Inherited Genetic Mutations and Polymorphisms in Malignant Mesothelioma: a Comprehensive Review
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Anti-ARL4A Antibody (ARG41291)
Product datasheet [email protected] ARG41291 Package: 100 μl anti-ARL4A antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes ARL4A Tested Reactivity Hu, Ms, Rat Tested Application ICC/IF, IHC-P Host Rabbit Clonality Polyclonal Isotype IgG Target Name ARL4A Antigen Species Human Immunogen Recombinant fusion protein corresponding to aa. 121-200 of Human ARL4A (NP_001032241.1). Conjugation Un-conjugated Alternate Names ARL4; ADP-ribosylation factor-like protein 4A Application Instructions Application table Application Dilution ICC/IF 1:50 - 1:200 IHC-P 1:50 - 1:200 Application Note * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 23 kDa Properties Form Liquid Purification Affinity purified. Buffer PBS (pH 7.3), 0.02% Sodium azide and 50% Glycerol. Preservative 0.02% Sodium azide Stabilizer 50% Glycerol Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. Note For laboratory research only, not for drug, diagnostic or other use. www.arigobio.com 1/2 Bioinformation Gene Symbol ARL4A Gene Full Name ADP-ribosylation factor-like 4A Background ADP-ribosylation factor-like 4A is a member of the ADP-ribosylation factor family of GTP-binding proteins. ARL4A is similar to ARL4C and ARL4D and each has a nuclear localization signal and an unusually high guaninine nucleotide exchange rate. -
Trajectory and Uniqueness of Mutational Signatures in Yeast Mutators
Trajectory and uniqueness of mutational signatures in yeast mutators Sophie Loeilleta,b, Mareike Herzogc, Fabio Pudduc, Patricia Legoixd, Sylvain Baulanded, Stephen P. Jacksonc, and Alain G. Nicolasa,b,1 aInstitut Curie, Paris Sciences et Lettres Research University, CNRS, UMR3244, 75248 Paris Cedex 05, France; bSorbonne Universités, Université Pierre et Marie Curie Paris 06, CNRS, UMR3244, 75248 Paris Cedex 05, France; cWellcome/Cancer Research UK Gurdon Institute and Department of Biochemistry, Cambridge CB2 1QN, United Kingdom; and dICGex NGS Platform, Institut Curie, 75248 Paris Cedex 05, France Edited by Richard D. Kolodner, Ludwig Institute for Cancer Research, La Jolla, CA, and approved August 24, 2020 (received for review June 2, 2020) The acquisition of mutations plays critical roles in adaptation, evolution, known (5, 15, 16). Here, we conducted a reciprocal functional ap- senescence, and tumorigenesis. Massive genome sequencing has proach to inactivate one or several genes involved in distinct ge- allowed extraction of specific features of many mutational landscapes nome maintenance processes (replication, repair, recombination, but it remains difficult to retrospectively determine the mechanistic oxidative stress response, or cell-cycle progression) in Saccharomy- origin(s), selective forces, and trajectories of transient or persistent ces cerevisiae diploids, establish the genome-wide mutational land- mutations and genome rearrangements. Here, we conducted a pro- scapes of mutation accumulation (MA) lines, explore the underlying spective reciprocal approach to inactivate 13 single or multiple evolu- mechanisms, and characterize the dynamics of mutation accumu- tionary conserved genes involved in distinct genome maintenance lation (and disappearance) along single-cell bottleneck passages. processes and characterize de novo mutations in 274 diploid Saccharo- myces cerevisiae mutation accumulation lines. -
Mediator of DNA Damage Checkpoint 1 (MDC1) Is a Novel Estrogen Receptor Co-Regulator in Invasive 6 Lobular Carcinoma of the Breast 7 8 Evelyn K
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423142; this version posted December 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 1 Running Title: MDC1 co-regulates ER in ILC 2 3 Research article 4 5 Mediator of DNA damage checkpoint 1 (MDC1) is a novel estrogen receptor co-regulator in invasive 6 lobular carcinoma of the breast 7 8 Evelyn K. Bordeaux1+, Joseph L. Sottnik1+, Sanjana Mehrotra1, Sarah E. Ferrara2, Andrew E. Goodspeed2,3, James 9 C. Costello2,3, Matthew J. Sikora1 10 11 +EKB and JLS contributed equally to this project. 12 13 Affiliations 14 1Dept. of Pathology, University of Colorado Anschutz Medical Campus 15 2Biostatistics and Bioinformatics Shared Resource, University of Colorado Comprehensive Cancer Center 16 3Dept. of Pharmacology, University of Colorado Anschutz Medical Campus 17 18 Corresponding author 19 Matthew J. Sikora, PhD.; Mail Stop 8104, Research Complex 1 South, Room 5117, 12801 E. 17th Ave.; Aurora, 20 CO 80045. Tel: (303)724-4301; Fax: (303)724-3712; email: [email protected]. Twitter: 21 @mjsikora 22 23 Authors' contributions 24 MJS conceived of the project. MJS, EKB, and JLS designed and performed experiments. JLS developed models 25 for the project. EKB, JLS, SM, and AEG contributed to data analysis and interpretation. SEF, AEG, and JCC 26 developed and performed informatics analyses. MJS wrote the draft manuscript; all authors read and revised the 27 manuscript and have read and approved of this version of the manuscript. -
Functional Parsing of Driver Mutations in the Colorectal Cancer Genome Reveals Numerous Suppressors of Anchorage-Independent
Supplementary information Functional parsing of driver mutations in the colorectal cancer genome reveals numerous suppressors of anchorage-independent growth Ugur Eskiocak1, Sang Bum Kim1, Peter Ly1, Andres I. Roig1, Sebastian Biglione1, Kakajan Komurov2, Crystal Cornelius1, Woodring E. Wright1, Michael A. White1, and Jerry W. Shay1. 1Department of Cell Biology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-9039. 2Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77054. Supplementary Figure S1. K-rasV12 expressing cells are resistant to p53 induced apoptosis. Whole-cell extracts from immortalized K-rasV12 or p53 down regulated HCECs were immunoblotted with p53 and its down-stream effectors after 10 Gy gamma-radiation. ! Supplementary Figure S2. Quantitative validation of selected shRNAs for their ability to enhance soft-agar growth of immortalized shTP53 expressing HCECs. Each bar represents 8 data points (quadruplicates from two separate experiments). Arrows denote shRNAs that failed to enhance anchorage-independent growth in a statistically significant manner. Enhancement for all other shRNAs were significant (two tailed Studentʼs t-test, compared to none, mean ± s.e.m., P<0.05)." ! Supplementary Figure S3. Ability of shRNAs to knockdown expression was demonstrated by A, immunoblotting for K-ras or B-E, Quantitative RT-PCR for ERICH1, PTPRU, SLC22A15 and SLC44A4 48 hours after transfection into 293FT cells. Two out of 23 tested shRNAs did not provide any knockdown. " ! Supplementary Figure S4. shRNAs against A, PTEN and B, NF1 do not enhance soft agar growth in HCECs without oncogenic manipulations (Student!s t-test, compared to none, mean ± s.e.m., ns= non-significant). -
A Curated Benchmark of Enhancer-Gene Interactions for Evaluating Enhancer-Target Gene Prediction Methods
University of Massachusetts Medical School eScholarship@UMMS Open Access Articles Open Access Publications by UMMS Authors 2020-01-22 A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods Jill E. Moore University of Massachusetts Medical School Et al. Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/oapubs Part of the Bioinformatics Commons, Computational Biology Commons, Genetic Phenomena Commons, and the Genomics Commons Repository Citation Moore JE, Pratt HE, Purcaro MJ, Weng Z. (2020). A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods. Open Access Articles. https://doi.org/10.1186/ s13059-019-1924-8. Retrieved from https://escholarship.umassmed.edu/oapubs/4118 Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License. This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Open Access Articles by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. Moore et al. Genome Biology (2020) 21:17 https://doi.org/10.1186/s13059-019-1924-8 RESEARCH Open Access A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods Jill E. Moore, Henry E. Pratt, Michael J. Purcaro and Zhiping Weng* Abstract Background: Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. Results: To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. -
Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights Into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle
animals Article Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle Masoumeh Naserkheil 1 , Abolfazl Bahrami 1 , Deukhwan Lee 2,* and Hossein Mehrban 3 1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; [email protected] (M.N.); [email protected] (A.B.) 2 Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do 17579, Korea 3 Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran; [email protected] * Correspondence: [email protected]; Tel.: +82-31-670-5091 Received: 25 August 2020; Accepted: 6 October 2020; Published: 9 October 2020 Simple Summary: Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. -
Cell Adhesion and Specificity
RESEARCH ARTICLE Molecular basis of sidekick-mediated cell- cell adhesion and specificity Kerry M Goodman1†, Masahito Yamagata2,3†, Xiangshu Jin1,4‡, Seetha Mannepalli1, Phinikoula S Katsamba4,5, Go¨ ran Ahlse´ n4,5, Alina P Sergeeva4,5, Barry Honig1,4,5,6,7*, Joshua R Sanes2,3*, Lawrence Shapiro1,5,7* 1Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States; 2Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States; 3Center for Brain Science, Harvard University, Cambridge, United States; 4Howard Hughes Medical Institute, Columbia University, New York, United States; 5Department of Systems Biology, Columbia University, New York, United States; 6Department of Medicine, Columbia University, New York, United States; 7Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States *For correspondence: bh6@cumc. Abstract Sidekick (Sdk) 1 and 2 are related immunoglobulin superfamily cell adhesion proteins columbia.edu (BH); sanesj@mcb. required for appropriate synaptic connections between specific subtypes of retinal neurons. Sdks harvard.edu (JRS); shapiro@ mediate cell-cell adhesion with homophilic specificity that underlies their neuronal targeting convex.hhmi.columbia.edu (LS) function. Here we report crystal structures of Sdk1 and Sdk2 ectodomain regions, revealing similar †These authors contributed homodimers mediated by the four N-terminal immunoglobulin domains (Ig1–4), arranged in a equally to this work horseshoe conformation. These Ig1–4 horseshoes interact in a novel back-to-back orientation in both homodimers through Ig1:Ig2, Ig1:Ig1 and Ig3:Ig4 interactions. Structure-guided mutagenesis Present address: ‡Department results show that this canonical dimer is required for both Sdk-mediated cell aggregation (via trans of Chemistry, Michigan State interactions) and Sdk clustering in isolated cells (via cis interactions). -
Bioinformatics Analyses of Genomic Imprinting
Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________ -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
The Ribosomal Protein Rpl22 Controls Ribosome Composition by Directly Repressing Expression of Its Own Paralog, Rpl22l1
The Ribosomal Protein Rpl22 Controls Ribosome Composition by Directly Repressing Expression of Its Own Paralog, Rpl22l1 Monique N. O’Leary1,2, Katherine H. Schreiber1,2, Yong Zhang3, Anne-Ce´cile E. Duc3, Shuyun Rao3,J. Scott Hale4¤, Emmeline C. Academia2, Shreya R. Shah1, John F. Morton5, Carly A. Holstein6, Dan B. Martin6, Matt Kaeberlein7, Warren C. Ladiges5, Pamela J. Fink4, Vivian L. MacKay1, David L. Wiest3, Brian K. Kennedy1,2* 1 Department of Biochemistry, University of Washington, Seattle, Washington, United States of America, 2 Buck Institute for Research on Aging, Novato, California, United States of America, 3 Blood Cell Development and Cancer Keystone, Immune Cell Development and Host Defense Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America, 4 Department of Immunology, University of Washington, Seattle, Washington, United States of America, 5 Department of Comparative Medicine, University of Washington, Seattle, Washington, United States of America, 6 Institute for Systems Biology, Seattle, Washington, United States of America, 7 Department of Pathology, University of Washington, Seattle, Washington, United States of America Abstract Most yeast ribosomal protein genes are duplicated and their characterization has led to hypotheses regarding the existence of specialized ribosomes with different subunit composition or specifically-tailored functions. In yeast, ribosomal protein genes are generally duplicated and evidence has emerged that paralogs might have specific roles. Unlike yeast, most mammalian ribosomal proteins are thought to be encoded by a single gene copy, raising the possibility that heterogenous populations of ribosomes are unique to yeast. Here, we examine the roles of the mammalian Rpl22, finding that Rpl222/2 mice have only subtle phenotypes with no significant translation defects. -
Assessment of Hematopoietic Failure Due to Rpl11 Deficiency in a Zebrafish Model of Diamond-Blackfan Anemia by Deep Sequencing
Zhang et al. BMC Genomics 2013, 14:896 http://www.biomedcentral.com/1471-2164/14/896 RESEARCH ARTICLE Open Access Assessment of hematopoietic failure due to Rpl11 deficiency in a zebrafish model of Diamond- Blackfan anemia by deep sequencing Zhaojun Zhang1†, Haibo Jia2†, Qian Zhang1, Yang Wan3, Yang Zhou2, Qiong Jia2, Wanguang Zhang4, Weiping Yuan3, Tao Cheng3, Xiaofan Zhu3* and Xiangdong Fang1* Abstract Background: Diamond–Blackfan anemia is a rare congenital red blood cell dysplasia that develops soon after birth. RPL11 mutations account for approximately 4.8% of human DBA cases with defective hematopoietic phenotypes. However, the mechanisms by which RPL11 regulates hematopoiesis in DBA remain elusive. In this study, we analyzed the transcriptome using deep sequencing data from an Rpl11-deficient zebrafish model to identify Rpl11-mediated hematopoietic failure and investigate the underlying mechanisms. Results: We characterized hematological defects in Rpl11-deficient zebrafish embryos by identifying affected hematological genes, hematopoiesis-associated pathways, and regulatory networks. We found that hemoglobin biosynthetic and hematological defects in Rpl11-deficient zebrafish were related to dysregulation of iron metabolism-related genes, including tfa, tfr1b, alas2 and slc25a37, which are involved in heme and hemoglobin biosynthesis. In addition, we found reduced expression of the hematopoietic stem cells (HSC) marker cmyb and HSC transcription factors tal1 and hoxb4a in Rpl11-deficient zebrafish embryos, indicating that the hematopoietic defects may be related to impaired HSC formation, differentiation, and proliferation. However, Rpl11 deficiency did not affect the development of other blood cell lineages such as granulocytes and myelocytes. Conclusion: We identified hematopoietic failure of Rpl11-deficient zebrafish embryos using transcriptome deep sequencing and elucidated potential underlying mechanisms.