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Bloom JS, Sathe L, Munugala C, Jones EM, Gasperini M, Lubock NB, Yarza F, Thompson EM, Kovary KM, Park J, Marquette D, Kay S, Lucas M, Love T, Sina Booeshaghi A, Brandenberg OF, Guo L, Boocock J, Hochman M, Simpkins SW, Lin I, LaPierre N, Hong D, Zhang Y, Oland G, Choe BJ, Chandrasekaran S, Hilt EE, Butte MJ, Damoiseaux R, Kravit C, Cooper AR, Yin Y, Pachter L, Garner OB, Flint J, Eskin E, Luo C, Kosuri S, Kruglyak L, Arboleda VA. Massively scaled-up testing for SARS-CoV-2 RNA via next-generation sequencing of pooled and barcoded nasal and saliva samples. Nat Biomed Eng 2021; 5:657-665. [PMID: 34211145 PMCID: PMC10810734 DOI: 10.1038/s41551-021-00754-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/20/2021] [Indexed: 02/02/2023]
Abstract
Frequent and widespread testing of members of the population who are asymptomatic for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for the mitigation of the transmission of the virus. Despite the recent increases in testing capacity, tests based on quantitative polymerase chain reaction (qPCR) assays cannot be easily deployed at the scale required for population-wide screening. Here, we show that next-generation sequencing of pooled samples tagged with sample-specific molecular barcodes enables the testing of thousands of nasal or saliva samples for SARS-CoV-2 RNA in a single run without the need for RNA extraction. The assay, which we named SwabSeq, incorporates a synthetic RNA standard that facilitates end-point quantification and the calling of true negatives, and that reduces the requirements for automation, purification and sample-to-sample normalization. We used SwabSeq to perform 80,000 tests, with an analytical sensitivity and specificity comparable to or better than traditional qPCR tests, in less than two months with turnaround times of less than 24 h. SwabSeq could be rapidly adapted for the detection of other pathogens.
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Affiliation(s)
- Joshua S Bloom
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
- Octant Inc., Emeryville, CA, USA.
| | - Laila Sathe
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Chetan Munugala
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | | | | | | | | | | | | | - Dawn Marquette
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Stephania Kay
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Mark Lucas
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - TreQuan Love
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | | | - Oliver F Brandenberg
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Longhua Guo
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - James Boocock
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | | | | | - Isabella Lin
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, UCLA, Los Angeles, CA, USA
| | - Duke Hong
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Yi Zhang
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Gabriel Oland
- Department of Surgery, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Bianca Judy Choe
- Department of Emergency Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sukantha Chandrasekaran
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Evann E Hilt
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Manish J Butte
- Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Microbiology, Immunology & Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Robert Damoiseaux
- California NanoSystems Institute, UCLA, Los Angeles, CA, USA
- Department of Bioengineering, Samueli School of Engineering, UCLA, Los Angeles, CA, USA
- Department of Medical and Molecular Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Clifford Kravit
- Department of Digital Technology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | | | - Yi Yin
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Lior Pachter
- Division of Biology and Bioengineering, Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
| | - Omai B Garner
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Eleazar Eskin
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Computer Science, Samueli School of Engineering, UCLA, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sriram Kosuri
- Octant Inc., Emeryville, CA, USA.
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.
| | - Leonid Kruglyak
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
| | - Valerie A Arboleda
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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2
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Bloom JS, Sathe L, Munugala C, Jones EM, Gasperini M, Lubock NB, Yarza F, Thompson EM, Kovary KM, Park J, Marquette D, Kay S, Lucas M, Love T, Booeshaghi AS, Brandenberg OF, Guo L, Boocock J, Hochman M, Simpkins SW, Lin I, LaPierre N, Hong D, Zhang Y, Oland G, Choe BJ, Chandrasekaran S, Hilt EE, Butte MJ, Damoiseaux R, Kravit C, Cooper AR, Yin Y, Pachter L, Garner OB, Flint J, Eskin E, Luo C, Kosuri S, Kruglyak L, Arboleda VA. Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing. medRxiv 2021. [PMID: 32909008 PMCID: PMC7480060 DOI: 10.1101/2020.08.04.20167874] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
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 transmission1,2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. 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. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens.
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Affiliation(s)
- Joshua S Bloom
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Octant, Inc
| | - Laila Sathe
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Chetan Munugala
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI
| | | | | | | | | | | | | | | | - Dawn Marquette
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Stephania Kay
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Mark Lucas
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - TreQuan Love
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | | | - Oliver F Brandenberg
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | - Longhua Guo
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | - James Boocock
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | | | | | - Isabella Lin
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, UCLA
| | - Duke Hong
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Yi Zhang
- Department of Human Genetics, David Geffen School of Medicine, UCLA
| | - Gabriel Oland
- Department of Surgery, David Geffen School of Medicine, UCLA
| | - Bianca Judy Choe
- Department of Emergency Medicine, David Geffen School of Medicine, UCLA
| | | | - Evann E Hilt
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Manish J Butte
- Department of Pediatrics, David Geffen School of Medicine, UCLA.,Department of Microbiology, Immunology & Molecular Genetics, David Geffen School of Medicine, UCLA
| | - Robert Damoiseaux
- California NanoSystems Institute, UCLA.,Department of Bioengineering, Samueli School of Engineering, UCLA.,David Geffen School of Medicine, Research Information Technology
| | - Clifford Kravit
- David Geffen School of Medicine, Research Information Technology
| | | | - Yi Yin
- Department of Human Genetics, David Geffen School of Medicine, UCLA
| | - Lior Pachter
- Division of Biology and Bioengineering & Department of Computing and Mathematical Sciences, Caltech
| | - Omai B Garner
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA
| | - Eleazar Eskin
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Computer Science, Samueli School of Engineering, UCLA.,Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, UCLA
| | - Sriram Kosuri
- Octant, Inc.,Department of Chemistry and Biochemistry, UCLA
| | - Leonid Kruglyak
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | - Valerie A Arboleda
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
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3
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Booeshaghi AS, Lubock NB, Cooper AR, Simpkins SW, Bloom JS, Gehring J, Luebbert L, Kosuri S, Pachter L. Reliable and accurate diagnostics from highly multiplexed sequencing assays. Sci Rep 2020; 10:21759. [PMID: 33303831 PMCID: PMC7730459 DOI: 10.1038/s41598-020-78942-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 11/24/2020] [Indexed: 11/09/2022] Open
Abstract
Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA.
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Affiliation(s)
- A Sina Booeshaghi
- Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | | | | | - Joshua S Bloom
- Octant Inc., Emeryville, CA, USA.,Department of Human Genetics, University of California, Los Angeles, Los Angeles, USA
| | - Jase Gehring
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. .,Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
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4
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Sidore AM, Plesa C, Samson JA, Lubock NB, Kosuri S. DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions. Nucleic Acids Res 2020; 48:e95. [PMID: 32692349 PMCID: PMC7498354 DOI: 10.1093/nar/gkaa600] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 06/13/2020] [Accepted: 07/11/2020] [Indexed: 01/12/2023] Open
Abstract
Multiplexed assays allow functional testing of large synthetic libraries of genetic elements, but are limited by the designability, length, fidelity and scale of the input DNA. Here, we improve DropSynth, a low-cost, multiplexed method that builds gene libraries by compartmentalizing and assembling microarray-derived oligonucleotides in vortexed emulsions. By optimizing enzyme choice, adding enzymatic error correction and increasing scale, we show that DropSynth can build thousands of gene-length fragments at >20% fidelity.
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Affiliation(s)
- Angus M Sidore
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Calin Plesa
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Joyce A Samson
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nathan B Lubock
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA.,UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
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5
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Jones EM, Lubock NB, Venkatakrishnan AJ, Wang J, Tseng AM, Paggi JM, Latorraca NR, Cancilla D, Satyadi M, Davis JE, Babu MM, Dror RO, Kosuri S. Structural and functional characterization of G protein-coupled receptors with deep mutational scanning. eLife 2020; 9:54895. [PMID: 33084570 PMCID: PMC7707821 DOI: 10.7554/elife.54895] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 10/16/2020] [Indexed: 01/14/2023] Open
Abstract
The >800 human 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 7800 of 7828 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 identify 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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Affiliation(s)
- Eric M Jones
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Nathan B Lubock
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - A J Venkatakrishnan
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.,Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Jeffrey Wang
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Alex M Tseng
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Joseph M Paggi
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Naomi R Latorraca
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Daniel Cancilla
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Megan Satyadi
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Jessica E Davis
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - M Madan Babu
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Ron O Dror
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
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6
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Jones EM, Jajoo R, Cancilla D, Lubock NB, Wang J, Satyadi M, Chong R, de March C, Bloom JS, Matsunami H, Kosuri S. A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing. Cell Syst 2019; 8:254-260.e6. [PMID: 30904378 PMCID: PMC6907015 DOI: 10.1016/j.cels.2019.02.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 01/16/2019] [Accepted: 02/26/2019] [Indexed: 12/13/2022]
Abstract
G protein-coupled receptors (GPCRs) are central to how mammalian cells sense and respond to chemicals. Mammalian olfactory receptors (ORs), the largest family of GPCRs, mediate the sense of smell through activation by small molecules, though for most bonafide ligands, they have not been identified. Here, we introduce a platform to screen large chemical panels against multiplexed GPCR libraries using next-generation sequencing of barcoded genetic reporters in stably engineered human cell lines. We mapped 39 mammalian ORs against 181 odorants and identified 79 interactions that have not been reported to our knowledge, including ligands for 15 previously orphaned receptors. This multiplexed receptor assay allows the cost-effective mapping of large chemical libraries to receptor repertoires at scale.
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Affiliation(s)
- Eric M Jones
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Rishi Jajoo
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Daniel Cancilla
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Nathan B Lubock
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Jeffrey Wang
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Megan Satyadi
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Rockie Chong
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Claire de March
- Department of Molecular Genetics and Microbiology, and Department of Neurobiology, and Duke Institute for Brain Sciences, Duke University Medical Center, Research Drive, Durham, NC 27710, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Hiroaki Matsunami
- Department of Molecular Genetics and Microbiology, and Department of Neurobiology, and Duke Institute for Brain Sciences, Duke University Medical Center, Research Drive, Durham, NC 27710, USA
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, UCLA-, DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA.
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7
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Plesa C, Sidore AM, Lubock NB, Zhang D, Kosuri S. Multiplexed gene synthesis in emulsions for exploring protein functional landscapes. Science 2018; 359:343-347. [PMID: 29301959 PMCID: PMC6261299 DOI: 10.1126/science.aao5167] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 12/18/2017] [Indexed: 12/14/2022]
Abstract
Improving our ability to construct and functionally characterize DNA sequences would broadly accelerate progress in biology. Here, we introduce DropSynth, a scalable, low-cost method to build thousands of defined gene-length constructs in a pooled (multiplexed) manner. DropSynth uses a library of barcoded beads that pull down the oligonucleotides necessary for a gene's assembly, which are then processed and assembled in water-in-oil emulsions. We used DropSynth to successfully build more than 7000 synthetic genes that encode phylogenetically diverse homologs of two essential genes in Escherichia coli We tested the ability of phosphopantetheine adenylyltransferase homologs to complement a knockout E. coli strain in multiplex, revealing core functional motifs and reasons underlying homolog incompatibility. DropSynth coupled with multiplexed functional assays allows us to rationally explore sequence-function relationships at an unprecedented scale.
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Affiliation(s)
- Calin Plesa
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA
| | - Angus M. Sidore
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Nathan B. Lubock
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA
| | - Di Zhang
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA
- UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, USA
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Lubock NB, Zhang D, Sidore AM, Church GM, Kosuri S. A systematic comparison of error correction enzymes by next-generation sequencing. Nucleic Acids Res 2017; 45:9206-9217. [PMID: 28911123 PMCID: PMC5587813 DOI: 10.1093/nar/gkx691] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 07/14/2017] [Accepted: 07/31/2017] [Indexed: 11/13/2022] Open
Abstract
Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality and cost of gene synthesis are limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment. Here, we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in model gene assemblies and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G transversions whereas T7 Endonuclease I preferentially corrects A/T transversions. More generally, this experimental and computational pipeline is a fast, scalable and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.
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Affiliation(s)
- Nathan B. Lubock
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
| | - Di Zhang
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Angus M. Sidore
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - George M. Church
- Wyss Institute for Biologically Inspired Engineering, Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
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Mabanglo MF, Hast MA, Lubock NB, Hellinga HW, Beese LS. Crystal structures of the fungal pathogen Aspergillus fumigatus protein farnesyltransferase complexed with substrates and inhibitors reveal features for antifungal drug design. Protein Sci 2014; 23:289-301. [PMID: 24347326 PMCID: PMC3945837 DOI: 10.1002/pro.2411] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Revised: 12/11/2013] [Accepted: 12/11/2013] [Indexed: 11/07/2022]
Abstract
Species of the fungal genus Aspergillus are significant human and agricultural pathogens that are often refractory to existing antifungal treatments. Protein farnesyltransferase (FTase), a critical enzyme in eukaryotes, is an attractive potential target for antifungal drug discovery. We report high-resolution structures of A. fumigatus FTase (AfFTase) in complex with substrates and inhibitors. Comparison of structures with farnesyldiphosphate (FPP) bound in the absence or presence of peptide substrate, corresponding to successive steps in ordered substrate binding, revealed that the second substrate-binding step is accompanied by motions of a loop in the catalytic site. Re-examination of other FTase structures showed that this motion is conserved. The substrate- and product-binding clefts in the AfFTase active site are wider than in human FTase (hFTase). Widening is a consequence of small shifts in the α-helices that comprise the majority of the FTase structure, which in turn arise from sequence variation in the hydrophobic core of the protein. These structural effects are key features that distinguish fungal FTases from hFTase. Their variation results in differences in steady-state enzyme kinetics and inhibitor interactions and presents opportunities for developing selective anti-fungal drugs by exploiting size differences in the active sites. We illustrate the latter by comparing the interaction of ED5 and Tipifarnib with hFTase and AfFTase. In AfFTase, the wider groove enables ED5 to bind in the presence of FPP, whereas in hFTase it binds only in the absence of substrate. Tipifarnib binds similarly to both enzymes but makes less extensive contacts in AfFTase with consequently weaker binding.
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Affiliation(s)
- Mark F Mabanglo
- Department of Biochemistry, Duke University Medical CenterDurham, North Carolina, 27710
| | - Michael A Hast
- Department of Biochemistry, Duke University Medical CenterDurham, North Carolina, 27710
| | - Nathan B Lubock
- Department of Biochemistry, Duke University Medical CenterDurham, North Carolina, 27710
| | - Homme W Hellinga
- Department of Biochemistry, Duke University Medical CenterDurham, North Carolina, 27710
| | - Lorena S Beese
- Department of Biochemistry, Duke University Medical CenterDurham, North Carolina, 27710
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Yoo PB, Lubock NB, Hincapie JG, Ruble SB, Hamann JJ, Grill WM. High-resolution measurement of electrically-evoked vagus nerve activity in the anesthetized dog. J Neural Eng 2013; 10:026003. [PMID: 23370017 DOI: 10.1088/1741-2560/10/2/026003] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Not fully understanding the type of axons activated during vagus nerve stimulation (VNS) is one of several factors that limit the clinical efficacy of VNS therapies. The main goal of this study was to characterize the electrical recruitment of both myelinated and unmyelinated fibers within the cervical vagus nerve. APPROACH In anesthetized dogs, recording nerve cuff electrodes were implanted on the vagus nerve following surgical excision of the epineurium. Both the vagal electroneurogram (ENG) and laryngeal muscle activity were recorded in response to stimulation of the right vagus nerve. MAIN RESULTS Desheathing the nerve significantly increased the signal-to-noise ratio of the ENG by 1.2 to 9.9 dB, depending on the nerve fiber type. Repeated VNS following nerve transection or neuromuscular block (1) enabled the characterization of A-fibers, two sub-types of B-fibers, and unmyelinated C-fibers, (2) confirmed the absence of stimulation-evoked reflex compound nerve action potentials in both the ipsilateral and contralateral vagus nerves, and (3) provided evidence of stimulus spillover into muscle tissue surrounding the stimulating electrode. SIGNIFICANCE Given the anatomical similarities between the canine and human vagus nerves, the results of this study provide a template for better understanding the nerve fiber recruitment patterns associated with VNS therapies.
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Affiliation(s)
- Paul B Yoo
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
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