Novel and Highly Recurrent Chromosomal Alterations in Se´Zary Syndrome
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Fish and Shellfish Immunology 95 (2019) 538–545
Fish and Shellfish Immunology 95 (2019) 538–545 Contents lists available at ScienceDirect Fish and Shellfish Immunology journal homepage: www.elsevier.com/locate/fsi Full length article Congenital asplenia due to a tlx1 mutation reduces resistance to Aeromonas T hydrophila infection in zebrafish ∗ Lang Xiea,b, Yixi Taoa,b, Ronghua Wua,b, Qin Yec, Hao Xua,b, Yun Lia,b, a Institute of Three Gorges Ecological Fisheries of Chongqing, College of Animal Science and Technology, Southwest University, Chongqing, 400715, China b Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, Southwest University, Chongqing, 400715, China c School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China ARTICLE INFO ABSTRACT Keywords: It is documented that tlx1, an orphan homeobox gene, plays critical roles in the regulation of early spleen tlx1 knock-out developmental in mammalian species. However, there is no direct evidence supporting the functions of tlx1 in Congenital asplenia non-mammalian species, especially in fish. In this study, we demonstrated that tlx1 is expressed in the splenic Aeromonas hydrophila primordia as early as 52 hours post-fertilization (hpf) in zebrafish. A tlx1−/− homozygous mutant line was Disease resistance generated via CRISPR/Cas9 to elucidate the roles of tlx1 in spleen development in zebrafish. In the tlx1−/− background, tlx1−/− cells persisted in the splenic primordia until 52 hpf but were no longer detectable after 53 hpf, suggesting perturbation of early spleen development. The zebrafish also exhibited congenital asplenia caused by the tlx1 mutation. Asplenic zebrafish can survive and breed normally under standard laboratory conditions, but the survival rate of animals infected with Aeromonas hydrophila was significantly lower than that of wild-type (WT) zebrafish. -
Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Multiple Activities of Arl1 Gtpase in the Trans-Golgi Network Chia-Jung Yu1,2 and Fang-Jen S
© 2017. Published by The Company of Biologists Ltd | Journal of Cell Science (2017) 130, 1691-1699 doi:10.1242/jcs.201319 COMMENTARY Multiple activities of Arl1 GTPase in the trans-Golgi network Chia-Jung Yu1,2 and Fang-Jen S. Lee3,4,* ABSTRACT typical features of an Arf-family GTPase, including an amphipathic ADP-ribosylation factors (Arfs) and ADP-ribosylation factor-like N-terminal helix and a consensus site for N-myristoylation (Lu et al., proteins (Arls) are highly conserved small GTPases that function 2001; Price et al., 2005). In yeast, recruitment of Arl1 to the Golgi as main regulators of vesicular trafficking and cytoskeletal complex requires a second Arf-like GTPase, Arl3 (Behnia et al., reorganization. Arl1, the first identified member of the large Arl family, 2004; Setty et al., 2003). Yeast Arl3 lacks a myristoylation site and is an important regulator of Golgi complex structure and function in is, instead, N-terminally acetylated; this modification is required for organisms ranging from yeast to mammals. Together with its effectors, its recruitment to the Golgi complex by Sys1. In mammalian cells, Arl1 has been shown to be involved in several cellular processes, ADP-ribosylation-factor-related protein 1 (Arfrp1), a mammalian including endosomal trans-Golgi network and secretory trafficking, lipid ortholog of yeast Arl3, plays a pivotal role in the recruitment of Arl1 droplet and salivary granule formation, innate immunity and neuronal to the trans-Golgi network (TGN) (Behnia et al., 2004; Panic et al., development, stress tolerance, as well as the response of the unfolded 2003b; Setty et al., 2003; Zahn et al., 2006). -
ACOX3 Antibody
Product Datasheet ACOX3 antibody Catalog No: #22136 Orders: [email protected] Description Support: [email protected] Product Name ACOX3 antibody Host Species Rabbit Clonality Polyclonal Purification Purified by antigen-affinity chromatography. Applications WB IHC Species Reactivity Hu Immunogen Type Recombinant protein Immunogen Description Recombinant protein fragment contain a sequence corresponding to a region within amino acids 408 and 613 of ACOX3 Target Name ACOX3 Accession No. Swiss-Prot:O15254Gene ID:8310 Concentration 1mg/ml Formulation Supplied in 0.1M Tris-buffered saline with 20% Glycerol (pH7.0). 0.01% Thimerosal was added as a preservative. Storage Store at -20°C for long term preservation (recommended). Store at 4°C for short term use. Application Details Predicted MW: 70kd Western blotting: 1:500-1:3000 Immunohistochemistry: 1:100-1:500 Images Sample (30 ug of whole cell lysate) A: A549 7.5% SDS PAGE Primary antibody diluted at 1: 1000 Address: 8400 Baltimore Ave., Suite 302, College Park, MD 20740, USA http://www.sabbiotech.com 1 Immunohistochemical analysis of paraffin-embedded H441 xenograft, using ACOX3 antibody at 1: 500 dilution. Background Acyl-Coenzyme A oxidase 3 also know as pristanoyl -CoA oxidase (ACOX3)is involved in the desaturation of 2-methyl branched fatty acids in peroxisomes. Unlike the rat homolog, the human gene is expressed in very low amounts in liver such that its mRNA was undetectable by routine Northern-blot analysis or its product by immunoblotting or by enzyme activity measurements. However the human cDNA encoding a 700 amino acid protein with a peroxisomal targeting C-terminal tripeptide S-K-L was isolated and is thought to be expressed under special conditions such as specific developmental stages or in a tissue specific manner in tissues that have not yet been examined. -
ACOX3 Human Shrna Plasmid Kit (Locus ID 8310) Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TL314988 ACOX3 Human shRNA Plasmid Kit (Locus ID 8310) Product data: Product Type: shRNA Plasmids Product Name: ACOX3 Human shRNA Plasmid Kit (Locus ID 8310) Locus ID: 8310 Vector: pGFP-C-shLenti (TR30023) Format: Lentiviral plasmids Components: ACOX3 - Human, 4 unique 29mer shRNA constructs in lentiviral GFP vector(Gene ID = 8310). 5µg purified plasmid DNA per construct Non-effective 29-mer scrambled shRNA cassette in pGFP-C-shLenti Vector, TR30021, included for free. RefSeq: NM_001101667, NM_003501, NM_003501.1, NM_003501.2, NM_001101667.1, BC017053, NM_003501.3, NM_001101667.2 Summary: Acyl-Coenzyme A oxidase 3 also know as pristanoyl -CoA oxidase (ACOX3)is involved in the desaturation of 2-methyl branched fatty acids in peroxisomes. Unlike the rat homolog, the human gene is expressed in very low amounts in liver such that its mRNA was undetectable by routine Northern-blot analysis or its product by immunoblotting or by enzyme activity measurements. However the human cDNA encoding a 700 amino acid protein with a peroxisomal targeting C-terminal tripeptide S-K-L was isolated and is thought to be expressed under special conditions such as specific developmental stages or in a tissue specific manner in tissues that have not yet been examined. [provided by RefSeq, Jul 2008] shRNA Design: These shRNA constructs were designed against multiple splice variants at this gene locus. To be certain that your variant of interest is targeted, please contact [email protected]. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Kids First Pediatric Research Program (Kids First) Poster Session at ASHG Accelerating Pediatric Genomics Research Through Collaboration October 15Th, 2019
The Gabriella Miller Kids First Pediatric Research Program (Kids First) Poster Session at ASHG Accelerating Pediatric Genomics Research through Collaboration October 15th, 2019 Background The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans- NIH Common Fund program initiated in response to the 2014 Gabriella Miller Kids First Research Act. The program’s vision is to alleviate suffering from childhood cancer and structural birth defects by fostering collaborative research to uncover the etiology of these diseases and support data sharing within the pediatric research community. This is implemented through developing the Gabriella Miller Kids First Data Resource (Kids First Data Resource) and populating this resource with whole genome sequence datasets and associated clinical and phenotypic information. Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. Purpose During this evening poster session, attendees will gain a broad understanding of the utility of the genomic data generated by Kids First, learn about the progress of Kids First X01 cohort projects, and observe demonstrations of the tools and functionalities of the recently launched Kids First Data Resource Portal. The session is an opportunity for the scientific community and public to engage with Kids First investigators, collaborators, and a growing community of researchers, patient foundations, and families. Several other NIH and external data efforts will present posters and be available to discuss collaboration opportunities as we work together to accelerate pediatric research. -
The Endocytic Membrane Trafficking Pathway Plays a Major Role
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Liverpool Repository RESEARCH ARTICLE The Endocytic Membrane Trafficking Pathway Plays a Major Role in the Risk of Parkinson’s Disease Sara Bandres-Ciga, PhD,1,2 Sara Saez-Atienzar, PhD,3 Luis Bonet-Ponce, PhD,4 Kimberley Billingsley, MSc,1,5,6 Dan Vitale, MSc,7 Cornelis Blauwendraat, PhD,1 Jesse Raphael Gibbs, PhD,7 Lasse Pihlstrøm, MD, PhD,8 Ziv Gan-Or, MD, PhD,9,10 The International Parkinson’s Disease Genomics Consortium (IPDGC), Mark R. Cookson, PhD,4 Mike A. Nalls, PhD,1,11 and Andrew B. Singleton, PhD1* 1Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 2Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain 3Transgenics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 4Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 5Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom 6Department of Pathophysiology, University of Tartu, Tartu, Estonia 7Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA 8Department of Neurology, Oslo University Hospital, Oslo, Norway 9Department of Neurology and Neurosurgery, Department of Human Genetics, McGill University, Montréal, Quebec, Canada 10Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada 11Data Tecnica International, Glen Echo, Maryland, USA ABSTRACT studies, summary-data based Mendelian randomization Background: PD is a complex polygenic disorder. -
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. -
ACOX3 (H-1): Sc-390624
SAN TA C RUZ BI OTEC HNOL OG Y, INC . ACOX3 (H-1): sc-390624 BACKGROUND APPLICATIONS ACOX3 (acyl-Coenzyme A oxidase 3), also known as BRCOX or PRCOX, is a ACOX3 (H-1) is recommended for detection of ACOX3 of mouse, rat and 700 amino acid protein that localizes to peroxisomes and belongs to the acyl- human origin by Western Blotting (starting dilution 1:100, dilution range CoA oxidase family. Using FAD as a cofactor, ACOX3 catalyzes the desatura - 1:100-1:1000), immunoprecipitation [1-2 µg per 100-500 µg of total protein tion of 2-methyl branched fatty acids in peroxisomes, thereby playing an impor - (1 ml of cell lysate)], immunofluorescence (starting dilution 1:50, dilution tant role in peroxisomal fatty acid β-oxidation. Human ACOX3 shares 75% range 1:50-1:500) and solid phase ELISA (starting dilution 1:30, dilution sequence identity with its rat counterpart, suggesting a conserved role range 1:30-1:3000). between species. Multiple isoforms of ACOX3 exist due to alternative splic - Suitable for use as control antibody for ACOX3 siRNA (h): sc-89236, ACOX3 ing events. The gene encoding ACOX3 maps to human chromosome 4, which siRNA (m): sc-140819, ACOX3 shRNA Plasmid (h): sc-89236-SH, ACOX3 encodes nearly 6% of the human genome and has the largest gene deserts shRNA Plasmid (m): sc-140819-SH, ACOX3 shRNA (h) Lentiviral Particles: (regions of the genome with no protein encoding genes) of all of the human sc-89236-V and ACOX3 shRNA (m) Lentiviral Particles: sc-140819-V. chromosomes. Defects in some of the genes located on chromosome 4 are associated with Huntington’s disease, Ellis-van Creveld syndrome, methyl - Molecular Weight of ACOX3: 78 kDa. -
The Genomic Landscape of Pancreatic and Periampullary Adenocarcinoma Vandana Sandhu1,2, David C
Published OnlineFirst August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Cancer Molecular and Cellular Pathobiology Research The Genomic Landscape of Pancreatic and Periampullary Adenocarcinoma Vandana Sandhu1,2, David C. Wedge3,4, Inger Marie Bowitz Lothe1,5, Knut Jørgen Labori6, Stefan C. Dentro3,4,Trond Buanes6,7, Martina L. Skrede1, Astrid M. Dalsgaard1, Else Munthe8, Ola Myklebost8, Ole Christian Lingjærde9, Anne-Lise Børresen-Dale1,7, Tone Ikdahl10,11, Peter Van Loo12,13, Silje Nord1, and Elin H. Kure1,2 Abstract Despite advances in diagnostics, less than 5% of patients with and Wnt signaling. By integrating genomics and transcrip- periampullary tumors experience an overall survival of five years tomics data from the same patients, we identified CCNE1 and or more. Periampullary tumors are neoplasms that arise in the ERBB2 as candidate driver genes. Morphologic subtypes of vicinity of the ampulla of Vater, an enlargement of liver and periampullary adenocarcinomas (i.e., pancreatobiliary or intes- pancreas ducts where they join and enter the small intestine. In tinal) harbor many common genomic aberrations. However, this study, we analyzed copy number aberrations using Affymetrix gain of 13q and 3q, and deletions of 5q were found specificto SNP 6.0 arrays in 60 periampullary adenocarcinomas from Oslo the intestinal subtype. Our study also implicated the use of the University Hospital to identify genome-wide copy number aber- PAM50 classifier in identifying a subgroup of patients with a rations, putative driver genes, deregulated pathways, and poten- high proliferation rate, which had impaired survival. Further- tial prognostic markers. Results were validated in a separate cohort more, gain of 18p11 (18p11.21-23, 18p11.31-32) and 19q13 derived from The Cancer Genome Atlas Consortium (n ¼ 127).