Sequencing, Analysis, and Annotation of Expressed Sequence Tags for Camelus Dromedarius
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The Switch from Fermentation to Respiration in Saccharomyces Cerevisiae Is Regulated by the Ert1 Transcriptional Activator/Repressor
INVESTIGATION The Switch from Fermentation to Respiration in Saccharomyces cerevisiae Is Regulated by the Ert1 Transcriptional Activator/Repressor Najla Gasmi,* Pierre-Etienne Jacques,† Natalia Klimova,† Xiao Guo,§ Alessandra Ricciardi,§ François Robert,†,** and Bernard Turcotte*,‡,§,1 ‡Department of Medicine, *Department of Biochemistry, and §Department of Microbiology and Immunology, McGill University Health Centre, McGill University, Montreal, QC, Canada H3A 1A1, †Institut de recherches cliniques de Montréal, Montréal, QC, Canada H2W 1R7, and **Département de Médecine, Faculté de Médecine, Université de Montréal, QC, Canada H3C 3J7 ABSTRACT In the yeast Saccharomyces cerevisiae, fermentation is the major pathway for energy production, even under aerobic conditions. However, when glucose becomes scarce, ethanol produced during fermentation is used as a carbon source, requiring a shift to respiration. This adaptation results in massive reprogramming of gene expression. Increased expression of genes for gluconeogenesis and the glyoxylate cycle is observed upon a shift to ethanol and, conversely, expression of some fermentation genes is reduced. The zinc cluster proteins Cat8, Sip4, and Rds2, as well as Adr1, have been shown to mediate this reprogramming of gene expression. In this study, we have characterized the gene YBR239C encoding a putative zinc cluster protein and it was named ERT1 (ethanol regulated transcription factor 1). ChIP-chip analysis showed that Ert1 binds to a limited number of targets in the presence of glucose. The strongest enrichment was observed at the promoter of PCK1 encoding an important gluconeogenic enzyme. With ethanol as the carbon source, enrichment was observed with many additional genes involved in gluconeogenesis and mitochondrial function. Use of lacZ reporters and quantitative RT-PCR analyses demonstrated that Ert1 regulates expression of its target genes in a manner that is highly redundant with other regulators of gluconeogenesis. -
Frequent Expression Loss of Inter-Alpha-Trypsin Inhibitor Heavy Chain (ITIH) Genes in Multiple Human Solid Tumors: a Systematic Expression Analysis
Hamm, A; Veeck, J; Bektas, N; Wild, P J; Hartmann, A; Heindrichs, U; Kristiansen, G; Werbowetski-Ogilvie, T; Del Maestro, R; Knuechel, R; Dahl, E (2008). Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis. BMC Cancer, 8:25. Postprint available at: http://www.zora.uzh.ch University of Zurich Posted at the Zurich Open Repository and Archive, University of Zurich. Zurich Open Repository and Archive http://www.zora.uzh.ch Originally published at: BMC Cancer 2008, 8:25. Winterthurerstr. 190 CH-8057 Zurich http://www.zora.uzh.ch Year: 2008 Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis Hamm, A; Veeck, J; Bektas, N; Wild, P J; Hartmann, A; Heindrichs, U; Kristiansen, G; Werbowetski-Ogilvie, T; Del Maestro, R; Knuechel, R; Dahl, E Hamm, A; Veeck, J; Bektas, N; Wild, P J; Hartmann, A; Heindrichs, U; Kristiansen, G; Werbowetski-Ogilvie, T; Del Maestro, R; Knuechel, R; Dahl, E (2008). Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis. BMC Cancer, 8:25. Postprint available at: http://www.zora.uzh.ch Posted at the Zurich Open Repository and Archive, University of Zurich. http://www.zora.uzh.ch Originally published at: BMC Cancer 2008, 8:25. Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis Abstract BACKGROUND: The inter-alpha-trypsin inhibitors (ITI) are a family of plasma protease inhibitors, assembled from a light chain - bikunin, encoded by AMBP - and five homologous heavy chains (encoded by ITIH1, ITIH2, ITIH3, ITIH4, and ITIH5), contributing to extracellular matrix stability by covalent linkage to hyaluronan. -
Transcriptomic Signatures of Brain Regional Vulnerability to Parkinsonâ
ARTICLE https://doi.org/10.1038/s42003-020-0804-9 OPEN Transcriptomic signatures of brain regional vulnerability to Parkinson’s disease Arlin Keo 1,2, Ahmed Mahfouz 1,2, Angela M.T. Ingrassia3, Jean-Pascal Meneboo4,5, Celine Villenet4, Eugénie Mutez6,7,8, Thomas Comptdaer 6,7, Boudewijn P.F. Lelieveldt2,9, Martin Figeac4,5, ✉ ✉ ✉ Marie-Christine Chartier-Harlin 6,7 , Wilma D.J. van de Berg 3 , Jacobus J. van Hilten 10 & 1234567890():,; ✉ Marcel J.T. Reinders 1,2 The molecular mechanisms underlying caudal-to-rostral progression of Lewy body pathology in Parkinson’s disease remain poorly understood. Here, we identified transcriptomic sig- natures across brain regions involved in Braak Lewy body stages in non-neurological adults from the Allen Human Brain Atlas. Among the genes that are indicative of regional vulner- ability, we found known genetic risk factors for Parkinson’s disease: SCARB2, ELOVL7, SH3GL2, SNCA, BAP1, and ZNF184. Results were confirmed in two datasets of non- neurological subjects, while in two datasets of Parkinson’s disease patients we found altered expression patterns. Co-expression analysis across vulnerable regions identified a module enriched for genes associated with dopamine synthesis and microglia, and another module related to the immune system, blood-oxygen transport, and endothelial cells. Both were highly expressed in regions involved in the preclinical stages of the disease. Finally, altera- tions in genes underlying these region-specific functions may contribute to the selective regional vulnerability in Parkinson’s disease brains. 1 Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands. 2 Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands. -
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. -
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. -
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. -
Comparison of Orthologs Across Multiple Species by Various Strategies
COMPARISON OF ORTHOLOGS ACROSS MULTIPLE SPECIES BY VARIOUS STRATEGIES BY HUI LIU DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biophysics and Computational Biology in the Graduate College of the University of Illinois at Urbana-Champaign, 2014 Urbana, Illinois Doctoral Committee: Professor Eric Jakobsson, Chair, Director of Research Professor Gene E. Robinson Associate Professor Saurabh Sinha Assistant Professor Jian Ma Abstract Thanks to the improvement of genome sequencing technology, abundant multi-species genomic data now became available and comparative genomics continues to be a fast prospering filed of biological research. Through the comparison of genomes of different organisms, we can understand what, at the molecular level, distinguishes different life forms from each other. It shed light on revealing the evolution of biology. And it also helps to refine the annotations and functions of individual genomes. For example, through comparisons across mammalian genomes, we can give an estimate of the conserved set of genes across mammals and correspondingly, find the species-specific sets of genes or functions. However, comparative genomics can be feasible only if a meaningful classification of genes exists. A natural way to do so is to delineate sets of orthologous genes. However, debates exist about the appropriate way to define orthologs. It is originally defined as genes in different species which derive from speciation events. But such definition is not sufficient to derive orthologous genes due to the complexity of evolutionary events such as gene duplication and gene loss. While it is possible to correctly figure out all the evolutionary events with the true phylogenetic tree, the true phylogenetic tree itself is impractical to be inferred. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
New Targets of Urocortin-Mediated Cardioprotection
69 New targets of urocortin-mediated cardioprotection Sea´n P Barry1, Kevin M Lawrence4, James McCormick1, Surinder M Soond5, Mike Hubank2, Simon Eaton3, Ahila Sivarajah6, Tiziano M Scarabelli7, Richard A Knight1, Christoph Thiemermann6, David S Latchman1, Paul A Townsend8 and Anastasis Stephanou1 1Medical Molecular Biology Unit, 2Department of Molecular Haematology and 3Department of Surgery, Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK 4Department of Cellular Pathology, St George’s, University of London, Cranmer Terrace, Tooting, London, SW17 0RE, UK 5School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK 6St Bartholomew’s and The Royal London School of Medicine and Dentistry, William Harvey Research Institute, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London, EC1M 7BQ, UK 7Center for Heart and Vessel Preclinical Studies, St John Hospital and Medical Center, Wayne State University School of Medicine, 22201 Moross Road, Detroit, Michigan 48336, USA 8Human Genetics Division, MP808, Southampton General Hospital, University of Southampton, Southampton SO16 6YD, UK (Correspondence should be addressed to S P Barry; Email: [email protected]) Abstract The urocortin (UCN) hormones UCN1 and UCN2 have been shown previously to confer significant protection against myocardial ischaemia/reperfusion (I/R) injury; however, the molecular mechanisms underlying their action are poorly understood. To further define the transcriptional effect of UCNs that underpins their cardioprotective activity, a microarray analysis was carried out using an in vivo rat coronary occlusion model of I/R injury. Infusion of UCN1 or UCN2 before the onset of reperfusion resulted in the differential regulation of 66 and 141 genes respectively, the majority of which have not been described previously. -
Identification of Candidate Genes and Pathways Associated with Obesity
animals Article Identification of Candidate Genes and Pathways Associated with Obesity-Related Traits in Canines via Gene-Set Enrichment and Pathway-Based GWAS Analysis Sunirmal Sheet y, Srikanth Krishnamoorthy y , Jihye Cha, Soyoung Choi and Bong-Hwan Choi * Animal Genome & Bioinformatics, National Institute of Animal Science, RDA, Wanju 55365, Korea; [email protected] (S.S.); [email protected] (S.K.); [email protected] (J.C.); [email protected] (S.C.) * Correspondence: [email protected]; Tel.: +82-10-8143-5164 These authors contributed equally. y Received: 10 October 2020; Accepted: 6 November 2020; Published: 9 November 2020 Simple Summary: Obesity is a serious health issue and is increasing at an alarming rate in several dog breeds, but there is limited information on the genetic mechanism underlying it. Moreover, there have been very few reports on genetic markers associated with canine obesity. These studies were limited to the use of a single breed in the association study. In this study, we have performed a GWAS and supplemented it with gene-set enrichment and pathway-based analyses to identify causative loci and genes associated with canine obesity in 18 different dog breeds. From the GWAS, the significant markers associated with obesity-related traits including body weight (CACNA1B, C22orf39, U6, MYH14, PTPN2, SEH1L) and blood sugar (PRSS55, GRIK2), were identified. Furthermore, the gene-set enrichment and pathway-based analysis (GESA) highlighted five enriched pathways (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms (fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface) which were found to be shared among all the traits.