1
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Mateyko N, de Boer CG. Culture Wars: Empirically Determining the Best Approach for Plasmid Library Amplification. ACS Synth Biol 2024; 13:2328-2334. [PMID: 39038190 DOI: 10.1021/acssynbio.4c00377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
DNA libraries are critical components of many biological assays. These libraries are often kept in plasmids that are amplified in E. coli to generate sufficient material for an experiment. Library uniformity is critical for ensuring that every element in the library is tested similarly and is thought to be influenced by the culture approach used during library amplification. We tested five commonly used culturing methods for their ability to uniformly amplify plasmid libraries: liquid, semisolid agar, cell spreader-spread plates with high or low colony density, and bead-spread plates. Each approach was evaluated with two library types: a random 80-mer library, representing high complexity and low coverage of similar sequence lengths, and a human TF ORF library, representing low complexity and high coverage of diverse sequence lengths. We found that no method was better than liquid culture, which produced relatively uniform libraries regardless of library type. However, when libraries were transformed with high coverage, the culturing method had minimal impact on uniformity or amplification bias. Plating libraries was the worst approach by almost every measure for both library types and, counterintuitively, produced the strongest biases against long sequence representation. Semisolid agar amplified most elements of the library uniformly but also included outliers with orders of magnitude higher abundance. For amplifying DNA libraries, liquid culture, the simplest method, appears to be best.
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Affiliation(s)
- Nicholas Mateyko
- Genome Science and Technology Graduate Program, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
| | - Carl G de Boer
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
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2
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Jiang Z, van Vlimmeren AE, Karandur D, Semmelman A, Shah NH. Revealing the principles of inter- and intra-domain regulation in a signaling enzyme via scanning mutagenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.13.593907. [PMID: 39091798 PMCID: PMC11291063 DOI: 10.1101/2024.05.13.593907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Multi-domain enzymes can be regulated by both inter-domain interactions and structural features intrinsic to the catalytic domain. The tyrosine phosphatase SHP2 is a quintessential example of a multi-domain protein that is regulated by inter-domain interactions. This enzyme has a protein tyrosine phosphatase (PTP) domain and two phosphotyrosine-recognition domains (N-SH2 and C-SH2) that regulate phosphatase activity through autoinhibitory interactions. SHP2 is canonically activated by phosphoprotein binding to the SH2 domains, which causes large inter-domain rearrangements, but autoinhibition can also be disrupted by disease-associated mutations. Many details of the SHP2 activation mechanism are still unclear, the physiologically-relevant active conformations remain elusive, and hundreds of human variants of SHP2 have not been functionally characterized. Here, we perform deep mutational scanning on both full-length SHP2 and its isolated PTP domain to examine mutational effects on inter-domain regulation and catalytic activity. Our experiments provide a comprehensive map of SHP2 mutational sensitivity, both in the presence and absence of inter-domain regulation. Coupled with molecular dynamics simulations, our investigation reveals novel structural features that govern the stability of the autoinhibited and active states of SHP2. Our analysis also identifies key residues beyond the SHP2 active site that control PTP domain dynamics and intrinsic catalytic activity. This work expands our understanding of SHP2 regulation and provides new insights into SHP2 pathogenicity.
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3
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McDonnell AF, Plech M, Livesey BJ, Gerasimavicius L, Owen LJ, Hall HN, FitzPatrick DR, Marsh JA, Kudla G. Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants. Mol Syst Biol 2024; 20:825-844. [PMID: 38849565 PMCID: PMC11219921 DOI: 10.1038/s44320-024-00043-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 04/05/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024] Open
Abstract
Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.
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Affiliation(s)
- Alexander F McDonnell
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Marcin Plech
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Benjamin J Livesey
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Lukas Gerasimavicius
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Liusaidh J Owen
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Hildegard Nikki Hall
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - David R FitzPatrick
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Grzegorz Kudla
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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4
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Arbesfeld JA, Da EY, Stevenson JS, Kuzma K, Paul A, Farris T, Capodanno BJ, Grindstaff SB, Riehle K, Saraiva-Agostinho N, Safer JF, Milosavljevic A, Foreman J, Firth HV, Hunt SE, Iqbal S, Cline MS, Rubin AF, Wagner AH. Mapping MAVE data for use in human genomics applications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.20.545702. [PMID: 38979347 PMCID: PMC11230167 DOI: 10.1101/2023.06.20.545702] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor.
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Affiliation(s)
- Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Estelle Y Da
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - James S Stevenson
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Kori Kuzma
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Anika Paul
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Tierra Farris
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | | | | | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Nuno Saraiva-Agostinho
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Jordan F Safer
- The Center for the Development of Therapeutics, The Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Julia Foreman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Helen V Firth
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Sumaiya Iqbal
- The Center for the Development of Therapeutics, The Broad Institute of MIT and Harvard, Cambridge, MA
| | - Melissa S Cline
- BRCA Exchange, University of California Santa Cruz, Santa Cruz, CA
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Australia
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH
- Department of Pediatrics and Biomedical Informatics, The Ohio State University, Columbus, OH
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5
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Ma K, Huang S, Ng KK, Lake NJ, Joseph S, Xu J, Lek A, Ge L, Woodman KG, Koczwara KE, Cohen J, Ho V, O'Connor CL, Brindley MA, Campbell KP, Lek M. Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.12.548370. [PMID: 37873263 PMCID: PMC10592615 DOI: 10.1101/2023.07.12.548370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.
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Affiliation(s)
- Kaiyue Ma
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Shushu Huang
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Equal second authors
| | - Kenneth K Ng
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Equal second authors
| | - Nicole J Lake
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Soumya Joseph
- Howard Hughes Medical Institute, Senator Paul D. Wellstone Muscular Dystrophy Specialized Research Center, Department of Molecular Physiology and Biophysics and Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Jenny Xu
- Yale University, New Haven, CT, USA
| | - Angela Lek
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Muscular Dystrophy Association, Chicago, IL, USA
| | - Lin Ge
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Keryn G Woodman
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | | | - Justin Cohen
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Vincent Ho
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | | | - Melinda A Brindley
- Department of Infectious Diseases, Department of Population Health, University of Georgia, Athens, GA, USA
- Senior Authors
| | - Kevin P Campbell
- Howard Hughes Medical Institute, Senator Paul D. Wellstone Muscular Dystrophy Specialized Research Center, Department of Molecular Physiology and Biophysics and Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
- Senior Authors
| | - Monkol Lek
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Senior Authors
- Lead Contact
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6
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Bendel AM, Skendo K, Klein D, Shimada K, Kauneckaite-Griguole K, Diss G. Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions. BMC Genomics 2024; 25:630. [PMID: 38914936 PMCID: PMC11194945 DOI: 10.1186/s12864-024-10524-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 06/14/2024] [Indexed: 06/26/2024] Open
Abstract
Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.
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Affiliation(s)
- Alexandra M Bendel
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
- University of Basel, Basel, Switzerland
| | | | - Dominique Klein
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
| | - Kenji Shimada
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
| | - Kotryna Kauneckaite-Griguole
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Guillaume Diss
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland.
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7
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Chen SK, Liu J, Van Nynatten A, Tudor-Price BM, Chang BSW. Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods. J Mol Evol 2024:10.1007/s00239-024-10179-8. [PMID: 38886207 DOI: 10.1007/s00239-024-10179-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024]
Abstract
Empirical studies of genotype-phenotype-fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.
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Affiliation(s)
- Steven K Chen
- Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Jing Liu
- Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Alexander Van Nynatten
- Department of Biological Science, University of Toronto Scarborough, Toronto, ON, Canada
| | | | - Belinda S W Chang
- Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada.
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON, Canada.
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, Toronto, ON, Canada.
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8
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Rao J, Xin R, Macdonald C, Howard MK, Estevam GO, Yee SW, Wang M, Fraser JS, Coyote-Maestas W, Pimentel H. Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage. Genome Biol 2024; 25:138. [PMID: 38789982 PMCID: PMC11127319 DOI: 10.1186/s13059-024-03279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.
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Affiliation(s)
- Jingyou Rao
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Ruiqi Xin
- Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Christian Macdonald
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
| | - Matthew K Howard
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Tetrad Graduate Program, UCSF, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA, USA
| | - Gabriella O Estevam
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Tetrad Graduate Program, UCSF, San Francisco, CA, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
| | - Mingsen Wang
- Department of Mathematics, Baruch College, CUNY, New York, NY, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA
| | - Willow Coyote-Maestas
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA.
- Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA.
| | - Harold Pimentel
- Department of Computer Science, UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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9
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Scott BM, Chen SK, Van Nynatten A, Liu J, Schott RK, Heon E, Peisajovich SG, Chang BSW. Scaling up Functional Analyses of the G Protein-Coupled Receptor Rhodopsin. J Mol Evol 2024; 92:61-71. [PMID: 38324225 DOI: 10.1007/s00239-024-10154-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/22/2023] [Indexed: 02/08/2024]
Abstract
Eukaryotic cells use G protein-coupled receptors (GPCRs) to convert external stimuli into internal signals to elicit cellular responses. However, how mutations in GPCR-coding genes affect GPCR activation and downstream signaling pathways remain poorly understood. Approaches such as deep mutational scanning show promise in investigations of GPCRs, but a high-throughput method to measure rhodopsin activation has yet to be achieved. Here, we scale up a fluorescent reporter assay in budding yeast that we engineered to study rhodopsin's light-activated signal transduction. Using this approach, we measured the mutational effects of over 1200 individual human rhodopsin mutants, generated by low-frequency random mutagenesis of the GPCR rhodopsin (RHO) gene. Analysis of the data in the context of rhodopsin's three-dimensional structure reveals that transmembrane helices are generally less tolerant to mutations compared to flanking helices that face the lipid bilayer, which suggest that mutational tolerance is contingent on both the local environment surrounding specific residues and the specific position of these residues in the protein structure. Comparison of functional scores from our screen to clinically identified rhodopsin disease variants found many pathogenic mutants to be loss of function. Lastly, functional scores from our assay were consistent with a complex counterion mechanism involved in ligand-binding and rhodopsin activation. Our results demonstrate that deep mutational scanning is possible for rhodopsin activation and can be an effective method for revealing properties of mutational tolerance that may be generalizable to other transmembrane proteins.
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Affiliation(s)
- Benjamin M Scott
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Steven K Chen
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | | | - Jing Liu
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Ryan K Schott
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
- Department of Biology and Centre for Vision Research, York University, Toronto, ON, Canada
- Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Elise Heon
- Department of Ophthalmology, Hospital for Sick Children, Toronto, ON, Canada
| | - Sergio G Peisajovich
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Belinda S W Chang
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada.
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada.
- Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
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10
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Irvine EB, Reddy ST. Advancing Antibody Engineering through Synthetic Evolution and Machine Learning. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:235-243. [PMID: 38166249 DOI: 10.4049/jimmunol.2300492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 01/04/2024]
Abstract
Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.
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Affiliation(s)
- Edward B Irvine
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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11
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Maes S, Deploey N, Peelman F, Eyckerman S. Deep mutational scanning of proteins in mammalian cells. CELL REPORTS METHODS 2023; 3:100641. [PMID: 37963462 PMCID: PMC10694495 DOI: 10.1016/j.crmeth.2023.100641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/06/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.
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Affiliation(s)
- Stefanie Maes
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biochemistry and Microbiology, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Nick Deploey
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Frank Peelman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Sven Eyckerman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium.
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12
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Staklinski SJ, Scheben A, Siepel A, Kilberg MS. Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564808. [PMID: 37961642 PMCID: PMC10634951 DOI: 10.1101/2023.10.30.564808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
AlphaMissense is a recently developed method that is designed to classify missense variants into pathogenic, benign, or ambiguous categories across the entire human proteome. Asparagine Synthetase Deficiency (ASNSD) is a developmental disorder associated with severe symptoms, including congenital microcephaly, seizures, and premature death. Diagnosing ASNSD relies on identifying mutations in the asparagine synthetase (ASNS) gene through DNA sequencing and determining whether these variants are pathogenic or benign. Pathogenic ASNS variants are predicted to disrupt the protein's structure and/or function, leading to asparagine depletion within cells and inhibition of cell growth. AlphaMissense offers a promising solution for the rapid classification of ASNS variants established by DNA sequencing and provides a community resource of pathogenicity scores and classifications for newly diagnosed ASNSD patients. Here, we assessed AlphaMissense's utility in ASNSD by benchmarking it against known critical residues in ASNS and evaluating its performance against a list of previously reported ASNSD-associated variants. We also present a pipeline to calculate AlphaMissense scores for any protein in the UniProt database. AlphaMissense accurately attributed a high average pathogenicity score to known critical residues within the two ASNS active sites and the connecting intramolecular tunnel. The program successfully categorized 78.9% of known ASNSD-associated missense variants as pathogenic. The remaining variants were primarily labeled as ambiguous, with a smaller proportion classified as benign. This study underscores the potential role of AlphaMissense in classifying ASNS variants in suspected cases of ASNSD, potentially providing clarity to patients and their families grappling with ongoing diagnostic uncertainty.
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Affiliation(s)
- Stephen J. Staklinski
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Armin Scheben
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Michael S. Kilberg
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Box 100245, Gainesville, FL 326010-0245
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13
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O'Neill MJ, Yang T, Laudeman J, Calandranis M, Solus J, Roden DM, Glazer AM. ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.04.23295019. [PMID: 37732247 PMCID: PMC10508793 DOI: 10.1101/2023.09.04.23295019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Background Interpreting the clinical significance of putative splice-altering variants outside 2-base pair canonical splice sites remains difficult without functional studies. Methods We developed Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed minigene-based assay, to test variant effects on RNA splicing quantified by high-throughput sequencing. We studied variants in SCN5A, an arrhythmia-associated gene which encodes the major cardiac voltage-gated sodium channel. We used the computational tool SpliceAI to prioritize exonic and intronic candidate splice variants, and ClinVar to select benign and pathogenic control variants. We generated a pool of 284 barcoded minigene plasmids, transfected them into Human Embryonic Kidney (HEK293) cells and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), sequenced the resulting pools of splicing products, and calibrated the assay to the American College of Medical Genetics and Genomics scheme. Variants were interpreted using the calibrated functional data, and experimental data were compared to SpliceAI predictions. We further studied some splice-altering missense variants by cDNA-based automated patch clamping (APC) in HEK cells and assessed splicing and sodium channel function in CRISPR-edited iPSC-CMs. Results ParSE-seq revealed the splicing effect of 224 SCN5A variants in iPSC-CMs and 244 variants in HEK293 cells. The scores between the cell types were highly correlated (R2=0.84). In iPSCs, the assay had concordant scores for 21/22 benign/likely benign and 24/25 pathogenic/likely pathogenic control variants from ClinVar. 43/112 exonic variants and 35/70 intronic variants with determinate scores disrupted splicing. 11 of 42 variants of uncertain significance were reclassified, and 29 of 34 variants with conflicting interpretations were reclassified using the functional data. SpliceAI computational predictions correlated well with experimental data (AUC = 0.96). We identified 20 unique SCN5A missense variants that disrupted splicing, and 2 clinically observed splice-altering missense variants of uncertain significance had normal function when tested with the cDNA-based APC assay. A splice-altering intronic variant detected by ParSE-seq, c.1891-5C>G, also disrupted splicing and sodium current when introduced into iPSC-CMs at the endogenous locus by CRISPR editing. Conclusions ParSE-seq is a calibrated, multiplexed, high-throughput assay to facilitate the classification of candidate splice-altering variants.
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Affiliation(s)
| | - Tao Yang
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Julie Laudeman
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Maria Calandranis
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Joseph Solus
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan M Roden
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Andrew M Glazer
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
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14
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Soneson C, Bendel AM, Diss G, Stadler MB. mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data. Genome Biol 2023; 24:132. [PMID: 37264470 DOI: 10.1186/s13059-023-02967-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/10/2023] [Indexed: 06/03/2023] Open
Abstract
Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan .
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Affiliation(s)
- Charlotte Soneson
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
| | - Alexandra M Bendel
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Guillaume Diss
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Michael B Stadler
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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15
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Moulana A, Dupic T, Phillips AM, Desai MM. Genotype-phenotype landscapes for immune-pathogen coevolution. Trends Immunol 2023; 44:384-396. [PMID: 37024340 PMCID: PMC10147585 DOI: 10.1016/j.it.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023]
Abstract
Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela M Phillips
- Department of Microbiology and Immunology, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA; NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA; Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA.
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16
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Mao L, Wang Y, An L, Zeng B, Wang Y, Frishman D, Liu M, Chen Y, Tang W, Xu H. Molecular Mechanisms and Clinical Phenotypes of GJB2 Missense Variants. BIOLOGY 2023; 12:biology12040505. [PMID: 37106706 PMCID: PMC10135792 DOI: 10.3390/biology12040505] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 03/29/2023]
Abstract
The GJB2 gene is the most common gene responsible for hearing loss (HL) worldwide, and missense variants are the most abundant type. GJB2 pathogenic missense variants cause nonsyndromic HL (autosomal recessive and dominant) and syndromic HL combined with skin diseases. However, the mechanism by which these different missense variants cause the different phenotypes is unknown. Over 2/3 of the GJB2 missense variants have yet to be functionally studied and are currently classified as variants of uncertain significance (VUS). Based on these functionally determined missense variants, we reviewed the clinical phenotypes and investigated the molecular mechanisms that affected hemichannel and gap junction functions, including connexin biosynthesis, trafficking, oligomerization into connexons, permeability, and interactions between other coexpressed connexins. We predict that all possible GJB2 missense variants will be described in the future by deep mutational scanning technology and optimizing computational models. Therefore, the mechanisms by which different missense variants cause different phenotypes will be fully elucidated.
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Affiliation(s)
- Lu Mao
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, China
| | - Yueqiang Wang
- Basecare Medical Device Co., Ltd., Suzhou 215000, China
| | - Lei An
- Translational Medicine Center, Huaihe Hospital of Henan University, Kaifeng 475000, China
| | - Beiping Zeng
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, China
| | - Yanyan Wang
- The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China
| | - Dmitrij Frishman
- Wissenschaftszentrum Weihenstephan, Technische Universitaet Muenchen, Am Staudengarten 2, 85354 Freising, Germany
| | - Mengli Liu
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, China
| | - Yanyu Chen
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, China
| | - Wenxue Tang
- The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China
| | - Hongen Xu
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, China
- The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China
- Correspondence:
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17
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Conti MM, Li R, Narváez Ramos MA, Zhu LJ, Fazzio TG, Benanti JA. Phosphosite Scanning reveals a complex phosphorylation code underlying CDK-dependent activation of Hcm1. Nat Commun 2023; 14:310. [PMID: 36658165 PMCID: PMC9852432 DOI: 10.1038/s41467-023-36035-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Abstract
Ordered cell cycle progression is coordinated by cyclin dependent kinases (CDKs). CDKs often phosphorylate substrates at multiple sites clustered within disordered regions. However, for most substrates, it is not known which phosphosites are functionally important. We developed a high-throughput approach, Phosphosite Scanning, that tests the importance of each phosphosite within a multisite phosphorylated domain. We show that Phosphosite Scanning identifies multiple combinations of phosphosites that can regulate protein function and reveals specific phosphorylations that are required for phosphorylation at additional sites within a domain. We applied this approach to the yeast transcription factor Hcm1, a conserved regulator of mitotic genes that is critical for accurate chromosome segregation. Phosphosite Scanning revealed a complex CDK-regulatory circuit that mediates Cks1-dependent phosphorylation of key activating sites in vivo. These results illuminate the mechanism of Hcm1 activation by CDK and establish Phosphosite Scanning as a powerful tool for decoding multisite phosphorylated domains.
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Affiliation(s)
- Michelle M Conti
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Rui Li
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Michelle A Narváez Ramos
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Lihua Julie Zhu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.,Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Thomas G Fazzio
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Jennifer A Benanti
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
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18
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Dewachter L, Brooks AN, Noon K, Cialek C, Clark-ElSayed A, Schalck T, Krishnamurthy N, Versées W, Vranken W, Michiels J. Deep mutational scanning of essential bacterial proteins can guide antibiotic development. Nat Commun 2023; 14:241. [PMID: 36646716 PMCID: PMC9842644 DOI: 10.1038/s41467-023-35940-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/09/2023] [Indexed: 01/18/2023] Open
Abstract
Deep mutational scanning is a powerful approach to investigate a wide variety of research questions including protein function and stability. Here, we perform deep mutational scanning on three essential E. coli proteins (FabZ, LpxC and MurA) involved in cell envelope synthesis using high-throughput CRISPR genome editing, and study the effect of the mutations in their original genomic context. We use more than 17,000 variants of the proteins to interrogate protein function and the importance of individual amino acids in supporting viability. Additionally, we exploit these libraries to study resistance development against antimicrobial compounds that target the selected proteins. Among the three proteins studied, MurA seems to be the superior antimicrobial target due to its low mutational flexibility, which decreases the chance of acquiring resistance-conferring mutations that simultaneously preserve MurA function. Additionally, we rank anti-LpxC lead compounds for further development, guided by the number of resistance-conferring mutations against each compound. Our results show that deep mutational scanning studies can be used to guide drug development, which we hope will contribute towards the development of novel antimicrobial therapies.
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Affiliation(s)
- Liselot Dewachter
- Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium. .,VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
| | | | | | | | | | - Thomas Schalck
- Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium.,VIB-KU Leuven Center for Microbiology, Leuven, Belgium
| | | | - Wim Versées
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.,VIB-VUB Center for Structural Biology, Brussels, Belgium
| | - Wim Vranken
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.,VIB-VUB Center for Structural Biology, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
| | - Jan Michiels
- Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium. .,VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
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19
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Zhou Y, Lauschke VM. Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy. Handb Exp Pharmacol 2023; 280:237-260. [PMID: 35792943 DOI: 10.1007/164_2022_596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
- University of Tuebingen, Tuebingen, Germany.
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20
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Haynes LM, Huttinger ZM, Yee A, Kretz CA, Siemieniak DR, Lawrence DA, Ginsburg D. Deep mutational scanning and massively parallel kinetics of plasminogen activator inhibitor-1 functional stability to probe its latency transition. J Biol Chem 2022; 298:102608. [PMID: 36257408 PMCID: PMC9667310 DOI: 10.1016/j.jbc.2022.102608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022] Open
Abstract
Plasminogen activator inhibitor-1 (PAI-1), a member of the serine protease inhibitor superfamily of proteins, is unique among serine protease inhibitors for exhibiting a spontaneous conformational change to a latent or inactive state. The functional half-life for this transition at physiologic temperature and pH is ∼1 to 2 h. To better understand the molecular mechanisms underlying this transition, we now report on the analysis of a comprehensive PAI-1 variant library expressed on filamentous phage and selected for functional stability after 48 h at 37 °C. Of the 7201 possible single amino acid substitutions in PAI-1, we identified 439 that increased the functional stability of PAI-1 beyond that of the WT protein. We also found 1549 single amino acid substitutions that retained inhibitory activity toward the canonical target protease of PAI-1 (urokinase-like plasminogen activator), whereas exhibiting functional stability less than or equal to that of WT PAI-1. Missense mutations that increase PAI-1 functional stability are concentrated in highly flexible regions within the PAI-1 structure. Finally, we developed a method for simultaneously measuring the functional half-lives of hundreds of PAI-1 variants in a multiplexed, massively parallel manner, quantifying the functional half-lives for 697 single missense variants of PAI-1 by this approach. Overall, these findings provide novel insight into the mechanisms underlying the latency transition of PAI-1 and provide a database for interpreting human PAI-1 genetic variants.
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Affiliation(s)
- Laura M Haynes
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - Zachary M Huttinger
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA; Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Andrew Yee
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
| | - Colin A Kretz
- Department of Medicine, McMaster University and the Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
| | - David R Siemieniak
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA; Howard Hughes Medical Institute
| | - Daniel A Lawrence
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - David Ginsburg
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA; Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, Michigan, USA; Howard Hughes Medical Institute; Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA; Departments of Human Genetics and Pediatrics, University of Michigan, Ann Arbor, Michigan, USA.
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21
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Valanciute A, Nygaard L, Zschach H, Maglegaard Jepsen M, Lindorff-Larsen K, Stein A. Accurate protein stability predictions from homology models. Comput Struct Biotechnol J 2022; 21:66-73. [PMID: 36514339 PMCID: PMC9729920 DOI: 10.1016/j.csbj.2022.11.048] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 11/27/2022] Open
Abstract
Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.
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Affiliation(s)
- Audrone Valanciute
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lasse Nygaard
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Henrike Zschach
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Michael Maglegaard Jepsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark,Corresponding authors.
| | - Amelie Stein
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark,Corresponding authors.
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22
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An L, Wang Y, Wu G, Wang Z, Shi Z, Liu C, Wang C, Yi M, Niu C, Duan S, Li X, Tang W, Wu K, Chen S, Xu H. Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors. Transl Res 2022; 255:14-25. [PMID: 36347492 DOI: 10.1016/j.trsl.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/06/2022] [Accepted: 11/01/2022] [Indexed: 11/08/2022]
Abstract
Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.
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Affiliation(s)
- Lei An
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | | | - Guangyao Wu
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Zhenxing Wang
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Zeyuan Shi
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Chang Liu
- School of Pharmacy, Henan University, Kaifeng 475000, China
| | - Chunli Wang
- School of Pharmacy, Henan University, Kaifeng 475000, China
| | - Ming Yi
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenguang Niu
- Key Laboratory of Clinical Resources Translation, The First Affiliated Hospital of Henan University, Kaifeng 475000, China
| | - Shaofeng Duan
- School of Pharmacy, Henan University, Kaifeng 475000, China
| | - Xiaodong Li
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Wenxue Tang
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450000, China; The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Kongming Wu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shuqing Chen
- Shenzhen Typhoon HealthCare, Shenzhen 518000, China.
| | - Hongen Xu
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450000, China; The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China.
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23
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Jayaraman V, Toledo‐Patiño S, Noda‐García L, Laurino P. Mechanisms of protein evolution. Protein Sci 2022; 31:e4362. [PMID: 35762715 PMCID: PMC9214755 DOI: 10.1002/pro.4362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/11/2022] [Accepted: 05/14/2022] [Indexed: 11/06/2022]
Abstract
How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.
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Affiliation(s)
- Vijay Jayaraman
- Department of Molecular Cell BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Saacnicteh Toledo‐Patiño
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Lianet Noda‐García
- Department of Plant Pathology and Microbiology, Institute of Environmental Sciences, Robert H. Smith Faculty of Agriculture, Food and EnvironmentHebrew University of JerusalemRehovotIsrael
| | - Paola Laurino
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
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24
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Kachroo AH, Vandeloo M, Greco BM, Abdullah M. Humanized yeast to model human biology, disease and evolution. Dis Model Mech 2022; 15:275614. [PMID: 35661208 PMCID: PMC9194483 DOI: 10.1242/dmm.049309] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
For decades, budding yeast, a single-cellular eukaryote, has provided remarkable insights into human biology. Yeast and humans share several thousand genes despite morphological and cellular differences and over a billion years of separate evolution. These genes encode critical cellular processes, the failure of which in humans results in disease. Although recent developments in genome engineering of mammalian cells permit genetic assays in human cell lines, there is still a need to develop biological reagents to study human disease variants in a high-throughput manner. Many protein-coding human genes can successfully substitute for their yeast equivalents and sustain yeast growth, thus opening up doors for developing direct assays of human gene function in a tractable system referred to as 'humanized yeast'. Humanized yeast permits the discovery of new human biology by measuring human protein activity in a simplified organismal context. This Review summarizes recent developments showing how humanized yeast can directly assay human gene function and explore variant effects at scale. Thus, by extending the 'awesome power of yeast genetics' to study human biology, humanizing yeast reinforces the high relevance of evolutionarily distant model organisms to explore human gene evolution, function and disease.
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25
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Park Y, Metzger BPH, Thornton JW. Epistatic drift causes gradual decay of predictability in protein evolution. Science 2022; 376:823-830. [PMID: 35587978 DOI: 10.1126/science.abn6895] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Epistatic interactions can make the outcomes of evolution unpredictable, but no comprehensive data are available on the extent and temporal dynamics of changes in the effects of mutations as protein sequences evolve. Here, we use phylogenetic deep mutational scanning to measure the functional effect of every possible amino acid mutation in a series of ancestral and extant steroid receptor DNA binding domains. Across 700 million years of evolution, epistatic interactions caused the effects of most mutations to become decorrelated from their initial effects and their windows of evolutionary accessibility to open and close transiently. Most effects changed gradually and without bias at rates that were largely constant across time, indicating a neutral process caused by many weak epistatic interactions. Our findings show that protein sequences drift inexorably into contingency and unpredictability, but that the process is statistically predictable, given sufficient phylogenetic and experimental data.
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Affiliation(s)
- Yeonwoo Park
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Brian P H Metzger
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Joseph W Thornton
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.,Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.,Department of Human Genetics, University of Chicago, Chicago, IL, USA
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26
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Horne J, Shukla D. Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering. Ind Eng Chem Res 2022; 61:6235-6245. [DOI: 10.1021/acs.iecr.1c04943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Jesse Horne
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Department of Bioengineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Department of Plant Biology, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Cancer Center at Illinois, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
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27
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Li B, Jin B, Capra JA, Bush WS. Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation. Annu Rev Biomed Data Sci 2022; 5:141-161. [PMID: 35508071 DOI: 10.1146/annurev-biodatasci-122220-112147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Bian Li
- Department of Biological Sciences and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Bowen Jin
- Graduate Program in Systems Biology and Bioinformatics, Department of Nutrition, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - John A Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA;
| | - William S Bush
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
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28
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Hsu TK, Asmussen J, Koire A, Choi BK, Gadhikar MA, Huh E, Lin CH, Konecki DM, Kim YW, Pickering CR, Kimmel M, Donehower LA, Frederick MJ, Myers JN, Katsonis P, Lichtarge O. A general calculus of fitness landscapes finds genes under selection in cancers. Genome Res 2022; 32:916-929. [PMID: 35301263 PMCID: PMC9104707 DOI: 10.1101/gr.275811.121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 03/14/2022] [Indexed: 11/24/2022]
Abstract
Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.
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Affiliation(s)
- Teng-Kuei Hsu
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jennifer Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Amanda Koire
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Byung-Kwon Choi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Mayur A Gadhikar
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Eunna Huh
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Chih-Hsu Lin
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Daniel M Konecki
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Young Won Kim
- Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Curtis R Pickering
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Marek Kimmel
- Departments of Statistics and Bioengineering, Rice University, Houston, Texas 77005, USA
- Department of Systems Engineering and Biology, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Lawrence A Donehower
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Mitchell J Frederick
- Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jeffrey N Myers
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Olivier Lichtarge
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, Texas 77030, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA
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29
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Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape. Nat Ecol Evol 2022; 6:427-438. [PMID: 35210579 DOI: 10.1038/s41559-022-01675-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.
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30
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Linking protein structural and functional change to mutation using amino acid networks. PLoS One 2022; 17:e0261829. [PMID: 35061689 PMCID: PMC8782487 DOI: 10.1371/journal.pone.0261829] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022] Open
Abstract
The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.
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31
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Roychowdury H, Romero PA. Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation. Cell Death Dis 2022; 8:7. [PMID: 35013287 PMCID: PMC8748541 DOI: 10.1038/s41420-021-00799-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/02/2021] [Accepted: 12/14/2021] [Indexed: 12/19/2022]
Abstract
The human caspase family comprises 12 cysteine proteases that are centrally involved in cell death and inflammation responses. The members of this family have conserved sequences and structures, highly similar enzymatic activities and substrate preferences, and overlapping physiological roles. In this paper, we present a deep mutational scan of the executioner caspases CASP3 and CASP7 to dissect differences in their structure, function, and regulation. Our approach leverages high-throughput microfluidic screening to analyze hundreds of thousands of caspase variants in tightly controlled in vitro reactions. The resulting data provides a large-scale and unbiased view of the impact of amino acid substitutions on the proteolytic activity of CASP3 and CASP7. We use this data to pinpoint key functional differences between CASP3 and CASP7, including a secondary internal cleavage site, CASP7 Q196 that is not present in CASP3. Our results will open avenues for inquiry in caspase function and regulation that could potentially inform the development of future caspase-specific therapeutics.
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Affiliation(s)
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA. .,The University of Wisconsin Carbone Cancer Center, Madison, WI, USA.
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32
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Dubé AK, Dandage R, Dibyachintan S, Dionne U, Després PC, Landry CR. Deep Mutational Scanning of Protein-Protein Interactions Between Partners Expressed from Their Endogenous Loci In Vivo. Methods Mol Biol 2022; 2477:237-259. [PMID: 35524121 DOI: 10.1007/978-1-0716-2257-5_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep mutational scanning (DMS) generates mutants of a protein of interest in a comprehensive manner. CRISPR-Cas9 technology enables large-scale genome editing with high efficiency. Using both DMS and CRISPR-Cas9 therefore allows us to investigate the effects of thousands of mutations inserted directly in the genome. Combined with protein-fragment complementation assay (PCA), which enables the quantitative measurement of protein-protein interactions (PPIs) in vivo, these methods allow for the systematic assessment of the effects of mutations on PPIs in living cells. Here, we describe a method leveraging DMS, CRISPR-Cas9, and PCA to study the effect of point mutations on PPIs mediated by protein domains in yeast.
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Affiliation(s)
- Alexandre K Dubé
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada.
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada.
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
| | - Rohan Dandage
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
| | - Soham Dibyachintan
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- Department of Chemical Engineering, Indian Institute of Technology Bombay (IIT), Powai, Mumbai, Maharashtra, India
| | - Ugo Dionne
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche du Centre Hospitalier Universitaire (CHU) de Québec, Université Laval, Québec, QC, Canada
- Centre de recherche sur le cancer de l'Université Laval, Québec, QC, Canada
| | - Philippe C Després
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada
| | - Christian R Landry
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada.
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada.
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
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Sharma P, Procko E, Kranz DM. Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display. Methods Mol Biol 2022; 2491:117-142. [PMID: 35482188 DOI: 10.1007/978-1-0716-2285-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein engineering using display platforms such as yeast display and phage display has allowed discovery of proteins with therapeutic and industrial applications. Antibodies and T cell receptors developed for therapeutic applications are often engineered by constructing libraries of mutations in loops of five to ten residues called complementarity determining regions that are in proximity to the antigen. In the past decade, deep mutational scanning has become a powerful tool in a protein engineer's toolbox, as it allows one to compare the impact of all 20 amino acids at each position, across the length of the protein. Thus, a single experiment can provide a sequence-activity landscape with information about hotspots or suboptimal binding sites in the original proteins. These residues or regions may be overlooked by engineering methods that are driven solely by structures or directed evolution of error-prone PCR libraries. Here, we describe experimental methods to engineer proteins by combining yeast display and deep mutational scanning mutagenesis, using T cell receptors as an example.
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Affiliation(s)
- Preeti Sharma
- Department of Biochemistry, University of Illinois, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois, Urbana, IL, USA
| | - Erik Procko
- Department of Biochemistry, University of Illinois, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois, Urbana, IL, USA
| | - David M Kranz
- Department of Biochemistry, University of Illinois, Urbana, IL, USA.
- Cancer Center at Illinois, University of Illinois, Urbana, IL, USA.
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34
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Hanning KR, Minot M, Warrender AK, Kelton W, Reddy ST. Deep mutational scanning for therapeutic antibody engineering. Trends Pharmacol Sci 2021; 43:123-135. [PMID: 34895944 DOI: 10.1016/j.tips.2021.11.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022]
Abstract
The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.
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Affiliation(s)
- Kyrin R Hanning
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand
| | - Mason Minot
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland
| | - Annmaree K Warrender
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand
| | - William Kelton
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand.
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland.
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35
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El Mouali Y, Ponath F, Scharrer V, Wenner N, Hinton JCD, Vogel J. Scanning mutagenesis of RNA-binding protein ProQ reveals a quality control role for the Lon protease. RNA (NEW YORK, N.Y.) 2021; 27:1512-1527. [PMID: 34497069 PMCID: PMC8594473 DOI: 10.1261/rna.078954.121] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 08/24/2021] [Indexed: 05/25/2023]
Abstract
The FinO-domain protein ProQ belongs to a widespread family of RNA-binding proteins (RBPs) involved in gene regulation in bacterial chromosomes and mobile elements. While the cellular RNA targets of ProQ have been established in diverse bacteria, the functionally crucial ProQ residues remain to be identified under physiological conditions. Following our discovery that ProQ deficiency alleviates growth suppression of Salmonella with succinate as the sole carbon source, an experimental evolution approach was devised to exploit this phenotype. By coupling mutational scanning with loss-of-function selection, we identified multiple ProQ residues in both the amino-terminal FinO domain and the variable carboxy-terminal region that are required for ProQ activity. Two carboxy-terminal mutations abrogated ProQ function and mildly impaired binding of a model RNA target. In contrast, several mutations in the FinO domain rendered ProQ both functionally inactive and unable to interact with target RNA in vivo. Alteration of the FinO domain stimulated the rapid turnover of ProQ by Lon-mediated proteolysis, suggesting a quality control mechanism that prevents the accumulation of nonfunctional ProQ molecules. We extend this observation to Hfq, the other major sRNA chaperone of enteric bacteria. The Hfq Y55A mutant protein, defective in RNA-binding and oligomerization, proved to be labile and susceptible to degradation by Lon. Taken together, our findings connect the major AAA+ family protease Lon with RNA-dependent quality control of Hfq and ProQ, the two major sRNA chaperones of Gram-negative bacteria.
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Affiliation(s)
- Youssef El Mouali
- Institute of Molecular Infection Biology (IMIB), University of Würzburg, D-97080 Würzburg, Germany
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), D-97080 Würzburg, Germany
| | - Falk Ponath
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), D-97080 Würzburg, Germany
| | - Vinzent Scharrer
- Institute of Molecular Infection Biology (IMIB), University of Würzburg, D-97080 Würzburg, Germany
| | - Nicolas Wenner
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, L7 3EA Liverpool, United Kingdom
| | - Jay C D Hinton
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, L7 3EA Liverpool, United Kingdom
| | - Jörg Vogel
- Institute of Molecular Infection Biology (IMIB), University of Würzburg, D-97080 Würzburg, Germany
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), D-97080 Würzburg, Germany
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36
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Oren O, Taube R, Papo N. Amyloid β structural polymorphism, associated toxicity and therapeutic strategies. Cell Mol Life Sci 2021; 78:7185-7198. [PMID: 34643743 PMCID: PMC11072899 DOI: 10.1007/s00018-021-03954-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/30/2021] [Accepted: 09/27/2021] [Indexed: 12/14/2022]
Abstract
A review of the multidisciplinary scientific literature reveals a large variety of amyloid-β (Aβ) oligomeric species, differing in molecular weight, conformation and morphology. These species, which may assemble via either on- or off-aggregation pathways, exhibit differences in stability, function and neurotoxicity, according to different experimental settings. The conformations of the different Aβ species are stabilized by intra- and inter-molecular hydrogen bonds and by electrostatic and hydrophobic interactions, all depending on the chemical and physical environment (e.g., solvent, ions, pH) and interactions with other molecules, such as lipids and proteins. This complexity and the lack of a complete understanding of the relationship between the different Aβ species and their toxicity is currently dictating the nature of the inhibitor (or inducer)-based approaches that are under development for interfering with (or inducing) the formation of specific species and Aβ oligomerization, and for interfering with the associated downstream neurotoxic effects. Here, we review the principles that underlie the involvement of different Aβ oligomeric species in neurodegeneration, both in vitro and in preclinical studies. In addition, we provide an overview of the existing inhibitors (or inducers) of Aβ oligomerization that serve as potential therapeutics for neurodegenerative diseases. The review, which covers the exciting studies that have been published in the past few years, comprises three main parts: 1) on- and off-fibrillar assembly mechanisms and Aβ structural polymorphism; 2) interactions of Aβ with other molecules and cell components that dictate the Aβ aggregation pathway; and 3) targeting the on-fibrillar Aβ assembly pathway as a therapeutic approach.
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Affiliation(s)
- Ofek Oren
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel
- Department of Biotechnology Engineering, Avram and Stella Goldstein-Goren, National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel
| | - Ran Taube
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel
| | - Niv Papo
- Department of Biotechnology Engineering, Avram and Stella Goldstein-Goren, National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel.
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37
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Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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38
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Heyne M, Shirian J, Cohen I, Peleg Y, Radisky ES, Papo N, Shifman JM. Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes. J Am Chem Soc 2021; 143:17261-17275. [PMID: 34609866 PMCID: PMC8532158 DOI: 10.1021/jacs.1c08707] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Indexed: 12/18/2022]
Abstract
Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.
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Affiliation(s)
- Michael Heyne
- Department
of Biological Chemistry, The Alexander Silberman Institute of Life
Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
- Avram
and Stella Goldstein-Goren Department of Biotechnology Engineering
and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Jason Shirian
- Department
of Biological Chemistry, The Alexander Silberman Institute of Life
Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Itay Cohen
- Avram
and Stella Goldstein-Goren Department of Biotechnology Engineering
and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Yoav Peleg
- Life
Sciences Core Facilities (LSCF) Structural Proteomics Unit (SPU), Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Evette S. Radisky
- Department
of Cancer Biology, Mayo Clinic Comprehensive
Cancer Center, Jacksonville, Florida 32224, United States
| | - Niv Papo
- Avram
and Stella Goldstein-Goren Department of Biotechnology Engineering
and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Julia M. Shifman
- Department
of Biological Chemistry, The Alexander Silberman Institute of Life
Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
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39
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Öztürk BE, Johnson ME, Kleyman M, Turunç S, He J, Jabalameli S, Xi Z, Visel M, Dufour VL, Iwabe S, Pompeo Marinho LFL, Aguirre GD, Sahel JA, Schaffer DV, Pfenning AR, Flannery JG, Beltran WA, Stauffer WR, Byrne LC. scAAVengr, a transcriptome-based pipeline for quantitative ranking of engineered AAVs with single-cell resolution. eLife 2021; 10:64175. [PMID: 34664552 PMCID: PMC8612735 DOI: 10.7554/elife.64175] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 10/11/2021] [Indexed: 12/14/2022] Open
Abstract
Background Adeno-associated virus (AAV)-mediated gene therapies are rapidly advancing to the clinic, and AAV engineering has resulted in vectors with increased ability to deliver therapeutic genes. Although the choice of vector is critical, quantitative comparison of AAVs, especially in large animals, remains challenging. Methods Here, we developed an efficient single-cell AAV engineering pipeline (scAAVengr) to simultaneously quantify and rank efficiency of competing AAV vectors across all cell types in the same animal. Results To demonstrate proof-of-concept for the scAAVengr workflow, we quantified - with cell-type resolution - the abilities of naturally occurring and newly engineered AAVs to mediate gene expression in primate retina following intravitreal injection. A top performing variant identified using this pipeline, K912, was used to deliver SaCas9 and edit the rhodopsin gene in macaque retina, resulting in editing efficiency similar to infection rates detected by the scAAVengr workflow. scAAVengr was then used to identify top-performing AAV variants in mouse brain, heart, and liver following systemic injection. Conclusions These results validate scAAVengr as a powerful method for development of AAV vectors. Funding This work was supported by funding from the Ford Foundation, NEI/NIH, Research to Prevent Blindness, Foundation Fighting Blindness, UPMC Immune Transplant and Therapy Center, and the Van Sloun fund for canine genetic research.
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Affiliation(s)
- Bilge E Öztürk
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States
| | - Molly E Johnson
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States
| | - Michael Kleyman
- Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
| | - Serhan Turunç
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States
| | - Jing He
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
| | - Sara Jabalameli
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States
| | - Zhouhuan Xi
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States.,Eye Center of Xiangya Hospital, Hunan Key Laboratory of Ophthalmology, Central South University, Changsha, China
| | - Meike Visel
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Valérie L Dufour
- Division of Experimental Retinal Therapies, Department of Clinical Sciences & Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, United States
| | - Simone Iwabe
- Division of Experimental Retinal Therapies, Department of Clinical Sciences & Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, United States
| | - Luis Felipe L Pompeo Marinho
- Division of Experimental Retinal Therapies, Department of Clinical Sciences & Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, United States
| | - Gustavo D Aguirre
- Division of Experimental Retinal Therapies, Department of Clinical Sciences & Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, United States
| | - José-Alain Sahel
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States
| | - David V Schaffer
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Chemical Engineering, University of California, Berkeley, Berkeley, United States
| | - Andreas R Pfenning
- Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
| | - John G Flannery
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Vision Science, Herbert Wertheim School of Optometry, University of California Berkeley, Berkeley, United States
| | - William A Beltran
- Division of Experimental Retinal Therapies, Department of Clinical Sciences & Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, United States
| | - William R Stauffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
| | - Leah C Byrne
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, United States.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States.,Division of Experimental Retinal Therapies, Department of Clinical Sciences & Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States
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40
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Huttinger ZM, Haynes LM, Yee A, Kretz CA, Holding ML, Siemieniak DR, Lawrence DA, Ginsburg D. Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape. Sci Rep 2021; 11:18827. [PMID: 34552126 PMCID: PMC8458277 DOI: 10.1038/s41598-021-97871-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/31/2021] [Indexed: 11/09/2022] Open
Abstract
The serine protease inhibitor (SERPIN) plasminogen activator inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic system, inhibiting the serine proteases tissue- and urokinase-type plasminogen activator (tPA and uPA, respectively). Missense variants render PAI-1 non-functional through misfolding, leading to its turnover as a protease substrate, or to a more rapid transition to the latent/inactive state. Deep mutational scanning was performed to evaluate the impact of amino acid sequence variation on PAI-1 inhibition of uPA using an M13 filamentous phage display system. Error prone PCR was used to construct a mutagenized PAI-1 library encompassing ~ 70% of potential single amino acid substitutions. The relative effects of 27% of all possible missense variants on PAI-1 inhibition of uPA were determined using high-throughput DNA sequencing. 826 missense variants demonstrated conserved inhibitory activity while 1137 resulted in loss of PAI-1 inhibitory function. The least evolutionarily conserved regions of PAI-1 were also identified as being the most tolerant of missense mutations. The results of this screen confirm previous low-throughput mutational studies, including those of the reactive center loop. These data provide a powerful resource for explaining structure-function relationships for PAI-1 and for the interpretation of human genomic sequence variants.
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Affiliation(s)
- Zachary M Huttinger
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA.,Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Otolaryngology, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Laura M Haynes
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA
| | - Andrew Yee
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Colin A Kretz
- Department of Medicine, McMaster University and the Thrombosis and Atherosclerosis Research Institute, Hamilton, ON, Canada
| | | | - David R Siemieniak
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA.,Howard Hughes Medical Institute, Ann Arbor, MI, USA
| | - Daniel A Lawrence
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - David Ginsburg
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. .,Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA. .,Howard Hughes Medical Institute, Ann Arbor, MI, USA. .,Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA. .,Departments of Human Genetics and Pediatrics, University of Michigan, Ann Arbor, MI, USA.
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41
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PacBio sequencing output increased through uniform and directional fivefold concatenation. Sci Rep 2021; 11:18065. [PMID: 34508117 PMCID: PMC8433307 DOI: 10.1038/s41598-021-96829-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/17/2021] [Indexed: 12/20/2022] Open
Abstract
Advances in sequencing technology have allowed researchers to sequence DNA with greater ease and at decreasing costs. Main developments have focused on either sequencing many short sequences or fewer large sequences. Methods for sequencing mid-sized sequences of 600-5,000 bp are currently less efficient. For example, the PacBio Sequel I system yields ~ 100,000-300,000 reads with an accuracy per base pair of 90-99%. We sought to sequence several DNA populations of ~ 870 bp in length with a sequencing accuracy of 99% and to the greatest depth possible. We optimised a simple, robust method to concatenate genes of ~ 870 bp five times and then sequenced the resulting DNA of ~ 5,000 bp by PacBioSMRT long-read sequencing. Our method improved upon previously published concatenation attempts, leading to a greater sequencing depth, high-quality reads and limited sample preparation at little expense. We applied this efficient concatenation protocol to sequence nine DNA populations from a protein engineering study. The improved method is accompanied by a simple and user-friendly analysis pipeline, DeCatCounter, to sequence medium-length sequences efficiently at one-fifth of the cost.
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42
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Burton TD, Eyre NS. Applications of Deep Mutational Scanning in Virology. Viruses 2021; 13:1020. [PMID: 34071591 PMCID: PMC8227372 DOI: 10.3390/v13061020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022] Open
Abstract
Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.
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Affiliation(s)
| | - Nicholas S. Eyre
- College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia;
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43
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Pucci F, Rooman M. Prediction and Evolution of the Molecular Fitness of SARS-CoV-2 Variants: Introducing SpikePro. Viruses 2021; 13:935. [PMID: 34070055 PMCID: PMC8158131 DOI: 10.3390/v13050935] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/11/2021] [Accepted: 05/14/2021] [Indexed: 12/18/2022] Open
Abstract
The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the inter-human transmissibility of the virus predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful, freely available instrument which predicts rapidly and with good accuracy the dangerousness of new viral strains. It can be integrated and play a fundamental role in the genomic surveillance programs of the SARS-CoV-2 virus that, despite all the efforts, remain time-consuming and expensive.
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Affiliation(s)
- Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium;
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium;
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
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44
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Pucci F, Rooman M. Prediction and Evolution of the Molecular Fitness of SARS-CoV-2 Variants: Introducing SpikePro. Viruses 2021; 13:v13050935. [PMID: 34070055 DOI: 10.1101/2021.04.11.439322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/11/2021] [Accepted: 05/14/2021] [Indexed: 05/25/2023] Open
Abstract
The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the inter-human transmissibility of the virus predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful, freely available instrument which predicts rapidly and with good accuracy the dangerousness of new viral strains. It can be integrated and play a fundamental role in the genomic surveillance programs of the SARS-CoV-2 virus that, despite all the efforts, remain time-consuming and expensive.
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Affiliation(s)
- Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
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45
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Routh S, Acharyya A, Dhar R. A two-step PCR assembly for construction of gene variants across large mutational distances. Biol Methods Protoc 2021; 6:bpab007. [PMID: 33928191 PMCID: PMC8062255 DOI: 10.1093/biomethods/bpab007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/09/2021] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Construction of empirical fitness landscapes has transformed our understanding of genotype-phenotype relationships across genes. However, most empirical fitness landscapes have been constrained to the local genotype neighbourhood of a gene primarily due to our limited ability to systematically construct genotypes that differ by a large number of mutations. Although a few methods have been proposed in the literature, these techniques are complex owing to several steps of construction or contain a large number of amplification cycles that increase chances of non-specific mutations. A few other described methods require amplification of the whole vector, thereby increasing the chances of vector backbone mutations that can have unintended consequences for study of fitness landscapes. Thus, this has substantially constrained us from traversing large mutational distances in the genotype network, thereby limiting our understanding of the interactions between multiple mutations and the role these interactions play in evolution of novel phenotypes. In the current work, we present a simple but powerful approach that allows us to systematically and accurately construct gene variants at large mutational distances. Our approach relies on building-up small fragments containing targeted mutations in the first step followed by assembly of these fragments into the complete gene fragment by polymerase chain reaction (PCR). We demonstrate the utility of our approach by constructing variants that differ by up to 11 mutations in a model gene. Our work thus provides an accurate method for construction of multi-mutant variants of genes and therefore will transform the studies of empirical fitness landscapes by enabling exploration of genotypes that are far away from a starting genotype.
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Affiliation(s)
- Shreya Routh
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Anamika Acharyya
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
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46
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Plemel RL, Odorizzi G, Merz AJ. Genetically encoded multimode reporter of adaptor complex 3 traffic in budding yeast. Traffic 2020; 22:38-44. [PMID: 33225520 DOI: 10.1111/tra.12772] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/19/2020] [Accepted: 11/20/2020] [Indexed: 11/29/2022]
Abstract
AP-3 (adaptor complex 3) mediates traffic from the late Golgi or early endosomes to late endosomal compartments. In mammals, mutations in AP-3 cause Hermansky-Pudlak syndrome type 2, cyclic neutropenias, and a form of epileptic encephalopathy. In budding yeast, AP-3 carries cargo directly from the trans-Golgi to the lysosomal vacuole. Despite the pathway's importance and its discovery two decades ago, rapid screens and selections for AP-3 mutants have not been available. We now report GNSI, a synthetic, genetically encoded reporter that allows rapid plate-based assessment of AP-3 functional deficiency, using either chromogenic or growth phenotype readouts. This system identifies defects in both the formation and consumption of AP-3 carrier vesicles and is adaptable to high-throughput screening or selection in both plate array and liquid batch culture formats. Episomal and integrating plasmids encoding GNSI have been submitted to the Addgene repository.
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Affiliation(s)
- Rachael L Plemel
- Department of Biochemistry, University of Washington, Seattle, Washington
| | - Greg Odorizzi
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, Colorado
| | - Alexey J Merz
- Department of Biochemistry, University of Washington, Seattle, Washington
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47
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Li X, Lehner B. Biophysical ambiguities prevent accurate genetic prediction. Nat Commun 2020; 11:4923. [PMID: 33004824 PMCID: PMC7529754 DOI: 10.1038/s41467-020-18694-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/04/2020] [Indexed: 12/27/2022] Open
Abstract
A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements.
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Affiliation(s)
- Xianghua Li
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain. .,ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
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48
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Newberry RW, Arhar T, Costello J, Hartoularos GC, Maxwell AM, Naing ZZC, Pittman M, Reddy NR, Schwarz DMC, Wassarman DR, Wu TS, Barrero D, Caggiano C, Catching A, Cavazos TB, Estes LS, Faust B, Fink EA, Goldman MA, Gomez YK, Gordon MG, Gunsalus LM, Hoppe N, Jaime-Garza M, Johnson MC, Jones MG, Kung AF, Lopez KE, Lumpe J, Martyn C, McCarthy EE, Miller-Vedam LE, Navarro EJ, Palar A, Pellegrino J, Saylor W, Stephens CA, Strickland J, Torosyan H, Wankowicz SA, Wong DR, Wong G, Redding S, Chow ED, DeGrado WF, Kampmann M. Robust Sequence Determinants of α-Synuclein Toxicity in Yeast Implicate Membrane Binding. ACS Chem Biol 2020; 15:2137-2153. [PMID: 32786289 DOI: 10.1021/acschembio.0c00339] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Protein conformations are shaped by cellular environments, but how environmental changes alter the conformational landscapes of specific proteins in vivo remains largely uncharacterized, in part due to the challenge of probing protein structures in living cells. Here, we use deep mutational scanning to investigate how a toxic conformation of α-synuclein, a dynamic protein linked to Parkinson's disease, responds to perturbations of cellular proteostasis. In the context of a course for graduate students in the UCSF Integrative Program in Quantitative Biology, we screened a comprehensive library of α-synuclein missense mutants in yeast cells treated with a variety of small molecules that perturb cellular processes linked to α-synuclein biology and pathobiology. We found that the conformation of α-synuclein previously shown to drive yeast toxicity-an extended, membrane-bound helix-is largely unaffected by these chemical perturbations, underscoring the importance of this conformational state as a driver of cellular toxicity. On the other hand, the chemical perturbations have a significant effect on the ability of mutations to suppress α-synuclein toxicity. Moreover, we find that sequence determinants of α-synuclein toxicity are well described by a simple structural model of the membrane-bound helix. This model predicts that α-synuclein penetrates the membrane to constant depth across its length but that membrane affinity decreases toward the C terminus, which is consistent with orthogonal biophysical measurements. Finally, we discuss how parallelized chemical genetics experiments can provide a robust framework for inquiry-based graduate coursework.
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Affiliation(s)
- Robert W. Newberry
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143, United States
| | - Taylor Arhar
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, California 94143, United States
| | - Jean Costello
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - George C. Hartoularos
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Alison M. Maxwell
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, California 94143, United States
| | - Zun Zar Chi Naing
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Maureen Pittman
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Nishith R. Reddy
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Daniel M. C. Schwarz
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, California 94143, United States
| | - Douglas R. Wassarman
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, California 94143, United States
| | - Taia S. Wu
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, California 94143, United States
| | - Daniel Barrero
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Christa Caggiano
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Adam Catching
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Taylor B. Cavazos
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Laurel S. Estes
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Bryan Faust
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Elissa A. Fink
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Miriam A. Goldman
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Yessica K. Gomez
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - M. Grace Gordon
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Laura M. Gunsalus
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Nick Hoppe
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Maru Jaime-Garza
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Matthew C. Johnson
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Matthew G. Jones
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Andrew F. Kung
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Kyle E. Lopez
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Jared Lumpe
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Calla Martyn
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Elizabeth E. McCarthy
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Lakshmi E. Miller-Vedam
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Erik J. Navarro
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Aji Palar
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Jenna Pellegrino
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Wren Saylor
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Christina A. Stephens
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Jack Strickland
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Hayarpi Torosyan
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Stephanie A. Wankowicz
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Daniel R. Wong
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Garrett Wong
- Integrative Program in Quantitative Biology, University of California, San Francisco, California 94143, United States
| | - Sy Redding
- Department of Biochemistry and Biophysics, University of California, San Francisco, California 94143, United States
| | - Eric D. Chow
- Department of Biochemistry and Biophysics, University of California, San Francisco, California 94143, United States
| | - William F. DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143, United States
| | - Martin Kampmann
- Department of Biochemistry and Biophysics, University of California, San Francisco, California 94143, United States
- Institute for Neurodegenerative Disease, University of California, San Francisco, California 94143, United States
- Chan Zuckerberg Biohub, San Francisco, California 94158, United States
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49
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DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biol 2020; 21:207. [PMID: 32799905 PMCID: PMC7429474 DOI: 10.1186/s13059-020-02091-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 07/05/2020] [Indexed: 12/30/2022] Open
Abstract
Deep mutational scanning (DMS) enables multiplexed measurement of the effects of thousands of variants of proteins, RNAs, and regulatory elements. Here, we present a customizable pipeline, DiMSum, that represents an end-to-end solution for obtaining variant fitness and error estimates from raw sequencing data. A key innovation of DiMSum is the use of an interpretable error model that captures the main sources of variability arising in DMS workflows, outperforming previous methods. DiMSum is available as an R/Bioconda package and provides summary reports to help researchers diagnose common DMS pathologies and take remedial steps in their analyses.
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50
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Zhang L, Sarangi V, Moon I, Yu J, Liu D, Devarajan S, Reid JM, Kalari KR, Wang L, Weinshilboum R. CYP2C9 and CYP2C19: Deep Mutational Scanning and Functional Characterization of Genomic Missense Variants. Clin Transl Sci 2020; 13:727-742. [PMID: 32004414 PMCID: PMC7359949 DOI: 10.1111/cts.12758] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 11/10/2019] [Indexed: 02/04/2023] Open
Abstract
Single nucleotide variants in the open reading frames (ORFs) of pharmacogenes are important causes of interindividual variability in drug response. The functional characterization of variants of unknown significance within ORFs remains a major challenge for pharmacogenomics. Deep mutational scanning (DMS) is a high-throughput technique that makes it possible to analyze the functional effect of hundreds of variants in a parallel and scalable fashion. We adapted a "landing pad" DMS system to study the function of missense variants in the ORFs of cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily C member 19 (CYP2C19). We studied 230 observed missense variants in the CYP2C9 and CYP2C19 ORFs and found that 19 of 109 CYP2C9 and 36 of 121 CYP2C19 variants displayed less than ~ 25% of the wild-type protein expression, a level that may have clinical relevance. Our results support DMS as an efficient method for the identification of damaging ORF variants that might have potential clinical pharmacogenomic application.
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Affiliation(s)
- Lingxin Zhang
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Vivekananda Sarangi
- Division of Biomedical Statistics and InformaticsDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesotaUSA
| | - Irene Moon
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Jia Yu
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Duan Liu
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Sandhya Devarajan
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Joel M. Reid
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Krishna R. Kalari
- Division of Biomedical Statistics and InformaticsDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesotaUSA
| | - Liewei Wang
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Richard Weinshilboum
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
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