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  • 1
    Publication Date: 2009-07-22
    Description: Acquired uniparental disomy (aUPD) is a common feature of cancer genomes, leading to loss of heterozygosity. aUPD is associated not only with loss-of-function mutations of tumour suppressor genes, but also with gain-of-function mutations of proto-oncogenes. Here we show unique gain-of-function mutations of the C-CBL (also known as CBL) tumour suppressor that are tightly associated with aUPD of the 11q arm in myeloid neoplasms showing myeloproliferative features. The C-CBL proto-oncogene, a cellular homologue of v-Cbl, encodes an E3 ubiquitin ligase and negatively regulates signal transduction of tyrosine kinases. Homozygous C-CBL mutations were found in most 11q-aUPD-positive myeloid malignancies. Although the C-CBL mutations were oncogenic in NIH3T3 cells, c-Cbl was shown to functionally and genetically act as a tumour suppressor. C-CBL mutants did not have E3 ubiquitin ligase activity, but inhibited that of wild-type C-CBL and CBL-B (also known as CBLB), leading to prolonged activation of tyrosine kinases after cytokine stimulation. c-Cbl(-/-) haematopoietic stem/progenitor cells (HSPCs) showed enhanced sensitivity to a variety of cytokines compared to c-Cbl(+/+) HSPCs, and transduction of C-CBL mutants into c-Cbl(-/-) HSPCs further augmented their sensitivities to a broader spectrum of cytokines, including stem-cell factor (SCF, also known as KITLG), thrombopoietin (TPO, also known as THPO), IL3 and FLT3 ligand (FLT3LG), indicating the presence of a gain-of-function that could not be attributed to a simple loss-of-function. The gain-of-function effects of C-CBL mutants on cytokine sensitivity of HSPCs largely disappeared in a c-Cbl(+/+) background or by co-transduction of wild-type C-CBL, which suggests the pathogenic importance of loss of wild-type C-CBL alleles found in most cases of C-CBL-mutated myeloid neoplasms. Our findings provide a new insight into a role of gain-of-function mutations of a tumour suppressor associated with aUPD in the pathogenesis of some myeloid cancer subsets.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sanada, Masashi -- Suzuki, Takahiro -- Shih, Lee-Yung -- Otsu, Makoto -- Kato, Motohiro -- Yamazaki, Satoshi -- Tamura, Azusa -- Honda, Hiroaki -- Sakata-Yanagimoto, Mamiko -- Kumano, Keiki -- Oda, Hideaki -- Yamagata, Tetsuya -- Takita, Junko -- Gotoh, Noriko -- Nakazaki, Kumi -- Kawamata, Norihiko -- Onodera, Masafumi -- Nobuyoshi, Masaharu -- Hayashi, Yasuhide -- Harada, Hiroshi -- Kurokawa, Mineo -- Chiba, Shigeru -- Mori, Hiraku -- Ozawa, Keiya -- Omine, Mitsuhiro -- Hirai, Hisamaru -- Nakauchi, Hiromitsu -- Koeffler, H Phillip -- Ogawa, Seishi -- 2R01CA026038-30/CA/NCI NIH HHS/ -- England -- Nature. 2009 Aug 13;460(7257):904-8. doi: 10.1038/nature08240. Epub 2009 Jul 20.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Cancer Genomics Project, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19620960" target="_blank"〉PubMed〈/a〉
    Keywords: Allelic Imbalance ; Amino Acid Sequence ; Animals ; Base Sequence ; Chromosomes, Human, Pair 11/genetics ; Female ; *Genes, Tumor Suppressor ; Humans ; Leukemia, Myeloid/*genetics/metabolism/pathology ; Male ; Mice ; Mice, Knockout ; Mice, Nude ; Models, Molecular ; Molecular Sequence Data ; Mutant Proteins/chemistry/genetics/*metabolism ; Mutation ; NIH 3T3 Cells ; Neoplasm Transplantation ; Oncogenes/genetics ; Phosphorylation ; Protein Conformation ; Proto-Oncogene Proteins c-cbl/antagonists & ; inhibitors/chemistry/deficiency/*genetics/*metabolism ; Ubiquitination ; Uniparental Disomy/genetics ; ras Proteins/genetics/metabolism
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2009-02-14
    Description: We often evaluate the self and others from social comparisons. We feel envy when the target person has superior and self-relevant characteristics. Schadenfreude occurs when envied persons fall from grace. To elucidate the neurocognitive mechanisms of envy and schadenfreude, we conducted two functional magnetic resonance imaging studies. In study one, the participants read information concerning target persons characterized by levels of possession and self-relevance of comparison domains. When the target person's possession was superior and self-relevant, stronger envy and stronger anterior cingulate cortex (ACC) activation were induced. In study two, stronger schadenfreude and stronger striatum activation were induced when misfortunes happened to envied persons. ACC activation in study one predicted ventral striatum activation in study two. Our findings document mechanisms of painful emotion, envy, and a rewarding reaction, schadenfreude.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Takahashi, Hidehiko -- Kato, Motoichiro -- Matsuura, Masato -- Mobbs, Dean -- Suhara, Tetsuya -- Okubo, Yoshiro -- Medical Research Council/United Kingdom -- New York, N.Y. -- Science. 2009 Feb 13;323(5916):937-9. doi: 10.1126/science.1165604.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Molecular Neuroimaging, National Institute of Radiological Sciences, 9-1, 4-chome, Anagawa, Inage-ku, Chiba, 263-8555, Japan. hidehiko@nirs.go.jp〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19213918" target="_blank"〉PubMed〈/a〉
    Keywords: Basal Ganglia/physiology ; Brain/*physiology ; *Brain Mapping ; *Emotions ; Female ; Gyrus Cinguli/physiology ; Happiness ; Humans ; *Jealousy ; Magnetic Resonance Imaging ; Male ; *Pain/psychology ; Reward ; Self Concept ; Social Behavior ; *Social Perception ; Young Adult
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2010-12-24
    Description: We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3142569/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3142569/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gerstein, Mark B -- Lu, Zhi John -- Van Nostrand, Eric L -- Cheng, Chao -- Arshinoff, Bradley I -- Liu, Tao -- Yip, Kevin Y -- Robilotto, Rebecca -- Rechtsteiner, Andreas -- Ikegami, Kohta -- Alves, Pedro -- Chateigner, Aurelien -- Perry, Marc -- Morris, Mitzi -- Auerbach, Raymond K -- Feng, Xin -- Leng, Jing -- Vielle, Anne -- Niu, Wei -- Rhrissorrakrai, Kahn -- Agarwal, Ashish -- Alexander, Roger P -- Barber, Galt -- Brdlik, Cathleen M -- Brennan, Jennifer -- Brouillet, Jeremy Jean -- Carr, Adrian -- Cheung, Ming-Sin -- Clawson, Hiram -- Contrino, Sergio -- Dannenberg, Luke O -- Dernburg, Abby F -- Desai, Arshad -- Dick, Lindsay -- Dose, Andrea C -- Du, Jiang -- Egelhofer, Thea -- Ercan, Sevinc -- Euskirchen, Ghia -- Ewing, Brent -- Feingold, Elise A -- Gassmann, Reto -- Good, Peter J -- Green, Phil -- Gullier, Francois -- Gutwein, Michelle -- Guyer, Mark S -- Habegger, Lukas -- Han, Ting -- Henikoff, Jorja G -- Henz, Stefan R -- Hinrichs, Angie -- Holster, Heather -- Hyman, Tony -- Iniguez, A Leo -- Janette, Judith -- Jensen, Morten -- Kato, Masaomi -- Kent, W James -- Kephart, Ellen -- Khivansara, Vishal -- Khurana, Ekta -- Kim, John K -- Kolasinska-Zwierz, Paulina -- Lai, Eric C -- Latorre, Isabel -- Leahey, Amber -- Lewis, Suzanna -- Lloyd, Paul -- Lochovsky, Lucas -- Lowdon, Rebecca F -- Lubling, Yaniv -- Lyne, Rachel -- MacCoss, Michael -- Mackowiak, Sebastian D -- Mangone, Marco -- McKay, Sheldon -- Mecenas, Desirea -- Merrihew, Gennifer -- Miller, David M 3rd -- Muroyama, Andrew -- Murray, John I -- Ooi, Siew-Loon -- Pham, Hoang -- Phippen, Taryn -- Preston, Elicia A -- Rajewsky, Nikolaus -- Ratsch, Gunnar -- Rosenbaum, Heidi -- Rozowsky, Joel -- Rutherford, Kim -- Ruzanov, Peter -- Sarov, Mihail -- Sasidharan, Rajkumar -- Sboner, Andrea -- Scheid, Paul -- Segal, Eran -- Shin, Hyunjin -- Shou, Chong -- Slack, Frank J -- Slightam, Cindie -- Smith, Richard -- Spencer, William C -- Stinson, E O -- Taing, Scott -- Takasaki, Teruaki -- Vafeados, Dionne -- Voronina, Ksenia -- Wang, Guilin -- Washington, Nicole L -- Whittle, Christina M -- Wu, Beijing -- Yan, Koon-Kiu -- Zeller, Georg -- Zha, Zheng -- Zhong, Mei -- Zhou, Xingliang -- modENCODE Consortium -- Ahringer, Julie -- Strome, Susan -- Gunsalus, Kristin C -- Micklem, Gos -- Liu, X Shirley -- Reinke, Valerie -- Kim, Stuart K -- Hillier, LaDeana W -- Henikoff, Steven -- Piano, Fabio -- Snyder, Michael -- Stein, Lincoln -- Lieb, Jason D -- Waterston, Robert H -- 054523/Wellcome Trust/United Kingdom -- R01 GM088565/GM/NIGMS NIH HHS/ -- R01 GM088565-03/GM/NIGMS NIH HHS/ -- R01GM088565/GM/NIGMS NIH HHS/ -- Howard Hughes Medical Institute/ -- Wellcome Trust/United Kingdom -- New York, N.Y. -- Science. 2010 Dec 24;330(6012):1775-87. doi: 10.1126/science.1196914. Epub 2010 Dec 22.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA. modencode.worm.pi@gersteinlab.org〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21177976" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Caenorhabditis elegans/*genetics/growth & development/metabolism ; Caenorhabditis elegans Proteins/genetics/metabolism ; Chromatin/genetics/metabolism/ultrastructure ; *Chromosomes/genetics/metabolism/ultrastructure ; Computational Biology/methods ; Conserved Sequence ; Evolution, Molecular ; *Gene Expression Profiling ; *Gene Expression Regulation ; Gene Regulatory Networks ; Genes, Helminth ; *Genome, Helminth ; Genomics/methods ; Histones/metabolism ; Models, Genetic ; *Molecular Sequence Annotation ; RNA, Helminth/genetics/metabolism ; RNA, Untranslated/genetics/metabolism ; Regulatory Sequences, Nucleic Acid ; Transcription Factors/genetics/metabolism
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 4
    Publication Date: 2014-12-17
    Description: Song-learning birds and humans share independently evolved similarities in brain pathways for vocal learning that are essential for song and speech and are not found in most other species. Comparisons of brain transcriptomes of song-learning birds and humans relative to vocal nonlearners identified convergent gene expression specializations in specific song and speech brain regions of avian vocal learners and humans. The strongest shared profiles relate bird motor and striatal song-learning nuclei, respectively, with human laryngeal motor cortex and parts of the striatum that control speech production and learning. Most of the associated genes function in motor control and brain connectivity. Thus, convergent behavior and neural connectivity for a complex trait are associated with convergent specialized expression of multiple genes.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4385736/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4385736/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Pfenning, Andreas R -- Hara, Erina -- Whitney, Osceola -- Rivas, Miriam V -- Wang, Rui -- Roulhac, Petra L -- Howard, Jason T -- Wirthlin, Morgan -- Lovell, Peter V -- Ganapathy, Ganeshkumar -- Mouncastle, Jacquelyn -- Moseley, M Arthur -- Thompson, J Will -- Soderblom, Erik J -- Iriki, Atsushi -- Kato, Masaki -- Gilbert, M Thomas P -- Zhang, Guojie -- Bakken, Trygve -- Bongaarts, Angie -- Bernard, Amy -- Lein, Ed -- Mello, Claudio V -- Hartemink, Alexander J -- Jarvis, Erich D -- DP1 OD000448/OD/NIH HHS/ -- R01 DC007218/DC/NIDCD NIH HHS/ -- R01DC007218/DC/NIDCD NIH HHS/ -- R21 DC007478/DC/NIDCD NIH HHS/ -- R24 GM092842/GM/NIGMS NIH HHS/ -- R24GM092842/GM/NIGMS NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2014 Dec 12;346(6215):1256846. doi: 10.1126/science.1256846.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Neurobiology, Howard Hughes Medical Institute, and Duke University Medical Center, Durham, NC 27710, USA. apfenning@csail.mit.edu amink@cs.duke.edu jarvis@neuro.duke.edu. ; Department of Neurobiology, Howard Hughes Medical Institute, and Duke University Medical Center, Durham, NC 27710, USA. ; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA. ; Duke Proteomics and Metabolomics Core Facility, Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27710, USA. ; Laboratory for Symbolic Cognitive Development, Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen, Denmark. Trace and Environmental DNA Laboratory, Department of Environment and Agriculture, Curtin University, Perth, Western Australia 6102, Australia. ; China National GeneBank, BGI-Shenzhen, Shenzhen 518083, China. Centre for Social Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark. ; Allen Institute for Brain Science, Seattle, WA 98103, USA. ; Department of Computer Science, Duke University, Durham, NC 27708, USA. apfenning@csail.mit.edu amink@cs.duke.edu jarvis@neuro.duke.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25504733" target="_blank"〉PubMed〈/a〉
    Keywords: Adult ; Animals ; Birds/genetics/physiology ; Brain/anatomy & histology/*physiology ; Brain Mapping ; Corpus Striatum/anatomy & histology/physiology ; Evolution, Molecular ; Finches/*genetics/*physiology ; *Gene Expression Regulation ; Humans ; *Learning ; Male ; Motor Cortex/anatomy & histology/physiology ; Neural Pathways ; Species Specificity ; *Speech ; Transcription, Genetic ; *Transcriptome ; *Vocalization, Animal
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 5
    Publication Date: 2014-08-29
    Description: The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155737/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155737/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gerstein, Mark B -- Rozowsky, Joel -- Yan, Koon-Kiu -- Wang, Daifeng -- Cheng, Chao -- Brown, James B -- Davis, Carrie A -- Hillier, LaDeana -- Sisu, Cristina -- Li, Jingyi Jessica -- Pei, Baikang -- Harmanci, Arif O -- Duff, Michael O -- Djebali, Sarah -- Alexander, Roger P -- Alver, Burak H -- Auerbach, Raymond -- Bell, Kimberly -- Bickel, Peter J -- Boeck, Max E -- Boley, Nathan P -- Booth, Benjamin W -- Cherbas, Lucy -- Cherbas, Peter -- Di, Chao -- Dobin, Alex -- Drenkow, Jorg -- Ewing, Brent -- Fang, Gang -- Fastuca, Megan -- Feingold, Elise A -- Frankish, Adam -- Gao, Guanjun -- Good, Peter J -- Guigo, Roderic -- Hammonds, Ann -- Harrow, Jen -- Hoskins, Roger A -- Howald, Cedric -- Hu, Long -- Huang, Haiyan -- Hubbard, Tim J P -- Huynh, Chau -- Jha, Sonali -- Kasper, Dionna -- Kato, Masaomi -- Kaufman, Thomas C -- Kitchen, Robert R -- Ladewig, Erik -- Lagarde, Julien -- Lai, Eric -- Leng, Jing -- Lu, Zhi -- MacCoss, Michael -- May, Gemma -- McWhirter, Rebecca -- Merrihew, Gennifer -- Miller, David M -- Mortazavi, Ali -- Murad, Rabi -- Oliver, Brian -- Olson, Sara -- Park, Peter J -- Pazin, Michael J -- Perrimon, Norbert -- Pervouchine, Dmitri -- Reinke, Valerie -- Reymond, Alexandre -- Robinson, Garrett -- Samsonova, Anastasia -- Saunders, Gary I -- Schlesinger, Felix -- Sethi, Anurag -- Slack, Frank J -- Spencer, William C -- Stoiber, Marcus H -- Strasbourger, Pnina -- Tanzer, Andrea -- Thompson, Owen A -- Wan, Kenneth H -- Wang, Guilin -- Wang, Huaien -- Watkins, Kathie L -- Wen, Jiayu -- Wen, Kejia -- Xue, Chenghai -- Yang, Li -- Yip, Kevin -- Zaleski, Chris -- Zhang, Yan -- Zheng, Henry -- Brenner, Steven E -- Graveley, Brenton R -- Celniker, Susan E -- Gingeras, Thomas R -- Waterston, Robert -- 1U01HG007031-01/HG/NHGRI NIH HHS/ -- 5U01HG004695-04/HG/NHGRI NIH HHS/ -- 5U54HG004555/HG/NHGRI NIH HHS/ -- HG007000/HG/NHGRI NIH HHS/ -- HG007355/HG/NHGRI NIH HHS/ -- K99 HG006698/HG/NHGRI NIH HHS/ -- P30 CA045508/CA/NCI NIH HHS/ -- R01 GM076655/GM/NIGMS NIH HHS/ -- RC2-HG005639/HG/NHGRI NIH HHS/ -- T15 LM007056/LM/NLM NIH HHS/ -- T32 HD060555/HD/NICHD NIH HHS/ -- U01 HG 004263/HG/NHGRI NIH HHS/ -- U01 HG004261/HG/NHGRI NIH HHS/ -- U01 HG004271/HG/NHGRI NIH HHS/ -- U01 HG007031/HG/NHGRI NIH HHS/ -- U01-HG004261/HG/NHGRI NIH HHS/ -- U01HG004258/HG/NHGRI NIH HHS/ -- U41 HG007000/HG/NHGRI NIH HHS/ -- U41 HG007234/HG/NHGRI NIH HHS/ -- U41 HG007355/HG/NHGRI NIH HHS/ -- U54 HG004555/HG/NHGRI NIH HHS/ -- U54 HG006944/HG/NHGRI NIH HHS/ -- U54 HG006994/HG/NHGRI NIH HHS/ -- U54 HG007004/HG/NHGRI NIH HHS/ -- U54 HG007005/HG/NHGRI NIH HHS/ -- U54HG007005/HG/NHGRI NIH HHS/ -- WT098051/Wellcome Trust/United Kingdom -- ZIA DK015600-18/Intramural NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2014 Aug 28;512(7515):445-8. doi: 10.1038/nature13424.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3] Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA [4] [5]. ; 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]. ; 1] Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA [2] Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA [3]. ; 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA [3]. ; 1] Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA [2]. ; 1] Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA [2]. ; 1] Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA [2] Department of Statistics, University of California, Los Angeles, California 90095-1554, USA [3] Department of Human Genetics, University of California, Los Angeles, California 90095-7088, USA [4]. ; 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2]. ; 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain [3]. ; 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA. ; Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, Massachusetts 02115, USA. ; Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA. ; Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA. ; Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA. ; 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Biostatistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA. ; Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA. ; 1] Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA [2] Center for Genomics and Bioinformatics, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA. ; MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China. ; National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. ; 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain. ; 1] Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland [2] Swiss Institute of Bioinformatics, Genopode building, Lausanne 1015, Switzerland. ; 1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK [2] Medical and Molecular Genetics, King's College London, London WC2R 2LS, UK. ; Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA. ; Department of Molecular, Cellular and Developmental Biology, PO Box 208103, Yale University, New Haven, Connecticut 06520, USA. ; Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA. ; Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, New York 10065, USA. ; 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2] Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA. ; Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA. ; 1] Developmental and Cell Biology, University of California, Irvine, California 92697, USA [2] Center for Complex Biological Systems, University of California, Irvine, California 92697, USA. ; Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA. ; Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA. ; 1] Department of Genetics and Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA [2] Howard Hughes Medical Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA. ; Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland. ; 1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK [2] European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. ; 1] Bioinformatics and Genomics Programme, Center for Genomic Regulation, Universitat Pompeu Fabra (CRG-UPF), 08003 Barcelona, Catalonia, Spain [2] Institute for Theoretical Chemistry, Theoretical Biochemistry Group (TBI), University of Vienna, Wahringerstrasse 17/3/303, A-1090 Vienna, Austria. ; 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2] Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. ; 1] Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong [2] 5 CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. ; 1] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA [2] Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA [3]. ; 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2].〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25164755" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Caenorhabditis elegans/embryology/*genetics/growth & development ; Chromatin/genetics ; Cluster Analysis ; Drosophila melanogaster/*genetics/growth & development ; *Gene Expression Profiling ; Gene Expression Regulation, Developmental/genetics ; Histones/metabolism ; Humans ; Larva/genetics/growth & development ; Models, Genetic ; Molecular Sequence Annotation ; Promoter Regions, Genetic/genetics ; Pupa/genetics/growth & development ; RNA, Untranslated/genetics ; Sequence Analysis, RNA ; Transcriptome/*genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Euphytica 25 (1976), S. 761-767 
    ISSN: 1573-5060
    Keywords: Brassicoraphanus ; seed fertility ; flower colour ; crossing-over
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary In Brassicoraphanus (amphidiploids between Brassica japonica Sieb. and Raphanus sativus L.), yellow-flowered plants that occurred among originally white-flowered plants showed an increased seed fertility. It is assumed that the gene Y (yellow-flower gene) from Brassica and the gene W (white-flower gene) from Raphanus are located at corresponding loci of only partially homologous chromosomes. W is dominant (epistatic) over Y. The normal white-flowered plants have the genotype YYWW. A YYYW-plant was found, which is assumed to have arisen through crossing-over following multivalent formation. In the progeny of this plant, yellow-flowered plants (YYYY) as well as white-flowered plants (YYWW, YYYW) appeared. The gene for flower colour is closely linked to a gene which controls the development of embryos (or endosperm). This gene promotes the development of embryos in homozygous condition. Therefore, the embryo having only the yellow-flower gene can develop more easily into viable seed than the embryo having the white-flower gene. It is also possible that the sterility of white-flowered plants is caused by a discordance between the cytoplasm of Brassica and W (or genes linked to W) of Raphanus.
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Euphytica 32 (1983), S. 415-423 
    ISSN: 1573-5060
    Keywords: Brassicoraphanus ; chromosome number ; aneuploid ; flower colour
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary To examine whether chromosome numbers of Brassicoraphanus (amphidiploids between Brassica japonica Sieb. and Raphanus sativus L.) are stable or not, the following four items were studied with some plants during the 2nd–11th generations: (1) chromosome numbers of open-pollinated progenies from eu-, hyper-, and hypoploids, (2) chromosome distribution at metaphase II in these plants, (3) frequency of euploids in relation to flower colour and generation, (4) seed fertility in eu-and aneuploids in relation to flower colour. In early generations, hyper-and hypoploids were frequently produced from euploids. In later generations, however, the chromosome number became less unstable. In euploids (2n=38), chromosome numbers at metaphase II showed some variation, and the mean frequency of the euploid chromosome number (n=19) was approximately 78%. This value was almost the same in white-and yellow-flowered plants through early and late generations. Nevertheless, yellow-flowered plants tended to produce euploids more frequently than did white-flowered ones. It is assumed that the difference in euploid productivity between yellow-and white-flowered plants is due to the difference in seed fertility between them. The progeny of each hypoploid showed higher chromosome numbers than their parents. The progeny of each hyperploid showed lower chromosome numbers than their parents: they were approaching to euploidy. This phenomenon, together with higher fertility of euploids and lower fertility of aneuploids, will favor the maintenance of euploidy of this strain.
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  • 8
    ISSN: 1573-5060
    Keywords: Brassicoraphanus ; amphidiploid ; euploid ; aneuploid ; chromosome number ; meiosis ; variation ; fertility
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary In order to elucidate the mechanism of low fertility of Brassicoraphanus, i.e., amphidiploids between Brassica japonica Sieb. and Raphanus sativus L., the chromosome number of 253 plants was studied during the 3rd–9th generations for their seed fertility. Meiotic irregularity showed no connection with degree of sterility. Brassicoraphanus consisted of euploids (2n=38), hyperploids (2n=39–43) and hypoploids (2n=34–37) with white or yellow flowers. The number of plants was highest in euploids and became lower as the chromosome number diverged from the euploid number. Further, seed fertility was highest and the range of its variation widest in euploids. The seed fertility of aneuploids became lower and its variation narrower in proportion to the number of chromosomes additional to or missing from the euploid number. Yellow-flowered plants were superior in seed fertility to white-flowered plants. Seed fertility of plants is primarily affected by their chromosome numbers and secondarily modified by genic effects. As a whole, seed fertility of Brassicoraphanus increased gradually and its variation widened with the advance of generations. This was explained mainly by the increase of balanced combinations of genes.
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Euphytica 28 (1979), S. 339-349 
    ISSN: 1573-5060
    Keywords: Brassicoraphanus ; esterase ; peroxidase ; isozymes ; electrophoresis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary A comparative electrophoretic study of esterase and peroxidase isozymes from the leaves of Brassicoraphanus and its parental species (Brassica japonica and Raphanus sativus) was carried out by means of the polyacrylamide gel isoelectrofocusing technique. The isozyme bands of Brassicoraphanus could be regarded as a summation of parent-derived bands, some of which were missing, in addition to some new bands. The qualitative and quantitative variation of isozyme patterns among individual plants was found within each strain of Brassicoraphanus as well as each parental species. The range of the enzymatic variation of a certain strain seemed to reflect the genetic homogeneity of the strain in question. Every strain of Brassicoraphanus was less variable in esterase patterns than the parental species, but in peroxidase patterns the variations of Brassicoraphanus were intermediate between those of both parents. Some strains of Brassicoraphanus were uniform in isozyme patterns, whereas others were variable. The difference in enzymatic variation among strains was perhaps due to the difference in the source of the strains and the selection which they received.
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  • 10
    ISSN: 1573-5060
    Keywords: Brassica japonica ; potherb mustard ; Raphanus sativus ; Japanese radish ; Brassicoraphanus ; nucleus substitution ; chlorophyll deficiency
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary Nucleus substitution of Brassica japonica (2n=20) with Raphanus sativus (2n=18) was carried out by means of repeated backcrossing of Brassicoraphavus (2n=37) to R. sativus as a pollen donor. In the course of nucleus substitution, chlorophyll deficiency appeared. Plants with more than 28 chromosomes, like their parents, had green leaves and those with 24 to 26 chromosomes had yellowish green ones. Almost all plants with 18 to 23 chromosomes showed yellow or whitish yellow. The R. sativus with B. japonica cytoplasm (2n=18) was obtained after four successive backerosses. The completely substituted R. sativus showed the same fertility as the true R. sativus used as a recurrent parent. It is assumed that the chlorophyll deficiency is caused by disharmony between the B. japonica cytoplasm and the R. sativus nucleus. The chlorophyll deficiency is discussed in comparison with male sterility or other characters which sometimes occur in alloplasmic Raphanus and Brassica species.
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