Kb Duplication at the FTO Locus in a Kindred with Obesity and a Distinct Body Fat Distribution
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Genetic Mutation Analysis of High and Low Igy Chickens by Capture Sequencing
animals Article Genetic Mutation Analysis of High and Low IgY Chickens by Capture Sequencing 1,2, 1,2, 1,3 1,2 1,2 1,2 Qiao Wang y , Fei Wang y, Lu Liu , Qinghe Li , Ranran Liu , Maiqing Zheng , Huanxian Cui 1,2, Jie Wen 1,2 and Guiping Zhao 1,2,4,* 1 Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; [email protected] (Q.W.); [email protected] (F.W.); [email protected] (L.L.); [email protected] (Q.L.); [email protected] (R.L.); [email protected] (M.Z.); [email protected] (H.C.); [email protected] (J.W.) 2 State Key Laboratory of Animal Nutrition, Beijing 100193, China 3 College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China 4 School of Life Science and Engineering, Foshan University, Foshan 528225, China * Correspondence: [email protected] These authors contributed equally to this work. y Received: 6 March 2019; Accepted: 20 May 2019; Published: 23 May 2019 Simple Summary: Immunoglobulin Y (IgY) is the major antibody produced by hens and it endows their offspring with effective humoral immunity against the pathogens. In previous research, we identified 13 genomic regions that were significantly associated with the serum IgY level or antibody responses to sheep red-blood-cells, but the specific mutations in these regions have not been reported. Therefore, we screened for variations in these regions in White Leghorn and Beijing-You chickens with high and low IgY. Our study identified 35,154 mutations and 829 Indels which were associated with IgY levels in both lines. -
Loss-Of-Function Mutation in the Dioxygenase-Encoding FTO Gene
Loss-of-Function Mutation in the Dioxygenase-Encoding FTO Gene Causes Severe Growth Retardation and Multiple Malformations Sarah Boissel, Orit Reish, Karine Proulx, Hiroko Kawagoe-Takaki, Barbara Sedgwick, Giles Yeo, David Meyre, Christelle Golzio, Florence Molinari, Noman Kadhom, et al. To cite this version: Sarah Boissel, Orit Reish, Karine Proulx, Hiroko Kawagoe-Takaki, Barbara Sedgwick, et al.. Loss-of- Function Mutation in the Dioxygenase-Encoding FTO Gene Causes Severe Growth Retardation and Multiple Malformations. American Journal of Human Genetics, Elsevier (Cell Press), 2009, 85 (1), pp.106-111. 10.1016/j.ajhg.2009.06.002. hal-02044723 HAL Id: hal-02044723 https://hal.archives-ouvertes.fr/hal-02044723 Submitted on 21 Feb 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. REPORT Loss-of-Function Mutation in the Dioxygenase-Encoding FTO Gene Causes Severe Growth Retardation and Multiple Malformations Sarah Boissel,1,7 Orit Reish,2,7 Karine Proulx,3,7 Hiroko Kawagoe-Takaki,4 Barbara Sedgwick,4 Giles S.H. Yeo,3 David Meyre,5 Christelle Golzio,1 Florence Molinari,1 Noman Kadhom,1 Heather C. Etchevers,1 Vladimir Saudek,3 I. Sadaf Farooqi,3 Philippe Froguel,5,6 Tomas Lindahl,4 Stephen O’Rahilly,3 Arnold Munnich,1 and Laurence Colleaux1,* FTO is a nuclear protein belonging to the AlkB-related non-haem iron- and 2-oxoglutarate-dependent dioxygenase family. -
Transcriptome Sequencing and Genome-Wide Association Analyses Reveal Lysosomal Function and Actin Cytoskeleton Remodeling in Schizophrenia and Bipolar Disorder
Molecular Psychiatry (2015) 20, 563–572 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp ORIGINAL ARTICLE Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder Z Zhao1,6,JXu2,6, J Chen3,6, S Kim4, M Reimers3, S-A Bacanu3,HYu1, C Liu5, J Sun1, Q Wang1, P Jia1,FXu2, Y Zhang2, KS Kendler3, Z Peng2 and X Chen3 Schizophrenia (SCZ) and bipolar disorder (BPD) are severe mental disorders with high heritability. Clinicians have long noticed the similarities of clinic symptoms between these disorders. In recent years, accumulating evidence indicates some shared genetic liabilities. However, what is shared remains elusive. In this study, we conducted whole transcriptome analysis of post-mortem brain tissues (cingulate cortex) from SCZ, BPD and control subjects, and identified differentially expressed genes in these disorders. We found 105 and 153 genes differentially expressed in SCZ and BPD, respectively. By comparing the t-test scores, we found that many of the genes differentially expressed in SCZ and BPD are concordant in their expression level (q ⩽ 0.01, 53 genes; q ⩽ 0.05, 213 genes; q ⩽ 0.1, 885 genes). Using genome-wide association data from the Psychiatric Genomics Consortium, we found that these differentially and concordantly expressed genes were enriched in association signals for both SCZ (Po10 − 7) and BPD (P = 0.029). To our knowledge, this is the first time that a substantially large number of genes show concordant expression and association for both SCZ and BPD. Pathway analyses of these genes indicated that they are involved in the lysosome, Fc gamma receptor-mediated phagocytosis, regulation of actin cytoskeleton pathways, along with several cancer pathways. -
Mouse Aktip Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Aktip Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Aktip conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Aktip gene (NCBI Reference Sequence: NM_001302267 ; Ensembl: ENSMUSG00000031667 ) is located on Mouse chromosome 8. 11 exons are identified, with the ATG start codon in exon 3 and the TAA stop codon in exon 11 (Transcript: ENSMUST00000120213). Exon 4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Aktip gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-149I24 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a hypomorphic allele exhibit impaired growth, skeletal and skin defects, abnormal heart tissue, and sterility. Exon 4 starts from about 4.91% of the coding region. The knockout of Exon 4 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 1235 bp, and the size of intron 4 for 3'-loxP site insertion: 2670 bp. The size of effective cKO region: ~706 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 3 4 11 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Aktip Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le May 2013 CMU-ML-13-101 Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le May 2013 CMU-ML-13-101 Machine Learning Department School of Computer Science Carnegie Mellon University Thesis Committee Ziv Bar-Joseph, Chair Christopher Langmead Roni Rosenfeld Quaid Morris Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy. Copyright @ 2013 Hai-Son Le This research was sponsored by the National Institutes of Health under grant numbers 5U01HL108642 and 1R01GM085022, the National Science Foundation under grant num- bers DBI0448453 and DBI0965316, and the Pittsburgh Life Sciences Greenhouse. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: genomics, gene expression, gene regulation, microarray, RNA-Seq, transcriptomics, error correction, comparative genomics, regulatory networks, cross-species, expression database, Gene Expression Omnibus, GEO, orthologs, microRNA, target prediction, Dirichlet Process, Indian Buffet Process, hidden Markov model, immune response, cancer. To Mom and Dad. i Abstract Advances in genomics allow researchers to measure the complete set of transcripts in cells. These transcripts include messenger RNAs (which encode for proteins) and microRNAs, short RNAs that play an important regulatory role in cellular networks. While this data is a great resource for reconstructing the activity of networks in cells, it also presents several computational challenges. These challenges include the data collection stage which often results in incomplete and noisy measurement, developing methods to integrate several experiments within and across species, and designing methods that can use this data to map the interactions and networks that are activated in specific conditions. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
FTS (AKTIP) (NM 022476) Human Untagged Clone 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 SC112492 FTS (AKTIP) (NM_022476) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: FTS (AKTIP) (NM_022476) Human Untagged Clone Tag: Tag Free Symbol: AKTIP Synonyms: FT1; FTS Vector: pCMV6-XL5 E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: None Fully Sequenced ORF: >NCBI ORF sequence for NM_022476, the custom clone sequence may differ by one or more nucleotides ATGAACCCTTTCTGGAGCATGTCTACAAGCTCTGTACGCAAACGATCTGAAGGTGAAGAGAAGACATTAA CAGGGGACGTGAAAACCAGTCCTCCACGAACTGCACCAAAGAAACAGCTGCCTTCTATTCCCAAAAATGC TTTGCCCATAACTAAGCCTACATCTCCTGCCCCAGCAGCACAGTCAACAAATGGCACGCATGCGTCCTAT GGACCCTTCTACCTGGAATACTCTCTTCTTGCAGAATTTACCTTGGTTGTGAAGCAGAAGCTACCAGGCG TCTATGTGCAGCCATCTTATCGCTCTGCATTAATGTGGTTTGGAGTAATATTCATACGGCATGGACTTTA CCAAGATGGCGTATTTAAGTTTACAGTTTACATCCCTGATAACTATCCAGATGGTGACTGTCCACGCTTG GTGTTCGATATTCCTGTCTTTCACCCGCTAGTTGATCCCACCTCAGGTGAGCTGGATGTGAAGAGAGCAT TTGCAAAATGGAGGCGGAACCATAATCATATTTGGCAGGTATTAATGTATGCAAGGAGAGTTTTCTACAA GATTGATACAGCAAGCCCCCTGAACCCAGAGGCTGCAGTACTGTATGAAAAAGATATTCAGCTTTTTAAA AGTAAAGTTGTTGACAGTGTTAAGGTGTGCACTGCTCGTTTGTTTGACCAACCTAAAATAGAAGACCCCT ATGCAATTAGCTTTTCTCCATGGAATCCTTCTGTACATGATGAAGCCAGAGAAAAGATGCTGACTCAGAA AAAGCCTGAAGAACAGCACAATAAAAGTGTTCATGTTGCTGGCCTGTCATGGGTAAAGCCTGGCTCAGTA CAGCCTTTCAGTAAAGAAGAGAAAACAGTGGCGACTTAA This product is to be used for laboratory only. Not for -
DIPPER, a Spatiotemporal Proteomics Atlas of Human Intervertebral Discs
TOOLS AND RESOURCES DIPPER, a spatiotemporal proteomics atlas of human intervertebral discs for exploring ageing and degeneration dynamics Vivian Tam1,2†, Peikai Chen1†‡, Anita Yee1, Nestor Solis3, Theo Klein3§, Mateusz Kudelko1, Rakesh Sharma4, Wilson CW Chan1,2,5, Christopher M Overall3, Lisbet Haglund6, Pak C Sham7, Kathryn Song Eng Cheah1, Danny Chan1,2* 1School of Biomedical Sciences, , The University of Hong Kong, Hong Kong; 2The University of Hong Kong Shenzhen of Research Institute and Innovation (HKU-SIRI), Shenzhen, China; 3Centre for Blood Research, Faculty of Dentistry, University of British Columbia, Vancouver, Canada; 4Proteomics and Metabolomics Core Facility, The University of Hong Kong, Hong Kong; 5Department of Orthopaedics Surgery and Traumatology, HKU-Shenzhen Hospital, Shenzhen, China; 6Department of Surgery, McGill University, Montreal, Canada; 7Centre for PanorOmic Sciences (CPOS), The University of Hong Kong, Hong Kong Abstract The spatiotemporal proteome of the intervertebral disc (IVD) underpins its integrity *For correspondence: and function. We present DIPPER, a deep and comprehensive IVD proteomic resource comprising [email protected] 94 genome-wide profiles from 17 individuals. To begin with, protein modules defining key †These authors contributed directional trends spanning the lateral and anteroposterior axes were derived from high-resolution equally to this work spatial proteomes of intact young cadaveric lumbar IVDs. They revealed novel region-specific Present address: ‡Department profiles of regulatory activities -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
Lack of Association Between Genetic Polymorphism of FTO
J Pediatr (Rio J). 2016;92(5):521---527 www.jped.com.br ORIGINAL ARTICLE Lack of association between genetic polymorphism of FTO, AKT1 and AKTIP in childhood overweight ଝ and obesity a,1 a,∗,1 Patrícia de Araújo Pereira , António Marcos Alvim-Soares Jr. , d c Valéria Cristina Sandrim , Carla Márcia Moreira Lanna , b b Débora Cristine Souza-Costa , Vanessa de Almeida Belo , a b Jonas Jardim de Paula , José Eduardo Tanus-Santos , a a Marco Aurélio Romano-Silva , Débora Marques de Miranda a Universidade Federal de Minas Gerais (UFMG), INCT de Medicina Molecular, Belo Horizonte, MG, Brazil b Universidade de São Paulo (USP), Faculdade de Medicina de Ribeirão Preto (FMRP), Departamento de Farmacologia, Ribeirão Preto, SP, Brazil c Universidade Federal de Juiz de Fora (UFJF), Instituto de Ciências Biológicas, Departamento de Fisiologia, Juiz de Fora, MG, Brazil d Universidade Estadual Paulista (UNESP), São Paulo, SP, Brazil Received 15 July 2015; accepted 15 December 2015 Available online 21 June 2016 KEYWORDS Abstract Single-nucleotide Objective: Obesity is a chronic disease caused by both environmental and genetic factors. polymorphisms; Epidemiological studies have documented that increased energy intake and sedentary lifestyle, as well as a genetic contribution, are forces behind the obesity epidemic. Knowledge about the Childhood obesity; interaction between genetic and environmental components can facilitate the choice of the Fat mass and obesity associated; most effective and specific measures for the prevention of obesity. The aim of this study was Gene; to assess the association between the FTO, AKT1, and AKTIP genes and childhood obesity and AKT1; insulin resistance. AKTIP Methods: This was a case---control study in which SNPs in the FTO (rs99396096), AKT1, and AKTIP genes were genotyped in groups of controls and obese/overweight children. -
A Causal Gene Network with Genetic Variations Incorporating Biological Knowledge and Latent Variables
A CAUSAL GENE NETWORK WITH GENETIC VARIATIONS INCORPORATING BIOLOGICAL KNOWLEDGE AND LATENT VARIABLES By Jee Young Moon A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2013 Date of final oral examination: 12/21/2012 The dissertation is approved by the following members of the Final Oral Committee: Brian S. Yandell. Professor, Statistics, Horticulture Alan D. Attie. Professor, Biochemistry Karl W. Broman. Professor, Biostatistics and Medical Informatics Christina Kendziorski. Associate Professor, Biostatistics and Medical Informatics Sushmita Roy. Assistant Professor, Biostatistics and Medical Informatics, Computer Science, Systems Biology in Wisconsin Institute of Discovery (WID) i To my parents and brother, ii ACKNOWLEDGMENTS I greatly appreciate my adviser, Prof. Brian S. Yandell, who has always encouraged, inspired and supported me. I am grateful to him for introducing me to the exciting research areas of statis- tical genetics and causal gene network analysis. He also allowed me to explore various statistical and biological problems on my own and guided me to see the problems in a bigger picture. Most importantly, he waited patiently as I progressed at my own pace. I would also like to thank Dr. Elias Chaibub Neto and Prof. Xinwei Deng who my adviser arranged for me to work together. These three improved my rigorous writing and thinking a lot when we prepared the second chapter of this dissertation for publication. It was such a nice opportunity for me to join the group of Prof. Alan D. Attie, Dr. Mark P. Keller, Prof. Karl W. Broman and Prof.