U1 Snrnp Regulates Cancer Cell Migration and Invasion in Vitro
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Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice
Loyola University Chicago Loyola eCommons Biology: Faculty Publications and Other Works Faculty Publications 2013 Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice Mihaela Palicev Gunter P. Wagner James P. Noonan Benedikt Hallgrimsson James M. Cheverud Loyola University Chicago, [email protected] Follow this and additional works at: https://ecommons.luc.edu/biology_facpubs Part of the Biology Commons Recommended Citation Palicev, M, GP Wagner, JP Noonan, B Hallgrimsson, and JM Cheverud. "Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice." Genome Biology and Evolution 5(10), 2013. This Article is brought to you for free and open access by the Faculty Publications at Loyola eCommons. It has been accepted for inclusion in Biology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. © Palicev et al., 2013. GBE Genomic Correlates of Relationship QTL Involved in Fore- versus Hind Limb Divergence in Mice Mihaela Pavlicev1,2,*, Gu¨ nter P. Wagner3, James P. Noonan4, Benedikt Hallgrı´msson5,and James M. Cheverud6 1Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria 2Department of Pediatrics, Cincinnati Children‘s Hospital Medical Center, Cincinnati, Ohio 3Yale Systems Biology Institute and Department of Ecology and Evolutionary Biology, Yale University 4Department of Genetics, Yale University School of Medicine 5Department of Cell Biology and Anatomy, The McCaig Institute for Bone and Joint Health and the Alberta Children’s Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Canada 6Department of Anatomy and Neurobiology, Washington University *Corresponding author: E-mail: [email protected]. -
Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma
Anatomy and Pathology Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma Sarah L. Lake,1 Sarah E. Coupland,1 Azzam F. G. Taktak,2 and Bertil E. Damato3 PURPOSE. To detect deletions and loss of heterozygosity of disease is fatal in 92% of patients within 2 years of diagnosis. chromosome 3 in a rare subset of fatal, disomy 3 uveal mela- Clinical and histopathologic risk factors for UM metastasis noma (UM), undetectable by fluorescence in situ hybridization include large basal tumor diameter (LBD), ciliary body involve- (FISH). ment, epithelioid cytomorphology, extracellular matrix peri- ϩ ETHODS odic acid-Schiff-positive (PAS ) loops, and high mitotic M . Multiplex ligation-dependent probe amplification 3,4 5 (MLPA) with the P027 UM assay was performed on formalin- count. Prescher et al. showed that a nonrandom genetic fixed, paraffin-embedded (FFPE) whole tumor sections from 19 change, monosomy 3, correlates strongly with metastatic death, and the correlation has since been confirmed by several disomy 3 metastasizing UMs. Whole-genome microarray analy- 3,6–10 ses using a single-nucleotide polymorphism microarray (aSNP) groups. Consequently, fluorescence in situ hybridization were performed on frozen tissue samples from four fatal dis- (FISH) detection of chromosome 3 using a centromeric probe omy 3 metastasizing UMs and three disomy 3 tumors with Ͼ5 became routine practice for UM prognostication; however, 5% years’ metastasis-free survival. to 20% of disomy 3 UM patients unexpectedly develop metas- tases.11 Attempts have therefore been made to identify the RESULTS. Two metastasizing UMs that had been classified as minimal region(s) of deletion on chromosome 3.12–15 Despite disomy 3 by FISH analysis of a small tumor sample were found these studies, little progress has been made in defining the key on MLPA analysis to show monosomy 3. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Subcellular RNA Profiling Links Splicing and Nuclear DICER1 to Alternative Cleavage and Polyadenylation
Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Subcellular RNA profiling links splicing and nuclear DICER1 to alternative cleavage and polyadenylation Jonathan Neve1#, Kaspar Burger2#, Wencheng Li3, Mainul Hoque3, Radhika Patel1, Bin Tian3, Monika Gullerova2* and Andre Furger1* Affiliations: 1 Department of Biochemistry, South Parks Road, University of Oxford, OX1 3QU, UK 2 Sir William Dunn School of Pathology, South Parks Road, University of Oxford, OX1 3RE, UK 3Department of Biochemistry and Molecular Biology, Rutgers New Jersey Medical School, Newark, NJ 07103, USA *Corresponding authors: Andre Furger Email: [email protected] Monika Gullerova Email: [email protected] # authors contributed equally Running Title: APA and subcellular fractionation Key words: alternative polyadenylation, mRNA, subcellular fractionation, DICER1 1 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press ABSTRACT Alternative cleavage and polyadenylation (APA) plays a crucial role in the regulation of gene expression across eukaryotes. Although APA is extensively studied, its regulation within cellular compartments and its physiological impact remains largely enigmatic. Here, we employed a rigorous subcellular fractionation approach to compare APA profiles of cytoplasmic and nuclear RNA fractions from human cell lines. This approach allowed us to extract APA isoforms that are subjected to differential regulation and provided us with a platform to interrogate the molecular regulatory pathways that shape APA profiles in different subcellular locations. Here we show that APA isoforms with shorter 3’UTRs tend to be overrepresented in the cytoplasm and appear to be cell type specific events. -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
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. -
CPSF6 Links Alternative Polyadenylation to Metabolism
Tan et al. Journal of Experimental & Clinical Cancer Research (2021) 40:85 https://doi.org/10.1186/s13046-021-01884-z RESEARCH Open Access CPSF6 links alternative polyadenylation to metabolism adaption in hepatocellular carcinoma progression Sheng Tan1†, Ming Zhang2†, Xinglong Shi3†, Keshuo Ding4, Qiang Zhao3, Qianying Guo4, Hao Wang4, Zhengsheng Wu4, Yani Kang3, Tao Zhu5*, Jielin Sun1* and Xiaodong Zhao1* Abstract Background: Alternative polyadenylation (APA) is an important mechanism of gene expression regulation through generation of RNA isoforms with distinct 3′ termini. Increasing evidence has revealed that APA is actively involved in development and disease, including hepatocellular carcinoma (HCC). However, how APA functions in tumor formation and progression remains elusive. In this study, we investigated the role of cleavage factor I (CFIm) subunit CPSF6 in human hepatocellular carcinoma (HCC). Methods: Expression levels of CPSF6 in clinical tissues and cell lines were determined by qRT-PCR and western blot. Functional assays, including the cell number, MTT, colony formation and transwell, were used to determine the oncogenic role of CPSF6 in HCC. Animal experiments were used to determine the role of CPSF6 in HCC tumorigenicity in vivo. Deep sequencing-based 3 T-seq was used to profile the transcriptome-wide APA sites in both HCC cells and CPSF6 knockdown HCC cells. The function of CPSF6-affected target NQO1 with distinct 3′UTRs was characterized by metabolism assays. Results: We observed CPSF6 was upregulated in HCC and the high expression of CPSF6 was associated with poor prognosis in patients. Overexpression of CPSF6 promoted proliferation, migration and invasion of HCC cells in vitro and in vivo. -
U6 Small Nuclear RNA Is Transcribed by RNA Polymerase III (Cloned Human U6 Gene/"TATA Box"/Intragenic Promoter/A-Amanitin/La Antigen) GARY R
Proc. Nati. Acad. Sci. USA Vol. 83, pp. 8575-8579, November 1986 Biochemistry U6 small nuclear RNA is transcribed by RNA polymerase III (cloned human U6 gene/"TATA box"/intragenic promoter/a-amanitin/La antigen) GARY R. KUNKEL*, ROBIN L. MASERt, JAMES P. CALVETt, AND THORU PEDERSON* *Cell Biology Group, Worcester Foundation for Experimental Biology, Shrewsbury, MA 01545; and tDepartment of Biochemistry, University of Kansas Medical Center, Kansas City, KS 66103 Communicated by Aaron J. Shatkin, August 7, 1986 ABSTRACT A DNA fragment homologous to U6 small 4A (20) was screened with a '251-labeled U6 RNA probe (21, nuclear RNA was isolated from a human genomic library and 22) using a modified in situ plaque hybridization protocol sequenced. The immediate 5'-flanking region of the U6 DNA (23). One of several positive clones was plaque-purified and clone had significant homology with a potential mouse U6 gene, subsequently shown by restriction mapping to contain a including a "TATA box" at a position 26-29 nucleotides 12-kilobase-pair (kbp) insert. A 3.7-kbp EcoRI fragment upstream from the transcription start site. Although this containing U6-hybridizing sequences was subcloned into sequence element is characteristic of RNA polymerase II pBR322 for further restriction mapping. An 800-base-pair promoters, the U6 gene also contained a polymerase III "box (bp) DNA fragment containing U6 homologous sequences A" intragenic control region and a typical run of five thymines was excised using Ava I and inserted into the Sma I site of at the 3' terminus (noncoding strand). The human U6 DNA M13mp8 replicative form DNA (M13/U6) (24). -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).