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Hasan MM, Adiba M, Rahman M, Akter H, Uddin M, Ebihara A, Nabi AN, Yasmin T. Mutational analyses of mitochondrial ATP6 gene reveal a possible association with abnormal levels of lactic acid and ammonia in Bangladeshi children with autism spectrum disorder: A case-control study. HUMAN GENE 2024; 42:201325. [DOI: 10.1016/j.humgen.2024.201325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
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2
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Koh HYK, Lam UTF, Ban KHK, Chen ES. Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers. Sci Rep 2024; 14:22618. [PMID: 39349509 PMCID: PMC11442673 DOI: 10.1038/s41598-024-71422-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 08/28/2024] [Indexed: 10/02/2024] Open
Abstract
The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).
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Affiliation(s)
- Herrick Yu Kan Koh
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ulysses Tsz Fung Lam
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kenneth Hon-Kim Ban
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- National University Health System (NUHS), Singapore, Singapore.
- NUS Center for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| | - Ee Sin Chen
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- National University Health System (NUHS), Singapore, Singapore.
- NUS Center for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore.
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Clarke B, Holtkamp E, Öztürk H, Mück M, Wahlberg M, Meyer K, Munzlinger F, Brechtmann F, Hölzlwimmer FR, Lindner J, Chen Z, Gagneur J, Stegle O. Integration of variant annotations using deep set networks boosts rare variant association testing. Nat Genet 2024:10.1038/s41588-024-01919-z. [PMID: 39322779 DOI: 10.1038/s41588-024-01919-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/20/2024] [Indexed: 09/27/2024]
Abstract
Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.
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Affiliation(s)
- Brian Clarke
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Eva Holtkamp
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Helmholtz Association-Munich School for Data Science (MUDS), Munich, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Hakime Öztürk
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcel Mück
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Magnus Wahlberg
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kayla Meyer
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Munzlinger
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Brechtmann
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Florian R Hölzlwimmer
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Jonas Lindner
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Zhifen Chen
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Julien Gagneur
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Munich Center for Machine Learning, Munich, Germany.
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
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4
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors. Hum Genomics 2024; 18:90. [PMID: 39198917 PMCID: PMC11360829 DOI: 10.1186/s40246-024-00663-z] [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: 06/22/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
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 three decades, 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 190 VIPs, resulting in a total of 407 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 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb 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, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Arul S Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA
- Illumina, Foster City, CA, 94404, USA
| | - Steven E Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA.
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA.
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA.
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5
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Liu Y, Sheng W, Hou S, Hou M, Zhang Y, Wang X, Zhang S, Zhou F, Cai C, Wang W. Functional Characterization of a Novel SLC4A4 Variant and Uniparental Isodisomy in Proximal Renal Tubular Acidosis Patient. Biochem Genet 2024; 62:2469-2481. [PMID: 37952039 DOI: 10.1007/s10528-023-10554-y] [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: 08/14/2023] [Accepted: 10/20/2023] [Indexed: 11/14/2023]
Abstract
SLC4A4 variants are the etiologies of inherited proximal renal tubular acidosis (pRTA), which results in metabolic acidosis, hypokalemia, glaucoma, band keratopathy, and cataract. This study aims to characterize SLC4A4 variant and uniparental isodisomy of chromosome 4 in a patient, and analyse the functional characterization of SLC4A4 variants. This study analyzed renal tubular acidosis disease genes by whole exome sequencing (WES). H3M2 algorithm was used to analyze the run of homozygosity region in chromosomal regions in trio-WES data. The pathogenicity analysis of variants was performed using bioinformatics tools. Additionally, protein stability was analyzed by cycloheximide chase assay. Whole-cell patch clamping was used to examine the electrophysiological properties of NBCe1-A. A novel homozygous SLC4A4 variant was identified in the patient: a missense variant c.496C > T, p. Arg166Trp (NM_003759.4). But the father was heterozygous variant carrier, and the mother did not detect the variant. The H3M2 and UPDio algorithm revealed paternal uniparental isodisomy on chromosome 4 in the patient. SIFT, Poly Phen-2, FATHMM and Mutant Taster showed that the variant might be pathogenic. The tertiary structure analysis showed that the variant could cause structural damage to NBCe1 protein. Foldx results showed that the protein stability of the variant was slightly reduced. Cycloheximide chase assay demonstrated that the variant affects protein stability. The result of electrophysiological studies showed that the variant altered Na+/HCO3- cotransport activity of protein. In conclusion, the study is the first to report a pRTA patient with Arg166Trp variant with UPiD (4) pat and analyze the function of Arg166Trp variant.
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Affiliation(s)
- Yan Liu
- Clinical Pediatric College of Tianjin Medical University, Tianjin Medical University, Tianjin, 300134, China
- Department of Nephrology, Tianjin Children's Hospital (Tianjin University Children's Hospital), No.238 Longyan Road, Beichen District, Tianjin, 300134, China
| | - Wenchao Sheng
- Clinical Pediatric College of Tianjin Medical University, Tianjin Medical University, Tianjin, 300134, China
| | - Shaowei Hou
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, China
| | - Mengzhu Hou
- Tianjin Pediatric Research Institute, Tianjin Children's Hospital (Tianjin University Children's Hospital), No.238 Longyan Road, Beichen District, Tianjin, 300134, China
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300134, China
| | - Ying Zhang
- Clinical Pediatric College of Tianjin Medical University, Tianjin Medical University, Tianjin, 300134, China
| | - Xuetao Wang
- Tianjin Pediatric Research Institute, Tianjin Children's Hospital (Tianjin University Children's Hospital), No.238 Longyan Road, Beichen District, Tianjin, 300134, China
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300134, China
| | - Shuyue Zhang
- Clinical Pediatric College of Tianjin Medical University, Tianjin Medical University, Tianjin, 300134, China
| | - Feiyu Zhou
- Clinical Pediatric College of Tianjin Medical University, Tianjin Medical University, Tianjin, 300134, China
| | - Chunquan Cai
- Tianjin Pediatric Research Institute, Tianjin Children's Hospital (Tianjin University Children's Hospital), No.238 Longyan Road, Beichen District, Tianjin, 300134, China.
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300134, China.
| | - Wenhong Wang
- Department of Nephrology, Tianjin Children's Hospital (Tianjin University Children's Hospital), No.238 Longyan Road, Beichen District, Tianjin, 300134, China.
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Stella S, Vitale SR, Massimino M, Martorana F, Tornabene I, Tomarchio C, Drago M, Pavone G, Gorgone C, Barone C, Bianca S, Manzella L. In Silico Prediction of BRCA1 and BRCA2 Variants with Conflicting Clinical Interpretation in a Cohort of Breast Cancer Patients. Genes (Basel) 2024; 15:943. [PMID: 39062721 PMCID: PMC11276437 DOI: 10.3390/genes15070943] [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: 06/28/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Germline BRCA1/2 alteration has been linked to an increased risk of hereditary breast and ovarian cancer syndromes. As a result, genetic testing, based on NGS, allows us to identify a high number of variants of uncertain significance (VUS) or conflicting interpretation of pathogenicity (CIP) variants. The identification of CIP/VUS is often considered inconclusive and clinically not actionable for the patients' and unaffected carriers' management. In this context, their assessment and classification remain a significant challenge. The aim of the study was to investigate whether the in silico prediction tools (PolyPhen-2, SIFT, Mutation Taster and PROVEAN) could predict the potential clinical impact and significance of BRCA1/2 CIP/VUS alterations, eventually impacting the clinical management of Breast Cancer subjects. In a cohort of 860 BC patients, 10.6% harbored BRCA1 or BRCA2 CIP/VUS alterations, mostly observed in BRCA2 sequences (85%). Among them, forty-two out of fifty-five alterations were predicted as damaging, with at least one in silico that used tools. Prediction agreement of the four tools was achieved in 45.5% of patients. Moreover, the highest consensus was obtained in twelve out of forty-two (28.6%) mutations by considering three out of four in silico algorithms. The use of prediction tools may help to identify variants with a potentially damaging effect. The lack of substantial agreement between the different algorithms suggests that the bioinformatic approaches should be combined with the personal and family history of the cancer patients.
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Affiliation(s)
- Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy; (S.R.V.); (M.M.)
| | - Silvia Rita Vitale
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy; (S.R.V.); (M.M.)
| | - Michele Massimino
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy; (S.R.V.); (M.M.)
- Department of General Surgery and Medical-Surgical Specialties, University of Catania, 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
| | - Irene Tornabene
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
- Division of Pathology, Humanitas Istituto Clinico Catanese, 95045 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy; (S.R.V.); (M.M.)
| | - Melissa Drago
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy; (S.R.V.); (M.M.)
| | - Giuliana Pavone
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
- Medical Oncology Unit, Humanitas Istituto Clinico Catanese, 95045 Catania, Italy
| | | | | | | | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; (F.M.); (I.T.); (C.T.); (M.D.); (G.P.); (L.M.)
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy; (S.R.V.); (M.M.)
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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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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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Alfayyadh MM, Maksemous N, Sutherland HG, Lea RA, Griffiths LR. Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches. Genes (Basel) 2024; 15:443. [PMID: 38674378 PMCID: PMC11049430 DOI: 10.3390/genes15040443] [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: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.
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Affiliation(s)
| | | | | | | | - Lyn R. Griffiths
- Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia; (M.M.A.); (N.M.); (H.G.S.); (R.A.L.)
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10
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Zhang M, Gong C, Ge F, Yu DJ. FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework. J Chem Inf Model 2024; 64:1394-1406. [PMID: 38349747 DOI: 10.1021/acs.jcim.3c02025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.
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Affiliation(s)
- Ming Zhang
- School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Chao Gong
- School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Fang Ge
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
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Xie CTY, Pastore SF, Vincent JB, Frankland PW, Hamel PA. Nonsynonymous Mutations in Intellectual Disability and Autism Spectrum Disorder Gene PTCHD1 Disrupt N-Glycosylation and Reduce Protein Stability. Cells 2024; 13:199. [PMID: 38275824 PMCID: PMC10814814 DOI: 10.3390/cells13020199] [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: 12/07/2023] [Revised: 01/14/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
PTCHD1 has been implicated in Autism Spectrum Disorders (ASDs) and/or intellectual disability, where copy-number-variant losses or loss-of-function coding mutations segregate with disease in an X-linked recessive fashion. Missense variants of PTCHD1 have also been reported in patients. However, the significance of these mutations remains undetermined since the activities, subcellular localization, and regulation of the PTCHD1 protein are currently unknown. This paucity of data concerning PTCHD1 prevents the effective evaluation of sequence variants identified during diagnostic screening. Here, we characterize PTCHD1 protein binding partners, extending previously reported interactions with postsynaptic scaffolding protein, SAP102. Six rare missense variants of PTCHD1 were also identified from patients with neurodevelopmental disorders. After modelling these variants on a hypothetical three-dimensional structure of PTCHD1, based on the solved structure of NPC1, PTCHD1 variants harboring these mutations were assessed for protein stability, post-translational processing, and protein trafficking. We show here that the wild-type PTCHD1 post-translational modification includes complex N-glycosylation and that specific mutant proteins disrupt normal N-link glycosylation processing. However, regardless of their processing, these mutants still localized to PSD95-containing dendritic processes and remained competent for complexing SAP102.
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Affiliation(s)
- Connie T. Y. Xie
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Stephen F. Pastore
- Molecular Neuropsychiatry & Development (MiND) Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1RS, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - John B. Vincent
- Molecular Neuropsychiatry & Development (MiND) Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1RS, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Paul W. Frankland
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Paul A. Hamel
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
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12
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Namme JN, Reza HM, Bepari AK. Computational analysis and molecular dynamics simulation of high-risk single nucleotide polymorphisms of the ADAM10 gene. J Biomol Struct Dyn 2024; 42:412-424. [PMID: 36995110 DOI: 10.1080/07391102.2023.2192890] [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: 09/01/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023]
Abstract
Polymorphisms of the disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) are linked to pathophysiological changes in lung inflammation, cancer, Alzheimer's disease (AD), encephalopathy, liver fibrosis, and cardiovascular diseases. In this study, we predicted the pathogenicity of ADAM10 non-synonymous single nucleotide polymorphisms (nsSNPs) in a wide array of mutation analyzing bioinformatics tools. We retrieved 423 nsSNPs from dbSNP-NCBI for the analysis, and 13 were predicted deleterious by each of the ten tools: SIFT, PROVEAN, CONDEL, PANTHER-PSEP, SNAP2, SuSPect, PolyPhen-2, Meta-SNP, Mutation Assessor and Predict-SNP. Further assessment of amino acid sequences, homology models, conservation profiles, and inter-atomic interactions identified C222G, G361E and C639Y as the most pathogenic mutations. We validated this prediction through structural stability analysis using DUET, I-Mutant Suite, SNPeffect and Dynamut. Molecular dynamics simulations and principal component analysis also indicated considerable instability of the C222G, G361E and C639Y variants. Therefore, these ADAM10 nsSNPs could be candidates for diagnostic genetic screening and therapeutic molecular targeting.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Jannatun Nayem Namme
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
| | - Hasan Mahmud Reza
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
| | - Asim Kumar Bepari
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
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13
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Stein D, Kars ME, Wu Y, Bayrak ÇS, Stenson PD, Cooper DN, Schlessinger A, Itan Y. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Genome Med 2023; 15:103. [PMID: 38037155 PMCID: PMC10688473 DOI: 10.1186/s13073-023-01261-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .
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Affiliation(s)
- David Stein
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Meltem Ece Kars
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yiming Wu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- College of Life Science, China West Normal University, Nan Chong, Si Chuan, 637009, China
| | - Çiğdem Sevim Bayrak
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Peter D Stenson
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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14
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Ge F, Arif M, Yan Z, Alahmadi H, Worachartcheewan A, Yu DJ, Shoombuatong W. MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction. J Chem Inf Model 2023; 63:7239-7257. [PMID: 37947586 PMCID: PMC10685454 DOI: 10.1021/acs.jcim.3c00950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/21/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.
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Affiliation(s)
- Fang Ge
- School
of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, 9 Wenyuanlu, Nanjing 210023, China
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Zihao Yan
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Hanin Alahmadi
- College of
Computer Science and Engineering, Taibah
University, Madinah 344, Saudi Arabia
| | - Apilak Worachartcheewan
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Dong-Jun Yu
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Watshara Shoombuatong
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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15
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Redouane S, Charoute H, Harmak H, Malki A, Barakat A, Rouba H. Computational study of the potential impact of AURKC missense SNPs on AURKC-INCENP interaction and their correlation to macrozoospermia. J Biomol Struct Dyn 2023; 41:9503-9522. [PMID: 36326488 DOI: 10.1080/07391102.2022.2142846] [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: 06/24/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
Aurora Kinase C (AURKC) is considered an important element in Chromosome Passenger Complex (CPC), its interaction with Inner Centromere Protein (INCENP) plays a critical role in the establishment and the recruitment of a stable CPC during spermatogenesis. Genetic variations of AURKC gene are susceptible to impact AURKC-INCENP interaction, which may affect CPC stability and predispose male subjects to macrozoospermia. In this study, we systematically applied computational approaches using different bioinformatic tools to predict the effect of missense SNPs reported on AURKC gene, we selected the deleterious ones and we introduced their corresponding amino acid substitutions on AURKC protein structure. Then we did a protein-protein docking between AURKC variants and INCENP followed by a structural assessment of each resulting complex using PRODIGY server, Yassara view, Ligplot + and we choose the complexes of the most impactful variants for molecular dynamics (MD) simulation study. Seventeen missense SNPs of AURKC were identified as deleterious between all reported ones. All of them were located on relatively conserved positions on AURKC protein according to Consurf server. Only the four missense SNPs; E91K, D166V, D221Y and G235V were ranked as the most impactful ones and were chosen for MD simulation. D221Y and G235V were responsible for the most remarkable changes on AURKC-INCENP structural stability, therefore, they were selected as the most deleterious ones. Experimental studies are recommended to test the actual effect of these two variants and their actual impact on the morphology of sperm cells.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Salaheddine Redouane
- Laboratory of Genomics and Human Genetics, Institut Pasteur du Maroc, Casablanca, Morocco
- Laboratory of Physiopathology and Molecular Genetics, Department of Biology, Faculty of Sciences Ben M'Sik, Hassan II University, Casablanca, Morocco
| | - Hicham Charoute
- Research Unit of Epidemiology, Biostatistics and Bioinformatics, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Houda Harmak
- Laboratory of Genomics and Human Genetics, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Abderrahim Malki
- Laboratory of Physiopathology and Molecular Genetics, Department of Biology, Faculty of Sciences Ben M'Sik, Hassan II University, Casablanca, Morocco
| | - Abdelhamid Barakat
- Laboratory of Genomics and Human Genetics, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Hassan Rouba
- Laboratory of Genomics and Human Genetics, Institut Pasteur du Maroc, Casablanca, Morocco
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16
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Arshad M, Noor N, Iqbal Z, Jaleel H. In silico analysis of missense SNPs in TNFR1a and their possible therapeutic or pathogenic role in immune diseases. Hum Immunol 2023; 84:609-617. [PMID: 37748952 DOI: 10.1016/j.humimm.2023.09.003] [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: 05/19/2023] [Revised: 09/12/2023] [Accepted: 09/19/2023] [Indexed: 09/27/2023]
Abstract
Tumor necrosis factor alpha (TNFa) is an inflammatory cytokine that is involved in the pathogenesis of various inflammatory disorders including rheumatoid arthritis. TNF-alpha receptor I (TNFR1a) is one of the receptors TNFa binds with for its activation. Any variation in this receptor might affect the role of TNFa in successive events. Amino acid residue substitutions might happen in TNFR1a through non-synonymous single nucleotide polymorphisms (nsSNPs) which may alter the functioning of TNFa, hence, identifying any such substitutions is of paramount significance. In this study, six nsSNPs at five different evolutionary conserved regions are predicted to be detrimental to the structure and/or function of TNFR1a by using numerous computational tools. Their 3D models are also proposed in this study. Besides, they were found to reduce the stability and affect the molecular mechanisms of this protein. Two contrasting possibilities might happen because of these substitutions. One, they might reduce the production of TNFa which is overexpressed in inflammatory diseases, hence can play therapeutic role in such diseases. Second, they might possibly hinder the apoptosis to occur which can effectuate the uncontrolled division of cells, hence can be pathogenic in diseases like cancer. Further investigations on these nsSNPs using animal models and at cellular level will open doors to understand the underlying mechanisms behind various diseases.
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Affiliation(s)
- Maria Arshad
- Department of Biochemistry and Molecular Biology, University of Iceland, Reykjavik, Iceland.
| | - Nabeel Noor
- Shalamar Medical & Dental College, Lahore, Pakistan
| | - Zunair Iqbal
- Shalamar Medical & Dental College, Lahore, Pakistan
| | - Hadiqa Jaleel
- Department of Research & Innovation, Shalamar Institute of Health Sciences, Lahore, Pakistan
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17
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Larrea-Sebal A, Jebari-Benslaiman S, Galicia-Garcia U, Jose-Urteaga AS, Uribe KB, Benito-Vicente A, Martín C. Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies. Curr Atheroscler Rep 2023; 25:839-859. [PMID: 37847331 PMCID: PMC10618353 DOI: 10.1007/s11883-023-01154-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/15/2023] [Indexed: 10/18/2023]
Abstract
PURPOSE OF REVIEW Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. RECENT FINDINGS In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.
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Affiliation(s)
- Asier Larrea-Sebal
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
- Fundación Biofisika Bizkaia, 48940, Leioa, Spain
| | - Shifa Jebari-Benslaiman
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Unai Galicia-Garcia
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Ane San Jose-Urteaga
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Kepa B Uribe
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Asier Benito-Vicente
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - César Martín
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain.
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain.
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18
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Alwehaidah MS, Alsabbagh M, Al-Kafaji G. Comprehensive analysis of mitochondrial DNA variants, mitochondrial DNA copy number and oxidative damage in psoriatic arthritis. Biomed Rep 2023; 19:85. [PMID: 37881602 PMCID: PMC10594069 DOI: 10.3892/br.2023.1667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/19/2023] [Indexed: 10/27/2023] Open
Abstract
Growing evidence suggests that abnormalities in mitochondrial DNA (mtDNA) are involved in the pathogenesis of various inflammatory and immuno-mediated diseases. The present study analysed the entire mitochondrial genome by next-generation sequencing (NGS) in 23 patients with psoriatic arthritis (PsA) and 20 healthy controls to identify PsA-related variants. Changes in mtDNA copy number (mtDNAcn) were also evaluated by quantitative polymerase chain reaction (qPCR) and mtDNA oxidative damage was measured using an 8-hydroxy-2'-deoxyguanosine assay. NGS analysis revealed a total of 435 variants including 187 in patients with PsA only and 122 in controls only. Additionally, 126 common variants were found, of which 2 variants differed significantly in their frequencies among patients and controls (P<0.05), and may be associated with susceptibility to PsA. A total of 33 missense variants in mtDNA-encoded genes for complexes I, III, IV and V were identified only in patients with PsA. Of them, 25 variants were predicted to be deleterious by affecting the functions and structures of encoded proteins, and 13 variants were predicted to affect protein's stability. mtDNAcn analysis revealed decreased mtDNA content in patients with PsA compared with controls (P=0.0001) but the decrease in mtDNAcn was not correlated with patients' age or inflammatory biomarkers (P>0.05). Moreover, a higher level of oxidative damage was observed in patients with PsA compared with controls (P=0.03). The results of the present comprehensive analysis of mtDNA in PsA revealed that certain mtDNA variants may be implicated in the predisposition/pathogenesis of PsA, highlighting the importance of NGS in the identification of mtDNA variants in PsA. The current results also demonstrated that decreased mtDNAcn in PsA may be a consequence of increased oxidative stress. These data provide valuable insights into the contribution of mtDNA defects to the pathogenesis of PsA. Additional studies in larger cohorts are needed to elucidate the role of mtDNA defects in PsA.
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Affiliation(s)
- Materah Salem Alwehaidah
- Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, Kuwait University, City of Kuwait 31470, State of Kuwait
| | - Manhel Alsabbagh
- Department of Molecular Medicine and Al-Jawhara Centre for Molecular Medicine, Genetics, and Inherited Disorders, College of Medicine and Medical Sciences, Arabian Gulf University, Manama 26671, Kingdom of Bahrain
| | - Ghada Al-Kafaji
- Department of Molecular Medicine and Al-Jawhara Centre for Molecular Medicine, Genetics, and Inherited Disorders, College of Medicine and Medical Sciences, Arabian Gulf University, Manama 26671, Kingdom of Bahrain
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19
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Corpas M, de Mendoza C, Moreno-Torres V, Pintos I, Seoane P, Perkins JR, Ranea JA, Fatumo S, Korcsmaros T, Martín-Villa JM, Barreiro P, Corral O, Soriano V. Genetic signature detected in T cell receptors from patients with severe COVID-19. iScience 2023; 26:107735. [PMID: 37720084 PMCID: PMC10504482 DOI: 10.1016/j.isci.2023.107735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/21/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Characterization of host genetic factors contributing to COVID-19 severity promises advances on drug discovery to fight the disease. Most genetic analyses to date have identified genome-wide significant associations involving loss-of-function variants for immune response pathways. Despite accumulating evidence supporting a role for T cells in COVID-19 severity, no definitive genetic markers have been found to support an involvement of T cell responses. We analyzed 205 whole exomes from both a well-characterized cohort of hospitalized severe COVID-19 patients and controls. Significantly enriched high impact alleles were found for 25 variants within the T cell receptor beta (TRB) locus on chromosome 7. Although most of these alleles were found in heterozygosis, at least three or more in TRBV6-5, TRBV7-3, TRBV7-6, TRBV7-7, and TRBV10-1 suggested a possible TRB loss of function via compound heterozygosis. This loss-of-function in TRB genes supports suboptimal or dysfunctional T cell responses as a major contributor to severe COVID-19 pathogenesis.
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Affiliation(s)
- Manuel Corpas
- School of Life Sciences, University of Westminster, London, UK
- Cambridge Precision Medicine Limited, ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK
- UNIR Health Sciences School & Medical Center, Madrid, Spain
- Institute of Continuing Education, University of Cambridge, Cambridge, UK
| | - Carmen de Mendoza
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Spain
| | - Víctor Moreno-Torres
- UNIR Health Sciences School & Medical Center, Madrid, Spain
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Spain
| | - Ilduara Pintos
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Spain
| | - Pedro Seoane
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain
- CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - James R. Perkins
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain
- CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- The Biomedical Research Institute of Málaga (IBIMA), Málaga, Spain
| | - Juan A.G. Ranea
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain
- CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- The Biomedical Research Institute of Málaga (IBIMA), Málaga, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Madrid, Spain
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, UK
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Tamas Korcsmaros
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Pablo Barreiro
- UNIR Health Sciences School & Medical Center, Madrid, Spain
- Emergency Hospital Isabel Zendal, Madrid, Spain
| | - Octavio Corral
- UNIR Health Sciences School & Medical Center, Madrid, Spain
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20
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Nijboer TCW, Hessel EVS, van Haaften GW, van Zandvoort MJ, van der Spek PJ, Troelstra C, de Kovel CGF, Koeleman BPC, van der Zwaag B, Brilstra EH, Burbach JPH. Identification of candidate genes for developmental colour agnosia in a single unique family. PLoS One 2023; 18:e0290013. [PMID: 37672513 PMCID: PMC10482254 DOI: 10.1371/journal.pone.0290013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 07/31/2023] [Indexed: 09/08/2023] Open
Abstract
Colour agnosia is a disorder that impairs colour knowledge (naming, recognition) despite intact colour perception. Previously, we have identified the first and only-known family with hereditary developmental colour agnosia. The aim of the current study was to explore genomic regions and candidate genes that potentially cause this trait in this family. For three family members with developmental colour agnosia and three unaffected family members CGH-array analysis and exome sequencing was performed, and linkage analysis was carried out using DominantMapper, resulting in the identification of 19 cosegregating chromosomal regions. Whole exome sequencing resulted in 11 rare coding variants present in all affected family members with developmental colour agnosia and absent in unaffected members. These variants affected genes that have been implicated in neural processes and functions (CACNA2D4, DDX25, GRINA, MYO15A) or that have an indirect link to brain function, development or disease (MAML2, STAU1, TMED3, RABEPK), and a remaining group lacking brain expression or involved in non-neural traits (DEPDC7, OR1J1, OR8D4). Although this is an explorative study, the small set of candidate genes that could serve as a starting point for unravelling mechanisms of higher level cognitive functions and cortical specialization, and disorders therein such as developmental colour agnosia.
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Affiliation(s)
- Tanja C. W. Nijboer
- UMCU Brain Center and Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and De Hoogstraat Rehabilitation, Utrecht, The Netherlands
- Department of Experimental Psychology and Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Ellen V. S. Hessel
- UMCU Brain Center and Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and De Hoogstraat Rehabilitation, Utrecht, The Netherlands
- Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gijs W. van Haaften
- Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martine J. van Zandvoort
- Department of Experimental Psychology and Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Peter J. van der Spek
- Department of Pathology, Erasmus Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Christine Troelstra
- Department of Pathology, Erasmus Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Carolien G. F. de Kovel
- Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bobby P. C. Koeleman
- Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bert van der Zwaag
- Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Eva H. Brilstra
- Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J. Peter H. Burbach
- UMCU Brain Center, Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands
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21
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Wan Y, Hong Z, Ma B, He X, Ma L, Wang M, Zhang Y. Identification of compound heterozygous variants in MSH4 as a novel genetic cause of diminished ovarian reserve. Reprod Biol Endocrinol 2023; 21:76. [PMID: 37620942 PMCID: PMC10464148 DOI: 10.1186/s12958-023-01127-0] [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: 05/30/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Diminished ovarian reserve (DOR) is a common cause of female infertility, with genetic factors being a significant contributor. However, due to high genetic heterogeneity, the etiology of DOR in many cases remains unknown. In this study, we analyzed the phenotype of a young woman with primary infertility and performed molecular genetic analysis to identify the genetic cause of her condition, thus providing important insights for genetic counseling and reproductive guidance. METHODS We collected the patient's basic information, clinical data, as well as diagnostic and therapeutic history and performed whole-exome sequencing on her peripheral blood. Candidate pathogenic variants were validated by Sanger sequencing in family members, and the pathogenicity of variants was analyzed using ACMG guidelines. We used bioinformatics tools to predict variant effects on splicing and protein function, and performed in vitro experiments including minigene assay and expression analysis to evaluate their functional effects on HEK293T. RESULTS We identified biallelic MSH4 variants, c.2374 A > G (p.Thr792Ala) and c.2222_2225delAAGA (p.Lys741Argfs*2) in the DOR patient. According to ACMG guidelines, the former was classified as likely pathogenic, while the latter was classified as pathogenic. The patient presented with poor oocyte quantity and quality, resulting in unsuccessful in vitro fertilization cycles. Bioinformatics and in vitro functional analysis showed that the c.2374 A > G variant altered the local conformation of the MutS_V domain without decreasing MSH4 protein expression, while the c.2222_2225delAAGA variant led to a reduction in MSH4 protein expression without impacting splicing. CONCLUSIONS In this study, we present evidence of biallelic variants in MSH4 as a potential cause of DOR. Our findings indicate a correlation between MSH4 variants and reduced oocyte quality, as well as abnormal morphology of the first polar body, thereby expanding the phenotypic spectrum associated with MSH4 variants. Furthermore, Our study emphasizes the importance of utilizing whole-exome sequencing and functional analysis in diagnosing genetic causes, as well as providing effective genetic counseling and reproductive guidance for DOR patients.
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Affiliation(s)
- Yingjing Wan
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China
| | - Zhidan Hong
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China
| | - Binyu Ma
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China
| | - Xuanyi He
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China
| | - Ling Ma
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China
| | - Mei Wang
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China.
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China.
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China.
| | - Yuanzhen Zhang
- Center for Reproductive Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, P.R. China.
- Clinical Medicine Research Center of Prenatal Diagnosis and Birth Health in Hubei Province, Wuhan, Hubei, P.R. China.
- Wuhan Clinical Research Center for Reproductive Science and Birth Health, Wuhan, Hubei, P.R. China.
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22
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Kumaran M, Devarajan B. eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease. Genet Med 2023; 25:100862. [PMID: 37092535 DOI: 10.1016/j.gim.2023.100862] [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: 07/06/2022] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/25/2023] Open
Abstract
PURPOSE Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model. METHODS We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure. RESULTS eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders. CONCLUSION eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.
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Affiliation(s)
- Manojkumar Kumaran
- Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, India; School of Chemical and Biotechnology, SASTRA (Deemed to be a university), Thanjavur, Tamil Nadu, India
| | - Bharanidharan Devarajan
- Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, India.
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23
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Dunham AS, Beltrao P, AlQuraishi M. High-throughput deep learning variant effect prediction with Sequence UNET. Genome Biol 2023; 24:110. [PMID: 37161576 PMCID: PMC10169183 DOI: 10.1186/s13059-023-02948-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/20/2023] [Indexed: 05/11/2023] Open
Abstract
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture. It achieves comparable pathogenicity prediction to recent methods. We demonstrate scalability by analysing 8.3B variants in 904,134 proteins detected through large-scale proteomics. Sequence UNET runs on modest hardware with a simple Python package.
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Affiliation(s)
- Alistair S Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1RQ, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
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Sobral PS, Luz VCC, Almeida JMGCF, Videira PA, Pereira F. Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors. Int J Mol Sci 2023; 24:ijms24065908. [PMID: 36982981 PMCID: PMC10054797 DOI: 10.3390/ijms24065908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.
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Affiliation(s)
- Patrícia S Sobral
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Vanessa C C Luz
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - João M G C F Almeida
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Paula A Videira
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Florbela Pereira
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
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Malhotra R, Javle V, Tanwar N, Gowda P, Varghese L, K A, Madhusudhan N, Jaiswal N, K. S. B, Chatterjee M, Prabhash K, Sreekanthreddy P, Rishi KD, Goswami HM, Veldore VH. An absolute approach to using whole exome DNA and RNA workflow for cancer biomarker testing. Front Oncol 2023; 13:1002792. [PMID: 36994199 PMCID: PMC10040847 DOI: 10.3389/fonc.2023.1002792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/24/2023] [Indexed: 03/16/2023] Open
Abstract
IntroductionThe concept of personalized medicine in cancer has emerged rapidly with the advancement of genome sequencing and the identification of clinically relevant variants that contribute to disease prognosis and facilitates targeted therapy options. In this study, we propose to validate a whole exome-based tumor molecular profiling for DNA and RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissue.MethodsThe study included 166 patients across 17 different cancer types. The scope of this study includes the identification of single-nucleotide variants (SNVs), insertions/deletions (INDELS), copy number alterations (CNAs), gene fusions, tumor mutational burden (TMB), and microsatellite instability (MSI). The assay yielded a mean read depth of 200×, with >80% of on-target reads and a mean uniformity of >90%. Clinical maturation of whole exome sequencing (WES) (DNA and RNA)- based assay was achieved by analytical and clinical validations for all the types of genomic alterations in multiple cancers. We here demonstrate a limit of detection (LOD) of 5% for SNVs and 10% for INDELS with 97.5% specificity, 100% sensitivity, and 100% reproducibility.ResultsThe results were >98% concordant with other orthogonal techniques and appeared to be more robust and comprehensive in detecting all the clinically relevant alterations. Our study demonstrates the clinical utility of the exome-based approach of comprehensive genomic profiling (CGP) for cancer patients at diagnosis and disease progression.DiscussionThe assay provides a consolidated picture of tumor heterogeneity and prognostic and predictive biomarkers, thus helping in precision oncology practice. The primary intended use of WES (DNA+RNA) assay would be for patients with rare cancers as well as for patients with unknown primary tumors, and this category constitutes nearly 20–30% of all cancers. The WES approach may also help us understand the clonal evolution during disease progression to precisely plan the treatment in advanced stage disease.
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Affiliation(s)
| | - Vyomesh Javle
- 4baseCare Onco Solutions Pvt. Ltd., Bangalore, India
| | | | - Pooja Gowda
- 4baseCare Onco Solutions Pvt. Ltd., Bangalore, India
| | - Linu Varghese
- 4baseCare Onco Solutions Pvt. Ltd., Bangalore, India
| | - Anju K
- 4baseCare Onco Solutions Pvt. Ltd., Bangalore, India
| | | | - Nupur Jaiswal
- 4baseCare Onco Solutions Pvt. Ltd., Bangalore, India
| | | | | | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Centre, Mumbai, India
| | | | | | | | - Vidya H. Veldore
- 4baseCare Onco Solutions Pvt. Ltd., Bangalore, India
- *Correspondence: Vidya H. Veldore,
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26
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Yu C, Deng XJ, Xu D. Gene mutations in comorbidity of epilepsy and arrhythmia. J Neurol 2023; 270:1229-1248. [PMID: 36376730 DOI: 10.1007/s00415-022-11430-2] [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: 07/31/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 11/16/2022]
Abstract
Epilepsy is one of the most common neurological disorders, and sudden unexpected death in epilepsy (SUDEP) is the most severe outcome of refractory epilepsy. Arrhythmia is one of the heterogeneous factors in the pathophysiological mechanism of SUDEP with a high incidence in patients with refractory epilepsy, increasing the risk of premature death. The gene co-expressed in the brain and heart is supposed to be the genetic basis between epilepsy and arrhythmia, among which the gene encoding ion channel contributes to the prevalence of "cardiocerebral channelopathy" theory. Nevertheless, this theory could only explain the molecular mechanism of comorbid arrhythmia in part of patients with epilepsy (PWE). Therefore, we summarized the mutant genes that can induce comorbidity of epilepsy and arrhythmia and the possible corresponding treatments. These variants involved the genes encoding sodium, potassium, calcium and HCN channels, as well as some non-ion channel coding genes such as CHD4, PKP2, FHF1, GNB5, and mitochondrial genes. The relationship between genotype and clinical phenotype was not simple linear. Indeed, genes co-expressed in the brain and heart could independently induce epilepsy and/or arrhythmia. Mutant genes in brain could affect cardiac rhythm through central or peripheral regulation, while in the heart it could also affect cerebral electrical activity by changing the hemodynamics or internal environment. Analysis of mutations in comorbidity of epilepsy and arrhythmia could refine and expand the theory of "cardiocerebral channelopathy" and provide new insights for risk stratification of premature death and corresponding precision therapy in PWE.
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Affiliation(s)
- Cheng Yu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China
| | - Xue-Jun Deng
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China
| | - Da Xu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China.
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27
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Cachau R, Shahsavari S, Cho SK. The in-silico evaluation of important GLUT9 residue for uric acid transport based on renal hypouricemia type 2. Chem Biol Interact 2023; 373:110378. [PMID: 36736875 PMCID: PMC10596759 DOI: 10.1016/j.cbi.2023.110378] [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: 06/27/2022] [Revised: 01/17/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023]
Abstract
Uric acid is the end product of purine metabolism. Uric acid transporters in the renal proximal tubule plays a key role in uric acid transport. Functional abnormalities in these transporters could lead to high or low levels of uric acid in the blood plasma, known as hyperuricemia and hypouricemia, respectively. GLUT9 has been reported as a key transporter for uric acid reuptake in renal proximal tubule. GLUT9 mutation is known as causal gene for renal hypouricemia due to defective uric acid uptake, with more severe cases resulting in urolithiasis and exercise induced acute kidney injury (EIAKI). However, the effect of mutation is not fully investigated and hard to predict the change of binding affinity. We comprehensively described the effect of GLUT9 mutation for uric acid transport using molecular dynamics and investigated the specific site for uric acid binding differences. R171C and R380W showed the significant disruption of the structure not affecting transport dynamics whereas L75R, G216R, N333S, and P412R showed the reduced affinity of the extracellular vestibular area towards urate. Interestingly, T125 M showed a significant increase in intracellular binding energy, associated with distorted geometries. We can use this classification to consider the effect mutations by comparing the transport profiles of mutants against those of chemical candidates for transport and providing new perspectives to urate lowering drug discovery using GLUT9.
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Affiliation(s)
- Raul Cachau
- Integrated Data Science Section, Research Technologies Branch, National Institute of Allergies and Infectious Diseases, Bethesda, MD, USA
| | | | - Sung Kweon Cho
- Center for Cancer Research, National Cancer Institute, Frederick, MD, USA; Department of Pharmacology Ajou University, School of Medicine, Suwon, South Korea.
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Structural Consequences of IRS-2 nsSNPs and Implication for Insulin Receptor Substrate-2 Protein Stability. Biochem Genet 2023; 61:69-86. [PMID: 35727487 DOI: 10.1007/s10528-022-10247-y] [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: 11/29/2021] [Accepted: 06/07/2022] [Indexed: 01/24/2023]
Abstract
Single-Nucleotide Polymorphisms (SNPs) are common genetic variations implicated in human diseases. The non-synonymous SNPs (nsSNPs) affect the proteins' structures and their molecular interactions with other interacting proteins during the accomplishment of biochemical processes. This ultimately causes proteins functional perturbation and disease phenotypes. The Insulin receptor substrate-2 (IRS-2) protein promotes glucose absorption and participates in the biological regulation of glucose metabolism and energy production. Several IRS-2 SNPs are reported in association with type 2 diabetes and obesity in human populations. However, there are no comprehensive reports about the protein structural consequences of these nsSNPs. Keeping in view the pathophysiological consequences of the IRS-2 nsSNPs, we designed the current study to understand their possible structural impact on coding protein. The prioritized list of the deleterious IRS-2 nsSNPs was acquired from multiple bioinformatics resources, including VEP (SIFT, PolyPhen, and Condel), PROVEAN, SNPs&GO, PMut, and SNAP2. The protein structure stability assessment of these nsSNPs was performed by MuPro and I-Mutant-3.0 servers via structural modeling approaches. The atomic-level structural and molecular dynamics (MD) impact of these nsSNPs were examined using GROMACS 2019.2 software package. The analyses initially predicted 8 high-risk nsSNPs located in the highly conserved regions of IRS-2. The MD simulation analysis eventually prioritized the N232Y, R218C, and R104H nsSNPs that predicted to significantly compromise the structure stability and may affect the biological function of IRS-2. These nsSNPs are predicted as high-risk candidates for diabetes and obesity. The validation of protein structural impact of these shortlisted nsSNPs may provide biochemical insight into the IRS-2-mediated type-2 diabetes.
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29
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Gonzalez B, Tare A, Ryu S, Johnson SC, Atzmon G, Barzilai N, Kaeberlein M, Suh Y. High-throughput sequencing analysis of nuclear-encoded mitochondrial genes reveals a genetic signature of human longevity. GeroScience 2023; 45:311-330. [PMID: 35948858 PMCID: PMC9886794 DOI: 10.1007/s11357-022-00634-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/28/2022] [Indexed: 02/03/2023] Open
Abstract
Mitochondrial dysfunction is a well-known contributor to aging and age-related diseases. The precise mechanisms through which mitochondria impact human lifespan, however, remain unclear. We hypothesize that humans with exceptional longevity harbor rare variants in nuclear-encoded mitochondrial genes (mitonuclear genes) that confer resistance against age-related mitochondrial dysfunction. Here we report an integrated functional genomics study to identify rare functional variants in ~ 660 mitonuclear candidate genes discovered by target capture sequencing analysis of 496 centenarians and 572 controls of Ashkenazi Jewish descent. We identify and prioritize longevity-associated variants, genes, and mitochondrial pathways that are enriched with rare variants. We provide functional gene variants such as those in MTOR (Y2396Lfs*29), CPS1 (T1406N), and MFN2 (G548*) as well as LRPPRC (S1378G) that is predicted to affect mitochondrial translation. Taken together, our results suggest a functional role for specific mitonuclear genes and pathways in human longevity.
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Affiliation(s)
- Brenda Gonzalez
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Archana Tare
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Seungjin Ryu
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Pharmacology, College of Medicine, Hallym University, Chuncheon, Gangwon, 24252, Republic of Korea
| | - Simon C Johnson
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Gil Atzmon
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Biology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Nir Barzilai
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Matt Kaeberlein
- Department of Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Yousin Suh
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Departments of Obstetrics and Gynecology, and Genetics and Development, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
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Ballinger ML, Pattnaik S, Mundra PA, Zaheed M, Rath E, Priestley P, Baber J, Ray-Coquard I, Isambert N, Causeret S, van der Graaf WTA, Puri A, Duffaud F, Le Cesne A, Seddon B, Chandrasekar C, Schiffman JD, Brohl AS, James PA, Kurtz JE, Penel N, Myklebost O, Meza-Zepeda LA, Pickett H, Kansara M, Waddell N, Kondrashova O, Pearson JV, Barbour AP, Li S, Nguyen TL, Fatkin D, Graham RM, Giannoulatou E, Green MJ, Kaplan W, Ravishankar S, Copty J, Powell JE, Cuppen E, van Eijk K, Veldink J, Ahn JH, Kim JE, Randall RL, Tucker K, Judson I, Sarin R, Ludwig T, Genin E, Deleuze JF, Haber M, Marshall G, Cairns MJ, Blay JY, Thomas DM, Tattersall M, Neuhaus S, Lewis C, Tucker K, Carey-Smith R, Wood D, Porceddu S, Dickinson I, Thorne H, James P, Ray-Coquard I, Blay JY, Cassier P, Le Cesne A, Duffaud F, Penel N, Isambert N, Kurtz JE, Puri A, Sarin R, Ahn JH, Kim JE, Ward I, Judson I, van der Graaf W, Seddon B, Chandrasekar C, Rickar R, Hennig I, Schiffman J, Randall RL, Silvestri A, Zaratzian A, Tayao M, Walwyn K, Niedermayr E, Mang D, Clark R, Thorpe T, MacDonald J, Riddell K, Mar J, Fennelly V, Wicht A, Zielony B, Galligan E, Glavich G, Stoeckert J, Williams L, Djandjgava L, Buettner I, Osinki C, Stephens S, Rogasik M, Bouclier L, Girodet M, Charreton A, Fayet Y, Crasto S, Sandupatla B, Yoon Y, Je N, Thompson L, Fowler T, Johnson B, Petrikova G, Hambridge T, Hutchins A, Bottero D, Scanlon D, Stokes-Denson J, Génin E, Campion D, Dartigues JF, Deleuze JF, Lambert JC, Redon R, Ludwig T, Grenier-Boley B, Letort S, Lindenbaum P, Meyer V, Quenez O, Dina C, Bellenguez C, Le Clézio CC, Giemza J, Chatel S, Férec C, Le Marec H, Letenneur L, Nicolas G, Rouault K. Heritable defects in telomere and mitotic function selectively predispose to sarcomas. Science 2023; 379:253-260. [PMID: 36656928 DOI: 10.1126/science.abj4784] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 11/16/2022] [Indexed: 01/20/2023]
Abstract
Cancer genetics has to date focused on epithelial malignancies, identifying multiple histotype-specific pathways underlying cancer susceptibility. Sarcomas are rare malignancies predominantly derived from embryonic mesoderm. To identify pathways specific to mesenchymal cancers, we performed whole-genome germline sequencing on 1644 sporadic cases and 3205 matched healthy elderly controls. Using an extreme phenotype design, a combined rare-variant burden and ontologic analysis identified two sarcoma-specific pathways involved in mitotic and telomere functions. Variants in centrosome genes are linked to malignant peripheral nerve sheath and gastrointestinal stromal tumors, whereas heritable defects in the shelterin complex link susceptibility to sarcoma, melanoma, and thyroid cancers. These studies indicate a specific role for heritable defects in mitotic and telomere biology in risk of sarcomas.
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Affiliation(s)
- Mandy L Ballinger
- Garvan Institute of Medical Research, Sydney 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
| | - Swetansu Pattnaik
- Garvan Institute of Medical Research, Sydney 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
| | - Piyushkumar A Mundra
- Garvan Institute of Medical Research, Sydney 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
| | - Milita Zaheed
- Hereditary Cancer Centre, Prince of Wales Hospital, Sydney 2031, Australia
| | - Emma Rath
- Garvan Institute of Medical Research, Sydney 2010, Australia
| | - Peter Priestley
- Hartwig Medical Foundation, 1098 XH Amsterdam, Netherlands
- Hartwig Medical Foundation Australia, Sydney 2000, Australia
| | - Jonathan Baber
- Hartwig Medical Foundation, 1098 XH Amsterdam, Netherlands
- Hartwig Medical Foundation Australia, Sydney 2000, Australia
| | - Isabelle Ray-Coquard
- Department of Adult Medical Oncology, Centre Leon Berard, University Claude Bernard, 69373 Lyon, France
| | | | | | | | - Ajay Puri
- Department of Orthopedic Oncology, Tata Memorial Hospital, Mumbai, Maharashtra 400012, India
| | | | | | - Beatrice Seddon
- Sarcoma Unit, University College Hospital, London NW1 2BU, UK
| | | | - Joshua D Schiffman
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Andrew S Brohl
- Sarcoma Department, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Paul A James
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Melbourne 3000, Australia
| | | | | | - Ola Myklebost
- Western Norway Familial Cancer Centre, Haukeland University Hospital, 5021 Bergen, Norway
- Department of Clinical Science, University of Bergen, 5007 Bergen, Norway
- Institute for Cancer Research, Oslo University Hospital, N-0424 Oslo, Norway
| | | | - Hilda Pickett
- Children's Medical Research Institute, The University of Sydney, Westmead 2145, Australia
| | - Maya Kansara
- Garvan Institute of Medical Research, Sydney 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
| | - Nicola Waddell
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - Olga Kondrashova
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - John V Pearson
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - Andrew P Barbour
- Faculty of Medicine. The University of Queensland, Brisbane 4072, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne 3010, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton 3800, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3051, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne 3010, Australia
| | - Diane Fatkin
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
- Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst 2010, Australia
- Cardiology Department, St Vincent's Hospital, Sydney 2010, Australia
| | - Robert M Graham
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
- Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst 2010, Australia
| | - Eleni Giannoulatou
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
- Computational Genomics Division, Victor Chang Cardiac Research Institute, Sydney 2010, Australia
| | - Melissa J Green
- School of Psychiatry, University of New South Wales, Sydney 2052, Australia
- Neuorscience Research Australia, Sydney 2031, Australia
| | - Warren Kaplan
- Garvan Institute of Medical Research, Sydney 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
| | | | - Joseph Copty
- Garvan Institute of Medical Research, Sydney 2010, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Sydney 2010, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney 2052, Australia
| | - Edwin Cuppen
- Hartwig Medical Foundation, 1098 XH Amsterdam, Netherlands
| | - Kristel van Eijk
- Department of Neurology, University Medical Centre Utrecht Brain Center, Utrecht University, 3584 CX Utrecht, Netherlands
| | - Jan Veldink
- Department of Neurology, University Medical Centre Utrecht Brain Center, Utrecht University, 3584 CX Utrecht, Netherlands
| | - Jin-Hee Ahn
- Department of Oncology, Asan Medical Centre, Seoul 05505, South Korea
| | - Jeong Eun Kim
- Department of Oncology, Asan Medical Centre, Seoul 05505, South Korea
| | - R Lor Randall
- Department of Orthopaedic Surgery, University of California, Davis Health, Sacramento, CA 95817, USA
| | - Kathy Tucker
- Hereditary Cancer Centre, Prince of Wales Hospital, Sydney 2031, Australia
| | - Ian Judson
- Sarcoma Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Rajiv Sarin
- Cancer Genetics Unit, ACTREC, Tata Memorial Centre, Mumbai, Maharashtra 410210, India
| | - Thomas Ludwig
- Université de Brest, Inserm, EFS, UMR 1078, GGB, CHU de Brest, 29200 Brest, France
| | - Emmanuelle Genin
- Université de Brest, Inserm, EFS, UMR 1078, GGB, CHU de Brest, 29200 Brest, France
| | - Jean-Francois Deleuze
- Centre National de Recherche en Génomique Humaine, Institut de Génomique, 91057 Evry, France
| | - Michelle Haber
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Kensington 2033, Australia
| | - Glenn Marshall
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Kensington 2033, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick 2031, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan 2308, Australia
- Centre for Brain and Mental Health Research, The Hunter Medical Research Institute, Newcastle 2305, Australia
| | - Jean-Yves Blay
- Department of Adult Medical Oncology, Centre Leon Berard, University Claude Bernard, 69373 Lyon, France
| | - David M Thomas
- Garvan Institute of Medical Research, Sydney 2010, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney 2010, Australia
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Nagata Y, Watanabe R, Eichhorn C, Ohno S, Aiba T, Ishikawa T, Nakano Y, Aizawa Y, Hayashi K, Murakoshi N, Nakajima T, Yagihara N, Mishima H, Sudo T, Higuchi C, Takahashi A, Sekine A, Makiyama T, Tanaka Y, Watanabe A, Tachibana M, Morita H, Yoshiura KI, Tsunoda T, Watanabe H, Kurabayashi M, Nogami A, Kihara Y, Horie M, Shimizu W, Makita N, Tanaka T. Targeted deep sequencing analyses of long QT syndrome in a Japanese population. PLoS One 2022; 17:e0277242. [PMID: 36480497 PMCID: PMC9731492 DOI: 10.1371/journal.pone.0277242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/22/2022] [Indexed: 12/13/2022] Open
Abstract
Long QT syndrome (LQTS) is one of the most common inherited arrhythmias and multiple genes have been reported as causative. Presently, genetic diagnosis for LQTS patients is becoming widespread and contributing to implementation of therapies. However, causative genetic mutations cannot be detected in about 20% of patients. To elucidate additional genetic mutations in LQTS, we performed deep-sequencing of previously reported 15 causative and 85 candidate genes for this disorder in 556 Japanese LQTS patients. We performed in-silico filtering of the sequencing data and found 48 novel variants in 33 genes of 53 cases. These variants were predicted to be damaging to coding proteins or to alter the binding affinity of several transcription factors. Notably, we found that most of the LQTS-related variants in the RYR2 gene were in the large cytoplasmic domain of the N-terminus side. They might be useful for screening of LQTS patients who had no known genetic factors. In addition, when the mechanisms of these variants in the development of LQTS are revealed, it will be useful for early diagnosis, risk stratification, and selection of treatment.
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Affiliation(s)
- Yuki Nagata
- Bioresourse Research Center, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Ryo Watanabe
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Christian Eichhorn
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
- Private University of the Principality of Liechtenstein, Triesen, Liechtenstein
| | - Seiko Ohno
- Department of Bioscience and Genetics, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Takeshi Aiba
- Devision of Arrhythmia, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Taisuke Ishikawa
- Omics Research Center, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yukiko Nakano
- Department of Cardiovascular Medicine, Hiroshima University, Hiroshima, Japan
| | - Yoshiyasu Aizawa
- Department of Cardiology, International University of Health and Welfare Narita Hospital, Narita, Japan
| | - Kenshi Hayashi
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Nobuyuki Murakoshi
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Tadashi Nakajima
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Nobue Yagihara
- Department of Cardiovascular Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hiroyuki Mishima
- Department of Human Genetics, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan
| | - Takeaki Sudo
- Institute of Education, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Chihiro Higuchi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Japan
| | - Atsushi Takahashi
- Department of Genomic Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Akihiro Sekine
- Department of Infection and Host Defense, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Takeru Makiyama
- Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshihiro Tanaka
- Center for Arrhythmia Research, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Atsuyuki Watanabe
- Department of Cardiology, National Hospital Organization Okayama Medical Center, Okayama, Japan
| | - Motomi Tachibana
- Department of Cardiology, Sakakibara heart institute of Okayama, Okayama, Japan
| | - Hiroshi Morita
- Department of Cardiovascular Therapeutics, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Koh-ichiro Yoshiura
- Department of Human Genetics, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan
- Division of Advanced Preventive Medical Sciences and Leading Medical Research Core Unit, Nagasaki Univerisity Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Hiroshi Watanabe
- Department of Cardiovascular Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masahiko Kurabayashi
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Akihiko Nogami
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Yasuki Kihara
- Department of Cardiovascular Medicine, Hiroshima University, Hiroshima, Japan
| | - Minoru Horie
- Department of Cardiovascular Medicine, Shiga University of Medical Science, Otsu, Japan
| | - Wataru Shimizu
- Department of Cardiovascular Medicine, Nippon Medical School, Tokyo, Japan
| | - Naomasa Makita
- Omics Research Center, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Toshihiro Tanaka
- Bioresourse Research Center, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
- * E-mail:
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Pharmacogenetics of CYP2A6, CYP2B6, and UGT2B7 in the Context of HIV Treatments in African Populations. J Pers Med 2022; 12:jpm12122013. [PMID: 36556234 PMCID: PMC9784060 DOI: 10.3390/jpm12122013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 12/07/2022] Open
Abstract
OBJECTIVES This study focuses on identifying variations in selected CYP genes related to treatment responses in patients with HIV in African populations by investigating variant characteristics and effects in African cohorts. DESIGN Cytochrome P450 (CYP) 2A6, 2B6, and Uridine 5'-diphospho-glucuronosyltransferase (UGT) 2B7 allele frequencies were studied using public-domain datasets obtained from the 1000 Genomes Phase 3 project, the African Genome Variation Project (AGVP), and the South African Human Genome Programme (SAHGP). METHODS Variant annotations were performed using self-identified ethnicities to conduct allele frequency analysis in a population-stratification-sensitive manner. The NCBI DB-SNP database was used to identify documented variants and standard frequencies, and the E! Ensembl Variant Effect Predictor tool was used to perform the prediction of possible deleterious variants. RESULTS A total of 4468 variants were identified across 3676 individuals following pre-filtering. Seventy-one variants were identified at an allelic frequency (1% or more in at least one population), which were predicted to be linked to existing disease associations and, in some cases, linked to drug metabolisms. This list was further studied to identify 23 alleles with disease considerations found at significantly different frequencies in one or more populations. CONCLUSIONS This study describes allele frequencies observed in African populations at significantly different frequencies relative to at least one other reference population and identifies a subset of variants of clinical interest. Despite the inclusion of mixed sequence coverage datasets, the variants identified pose notable avenues for future inquiries. A subset of variants of clinical interest with statistically significant inter-population frequency differences was identified for further inspection, which provides evidence of an African population-specific variant frequency profile. This study highlights the need for additional research and African genetics data given the presence of this unique frequency profile to better facilitate the genetic pre-screening of patients as a standard of practice in HIV care, particularly on the African continent where HIV is highly prevalent.
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Jawad Ul Hasnain M, Amin F, Ghani A, Ahmad S, Rahman Z, Aslam T, Pervez MT. Structural and Functional Impact of Damaging Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) on Human VPS35 Protein Using Computational Approaches. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3715-3724. [PMID: 34613918 DOI: 10.1109/tcbb.2021.3118054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Parkinson's disease is the second most common progressive neurodegenerative movement disorder. Mutations in retromer complex subunit and VPS35 represent the second most common cause of late-onset familial Parkinson's disease. The mutation in VPS35 can disrupt the normal protein functions resulting in Parkinson's disease. The aim of this study was the identification of deleterious missense Single Nucleotide Polymorphisms (nsSNPs) and their structural and functional impact on the VPS35 protein. In this study, several insilico tools were used to identify deleterious and disease-associated nsSNPs. 3D structure of VPS35 protein was constructed through MODELLER 9.2, normalized using FOLDX, and evaluated through RAMPAGE and ERRAT whereas, FOLDX was used for mutagenesis. 25 ligands were obtained from literature and docked using PyRx 0.8 software. Based on the binding affinity, five ligands i.e., PG4, MSE, GOL, EDO, and CAF were further analyzed. Molecular Dynamic simulation analysis was performed using GROMACS 5.1.4, where temperature, pressure, density, RMSD, RMSF, Rg, and SASA graphs were analyzed. The results showed that the mutations Y67H, R524W, and D620N had a structural and functional impact on the VPS35 protein. The current findings will help in appropriate drug design against the disease caused by these mutations in a large population using in-vitro study.
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Tirtakusuma R, Szoltysek K, Milne P, Grinev VV, Ptasinska A, Chin PS, Meyer C, Nakjang S, Hehir-Kwa JY, Williamson D, Cauchy P, Keane P, Assi SA, Ashtiani M, Kellaway SG, Imperato MR, Vogiatzi F, Schweighart EK, Lin S, Wunderlich M, Stutterheim J, Komkov A, Zerkalenkova E, Evans P, McNeill H, Elder A, Martinez-Soria N, Fordham SE, Shi Y, Russell LJ, Pal D, Smith A, Kingsbury Z, Becq J, Eckert C, Haas OA, Carey P, Bailey S, Skinner R, Miakova N, Collin M, Bigley V, Haniffa M, Marschalek R, Harrison CJ, Cargo CA, Schewe D, Olshanskaya Y, Thirman MJ, Cockerill PN, Mulloy JC, Blair HJ, Vormoor J, Allan JM, Bonifer C, Heidenreich O, Bomken S. Epigenetic regulator genes direct lineage switching in MLL/AF4 leukemia. Blood 2022; 140:1875-1890. [PMID: 35839448 PMCID: PMC10488321 DOI: 10.1182/blood.2021015036] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 06/30/2022] [Indexed: 11/20/2022] Open
Abstract
The fusion gene MLL/AF4 defines a high-risk subtype of pro-B acute lymphoblastic leukemia. Relapse can be associated with a lineage switch from acute lymphoblastic to acute myeloid leukemia, resulting in poor clinical outcomes caused by resistance to chemotherapies and immunotherapies. In this study, the myeloid relapses shared oncogene fusion breakpoints with their matched lymphoid presentations and originated from various differentiation stages from immature progenitors through to committed B-cell precursors. Lineage switching is linked to substantial changes in chromatin accessibility and rewiring of transcriptional programs, including alternative splicing. These findings indicate that the execution and maintenance of lymphoid lineage differentiation is impaired. The relapsed myeloid phenotype is recurrently associated with the altered expression, splicing, or mutation of chromatin modifiers, including CHD4 coding for the ATPase/helicase of the nucleosome remodelling and deacetylation complex. Perturbation of CHD4 alone or in combination with other mutated epigenetic modifiers induces myeloid gene expression in MLL/AF4+ cell models, indicating that lineage switching in MLL/AF4 leukemia is driven and maintained by disrupted epigenetic regulation.
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Affiliation(s)
- Ricky Tirtakusuma
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Katarzyna Szoltysek
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Maria Sklodowska-Curie Institute, Oncology Center, Gliwice Branch, Gliwice, Poland
| | - Paul Milne
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Vasily V. Grinev
- Department of Genetics, the Faculty of Biology, Belarusian State University, Minsk, Republic of Belarus
| | - Anetta Ptasinska
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Paulynn S. Chin
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Claus Meyer
- Institute of Pharmaceutical Biology/DCAL, Goethe-University, Frankfurt/Main, Germany
| | - Sirintra Nakjang
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Daniel Williamson
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Pierre Cauchy
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Peter Keane
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Salam A. Assi
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Minoo Ashtiani
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Sophie G. Kellaway
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Maria R. Imperato
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Fotini Vogiatzi
- ALL-BFM Study Group, Pediatric Hematology/Oncology, Christian Albrechts University Kiel and University Hospital Schleswig-Holstein, Campus Kiel, Germany
| | | | - Shan Lin
- Experimental Hematology and Cancer Biology, Cancer and Blood Disease Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Mark Wunderlich
- Experimental Hematology and Cancer Biology, Cancer and Blood Disease Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | | | - Alexander Komkov
- Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology, and Immunology, Moscow, Russia
| | - Elena Zerkalenkova
- Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology, and Immunology, Moscow, Russia
| | - Paul Evans
- Haematological Malignancy Diagnostic Service, St James’s University Hospital, Leeds, United Kingdom
| | - Hesta McNeill
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alex Elder
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Natalia Martinez-Soria
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sarah E. Fordham
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yuzhe Shi
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Lisa J. Russell
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Deepali Pal
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alex Smith
- Epidemiology and Cancer Statistics Group, University of York, York, United Kingdom
| | | | - Jennifer Becq
- Illumina Cambridge Ltd., Great Abington, United Kingdom
| | - Cornelia Eckert
- Department of Pediatric Oncology/Hematology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Oskar A. Haas
- St Anna Children’s Cancer Research Institute (CCRI), Vienna, Austria
| | - Peter Carey
- Department of Paediatric Haematology and Oncology, The Great North Children’s Hospital, Newcastle upon Tyne, United Kingdom
| | - Simon Bailey
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Paediatric Haematology and Oncology, The Great North Children’s Hospital, Newcastle upon Tyne, United Kingdom
| | - Roderick Skinner
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Paediatric Haematology and Oncology, The Great North Children’s Hospital, Newcastle upon Tyne, United Kingdom
| | - Natalia Miakova
- Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology, and Immunology, Moscow, Russia
| | - Matthew Collin
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Venetia Bigley
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Muzlifah Haniffa
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
- Department of Dermatology and Newcastle National Institute of Health Research (NIHR), Newcastle Biomedical Research Centre, Newcastle Hospitals National Health Service (NHS) Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Rolf Marschalek
- Institute of Pharmaceutical Biology/DCAL, Goethe-University, Frankfurt/Main, Germany
| | - Christine J. Harrison
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Catherine A. Cargo
- Haematological Malignancy Diagnostic Service, St James’s University Hospital, Leeds, United Kingdom
| | - Denis Schewe
- Department of Pediatrics, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Yulia Olshanskaya
- Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology, and Immunology, Moscow, Russia
| | - Michael J. Thirman
- Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Peter N. Cockerill
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - James C. Mulloy
- Experimental Hematology and Cancer Biology, Cancer and Blood Disease Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Helen J. Blair
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Josef Vormoor
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - James M. Allan
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Constanze Bonifer
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Olaf Heidenreich
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Simon Bomken
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Paediatric Haematology and Oncology, The Great North Children’s Hospital, Newcastle upon Tyne, United Kingdom
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35
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Aldosary M, Alsagob M, AlQudairy H, González-Álvarez AC, Arold ST, Dababo MA, Alharbi OA, Almass R, AlBakheet A, AlSarar D, Qari A, Al-Ansari MM, Oláhová M, Al-Shahrani SA, AlSayed M, Colak D, Taylor RW, AlOwain M, Kaya N. A Novel Homozygous Founder Variant of RTN4IP1 in Two Consanguineous Saudi Families. Cells 2022; 11:3154. [PMID: 36231115 PMCID: PMC9563936 DOI: 10.3390/cells11193154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/26/2022] [Accepted: 10/01/2022] [Indexed: 11/25/2022] Open
Abstract
The genetic architecture of mitochondrial disease continues to expand and currently exceeds more than 350 disease-causing genes. Bi-allelic variants in RTN4IP1, also known as Optic Atrophy-10 (OPA10), lead to early-onset recessive optic neuropathy, atrophy, and encephalopathy in the afflicted patients. The gene is known to encode a mitochondrial ubiquinol oxidoreductase that interacts with reticulon 4 and is thought to be a mitochondrial antioxidant NADPH oxidoreductase. Here, we describe two unrelated consanguineous families from the northern region of Saudi Arabia harboring a missense variant (RTN4IP1:NM_032730.5; c.475G
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Affiliation(s)
- Mazhor Aldosary
- Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Maysoon Alsagob
- Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
- Center of Excellence for Biomedicine, Joint Centers for Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Hanan AlQudairy
- Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Ana C. González-Álvarez
- Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Stefan T. Arold
- Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
- Centre de Biologie Structurale (CBS), INSERM, CNRS, Université de Montpellier, F-34090 Montpellier, France
| | - Mohammad Anas Dababo
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Omar A. Alharbi
- Radiology Department, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Rawan Almass
- Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - AlBandary AlBakheet
- Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Dalia AlSarar
- Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Alya Qari
- Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Mysoon M. Al-Ansari
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
- Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Monika Oláhová
- Welcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Saif A. Al-Shahrani
- Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Moeenaldeen AlSayed
- Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
- College of Medicine, Alfaisal University, P.O. Box 50927, Riyadh 11533, Saudi Arabia
| | - Dilek Colak
- Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Robert W. Taylor
- Welcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- NHS Highly Specialized Service for Rare Mitochondrial Disorders, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK
| | - Mohammed AlOwain
- Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Namik Kaya
- Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
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36
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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: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [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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37
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Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022; 13:981005. [PMID: 36246661 PMCID: PMC9559863 DOI: 10.3389/fgene.2022.981005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Affiliation(s)
- Ye Liu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Philip C. N. Chiu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Dandan Cao
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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Barbosa P, Ribeiro M, Carmo-Fonseca M, Fonseca A. Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants. Front Cardiovasc Med 2022; 9:975478. [PMID: 36061567 PMCID: PMC9433717 DOI: 10.3389/fcvm.2022.975478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.
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Affiliation(s)
- Pedro Barbosa
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Marta Ribeiro
- Department of Bioengineering and iBB-Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Maria Carmo-Fonseca
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Alcides Fonseca
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
- GenoMed - Diagnósticos de Medicina Molecular, Lisboa, Portugal
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39
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Mahmood MS, Afzal M, Batool H, Saif A, Aqdas T, Ashraf NM, Saleem M. Screening of Pathogenic Missense Single Nucleotide Variants From LHPP Gene Associated With the Hepatocellular Carcinoma: An In silico Approach. Bioinform Biol Insights 2022; 16:11779322221115547. [PMID: 35966807 PMCID: PMC9373111 DOI: 10.1177/11779322221115547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/11/2022] [Indexed: 11/15/2022] Open
Abstract
LHPP gene encodes a phospholysine phosphohistidine inorganic pyrophosphate phosphatase, which functions as a tumor-suppressor protein. The tumor suppression by this protein has been confirmed in various cancers, including hepatocellular carcinoma (HCC). LHPP downregulation promotes cell growth and proliferation by modulating the PI3K/AKT signaling pathway. This study identifies potentially deleterious missense single nucleotide variants (SNVs) associated with the LHPP gene using multiple computational tools based on different algorithms. A total of 4 destabilizing mutants are identified as L22P, I212T, G227R, and G236R, from the conserved region of the phosphatase. The 3-dimensional (3D) modeling and structural comparison of variants with the native protein reveals significant structural and conformational variations after mutations, suggesting disruption in the function of phospholysine phosphohistidine inorganic pyrophosphate phosphatase. The identified mutations might, therefore, participate in the cause of HCC.
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Affiliation(s)
- Malik Siddique Mahmood
- School of Biochemistry & Biotechnology, University of the Punjab, Lahore, Pakistan.,Department of Biochemistry, NUR International University, Lahore, Pakistan
| | - Maryam Afzal
- School of Biochemistry & Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Hina Batool
- Department of Life Sciences, University of Management and Technology, Lahore, Pakistan
| | - Amara Saif
- Department of Life Sciences, University of Management and Technology, Lahore, Pakistan
| | - Tahreem Aqdas
- School of Biochemistry & Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Naeem Mahmood Ashraf
- Department of Biochemistry & Biotechnology, University of Gujrat, Gujrat, Pakistan
| | - Mahjabeen Saleem
- School of Biochemistry & Biotechnology, University of the Punjab, Lahore, Pakistan
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40
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Dash R, Munni YA, Mitra S, Choi HJ, Jahan SI, Chowdhury A, Jang TJ, Moon IS. Dynamic insights into the effects of nonsynonymous polymorphisms (nsSNPs) on loss of TREM2 function. Sci Rep 2022; 12:9378. [PMID: 35672339 PMCID: PMC9174165 DOI: 10.1038/s41598-022-13120-5] [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: 12/03/2021] [Accepted: 05/16/2022] [Indexed: 11/09/2022] Open
Abstract
Single nucleotide variations in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) are associated with many neurodegenerative diseases, including Nasu-Hakola disease (NHD), frontotemporal dementia (FTD), and late-onset Alzheimer's disease because they disrupt ligand binding to the extracellular domain of TREM2. However, the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) in TREM2 on disease progression remain unknown. In this study, we identified several high-risk nsSNPs in the TREM2 gene using various deleterious SNP predicting algorithms and analyzed their destabilizing effects on the ligand recognizing region of the TREM2 immunoglobulin (Ig) domain by molecular dynamics (MD) simulation. Cumulative prediction by all tools employed suggested the three most deleterious nsSNPs involved in loss of TREM2 function are rs549402254 (W50S), rs749358844 (R52C), and rs1409131974 (D104G). MD simulation showed that these three variants cause substantial structural alterations and conformational remodeling of the apical loops of the TREM2 Ig domain, which is responsible for ligand recognition. Detailed analysis revealed that these variants substantially increased distances between apical loops and induced conformation remodeling by changing inter-loop nonbonded contacts. Moreover, all nsSNPs changed the electrostatic potentials near the putative ligand-interacting region (PLIR), which suggested they might reduce specificity or loss of binding affinity for TREM2 ligands. Overall, this study identifies three potential high-risk nsSNPs in the TREM2 gene. We propose further studies on the molecular mechanisms responsible for loss of TREM2 function and the associations between TREM2 nsSNPs and neurodegenerative diseases.
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Affiliation(s)
- Raju Dash
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, 38066, Republic of Korea
| | - Yeasmin Akter Munni
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, 38066, Republic of Korea
| | - Sarmistha Mitra
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, 38066, Republic of Korea
| | - Ho Jin Choi
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, 38066, Republic of Korea
| | - Sultana Israt Jahan
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - Apusi Chowdhury
- Department of Pharmaceutical Science, North-South University, Dhaka, 1229, Bangladesh
| | - Tae Jung Jang
- Department of Pathology, Dongguk University College of Medicine, Gyeongju, 38066, Republic of Korea
| | - Il Soo Moon
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, 38066, Republic of Korea.
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41
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Livesey BJ, Marsh JA. Interpreting protein variant effects with computational predictors and deep mutational scanning. Dis Model Mech 2022; 15:275742. [PMID: 35736673 PMCID: PMC9235876 DOI: 10.1242/dmm.049510] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.
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Affiliation(s)
- Benjamin J Livesey
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
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42
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Paiva VDA, Gomes IDS, Monteiro CR, Mendonça MV, Martins PM, Santana CA, Gonçalves-Almeida V, Izidoro SC, Melo-Minardi RCD, Silveira SDA. Protein structural bioinformatics: An overview. Comput Biol Med 2022; 147:105695. [DOI: 10.1016/j.compbiomed.2022.105695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 11/27/2022]
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43
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Zhang C, Verma A, Feng Y, Melo MCR, McQuillan M, Hansen M, Lucas A, Park J, Ranciaro A, Thompson S, Rubel MA, Campbell MC, Beggs W, Hirbo J, Wata Mpoloka S, George Mokone G, Nyambo T, Wolde Meskel D, Belay G, Fokunang C, Njamnshi AK, Omar SA, Williams SM, Rader DJ, Ritchie MD, de la Fuente-Nunez C, Sirugo G, Tishkoff SA. Impact of natural selection on global patterns of genetic variation and association with clinical phenotypes at genes involved in SARS-CoV-2 infection. Proc Natl Acad Sci U S A 2022; 119:e2123000119. [PMID: 35580180 PMCID: PMC9173769 DOI: 10.1073/pnas.2123000119] [Citation(s) in RCA: 4] [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: 12/22/2021] [Accepted: 03/29/2022] [Indexed: 01/09/2023] Open
Abstract
Human genomic diversity has been shaped by both ancient and ongoing challenges from viruses. The current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a devastating impact on population health. However, genetic diversity and evolutionary forces impacting host genes related to SARS-CoV-2 infection are not well understood. We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (angiotensin converting enzyme 2 [ACE2], transmembrane protease serine 2 [TMPRSS2], dipeptidyl peptidase 4 [DPP4], and lymphocyte antigen 6 complex locus E [LY6E]). We analyzed data from 2,012 ethnically diverse Africans and 15,977 individuals of European and African ancestry with electronic health records and integrated with global data from the 1000 Genomes Project. At ACE2, we identified 41 nonsynonymous variants that were rare in most populations, several of which impact protein function. However, three nonsynonymous variants (rs138390800, rs147311723, and rs145437639) were common among central African hunter-gatherers from Cameroon (minor allele frequency 0.083 to 0.164) and are on haplotypes that exhibit signatures of positive selection. We identify signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage compared with the chimpanzee genome. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19. Our study provides insights into global variation at host genes related to SARS-CoV-2 infection, which have been shaped by natural selection in some populations, possibly due to prior viral infections.
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Affiliation(s)
- Chao Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Yuanqing Feng
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Marcelo C. R. Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael McQuillan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Matthew Hansen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Joseph Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Alessia Ranciaro
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Simon Thompson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Meagan A. Rubel
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael C. Campbell
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089
| | - William Beggs
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Jibril Hirbo
- Department of Medicine, Vanderbilt University, Nashville, TN 37232
| | | | | | | | - Thomas Nyambo
- Department of Biochemistry, Kampala International University in Tanzania, Dar es Salaam, Tanzania
| | - Dawit Wolde Meskel
- Department of Microbial Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Gurja Belay
- Department of Microbial Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Charles Fokunang
- Department of Pharmacotoxicology and Pharmacokinetics, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon
| | - Alfred K. Njamnshi
- Department of Neurology, Central Hospital Yaoundé, Yaoundé, Cameroon
- Brain Research Africa Initiative, Neuroscience Laboratory, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon
| | - Sabah A. Omar
- Center for Biotechnology Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - Scott M. Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106
| | - Daniel J. Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104
| | - Giorgio Sirugo
- Division of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sarah A. Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA 19104
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Bhat GR, Sethi I, Rah B, Kumar R, Afroze D. Innovative in Silico Approaches for Characterization of Genes and Proteins. Front Genet 2022; 13:865182. [PMID: 35664302 PMCID: PMC9159363 DOI: 10.3389/fgene.2022.865182] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.
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Affiliation(s)
- Gh. Rasool Bhat
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
| | - Itty Sethi
- Institute of Human Genetics, University of Jammu, Jammu, India
| | - Bilal Rah
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
| | - Rakesh Kumar
- School of Biotechnology, Shri Mata Vaishno Devi University, Katra, India
| | - Dil Afroze
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
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David A, Parkinson N, Peacock TP, Pairo-Castineira E, Khanna T, Cobat A, Tenesa A, Sancho-Shimizu V, Casanova JL, Abel L, Barclay WS, Baillie JK, Sternberg MJ. A common TMPRSS2 variant has a protective effect against severe COVID-19. Curr Res Transl Med 2022; 70:103333. [PMID: 35104687 PMCID: PMC8743599 DOI: 10.1016/j.retram.2022.103333] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/22/2021] [Accepted: 01/06/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND The human protein transmembrane protease serine type 2 (TMPRSS2) plays a key role in SARS-CoV-2 infection, as it is required to activate the virus' spike protein, facilitating entry into target cells. We hypothesized that naturally-occurring TMPRSS2 human genetic variants affecting the structure and function of the TMPRSS2 protein may modulate the severity of SARS-CoV-2 infection. METHODS We focused on the only common TMPRSS2 non-synonymous variant predicted to be damaging (rs12329760 C>T, p.V160M), which has a minor allele frequency ranging from 0.14 in Ashkenazi Jewish to 0.38 in East Asians. We analysed the association between the rs12329760 and COVID-19 severity in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units recruited as part of the GenOMICC (Genetics Of Mortality In Critical Care) study. Logistic regression analyses were adjusted for sex, age and deprivation index. For in vitro studies, HEK293 cells were co-transfected with ACE2 and either TMPRSS2 wild type or mutant (TMPRSS2V160M). A SARS-CoV-2 pseudovirus entry assay was used to investigate the ability of TMPRSS2V160M to promote viral entry. RESULTS We show that the T allele of rs12329760 is associated with a reduced likelihood of developing severe COVID-19 (OR 0.87, 95%CI:0.79-0.97, p = 0.01). This association was stronger in homozygous individuals when compared to the general population (OR 0.65, 95%CI:0.50-0.84, p = 1.3 × 10-3). We demonstrate in vitro that this variant, which causes the amino acid substitution valine to methionine, affects the catalytic activity of TMPRSS2 and is less able to support SARS-CoV-2 spike-mediated entry into cells. CONCLUSION TMPRSS2 rs12329760 is a common variant associated with a significantly decreased risk of severe COVID-19. Further studies are needed to assess the expression of TMPRSS2 across different age groups. Moreover, our results identify TMPRSS2 as a promising drug target, with a potential role for camostat mesilate, a drug approved for the treatment of chronic pancreatitis and postoperative reflux esophagitis, in the treatment of COVID-19. Clinical trials are needed to confirm this.
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Affiliation(s)
- Alessia David
- Centre for Integrative System Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Nicholas Parkinson
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK
| | - Thomas P Peacock
- Department of Infectious Diseases, Imperial College London, London, W2 1PG, UK
| | | | - Tarun Khanna
- Centre for Integrative System Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Aurelie Cobat
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, EU France; University of Paris, Imagine Institute, Paris, EU France
| | - Albert Tenesa
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK
| | - Vanessa Sancho-Shimizu
- Department of Paediatric Infectious Diseases & Virology, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Faculty of Medicine, Imperial College London, London, UK
| | - Jean-Laurent Casanova
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, EU France; University of Paris, Imagine Institute, Paris, EU France; Howard Hughes Medical Institute, New York, NY, USA
| | - Laurent Abel
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, EU France; University of Paris, Imagine Institute, Paris, EU France
| | - Wendy S Barclay
- Department of Infectious Diseases, Imperial College London, London, W2 1PG, UK
| | - J Kenneth Baillie
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK; Intenstive Care Unit, Royal Infirmary of Edinburgh, 54 Little France Drive, Edinburgh, EH16 5SA, UK
| | - Michael Je Sternberg
- Centre for Integrative System Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
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The Genetic and Molecular Analyses of RAD51C and RAD51D Identifies Rare Variants Implicated in Hereditary Ovarian Cancer from a Genetically Unique Population. Cancers (Basel) 2022; 14:cancers14092251. [PMID: 35565380 PMCID: PMC9104874 DOI: 10.3390/cancers14092251] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 12/03/2022] Open
Abstract
To identify candidate variants in RAD51C and RAD51D ovarian cancer (OC) predisposing genes by investigating French Canadians (FC) exhibiting unique genetic architecture. Candidates were identified by whole exome sequencing analysis of 17 OC families and 53 early-onset OC cases. Carrier frequencies were determined by the genetic analysis of 100 OC or HBOC families, 438 sporadic OC cases and 1025 controls. Variants of unknown function were assayed for their biological impact and/or cellular sensitivity to olaparib. RAD51C c.414G>C;p.Leu138Phe and c.705G>T;p.Lys235Asn and RAD51D c.137C>G;p.Ser46Cys, c.620C>T;p.Ser207Leu and c.694C>T;p.Arg232Ter were identified in 17.6% of families and 11.3% of early-onset cases. The highest carrier frequency was observed in OC families (1/44, 2.3%) and sporadic cases (15/438, 3.4%) harbouring RAD51D c.620C>T versus controls (1/1025, 0.1%). Carriers of c.620C>T (n = 7), c.705G>T (n = 2) and c.137C>G (n = 1) were identified in another 538 FC OC cases. RAD51C c.705G>T affected splicing by skipping exon four, while RAD51D p.Ser46Cys affected protein stability and conferred olaparib sensitivity. Genetic and functional assays implicate RAD51C c.705G>T and RAD51D c.137C>G as likely pathogenic variants in OC. The high carrier frequency of RAD51D c.620C>T in FC OC cases validates previous findings. Our findings further support the role of RAD51C and RAD51D in hereditary OC.
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AlGhamdi NA, Alsuwat HS, Borgio JF, AbdulAzeez S. Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: in silico approaches. J Biomol Struct Dyn 2022; 40:2635-2646. [PMID: 33138699 PMCID: PMC7651216 DOI: 10.1080/07391102.2020.1841032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 10/19/2020] [Indexed: 12/15/2022]
Abstract
SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein-protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (n = 7489) in hACE2 using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (-3.93 to -18.43), weak (-18.42 to -32.94), moderate (-32.94 to -47.44), strong (-47.44 to -61.95) and very strong (-61.95 to -76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (p < 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Norah Ali AlGhamdi
- Department of Genetic Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
- College of Science, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Hind Saleh Alsuwat
- Department of Genetic Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - J. Francis Borgio
- Department of Genetic Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
- Department of Epidemic Diseases Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Sayed AbdulAzeez
- Department of Genetic Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Andrades R, Recamonde-Mendoza M. Machine learning methods for prediction of cancer driver genes: a survey paper. Brief Bioinform 2022; 23:6551145. [PMID: 35323900 DOI: 10.1093/bib/bbac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
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Affiliation(s)
- Renan Andrades
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
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Structural Consequence of Non-Synonymous Single-Nucleotide Variants in the N-Terminal Domain of LIS1. Int J Mol Sci 2022; 23:ijms23063109. [PMID: 35328531 PMCID: PMC8955593 DOI: 10.3390/ijms23063109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 02/04/2023] Open
Abstract
Disruptive neuronal migration during early brain development causes severe brain malformation. Characterized by mislocalization of cortical neurons, this condition is a result of the loss of function of migration regulating genes. One known neuronal migration disorder is lissencephaly (LIS), which is caused by deletions or mutations of the LIS1 (PAFAH1B1) gene that has been implicated in regulating the microtubule motor protein cytoplasmic dynein. Although this class of diseases has recently received considerable attention, the roles of non-synonymous polymorphisms (nsSNPs) in LIS1 on lissencephaly progression remain elusive. Therefore, the present study employed combined bioinformatics and molecular modeling approach to identify potential damaging nsSNPs in the LIS1 gene and provide atomic insight into their roles in LIS1 loss of function. Using this approach, we identified three high-risk nsSNPs, including rs121434486 (F31S), rs587784254 (W55R), and rs757993270 (W55L) in the LIS1 gene, which are located on the N-terminal domain of LIS1. Molecular dynamics simulation highlighted that all variants decreased helical conformation, increased the intermonomeric distance, and thus disrupted intermonomeric contacts in the LIS1 dimer. Furthermore, the presence of variants also caused a loss of positive electrostatic potential and reduced dimer binding potential. Since self-dimerization is an essential aspect of LIS1 to recruit interacting partners, thus these variants are associated with the loss of LIS1 functions. As a corollary, these findings may further provide critical insights on the roles of LIS1 variants in brain malformation.
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Soussi T, Baliakas P. Landscape of TP53 Alterations in Chronic Lymphocytic Leukemia via Data Mining Mutation Databases. Front Oncol 2022; 12:808886. [PMID: 35251978 PMCID: PMC8890000 DOI: 10.3389/fonc.2022.808886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/20/2022] [Indexed: 11/16/2022] Open
Abstract
Locus-specific databases are invaluable tools for both basic and clinical research. The extensive information they contain is gathered from the literature and manually curated by experts. Cancer genome sequencing projects generate an immense amount of data, which are stored directly in large repositories (cancer genome databases). The presence of a TP53 defect (17p deletion and/or TP53 mutations) is an independent prognostic factor in chronic lymphocytic leukemia (CLL) and TP53 status analysis has been adopted in routine clinical practice. For that reason, TP53 mutation databases have become essential for the validation of the plethora of TP53 variants detected in tumor samples. TP53 profiles in CLL are characterized by a great number of subclonal TP53 mutations with low variant allelic frequencies and the presence of multiple minor subclones harboring different TP53 mutations. In this review, we describe the various characteristics of the multiple levels of heterogeneity of TP53 variants in CLL through the analysis of TP53 mutation databases and the utility of their diagnosis in the clinic.
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Affiliation(s)
- Thierry Soussi
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.,Sorbonne Université, UPMC Univ Paris 06, Paris, France
| | - Panagiotis Baliakas
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
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