Related Deletions on Human Chromosome 3
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Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma
Anatomy and Pathology Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma Sarah L. Lake,1 Sarah E. Coupland,1 Azzam F. G. Taktak,2 and Bertil E. Damato3 PURPOSE. To detect deletions and loss of heterozygosity of disease is fatal in 92% of patients within 2 years of diagnosis. chromosome 3 in a rare subset of fatal, disomy 3 uveal mela- Clinical and histopathologic risk factors for UM metastasis noma (UM), undetectable by fluorescence in situ hybridization include large basal tumor diameter (LBD), ciliary body involve- (FISH). ment, epithelioid cytomorphology, extracellular matrix peri- ϩ ETHODS odic acid-Schiff-positive (PAS ) loops, and high mitotic M . Multiplex ligation-dependent probe amplification 3,4 5 (MLPA) with the P027 UM assay was performed on formalin- count. Prescher et al. showed that a nonrandom genetic fixed, paraffin-embedded (FFPE) whole tumor sections from 19 change, monosomy 3, correlates strongly with metastatic death, and the correlation has since been confirmed by several disomy 3 metastasizing UMs. Whole-genome microarray analy- 3,6–10 ses using a single-nucleotide polymorphism microarray (aSNP) groups. Consequently, fluorescence in situ hybridization were performed on frozen tissue samples from four fatal dis- (FISH) detection of chromosome 3 using a centromeric probe omy 3 metastasizing UMs and three disomy 3 tumors with Ͼ5 became routine practice for UM prognostication; however, 5% years’ metastasis-free survival. to 20% of disomy 3 UM patients unexpectedly develop metas- tases.11 Attempts have therefore been made to identify the RESULTS. Two metastasizing UMs that had been classified as minimal region(s) of deletion on chromosome 3.12–15 Despite disomy 3 by FISH analysis of a small tumor sample were found these studies, little progress has been made in defining the key on MLPA analysis to show monosomy 3. -
Methods Mouse Strains and Housing Male Wild-Type Mice
Methods Mouse strains and housing Male wild-type mice (C57BL/6J background) and B6.Cg-Tg (APPSwFlLon, PSEN1*M146L*L286V) 6799Vas/Mmjax (5xFAD, JAX 008730) were purchased from the Jackson Laboratory. μMT-/- mice which are deficient in B cells were purchased from Shanghai Model Organisms Center, Inc. Both B6.Cg-Tg and μMT-/- mice were bred on a C57BL/6J background. 5xFAD mice and μMT-/- mice were crossed to generate μMT-/-/5xFAD mice. The mice were used at different ages that are indicated throughout the manuscript. Mice of all strains were specific pathogen free environment with controlled temperature and humidity, on 12 h light: dark cycles (lights on at 7:00), and fed with regular rodent’s chow and sterilized tap water ad libitum. All experiments were approved by the Institutional Animal Care and Use Committee of the Nanjing Medical University. Human samples Frontal cortex tissues were obtained from 4 cases within 4-6 h of death, via informed donation for the Medical Education and Research of Nanjing Medical University, with corresponding written consents prepared by the donors and their families. Cases that were died from brain associated diseases were excluded from this study. The utilization of human tissues was approved by the Ethics Committee of Nanjing Medical University. All obtained samples were fixed in a 4% formalin solution and kept in paraffin blocks until further sectioning. Animal surgery DcLN ligation: The procedure of surgical ligation of the lymphatics afferent to the dcLNs was according to published literature (3, 30). In brief, mice were anaesthetized by i.p. injection with ketamine and xylazine in saline, and fixed on a stereotaxic apparatus in a supine position. -
AGBT-2018-Andy-Pang.Pdf
Efficient Structural Variation Detection and Annotation Using Bionano Genome Mapping A. W. C. Pang1, J. Lee1, A. Hastie1, E. Lam1, E. Chan2, V. Hayes2, H. Cao1, M. Borodkin1 1) Bionano Genomics, San Diego, California, USA; 2) Garvan Institute of Medical Research, Darlinghurst, Australia Abstract Background Structural variation (SV) detection is fundamental to understanding cancer workflow can compare against a control sample, and examines whether the Generating high-quality finished genomes replete with accurate genomes. While karyotyping and conventional molecular detection approaches cancer mutations are present in low fraction among the control’s molecules. identification of structural variation and high completion (minimal are robust, they can be manually intensive, biased towards targeted loci, and Using this pipeline, whose runtime is only a few hours, we can efficiently focus gaps) remains challenging using short read sequencing cannot determine the copy number of long repeats. on several dozen significant candidates for further analysis. technologies alone. Bionano mapping provides direct visualization of long DNA molecules in their native state, bypassing the statistical inference needed to align Bionano Genomics’ Saphyr System offers a sensitive method for detecting large We ran multiple solid and hematologic cancer samples. First, we generated a paired-end reads with an uncertain insert size distribution. These long labeled SVs. DNA molecules larger than 100 kbp are extracted, labelled at specific highly contiguous genome map assembly on a solid tumor sample. The ultra molecules are de novo assembled into physical maps spanning the whole genome. motifs, and linearized through NanoChannels arrays for subsequent long maps – with lengths encompassing entire chromosomal arms – were able The resulting order and orientation of sequence elements in the map can be used visualization. -
Supplemental Figure and Table Legends
Supplemental figure and table legends Supplementary Figure 1: KIAA1841 is well conserved among vertebrates. NCBI HomoloGene pairwise alignment scores of human KIAA1841 sequence compared to other vertebrate orthologs. Supplementary Figure 2: µ-germline transcripts (GLT) and AID mRNA expression are not affected by overexpression of KIAA1841. Splenic B cells were isolated from wild-type mice, and transduced with retroviral vector control (pMIG) or a vector expressing KIAA1841. Levels of µ-GLT and AID mRNA were determined at 72h post-infection by RT-qPCR, and normalized to -actin mRNA and the pMIG control. The mean of three independent experiments +/- SD is shown. NS, p = not significant, p 0.05, two-tailed paired student’s t-test. Supplementary Figure 3: Overexpression of untagged and Xpress-tagged KIAA1841 does not affect cell proliferation. Splenic B cells were isolated from wild-type mice, stimulated with LPS+IL4, and transduced with retroviral vector control (pMIG) or a vector expressing KIAA1841 or Xpress (Xp)-tagged KIAA1841. Cells are labeled with seminaphthorhodafluor (SNARF) cell tracking dye and SNARF intensity was measured at 0h, 24h, and 48h after retroviral infection. Histograms of transduced cells (GFP+) for pMIG control, KIAA1841 and Xp-KIAA1841 were superimposed at each time point. Three independent retroviral infection experiments are shown. Supplementary Figure 4: Sequence alignment of the putative SANT domain of KIAA1841 with the SANT domain of SWI3. Alignment was performed using ClustalOmega; *, conserved residue, :, strongly similar residues, ., weakly similar residues. Numbers indicate amino acid residues in each sequence. Helix 3, which has been reported to be important for the chromatin remodeling function of SWI3 (Boyer et. -
Transcriptional Recapitulation and Subversion Of
Open Access Research2007KaiseretVolume al. 8, Issue 7, Article R131 Transcriptional recapitulation and subversion of embryonic colon comment development by mouse colon tumor models and human colon cancer Sergio Kaiser¤*, Young-Kyu Park¤†, Jeffrey L Franklin†, Richard B Halberg‡, Ming Yu§, Walter J Jessen*, Johannes Freudenberg*, Xiaodi Chen‡, Kevin Haigis¶, Anil G Jegga*, Sue Kong*, Bhuvaneswari Sakthivel*, Huan Xu*, Timothy Reichling¥, Mohammad Azhar#, Gregory P Boivin**, reviews Reade B Roberts§, Anika C Bissahoyo§, Fausto Gonzales††, Greg C Bloom††, Steven Eschrich††, Scott L Carter‡‡, Jeremy E Aronow*, John Kleimeyer*, Michael Kleimeyer*, Vivek Ramaswamy*, Stephen H Settle†, Braden Boone†, Shawn Levy†, Jonathan M Graff§§, Thomas Doetschman#, Joanna Groden¥, William F Dove‡, David W Threadgill§, Timothy J Yeatman††, reports Robert J Coffey Jr† and Bruce J Aronow* Addresses: *Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA. †Departments of Medicine, and Cell and Developmental Biology, Vanderbilt University and Department of Veterans Affairs Medical Center, Nashville, TN 37232, USA. ‡McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI 53706, USA. §Department of Genetics and Lineberger Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA. ¶Molecular Pathology Unit and Center for Cancer Research, Massachusetts deposited research General Hospital, Charlestown, MA 02129, USA. ¥Division of Human Cancer Genetics, The Ohio State University College of Medicine, Columbus, Ohio 43210-2207, USA. #Institute for Collaborative BioResearch, University of Arizona, Tucson, AZ 85721-0036, USA. **University of Cincinnati, Department of Pathology and Laboratory Medicine, Cincinnati, OH 45267, USA. ††H Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA. ‡‡Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, Massachusetts 02115, USA. -
Multi-Dimensional Genomic Analysis of Myoepithelial Carcinoma Identifies Prevalent Oncogenic Gene Fusions
ARTICLE DOI: 10.1038/s41467-017-01178-z OPEN Multi-dimensional genomic analysis of myoepithelial carcinoma identifies prevalent oncogenic gene fusions Martin G. Dalin1,2,3, Nora Katabi4, Marta Persson5, Ken-Wing Lee1, Vladimir Makarov1,6, Alexis Desrichard 1,6, Logan A. Walsh1, Lyndsay West7, Zaineb Nadeem1,7, Deepa Ramaswami1,7, Jonathan J. Havel1,6, Fengshen Kuo 1,6, Kalyani Chadalavada8, Gouri J. Nanjangud 8, Ian Ganly7, Nadeem Riaz6,9, Alan L. Ho10, Cristina R. Antonescu4, Ronald Ghossein4, Göran Stenman 5, Timothy A. Chan1,6,9 & Luc G.T. Morris 1,6,7 1234567890 Myoepithelial carcinoma (MECA) is an aggressive salivary gland cancer with largely unknown genetic features. Here we comprehensively analyze molecular alterations in 40 MECAs using integrated genomic analyses. We identify a low mutational load, and high prevalence (70%) of oncogenic gene fusions. Most fusions involve the PLAG1 oncogene, which is associated with PLAG1 overexpression. We find FGFR1-PLAG1 in seven (18%) cases, and the novel TGFBR3-PLAG1 fusion in six (15%) cases. TGFBR3-PLAG1 promotes a tumorigenic phenotype in vitro, and is absent in 723 other salivary gland tumors. Other novel PLAG1 fusions include ND4-PLAG1; a fusion between mitochondrial and nuclear DNA. We also identify higher number of copy number alterations as a risk factor for recurrence, independent of tumor stage at diagnosis. Our findings indicate that MECA is a fusion-driven disease, nominate TGFBR3-PLAG1 as a hallmark of MECA, and provide a framework for future diagnostic and therapeutic research in this lethal cancer. 1 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. -
Complex Humoral Immune Response Against a Benign Tumor: Frequent Antibody Response Against Specific Antigens As Diagnostic Targets
Complex humoral immune response against a benign tumor: Frequent antibody response against specific antigens as diagnostic targets Nicole Comtesse*, Andrea Zippel*, Sascha Walle*, Dominik Monz*, Christina Backes*, Ulrike Fischer*, Jens Mayer*, Nicole Ludwig*, Andreas Hildebrandt†, Andreas Keller†, Wolf-Ingo Steudel‡, Hans-Peter Lenhof†, and Eckart Meese*§ *Department of Human Genetics, Medical School, University of Saarland, Building 60, 66421 Homburg͞Saar, Germany; ‡Department of Neurosurgery, Medical School, University of Saarland, Building 90, 66421 Homburg͞Saar, Germany; and †Department of Bioinformatics, University of Saarland, Building 36.1, 66041 Saarbru¨cken, Germany Edited by George Klein, Karolinska Institutet, Stockholm, Sweden, and approved May 16, 2005 (received for review January 17, 2005) There are numerous studies on the immune response against are likely attributed to overexpression of MGEA6͞11 protein in malignant human tumors. This study was aimed to address the tumor cells (12). complexity and specificity of humoral immune response against a Immunogenic tumor-associated antigens have been reported for benign human tumor. We assembled a panel of 62 meningioma- a large variety of malignant tumors, including melanomas and colon expressed antigens that show reactivity with serum antibodies of cancer. The finding of immunogenic antigens in meningioma leaves meningioma patients, including 41 previously uncharacterized an- several questions. Are benign tumors associated with a frequent tigens by screening of a fetal brain -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
The Human Gene Connectome As a Map of Short Cuts for Morbid Allele Discovery
The human gene connectome as a map of short cuts for morbid allele discovery Yuval Itana,1, Shen-Ying Zhanga,b, Guillaume Vogta,b, Avinash Abhyankara, Melina Hermana, Patrick Nitschkec, Dror Friedd, Lluis Quintana-Murcie, Laurent Abela,b, and Jean-Laurent Casanovaa,b,f aSt. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; bLaboratory of Human Genetics of Infectious Diseases, Necker Branch, Paris Descartes University, Institut National de la Santé et de la Recherche Médicale U980, Necker Medical School, 75015 Paris, France; cPlateforme Bioinformatique, Université Paris Descartes, 75116 Paris, France; dDepartment of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel; eUnit of Human Evolutionary Genetics, Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, Institut Pasteur, F-75015 Paris, France; and fPediatric Immunology-Hematology Unit, Necker Hospital for Sick Children, 75015 Paris, France Edited* by Bruce Beutler, University of Texas Southwestern Medical Center, Dallas, TX, and approved February 15, 2013 (received for review October 19, 2012) High-throughput genomic data reveal thousands of gene variants to detect a single mutated gene, with the other polymorphic genes per patient, and it is often difficult to determine which of these being of less interest. This goes some way to explaining why, variants underlies disease in a given individual. However, at the despite the abundance of NGS data, the discovery of disease- population level, there may be some degree of phenotypic homo- causing alleles from such data remains somewhat limited. geneity, with alterations of specific physiological pathways under- We developed the human gene connectome (HGC) to over- come this problem. -
The Human Gene Connectome As a Map of Short Cuts for Morbid Allele Discovery
The human gene connectome as a map of short cuts for morbid allele discovery Yuval Itana,1, Shen-Ying Zhanga,b, Guillaume Vogta,b, Avinash Abhyankara, Melina Hermana, Patrick Nitschkec, Dror Friedd, Lluis Quintana-Murcie, Laurent Abela,b, and Jean-Laurent Casanovaa,b,f aSt. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; bLaboratory of Human Genetics of Infectious Diseases, Necker Branch, Paris Descartes University, Institut National de la Santé et de la Recherche Médicale U980, Necker Medical School, 75015 Paris, France; cPlateforme Bioinformatique, Université Paris Descartes, 75116 Paris, France; dDepartment of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel; eUnit of Human Evolutionary Genetics, Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, Institut Pasteur, F-75015 Paris, France; and fPediatric Immunology-Hematology Unit, Necker Hospital for Sick Children, 75015 Paris, France Edited* by Bruce Beutler, University of Texas Southwestern Medical Center, Dallas, TX, and approved February 15, 2013 (received for review October 19, 2012) High-throughput genomic data reveal thousands of gene variants to detect a single mutated gene, with the other polymorphic genes per patient, and it is often difficult to determine which of these being of less interest. This goes some way to explaining why, variants underlies disease in a given individual. However, at the despite the abundance of NGS data, the discovery of disease- population level, there may be some degree of phenotypic homo- causing alleles from such data remains somewhat limited. geneity, with alterations of specific physiological pathways under- We developed the human gene connectome (HGC) to over- come this problem. -
Use of IBM Watson to Identify Additional RNA-Binding Proteins
Acta Neuropathol DOI 10.1007/s00401-017-1785-8 ORIGINAL PAPER Artifcial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA‑binding proteins altered in amyotrophic lateral sclerosis Nadine Bakkar1 · Tina Kovalik1 · Ileana Lorenzini1 · Scott Spangler2 · Alix Lacoste3 · Kyle Sponaugle1 · Philip Ferrante1 · Elenee Argentinis3 · Rita Sattler1 · Robert Bowser1 Received: 29 September 2017 / Revised: 4 November 2017 / Accepted: 4 November 2017 © The Author(s) 2017. This article is an open access publication Abstract Amyotrophic lateral sclerosis (ALS) is a dev- to controls. Overall, we successfully used IBM Watson to astating neurodegenerative disease with no efective treat- help identify additional RBPs altered in ALS, highlighting ments. Numerous RNA-binding proteins (RBPs) have been the use of artifcial intelligence tools to accelerate scientifc shown to be altered in ALS, with mutations in 11 RBPs discovery in ALS and possibly other complex neurological causing familial forms of the disease, and 6 more RBPs disorders. showing abnormal expression/distribution in ALS albeit without any known mutations. RBP dysregulation is widely Keywords Amyotrophic lateral sclerosis · RNA-binding accepted as a contributing factor in ALS pathobiology. There protein · Artifcial intelligence · Protein aggregation · are at least 1542 RBPs in the human genome; therefore, Motor neuron other unidentifed RBPs may also be linked to the patho- genesis of ALS. We used IBM Watson® to sieve through all RBPs in the genome and identify new RBPs linked to ALS Introduction (ALS-RBPs). IBM Watson extracted features from pub- lished literature to create semantic similarities and identify Amyotrophic lateral sclerosis (ALS) is characterized by new connections between entities of interest. -
Mouse Rbm6 Knockout Project (CRISPR/Cas9)
http://www.alphaknockout.com/ Mouse Rbm6 Knockout Project (CRISPR/Cas9) Objective: To create a Rbm6 knockout Mouse model (C57BL/6N) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Rbm6 gene (NCBI Reference Sequence: NM_029169 ; Ensembl: ENSMUSG00000032582 ) is located on Mouse chromosome 9. 21 exons are identified, with the ATG start codon in exon 3 and the TAA stop codon in exon 21 (Transcript: ENSMUST00000035201). Exon 3~17 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 0.1% of the coding region. Exon 3~17 covers 85.6% of the coding region. The size of effective KO region: ~71065 bp. The KO region does not have any other known gene. Page 1 of 9 http://www.alphaknockout.com/ Overview of the Targeting Strategy Wildtype allele 5' gRNA region 15 3' 13 gRNA region 10 12 17 1 3 4 5 6 7 8 9 11 14 16 21 Legends Exon of mouse Rbm6 Knockout region Page 2 of 9 http://www.alphaknockout.com/ Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section of Exon 3 is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section of Exon 17 is aligned with itself to determine if there are tandem repeats.