Sequencing, Analysis, and Annotation of Expressed Sequence Tags for Camelus Dromedarius
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NIH Public Access Author Manuscript Reprod Biomed Online
NIH Public Access Author Manuscript Reprod Biomed Online. Author manuscript; available in PMC 2014 October 01. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Reprod Biomed Online. 2013 October ; 27(4): 423–435. doi:10.1016/j.rbmo.2013.06.013. The human oviduct transcriptome reveals an anti-inflammatory, anti-angiogenic, secretory and matrix-stable environment during embryo transit AP Hessa,b, S Talbia, AE Hamiltona, DM Baston-Buestb, M Nyegaarda, JC Irwind, F Barragand, JS Kruesselb, A Germeyera,c, and LC Giudicea,d,* aDepartment of Gynecology and Obstetrics, Stanford University Medical School, California, USA bUniversity of Dusseldorf, Medical Faculty, Department of Gynecology, Obstetrics and REI (UniKiD), Dusseldorf, Germany cDepartment of Gynecological Endocrinology and Reproductive Medicine, University Medical Center, Heidelberg, Germany Abstract The human oviduct serves as a conduit for spermatozoa in the periovulatory phase and nurtures and facilitates transport of the developing embryo for nidation during the luteal phase. Interactions between the embryo and oviductal epithelial surface proteins and secreted products during embryo transit are largely undefined. This study investigated gene expression in the human oviduct in the early luteal versus follicular phases to identify candidate genes and biomolecular processes that may participate in maturation and transport of the embryo as it traverses this tissue. Oviductal RNA was hybridized to oligonucleotide arrays and resulting data were analysed by bioinformatic approaches. There were 650 genes significantly down-regulated and 683 genes significantly up- regulated (P < 0.05) in the luteal versus follicular phase. Quantitative real-time PCR, immunoblot analysis and immunohistochemistry confirmed selected gene expression and cellular protein localization. -
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. -
Supplemental Figures Tables Importance of the MHC (SLA) in Swine Health and Biomedical Research
Supplemental Material: Annu. Rev. Anim. Biosci. 2020. 8:171-198 https://doi.org/10.1146/annurev-animal-020518-115014 Importance of the Major Histocompatibility Complex (Swine Leukocyte Antigen) in Swine Health and Biomedical Research Hammer, Ho, Ando, Rogel-Gaillard, Charles, Tector, Tector, and Lunney Supplemental Figures Tables Importance of the MHC (SLA) in swine health and biomedical research Annual Review Animal Biosciences AV08 Lunney Supplemental Figure 1. Chromosomal mapping of the human (HLA complex) and swine MHC (SLA complex) a b HSA 6p21 SSC 7p11-7q11 MOG MOG Class I Class III Class I Class II Class III RING1 Centromere Class II RING1 HLA complex SLA complex Human Leucocyte Antigen Swine Leucocyte Antigen Supplemental Figure 1. Chromosomal mapping of the human (HLA complex) and swine MHC (SLA complex). A. Schematic representation of the chromosome mapping and orientation of the HLA complex on HSA 6p21 and of the pig SLA complex on both sides of the centromere on swine chromosome 7 (SSC7). B. Fluorescent in situ hybridization (FISH) map demonstrating SLA location on SSC7 p11 using a YAC clone containing SLA class Ia genes (adapted from Velten F, Rogel-Gaillard C, Renard C, Pontarotti P, Tazi-Ahnini R, et al. 1998. A first map of the porcine major histocompatibility complex class I region. Tissue Antigens 51:183–94). Supplemental Figure 2. Detailed Physical Map of SLA genes Position Name Position Name Position Name Position Name Position Name 7 22 595 564 22 606 449+ MOG 23 190 757 23 207 872- DHX16 23 705 030 23 706 737- LTB -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Sequences, Annotation and Single Nucleotide Polymorphism of The
Nova Southeastern University NSUWorks Biology Faculty Articles Department of Biological Sciences 6-2008 Sequences, Annotation and Single Nucleotide Polymorphism of the Major Histocompatibility Complex in the Domestic Cat Naoya Yuhki National Cancer Institute at Frederick James C. Mullikin National Human Genome Research Institute Thomas W. Beck National Cancer Institute at Frederick Robert M. Stephens National Cancer Institute at Frederick Stephen J. O'Brien National Cancer Institute at Frederick, [email protected] Follow this and additional works at: https://nsuworks.nova.edu/cnso_bio_facarticles Part of the Genetics and Genomics Commons, and the Zoology Commons NSUWorks Citation Yuhki, Naoya; James C. Mullikin; Thomas W. Beck; Robert M. Stephens; and Stephen J. O'Brien. 2008. "Sequences, Annotation and Single Nucleotide Polymorphism of the Major Histocompatibility Complex in the Domestic Cat." PLoS ONE 7, (3 e2674): 1-17. https://nsuworks.nova.edu/cnso_bio_facarticles/773 This Article is brought to you for free and open access by the Department of Biological Sciences at NSUWorks. It has been accepted for inclusion in Biology Faculty Articles by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Sequences, Annotation and Single Nucleotide Polymorphism of the Major Histocompatibility Complex in the Domestic Cat Naoya Yuhki1*, James C. Mullikin2, Thomas Beck3, Robert Stephens4, Stephen J. O’Brien1 1 Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland, -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
The Interactome of KRAB Zinc Finger Proteins Reveals the Evolutionary History of Their Functional Diversification
Resource The interactome of KRAB zinc finger proteins reveals the evolutionary history of their functional diversification Pierre-Yves Helleboid1,†, Moritz Heusel2,†, Julien Duc1, Cécile Piot1, Christian W Thorball1, Andrea Coluccio1, Julien Pontis1, Michaël Imbeault1, Priscilla Turelli1, Ruedi Aebersold2,3,* & Didier Trono1,** Abstract years ago (MYA) (Imbeault et al, 2017). Their products harbor an N-terminal KRAB (Kru¨ppel-associated box) domain related to that of Krüppel-associated box (KRAB)-containing zinc finger proteins Meisetz (a.k.a. PRDM9), a protein that originated prior to the diver- (KZFPs) are encoded in the hundreds by the genomes of higher gence of chordates and echinoderms, and a C-terminal array of zinc vertebrates, and many act with the heterochromatin-inducing fingers (ZNF) with sequence-specific DNA-binding potential (Urru- KAP1 as repressors of transposable elements (TEs) during early tia, 2003; Birtle & Ponting, 2006; Imbeault et al, 2017). KZFP genes embryogenesis. Yet, their widespread expression in adult tissues multiplied by gene and segment duplication to count today more and enrichment at other genetic loci indicate additional roles. than 350 and 700 representatives in the human and mouse Here, we characterized the protein interactome of 101 of the ~350 genomes, respectively (Urrutia, 2003; Kauzlaric et al, 2017). A human KZFPs. Consistent with their targeting of TEs, most KZFPs majority of human KZFPs including all primate-restricted family conserved up to placental mammals essentially recruit KAP1 and members target sequences derived from TEs, that is, DNA trans- associated effectors. In contrast, a subset of more ancient KZFPs posons, ERVs (endogenous retroviruses), LINEs, SINEs (long and rather interacts with factors related to functions such as genome short interspersed nuclear elements, respectively), or SVAs (SINE- architecture or RNA processing. -
Learning from Cadherin Structures and Sequences: Affinity Determinants and Protein Architecture
Learning from cadherin structures and sequences: affinity determinants and protein architecture Klára Fels ıvályi Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Klara Felsovalyi All rights reserved ABSTRACT Learning from cadherin structures and sequences: affinity determinants and protein architecture Klara Felsovalyi Cadherins are a family of cell-surface proteins mediating adhesion that are important in development and maintenance of tissues. The family is defined by the repeating cadherin domain (EC) in their extracellular region, but they are diverse in terms of protein size, architecture and cellular function. The best-understood subfamily is the type I classical cadherins, which are found in vertebrates and have five EC domains. Among the five different type I classical cadherins, the binding interactions are highly specific in their homo- and heterophilic binding affinities, though their sequences are very similar. As previously shown, E- and N-cadherins, two prototypic members of the subfamily, differ in their homophilic K D by about an order of magnitude, while their heterophilic affinity is intermediate. To examine the source of the binding affinity differences among type I cadherins, we used crystal structures, analytical ultracentrifugation (AUC), surface plasmon resonance (SPR), and electron paramagnetic resonance (EPR) studies. Phylogenetic analysis and binding affinity behavior show that the type I cadherins can be further divided into two subgroups, with E- and N-cadherin representing each. In addition to the affinity differences in their wild-type binding through the strand-swapped interface, a second interface also shows an affinity difference between E- and N-cadherin. -
Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors
ARTICLE DOI: 10.1038/s41467-018-06366-z OPEN Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors Sudheer Kumar Gara1, Justin Lack2, Lisa Zhang1, Emerson Harris1, Margaret Cam2 & Electron Kebebew1,3 Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is 1234567890():,; unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37–57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs. 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2 Center for Cancer Research, Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3 Department of Surgery and Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Correspondence and requests for materials should be addressed to E.K. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4172 | DOI: 10.1038/s41467-018-06366-z | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06366-z drenocortical carcinoma (ACC) is a rare malignancy with types including primary ACC from the TCGA to understand our A0.7–2 cases per million per year1,2. -
Journal of Translational Medicine Biomed Central
Journal of Translational Medicine BioMed Central Review Open Access CD177: A member of the Ly-6 gene superfamily involved with neutrophil proliferation and polycythemia vera David F Stroncek*, Lorraine Caruccio and Maria Bettinotti Address: From the Department of Transfusion Medicine, Warren G. Magnuson Clinical Center, National Institutes of Health, Bethesda, MD, USA Email: David F Stroncek* - [email protected]; Lorraine Caruccio - [email protected]; Maria Bettinotti - [email protected] * Corresponding author Published: 29 March 2004 Received: 22 December 2003 Accepted: 29 March 2004 Journal of Translational Medicine 2004, 2:8 This article is available from: http://www.translational-medicine.com/content/2/1/8 © 2004 Stroncek et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. CD177PRV-1NB1neutrophilspolycythemia veramyelopoiesis Abstract Genes in the Leukocyte Antigen 6 (Ly-6) superfamily encode glycosyl-phosphatidylinositol (GPI) anchored glycoproteins (gp) with conserved domains of 70 to 100 amino acids and 8 to 10 cysteine residues. Murine Ly-6 genes encode important lymphocyte and hematopoietic stem cell antigens. Recently, a new member of the human Ly-6 gene superfamily has been described, CD177. CD177 is polymorphic and has at least two alleles, PRV-1 and NB1. CD177 was first described as PRV-1, a gene that is overexpressed in neutrophils from approximately 95% of patients with polycythemia vera and from about half of patients with essential thrombocythemia. CD177 encodes NB1 gp, a 58–64 kD GPI gp that is expressed by neutrophils and neutrophil precursors. -
Supp Table 6.Pdf
Supplementary Table 6. Processes associated to the 2037 SCL candidate target genes ID Symbol Entrez Gene Name Process NM_178114 AMIGO2 adhesion molecule with Ig-like domain 2 adhesion NM_033474 ARVCF armadillo repeat gene deletes in velocardiofacial syndrome adhesion NM_027060 BTBD9 BTB (POZ) domain containing 9 adhesion NM_001039149 CD226 CD226 molecule adhesion NM_010581 CD47 CD47 molecule adhesion NM_023370 CDH23 cadherin-like 23 adhesion NM_207298 CERCAM cerebral endothelial cell adhesion molecule adhesion NM_021719 CLDN15 claudin 15 adhesion NM_009902 CLDN3 claudin 3 adhesion NM_008779 CNTN3 contactin 3 (plasmacytoma associated) adhesion NM_015734 COL5A1 collagen, type V, alpha 1 adhesion NM_007803 CTTN cortactin adhesion NM_009142 CX3CL1 chemokine (C-X3-C motif) ligand 1 adhesion NM_031174 DSCAM Down syndrome cell adhesion molecule adhesion NM_145158 EMILIN2 elastin microfibril interfacer 2 adhesion NM_001081286 FAT1 FAT tumor suppressor homolog 1 (Drosophila) adhesion NM_001080814 FAT3 FAT tumor suppressor homolog 3 (Drosophila) adhesion NM_153795 FERMT3 fermitin family homolog 3 (Drosophila) adhesion NM_010494 ICAM2 intercellular adhesion molecule 2 adhesion NM_023892 ICAM4 (includes EG:3386) intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)adhesion NM_001001979 MEGF10 multiple EGF-like-domains 10 adhesion NM_172522 MEGF11 multiple EGF-like-domains 11 adhesion NM_010739 MUC13 mucin 13, cell surface associated adhesion NM_013610 NINJ1 ninjurin 1 adhesion NM_016718 NINJ2 ninjurin 2 adhesion NM_172932 NLGN3 neuroligin