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Chen Y, Lee K, Woo J, Kim DW, Keum C, Babbi G, Casadio R, Martelli PL, Savojardo C, Manfredi M, Shen Y, Sun Y, Katsonis P, Lichtarge O, Pejaver V, Seward DJ, Kamandula A, Bakolitsa C, Brenner SE, Radivojac P, O’Donnell-Luria A, Mooney SD, Jain S. Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers. RESEARCH SQUARE 2024:rs.3.rs-4587317. [PMID: 39011112 PMCID: PMC11247923 DOI: 10.21203/rs.3.rs-4587317/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.
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Affiliation(s)
- Yile Chen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, 98105, WA, USA
| | - Kyoungyeul Lee
- 3billion, 3billion Biotechnology company, Seoul, South Korea
| | - Junwoo Woo
- 3billion, 3billion Biotechnology company, Seoul, South Korea
| | - Dong-wook Kim
- 3billion, 3billion Biotechnology company, Seoul, South Korea
| | - Changwon Keum
- 3billion, 3billion Biotechnology company, Seoul, South Korea
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Matteo Manfredi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA
| | - Panagiotis Katsonis
- Molecular and Human Genetics, Baylor College of Medicine, Houston, 77030, TX, USA
| | - Olivier Lichtarge
- Molecular and Human Genetics, Baylor College of Medicine, Houston, 77030, TX, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - David J. Seward
- Department of Pathology, University of Vermont, Burlington, 5445, VT, USA
| | - Akash Kamandula
- Khoury College of Computer Sciences, Northeastern University, Boston, 02115, MA, USA
| | | | | | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, 02115, MA, USA
| | - Anne O’Donnell-Luria
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, 02115, MA, USA
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, 02142, MA, USA
| | - Sean D. Mooney
- Center for Information Technology, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Shantanu Jain
- Khoury College of Computer Sciences, Northeastern University, Boston, 02115, MA, USA
- The Institute for Experiential AI, Northeastern University, Boston, 02115, MA, USA
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2
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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3
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Kaur A, Rojek AE, Symes E, Nawas MT, Patel AA, Patel JL, Sojitra P, Aqil B, Sukhanova M, McNerney ME, Wu LP, Akmatbekov A, Segal J, Tjota MY, Gurbuxani S, Cheng JX, Yeon SY, Ravisankar HV, Fitzpatrick C, Lager A, Drazer MW, Saygin C, Wanjari P, Katsonis P, Lichtarge O, Churpek JE, Ghosh SB, Patel AB, Menon MP, Arber DA, Wang P, Venkataraman G. Real world predictors of response and 24-month survival in high-grade TP53-mutated myeloid neoplasms. Blood Cancer J 2024; 14:99. [PMID: 38890297 PMCID: PMC11189545 DOI: 10.1038/s41408-024-01077-9] [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: 04/08/2024] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024] Open
Abstract
Current therapies for high-grade TP53-mutated myeloid neoplasms (≥10% blasts) do not offer a meaningful survival benefit except allogeneic stem cell transplantation in the minority who achieve a complete response to first line therapy (CR1). To identify reliable pre-therapy predictors of complete response to first-line therapy (CR1) and outcomes, we assembled a cohort of 242 individuals with TP53-mutated myeloid neoplasms and ≥10% blasts with well-annotated clinical, molecular and pathology data. Key outcomes examined were CR1 & 24-month survival (OS24). In this elderly cohort (median age 68.2 years) with 74.0% receiving frontline non-intensive regimens (hypomethylating agents +/- venetoclax), the overall cohort CR1 rate was 25.6% (50/195). We additionally identified several pre-therapy factors predictive of inferior CR1 including male gender (P = 0.026), ≥2 autosomal monosomies (P < 0.001), -17/17p (P = 0.011), multi-hit TP53 allelic state (P < 0.001) and CUX1 co-alterations (P = 0.010). In univariable analysis of the entire cohort, inferior OS24 was predicated by ≥2 monosomies (P = 0.004), TP53 VAF > 25% (P = 0.002), TP53 splice junction mutations (P = 0.007) and antecedent treated myeloid neoplasm (P = 0.001). In addition, mutations/deletions in CUX1, U2AF1, EZH2, TET2, CBL, or KRAS ('EPI6' signature) predicted inferior OS24 (HR = 2.0 [1.5-2.8]; P < 0.0001). In a subgroup analysis of HMA +/-Ven treated individuals (N = 144), TP53 VAF and monosomies did not impact OS24. A risk score for HMA +/-Ven treated individuals incorporating three pre-therapy predictors including TP53 splice junction mutations, EPI6 and antecedent treated myeloid neoplasm stratified 3 prognostic distinct groups: intermediate, intermediate-poor, and poor with significantly different median (12.8, 6.0, 4.3 months) and 24-month (20.9%, 5.7%, 0.5%) survival (P < 0.0001). For the first time, in a seemingly monolithic high-risk cohort, our data identifies several baseline factors that predict response and 24-month survival.
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Affiliation(s)
- Amandeep Kaur
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Alexandra E Rojek
- Hematology/Oncology Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Emily Symes
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Mariam T Nawas
- Hematology/Oncology Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Anand A Patel
- Hematology/Oncology Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Jay L Patel
- Departments of Pathology and Hematology/Oncology, University of Utah/ARUP, Salt Lake City, UT, USA
| | - Payal Sojitra
- Department of Pathology, Rutgers Robert Wood Johnson Medical School New Brunswick NJ, New Brunswick, NJ, USA
| | - Barina Aqil
- Department of Pathology, Northwestern Memorial Hospital, Chicago, IL, USA
| | - Madina Sukhanova
- Department of Pathology, Northwestern Memorial Hospital, Chicago, IL, USA
| | - Megan E McNerney
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Leo P Wu
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Aibek Akmatbekov
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Jeremy Segal
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Melissa Y Tjota
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Sandeep Gurbuxani
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Jason X Cheng
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Su-Yeon Yeon
- Department of Pathology, University of Illinois, Chicago, IL, USA
| | - Harini V Ravisankar
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Carrie Fitzpatrick
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Angela Lager
- Hematology/Oncology Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Michael W Drazer
- Hematology/Oncology Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Caner Saygin
- Hematology/Oncology Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Pankhuri Wanjari
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | | | - Olivier Lichtarge
- Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jane E Churpek
- Division of Hematology, Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Sharmila B Ghosh
- Department of Pathology, Henry Ford Health Systems, Detroit, MI, USA
| | - Ami B Patel
- Departments of Pathology and Hematology/Oncology, University of Utah/ARUP, Salt Lake City, UT, USA
| | - Madhu P Menon
- Departments of Pathology and Hematology/Oncology, University of Utah/ARUP, Salt Lake City, UT, USA
| | - Daniel A Arber
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Peng Wang
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Girish Venkataraman
- Department of Pathology, Sections of Hematopathology and Genomic Pathology, University of Chicago Medicine, Chicago, IL, USA.
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4
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Jain S, Trinidad M, Nguyen TB, Jones K, Neto SD, Ge F, Glagovsky A, Jones C, Moran G, Wang B, Rahimi K, Çalıcı SZ, Cedillo LR, Berardelli S, Özden B, Chen K, Katsonis P, Williams A, Lichtarge O, Rana S, Pradhan S, Srinivasan R, Sajeed R, Joshi D, Faraggi E, Jernigan R, Kloczkowski A, Xu J, Song Z, Özkan S, Padilla N, de la Cruz X, Acuna-Hidalgo R, Grafmüller A, Jiménez Barrón LT, Manfredi M, Savojardo C, Babbi G, Martelli PL, Casadio R, Sun Y, Zhu S, Shen Y, Pucci F, Rooman M, Cia G, Raimondi D, Hermans P, Kwee S, Chen E, Astore C, Kamandula A, Pejaver V, Ramola R, Velyunskiy M, Zeiberg D, Mishra R, Sterling T, Goldstein JL, Lugo-Martinez J, Kazi S, Li S, Long K, Brenner SE, Bakolitsa C, Radivojac P, Suhr D, Suhr T, Clark WT. Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594558. [PMID: 38798479 PMCID: PMC11118473 DOI: 10.1101/2024.05.16.594558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.
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Affiliation(s)
- Shantanu Jain
- The Institute for Experiential AI, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Marena Trinidad
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Thanh Binh Nguyen
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia
| | | | | | - Fang Ge
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | | | | | | | - Boqi Wang
- Department of Bioinformatics and System Biology, University of California, San Diego, La Jolla, CA, USA
| | - Kobra Rahimi
- Department of Computational Biology, School of Life Sciences, Ochanomizu University, Tokyo, Japan
| | - Sümeyra Zeynep Çalıcı
- Department of Genomics, Faculty of Aquatic Science, Istanbul University, Istanbul, Türkiye
| | | | - Silvia Berardelli
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- enGenome srl, Pavia, Italy
| | - Buse Özden
- Program of Molecular Biotechnology and Genetics, Institute of Science, Istanbul University, Istanbul, Türkiye
| | - Ken Chen
- University of California, Berkeley, Berkeley, CA, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | | | | | | | | | - Eshel Faraggi
- Research and Information Systems LLC, Indianapolis, IN, USA
- Physics Department, Indiana University-Purdue University, Indianapolis, IN, USA
| | - Robert Jernigan
- Roy J. Carver Department of Biochemistry, Iowa State University, Ames, IA, USA
| | - Andrzej Kloczkowski
- Institute for Genomic Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Jierui Xu
- University of California, Berkeley, Berkeley, CA, USA
| | | | - Selen Özkan
- Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Natàlia Padilla
- Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier de la Cruz
- Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
- Institucío Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | | | | | | | | | | | - Giulia Babbi
- Biocomputing Group, University of Bologna, Bologna, Italy
| | | | - Rita Casadio
- Biocomputing Group, University of Bologna, Bologna, Italy
| | - Yuanfei Sun
- Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Shaowen Zhu
- Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Yang Shen
- Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Bruxelles, Belgium
| | | | - Pauline Hermans
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Sofia Kwee
- University of California, Berkeley, Berkeley, CA, USA
| | - Ella Chen
- University of California, Berkeley, Berkeley, CA, USA
| | | | - Akash Kamandula
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Vikas Pejaver
- Institute for Genomic Health, 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
| | - Rashika Ramola
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Michelle Velyunskiy
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Daniel Zeiberg
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Reet Mishra
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Department of Bioengineering, University of California, San Francisco, CA, USA
| | | | - Jennifer L Goldstein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jose Lugo-Martinez
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Sindy Li
- University of California, Berkeley, Berkeley, CA, USA
| | - Kinsey Long
- University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
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5
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Chinnam NB, Thapar R, Arvai AS, Sarker AH, Soll JM, Paul T, Syed A, Rosenberg DJ, Hammel M, Bacolla A, Katsonis P, Asthana A, Tsai MS, Ivanov I, Lichtarge O, Silverman RH, Mosammaparast N, Tsutakawa SE, Tainer JA. ASCC1 structures and bioinformatics reveal a novel helix-clasp-helix RNA-binding motif linked to a two-histidine phosphodiesterase. J Biol Chem 2024; 300:107368. [PMID: 38750793 PMCID: PMC11214414 DOI: 10.1016/j.jbc.2024.107368] [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: 01/22/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024] Open
Abstract
Activating signal co-integrator complex 1 (ASCC1) acts with ASCC-ALKBH3 complex in alkylation damage responses. ASCC1 uniquely combines two evolutionarily ancient domains: nucleotide-binding K-Homology (KH) (associated with regulating splicing, transcriptional, and translation) and two-histidine phosphodiesterase (PDE; associated with hydrolysis of cyclic nucleotide phosphate bonds). Germline mutations link loss of ASCC1 function to spinal muscular atrophy with congenital bone fractures 2 (SMABF2). Herein analysis of The Cancer Genome Atlas (TCGA) suggests ASCC1 RNA overexpression in certain tumors correlates with poor survival, Signatures 29 and 3 mutations, and genetic instability markers. We determined crystal structures of Alvinella pompejana (Ap) ASCC1 and Human (Hs) PDE domain revealing high-resolution details and features conserved over 500 million years of evolution. Extending our understanding of the KH domain Gly-X-X-Gly sequence motif, we define a novel structural Helix-Clasp-Helix (HCH) nucleotide binding motif and show ASCC1 sequence-specific binding to CGCG-containing RNA. The V-shaped PDE nucleotide binding channel has two His-Φ-Ser/Thr-Φ (HXT) motifs (Φ being hydrophobic) positioned to initiate cyclic phosphate bond hydrolysis. A conserved atypical active-site histidine torsion angle implies a novel PDE substrate. Flexible active site loop and arginine-rich domain linker appear regulatory. Small-angle X-ray scattering (SAXS) revealed aligned KH-PDE RNA binding sites with limited flexibility in solution. Quantitative evolutionary bioinformatic analyses of disease and cancer-associated mutations support implied functional roles for RNA binding, phosphodiesterase activity, and regulation. Collective results inform ASCC1's roles in transactivation and alkylation damage responses, its targeting by structure-based inhibitors, and how ASCC1 mutations may impact inherited disease and cancer.
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Affiliation(s)
- Naga Babu Chinnam
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Roopa Thapar
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Andrew S Arvai
- Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Altaf H Sarker
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Jennifer M Soll
- Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tanmoy Paul
- Department of Chemistry, Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, Georgia, USA
| | - Aleem Syed
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Daniel J Rosenberg
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Michal Hammel
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Albino Bacolla
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Panagiotis Katsonis
- Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Abhishek Asthana
- Department Cancer Biology, Cleveland Clinic Foundation, Lerner Research Institute, Cleveland, Ohio, USA
| | - Miaw-Sheue Tsai
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Ivaylo Ivanov
- Department of Chemistry, Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, Georgia, USA
| | - Olivier Lichtarge
- Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Robert H Silverman
- Department Cancer Biology, Cleveland Clinic Foundation, Lerner Research Institute, Cleveland, Ohio, USA
| | - Nima Mosammaparast
- Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
| | - John A Tainer
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Department of Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
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6
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Álvarez-Machancoses Ó, Faraggi E, deAndrés-Galiana EJ, Fernández-Martínez JL, Kloczkowski A. Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler. Curr Genomics 2024; 25:171-184. [PMID: 39086995 PMCID: PMC11288160 DOI: 10.2174/0113892029236347240308054538] [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: 02/03/2023] [Revised: 08/03/2023] [Accepted: 09/22/2023] [Indexed: 08/02/2024] Open
Abstract
Background Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem. Methods Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods. Results We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs. Conclusion Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.
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Affiliation(s)
- Óscar Álvarez-Machancoses
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain
| | - Eshel Faraggi
- School of Science, Indiana University–Purdue University Indianapolis, IN, USA
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain
- Department of Computer Science, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain
| | - Juan L. Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain
| | - Andrzej Kloczkowski
- Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University, Columbus, OH, USA
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7
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Stenton SL, O'Leary MC, Lemire G, VanNoy GE, DiTroia S, Ganesh VS, Groopman E, O'Heir E, Mangilog B, Osei-Owusu I, Pais LS, Serrano J, Singer-Berk M, Weisburd B, Wilson MW, Austin-Tse C, Abdelhakim M, Althagafi A, Babbi G, Bellazzi R, Bovo S, Carta MG, Casadio R, Coenen PJ, De Paoli F, Floris M, Gajapathy M, Hoehndorf R, Jacobsen JOB, Joseph T, Kamandula A, Katsonis P, Kint C, Lichtarge O, Limongelli I, Lu Y, Magni P, Mamidi TKK, Martelli PL, Mulargia M, Nicora G, Nykamp K, Pejaver V, Peng Y, Pham THC, Podda MS, Rao A, Rizzo E, Saipradeep VG, Savojardo C, Schols P, Shen Y, Sivadasan N, Smedley D, Soru D, Srinivasan R, Sun Y, Sunderam U, Tan W, Tiwari N, Wang X, Wang Y, Williams A, Worthey EA, Yin R, You Y, Zeiberg D, Zucca S, Bakolitsa C, Brenner SE, Fullerton SM, Radivojac P, Rehm HL, O'Donnell-Luria A. Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project. Hum Genomics 2024; 18:44. [PMID: 38685113 PMCID: PMC11057178 DOI: 10.1186/s40246-024-00604-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: 08/11/2023] [Accepted: 04/02/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.
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Affiliation(s)
- Sarah L Stenton
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Melanie C O'Leary
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabrielle Lemire
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Grace E VanNoy
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie DiTroia
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay S Ganesh
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emily Groopman
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emily O'Heir
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Mangilog
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ikeoluwa Osei-Owusu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lynn S Pais
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jillian Serrano
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ben Weisburd
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael W Wilson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christina Austin-Tse
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Marwa Abdelhakim
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Azza Althagafi
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
- Computer Science Department, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Riccardo Bellazzi
- enGenome Srl, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Samuele Bovo
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Maria Giulia Carta
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | | | - Matteo Floris
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Manavalan Gajapathy
- Center for Computational Genomics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
- Hugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Julius O B Jacobsen
- William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK
| | - Thomas Joseph
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Akash Kamandula
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Structural and Computational Biology and Molecular Biophysics Program, Baylor College of Medicine, Houston, TX, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
| | | | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children's Hospital of Fudan University, Shanghai, China
| | - Paolo Magni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Tarun Karthik Kumar Mamidi
- Center for Computational Genomics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
- Hugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Marta Mulargia
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Giovanna Nicora
- enGenome Srl, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - Vikas Pejaver
- Institute for Genomic Health, 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
| | - Yisu Peng
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Maurizio S Podda
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Institute of Clinical Physiology (IFC), CNR, Via Moruzzi 1, 56124, Pisa, Italy
- University of Siena, Siena, Italy
- CTGLab, Institute of Informatics and Telematics (IIT), CNR, ViaMoruzzi 1, 56124, Pisa, Italy
| | - Aditya Rao
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | | | - Vangala G Saipradeep
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Peter Schols
- Invitae, San Francisco, CA, USA
- Codon One, Louvain, EU, Belgium
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
- Institute of Biosciences and Technology and Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA
| | - Naveen Sivadasan
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Damian Smedley
- William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK
| | | | - Rajgopal Srinivasan
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Uma Sunderam
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Wuwei Tan
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Naina Tiwari
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Xiao Wang
- Center for Molecular Medicine, Pediatric Research Institute, Children's Hospital of Fudan University, Shanghai, China
| | - Yaqiong Wang
- Center for Molecular Medicine, Pediatric Research Institute, Children's Hospital of Fudan University, Shanghai, China
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Elizabeth A Worthey
- Center for Computational Genomics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
- Hugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rujie Yin
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Yuning You
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Daniel Zeiberg
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Constantina Bakolitsa
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Steven E Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Stephanie M Fullerton
- Department of Bioethics and Humanities, University of Washington School of Medicine, Seattle, WA, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
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8
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Zucca S, Nicora G, De Paoli F, Carta MG, Bellazzi R, Magni P, Rizzo E, Limongelli I. An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases. Hum Genet 2024:10.1007/s00439-023-02638-x. [PMID: 38520562 DOI: 10.1007/s00439-023-02638-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/27/2023] [Indexed: 03/25/2024]
Abstract
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.
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Affiliation(s)
- S Zucca
- enGenome Srl, 27100, Pavia, Italy
| | - G Nicora
- enGenome Srl, 27100, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - M G Carta
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - R Bellazzi
- enGenome Srl, 27100, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - P Magni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
- University of Pavia, 27100, Pavia, Italy.
| | - E Rizzo
- enGenome Srl, 27100, Pavia, Italy
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9
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Wang X, Li A, Li X, Cui H. Empowering Protein Engineering through Recombination of Beneficial Substitutions. Chemistry 2024; 30:e202303889. [PMID: 38288640 DOI: 10.1002/chem.202303889] [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: 01/04/2024] [Indexed: 02/24/2024]
Abstract
Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.
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Affiliation(s)
- Xinyue Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Anni Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Xiujuan Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Haiyang Cui
- School of Life Sciences, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
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10
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Shepherdson JL, Hutchison K, Don DW, McGillivray G, Choi TI, Allan CA, Amor DJ, Banka S, Basel DG, Buch LD, Carere DA, Carroll R, Clayton-Smith J, Crawford A, Dunø M, Faivre L, Gilfillan CP, Gold NB, Gripp KW, Hobson E, Holtz AM, Innes AM, Isidor B, Jackson A, Katsonis P, Amel Riazat Kesh L, Küry S, Lecoquierre F, Lockhart P, Maraval J, Matsumoto N, McCarrier J, McCarthy J, Miyake N, Moey LH, Németh AH, Østergaard E, Patel R, Pope K, Posey JE, Schnur RE, Shaw M, Stolerman E, Taylor JP, Wadman E, Wakeling E, White SM, Wong LC, Lupski JR, Lichtarge O, Corbett MA, Gecz J, Nicolet CM, Farnham PJ, Kim CH, Shinawi M. Variants in ZFX are associated with an X-linked neurodevelopmental disorder with recurrent facial gestalt. Am J Hum Genet 2024; 111:487-508. [PMID: 38325380 PMCID: PMC10940019 DOI: 10.1016/j.ajhg.2024.01.007] [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: 08/06/2023] [Revised: 01/14/2024] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
Pathogenic variants in multiple genes on the X chromosome have been implicated in syndromic and non-syndromic intellectual disability disorders. ZFX on Xp22.11 encodes a transcription factor that has been linked to diverse processes including oncogenesis and development, but germline variants have not been characterized in association with disease. Here, we present clinical and molecular characterization of 18 individuals with germline ZFX variants. Exome or genome sequencing revealed 11 variants in 18 subjects (14 males and 4 females) from 16 unrelated families. Four missense variants were identified in 11 subjects, with seven truncation variants in the remaining individuals. Clinical findings included developmental delay/intellectual disability, behavioral abnormalities, hypotonia, and congenital anomalies. Overlapping and recurrent facial features were identified in all subjects, including thickening and medial broadening of eyebrows, variations in the shape of the face, external eye abnormalities, smooth and/or long philtrum, and ear abnormalities. Hyperparathyroidism was found in four families with missense variants, and enrichment of different tumor types was observed. In molecular studies, DNA-binding domain variants elicited differential expression of a small set of target genes relative to wild-type ZFX in cultured cells, suggesting a gain or loss of transcriptional activity. Additionally, a zebrafish model of ZFX loss displayed an altered behavioral phenotype, providing additional evidence for the functional significance of ZFX. Our clinical and experimental data support that variants in ZFX are associated with an X-linked intellectual disability syndrome characterized by a recurrent facial gestalt, neurocognitive and behavioral abnormalities, and an increased risk for congenital anomalies and hyperparathyroidism.
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Affiliation(s)
- James L Shepherdson
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO, USA
| | - Katie Hutchison
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - George McGillivray
- Victorian Clinical Genetics Services, Parkville, VIC 3052, Australia; Murdoch Children's Research Institute, Parkville, VIC 3052, Australia
| | - Tae-Ik Choi
- Department of Biology, Chungnam National University, Daejeon 34134, Korea
| | - Carolyn A Allan
- Hudson Institute of Medical Research, Monash University, and Department of Endocrinology, Monash Health, Melbourne, Australia
| | - David J Amor
- Murdoch Children's Research Institute, Parkville, VIC 3052, Australia; Department of Paediatrics, The University of Melbourne, Parkville 3052, VIC, Australia
| | - Siddharth Banka
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK
| | - Donald G Basel
- Division of Genetics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | | | - Renée Carroll
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
| | - Jill Clayton-Smith
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK
| | - Ali Crawford
- Medical Genomics Research, Illumina Inc, San Diego, CA, USA
| | - Morten Dunø
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Laurence Faivre
- Centre de Référence Anomalies du Développement et Syndromes Malformatifs, FHU TRANSLAD, Hôpital d'Enfants, Dijon, France; INSERM UMR1231, Equipe GAD, Université de Bourgogne-Franche Comté, 21000 Dijon, France
| | - Christopher P Gilfillan
- Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia; Department of Endocrinology, Eastern Health, Box Hill Hospital, Melbourne, VIC, Australia
| | - Nina B Gold
- Harvard Medical School, Boston, MA, USA; Division of Medical Genetics and Metabolism, Massachusetts General Hospital, Boston, MA, USA
| | - Karen W Gripp
- Division of Medical Genetics, Nemours Children's Hospital, Wilmington, DE, USA
| | - Emma Hobson
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Department of Clinical Genetics, Chapel Allerton Hospital, Leeds, UK
| | - Alexander M Holtz
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - A Micheil Innes
- Departments of Medical Genetics and Pediatrics and Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bertrand Isidor
- Nantes Université, CHU Nantes, Service de Génétique Médicale, 44000 Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, 44000 Nantes, France
| | - Adam Jackson
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Leila Amel Riazat Kesh
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Department of Clinical Genetics, Chapel Allerton Hospital, Leeds, UK
| | - Sébastien Küry
- Nantes Université, CHU Nantes, Service de Génétique Médicale, 44000 Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, 44000 Nantes, France
| | - François Lecoquierre
- Univ Rouen Normandie, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, 76000 Rouen, France
| | - Paul Lockhart
- Murdoch Children's Research Institute, Parkville, VIC 3052, Australia; Department of Paediatrics, The University of Melbourne, Parkville 3052, VIC, Australia
| | - Julien Maraval
- Centre de Référence Anomalies du Développement et Syndromes Malformatifs, FHU TRANSLAD, Hôpital d'Enfants, Dijon, France; INSERM UMR1231, Equipe GAD, Université de Bourgogne-Franche Comté, 21000 Dijon, France
| | - Naomichi Matsumoto
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Julie McCarrier
- Division of Genetics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Josephine McCarthy
- Department of Endocrinology, Eastern Health, Box Hill Hospital, Melbourne, VIC, Australia
| | - Noriko Miyake
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan; Department of Human Genetics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
| | - Lip Hen Moey
- Department of Genetics, Penang General Hospital, George Town, Penang, Malaysia
| | - Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Elsebet Østergaard
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Rushina Patel
- Medical Genetics, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA
| | - Kate Pope
- Murdoch Children's Research Institute, Parkville, VIC 3052, Australia
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Marie Shaw
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
| | | | - Julie P Taylor
- Medical Genomics Research, Illumina Inc, San Diego, CA, USA
| | - Erin Wadman
- Division of Medical Genetics, Nemours Children's Hospital, Wilmington, DE, USA
| | - Emma Wakeling
- North East Thames Regional Genetic Service, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Susan M White
- Victorian Clinical Genetics Services, Parkville, VIC 3052, Australia; Murdoch Children's Research Institute, Parkville, VIC 3052, Australia; Department of Paediatrics, The University of Melbourne, Parkville 3052, VIC, Australia
| | - Lawrence C Wong
- Medical Genetics, Kaiser Permanente Downey Medical Center, Downey, CA, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Mark A Corbett
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
| | - Jozef Gecz
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Charles M Nicolet
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peggy J Farnham
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Cheol-Hee Kim
- Department of Biology, Chungnam National University, Daejeon 34134, Korea.
| | - Marwan Shinawi
- Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.
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Jain S, Bakolitsa C, Brenner SE, Radivojac P, Moult J, Repo S, Hoskins RA, Andreoletti G, Barsky D, Chellapan A, Chu H, Dabbiru N, Kollipara NK, Ly M, Neumann AJ, Pal LR, Odell E, Pandey G, Peters-Petrulewicz RC, Srinivasan R, Yee SF, Yeleswarapu SJ, Zuhl M, Adebali O, Patra A, Beer MA, Hosur R, Peng J, Bernard BM, Berry M, Dong S, Boyle AP, Adhikari A, Chen J, Hu Z, Wang R, Wang Y, Miller M, Wang Y, Bromberg Y, Turina P, Capriotti E, Han JJ, Ozturk K, Carter H, Babbi G, Bovo S, Di Lena P, Martelli PL, Savojardo C, Casadio R, Cline MS, De Baets G, Bonache S, Díez O, Gutiérrez-Enríquez S, Fernández A, Montalban G, Ootes L, Özkan S, Padilla N, Riera C, De la Cruz X, Diekhans M, Huwe PJ, Wei Q, Xu Q, Dunbrack RL, Gotea V, Elnitski L, Margolin G, Fariselli P, Kulakovskiy IV, Makeev VJ, Penzar DD, Vorontsov IE, Favorov AV, Forman JR, Hasenahuer M, Fornasari MS, Parisi G, Avsec Z, Çelik MH, Nguyen TYD, Gagneur J, Shi FY, Edwards MD, Guo Y, Tian K, Zeng H, Gifford DK, Göke J, Zaucha J, Gough J, Ritchie GRS, Frankish A, Mudge JM, Harrow J, Young EL, Yu Y, Huff CD, Murakami K, Nagai Y, Imanishi T, Mungall CJ, Jacobsen JOB, Kim D, Jeong CS, Jones DT, Li MJ, Guthrie VB, Bhattacharya R, Chen YC, Douville C, Fan J, Kim D, Masica D, Niknafs N, Sengupta S, Tokheim C, Turner TN, Yeo HTG, Karchin R, Shin S, Welch R, Keles S, Li Y, Kellis M, Corbi-Verge C, Strokach AV, Kim PM, Klein TE, Mohan R, Sinnott-Armstrong NA, Wainberg M, Kundaje A, Gonzaludo N, Mak ACY, Chhibber A, Lam HYK, Dahary D, Fishilevich S, Lancet D, Lee I, Bachman B, Katsonis P, Lua RC, Wilson SJ, Lichtarge O, Bhat RR, Sundaram L, Viswanath V, Bellazzi R, Nicora G, Rizzo E, Limongelli I, Mezlini AM, Chang R, Kim S, Lai C, O’Connor R, Topper S, van den Akker J, Zhou AY, Zimmer AD, Mishne G, Bergquist TR, Breese MR, Guerrero RF, Jiang Y, Kiga N, Li B, Mort M, Pagel KA, Pejaver V, Stamboulian MH, Thusberg J, Mooney SD, Teerakulkittipong N, Cao C, Kundu K, Yin Y, Yu CH, Kleyman M, Lin CF, Stackpole M, Mount SM, Eraslan G, Mueller NS, Naito T, Rao AR, Azaria JR, Brodie A, Ofran Y, Garg A, Pal D, Hawkins-Hooker A, Kenlay H, Reid J, Mucaki EJ, Rogan PK, Schwarz JM, Searls DB, Lee GR, Seok C, Krämer A, Shah S, Huang CV, Kirsch JF, Shatsky M, Cao Y, Chen H, Karimi M, Moronfoye O, Sun Y, Shen Y, Shigeta R, Ford CT, Nodzak C, Uppal A, Shi X, Joseph T, Kotte S, Rana S, Rao A, Saipradeep VG, Sivadasan N, Sunderam U, Stanke M, Su A, Adzhubey I, Jordan DM, Sunyaev S, Rousseau F, Schymkowitz J, Van Durme J, Tavtigian SV, Carraro M, Giollo M, Tosatto SCE, Adato O, Carmel L, Cohen NE, Fenesh T, Holtzer T, Juven-Gershon T, Unger R, Niroula A, Olatubosun A, Väliaho J, Yang Y, Vihinen M, Wahl ME, Chang B, Chong KC, Hu I, Sun R, Wu WKK, Xia X, Zee BC, Wang MH, Wang M, Wu C, Lu Y, Chen K, Yang Y, Yates CM, Kreimer A, Yan Z, Yosef N, Zhao H, Wei Z, Yao Z, Zhou F, Folkman L, Zhou Y, Daneshjou R, Altman RB, Inoue F, Ahituv N, Arkin AP, Lovisa F, Bonvini P, Bowdin S, Gianni S, Mantuano E, Minicozzi V, Novak L, Pasquo A, Pastore A, Petrosino M, Puglisi R, Toto A, Veneziano L, Chiaraluce R, Ball MP, Bobe JR, Church GM, Consalvi V, Cooper DN, Buckley BA, Sheridan MB, Cutting GR, Scaini MC, Cygan KJ, Fredericks AM, Glidden DT, Neil C, Rhine CL, Fairbrother WG, Alontaga AY, Fenton AW, Matreyek KA, Starita LM, Fowler DM, Löscher BS, Franke A, Adamson SI, Graveley BR, Gray JW, Malloy MJ, Kane JP, Kousi M, Katsanis N, Schubach M, Kircher M, Mak ACY, Tang PLF, Kwok PY, Lathrop RH, Clark WT, Yu GK, LeBowitz JH, Benedicenti F, Bettella E, Bigoni S, Cesca F, Mammi I, Marino-Buslje C, Milani D, Peron A, Polli R, Sartori S, Stanzial F, Toldo I, Turolla L, Aspromonte MC, Bellini M, Leonardi E, Liu X, Marshall C, McCombie WR, Elefanti L, Menin C, Meyn MS, Murgia A, Nadeau KCY, Neuhausen SL, Nussbaum RL, Pirooznia M, Potash JB, Dimster-Denk DF, Rine JD, Sanford JR, Snyder M, Cote AG, Sun S, Verby MW, Weile J, Roth FP, Tewhey R, Sabeti PC, Campagna J, Refaat MM, Wojciak J, Grubb S, Schmitt N, Shendure J, Spurdle AB, Stavropoulos DJ, Walton NA, Zandi PP, Ziv E, Burke W, Chen F, Carr LR, Martinez S, Paik J, Harris-Wai J, Yarborough M, Fullerton SM, Koenig BA, McInnes G, Shigaki D, Chandonia JM, Furutsuki M, Kasak L, Yu C, Chen R, Friedberg I, Getz GA, Cong Q, Kinch LN, Zhang J, Grishin NV, Voskanian A, Kann MG, Tran E, Ioannidis NM, Hunter JM, Udani R, Cai B, Morgan AA, Sokolov A, Stuart JM, Minervini G, Monzon AM, Batzoglou S, Butte AJ, Greenblatt MS, Hart RK, Hernandez R, Hubbard TJP, Kahn S, O’Donnell-Luria A, Ng PC, Shon J, Veltman J, Zook JM. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Genome Biol 2024; 25:53. [PMID: 38389099 PMCID: PMC10882881 DOI: 10.1186/s13059-023-03113-6] [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: 01/21/2023] [Accepted: 11/17/2023] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. RESULTS Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. CONCLUSIONS Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
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12
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Gao W, Liu L, Huh E, Gbahou F, Cecon E, Oshima M, Houzé L, Katsonis P, Hegron A, Fan Z, Hou G, Charpentier G, Boissel M, Derhourhi M, Marre M, Balkau B, Froguel P, Scharfmann R, Lichtarge O, Dam J, Bonnefond A, Liu J, Jockers R. Human GLP1R variants affecting GLP1R cell surface expression are associated with impaired glucose control and increased adiposity. Nat Metab 2023; 5:1673-1684. [PMID: 37709961 DOI: 10.1038/s42255-023-00889-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 08/09/2023] [Indexed: 09/16/2023]
Abstract
The glucagon-like peptide 1 receptor (GLP1R) is a major drug target with several agonists being prescribed in individuals with type 2 diabetes and obesity1,2. The impact of genetic variability of GLP1R on receptor function and its association with metabolic traits are unclear with conflicting reports. Here, we show an unexpected diversity of phenotypes ranging from defective cell surface expression to complete or pathway-specific gain of function (GoF) and loss of function (LoF), after performing a functional profiling of 60 GLP1R variants across four signalling pathways. The defective insulin secretion of GLP1R LoF variants is rescued by allosteric GLP1R ligands or high concentrations of exendin-4/semaglutide in INS-1 823/3 cells. Genetic association studies in 200,000 participants from the UK Biobank show that impaired GLP1R cell surface expression contributes to poor glucose control and increased adiposity with increased glycated haemoglobin A1c and body mass index. This study defines impaired GLP1R cell surface expression as a risk factor for traits associated with type 2 diabetes and obesity and provides potential treatment options for GLP1R LoF variant carriers.
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Affiliation(s)
- Wenwen Gao
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, China
| | - Lei Liu
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
- Cellular Signaling Laboratory, International Research Center for Sensory Biology and Technology of MOST, Key Laboratory of Molecular Biophysics of Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Eunna Huh
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA
| | - Florence Gbahou
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
| | - Erika Cecon
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
| | - Masaya Oshima
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
| | - Ludivine Houzé
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alan Hegron
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, Quebec, Canada
- Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Zhiran Fan
- Cellular Signaling Laboratory, International Research Center for Sensory Biology and Technology of MOST, Key Laboratory of Molecular Biophysics of Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Guofei Hou
- Cellular Signaling Laboratory, International Research Center for Sensory Biology and Technology of MOST, Key Laboratory of Molecular Biophysics of Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Guillaume Charpentier
- CERITD (Centre d'Étude et de Recherche pour l'Intensification du Traitement du Diabète), Evry, France
| | - Mathilde Boissel
- University of Lille, Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
| | - Mehdi Derhourhi
- University of Lille, Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
| | - Michel Marre
- Institut Necker-Enfants Malades, INSERM, Université Paris Cité, Paris, France
- Clinique Ambroise Paré, Neuilly-sur-Seine, France
| | - Beverley Balkau
- Inserm U1018, Center for Research in Epidemiology and Population Health, Villejuif, France
- University Paris-Saclay, University Paris-Sud, Villejuif, France
| | - Philippe Froguel
- University of Lille, Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Imperial College London, London, UK
| | | | - Olivier Lichtarge
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Julie Dam
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
| | - Amélie Bonnefond
- University of Lille, Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Imperial College London, London, UK
| | - Jianfeng Liu
- Cellular Signaling Laboratory, International Research Center for Sensory Biology and Technology of MOST, Key Laboratory of Molecular Biophysics of Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
| | - Ralf Jockers
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France.
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Shapiro D, Lee K, Asmussen J, Bourquard T, Lichtarge O. Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease. J Am Heart Assoc 2023; 12:e029103. [PMID: 37642027 PMCID: PMC10547338 DOI: 10.1161/jaha.122.029103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/11/2023] [Indexed: 08/31/2023]
Abstract
Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.
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Affiliation(s)
- Dillon Shapiro
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Kwanghyuk Lee
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Jennifer Asmussen
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Thomas Bourquard
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Olivier Lichtarge
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
- Computational & Integrative Biomedical Research CenterBaylor College of MedicineHoustonTXUSA
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14
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Stenton SL, O’Leary M, Lemire G, VanNoy GE, DiTroia S, Ganesh VS, Groopman E, O’Heir E, Mangilog B, Osei-Owusu I, Pais LS, Serrano J, Singer-Berk M, Weisburd B, Wilson M, Austin-Tse C, Abdelhakim M, Althagafi A, Babbi G, Bellazzi R, Bovo S, Carta MG, Casadio R, Coenen PJ, De Paoli F, Floris M, Gajapathy M, Hoehndorf R, Jacobsen JO, Joseph T, Kamandula A, Katsonis P, Kint C, Lichtarge O, Limongelli I, Lu Y, Magni P, Mamidi TKK, Martelli PL, Mulargia M, Nicora G, Nykamp K, Pejaver V, Peng Y, Pham THC, Podda MS, Rao A, Rizzo E, Saipradeep VG, Savojardo C, Schols P, Shen Y, Sivadasan N, Smedley D, Soru D, Srinivasan R, Sun Y, Sunderam U, Tan W, Tiwari N, Wang X, Wang Y, Williams A, Worthey EA, Yin R, You Y, Zeiberg D, Zucca S, Bakolitsa C, Brenner SE, Fullerton SM, Radivojac P, Rehm HL, O’Donnell-Luria A. Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.02.23293212. [PMID: 37577678 PMCID: PMC10418577 DOI: 10.1101/2023.08.02.23293212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Background A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.
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Affiliation(s)
- Sarah L. Stenton
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Melanie O’Leary
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabrielle Lemire
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Grace E. VanNoy
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie DiTroia
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay S. Ganesh
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emily Groopman
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emily O’Heir
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Mangilog
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ikeoluwa Osei-Owusu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lynn S. Pais
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jillian Serrano
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ben Weisburd
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Wilson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christina Austin-Tse
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Marwa Abdelhakim
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer Science Department, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Riccardo Bellazzi
- enGenome Srl, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Samuele Bovo
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Maria Giulia Carta
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | | | - Matteo Floris
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Manavalan Gajapathy
- Center for Computational Genomics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
- Hugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Julius O.B. Jacobsen
- William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK
| | - Thomas Joseph
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Akash Kamandula
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Panagiotis Katsonis
- Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Olivier Lichtarge
- Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Structural and Computational Biology & Molecular Biophysics Program, Baylor College of Medicine, Houston, TX, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
| | | | - Yulan Lu
- Center for molecular medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, Shanghai, China
| | - Paolo Magni
- enGenome Srl, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Tarun Karthik Kumar Mamidi
- Center for Computational Genomics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
- Hugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Marta Mulargia
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | | | | | - Vikas Pejaver
- Institute for Genomic Health, 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
| | - Yisu Peng
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Thi Hong Cam Pham
- Anatomy and Surgical Training Department, University of Medicine and Pharmacy, Hue University, Vietnam
| | - Maurizio S. Podda
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Aditya Rao
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | | | - Vangala G Saipradeep
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
- Institute of Biosciences and Technology and Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, Texas, USA
| | - Naveen Sivadasan
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Damian Smedley
- William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK
| | | | - Rajgopal Srinivasan
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Uma Sunderam
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Wuwei Tan
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Naina Tiwari
- TCS Research, Tata Consultancy Services (TCS) Ltd, Deccan Park, Madhapur, Hyderabad, India
| | - Xiao Wang
- Center for molecular medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, Shanghai, China
| | - Yaqiong Wang
- Center for molecular medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, Shanghai, China
| | - Amanda Williams
- Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Elizabeth A. Worthey
- Center for Computational Genomics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
- Hugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rujie Yin
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Yuning You
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Daniel Zeiberg
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Constantina Bakolitsa
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Steven E. Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O’Donnell-Luria
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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15
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Aspromonte MC, Conte AD, Zhu S, Tan W, Shen Y, Zhang Y, Li Q, Wang MH, Babbi G, Bovo S, Martelli PL, Casadio R, Althagafi A, Toonsi S, Kulmanov M, Hoehndorf R, Katsonis P, Williams A, Lichtarge O, Xian S, Surento W, Pejaver V, Mooney SD, Sunderam U, Srinivasan R, Murgia A, Piovesan D, Tosatto SCE, Leonardi E. CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs). RESEARCH SQUARE 2023:rs.3.rs-3209168. [PMID: 37577579 PMCID: PMC10418555 DOI: 10.21203/rs.3.rs-3209168/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.
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Affiliation(s)
| | | | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
| | - Wuwei Tan
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
| | | | - Qi Li
- CUHK Shenzhen Research Institute, Shenzhen
| | | | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna
| | - Samuele Bovo
- Department of Agricultural and Food Sciences, University of Bologna
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Sumyyah Toonsi
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Maxat Kulmanov
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030
| | - Su Xian
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195
| | - Wesley Surento
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195
| | - Uma Sunderam
- Innovation Labs, Tata Consultancy Services, Hyderabad
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16
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Wang C, Govindarajan H, Katsonis P, Lichtarge O. ShinyBioHEAT: an interactive shiny app to identify phenotype driver genes in E.coli and B.subtilis. Bioinformatics 2023; 39:btad467. [PMID: 37522889 PMCID: PMC10412404 DOI: 10.1093/bioinformatics/btad467] [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: 01/06/2023] [Revised: 07/13/2023] [Accepted: 07/28/2023] [Indexed: 08/01/2023] Open
Abstract
SUMMARY In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers. AVAILABILITY AND IMPLEMENTATION Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.
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Affiliation(s)
- Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Harikumar Govindarajan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, United States
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, United States
- Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, United States
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, United States
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17
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Konecki DM, Hamrick S, Wang C, Agosto MA, Wensel TG, Lichtarge O. CovET: A covariation-evolutionary trace method that identifies protein structure-function modules. J Biol Chem 2023; 299:104896. [PMID: 37290531 PMCID: PMC10338321 DOI: 10.1016/j.jbc.2023.104896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/10/2023] Open
Abstract
Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.
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Affiliation(s)
- Daniel M Konecki
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Spencer Hamrick
- Chemical, Physical, and Structural Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Melina A Agosto
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Theodore G Wensel
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA; Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Olivier Lichtarge
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA; Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA.
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18
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Yu W, Chen Y, Putluri N, Osman A, Coarfa C, Putluri V, Kamal AHM, Asmussen JK, Katsonis P, Myers JN, Lai SY, Lu W, Stephan CC, Powell RT, Johnson FM, Skinner HD, Kazi J, Ahmed KM, Hu L, Threet A, Meyer MD, Bankson JA, Wang T, Davis J, Parker KR, Harris MA, Baek ML, Echeverria GV, Qi X, Wang J, Frederick AI, Walsh AJ, Lichtarge O, Frederick MJ, Sandulache VC. Evolution of cisplatin resistance through coordinated metabolic reprogramming of the cellular reductive state. Br J Cancer 2023; 128:2013-2024. [PMID: 37012319 PMCID: PMC10205814 DOI: 10.1038/s41416-023-02253-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Cisplatin (CDDP) is a mainstay treatment for advanced head and neck squamous cell carcinomas (HNSCC) despite a high frequency of innate and acquired resistance. We hypothesised that tumours acquire CDDP resistance through an enhanced reductive state dependent on metabolic rewiring. METHODS To validate this model and understand how an adaptive metabolic programme might be imprinted, we performed an integrated analysis of CDDP-resistant HNSCC clones from multiple genomic backgrounds by whole-exome sequencing, RNA-seq, mass spectrometry, steady state and flux metabolomics. RESULTS Inactivating KEAP1 mutations or reductions in KEAP1 RNA correlated with Nrf2 activation in CDDP-resistant cells, which functionally contributed to resistance. Proteomics identified elevation of downstream Nrf2 targets and the enrichment of enzymes involved in generation of biomass and reducing equivalents, metabolism of glucose, glutathione, NAD(P), and oxoacids. This was accompanied by biochemical and metabolic evidence of an enhanced reductive state dependent on coordinated glucose and glutamine catabolism, associated with reduced energy production and proliferation, despite normal mitochondrial structure and function. CONCLUSIONS Our analysis identified coordinated metabolic changes associated with CDDP resistance that may provide new therapeutic avenues through targeting of these convergent pathways.
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Affiliation(s)
- Wangie Yu
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Yunyun Chen
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nagireddy Putluri
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Abdullah Osman
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cristian Coarfa
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Vasanta Putluri
- Advanced Technology core, Dan Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Abu H M Kamal
- Advanced Technology core, Dan Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Jennifer Kay Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey N Myers
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stephen Y Lai
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wuhao Lu
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Clifford C Stephan
- Institute of Biosciences and Technology, Texas A&M Health Science Center, Houston, TX, USA
- Department of Translational Medical Sciences, School of Medicine, Texas A&M University, Houston, TX, USA
| | - Reid T Powell
- Institute of Biosciences and Technology, Texas A&M Health Science Center, Houston, TX, USA
- Department of Translational Medical Sciences, School of Medicine, Texas A&M University, Houston, TX, USA
| | - Faye M Johnson
- Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Heath D Skinner
- Department of Radiation Oncology, UPMC Hillman Cancer Center, Pittsburgh, PA, USA
| | - Jawad Kazi
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Kazi Mokim Ahmed
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Linghao Hu
- Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Addison Threet
- Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Matthew D Meyer
- Shared Equipment Authority, Rice University, Houston, TX, USA
| | - James A Bankson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tony Wang
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Jack Davis
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Kirby R Parker
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Madison A Harris
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Mokryun L Baek
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Gloria V Echeverria
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Xiaoli Qi
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA
| | - Jin Wang
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA
| | - Andy I Frederick
- School of Electrical and Computer Engineering Undergraduate Department, Cornell University, NY, USA
| | - Alex J Walsh
- Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA
- Department of Biochemistry & Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
- Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
| | - Mitchell J Frederick
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA.
| | - Vlad C Sandulache
- Bobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
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19
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Bourquard T, Lee K, Al-Ramahi I, Pham M, Shapiro D, Lagisetty Y, Soleimani S, Mota S, Wilhelm K, Samieinasab M, Kim YW, Huh E, Asmussen J, Katsonis P, Botas J, Lichtarge O. Functional variants identify sex-specific genes and pathways in Alzheimer's Disease. Nat Commun 2023; 14:2765. [PMID: 37179358 PMCID: PMC10183026 DOI: 10.1038/s41467-023-38374-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
The incidence of Alzheimer's Disease in females is almost double that of males. To search for sex-specific gene associations, we build a machine learning approach focused on functionally impactful coding variants. This method can detect differences between sequenced cases and controls in small cohorts. In the Alzheimer's Disease Sequencing Project with mixed sexes, this approach identified genes enriched for immune response pathways. After sex-separation, genes become specifically enriched for stress-response pathways in male and cell-cycle pathways in female. These genes improve disease risk prediction in silico and modulate Drosophila neurodegeneration in vivo. Thus, a general approach for machine learning on functionally impactful variants can uncover sex-specific candidates towards diagnostic biomarkers and therapeutic targets.
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Affiliation(s)
- Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kwanghyuk Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Ismael Al-Ramahi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, 77030, USA
- Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Minh Pham
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dillon Shapiro
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yashwanth Lagisetty
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Biology and Pharmacology, UTHealth McGovern Medical School, Houston, TX, 77030, USA
| | - Shirin Soleimani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Samantha Mota
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kevin Wilhelm
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Maryam Samieinasab
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Young Won Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Eunna Huh
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jennifer Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Juan Botas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, 77030, USA
- Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
- Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA.
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, 77030, USA.
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20
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Pejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R, O'Donnell-Luria A, Harrison SM, Tavtigian SV, Greenblatt MS, Biesecker LG, Radivojac P, Brenner SE. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet 2022; 109:2163-2177. [PMID: 36413997 PMCID: PMC9748256 DOI: 10.1016/j.ajhg.2022.10.013] [Citation(s) in RCA: 142] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022] Open
Abstract
Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.
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Affiliation(s)
- Vikas Pejaver
- Institute for Genomic Health, 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; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Alicia B Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bing-Jian Feng
- Department of Dermatology, University of Utah, Salt Lake City, UT 84132, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Kymberleigh A Pagel
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Rachel Karchin
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Biomedical Engineering, Oncology, and Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Ambry Genetics, Aliso Viejo, CA 92656, USA
| | - Sean V Tavtigian
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Marc S Greenblatt
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA
| | - Leslie G Biesecker
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
| | - Steven E Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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21
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Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, Nechaev D, Rost B. Embeddings from protein language models predict conservation and variant effects. Hum Genet 2022; 141:1629-1647. [PMID: 34967936 PMCID: PMC8716573 DOI: 10.1007/s00439-021-02411-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/06/2021] [Indexed: 12/13/2022]
Abstract
The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.
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Affiliation(s)
- Céline Marquet
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Tobias Olenyi
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Kyra Erckert
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Dmitrii Nechaev
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748, Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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22
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Gress A, Srikakulam SK, Keller S, Ramensky V, Kalinina OV. d-StructMAn: Containerized structural annotation on the scale from genetic variants to whole proteomes. Gigascience 2022; 11:6706670. [PMID: 36130085 PMCID: PMC9487898 DOI: 10.1093/gigascience/giac086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/06/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022] Open
Abstract
Background Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. Results We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. Conclusions d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.
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Affiliation(s)
- Alexander Gress
- Correspondence address. Alexander Gress, Campus Saarland University 66123 Saarbrücken Building E2.1 Room 101; E-mail:
| | - Sanjay K Srikakulam
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
- Interdisciplinary Graduate School of Natural Product Research, Saarland University, Saarbrücken 6: 119991, Germany
| | - Sebastian Keller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
- Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken 7: 66421, Germany
| | - Vasily Ramensky
- National Medical Research Center for Therapy and Preventive Medicine of the Ministry of Healthcare of Russian Federation, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Medical Faculty, Saarland University, Homburg, Germany
- Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany
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23
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Abstract
Identifying triple negative breast cancer (TNBC) patients expected to have poor outcomes provides an opportunity to enhance clinical management. We applied an Evolutionary Action Score to functionally characterize TP53 mutations (EAp53) in 96 TNBC patients and observed that EAp53 stratification may identify TP53 mutations associated with worse outcomes. These findings merit further exploration in larger TNBC cohorts and in patients treated with neoadjuvant chemotherapy regimens.
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24
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Lagisetty Y, Bourquard T, Al-Ramahi I, Mangleburg CG, Mota S, Soleimani S, Shulman JM, Botas J, Lee K, Lichtarge O. Identification of risk genes for Alzheimer's disease by gene embedding. CELL GENOMICS 2022; 2:100162. [PMID: 36268052 PMCID: PMC9581494 DOI: 10.1016/j.xgen.2022.100162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Most disease-gene association methods do not account for gene-gene interactions, even though these play a crucial role in complex, polygenic diseases like Alzheimer's disease (AD). To discover new genes whose interactions may contribute to pathology, we introduce GeneEMBED. This approach compares the functional perturbations induced in gene interaction network neighborhoods by coding variants from disease versus healthy subjects. In two independent AD cohorts of 5,169 exomes and 969 genomes, GeneEMBED identified novel candidates. These genes were differentially expressed in post mortem AD brains and modulated neurological phenotypes in mice. Four that were differentially overexpressed and modified neurodegeneration in vivo are PLEC, UTRN, TP53, and POLD1. Notably, TP53 and POLD1 are involved in DNA break repair and inhibited by approved drugs. While these data show proof of concept in AD, GeneEMBED is a general approach that should be broadly applicable to identify genes relevant to risk mechanisms and therapy of other complex diseases.
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Affiliation(s)
- Yashwanth Lagisetty
- Department of Biology and Pharmacology, UTHealth McGovern Medical School, Houston, TX 77030, USA,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ismael Al-Ramahi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA,Center for Alzheimer’s and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX 77030, USA
| | - Carl Grant Mangleburg
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Samantha Mota
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shirin Soleimani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Joshua M. Shulman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA,Center for Alzheimer’s and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX 77030, USA,Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA,Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Juan Botas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA,Center for Alzheimer’s and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kwanghyuk Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Center for Alzheimer’s and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX 77030, USA,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA,Corresponding author
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25
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Marciano DC, Wang C, Hsu TK, Bourquard T, Atri B, Nehring RB, Abel NS, Bowling EA, Chen TJ, Lurie PD, Katsonis P, Rosenberg SM, Herman C, Lichtarge O. Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli. Nat Commun 2022; 13:3189. [PMID: 35680894 PMCID: PMC9184624 DOI: 10.1038/s41467-022-30889-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/24/2022] [Indexed: 11/08/2022] Open
Abstract
Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes.
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Affiliation(s)
- David C Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Teng-Kuei Hsu
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Benu Atri
- Structural and Computational Biology & Molecular Biophysics Program, Baylor College of Medicine, Houston, TX, 77030, USA
- Clara Analytics Inc., 451 El Camino Real #201, Santa Clara, CA, 95050, USA
| | - Ralf B Nehring
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Nicholas S Abel
- Department of Pharmacology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Elizabeth A Bowling
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Taylor J Chen
- Integrative Molecular & Biomedical Biosciences Program, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Pamela D Lurie
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Susan M Rosenberg
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
- Integrative Molecular & Biomedical Biosciences Program, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christophe Herman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
- Structural and Computational Biology & Molecular Biophysics Program, Baylor College of Medicine, Houston, TX, 77030, USA.
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, 77030, USA.
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26
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Michikawa C, Torres-Saavedra PA, Silver NL, Harari PM, Kies MS, Rosenthal DI, Le QT, Jordan RC, Duose DY, Mallampati S, Trivedi S, Luthra R, Wistuba II, Osman AA, Lichtarge O, Foote RL, Parvathaneni U, Hayes DN, Pickering CR, Myers JN. Evolutionary Action Score of TP53 Analysis in Pathologically High-Risk Human Papillomavirus-Negative Head and Neck Cancer From a Phase 2 Clinical Trial: NRG Oncology Radiation Therapy Oncology Group 0234. Adv Radiat Oncol 2022; 7:100989. [PMID: 36420184 PMCID: PMC9677209 DOI: 10.1016/j.adro.2022.100989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/04/2022] [Indexed: 12/15/2022] Open
Abstract
Purpose An evolutionary action scoring algorithm (EAp53) based on phylogenetic sequence variations stratifies patients with head and neck squamous cell carcinoma (HNSCC) bearing TP53 missense mutations as high-risk, associated with poor outcomes, or low-risk, with similar outcomes as TP53 wild-type, and has been validated as a reliable prognostic marker. We performed this study to further validate prior findings demonstrating that EAp53 is a prognostic marker for patients with locally advanced HNSCC and explored its predictive value for treatment outcomes to adjuvant bio-chemoradiotherapy. Methods and Materials Eighty-one resection samples from patients treated surgically for stage III or IV human papillomavirus-negative HNSCC with high-risk pathologic features, who received either radiation therapy + cetuximab + cisplatin (cisplatin) or radiation therapy + cetuximab + docetaxel (docetaxel) as adjuvant treatment in a phase 2 study were subjected to TP53 targeted sequencing and EAp53 scoring to correlate with clinical outcomes. Due to the limited sample size, patients were combined into 2 EAp53 groups: (1) wild-type or low-risk; and (2) high-risk or other. Results At a median follow-up of 9.8 years, there was a significant interaction between EAp53 group and treatment for overall survival (P = .008), disease-free survival (P = .05), and distant metastasis (DM; P = .004). In wild-type or low-risk group, the docetaxel arm showed significantly better overall survival (hazard ratio [HR] 0.11, [0.03-0.36]), disease-free survival (HR 0.24, [0.09-0.61]), and less DM (HR 0.04, [0.01-0.31]) than the cisplatin arm. In high-risk or other group, differences between treatments were not statistically significant. Conclusions The docetaxel arm was associated with better survival than the cisplatin arm for patients with wild-type or low-risk EAp53. These benefits appear to be largely driven by a reduction in DM.
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Affiliation(s)
- Chieko Michikawa
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas,Department of Maxillofacial Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Pedro A. Torres-Saavedra
- NRG Oncology Statistics and Data Management Center, American College of Radiology, Philadelphia, Pennsylvania
| | - Natalie L. Silver
- Cleveland Clinic, Head and Neck Institute/Lerner Research Institute, Cleveland, Ohio
| | - Paul M. Harari
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Merrill S. Kies
- Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David I. Rosenthal
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, California
| | - Richard C. Jordan
- NRG Oncology Biospecimen Bank and University of California, San Francisco, San Francisco, California
| | | | | | - Sanchit Trivedi
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rajyalakshmi Luthra
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Abdullah A. Osman
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Olivier Lichtarge
- Departments of Molecular and Human Genetics, Pharmacology, and Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas
| | - Robert L. Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Upendra Parvathaneni
- Radiation Oncology Center, University of Washington Medical Center, Seattle, Washington
| | - D. Neil Hayes
- Division of Medical Oncology, The University of Tennessee Health Science Center, Memphis, Tennessee
| | - Curtis R. Pickering
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jeffrey N. Myers
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas,Corresponding author: Jeffrey N. Myers, MD, PhD
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27
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Hsu TK, Asmussen J, Koire A, Choi BK, Gadhikar MA, Huh E, Lin CH, Konecki DM, Kim YW, Pickering CR, Kimmel M, Donehower LA, Frederick MJ, Myers JN, Katsonis P, Lichtarge O. A general calculus of fitness landscapes finds genes under selection in cancers. Genome Res 2022; 32:916-929. [PMID: 35301263 PMCID: PMC9104707 DOI: 10.1101/gr.275811.121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 03/14/2022] [Indexed: 11/24/2022]
Abstract
Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.
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Affiliation(s)
- Teng-Kuei Hsu
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jennifer Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Amanda Koire
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Byung-Kwon Choi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Mayur A Gadhikar
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Eunna Huh
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Chih-Hsu Lin
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Daniel M Konecki
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Young Won Kim
- Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Curtis R Pickering
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Marek Kimmel
- Departments of Statistics and Bioengineering, Rice University, Houston, Texas 77005, USA
- Department of Systems Engineering and Biology, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Lawrence A Donehower
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Mitchell J Frederick
- Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jeffrey N Myers
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Olivier Lichtarge
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, Texas 77030, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA
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28
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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29
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Parvandeh S, Donehower LA, Katsonis P, Hsu TK, Asmussen J, Lee K, Lichtarge O. EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants. Nucleic Acids Res 2022; 50:e70. [PMID: 35412634 PMCID: PMC9262594 DOI: 10.1093/nar/gkac215] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 02/01/2023] Open
Abstract
Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.
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Affiliation(s)
- Saeid Parvandeh
- To whom correspondence should be addressed. Tel: +1 713 798 7677;
| | - Lawrence A Donehower
- Department of Molecular Virology and Microbiology, Houston, TX 77030, USA,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Teng-Kuei Hsu
- Department of Biochemistry & Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Jennifer K Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kwanghyuk Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Correspondence may also be addressed to Olivier Lichtarge. Tel: +1 713 798 5646;
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30
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Lee W, de Prisco N, Gennarino VA. Identifying patients and assessing variant pathogenicity for an autosomal dominant disease-driving gene. STAR Protoc 2022; 3:101150. [PMID: 35146449 PMCID: PMC8819039 DOI: 10.1016/j.xpro.2022.101150] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches. For complete details on the use and execution of this protocol, please refer to Gennarino et al. (2018).
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Affiliation(s)
- Winston Lee
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Nicola de Prisco
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Vincenzo A. Gennarino
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA
- Departments of Pediatrics and Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY 10032, USA
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31
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Extracting phylogenetic dimensions of coevolution reveals hidden functional signals. Sci Rep 2022; 12:820. [PMID: 35039514 PMCID: PMC8764114 DOI: 10.1038/s41598-021-04260-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/17/2021] [Indexed: 11/08/2022] Open
Abstract
Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein's phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a background model for phylogenetic contributions of statistical coupling that separates the coevolution signal due to inter-clade and intra-clade sequence comparisons and demonstrate that coevolution can be measured on multiple phylogenetic timescales within a single protein. Our method, nested coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved the structural-contact predictions of several coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after application of NC were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Thus, our conceptualization of the phylogenetic separation of coevolution provides the potential to further elucidate relationships among protein evolution, function, and genetic diseases.
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32
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Tsutakawa SE, Bacolla A, Katsonis P, Bralić A, Hamdan SM, Lichtarge O, Tainer JA, Tsai CL. Decoding Cancer Variants of Unknown Significance for Helicase-Nuclease-RPA Complexes Orchestrating DNA Repair During Transcription and Replication. Front Mol Biosci 2021; 8:791792. [PMID: 34966786 PMCID: PMC8710748 DOI: 10.3389/fmolb.2021.791792] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 11/16/2021] [Indexed: 01/13/2023] Open
Abstract
All tumors have DNA mutations, and a predictive understanding of those mutations could inform clinical treatments. However, 40% of the mutations are variants of unknown significance (VUS), with the challenge being to objectively predict whether a VUS is pathogenic and supports the tumor or whether it is benign. To objectively decode VUS, we mapped cancer sequence data and evolutionary trace (ET) scores onto crystallography and cryo-electron microscopy structures with variant impacts quantitated by evolutionary action (EA) measures. As tumors depend on helicases and nucleases to deal with transcription/replication stress, we targeted helicase–nuclease–RPA complexes: (1) XPB-XPD (within TFIIH), XPF-ERCC1, XPG, and RPA for transcription and nucleotide excision repair pathways and (2) BLM, EXO5, and RPA plus DNA2 for stalled replication fork restart. As validation, EA scoring predicts severe effects for most disease mutations, but disease mutants with low ET scores not only are likely destabilizing but also disrupt sophisticated allosteric mechanisms. For sites of disease mutations and VUS predicted to be severe, we found strong co-localization to ordered regions. Rare discrepancies highlighted the different survival requirements between disease and tumor mutations, as well as the value of examining proteins within complexes. In a genome-wide analysis of 33 cancer types, we found correlation between the number of mutations in each tumor and which pathways or functional processes in which the mutations occur, revealing different mutagenic routes to tumorigenesis. We also found upregulation of ancient genes including BLM, which supports a non-random and concerted cancer process: reversion to a unicellular, proliferation-uncontrolled, status by breaking multicellular constraints on cell division. Together, these genes and global analyses challenge the binary “driver” and “passenger” mutation paradigm, support a gradient impact as revealed by EA scoring from moderate to severe at a single gene level, and indicate reduced regulation as well as activity. The objective quantitative assessment of VUS scoring and gene overexpression in the context of functional interactions and pathways provides insights for biology, oncology, and precision medicine.
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Affiliation(s)
- Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Albino Bacolla
- Department of Molecular and Cellular Oncology, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Amer Bralić
- Laboratory of DNA Replication and Recombination, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Samir M Hamdan
- Laboratory of DNA Replication and Recombination, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - John A Tainer
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.,Department of Molecular and Cellular Oncology, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States.,Department of Cancer Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Chi-Lin Tsai
- Department of Molecular and Cellular Oncology, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
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33
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Recurrent high-impact mutations at cognate structural positions in class A G protein-coupled receptors expressed in tumors. Proc Natl Acad Sci U S A 2021; 118:2113373118. [PMID: 34916293 PMCID: PMC8713800 DOI: 10.1073/pnas.2113373118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 12/23/2022] Open
Abstract
GPCRs and GPCR pathways are increasingly being implicated in human malignancies, placing them among the most promising cancer drug candidates. Our results reveal enrichment of highly impactful, recurrent GPCR mutations within cancers. We found that cognate mutations in selected class A GPCRs have deleterious effects on signaling function. The results also suggest that olfactory receptors, often considered inconsequential, display a nonrandom mutation pattern in tumors in which they are expressed. These findings support the idea that protein paralogs can act in parallel as members of an onco-group. G protein-coupled receptors (GPCRs) are the largest family of human proteins. They have a common structure and, signaling through a much smaller set of G proteins, arrestins, and effectors, activate downstream pathways that often modulate hallmark mechanisms of cancer. Because there are many more GPCRs than effectors, mutations in different receptors could perturb signaling similarly so as to favor a tumor. We hypothesized that somatic mutations in tumor samples may not be enriched within a single gene but rather that cognate mutations with similar effects on GPCR function are distributed across many receptors. To test this possibility, we systematically aggregated somatic cancer mutations across class A GPCRs and found a nonrandom distribution of positions with variant amino acid residues. Individual cancer types were enriched for highly impactful, recurrent mutations at selected cognate positions of known functional motifs. We also discovered that no single receptor drives this pattern, but rather multiple receptors contain amino acid substitutions at a few cognate positions. Phenotypic characterization suggests these mutations induce perturbation of G protein activation and/or β-arrestin recruitment. These data suggest that recurrent impactful oncogenic mutations perturb different GPCRs to subvert signaling and promote tumor growth or survival. The possibility that multiple different GPCRs could moonlight as drivers or enablers of a given cancer through mutations located at cognate positions across GPCR paralogs opens a window into cancer mechanisms and potential approaches to therapeutics.
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34
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Wang C, Konecki DM, Marciano DC, Govindarajan H, Williams AM, Wastuwidyaningtyas B, Bourquard T, Katsonis P, Lichtarge O. Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and greater coronavirus family. Bioinformatics 2021; 37:4033-4040. [PMID: 34043002 PMCID: PMC8243408 DOI: 10.1093/bioinformatics/btab406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/10/2021] [Accepted: 05/26/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. RESULTS Combining coronavirus family sequence information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent. AVAILABILITY AND IMPLEMENTATION The results of this work are made interactively available at http://cov.lichtargelab.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel M Konecki
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - David C Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Harikumar Govindarajan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Amanda M Williams
- Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
- Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA
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35
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Munari FF, Dos Santos W, Evangelista AF, Carvalho AC, Pastrez PA, Bugatti D, Wohnrath DR, Scapulatempo-Neto C, Guimarães DP, Longatto-Filho A, Reis RM. Profile of esophageal squamous cell carcinoma mutations in Brazilian patients. Sci Rep 2021; 11:20596. [PMID: 34663841 PMCID: PMC8523676 DOI: 10.1038/s41598-021-00208-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 10/08/2021] [Indexed: 12/24/2022] Open
Abstract
Esophageal cancer is an aggressive tumor that has a high rate of incidence and mortality worldwide. It is the 10th most frequent type in Brazil, being squamous cell carcinoma (ESCC) the predominant subtype. There is currently an incessant search to identify the frequently altered genes associated with esophageal squamous cell carcinoma biology that could be druggable. This study aimed to analyze the somatic mutation profile of a large panel of cancer-related genes in Brazilian ESCC. In a series of 46 ESCC diagnoses at Barretos Cancer Hospital, DNA isolated from paired fresh-frozen and blood tissue, a panel of 150 cancer-related genes was analyzed by next-generation sequencing. The genes with the highest frequency of mutations were TP53 (39/46, 84.8%), followed by NOTCH1 (7/46, 15.2%), NFE2L2 (5/46, 10.8%), RB1 (3/46, 6.5%), PTEN (3/46, 6.5%), CDKN2A (3/46, 6.5%), PTCH1 (2/46, 4.3%) and PIK3CA (2/46, 4.3%). There was no significant association between molecular and patients' clinicopathological features. Applying an evolutionary action score of p53 (EAp53), we observed that 14 (35.9%) TP53 mutations were classified as high-risk, yet no association with overall survival was observed. Concluding, this the largest mutation profile of Brazilian ESCC patients, which helps in the elucidation of the major cancer-related genes in this population.
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Affiliation(s)
- Fernanda Franco Munari
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil
| | - Wellington Dos Santos
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil
| | - Adriane Feijó Evangelista
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil
| | - Ana Carolina Carvalho
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil
| | - Paula Aguiar Pastrez
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil
| | - Diego Bugatti
- Department of Upper Digestive, Barretos Cancer Hospital, Barretos, Brazil
| | - Durval R Wohnrath
- Department of Upper Digestive, Barretos Cancer Hospital, Barretos, Brazil
| | - Cristovam Scapulatempo-Neto
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil.,Department of Pathology, Barretos Cancer Hospital, Barretos, Brazil
| | - Denise Peixoto Guimarães
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil.,Department of Endoscopy, Barretos Cancer Hospital, Barretos, Brazil
| | - Adhemar Longatto-Filho
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil.,Medical Laboratory of Medical Investigation (LIM) 14, Department of Pathology, Medical School, University of São Paulo, São Paulo, Brazil.,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga, Guimarães, Portugal
| | - Rui Manuel Reis
- Molecular Oncology Research Center, Barretos Cancer Hospital, Antenor Duarte Villela, 1331, Barretos, São Paulo, 14784 400, Brazil. .,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal. .,ICVS/3B's-PT Government Associate Laboratory, Braga, Guimarães, Portugal.
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36
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Hegron A, Huh E, Deupi X, Sokrat B, Gao W, Le Gouill C, Canouil M, Boissel M, Charpentier G, Roussel R, Balkau B, Froguel P, Plouffe B, Bonnefond A, Lichtarge O, Jockers R, Bouvier M. Identification of Key Regions Mediating Human Melatonin Type 1 Receptor Functional Selectivity Revealed by Natural Variants. ACS Pharmacol Transl Sci 2021; 4:1614-1627. [PMID: 34661078 PMCID: PMC8507577 DOI: 10.1021/acsptsci.1c00157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Indexed: 11/30/2022]
Abstract
Melatonin is a hormone mainly produced by the pineal gland and MT1 is one of the two G protein-coupled receptors (GPCRs) mediating its action. Despite an increasing number of available GPCR crystal structures, the molecular mechanism of activation of a large number of receptors, including MT1, remains poorly understood. The purpose of this study is to elucidate the structural elements involved in the process of MT1's activation using naturally occurring variants affecting its function. Thirty-six nonsynonymous variants, including 34 rare ones, were identified in MTNR1A (encoding MT1) from a cohort of 8687 individuals and their signaling profiles were characterized using Bioluminescence Resonance Energy Transfer-based sensors probing 11 different signaling pathways. Computational analysis of the experimental data allowed us to group the variants in clusters according to their signaling profiles and to analyze the position of each variant in the context of the three-dimensional structure of MT1 to link functional selectivity to structure. MT1 variant signaling profiles revealed three clusters characterized by (1) wild-type-like variants, (2) variants with selective defect of βarrestin-2 recruitment, and (3) severely defective variants on all pathways. Our structural analysis allows us to identify important regions for βarrestin-2 recruitment as well as for Gα12 and Gα15 activation. In addition to identifying MT1 domains differentially controlling the activation of the various signaling effectors, this study illustrates how natural variants can be used as tools to study the molecular mechanisms of receptor activation.
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Affiliation(s)
- Alan Hegron
- Université
de Paris, Institut Cochin, CNRS, INSERM, F-75014 Paris, France
- Department
of Biochemistry and Molecular Medicine, University de Montréal, Montreal, Quebec, H3T 1J4 Canada
- Institute
for Research in Immunology and Cancer, University
of Montreal, Montreal, Quebec, H3T 1J4 Canada
| | - Eunna Huh
- Department
of Pharmacology and Chemical Biology, Baylor
College of Medicine, Houston, Texas 77030, United States of America
| | - Xavier Deupi
- Laboratory
of Biomolecular Research, Paul Scherrer
Institute (PSI), 5232 Villigen, Switzerland
- Condensed
Matter Theory group, Paul Scherrer Institute
(PSI), 5232 Villigen, Switzerland
| | - Badr Sokrat
- Department
of Biochemistry and Molecular Medicine, University de Montréal, Montreal, Quebec, H3T 1J4 Canada
- Institute
for Research in Immunology and Cancer, University
of Montreal, Montreal, Quebec, H3T 1J4 Canada
| | - Wenwen Gao
- Université
de Paris, Institut Cochin, CNRS, INSERM, F-75014 Paris, France
| | - Christian Le Gouill
- Institute
for Research in Immunology and Cancer, University
of Montreal, Montreal, Quebec, H3T 1J4 Canada
| | - Mickaël Canouil
- Inserm
UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, 59000, France
- University
of Lille, Lille University
Hospital, Lille, 59000, France
| | - Mathilde Boissel
- Inserm
UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, 59000, France
- University
of Lille, Lille University
Hospital, Lille, 59000, France
| | - Guillaume Charpentier
- Centre d’Étude et de Recherche pour l’Intensification
du Traitement du Diabète, 91000, Evry, France
| | - Ronan Roussel
- Department
of Diabetology Endocrinology Nutrition, Hôpital Bichat, DHU FIRE, Assistance Publique Hôpitaux
de Paris, 75004 Paris, France
- Inserm U1138, Centre de Recherche des Cordeliers, 75006 Paris, France
- UFR de Médecine, University Paris
Diderot, Sorbonne Paris Cité, 75006 Paris, France
| | - Beverley Balkau
- Inserm U1018, Center for Research in Epidemiology and Population
Health, 94805 Villejuif, France
- University
Paris-Saclay, University Paris-Sud, 94270 Villejuif, France
| | - Philippe Froguel
- Inserm
UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, 59000, France
- University
of Lille, Lille University
Hospital, Lille, 59000, France
- Department
of Metabolism, Imperial College London, London, W12 0NN, United Kingdom
| | - Bianca Plouffe
- Department
of Biochemistry and Molecular Medicine, University de Montréal, Montreal, Quebec, H3T 1J4 Canada
- Institute
for Research in Immunology and Cancer, University
of Montreal, Montreal, Quebec, H3T 1J4 Canada
| | - Amélie Bonnefond
- Inserm
UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, 59000, France
- University
of Lille, Lille University
Hospital, Lille, 59000, France
- Department
of Metabolism, Imperial College London, London, W12 0NN, United Kingdom
| | - Olivier Lichtarge
- Department
of Pharmacology and Chemical Biology, Baylor
College of Medicine, Houston, Texas 77030, United States of America
- Department
of Molecular and Human Genetics, Baylor
College of Medicine, Houston, Texas 77030, United States
| | - Ralf Jockers
- Université
de Paris, Institut Cochin, CNRS, INSERM, F-75014 Paris, France
| | - Michel Bouvier
- Department
of Biochemistry and Molecular Medicine, University de Montréal, Montreal, Quebec, H3T 1J4 Canada
- Institute
for Research in Immunology and Cancer, University
of Montreal, Montreal, Quebec, H3T 1J4 Canada
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37
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Saleh MM, Scheffler M, Merkelbach-Bruse S, Scheel AH, Ulmer B, Wolf J, Buettner R. Comprehensive Analysis of TP53 and KEAP1 Mutations and Their Impact on Survival in Localized- and Advanced-Stage NSCLC. J Thorac Oncol 2021; 17:76-88. [PMID: 34601169 DOI: 10.1016/j.jtho.2021.08.764] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/12/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022]
Abstract
INTRODUCTION TP53 and KEAP1 are frequently mutated in NSCLC, but their prognostic value is ambiguous, particularly in localized stage tumors. METHODS This retrospective cohort study included a total of 6297 patients with NSCLC who were diagnosed between November 1998 and February 2020. The primary end point was overall survival. Patients were diagnosed in a central pathology laboratory as part of the Network Genomic Medicine collaboration, encompassing more than 300 lung cancer-treating oncology centers in Germany. All patients underwent molecular testing, including targeted next-generation panel sequencing and in situ hybridization. RESULTS A total of 6297 patients with NSCLC were analyzed. In 1518 surgically treated patients (Union for International Cancer Control [UICC] I-IIIA), truncating TP53 mutations and KEAP1 mutations were independent negative prognostic markers in multivariable analysis (hazard ratio [HR]TP53truncating = 1.43, 95% confidence interval [CI]: 1.07-1.91, p = 0.015; HRKEAP1mut = 1.68, 95% CI:1.24-2.26, p = 0.001). Consistently, these mutations were associated with shorter disease-free survival. In 4779 patients with advanced-stage (UICC IIIB-IV) tumors, TP53 mutations did not predict outcome in univariable analysis. In contrast, KEAP1 mutations remained a negative prognostic factor (HRKEAP1mut = 1.40, 95% CI: 1.23-1.61, p < 0.001) in patients with advanced-stage tumors. Furthermore, those with KEAP1-mutant tumors with co-occurring TP53 missense mutations had longer overall survival than those with KEAP1-mutant tumors with wild-type or truncating TP53 mutations. CONCLUSIONS This study found that TP53 and KEAP1 mutations were prognostic for localized and advanced-stage NSCLC. The increased relative hazard of harboring TP53 or KEAP1 mutations was comparable to an increase in one UICC stage. Our data suggest that molecular stratification on the basis of TP53 and KEAP1 mutation status should be implemented for localized and advanced-stage NSCLC to optimize and modify clinical decision-making.
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Affiliation(s)
- Mohamed Mahde Saleh
- Lung Cancer Group Cologne, Institute of Pathology, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany
| | - Matthias Scheffler
- Lung Cancer Group Cologne, Department I for Internal Medicine, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany
| | - Sabine Merkelbach-Bruse
- Lung Cancer Group Cologne, Institute of Pathology, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany
| | - Andreas Hans Scheel
- Lung Cancer Group Cologne, Institute of Pathology, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany
| | - Bastian Ulmer
- Lung Cancer Group Cologne, Institute of Pathology, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany
| | - Jürgen Wolf
- Lung Cancer Group Cologne, Department I for Internal Medicine, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany
| | - Reinhard Buettner
- Lung Cancer Group Cologne, Institute of Pathology, Center for Integrated Oncology Cologne/Bonn, University Hospital Cologne, Cologne, Germany.
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PPAR-Responsive Elements Enriched with Alu Repeats May Contribute to Distinctive PPARγ-DNMT1 Interactions in the Genome. Cancers (Basel) 2021; 13:cancers13163993. [PMID: 34439147 PMCID: PMC8391462 DOI: 10.3390/cancers13163993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/02/2021] [Accepted: 08/05/2021] [Indexed: 01/11/2023] Open
Abstract
Simple Summary This study aimed to explore the potential role of PPARγ–DNMT1 interaction through PPAR-responsive elements (PPREs), which we have found to be enriched with Alu repeats. Apart from protein–protein interactions and co-expression in multiple cancer types, we exclusively described a prognostic role for PPARγ in uveal melanoma (UM). Abstract Background: PPARγ (peroxisome proliferator-activated receptor gamma) is involved in the pathology of numerous diseases, including UM and other types of cancer. Emerging evidence suggests that an interaction between PPARγ and DNMTs (DNA methyltransferase) plays a role in cancer that is yet to be defined. Methods: The configuration of the repeating elements was performed with CAP3 and MAFFT, and the structural modelling was conducted with HDOCK. An evolutionary action scores algorithm was used to identify oncogenic variants. A systematic bioinformatic appraisal of PPARγ and DNMT1 was performed across 29 tumor types and UM available in The Cancer Genome Atlas (TCGA). Results: PPAR-responsive elements (PPREs) enriched with Alu repeats are associated with different genomic regions, particularly the promotor region of DNMT1. PPARγ–DNMT1 co-expression is significantly associated with several cancers. C-terminals of PPARγ and DNMT1 appear to be the potential protein–protein interaction sites where disease-specific mutations may directly impair the respective protein functions. Furthermore, PPARγ expression could be identified as an additional prognostic marker for UM. Conclusions: We hypothesize that the function of PPARγ requires an additional contribution of Alu repeats which may directly influence the DNMT1 network. Regarding UM, PPARγ appears to be an additional discriminatory prognostic marker, in particular in disomy 3 tumors.
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Marbach F, Stoyanov G, Erger F, Stratakis CA, Settas N, London E, Rosenfeld JA, Torti E, Haldeman-Englert C, Sklirou E, Kessler E, Ceulemans S, Nelson SF, Martinez-Agosto JA, Palmer CGS, Signer RH, Andrews MV, Grange DK, Willaert R, Person R, Telegrafi A, Sievers A, Laugsch M, Theiß S, Cheng Y, Lichtarge O, Katsonis P, Stocco A, Schaaf CP. Variants in PRKAR1B cause a neurodevelopmental disorder with autism spectrum disorder, apraxia, and insensitivity to pain. Genet Med 2021; 23:1465-1473. [PMID: 33833410 PMCID: PMC8354857 DOI: 10.1038/s41436-021-01152-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 03/06/2021] [Accepted: 03/08/2021] [Indexed: 11/28/2022] Open
Abstract
PURPOSE We characterize the clinical and molecular phenotypes of six unrelated individuals with intellectual disability and autism spectrum disorder who carry heterozygous missense variants of the PRKAR1B gene, which encodes the R1β subunit of the cyclic AMP-dependent protein kinase A (PKA). METHODS Variants of PRKAR1B were identified by single- or trio-exome analysis. We contacted the families and physicians of the six individuals to collect phenotypic information, performed in vitro analyses of the identified PRKAR1B-variants, and investigated PRKAR1B expression during embryonic development. RESULTS Recent studies of large patient cohorts with neurodevelopmental disorders found significant enrichment of de novo missense variants in PRKAR1B. In our cohort, de novo origin of the PRKAR1B variants could be confirmed in five of six individuals, and four carried the same heterozygous de novo variant c.1003C>T (p.Arg335Trp; NM_001164760). Global developmental delay, autism spectrum disorder, and apraxia/dyspraxia have been reported in all six, and reduced pain sensitivity was found in three individuals carrying the c.1003C>T variant. PRKAR1B expression in the brain was demonstrated during human embryonal development. Additionally, in vitro analyses revealed altered basal PKA activity in cells transfected with variant-harboring PRKAR1B expression constructs. CONCLUSION Our study provides strong evidence for a PRKAR1B-related neurodevelopmental disorder.
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Affiliation(s)
- Felix Marbach
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Georgi Stoyanov
- Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Florian Erger
- Faculty of Medicine, University of Cologne, Cologne, Germany
- Institute of Human Genetics, University Hospital Cologne, Cologne, Germany
| | - Constantine A Stratakis
- Section on Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - Nikolaos Settas
- Section on Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - Edra London
- Section on Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Baylor Genetics Laboratory, Houston, TX, USA
| | | | | | - Evgenia Sklirou
- Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elena Kessler
- Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sophia Ceulemans
- Genetics/Dysmorphology, Rady Children's Hospital, San Diego, CA, USA
| | - Stanley F Nelson
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - Christina G S Palmer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Institute for Society and Genetics, UCLA, Los Angeles, CA, USA
| | - Rebecca H Signer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Marisa V Andrews
- Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO, USA
| | - Dorothy K Grange
- Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO, USA
| | | | | | | | - Aaron Sievers
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Magdalena Laugsch
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Susanne Theiß
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - YuZhu Cheng
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Biomedicine West Wing, International Centre for Life, Times Square, Newcastle upon Tyne, UK
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Amber Stocco
- INTEGRIS Pediatric Neurology, Oklahoma City, OK, USA
| | - Christian P Schaaf
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany.
- Institute of Human Genetics, University Hospital Cologne, Cologne, Germany.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
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40
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Koire A, Katsonis P, Kim YW, Buchovecky C, Wilson SJ, Lichtarge O. A method to delineate de novo missense variants across pathways prioritizes genes linked to autism. Sci Transl Med 2021; 13:13/594/eabc1739. [PMID: 34011629 DOI: 10.1126/scitranslmed.abc1739] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 03/01/2021] [Indexed: 12/31/2022]
Abstract
Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.
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Affiliation(s)
- Amanda Koire
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.,Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA.,Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Young Won Kim
- Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Christie Buchovecky
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.,Division of Carrier Screening and Prenatal Testing, SEMA4, Stamford, CT, USA
| | - Stephen J Wilson
- Department of Biochemistry, Baylor College of Medicine, Houston, TX, USA
| | - Olivier Lichtarge
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA. .,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.,Department of Biochemistry, Baylor College of Medicine, Houston, TX, USA
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41
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Cea-Rama I, Coscolín C, Katsonis P, Bargiela R, Golyshin PN, Lichtarge O, Ferrer M, Sanz-Aparicio J. Structure and evolutionary trace-assisted screening of a residue swapping the substrate ambiguity and chiral specificity in an esterase. Comput Struct Biotechnol J 2021; 19:2307-2317. [PMID: 33995922 PMCID: PMC8105184 DOI: 10.1016/j.csbj.2021.04.041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 01/02/2023] Open
Abstract
Our understanding of enzymes with high substrate ambiguity remains limited because their large active sites allow substrate docking freedom to an extent that seems incompatible with stereospecificity. One possibility is that some of these enzymes evolved a set of evolutionarily fitted sequence positions that stringently allow switching substrate ambiguity and chiral specificity. To explore this hypothesis, we targeted for mutation a serine ester hydrolase (EH3) that exhibits an impressive 71-substrate repertoire but is not stereospecific (e.e. 50%). We used structural actions and the computational evolutionary trace method to explore specificity-swapping sequence positions and hypothesized that position I244 was critical. Driven by evolutionary action analysis, this position was substituted to leucine, which together with isoleucine appears to be the amino acid most commonly present in the closest homologous sequences (max. identity, ca. 67.1%), and to phenylalanine, which appears in distant homologues. While the I244L mutation did not have any functional consequences, the I244F mutation allowed the esterase to maintain a remarkable 53-substrate range while gaining stereospecificity properties (e.e. 99.99%). These data support the possibility that some enzymes evolve sequence positions that control the substrate scope and stereospecificity. Such residues, which can be evolutionarily screened, may serve as starting points for further designing substrate-ambiguous, yet chiral-specific, enzymes that are greatly appreciated in biotechnology and synthetic chemistry.
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Affiliation(s)
- Isabel Cea-Rama
- Institute of Physical Chemistry “Rocasolano”, CSIC, 28006 Madrid, Spain
| | | | | | - Rafael Bargiela
- Centre for Environmental Biotechnology, Bangor University, LL57 2UW Bangor, UK
| | - Peter N. Golyshin
- Centre for Environmental Biotechnology, Bangor University, LL57 2UW Bangor, UK
- School of Natural Sciences, Bangor University, LL57 2UW Bangor, UK
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42
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Kanagal-Shamanna R, Montalban-Bravo G, Katsonis P, Sasaki K, Class CA, Jabbour E, Sallman D, Hunter AM, Benton C, Chien KS, Luthra R, Bueso-Ramos CE, Kadia T, Andreeff M, Komrokji RS, Al Ali NH, Short N, Daver N, Routbort MJ, Khoury JD, Patel K, Ganan-Gomez I, Wei Y, Borthakur G, Ravandi F, Do KA, Soltysiak KA, Lichtarge O, Medeiros LJ, Kantarjian H, Garcia-Manero G. Evolutionary action score identifies a subset of TP53 mutated myelodysplastic syndrome with favorable prognosis. Blood Cancer J 2021; 11:52. [PMID: 33677472 PMCID: PMC7936977 DOI: 10.1038/s41408-021-00446-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/16/2021] [Accepted: 02/19/2021] [Indexed: 12/25/2022] Open
Affiliation(s)
- Rashmi Kanagal-Shamanna
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
| | - Guillermo Montalban-Bravo
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Koji Sasaki
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Caleb A Class
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Elias Jabbour
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - David Sallman
- Malignant Hematology Department, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | | | - Christopher Benton
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kelly S Chien
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Rajyalakshmi Luthra
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carlos E Bueso-Ramos
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Tapan Kadia
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Michael Andreeff
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Rami S Komrokji
- Malignant Hematology Department, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Najla H Al Ali
- Malignant Hematology Department, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Nicholas Short
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Naval Daver
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark J Routbort
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Joseph D Khoury
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyur Patel
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Irene Ganan-Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Yue Wei
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Gautam Borthakur
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Farhad Ravandi
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kim-Anh Do
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kelly A Soltysiak
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - L Jeffrey Medeiros
- Department of Hematopathology and Molecular Diagnostics, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hagop Kantarjian
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Guillermo Garcia-Manero
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Wang C, Konecki DM, Marciano DC, Govindarajan H, Williams AM, Wastuwidyaningtyas B, Bourquard T, Katsonis P, Lichtarge O. Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and the greater coronavirus family. RESEARCH SQUARE 2021:rs.3.rs-95030. [PMID: 33106800 PMCID: PMC7587783 DOI: 10.21203/rs.3.rs-95030/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent.
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Affiliation(s)
- Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel M. Konecki
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - David C. Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Harikumar Govindarajan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Amanda M. Williams
- Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- MAbSilico, Nouzilly, Centre, France, EU
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
- Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA
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44
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Wang C, Konecki DM, Marciano DC, Govindarajan H, Williams AM, Wastuwidyaningtyas B, Bourquard T, Katsonis P, Lichtarge O. Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and the greater coronavirus family. RESEARCH SQUARE 2021:rs.3.rs-95030. [PMID: 36575762 PMCID: PMC9793837 DOI: 10.21203/rs.3.rs-95030/v3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent.
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Affiliation(s)
- Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel M. Konecki
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - David C. Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Correspondence: (D.C.M), (O.L.)
| | - Harikumar Govindarajan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Amanda M. Williams
- Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,MAbSilico, Nouzilly, Centre, France, EU
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA,Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA,Correspondence: (D.C.M), (O.L.)
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45
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Munro D, Singh M. DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction. Bioinformatics 2020; 36:5322-5329. [PMID: 33325500 PMCID: PMC8016454 DOI: 10.1093/bioinformatics/btaa1030] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/16/2020] [Accepted: 11/30/2020] [Indexed: 01/27/2023] Open
Abstract
Motivation Accurately predicting the quantitative impact of a substitution on a protein’s molecular function would be a great aid in understanding the effects of observed genetic variants across populations. While this remains a challenging task, new approaches can leverage data from the increasing numbers of comprehensive deep mutational scanning (DMS) studies that systematically mutate proteins and measure fitness. Results We introduce DeMaSk, an intuitive and interpretable method based only upon DMS datasets and sequence homologs that predicts the impact of missense mutations within any protein. DeMaSk first infers a directional amino acid substitution matrix from DMS datasets and then fits a linear model that combines these substitution scores with measures of per-position evolutionary conservation and variant frequency across homologs. Despite its simplicity, DeMaSk has state-of-the-art performance in predicting the impact of amino acid substitutions, and can easily and rapidly be applied to any protein sequence. Availability and implementation https://demask.princeton.edu generates fitness impact predictions and visualizations for any user-submitted protein sequence. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Munro
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08544, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08544, USA.,Department of Computer Science, Princeton University, Princeton, 08544, USA
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46
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Kim YW, Al‐Ramahi I, Koire A, Wilson SJ, Konecki DM, Mota S, Soleimani S, Botas J, Lichtarge O. Harnessing the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer's disease. Alzheimers Dement 2020; 17:831-846. [PMID: 33576571 PMCID: PMC8247413 DOI: 10.1002/alz.12240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/08/2020] [Accepted: 10/22/2020] [Indexed: 01/05/2023]
Abstract
The strongest genetic risk factor for idiopathic late‐onset Alzheimer's disease (LOAD) is apolipoprotein E (APOE) ɛ4, while the APOE ɛ2 allele is protective. However, there are paradoxical APOE ɛ4 carriers who remain disease‐free and APOE ɛ2 carriers with LOAD. We compared exomes of healthy APOE ɛ4 carriers and APOE ɛ2 Alzheimer's disease (AD) patients, prioritizing coding variants based on their predicted functional impact, and identified 216 genes with differential mutational load between these two populations. These candidate genes were significantly dysregulated in LOAD brains, and many modulated tau‐ or β42‐induced neurodegeneration in Drosophila. Variants in these genes were associated with AD risk, even in APOE ɛ3 homozygotes, showing robust predictive power for risk stratification. Network analyses revealed involvement of candidate genes in brain cell type‐specific pathways including synaptic biology, dendritic spine pruning and inflammation. These potential modifiers of LOAD may constitute novel biomarkers, provide potential therapeutic intervention avenues, and support applying this approach as larger whole exome sequencing cohorts become available.
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Affiliation(s)
- Young Won Kim
- Program in Integrative Molecular and Biomedical SciencesBaylor College of MedicineHoustonTexasUSA
| | - Ismael Al‐Ramahi
- Jan and Dan Duncan Neurological Research InstituteHoustonTexasUSA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Amanda Koire
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
- Medical Scientist Training ProgramBaylor College of MedicineHoustonTexasUSA
| | - Stephen J. Wilson
- Biochemistry and Molecular BiologyBaylor College of MedicineHoustonTexasUSA
| | - Daniel M. Konecki
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
| | - Samantha Mota
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Shirin Soleimani
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Juan Botas
- Jan and Dan Duncan Neurological Research InstituteHoustonTexasUSA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
| | - Olivier Lichtarge
- Program in Integrative Molecular and Biomedical SciencesBaylor College of MedicineHoustonTexasUSA
- Jan and Dan Duncan Neurological Research InstituteHoustonTexasUSA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
- Medical Scientist Training ProgramBaylor College of MedicineHoustonTexasUSA
- Biochemistry and Molecular BiologyBaylor College of MedicineHoustonTexasUSA
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47
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Lees-Miller JP, Cobban A, Katsonis P, Bacolla A, Tsutakawa SE, Hammel M, Meek K, Anderson DW, Lichtarge O, Tainer JA, Lees-Miller SP. Uncovering DNA-PKcs ancient phylogeny, unique sequence motifs and insights for human disease. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2020; 163:87-108. [PMID: 33035590 PMCID: PMC8021618 DOI: 10.1016/j.pbiomolbio.2020.09.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 09/12/2020] [Accepted: 09/29/2020] [Indexed: 01/26/2023]
Abstract
DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is a key member of the phosphatidylinositol-3 kinase-like (PIKK) family of protein kinases with critical roles in DNA-double strand break repair, transcription, metastasis, mitosis, RNA processing, and innate and adaptive immunity. The absence of DNA-PKcs from many model organisms has led to the assumption that DNA-PKcs is a vertebrate-specific PIKK. Here, we find that DNA-PKcs is widely distributed in invertebrates, fungi, plants, and protists, and that threonines 2609, 2638, and 2647 of the ABCDE cluster of phosphorylation sites are highly conserved amongst most Eukaryotes. Furthermore, we identify highly conserved amino acid sequence motifs and domains that are characteristic of DNA-PKcs relative to other PIKKs. These include residues in the Forehead domain and a novel motif we have termed YRPD, located in an α helix C-terminal to the ABCDE phosphorylation site loop. Combining sequence with biochemistry plus structural data on human DNA-PKcs unveils conserved sequence and conformational features with functional insights and implications. The defined generally progressive DNA-PKcs sequence diversification uncovers conserved functionality supported by Evolutionary Trace analysis, suggesting that for many organisms both functional sites and evolutionary pressures remain identical due to fundamental cell biology. The mining of cancer genomic data and germline mutations causing human inherited disease reveal that robust DNA-PKcs activity in tumors is detrimental to patient survival, whereas germline mutations compromising function are linked to severe immunodeficiency and neuronal degeneration. We anticipate that these collective results will enable ongoing DNA-PKcs functional analyses with biological and medical implications.
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Affiliation(s)
- James P Lees-Miller
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 4N1, Canada
| | - Alexander Cobban
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 4N1, Canada
| | - Panagiotis Katsonis
- Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Albino Bacolla
- Departments of Cancer Biology and of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, 6767 Bertner Avenue, Houston, TX, 77030, USA
| | - Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Michal Hammel
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Katheryn Meek
- College of Veterinary Medicine, Department of Microbiology & Molecular Genetics, And Department of Pathobiology & Diagnostic Investigation, Michigan State University, East Lansing, MI, 48824, USA
| | - Dave W Anderson
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 4N1, Canada
| | - Olivier Lichtarge
- Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - John A Tainer
- Departments of Cancer Biology and of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, 6767 Bertner Avenue, Houston, TX, 77030, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
| | - Susan P Lees-Miller
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 4N1, Canada.
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48
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Gleber-Netto FO, Neskey D, Costa AFDM, Kataria P, Rao X, Wang J, Kowalski LP, Pickering CR, Dias-Neto E, Myers JN. Functionally impactful TP53 mutations are associated with increased risk of extranodal extension in clinically advanced oral squamous cell carcinoma. Cancer 2020; 126:4498-4510. [PMID: 32797678 DOI: 10.1002/cncr.33101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/24/2020] [Accepted: 06/20/2020] [Indexed: 11/10/2022]
Abstract
BACKGROUND The treatment of advanced oral squamous cell carcinoma (OSCC) is a clinical challenge because it is unclear which therapeutic approaches are the best for this highly heterogeneous group of patients. Because TP53 mutations are the most common genetic event in these tumors, the authors investigated whether they could represent an ancillary biomarker in the management of advanced OSCC. METHODS The TP53 gene was sequenced in 78 samples from patients with advanced OSCC who received treatment at 2 institutions located in the United States and Brazil. TP53 mutations were classified according to an in-silico impact score (the evolutionary action score of p53 [EAp53]), which identifies mutations that have greater alterations of p53 protein function (high-risk). Associations between TP53 mutation status/characteristics and clinicopathologic characteristics were investigated. The relevant findings were validated in silico by analyzing 197 samples from patients with advanced OSCC from The Cancer Genome Atlas. RESULTS No differences in clinical outcomes were detected between patients with TP53-mutant and wild-type TP53 disease. However, patients who had tumors carrying high-risk TP53 mutations had a significantly increased risk of developing extranodal extension (ENE) compared with those who had wild-type TP53-bearing tumors. The increased chances of detecting ENE among patients who had high-risk TP53 mutations was validated among patients with advanced OSCC from The Cancer Genome Atlas cohort. CONCLUSIONS High-risk TP53 mutations are associated with an increased chance of detecting ENE in patients with advanced OSCC. Because ENE is 1 of the major factors considered for OSCC patient management, TP53 mutation status may represent a potential ancillary biomarker for treatment decisions regarding postoperative adjuvant therapy.
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Affiliation(s)
- Frederico O Gleber-Netto
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David Neskey
- Department of Otolaryngology, Head and Neck Surgery, Medical University of South Carolina, Charleston, South Carolina.,Department of Cell and Molecular Pharmacology and Developmental Therapeutics, Medical University of South Carolina, Charleston, South Carolina
| | - Ana Flávia de Mattos Costa
- Laboratory of Medical Genomics, International Research Center, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Pranav Kataria
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Xiayu Rao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jing Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Curtis R Pickering
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas Graduate School of Biomedical Sciences, Houston, Texas
| | - Emmanuel Dias-Neto
- Laboratory of Medical Genomics, International Research Center, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Jeffrey N Myers
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas Graduate School of Biomedical Sciences, Houston, Texas
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49
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Zhao Y, Han H, Gao Z, Hu H, Xiang J, Sun Y, Chen H. Evolutionary Action Score of TP53 Enhances the Prognostic Prediction for Stage I Lung Adenocarcinoma. Semin Thorac Cardiovasc Surg 2020; 33:221-229. [PMID: 32450216 DOI: 10.1053/j.semtcvs.2020.04.005] [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: 04/11/2020] [Accepted: 04/20/2020] [Indexed: 11/11/2022]
Abstract
Stage I lung adenocarcinoma usually has a good prognosis after surgery. However, some patients do suffer disease recurrence during follow-up. Here, we report the prognostic value of evolutionary action score of TP53, which calculates the functional prediction of TP53, in patients with stage I lung adenocarcinoma. From January 2011 to August 2013, 83 patients with a complete follow-up history (36 with a disease recurrence and 47 without recurrence during follow-up) who were pathologically confirmed stage I lung adenocarcinoma were included. Whole-exome sequencing were performed on those paired tumor-normal specimens. Evolutionary action score of TP53 (EAp53) was calculated and patients were divided into groups according to their TP53 mutational status. Tumor mutational burden and survival analyses were performed to assess the prognostic value of EAp53. TP53 mutation was identified in 31 patients (37.3%). Of them, 11 were high-risk point mutations, 9 were low-risk point mutations, and 11 were truncating mutations. The high-risk group showed a poorer recurrence-free survival compared with the low-risk group (P = 0.046) and the wild-type group (P = 0.007). In multivariable analysis, the high-risk/truncating group showed a poorer recurrence-free survival (P = 0.007) and overall survival (P = 0.009) compared with the low-risk/wild-type group. Moreover, tumor mutational burden was higher in the high-risk/truncating group (P < 0.001). EAp53 is of prognostic value in patients with stage I lung adenocarcinoma. The mutational type of TP53 should be paid attention to when predicting the prognosis of patients with stage I lung adenocarcinoma.
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Affiliation(s)
- Yue Zhao
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Han Han
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhendong Gao
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hong Hu
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaqing Xiang
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yihua Sun
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haiquan Chen
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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50
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Novikov IB, Wilkins AD, Lichtarge O. An Evolutionary Trace method defines functionally important bases and sites common to RNA families. PLoS Comput Biol 2020; 16:e1007583. [PMID: 32208421 PMCID: PMC7092961 DOI: 10.1371/journal.pcbi.1007583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 11/27/2019] [Indexed: 11/18/2022] Open
Abstract
Functional non-coding (fnc)RNAs are nucleotide sequences of varied lengths, structures, and mechanisms that ubiquitously influence gene expression and translation, genome stability and dynamics, and human health and disease. Here, to shed light on their functional determinants, we seek to exploit the evolutionary record of variation and divergence read from sequence comparisons. The approach follows the phylogenetic Evolutionary Trace (ET) paradigm, first developed and extensively validated on proteins. We assigned a relative rank of importance to every base in a study of 1070 functional RNAs, including the ribosome, and observed evolutionary patterns strikingly similar to those seen in proteins, namely, (1) the top-ranked bases clustered in secondary and tertiary structures. (2) In turn, these clusters mapped functional regions for catalysis, binding proteins and drugs, post-transcriptional modification, and deleterious mutations. (3) Moreover, the quantitative quality of these clusters correlated with the identification of functional regions. (4) As a result of this correlation, smoother structural distributions of evolutionary important nucleotides improved functional site predictions. Thus, in practice, phylogenetic analysis can broadly identify functional determinants in RNA sequences and functional sites in RNA structures, and reveal details on the basis of RNA molecular functions. As example of application, we report several previously undocumented and potentially functional ET nucleotide clusters in the ribosome. This work is broadly relevant to studies of structure-function in ribonucleic acids. Additionally, this generalization of ET shows that evolutionary constraints among sequence, structure, and function are similar in structured RNA and proteins. RNA ET is currently available as part of the ET command-line package, and will be available as a web-server. Traditionally, RNA has been delegated to the role of an intermediate between DNA and proteins. However, we now recognize that RNAs are broadly functional beyond their role in translation, and that a number of diverse classes exist. Because functional, non-coding RNAs are prevalent in biology and impact human health, it is important to better understand their functional determinants. However, the classical solution to this problem, targeted mutagenesis, is time-consuming and scales poorly. We propose an alternative computational approach to this problem, the Evolutionary Trace method. Previously developed and validated for proteins, Evolutionary Trace examines evolutionary history of a molecule and predicts evolutionarily important residues in the sequence. We apply Evolutionary Trace to a set of diverse RNAs, and find that the evolutionarily important nucleotides cluster on the three-dimensional structure, and that these clusters closely overlap functional sites. We also find that the clustering property can be used to refine and improve predictions. These findings are in close agreement with our observations of Evolutionary Trace in proteins, and suggest that structured functional RNAs and proteins evolve under similar constraints. In practice, the approach is to be used by RNA researches seeking insight into their molecule of interest, and the Evolutionary Trace program, along with a working example, is available at https://github.com/LichtargeLab/RNA_ET_ms.
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Affiliation(s)
- Ilya B. Novikov
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Angela D. Wilkins
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- * E-mail:
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