A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing

Jones, Eric M.; Jajoo, Rishi; Cancilla, Daniel; Lubock, Nathan B.; Wang, Jeffrey; Satyadi, Megan; Cheung, Rocky; March, Claire de; Bloom, Joshua S.; Matsunami, Hiroaki; Kosuri, Sriram

doi:10.1016/j.cels.2019.02.009

Cited by 28 publications

(12 citation statements)

References 38 publications

(51 reference statements)

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“…Third, the identification of mutations that can stabilize specific conformations or increase receptor expression can aid in GPCR structure determination ( Serrano-Vega et al, 2008 ; Tate and Schertler, 2009 ). Fourth, the development of stable cell libraries expressing human medicinally related GPCR variants can be combined with large-scale profiling against small molecule libraries to build very large-scale empirical maps for how small molecules modulate this broad class of receptors ( Botvinik and Rossner, 2012 ; Galinski et al, 2018 ; Jones et al, 2019 ). Finally, our approach is generalizable to many classes of drug receptors where transcriptional reporters exist or can be developed ( O'Connell et al, 2016 ), enabling the functional profiling, structural characterization, and pharmacogenomic analysis for most major drug target classes.…”

Section: Discussionmentioning

confidence: 99%

“…Here, we develop an experimental approach to simultaneously profile variant libraries with barcoded transcriptional reporters in human cell lines using RNA-seq. Methods to detect GPCR activation in multiplex have been previously described by us and others ( Botvinnik et al, 2010 ; Galinski et al, 2018 ; Jones et al, 2019 ). Galinski et al’s method reports on GPCR activity with a β-arrestin proximity sensor, requiring engineering of both arrestin and the GPCR, and enabling broad detection of GPCR activation across multiple signaling modalities.…”

Section: Introductionmentioning

confidence: 99%

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Structural and functional characterization of G protein–coupled receptors with deep mutational scanning

Jones

Lubock

Venkatakrishnan

et al. 2020

eLife

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114

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In humans, the >800 G protein-coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state, and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G-protein signal transduction. We tested 7,800 of 7,828 possible single amino acid substitutions to the beta-2 adrenergic receptor (β2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for β2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we resolve residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural and functional characterization of G protein–coupled receptors with deep mutational scanning

Jones

Lubock

Venkatakrishnan

et al. 2020

eLife

Self Cite

114

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“…Here we describe SwabSeq, a SARS-CoV-2 testing platform that leverages next-generation sequencing to massively scale up testing capacity 14,15 .…”

Section: Introductionmentioning

confidence: 99%

“…Frequent, low-cost population testing, combined with contact tracing and isolation of infected individuals, would help to halt the spread of COVID-19 and reopen society 12, 13 . Here we describe SwabSeq, a SARS-CoV-2 testing platform that leverages next-generation sequencing to massively scale up testing capacity 14, 15 .…”

Section: Introductionmentioning

confidence: 99%

Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing

Bloom

Sathe

Munugala

et al. 2020

Preprint

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The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission and is a key element in safely reopening society. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. We show that SwabSeq can test nasal and oral specimens for SARS-CoV-2 with or without RNA extraction while maintaining analytical sensitivity better than or comparable to that of fluorescence-based RT-qPCR tests. SwabSeq is simple, sensitive, flexible, rapidly scalable, inexpensive enough to test widely and frequently, and can provide a turn around time of 12 to 24 hours.

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“…However, such experiments need to be performed separately for each receptor and are time consuming; moreover, some receptors do not express well in the heterologous systems (Ronderos et al 2014). High-throughput methods for identifying OR-odor interactions have also been developed recently, but these require elaborate experimental pipelines for each species (Jiang et al 2015;Hu and Matsunami 2018;Jones et al 2019). The high-throughput methods also generate many false positives, so it is recommended that the identified responses be further verified with targeted experiments (Nishizumi and Sakano 2015).…”

Section: Introductionmentioning

confidence: 99%

Sequence-Based Prediction of Olfactory Receptor Responses

et al. 2019

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Computational prediction of how strongly an olfactory receptor (OR) responds to various odors can help in bridging the widening gap between the large number of receptors that have been sequenced and the small number of experiments measuring their responses. Previous efforts in this area have predicted the responses of a receptor to some odors, using the known responses of the same receptor to other odors. Here, we present a method to predict the responses of a receptor without any known responses by using available data about the responses of other conspecific receptors and their sequences. We applied this method to ORs in insects Drosophila melanogaster (both adult and larva) and Anopheles gambiae and to mouse and human ORs. We found the predictions to be in significant agreement with the experimental measurements. The method also provides clues about the response-determining positions within the receptor sequences.

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A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing

Cited by 28 publications

References 38 publications

Structural and functional characterization of G protein–coupled receptors with deep mutational scanning

Structural and functional characterization of G protein–coupled receptors with deep mutational scanning

Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing

Sequence-Based Prediction of Olfactory Receptor Responses

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