301
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Xu X, Wang BS, Yu H. Intraspecies Genomic Divergence of a Fig Wasp Species Is Due to Geographical Barrier and Adaptation. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.764828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Understanding how intraspecies divergence results in speciation has great importance for our knowledge of evolutionary biology. Here we applied population genomics approaches to a fig wasp species (Valisia javana complex sp 1) to reveal its intraspecies differentiation and the underlying evolutionary dynamics. With re-sequencing data, we prove the Hainan Island population (DA) of sp1 genetically differ from the continental ones, then reveal the differed divergence pattern. DA has reduced SNP diversity but a higher proportion of population-specific structural variations (SVs), implying a restricted gene exchange. Based on SNPs, 32 differentiated islands containing 204 genes were detected, along with 1,532 population-specific SVs of DA overlapping 4,141 genes. The gene ontology (GO) enrichment analysis performed on differentiated islands linked to three significant GO terms on a basic metabolism process, with most of the genes failing to enrich. In contrast, population-specific SVs contributed more to the adaptation than the SNPs by linking to 59 terms that are crucial for wasp speciation, such as host reorganization and development regulation. In addition, the generalized dissimilarity modeling confirms the importance of environment difference on the genetic divergence within sp1. Hence, we assume the genetic divergence between DA and the continent due to not only the strait as a geographic barrier, but also adaptation. We reconstruct the demographic history within sp1. DA shares a similar population history with the nearby continental population, suggesting an incomplete divergence. Summarily, our results reveal how geographic barriers and adaptation both influence the genetic divergence at population-level, thereby increasing our knowledge on the potential speciation of non-model organisms.
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302
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Wang Y, Wang Q, Wu Z, Han GZ. Segregating Complete Tf2 Elements Are Largely Neutral in Fission Yeast. Genome Biol Evol 2021; 13:6430117. [PMID: 34791222 PMCID: PMC8634392 DOI: 10.1093/gbe/evab254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/10/2021] [Indexed: 11/14/2022] Open
Abstract
Transposable elements (TEs) comprise a large proportion of the eukaryote genomes. Yet it remains poorly understood how TEs influence the fitness of the hosts carrying them. Here, we empirically test the impact of TEs on the host fitness in the fission yeast Schizosaccharomyces pombe. We find that two families of TEs (Tf1 and Tf2 elements), both of which belong to long terminal repeat retrotransposons, are highly polymorphic among individual S. pombe strains. Only 13 complete Tf2 elements are identified in S. pombe laboratory strain 972. These 13 Tf2 elements integrated into host genomes in very recent time and are segregating within the S. pombe population. Through knocking out each of the 13 Tf2 elements in S. pombe strain 972, we find Tf2 knockout does not affect the host fitness, and Tf2 elements do not alter the expression of nearby genes. Challenged by diverse forms of stress, the Tf2 knockout strains do not exhibit different growth rates from wild-type strain. Together, we conclude that segregating complete Tf2 elements insertions are largely neutral to host fitness in the fission yeast. Our study provides genome-wide empirical support for the selfish nature of TEs in fission yeast.
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Affiliation(s)
- Yan Wang
- College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Qin Wang
- College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Zhiwei Wu
- College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Guan-Zhu Han
- College of Life Sciences, Nanjing Normal University, Nanjing, China
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303
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Bornowski N, Michel KJ, Hamilton JP, Ou S, Seetharam AS, Jenkins J, Grimwood J, Plott C, Shu S, Talag J, Kennedy M, Hundley H, Singan VR, Barry K, Daum C, Yoshinaga Y, Schmutz J, Hirsch CN, Hufford MB, de Leon N, Kaeppler SM, Buell CR. Genomic variation within the maize stiff-stalk heterotic germplasm pool. THE PLANT GENOME 2021; 14:e20114. [PMID: 34275202 DOI: 10.1002/tpg2.20114] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/06/2021] [Indexed: 05/28/2023]
Abstract
The stiff-stalk heterotic group in Maize (Zea mays L.) is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and, to a lesser extent, B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four expired Plant Variety Protection (ex-PVP) lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40, which is a Pioneer Hi-Bred International (PHI) early stiff-stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13-2.18 Gbp with N50 scaffold lengths >200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas, whereas transposable element (TE) annotation was conducted using de novo and homology-directed methodologies. Compared with the reference inbred B73, synteny analyses revealed extensive collinearity across the five stiff-stalk genomes, although unique components of the maize pangenome were detected. Comparison of this set of stiff-stalk inbreds with the original Iowa Stiff Stalk Synthetic breeding population revealed that these inbreds represent only a proportion of variation in the original stiff-stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original stiff-stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent stiff-stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pangenome.
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Affiliation(s)
- Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
| | - Kathryn J Michel
- Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
| | - John P Hamilton
- Dep. of Plant Biology, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
| | - Shujun Ou
- Dep. of Ecology, Evolution, and Organismal Biology, Iowa State Univ., 2200 Osborn Drive, Ames, IA, 50011, USA
| | - Arun S Seetharam
- Dep. of Ecology, Evolution, and Organismal Biology, Iowa State Univ., 2200 Osborn Drive, Ames, IA, 50011, USA
| | - Jerry Jenkins
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
| | - Jane Grimwood
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
| | - Chris Plott
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
| | - Shengqiang Shu
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Jayson Talag
- Arizona Genomics Institute, School of Plant Sciences, Univ. of Arizona, 1657 E Helen Street, Tucson, AZ, 85721, USA
| | - Megan Kennedy
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Hope Hundley
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Vasanth R Singan
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Kerrie Barry
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Chris Daum
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Yuko Yoshinaga
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Jeremy Schmutz
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Candice N Hirsch
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Matthew B Hufford
- Dep. of Ecology, Evolution, and Organismal Biology, Iowa State Univ., 2200 Osborn Drive, Ames, IA, 50011, USA
| | - Natalia de Leon
- Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Dep. of Energy, Great Lakes Bioenergy Research Center, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Dep. of Energy, Great Lakes Bioenergy Research Center, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Wisconsin Crop Innovation Center, Univ. of Wisconsin - Madison, 8520 University Green, Middleton, WI, 53562, USA
| | - C Robin Buell
- Dep. of Plant Biology, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
- Dep. of Energy, Great Lakes Bioenergy Research Center, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
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304
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Campbell-Staton SC, Arnold BJ, Gonçalves D, Granli P, Poole J, Long RA, Pringle RM. Ivory poaching and the rapid evolution of tusklessness in African elephants. Science 2021; 374:483-487. [PMID: 34672738 DOI: 10.1126/science.abe7389] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Shane C Campbell-Staton
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA.,Institute for Society and Genetics, University of California, Los Angeles, CA 90095, USA
| | - Brian J Arnold
- Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08540, USA
| | - Dominique Gonçalves
- Gorongosa National Park, Sofala 00000, Mozambique.,Durrell Institute of Conservation and Ecology, University of Kent, Canterbury CT2 7NR, UK
| | | | - Joyce Poole
- ElephantVoices, San Francisco, CA 94111, USA
| | - Ryan A Long
- Department of Fish and Wildlife Sciences, University of Idaho, Moscow, ID 83844, USA
| | - Robert M Pringle
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
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305
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Micklethwaite KP, Gowrishankar K, Gloss BS, Li Z, Street JA, Moezzi L, Mach MA, Sutrave G, Clancy LE, Bishop DC, Louie RHY, Cai C, Foox J, MacKay M, Sedlazeck FJ, Blombery P, Mason CE, Luciani F, Gottlieb DJ, Blyth E. Investigation of product-derived lymphoma following infusion of piggyBac-modified CD19 chimeric antigen receptor T cells. Blood 2021; 138:1391-1405. [PMID: 33974080 PMCID: PMC8532197 DOI: 10.1182/blood.2021010858] [Citation(s) in RCA: 143] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/24/2021] [Indexed: 11/20/2022] Open
Abstract
We performed a phase 1 clinical trial to evaluate outcomes in patients receiving donor-derived CD19-specific chimeric antigen receptor (CAR) T cells for B-cell malignancy that relapsed or persisted after matched related allogeneic hemopoietic stem cell transplant. To overcome the cost and transgene-capacity limitations of traditional viral vectors, CAR T cells were produced using the piggyBac transposon system of genetic modification. Following CAR T-cell infusion, 1 patient developed a gradually enlarging retroperitoneal tumor due to a CAR-expressing CD4+ T-cell lymphoma. Screening of other patients led to the detection, in an asymptomatic patient, of a second CAR T-cell tumor in thoracic para-aortic lymph nodes. Analysis of the first lymphoma showed a high transgene copy number, but no insertion into typical oncogenes. There were also structural changes such as altered genomic copy number and point mutations unrelated to the insertion sites. Transcriptome analysis showed transgene promoter-driven upregulation of transcription of surrounding regions despite insulator sequences surrounding the transgene. However, marked global changes in transcription predominantly correlated with gene copy number rather than insertion sites. In both patients, the CAR T-cell-derived lymphoma progressed and 1 patient died. We describe the first 2 cases of malignant lymphoma derived from CAR gene-modified T cells. Although CAR T cells have an enviable record of safety to date, our results emphasize the need for caution and regular follow-up of CAR T recipients, especially when novel methods of gene transfer are used to create genetically modified immune therapies. This trial was registered at www.anzctr.org.au as ACTRN12617001579381.
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MESH Headings
- Aged
- DNA Transposable Elements
- Gene Expression Regulation, Neoplastic
- Gene Transfer Techniques
- Humans
- Immunotherapy, Adoptive/adverse effects
- Immunotherapy, Adoptive/methods
- Leukemia, B-Cell/genetics
- Leukemia, B-Cell/therapy
- Lymphoma/etiology
- Lymphoma/genetics
- Lymphoma, B-Cell/genetics
- Lymphoma, B-Cell/therapy
- Male
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/therapeutic use
- T-Lymphocytes/metabolism
- Transcriptome
- Transgenes
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Affiliation(s)
- Kenneth P Micklethwaite
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology-ICPMR Westmead, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Kavitha Gowrishankar
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Brian S Gloss
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Ziduo Li
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Janine A Street
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Leili Moezzi
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Melanie A Mach
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Gaurav Sutrave
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Leighton E Clancy
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology-ICPMR Westmead, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - David C Bishop
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Raymond H Y Louie
- Kirby Institute, University of New South Wales, Sydney. NSW, Australia
| | - Curtis Cai
- Kirby Institute, University of New South Wales, Sydney. NSW, Australia
| | - Jonathan Foox
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY
| | - Matthew MacKay
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, College of Medicine, Baylor University, Houston, TX
| | - Piers Blombery
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Clinical Haematology, The Royal Melbourne Hospital and Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher E Mason
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY
- The Feil Family Brain and Mind Research Institute, New York, NY; and
- The WorldQuant Initiative for Quantitative Prediction, New York, NY
| | - Fabio Luciani
- Kirby Institute, University of New South Wales, Sydney. NSW, Australia
| | - David J Gottlieb
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Emily Blyth
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology-ICPMR Westmead, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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306
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Genome Comparisons of the Fission Yeasts Reveal Ancient Collinear Loci Maintained by Natural Selection. J Fungi (Basel) 2021; 7:jof7100864. [PMID: 34682285 PMCID: PMC8537764 DOI: 10.3390/jof7100864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
Fission yeasts have a unique life history and exhibit distinct evolutionary patterns from other yeasts. Besides, the species demonstrate stable genome structures despite the relatively fast evolution of their genomic sequences. To reveal what could be the reason for that, comparative genomic analyses were carried out. Our results provided evidence that the structural and sequence evolution of the fission yeasts were correlated. Moreover, we revealed ancestral locally collinear blocks (aLCBs), which could have been inherited from their last common ancestor. These aLCBs proved to be the most conserved regions of the genomes as the aLCBs contain almost eight genes/blocks on average in the same orientation and order across the species. Gene order of the aLCBs is mainly fission-yeast-specific but supports the idea of filamentous ancestors. Nevertheless, the sequences and gene structures within the aLCBs are as mutable as any sequences in other parts of the genomes. Although genes of certain Gene Ontology (GO) categories tend to cluster at the aLCBs, those GO enrichments are not related to biological functions or high co-expression rates, they are, rather, determined by the density of essential genes and Rec12 cleavage sites. These data and our simulations indicated that aLCBs might not only be remnants of ancestral gene order but are also maintained by natural selection.
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307
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Li H. New strategies to improve minimap2 alignment accuracy. Bioinformatics 2021; 37:4572-4574. [PMID: 34623391 PMCID: PMC8652018 DOI: 10.1093/bioinformatics/btab705] [Citation(s) in RCA: 512] [Impact Index Per Article: 128.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 11/13/2022] Open
Abstract
SUMMARY We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences. Now minimap2 v2.22 can more accurately map long reads to highly repetitive regions and align through insertions or deletions up to 100kb by default, addressing major weakness in minimap2 v2.18 or earlier. AVAILABILITY AND IMPLEMENTATION https://github.com/lh3/minimap2.
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Affiliation(s)
- Heng Li
- Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215, USA.,Harvard Medical School, 10 Shattuck St, Boston, MA 02215, USA
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308
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Chen Z, Guo X, Long J, Ping J, Li B, Fadden MK, Ahearn TU, Stram DO, Shu XO, Jia G, Figueroa J, Palmer JR, Sanderson M, Haiman CA, Blot WJ, Garcia-Closas M, Cai Q, Zheng W. Discovery of structural deletions in breast cancer predisposition genes using whole genome sequencing data from > 2000 women of African-ancestry. Hum Genet 2021; 140:1449-1457. [PMID: 34487234 PMCID: PMC9109261 DOI: 10.1007/s00439-021-02342-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/10/2021] [Indexed: 12/28/2022]
Abstract
Single germline nucleotide pathogenic variants have been identified in 12 breast cancer predisposition genes, but structural deletions in these genes remain poorly characterized. We conducted in-depth whole genome sequencing (WGS) in genomic DNA samples obtained from 1340 invasive breast cancer cases and 675 controls of African ancestry. We identified 25 deletions in the intragenic regions of ten established breast cancer predisposition genes based on a consensus call from six state-of-the-art SV callers. Overall, no significant case-control difference was found in the frequency of these deletions. However, 1.0% of cases and 0.3% of controls carried any of the eight putative protein-truncating rare deletions located in BRCA1, BRCA2, CDH1, TP53, NF1, RAD51D, RAD51C and CHEK2, resulting in an odds ratio (OR) of 3.29 (95% CI 0.74-30.16). We also identified a low-frequency deletion in NF1 associated with breast cancer risk (OR 1.93, 95% CI 1.14-3.42). In addition, we detected 56 deletions, including six putative protein-truncating deletions, in suspected breast predisposition genes. This is the first large study to systematically search for structural deletions in breast cancer predisposition genes. Many of the deletions, particularly those resulting in protein truncations, are likely to be pathogenic. Results from this study, if confirmed in future large-scale studies, could have significant implications for genetic testing for this common cancer.
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Affiliation(s)
- Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Mary Kay Fadden
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Jonine Figueroa
- Usher Institute and CRUK Edinburgh Centre, University of Edinburgh, Edinburgh, UK
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - William J Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | | | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, TN, 37203-1738, Nashville, USA.
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309
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Xu L, Yin L, Qi Y, Tan X, Gao M, Peng J. 3D disorganization and rearrangement of genome provide insights into pathogenesis of NAFLD by integrated Hi-C, Nanopore, and RNA sequencing. Acta Pharm Sin B 2021; 11:3150-3164. [PMID: 34729306 PMCID: PMC8546856 DOI: 10.1016/j.apsb.2021.03.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 01/30/2021] [Accepted: 02/07/2021] [Indexed: 12/12/2022] Open
Abstract
The three-dimensional (3D) conformation of chromatin is integral to the precise regulation of gene expression. The 3D genome and genomic variations in non-alcoholic fatty liver disease (NAFLD) are largely unknown, despite their key roles in cellular function and physiological processes. High-throughput chromosome conformation capture (Hi-C), Nanopore sequencing, and RNA-sequencing (RNA-seq) assays were performed on the liver of normal and NAFLD mice. A high-resolution 3D chromatin interaction map was generated to examine different 3D genome hierarchies including A/B compartments, topologically associated domains (TADs), and chromatin loops by Hi-C, and whole genome sequencing identifying structural variations (SVs) and copy number variations (CNVs) by Nanopore sequencing. We identified variations in thousands of regions across the genome with respect to 3D chromatin organization and genomic rearrangements, between normal and NAFLD mice, and revealed gene dysregulation frequently accompanied by these variations. Candidate target genes were identified in NAFLD, impacted by genetic rearrangements and spatial organization disruption. Our data provide a high-resolution 3D genome interaction resource for NAFLD investigations, revealed the relationship among genetic rearrangements, spatial organization disruption, and gene regulation, and identified candidate genes associated with these variations implicated in the pathogenesis of NAFLD. The newly findings offer insights into novel mechanisms of NAFLD pathogenesis and can provide a new conceptual framework for NAFLD therapy.
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Key Words
- 3C, chromosome conformation capture
- 3D genome
- 3D, three-dimensional
- ALT, alanine aminotransferase
- AST, aspartate aminotransferase
- Abcg5, ATP-binding cassette sub-family G member 5
- BWA, Burrows-Wheeler Aligner
- CNV, copy number variation
- Camk1d, calcium/calmodulin-dependent protein kinase type 1D
- Chr, chromosome
- Chromatin looping
- DEG, differentially expressed gene
- DEL, deletion
- DI, directionality index
- DUP, duplication
- Elovl6, elongation of very long chain fatty acids protein 6
- FDR, false discovery rate
- FFA, free fatty acid
- Fgfr2, fibroblast growth factor receptor 2
- GCKR, glucokinase regulator
- GO, gene ontology
- GSH, glutathione
- Gadd45g, growth arrest and DNA damage-inducible protein GADD45 gamma
- Grm8, metabotropic glutamate receptor 8
- Gsta1, glutathione S-transferase A1
- H&E, hematoxylin-eosin
- HFD, high-fat diet
- HSD17B13, hydroxysteroid 17-beta dehydrogenase 13
- Hi-C, high-throughput chromosome conformation capture
- IDE, interaction decay exponent
- INS, insertion
- INV, inversion
- IR, inclusion ratio
- IRGM, immunity related GTPase M
- IRS4, insulin receptor substrate 4
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- Kcnma1, calcium-activated potassium channel subunit alpha-1
- LPIN1, lipin 1
- MBOAT7, membrane bound O-acyltransferase domain containing 7
- MDA, malondialdehyde
- NAFLD, non-alcoholic fatty liver disease
- NF1, neurofibromin 1
- NGS, next-generation sequencing
- NOTCH1, notch receptor 1
- ONT, Oxford Nanopore Technologies
- PCA, principal component analysis
- PNPLA3, patatin like phospholipase domain containing 3
- PPP1R3B, protein phosphatase 1 regulatory subunit 3B
- PTEN, phosphatase and tensin homolog
- Pde4b, phosphodiesterase 4B
- Plce1, 1-phosphat-idylinositol 4,5-bisphosphate phosphodiesterase epsilon-1
- Plxnb1, Plexin-B1
- RB1, RB transcriptional corepressor 1
- RNA-seq, RNA-sequencing
- SD, standard deviation
- SOD, superoxide dismutase
- SV, structural variation
- Scd1, acyl-CoA desaturase 1
- Sugct, succinate-hydroxymethylglutarate CoA-transferase
- TAD, topologically associated domain
- TC, total cholesterol
- TG, triglyceride
- TM6SF2, transmembrane 6 superfamily member 2
- TP53, tumor protein p53
- TRA, translocation
- Topologically associated domain
- Transcriptome
- WGS, whole-genome sequencing
- Whole-genome sequencing
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Affiliation(s)
- Lina Xu
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Lianhong Yin
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Yan Qi
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Xuemei Tan
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Meng Gao
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Jinyong Peng
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
- Key Laboratory for Basic and Applied Research on Pharmacodynamics Substances of Traditional Chinese Medicine of Liaoning Province, Dalian Medical University, Dalian 116044, China
- National-Local Joint Engineering Research Center for Drug Development (R&D) of Neurodegenerative Diseases, Dalian Medical University, Dalian 116044, China
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310
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Cisarova K, Garavelli L, Caraffi SG, Peluso F, Valeri L, Gargano G, Gavioli S, Trimarchi G, Neri A, Campos-Xavier B, Superti-Furga A. A monoallelic SEC23A variant E599K associated with cranio-lenticulo-sutural dysplasia. Am J Med Genet A 2021; 188:319-325. [PMID: 34580982 PMCID: PMC9291540 DOI: 10.1002/ajmg.a.62506] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 11/06/2022]
Abstract
Cranio-lenticulo-sutural dysplasia (CLSD; MIM 607812) is a rare or underdiagnosed condition, as only two families have been reported. The original family (Boyadjiev et al., Human Genetics, 2003, 113, 1-9 and Boyadjiev et al., Nature Genetics, 2006, 38, 1192-1197) showed recessive inheritance of the condition with a biallelic SEC23A missense variant in affected individuals. In contrast, another child with sporadic CLSD had a monoallelic SEC23A variant inherited from the reportedly unaffected father (Boyadjiev et al., Clinical Genetics, 2011, 80, 169-176), raising questions on possible digenism. Here, we report a 2-month-old boy seen because of large fontanels with wide cranial sutures, a large forehead, hypertelorism, a thin nose, a high arched palate, and micrognathia. His mother was clinically unremarkable, while his father had a history of large fontanels in infancy who had closed only around age 10 years; he also had a large forehead, hypertelorism, a thin, beaked nose and was operated for bilateral glaucoma with exfoliation of the lens capsule. Trio genome sequencing and familial segregation revealed a monoallelic c.1795G > A transition in SEC23A that was de novo in the father and transmitted to the proband. The variant predicts a nonconservative substitution (p.E599K) in an ultra-conserved residue that is seen in 3D models of yeast SEC23 to be involved in direct binding between SEC23 and SAR1 subunits of the coat protein complex II coat. This observation confirms the link between SEC23A variants and CLSD but suggests that in addition to the recessive inheritance described in the original family, SEC23A variants may result in dominant inheritance of CLSD, possibly by a dominant-negative disruptive effect on the SEC23 multimer.
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Affiliation(s)
- Katarina Cisarova
- Division of Genetic Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Livia Garavelli
- Clinical Genetics Unit, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | | | - Francesca Peluso
- Clinical Genetics Unit, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Lara Valeri
- Clinical Genetics Unit, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Giancarlo Gargano
- Neonatal Intensive Care Unit, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Sara Gavioli
- Neonatal Intensive Care Unit, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Gabriele Trimarchi
- Clinical Genetics Unit, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Alberto Neri
- Ophthalmology Unit, Department of Surgery, Azienda USL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Belinda Campos-Xavier
- Division of Genetic Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Andrea Superti-Furga
- Division of Genetic Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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311
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Geryk J, Zinkova A, Zedníková I, Simková H, Stenzl V, Korabecna M. Improving structural variant clustering to reduce the negative effect of the breakpoint uncertainty problem. BMC Bioinformatics 2021; 22:464. [PMID: 34579642 PMCID: PMC8474851 DOI: 10.1186/s12859-021-04374-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/15/2021] [Indexed: 11/12/2022] Open
Abstract
Background Structural variants (SVs) represent an important source of genetic variation. One of the most critical problems in their detection is breakpoint uncertainty associated with the inability to determine their exact genomic position. Breakpoint uncertainty is a characteristic issue of structural variants detected via short-read sequencing methods and complicates subsequent population analyses. The commonly used heuristic strategy reduces this issue by clustering/merging nearby structural variants of the same type before the data from individual samples are merged. Results We compared the two most used dissimilarity measures for SV clustering in terms of Mendelian inheritance errors (MIE), kinship prediction, and deviation from Hardy–Weinberg equilibrium. We analyzed the occurrence of Mendelian-inconsistent SV clusters that can be collapsed into one Mendelian-consistent SV as a new measure of dataset consistency. We also developed a new method based on constrained clustering that explicitly identifies these types of clusters. Conclusions We found that the dissimilarity measure based on the distance between SVs breakpoints produces slightly better results than the measure based on SVs overlap. This difference is evident in trivial and corrected clustering strategy, but not in constrained clustering strategy. However, constrained clustering strategy provided the best results in all aspects, regardless of the dissimilarity measure used. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04374-3.
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Affiliation(s)
- Jan Geryk
- Department of Biology and Medical Genetics, Second Faculty of Medicine, Charles University and University Hospital Motol, V Úvalu 84, 15006, Prague, Czech Republic. .,Department of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Albertov 4, 128 00, Prague, Czech Republic.
| | - Alzbeta Zinkova
- Department of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Albertov 4, 128 00, Prague, Czech Republic
| | - Iveta Zedníková
- Department of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Albertov 4, 128 00, Prague, Czech Republic
| | - Halina Simková
- Department of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Albertov 4, 128 00, Prague, Czech Republic
| | - Vlastimil Stenzl
- Department of Forensic Genetics, Institute of Criminalistics, Strojnická 27, 170 89, Prague, Czech Republic
| | - Marie Korabecna
- Department of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Albertov 4, 128 00, Prague, Czech Republic
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312
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Fu Y, Mahmoud M, Muraliraman VV, Sedlazeck FJ, Treangen TJ. Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment. Gigascience 2021; 10:giab063. [PMID: 34561697 PMCID: PMC8463296 DOI: 10.1093/gigascience/giab063] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/22/2021] [Accepted: 08/29/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. FINDINGS We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. CONCLUSIONS Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.
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Affiliation(s)
- Yilei Fu
- Department of Computer Science, Rice University, Houston, TX 77251-1892, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX 77251-1892, USA
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313
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Yan SM, Sherman RM, Taylor DJ, Nair DR, Bortvin AN, Schatz MC, McCoy RC. Local adaptation and archaic introgression shape global diversity at human structural variant loci. eLife 2021; 10:e67615. [PMID: 34528508 PMCID: PMC8492059 DOI: 10.7554/elife.67615] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/14/2021] [Indexed: 12/13/2022] Open
Abstract
Large genomic insertions and deletions are a potent source of functional variation, but are challenging to resolve with short-read sequencing, limiting knowledge of the role of such structural variants (SVs) in human evolution. Here, we used a graph-based method to genotype long-read-discovered SVs in short-read data from diverse human genomes. We then applied an admixture-aware method to identify 220 SVs exhibiting extreme patterns of frequency differentiation - a signature of local adaptation. The top two variants traced to the immunoglobulin heavy chain locus, tagging a haplotype that swept to near fixation in certain southeast Asian populations, but is rare in other global populations. Further investigation revealed evidence that the haplotype traces to gene flow from Neanderthals, corroborating the role of immune-related genes as prominent targets of adaptive introgression. Our study demonstrates how recent technical advances can help resolve signatures of key evolutionary events that remained obscured within technically challenging regions of the genome.
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Affiliation(s)
- Stephanie M Yan
- Department of Biology, Johns Hopkins University, BaltimoreBaltimoreUnited States
| | - Rachel M Sherman
- Department of Computer Science, Johns Hopkins UniversityBaltimoreUnited States
| | - Dylan J Taylor
- Department of Biology, Johns Hopkins University, BaltimoreBaltimoreUnited States
| | - Divya R Nair
- Department of Biology, Johns Hopkins University, BaltimoreBaltimoreUnited States
| | - Andrew N Bortvin
- Department of Biology, Johns Hopkins University, BaltimoreBaltimoreUnited States
| | - Michael C Schatz
- Department of Biology, Johns Hopkins University, BaltimoreBaltimoreUnited States
- Department of Computer Science, Johns Hopkins UniversityBaltimoreUnited States
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, BaltimoreBaltimoreUnited States
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314
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Zhao H, Sun S, Ding Y, Wang Y, Yue X, Du X, Wei Q, Fan G, Sun H, Lou Y, Yang H, Wang J, Xu X, Li L, Yang K, Xu H, Wang J, Zhu C, Wang S, Shan X, Hou Y, Wang Y, Fei B, Liu X, Jiang Z, Gao Z. Analysis of 427 genomes reveals moso bamboo population structure and genetic basis of property traits. Nat Commun 2021; 12:5466. [PMID: 34526499 PMCID: PMC8443721 DOI: 10.1038/s41467-021-25795-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 08/24/2021] [Indexed: 02/08/2023] Open
Abstract
Moso bamboo (Phyllostachys edulis) is an economically and ecologically important nontimber forestry species. Further development of this species as a sustainable bamboo resource has been hindered by a lack of population genome information. Here, we report a moso bamboo genomic variation atlas of 5.45 million single-nucleotide polymorphisms (SNPs) from whole-genome resequencing of 427 individuals covering 15 representative geographic areas. We uncover low genetic diversity, high genotype heterozygosity, and genes under balancing selection underlying moso bamboo population adaptation. We infer its demographic history with one bottleneck and its recently small population without a rebound. We define five phylogenetic groups and infer that one group probably originated by a single-origin event from East China. Finally, we conduct genome-wide association analysis of nine important property-related traits to identify candidate genes, many of which are involved in cell wall, carbohydrate metabolism, and environmental adaptation. These results provide a foundation and resources for understanding moso bamboo evolution and the genetic mechanisms of agriculturally important traits.
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Affiliation(s)
- Hansheng Zhao
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Shuai Sun
- grid.21155.320000 0001 2034 1839BGI-Qingdao, BGI-Shenzhen, 266555 Qingdao, China ,China National GeneBank, BGI-Shenzhen, 518120 Shenzhen, China ,grid.410726.60000 0004 1797 8419College of Life Sciences, University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Yulong Ding
- grid.410625.40000 0001 2293 4910Bamboo Research Institute, Nanjing Forestry University, 210037 Nanjing, China
| | - Yue Wang
- grid.21155.320000 0001 2034 1839BGI-Qingdao, BGI-Shenzhen, 266555 Qingdao, China ,China National GeneBank, BGI-Shenzhen, 518120 Shenzhen, China
| | - Xianghua Yue
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Xiao Du
- grid.21155.320000 0001 2034 1839BGI-Qingdao, BGI-Shenzhen, 266555 Qingdao, China ,China National GeneBank, BGI-Shenzhen, 518120 Shenzhen, China ,grid.21155.320000 0001 2034 1839BGI-Shenzhen, 518083 Shenzhen, China
| | - Qiang Wei
- grid.410625.40000 0001 2293 4910Bamboo Research Institute, Nanjing Forestry University, 210037 Nanjing, China
| | - Guangyi Fan
- grid.21155.320000 0001 2034 1839BGI-Qingdao, BGI-Shenzhen, 266555 Qingdao, China ,China National GeneBank, BGI-Shenzhen, 518120 Shenzhen, China ,grid.21155.320000 0001 2034 1839BGI-Shenzhen, 518083 Shenzhen, China ,grid.21155.320000 0001 2034 1839State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, 518083 Shenzhen, China
| | - Huayu Sun
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Yongfeng Lou
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Huanming Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, 518083 Shenzhen, China ,grid.21155.320000 0001 2034 1839Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI-Shenzhen, 518120 Shenzhen, China
| | - Jian Wang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, 518083 Shenzhen, China ,grid.13402.340000 0004 1759 700XJames D. Watson Institute of Genome Science, 310008 Hangzhou, China
| | - Xun Xu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, 518083 Shenzhen, China ,grid.21155.320000 0001 2034 1839Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI-Shenzhen, 518120 Shenzhen, China
| | - Lichao Li
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Kebin Yang
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Hao Xu
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Jiongliang Wang
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Chenglei Zhu
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Sining Wang
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Xuemeng Shan
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Yinguang Hou
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Yu Wang
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Benhua Fei
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Xin Liu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, 518083 Shenzhen, China ,grid.21155.320000 0001 2034 1839State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, 518083 Shenzhen, China ,grid.21155.320000 0001 2034 1839BGI-Beijing, BGI-Shenzhen, 100101 Beijing, China ,grid.21155.320000 0001 2034 1839BGI-Fuyang, BGI-Shenzhen, 236009 Fuyang, China
| | - Zehui Jiang
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
| | - Zhimin Gao
- Institute of Gene Science and Industrialization for Bamboo and Rattan Resources, International Center for Bamboo and Rattan, 100102 Beijing, China ,Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, 100102 Beijing, China
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315
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Li G, Jiang T, Li J, Wang Y. PanSVR: Pan-Genome Augmented Short Read Realignment for Sensitive Detection of Structural Variations. Front Genet 2021; 12:731515. [PMID: 34490049 PMCID: PMC8417358 DOI: 10.3389/fgene.2021.731515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 07/26/2021] [Indexed: 01/10/2023] Open
Abstract
The comprehensive discovery of structure variations (SVs) is fundamental to many genomics studies and high-throughput sequencing has become a common approach to this task. However, due the limited length, it is still non-trivial to state-of-the-art tools to accurately align short reads and produce high-quality SV callsets. Pan-genome provides a novel and promising framework to short read-based SV calling since it enables to comprehensively integrate known variants to reduce the incompleteness and bias of single reference to breakthrough the bottlenecks of short read alignments and provide new evidences to the detection of SVs. However, it is still an open problem to develop effective computational approaches to fully take the advantage of pan-genomes. Herein, we propose Pan-genome augmented Structure Variation calling tool with read Re-alignment (PanSVR), a novel pan-genome-based SV calling approach. PanSVR uses several tailored methods to implement precise re-alignment for SV-spanning reads against well-organized pan-genome reference with plenty of known SVs. PanSVR enables to greatly improve the quality of short read alignments and produce clear and homogenous SV signatures which facilitate SV calling. Benchmark results on real sequencing data suggest that PanSVR is able to largely improve the sensitivity of SV calling than that of state-of-the-art SV callers, especially for the SVs from repeat-rich regions and/or novel insertions which are difficult to existing tools.
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Affiliation(s)
- Gaoyang Li
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tao Jiang
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Junyi Li
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.,School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
| | - Yadong Wang
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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316
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Xia L, Wang Z, Wu X, Zeng T, Luo W, Hu X, Ni Y, Che G, Liu L, Zhang W, Xie D, Li W. Multiplatform discovery and regulatory function analysis of structural variations in non-small cell lung carcinoma. Cell Rep 2021; 36:109660. [PMID: 34496260 DOI: 10.1016/j.celrep.2021.109660] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/06/2021] [Accepted: 08/11/2021] [Indexed: 02/08/2023] Open
Abstract
Non-small cell lung carcinoma (NSCLC), the most common form of lung cancer, is the leading cause of cancer-related death worldwide. We perform whole-genome sequencing (WGS) on samples from 43 primary patients with NSCLC and matched normal samples and analyze their matched open chromatin data and transcriptome data. Our results indicate that next-generation sequencing (NGS) and the Bionano Genomics (BNG) platform should be viewed as complementary technologies in terms of structural variations detection. By creating a framework integrating these two platforms, we detect high-technical-confidence somatic structural variations (SVs) in NSCLC cases, which could aid in the efficient investigation of new candidate oncogenes, such as TRIO and SESTD1. Our findings highlight the impact of somatic SVs on NSCLC oncogenesis and lay a foundation for exploring associations among somatic SVs, gene expression, and regulatory networks in patients with NSCLC.
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Affiliation(s)
- Lin Xia
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China
| | - Zhoufeng Wang
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China; Precision Medicine Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China; The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Xinyue Wu
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China
| | - Tianfu Zeng
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China
| | - Wenxin Luo
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Xinlei Hu
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China
| | - Yinyun Ni
- Precision Medicine Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Guowei Che
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Lunxu Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Wei Zhang
- Department of Respiratory and Critical Care Medicine, Shanghai Changhai Hospital, the Second Military Medical University, No. 168 Changhai Road, Shanghai 200433, China
| | - Dan Xie
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China; Precision Medicine Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China.
| | - Weimin Li
- Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China; Precision Medicine Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China; Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China; The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China.
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317
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Foox J, Tighe SW, Nicolet CM, Zook JM, Byrska-Bishop M, Clarke WE, Khayat MM, Mahmoud M, Laaguiby PK, Herbert ZT, Warner D, Grills GS, Jen J, Levy S, Xiang J, Alonso A, Zhao X, Zhang W, Teng F, Zhao Y, Lu H, Schroth GP, Narzisi G, Farmerie W, Sedlazeck FJ, Baldwin DA, Mason CE. Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study. Nat Biotechnol 2021; 39:1129-1140. [PMID: 34504351 PMCID: PMC8985210 DOI: 10.1038/s41587-021-01049-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 08/05/2021] [Indexed: 02/08/2023]
Abstract
Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2 × 250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2 × 250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling.
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Affiliation(s)
- Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Scott W Tighe
- University of Vermont Cancer Center, Vermont Integrative Genomics Resource, University of Vermont, Burlington, VT, USA
| | - Charles M Nicolet
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Justin M Zook
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | | | - Michael M Khayat
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Phoebe K Laaguiby
- University of Vermont Cancer Center, Vermont Integrative Genomics Resource, University of Vermont, Burlington, VT, USA
| | - Zachary T Herbert
- Molecular Biology Core Facilities, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Derek Warner
- DNA Sequencing Core, University of Utah, Salt Lake City, UT, USA
| | - George S Grills
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Jin Jen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Jenny Xiang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Alicia Alonso
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Xia Zhao
- BGI-Shenzhen, Shenzhen, China
- MGI, BGI-Shenzhen, Shenzhen, China
| | | | | | - Yonggang Zhao
- BGI-Shenzhen, Shenzhen, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Haorong Lu
- BGI-Shenzhen, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
| | | | | | - William Farmerie
- Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Don A Baldwin
- Department of Pathology, Fox Chase Cancer Center, Philadelphia, PA, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- The Feil Family Brain and Mind Research Institute, New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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318
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Abstract
Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale.
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Affiliation(s)
- Wouter De Coster
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Applied and Translational Neurogenomics Group, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
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319
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Hufford MB, Seetharam AS, Woodhouse MR, Chougule KM, Ou S, Liu J, Ricci WA, Guo T, Olson A, Qiu Y, Della Coletta R, Tittes S, Hudson AI, Marand AP, Wei S, Lu Z, Wang B, Tello-Ruiz MK, Piri RD, Wang N, Kim DW, Zeng Y, O'Connor CH, Li X, Gilbert AM, Baggs E, Krasileva KV, Portwood JL, Cannon EKS, Andorf CM, Manchanda N, Snodgrass SJ, Hufnagel DE, Jiang Q, Pedersen S, Syring ML, Kudrna DA, Llaca V, Fengler K, Schmitz RJ, Ross-Ibarra J, Yu J, Gent JI, Hirsch CN, Ware D, Dawe RK. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science 2021; 373:655-662. [PMID: 34353948 PMCID: PMC8733867 DOI: 10.1126/science.abg5289] [Citation(s) in RCA: 325] [Impact Index Per Article: 81.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 06/24/2021] [Indexed: 12/24/2022]
Abstract
We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.
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Affiliation(s)
- Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Arun S Seetharam
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
- Genome Informatics Facility, Iowa State University, Ames, IA 50011, USA
| | - Margaret R Woodhouse
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | | | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Jianing Liu
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - William A Ricci
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Andrew Olson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Silas Tittes
- Center for Population Biology, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Center for Population Biology, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | | | - Sharon Wei
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Zhenyuan Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Bo Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | | | - Rebecca D Piri
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | - Na Wang
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Dong Won Kim
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Yibing Zeng
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108, USA
| | - Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Erin Baggs
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Ksenia V Krasileva
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - John L Portwood
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - Ethalinda K S Cannon
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - Carson M Andorf
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - Nancy Manchanda
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Samantha J Snodgrass
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - David E Hufnagel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| | - Qiuhan Jiang
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Sarah Pedersen
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Michael L Syring
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - David A Kudrna
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
| | | | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - Jeffrey Ross-Ibarra
- Center for Population Biology, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
- Genome Center, University of California, Davis, CA 95616, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Jonathan I Gent
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Doreen Ware
- USDA-ARS NAA Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, NY 14853, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - R Kelly Dawe
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA.
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320
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Jun G, Sedlazeck F, Zhu Q, English A, Metcalf G, Kang HM, Human Genome Structural Variation Consortium (HGSVC), Lee C, Gibbs R, Boerwinkle E. muCNV: Genotyping Structural Variants for Population-level Sequencing. Bioinformatics 2021; 37:2055–2057. [PMID: 33760063 PMCID: PMC8496513 DOI: 10.1093/bioinformatics/btab199] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 01/31/2021] [Accepted: 03/13/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION There are high demands for joint genotyping of structural variations with short-read sequencing, but efficient and accurate genotyping in population scale is a challenging task. RESULTS We developed muCNV that aggregates per-sample summary pileups for joint genotyping of > 100,000 samples. Pilot results show very low Mendelian inconsistencies. Applications to large-scale projects in cloud show the computational efficiencies of muCNV genotyping pipeline. AVAILABILITY muCNV is publicly available for download at: https://github.com/gjun/muCNV. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Goo Jun
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fritz Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Qihui Zhu
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Adam English
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hyun Min Kang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Charles Lee
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Richard Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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321
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Rajamäki K, Taira A, Katainen R, Välimäki N, Kuosmanen A, Plaketti RM, Seppälä TT, Ahtiainen M, Wirta EV, Vartiainen E, Sulo P, Ravantti J, Lehtipuro S, Granberg KJ, Nykter M, Tanskanen T, Ristimäki A, Koskensalo S, Renkonen-Sinisalo L, Lepistö A, Böhm J, Taipale J, Mecklin JP, Aavikko M, Palin K, Aaltonen LA. Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease-Associated Colorectal Cancer. Gastroenterology 2021; 161:592-607. [PMID: 33930428 DOI: 10.1053/j.gastro.2021.04.042] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/16/2021] [Accepted: 04/16/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Inflammatory bowel disease (IBD) is a chronic, relapsing inflammatory disorder associated with an elevated risk of colorectal cancer (CRC). IBD-associated CRC (IBD-CRC) may represent a distinct pathway of tumorigenesis compared to sporadic CRC (sCRC). Our aim was to comprehensively characterize IBD-associated tumorigenesis integrating multiple high-throughput approaches, and to compare the results with in-house data sets from sCRCs. METHODS Whole-genome sequencing, single nucleotide polymorphism arrays, RNA sequencing, genome-wide methylation analysis, and immunohistochemistry were performed using fresh-frozen and formalin-fixed tissue samples of tumor and corresponding normal tissues from 31 patients with IBD-CRC. RESULTS Transcriptome-based tumor subtyping revealed the complete absence of canonical epithelial tumor subtype associated with WNT signaling in IBD-CRCs, dominated instead by mesenchymal stroma-rich subtype. Negative WNT regulators AXIN2 and RNF43 were strongly down-regulated in IBD-CRCs and chromosomal gains at HNF4A, a negative regulator of WNT-induced epithelial-mesenchymal transition (EMT), were less frequent compared to sCRCs. Enrichment of hypomethylation at HNF4α binding sites was detected solely in sCRC genomes. PIGR and OSMR involved in mucosal immunity were dysregulated via epigenetic modifications in IBD-CRCs. Genome-wide analysis showed significant enrichment of noncoding mutations to 5'untranslated region of TP53 in IBD-CRCs. As reported previously, somatic mutations in APC and KRAS were less frequent in IBD-CRCs compared to sCRCs. CONCLUSIONS Distinct mechanisms of WNT pathway dysregulation skew IBD-CRCs toward mesenchymal tumor subtype, which may affect prognosis and treatment options. Increased OSMR signaling may favor the establishment of mesenchymal tumors in patients with IBD.
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Affiliation(s)
- Kristiina Rajamäki
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.
| | - Aurora Taira
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Riku Katainen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Niko Välimäki
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Anna Kuosmanen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Roosa-Maria Plaketti
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Toni T Seppälä
- Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland; Department of Surgery, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland; Department of Surgical Oncology, Johns Hopkins University, Baltimore, Maryland
| | - Maarit Ahtiainen
- Department of Pathology, Central Finland Health Care District, Jyväskylä, Finland
| | - Erkki-Ville Wirta
- Department of Gastroenterology and Alimentary Tract Surgery, Tampere University Hospital, Tampere, Finland
| | - Emilia Vartiainen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Päivi Sulo
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Janne Ravantti
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Suvi Lehtipuro
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Tays Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Kirsi J Granberg
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Tays Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Matti Nykter
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Tays Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Tomas Tanskanen
- Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland
| | - Ari Ristimäki
- Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland; Department of Pathology, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Selja Koskensalo
- Department of Gastrointestinal Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Laura Renkonen-Sinisalo
- Department of Gastrointestinal Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Anna Lepistö
- Department of Gastrointestinal Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Jan Böhm
- Department of Pathology, Central Finland Health Care District, Jyväskylä, Finland
| | - Jussi Taipale
- Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland; Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden; Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Jukka-Pekka Mecklin
- Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland; Department of Education and Research, Central Finland Central Hospital, Jyväskylä, Finland
| | - Mervi Aavikko
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland; Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kimmo Palin
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Lauri A Aaltonen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.
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322
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Ellis DA, Reyes-Martín F, Rodríguez-López M, Cotobal C, Sun XM, Saintain Q, Jeffares DC, Marguerat S, Tallada VA, Bähler J. R-loops and regulatory changes in chronologically ageing fission yeast cells drive non-random patterns of genome rearrangements. PLoS Genet 2021; 17:e1009784. [PMID: 34464389 PMCID: PMC8437301 DOI: 10.1371/journal.pgen.1009784] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 09/13/2021] [Accepted: 08/18/2021] [Indexed: 12/03/2022] Open
Abstract
Aberrant repair of DNA double-strand breaks can recombine distant chromosomal breakpoints. Chromosomal rearrangements compromise genome function and are a hallmark of ageing. Rearrangements are challenging to detect in non-dividing cell populations, because they reflect individually rare, heterogeneous events. The genomic distribution of de novo rearrangements in non-dividing cells, and their dynamics during ageing, remain therefore poorly characterized. Studies of genomic instability during ageing have focussed on mitochondrial DNA, small genetic variants, or proliferating cells. To characterize genome rearrangements during cellular ageing in non-dividing cells, we interrogated a single diagnostic measure, DNA breakpoint junctions, using Schizosaccharomyces pombe as a model system. Aberrant DNA junctions that accumulated with age were associated with microhomology sequences and R-loops. Global hotspots for age-associated breakpoint formation were evident near telomeric genes and linked to remote breakpoints elsewhere in the genome, including the mitochondrial chromosome. Formation of breakpoint junctions at global hotspots was inhibited by the Sir2 histone deacetylase and might be triggered by an age-dependent de-repression of chromatin silencing. An unexpected mechanism of genomic instability may cause more local hotspots: age-associated reduction in an RNA-binding protein triggering R-loops at target loci. This result suggests that biological processes other than transcription or replication can drive genome rearrangements. Notably, we detected similar signatures of genome rearrangements that accumulated in old brain cells of humans. These findings provide insights into the unique patterns and possible mechanisms of genome rearrangements in non-dividing cells, which can be promoted by ageing-related changes in gene-regulatory proteins.
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Affiliation(s)
- David A. Ellis
- Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
| | - Félix Reyes-Martín
- Centro Andaluz de Biología del Desarrollo, Universidad Pablo de Olavide/Consejo Superior de Investigaciones Científicas, Seville, Spain
| | - María Rodríguez-López
- Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
| | - Cristina Cotobal
- Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
| | - Xi-Ming Sun
- MRC London Institute of Medical Sciences, London, United Kingdom
- Institute of Clinical Sciences, Imperial College London, London, United Kingdom
| | - Quentin Saintain
- Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
| | - Daniel C. Jeffares
- Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
| | - Samuel Marguerat
- MRC London Institute of Medical Sciences, London, United Kingdom
- Institute of Clinical Sciences, Imperial College London, London, United Kingdom
| | - Víctor A. Tallada
- Centro Andaluz de Biología del Desarrollo, Universidad Pablo de Olavide/Consejo Superior de Investigaciones Científicas, Seville, Spain
| | - Jürg Bähler
- Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
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323
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Retinoblastoma from human stem cell-derived retinal organoids. Nat Commun 2021; 12:4535. [PMID: 34315877 PMCID: PMC8316454 DOI: 10.1038/s41467-021-24781-7] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/30/2021] [Indexed: 12/13/2022] Open
Abstract
Retinoblastoma is a childhood cancer of the developing retina that initiates with biallelic inactivation of the RB1 gene. Children with germline mutations in RB1 have a high likelihood of developing retinoblastoma and other malignancies later in life. Genetically engineered mouse models of retinoblastoma share some similarities with human retinoblastoma but there are differences in their cellular differentiation. To develop a laboratory model of human retinoblastoma formation, we make induced pluripotent stem cells (iPSCs) from 15 participants with germline RB1 mutations. Each of the stem cell lines is validated, characterized and then differentiated into retina using a 3-dimensional organoid culture system. After 45 days in culture, the retinal organoids are dissociated and injected into the vitreous of eyes of immunocompromised mice to support retinoblastoma tumor growth. Retinoblastomas formed from retinal organoids made from patient-derived iPSCs have molecular, cellular and genomic features indistinguishable from human retinoblastomas. This model of human cancer based on patient-derived iPSCs with germline cancer predisposing mutations provides valuable insights into the cellular origins of this debilitating childhood disease as well as the mechanism of tumorigenesis following RB1 gene inactivation. Retinoblastoma is a heritable pediatric cancer driven by mutations in RB1. Here, the authors demonstrate the first patient derived model of retinoblastoma using iPSCs from patients with germline mutations in RB1.
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Abstract
Mycobacterium tuberculosis complex (MTBC) species are classic examples of genetically monomorphic microorganisms due to their low genetic variability. Whole-genome sequencing made it possible to describe both the main species within the complex and M. tuberculosis lineages and sublineages. This differentiation is based on single nucleotide polymorphisms (SNPs) and large sequence polymorphisms in the so-called regions of difference (RDs). Although a number of studies have been performed to elucidate RD localizations, their distribution among MTBC species, and their role in the bacterial life cycle, there are some inconsistencies and ambiguities in the localization of RDs in different members of the complex. To address this issue, we conducted a thorough search for all possible deletions in the WGS data collection comprising 721 samples representing the full MTBC diversity. Discovered deletions were compared with a list of all previously described RDs. As with the SNP-based analysis, we confirmed the specificities of 79 regions at the species, lineage, or sublineage level, 17 of which are described for the first time. We also present RDscan (https://github.com/dbespiatykh/RDscan), an open-source workflow, which detects deletions from short-read sequencing data and correlates the results with high-specificity RDs, curated in this study. Testing of the workflow on a collection comprising ∼7,000 samples showed a high specificity of the found RDs. This study provides novel details that can contribute to a better understanding of the species differentiation within the MTBC and can help to determine how individual clusters evolve within various MTBC species. IMPORTANCE Reductive genome evolution is one of the most important and intriguing adaptation strategies of different living organisms to their environment. Mycobacterium offers several notorious examples of either naturally reduced (Mycobacterium leprae) or laboratory-reduced (Mycobacterium bovis BCG) genomes. Mycobacterium tuberculosis complex has its phylogeny unambiguously framed by large sequence polymorphisms that present unidirectional unique event changes. In the present study, we curated all known regions of difference and analyzed both Mycobacterium tuberculosis and animal-adapted MTBC species. For 79 loci, we have shown a relationship with phylogenetic units, which can serve as a marker for diagnosing or studying biological effects. Moreover, intersections were found for some loci, which may indicate the nonrandomness of these processes and the involvement of these regions in the adaptation of bacteria to external conditions.
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325
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How Important Are Structural Variants for Speciation? Genes (Basel) 2021; 12:genes12071084. [PMID: 34356100 PMCID: PMC8305853 DOI: 10.3390/genes12071084] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/04/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Understanding the genetic basis of reproductive isolation is a central issue in the study of speciation. Structural variants (SVs); that is, structural changes in DNA, including inversions, translocations, insertions, deletions, and duplications, are common in a broad range of organisms and have been hypothesized to play a central role in speciation. Recent advances in molecular and statistical methods have identified structural variants, especially inversions, underlying ecologically important traits; thus, suggesting these mutations contribute to adaptation. However, the contribution of structural variants to reproductive isolation between species—and the underlying mechanism by which structural variants most often contribute to speciation—remain unclear. Here, we review (i) different mechanisms by which structural variants can generate or maintain reproductive isolation; (ii) patterns expected with these different mechanisms; and (iii) relevant empirical examples of each. We also summarize the available sequencing and bioinformatic methods to detect structural variants. Lastly, we suggest empirical approaches and new research directions to help obtain a more complete assessment of the role of structural variants in speciation.
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326
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Torres DE, Thomma BPHJ, Seidl MF. Transposable Elements Contribute to Genome Dynamics and Gene Expression Variation in the Fungal Plant Pathogen Verticillium dahliae. Genome Biol Evol 2021; 13:evab135. [PMID: 34100895 PMCID: PMC8290119 DOI: 10.1093/gbe/evab135] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 12/12/2022] Open
Abstract
Transposable elements (TEs) are a major source of genetic and regulatory variation in their host genome and are consequently thought to play important roles in evolution. Many fungal and oomycete plant pathogens have evolved dynamic and TE-rich genomic regions containing genes that are implicated in host colonization and adaptation. TEs embedded in these regions have typically been thought to accelerate the evolution of these genomic compartments, but little is known about their dynamics in strains that harbor them. Here, we used whole-genome sequencing data of 42 strains of the fungal plant pathogen Verticillium dahliae to systematically identify polymorphic TEs that may be implicated in genomic as well as in gene expression variation. We identified 2,523 TE polymorphisms and characterize a subset of 8% of the TEs as polymorphic elements that are evolutionary younger, less methylated, and more highly expressed when compared with the remaining 92% of the total TE complement. As expected, the polyrmorphic TEs are enriched in the adaptive genomic regions. Besides, we observed an association of polymorphic TEs with pathogenicity-related genes that localize nearby and that display high expression levels. Collectively, our analyses demonstrate that TE dynamics in V. dahliae contributes to genomic variation, correlates with expression of pathogenicity-related genes, and potentially impacts the evolution of adaptive genomic regions.
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Affiliation(s)
- David E Torres
- Theoretical Biology and Bioinformatics Group, Department of Biology, Utrecht University, The Netherlands
- Laboratory of Phytopathology, Wageningen University and Research, The Netherlands
| | - Bart P H J Thomma
- Laboratory of Phytopathology, Wageningen University and Research, The Netherlands
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute for Plant Sciences, University of Cologne, Germany
| | - Michael F Seidl
- Theoretical Biology and Bioinformatics Group, Department of Biology, Utrecht University, The Netherlands
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327
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Fan X, Yang C, Li W, Bai X, Zhou X, Xie H, Wen L, Tang F. SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform. Genome Biol 2021; 22:195. [PMID: 34193237 PMCID: PMC8247186 DOI: 10.1186/s13059-021-02406-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 06/08/2021] [Indexed: 04/27/2023] Open
Abstract
There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show that SMOOTH-seq reliably and effectively detects SVs and ecDNAs in individual cells, but shows relatively limited accuracy in detection of CNVs and SNVs. SMOOTH-seq opens a new chapter in scWGS as it generates high fidelity reads of kilobases long.
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Affiliation(s)
- Xiaoying Fan
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Cheng Yang
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China
| | - Wen Li
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Xiuzhen Bai
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China
| | - Xin Zhou
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China
| | - Haoling Xie
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China
| | - Lu Wen
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China
| | - Fuchou Tang
- Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Department of General Surgery, Third Hospital, Peking University, Beijing, 100871, China.
- Biomedical Pioneering Innovation Center, Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, 100871, China.
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
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328
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Akbarinejad S, Hadadian Nejad Yousefi M, Goudarzi M. SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks. BMC Bioinformatics 2021; 22:335. [PMID: 34147063 PMCID: PMC8214287 DOI: 10.1186/s12859-021-04184-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 05/11/2021] [Indexed: 11/10/2022] Open
Abstract
Background Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads that takes raw reads as the input and detects structural variants of size larger than 50 bp. Our pipeline utilizes state-of-the-art long-read aligners, namely NGMLR and Minimap2, and structural variation callers, videlicet Sniffle and SVIM. We found that by using a neural network, we can extract features from Minimap2 output to detect a subset of reads that provide useful information for structural variation detection. By only mapping this subset with NGMLR, which is far slower than Minimap2 but better serves downstream structural variation detection, we can increase the sensitivity in an efficient way. As a result of using multiple tools intelligently, SVNN achieves up to 20 percentage points of sensitivity improvement in comparison with state-of-the-art methods and is three times faster than a naive combination of state-of-the-art tools to achieve almost the same accuracy. Conclusion Since prohibitive costs of using high-coverage data have impeded long-read applications, with SVNN, we provide the users with a much faster structural variation detection platform for PacBio reads with high precision and sensitivity in low-coverage scenarios.
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Affiliation(s)
- Shaya Akbarinejad
- Department of Computer Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran
| | | | - Maziar Goudarzi
- Department of Computer Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran.
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329
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Badet T, Fouché S, Hartmann FE, Zala M, Croll D. Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen. Nat Commun 2021; 12:3551. [PMID: 34112792 PMCID: PMC8192914 DOI: 10.1038/s41467-021-23862-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 05/11/2021] [Indexed: 02/05/2023] Open
Abstract
Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.
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Affiliation(s)
- Thomas Badet
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
| | - Simone Fouché
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
- Plant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Fanny E Hartmann
- Ecologie Systématique Evolution, Bâtiment 360, Univ. Paris-Sud, AgroParisTech, CNRS, Université Paris-Saclay, Orsay, France
| | - Marcello Zala
- Plant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland.
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330
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Pereira L, Sapkota M, Alonge M, Zheng Y, Zhang Y, Razifard H, Taitano NK, Schatz MC, Fernie AR, Wang Y, Fei Z, Caicedo AL, Tieman DM, van der Knaap E. Natural Genetic Diversity in Tomato Flavor Genes. FRONTIERS IN PLANT SCIENCE 2021; 12:642828. [PMID: 34149747 PMCID: PMC8212054 DOI: 10.3389/fpls.2021.642828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/23/2021] [Indexed: 05/22/2023]
Abstract
Fruit flavor is defined as the perception of the food by the olfactory and gustatory systems, and is one of the main determinants of fruit quality. Tomato flavor is largely determined by the balance of sugars, acids and volatile compounds. Several genes controlling the levels of these metabolites in tomato fruit have been cloned, including LIN5, ALMT9, AAT1, CXE1, and LoxC. The aim of this study was to identify any association of these genes with trait variation and to describe the genetic diversity at these loci in the red-fruited tomato clade comprised of the wild ancestor Solanum pimpinellifolium, the semi-domesticated species Solanum lycopersicum cerasiforme and early domesticated Solanum lycopersicum. High genetic diversity was observed at these five loci, including novel haplotypes that could be incorporated into breeding programs to improve fruit quality of modern tomatoes. Using newly available high-quality genome assemblies, we assayed each gene for potential functional causative polymorphisms and resolved a duplication at the LoxC locus found in several wild and semi-domesticated accessions which caused lower accumulation of lipid derived volatiles. In addition, we explored gene expression of the five genes in nine phylogenetically diverse tomato accessions. In general, the expression patterns of these genes increased during fruit ripening but diverged between accessions without clear relationship between expression and metabolite levels.
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Affiliation(s)
- Lara Pereira
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, United States
| | - Manoj Sapkota
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
| | - Michael Alonge
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - Yi Zheng
- Boyce Thompson Institute, Ithaca, NY, United States
| | - Youjun Zhang
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Hamid Razifard
- Department of Biological Sciences, Mississippi State University, Starkville, MS, United States
| | - Nathan K. Taitano
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - Alisdair R. Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Ying Wang
- Department of Biological Sciences, Mississippi State University, Starkville, MS, United States
| | - Zhangjun Fei
- Boyce Thompson Institute, Ithaca, NY, United States
- U.S. Department of Agriculture, Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
| | - Ana L. Caicedo
- Biology Department, University of Massachusetts Amherst, Amherst, MA, United States
| | - Denise M. Tieman
- Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Esther van der Knaap
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, United States
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, United States
- Department of Horticulture, University of Georgia, Athens, GA, United States
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331
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Huang Q, Wu ZH, Li WF, Guo R, Xu JS, Dang XQ, Ma ZG, Chen YP, Evans JD. Genome and Evolutionary Analysis of Nosema ceranae: A Microsporidian Parasite of Honey Bees. Front Microbiol 2021; 12:645353. [PMID: 34149635 PMCID: PMC8206274 DOI: 10.3389/fmicb.2021.645353] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/29/2021] [Indexed: 01/18/2023] Open
Abstract
Microsporidia comprise a phylum of single cell, intracellular parasites and represent the earliest diverging branch in the fungal kingdom. The microsporidian parasite Nosema ceranae primarily infects honey bee gut epithelial cells, leading to impaired memory, suppressed host immune responses and colony collapse under certain circumstances. As the genome of N. ceranae is challenging to assembly due to very high genetic diversity and repetitive region, the genome was re-sequenced using long reads. We present a robust 8.8 Mbp genome assembly of 2,280 protein coding genes, including a high number of genes involved in transporting nutrients and energy, as well as drug resistance when compared with sister species Nosema apis. We also describe the loss of the critical protein Dicer in approximately half of the microsporidian species, giving new insights into the availability of RNA interference pathway in this group. Our results provided new insights into the pathogenesis of N. ceranae and a blueprint for treatment strategies that target this parasite without harming honey bees. The unique infectious apparatus polar filament and transportation pathway members can help to identify treatments to control this parasite.
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Affiliation(s)
- Qiang Huang
- Honeybee Research Institute, Jiangxi Agricultural University, Nanchang, China.,Jiangxi Province Key Laboratory of Honeybee Biology and Beekeeping, Jiangxi Agricultural University, Nanchang, China
| | - Zhi Hao Wu
- Honeybee Research Institute, Jiangxi Agricultural University, Nanchang, China.,Jiangxi Province Key Laboratory of Honeybee Biology and Beekeeping, Jiangxi Agricultural University, Nanchang, China
| | - Wen Feng Li
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China
| | - Rui Guo
- College of Animal Sciences (College of Bee Science), Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jin Shan Xu
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Xiao Qun Dang
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Zheng Gang Ma
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Yan Ping Chen
- US Department of Agriculture-Aricultural Research Service (USDA-ARS) Bee Research Laboratory, Beltsville, MD, United States
| | - Jay D Evans
- US Department of Agriculture-Aricultural Research Service (USDA-ARS) Bee Research Laboratory, Beltsville, MD, United States
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332
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Gorkovskiy A, Verstrepen KJ. The Role of Structural Variation in Adaptation and Evolution of Yeast and Other Fungi. Genes (Basel) 2021; 12:699. [PMID: 34066718 PMCID: PMC8150848 DOI: 10.3390/genes12050699] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 01/12/2023] Open
Abstract
Mutations in DNA can be limited to one or a few nucleotides, or encompass larger deletions, insertions, duplications, inversions and translocations that span long stretches of DNA or even full chromosomes. These so-called structural variations (SVs) can alter the gene copy number, modify open reading frames, change regulatory sequences or chromatin structure and thus result in major phenotypic changes. As some of the best-known examples of SV are linked to severe genetic disorders, this type of mutation has traditionally been regarded as negative and of little importance for adaptive evolution. However, the advent of genomic technologies uncovered the ubiquity of SVs even in healthy organisms. Moreover, experimental evolution studies suggest that SV is an important driver of evolution and adaptation to new environments. Here, we provide an overview of the causes and consequences of SV and their role in adaptation, with specific emphasis on fungi since these have proven to be excellent models to study SV.
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Affiliation(s)
- Anton Gorkovskiy
- Laboratory for Genetics and Genomics, Centre of Microbial and Plant Genetics (CMPG), KU Leuven, Gaston Geenslaan 1, 3001 Leuven, Belgium;
- Laboratory for Systems Biology, VIB—KU Leuven Center for Microbiology, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium
| | - Kevin J. Verstrepen
- Laboratory for Genetics and Genomics, Centre of Microbial and Plant Genetics (CMPG), KU Leuven, Gaston Geenslaan 1, 3001 Leuven, Belgium;
- Laboratory for Systems Biology, VIB—KU Leuven Center for Microbiology, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium
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333
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Wei T, van Treuren R, Liu X, Zhang Z, Chen J, Liu Y, Dong S, Sun P, Yang T, Lan T, Wang X, Xiong Z, Liu Y, Wei J, Lu H, Han S, Chen JC, Ni X, Wang J, Yang H, Xu X, Kuang H, van Hintum T, Liu X, Liu H. Whole-genome resequencing of 445 Lactuca accessions reveals the domestication history of cultivated lettuce. Nat Genet 2021; 53:752-760. [PMID: 33846635 DOI: 10.1038/s41588-021-00831-0] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 03/01/2021] [Indexed: 02/01/2023]
Abstract
Lettuce (Lactuca sativa) is an important vegetable crop worldwide. Cultivated lettuce is believed to be domesticated from L. serriola; however, its origins and domestication history remain to be elucidated. Here, we sequenced a total of 445 Lactuca accessions, including major lettuce crop types and wild relative species, and generated a comprehensive map of lettuce genome variations. In-depth analyses of population structure and demography revealed that lettuce was first domesticated near the Caucasus, which was marked by loss of seed shattering. We also identified the genetic architecture of other domestication traits and wild introgressions in major resistance clusters in the lettuce genome. This study provides valuable genomic resources for crop breeding and sheds light on the domestication history of cultivated lettuce.
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Affiliation(s)
- Tong Wei
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
| | - Rob van Treuren
- Centre for Genetic Resources, the Netherlands, Wageningen, the Netherlands.
| | - Xinjiang Liu
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
| | - Zhaowu Zhang
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | | | - Yang Liu
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
| | | | - Peinan Sun
- Huazhong Agricultural University, Wuhan, China
| | - Ting Yang
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
| | - Tianming Lan
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
- University of Copenhagen, Copenhagen, Denmark
| | | | | | | | - Jinpu Wei
- China National GeneBank, Shenzhen, China
| | - Haorong Lu
- China National GeneBank, Shenzhen, China
| | | | | | - Xuemei Ni
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
| | - Jian Wang
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
- James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Huanming Yang
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
- James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Xun Xu
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
| | | | - Theo van Hintum
- Centre for Genetic Resources, the Netherlands, Wageningen, the Netherlands
| | - Xin Liu
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China.
| | - Huan Liu
- State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China.
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334
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Yan C, He J, Luo J, Wang J, Zhang G, Luo H. SIns: A Novel Insertion Detection Approach Based on Soft-Clipped Reads. Front Genet 2021; 12:665812. [PMID: 33995493 PMCID: PMC8120196 DOI: 10.3389/fgene.2021.665812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/06/2021] [Indexed: 11/13/2022] Open
Abstract
As a common type of structural variation, an insertion refers to the addition of a DNA sequence into an individual genome and is usually associated with some inherited diseases. In recent years, many methods have been proposed for detecting insertions. However, the accurate calling of insertions is also a challenging task. In this study, we propose a novel insertion detection approach based on soft-clipped reads, which is called SIns. First, based on the alignments between paired reads and the reference genome, SIns extracts breakpoints from soft-clipped reads and determines insertion locations. The insert size information about paired reads is then further clustered to determine the genotype, and SIns subsequently adopts Minia to assemble the insertion sequences. Experimental results show that SIns can achieve better performance than other methods in terms of the F-score value for simulated and true datasets.
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Affiliation(s)
- Chaokun Yan
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Junyi He
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Junwei Luo
- College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Ge Zhang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Huimin Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
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335
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Daw Elbait G, Henschel A, Tay GK, Al Safar HS. A Population-Specific Major Allele Reference Genome From The United Arab Emirates Population. Front Genet 2021; 12:660428. [PMID: 33968136 PMCID: PMC8102833 DOI: 10.3389/fgene.2021.660428] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/19/2021] [Indexed: 12/30/2022] Open
Abstract
The ethnic composition of the population of a country contributes to the uniqueness of each national DNA sequencing project and, ideally, individual reference genomes are required to reduce the confounding nature of ethnic bias. This work represents a representative Whole Genome Sequencing effort of an understudied population. Specifically, high coverage consensus sequences from 120 whole genomes and 33 whole exomes were used to construct the first ever population specific major allele reference genome for the United Arab Emirates (UAE). When this was applied and compared to the archetype hg19 reference, assembly of local Emirati genomes was reduced by ∼19% (i.e., some 1 million fewer calls). In compiling the United Arab Emirates Reference Genome (UAERG), sets of annotated 23,038,090 short (novel: 1,790,171) and 137,713 structural (novel: 8,462) variants; their allele frequencies (AFs) and distribution across the genome were identified. Population-specific genetic characteristics including loss-of-function variants, admixture, and ancestral haplogroup distribution were identified and reported here. We also detect a strong correlation between F ST and admixture components in the UAE. This baseline study was conceived to establish a high-quality reference genome and a genetic variations resource to enable the development of regional population specific initiatives and thus inform the application of population studies and precision medicine in the UAE.
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Affiliation(s)
- Gihan Daw Elbait
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Andreas Henschel
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Guan K. Tay
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Habiba S. Al Safar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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336
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Göktay M, Fulgione A, Hancock AM. A New Catalog of Structural Variants in 1,301 A. thaliana Lines from Africa, Eurasia, and North America Reveals a Signature of Balancing Selection at Defense Response Genes. Mol Biol Evol 2021; 38:1498-1511. [PMID: 33247723 PMCID: PMC8042739 DOI: 10.1093/molbev/msaa309] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Genomic variation in the model plant Arabidopsis thaliana has been extensively used to understand evolutionary processes in natural populations, mainly focusing on single-nucleotide polymorphisms. Conversely, structural variation has been largely ignored in spite of its potential to dramatically affect phenotype. Here, we identify 155,440 indels and structural variants ranging in size from 1 bp to 10 kb, including presence/absence variants (PAVs), inversions, and tandem duplications in 1,301 A. thaliana natural accessions from Morocco, Madeira, Europe, Asia, and North America. We show evidence for strong purifying selection on PAVs in genes, in particular for housekeeping genes and homeobox genes, and we find that PAVs are concentrated in defense-related genes (R-genes, secondary metabolites) and F-box genes. This implies the presence of a "core" genome underlying basic cellular processes and a "flexible" genome that includes genes that may be important in spatially or temporally varying selection. Further, we find an excess of intermediate frequency PAVs in defense response genes in nearly all populations studied, consistent with a history of balancing selection on this class of genes. Finally, we find that PAVs in genes involved in the cold requirement for flowering (vernalization) and drought response are strongly associated with temperature at the sites of origin.
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Affiliation(s)
- Mehmet Göktay
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Andrea Fulgione
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Angela M Hancock
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
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337
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Sapoval N, Mahmoud M, Jochum MD, Liu Y, Elworth RAL, Wang Q, Albin D, Ogilvie HA, Lee MD, Villapol S, Hernandez KM, Maljkovic Berry I, Foox J, Beheshti A, Ternus K, Aagaard KM, Posada D, Mason CE, Sedlazeck FJ, Treangen TJ. SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission. Genome Res 2021; 31:635-644. [PMID: 33602693 PMCID: PMC8015855 DOI: 10.1101/gr.268961.120] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 02/12/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.
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Affiliation(s)
- Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Michael D Jochum
- Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas 77030, USA
| | - Yunxi Liu
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - R A Leo Elworth
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Qi Wang
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Rice University, Houston, Texas 77005, USA
| | - Dreycey Albin
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Rice University, Houston, Texas 77005, USA
| | - Huw A Ogilvie
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Michael D Lee
- Exobiology Branch, NASA Ames Research Center, Mountain View, California 94043, USA
- Blue Marble Space Institute of Science, Seattle, Washington 98104, USA
| | - Sonia Villapol
- Department of Neurosurgery, Houston Methodist Research Institute, Houston, Texas 77030, USA
| | - Kyle M Hernandez
- Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA
- Center for Translational Data Science, University of Chicago, Chicago, Illinois 60637, USA
| | | | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York 10021, USA
| | - Afshin Beheshti
- KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, California 94035, USA
| | | | - Kjersti M Aagaard
- Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas 77030, USA
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York 10021, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
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338
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Kovaka S, Fan Y, Ni B, Timp W, Schatz MC. Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED. Nat Biotechnol 2021; 39:431-441. [PMID: 33257863 PMCID: PMC8567335 DOI: 10.1038/s41587-020-0731-9] [Citation(s) in RCA: 132] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 10/07/2020] [Indexed: 02/07/2023]
Abstract
Conventional targeted sequencing methods eliminate many of the benefits of nanopore sequencing, such as the ability to accurately detect structural variants or epigenetic modifications. The ReadUntil method allows nanopore devices to selectively eject reads from pores in real time, which could enable purely computational targeted sequencing. However, this requires rapid identification of on-target reads while most mapping methods require computationally intensive basecalling. We present UNCALLED ( https://github.com/skovaka/UNCALLED ), an open source mapper that rapidly matches streaming of nanopore current signals to a reference sequence. UNCALLED probabilistically considers k-mers that could be represented by the signal and then prunes the candidates based on the reference encoded within a Ferragina-Manzini index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species 4.46-fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6× coverage using one MinION flowcell, enabling accurate detection of single-nucleotide polymorphisms, insertions and deletions, structural variants and methylation in these genes.
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Affiliation(s)
- Sam Kovaka
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Yunfan Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Bohan Ni
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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339
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Payne A, Holmes N, Clarke T, Munro R, Debebe BJ, Loose M. Readfish enables targeted nanopore sequencing of gigabase-sized genomes. Nat Biotechnol 2021; 39:442-450. [PMID: 33257864 PMCID: PMC7610616 DOI: 10.1038/s41587-020-00746-x] [Citation(s) in RCA: 179] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 10/21/2020] [Accepted: 10/21/2020] [Indexed: 01/07/2023]
Abstract
Nanopore sequencers can be used to selectively sequence certain DNA molecules in a pool by reversing the voltage across individual nanopores to reject specific sequences, enabling enrichment and depletion to address biological questions. Previously, we achieved this using dynamic time warping to map the signal to a reference genome, but the method required substantial computational resources and did not scale to gigabase-sized references. Here we overcome this limitation by using graphical processing unit (GPU) base-calling. We show enrichment of specific chromosomes from the human genome and of low-abundance organisms in mixed populations without a priori knowledge of sample composition. Finally, we enrich targeted panels comprising 25,600 exons from 10,000 human genes and 717 genes implicated in cancer, identifying PML-RARA fusions in the NB4 cell line in <15 h sequencing. These methods can be used to efficiently screen any target panel of genes without specialized sample preparation using any computer and a suitable GPU. Our toolkit, readfish, is available at https://www.github.com/looselab/readfish .
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Affiliation(s)
- Alexander Payne
- DeepSeq, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | - Nadine Holmes
- DeepSeq, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | - Thomas Clarke
- DeepSeq, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | - Rory Munro
- DeepSeq, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | - Bisrat J Debebe
- DeepSeq, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | - Matthew Loose
- DeepSeq, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK.
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340
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Mc Cartney AM, Mahmoud M, Jochum M, Agustinho DP, Zorman B, Al Khleifat A, Dabbaghie F, K Kesharwani R, Smolka M, Dawood M, Albin D, Aliyev E, Almabrazi H, Arslan A, Balaji A, Behera S, Billingsley K, L Cameron D, Daw J, T. Dawson E, De Coster W, Du H, Dunn C, Esteban R, Jolly A, Kalra D, Liao C, Liu Y, Lu TY, M Havrilla J, M Khayat M, Marin M, Monlong J, Price S, Rafael Gener A, Ren J, Sagayaradj S, Sapoval N, Sinner C, C. Soto D, Soylev A, Subramaniyan A, Syed N, Tadimeti N, Tater P, Vats P, Vaughn J, Walker K, Wang G, Zeng Q, Zhang S, Zhao T, Kille B, Biederstedt E, Chaisson M, English A, Kronenberg Z, J. Treangen T, Hefferon T, Chin CS, Busby B, J Sedlazeck F. An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates. F1000Res 2021; 10:246. [PMID: 34621504 PMCID: PMC8479851 DOI: 10.12688/f1000research.51477.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 11/20/2022] Open
Abstract
In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.
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Affiliation(s)
| | | | | | | | | | | | - Fawaz Dabbaghie
- Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | | | | | | | | | | | | | - Ahmed Arslan
- Stanford University School of Medicine, California, USA
| | | | | | | | - Daniel L Cameron
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Joyjit Daw
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | - Haowei Du
- Baylor College of Medicine, Houston, USA
| | | | | | | | | | | | | | | | | | | | | | - Jean Monlong
- UC Santa Cruz Genomics Institute, Santa Cruz, USA
| | | | | | | | | | | | | | | | - Arda Soylev
- Konya Food and Agriculture University, Konya, Turkey
| | | | | | | | | | - Pankaj Vats
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | | | - Qiandong Zeng
- Laboratory Corporation of America Holdings, Westborough, USA
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341
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Mc Cartney AM, Mahmoud M, Jochum M, Agustinho DP, Zorman B, Al Khleifat A, Dabbaghie F, K Kesharwani R, Smolka M, Dawood M, Albin D, Aliyev E, Almabrazi H, Arslan A, Balaji A, Behera S, Billingsley K, L Cameron D, Daw J, T. Dawson E, De Coster W, Du H, Dunn C, Esteban R, Jolly A, Kalra D, Liao C, Liu Y, Lu TY, M Havrilla J, M Khayat M, Marin M, Monlong J, Price S, Rafael Gener A, Ren J, Sagayaradj S, Sapoval N, Sinner C, C. Soto D, Soylev A, Subramaniyan A, Syed N, Tadimeti N, Tater P, Vats P, Vaughn J, Walker K, Wang G, Zeng Q, Zhang S, Zhao T, Kille B, Biederstedt E, Chaisson M, English A, Kronenberg Z, J. Treangen T, Hefferon T, Chin CS, Busby B, J Sedlazeck F. An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates. F1000Res 2021; 10:246. [PMID: 34621504 PMCID: PMC8479851 DOI: 10.12688/f1000research.51477.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/04/2021] [Indexed: 11/08/2023] Open
Abstract
In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.
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Affiliation(s)
| | | | | | | | | | | | - Fawaz Dabbaghie
- Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | | | | | | | | | | | | | - Ahmed Arslan
- Stanford University School of Medicine, California, USA
| | | | | | | | - Daniel L Cameron
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Joyjit Daw
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | - Haowei Du
- Baylor College of Medicine, Houston, USA
| | | | | | | | | | | | | | | | | | | | | | - Jean Monlong
- UC Santa Cruz Genomics Institute, Santa Cruz, USA
| | | | | | | | | | | | | | | | - Arda Soylev
- Konya Food and Agriculture University, Konya, Turkey
| | | | | | | | | | - Pankaj Vats
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | | | - Qiandong Zeng
- Laboratory Corporation of America Holdings, Westborough, USA
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342
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Butler D, Mozsary C, Meydan C, Foox J, Rosiene J, Shaiber A, Danko D, Afshinnekoo E, MacKay M, Sedlazeck FJ, Ivanov NA, Sierra M, Pohle D, Zietz M, Gisladottir U, Ramlall V, Sholle ET, Schenck EJ, Westover CD, Hassan C, Ryon K, Young B, Bhattacharya C, Ng DL, Granados AC, Santos YA, Servellita V, Federman S, Ruggiero P, Fungtammasan A, Chin CS, Pearson NM, Langhorst BW, Tanner NA, Kim Y, Reeves JW, Hether TD, Warren SE, Bailey M, Gawrys J, Meleshko D, Xu D, Couto-Rodriguez M, Nagy-Szakal D, Barrows J, Wells H, O'Hara NB, Rosenfeld JA, Chen Y, Steel PAD, Shemesh AJ, Xiang J, Thierry-Mieg J, Thierry-Mieg D, Iftner A, Bezdan D, Sanchez E, Campion TR, Sipley J, Cong L, Craney A, Velu P, Melnick AM, Shapira S, Hajirasouliha I, Borczuk A, Iftner T, Salvatore M, Loda M, Westblade LF, Cushing M, Wu S, Levy S, Chiu C, Schwartz RE, Tatonetti N, Rennert H, Imielinski M, Mason CE. Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. Nat Commun 2021; 12:1660. [PMID: 33712587 PMCID: PMC7954844 DOI: 10.1038/s41467-021-21361-7] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 01/25/2021] [Indexed: 02/08/2023] Open
Abstract
In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.
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Affiliation(s)
- Daniel Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Christopher Mozsary
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Joel Rosiene
- New York Genome Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alon Shaiber
- New York Genome Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine and the Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - David Danko
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Matthew MacKay
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Nikolay A Ivanov
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY, USA
| | - Maria Sierra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Diana Pohle
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | - Michael Zietz
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
| | - Undina Gisladottir
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
| | - Vijendra Ramlall
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
- Department of Cellular, Molecular Physiology & Biophysics, Columbia University, Columbia, NY, USA
| | - Evan T Sholle
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Craig D Westover
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ciaran Hassan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Krista Ryon
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin Young
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | | | - Dianna L Ng
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Andrea C Granados
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Yale A Santos
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Venice Servellita
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Scot Federman
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Phyllis Ruggiero
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | | | | | | | | | | | | | | | | | | | - Justyna Gawrys
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Dmitry Meleshko
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medicine, New York, NY, USA
| | - Dong Xu
- Genomics Resources Core Facility, Weill Cornell Medicine, New York, NY, USA
| | | | - Dorottya Nagy-Szakal
- Biotia, Inc., New York, NY, USA
- Department of Cell Biology, SUNY Downstate Health Sciences University, New York, NY, USA
| | | | | | - Niamh B O'Hara
- Biotia, Inc., New York, NY, USA
- Department of Cell Biology, SUNY Downstate Health Sciences University, New York, NY, USA
| | - Jeffrey A Rosenfeld
- Rutgers Cancer Institute of New Jersey, New York, NJ, USA
- Department of Pathology, Robert Wood Johnson Medical School, New York, NJ, USA
| | - Ying Chen
- Rutgers Cancer Institute of New Jersey, New York, NJ, USA
| | - Peter A D Steel
- Department of Emergency Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Amos J Shemesh
- Department of Emergency Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jenny Xiang
- Genomics Resources Core Facility, Weill Cornell Medicine, New York, NY, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Angelika Iftner
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | - Daniela Bezdan
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | | | - Thomas R Campion
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - John Sipley
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lin Cong
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Arryn Craney
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ari M Melnick
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sagi Shapira
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
| | - Iman Hajirasouliha
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine and the Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Alain Borczuk
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Thomas Iftner
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | - Mirella Salvatore
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lars F Westblade
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Melissa Cushing
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Shixiu Wu
- Hangzhou Cancer Institute, Hangzhou Cancer Hospital, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Hangzhou, China
| | - Shawn Levy
- HudsonAlpha Discovery Institute, Huntsville, AL, USA
| | - Charles Chiu
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
- Department of Medicine, Division of Infectious Diseases, University of California, San Francisco, CA, USA
| | | | - Nicholas Tatonetti
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA.
| | - Hanna Rennert
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
| | - Marcin Imielinski
- New York Genome Center, New York, NY, USA.
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
- Englander Institute for Precision Medicine and the Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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343
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Franceschini N, Verbruggen B, Tryfonidou MA, Kruisselbrink AB, Baelde H, de Visser KE, Szuhai K, Cleton-Jansen AM, Bovée JVMG. Transformed Canine and Murine Mesenchymal Stem Cells as a Model for Sarcoma with Complex Genomics. Cancers (Basel) 2021; 13:cancers13051126. [PMID: 33807947 PMCID: PMC7961539 DOI: 10.3390/cancers13051126] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/23/2021] [Accepted: 02/28/2021] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Sarcomas are rare cancers of mesenchymal origin, the majority of which are characterized by many copy number alterations, amplifications, or deletions. Because of these complex genomics, it is notoriously difficult to identify driver events of malignant transformation. In this study, we show that murine and canine mesenchymal stem cells (MSCs) can be used to model spontaneous malignant transformation towards sarcomas with complex genomics. We show that these MSCs have an abnormal karyotype, many structural variants, and point mutations at whole genome sequencing analysis, and form sarcomas after injection into mice. Our cross-species analysis reveals that p53 loss is an early event in sarcomagenesis, and it was shown that MSCs with a knock-out in Trp53 transform earlier compared to wild-type MSCs. Our study points to the importance of p53 loss in the transformation process towards sarcomas with complex genomics. Abstract Sarcomas are rare mesenchymal tumors with a broad histological spectrum, but they can be divided into two groups based on molecular pathology: sarcomas with simple or complex genomics. Tumors with complex genomics can have aneuploidy and copy number gains and losses, which hampers the detection of early, initiating events in tumorigenesis. Often, no benign precursors are known, which is why good models are essential. The mesenchymal stem cell (MSC) is the presumed cell of origin of sarcoma. In this study, MSCs of murine and canine origin are used as a model to identify driver events for sarcomas with complex genomic alterations as they transform spontaneously after long-term culture. All transformed murine but not canine MSCs formed sarcomas after subcutaneous injection in mice. Using whole genome sequencing, spontaneously transformed murine and canine MSCs displayed a complex karyotype with aneuploidy, point mutations, structural variants, inter-chromosomal translocations, and copy number gains and losses. Cross-species analysis revealed that point mutations in Tp53/Trp53 are common in transformed murine and canine MSCs. Murine MSCs with a cre-recombinase induced deletion of exon 2–10 of Trp53 transformed earlier compared to wild-type murine MSCs, confirming the contribution of loss of p53 to spontaneous transformation. Our comparative approach using transformed murine and canine MSCs points to a crucial role for p53 loss in the formation of sarcomas with complex genomics.
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Affiliation(s)
- Natasja Franceschini
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (N.F.); (B.V.); (A.B.K.); (H.B.); (A.-M.C.-J.)
| | - Bas Verbruggen
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (N.F.); (B.V.); (A.B.K.); (H.B.); (A.-M.C.-J.)
| | - Marianna A. Tryfonidou
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CL Utrecht, The Netherlands;
| | - Alwine B. Kruisselbrink
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (N.F.); (B.V.); (A.B.K.); (H.B.); (A.-M.C.-J.)
| | - Hans Baelde
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (N.F.); (B.V.); (A.B.K.); (H.B.); (A.-M.C.-J.)
| | - Karin E. de Visser
- Division of Tumour Biology & Immunology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;
- Oncode Institute, Office Jaarbeurs Innovation Mile (JIM), Jaarbeursplein 6, 3521 AL Utrecht, The Netherlands
| | - Karoly Szuhai
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands;
| | - Anne-Marie Cleton-Jansen
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (N.F.); (B.V.); (A.B.K.); (H.B.); (A.-M.C.-J.)
| | - Judith V. M. G. Bovée
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (N.F.); (B.V.); (A.B.K.); (H.B.); (A.-M.C.-J.)
- Correspondence: ; Tel.: +31-715266622
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344
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Krishnan V, Utiramerur S, Ng Z, Datta S, Snyder MP, Ashley EA. Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays. BMC Bioinformatics 2021; 22:85. [PMID: 33627090 PMCID: PMC7903625 DOI: 10.1186/s12859-020-03934-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/15/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Benchmarking the performance of complex analytical pipelines is an essential part of developing Lab Developed Tests (LDT). Reference samples and benchmark calls published by Genome in a Bottle (GIAB) consortium have enabled the evaluation of analytical methods. The performance of such methods is not uniform across the different genomic regions of interest and variant types. Several benchmarking methods such as hap.py, vcfeval, and vcflib are available to assess the analytical performance characteristics of variant calling algorithms. However, assessing the performance characteristics of an overall LDT assay still requires stringing together several such methods and experienced bioinformaticians to interpret the results. In addition, these methods are dependent on the hardware, operating system and other software libraries, making it impossible to reliably repeat the analytical assessment, when any of the underlying dependencies change in the assay. Here we present a scalable and reproducible, cloud-based benchmarking workflow that is independent of the laboratory and the technician executing the workflow, or the underlying compute hardware used to rapidly and continually assess the performance of LDT assays, across their regions of interest and reportable range, using a broad set of benchmarking samples. RESULTS The benchmarking workflow was used to evaluate the performance characteristics for secondary analysis pipelines commonly used by Clinical Genomics laboratories in their LDT assays such as the GATK HaplotypeCaller v3.7 and the SpeedSeq workflow based on FreeBayes v0.9.10. Five reference sample truth sets generated by Genome in a Bottle (GIAB) consortium, six samples from the Personal Genome Project (PGP) and several samples with validated clinically relevant variants from the Centers for Disease Control were used in this work. The performance characteristics were evaluated and compared for multiple reportable ranges, such as whole exome and the clinical exome. CONCLUSIONS We have implemented a benchmarking workflow for clinical diagnostic laboratories that generates metrics such as specificity, precision and sensitivity for germline SNPs and InDels within a reportable range using whole exome or genome sequencing data. Combining these benchmarking results with validation using known variants of clinical significance in publicly available cell lines, we were able to establish the performance of variant calling pipelines in a clinical setting.
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Affiliation(s)
- Vandhana Krishnan
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.,Stanford Center for Genomics and Personalized Medicine, Stanford University, Palo Alto, CA, USA
| | - Sowmithri Utiramerur
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Palo Alto, CA, USA. .,Clinical Genomics Program, Stanford Health Care, Stanford, CA, USA. .,Roche Diagnostics Solutions, Research and Early Development, Pleasanton, CA, USA.
| | - Zena Ng
- Clinical Genomics Program, Stanford Health Care, Stanford, CA, USA
| | - Somalee Datta
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Palo Alto, CA, USA.,School of Medicine, Research IT - Technology and Digital Solutions, Stanford University, Redwood City, CA, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.,Stanford Center for Genomics and Personalized Medicine, Stanford University, Palo Alto, CA, USA
| | - Euan A Ashley
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA. .,Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA. .,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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345
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Wang C, Wallerman O, Arendt ML, Sundström E, Karlsson Å, Nordin J, Mäkeläinen S, Pielberg GR, Hanson J, Ohlsson Å, Saellström S, Rönnberg H, Ljungvall I, Häggström J, Bergström TF, Hedhammar Å, Meadows JRS, Lindblad-Toh K. A novel canine reference genome resolves genomic architecture and uncovers transcript complexity. Commun Biol 2021; 4:185. [PMID: 33568770 PMCID: PMC7875987 DOI: 10.1038/s42003-021-01698-x] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/17/2020] [Indexed: 12/13/2022] Open
Abstract
We present GSD_1.0, a high-quality domestic dog reference genome with chromosome length scaffolds and contiguity increased 55-fold over CanFam3.1. Annotation with generated and existing long and short read RNA-seq, miRNA-seq and ATAC-seq, revealed that 32.1% of lifted over CanFam3.1 gaps harboured previously hidden functional elements, including promoters, genes and miRNAs in GSD_1.0. A catalogue of canine "dark" regions was made to facilitate mapping rescue. Alignment in these regions is difficult, but we demonstrate that they harbour trait-associated variation. Key genomic regions were completed, including the Dog Leucocyte Antigen (DLA), T Cell Receptor (TCR) and 366 COSMIC cancer genes. 10x linked-read sequencing of 27 dogs (19 breeds) uncovered 22.1 million SNPs, indels and larger structural variants. Subsequent intersection with protein coding genes showed that 1.4% of these could directly influence gene products, and so provide a source of normal or aberrant phenotypic modifications.
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Affiliation(s)
- Chao Wang
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
| | - Ola Wallerman
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Maja-Louise Arendt
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- Department of Veterinary Clinical Sciences, University of Copenhagen, Frederiksberg D, Denmark
| | - Elisabeth Sundström
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Åsa Karlsson
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Jessika Nordin
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Suvi Mäkeläinen
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Gerli Rosengren Pielberg
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Jeanette Hanson
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Åsa Ohlsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Sara Saellström
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Henrik Rönnberg
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Ingrid Ljungvall
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Jens Häggström
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Tomas F Bergström
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Åke Hedhammar
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Jennifer R S Meadows
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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346
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Chawla HS, Lee H, Gabur I, Vollrath P, Tamilselvan‐Nattar‐Amutha S, Obermeier C, Schiessl SV, Song J, Liu K, Guo L, Parkin IAP, Snowdon RJ. Long-read sequencing reveals widespread intragenic structural variants in a recent allopolyploid crop plant. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:240-250. [PMID: 32737959 PMCID: PMC7868984 DOI: 10.1111/pbi.13456] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/12/2020] [Accepted: 07/21/2020] [Indexed: 05/05/2023]
Abstract
Genome structural variation (SV) contributes strongly to trait variation in eukaryotic species and may have an even higher functional significance than single-nucleotide polymorphism (SNP). In recent years, there have been a number of studies associating large chromosomal scale SV ranging from hundreds of kilobases all the way up to a few megabases to key agronomic traits in plant genomes. However, there have been little or no efforts towards cataloguing small- (30-10 000 bp) to mid-scale (10 000-30 000 bp) SV and their impact on evolution and adaptation-related traits in plants. This might be attributed to complex and highly duplicated nature of plant genomes, which makes them difficult to assess using high-throughput genome screening methods. Here, we describe how long-read sequencing technologies can overcome this problem, revealing a surprisingly high level of widespread, small- to mid-scale SV in a major allopolyploid crop species, Brassica napus. We found that up to 10% of all genes were affected by small- to mid-scale SV events. Nearly half of these SV events ranged between 100 bp and 1000 bp, which makes them challenging to detect using short-read Illumina sequencing. Examples demonstrating the contribution of such SV towards eco-geographical adaptation and disease resistance in oilseed rape suggest that revisiting complex plant genomes using medium-coverage long-read sequencing might reveal unexpected levels of functional gene variation, with major implications for trait regulation and crop improvement.
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Affiliation(s)
| | - HueyTyng Lee
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
| | - Iulian Gabur
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
| | - Paul Vollrath
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
| | | | | | - Sarah V. Schiessl
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
- Department of Botany and Molecular EvolutionSenckenberg Research Institute and Natural History Museum FrankfurtFrankfurt am MainGermany
| | - Jia‐Ming Song
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Kede Liu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Liang Guo
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | | | - Rod J. Snowdon
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
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347
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Jiang Q, Isquith J, Ladel L, Mark A, Holm F, Mason C, He Y, Mondala P, Oliver I, Pham J, Ma W, Reynoso E, Ali S, Morris IJ, Diep R, Nasamran C, Xu G, Sasik R, Rosenthal SB, Birmingham A, Coso S, Pineda G, Crews L, Donohoe ME, Venter JC, Whisenant T, Mesa RA, Alexandrov LB, Fisch KM, Jamieson C. Inflammation-driven deaminase deregulation fuels human pre-leukemia stem cell evolution. Cell Rep 2021; 34:108670. [PMID: 33503434 PMCID: PMC8477897 DOI: 10.1016/j.celrep.2020.108670] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 12/03/2020] [Accepted: 12/29/2020] [Indexed: 12/11/2022] Open
Abstract
Inflammation-dependent base deaminases promote therapeutic resistance in many malignancies. However, their roles in human pre-leukemia stem cell (pre-LSC) evolution to acute myeloid leukemia stem cells (LSCs) had not been elucidated. Comparative whole-genome and whole-transcriptome sequencing analyses of FACS-purified pre-LSCs from myeloproliferative neoplasm (MPN) patients reveal APOBEC3C upregulation, an increased C-to-T mutational burden, and hematopoietic stem and progenitor cell (HSPC) proliferation during progression, which can be recapitulated by lentiviral APOBEC3C overexpression. In pre-LSCs, inflammatory splice isoform overexpression coincides with APOBEC3C upregulation and ADAR1p150-induced A-to-I RNA hyper-editing. Pre-LSC evolution to LSCs is marked by STAT3 editing, STAT3β isoform switching, elevated phospho-STAT3, and increased ADAR1p150 expression, which can be prevented by JAK2/STAT3 inhibition with ruxolitinib or fedratinib or lentiviral ADAR1 shRNA knockdown. Conversely, lentiviral ADAR1p150 expression enhances pre-LSC replating and STAT3 splice isoform switching. Thus, pre-LSC evolution to LSCs is fueled by primate-specific APOBEC3C-induced pre-LSC proliferation and ADAR1-mediated splicing deregulation.
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Affiliation(s)
- Qingfei Jiang
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Jane Isquith
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Luisa Ladel
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Adam Mark
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Frida Holm
- Karolinska Institutet, Stockholm, Sweden
| | - Cayla Mason
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Yudou He
- Department of Cellular and Molecular Medicine and Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Phoebe Mondala
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Isabelle Oliver
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Jessica Pham
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Wenxue Ma
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Eduardo Reynoso
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Shawn Ali
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Isabella Jamieson Morris
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Raymond Diep
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Chanond Nasamran
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Guorong Xu
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Roman Sasik
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Amanda Birmingham
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Sanja Coso
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Gabriel Pineda
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Leslie Crews
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Mary E Donohoe
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | | | - Thomas Whisenant
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA
| | - Ruben A Mesa
- Mays Cancer Center at UT Health San Antonio MD Anderson, UT Health San Antonio, San Antonio, TX 78229, USA
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine and Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Kathleen M Fisch
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0681, USA.
| | - Catriona Jamieson
- Division of Regenerative Medicine, Department of Medicine, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA.
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348
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Takayama J, Tadaka S, Yano K, Katsuoka F, Gocho C, Funayama T, Makino S, Okamura Y, Kikuchi A, Sugimoto S, Kawashima J, Otsuki A, Sakurai-Yageta M, Yasuda J, Kure S, Kinoshita K, Yamamoto M, Tamiya G. Construction and integration of three de novo Japanese human genome assemblies toward a population-specific reference. Nat Commun 2021; 12:226. [PMID: 33431880 PMCID: PMC7801658 DOI: 10.1038/s41467-020-20146-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 11/17/2020] [Indexed: 12/21/2022] Open
Abstract
The complete human genome sequence is used as a reference for next-generation sequencing analyses. However, some ethnic ancestries are under-represented in the reference genome (e.g., GRCh37) due to its bias toward European and African ancestries. Here, we perform de novo assembly of three Japanese male genomes using > 100× Pacific Biosciences long reads and Bionano Genomics optical maps per sample. We integrate the genomes using the major allele for consensus and anchor the scaffolds using genetic and radiation hybrid maps to reconstruct each chromosome. The resulting genome sequence, JG1, is contiguous, accurate, and carries the Japanese major allele at most loci. We adopt JG1 as the reference for confirmatory exome re-analyses of seven rare-disease Japanese families and find that re-analysis using JG1 reduces total candidate variant calls versus GRCh37 while retaining disease-causing variants. These results suggest that integrating multiple genomes from a single population can aid genome analyses of that population.
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Affiliation(s)
- Jun Takayama
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Kenji Yano
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Fumiki Katsuoka
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Chinatsu Gocho
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Takamitsu Funayama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Satoshi Makino
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Yasunobu Okamura
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Atsuo Kikuchi
- Department of Pediatrics, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Sachiyo Sugimoto
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Junko Kawashima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Akihito Otsuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Mika Sakurai-Yageta
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Jun Yasuda
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Division of Molecular and Cellular Oncology, Miyagi Cancer Center Research Institute, 47-1, Nodayama, Medeshima-Shiode, Natori, Miyagi, 981-1293, Japan
| | - Shigeo Kure
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Department of Pediatrics, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Kengo Kinoshita
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki Aza-Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan.
| | - Masayuki Yamamoto
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
| | - Gen Tamiya
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
- Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
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349
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Bendixsen DP, Gettle N, Gilchrist C, Zhang Z, Stelkens R. Genomic Evidence of an Ancient East Asian Divergence Event in Wild Saccharomyces cerevisiae. Genome Biol Evol 2021; 13:6081032. [PMID: 33432360 PMCID: PMC7874999 DOI: 10.1093/gbe/evab001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/21/2020] [Accepted: 01/05/2021] [Indexed: 12/12/2022] Open
Abstract
Comparative genome analyses have suggested East Asia to be the cradle of the domesticated microbe Brewer's yeast (Saccharomyces cerevisiae), used in the food and biotechnology industry worldwide. Here, we provide seven new, high-quality long-read genomes of nondomesticated yeast strains isolated from primeval forests and other natural environments in China and Taiwan. In a comprehensive analysis of our new genome assemblies, along with other long-read Saccharomycetes genomes available, we show that the newly sequenced East Asian strains are among the closest living relatives of the ancestors of the global diversity of Brewer's yeast, confirming predictions made from short-read genomic data. Three of these strains (termed the East Asian Clade IX Complex here) share a recent ancestry and evolutionary history suggesting an early divergence from other S. cerevisiae strains before the larger radiation of the species, and prior to its domestication. Our genomic analyses reveal that the wild East Asian strains contain elevated levels of structural variations. The new genomic resources provided here contribute to our understanding of the natural diversity of S. cerevisiae, expand the intraspecific genetic variation found in this heavily domesticated microbe, and provide a foundation for understanding its origin and global colonization history.
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350
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Guan J, Xu Y, Yu Y, Fu J, Ren F, Guo J, Zhao J, Jiang Q, Wei J, Xie H. Genome structure variation analyses of peach reveal population dynamics and a 1.67 Mb causal inversion for fruit shape. Genome Biol 2021; 22:13. [PMID: 33402202 PMCID: PMC7784018 DOI: 10.1186/s13059-020-02239-1] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Structural variations (SVs), a major resource of genomic variation, can have profound consequences on phenotypic variation, yet the impacts of SVs remain largely unexplored in crops. RESULTS Here, we generate a high-quality de novo genome assembly for a flat-fruit peach cultivar and produce a comprehensive SV map for peach, as a high proportion of genomic sequence is occupied by heterozygous SVs in the peach genome. We conduct population-level analyses that indicate SVs have undergone strong purifying selection during peach domestication, and find evidence of positive selection, with a significant preference for upstream and intronic regions during later peach improvement. We perform a SV-based GWAS that identifies a large 1.67-Mb heterozygous inversion that segregates perfectly with flat-fruit shape. Mechanistically, this derived allele alters the expression of the PpOFP2 gene positioned near the proximal breakpoint of the inversion, and we confirm in transgenic tomatoes that PpOFP2 is causal for flat-fruit shape. CONCLUSIONS Thus, beyond introducing new genomics resources for peach research, our study illustrates how focusing on SV data can drive basic functional discoveries in plant science.
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Affiliation(s)
- Jiantao Guan
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing, People's Republic of China
| | - Yaoguang Xu
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing, People's Republic of China
| | - Yang Yu
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing, People's Republic of China
| | - Jun Fu
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing, People's Republic of China
| | - Fei Ren
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
| | - Jiying Guo
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
| | - Jianbo Zhao
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
| | - Quan Jiang
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China.
| | - Jianhua Wei
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China.
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing, People's Republic of China.
| | - Hua Xie
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China.
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing, People's Republic of China.
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