slope is <1, as determined by nonlinear regression curve four-parameter variable slope analysis.

We next assayed the affinity of the quinazoline α1-AR–selective antagonists doxazosin (Fig. 3, A and B), terazosin (Fig. 3, C and D), and prazosin (Fig. 3, E and F) for inhibiting phenylephrine DMR responses. All three antagonists produced progressive rightward shifts in DMR concentration–response curves facilitating Schild regression analysis to calculate antagonist pA2 values (listed in Table 2). The affinities for terazosin and doxazosin are consistent with previously reported affinity values for antagonizing α1-AR–stimulated contractions of the rabbit (Martin et al., 1997) and human prostate (Kenny et al., 1996; Muramatsu et al., 1998). However, the observed prazosin DMR affinity is appreciably lower than what is reported in isolated tissue in vitro contraction assays examining the three α1-AR subtypes (Docherty, 2010). Rather, our observed prazosin affinity value is within the range of the elusive α1L-adrenoceptor, or low-affinity α1-AR prazosin binding site previously documented to be present in dog saphenous vein (Muramatsu et al., 1990), rat small mesenteric artery (Stam et al., 1999), rabbit urinary tract (Van der Graaf et al., 1997), rat portal vein (Digges and Summers, 1983), and rat vas deferens (Muramatsu et al., 1996). Thus, the lack of effect of β- and α2-AR antagonists, and the pronounced inhibition of phenylephrine DMR responses by mixed α1/α2-AR– and α1-AR–selective antagonists, indicate SW480 cells express functional α1-ARs.

Fig. 3.

Fig. 3.

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Phenylephrine-stimulated SW480 DMR responses are blocked with high affinity by α1-AR antagonists. Label-free DMR responses were measured for the α1-AR–selective agonist phenylephrine in the absence and presence of the α1-AR–selective antagonists (A) doxazosin, (C) terazosin, and (E) prazosin. For each condition, concentration–response curves were constructed using DMR values obtained at t = 60 minutes, from which agonist potencies were calculated for subsequent Schild regression analysis of affinity for doxazosin (B), terazosin (D), and prazosin (F). Data are mean ± S.E.M. from three independent experiments performed with four replicates.

We next sought to identify the specific α1-AR subtype(s) expressed by comparing the ability of various α1-AR subtype-selective antagonists to inhibit phenylephrine DMR responses. The antagonists tested included tamsulosin (α1A/D-AR selective), 5-methylurapidil and niguldipine (α1A-AR selective), cyclazosin (α1B-AR selective), and BMY7378 (α1D-AR selective). All antagonist functional data are compiled in Table 2.

BMY7378 inhibited DMR responses with pA2 = −6.87, or 134 nM (Fig. 4, A and B), which is significantly lower than previously reported BMY 7378 functional affinities for inhibiting α1D-AR–mediated contraction of rat (Goetz et al., 1995; Kenny et al., 1995; Indra et al., 2002; Cleary et al., 2005) or mouse thoracic aorta (Yamamoto and Koike, 2001), and more indicative of an α1A or α1B affinity for this antagonist. Accordingly, the α1A/D-AR antagonist tamsulosin blocked phenylephrine-stimulated DMR (Fig. 4C) with significantly lower apparent affinity (1 μM tamsulosin pKB = −6.83 ± 0.08) than previously reported tamsulosin pA2 values for blocking the α1A- or α1D-AR subtypes (Noble et al., 1997). The lack of α1A-AR contribution to phenylephrine responses was supported by the limited ability of α1A-AR antagonists 5-methylurapidil (Fig. 4D) and niguldipine (Fig. 4E) to antagonize phenylephrine-stimulated DMR responses. Interestingly, maximum phenylephrine DMR values were significantly decreased with increasing concentrations of tamsulosin, 5-methylurapidil, and niguldpine. This observation demonstrates a potential limitation of using label-free DMR assays to calculate antagonist pA2 values. Label-free DMR assays are thought to measure the summation of all downstream signaling events following GPCR activation (Fang et al, 2006, 2007; Schroder et al., 2010). Thus, if an α1-AR antagonist binds and alters the activity of off-target proteins essential to the α1-AR signaling cascade, this may result in an alteration in the magnitude of the DMR output. For example, niguldipine, a member of the dihydropyridine family of antihypertensives, inhibits both L-type Ca2+ channels (Boer et al., 1989) and bTREK-1 K+ channels (Liu et al., 2007), such that decreases in maximum phenylephrine DMR responses observed at μM concentrations of niguldipine may be a result of disrupted electrochemical gradients. As such, the observed antagonist-dependent decreases in phenylephrine maximal DMR responses preclude pA2 determination for tamsulosin, 5-methylurapidil, or niguldipine. Regardless, the inability of BMY7378, tamsulosin, 5-methylurapidil, or niguldipine to antagonize phenylephrine-stimulated DMR responses in SW480 with high affinity strongly suggests the α1A and α1D subtypes do not functionally contribute to phenylephrine responses. Conversely, the α1B-AR–selective antagonist cyclazosin potently inhibited phenylephrine responses with pA2 = −8.37, or 4.26 nM (Fig. 4, F and G), consistent with reported affinities for blocking α1B-AR–mediated contraction of rabbit thoracic aorta (Marucci et al., 2005). Given the α1B-AR antagonist cyclazosin produced the most potent inhibition of phenylephrine DMR responses in SW480 cells, we hypothesize SW480 cells express predominantly the α1B-AR subtype.

Fig. 4.

Fig. 4.

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Phenylephrine-stimulated SW480 DMR responses are blocked with high affinity by the α1B-AR–selective antagonist cyclazosin. Label-free DMR responses were measured for the α1-AR–selective agonist phenylephrine in the absence and presence of the α1D-AR subtype-selective antagonist BMY7378 [(A) Schild regression analysis in (B)]; the α1A/D-AR subtype-selective antagonist tamsulosin (C); the α1A-AR subtype-selective antagonists 5-methylurapidil (D) and niguldipine (E); and the α1B-AR subtype-selective antagonist cyclazosin [(F) Schild regression analysis in (G)]. For each condition, concentration–response curves were constructed using DMR values obtained at t = 60 minutes. When appropriate, agonist potencies were calculated for subsequent Schild regression analysis of affinity. Data are mean ± S.E.M. from three independent experiments performed with four replicates.

Label-free DMR pharmacological results were subsequently validated with quantitative reverse-transcriptase polymerase chain reaction assays. Internal primers directed against individual α1-AR subtypes and the β2-AR were used to measure relative mRNA concentrations expressed as cycle threshold fold. All values were compared with APRT levels as a reference housekeeping gene. As shown in Fig. 5A, α1B-AR mRNA levels were 2.53 ± 0.09-fold greater than APRT, whereas α1A-AR (0.02 ± 0.06) and α1D-AR (6 × 10−4 ± 0.28) mRNA levels were barely detectable. β2-AR mRNA levels (0.09 ± 0.04) were greater than α1A- and α1D-AR, yet significantly lower than α1B-AR.

Fig. 5.

Fig. 5.

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SW480 cells express high levels of α1B-AR mRNA and low densities of functional α1B-ARs. (A) Quantitative reverse-transcriptase polymerase chain reaction was performed on mRNA isolated from SW480 cell lysates using internal primers targeted to the α1A (ADRA1A), α1B (ADRA1B), α1D (ADRA1D), and β2 (ADRB2)-AR subtypes. Data were normalized as cycle threshold (CT) fold change relative to APRT mRNA levels and are as expressed as mean ± S.E.M. (n = 2 with three replicates). (B) Polyacrylamide gel electrophoresis of wild-type SW480 cell lysates (MOCK lane), or SW480 cell lysates following transfection with empty pSNAP vector (SNAP), and 2, 3, or 5 μg N-terminal SNAP-epitope–tagged α1B-AR cDNA. SNAP–α1B-AR protein bands are denoted with black arrow on right (76.6 kDa). (C) Saturation [3H]-prazosin radioligand-binding assays were performed on SW480 cell lysates transfected with empty pSNAP vector (black ▪) or 3 μg SNAP-α1B-AR cDNA (red ▪). Nonspecific binding was determined with 10 μM phentolamine. Data are the mean ± S.E.M. of three experiments with three replicates. (D) Phosphoinositol hydrolysis assays were performed on SW480 cells transfected with empty pSNAP vector or 3 μg SNAP–α1B-AR cDNA. Cells were preincubated with 1 μCi [3H]-myoinositol for 48 hours and treated with HBSS buffer or 100 μM phenylephrine for 1 hour. Data are expressed as the mean ± S.E.M. of three experiments performed in triplicate. **Student’s t test, P < 0.05. (E) Label-free DMR responses were measured for the α1-AR–selective agonist phenylephrine in SW480 cells transfected with empty pSNAP vector (black ▪) or 3 μg SNAP–α1B-AR cDNA (red ▪). Data are the mean ± S.E.M. of two experiments with four replicates.

Thus, SW480 cells produce relatively high levels of α1B-AR mRNA and robust phenylephrine-stimulated label-free DMR responses inhibited by the α1B-AR–selective antagonist cyclazosin. Yet, α1-ARs are reported as minimally detectable in previous flow cytometry assays (Masur et al., 2001). To clarify this discrepancy, we quantified endogenous α1B-AR functional receptor density with [3H]-prazosin radioligand-binding and phosphoinositol hydrolysis assays. As a positive control, we included SW480 cells transfected with N-terminal SNAP-epitope–tagged α1B-ARs. SNAP protein gels demonstrated 3 μg SNAP–α1B-AR cDNA was optimal for ensuring SW480 cell viability and maximal SNAP–α1B-AR protein levels (Fig. 5B, denoted with arrow at ∼76.6 kDa). Remarkably, we did not detect significant levels of endogenous α1B-AR binding sites (<10 fmol/mg protein) in SW480 cell lysates with [3H]-prazosin saturation radioligand-binding assays, whereas transfecting SNAP–α1B-AR induced a robust increase in receptor density (Bmax to 59.3 ± 10.7 fmol/mg protein; Fig. 5C). Phosphoinositol hydrolysis assays produced equivalent results (Fig. 5D). Specifically, application of 100 μM phenylephrine did not generate significant increases in wild-type SW480 cellular inositol phosphate levels, yet produced a 62.1% increase in inositol phosphate formation in SW480 cells transfected with SNAP–α1B-AR. Congruent with this finding, maximal phenylephrine-stimulated DMR responses were enhanced by 25.8% in SW480 cells when transfected with SNAP–α1B-AR (Fig. 5E). Taken together, these data demonstrate that SW480 cells express low levels of functional α1B-ARs that are undetectable with radioligand-binding and reductionist functional assays, but are robustly detectable with label-free DMR assays.

Although it is has been shown that AR stimulation induces migration of cultured SW480 cells (Masur et al., 2001), this study used NE as the adrenergic agonist, and thus the AR subtype(s) involved remains unclear. With the discovery that SW480 cells express low level of functional α1B-ARs, we sought to delineate the effect of β- and α1-AR stimulation on SW480 cell viability and proliferation. β-AR stimulation with isoproterenol produced a dose-dependent decrease in both cell viability and proliferation (Fig. 6A). Note that the antiproliferative effects of isoproterenol were marginally greater than its antiviability effects. Conversely, α1-AR stimulation with phenylephrine produced a striking increase in cell viability, with no significant effects on cell proliferation (Fig. 6B). The prosurvival effects of phenylephrine were antagonized by the α1-AR antagonists terazosin, cyclazosin, phenoxybenzamine, and phentolamine (Fig. 6C). These results show that SW480 cells express low densities of functional α1B-ARs that, when activated, are prosurvival.

Fig. 6.

Fig. 6.

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Stimulation of β-AR decreases cell proliferation and viability, and stimulation of α1B-AR increases cell viability of water soluble tetrazolium salt (SW480) cells. Changes in SW480 cell proliferation and viability in response to drug treatment were measured using bromodeoxyuridine and WST-1 assays, respectively. The β-AR–selective agonist isoproterenol produces concentration-dependent decreases in both cell proliferation and viability (A), whereas the α1-AR–selective agonist phenylephrine increases cell viability without affecting proliferation (B). (C) Phenylephrine-stimulated increases in cell viability (100 μM) were antagonized by 10-minute pretreatment with cyclazosin (CYS, 100 nM) terazosin (TRZ, 1 µM), phenoxybenzamine (PBZ, 300 nM), or phentolamine (PHN, 10 µM). Data are the mean ± S.E.M. of three experiments with four replicates. **P < 0.01 compared with vehicle-treated cells (control) (one-way analysis of variance with Dunnett’s post hoc test).

Discussion

With the ongoing and increasing usage of small molecules targeting adrenergic signaling mechanisms for the treatment of cardiovascular disease, central nervous system disorders, and numerous other indications, understanding whether these medicines also influence tumor growth and/or metastasis is of critical importance. Accordingly, identifying subtypes of ARs expressed by specific cancer cells and characterizing the action of small molecules targeting this receptor family and their resulting effect on tumor cell fate will provide critical information that might drive patient-specific pharmacotherapy. In this study, we illustrate the inherent power of label-free DMR signaling technology to identify low-density, yet highly-functional ARs in SW480 carcinoma cancer cells. Moreover, to the best of our knowledge, our study represents the first to combine label-free DMR assays with Schild regression analysis of antagonist affinities to facilitate pharmacological characterization of cancer cell–specific expression of endogenous GPCR subtypes. Based on these results, we provide evidence that antagonists targeting both α1B-AR and β-AR may significantly affect cancer cell fate.

The effects of chronic AR stimulation on human health and disease have been extensively studied and documented. To date, the overwhelming majority of the data indicates chronic β-AR stimulation produces negative outcomes on cancer prognosis. Epidemiologic data compiled from breast cancer outcomes of patients on chronic β-blocker therapy had a 57% reduced risk of metastasis and 71% reduction in mortality after 10 years (Powe et al., 2010). A subsequent study discovered brain metastasis derived from breast cancer cells have increased β2-AR mRNA and protein expression levels, and exhibit enhanced cell proliferation and migration (Choy et al., 2016). Chronic β-AR activation induced by stress promotes colon cancer metastasis (Zhao et al., 2015), possibly through trans-activation of the epidermal growth factor receptor-Akt/extracellular signal-regulated kinase pathway (Chin et al., 2016). Accordingly, β-AR blockade has been shown to be an effective treatment in experimental models of colon cancer (Barbieri et al., 2015; Sorski et al., 2016) and reduces the incidence of colon cancer in human populations (Chang et al., 2015). Contrary to these findings, we found β-AR activation decreased SW480 cell viability and proliferation, similar to what has been reported in cardiomyocytes during heart failure, where prolonged β-AR stimulation leads cell death, fibrosis, and adverse remodeling (reviewed in Lefkowitz et al., 2000).

Significantly less information exists on the role of α1-AR activation in cancer outcomes. Studies investigating the effects of chronic α1-AR stimulation on cell survival from cardiovascular studies provide valuable insights (ALLHAT, 2002). A major revelation of the groundbreaking Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) was the increased incidence of deleterious cardiovascular events in the doxazosin-treatment arm, forcing an early termination of this portion of the study (Piller et al., 2002). This single observation has significantly diminished the use of α1-AR antagonists for hypertension, heart failure, and other cardiovascular diseases. Subsequent basic science studies showed that α1-AR stimulation inhibits apoptosis in in vitro and in vivo and enhances ischemic preconditioning in human volunteers via activating prosurvival mechanisms such as induction of fetal gene transcription, increased protein synthesis, enhanced glycolysis, activation of extracellular signal-regulated kinase, protein kinase C, and others (reviewed in Jensen et al., 2011). Additionally, a series of elegant knockout mice studies show that knocking the α1A- and α1B-AR subtypes induces maladaptive cardiac morphologic alterations, resulting in diminished cardiac output, impaired exercise tolerance, and enhanced mortality induced by transverse aortic constriction (TAC), suggesting α1-ARs are prosurvival in cardiomyocytes (O’Connell et al., 2006). Despite these significant studies, the specific contributions of individual α1-AR subtypes to the prosurvival phenotype in various cancers remain unclear.

We found that α1B-AR stimulation enhanced SW480 cell survival, despite their relatively low expression. Interestingly, mice overexpressing constitutively active α1B-AR mutants display a host of maladaptive phenotypes, including cardiac hypertrophy post-TAC (Wang et al., 2000), depression-like behavior (Doze et al., 2009), age-related apoptotic neurodegeneration (Zuscik et al., 2000; Papay et al., 2002), and reduced life span (Collette et al., 2014). Taken together, the effects of α1B-AR stimulation on cell outcome may be directly correlated to their functional expression levels, with low to moderate expression of α1B-ARs being prosurvival, whereas overexpression promotes morphologic changes leading to cell death. Further studies are needed to clarify the effect of chronic α1B-AR stimulation on cancer cell fate, and whether these effects are specific to colon carcinoma cells.

In summary, we leveraged the power of label-free DMR signaling technology to identify and characterize low-density α1B-ARs previously undetectable with traditional experimental approaches used to quantify GPCR functional expression levels in cell culture (i.e., as measured by flow cytometry, radioligand binding, or reductionist functional assays), which, when activated, increase the survival of human SW480 colon carcinoma cells. To our knowledge, this study is the first to examine the effects of α1B-AR stimulation on human colon carcinoma cell fate. Thus, label-free DMR should prove useful for characterizing membrane-signaling proteins functionally expressed on specific carcinoma cell types, thereby facilitating their investigation as potential antineoplastic targets using available therapeutic agents, or to predict the side effects of concurrently used medications on tumor cell outcome. As health-care systems move toward the era of precision medicine, the translational advantages of label-free technology provide a unique opportunity to drive patient-specific pharmacotherapy. Our discovery that α1B-ARs are prosurvival in SW480 colon carcinoma cells may have relevance for the increasing number of patients taking α1-AR antagonists for benign prostatic hypertrophy, post-traumatic stress disorder, and/or cardiovascular disease. Thus, further investigation into the effects of α1-AR stimulation on colon carcinoma cell fate is warranted.

Abbreviations

APRT

adenine phosphoribosyltransferase

AR

adrenergic receptor

DMEM

Dulbecco’s modified Eagle’s medium

DMR

dynamic mass redistribution

GPCR

G protein–coupled receptor

HBSS

Hanks’ balanced salt solution

NE

norepinephrine

pA2

affinity constant

pKB

apparent affinity

qRT-PCR

quantitative reverse-transcriptase polymerase chain reaction

TAC

transverse aortic constriction

Authorship Contributions

Participated in research design: Harris, Lee, Stella, Hague.

Conducted experiments: Harris, Park, Lee, Xu, Hague.

Performed data analysis: Harris, Lee, Stella, Hague.

Wrote or contributed to the writing of the manuscript: Harris, Stella, Hague.

Footnotes

This work was supported by the National Institutes of Health [Grants GM100893, DA014486, and 5T32GM00775].

Inline graphicThis article has supplemental material available at jpet.aspetjournals.org.

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