Mouse Mgst3 Knockout Project (CRISPR/Cas9)
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Meta-Analysis of Nasopharyngeal Carcinoma
BMC Genomics BioMed Central Research article Open Access Meta-analysis of nasopharyngeal carcinoma microarray data explores mechanism of EBV-regulated neoplastic transformation Xia Chen†1,2, Shuang Liang†1, WenLing Zheng1,3, ZhiJun Liao1, Tao Shang1 and WenLi Ma*1 Address: 1Institute of Genetic Engineering, Southern Medical University, Guangzhou, PR China, 2Xiangya Pingkuang associated hospital, Pingxiang, Jiangxi, PR China and 3Southern Genomics Research Center, Guangzhou, Guangdong, PR China Email: Xia Chen - [email protected]; Shuang Liang - [email protected]; WenLing Zheng - [email protected]; ZhiJun Liao - [email protected]; Tao Shang - [email protected]; WenLi Ma* - [email protected] * Corresponding author †Equal contributors Published: 7 July 2008 Received: 16 February 2008 Accepted: 7 July 2008 BMC Genomics 2008, 9:322 doi:10.1186/1471-2164-9-322 This article is available from: http://www.biomedcentral.com/1471-2164/9/322 © 2008 Chen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Epstein-Barr virus (EBV) presumably plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC), but the molecular mechanism of EBV-dependent neoplastic transformation is not well understood. The combination of bioinformatics with evidences from biological experiments paved a new way to gain more insights into the molecular mechanism of cancer. Results: We profiled gene expression using a meta-analysis approach. Two sets of meta-genes were obtained. Meta-A genes were identified by finding those commonly activated/deactivated upon EBV infection/reactivation. -
Evidence for Differential Alternative Splicing in Blood of Young Boys With
Stamova et al. Molecular Autism 2013, 4:30 http://www.molecularautism.com/content/4/1/30 RESEARCH Open Access Evidence for differential alternative splicing in blood of young boys with autism spectrum disorders Boryana S Stamova1,2,5*, Yingfang Tian1,2,4, Christine W Nordahl1,3, Mark D Shen1,3, Sally Rogers1,3, David G Amaral1,3 and Frank R Sharp1,2 Abstract Background: Since RNA expression differences have been reported in autism spectrum disorder (ASD) for blood and brain, and differential alternative splicing (DAS) has been reported in ASD brains, we determined if there was DAS in blood mRNA of ASD subjects compared to typically developing (TD) controls, as well as in ASD subgroups related to cerebral volume. Methods: RNA from blood was processed on whole genome exon arrays for 2-4–year-old ASD and TD boys. An ANCOVA with age and batch as covariates was used to predict DAS for ALL ASD (n=30), ASD with normal total cerebral volumes (NTCV), and ASD with large total cerebral volumes (LTCV) compared to TD controls (n=20). Results: A total of 53 genes were predicted to have DAS for ALL ASD versus TD, 169 genes for ASD_NTCV versus TD, 1 gene for ASD_LTCV versus TD, and 27 genes for ASD_LTCV versus ASD_NTCV. These differences were significant at P <0.05 after false discovery rate corrections for multiple comparisons (FDR <5% false positives). A number of the genes predicted to have DAS in ASD are known to regulate DAS (SFPQ, SRPK1, SRSF11, SRSF2IP, FUS, LSM14A). In addition, a number of genes with predicted DAS are involved in pathways implicated in previous ASD studies, such as ROS monocyte/macrophage, Natural Killer Cell, mTOR, and NGF signaling. -
Computational and Experimental Approaches for Evaluating the Genetic Basis of Mitochondrial Disorders
Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Lieber, Daniel Solomon. 2013. Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders. Doctoral dissertation, Harvard University. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11158264 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders A dissertation presented by Daniel Solomon Lieber to The Committee on Higher Degrees in Systems Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Systems Biology Harvard University Cambridge, Massachusetts April 2013 © 2013 - Daniel Solomon Lieber All rights reserved. Dissertation Adviser: Professor Vamsi K. Mootha Daniel Solomon Lieber Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders Abstract Mitochondria are responsible for some of the cell’s most fundamental biological pathways and metabolic processes, including aerobic ATP production by the mitochondrial respiratory chain. In humans, mitochondrial dysfunction can lead to severe disorders of energy metabolism, which are collectively referred to as mitochondrial disorders and affect approximately 1:5,000 individuals. These disorders are clinically heterogeneous and can affect multiple organ systems, often within a single individual. Symptoms can include myopathy, exercise intolerance, hearing loss, blindness, stroke, seizures, diabetes, and GI dysmotility. -
The Draft Genomes of Softshell Turtle and Green Sea Turtle Yield Insights
LETTERS OPEN The draft genomes of soft-shell turtle and green sea turtle yield insights into the development and evolution of the turtle-specific body plan Zhuo Wang1,12, Juan Pascual-Anaya2,12, Amonida Zadissa3,12, Wenqi Li4,12, Yoshihito Niimura5, Zhiyong Huang1, Chunyi Li4, Simon White3, Zhiqiang Xiong1, Dongming Fang1, Bo Wang1, Yao Ming1, Yan Chen1, Yuan Zheng1, Shigehiro Kuraku2, Miguel Pignatelli6, Javier Herrero6, Kathryn Beal6, Masafumi Nozawa7, Qiye Li1, Juan Wang1, Hongyan Zhang4, Lili Yu1, Shuji Shigenobu7, Junyi Wang1, Jiannan Liu4, Paul Flicek6, Steve Searle3, Jun Wang1,8,9, Shigeru Kuratani2, Ye Yin4, Bronwen Aken3, Guojie Zhang1,10,11 & Naoki Irie2 The unique anatomical features of turtles have raised Three major hypotheses have been proposed for the evolutionary unanswered questions about the origin of their unique body origin of turtles, including that they (i) constitute early-diverged rep- plan. We generated and analyzed draft genomes of the soft- tiles, called anapsids3, (ii) are a sister group of the lizard-snake-tuatara shell turtle (Pelodiscus sinensis) and the green sea turtle (Lepidosauria) clade4 or (iii) are closely related to a lineage that (Chelonia mydas); our results indicated the close relationship includes crocodilians and birds (Archosauria)5–8. Even using molecular of the turtles to the bird-crocodilian lineage, from which they approaches, inconsistency still remains6–9. To clarify the evolution of split ~267.9–248.3 million years ago (Upper Permian to Triassic). the turtle-specific body plan, we first addressed the question of evolu- We also found extensive expansion of olfactory receptor genes tionary origin of the turtle by performing the first genome-wide phylo- in these turtles. -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2- -
A Systems-Genetics Analyses of Complex Phenotypes
A systems-genetics analyses of complex phenotypes A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Life Sciences 2015 David Ashbrook Table of contents Table of contents Table of contents ............................................................................................... 1 Tables and figures ........................................................................................... 10 General abstract ............................................................................................... 14 Declaration ....................................................................................................... 15 Copyright statement ........................................................................................ 15 Acknowledgements.......................................................................................... 16 Chapter 1: General introduction ...................................................................... 17 1.1 Overview................................................................................................... 18 1.2 Linkage, association and gene annotations .............................................. 20 1.3 ‘Big data’ and ‘omics’ ................................................................................ 22 1.4 Systems-genetics ..................................................................................... 24 1.5 Recombinant inbred (RI) lines and the BXD .............................................. 25 Figure 1.1: -
Signature Redacted Author
Automated, highly scalable RNA-seq analysis ARCHNES M ASSA HUSETS INS ITUTE by Rory Kirchner RSEP 24 2015 B.S., Rochester Institute of Technology (1999) LIBRARIES Submitted to the Department of Health Sciences and Technology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Health Sciences and Technology at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2015 D Massachusetts Institute of Technology 2015. All rights reserved. Signature redacted Author. Department of Health S ences and Technology Septem 2015 Signature redacted Certified by... Martha Constantine-Paton Professor of E rain and Cognitive Science Thesis Supervisor Signature redacted Acrented by ...... ........ Emery N. Brown Director, Harvard- Program in Health Sciences and Technology Professor of Computational Neuroscience and Health Sciences and Technology F Automated, highly scalable RNA-seq analysis by Rory Kirchner Submitted to the Department of Health Sciences and Technology on September 1, 2015, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Health Sciences and Technology Abstract RNA-sequencing is a sensitive method for inferring gene expression and provides ad- ditional information regarding splice variants, polymorphisms and novel genes and isoforms. Using this extra information greatly increases the complexity of an analysis and prevents novice investigators from analyzing their own data. The first chapter of this work introduces a solution to this issue. It describes a community-curated, scal- able RNA-seq analysis framework for performing differential transcriptome expres- sion, transcriptome assembly, variant and RNA-editing calling. It handles the entire stack of an analysis, from downloading and installing hundreds of tools, libraries and genomes to running an analysis that is able to be scaled to handle thousands of samples simultaneously. -
1 Supplementary Information ADCK2 Haploinsufficiency Reduces
Supplementary information ADCK2 haploinsufficiency reduces mitochondrial lipid oxidation and causes myopathy associated with CoQ deficiency.. Luis Vázquez-Fonseca1,8,§, Jochen Schäfer2,§, Ignacio Navas-Enamorado1,7, Carlos Santos-Ocaña1,10, Juan D. Hernández-Camacho1,10, Ignacio Guerra1, María V. Cascajo1,10, Ana Sánchez-Cuesta1,10, Zoltan Horvath2#, Emilio Siendones1, Cristina Jou3,10, Mercedes Casado3,10, Purificación Gutiérrez1, Gloria Brea-Calvo1,10, Guillermo López-Lluch1,10, Daniel M. Fernández-Ayala1,10, Ana B. Cortés- Rodríguez1,10, Juan C. Rodríguez-Aguilera1,10, Cristiane Matté4, Antonia Ribes5,10, Sandra Y. Prieto- Soler6, Eduardo Dominguez-del-Toro6, Andrea di Francesco8, Miguel A. Aon8, Michel Bernier8, Leonardo Salviati9, Rafael Artuch3,10, Rafael de Cabo8, Sandra Jackson2 and Plácido Navas1,10 1 Supplementary Results Case report The male index patient (subject II-3, Fig. S1A) presented to our clinic at 45 years of age with a 15-year history of slowly progressive muscle weakness and myalgia, which occurred at rest but worsened with exercise. Past medical history was unremarkable except for renal disease of unknown cause in childhood, which spontaneously improved. Family history was negative for neurological disease. On examination, moderate proximal symmetrical myopathy, more pronounced in the arms, was noted and the patient was unable to lift his arms above the horizontal position. The patient had a hyperlordotic, waddling gait and was only able to walk 100 meters without the aid of crutches. Bilateral scapular winging was present, and bilateral atrophy of the biceps, triceps, and quadriceps was noted, whilst the deltoid muscles were well preserved. Calf hypertrophy was present. The Trendelenburg sign was positive, and the patient was unable to rise from squatting. -
Loss of Mecp2 Function Is Associated with Distinct Gene Expression Changes in the Striatum
Neurobiology of Disease 59 (2013) 257–266 Contents lists available at ScienceDirect Neurobiology of Disease journal homepage: www.elsevier.com/locate/ynbdi Loss of MeCP2 function is associated with distinct gene expression changes in the striatum Ying-Tao Zhao 1,DarrenGoffin 1, Brian S. Johnson 1, Zhaolan Zhou ⁎ Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA article info abstract Article history: Rett syndrome (RTT) is a neurodevelopmental disorder characterized by developmental regression beginning Received 1 May 2013 6–18 months after birth, followed by a lifetime of intellectual disability, stereotyped behaviors, and motor defi- Revised 5 July 2013 cits. RTT is caused by mutations in the gene encoding MeCP2, a methyl-CpG binding protein believed to modulate Accepted 2 August 2013 gene transcription. Gene expression studies of individual brain regions have reported that Mecp2 loss-of-function Available online 13 August 2013 leads to both activation and repression of its gene targets in mice. Conditional deletion of MeCP2 from different brain regions has revealed unique insights into the role of these structures in mediating particular RTT-like phe- Keywords: Methyl-CpG binding protein 2 notypes. However, the function of MeCP2 in the striatum, a major brain region involved in motor control and MeCP2 executive cognitive functions, has yet to be studied. Here, we characterized the gene expression changes in the Rett syndrome striatum of Mecp2 mutant mice. We found a number of differentially expressed genes in the striatum of both con- RTT stitutive Mecp2-null mice and mice lacking MeCP2 only from forebrain GABAergic neurons. -
Estrogen's Impact on the Specialized Transcriptome, Brain, and Vocal
Estrogen’s Impact on the Specialized Transcriptome, Brain, and Vocal Learning Behavior of a Sexually Dimorphic Songbird by Ha Na Choe Department of Molecular Genetics & Microbiology Duke University Date:_____________________ Approved: ___________________________ Erich D. Jarvis, Supervisor ___________________________ Hiroaki Matsunami ___________________________ Debra Silver ___________________________ Dong Yan ___________________________ Gregory Crawford Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Molecular Genetics & Microbiology in the Graduate School of Duke University 2020 ABSTRACT Estrogen’s Impact on the Specialized Transcriptome, Brain, and Vocal Learning Behavior of a Sexually Dimorphic Songbird by Ha Na Choe Department of Molecular Genetics & Microbiology Duke University Date:_________________________ Approved: ___________________________ Erich D. Jarvis, Supervisor ___________________________ Hiroaki Matsunami ___________________________ Debra Silver ___________________________ Dong Yan ___________________________ Gregory Crawford Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Molecular Genetics & Microbiology in the Graduate School of Duke University 2020 Copyright by Ha Na Choe 2020 Abstract The song system of the zebra finch (Taeniopygia guttata) is highly sexually dimorphic, where only males develop the neural structures necessary to learn and produce learned vocalizations -
Do Non-Genomically Encoded Fusion Transcripts Cause Recurrent Chromosomal Translocations?
Cancers 2012, 4, 1036-1049; doi:10.3390/cancers4041036 OPEN ACCESS cancers ISSN 2072-6694 www.mdpi.com/journal/cancers Article Do Non-Genomically Encoded Fusion Transcripts Cause Recurrent Chromosomal Translocations? Eric Kowarz, Theo Dingermann and Rolf Marschalek * Institute of Pharmaceutical Biology/ZAFES/DCAL, Goethe-University of Frankfurt, Biocenter, Max-von-Laue-Str. 9, D-60438 Frankfurt/Main, Germany; E-Mails: [email protected] (E.K.); [email protected] (T.D.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel: +49-69-798-29647; Fax: +49-69-798-29662. Received: 26 July 2012; in revised form: 14 September 2012 / Accepted: 9 October 2012 / Published: 18 October 2012 Abstract: We among others have recently demonstrated that normal cells produce ―fusion mRNAs‖. These fusion mRNAs do not derive from rearranged genomic loci, but rather they are derived from ―early-terminated transcripts‖ (ETTs). Premature transcriptional termination takes place in intronic sequences that belong to ―breakpoint cluster regions‖. One important property of ETTs is that they exhibit an unsaturated splice donor site. This results in: (1) splicing to ―cryptic exons‖ present in the final intron; (2) Splicing to another transcript of the same gene (intragenic trans-splicing), resulting in ―exon repetitions‖; (3) splicing to a transcript of another gene (intergenic trans-splicing), leading to ―non-genomically encoded fusion transcripts‖ (NGEFTs). These NGEFTs bear the potential risk to influence DNA repair processes, since they share identical nucleotides with their DNA of origin, and thus, could be used as ―guidance RNA‖ for DNA repair processes. Here, we present experimental data about four other genes. -
Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein