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A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Synovial Sarcoma: Recent Discoveries As a Roadmap to New Avenues for Therapy
Published OnlineFirst January 22, 2015; DOI: 10.1158/2159-8290.CD-14-1246 REVIEW Synovial Sarcoma: Recent Discoveries as a Roadmap to New Avenues for Therapy Torsten O. Nielsen 1 , Neal M. Poulin 1 , and Marc Ladanyi 2 ABSTRACT Oncogenesis in synovial sarcoma is driven by the chromosomal translocation t(X,18; p11,q11), which generates an in-frame fusion of the SWI/SNF subunit SS18 to the C-terminal repression domains of SSX1 or SSX2. Proteomic studies have identifi ed an integral role of SS18–SSX in the SWI/SNF complex, and provide new evidence for mistargeting of polycomb repression in synovial sarcoma. Two recent in vivo studies are highlighted, providing additional support for the importance of WNT signaling in synovial sarcoma: One used a conditional mouse model in which knock- out of β-catenin prevents tumor formation, and the other used a small-molecule inhibitor of β-catenin in xenograft models. Signifi cance: Synovial sarcoma appears to arise from still poorly characterized immature mesenchymal progenitor cells through the action of its primary oncogenic driver, the SS18–SSX fusion gene, which encodes a multifaceted disruptor of epigenetic control. The effects of SS18–SSX on polycomb-mediated gene repression and SWI/SNF chromatin remodeling have recently come into focus and may offer new insights into the basic function of these processes. A central role for deregulation of WNT–β-catenin sig- naling in synovial sarcoma has also been strengthened by recent in vivo studies. These new insights into the the biology of synovial sarcoma are guiding novel preclinical and clinical studies in this aggressive cancer. -
MLLT10 (AF10) Antibody (Center) Purified Rabbit Polyclonal Antibody (Pab) Catalog # Ap1906b
苏州工业园区双圩路9号1幢 邮 编 : 215000 电 话 : 0512-88856768 MLLT10 (AF10) Antibody (Center) Purified Rabbit Polyclonal Antibody (Pab) Catalog # AP1906b Specification MLLT10 (AF10) Antibody (Center) - Product info Application WB, IF Primary Accession P55197 Reactivity Human Host Rabbit Clonality Polyclonal Isotype Rabbit Ig MLLT10 (AF10) Antibody (Center) - Additional info Gene ID 8028 Other Names Protein AF-10, ALL1-fused gene from chromosome 10 protein, MLLT10, AF10 Western blot analysis of MLLT10 (Cat. Target/Specificity #AP1906b) in K562 cell line lysates This MLLT10 (AF10) antibody is generated from rabbits (35ug/lane). MLLT10 (arrow) was immunized with a KLH conjugated synthetic peptide between detected using the purified Pab.(2ug/ml) 294-323 amino acids from the Central region of human MLLT10 (AF10). Dilution WB~~1:1000 IF~~1:10~50 Format Purified polyclonal antibody supplied in PBS with 0.09% (W/V) sodium azide. This antibody is prepared by Saturated Ammonium Sulfate (SAS) precipitation followed by dialysis against PBS. Storage Maintain refrigerated at 2-8°C for up to 2 weeks. For long term storage store at -20°C in small aliquots to prevent freeze-thaw cycles. Precautions MLLT10 (AF10) Antibody (Center) is for research use only and Fluorescent confocal image of Hela cell not for use in diagnostic or therapeutic procedures. stained with MLLT10 (AF10) Antibody (Center)(Cat#AP1906b).HeLa cells were fixed with 4% PFA (20 min), MLLT10 (AF10) Antibody (Center) - Protein Information permeabilized with Triton X-100 (0.1%, 10 min), then incubated with MLLT10 Name MLLT10 (HGNC:16063) primary antibody (1:25, 1 h at 37℃). For secondary antibody, Alexa Fluor® 488 Function conjugated donkey anti-rabbit antibody Probably involved in transcriptional regulation. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response
Supplementary Online Content Beltran H, Eng K, Mosquera JM, et al. Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol. Published online May 28, 2015. doi:10.1001/jamaoncol.2015.1313 eMethods eFigure 1. A schematic of the IPM Computational Pipeline eFigure 2. Tumor purity analysis eFigure 3. Tumor purity estimates from Pathology team versus computationally (CLONET) estimated tumor purities values for frozen tumor specimens (Spearman correlation 0.2765327, p- value = 0.03561) eFigure 4. Sequencing metrics Fresh/frozen vs. FFPE tissue eFigure 5. Somatic copy number alteration profiles by tumor type at cytogenetic map location resolution; for each cytogenetic map location the mean genes aberration frequency is reported eFigure 6. The 20 most frequently aberrant genes with respect to copy number gains/losses detected per tumor type eFigure 7. Top 50 genes with focal and large scale copy number gains (A) and losses (B) across the cohort eFigure 8. Summary of total number of copy number alterations across PM tumors eFigure 9. An example of tumor evolution looking at serial biopsies from PM222, a patient with metastatic bladder carcinoma eFigure 10. PM12 somatic mutations by coverage and allele frequency (A) and (B) mutation correlation between primary (y- axis) and brain metastasis (x-axis) eFigure 11. Point mutations across 5 metastatic sites of a 55 year old patient with metastatic prostate cancer at time of rapid autopsy eFigure 12. CT scans from patient PM137, a patient with recurrent platinum refractory metastatic urothelial carcinoma eFigure 13. Tracking tumor genomics between primary and metastatic samples from patient PM12 eFigure 14. -
Usbiological Datasheet
MLLT10, CT (AF10) (Protein AF-10, AF10, ALL1-fused Gene From Chromosome 10 Protein) (Biotin) Catalog number A0923-60C-Biotin Supplier United States Biological Translocations affecting chromosome 11q23 involve many partner chromosome regions and occur in various leukemias. The 11q23 gene involved in the translocations is MLL. MLLT10 is the partner gene to MLL1 involved in t(10;11)(p12;q23) translocations. In an analysis of two leukemia patients, the in t(10;11)(p12;q23) translocation fuses MLL1, a SET domain containg histone methyltransferase, to the MLLT10 gene. The MLLT10 gene encodes a predicted 1,027aa protein containing an N-terminal zinc finger and a C-terminal leucine zipper domain. The MLLT10 gene is one of the few MLL partner genes to be independently rearranged with a third gene in leukemia, the CALM gene in the t(10;11)(p12;q14) translocation. Chimeric fusion proteins MLL/AF10 and CALM/AF10 consistently retain the leucine zipper motif of MLLT10. The leucine zipper interacts with GAS41, a protein previously identified as the product of an amplified gene in a glioblastoma. GAS41 interacts with integrase interactor-1 (INI1), a component of the SWI/SNF complex, which acts to remodel chromatin and to modulate transcription. Retention of the leucine zipper in the MLL and CALM fusions suggested that a key feature of these chimeric proteins may be their ability to interfere in normal gene regulation through interaction with the adenosine triphosphate-dependent chromatin remodeling complexes. Applications Suitable for use in ELISA, Western Blot, and Immunohistochemistry. Other applications not tested. Recommended Dilution ELISA: 1:1,000 Western Blot: 1:100-1:500 Immunohistochemistry: 1:10-1:50 Optimal dilutions to be determined by the researcher. -
Recurrent Involvement of Ring-Type Zinc Finger Genes in Complex
Leukemia (2013) 27, 1745–1791 & 2013 Macmillan Publishers Limited All rights reserved 0887-6924/13 www.nature.com/leu LETTERS TO THE EDITOR Recurrent involvement of ring-type zinc finger genes in complex molecular rearrangements in childhood acute myelogeneous leukemia with translocation t(10;11)(p12;q23) Leukemia (2013) 27, 1745–1791; doi:10.1038/leu.2013.1 the MLL/MLLT10 fusion gene had been detected by conventional FISH and RT-PCR (Table S1). A specimen of the initial and remission sample was obtained in six children; in three of them a Complex rearrangements involving the MLL gene on chromosome relapse sample was available and additionally analyzed after 11q23 and MLLT10 on 10p have been reported in 15% of pediatric informed consent. By analyzing paired-end reads, we were able to patients with MLL rearranged acute myelogeneous leukemia describe these alterations in depth, revealing the precise pattern (AML). Owing to the opposite direction of MLL and MLLT10 of molecular rearrangement and finding other involved chromo- rearrangements (inversion and subsequent translocation or somes. For paired-end sequencing, DNA was isolated from insertion) with three or more breaks are required to result in a peripheral blood lymphocytes, and fragment libraries with a fusion gene.1,2 There are several additional reported median insert size of 450 bp were prepared. Samples of patients recombination partners of the MLL gene, in which a simple 1–3 were sequenced on a GAIIx platform; samples of patients 4–6 reciprocal translocation is insufficient due to incompatible on a HiSeq 2000 (Illumina Inc., San Diego, CA, USA). -
MLLT10 and IL3 Rearrangement Together with a Complex Four-Way
ONCOLOGY REPORTS 33: 625-630, 2015 MLLT10 and IL3 rearrangement together with a complex four-way translocation and trisomy 4 in a patient with early T-cell precursor acute lymphoblastic leukemia: A case report MoNEEB A.K. oTHMAN1, JoANA B. MELo2,3, ISABEL M. CARREIRA2,3, Martina RINCIC1,4, EyAd ALHouRANI1, Kathleen WILHELM1,5, BERNd GRuHN5, Anita GLASER1 and THoMAS LIEHR1 1Jena university Hospital, Friedrich Schiller university, Institute of Human Genetics, Jena, Germany; 2Laboratory of Cytogenetics and Genomics, Faculty of Medicine, university of Coimbra, Coimbra; 3CIMAGo, Centro de Investigação em Meio Ambiente, Genéticae oncobiologia, Coimbra, Portugal; 4Croatian Institute of Brain Research, Zagreb, Croatia; 5department of Pediatrics (oncology and Hematology), Jena university Hospital, Friedrich Schiller university, Jena, Germany Received September 3, 2014; Accepted october 13, 2014 DOI: 10.3892/or.2014.3624 Abstract. Cytogenetic classification of acute lymphoblastic Introduction leukemia (ALL) is primarily based on numerical and struc- tural chromosomal abnormalities. In T-cell ALL (T-ALL), T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive chromosomal rearrangements are identified in up to 70% leukemia derived from malignant transformation of T cell of the patients while the remaining patients show a normal progenitors and is more common in males than in females. karyotype. In the present study, a 16-year-old male was diag- T-ALL affects mainly older children and adolescents and nosed with T-precursor cell ALL and a normal karyotype represents 10-15% of pediatric and 25% of young adult ALL after standard GTG-banding, was studied retrospectively cases (1). Hyperdiploidy (>46 chromosomes) is found in 30% (>10 years after diagnosis) in frame of a research project of childhood and 10% of adulthood ALL cases. -
Cryptic Genomic Lesions in Adverse-Risk Acute Myeloid Leukemia Identified by Integrated Whole Genome and Transcriptome Sequencing
Leukemia (2020) 34:306–311 https://doi.org/10.1038/s41375-019-0546-1 LETTER Cytogenetics and molecular genetics Cryptic genomic lesions in adverse-risk acute myeloid leukemia identified by integrated whole genome and transcriptome sequencing 1,2,3 2 3 3,4 2 Jaeseung C. Kim ● Philip C. Zuzarte ● Tracy Murphy ● Michelle Chan-Seng-Yue ● Andrew M. K. Brown ● 2 5 1,3,4 1,3 1,2,6 Paul M. Krzyzanowski ● Adam C. Smith ● Faiyaz Notta ● Mark D. Minden ● John D. McPherson Received: 15 March 2019 / Revised: 23 April 2019 / Accepted: 17 June 2019 / Published online: 21 August 2019 © The Author(s) 2019. This article is published with open access To the Editor: treated accordingly [1]. In recent years, advancements in next-generation sequencing and efforts by large genomics Acute myeloid leukemia (AML) is a heterogeneous group studies have led to a classification of 11 AML subgroups of hematologic malignancies characterized by the pro- based on cytogenetic abnormalities as well as mutations in liferation of myeloid cells blocked in their ability to dif- genes, such as NPM1 or CEBPA [1]. 1234567890();,: 1234567890();,: ferentiate. Evaluation by G-banding and fluorescence in situ AML with a complex karyotype, defined by the presence hybridization is an essential aspect in the initial disease of three or more unrelated chromosomal aberrations and the characterization and even now is fundamental in identifying absence of favorable cytogenetic rearrangements, is asso- cytogenetic abnormalities that can inform disease diagnosis, ciated with TP53 mutations and strikingly poor outcome prognosis, and treatment decision [1, 2]. For instance, the [1, 3]. -
Detection of Novel Fusion-Transcripts by RNA-Seq in T-Cell Lymphoblastic
www.nature.com/scientificreports OPEN Detection of novel fusion- transcripts by RNA-Seq in T-cell lymphoblastic lymphoma Received: 3 July 2018 Pilar López-Nieva 1,2,3, Pablo Fernández-Navarro4,5, Osvaldo Graña-Castro6, Accepted: 14 March 2019 Eduardo Andrés-León7, Javier Santos1,2,3, María Villa-Morales 1,2,3, María Ángeles Cobos- Published: xx xx xxxx Fernández1,2,3, Laura González-Sánchez1,2,3, Marcos Malumbres8, María Salazar-Roa 8 & José Fernández-Piqueras 1,2,3 Fusions transcripts have been proven to be strong drivers for neoplasia-associated mutations, although their incidence in T-cell lymphoblastic lymphoma needs to be determined yet. Using RNA-Seq we have selected 55 fusion transcripts identifed by at least two of three detection methods in the same tumour. We confrmed the existence of 24 predicted novel fusions that had not been described in cancer or normal tissues yet, indicating the accuracy of the prediction. Of note, one of them involves the proto oncogene TAL1. Other confrmed fusions could explain the overexpression of driver genes such as COMMD3-BMI1, LMO1 or JAK3. Five fusions found exclusively in tumour samples could be considered pathogenic (NFYG-TAL1, RIC3-TCRBC2, SLC35A3-HIAT1, PICALM MLLT10 and MLLT10- PICALM). However, other fusions detected simultaneously in normal and tumour samples (JAK3-INSL3, KANSL1-ARL17A/B and TFG-ADGRG7) could be germ-line fusions genes involved in tumour-maintaining tasks. Notably, some fusions were confrmed in more tumour samples than predicted, indicating that the detection methods underestimated the real number of existing fusions. Our results highlight the potential of RNA-Seq to identify new cryptic fusions, which could be drivers or tumour-maintaining passenger genes.