Convergent and Distributed Effects of the 3Q29 Deletion on the Human Neural Transcriptome ✉ Esra Sefik1,2,7, Ryan H
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Refinement and Discovery of New Hotspots of Copy-Number Variation
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector ARTICLE Refinement and Discovery of New Hotspots of Copy-Number Variation Associated with Autism Spectrum Disorder Santhosh Girirajan,1,5 Megan Y. Dennis,1,5 Carl Baker,1 Maika Malig,1 Bradley P. Coe,1 Catarina D. Campbell,1 Kenneth Mark,1 Tiffany H. Vu,1 Can Alkan,1 Ze Cheng,1 Leslie G. Biesecker,2 Raphael Bernier,3 and Evan E. Eichler1,4,* Rare copy-number variants (CNVs) have been implicated in autism and intellectual disability. These variants are large and affect many genes but lack clear specificity toward autism as opposed to developmental-delay phenotypes. We exploited the repeat architecture of the genome to target segmental duplication-mediated rearrangement hotspots (n ¼ 120, median size 1.78 Mbp, range 240 kbp to 13 Mbp) and smaller hotspots flanked by repetitive sequence (n ¼ 1,247, median size 79 kbp, range 3–96 kbp) in 2,588 autistic individuals from simplex and multiplex families and in 580 controls. Our analysis identified several recurrent large hotspot events, including association with 1q21 duplications, which are more likely to be identified in individuals with autism than in those with developmental delay (p ¼ 0.01; OR ¼ 2.7). Within larger hotspots, we also identified smaller atypical CNVs that implicated CHD1L and ACACA for the 1q21 and 17q12 deletions, respectively. Our analysis, however, suggested no overall increase in the burden of smaller hotspots in autistic individuals as compared to controls. By focusing on gene-disruptive events, we identified recurrent CNVs, including DPP10, PLCB1, TRPM1, NRXN1, FHIT, and HYDIN, that are enriched in autism. -
Clinical Application of Chromosomal Microarray Analysis for Fetuses With
Xu et al. Molecular Cytogenetics (2020) 13:38 https://doi.org/10.1186/s13039-020-00502-5 RESEARCH Open Access Clinical application of chromosomal microarray analysis for fetuses with craniofacial malformations Chenyang Xu1†, Yanbao Xiang1†, Xueqin Xu1, Lili Zhou1, Huanzheng Li1, Xueqin Dong1 and Shaohua Tang1,2* Abstract Background: The potential correlations between chromosomal abnormalities and craniofacial malformations (CFMs) remain a challenge in prenatal diagnosis. This study aimed to evaluate 118 fetuses with CFMs by applying chromosomal microarray analysis (CMA) and G-banded chromosome analysis. Results: Of the 118 cases in this study, 39.8% were isolated CFMs (47/118) whereas 60.2% were non-isolated CFMs (71/118). The detection rate of chromosomal abnormalities in non-isolated CFM fetuses was significantly higher than that in isolated CFM fetuses (26/71 vs. 7/47, p = 0.01). Compared to the 16 fetuses (16/104; 15.4%) with pathogenic chromosomal abnormalities detected by karyotype analysis, CMA identified a total of 33 fetuses (33/118; 28.0%) with clinically significant findings. These 33 fetuses included cases with aneuploidy abnormalities (14/118; 11.9%), microdeletion/microduplication syndromes (9/118; 7.6%), and other pathogenic copy number variations (CNVs) only (10/118; 8.5%).We further explored the CNV/phenotype correlation and found a series of clear or suspected dosage-sensitive CFM genes including TBX1, MAPK1, PCYT1A, DLG1, LHX1, SHH, SF3B4, FOXC1, ZIC2, CREBBP, SNRPB, and CSNK2A1. Conclusion: These findings enrich our understanding of the potential causative CNVs and genes in CFMs. Identification of the genetic basis of CFMs contributes to our understanding of their pathogenesis and allows detailed genetic counselling. -
Visual Exploration Across Biomedical Databases
1 Visual Exploration Across Biomedical Databases Michael D. Lieberman∗†‡ Sima Taheri∗ Huimin Guo∗ Fatemeh Mir-Rashed∗ [email protected] [email protected] [email protected] [email protected] Inbal Yahav§ Aleks Aris∗ Ben Shneiderman∗† [email protected] [email protected] [email protected] Abstract—Though biomedical research often draws on knowl- mentioned in d. d may also have associations with other edge from a wide variety of fields, few visualization methods PubMed documents that cite d as a reference, as well as for biomedical data incorporate meaningful cross-database ex- associations to the PubMed documents that d itself cites. ploration. A new approach is offered for visualizing and explor- ∈ ing a query-based subset of multiple heterogeneous biomedical Furthermore, each gene g G could have associations with databases. Databases are modeled as an entity-relation graph the proteins for which g codes, or the DNA sequences in containing nodes (database records) and links (relationships which g’s code appears. Usually, the various types of records between records). Users specify a keyword search string to in these databases also have many attributes associated with retrieve an initial set of nodes, and then explore intra- and inter- them. For example, PubMed documents might be annotated database links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually with the date of publication, authors, and general topics, while present in biomedical data. Comments from domain experts gene records could be annotated with the relevant species, indicate that this visualization method is potentially advantageous location on chromosome, or function. -
Dynamic Repression by BCL6 Controls the Genome-Wide Liver Response To
RESEARCH ARTICLE Dynamic repression by BCL6 controls the genome-wide liver response to fasting and steatosis Meredith A Sommars1, Krithika Ramachandran1, Madhavi D Senagolage1, Christopher R Futtner1, Derrik M Germain1, Amanda L Allred1, Yasuhiro Omura1, Ilya R Bederman2, Grant D Barish1,3,4* 1Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, United States; 2Department of Pediatrics, Case Western Reserve University, Cleveland, United States; 3Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, United States; 4Jesse Brown VA Medical Center, Chicago, United States Abstract Transcription is tightly regulated to maintain energy homeostasis during periods of feeding or fasting, but the molecular factors that control these alternating gene programs are incompletely understood. Here, we find that the B cell lymphoma 6 (BCL6) repressor is enriched in the fed state and converges genome-wide with PPARa to potently suppress the induction of fasting transcription. Deletion of hepatocyte Bcl6 enhances lipid catabolism and ameliorates high- fat-diet-induced steatosis. In Ppara-null mice, hepatocyte Bcl6 ablation restores enhancer activity at PPARa-dependent genes and overcomes defective fasting-induced fatty acid oxidation and lipid accumulation. Together, these findings identify BCL6 as a negative regulator of oxidative metabolism and reveal that alternating recruitment of repressive and activating transcription factors to -
University of California Santa Cruz Sample
UNIVERSITY OF CALIFORNIA SANTA CRUZ SAMPLE-SPECIFIC CANCER PATHWAY ANALYSIS USING PARADIGM A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in BIOMOLECULAR ENGINEERING AND BIOINFORMATICS by Stephen C. Benz June 2012 The Dissertation of Stephen C. Benz is approved: Professor David Haussler, Chair Professor Joshua Stuart Professor Nader Pourmand Dean Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Stephen C. Benz 2012 Table of Contents List of Figures v List of Tables xi Abstract xii Dedication xiv Acknowledgments xv 1 Introduction 1 1.1 Identifying Genomic Alterations . 2 1.2 Pathway Analysis . 5 2 Methods to Integrate Cancer Genomics Data 10 2.1 UCSC Cancer Genomics Browser . 11 2.2 BioIntegrator . 16 3 Pathway Analysis Using PARADIGM 20 3.1 Method . 21 3.2 Comparisons . 26 3.2.1 Distinguishing True Networks From Decoys . 27 3.2.2 Tumor versus Normal - Pathways associated with Ovarian Cancer 29 3.2.3 Differentially Regulated Pathways in ER+ve vs ER-ve breast can- cers . 36 3.2.4 Therapy response prediction using pathways (Platinum Free In- terval in Ovarian Cancer) . 38 3.3 Unsupervised Stratification of Cancer Patients by Pathway Activities . 42 4 SuperPathway - A Global Pathway Model for Cancer 51 4.1 SuperPathway in Ovarian Cancer . 55 4.2 SuperPathway in Breast Cancer . 61 iii 4.2.1 Chin-Naderi Cohort . 61 4.2.2 TCGA Breast Cancer . 63 4.3 Cross-Cancer SuperPathway . 67 5 Pathway Analysis of Drug Effects 74 5.1 SuperPathway on Breast Cell Lines . -
A Molecular Quantitative Trait Locus Map for Osteoarthritis
A molecular quantitative trait locus map for osteoarthritis Julia Steinberg1,2,3,4, Lorraine Southam1,3, Theodoros I Roumeliotis3,5, Matthew J Clark6, Raveen L Jayasuriya6, Diane Swift6, Karan M Shah6, Natalie C Butterfield7, Roger A Brooks8, Andrew W McCaskie8, JH Duncan Bassett7, Graham R Williams7, Jyoti S Choudhary3,5, J Mark Wilkinson6,9,11, Eleftheria Zeggini1,3,10,11 1 Institute of Translational Genomics, Helmholtz Zentrum München – German Research Center for Environmental Health, 85764 Neuherberg, Germany 2 Cancer Research Division, Cancer Council NSW, Sydney, New South Wales 2011, Australia 3 Wellcome Sanger Institute, Hinxton CB10 1SA, United Kingdom 4 School of Public Health, The University of Sydney, Sydney, New South Wales 2006, Australia 5 The Institute of Cancer Research, London SW7 3RP, UK 6 Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2RX, UK 7 Molecular Endocrinology Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 ONN, UK 8 Division of Trauma & Orthopaedic Surgery, Department of Surgery, University of Cambridge, Cambridge CB2 2QQ, UK 9 Centre for Integrated Research into Musculoskeletal Ageing and Sheffield Healthy Lifespan Institute, University of Sheffield, Sheffield S10 2TN, UK 10 TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany 11 These authors contributed equally Correspondence to: [email protected]; eleftheria.zeggini@helmholtz- muenchen.de Supplementary Information Table of Contents Supplementary Notes ................................................................................................................... 3 Supplementary Note 1: Identification of cis-eQTLs and cis-pQTLs in osteoarthritis tissues ..................... 3 Supplementary Note 2: Differential eQTLs between low-grade and high-grade cartilage ....................... 4 Supplementary Note 3: Differential gene expression between high-grade and low-grade cartilage ...... -
BDH1 (G-5): Sc-514413
SAN TA C RUZ BI OTEC HNOL OG Y, INC . BDH1 (G-5): sc-514413 BACKGROUND APPLICATIONS BDH1 (3-hydroxybutyrate dehydrogenase, type 1), also known as BDH or BDH1 (G-5) is recommended for detection of BDH1 of mouse, rat and human SDR9C1, is a 343 amino acid protein that localizes to the mitochondrial origin by Western Blotting (starting dilution 1:100, dilution range 1:100- matrix and belongs to the short-chain dehydrogenases/reductases (SDR) 1:1000), immunoprecipitation [1-2 µg per 100-500 µg of total protein (1 ml of family. Existing as a homotetramer, BDH1 functions to catalyze the NAD +- cell lysate)], immunofluorescence (starting dilution 1:50, dilution range 1:50- dependent interconversion of (R)-3-hydroxybutanoate and acetoacetate, a 1:500) and solid phase ELISA (starting dilution 1:30, dilution range 1:30- reaction that is allosterically activated by phosphatidylcholine. As both 1:3000). (R)-3-hydroxybutanoate and acetoacetate are two major ketone bodies pro - Suitable for use as control antibody for BDH1 siRNA (h): sc-78262, BDH1 duced during fatty acid catabolism, BDH1 plays an important role in the meta - siRNA (m): sc-141681, BDH1 shRNA Plasmid (h): sc-78262-SH, BDH1 shRNA bolic degradation of fatty acids. The gene encoding BDH1 maps to human Plasmid (m): sc-141681-SH, BDH1 shRNA (h) Lentiviral Particles: sc-78262-V chromosome 3, which houses over 1,100 genes, including a chemokine and BDH1 shRNA (m) Lentiviral Particles: sc-141681-V. receptor (CKR) gene cluster and a variety of human cancer-related gene loci. Molecular Weight of mature BDH1: 38 kDa. -
Contrasting Evolutionary Genome Dynamics Between Domesticated and Wild Yeasts
ARTICLES OPEN Contrasting evolutionary genome dynamics between domesticated and wild yeasts Jia-Xing Yue1 , Jing Li1, Louise Aigrain2, Johan Hallin1 , Karl Persson3 , Karen Oliver2, Anders Bergström2, Paul Coupland2,5, Jonas Warringer3 , Marco Cosentino Lagomarsino4, Gilles Fischer4, Richard Durbin2 & Gianni Liti1 Structural rearrangements have long been recognized as an important source of genetic variation, with implications in phenotypic diversity and disease, yet their detailed evolutionary dynamics remain elusive. Here we use long-read sequencing to generate end- to-end genome assemblies for 12 strains representing major subpopulations of the partially domesticated yeast Saccharomyces cerevisiae and its wild relative Saccharomyces paradoxus. These population-level high-quality genomes with comprehensive annotation enable precise definition of chromosomal boundaries between cores and subtelomeres and a high-resolution view of evolutionary genome dynamics. In chromosomal cores, S. paradoxus shows faster accumulation of balanced rearrangements (inversions, reciprocal translocations and transpositions), whereas S. cerevisiae accumulates unbalanced rearrangements (novel insertions, deletions and duplications) more rapidly. In subtelomeres, both species show extensive interchromosomal reshuffling, with a higher tempo in S. cerevisiae. Such striking contrasts between wild and domesticated yeasts are likely to reflect the influence of human activities on structural genome evolution. Understanding how genetic variation translates into phenotypic biology and genetics. It was the first eukaryote to have its genome diversity is a central theme in biology. With the rapid advancement of sequence, population genomics and genotype–phenotype map exten- sequencing technology, genetic variation in large natural populations sively explored1,20,21. Here we applied PacBio sequencing to 12 repre- has been explored extensively for humans and several model organ- sentative strains of S. -
BDH1 Antibody
Efficient Professional Protein and Antibody Platforms BDH1 Antibody Basic information: Catalog No.: UMA60427 Source: Mouse Size: 50ul/100ul Clonality: Monoclonal Concentration: 1mg/ml Isotype: Mouse IgG1 Purification: Protein A affinity purified Useful Information: WB:1:500-1:1000 Applications: IHC:1:50-1:200 Reactivity: Human, Mouse Specificity: This antibody recognizes BDH1 protein. Immunogen: Recombinant protein BDH1 (3-hydroxybutyrate dehydrogenase, type 1), also known as BDH or SDR9C1, is a 343 amino acid protein that localizes to the mitochondrial ma- trix and belongs to the short-chain dehydrogenases/reductases (SDR) family. Existing as a homotetramer, BDH1 functions to catalyze the NAD+-dependent interconversion of (R)-3-hydroxybutanoate and acetoace- Description: tate, a reaction that is allosterically activated by phosphatidylcholine. As both (R)-3-hydroxybutanoate and acetoacetate are two major ketone bodies produced during fatty acid catabolism, BDH1 plays an important role in the metabolic degradation of fatty acids. The gene encoding BDH1 maps to hu- man chromosome 3, which houses over 1,100 genes, including a chemokine receptor (CKR) gene cluster and a variety of human cancer-related gene loci. Uniprot: Q02338(Human) Q80XN0(Mouse) BiowMW: 45 kDa Buffer: 1*TBS (pH7.4), 1%BSA, Preservative: 0.05% Sodium Azide. Storage: Store at 4°C short term and -20°C long term. Avoid freeze-thaw cycles. Note: For research use only, not for use in diagnostic procedure. Data: Western blot analysis of BDH1 on HepG2 and NIH/3T3 cell lysate using anti-BDH1 antibody at 1/1,000 dilution. Gene Universal Technology Co. Ltd www.universalbiol.com Tel: 0550-3121009 E-mail: [email protected] Efficient Professional Protein and Antibody Platforms Immunohistochemical analysis of paraf- fin-embedded human prostate tissue using an- ti-BDH1 antibody. -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility. -
A Novel Transmembrane Glycoprotein Cancer Biomarker Present in the X Chromosome ANA PAULA DELGADO, SHEILIN HAMID, PAMELA BRANDAO and RAMASWAMY NARAYANAN
CANCER GENOMICS & PROTEOMICS 11 : 81-92 (2014) A Novel Transmembrane Glycoprotein Cancer Biomarker Present in the X Chromosome ANA PAULA DELGADO, SHEILIN HAMID, PAMELA BRANDAO and RAMASWAMY NARAYANAN Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A. Abstract. Background: The uncharacterized proteins of the genome are of unknown nature (10). These proteins and the human proteome offer an untapped potential for cancer non-coding RNAs are called the ‘dark matter’ of the genome biomarker discovery. Numerous predicted open reading (11-13). Whereas in the past most gene discovery has frames (ORFs) are present in diverse chromosomes. The revolved around known genes due to the ease of follow-up mRNA and protein expression data, as well as the mutational studies (14-17), the dark matter of the genome offers an and variant information for these ORF proteins are available untapped potential (18). Realizing the importance of this area, in the cancer-related bioinformatics databases. Materials the US National Cancer Institute has recently announced a and Methods: ORF proteins were mined using bioinformatics major initiative called illuminating the dark matter for and proteomic tools to predict motifs and domains, and druggable targets (http://commonfund.nih.gov/idg/index). cancer relevance was established using cancer genome, Establishing cancer relevance for novel or uncharacterized transcriptome and proteome analysis tools. Results: A novel proteins is a crucial first step in lead discovery. The cancer testis-restricted ORF protein present in chromosome X called genomes of patients from around the world can be readily CXorf66 was detected in the serum, plasma and neutrophils.