1
|
Padigepati SR, Stafford DA, Tan CA, Silvis MR, Jamieson K, Keyser A, Nunez PAC, Nicoludis JM, Manders T, Fresard L, Kobayashi Y, Araya CL, Aradhya S, Johnson B, Nykamp K, Reuter JA. Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification. Hum Genet 2024:10.1007/s00439-024-02691-0. [PMID: 39085601 DOI: 10.1007/s00439-024-02691-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 07/12/2024] [Indexed: 08/02/2024]
Abstract
As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.
Collapse
Affiliation(s)
| | | | | | - Melanie R Silvis
- Invitae Corporation, San Francisco, CA, 94103, USA
- Epic Bio, South San Francisco, CA, 94080, USA
| | - Kirsty Jamieson
- Invitae Corporation, San Francisco, CA, 94103, USA
- Epic Bio, South San Francisco, CA, 94080, USA
| | - Andrew Keyser
- Invitae Corporation, San Francisco, CA, 94103, USA
- Calico Life Sciences, South San Francisco, CA, 94080, USA
| | | | - John M Nicoludis
- Invitae Corporation, San Francisco, CA, 94103, USA
- Department of Structural Biology, Genentech, South San Francisco, CA, 94080, USA
| | - Toby Manders
- Invitae Corporation, San Francisco, CA, 94103, USA
| | | | | | - Carlos L Araya
- Invitae Corporation, San Francisco, CA, 94103, USA
- Tapanti.org, Santa Barbara, CA, 93108, USA
| | | | - Britt Johnson
- Invitae Corporation, San Francisco, CA, 94103, USA
- GeneDx, Stamford, CT, 06902, USA
| | - Keith Nykamp
- Invitae Corporation, San Francisco, CA, 94103, USA.
| | | |
Collapse
|
2
|
Li G, Zhang N, Dai X, Fan L. EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity. J Chem Inf Model 2024. [PMID: 39038814 DOI: 10.1021/acs.jcim.4c00920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enhancement of one often leads to the reduction of the other, also called the trade-off mechanism. Although dozens of methods that predict the change of protein stability upon mutations have been developed, the prediction of the effect on activity is still in its early stage. Therefore, developing a fast and accurate method to predict the impact of the mutations on enzyme activity is helpful for enzyme design and understanding of the trade-off mechanism. Here, we introduce a novel approach, EnzyACT, a deep learning method that fuses graph technique and protein embedding to predict activity changes upon single or multiple mutations. Our model combines graph-based techniques and language models to predict the activity changes. Moreover, EnzyACT is trained on a new curated data set including both single- and multiple-point mutations. When benchmarked on multiple independent data sets, it shows uniform performance on problems affected by mutations. This work also provides insights into the impact of distant mutations within activity design, which could also be useful for predicting catalytic residues and developing improved enzyme-engineering strategies.
Collapse
Affiliation(s)
- Gen Li
- Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co.,Ltd., Shanghai 200131, China
| | - Ning Zhang
- Production and R&D Center I of LSS, GenScript Biotech Corporation, Nanjing 211122, China
| | - Xiaowen Dai
- Production and R&D Center I of LSS, GenScript Biotech Corporation, Nanjing 211122, China
| | - Long Fan
- Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co.,Ltd., Shanghai 200131, China
| |
Collapse
|
3
|
Mermet-Meillon F, Mercan S, Bauer-Probst B, Allard C, Bleu M, Calkins K, Knehr J, Altorfer M, Naumann U, Sprouffske K, Barys L, Sesterhenn F, Galli GG. Protein destabilization underlies pathogenic missense mutations in ARID1B. Nat Struct Mol Biol 2024; 31:1018-1022. [PMID: 38347147 PMCID: PMC11257965 DOI: 10.1038/s41594-024-01229-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 01/18/2024] [Indexed: 07/20/2024]
Abstract
ARID1B is a SWI/SNF subunit frequently mutated in human Coffin-Siris syndrome (CSS) and it is necessary for proliferation of ARID1A mutant cancers. While most CSS ARID1B aberrations introduce frameshifts or stop codons, the functional consequence of missense mutations found in ARID1B is unclear. We here perform saturated mutagenesis screens on ARID1B and demonstrate that protein destabilization is the main mechanism associated with pathogenic missense mutations in patients with Coffin-Siris Syndrome.
Collapse
Affiliation(s)
| | - Samuele Mercan
- Disease Area Oncology, Novartis Biomedical Research, Basel, Switzerland
| | | | - Cyril Allard
- Disease Area Immunology, Novartis Biomedical Research, Basel, Switzerland
| | - Melusine Bleu
- Disease Area Oncology, Novartis Biomedical Research, Basel, Switzerland
| | - Keith Calkins
- Disease Area Oncology, Novartis Biomedical Research, Basel, Switzerland
| | - Judith Knehr
- Discovery Sciences, Novartis Biomedical Research, Basel, Switzerland
| | - Marc Altorfer
- Discovery Sciences, Novartis Biomedical Research, Basel, Switzerland
| | - Ulrike Naumann
- Discovery Sciences, Novartis Biomedical Research, Basel, Switzerland
| | | | - Louise Barys
- Disease Area Oncology, Novartis Biomedical Research, Basel, Switzerland
| | - Fabian Sesterhenn
- Discovery Sciences, Novartis Biomedical Research, Basel, Switzerland.
| | - Giorgio G Galli
- Disease Area Oncology, Novartis Biomedical Research, Basel, Switzerland.
| |
Collapse
|
4
|
Zhou Y, Pirmann S, Lauschke VM. APF2: an improved ensemble method for pharmacogenomic variant effect prediction. THE PHARMACOGENOMICS JOURNAL 2024; 24:17. [PMID: 38802404 PMCID: PMC11129946 DOI: 10.1038/s41397-024-00338-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/26/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024]
Abstract
Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.
Collapse
Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Sebastian Pirmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
- Center for Molecular Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden.
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
- University of Tübingen, Tübingen, Germany.
| |
Collapse
|
5
|
Yee SW, Macdonald CB, Mitrovic D, Zhou X, Koleske ML, Yang J, Buitrago Silva D, Rockefeller Grimes P, Trinidad DD, More SS, Kachuri L, Witte JS, Delemotte L, Giacomini KM, Coyote-Maestas W. The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics. Mol Cell 2024; 84:1932-1947.e10. [PMID: 38703769 DOI: 10.1016/j.molcel.2024.04.008] [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: 07/11/2023] [Revised: 01/04/2024] [Accepted: 04/15/2024] [Indexed: 05/06/2024]
Abstract
Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.
Collapse
Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Christian B Macdonald
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Darko Mitrovic
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, 12121 Solna, Stockholm, Stockholm County 114 28, Sweden
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Megan L Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jia Yang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Dina Buitrago Silva
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Patrick Rockefeller Grimes
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Donovan D Trinidad
- Department of Medicine, Division of Infectious Disease, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Swati S More
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Lucie Delemotte
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, 12121 Solna, Stockholm, Stockholm County 114 28, Sweden.
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Willow Coyote-Maestas
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Chan Zuckerberg Biohub, San Francisco, CA 94148, USA.
| |
Collapse
|
6
|
Simon JJ, Fowler DM, Maly DJ. Multiplexed, multimodal profiling of the intracellular activity, interactions, and druggability of protein variants using LABEL-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590094. [PMID: 38659825 PMCID: PMC11042325 DOI: 10.1101/2024.04.19.590094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Multiplexed assays of variant effect are powerful tools for assessing the impact of protein sequence variation, but are limited to measuring a single protein property and often rely on indirect readouts of intracellular protein function. Here, we developed LAbeling with Barcodes and Enrichment for biochemicaL analysis by sequencing (LABEL-seq), a platform for the multimodal profiling of thousands of protein variants in cultured human cells. Multimodal measurement of ~20,000 variant effects for ~1,600 BRaf variants using LABEL-seq revealed that variation at positions that are frequently mutated in cancer had minimal effects on folding and intracellular abundance but could dramatically alter activity, protein-protein interactions, and druggability. Integrative analysis of our multimodal measurements identified networks of positions with similar roles in regulating BRaf's signaling properties and enabled predictive modeling of variant effects on complex processes such as cell proliferation and small molecule-promoted degradation. LABEL-seq provides a scalable approach for the direct measurement of multiple biochemical effects of protein variants in their native cellular context, yielding insight into protein function, disease mechanisms, and druggability.
Collapse
Affiliation(s)
- Jessica J Simon
- Department of Chemistry, University of Washington, Seattle, WA, United States
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Co-corresponding authors: ,
| | - Dustin J Maly
- Department of Chemistry, University of Washington, Seattle, WA, United States
- Department of Biochemistry, University of Washington, Seattle, WA, United States
- Co-corresponding authors: ,
| |
Collapse
|
7
|
Gersing S, Schulze TK, Cagiada M, Stein A, Roth FP, Lindorff-Larsen K, Hartmann-Petersen R. Characterizing glucokinase variant mechanisms using a multiplexed abundance assay. Genome Biol 2024; 25:98. [PMID: 38627865 PMCID: PMC11021015 DOI: 10.1186/s13059-024-03238-2] [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: 05/30/2023] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. RESULTS Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity. CONCLUSIONS In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.
Collapse
Affiliation(s)
- Sarah Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| | - Thea K Schulze
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Matteo Cagiada
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, M5S 3E1, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, M5G 1X5, Toronto, ON, Canada
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, 15213, Pittsburgh, USA
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| |
Collapse
|
8
|
Weile J, Ferra G, Boyle G, Pendyala S, Amorosi C, Yeh CL, Cote AG, Kishore N, Tabet D, van Loggerenberg W, Rayhan A, Fowler DM, Dunham MJ, Roth FP. Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries. Bioinformatics 2024; 40:btae182. [PMID: 38569896 PMCID: PMC11021806 DOI: 10.1093/bioinformatics/btae182] [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: 12/19/2023] [Revised: 03/05/2024] [Accepted: 04/02/2024] [Indexed: 04/05/2024] Open
Abstract
MOTIVATION Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. RESULTS Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues. AVAILABILITY AND IMPLEMENTATION Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.
Collapse
Affiliation(s)
- Jochen Weile
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Gabrielle Ferra
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Gabriel Boyle
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Sriram Pendyala
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Clara Amorosi
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Chiann-Ling Yeh
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Atina G Cote
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Nishka Kishore
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Daniel Tabet
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Warren van Loggerenberg
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Ashyad Rayhan
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
- Department of Bioengineering, University of Washington, Seattle, WA 98195, United States
- Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, United States
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Frederick P Roth
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, United States
| |
Collapse
|
9
|
Garge RK, Geck RC, Armstrong JO, Dunn B, Boutz DR, Battenhouse A, Leutert M, Dang V, Jiang P, Kwiatkowski D, Peiser T, McElroy H, Marcotte EM, Dunham MJ. Systematic profiling of ale yeast protein dynamics across fermentation and repitching. G3 (BETHESDA, MD.) 2024; 14:jkad293. [PMID: 38135291 PMCID: PMC10917522 DOI: 10.1093/g3journal/jkad293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/28/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023]
Abstract
Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is among the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout 2 fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.
Collapse
Affiliation(s)
- Riddhiman K Garge
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Renee C Geck
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Joseph O Armstrong
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Barbara Dunn
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Daniel R Boutz
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
- Antibody Discovery and Accelerated Protein Therapeutics, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Anna Battenhouse
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Institute of Molecular Systems Biology, ETH Zürich, Zürich 8049, Switzerland
| | - Vy Dang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Pengyao Jiang
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | | | | | | | - Edward M Marcotte
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| |
Collapse
|
10
|
Swint-Kruse L, Fenton AW. Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function. J Biol Chem 2024; 300:105736. [PMID: 38336297 PMCID: PMC10914490 DOI: 10.1016/j.jbc.2024.105736] [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: 11/15/2023] [Revised: 01/09/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.
Collapse
Affiliation(s)
- Liskin Swint-Kruse
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas, USA.
| | - Aron W Fenton
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas, USA
| |
Collapse
|
11
|
Kamath ND, Matreyek KA. Multiplex Functional Characterization of Protein Variant Libraries in Mammalian Cells with Single-Copy Genomic Integration and High-Throughput DNA Sequencing. Methods Mol Biol 2024; 2774:135-152. [PMID: 38441763 DOI: 10.1007/978-1-0716-3718-0_10] [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: 03/07/2024]
Abstract
Sequencing-based, massively parallel genetic assays have enabled simultaneous characterization of the genotype-phenotype relationships for libraries encoding thousands of unique protein variants. Since plasmid transfection and lentiviral transduction have characteristics that limit multiplexing with pooled libraries, we developed a mammalian synthetic biology platform that harnesses the Bxb1 bacteriophage DNA recombinase to insert single promoterless plasmids encoding a transgene of interest into a pre-engineered "landing pad" site within the cell genome. The transgene is expressed behind a genomically integrated promoter, ensuring only one transgene is expressed per cell, preserving a strict genotype-phenotype link. Upon selecting cells based on a desired phenotype, the transgene can be sequenced to ascribe each variant a phenotypic score. We describe how to create and utilize landing pad cells for large-scale, library-based genetic experiments. Using the provided examples, the experimental template can be adapted to explore protein variants in diverse biological problems within mammalian cells.
Collapse
Affiliation(s)
- Nisha D Kamath
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kenneth A Matreyek
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
| |
Collapse
|
12
|
Zhao Y, Zhong G, Hagen J, Pan H, Chung WK, Shen Y. A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.11.23299809. [PMID: 38168397 PMCID: PMC10760286 DOI: 10.1101/2023.12.11.23299809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.
Collapse
Affiliation(s)
- Yige Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- The Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY 10032
| | - Guojie Zhong
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- The Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY 10032
| | - Jake Hagen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY 10032
| | - Hongbing Pan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032
| | - Wendy K. Chung
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115
| | - Yufeng Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032
- JP Sulzberger Columbia Genome Center, Columbia University, New York, NY 10032
| |
Collapse
|
13
|
Notin P, Kollasch AW, Ritter D, van Niekerk L, Paul S, Spinner H, Rollins N, Shaw A, Weitzman R, Frazer J, Dias M, Franceschi D, Orenbuch R, Gal Y, Marks DS. ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570727. [PMID: 38106144 PMCID: PMC10723403 DOI: 10.1101/2023.12.07.570727] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Ada Shaw
- Applied Mathematics, Harvard University
| | | | | | - Mafalda Dias
- Centre for Genomic Regulation, Universitat Pompeu Fabra
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
| | | |
Collapse
|
14
|
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.
Collapse
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.
| |
Collapse
|
15
|
Qu Y, Niu Z, Ding Q, Zhao T, Kong T, Bai B, Ma J, Zhao Y, Zheng J. Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction. Int J Mol Sci 2023; 24:16496. [PMID: 38003686 PMCID: PMC10671426 DOI: 10.3390/ijms242216496] [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: 10/13/2023] [Revised: 11/11/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023] Open
Abstract
Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.
Collapse
Affiliation(s)
- Yang Qu
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Zitong Niu
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Qiaojiao Ding
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Taowa Zhao
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Tong Kong
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Bing Bai
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Jianwei Ma
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Yitian Zhao
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Jianping Zheng
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| |
Collapse
|
16
|
Xie MJ, Cromie GA, Owens K, Timour MS, Tang M, Kutz JN, El-Hattab AW, McLaughlin RN, Dudley AM. Constructing and interpreting a large-scale variant effect map for an ultrarare disease gene: Comprehensive prediction of the functional impact of PSAT1 genotypes. PLoS Genet 2023; 19:e1010972. [PMID: 37812589 PMCID: PMC10561871 DOI: 10.1371/journal.pgen.1010972] [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: 06/13/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
Reduced activity of the enzymes encoded by PHGDH, PSAT1, and PSPH causes a set of ultrarare, autosomal recessive diseases known as serine biosynthesis defects. These diseases present in a broad phenotypic spectrum: at the severe end is Neu-Laxova syndrome, in the intermediate range are infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end is childhood disease with intellectual disability. However, L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms. Therefore, knowledge of pathogenic variants can improve clinical outcomes. Here, we use a yeast-based assay to individually measure the functional impact of 1,914 SNV-accessible amino acid substitutions in PSAT. Results of our assay agree well with clinical interpretations and protein structure-function relationships, supporting the inclusion of our data as functional evidence as part of the ACMG variant interpretation guidelines. We use existing ClinVar variants, disease alleles reported in the literature and variants present as homozygotes in the primAD database to define assay ranges that could aid clinical variant interpretation for up to 98% of the tested variants. In addition to measuring the functional impact of individual variants in yeast haploid cells, we also assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. Results from our diploid assay successfully distinguish the genotypes of affected individuals from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements to predict the biallelic function of ~1.8 million allele combinations corresponding to potential human genotypes. Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of ultrarare diseases.
Collapse
Affiliation(s)
- Michael J. Xie
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
- Molecular Engineering Graduate Program, University of Washington, Seattle, Washington, United States of America
| | - Gareth A. Cromie
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Katherine Owens
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Martin S. Timour
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Michelle Tang
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Ayman W. El-Hattab
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | | | - Aimée M. Dudley
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
- Molecular Engineering Graduate Program, University of Washington, Seattle, Washington, United States of America
| |
Collapse
|
17
|
Tremmel R, Pirmann S, Zhou Y, Lauschke VM. Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance. Br J Clin Pharmacol 2023. [PMID: 37759374 DOI: 10.1111/bcp.15915] [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: 09/07/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.
Collapse
Affiliation(s)
- Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Sebastian Pirmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
18
|
McConnell A, Hackel BJ. Protein engineering via sequence-performance mapping. Cell Syst 2023; 14:656-666. [PMID: 37494931 PMCID: PMC10527434 DOI: 10.1016/j.cels.2023.06.009] [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: 02/27/2023] [Revised: 05/10/2023] [Accepted: 06/21/2023] [Indexed: 07/28/2023]
Abstract
Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.
Collapse
Affiliation(s)
- Adam McConnell
- Department of Biomedical Engineering, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA
| | - Benjamin J Hackel
- Department of Biomedical Engineering, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA; Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA.
| |
Collapse
|
19
|
Livesey BJ, Marsh JA. Updated benchmarking of variant effect predictors using deep mutational scanning. Mol Syst Biol 2023; 19:e11474. [PMID: 37310135 PMCID: PMC10407742 DOI: 10.15252/msb.202211474] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.
Collapse
Affiliation(s)
- Benjamin J Livesey
- MRC Human Genetics Unit, Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| |
Collapse
|
20
|
Cagiada M, Bottaro S, Lindemose S, Schenstrøm SM, Stein A, Hartmann-Petersen R, Lindorff-Larsen K. Discovering functionally important sites in proteins. Nat Commun 2023; 14:4175. [PMID: 37443362 PMCID: PMC10345196 DOI: 10.1038/s41467-023-39909-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/02/2023] [Indexed: 07/15/2023] Open
Abstract
Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.
Collapse
Affiliation(s)
- Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sandro Bottaro
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Søren Lindemose
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Signe M Schenstrøm
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- 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.
| |
Collapse
|
21
|
Mighell TL, Toledano I, Lehner B. SUNi mutagenesis: Scalable and uniform nicking for efficient generation of variant libraries. PLoS One 2023; 18:e0288158. [PMID: 37418460 DOI: 10.1371/journal.pone.0288158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 06/20/2023] [Indexed: 07/09/2023] Open
Abstract
Multiplexed assays of variant effects (MAVEs) have made possible the functional assessment of all possible mutations to genes and regulatory sequences. A core pillar of the approach is generation of variant libraries, but current methods are either difficult to scale or not uniform enough to enable MAVEs at the scale of gene families or beyond. We present an improved method called Scalable and Uniform Nicking (SUNi) mutagenesis that combines massive scalability with high uniformity to enable cost-effective MAVEs of gene families and eventually genomes.
Collapse
Affiliation(s)
- Taylor L Mighell
- The Barcelona Institute of Science and Technology, Center for Genomic Regulation (CRG), Barcelona, Spain
| | - Ignasi Toledano
- The Barcelona Institute of Science and Technology, Center for Genomic Regulation (CRG), Barcelona, Spain
| | - Ben Lehner
- The Barcelona Institute of Science and Technology, Center for Genomic Regulation (CRG), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| |
Collapse
|
22
|
Khoza N, Twesigomwe D, Othman H. Characterizing the combined effects of cytochrome P450 missense variation within star allele definitions. Pharmacogenomics 2023; 24:561-578. [PMID: 37503750 DOI: 10.2217/pgs-2023-0068] [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: 07/29/2023] Open
Abstract
Background: Cytochrome P450 (CYP) genetic variation largely impacts drug response. However, many CYP star alleles (haplotypes) lack functional annotation, impeding our understanding of drug metabolism mechanisms. We aimed to investigate the impact of missense variant combinations on CYP protein structures. Methods: Normal mode analysis was conducted on 261 missense variants within 91 CYP haplotypes. CYP2D6*2 and CYP2D6*17 were prioritized for molecular dynamics simulation. Results: Normal mode analysis and molecular dynamics highlight the effects of known CYP missense variants on protein stability and conformational dynamics. Missense variants within haplotypes may have intermodulating effects on protein structure and function. Conclusion: This study highlights the utility of multiscale modeling in interpreting CYP missense variants and particularly their combinations within various star alleles.
Collapse
Affiliation(s)
- Nhlamulo Khoza
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, 2193, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 2001, South Africa
| | - David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, 2193, South Africa
| | - Houcemeddine Othman
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, 2193, South Africa
- Laboratory of Human Cytogenetics, Molecular Genetics and Reproductive Biology, Farhat Hached University Hospital, Sousse, 4000, Tunisia
| |
Collapse
|
23
|
Geng L, Gu J, Li M, Liu H, Sun H, Ni B, Gu W, Shao Y, Li M, Chen M. Frequency of prothrombin time-international normalized ratio monitoring and the long-term prognosis in patients with mechanical valve replacement. BMC Cardiovasc Disord 2023; 23:322. [PMID: 37355558 PMCID: PMC10290782 DOI: 10.1186/s12872-023-03293-w] [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: 12/11/2022] [Accepted: 05/11/2023] [Indexed: 06/26/2023] Open
Abstract
BACKGROUND The study aimed to assess the correlation between the monitoring frequency of PT-INR and the long-term prognosis in patients with mechanical heart valve (MHV) replacement after discharge. METHODS This single-center, observational study enrolled patients who underwent MHV replacement and discharged from June 2015 to May 2018. Patients or their corresponding family members were followed with a telephone questionnaire survey in July-October 2020. Based on monitoring intervals, patients were divided into frequent monitoring (FM) group (≤ 1 month) and less frequent monitoring (LFM) group (> 1 month). The primary endpoint was the composite of thromboembolic event, major bleeding or all-cause death. The secondary endpoints were thromboembolic event, major bleeding or all-cause death, respectively. RESULTS A total of 188 patients were included in the final analysis. The median follow-up duration was 3.6 years (Interquartile range: 2.6 to 4.4 years). 104 (55.3%) patients and 84 (44.7%) patients were classified into the FM group and the LFM group, respectively. The FM group had a significantly lower incidence of the primary endpoint than the LFM group (3.74 vs. 1.16 per 100 patient-years, adjusted HR: 3.31 [95% CI 1.05-10.42, P = 0.041]). Secondary analysis revealed that the risk of thromboembolic events and all-cause death were also reduced in the FM group. CONCLUSIONS The management of warfarin treatment in patients after MHV replacement remains challenging. Patients with less frequent monitoring of PT-INR might have worse clinical prognosis than those with frequent PT-INR monitoring.
Collapse
Affiliation(s)
- Le Geng
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Jiaxi Gu
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Minghui Li
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Hong Liu
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Haoliang Sun
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Buqing Ni
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Weidong Gu
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| | - Yongfeng Shao
- Division of Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China.
| | - Mingfang Li
- Division of Cardiology, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China.
| | - Minglong Chen
- Division of Cardiology, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, P.R. China
| |
Collapse
|
24
|
Yee SW, Macdonald C, Mitrovic D, Zhou X, Koleske ML, Yang J, Silva DB, Grimes PR, Trinidad D, More SS, Kachuri L, Witte JS, Delemotte L, Giacomini KM, Coyote-Maestas W. The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543963. [PMID: 37333090 PMCID: PMC10274788 DOI: 10.1101/2023.06.06.543963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Membrane transporters play a fundamental role in the tissue distribution of endogenous compounds and xenobiotics and are major determinants of efficacy and side effects profiles. Polymorphisms within these drug transporters result in inter-individual variation in drug response, with some patients not responding to the recommended dosage of drug whereas others experience catastrophic side effects. For example, variants within the major hepatic Human organic cation transporter OCT1 (SLC22A1) can change endogenous organic cations and many prescription drug levels. To understand how variants mechanistically impact drug uptake, we systematically study how all known and possible single missense and single amino acid deletion variants impact expression and substrate uptake of OCT1. We find that human variants primarily disrupt function via folding rather than substrate uptake. Our study revealed that the major determinants of folding reside in the first 300 amino acids, including the first 6 transmembrane domains and the extracellular domain (ECD) with a stabilizing and highly conserved stabilizing helical motif making key interactions between the ECD and transmembrane domains. Using the functional data combined with computational approaches, we determine and validate a structure-function model of OCT1s conformational ensemble without experimental structures. Using this model and molecular dynamic simulations of key mutants, we determine biophysical mechanisms for how specific human variants alter transport phenotypes. We identify differences in frequencies of reduced function alleles across populations with East Asians vs European populations having the lowest and highest frequency of reduced function variants, respectively. Mining human population databases reveals that reduced function alleles of OCT1 identified in this study associate significantly with high LDL cholesterol levels. Our general approach broadly applied could transform the landscape of precision medicine by producing a mechanistic basis for understanding the effects of human mutations on disease and drug response.
Collapse
Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Christian Macdonald
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Darko Mitrovic
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, 12121 Solna, Sweden
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Megan L Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Jia Yang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Dina Buitrago Silva
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Patrick Rockefeller Grimes
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Donovan Trinidad
- Department of Medicine, Division of Infectious Disease, University of California, San Francisco, United States
| | - Swati S More
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
- Current address: Center for Drug Design (CDD), College of Pharmacy, University of Minnesota, Minnesota, United States
| | - Linda Kachuri
- Epidemiology and Population Health, Stanford University, California, United States
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States
| | - John S Witte
- Epidemiology and Population Health, Stanford University, California, United States
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States
| | - Lucie Delemotte
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, 12121 Solna, Sweden
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
| | - Willow Coyote-Maestas
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
- Quantitative Biosciences Institute, University of California, San Francisco, United States
| |
Collapse
|
25
|
Gersing S, Schulze TK, Cagiada M, Stein A, Roth FP, Lindorff-Larsen K, Hartmann-Petersen R. Characterizing glucokinase variant mechanisms using a multiplexed abundance assay. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.24.542036. [PMID: 37292969 PMCID: PMC10245906 DOI: 10.1101/2023.05.24.542036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.
Collapse
Affiliation(s)
- Sarah Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Thea K. Schulze
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Matteo Cagiada
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Frederick P. Roth
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| |
Collapse
|
26
|
Zou LL, Zhao FL, Qi YY, Wang SH, Zhou Q, Geng PW, Zhou YF, Zhang Q, Chen H, Dai DP, Cai JP, Ji FS. Characterization of 15 CYP2J2 variants identified in the Chinese Han population on the metabolism of ebastine and terfenadine in vitro. Front Pharmacol 2023; 14:1186824. [PMID: 37288113 PMCID: PMC10242136 DOI: 10.3389/fphar.2023.1186824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023] Open
Abstract
Genetic polymorphism of the cytochrome P450 (CYP) gene can significantly influence the metabolism of endogenous and xenobiotic compounds. However, few studies have focused on the polymorphism of CYP2J2 and its impact on drug catalytic activity, especially in the Chinese Han population. In this study, we sequenced the promoter and exon regions of CYP2J2 in 1,163 unrelated healthy Chinese Han individuals using the multiplex PCR amplicon sequencing method. Then, the catalytic activities of the detected CYP2J2 variants were evaluated after recombinant expression in S. cerevisiae microsomes. As a result, CYP2J2*7, CYP2J2*8, 13 variations in the promoter region and 15 CYP2J2 nonsynonymous variants were detected, of which V15A, G24R, V68A, L166F and A391T were novel missense variations. Immunoblotting results showed that 11 of 15 CYP2J2 variants exhibited lower protein expression than wild-type CYP2J2.1. In vitro functional analysis results revealed that the amino acid changes of 14 variants could significantly influence the drug metabolic activity of CYP2J2 toward ebastine or terfenadine. Specifically, 4 variants with relatively higher allele frequencies, CYP2J2.8, 173_173del, K267fs and R446W, exhibited extremely low protein expression and defective catalytic activities for both substrates. Our results indicated that a high genetic polymorphism of CYP2J2 could be detected in the Chinese Han population, and most genetic variations in CYP2J2 could influence the expression and catalytic activity of CYP2J2. Our data significantly enrich the knowledge of genetic polymorphisms in CYP2J2 and provide new theoretical information for corresponding individualized medication in Chinese and other Asian populations.
Collapse
Affiliation(s)
- Li-Li Zou
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Fang-Ling Zhao
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yu-Ying Qi
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuang-Hu Wang
- The Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Quan Zhou
- The Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Pei-Wu Geng
- The Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Yun-Fang Zhou
- The Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Qing Zhang
- Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Chen
- Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Da-Peng Dai
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jian-Ping Cai
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Fu-Sui Ji
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| |
Collapse
|
27
|
Twesigomwe D, Drögemöller BI, Wright GE, Adebamowo C, Agongo G, Boua PR, Matshaba M, Paximadis M, Ramsay M, Simo G, Simuunza MC, Tiemessen CT, Lombard Z, Hazelhurst S. Characterization of CYP2D6 Pharmacogenetic Variation in Sub-Saharan African Populations. Clin Pharmacol Ther 2023; 113:643-659. [PMID: 36111505 PMCID: PMC9957841 DOI: 10.1002/cpt.2749] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 09/11/2022] [Indexed: 11/07/2022]
Abstract
Cytochrome P450 2D6 (CYP2D6) is a key enzyme in drug response owing to its involvement in the metabolism of ~ 25% of clinically prescribed medications. The encoding CYP2D6 gene is highly polymorphic, and many pharmacogenetics studies have been performed worldwide to investigate the distribution of CYP2D6 star alleles (haplotypes); however, African populations have been relatively understudied to date. In this study, the distributions of CYP2D6 star alleles and predicted drug metabolizer phenotypes-derived from activity scores-were examined across multiple sub-Saharan African populations based on bioinformatics analysis of 961 high-depth whole genome sequences. This was followed by characterization of novel star alleles and suballeles in a subset of the participants via targeted high-fidelity Single-Molecule Real-Time resequencing (Pacific Biosciences). This study revealed varying frequencies of known CYP2D6 alleles and predicted phenotypes across different African ethnolinguistic groups. Twenty-seven novel CYP2D6 star alleles were predicted computationally and two of them were further validated. This study highlights the importance of studying variation in key pharmacogenes such as CYP2D6 in the African context to better understand population-specific allele frequencies. This will aid in the development of better genotyping panels and star allele detection approaches with a view toward supporting effective implementation of precision medicine strategies in Africa and across the African diaspora.
Collapse
Affiliation(s)
- David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
- Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
| | - Britt I. Drögemöller
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health SciencesUniversity of ManitobaWinnipegManitobaCanada
| | - Galen E.B. Wright
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre and Max Rady College of MedicineUniversity of ManitobaWinnipegManitobaCanada
- Department of Pharmacology and Therapeutics, Rady Faculty of Health SciencesUniversity of ManitobaWinnipegManitobaCanada
| | - Clement Adebamowo
- Institute for Human VirologyAbujaNigeria
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, and the Marlene and Stewart Greenebaum Comprehensive Cancer CentreUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Godfred Agongo
- Navrongo Health Research CentreGhana Health ServiceNavrongoGhana
- C.K. Tedam University of Technology and Applied SciencesNavrongoGhana
| | - Palwendé R. Boua
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
- Clinical Research Unit of NanoroInstitut de Recherche en Sciences de la SantéNanoroBurkina Faso
| | - Mogomotsi Matshaba
- Botswana‐Baylor Children's Clinical Centre of ExcellenceGaboroneBotswana
- RetrovirologyDepartment of Pediatrics, Baylor College of MedicineHoustonTexasUSA
| | - Maria Paximadis
- Centre for HIV and STIs, National Institute for Communicable Diseases, National Health Laboratory Services and Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
- School of Molecular and Cell BiologyUniversity of the WitwatersrandJohannesburgSouth Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
- Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
| | - Gustave Simo
- Molecular Parasitology and Entomology Unit, Department of Biochemistry, Faculty of ScienceUniversity of DschangDschangCameroon
| | - Martin C. Simuunza
- Department of Disease Control, School of Veterinary MedicineUniversity of ZambiaLusakaZambia
| | - Caroline T. Tiemessen
- Centre for HIV and STIs, National Institute for Communicable Diseases, National Health Laboratory Services and Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
| | - Zané Lombard
- Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa
- School of Electrical and Information EngineeringUniversity of the WitwatersrandJohannesburgSouth Africa
| |
Collapse
|
28
|
Krammer L, Breinbauer R. Activity‐Based Protein Profiling of Oxidases and Reductases. Isr J Chem 2023. [DOI: 10.1002/ijch.202200086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Leo Krammer
- Institute of Organic Chemistry Graz University of Technology Stremayrgasse 9 A-8010 Graz Austria
| | - Rolf Breinbauer
- Institute of Organic Chemistry Graz University of Technology Stremayrgasse 9 A-8010 Graz Austria
- BIOTECHMED Graz A-8010 Graz Austria
| |
Collapse
|
29
|
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.
Collapse
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.
| |
Collapse
|
30
|
Fu Y, Bedő J, Papenfuss AT, Rubin AF. Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants. Gigascience 2022; 12:giad073. [PMID: 37721410 PMCID: PMC10506130 DOI: 10.1093/gigascience/giad073] [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: 02/14/2023] [Revised: 07/02/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. RESULTS In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. CONCLUSIONS We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.
Collapse
Affiliation(s)
- Yunfan Fu
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
| | - Justin Bedő
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
| | - Anthony T Papenfuss
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
- Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Alan F Rubin
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
| |
Collapse
|
31
|
Zhao FL, Zhang Q, Wang SH, Hong Y, Zhou S, Zhou Q, Geng PW, Luo QF, Yang JF, Chen H, Cai JP, Dai DP. Identification and drug metabolic characterization of four new CYP2C9 variants CYP2C9*72- *75 in the Chinese Han population. Front Pharmacol 2022; 13:1007268. [PMID: 36582532 PMCID: PMC9792615 DOI: 10.3389/fphar.2022.1007268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022] Open
Abstract
Cytochrome 2C9 (CYP2C9), one of the most important drug metabolic enzymes in the human hepatic P450 superfamily, is required for the metabolism of 15% of clinical drugs. Similar to other CYP2C family members, CYP2C9 gene has a high genetic polymorphism which can cause significant racial and inter-individual differences in drug metabolic activity. To better understand the genetic distribution pattern of CYP2C9 in the Chinese Han population, 931 individuals were recruited and used for the genotyping in this study. As a result, seven synonymous and 14 non-synonymous variations were identified, of which 4 missense variants were designated as new alleles CYP2C9*72, *73, *74 and *75, resulting in the amino acid substitutions of A149V, R150C, Q214H and N418T, respectively. When expressed in insect cell microsomes, all four variants exhibited comparable protein expression levels to that of the wild-type CYP2C9 enzyme. However, drug metabolic activity analysis revealed that these variants exhibited significantly decreased catalytic activities toward three CYP2C9 specific probe drugs, as compared with that of the wild-type enzyme. These data indicate that the amino acid substitution in newly designated variants can cause reduced function of the enzyme and its clinical significance still needs further investigation in the future.
Collapse
Affiliation(s)
- Fang-Ling Zhao
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China,Peking University Fifth School of Clinical Medicine, Beijing, China
| | - Qing Zhang
- Department of Cardiovascular, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuang-Hu Wang
- Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Yun Hong
- Department of Gastroenterology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Shan Zhou
- Peking University Fifth School of Clinical Medicine, Beijing, China
| | - Quan Zhou
- Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Pei-Wu Geng
- Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Qing-Feng Luo
- Department of Gastroenterology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jie-Fu Yang
- Department of Cardiovascular, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Chen
- Department of Cardiovascular, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China,*Correspondence: Da-Peng Dai, ; Jian-Ping Cai, ; Hao Chen,
| | - Jian-Ping Cai
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China,*Correspondence: Da-Peng Dai, ; Jian-Ping Cai, ; Hao Chen,
| | - Da-Peng Dai
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China,Peking University Fifth School of Clinical Medicine, Beijing, China,*Correspondence: Da-Peng Dai, ; Jian-Ping Cai, ; Hao Chen,
| |
Collapse
|
32
|
Tabet D, Parikh V, Mali P, Roth FP, Claussnitzer M. Scalable Functional Assays for the Interpretation of Human Genetic Variation. Annu Rev Genet 2022; 56:441-465. [PMID: 36055970 DOI: 10.1146/annurev-genet-072920-032107] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.
Collapse
Affiliation(s)
- Daniel Tabet
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Victoria Parikh
- Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Frederick P Roth
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine and Endocrine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA;
| |
Collapse
|
33
|
Aldiban W, Altawil Y, Hussein S, Aljamali M, Youssef LA. Hyper-responsiveness to warfarin in a young patient with the VKORC1 -1639GA/CYP2C9*1*46 genotype: a case report. Thromb J 2022; 20:65. [PMID: 36303140 PMCID: PMC9608913 DOI: 10.1186/s12959-022-00425-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Warfarin is the most widely used oral anticoagulant; nevertheless, dosing of warfarin is problematic for clinicians worldwide. Inter-individual variability in response to warfarin is attributed to genetic as well as non-genetic factors. Pharmacogenomics studies have identified variants in CYP2C9 and VKORC1 genes as significant predictors of warfarin dose, however, phenotypes of rare variants are not well characterized. CASE PRESENTATION We report a case of hyper-responsiveness to warfarin in a 22-year-old outpatient with Crohn's disease who presented with a swollen, red, and painful left calf. Deep venous thrombosis (DVT) in the left lower extremity was confirmed via ultrasonography, and hence, anticoagulation therapy of heparin and concomitant warfarin was initiated. Warfarin dose of 7.5 mg/day was estimated by the physician based on clinical factors. Higher than the expected international normalized ratio (INR) value of 4.5 necessitated the reduction of the warfarin dose to 5 and eventually to 2.5 mg/day to reach a therapeutic INR value of 2.6. Pharmacogenetic profiling of the VKORC1 -1639G > A and CYP2C9 *2, *3, *4, *5, *8, *14, *20, *24, *26, *33, *40, *41, *42, *43, *45, *46, *55, *62, *63, *66, *68, *72, *73 and *78 revealed a VKORC1-1639GA/CYP2C9*1*46 genotype. The lower catalytic activity of the CYP2C9*46 (A149T) variant was previously reported in in vitro settings. CONCLUSIONS This is the first report on a case of warfarin hyper-responsive phenotype of a patient with the heterozygous CYP2C9*1*46 polymorphism.
Collapse
Affiliation(s)
- Weam Aldiban
- grid.8192.20000 0001 2353 3326Program of Clinical and Hospital Pharmacy, Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic ,grid.461272.40000 0004 0417 813XFaculty of Pharmacy, International University for Science and Technology (IUST), Daraa, Syrian Arab Republic
| | - Yara Altawil
- grid.8192.20000 0001 2353 3326Program of Clinical and Hospital Pharmacy, Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | | | - Majd Aljamali
- grid.8192.20000 0001 2353 3326Department of Biochemistry and Microbiology, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic ,National Commission for Biotechnology, Damascus, Syrian Arab Republic
| | - Lama A. Youssef
- grid.8192.20000 0001 2353 3326Program of Clinical and Hospital Pharmacy, Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic ,grid.461272.40000 0004 0417 813XFaculty of Pharmacy, International University for Science and Technology (IUST), Daraa, Syrian Arab Republic ,National Commission for Biotechnology, Damascus, Syrian Arab Republic
| |
Collapse
|
34
|
High-throughput approaches to functional characterization of genetic variation in yeast. Curr Opin Genet Dev 2022; 76:101979. [PMID: 36075138 DOI: 10.1016/j.gde.2022.101979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/29/2022] [Accepted: 08/02/2022] [Indexed: 11/20/2022]
Abstract
Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.
Collapse
|
35
|
Zhou Y, Tremmel R, Schaeffeler E, Schwab M, Lauschke VM. Challenges and opportunities associated with rare-variant pharmacogenomics. Trends Pharmacol Sci 2022; 43:852-865. [PMID: 36008164 DOI: 10.1016/j.tips.2022.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/15/2022] [Accepted: 07/29/2022] [Indexed: 12/26/2022]
Abstract
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.
Collapse
Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany
| | - Elke Schaeffeler
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany; Department of Clinical Pharmacology, and Department of Biochemistry and Pharmacy, University of Tübingen, Tübingen, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden; Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany.
| |
Collapse
|
36
|
Blee AM, Li B, Pecen T, Meiler J, Nagel ZD, Capra JA, Chazin WJ. An Active Learning Framework Improves Tumor Variant Interpretation. Cancer Res 2022; 82:2704-2715. [PMID: 35687855 PMCID: PMC9357215 DOI: 10.1158/0008-5472.can-21-3798] [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: 11/08/2021] [Revised: 04/26/2022] [Accepted: 06/07/2022] [Indexed: 02/05/2023]
Abstract
SIGNIFICANCE A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.
Collapse
Affiliation(s)
- Alexandra M. Blee
- Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
| | - Bian Li
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Turner Pecen
- John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, SAC 04103, Germany
| | - Zachary D. Nagel
- John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - John A. Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94107, USA
| | - Walter J. Chazin
- Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
| |
Collapse
|
37
|
Daly AK. Pharmacogenetics of the cytochromes P450: Selected pharmacological and toxicological aspects. ADVANCES IN PHARMACOLOGY (SAN DIEGO, CALIF.) 2022; 95:49-72. [PMID: 35953163 DOI: 10.1016/bs.apha.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the availability of detailed genomic data on all 57 human cytochrome P450 genes, it is clear that there is substantial variability in gene product activity with functionally significant polymorphisms reported across almost all isoforms. This article is concerned mainly with 13 P450 isoforms of particular relevance to xenobiotic metabolism. After brief review of the extent of polymorphism in each, the relevance of selected P450 isoforms to both adverse drug reaction and disease susceptibility is considered in detail. Bleeding due to warfarin and other coumarin anticoagulants is considered as an example of a type A reaction with idiosyncratic adverse drug reactions affecting the liver and skin as type B. It is clear that CYP2C9 variants contribute significantly to warfarin dose requirement and also risk of bleeding, with a minor contribution from CYP4F2. In the case of idiosyncratic adverse drug reactions, CYP2B6 variants appear relevant to both liver and skin reactions to several drugs with CYP2C9 variants also relevant to phenytoin-related skin rash. The relevance of P450 genotype to disease susceptibility is also considered but detailed genetic studies now suggest that CYP2A6 is the only P450 relevant to risk of lung cancer with alleles associated with low or absent activity clearly protective against disease. Other cytochrome P450 genotypes are generally not predictors for risk of cancer or other complex disease development.
Collapse
Affiliation(s)
- Ann K Daly
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom.
| |
Collapse
|
38
|
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.
Collapse
|
39
|
Yeh CLC, Amorosi CJ, Showman S, Dunham MJ. PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment. Bioinformatics 2022; 38:2927-2929. [PMID: 35561209 PMCID: PMC9306489 DOI: 10.1093/bioinformatics/btac165] [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: 11/22/2021] [Revised: 03/02/2022] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Use of PacBio sequencing for characterizing barcoded libraries of genetic variants is on the rise. However, current approaches in resolving PacBio sequencing artifacts can result in a high number of incorrectly identified or unusable reads. Here, we developed a PacBio Read Alignment Tool (PacRAT) that improves the accuracy of barcode-variant mapping through several steps of read alignment and consensus calling. To quantify the performance of our approach, we simulated PacBio reads from eight variant libraries of various lengths and showed that PacRAT improves the accuracy in pairing barcodes and variants across these libraries. Analysis of real (non-simulated) libraries also showed an increase in the number of reads that can be used for downstream analyses when using PacRAT. AVAILABILITY AND IMPLEMENTATION PacRAT is written in Python and is freely available (https://github.com/dunhamlab/PacRAT). SUPPLEMENTARY INFORMATION Supplemental data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | | | - Soyeon Showman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA
| | | |
Collapse
|
40
|
Brown KE, Staples JW, Woodahl EL. Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function. Pharmacogenomics 2022; 23:255-262. [PMID: 35083931 PMCID: PMC8890136 DOI: 10.2217/pgs-2021-0149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The discovery of haplotypes with unknown or uncertain function in the CYP2D6 pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanning and phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.
Collapse
Affiliation(s)
- Karen E Brown
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Jack W Staples
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Erica L Woodahl
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA,Author for correspondence: Tel.: +1 406 243 4129;
| |
Collapse
|
41
|
Auwerx C, Sadler MC, Reymond A, Kutalik Z. From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
Collapse
|
42
|
Li C, Haller G, Weihl CC. Current and Future Approaches to Classify VUSs in LGMD-Related Genes. Genes (Basel) 2022; 13:genes13020382. [PMID: 35205425 PMCID: PMC8871643 DOI: 10.3390/genes13020382] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/11/2022] [Accepted: 02/16/2022] [Indexed: 01/09/2023] Open
Abstract
Next-generation sequencing (NGS) has revealed large numbers of genetic variants in LGMD-related genes, with most of them classified as variants of uncertain significance (VUSs). VUSs are genetic changes with unknown pathological impact and present a major challenge in genetic test interpretation and disease diagnosis. Understanding the phenotypic consequences of VUSs can provide clinical guidance regarding LGMD risk and therapy. In this review, we provide a brief overview of the subtypes of LGMD, disease diagnosis, current classification systems for investigating VUSs, and a potential deep mutational scanning approach to classify VUSs in LGMD-related genes.
Collapse
Affiliation(s)
- Chengcheng Li
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA; (C.L.); (G.H.)
| | - Gabe Haller
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA; (C.L.); (G.H.)
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO 63110, USA
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Conrad C. Weihl
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA; (C.L.); (G.H.)
- Correspondence:
| |
Collapse
|
43
|
Høie MH, Cagiada M, Beck Frederiksen AH, Stein A, Lindorff-Larsen K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Rep 2022; 38:110207. [PMID: 35021073 DOI: 10.1016/j.celrep.2021.110207] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/01/2021] [Accepted: 12/13/2021] [Indexed: 01/23/2023] Open
Abstract
Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.
Collapse
Affiliation(s)
- Magnus Haraldson Høie
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Anders Haagen Beck Frederiksen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
| |
Collapse
|
44
|
Matreyek KA, Stephany JJ, Ahler E, Fowler DM. Integrating thousands of PTEN variant activity and abundance measurements reveals variant subgroups and new dominant negatives in cancers. Genome Med 2021; 13:165. [PMID: 34649609 PMCID: PMC8518224 DOI: 10.1186/s13073-021-00984-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 09/30/2021] [Indexed: 01/22/2023] Open
Abstract
Background PTEN is a multi-functional tumor suppressor protein regulating cell growth, immune signaling, neuronal function, and genome stability. Experimental characterization can help guide the clinical interpretation of the thousands of germline or somatic PTEN variants observed in patients. Two large-scale mutational datasets, one for PTEN variant intracellular abundance encompassing 4112 missense variants and one for lipid phosphatase activity encompassing 7244 variants, were recently published. The combined information from these datasets can reveal variant-specific phenotypes that may underlie various clinical presentations, but this has not been comprehensively examined, particularly for somatic PTEN variants observed in cancers. Methods Here, we add to these efforts by measuring the intracellular abundance of 764 new PTEN variants and refining abundance measurements for 3351 previously studied variants. We use this expanded and refined PTEN abundance dataset to explore the mutational patterns governing PTEN intracellular abundance, and then incorporate the phosphatase activity data to subdivide PTEN variants into four functionally distinct groups. Results This analysis revealed a set of highly abundant but lipid phosphatase defective variants that could act in a dominant-negative fashion to suppress PTEN activity. Two of these variants were, indeed, capable of dysregulating Akt signaling in cells harboring a WT PTEN allele. Both variants were observed in multiple breast or uterine tumors, demonstrating the disease relevance of these high abundance, inactive variants. Conclusions We show that multidimensional, large-scale variant functional data, when paired with public cancer genomics datasets and follow-up assays, can improve understanding of uncharacterized cancer-associated variants, and provide better insights into how they contribute to oncogenesis. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00984-x.
Collapse
Affiliation(s)
- Kenneth A Matreyek
- Department of Genome Sciences, University of Washington, Seattle, WA, USA. .,Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | - Jason J Stephany
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Ethan Ahler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Present Address: Revolution Medicines, Redwood City, CA, 94063, USA
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA. .,Department of Bioengineering, University of Washington, Seattle, WA, USA.
| |
Collapse
|
45
|
Powell NR, Zhao H, Ipe J, Liu Y, Skaar TC. Mapping the miRNA-mRNA Interactome in Human Hepatocytes and Identification of Functional mirSNPs in Pharmacogenes. Clin Pharmacol Ther 2021; 110:1106-1118. [PMID: 34314509 PMCID: PMC9007393 DOI: 10.1002/cpt.2379] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/21/2021] [Indexed: 12/21/2022]
Abstract
MiRNAs regulate the expression of hepatic genes involved in pharmacokinetics and pharmacodynamics. Genetic variants affecting miRNA binding (mirSNPs) have been associated with altered drug response, but previously used methods to identify miRNA binding sites and functional mirSNPs in pharmacogenes are indirect and limited by low throughput. We utilized the high-throughput chimeric-eCLIP assay to directly map thousands of miRNA-mRNA interactions and define the miRNA binding sites in primary hepatocytes. We then used the high-throughput PASSPORT-seq assay to functionally test 262 potential mirSNPs with coordinates overlapping the identified miRNA binding sites. Using chimeric-eCLIP, we identified a network of 448 miRNAs that collectively target 11,263 unique genes in primary hepatocytes pooled from 100 donors. Our data provide an extensive map of miRNA binding of each gene, including pharmacogenes, expressed in primary hepatocytes. For example, we identified the hsa-mir-27b-DPYD interaction at a previously validated binding site. A second example is our identification of 19 unique miRNAs that bind to CYP2B6 across 20 putative binding sites on the transcript. Using PASSPORT-seq, we then identified 24 mirSNPs that functionally impacted reporter mRNA levels. To our knowledge, this is the most comprehensive identification of miRNA binding sites in pharmacogenes. Combining chimeric-eCLIP with PASSPORT-seq successfully identified functional mirSNPs in pharmacogenes that may affect transcript levels through altered miRNA binding. These results provide additional insights into potential mechanisms contributing to interindividual variability in drug response.
Collapse
Affiliation(s)
- Nicholas R. Powell
- Indiana University School of Medicine, Department of Medicine, Division of Clinical Pharmacology, Indianapolis, Indiana, USA
| | - Harrison Zhao
- Indiana University School of Medicine, Department of Medical and Molecular Genetics, Indianapolis, Indiana, USA
| | - Joseph Ipe
- Indiana University School of Medicine, Department of Medicine, Division of Clinical Pharmacology, Indianapolis, Indiana, USA
| | - Yunlong Liu
- Indiana University School of Medicine, Department of Medical and Molecular Genetics, Indianapolis, Indiana, USA
| | - Todd C. Skaar
- Indiana University School of Medicine, Department of Medicine, Division of Clinical Pharmacology, Indianapolis, Indiana, USA
| |
Collapse
|
46
|
Geck RC, Boyle G, Amorosi CJ, Fowler DM, Dunham MJ. Measuring Pharmacogene Variant Function at Scale Using Multiplexed Assays. Annu Rev Pharmacol Toxicol 2021; 62:531-550. [PMID: 34516287 DOI: 10.1146/annurev-pharmtox-032221-085807] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As costs of next-generation sequencing decrease, identification of genetic variants has far outpaced our ability to understand their functional consequences. This lack of understanding is a central challenge to a key promise of pharmacogenomics: using genetic information to guide drug selection and dosing. Recently developed multiplexed assays of variant effect enable experimental measurement of the function of thousands of variants simultaneously. Here, we describe multiplexed assays that have been performed on nearly 25,000 variants in eight key pharmacogenes (ADRB2, CYP2C9, CYP2C19, NUDT15, SLCO1B1, TMPT, VKORC1, and the LDLR promoter), discuss advances in experimental design, and explore key challenges that must be overcome to maximize the utility of multiplexed functional data. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- Renee C Geck
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| | - Gabriel Boyle
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| | - Clara J Amorosi
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; , .,Department of Bioengineering, University of Washington, Seattle, Washington 98195, USA
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| |
Collapse
|
47
|
McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide Association Studies in Pharmacogenomics. Clin Pharmacol Ther 2021; 110:637-648. [PMID: 34185318 PMCID: PMC8376796 DOI: 10.1002/cpt.2349] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/15/2021] [Indexed: 12/24/2022]
Abstract
The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.
Collapse
Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California, USA
| | - Yash Pershad
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Departments of Genetics, Medicine, Biomedical Data Science, Stanford, California, USA
| |
Collapse
|