Supplementary Information Table S1. Confirmed and Predicted Imprinted
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Analyses of Allele-Specific Gene Expression in Highly Divergent
ARTICLES Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance James J Crowley1,10, Vasyl Zhabotynsky1,10, Wei Sun1,2,10, Shunping Huang3, Isa Kemal Pakatci3, Yunjung Kim1, Jeremy R Wang3, Andrew P Morgan1,4,5, John D Calaway1,4,5, David L Aylor1,9, Zaining Yun1, Timothy A Bell1,4,5, Ryan J Buus1,4,5, Mark E Calaway1,4,5, John P Didion1,4,5, Terry J Gooch1,4,5, Stephanie D Hansen1,4,5, Nashiya N Robinson1,4,5, Ginger D Shaw1,4,5, Jason S Spence1, Corey R Quackenbush1, Cordelia J Barrick1, Randal J Nonneman1, Kyungsu Kim2, James Xenakis2, Yuying Xie1, William Valdar1,4, Alan B Lenarcic1, Wei Wang3,9, Catherine E Welsh3, Chen-Ping Fu3, Zhaojun Zhang3, James Holt3, Zhishan Guo3, David W Threadgill6, Lisa M Tarantino7, Darla R Miller1,4,5, Fei Zou2,11, Leonard McMillan3,11, Patrick F Sullivan1,5,7,8,11 & Fernando Pardo-Manuel de Villena1,4,5,11 Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Because regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in further characterizing these mechanisms. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. Effects from these variants influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. -
Core Transcriptional Regulatory Circuitries in Cancer
Oncogene (2020) 39:6633–6646 https://doi.org/10.1038/s41388-020-01459-w REVIEW ARTICLE Core transcriptional regulatory circuitries in cancer 1 1,2,3 1 2 1,4,5 Ye Chen ● Liang Xu ● Ruby Yu-Tong Lin ● Markus Müschen ● H. Phillip Koeffler Received: 14 June 2020 / Revised: 30 August 2020 / Accepted: 4 September 2020 / Published online: 17 September 2020 © The Author(s) 2020. This article is published with open access Abstract Transcription factors (TFs) coordinate the on-and-off states of gene expression typically in a combinatorial fashion. Studies from embryonic stem cells and other cell types have revealed that a clique of self-regulated core TFs control cell identity and cell state. These core TFs form interconnected feed-forward transcriptional loops to establish and reinforce the cell-type- specific gene-expression program; the ensemble of core TFs and their regulatory loops constitutes core transcriptional regulatory circuitry (CRC). Here, we summarize recent progress in computational reconstitution and biologic exploration of CRCs across various human malignancies, and consolidate the strategy and methodology for CRC discovery. We also discuss the genetic basis and therapeutic vulnerability of CRC, and highlight new frontiers and future efforts for the study of CRC in cancer. Knowledge of CRC in cancer is fundamental to understanding cancer-specific transcriptional addiction, and should provide important insight to both pathobiology and therapeutics. 1234567890();,: 1234567890();,: Introduction genes. Till now, one critical goal in biology remains to understand the composition and hierarchy of transcriptional Transcriptional regulation is one of the fundamental mole- regulatory network in each specified cell type/lineage. -
BIOINFORMATICS Pages 1–7
Vol. 00 no. 00 2010 BIOINFORMATICS Pages 1–7 Integrative classification and analysis of multiple arrayCGH datasets with probe alignment Ze Tian and Rui Kuang∗ Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, USA Received on XXXXX; revised on XXXXX; accepted on XXXXX Associate Editor: XXXXXXX ABSTRACT 2009). Chromosome copy number variations can be measured by Motivation: Array comparative genomic hybridization (ArrayCGH) comparative genomic hybridization (CGH), which compares the is widely used to measure DNA copy numbers in cancer research. copy number of a differentially labeled case sample with a reference ArrayCGH data report log-ratio intensities of thousands of probes DNA from a normal individual. ArrayCGH technology based on sampled along the chromosomes. Typically, the choices of the DNA microarray can currently allow genome-wide identification locations and the lengths of the probes vary in different experiments. of regions with copy number variations at different resolutions This discrepancy in choosing probes poses a challenge in integrated (Carter, 2007). The arrayCGH data was used to discriminate healthy classification or analysis across multiple arrayCGH datasets. We patients from cancer patients and classify patients of different cancer propose an alignment based framework to integrate arrayCGH subtypes. Thus, arrayCGH data is considered as a new source of samples generated from different probe sets. The alignment biomarkers that provide important information of candidate cancer framework seeks an optimal alignment between the probe series loci for the classification of patients and discovery of molecular of one arrayCGH sample and the probe series of another sample, mechanisms of cancers (Sykes et al., 2009). -
PDF Datasheet
Product Datasheet C9orf85 Overexpression Lysate NBL1-08604 Unit Size: 0.1 mg Store at -80C. Avoid freeze-thaw cycles. Protocols, Publications, Related Products, Reviews, Research Tools and Images at: www.novusbio.com/NBL1-08604 Updated 3/17/2020 v.20.1 Earn rewards for product reviews and publications. Submit a publication at www.novusbio.com/publications Submit a review at www.novusbio.com/reviews/destination/NBL1-08604 Page 1 of 2 v.20.1 Updated 3/17/2020 NBL1-08604 C9orf85 Overexpression Lysate Product Information Unit Size 0.1 mg Concentration The exact concentration of the protein of interest cannot be determined for overexpression lysates. Please contact technical support for more information. Storage Store at -80C. Avoid freeze-thaw cycles. Buffer RIPA buffer Target Molecular Weight 18.1 kDa Product Description Description Transient overexpression lysate of chromosome 9 open reading frame 85 (C9orf85) The lysate was created in HEK293T cells, using Plasmid ID RC208807 and based on accession number NM_182505. The protein contains a C- MYC/DDK Tag. Gene ID 138241 Gene Symbol C9ORF85 Species Human Notes HEK293T cells in 10-cm dishes were transiently transfected with a non-lipid polymer transfection reagent specially designed and manufactured for large volume DNA transfection. Transfected cells were cultured for 48hrs before collection. The cells were lysed in modified RIPA buffer (25mM Tris-HCl pH7.6, 150mM NaCl, 1% NP-40, 1mM EDTA, 1xProteinase inhibitor cocktail mix, 1mM PMSF and 1mM Na3VO4, and then centrifuged to clarify the lysate. Protein concentration was measured by BCA protein assay kit.This product is manufactured by and sold under license from OriGene Technologies and its use is limited solely for research purposes. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
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. -
Genetics of Familial Non-Medullary Thyroid Carcinoma (FNMTC)
cancers Review Genetics of Familial Non-Medullary Thyroid Carcinoma (FNMTC) Chiara Diquigiovanni * and Elena Bonora Unit of Medical Genetics, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-051-208-8418 Simple Summary: Non-medullary thyroid carcinoma (NMTC) originates from thyroid follicular epithelial cells and is considered familial when occurs in two or more first-degree relatives of the patient, in the absence of predisposing environmental factors. Familial NMTC (FNMTC) cases show a high genetic heterogeneity, thus impairing the identification of pivotal molecular changes. In the past years, linkage-based approaches identified several susceptibility loci and variants associated with NMTC risk, however only few genes have been identified. The advent of next-generation sequencing technologies has improved the discovery of new predisposing genes. In this review we report the most significant genes where variants predispose to FNMTC, with the perspective that the integration of these new molecular findings in the clinical data of patients might allow an early detection and tailored therapy of the disease, optimizing patient management. Abstract: Non-medullary thyroid carcinoma (NMTC) is the most frequent endocrine tumor and originates from the follicular epithelial cells of the thyroid. Familial NMTC (FNMTC) has been defined in pedigrees where two or more first-degree relatives of the patient present the disease in absence of other predisposing environmental factors. Compared to sporadic cases, FNMTCs are often multifocal, recurring more frequently and showing an early age at onset with a worse outcome. FNMTC cases Citation: Diquigiovanni, C.; Bonora, E. -
Apba, a New Genetic Locus Involved in Thiamine Biosynthesis in Salmonella Typhimurium DIANA M
JOURNAL OF BACrERIOLOGY, Aug. 1994, p. 4858-4864 Vol. 176, No. 16 0021-9193/94/$04.00+0 Copyright X 1994, American Society for Microbiology apbA, a New Genetic Locus Involved in Thiamine Biosynthesis in Salmonella typhimurium DIANA M. DOWNS* AND LESLIE PETERSEN Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706 Received 3 February 1994/Returned for modification 14 April 1994/Accepted 3 June 1994 In Salnonella typhimurium, the synthesis of the pyrimidine moiety of thiamine can occur by utilization of the first five steps in de novo purine biosynthesis or independently of the pur genes through the alternative pyrimidine biosynthetic, or APB, pathway (D. M. Downs, J. Bacteriol. 174:1515-1521, 1992). We have isolated the first mutations defective in the APB pathway. These mutations define the apbA locus and map at 10.5 min on the S. typhimurium chromosome. We have cloned and sequenced the apbA gene and found it to encode a 32-kDa polypeptide whose sequence predicts an NAD/flavin adenine dinucleotide-binding pocket in the protein. The phenotypes of apbA mutants suggest that, under some conditions, the APB pathway is the sole source of the pyrimidine moiety of thiamine in wild-type S. typhimurium, and furthermore, the pur genetic background of the strain influences whether this pathway can function under aerobic and/or anaerobic growth conditions. Thiamine (vitamin B1) is a required nutrient for the cell and thiamine biosynthesis still required the remainingpur genes for in its coenzymic form, thiamine pyrophosphate, participates as the formation of HMP (9). a carrier of C2 units in reactions such as the ones catalyzed by Recently, we demonstrated the existence of a pathway that transketolase and pyruvate dehydrogenase. -
CREB3L2-Pparg Fusion Mutation Identifies a Thyroid Signaling Pathway Regulated by Intramembrane Proteolysis
Research Article CREB3L2-PPARg Fusion Mutation Identifies a Thyroid Signaling Pathway Regulated by Intramembrane Proteolysis Weng-Onn Lui,3 Lingchun Zeng,1 Victoria Rehrmann,1 Seema Deshpande,5 Maria Tretiakova,1 Edwin L. Kaplan,2 Ingo Leibiger,3 Barbara Leibiger,3 Ulla Enberg,3 Anders Ho¨o¨g,4 Catharina Larsson,3 and Todd G. Kroll1 Departments of 1Pathology and 2Surgery, University of Chicago Medical Center, Chicago, Illinois; Departments of 3Molecular Medicine and Surgery and 4Oncology-Pathology, Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden; and 5Department of Pathology, Emory University School of Medicine, Atlanta, Georgia Abstract cancer in patients; and (c) the implementation of effective molecular-targeted chemotherapies that have relatively few side The discovery of gene fusion mutations, particularly in effects. The discovery of fusion mutations is therefore important, leukemia, has consistently identified new cancer pathways particularly in carcinoma, the most common cancer group in and led to molecular diagnostic assays and molecular-targeted which few gene fusions have been identified (1). The recent chemotherapies for cancer patients. Here, we report our discoveries of ERG (2) and ALK (3) gene fusions in prostate and discovery of a novel CREB3L2-PPARg fusion mutation in lung carcinoma, respectively, increase the prospect that new thyroid carcinoma with t(3;7)(p25;q34), showing that a family diagnostic and therapeutic strategies based on gene fusions will of somatic PPARg fusion mutations exist in thyroid cancer. The be applicable to common epithelial cancers. CREB3L2-PPARg fusion encodes a CREB3L2-PPAR; fusion Families of gene fusions tend to characterize specific cancer protein that is composed of the transactivation domain of types. -
Product Description SALSA® MS-MLPA® Probemix ME031-C1 GNAS to Be Used with the MS-MLPA General Protocol
Product description version C1-01; Issued 19 March 2021 Product Description SALSA® MS-MLPA® Probemix ME031-C1 GNAS To be used with the MS-MLPA General Protocol. Version C1 As compared to version B2, probemix completely revised, details are shown in complete product history see page 11. Catalogue numbers: ME031-025R: SALSA MS-MLPA Probemix ME031 GNAS, 25 reactions. ME031-050R: SALSA MS-MLPA Probemix ME031 GNAS, 50 reactions. ME031-100R: SALSA MS-MLPA Probemix ME031 GNAS, 100 reactions. To be used in combination with a SALSA MLPA reagent kit, SALSA HhaI (SMR50) and Coffalyser.Net data analysis software. MLPA reagent kits are either provided with FAM or Cy5.0 dye-labelled PCR primer, suitable for Applied Biosystems and Beckman/SCIEX capillary sequencers, respectively (see www.mrcholland.com). Certificate of Analysis Information regarding storage conditions, quality tests, and a sample electropherogram from the current sales lot is available at www.mrcholland.com. Precautions and warnings For professional use only. Always consult the most recent product description AND the MS-MLPA General Protocol before use: www.mrcholland.com. It is the responsibility of the user to be aware of the latest scientific knowledge of the application before drawing any conclusions from findings generated with this product. This SALSA MS-MLPA probemix is intended for experienced MLPA users only! The exact link between the GNAS complex locus genotype and phenotype is still being investigated. Use of this ME031 GNAS probemix will not always provide you with clear-cut answers and interpretation of results can therefore be complicated. MRC Holland can only provide limited support with interpretation of results obtained with this product, and recommends thoroughly screening any available literature. -
Functional Characterisation of Human Synaptic Genes Expressed in the Drosophila Brain Lysimachos Zografos1,2, Joanne Tang1, Franziska Hesse3, Erich E
© 2016. Published by The Company of Biologists Ltd | Biology Open (2016) 5, 662-667 doi:10.1242/bio.016261 METHODS & TECHNIQUES Functional characterisation of human synaptic genes expressed in the Drosophila brain Lysimachos Zografos1,2, Joanne Tang1, Franziska Hesse3, Erich E. Wanker3, Ka Wan Li4, August B. Smit4, R. Wayne Davies1,5 and J. Douglas Armstrong1,5,* ABSTRACT systems biology approaches are likely to be the best route to unlock a Drosophila melanogaster is an established and versatile model new generation of neuroscience research and CNS drug organism. Here we describe and make available a collection of development that society so urgently demands (Catalá-López transgenic Drosophila strains expressing human synaptic genes. The et al., 2013). Yet these modelling type approaches also need fast, collection can be used to study and characterise human synaptic tractable in vivo models for validation. genes and their interactions and as controls for mutant studies. It was More than 100 years after the discovery of the white gene in generated in a way that allows the easy addition of new strains, as well Drosophila melanogaster, the common fruit fly remains a key tool as their combination. In order to highlight the potential value of the for the study of neuroscience and neurobiology. The fruit fly collection for the characterisation of human synaptic genes we also genome is well annotated and there is a vast genetic manipulation use two assays, investigating any gain-of-function motor and/or toolkit available. This allows interventions such as high throughput cognitive phenotypes in the strains in this collection. Using these cloning (Bischof et al., 2013; Wang et al., 2012) and the precise assays we show that among the strains made there are both types of insertion of transgenes in the genome (Groth et al., 2004; Venken gain-of-function phenotypes investigated.