Title: Glucocorticoids rapidly activate cAMP production via G<sub>αs</sub>to initiate non‐genomic signaling that contributes to one‐third of their canonical genomic effects
Abstract: The FASEB JournalVolume 34, Issue 2 p. 2882-2895 RESEARCH ARTICLEOpen Access Glucocorticoids rapidly activate cAMP production via Gαs to initiate non-genomic signaling that contributes to one-third of their canonical genomic effects Francisco J. Nuñez, Francisco J. Nuñez Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorTimothy B. Johnstone, Timothy B. Johnstone Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorMaia L. Corpuz, Maia L. Corpuz Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorAustin G. Kazarian, Austin G. Kazarian Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorNicole N. Mohajer, Nicole N. Mohajer Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorOmar Tliba, Omar Tliba Department of Biomedical Sciences, College of Veterinary Medicine, Long Island University, Brookville, NY, USASearch for more papers by this authorReynold A. Panettieri Jr., Reynold A. Panettieri Jr. Rutgers Institute for Translational Medicine and Science, Child Health Institute, Rutgers University, New Brunswick, NJ, USASearch for more papers by this authorCynthia Koziol-White, Cynthia Koziol-White Rutgers Institute for Translational Medicine and Science, Child Health Institute, Rutgers University, New Brunswick, NJ, USASearch for more papers by this authorMoom R. Roosan, Moom R. Roosan Department of Pharmacy Practice, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorRennolds S. Ostrom, Corresponding Author Rennolds S. Ostrom [email protected] Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USA Correspondence Rennolds S. Ostrom, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, 9401 Jeronimo Road, Irvine, CA 92618, USA. Email: [email protected] for more papers by this author Francisco J. Nuñez, Francisco J. Nuñez Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorTimothy B. Johnstone, Timothy B. Johnstone Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorMaia L. Corpuz, Maia L. Corpuz Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorAustin G. Kazarian, Austin G. Kazarian Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorNicole N. Mohajer, Nicole N. Mohajer Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorOmar Tliba, Omar Tliba Department of Biomedical Sciences, College of Veterinary Medicine, Long Island University, Brookville, NY, USASearch for more papers by this authorReynold A. Panettieri Jr., Reynold A. Panettieri Jr. Rutgers Institute for Translational Medicine and Science, Child Health Institute, Rutgers University, New Brunswick, NJ, USASearch for more papers by this authorCynthia Koziol-White, Cynthia Koziol-White Rutgers Institute for Translational Medicine and Science, Child Health Institute, Rutgers University, New Brunswick, NJ, USASearch for more papers by this authorMoom R. Roosan, Moom R. Roosan Department of Pharmacy Practice, Chapman University School of Pharmacy, Irvine, CA, USASearch for more papers by this authorRennolds S. Ostrom, Corresponding Author Rennolds S. Ostrom [email protected] Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USA Correspondence Rennolds S. Ostrom, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, 9401 Jeronimo Road, Irvine, CA 92618, USA. Email: [email protected] for more papers by this author First published: 27 December 2019 https://doi.org/10.1096/fj.201902521RCitations: 2 Francisco J. Nuñez and Timothy B. Johnstone contributed equally to this work. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Glucocorticoids are widely used for the suppression of inflammation, but evidence is growing that they can have rapid, non-genomic actions that have been unappreciated. Diverse cell signaling effects have been reported for glucocorticoids, leading us to hypothesize that glucocorticoids alone can swiftly increase the 3′,5′-cyclic adenosine monophosphate (cAMP) production. We found that prednisone, fluticasone, budesonide, and progesterone each increased cAMP levels within 3 minutes without phosphodiesterase inhibitors by measuring real-time cAMP dynamics using the cAMP difference detector in situ assay in a variety of immortalized cell lines and primary human airway smooth muscle (HASM) cells. A membrane- impermeable glucocorticoid showed similarly rapid stimulation of cAMP, implying that responses are initiated at the cell surface. siRNA knockdown of Gαs virtually eliminated glucocorticoid-stimulated cAMP responses, suggesting that these drugs activate the cAMP production via a G protein-coupled receptor. Estradiol had small effects on cAMP levels but G protein estrogen receptor antagonists had little effect on responses to any of the glucocorticoids tested. The genomic and non-genomic actions of budesonide were analyzed by RNA-Seq analysis of 24 hours treated HASM, with and without knockdown of Gαs. A 140-gene budesonide signature was identified, of which 48 genes represent a non-genomic signature that requires Gαs signaling. Collectively, this non-genomic cAMP signaling modality contributes to one-third of the gene expression changes induced by glucocorticoid treatment and shifts the view of how this important class of drugs exerts its effects. Abbreviations ßAR ß-adrenergic receptor cAMP 3′,5′-cyclic adenosine monophosphate CS connectivity score Fsk forskolin GPCR G protein-coupled receptor GPER G protein estrogen receptor GR glucocorticoid receptor GRE glucocorticoid response element HASM airway smooth muscle IBMX 3-isobutyl-1-methylxanthine Iso isoproterenol mGR membrane-bound glucocorticoid receptor PDE phosphodiesterase 1 INTRODUCTION Glucocorticoids are used in the treatment of a wide array of diseases, including rheumatoid arthritis, autoimmune disorders, allergy, cancer, and respiratory syndromes. The occurrence of side effects and glucocorticoid resistance, particularly with systemic use, greatly hamper their use.1 The combination of glucocorticoids ß-agonists has long been considered the most effective means for controlling asthma and chronic obstructive pulmonary disease, even more than using either alone.2-5 While the mechanisms underlying glucocorticoid genomic effects on enhancing the clinical efficacy of ß-agonist have been previously studied,6 the possibility that non-genomic signaling by glucocorticoids enhance the clinical efficacy of ß-agonists has not been investigated due to the limited tools to measure real time kinetics of intracellular changes in 3′,5′-cyclic adenosine monophosphate (cAMP) concentration. Conventional thought suggests that glucocorticoids alter the cell function through changes in the gene expression that occur via activation of ubiquitously expressed intracellular glucocorticoid receptors (GR).7 In the absence of glucocorticoid, the GR resides in the cytoplasm then translocates to the cell nucleus upon binding of ligand. Nuclear GR then interacts with glucocorticoid response elements (GREs) to alter the gene expression. Various reports, some published over 25 years ago, suggest that glucocorticoids also induce rapid alterations in various signaling processes that appear to be non-genomic in nature.5, 8, 9 The human skin blanching assay (often called the vasoconstrictor assay) has been used for nearly 50 years as a means of qualitatively assessing the topical availability and potency of glucocorticoids.10 This test characterizes the potency of glucocorticoids through non-genomic effects on vasoconstriction. Some of these non-genomic effects may require specific interactions with membrane-bound versions of GR (mGR) or other undefined membrane components.5 Short duration treatment of various cell types with glucocorticoid affect many different signaling events, including agonist-induced calcium release, reactive oxygen species, and arachidonic acid release.11-16 BSA-conjugated cortisol, a steroid unable to cross the cell membrane, has been used as a tool to differentiate plasma membrane GR effects from those of the cytosolic GR. For instance, the effects of short (5 to 90 minutes) exposure of BSA-cortisol on leukemia cells was studied using proteomic tools and 128 unique proteins were found to be specifically upregulated.17 Interestingly, the putative mGR may interact with the NMDA receptor or may directly activate a G protein-coupled receptor (GPCR) coupled to Gαs and/or Gq/11.18 While these and other studies support the idea that glucocorticoids possess the ability to modulate rapid, non-genomic signaling, none reveal the specific signaling pathways or receptor(s) responsible. Our studies demonstrate that glucocorticoids rapidly increase the cAMP levels in a variety of cell types. In human airway smooth muscle (HASM) glucocorticoids trigger this non-genomic signaling via binding to an extracellular site and activating the stimulatory G protein, Gαs. While a GPCR might be involved, our data suggest that the G protein estrogen receptor (GPER) does not mediate this response. Removal of this rapid, non-genomic signal via siRNA knockdown of Gαs modifies the transcriptomic response to the glucocorticoid, budesonide. Out of a 140 gene budesonide signature, the alteration of 48 of these genes was dependent upon the Gαs-cAMP signal. Thus, of all the canonical changes in gene transcription by glucocorticoid, a full one-third of them require this rapid, non-genomic signal. 2 MATERIALS AND METHODS 2.1 Materials Forskolin (Fsk) was purchased from LC Laboratories. Cell culture media and components were purchased from Thermo Fisher. Fetal bovine serum was purchased from Atlanta Biologicals. siRNA construct for silencing GNAS were obtained from Dharmacon. The sense sequence used was CGAUGUGACUGCCAUCAUCUU. Secondary antibodies were obtained from Santa Cruz Biotechnology. All other drugs and chemicals were purchased from Sigma-Aldrich unless otherwise noted. 2.2 Cell culture HASM cells were isolated from deceased, de-identified lung donors by enzymatic dissociation in accordance with Institutional Review Board approval and as described previously.19 HASM cells were grown in Ham's F-12 media (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum, pen/strep, 25 mM HEPES, 1.7 mM CaCl2, and l-glutamine. Cells were kept at 5% CO2 and 37°C. Experiments were performed on cells from passage 3-7 using cells from 10 different donors in total, and at least three different donors for each study. Patient demographics are described in Table 1. Human fetal lung (HFL-1) fibroblasts (American Type Culture Collection) were grown in Ham's F12 medium with 10% fetal bovine serum and 1% antibiotic-antimycotic solution. HEK-293 cells (American Type Culture Collection) were cultured in Dulbecco's modified Eagle's medium (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum. All cells were kept in a humidified incubator with 5% CO2 at 37°C. Table 1. HASM cell patient demographics Designation Age Sex Race N100217 39 M Black N041717 19 M Caucasian N021014 54 M Caucasian N101317K 54 F Hispanic N012317 29 F Caucasian N012414 20 F Black N030116 69 M Caucasian N062017 47 M Hispanic N080817 23 M Black N012518K 18 M Caucasian N112017K 53 M Asian N011118K 14 M Caucasian Notes HASM cells were derived from the following patients who had no history of asthma or chronic illness. RNA-seq analysis was performed using the cell lines from the first six rows. 2.3 Transfection To transfect HASM cells with siRNA, 250 000 cells were incubated with 100 nM siRNA (target or scrambled) for 30 minutes at room temperature using HiPerFect transfection reagent (Qiagen) following the manufacturer's instructions. Cells were then transferred to 6-well plates. After 5 hours incubation at 37°C with 5% CO2, HASM growth media containing 5% fetal bovine serum was added for 48 hours. Media was replaced with serum-free media for 24 hours prior to drug treatment or assay. 2.4 cADDis cAMP assays We performed kinetic measurements of cAMP production in live cells using the green cAMP difference detector in situ (cADDis) cAMP sensor (Montana Molecular, Bozeman, MT) as described previously.20 Briefly, sub-confluent HASM, HFL-1, or HEK-293 cells were plated on a black-walled, clear flat bottom 96-well plates along with recombinant BacMam virus expressing the cADDis sensor and 1 µM trichostatin-A. Cells were grown overnight at 5% CO2 and 37°C. Media was aspirated and replaced with 180 µL per well of 1X Dulbecco's Phosphate Buffered Saline Solution without calcium and magnesium. The 96-well plate was covered with aluminum foil and incubated at room temperature for 30 minutes. Cell fluorescence was read from the plate bottom using excitation/emission wavelengths of 494 and 522 nm, respectively, using a SpectraMax M5 plate reader (Molecular Devices). A 5 minutes kinetic read on unstimulated cells was monitored until the variability in each well's fluorescence was ≤5%. Cells were then stimulated with the indicated drug and fluorescence changes in each well were read at 30 seconds intervals for 30 minutes. Data were transformed to the change in fluorescence over the initial fluorescence (ΔF/F0) then plotted and fit to a single site decay model using GraphPad Prism 8.0 (GraphPad Software). The K value (slope) and the plateau from this one-site decay fit are reported. To create a concentration-response curve, the K was multiplied by the plateau for each drug concentration and plotted on a log scale. 2.5 Immunoblot analysis Whole cell lysates were obtained by scraping cells in modified RIPA lysis buffer (50 mM Tris–HCl, pH 7.5, 150 mM NaCl, 1% Igepal CA-630, plus mammalian protease inhibitor cocktail). Whole cell lysates were separated on 10% SDS-polyacrylamide gels by electrophoresis before being transferred to PVDF membranes (Millipore) by electroblotting. ß-actin (Santa Cruz Biotechnology sc-47778, 1:1000 dilution) and Gαs (Santa Cruz Biotechnology sc-135914, 1:500 dilution) antibodies were simultaneously incubated overnight at 4°C following block in nonfat milk. The appropriate secondary antibodies with conjugated horseradish peroxidase were purchased from Santa Cruz Biotechnology. Images were captured using a BioRad Gel Doc system then the alignment, exposure, and contrast of each image was optimized using Adobe Photoshop CS4. Immunoreactive bands were analyzed by densitometric analysis using the volume plus density method and normalized to ß-actin, as described previously.21 2.6 RNA-Seq Control or Gαs-knockdown HASM cells (see transfection procedure above) from six different donors (see Table 1) at the same passage number were treated with either vehicle or 1 µM budesonide for 24 hours. Total RNA was extracted using the miRNAeasy mini kit (Qiagen). Approximately 1 µg of RNA from each sample was used to generate RNA-Seq cDNA libraries for sequencing using the TruSeq RNA Sample Prep Kit v2 (Illumina, Inc). Sequencing of 100 bp single-end reads was performed with an Illumina HiSeq 4000 instrument at the University of California, Irvine Genomics High-Throughput Facility. 2.7 Data analysis and statistics Data analysis and statistics: Standard curves were fit and unknown values were extrapolated using GraphPad Prism 8.0. Data were presented as the mean ± SEM. Statistical comparisons (t tests and one-way analysis of variance) were performed and graphics were generated using GraphPad Prism 8.0. RNA-Seq alignment and quantification: RNA-Seq data quality was checked using FastQC and all samples had high quality score (Phred score >28) for all nucleotides sequenced. FastQC analysis showed Illumina TrueSeq adapters were overrepresented in two samples. Cutadapt software was used to remove the identified adapters and reads were filtered for a minimum length of 20 bp. The Rsubread R package (version v1.30.6; Liao et al22) was used to align the reads and to produce the gene-level summarized values using hg38 annotation from the Rsubread package. Integer-based gene counts were generated using the featureCounts function in the Rsubread package.22, 23 limma24 and edgeR (version v3.22.3) 25, 26 ) packages were used to calculate FPKM values27 and a custom script to convert FPKM to TPM values.28 Ensembl Genome Reference Consortium Human Build 38 patch 12 (GRCh38.p12) database was used to convert gene IDs to Hugo Gene Nomenclature Commitee (HGNC) HCNC gene symbols.29 RNA-Seq data was available in GEO under accession number GSE130715. GSE94335, an independent dataset including 34 samples from fatal-asthma and non-asthma donors treated with control and budesonide,40 was also processed with the same alignment and quantification pipeline to minimize technical and analytical bias. Differential gene expression and pathway analysis: Genes with less than 100 counts in 50% of our samples were filtered out prior to any analysis. Principal Component Analysis (PCA) was used to investigate interpatient variability compared to treatment-specific variability. The prcromp function from the stats R package was used to compute PCAs. Plotting of first two PCAs showed intended treatment-specific variability was more dominant than interpatient variability. Therefore, no adjustment was necessary. DESeq2 R package was used to generate differential gene-lists between various treatment conditions: (a) vehicle-treated and Gαs-knockdown vehicle-treated HASM cells, (b) budesonide-treated and Gαs-knockdown budesonide-treated HASM cells, (c) vehicle-treated and budesonide-treated HASM cells, and (d) Gαs-knockdown vehicle-treated and Gαs-knockdown budesonide-treated HASM cells. Gene-lists from (a) and (b) were used for in silico validation of Gαs (GNAS gene) knockdown. Differential gene-lists from (c) and (d) represent the budesonide induced transcriptional activity in control (genomic + non-genomic) and Gαs-knockdown (genomic only) HASM cells, respectively. (c) and (d) were compared to previously published budesonide-associated differentially expressed genes by Himes et al30 for validation of our budesonide signature (Figure S1). Overlap analysis of signature gene-lists was performed using a Venn diagram. Then, ASSIGN, a pathway profiling toolkit, was used to evaluate the gene-lists (c) and (d) in predicting budesonide-induced transcriptional activity in HASM.31 (c) and (d) gene-lists were budesonide signatures due to Gαs-independent and -dependent transcriptional changes due to 24-hour post-budesonide treatment, respectively. An independent HASM dataset, GSE94335,40 was used to validate both budesonide signatures. Predicted budesonide activity was correlated using Pearson’s correlation to evaluate budesonide and budesonide-Gαs knockdown signatures. Gene set enrichment analysis: Using fgsea function, a gene set enrichment analyses were performed against KEGG molecular pathways and gene ontology gene annotations for both budesonide signatures. Cutoff values of P < .05 and a false discovery rate (FDR) < 0.05 were used to assess significant enrichment. Analysis of budesonide transcriptional activity in publicly available data: Connectivity scores (CSs) were assessed using the gene-list that was unique to differentially expressed gene- list (3) using a ConnectivityMap (CMAP) query to identify most similar and dissimilar perturbagen signatures in a publicly available database.32 All RNA-Seq data analyses except the CMAP query were performed in R version 3.6.0 and Bioconductor version 3.733 (R Core Team, 2014; http://www.R-project.org/). All codes are available at https://github.com/mumtahena/gluc_HASMs. 3 RESULTS 3.1 Rapid effect of glucocorticoids on cellular production of cAMP Since glucocorticoids have been found to rapidly activate different signaling pathways in neurons,9, 18 we hypothesized that glucocorticoids stimulate the cAMP production in mammalian cells. Using a highly sensitive cAMP biosensor capable of displaying rapid cAMP kinetics in live HEK-293 cells (cADDis, Montana Molecular), we examined responses to two commonly prescribed glucocorticoids. As shown in Figure 1A, the addition of 10 µM fluticasone increased cAMP levels (reflected as a decay in cADDis fluorescence) within 30-60 seconds of drug exposure. This cAMP response reached a plateau at approximately 12 minutes and the maximal effect was nearly as efficacious as the response to a maximal concentration of the direct adenylyl cyclase activator, Fsk (10 µM, Figure 1A). A 10-fold lower concentration of fluticasone (1 µM) also stimulated the cAMP levels, but at a somewhat slower rate of decay and smaller plateau. Fluticasone concentrations lower than 1 µM induced responses that were not statistically significant when compared to vehicle control (0.1 µM fluticasone is shown). The addition of budesonide (0.1, 1, or 10 µM) elicited responses similar to fluticasone (Figure 1B). We also expressed the cADDis sensor in HFL-1 cells, a human fetal lung fibroblast cell line, and measured cAMP responses to glucocorticoids. Both budesonide and fluticasone induced rapid increases in cAMP levels in HFL-1 cells that were similar to that seen in HEK-293 cells (10 µM budesonide plateau was −0.251 in HFL-1 cells compared to −0.395 in HEK-293 cells; 10 µM fluticasone plateau was −0.341 in HFL-1 cells compared −0.457 in HEK-293 cells). Thus, two different glucocorticoids induce rapid increases in cAMP levels in two different cell lines (HEK-293 and HFL-1 cells) and these responses were large enough to observe without the presence of phosphodiesterase (PDE) inhibitors. Figure 1Open in figure viewer Glucocorticoids stimulate rapid cAMP responses in HEK-293 cells. Cells were incubated with recombinant BacMam virus expressing the cADDis cAMP sensor. After establishing baseline, fluorescence decay was monitored for 30 minutes after addition of drug. cADDis sensor fluorescent decay curves elicited by 1 or 10 µM fluticasone (A) 1 or 10 µM budesonide (B) are shown. Fluorescence decay curves elicited by vehicle and 10 µM forskolin are shown as reference to the minimal and maximal responses. Each point represents the mean ± SEM of n = 5 experiments and lines represent the fit by one-phase decay non-linear regression analysis. * denotes P < .05 for the 1 µM glucocorticoid conditions at the indicated time points, # denotes P < .05 for the 10 µM glucocorticoid conditions at the indicated time points compared to vehicle using multiple t tests and the Holm-Sidak method for correction of multiple comparisons. The 0.1 µM glucocorticoid conditions were not significantly different than vehicle When primary cultured HASM cells obtained from several donors were treated with various glucocorticoids, a rapid production of cAMP was again observed. Prednisone (Figure 2A), fluticasone (Figure 2C), and budesonide (Figure 2D) elicited cAMP responses in HASM within minutes of drug addition. Prednisone induced smaller responses than fluticasone or budesonide, but significantly increased cAMP within 8 minutes of treatment. We also examined other steroids and found that progesterone (Figure 2B) stimulated the cAMP production in HASM cells. Estradiol did not stimulate the cAMP responses that were significantly different than vehicle (Figure 4A). To determine if glucocorticoids increase cAMP via inhibition of PDEs, we preincubated HASM with a broad-spectrum PDE inhibitor, 3-isobutyl-1-methylxanthine (IBMX). Fluticasone retained cAMP stimulating activity in the presence of 10 µM IBMX (Figure 2E). IBMX stimulated cADDis responses on its own, so once the baseline was set following IBMX addition, the maximal response to Fsk was diminished as compared to control (shown in Figure 2C). All these responses occurred within minutes of drug addition, indicating a non-genomic mode of action. Figure 2Open in figure viewer Glucocorticoids stimulate rapid cAMP responses in HASM. Primary HASM cells were incubated with recombinant BacMam virus expressing the cADDis cAMP sensor. After establishing baseline, fluorescence decay was monitored for 30 minutes after addition of drug. cADDis sensor fluorescent decay curves elicited by 1 or 10 µM prednisone (A), 1 or 10 µM progesterone (B), 1 or 10 µM fluticasone (C), 1 or 10 µM budesonide (D), and 1 or 10 µM fluticasone in cell preincubated with 10 µM IBMX (E). Fluorescence decay curves elicited by 10 µM forskolin are shown in each panel as reference to the maximal response. Fluorescence decay by cADDis was monitored for 30 minutes after addition of either vehicle, 1 µM forskolin, 10 µM cortisol, or 10 µM cortisol-BSA (F). A different Y axis scale is used on panel E to better visualize these responses. Each point represents the mean ± SEM of n = 4-6 donors and lines represent the fit by one-phase decay non-linear regression analysis. * denotes P < .05, ** denotes P < .01 of each time point compared to vehicle using multiple t tests and the Holm-Sidak method for correction of multiple comparisons We have recently observed that this non-genomic action of glucocorticoids in HASM is blocked by RU486 but not altered by knockdown of GRα.34 Therefore, we posited that this rapid stimulation of cAMP levels involves glucocorticoid binding to a plasma membrane receptor. Addition of 10 µM cortisol or 10 µM cortisol-BSA conjugate (the latter drug is unable to cross cell membranes) elicited identical cAMP responses in HASM (Figure 2F). Cortisol and cortisol-BSA induced significant reductions in cADDis fluorescence within 4 minutes (as compared to vehicle) and reached a maximal response that was about half of that induced by a near-maximal concentration of Fsk (1 µM). These data were consistent with the idea that glucocorticoids activate a membrane-bound receptor via an outward-facing binding site to stimulate cAMP production. 3.2 Role of Gαs in mediating glucocorticoid effects on production of cAMP Since the stimulation of cAMP by glucocorticoids is rapid and plasma membrane delimited, we hypothesized that the response involves the direct activation of the stimulatory G protein, Gαs. To this end, we used siRNA strategy to knockdown the expression of Gαs in HASM cells. HASM cells were transfected with validated siRNA sequences specific for GNAS or scrambled siRNA.35 As shown in Figure 3A, immunoblot analysis indicated a reduction in expression of both the long and short forms of Gαs in siRNA transfected HASM as compared to scrambled control. We consistently observed a reduction in the cADDis sensor expression levels following the transfection procedure, which resulted in the maximal cADDis responses being reduced by about 50% (comparing the response to 10 µM Fsk in Figure 2 vs Figure 3). cAMP responses to vehicle or Fsk (10 µM) were unaffected by Gαs knockdown, indicating that adenylyl cyclase expression and total activity were unaffected (Figure 3B). In contrast, cAMP responses to 100 nM formoterol (a long-acting ß2-adrenoceptor agonist approved for the treatment of asthma) were significantly reduced in Gαs-knockdown HASM as compared to control, consistent with ß-adrenoceptors stimulating cAMP production via activation of Gαs (Figure 3C). cAMP responses to budesonide (10 µM, Figure 3D) or fluticasone (10 µM, Figure 3E) were also significantly diminished in cells with Gαs knockdown. Taken together, these results indicate that glucocorticoids activate a rapid, non-genomic signaling pathway that stimulates Gαs and the production of cAMP. Figure 3Open in figure viewer Glucocorticoid stimulation of cAMP depends upon Gαs expression. A, HASM were transfected with siRNA specific for GNAS or scrambled control for 48 hours and lysates analyzed by SDS-PAGE and immunoblotting simultaneously with antibodies specific for Gαs and ß-actin. Image is representative of n = 3 experiments on separate donor cells. RNA sequencing revealed GNAS transcript was reduced 6.07 ± 0.35 fold (n = 6) following transfection with siRNA. B-E, cADDis sensor was expressed in control or Gαs-knockdown HASM using a recombinant BacMam virus then responses to vehicle, forskolin (C), formoterol (C), budesonide (D), or fluticasone (E) were measured. Each point represents the mean ± SEM of n = 4-5 donors. * denotes P < .05, ** denotes P < .01 of each time point 3.3 Role of GPER in mediating glucocorticoid effects on production of cAMP Given that glucocorticoids act via an e