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Haver A, Theijn R, Grift ID, Raaijmakers G, Poorter E, Laros JFJ, van Dissel JT, Lodder WJ. Regional reemergence of a SARS-CoV-2 Delta lineage amid an Omicron wave detected by wastewater sequencing. Sci Rep 2023; 13:17870. [PMID: 37857658 PMCID: PMC10587120 DOI: 10.1038/s41598-023-44500-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 10/09/2023] [Indexed: 10/21/2023] Open
Abstract
The implementation and integration of wastewater-based epidemiology constitutes a valuable addition to existing pathogen surveillance systems, such as clinical surveillance for SARS-CoV-2. In the Netherlands, SARS-CoV-2 variant circulation is monitored by performing whole-genome sequencing on wastewater samples. In this manuscript, we describe the detection of an AY.43 lineage (Delta variant) amid a period of BA.5 (Omicron variant) dominance in wastewater samples from two wastewater treatment plants (WWTPs) during the months of August and September of 2022. Our results describe a temporary emergence, which was absent in samples from other WWTPs, and which coincided with peaks in viral load. We show how these lineage estimates can be traced back to lineage-specific substitution patterns. The absence of this variant from reported clinical data, but high associated viral loads suggest cryptic transmission. Our findings highlight the additional value of wastewater surveillance for generating insights into circulating pathogens.
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Affiliation(s)
- Auke Haver
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Human Genetics (HG), Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Rick Theijn
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Ivo D Grift
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Gino Raaijmakers
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Elsa Poorter
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics (HG), Leiden University Medical Center (LUMC), Leiden, The Netherlands
- Department of BioInformatics and Computational Services (BIR), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Jaap T van Dissel
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Willemijn J Lodder
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
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Schmitz D, Zwagemaker F, van der Veer B, Vennema H, Laros JFJ, Koopmans MPG, De Graaf M, Kroneman A. Metagenomic Surveillance of Viral Gastroenteritis in a Public Health Setting. Microbiol Spectr 2023; 11:e0502222. [PMID: 37432120 PMCID: PMC10434279 DOI: 10.1128/spectrum.05022-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/06/2023] [Indexed: 07/12/2023] Open
Abstract
Norovirus is the primary cause of viral gastroenteritis (GE). To investigate norovirus epidemiology, there is a need for whole-genome sequencing and reference sets consisting of complete genomes. To investigate the potential of shotgun metagenomic sequencing on the Illumina platform for whole-genome sequencing, 71 reverse transcriptase quantitative PCR (RT-qPCR) norovirus positive-feces (threshold cycle [CT], <30) samples from norovirus surveillance within The Netherlands were subjected to metagenomic sequencing. Data were analyzed through an in-house next-generation sequencing (NGS) analysis workflow. Additionally, we assessed the potential of metagenomic sequencing for the surveillance of off-target viruses that are of importance for public health, e.g., sapovirus, rotavirus A, enterovirus, parechovirus, aichivirus, adenovirus, and bocaparvovirus. A total of 60 complete and 10 partial norovirus genomes were generated, representing 7 genogroup I capsid genotypes and 12 genogroup II capsid genotypes. In addition to the norovirus genomes, the metagenomic approach yielded partial or complete genomes of other viruses for 39% of samples from children and 6.7% of samples from adults, including adenovirus 41 (N = 1); aichivirus 1 (N = 1); coxsackievirus A2 (N = 2), A4 (N = 2), A5 (N = 1), and A16 (N = 1); bocaparvovirus 1 (N = 1) and 3 (N = 1); human parechovirus 1 (N = 2) and 3 (N = 1); Rotavirus A (N = 1); and a sapovirus GI.7 (N = 1). The sapovirus GI.7 was initially not detected through RT-qPCR and warranted an update of the primer and probe set. Metagenomic sequencing on the Illumina platform robustly determines complete norovirus genomes and may be used to broaden gastroenteritis surveillance by capturing off-target enteric viruses. IMPORTANCE Viral gastroenteritis results in significant morbidity and mortality in vulnerable individuals and is primarily caused by norovirus. To investigate norovirus epidemiology, there is a need for whole-genome sequencing and reference sets consisting of full genomes. Using surveillance samples sent to the Dutch National Institute for Public Health and the Environment (RIVM), we compared metagenomics against conventional techniques, such as RT-qPCR and Sanger-sequencing, with norovirus as the target pathogen. We determined that metagenomics is a robust method to generate complete norovirus genomes, in parallel to many off-target pathogenic enteric virus genomes, thereby broadening our surveillance efforts. Moreover, we detected a sapovirus that was not detected by our validated gastroenteritis RT-qPCR panel, which exemplifies the strength of metagenomics. Our study shows that metagenomics can be used for public health gastroenteritis surveillance, the generation of reference-sets for molecular epidemiology, and how it compares to current surveillance strategies.
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Affiliation(s)
- Dennis Schmitz
- National Institute of Public Health and the Environment, Center for Infectious Disease Control, Bilthoven, The Netherlands
- Erasmus Medical Center, Viroscience, Rotterdam, The Netherlands
| | - Florian Zwagemaker
- National Institute of Public Health and the Environment, Center for Infectious Disease Control, Bilthoven, The Netherlands
| | - Bas van der Veer
- National Institute of Public Health and the Environment, Center for Infectious Disease Control, Bilthoven, The Netherlands
| | - Harry Vennema
- National Institute of Public Health and the Environment, Center for Infectious Disease Control, Bilthoven, The Netherlands
| | - Jeroen F. J. Laros
- National Institute of Public Health and the Environment, Center for Infectious Disease Control, Bilthoven, The Netherlands
- Leiden University Medical Center, Department of Human Genetics, Leiden, The Netherlands
| | | | | | - Annelies Kroneman
- National Institute of Public Health and the Environment, Center for Infectious Disease Control, Bilthoven, The Netherlands
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3
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Vis JK, Santcroos MA, Kosters WA, Laros JFJ. A Boolean algebra for genetic variants. Bioinformatics 2023; 39:6967432. [PMID: 36594541 PMCID: PMC9879725 DOI: 10.1093/bioinformatics/btad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 12/06/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Beyond identifying genetic variants, we introduce a set of Boolean relations, which allows for a comprehensive classification of the relations of every pair of variants by taking all minimal alignments into account. We present an efficient algorithm to compute these relations, including a novel way of efficiently computing all minimal alignments within the best theoretical complexity bounds. RESULTS We show that these relations are common, and many non-trivial, for variants of the CFTR gene in dbSNP. Ultimately, we present an approach for the storing and indexing of variants in the context of a database that enables efficient querying for all these relations. AVAILABILITY AND IMPLEMENTATION A Python implementation is available at https://github.com/mutalyzer/algebra/tree/v0.2.0 as well as an interface at https://mutalyzer.nl/algebra.
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Affiliation(s)
| | - Mark A Santcroos
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands,Department of Clinical Genetics, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - Walter A Kosters
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 CA Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands,National Institute for Public Health and the Environment (RIVM), 3721 MA Bilthoven, The Netherlands
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4
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Marissen R, Varunjikar MS, Laros JFJ, Rasinger JD, Neely BA, Palmblad M. compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets. J Proteome Res 2022; 22:514-519. [PMID: 36173614 PMCID: PMC9903320 DOI: 10.1021/acs.jproteome.2c00457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.
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Affiliation(s)
- Rob Marissen
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Postbus 9600, 2300 RC Leiden, The Netherlands
| | | | - Jeroen F. J. Laros
- National
Institute for Public Health and the Environment, 3720 BA Bilthoven, The Netherlands,Department
of Human Genetics, Leiden University Medical
Center, Postbus 9600, 2300
RC Leiden, The Netherlands
| | - Josef D. Rasinger
- Institute
of Marine Research, P.O. Box 1870
Nordnes, 5817 Bergen, Norway
| | - Benjamin A. Neely
- National
Institute of Standards and Technology, Charleston, South Carolina 29412, United States
| | - Magnus Palmblad
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Postbus 9600, 2300 RC Leiden, The Netherlands,. Phone: +31 71 5266969
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5
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Bertola LD, Vermaat M, Lesilau F, Chege M, Tumenta PN, Sogbohossou EA, Schaap OD, Bauer H, Patterson BD, White PA, de Iongh HH, Laros JFJ, Vrieling K. Whole genome sequencing and the application of a SNP panel reveal primary evolutionary lineages and genomic variation in the lion (Panthera leo). BMC Genomics 2022; 23:321. [PMID: 35459090 PMCID: PMC9027350 DOI: 10.1186/s12864-022-08510-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/29/2022] [Indexed: 11/23/2022] Open
Abstract
Background Previous phylogeographic studies of the lion (Panthera leo) have improved our insight into the distribution of genetic variation, as well as a revised taxonomy which now recognizes a northern (Panthera leo leo) and a southern (Panthera leo melanochaita) subspecies. However, existing whole range phylogeographic studies on lions either consist of very limited numbers of samples, or are focused on mitochondrial DNA and/or a limited set of microsatellites. The geographic extent of genetic lineages and their phylogenetic relationships remain uncertain, clouded by massive sampling gaps, sex-biased dispersal and incomplete lineage sorting. Results In this study we present results of low depth whole genome sequencing and subsequent variant calling in ten lions sampled throughout the geographic range, resulting in the discovery of >150,000 Single Nucleotide Polymorphisms (SNPs). Phylogenetic analyses revealed the same basal split between northern and southern populations, as well as four population clusters on a more local scale. Further, we designed a SNP panel, including 125 autosomal and 14 mitochondrial SNPs, which was tested on >200 lions from across their range. Results allow us to assign individuals to one of these four major clades (West & Central Africa, India, East Africa, or Southern Africa) and delineate these clades in more detail. Conclusions The results presented here, particularly the validated SNP panel, have important applications, not only for studying populations on a local geographic scale, but also for tracing samples of unknown origin for forensic purposes, and for guiding conservation management of ex situ populations. Thus, these genomic resources not only contribute to our understanding of the evolutionary history of the lion, but may also play a crucial role in conservation efforts aimed at protecting the species in its full diversity. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08510-y.
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Affiliation(s)
- L D Bertola
- City University of New York, City College of New York, 160 Convent Avenue, New York, NY, 10031, USA. .,Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, The Netherlands. .,Institute of Biology Leiden (IBL), Leiden University, PO Box 9505, 2300 RA, Leiden, The Netherlands.
| | - M Vermaat
- Department of Human Genetics, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands.,Leiden Genome Technology Center, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - F Lesilau
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, The Netherlands.,Kenya Wildlife Service, Nairobi, Kenya
| | - M Chege
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, The Netherlands.,Kenya Wildlife Service, Nairobi, Kenya
| | - P N Tumenta
- Centre for Environment and Developmental Studies, Cameroon (CEDC), Yaounde, Cameroon.,Regional Training Centre Specialized in Agriculture, Forest and Wood, University of Dschang, BP 138, Yaounde, Cameroon
| | - E A Sogbohossou
- Laboratoire d'Ecologie Appliquée, Université d'Abomey-Calavi, 03 BP 294, Cotonou, Benin
| | - O D Schaap
- Institute of Biology Leiden (IBL), Leiden University, PO Box 9505, 2300 RA, Leiden, The Netherlands
| | - H Bauer
- Wildlife Conservation Research Unit, Zoology, University of Oxford Recanati-Kaplan Centre, Tubney, OX13 5QL, UK
| | - B D Patterson
- Negaunee Integrative Research Center, Field Museum of Natural History, Chicago, IL, 60605, USA
| | - P A White
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, 90095-1496, USA
| | - H H de Iongh
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, The Netherlands.,Department of Biology, Evolutionary Ecology Group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerpen, Belgium
| | - J F J Laros
- Department of Human Genetics, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands.,Leiden Genome Technology Center, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - K Vrieling
- Institute of Biology Leiden (IBL), Leiden University, PO Box 9505, 2300 RA, Leiden, The Netherlands
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6
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van der Velde KJ, Singh G, Kaliyaperumal R, Liao X, de Ridder S, Rebers S, Kerstens HHD, de Andrade F, van Reeuwijk J, De Gruyter FE, Hiltemann S, Ligtvoet M, Weiss MM, van Deutekom HWM, Jansen AML, Stubbs AP, Vissers LELM, Laros JFJ, van Enckevort E, Stemkens D, 't Hoen PAC, Beliën JAM, van Gijn ME, Swertz MA. FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research. Sci Data 2022; 9:169. [PMID: 35418585 PMCID: PMC9008059 DOI: 10.1038/s41597-022-01265-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 03/25/2022] [Indexed: 11/08/2022] Open
Abstract
The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .
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Affiliation(s)
- K Joeri van der Velde
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Gurnoor Singh
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Rajaram Kaliyaperumal
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - XiaoFeng Liao
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Sander de Ridder
- Amsterdam University Medical Center, University of Amsterdam, Department of Pathology, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Susanne Rebers
- The Netherlands Cancer Institute, Division of Molecular Pathology, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Hindrik H D Kerstens
- Prinses Máxima Center for Pediatric Oncology, Kemmeren group, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands
| | - Fernanda de Andrade
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Jeroen van Reeuwijk
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Fini E De Gruyter
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Saskia Hiltemann
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Maarten Ligtvoet
- Nictiz - Dutch competence centre for electronic exchange of health and care information, Oude Middenweg 55, 2491 AC, The Hague, The Netherlands
| | - Marjan M Weiss
- Radboud University Medical Center, Department of Human Genetics, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Hanneke W M van Deutekom
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Anne M L Jansen
- University Medical Center Utrecht, Department of Pathology, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Andrew P Stubbs
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Lisenka E L M Vissers
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen F J Laros
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Leiden University Medical Center, Department of Clinical Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Rijksinstituut voor Volksgezondheid en Milieu, Antonie van Leeuwenhoeklaan 9, 3721 MA, Bilthoven, The Netherlands
| | - Esther van Enckevort
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Daphne Stemkens
- VSOP - Patient Alliance for Rare and Genetic Diseases The Netherlands, Koninginnelaan 23, 3762 DA, Soest, The Netherlands
| | - Peter A C 't Hoen
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen A M Beliën
- Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Department of Pathology, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Mariëlle E van Gijn
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Morris A Swertz
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
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7
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Nooij S, Ducarmon QR, Laros JFJ, Zwittink RD, Norman JM, Smits WK, Verspaget HW, Keller JJ, Terveer EM, Kuijper EJ. Fecal Microbiota Transplantation Influences Procarcinogenic Escherichia coli in Recipient Recurrent Clostridioides difficile Patients. Gastroenterology 2021; 161:1218-1228.e5. [PMID: 34126062 DOI: 10.1053/j.gastro.2021.06.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/18/2021] [Accepted: 06/03/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND & AIMS Patients with multiple recurrent Clostridioides difficile infection (rCDI) have a disturbed gut microbiota that can be restored by fecal microbiota transplantation (FMT). Despite extensive screening, healthy feces donors may carry bacteria in their intestinal tract that could have long-term health effects, such as potentially procarcinogenic polyketide synthase-positive (pks+) Escherichia coli. Here, we aim to determine whether the pks abundance and persistence of pks+E coli is influenced by pks status of the donor feces. METHODS In a cohort of 49 patients with rCDI treated with FMT and matching donor samples-the largest cohort of its kind, to our knowledge-we retrospectively screened fecal metagenomes for pks+E coli and compared the presence of pks in patients before and after treatment and to their respective donors. RESULTS The pks island was more prevalent (P = .026) and abundant (P < .001) in patients with rCDI (pre-FMT, 27 of 49 [55%]; median, 0.46 reads per kilobase per million [RPKM] pks) than in healthy donors (3 of 8 donors [37.5%], 11 of 38 samples [29%]; median, 0.01 RPKM pks). The pks status of patients post-FMT depended on the pks status of the donor suspension with which the patient was treated (P = .046). Particularly, persistence (8 of 9 cases) or clearance (13 of 18) of pks+E coli in pks+ patients was correlated to pks in the donor (P = .004). CONCLUSIONS We conclude that FMT contributes to pks+E coli persistence or eradication in patients with rCDI but that donor-to-patient transmission of pks+E coli is unlikely.
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Affiliation(s)
- Sam Nooij
- Experimental Bacteriology, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands; Netherlands Donor Feces Bank, Leiden, the Netherlands; Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Quinten R Ducarmon
- Experimental Bacteriology, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands; Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jeroen F J Laros
- Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands; National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Romy D Zwittink
- Experimental Bacteriology, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands; Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Wiep Klaas Smits
- Experimental Bacteriology, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands; Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands
| | - Hein W Verspaget
- Department of Gastroenterology, Leiden University Medical Center, Leiden, the Netherlands; Department of Biobanking, Leiden University Medical Center, Leiden, the Netherlands
| | - Josbert J Keller
- Department of Gastroenterology, Leiden University Medical Center, Leiden, the Netherlands; Department of Gastroenterology, Haaglanden Medical Center, The Hague, the Netherlands
| | - Elisabeth M Terveer
- Experimental Bacteriology, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands; Netherlands Donor Feces Bank, Leiden, the Netherlands; Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands
| | - Ed J Kuijper
- Experimental Bacteriology, Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands; Netherlands Donor Feces Bank, Leiden, the Netherlands; Center for Microbiome Analyses and Therapeutics, Leiden University Medical Center, Leiden, the Netherlands; National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
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8
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Lefter M, Vis JK, Vermaat M, den Dunnen JT, Taschner PEM, Laros JFJ. Mutalyzer 2: next generation HGVS nomenclature checker. Bioinformatics 2021; 37:2811-2817. [PMID: 33538839 PMCID: PMC8479679 DOI: 10.1093/bioinformatics/btab051] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 12/02/2020] [Accepted: 01/22/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Unambiguous variant descriptions are of utmost importance in clinical genetic diagnostics, scientific literature and genetic databases. The Human Genome Variation Society (HGVS) publishes a comprehensive set of guidelines on how variants should be correctly and unambiguously described. We present the implementation of the Mutalyzer 2 tool suite, designed to automatically apply the HGVS guidelines so users do not have to deal with the HGVS intricacies explicitly to check and correct their variant descriptions. RESULTS Mutalyzer is profusely used by the community, having processed over 133 million descriptions since its launch. Over a five year period, Mutalyzer reported a correct input in ∼50% of cases. In 41% of the cases either a syntactic or semantic error was identified and for ∼7% of cases, Mutalyzer was able to automatically correct the description. AVAILABILITY AND IMPLEMENTATION Mutalyzer is an Open Source project under the GNU Affero General Public License. The source code is available on GitHub (https://github.com/mutalyzer/mutalyzer) and a running instance is available at: https://mutalyzer.nl.
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Affiliation(s)
- Mihai Lefter
- Department of Human Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands,To whom correspondence should be addressed.
| | - Jonathan K Vis
- Department of Human Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands,Department of Clinical Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands
| | - Martijn Vermaat
- Department of Human Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands
| | - Johan T den Dunnen
- Department of Human Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands,Department of Clinical Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands
| | - Peter E M Taschner
- Department of Human Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands,Generade Centre of Expertise Genomics and Leiden Centre for Applied Bioscience, University of Applied Sciences Leiden, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands,Department of Clinical Genetics, Leiden University Medical Center (LUMC)Leiden, The Netherlands,National Institute for Public Health and the Environment (RIVM), Bthoven, The Netherlands
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9
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Ehrhart F, Jacobsen A, Rigau M, Bosio M, Kaliyaperumal R, Laros JFJ, Willighagen EL, Valencia A, Roos M, Capella-Gutierrez S, Curfs LMG, Evelo CT. A catalogue of 863 Rett-syndrome-causing MECP2 mutations and lessons learned from data integration. Sci Data 2021; 8:10. [PMID: 33452270 PMCID: PMC7810705 DOI: 10.1038/s41597-020-00794-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/30/2020] [Indexed: 11/09/2022] Open
Abstract
Rett syndrome (RTT) is a rare neurological disorder mostly caused by a genetic variation in MECP2. Making new MECP2 variants and the related phenotypes available provides data for better understanding of disease mechanisms and faster identification of variants for diagnosis. This is, however, currently hampered by the lack of interoperability between genotype-phenotype databases. Here, we demonstrate on the example of MECP2 in RTT that by making the genotype-phenotype data more Findable, Accessible, Interoperable, and Reusable (FAIR), we can facilitate prioritization and analysis of variants. In total, 10,968 MECP2 variants were successfully integrated. Among these variants 863 unique confirmed RTT causing and 209 unique confirmed benign variants were found. This dataset was used for comparison of pathogenicity predicting tools, protein consequences, and identification of ambiguous variants. Prediction tools generally recognised the RTT causing and benign variants, however, there was a broad range of overlap Nineteen variants were identified that were annotated as both disease-causing and benign, suggesting that there are additional factors in these cases contributing to disease development. Measurement(s) | Rett syndrome • phenotype • MECP2 Gene | Technology Type(s) | digital curation • network analysis | Sample Characteristic - Organism | Homo sapiens |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13359476
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Affiliation(s)
- Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, MHeNS School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands. .,GKC - Rett Expertise Centre, Maastricht University Medical Center, Maastricht, The Netherlands.
| | - Annika Jacobsen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Maria Rigau
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain
| | - Mattia Bosio
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain
| | - Rajaram Kaliyaperumal
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, MHeNS School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alfonso Valencia
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Leopold M G Curfs
- GKC - Rett Expertise Centre, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, MHeNS School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.,GKC - Rett Expertise Centre, Maastricht University Medical Center, Maastricht, The Netherlands
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10
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Arindrarto W, Borràs DM, de Groen RAL, van den Berg RR, Locher IJ, van Diessen SAME, van der Holst R, van der Meijden ED, Honders MW, de Leeuw RH, Verlaat W, Jedema I, Kroes WGM, Knijnenburg J, van Wezel T, Vermaat JSP, Valk PJM, Janssen B, de Knijff P, van Bergen CAM, van den Akker EB, Hoen PAC', Kiełbasa SM, Laros JFJ, Griffioen M, Veelken H. Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing. Leukemia 2020; 35:47-61. [PMID: 32127641 PMCID: PMC7787979 DOI: 10.1038/s41375-020-0762-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 01/17/2020] [Accepted: 02/12/2020] [Indexed: 01/12/2023]
Abstract
Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform.
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Affiliation(s)
- Wibowo Arindrarto
- Center for Computational Biology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Daniel M Borràs
- GenomeScan B.V, 2333 BZ, Leiden, The Netherlands.,Department of Chemical Cell Biology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Ruben A L de Groen
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Redmar R van den Berg
- Department of Human Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Irene J Locher
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | | | - Rosalie van der Holst
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | | | - M Willy Honders
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Rick H de Leeuw
- Forensic Laboratory for DNA Research, Department of Human Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Wina Verlaat
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Inge Jedema
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Wilma G M Kroes
- Department of Clinical Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Jeroen Knijnenburg
- Department of Clinical Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Tom van Wezel
- Department of Pathology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Joost S P Vermaat
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Peter J M Valk
- Department of Hematology, Erasmus University Medical Center, 3015CN, Rotterdam, The Netherlands
| | - Bart Janssen
- GenomeScan B.V, 2333 BZ, Leiden, The Netherlands
| | - Peter de Knijff
- Forensic Laboratory for DNA Research, Department of Human Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | | | - Erik B van den Akker
- Center for Computational Biology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands.,The Delft Bioinformatics Lab, Delft University of Technology, 2628CD, Delft, The Netherlands.,Section of Molecular Epidemiology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands.,The Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Szymon M Kiełbasa
- Center for Computational Biology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - Marieke Griffioen
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands.
| | - Hendrik Veelken
- Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
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11
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Khachatryan L, de Leeuw RH, Kraakman MEM, Pappas N, Te Raa M, Mei H, de Knijff P, Laros JFJ. Taxonomic classification and abundance estimation using 16S and WGS-A comparison using controlled reference samples. Forensic Sci Int Genet 2020; 46:102257. [PMID: 32058299 DOI: 10.1016/j.fsigen.2020.102257] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 12/30/2019] [Accepted: 01/27/2020] [Indexed: 12/30/2022]
Abstract
The assessment of microbiome biodiversity is the most common application of metagenomics. While 16S sequencing remains standard procedure for taxonomic profiling of metagenomic data, a growing number of studies have clearly demonstrated biases associated with this method. By using Whole Genome Shotgun sequencing (WGS) metagenomics, most of the known restrictions associated with 16S data are alleviated. However, due to the computationally intensive data analyses and higher sequencing costs, WGS based metagenomics remains a less popular option. Selecting the experiment type that provides a comprehensive, yet manageable amount of information is a challenge encountered in many metagenomics studies. In this work, we created a series of artificial bacterial mixes, each with a different distribution of skin-associated microbial species. These mixes were used to estimate the resolution of two different metagenomic experiments - 16S and WGS - and to evaluate several different bioinformatics approaches for taxonomic read classification. In all test cases, WGS approaches provide much more accurate results, in terms of taxa prediction and abundance estimation, in comparison to those of 16S. Furthermore, we demonstrate that a 16S dataset, analysed using different state of the art techniques and reference databases, can produce widely different results. In light of the fact that most forensic metagenomic analysis are still performed using 16S data, our results are especially important.
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Affiliation(s)
- Lusine Khachatryan
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Rick H de Leeuw
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Margriet E M Kraakman
- Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Nikos Pappas
- Sequencing Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Marije Te Raa
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Peter de Knijff
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
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12
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Van Dissel JT, Pieters T, Geluk A, Maat G, Menke HE, Tió-Coma M, Altena E, Laros JFJ, Adhin MR. Archival, paleopathological and aDNA-based techniques in leprosy research and the case of Father Petrus Donders at the Leprosarium 'Batavia', Suriname. Int J Paleopathol 2019; 27:1-8. [PMID: 31430635 DOI: 10.1016/j.ijpp.2019.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 08/02/2019] [Accepted: 08/03/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVE We assessed whether Petrus Donders (died 1887), a Dutch priest who for 27 years cared for people with leprosy in the leprosarium Batavia, Suriname, had evidence of Mycobacterium (M.) leprae infection. A positive finding of M. leprae ancient (a)DNA would contribute to the origin of leprosy in Suriname. MATERIALS Skeletal remains of Father Petrus Donders; two additional skeletons excavated from the Batavia cemetery were used as controls. METHODS Archival research, paleopathological evaluation and aDNA-based testing of skeletal remains. RESULTS Neither archives nor inspection of Donders skeletal remains revealed evidence of leprosy, and aDNA-based testing for M. leprae was negative. We detected M. leprae aDNA by RLEP PCR in one control skeleton, which also displayed pathological lesions compatible with leprosy. The M. leprae aDNA was genotyped by Sanger sequencing as SNP type 4; the skeleton displayed mitochondrial haplogroup L3. CONCLUSION We found no evidence that Donders contracted leprosy despite years of intense leprosy contact, but we successfully isolated an archaeological M. leprae aDNA sample from a control skeleton from South America. SIGNIFICANCE We successfully genotyped recovered aDNA to a M. leprae strain that likely originated in West Africa. The detected human mitochondrial haplogroup L3 is also associated with this geographical region. This suggests that slave trade contributed to leprosy in Suriname. LIMITATIONS A limited number of skeletons was examined. SUGGESTIONS FOR FURTHER RESEARCH Broader review of skeletal collections is advised to expand on diversity of the M. leprae aDNA database.
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Affiliation(s)
- Jaap T Van Dissel
- Dept Infectious Diseases, Leiden University Medical Centre, Leiden, the Netherlands.
| | - Toine Pieters
- Freudenthal Institute for Science and Mathematics Education, Utrecht University, Utrecht, the Netherlands
| | - Annemieke Geluk
- Dept Infectious Diseases, Leiden University Medical Centre, Leiden, the Netherlands
| | - George Maat
- Dept Anatomy, Leiden University Medical Center, Leiden, the Netherlands
| | - Henk E Menke
- Dermatology Service, Ministry of Health, Paramaribo, Suriname(2)
| | - Maria Tió-Coma
- Dept Infectious Diseases, Leiden University Medical Centre, Leiden, the Netherlands
| | - Eveline Altena
- Dept Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jeroen F J Laros
- Dept Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Dept Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Malti R Adhin
- Dept Biochemistry, Faculty of Medical Sciences, Anton de Kom Universiteit van Suriname, Paramaribo, Suriname
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13
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Fokkema IFAC, van der Velde KJ, Slofstra MK, Ruivenkamp CAL, Vogel MJ, Pfundt R, Blok MJ, Lekanne Deprez RH, Waisfisz Q, Abbott KM, Sinke RJ, Rahman R, Nijman IJ, de Koning B, Thijs G, Wieskamp N, Moritz RJG, Charbon B, Saris JJ, den Dunnen JT, Laros JFJ, Swertz MA, van Gijn ME. Dutch genome diagnostic laboratories accelerated and improved variant interpretation and increased accuracy by sharing data. Hum Mutat 2019; 40:2230-2238. [PMID: 31433103 PMCID: PMC6900155 DOI: 10.1002/humu.23896] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 08/05/2019] [Accepted: 08/14/2019] [Indexed: 11/06/2022]
Abstract
Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next-generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5-tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as "consensus" when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled "conflicting", while other nonconsensus observations were labeled "no consensus". We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5-tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.
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Affiliation(s)
- Ivo F A C Fokkema
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Kasper J van der Velde
- Genomics Coordination Center & Department of Genetics, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - Mariska K Slofstra
- Genomics Coordination Center & Department of Genetics, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - Claudia A L Ruivenkamp
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Maartje J Vogel
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marinus J Blok
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Ronald H Lekanne Deprez
- Department of Clinical Genetics, Academic Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Quinten Waisfisz
- Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Kristin M Abbott
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Richard J Sinke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rubayte Rahman
- Department of Research IT, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Isaäc J Nijman
- Medicine Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bart de Koning
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Gert Thijs
- DGG-Genomics Software Solutions, Agilent Technologies, Leuven, Belgium
| | - Nienke Wieskamp
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ruben J G Moritz
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Bart Charbon
- Genomics Coordination Center & Department of Genetics, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - Jasper J Saris
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Johan T den Dunnen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.,Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Morris A Swertz
- Genomics Coordination Center & Department of Genetics, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - Marielle E van Gijn
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Medicine Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
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14
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Khachatryan L, Kraakman MEM, Bernards AT, Laros JFJ. BacTag - a pipeline for fast and accurate gene and allele typing in bacterial sequencing data based on database preprocessing. BMC Genomics 2019; 20:338. [PMID: 31060512 PMCID: PMC6501397 DOI: 10.1186/s12864-019-5723-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 04/22/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Bacteria carry a wide array of genes, some of which have multiple alleles. These different alleles are often responsible for distinct types of virulence and can determine the classification at the subspecies levels (e.g., housekeeping genes for Multi Locus Sequence Typing, MLST). Therefore, it is important to rapidly detect not only the gene of interest, but also the relevant allele. Current sequencing-based methods are limited to mapping reads to each of the known allele reference, which is a time-consuming procedure. RESULTS To address this limitation, we developed BacTag - a pipeline that rapidly and accurately detects which genes are present in a sequencing dataset and reports the allele of each of the identified genes. We exploit the fact that different alleles of the same gene have a high similarity. Instead of mapping the reads to each of the allele reference sequences, we preprocess the database prior to the analysis, which makes the subsequent gene and allele identification efficient. During the preprocessing, we determine a representative reference sequence for each gene and store the differences between all alleles and this chosen reference. Throughout the analysis we estimate whether the gene is present in the sequencing data by mapping the reads to this reference sequence; if the gene is found, we compare the variants to those in the preprocessed database. This allows to detect which specific allele is present in the sequencing data. Our pipeline was successfully tested on artificial WGS E. coli, S. pseudintermedius, P. gingivalis, M. bovis, Borrelia spp. and Streptomyces spp. data and real WGS E. coli and K. pneumoniae data in order to report alleles of MLST house-keeping genes. CONCLUSIONS We developed a new pipeline for fast and accurate gene and allele recognition based on database preprocessing and parallel computing and performed better or comparable to the current popular tools. We believe that our approach can be useful for a wide range of projects, including bacterial subspecies classification, clinical diagnostics of bacterial infections, and epidemiological studies.
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Affiliation(s)
- Lusine Khachatryan
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
| | - Margriet E M Kraakman
- Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexandra T Bernards
- Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.,Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.,GenomeScan, Leiden, The Netherlands
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15
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Horn IR, Kenens Y, Palmblad NM, van der Plas-Duivesteijn SJ, Langeveld BW, Meijer HJM, Dalebout H, Marissen RJ, Fischer A, Vincent Florens FB, Niemann J, Rijsdijk KF, Schulp AS, Laros JFJ, Gravendeel B. Palaeoproteomics of bird bones for taxonomic classification. Zool J Linn Soc 2019. [DOI: 10.1093/zoolinnean/zlz012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Ivo R Horn
- University of Applied Sciences Leiden, Faculty of Science and Technology, Zernikedreef, CK, Leiden, The Netherlands
- Naturalis Biodiversity Center, Endless Forms Group, Darwinweg, CR Leiden, The Netherlands
| | - Yvo Kenens
- University of Applied Sciences Leiden, Faculty of Science and Technology, Zernikedreef, CK, Leiden, The Netherlands
| | - N Magnus Palmblad
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Bram W Langeveld
- Natural History Museum Rotterdam, Museumpark, Rotterdam, The Netherlands
| | - Hanneke J M Meijer
- Naturalis Biodiversity Center, Endless Forms Group, Darwinweg, CR Leiden, The Netherlands
- University Museum, Department of Natural History, University of Bergen, Bergen, Norway
| | - Hans Dalebout
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Rob J Marissen
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Anja Fischer
- University of Amsterdam, Faculty of Humanities, Amsterdam, The Netherlands
| | - F B Vincent Florens
- Tropical Island Biodiversity, Ecology and Conservation Pole of Research, University of Mauritius, Réduit, Mauritius
| | - Jonas Niemann
- Natural History Museum of Denmark, Copenhagen, Denmark
| | - Kenneth F Rijsdijk
- BIOMAC group, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Faculty of Natural Sciences, Science Park, Amsterdam, The Netherlands
| | - Anne S Schulp
- Naturalis Biodiversity Center, Endless Forms Group, Darwinweg, CR Leiden, The Netherlands
| | | | - Barbara Gravendeel
- University of Applied Sciences Leiden, Faculty of Science and Technology, Zernikedreef, CK, Leiden, The Netherlands
- Naturalis Biodiversity Center, Endless Forms Group, Darwinweg, CR Leiden, The Netherlands
- Institute of Biology Leiden, Leiden University, Sylviusweg, BE Leiden, The Netherlands
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16
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den Hollander W, Pulyakhina I, Boer C, Bomer N, van der Breggen R, Arindrarto W, Couthino de Almeida R, Lakenberg N, Sentner T, Laros JFJ, ‘t Hoen PAC, Slagboom EPE, Nelissen RGHH, van Meurs J, Ramos YFM, Meulenbelt I. Annotating Transcriptional Effects of Genetic Variants in Disease-Relevant Tissue: Transcriptome-Wide Allelic Imbalance in Osteoarthritic Cartilage. Arthritis Rheumatol 2019; 71:561-570. [PMID: 30298554 PMCID: PMC6593438 DOI: 10.1002/art.40748] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 10/02/2018] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Multiple single-nucleotide polymorphisms (SNPs) conferring susceptibility to osteoarthritis (OA) mark imbalanced expression of positional genes in articular cartilage, reflected by unequally expressed alleles among heterozygotes (allelic imbalance [AI]). We undertook this study to explore the articular cartilage transcriptome from OA patients for AI events to identify putative disease-driving genetic variation. METHODS AI was assessed in 42 preserved and 5 lesioned OA cartilage samples (from the Research Arthritis and Articular Cartilage study) for which RNA sequencing data were available. The count fraction of the alternative alleles among the alternative and reference alleles together (φ) was determined for heterozygous individuals. A meta-analysis was performed to generate a meta-φ and P value for each SNP with a false discovery rate (FDR) correction for multiple comparisons. To further validate AI events, we explored them as a function of multiple additional OA features. RESULTS We observed a total of 2,070 SNPs that consistently marked AI of 1,031 unique genes in articular cartilage. Of these genes, 49 were found to be significantly differentially expressed (fold change <0.5 or >2, FDR <0.05) between preserved and paired lesioned cartilage, and 18 had previously been reported to confer susceptibility to OA and/or related phenotypes. Moreover, we identified notable highly significant AI SNPs in the CRLF1, WWP2, and RPS3 genes that were related to multiple OA features. CONCLUSION We present a framework and resulting data set for researchers in the OA research field to probe for disease-relevant genetic variation that affects gene expression in pivotal disease-affected tissue. This likely includes putative novel compelling OA risk genes such as CRLF1, WWP2, and RPS3.
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Affiliation(s)
| | - Irina Pulyakhina
- Radboud University Medical Center Nijmegen, The Netherlands, and Wellcome Trust Centre for Human GeneticsOxfordUK
| | - Cindy Boer
- Erasmus Medical CenterRotterdamThe Netherlands
| | - Nils Bomer
- Leiden University Medical CenterLeidenThe Netherlands
| | | | | | | | | | - Thom Sentner
- Leiden University Medical CenterLeidenThe Netherlands
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17
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van der Gaag KJ, de Leeuw RH, Laros JFJ, den Dunnen JT, de Knijff P. Short hypervariable microhaplotypes: A novel set of very short high discriminating power loci without stutter artefacts. Forensic Sci Int Genet 2018; 35:169-175. [PMID: 29852469 DOI: 10.1016/j.fsigen.2018.05.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/03/2018] [Accepted: 05/16/2018] [Indexed: 12/12/2022]
Abstract
Since two decades, short tandem repeats (STRs) are the preferred markers for human identification, routinely analysed by fragment length analysis. Here we present a novel set of short hypervariable autosomal microhaplotypes (MH) that have four or more SNPs in a span of less than 70 nucleotides (nt). These MHs display a discriminating power approaching that of STRs and provide a powerful alternative for the analysis;1;is of forensic samples that are problematic when the STR fragment size range exceeds the integrity range of severely degraded DNA or when multiple donors contribute to an evidentiary stain and STR stutter artefacts complicate profile interpretation. MH typing was developed using the power of massively parallel sequencing (MPS) enabling new powerful, fast and efficient SNP-based approaches. MH candidates were obtained from queries in data of the 1000 Genomes, and Genome of the Netherlands (GoNL) projects. Wet-lab analysis of 276 globally dispersed samples and 97 samples of nine large CEPH families assisted locus selection and corroboration of informative value. We infer that MHs represent an alternative marker type with good discriminating power per locus (allowing the use of a limited number of loci), small amplicon sizes and absence of stutter artefacts that can be especially helpful when unbalanced mixed samples are submitted for human identification.
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Affiliation(s)
- Kristiaan J van der Gaag
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands; Division of Biological Traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497GB, The Hague, The Netherlands.
| | - Rick H de Leeuw
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands.
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands.
| | - Johan T den Dunnen
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands.
| | - Peter de Knijff
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands.
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18
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Grand Moursel L, van Roon-Mom WMC, Kiełbasa SM, Mei H, Buermans HPJ, van der Graaf LM, Hettne KM, de Meijer EJ, van Duinen SG, Laros JFJ, van Buchem MA, 't Hoen PAC, van der Maarel SM, van der Weerd L. Brain Transcriptomic Analysis of Hereditary Cerebral Hemorrhage With Amyloidosis-Dutch Type. Front Aging Neurosci 2018; 10:102. [PMID: 29706885 PMCID: PMC5908973 DOI: 10.3389/fnagi.2018.00102] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 03/26/2018] [Indexed: 11/23/2022] Open
Abstract
Hereditary cerebral hemorrhage with amyloidosis-Dutch type (HCHWA-D) is an early onset hereditary form of cerebral amyloid angiopathy (CAA) caused by a point mutation resulting in an amino acid change (NP_000475.1:p.Glu693Gln) in the amyloid precursor protein (APP). Post-mortem frontal and occipital cortical brain tissue from nine patients and nine age-related controls was used for RNA sequencing to identify biological pathways affected in HCHWA-D. Although previous studies indicated that pathology is more severe in the occipital lobe in HCHWA-D compared to the frontal lobe, the current study showed similar changes in gene expression in frontal and occipital cortex and the two brain regions were pooled for further analysis. Significantly altered pathways were analyzed using gene set enrichment analysis (GSEA) on 2036 significantly differentially expressed genes. Main pathways over-represented by down-regulated genes were related to cellular aerobic respiration (including ATP synthesis and carbon metabolism) indicating a mitochondrial dysfunction. Principal up-regulated pathways were extracellular matrix (ECM)–receptor interaction and ECM proteoglycans in relation with an increase in the transforming growth factor beta (TGFβ) signaling pathway. Comparison with the publicly available dataset from pre-symptomatic APP-E693Q transgenic mice identified overlap for the ECM–receptor interaction pathway, indicating that ECM modification is an early disease specific pathomechanism.
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Affiliation(s)
- Laure Grand Moursel
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Szymon M Kiełbasa
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
| | - Hailiang Mei
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
| | - Henk P J Buermans
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Linda M van der Graaf
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Kristina M Hettne
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Emile J de Meijer
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Sjoerd G van Duinen
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands.,Department of Clinical Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | | | - Louise van der Weerd
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
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19
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Suurmond J, Habets KLL, Tatum Z, Schonkeren JJ, Hoen PAC', Huizinga TWJ, Laros JFJ, Toes REM, Kurreeman F. Repeated FcεRI triggering reveals modified mast cell function related to chronic allergic responses in tissue. J Allergy Clin Immunol 2016; 138:869-880. [PMID: 27033170 DOI: 10.1016/j.jaci.2016.01.017] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2015] [Revised: 12/18/2015] [Accepted: 01/07/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND Activation of mast cells through FcεRI plays an important role in acute allergic reactions. However, little is known about the function of mast cells in patients with chronic allergic inflammation or the effect of repeated FcεRI triggering occurring in such responses. OBJECTIVE We aimed to identify changes in mast cell function after repeated FcεRI triggering and to correlate these changes to chronic allergic responses in tissue. METHODS Human cord blood-derived mast cells were treated for 2 weeks with anti-IgE. The function of naive or treated mast cells was analyzed by means of RNA sequencing, quantitative RT-PCR, flow cytometry, and functional assays. Protein secretion was measured with ELISAs and multiplex assays. RESULTS We observed several changes in mast cell function after repeated anti-IgE triggering. Although the acute response was dampened, we identified 289 genes significantly upregulated after repeated anti-IgE. Most of these genes (84%) were not upregulated after a single anti-IgE stimulus, indicating a significantly different response mode characterized by increased antigen presentation, response to bacteria, and chemotaxis. Changes in mast cell function were related to changes in expression of the transcription factors RXRA and BATF and others. Importantly, we found a substantial overlap between genes upregulated after repeated anti-IgE triggering and genes upregulated in tissue from patients with chronic allergy, in particular those of patients with chronic rhinosinusitis. CONCLUSION Our study provides evidence for intrinsic modulation of mast cell function on repeated FcεRI-mediated activation. The overlap with gene expression in tissues is suggestive of a direct link between repeated IgE-mediated activation of mast cells and chronic allergy.
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Affiliation(s)
- Jolien Suurmond
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Kim L L Habets
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zuotian Tatum
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Joris J Schonkeren
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Tom W J Huizinga
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - René E M Toes
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Fina Kurreeman
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands.
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20
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Hettne KM, Thompson M, van Haagen HHHBM, van der Horst E, Kaliyaperumal R, Mina E, Tatum Z, Laros JFJ, van Mulligen EM, Schuemie M, Aten E, Li TS, Bruskiewich R, Good BM, Su AI, Kors JA, den Dunnen J, van Ommen GJB, Roos M, ‘t Hoen PA, Mons B, Schultes EA. The Implicitome: A Resource for Rationalizing Gene-Disease Associations. PLoS One 2016; 11:e0149621. [PMID: 26919047 PMCID: PMC4769089 DOI: 10.1371/journal.pone.0149621] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 02/03/2016] [Indexed: 11/19/2022] Open
Abstract
High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.
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Affiliation(s)
- Kristina M. Hettne
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- * E-mail:
| | - Mark Thompson
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Eelke van der Horst
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Rajaram Kaliyaperumal
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Eleni Mina
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Zuotian Tatum
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen F. J. Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik M. van Mulligen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martijn Schuemie
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emmelien Aten
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Tong Shu Li
- Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, United States of America
| | | | - Benjamin M. Good
- Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Andrew I. Su
- Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Jan A. Kors
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Johan den Dunnen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gert-Jan B. van Ommen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A.C. ‘t Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Barend Mons
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Erik A. Schultes
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Institute for Advanced Computer Science, Leiden, The Netherlands
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21
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Li M, Rothwell R, Vermaat M, Wachsmuth M, Schröder R, Laros JFJ, van Oven M, de Bakker PIW, Bovenberg JA, van Duijn CM, van Ommen GJB, Slagboom PE, Swertz MA, Wijmenga C, Kayser M, Boomsma DI, Zöllner S, de Knijff P, Stoneking M. Transmission of human mtDNA heteroplasmy in the Genome of the Netherlands families: support for a variable-size bottleneck. Genome Res 2016; 26:417-26. [PMID: 26916109 PMCID: PMC4817766 DOI: 10.1101/gr.203216.115] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 01/21/2016] [Indexed: 12/17/2022]
Abstract
Although previous studies have documented a bottleneck in the transmission of mtDNA genomes from mothers to offspring, several aspects remain unclear, including the size and nature of the bottleneck. Here, we analyze the dynamics of mtDNA heteroplasmy transmission in the Genomes of the Netherlands (GoNL) data, which consists of complete mtDNA genome sequences from 228 trios, eight dizygotic (DZ) twin quartets, and 10 monozygotic (MZ) twin quartets. Using a minor allele frequency (MAF) threshold of 2%, we identified 189 heteroplasmies in the trio mothers, of which 59% were transmitted to offspring, and 159 heteroplasmies in the trio offspring, of which 70% were inherited from the mothers. MZ twin pairs exhibited greater similarity in MAF at heteroplasmic sites than DZ twin pairs, suggesting that the heteroplasmy MAF in the oocyte is the major determinant of the heteroplasmy MAF in the offspring. We used a likelihood method to estimate the effective number of mtDNA genomes transmitted to offspring under different bottleneck models; a variable bottleneck size model provided the best fit to the data, with an estimated mean of nine individual mtDNA genomes transmitted. We also found evidence for negative selection during transmission against novel heteroplasmies (in which the minor allele has never been observed in polymorphism data). These novel heteroplasmies are enhanced for tRNA and rRNA genes, and mutations associated with mtDNA diseases frequently occur in these genes. Our results thus suggest that the female germ line is able to recognize and select against deleterious heteroplasmies.
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Affiliation(s)
- Mingkun Li
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany; Fondation Mérieux, 69002 Lyon, France
| | - Rebecca Rothwell
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Martijn Vermaat
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Manja Wachsmuth
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Roland Schröder
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Jeroen F J Laros
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Mannis van Oven
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, The Netherlands
| | - Paul I W de Bakker
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands; Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands
| | - Jasper A Bovenberg
- Department of Biological Psychology, VU University Amsterdam, Amsterdam 1081 BT, The Netherlands
| | - Cornelia M van Duijn
- Legal Pathways Institute for Health and Bio Law, Aerdenhout 2111, The Netherlands
| | - Gert-Jan B van Ommen
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, The Netherlands
| | - P Eline Slagboom
- Department of Human Genetics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Morris A Swertz
- Section of Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands; Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | - Cisca Wijmenga
- Section of Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands; Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | | | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, The Netherlands
| | - Dorret I Boomsma
- Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Peter de Knijff
- Department of Human Genetics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Mark Stoneking
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
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22
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Abstract
The dystrophin protein encoding DMD gene is the longest human gene. The 2.2 Mb long human dystrophin transcript takes 16 hours to be transcribed and is co-transcriptionally spliced. It contains long introns (24 over 10kb long, 5 over 100kb long) and the heterogeneity in intron size makes it an ideal transcript to study different aspects of the human splicing process. Splicing is a complex process and much is unknown regarding the splicing of long introns in human genes. Here, we used ultra-deep transcript sequencing to characterize splicing of the dystrophin transcripts in 3 different human skeletal muscle cell lines, and explored the order of intron removal and multi-step splicing. Coverage and read pair analyses showed that around 40% of the introns were not always removed sequentially. Additionally, for the first time, we report that non-consecutive intron removal resulted in 3 or more joined exons which are flanked by unspliced introns and we defined these joined exons as an exon block. Lastly, computational and experimental data revealed that, for the majority of dystrophin introns, multistep splicing events are used to splice out a single intron. Overall, our data show for the first time in a human transcript, that multi-step intron removal is a general feature of mRNA splicing.
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Affiliation(s)
- Isabella Gazzoli
- a Department of Human Genetics , Leiden University Medical Center , Leiden , the Netherlands
| | - Irina Pulyakhina
- a Department of Human Genetics , Leiden University Medical Center , Leiden , the Netherlands
| | - Nisha E Verwey
- a Department of Human Genetics , Leiden University Medical Center , Leiden , the Netherlands
| | - Yavuz Ariyurek
- b Leiden Genome Technology Center, Leiden University Medical Center , Leiden , The Netherlands
| | - Jeroen F J Laros
- a Department of Human Genetics , Leiden University Medical Center , Leiden , the Netherlands.,b Leiden Genome Technology Center, Leiden University Medical Center , Leiden , The Netherlands
| | - Peter A C 't Hoen
- a Department of Human Genetics , Leiden University Medical Center , Leiden , the Netherlands
| | - Annemieke Aartsma-Rus
- a Department of Human Genetics , Leiden University Medical Center , Leiden , the Netherlands
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23
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Pulyakhina I, Gazzoli I, 't Hoen PAC, Verwey N, den Dunnen JT, Aartsma-Rus A, Laros JFJ. SplicePie: a novel analytical approach for the detection of alternative, non-sequential and recursive splicing. Nucleic Acids Res 2015; 43:11068. [PMID: 26450963 PMCID: PMC4678815 DOI: 10.1093/nar/gkv1062] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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24
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Vis JK, Vermaat M, Taschner PEM, Kok JN, Laros JFJ. An efficient algorithm for the extraction of HGVS variant descriptions from sequences. Bioinformatics 2015; 31:3751-7. [PMID: 26231427 DOI: 10.1093/bioinformatics/btv443] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 07/22/2015] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Unambiguous sequence variant descriptions are important in reporting the outcome of clinical diagnostic DNA tests. The standard nomenclature of the Human Genome Variation Society (HGVS) describes the observed variant sequence relative to a given reference sequence. We propose an efficient algorithm for the extraction of HGVS descriptions from two sequences with three main requirements in mind: minimizing the length of the resulting descriptions, minimizing the computation time and keeping the unambiguous descriptions biologically meaningful. RESULTS Our algorithm is able to compute the HGVS descriptions of complete chromosomes or other large DNA strings in a reasonable amount of computation time and its resulting descriptions are relatively small. Additional applications include updating of gene variant database contents and reference sequence liftovers. AVAILABILITY The algorithm is accessible as an experimental service in the Mutalyzer program suite (https://mutalyzer.nl). The C++ source code and Python interface are accessible at: https://github.com/mutalyzer/description-extractor. CONTACT j.k.vis@lumc.nl.
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Affiliation(s)
- Jonathan K Vis
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
| | - Martijn Vermaat
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter E M Taschner
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands, Generade Center of Expertise Genomics, University of Applied Sciences Leiden, Leiden, The Netherlands and
| | - Joost N Kok
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands, Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
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25
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Pulyakhina I, Gazzoli I, 't Hoen PAC, Verwey N, den Dunnen JT, den Dunnen J, Aartsma-Rus A, Laros JFJ. SplicePie: a novel analytical approach for the detection of alternative, non-sequential and recursive splicing. Nucleic Acids Res 2015; 43:e80. [PMID: 25800735 PMCID: PMC4499118 DOI: 10.1093/nar/gkv242] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 03/09/2015] [Indexed: 11/20/2022] Open
Abstract
Alternative splicing is a powerful mechanism present in eukaryotic cells to obtain a wide range of transcripts and protein isoforms from a relatively small number of genes. The mechanisms regulating (alternative) splicing and the paradigm of consecutive splicing have recently been challenged, especially for genes with a large number of introns. RNA-Seq, a powerful technology using deep sequencing in order to determine transcript structure and expression levels, is usually performed on mature mRNA, therefore not allowing detailed analysis of splicing progression. Sequencing pre-mRNA at different stages of splicing potentially provides insight into mRNA maturation. Although the number of tools that analyze total and cytoplasmic RNA in order to elucidate the transcriptome composition is rapidly growing, there are no tools specifically designed for the analysis of nuclear RNA (which contains mixtures of pre- and mature mRNA). We developed dedicated algorithms to investigate the splicing process. In this paper, we present a new classification of RNA-Seq reads based on three major stages of splicing: pre-, intermediate- and post-splicing. Applying this novel classification we demonstrate the possibility to analyze the order of splicing. Furthermore, we uncover the potential to investigate the multi-step nature of splicing, assessing various types of recursive splicing events. We provide the data that gives biological insight into the order of splicing, show that non-sequential splicing of certain introns is reproducible and coinciding in multiple cell lines. We validated our observations with independent experimental technologies and showed the reliability of our method. The pipeline, named SplicePie, is freely available at: https://github.com/pulyakhina/splicing_analysis_pipeline. The example data can be found at: https://barmsijs.lumc.nl/HG/irina/example_data.tar.gz.
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Affiliation(s)
- Irina Pulyakhina
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Isabella Gazzoli
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Nisha Verwey
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan T den Dunnen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan den Dunnen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Annemieke Aartsma-Rus
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
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Lips MA, Van Klinken JB, van Harmelen V, Dharuri HK, 't Hoen PAC, Laros JFJ, van Ommen GJ, Janssen IM, Van Ramshorst B, Van Wagensveld BA, Swank DJ, Van Dielen F, Dane A, Harms A, Vreeken R, Hankemeier T, Smit JWA, Pijl H, Willems van Dijk K. Roux-en-Y gastric bypass surgery, but not calorie restriction, reduces plasma branched-chain amino acids in obese women independent of weight loss or the presence of type 2 diabetes. Diabetes Care 2014; 37:3150-6. [PMID: 25315204 DOI: 10.2337/dc14-0195] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Obesity and type 2 diabetes mellitus (T2DM) have been associated with increased levels of circulating branched-chain amino acids (BCAAs) that may be involved in the pathogenesis of insulin resistance. However, weight loss has not been consistently associated with the reduction of BCAA levels. RESEARCH DESIGN AND METHODS We included 30 obese normal glucose-tolerant (NGT) subjects, 32 obese subjects with T2DM, and 12 lean female subjects. Obese subjects underwent either a restrictive procedure (gastric banding [GB], a very low-calorie diet [VLCD]), or a restrictive/bypass procedure (Roux-en-Y gastric bypass [RYGB] surgery). Fasting blood samples were taken for the determination of amine group containing metabolites 4 weeks before, as well as 3 weeks and 3 months after the intervention. RESULTS BCAA levels were higher in T2DM subjects, but not in NGT subjects, compared with lean subjects. Principal component (PC) analysis revealed a concise PC consisting of all BCAAs, which showed a correlation with measures of insulin sensitivity and glucose tolerance. Only after the RYGB procedure, and at both 3 weeks and 3 months, were circulating BCAA levels reduced. CONCLUSIONS Our data confirm an association between deregulation of BCAA metabolism in plasma and insulin resistance and glucose intolerance. Three weeks after undergoing RYGB surgery, a significant decrease in BCAAs in both NGT as well as T2DM subjects was observed. After 3 months, despite inducing significant weight loss, neither GB nor VLCD induced a reduction in BCAA levels. Our results indicate that the bypass procedure of RYGB surgery, independent of weight loss or the presence of T2DM, reduces BCAA levels in obese subjects.
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Affiliation(s)
- Mirjam A Lips
- Department of Endocrinology and Metabolism, Leiden University Medical Center, Leiden, the Netherlands
| | - Jan B Van Klinken
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Vanessa van Harmelen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Harish K Dharuri
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Gert-Jan van Ommen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Ignace M Janssen
- Department of Surgery, Rijnstate Ziekenhuis, Arnhem, the Netherlands
| | - Bert Van Ramshorst
- Department of Surgery, St. Antonius Ziekenhuis, Nieuwegein, the Netherlands
| | | | | | | | - Adrie Dane
- Leiden Amsterdam Centre for Drug Research, Netherlands Metabolomics Centre, Leiden, the Netherlands
| | - Amy Harms
- Leiden Amsterdam Centre for Drug Research, Netherlands Metabolomics Centre, Leiden, the Netherlands
| | - Rob Vreeken
- Leiden Amsterdam Centre for Drug Research, Netherlands Metabolomics Centre, Leiden, the Netherlands
| | - Thomas Hankemeier
- Leiden Amsterdam Centre for Drug Research, Netherlands Metabolomics Centre, Leiden, the Netherlands
| | - Johannes W A Smit
- Department of Endocrinology and Metabolism, Leiden University Medical Center, Leiden, the Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden, the Netherlands
| | - Hanno Pijl
- Department of Endocrinology and Metabolism, Leiden University Medical Center, Leiden, the Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden, the Netherlands
| | - Ko Willems van Dijk
- Department of Endocrinology and Metabolism, Leiden University Medical Center, Leiden, the Netherlands Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden, the Netherlands
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27
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Dharuri H, 't Hoen PAC, van Klinken JB, Henneman P, Laros JFJ, Lips MA, El Bouazzaoui F, van Ommen GJB, Janssen I, van Ramshorst B, van Wagensveld BA, Pijl H, Willems van Dijk K, van Harmelen V. Downregulation of the acetyl-CoA metabolic network in adipose tissue of obese diabetic individuals and recovery after weight loss. Diabetologia 2014; 57:2384-92. [PMID: 25099943 DOI: 10.1007/s00125-014-3347-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 07/10/2014] [Indexed: 01/18/2023]
Abstract
AIMS/HYPOTHESIS Not all obese individuals develop type 2 diabetes. Why some obese individuals retain normal glucose tolerance (NGT) is not well understood. We hypothesise that the biochemical mechanisms that underlie the function of adipose tissue can help explain the difference between obese individuals with NGT and those with type 2 diabetes. METHODS RNA sequencing was used to analyse the transcriptome of samples extracted from visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) of obese women with NGT or type 2 diabetes who were undergoing bariatric surgery. The gene expression data was analysed by bioinformatic visualisation and statistical analyses techniques. RESULTS A network-based approach to distinguish obese individuals with NGT from obese individuals with type 2 diabetes identified acetyl-CoA metabolic network downregulation as an important feature in the pathophysiology of type 2 diabetes in obese individuals. In general, genes within two reaction steps of acetyl-CoA were found to be downregulated in the VAT and SAT of individuals with type 2 diabetes. Upon weight loss and amelioration of metabolic abnormalities three months following bariatric surgery, the expression level of these genes recovered to levels seen in individuals with NGT. We report four novel genes associated with type 2 diabetes and recovery upon weight loss: ACAT1 (encoding acetyl-CoA acetyltransferase 1), ACACA (encoding acetyl-CoA carboxylase α), ALDH6A1 (encoding aldehyde dehydrogenase 6 family, member A1) and MTHFD1 (encoding methylenetetrahydrofolate dehydrogenase). CONCLUSIONS/INTERPRETATION Downregulation of the acetyl-CoA network in VAT and SAT is an important feature in the pathophysiology of type 2 diabetes in obese individuals. ACAT1, ACACA, ALDH6A1 and MTHFD1 represent novel biomarkers in adipose tissue associated with type 2 diabetes in obese individuals.
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Affiliation(s)
- Harish Dharuri
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, P.O. Box 9600, 2300 RC, Leiden, the Netherlands
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Anvar SY, van der Gaag KJ, van der Heijden JWF, Veltrop MHAM, Vossen RHAM, de Leeuw RH, Breukel C, Buermans HPJ, Verbeek JS, de Knijff P, den Dunnen JT, Laros JFJ. TSSV: a tool for characterization of complex allelic variants in pure and mixed genomes. Bioinformatics 2014; 30:1651-9. [DOI: 10.1093/bioinformatics/btu068] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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29
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Boomsma DI, Wijmenga C, Slagboom EP, Swertz MA, Karssen LC, Abdellaoui A, Ye K, Guryev V, Vermaat M, van Dijk F, Francioli LC, Hottenga JJ, Laros JFJ, Li Q, Li Y, Cao H, Chen R, Du Y, Li N, Cao S, van Setten J, Menelaou A, Pulit SL, Hehir-Kwa JY, Beekman M, Elbers CC, Byelas H, de Craen AJM, Deelen P, Dijkstra M, den Dunnen JT, de Knijff P, Houwing-Duistermaat J, Koval V, Estrada K, Hofman A, Kanterakis A, Enckevort DV, Mai H, Kattenberg M, van Leeuwen EM, Neerincx PBT, Oostra B, Rivadeneira F, Suchiman EHD, Uitterlinden AG, Willemsen G, Wolffenbuttel BH, Wang J, de Bakker PIW, van Ommen GJ, van Duijn CM. The Genome of the Netherlands: design, and project goals. Eur J Hum Genet 2014; 22:221-7. [PMID: 23714750 PMCID: PMC3895638 DOI: 10.1038/ejhg.2013.118] [Citation(s) in RCA: 178] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2012] [Revised: 02/28/2013] [Accepted: 03/24/2013] [Indexed: 11/09/2022] Open
Abstract
Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.
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Affiliation(s)
- Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Eline P Slagboom
- Molecular Epidemiology Section, Leiden University Medical Center, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Morris A Swertz
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Lennart C Karssen
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Kai Ye
- Molecular Epidemiology Section, Leiden University Medical Center, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn Vermaat
- Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Freerk van Dijk
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Laurent C Francioli
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Jeroen F J Laros
- Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | | | | | | | - Ning Li
- BGI-Europe, Copenhagen, Denmark
| | | | - Jessica van Setten
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Androniki Menelaou
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sara L Pulit
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jayne Y Hehir-Kwa
- Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Marian Beekman
- Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Clara C Elbers
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Heorhiy Byelas
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Anton J M de Craen
- Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Patrick Deelen
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Martijn Dijkstra
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Johan T den Dunnen
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter de Knijff
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeanine Houwing-Duistermaat
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Vyacheslav Koval
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Karol Estrada
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Alexandros Kanterakis
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | | | - Hailiang Mai
- Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
| | - Mathijs Kattenberg
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | | | - Pieter B T Neerincx
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Ben Oostra
- Department of Clinical Genetics, Erasmus University Medical School, Rotterdam, The Netherlands
| | - Fernanodo Rivadeneira
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Eka H D Suchiman
- Molecular Epidemiology Section, Leiden University Medical Center, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Andre G Uitterlinden
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Bruce H Wolffenbuttel
- LifeLines Cohort Study & Department of Endocrinology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jun Wang
- BGI-Shenzhen, Shenzhen, China
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
- The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Paul I W de Bakker
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gert-Jan van Ommen
- Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
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't Hoen PAC, Friedländer MR, Almlöf J, Sammeth M, Pulyakhina I, Anvar SY, Laros JFJ, Buermans HPJ, Karlberg O, Brännvall M, den Dunnen JT, van Ommen GJB, Gut IG, Guigó R, Estivill X, Syvänen AC, Dermitzakis ET, Lappalainen T. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat Biotechnol 2013; 31:1015-22. [DOI: 10.1038/nbt.2702] [Citation(s) in RCA: 193] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Accepted: 08/21/2013] [Indexed: 02/07/2023]
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Spitali P, van den Bergen JC, Verhaart IEC, Wokke B, Janson AAM, van den Eijnde R, den Dunnen JT, Laros JFJ, Verschuuren JJGM, 't Hoen PAC, Aartsma-Rus A. DMD transcript imbalance determines dystrophin levels. FASEB J 2013; 27:4909-16. [PMID: 23975932 DOI: 10.1096/fj.13-232025] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Duchenne and Becker muscular dystrophies are caused by out-of-frame and in-frame mutations, respectively, in the dystrophin encoding DMD gene. Molecular therapies targeting the precursor-mRNA are in clinical trials and show promising results. These approaches will depend on the stability and expression levels of dystrophin mRNA in skeletal muscles and heart. We report that the DMD gene is more highly expressed in heart than in skeletal muscles, in mice and humans. The transcript mutated in the mdx mouse model shows a 5' to 3' imbalance compared with that of its wild-type counterpart and reading frame restoration via antisense-mediated exon skipping does not correct this event. We also report significant transcript instability in 22 patients with Becker dystrophy, clarifying the fact that transcript imbalance is not caused by premature nonsense mutations. Finally, we demonstrate that transcript stability, rather than transcriptional rate, is an important determinant of dystrophin protein levels in patients with Becker dystrophy. We suggest that the availability of the complete transcript is a key factor to determine protein abundance and thus will influence the outcome of mRNA-targeting therapies.
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Affiliation(s)
- Pietro Spitali
- 1Department of Human Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands.
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Hilbers FS, Meijers CM, Laros JFJ, van Galen M, Hoogerbrugge N, Vasen HFA, Nederlof PM, Wijnen JT, van Asperen CJ, Devilee P. Exome sequencing of germline DNA from non-BRCA1/2 familial breast cancer cases selected on the basis of aCGH tumor profiling. PLoS One 2013; 8:e55734. [PMID: 23383274 PMCID: PMC3561352 DOI: 10.1371/journal.pone.0055734] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Accepted: 12/30/2012] [Indexed: 12/17/2022] Open
Abstract
The bulk of familial breast cancer risk (∼70%) cannot be explained by mutations in the known predisposition genes, primarily BRCA1 and BRCA2. Underlying genetic heterogeneity in these cases is the probable explanation for the failure of all attempts to identify further high-risk alleles. While exome sequencing of non-BRCA1/2 breast cancer cases is a promising strategy to detect new high-risk genes, rational approaches to the rigorous pre-selection of cases are needed to reduce heterogeneity. We selected six families in which the tumours of multiple cases showed a specific genomic profile on array comparative genomic hybridization (aCGH). Linkage analysis in these families revealed a region on chromosome 4 with a LOD score of 2.49 under homogeneity. We then analysed the germline DNA of two patients from each family using exome sequencing. Initially focusing on the linkage region, no potentially pathogenic variants could be identified in more than one family. Variants outside the linkage region were then analysed, and we detected multiple possibly pathogenic variants in genes that encode DNA integrity maintenance proteins. However, further analysis led to the rejection of all variants due to poor co-segregation or a relatively high allele frequency in a control population. We concluded that using CGH results to focus on a sub-set of families for sequencing analysis did not enable us to identify a common genetic change responsible for the aggregation of breast cancer in these families. Our data also support the emerging view that non-BRCA1/2 hereditary breast cancer families have a very heterogeneous genetic basis.
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Affiliation(s)
- Florentine S Hilbers
- Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands.
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Sun Y, Bak B, Schoenmakers N, van Trotsenburg ASP, Oostdijk W, Voshol P, Cambridge E, White JK, le Tissier P, Gharavy SNM, Martinez-Barbera JP, Stokvis-Brantsma WH, Vulsma T, Kempers MJ, Persani L, Campi I, Bonomi M, Beck-Peccoz P, Zhu H, Davis TME, Hokken-Koelega ACS, Del Blanco DG, Rangasami JJ, Ruivenkamp CAL, Laros JFJ, Kriek M, Kant SG, Bosch CAJ, Biermasz NR, Appelman-Dijkstra NM, Corssmit EP, Hovens GCJ, Pereira AM, den Dunnen JT, Wade MG, Breuning MH, Hennekam RC, Chatterjee K, Dattani MT, Wit JM, Bernard DJ. Loss-of-function mutations in IGSF1 cause an X-linked syndrome of central hypothyroidism and testicular enlargement. Nat Genet 2012; 44:1375-81. [PMID: 23143598 PMCID: PMC3511587 DOI: 10.1038/ng.2453] [Citation(s) in RCA: 143] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Accepted: 10/03/2012] [Indexed: 11/09/2022]
Abstract
Congenital central hypothyroidism occurs either in isolation or in conjunction with other pituitary hormone deficits. Using exome and candidate gene sequencing, we identified 8 distinct mutations and 2 deletions in IGSF1 in males from 11 unrelated families with central hypothyroidism, testicular enlargement and variably low prolactin concentrations. IGSF1 is a membrane glycoprotein that is highly expressed in the anterior pituitary gland, and the identified mutations impair its trafficking to the cell surface in heterologous cells. Igsf1-deficient male mice show diminished pituitary and serum thyroid-stimulating hormone (TSH) concentrations, reduced pituitary thyrotropin-releasing hormone (TRH) receptor expression, decreased triiodothyronine concentrations and increased body mass. Collectively, our observations delineate a new X-linked disorder in which loss-of-function mutations in IGSF1 cause central hypothyroidism, likely secondary to an associated impairment in pituitary TRH signaling.
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Affiliation(s)
- Yu Sun
- Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
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Laros JFJ, Blavier A, den Dunnen JT, Taschner PEM. A formalized description of the standard human variant nomenclature in Extended Backus-Naur Form. BMC Bioinformatics 2011; 12 Suppl 4:S5. [PMID: 21992071 PMCID: PMC3194197 DOI: 10.1186/1471-2105-12-s4-s5] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background The use of a standard human sequence variant nomenclature is advocated by the Human Genome Variation Society in order to unambiguously describe genetic variants in databases and literature. There is a clear need for tools that allow the mining of data about human sequence variants and their functional consequences from databases and literature. Existing text mining focuses on the recognition of protein variants and their effects. The recognition of variants at the DNA and RNA levels is essential for dissemination of variant data for diagnostic purposes. Development of new tools is hampered by the complexity of the current nomenclature, which requires processing at the character level to recognize the specific syntactic constructs used in variant descriptions. Results We approached the gene variant nomenclature as a scientific sublanguage and created two formal descriptions of the syntax in Extended Backus-Naur Form: one at the DNA-RNA level and one at the protein level. To ensure compatibility to older versions of the human sequence variant nomenclature, previously recommended variant description formats have been included. The first grammar versions were designed to help build variant description handling in the Alamut mutation interpretation software. The DNA and RNA level descriptions were then updated and used to construct the context-free parser of the Mutalyzer 2 sequence variant nomenclature checker, which has already been used to check more than one million variant descriptions. Conclusions The Extended Backus-Naur Form provided an overview of the full complexity of the syntax of the sequence variant nomenclature, which remained hidden in the textual format and the division of the recommendations across the DNA, RNA and protein sections of the Human Genome Variation Society nomenclature website (http://www.hgvs.org/mutnomen/). This insight into the syntax of the nomenclature could be used to design detailed and clear rules for software development. The Mutalyzer 2 parser demonstrated that it facilitated decomposition of complex variant descriptions into their individual parts. The Extended Backus-Naur Form or parts of it can be used or modified by adding rules, allowing the development of specific sequence variant text mining tools and other programs, which can generate or handle sequence variant descriptions.
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Affiliation(s)
- Jeroen F J Laros
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
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Fokkema IFAC, Taschner PEM, Schaafsma GCP, Celli J, Laros JFJ, den Dunnen JT. LOVD v.2.0: the next generation in gene variant databases. Hum Mutat 2011; 32:557-63. [PMID: 21520333 DOI: 10.1002/humu.21438] [Citation(s) in RCA: 718] [Impact Index Per Article: 55.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2010] [Accepted: 12/14/2010] [Indexed: 01/14/2023]
Abstract
Locus-Specific DataBases (LSDBs) store information on gene sequence variation associated with human phenotypes and are frequently used as a reference by researchers and clinicians. We developed the Leiden Open-source Variation Database (LOVD) as a platform-independent Web-based LSDB-in-a-Box package. LOVD was designed to be easy to set up and maintain and follows the Human Genome Variation Society (HGVS) recommendations. Here we describe LOVD v.2.0, which adds enhanced flexibility and functionality and has the capacity to store sequence variants in multiple genes per patient. To reduce redundancy, patient and sequence variant data are stored in separate tables. Tables are linked to generate connections between sequence variant data for each gene and every patient. The dynamic structure allows database managers to add custom columns. The database structure supports fast queries and allows storage of sequence variants from high-throughput sequence analysis, as demonstrated by the X-chromosomal Mental Retardation LOVD installation. LOVD contains measures to ensure database security from unauthorized access. Currently, the LOVD Website (http://www.LOVD.nl/) lists 71 public LOVD installations hosting 3,294 gene variant databases with 199,000 variants in 84,000 patients. To promote LSDB standardization and thereby database interoperability, we offer free server space and help to establish an LSDB on our Leiden server.
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Affiliation(s)
- Ivo F A C Fokkema
- Center of Human and Clinical Genetics, Department of Human Genetics, Leiden University Medical Center, Leiden, Nederland
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