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Horesh ME, Martin-Fernandez M, Gruber C, Buta S, Le Voyer T, Puzenat E, Lesmana H, Wu Y, Richardson A, Stein D, Hodeib S, Youssef M, Kurowski JA, Feuille E, Pedroza LA, Fuleihan RL, Haseley A, Hovnanian A, Quartier P, Rosain J, Davis G, Mullan D, Stewart O, Patel R, Lee AE, Rubinstein R, Ewald L, Maheshwari N, Rahming V, Chinn IK, Lupski JR, Orange JS, Sancho-Shimizu V, Casanova JL, Abul-Husn NS, Itan Y, Milner JD, Bustamante J, Bogunovic D. Individuals with JAK1 variants are affected by syndromic features encompassing autoimmunity, atopy, colitis, and dermatitis. J Exp Med 2024; 221:e20232387. [PMID: 38563820 PMCID: PMC10986756 DOI: 10.1084/jem.20232387] [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: 12/29/2023] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 04/04/2024] Open
Abstract
Inborn errors of immunity lead to autoimmunity, inflammation, allergy, infection, and/or malignancy. Disease-causing JAK1 gain-of-function (GoF) mutations are considered exceedingly rare and have been identified in only four families. Here, we use forward and reverse genetics to identify 59 individuals harboring one of four heterozygous JAK1 variants. In vitro and ex vivo analysis of these variants revealed hyperactive baseline and cytokine-induced STAT phosphorylation and interferon-stimulated gene (ISG) levels compared with wild-type JAK1. A systematic review of electronic health records from the BioME Biobank revealed increased likelihood of clinical presentation with autoimmunity, atopy, colitis, and/or dermatitis in JAK1 variant-positive individuals. Finally, treatment of one affected patient with severe atopic dermatitis using the JAK1/JAK2-selective inhibitor, baricitinib, resulted in clinically significant improvement. These findings suggest that individually rare JAK1 GoF variants may underlie an emerging syndrome with more common presentations of autoimmune and inflammatory disease (JAACD syndrome). More broadly, individuals who present with such conditions may benefit from genetic testing for the presence of JAK1 GoF variants.
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Affiliation(s)
- Michael E. Horesh
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marta Martin-Fernandez
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Conor Gruber
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sofija Buta
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tom Le Voyer
- Laboratory of Human Genetics of Infectious Diseases, INSERM UMR1163, Paris, France
- Imagine Institute, University of Paris, Paris, France
- Clinical Immunology Department, Assistance Publique Hôpitaux de Paris (AP-HP), Saint-Louis Hospital, Paris, France
| | - Eve Puzenat
- Department of Dermatology and INSERM 1098, University of Bourgogne-Franche Comté, Besançon, France
| | - Harry Lesmana
- Genomic Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Pediatric Hematology, Oncology and Bone Marrow Transplantation, Cleveland Clinic, Cleveland, OH, USA
| | - Yiming Wu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashley Richardson
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David Stein
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephanie Hodeib
- Department of Paediatric Infectious Diseases and Virology, Imperial College London, London, UK
- Imperial College London, Centre for Paediatrics and Child Health, London, UK
| | - Mariam Youssef
- Department of Pediatrics, Division of Pediatric Allergy, Immunology and Rheumatology, Columbia University, New York, NY, USA
| | - Jacob A. Kurowski
- Department of Pediatric Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, OH, USA
| | | | - Luis A. Pedroza
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Ramsay L. Fuleihan
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Alexandria Haseley
- Center for Personalized Genetic Healthcare, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Alain Hovnanian
- Imagine Institute, University of Paris, Paris, France
- Laboratory of Genetic Skin Diseases, INSERM U1163, Paris, France
| | - Pierre Quartier
- Université Paris-Cité, Paris, France
- Paediatric Hematology-Immunology and Rheumatology Unit, Hopital Necker-Enfants Malades, Assistance Publique-Hopitaux de Paris, Paris, Fance
| | - Jérémie Rosain
- Laboratory of Human Genetics of Infectious Diseases, INSERM UMR1163, Paris, France
- Imagine Institute, University of Paris, Paris, France
- Center for the Study of Primary Immunodeficiencies, Necker Hospital for Sick Children, Paris, France
| | - Georgina Davis
- Department of Immunology, Derriford Hospital, Plymouth, UK
| | - Daniel Mullan
- Department of Immunology, Derriford Hospital, Plymouth, UK
| | - O’Jay Stewart
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roosheel Patel
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Angelica E. Lee
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rebecca Rubinstein
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leyla Ewald
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nikhil Maheshwari
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Ivan K. Chinn
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Immunology, Allergy, and Retrovirology, Texas Children’s Hospital, Houston, TX, USA
| | - James R. Lupski
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jordan S. Orange
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Vanessa Sancho-Shimizu
- Department of Paediatric Infectious Diseases and Virology, Imperial College London, London, UK
- Imperial College London, Centre for Paediatrics and Child Health, London, UK
| | - Jean-Laurent Casanova
- Laboratory of Human Genetics of Infectious Diseases, INSERM UMR1163, Paris, France
- Imagine Institute, University of Paris, Paris, France
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, New Yor, NY, USA
- Department of Pediatrics, Necker Hospital for Sick Children, Paris, France
| | - Noura S. Abul-Husn
- Department of Medicine, Division of Genomic Medicine, Icahn School of Medicine at Mount Sinai, Institute for Genomic Health, New York, NY, USA
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua D. Milner
- Department of Pediatrics, Division of Pediatric Allergy, Immunology and Rheumatology, Columbia University, New York, NY, USA
| | - Jacinta Bustamante
- Laboratory of Human Genetics of Infectious Diseases, INSERM UMR1163, Paris, France
- Imagine Institute, University of Paris, Paris, France
- Center for the Study of Primary Immunodeficiencies, Necker Hospital for Sick Children, Paris, France
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA
| | - Dusan Bogunovic
- Center for Inborn Errors of Immunity, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pediatrics, Hiroshima University, Hiroshima, Japan
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2
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Montanucci L, Brünger T, Bhattarai N, Boßelmann CM, Kim S, Allen JP, Zhang J, Klöckner C, Fariselli P, May P, Lemke JR, Myers SJ, Yuan H, Traynelis SF, Lal D. Distances from ligands as main predictive features for pathogenicity and functional effect of variants in NMDA receptors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306939. [PMID: 38766179 PMCID: PMC11100844 DOI: 10.1101/2024.05.06.24306939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Genetic variants in genes GRIN1 , GRIN2A , GRIN2B , and GRIN2D , which encode subunits of the N-methyl-D-aspartate receptor (NMDAR), have been associated with severe and heterogeneous neurologic diseases. Missense variants in these genes can result in gain or loss of the NMDAR function, requiring opposite therapeutic treatments. Computational methods that predict pathogenicity and molecular functional effects are therefore crucial for accurate diagnosis and therapeutic applications. We assembled missense variants: 201 from patients, 631 from general population, and 159 characterized by electrophysiological readouts showing whether they can enhance or reduce the receptor function. This includes new functional data from 47 variants reported here, for the first time. We found that pathogenic/benign variants and variants that increase/decrease the channel function were distributed unevenly on the protein structure, with spatial proximity to ligands bound to the agonist and antagonist binding sites being key predictive features. Leveraging distances from ligands, we developed two independent machine learning-based predictors for NMDAR missense variants: a pathogenicity predictor which outperforms currently available predictors (AUC=0.945, MCC=0.726), and the first binary predictor of molecular function (increase or decrease) (AUC=0.809, MCC=0.523). Using these, we reclassified variants of uncertain significance in the ClinVar database and refined a previous genome-informed epidemiological model to estimate the birth incidence of molecular mechanism-defined GRIN disorders. Our findings demonstrate that distance from ligands is an important feature in NMDARs that can enhance variant pathogenicity prediction and enable functional prediction. Further studies with larger numbers of phenotypically and functionally characterized variants will enhance the potential clinical utility of this method.
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Zhong G, Zhao Y, Zhuang D, Chung WK, Shen Y. PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.20.581321. [PMID: 38746140 PMCID: PMC11092447 DOI: 10.1101/2024.02.20.581321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.
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4
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Ablooglu AJ, Chen WS, Xie Z, Desai A, Paul S, Lack JB, Scott LA, Eisch AR, Dudek AZ, Parikh SM, Druey KM. Intrinsic endothelial hyperresponsiveness to inflammatory mediators drives acute episodes in models of Clarkson disease. J Clin Invest 2024; 134:e169137. [PMID: 38502192 DOI: 10.1172/jci169137] [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: 01/26/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
Clarkson disease, or monoclonal gammopathy-associated idiopathic systemic capillary leak syndrome (ISCLS), is a rare, relapsing-remitting disorder featuring the abrupt extravasation of fluids and proteins into peripheral tissues, which in turn leads to hypotensive shock, severe hemoconcentration, and hypoalbuminemia. The specific leakage factor(s) and pathways in ISCLS are unknown, and there is no effective treatment for acute flares. Here, we characterize an autonomous vascular endothelial defect in ISCLS that was recapitulated in patient-derived endothelial cells (ECs) in culture and in a mouse model of disease. ISCLS-derived ECs were functionally hyperresponsive to permeability-inducing factors like VEGF and histamine, in part due to increased endothelial nitric oxide synthase (eNOS) activity. eNOS blockade by administration of N(γ)-nitro-l-arginine methyl ester (l-NAME) ameliorated vascular leakage in an SJL/J mouse model of ISCLS induced by histamine or VEGF challenge. eNOS mislocalization and decreased protein phosphatase 2A (PP2A) expression may contribute to eNOS hyperactivation in ISCLS-derived ECs. Our findings provide mechanistic insights into microvascular barrier dysfunction in ISCLS and highlight a potential therapeutic approach.
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Affiliation(s)
- Ararat J Ablooglu
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
| | - Wei-Sheng Chen
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
| | - Zhihui Xie
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
| | - Abhishek Desai
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
| | - Subrata Paul
- Integrative Data Sciences Section, National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Justin B Lack
- Integrative Data Sciences Section, National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Linda A Scott
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
| | - A Robin Eisch
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
| | - Arkadiusz Z Dudek
- Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Samir M Parikh
- Division of Nephrology, Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Kirk M Druey
- Lung and Vascular Inflammation Section, Laboratory of Allergic Diseases, and
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5
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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.
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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
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Zhao H, Du H, Zhao S, Chen Z, Li Y, Xu K, Liu B, Cheng X, Wen W, Li G, Chen G, Zhao Z, Qiu G, Liu P, Zhang TJ, Wu Z, Wu N. SIGMA leverages protein structural information to predict the pathogenicity of missense variants. CELL REPORTS METHODS 2024; 4:100687. [PMID: 38211594 PMCID: PMC10831939 DOI: 10.1016/j.crmeth.2023.100687] [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: 03/16/2023] [Revised: 08/15/2023] [Accepted: 12/14/2023] [Indexed: 01/13/2024]
Abstract
Leveraging protein structural information to evaluate pathogenicity has been hindered by the scarcity of experimentally determined 3D protein. With the aid of AlphaFold2 predictions, we developed the structure-informed genetic missense mutation assessor (SIGMA) to predict missense variant pathogenicity. In comparison with existing predictors across labeled variant datasets and experimental datasets, SIGMA demonstrates superior performance in predicting missense variant pathogenicity (AUC = 0.933). We found that the relative solvent accessibility of the mutated residue contributed greatly to the predictive ability of SIGMA. We further explored combining SIGMA with other top-tier predictors to create SIGMA+, proving highly effective for variant pathogenicity prediction (AUC = 0.966). To facilitate the application of SIGMA, we pre-computed SIGMA scores for over 48 million possible missense variants across 3,454 disease-associated genes and developed an interactive online platform (https://www.sigma-pred.org/). Overall, by leveraging protein structure information, SIGMA offers an accurate structure-based approach to evaluating the pathogenicity of missense variants.
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Affiliation(s)
- Hengqiang Zhao
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Huakang Du
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Sen Zhao
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Zefu Chen
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Yaqi Li
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Kexin Xu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Bowen Liu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Xi Cheng
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Wen Wen
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Guozhuang Li
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Guilin Chen
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Zhengye Zhao
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Guixing Qiu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Baylor Genetics, Houston, TX 77021, USA
| | - Terry Jianguo Zhang
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China.
| | - Zhihong Wu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; Medical Research Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Medical Research Center of Orthopedics, Chinese Academy of Medical Sciences, Beijing 100730, China.
| | - Nan Wu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China.
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7
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Ge F, Arif M, Yan Z, Alahmadi H, Worachartcheewan A, Shoombuatong W. Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations. ACS OMEGA 2024; 9:2032-2047. [PMID: 38250421 PMCID: PMC10795160 DOI: 10.1021/acsomega.3c07662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/23/2024]
Abstract
Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.
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Affiliation(s)
- Fang Ge
- State
Key Laboratory of Organic Electronics and lnformation Displays &
lnstitute of Advanced Materials (IAM), Nanjing University of Posts
& Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
| | - Zihao Yan
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Hanin Alahmadi
- College
of Computer Science and Engineering, Taibah
University, Madinah 344, Saudi Arabia
| | - Apilak Worachartcheewan
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Watshara Shoombuatong
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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8
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Stein D, Kars ME, Wu Y, Bayrak ÇS, Stenson PD, Cooper DN, Schlessinger A, Itan Y. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Genome Med 2023; 15:103. [PMID: 38037155 PMCID: PMC10688473 DOI: 10.1186/s13073-023-01261-9] [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: 05/29/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .
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Affiliation(s)
- David Stein
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Meltem Ece Kars
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yiming Wu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- College of Life Science, China West Normal University, Nan Chong, Si Chuan, 637009, China
| | - Çiğdem Sevim Bayrak
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Peter D Stenson
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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9
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Ge F, Arif M, Yan Z, Alahmadi H, Worachartcheewan A, Yu DJ, Shoombuatong W. MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction. J Chem Inf Model 2023; 63:7239-7257. [PMID: 37947586 PMCID: PMC10685454 DOI: 10.1021/acs.jcim.3c00950] [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: 06/22/2023] [Revised: 10/21/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.
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Affiliation(s)
- Fang Ge
- School
of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, 9 Wenyuanlu, Nanjing 210023, China
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Zihao Yan
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Hanin Alahmadi
- College of
Computer Science and Engineering, Taibah
University, Madinah 344, Saudi Arabia
| | - Apilak Worachartcheewan
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Dong-Jun Yu
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Watshara Shoombuatong
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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10
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Tu KJ, Diplas BH, Regal JA, Waitkus MS, Pirozzi CJ, Reitman ZJ. Mining cancer genomes for change-of-metabolic-function mutations. Commun Biol 2023; 6:1143. [PMID: 37950065 PMCID: PMC10638295 DOI: 10.1038/s42003-023-05475-w] [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: 04/20/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
Enzymes with novel functions are needed to enable new organic synthesis techniques. Drawing inspiration from gain-of-function cancer mutations that functionally alter proteins and affect cellular metabolism, we developed METIS (Mutated Enzymes from Tumors In silico Screen). METIS identifies metabolism-altering cancer mutations using mutation recurrence rates and protein structure. We used METIS to screen 298,517 cancer mutations and identify 48 candidate mutations, including those previously identified to alter enzymatic function. Unbiased metabolomic profiling of cells exogenously expressing a candidate mutant (OGDHLp.A400T) supports an altered phenotype that boosts in vitro production of xanthosine, a pharmacologically useful chemical that is currently produced using unsustainable, water-intensive methods. We then applied METIS to 49 million cancer mutations, yielding a refined set of candidates that may impart novel enzymatic functions or contribute to tumor progression. Thus, METIS can be used to identify and catalog potentially-useful cancer mutations for green chemistry and therapeutic applications.
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Affiliation(s)
- Kevin J Tu
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 21044, USA
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Bill H Diplas
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joshua A Regal
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA
| | | | | | - Zachary J Reitman
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA.
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA.
- Department of Pathology, Duke University, Durham, NC, 27710, USA.
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11
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Similuk M, Kuijpers T. Nature and nurture: understanding phenotypic variation in inborn errors of immunity. Front Cell Infect Microbiol 2023; 13:1183142. [PMID: 37780853 PMCID: PMC10538643 DOI: 10.3389/fcimb.2023.1183142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
The overall disease burden of pediatric infection is high, with widely varying clinical outcomes including death. Among the most vulnerable children, those with inborn errors of immunity, reduced penetrance and variable expressivity are common but poorly understood. There are several genetic mechanisms that influence phenotypic variation in inborn errors of immunity, as well as a body of knowledge on environmental influences and specific pathogen triggers. Critically, recent advances are illuminating novel nuances for fundamental concepts on disease penetrance, as well as raising new areas of inquiry. The last few decades have seen the identification of almost 500 causes of inborn errors of immunity, as well as major advancements in our ability to characterize somatic events, the microbiome, and genotypes across large populations. The progress has not been linear, and yet, these developments have accumulated into an enhanced ability to diagnose and treat inborn errors of immunity, in some cases with precision therapy. Nonetheless, many questions remain regarding the genetic and environmental contributions to phenotypic variation both within and among families. The purpose of this review is to provide an updated summary of key concepts in genetic and environmental contributions to phenotypic variation within inborn errors of immunity, conceptualized as including dynamic, reciprocal interplay among factors unfolding across the key dimension of time. The associated findings, potential gaps, and implications for research are discussed in turn for each major influencing factor. The substantial challenge ahead will be to organize and integrate information in such a way that accommodates the heterogeneity within inborn errors of immunity to arrive at a more comprehensive and accurate understanding of how the immune system operates in health and disease. And, crucially, to translate this understanding into improved patient care for the millions at risk for serious infection and other immune-related morbidity.
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Affiliation(s)
- Morgan Similuk
- Centralized Sequencing Program, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Taco Kuijpers
- Department of Pediatric Immunology, Rheumatology and Infectious Diseases, Emma Children’s Hospital, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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12
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Ramakrishnan G, Baakman C, Heijl S, Vroling B, van Horck R, Hiraki J, Xue LC, Huynen MA. Understanding structure-guided variant effect predictions using 3D convolutional neural networks. Front Mol Biosci 2023; 10:1204157. [PMID: 37475887 PMCID: PMC10354367 DOI: 10.3389/fmolb.2023.1204157] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/22/2023] [Indexed: 07/22/2023] Open
Abstract
Predicting pathogenicity of missense variants in molecular diagnostics remains a challenge despite the available wealth of data, such as evolutionary information, and the wealth of tools to integrate that data. We describe DeepRank-Mut, a configurable framework designed to extract and learn from physicochemically relevant features of amino acids surrounding missense variants in 3D space. For each variant, various atomic and residue-level features are extracted from its structural environment, including sequence conservation scores of the surrounding amino acids, and stored in multi-channel 3D voxel grids which are then used to train a 3D convolutional neural network (3D-CNN). The resultant model gives a probabilistic estimate of whether a given input variant is disease-causing or benign. We find that the performance of our 3D-CNN model, on independent test datasets, is comparable to other widely used resources which also combine sequence and structural features. Based on the 10-fold cross-validation experiments, we achieve an average accuracy of 0.77 on the independent test datasets. We discuss the contribution of the variant neighborhood in the model's predictive power, in addition to the impact of individual features on the model's performance. Two key features: evolutionary information of residues in the variant neighborhood and their solvent accessibilities were observed to influence the predictions. We also highlight how predictions are impacted by the underlying disease mechanisms of missense mutations and offer insights into understanding these to improve pathogenicity predictions. Our study presents aspects to take into consideration when adopting deep learning approaches for protein structure-guided pathogenicity predictions.
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Affiliation(s)
- Gayatri Ramakrishnan
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Coos Baakman
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
| | | | | | | | | | - Li C. Xue
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Martijn A. Huynen
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
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13
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Mendelian inheritance revisited: dominance and recessiveness in medical genetics. Nat Rev Genet 2023:10.1038/s41576-023-00574-0. [PMID: 36806206 DOI: 10.1038/s41576-023-00574-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 02/22/2023]
Abstract
Understanding the consequences of genotype for phenotype (which ranges from molecule-level effects to whole-organism traits) is at the core of genetic diagnostics in medicine. Many measures of the deleteriousness of individual alleles exist, but these have limitations for predicting the clinical consequences. Various mechanisms can protect the organism from the adverse effects of functional variants, especially when the variant is paired with a wild type allele. Understanding why some alleles are harmful in the heterozygous state - representing dominant inheritance - but others only with the biallelic presence of pathogenic variants - representing recessive inheritance - is particularly important when faced with the deluge of rare genetic alterations identified by high throughput DNA sequencing. Both awareness of the specific quantitative and/or qualitative effects of individual variants and the elucidation of allelic and non-allelic interactions are essential to optimize genetic diagnosis and counselling.
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14
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Integrative proteomic characterization of adenocarcinoma of esophagogastric junction. Nat Commun 2023; 14:778. [PMID: 36774361 PMCID: PMC9922290 DOI: 10.1038/s41467-023-36462-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 02/02/2023] [Indexed: 02/13/2023] Open
Abstract
The incidence of adenocarcinoma of the esophagogastric junction (AEG) has been rapidly increasing in recent decades, but its molecular alterations and subtypes are still obscure. Here, we conduct proteomics and phosphoproteomics profiling of 103 AEG tumors with paired normal adjacent tissues (NATs), whole exome sequencing of 94 tumor-NAT pairs, and RNA sequencing in 83 tumor-NAT pairs. Our analysis reveals an extensively altered proteome and 252 potential druggable proteins in AEG tumors. We identify three proteomic subtypes with significant clinical and molecular differences. The S-II subtype signature protein, FBXO44, is demonstrated to promote tumor progression and metastasis in vitro and in vivo. Our comparative analyses reveal distinct genomic features in AEG subtypes. We find a specific decrease of fibroblasts in the S-III subtype. Further phosphoproteomic comparisons reveal different kinase-phosphosubstrate regulatory networks among AEG subtypes. Our proteogenomics dataset provides valuable resources for understanding molecular mechanisms and developing precision treatment strategies of AEG.
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15
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Ge F, Li C, Iqbal S, Muhammad A, Li F, Thafar MA, Yan Z, Worachartcheewan A, Xu X, Song J, Yu DJ. VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants. Brief Bioinform 2023; 24:6931725. [PMID: 36528806 DOI: 10.1093/bib/bbac535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/17/2022] [Accepted: 11/08/2022] [Indexed: 12/23/2022] Open
Abstract
Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.
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Affiliation(s)
- Fang Ge
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China.,School of Computer Science and Information Engineering, Bengbu University, 1866 Caoshan Road, Bengbu, 233030, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Shahid Iqbal
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Arif Muhammad
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia.,College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Maha A Thafar
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box 110099, Taif 21944, Saudi Arabia
| | - Zihao Yan
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Apilak Worachartcheewan
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Xiaofeng Xu
- School of Computer and Information, Anhui Polytechnic University, Beijingzhong Road, Wuhu 241000, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
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16
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Margraf RL, Alexander RZ, Fulmer ML, Miller CE, Coupal E, Mao R. Multiple endocrine neoplasia type 2 (MEN2) and RET specific modifications of the ACMG/AMP variant classification guidelines and impact on the MEN2 RET database. Hum Mutat 2022; 43:1780-1794. [PMID: 36251279 DOI: 10.1002/humu.24486] [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: 04/20/2022] [Revised: 09/20/2022] [Accepted: 10/04/2022] [Indexed: 01/24/2023]
Abstract
The Multiple Endocrine Neoplasia type 2 (MEN2) RET proto-oncogene database, originally published in 2008, is a comprehensive repository of all publicly available RET gene variations associated with MEN2 syndromes. The variant-specific genotype/phenotype information, age of earliest reported medullary thyroid carcinoma (MTC) onset, and relevant references with a brief summary of findings are cataloged. The ACMG/AMP 2015 consensus statement on variant classification was modified specifically for MEN2 syndromes and RET variants using ClinGen sequence variant interpretation working group recommendations and ClinGen expert panel manuscripts, as well as manuscripts from the American Thyroid Association Guidelines Task Force on Medullary Thyroid Carcinoma and other MEN2 RET literature. The classifications for the 166 single unique variants in the MEN2 RET database were reanalyzed using the MEN2 RET specifically modified ACMG/AMP classification guidelines (version 1). Applying these guidelines added two new variant classifications to the database (likely benign and likely pathogenic) and resulted in clinically significant classification changes (e.g., from pathogenic to uncertain) in 15.7% (26/166) of the original variants. Of those clinically significant changes, the highest percentage of changes, 46.2% (12/26), were changes from uncertain to benign or likely benign. The modified ACMG/AMP criteria with MEN2 RET specifications will optimize and standardize RET variant classifications.
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Affiliation(s)
- Rebecca L Margraf
- ARUP Institute for Clinical and Experimental Pathology®, Salt Lake City, Utah, USA
| | | | - Makenzie L Fulmer
- ARUP Institute for Clinical and Experimental Pathology®, Salt Lake City, Utah, USA.,Department of Pathology, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Christine E Miller
- ARUP Institute for Clinical and Experimental Pathology®, Salt Lake City, Utah, USA
| | - Elena Coupal
- ARUP Institute for Clinical and Experimental Pathology®, Salt Lake City, Utah, USA
| | - Rong Mao
- ARUP Institute for Clinical and Experimental Pathology®, Salt Lake City, Utah, USA.,Department of Pathology, School of Medicine, University of Utah, Salt Lake City, Utah, USA
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17
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Mittal S, Tang I, Gleeson JG. Evaluating human mutation databases for “treatability” using patient-customized therapy. MED 2022; 3:740-759. [DOI: 10.1016/j.medj.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 08/04/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022]
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18
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Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nat Commun 2022; 13:3895. [PMID: 35794153 PMCID: PMC9259657 DOI: 10.1038/s41467-022-31686-6] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/29/2022] [Indexed: 12/12/2022] Open
Abstract
Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we investigate the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors, even those based solely on sequence conservation, underperform on non-LOF mutations. However, we do show that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mechanisms are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms. Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Here the authors analyse the locations of thousands of human disease mutations and their predicted effects on protein structure and show that,while loss-of-function mutations tend to be highly disruptive, non-loss-of-function mutations are in general much milder at a protein structural level.
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19
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Sen N, Anishchenko I, Bordin N, Sillitoe I, Velankar S, Baker D, Orengo C. Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs. Brief Bioinform 2022; 23:6596316. [PMID: 35641150 PMCID: PMC9294430 DOI: 10.1093/bib/bbac187] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 04/23/2022] [Accepted: 04/27/2022] [Indexed: 12/12/2022] Open
Abstract
Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.
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Affiliation(s)
- Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.,Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.,Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
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