Genome Wide Association Study of Response to Interval and Continuous Exercise Training: the Predict‑HIIT Study Camilla J
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BIRC7 Sirna (Human)
For research purposes only, not for human use Product Data Sheet BIRC7 siRNA (Human) Catalog # Source Reactivity Applications CRJ2387 Synthetic H RNAi Description siRNA to inhibit BIRC7 expression using RNA interference Specificity BIRC7 siRNA (Human) is a target-specific 19-23 nt siRNA oligo duplexes designed to knock down gene expression. Form Lyophilized powder Gene Symbol BIRC7 Alternative Names KIAP; LIVIN; MLIAP; RNF50; Baculoviral IAP repeat-containing protein 7; Kidney inhibitor of apoptosis protein; KIAP; Livin; Melanoma inhibitor of apoptosis protein; ML-IAP; RING finger protein 50 Entrez Gene 79444 (Human) SwissProt Q96CA5 (Human) Purity > 97% Quality Control Oligonucleotide synthesis is monitored base by base through trityl analysis to ensure appropriate coupling efficiency. The oligo is subsequently purified by affinity-solid phase extraction. The annealed RNA duplex is further analyzed by mass spectrometry to verify the exact composition of the duplex. Each lot is compared to the previous lot by mass spectrometry to ensure maximum lot-to-lot consistency. Components We offers pre-designed sets of 3 different target-specific siRNA oligo duplexes of human BIRC7 gene. Each vial contains 5 nmol of lyophilized siRNA. The duplexes can be transfected individually or pooled together to achieve knockdown of the target gene, which is most commonly assessed by qPCR or western blot. Our siRNA oligos are also chemically modified (2’-OMe) at no extra charge for increased stability and Application key: E- ELISA, WB- Western blot, IH- Immunohistochemistry, -
A Comparative Analysis of Transcribed Genes in the Mouse Hypothalamus and Neocortex Reveals Chromosomal Clustering
A comparative analysis of transcribed genes in the mouse hypothalamus and neocortex reveals chromosomal clustering Wee-Ming Boon*, Tim Beissbarth†, Lavinia Hyde†, Gordon Smyth†, Jenny Gunnersen*, Derek A. Denton*‡, Hamish Scott†, and Seong-Seng Tan* *Howard Florey Institute, University of Melbourne, Parkville 3052, Australia; and †Genetics and Bioinfomatics Division, Walter and Eliza Hall Institute of Medical Research, Royal Parade, Parkville 3050, Australia Contributed by Derek A. Denton, August 26, 2004 The hypothalamus and neocortex are subdivisions of the mamma- representing all of the genes that are expressed (qualitative and lian forebrain, and yet, they have vastly different evolutionary quantitative) in the hypothalamus and neocortex under standard histories, cytoarchitecture, and biological functions. In an attempt conditions. to define these attributes in terms of their genetic activity, we have In the current study, we describe the use of the Serial Analysis compared their genetic repertoires by using the Serial Analysis of of Gene Expression (SAGE) database, which allows simulta- Gene Expression database. From a comparison of 78,784 hypothal- neous detection of the expression levels of the entire genome amus tags with 125,296 neocortical tags, we demonstrate that each without a priori knowledge of gene sequences (13). SAGE takes structure possesses a different transcriptional profile in terms of advantage of the fact that a small sequence tag taken from a gene ontological characteristics and expression levels. Despite its defined position within the transcript is sufficient to identify the more recent evolutionary history, the neocortex has a more com- gene (from known cDNA or EST sequences), and up to 40 tags plex pattern of gene activity. -
GCAT|Panel, a Comprehensive Structural Variant Haplotype Map of the Iberian Population from High-Coverage Whole-Genome Sequencing
bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453041; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing Jordi Valls-Margarit1,#, Iván Galván-Femenía2,3,#, Daniel Matías-Sánchez1,#, Natalia Blay2, Montserrat Puiggròs1, Anna Carreras2, Cecilia Salvoro1, Beatriz Cortés2, Ramon Amela1, Xavier Farre2, Jon Lerga- Jaso4, Marta Puig4, Jose Francisco Sánchez-Herrero5, Victor Moreno6,7,8,9, Manuel Perucho10,11, Lauro Sumoy5, Lluís Armengol12, Olivier Delaneau13,14, Mario Cáceres4,15, Rafael de Cid2,*,† & David Torrents1,15,* 1. Life sciences dept, Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain. 2. Genomes for Life-GCAT lab Group, Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, 08916, Spain. 3. Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, 08028, Barcelona, Spain (current affiliation). 4. Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain. 5. High Content Genomics and Bioinformatics Unit, Institute for Health Science Research Germans Trias i Pujol (IGTP), 08916, Badalona, Spain. 6. Catalan Institute of Oncology, Hospitalet del Llobregat, 08908, Spain. 7. Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet del Llobregat, 08908, Spain. 8. CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, 28029, Spain. 9. Universitat de Barcelona (UB), Barcelona, 08007, Spain. -
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. -
Piezo2 Mediates Low-Threshold Mechanically Evoked Pain in the Cornea
8976 • The Journal of Neuroscience, November 18, 2020 • 40(47):8976–8993 Cellular/Molecular Piezo2 Mediates Low-Threshold Mechanically Evoked Pain in the Cornea Jorge Fernández-Trillo, Danny Florez-Paz, Almudena Íñigo-Portugués, Omar González-González, Ana Gómez del Campo, Alejandro González, Félix Viana, Carlos Belmonte, and Ana Gomis Instituto de Neurociencias, Universidad Miguel Hernández-Consejo Superior de Investigaciones Científicas, 03550 San Juan de Alicante, Alicante,Spain Mammalian Piezo2 channels are essential for transduction of innocuous mechanical forces by proprioceptors and cutaneous touch receptors. In contrast, mechanical responses of somatosensory nociceptor neurons evoking pain, remain intact or are only partially reduced in Piezo2-deficient mice. In the eye cornea, comparatively low mechanical forces are detected by polymodal and pure mecha- nosensory trigeminal ganglion neurons. Their activation always evokes ocular discomfort or pain and protective reflexes, thus being a unique model to study mechanotransduction mechanisms in this particular class of nociceptive neurons. Cultured male and female mouse mechano- and polymodal nociceptor corneal neurons display rapidly, intermediately and slowly adapting mechanically activated currents. Immunostaining of the somas and peripheral axons of corneal neurons responding only to mechanical force (pure mechano-nociceptor) or also exhibiting TRPV1 (transient receptor potential cation channel subfamily V member 1) immunoreactivity (polymodal nociceptor) revealed that they express -
Circular RNA Hsa Circ 0005114‑Mir‑142‑3P/Mir‑590‑5P‑ Adenomatous
ONCOLOGY LETTERS 21: 58, 2021 Circular RNA hsa_circ_0005114‑miR‑142‑3p/miR‑590‑5p‑ adenomatous polyposis coli protein axis as a potential target for treatment of glioma BO WEI1*, LE WANG2* and JINGWEI ZHAO1 1Department of Neurosurgery, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033; 2Department of Ophthalmology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin 130021, P.R. China Received September 12, 2019; Accepted October 22, 2020 DOI: 10.3892/ol.2020.12320 Abstract. Glioma is the most common type of brain tumor APC expression with a good overall survival rate. UALCAN and is associated with a high mortality rate. Despite recent analysis using TCGA data of glioblastoma multiforme and the advances in treatment options, the overall prognosis in patients GSE25632 and GSE103229 microarray datasets showed that with glioma remains poor. Studies have suggested that circular hsa‑miR‑142‑3p/hsa‑miR‑590‑5p was upregulated and APC (circ)RNAs serve important roles in the development and was downregulated. Thus, hsa‑miR‑142‑3p/hsa‑miR‑590‑5p‑ progression of glioma and may have potential as therapeutic APC‑related circ/ceRNA axes may be important in glioma, targets. However, the expression profiles of circRNAs and their and hsa_circ_0005114 interacted with both of these miRNAs. functions in glioma have rarely been studied. The present study Functional analysis showed that hsa_circ_0005114 was aimed to screen differentially expressed circRNAs (DECs) involved in insulin secretion, while APC was associated with between glioma and normal brain tissues using sequencing the Wnt signaling pathway. In conclusion, hsa_circ_0005114‑ data collected from the Gene Expression Omnibus database miR‑142‑3p/miR‑590‑5p‑APC ceRNA axes may be potential (GSE86202 and GSE92322 datasets) and explain their mecha‑ targets for the treatment of glioma. -
XIAP's Profile in Human Cancer
biomolecules Review XIAP’s Profile in Human Cancer Huailu Tu and Max Costa * Department of Environmental Medicine, Grossman School of Medicine, New York University, New York, NY 10010, USA; [email protected] * Correspondence: [email protected] Received: 16 September 2020; Accepted: 25 October 2020; Published: 29 October 2020 Abstract: XIAP, the X-linked inhibitor of apoptosis protein, regulates cell death signaling pathways through binding and inhibiting caspases. Mounting experimental research associated with XIAP has shown it to be a master regulator of cell death not only in apoptosis, but also in autophagy and necroptosis. As a vital decider on cell survival, XIAP is involved in the regulation of cancer initiation, promotion and progression. XIAP up-regulation occurs in many human diseases, resulting in a series of undesired effects such as raising the cellular tolerance to genetic lesions, inflammation and cytotoxicity. Hence, anti-tumor drugs targeting XIAP have become an important focus for cancer therapy research. RNA–XIAP interaction is a focus, which has enriched the general profile of XIAP regulation in human cancer. In this review, the basic functions of XIAP, its regulatory role in cancer, anti-XIAP drugs and recent findings about RNA–XIAP interactions are discussed. Keywords: XIAP; apoptosis; cancer; therapeutics; non-coding RNA 1. Introduction X-linked inhibitor of apoptosis protein (XIAP), also known as inhibitor of apoptosis protein 3 (IAP3), baculoviral IAP repeat-containing protein 4 (BIRC4), and human IAPs like protein (hILP), belongs to IAP family which was discovered in insect baculovirus [1]. Eight different IAPs have been isolated from human tissues: NAIP (BIRC1), BIRC2 (cIAP1), BIRC3 (cIAP2), XIAP (BIRC4), BIRC5 (survivin), BIRC6 (apollon), BIRC7 (livin) and BIRC8 [2]. -
Gene Prioritization in Type 2 Diabetes Using Domain Interactions And
Sharma et al. BMC Genomics 2010, 11:84 http://www.biomedcentral.com/1471-2164/11/84 RESEARCH ARTICLE Open Access Gene prioritization in Type 2 Diabetes using domain interactions and network analysis Amitabh Sharma1†, Sreenivas Chavali1†, Rubina Tabassum1, Nikhil Tandon2, Dwaipayan Bharadwaj1* Abstract Background: Identification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes. Results: Our approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (Ci). Each HWE with a Ci < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average Ci of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. -
Bioinformatics Analyses of Genomic Imprinting
Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________ -
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, -
'Next- Generation' Sequencing Data Analysis
Novel Algorithm Development for ‘Next- Generation’ Sequencing Data Analysis Agne Antanaviciute Submitted in accordance with the requirements for the degree of Doctor of Philosophy University of Leeds School of Medicine Leeds Institute of Biomedical and Clinical Sciences 12/2017 ii The candidate confirms that the work submitted is her own, except where work which has formed part of jointly-authored publications has been included. The contribution of the candidate and the other authors to this work has been explicitly given within the thesis where reference has been made to the work of others. This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement ©2017 The University of Leeds and Agne Antanaviciute The right of Agne Antanaviciute to be identified as Author of this work has been asserted by her in accordance with the Copyright, Designs and Patents Act 1988. Acknowledgements I would like to thank all the people who have contributed to this work. First and foremost, my supervisors Dr Ian Carr, Professor David Bonthron and Dr Christopher Watson, who have provided guidance, support and motivation. I could not have asked for a better supervisory team. I would also like to thank my collaborators Dr Belinda Baquero and Professor Adrian Whitehouse for opening new, interesting research avenues. A special thanks to Dr Belinda Baquero for all the hard wet lab work without which at least half of this thesis would not exist. Thanks to everyone at the NGS Facility – Carolina Lascelles, Catherine Daley, Sally Harrison, Ummey Hany and Laura Crinnion – for the generation of NGS data used in this work and creating a supportive and stimulating work environment. -
The Landscape of Genomic Imprinting Across Diverse Adult Human Tissues
Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Research The landscape of genomic imprinting across diverse adult human tissues Yael Baran,1 Meena Subramaniam,2 Anne Biton,2 Taru Tukiainen,3,4 Emily K. Tsang,5,6 Manuel A. Rivas,7 Matti Pirinen,8 Maria Gutierrez-Arcelus,9 Kevin S. Smith,5,10 Kim R. Kukurba,5,10 Rui Zhang,10 Celeste Eng,2 Dara G. Torgerson,2 Cydney Urbanek,11 the GTEx Consortium, Jin Billy Li,10 Jose R. Rodriguez-Santana,12 Esteban G. Burchard,2,13 Max A. Seibold,11,14,15 Daniel G. MacArthur,3,4,16 Stephen B. Montgomery,5,10 Noah A. Zaitlen,2,19 and Tuuli Lappalainen17,18,19 1The Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel; 2Department of Medicine, University of California San Francisco, San Francisco, California 94158, USA; 3Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA; 4Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA; 5Department of Pathology, Stanford University, Stanford, California 94305, USA; 6Biomedical Informatics Program, Stanford University, Stanford, California 94305, USA; 7Wellcome Trust Center for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom; 8Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; 9Department of Genetic Medicine and Development, University of Geneva, 1211 Geneva, Switzerland;