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bioRxiv · 10.1101/734863

Detection and quantification of GPCR mRNA: An assessment and implications of data from high-content methods

Abstract

G protein-coupled receptors (GPCRs) are the largest family of membrane receptors and targets for approved drugs. Analysis of GPCR expression is thus important for drug discovery and typically involves mRNA-based methods. We compared transcriptomic cDNA [Affymetrix] microarrays, RNA-seq and qPCR-based TaqMan arrays for their ability to detect and quantify expression of endoGPCRs (non-chemosensory GPCRs with endogenous agonists). In human pancreatic cancer-associated fibroblasts, RNA-seq and TaqMan arrays yielded closely correlated values for GPCR number (~100) and expression levels, as validated by independent qPCR. By contrast, the microarrays failed to identify ~30 such GPCRs and generated data poorly correlated with results from those methods. RNA-seq and TaqMan arrays also yielded comparable results for GPCRs in human cardiac fibroblasts, pancreatic stellate cells, cancer cell lines and pulmonary arterial smooth muscle cells. The magnitude of mRNA expression for several Gq/11-coupled GPCRs predicted cytosolic calcium increase and cell migration by cognate agonists. RNA-seq also revealed splice variants for endoGPCRs. Thus, RNA-seq and qPCR-based arrays are better suited than microarrays for assessing GPCR expression and can yield results predictive of functional responses--findings that have implications for GPCR biology and drug discovery.\n\nAbstract Graphic\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC=\"FIGDIR/small/734863v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@1a47e41org.highwire.dtl.DTLVardef@5ce56corg.highwire.dtl.DTLVardef@da5330org.highwire.dtl.DTLVardef@18b8215_HPS_FORMAT_FIGEXP M_FIG C_FIG

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BibTeXRIS

Sriram, K., Wiley, S. Z., Moyung, K., Gorr, M. W., Salmeron, C., Marucut, J., French, R. P., Lowy, A. M., Insel, P. A.. 2019-08-15. Detection and quantification of GPCR mRNA: An assessment and implications of data from high-content methods. https://doi.org/10.1101/734863

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