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Walsh ZH, Shah P, Kothapalli N, Shah SB, Nikolenyi G, Brodtman DZ, Leuzzi G, Rogava M, Mu M, Ho P, Abuzaid S, Vasan N, AlQuraishi M, Milner JD, Ciccia A, Melms JC, Izar B. Mapping variant effects on anti-tumor hallmarks of primary human T cells with base-editing screens. Nat Biotechnol 2024:10.1038/s41587-024-02235-x. [PMID: 38783148 DOI: 10.1038/s41587-024-02235-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/10/2024] [Indexed: 05/25/2024]
Abstract
Single-nucleotide variants (SNVs) in key T cell genes can drive clinical pathologies and could be repurposed to improve cellular cancer immunotherapies. Here, we perform massively parallel base-editing screens to generate thousands of variants at gene loci annotated with known or potential clinical relevance. We discover a broad landscape of putative gain-of-function (GOF) and loss-of-function (LOF) mutations, including in PIK3CD and the gene encoding its regulatory subunit, PIK3R1, LCK, SOS1, AKT1 and RHOA. Base editing of PIK3CD and PIK3R1 variants in T cells with an engineered T cell receptor specific to a melanoma epitope or in different generations of CD19 chimeric antigen receptor (CAR) T cells demonstrates that discovered GOF variants, but not LOF or silent mutation controls, enhanced signaling, cytokine production and lysis of cognate melanoma and leukemia cell models, respectively. Additionally, we show that generations of CD19 CAR T cells engineered with PIK3CD GOF mutations demonstrate enhanced antigen-specific signaling, cytokine production and leukemia cell killing, including when benchmarked against other recent strategies.
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Affiliation(s)
- Zachary H Walsh
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Parin Shah
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Neeharika Kothapalli
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Shivem B Shah
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Gergo Nikolenyi
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - D Zack Brodtman
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Giuseppe Leuzzi
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics and Development, Columbia University Medical Center, New York, NY, USA
| | - Meri Rogava
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Michael Mu
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patricia Ho
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Sinan Abuzaid
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Neil Vasan
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Mohammed AlQuraishi
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Joshua D Milner
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - Alberto Ciccia
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics and Development, Columbia University Medical Center, New York, NY, USA
| | - Johannes C Melms
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Benjamin Izar
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
- Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA.
- Columbia Center for Translational Immunology, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
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Woods E, Holmes N, Albaba S, Evans IR, Balasubramanian M. ASXL3-related disorder: Molecular phenotyping and comprehensive review providing insights into disease mechanism. Clin Genet 2024; 105:470-487. [PMID: 38420660 DOI: 10.1111/cge.14506] [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: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 03/02/2024]
Abstract
ASXL3-related disorder, sometimes referred to as Bainbridge-Ropers syndrome, was first identified as a distinct neurodevelopmental disorder by Bainbridge et al. in 2013. Since then, there have been a number of case series and single case reports published worldwide. A comprehensive review of the literature was carried out. Abstracts were screened, relevant literature was analysed, and descriptions of common phenotypic features were quantified. ASXL3 variants were collated and categorised. Common phenotypic features comprised global developmental delay or intellectual disability (97%), feeding problems (76%), hypotonia (88%) and characteristic facial features (93%). The majority of genetic variants were de novo truncating variants in exon 11 or 12 of the ASXL3 gene. Several gaps in our knowledge of this disorder were identified, namely, underlying pathophysiology and disease mechanism, disease contribution of missense variants, relevance of variant location, prevalence and penetrance data. Clinical information is currently limited by patient numbers and lack of longitudinal data, which this review aims to address.
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Affiliation(s)
- Emily Woods
- Sheffield Clinical Genetics Service, Sheffield Children's Hospital, Sheffield, UK
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | - Nicola Holmes
- Sheffield Diagnostic Genetics Service, Sheffield Children's Hospital, Sheffield, UK
| | - Shadi Albaba
- Sheffield Diagnostic Genetics Service, Sheffield Children's Hospital, Sheffield, UK
| | - Iwan R Evans
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
- The Bateson Centre, University of Sheffield, Sheffield, UK
| | - Meena Balasubramanian
- Sheffield Clinical Genetics Service, Sheffield Children's Hospital, Sheffield, UK
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
- The Bateson Centre, University of Sheffield, Sheffield, UK
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Zhang J, Durham J, Qian Cong. Revolutionizing protein-protein interaction prediction with deep learning. Curr Opin Struct Biol 2024; 85:102775. [PMID: 38330793 DOI: 10.1016/j.sbi.2024.102775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 02/10/2024]
Abstract
Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.
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Affiliation(s)
- Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA. https://twitter.com/jzhang_genome
| | - Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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8
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Durham J, Zhang J, Humphreys IR, Pei J, Cong Q. Recent advances in predicting and modeling protein-protein interactions. Trends Biochem Sci 2023; 48:527-538. [PMID: 37061423 DOI: 10.1016/j.tibs.2023.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/17/2023]
Abstract
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
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Affiliation(s)
- Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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