And Hypermethylated Genes Related to Atherosclerosis by a Genome-Wide Analysis of DNA Methylation
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Core Lab Brochure
CHOOSE THE MOST TRUSTED LONG-READ TECHNOLOGY FOR YOUR CORE Sequence with Confidence The Sequel® II and IIe Systems are powered by Single Molecule, Real-Time (SMRT®) Sequencing, a technology proven to produce highly accurate long reads, known as HiFi reads, for sequencing data you and your customers can trust. SMRT SEQUENCING IS SMART BUSINESS HiFi Reads: PacBio is the only sequencing technology to offer highly accurate long reads. Because HiFi reads are extremely accurate, downstream analysis is simplified and streamlined, requiring less compute time than the error-prone long reads of other technologies. High Throughput: The Sequel II and IIe Systems have high data yields on robust, highly automated platforms to increase productivity and reduce project costs. Efficient and Easy-To-Use Workflows: Our end-to-end solutions feature library preparation in <3 hours and many push- button analysis workflows, so you can run projects quickly and easily. Support: All of our products are backed by a global team of scientists, bioinformaticians, and engineers who stand ready to provide you with outstanding service. OUTSTANDING PERFORMANCE AND RELIABILITY 99% of runs on the Sequel II System completed successfully Sequel II Systems provide reliable performance with the total bases produced by the PacBio fleet steadily increasing, and 99% of runs completed successfully. “In our experience, the Sequel II System was essentially production-ready right out of the box. We have used it for a range of applications and sample types — from human genome sequencing to metagenome and microbiome profiling to non-model plant and animal genomes — and results have been very good.” — Luke Tallon, Director of the Genomics Resource Center at Maryland Genomics pacb.com/Sequel SMRT SEQUENCING APPLICATIONS – EFFICIENT AND COST EFFECTIVE The Sequel II and IIe Systems support a wide range of applications, each adding unique value to a sequencing study. -
Detection of Driver Mutations in FFPE Samples from Patients with Verified Malignant Melanoma
Neoplasma 2019; 66(1): 33–38 33 doi:10.4149/neo_2018_180115N31 Detection of driver mutations in FFPE samples from patients with verified malignant melanoma B. MALICHEROVA1,2,#,*, T. BURJANIVOVA1,2,#, E. MINARIKOVA3, I. KASUBOVA1,2, T. PECOVA3, M. BOBROVSKA4, I. HOMOLA5, Z. LASABOVA1,2,6, L. PLANK1,2,4 1Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Commenius University in Bratislava (JFM CU), Slovakia; 2Division of Oncology JFM CU, Martin, Slovakia; 3Clinic of Dermatovenerology, Jessenius Faculty of Medicine and University Hospital in Martin, Martin, Slovakia; 4Department of Pathological Anatomy, Jessenius Faculty of Medicine and University Hospital in Martin, Martin, Slovakia; 5Depart- ment of Plastic Surgery, Jessenius Faculty of Medicine and University Hospital in Martin, Martin, Slovakia; 6Department of Molecular Biology, JFM CU, Martin Slovakia *Correspondence: [email protected] #Contributed equally to this work. Received January 15, 2018 / Accepted April 24, 2018 Malignant melanoma is an oncological disease characterized by etiologic heterogeneity and it has increasing incidence and mortality in the Slovak Republic. While it is treated surgically in combination with chemotherapy, targeted therapy, and immunotherapy, malignant melanomas can ulcerate and are susceptible to infections. These are highly aggressive cancers with metastasis, and recent studies have shown the presence of mutations in RAC1, PPP6C and STK19 genes in melanoma patients. Mutations in these genes are driver mutations; important in oncogenesis and providing selective advantage to tumor cells. The aim of our study is to establish a method to detect driver mutations in formalin-fixed, paraffin embedded (FFPE) tissue DNA. We applied Sanger sequencing to detect driver somatic mutations in RAC1, PPP6C, STK19 and BRAF genes in patients with malignant melanoma. -
Next-Generation Sequencing
Tinhofer et al. Radiation Oncology (2015) 10:183 DOI 10.1186/s13014-015-0481-x REVIEW Open Access Next-generation sequencing: hype and hope for development of personalized radiation therapy? Ingeborg Tinhofer1,2*, Franziska Niehr1,2, Robert Konschak1,2, Sandra Liebs2, Matthias Munz3, Albrecht Stenzinger4, Wilko Weichert4,5, Ulrich Keilholz6 and Volker Budach1,2 Abstract The introduction of next-generation sequencing (NGS) in the field of cancer research has boosted worldwide efforts of genome-wide personalized oncology aiming at identifying predictive biomarkers and novel actionable targets. Despite considerable progress in understanding the molecular biology of distinct cancer entities by the use of this revolutionary technology and despite contemporaneous innovations in drug development, translation of NGS findings into improved concepts for cancer treatment remains a challenge. The aim of this article is to describe shortly the NGS platforms for DNA sequencing and in more detail key achievements and unresolved hurdles. A special focus will be given on potential clinical applications of this innovative technique in the field of radiation oncology. Introduction and Wong et al. [11]. We will instead focus on key achieve- Recent technological advances in DNA sequencing with ments in cancer genetics and potential clinical applications of greater speed and resolution at lower costs has provided this innovative technique in the field of radiation oncology. new insights in cancer genetics. The next-generation se- quencing (NGS) technology is tremendously facilitating the in-depth genome-wide search for genetic alterations The advantages of NGS which might significantly contribute to aggressive and/or Next-generation sequencing has rapidly been evolv- treatment-resistant phenotypes of cancers, thereby es- ing within the last decade [10]. -
Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers
Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers. by Kelly Kennaley A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Molecular and Cellular Pathology) in the University of Michigan 2019 Doctoral Committee: Associate Professor Zaneta Nikolovska-Coleska, Co-Chair Adjunct Associate Professor Scott A. Tomlins, Co-Chair Associate Professor Eric R. Fearon Associate Professor Alexey I. Nesvizhskii Kelly R. Kennaley [email protected] ORCID iD: 0000-0003-2439-9020 © Kelly R. Kennaley 2019 Acknowledgements I have immeasurable gratitude for the unwavering support and guidance I received throughout my dissertation. First and foremost, I would like to thank my thesis advisor and mentor Dr. Scott Tomlins for entrusting me with a challenging, interesting, and impactful project. He taught me how to drive a project forward through set-backs, ask the important questions, and always consider the impact of my work. I’m truly appreciative for his commitment to ensuring that I would get the most from my graduate education. I am also grateful to the many members of the Tomlins lab that made it the supportive, collaborative, and educational environment that it was. I would like to give special thanks to those I’ve worked closely with on this project, particularly Dr. Moloy Goswami for his mentorship, Lei Lucy Wang, Dr. Sumin Han, and undergraduate students Bhavneet Singh, Travis Weiss, and Myles Barlow. I am also grateful for the support of my thesis committee, Dr. Eric Fearon, Dr. Alexey Nesvizhskii, and my co-mentor Dr. Zaneta Nikolovska-Coleska, who have offered guidance and critical evaluation since project inception. -
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. -
IL21R Expressing CD14+CD16+ Monocytes Expand in Multiple
Plasma Cell Disorders SUPPLEMENTARY APPENDIX IL21R expressing CD14 +CD16 + monocytes expand in multiple myeloma patients leading to increased osteoclasts Marina Bolzoni, 1 Domenica Ronchetti, 2,3 Paola Storti, 1,4 Gaetano Donofrio, 5 Valentina Marchica, 1,4 Federica Costa, 1 Luca Agnelli, 2,3 Denise Toscani, 1 Rosanna Vescovini, 1 Katia Todoerti, 6 Sabrina Bonomini, 7 Gabriella Sammarelli, 1,7 Andrea Vecchi, 8 Daniela Guasco, 1 Fabrizio Accardi, 1,7 Benedetta Dalla Palma, 1,7 Barbara Gamberi, 9 Carlo Ferrari, 8 Antonino Neri, 2,3 Franco Aversa 1,4,7 and Nicola Giuliani 1,4,7 1Myeloma Unit, Dept. of Medicine and Surgery, University of Parma; 2Dept. of Oncology and Hemato-Oncology, University of Milan; 3Hematology Unit, “Fondazione IRCCS Ca’ Granda”, Ospedale Maggiore Policlinico, Milan; 4CoreLab, University Hospital of Parma; 5Dept. of Medical-Veterinary Science, University of Parma; 6Laboratory of Pre-clinical and Translational Research, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture; 7Hematology and BMT Center, University Hospital of Parma; 8Infectious Disease Unit, University Hospital of Parma and 9“Dip. Oncologico e Tecnologie Avanzate”, IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy ©2017 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2016.153841 Received: August 5, 2016. Accepted: December 23, 2016. Pre-published: January 5, 2017. Correspondence: [email protected] SUPPLEMENTAL METHODS Immunophenotype of BM CD14+ in patients with monoclonal gammopathies. Briefly, 100 μl of total BM aspirate was incubated in the dark with anti-human HLA-DR-PE (clone L243; BD), anti-human CD14-PerCP-Cy 5.5, anti-human CD16-PE-Cy7 (clone B73.1; BD) and anti-human CD45-APC-H 7 (clone 2D1; BD) for 20 min. -
Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Anti-Histone H4 Acetyl (Lys8) Antibody (ARG54759)
Product datasheet [email protected] ARG54759 Package: 100 μl anti-Histone H4 acetyl (Lys8) antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes Histone H4 acetyl (Lys8) Tested Reactivity Hu Tested Application WB Host Rabbit Clonality Polyclonal Isotype IgG Target Name Histone H4 Antigen Species Human Immunogen Synthetic acetylated peptide around Lys8 of Human histone H4 (NP_003539.1) Conjugation Un-conjugated Alternate Names H4/p; Histone H4 Application Instructions Application table Application Dilution WB 1:1000 - 1:3000 Application Note * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Positive Control 293T Calculated Mw 11 kDa Properties Form Liquid Purification Affinity purification with immunogen. 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 Database links GeneID: 8370 Human Swiss-port # P62805 Human Gene Symbol HIST2H4A Gene Full Name histone cluster 2, H4a Background Histones are basic nuclear proteins that are responsible for the nucleosome structure of the chromosomal fiber in eukaryotes. This structure consists of approximately 146 bp of DNA wrapped around a nucleosome, an octamer composed of pairs of each of the four core histones (H2A, H2B, H3, and H4). -
Strategies and Opportunities for Small Molecule Drug Discovery to Target Neurodegenerative Diseases Andrea I
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.01.020206; this version posted April 2, 2020. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. Defining the Neural Kinome: Strategies and Opportunities for Small Molecule Drug Discovery to Target Neurodegenerative Diseases Andrea I. Krahn, Carrow Wells, David H. Drewry, Lenore K. Beitel, Thomas M. Durcan, Alison D. Axtman* ABSTRACT: Kinases are highly tractable drug targets that have reached unparalleled success in fields such as cancer but whose potential has not yet been realized in neuroscience. There are currently 55 approved small molecule kinase-targeting drugs, 48 of which have an anti-cancer indication. The intrinsic complexity linked to central nervous system (CNS) drug development and a lack of validated targets has hindered progress in developing kinase inhibitors for CNS disorders when compared to other therapeutic areas such as oncology. Identification and/or characterization of new kinases as potential drug targets for neurodegenerative diseases will create opportunities for development of CNS drugs in the future. The track record of kinase inhibitors in other disease indications supports the idea that with the best targets identified small molecule kinase modulators will become impactful therapeutics for neurodegenerative diseases. KEYWORDS: kinase, neurodegeneration, -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases
Journal of Clinical Medicine Review Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases Anna Wajda 1 , Larysa Sivitskaya 2,* and Agnieszka Paradowska-Gorycka 1 1 Department of Molecular Biology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland; [email protected] (A.W.); [email protected] (A.P.-G.) 2 Institute of Genetics and Cytology, National Academy of Sciences of Belarus, 220072 Minsk, Belarus * Correspondence: [email protected] Abstract: NGS technologies have transformed clinical diagnostics and broadly used from neonatal emergencies to adult conditions where the diagnosis cannot be made based on clinical symptoms. Autoimmune diseases reveal complicate molecular background and traditional methods could not fully capture them. Certainly, NGS technologies meet the needs of modern exploratory research, diagnostic and pharmacotherapy. Therefore, the main purpose of this review was to briefly present the application of NGS technology used in recent years in the understanding of autoimmune diseases paying particular attention to autoimmune connective tissue diseases. The main issues are presented in four parts: (a) panels, whole-genome and -exome sequencing (WGS and WES) in diagnostic, (b) Human leukocyte antigens (HLA) as a diagnostic tool, (c) RNAseq, (d) microRNA and (f) microbiome. Although all these areas of research are extensive, it seems that epigenetic impact on the development of systemic autoimmune diseases will set trends for future studies on this area. Citation: Wajda, A.; Sivitskaya, L.; Keywords: next-generation sequencing; autoimmune diseases; autoimmune connective tissue dis- Paradowska-Gorycka, A. Application eases; HLA; microRNA; microbiome of NGS Technology in Understanding the Pathology of Autoimmune Diseases. J. Clin.