Structure Characterization of a Peroxidase from The
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Synergistic Cytotoxicity of Renieramycin M and Doxorubicin in MCF-7 Breast Cancer Cells
marine drugs Article Synergistic Cytotoxicity of Renieramycin M and Doxorubicin in MCF-7 Breast Cancer Cells Jortan O. Tun 1,*, Lilibeth A. Salvador-Reyes 1,2 , Michael C. Velarde 3 , Naoki Saito 4 , Khanit Suwanborirux 5 and Gisela P. Concepcion 1,2,* 1 The Marine Science Institute, University of the Philippines Diliman, Quezon City 1101, Philippines; [email protected] 2 Philippine Genome Center, University of the Philippines Diliman, Quezon City 1101, Philippines 3 Institute of Biology, University of the Philippines Diliman, Quezon City 1101, Philippines; [email protected] 4 Graduate School of Pharmaceutical Sciences, Meiji Pharmaceutical University, Tokyo 204-8588, Japan; [email protected] 5 Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Center for Bioactive Natural Products from Marine Organisms and Endophytic Fungi (BNPME), Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand; [email protected] * Correspondence: [email protected] (J.O.T.); [email protected] (G.P.C.); Tel.: +632-922-3959 (J.O.T. & G.P.C.) Received: 23 July 2019; Accepted: 7 August 2019; Published: 16 September 2019 Abstract: Renieramycin M (RM) is a KCN-stabilized tetrahydroisoquinoline purified from the blue sponge Xestospongia sp., with nanomolar IC50s against several cancer cell lines. Our goal is to evaluate its combination effects with doxorubicin (DOX) in estrogen receptor positive MCF-7 breast cancer cells. MCF-7 cells were treated simultaneously or sequentially with various combination ratios of RM and DOX for 72 h. Cell viability was determined using the MTT assay. Synergism or antagonism was determined using curve-shift analysis, combination index method and isobologram analysis. -
CURRICULUM VITAE George M. Weinstock, Ph.D
CURRICULUM VITAE George M. Weinstock, Ph.D. DATE September 26, 2014 BIRTHDATE February 6, 1949 CITIZENSHIP USA ADDRESS The Jackson Laboratory for Genomic Medicine 10 Discovery Drive Farmington, CT 06032 [email protected] phone: 860-837-2420 PRESENT POSITION Associate Director for Microbial Genomics Professor Jackson Laboratory for Genomic Medicine UNDERGRADUATE 1966-1967 Washington University EDUCATION 1967-1970 University of Michigan 1970 B.S. (with distinction) Biophysics, Univ. Mich. GRADUATE 1970-1977 PHS Predoctoral Trainee, Dept. Biology, EDUCATION Mass. Institute of Technology, Cambridge, MA 1977 Ph.D., Advisor: David Botstein Thesis title: Genetic and physical studies of bacteriophage P22 genomes containing translocatable drug resistance elements. POSTDOCTORAL 1977-1980 Postdoctoral Fellow, Department of Biochemistry TRAINING Stanford University Medical School, Stanford, CA. Advisor: Dr. I. Robert Lehman. ACADEMIC POSITIONS/EMPLOYMENT/EXPERIENCE 1980-1981 Staff Scientist, Molec. Gen. Section, NCI-Frederick Cancer Research Facility, Frederick, MD 1981-1983 Staff Scientist, Laboratory of Genetics and Recombinant DNA, NCI-Frederick Cancer Research Facility, Frederick, MD 1981-1984 Adjunct Associate Professor, Department of Biological Sciences, University of Maryland, Baltimore County, Catonsville, MD 1983-1984 Senior Scientist and Head, DNA Metabolism Section, Lab. Genetics and Recombinant DNA, NCI-Frederick Cancer Research Facility, Frederick, MD 1984-1990 Associate Professor with tenure (1985) Department of Biochemistry -
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We gratefully acknowledge the following Authors from the Originating laboratories responsible for obtaining the specimens, as well as the Submitting laboratories where the genome data were generated and shared via GISAID, on which this research is based. All Submitters of data may be contacted directly via www.gisaid.org Authors are sorted alphabetically. Accession ID Originating Laboratory Submitting Laboratory Authors EPI_ISL_1562503 Aegis Sciences Corporation Centers for Disease Control and Prevention Division of Viral Dakota Howard, Dhwani Batra, Peter W. Cook, Kara Moser, Adrian Paskey, Jason Caravas, Benjamin Rambo-Martin, Shatavia Morrison, Christopher Diseases, Pathogen Discovery Gulvick, Scott Sammons, Yvette Unoarumhi, Darlene Wagner, Matthew Schmerer, Cyndi Clark, Patrick Campbell, Rob Case, Vikramsinha Ghorpade, Holly Houdeshell, Ola Kvalvaag, Dillon Nall, Ethan Sanders, Alec Vest, Shaun Westlund, Matthew Hardison, Clinton R. Paden, Duncan MacCannell EPI_ISL_1648208 Quest Diagnostics Incorporated Centers for Disease Control and Prevention Division of Viral Dakota Howard, Dhwani Batra, Peter W. Cook, Kara Moser, Adrian Paskey, Jason Caravas, Benjamin Rambo-Martin, Shatavia Morrison, Christopher Diseases, Pathogen Discovery Gulvick, Scott Sammons, Yvette Unoarumhi, Darlene Wagner, Matthew Schmerer, S. H. Rosenthal, A. Gerasimova, R. M. Kagan, B. Anderson, M. Hua, Y. Liu, L.E. Bernstein, K.E. Livingston, A. Perez, I. A. Shlyakhter, R. V. Rolando, R. Owen, P. Tanpaiboon, F. Lacbawan, Clinton R. Paden, Duncan MacCannell EPI_ISL_1660458 -
28 June 2019 25 – 28 Fjuneaculty of 2019 Scienc E Kasetsart University, Bangkok Faculty of Science Kasetsart University, Bangkok
25 - 28 June 2019 25 – 28 FJuneaculty of 2019 Scienc e Kasetsart University, Bangkok Faculty of Science Kasetsart University, Bangkok This page is intentionally left blank. T A B L E O F C ONTENT Workshop schedule 2 Opening and Welcome speeches 6 Sponsors’ Info 11 Organizing Committee 15 Presenters’ Biographies 17 Participants’ Info 27 Invited Talks and Talks 31 Lecture Summary 44 Poster Abstracts 50 1 W O R K S H O P S CHEDULE 08:00-08:40 Registration 08:40-08:45 Celebration of Coronation of King Rama X 08:45-09:05 Welcome and opening ceremony Group Photo 09:05-09:15 Overview of the Empowering agricultural research through (meta)genomics workshop Alexie Papanicolaou 09:15-10:15 Plenary Talk: “Genomes, Genes, Allele and Mechanisms.” Roger Hellens 10:15:10:30 Tea break and networking (Room 352) 10:30-10:50 Invited Talk: High Resolution Profiling of Bacterial Communities using Full-Length 16S rRNA Sequence Data from PacBio SMRT Sequencing System Wirulda Pootakarm 10:50-11:10 Invited Talk: From Omics to System Biology: Impact on Industrial Biotechnology and Human Health Wanwipa Vongsangnak 11:10-11:50 Invited talk: Functional Genomics of Tropical Plants Goh Hoe Han 11:50-13:00 Lunch and Product presentation by Sponsors (Room 352) 12:00-12.20: Advancing metagenomics research with Illumina solutions - Cara Lim 12:20-12:40: QIAGEN’s Innovative Solutions for Advancing Microbiome Research from Challenging Samples to Insight with Confidence - Wasin Sakulkoo 2 12:40-12:50: Sequencing solution on DNBSEQ platform - Honghong Liu 13:00:13:40 Invited -
Description of a New Species of the Genus Raphitoma Bellardi, 1847 from the Mediterranean Sea (Mollusca Neogastropoda Conoidea Raphitomidae)
Biodiversity Journal, 2017, 8 (1): 205–210 MONOGRAPH Description of a new species of the genus Raphitoma Bellardi, 1847 from the Mediterranean Sea (Mollusca Neogastropoda Conoidea Raphitomidae) Francesco Pusateri1, Riccardo Giannuzzi Savelli2* & Peter Stahlschmidt3 1via Castellana 64, 90135 Palermo, Italy; e-mail: [email protected] 2via Mater Dolorosa 54, 90146 Palermo, Italy; e-mail: [email protected] 3University of Koblenz-Landau, Institute for Environmental Sciences, Fortstraße 7 - 76829 Landau, Germany; e-mail: [email protected] *Corresponding author ABSTRACT The family of Raphitomidae is currently considered a well supported clade of the Conoidea. The type genus Raphitoma Bellardi, 1847 is well known in the mediterranen Seas with about 40 species, some of which are still undescribed. Morphological analyses carried out on the genus Raphitoma Bellardi, 1847 (Mollusca Neogastropoda Conoidea Raphitomidae) from Mediterranean Sea allowed to identify a new species which is described in the present paper. KEY WORDS Raphitoma; Conoidea; new species; Mediterranean Sea. Received 12.01.2016; accepted 28.02.2017; printed 30.03.2017 Proceedings of the 3rd International Congress “Biodiversity, Mediterranean, Society”, September 4th-6th 2015, Noto- Vendicari (Italy) INTRODUCTION as “turrids”, and Turridae s.s. including some of the traditional “turrids”. More recently, Puillandre et al. The Raphitomidae Bellardi, 1875 are currently (2008) and Bouchet et al. (2011), based on DNA considered a well supported clade of the Conoidea phylogeny, have provided a major update of con- (Bouchet et al., 2011). oidean classification. Although a larger taxonomic The superfamily Conoidea, with over 300 gen- coverage would be desirable to further stabilize the era and 4,000 recognised species, but probably over molecular phylogeny, however, the position of the 12,000 extant species (Bouchet, 1990; Tucker, Raphitomidae as a clade of the Conoidea is suffi- 2004), represents the largest radiation of the entire ciently supported. -
Choudhury 22.11.2018 Suppl
The Set1 complex is dimeric and acts with Jhd2 demethylation to convey symmetrical H3K4 trimethylation Rupam Choudhury, Sukdeep Singh, Senthil Arumugam, Assen Roguev and A. Francis Stewart Supplementary Material 9 Supplementary Figures 1 Supplementary Table – an Excel file not included in this document Extended Materials and Methods Supplementary Figure 1. Sdc1 mediated dimerization of yeast Set1 complex. (A) Multiple sequence alignment of the dimerizaton region of the protein kinase A regulatory subunit II with Dpy30 homologues and other proteins (from Roguev et al, 2001). Arrows indicate the three amino acids that were changed to alanine in sdc1*. (B) Expression levels of TAP-Set1 protein in wild type and sdc1 mutant strains evaluated by Westerns using whole cell extracts and beta-actin (B-actin) as loading control. (C) Spp1 associates with the monomeric Set1 complex. Spp1 was tagged with a Myc epitope in wild type and sdc1* strains that carried TAP-Set1. TAP-Set1 was immunoprecipitated from whole cell extracts and evaluated by Western with an anti-myc antibody. Input, whole cell extract from the TAP-set1; spp1-myc strain. W303, whole cell extract from the W303 parental strain. a Focal Volume Fluorescence signal PCH Molecular brightness comparison Intensity 25 20 Time 15 10 Frequency Brightness (au) 5 0 Intensity Intensity (counts /ms) GFP1 GFP-GFP2 Time c b 1x yEGFP 2x yEGFP 3x yEGFP 1 X yEGFP ADH1 yEGFP CYC1 2 X yEGFP ADH1 yEGFP yEGFP CYC1 3 X yEGFP ADH1 yEGFP yEGFP yEGFP CYC1 d 121-165 sdc1 wt sdc1 sdc1 Swd1-yEGFP B-Actin Supplementary Figure 2. Molecular analysis of Sdc1 mediated dimerization of Set1C by Fluorescence Correlation Spectroscopy (FCS). -
RNA-‐Seq: Revelation of the Messengers
Supplementary Material Hands-On Tutorial RNA-Seq: revelation of the messengers Marcel C. Van Verk†,1, Richard Hickman†,1, Corné M.J. Pieterse1,2, Saskia C.M. Van Wees1 † Equal contributors Corresponding author: Van Wees, S.C.M. ([email protected]). Bioinformatics questions: Van Verk, M.C. ([email protected]) or Hickman, R. ([email protected]). 1 Plant-Microbe Interactions, Department of Biology, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands 2 Centre for BioSystems Genomics, PO Box 98, 6700 AB Wageningen, The Netherlands INDEX 1. RNA-Seq tools websites……………………………………………………………………………………………………………………………. 2 2. Installation notes……………………………………………………………………………………………………………………………………… 3 2.1. CASAVA 1.8.2………………………………………………………………………………………………………………………………….. 3 2.2. Samtools…………………………………………………………………………………………………………………………………………. 3 2.3. MiSO……………………………………………………………………………………………………………………………………………….. 4 3. Quality Control: FastQC……………………………………………………………………………………………………………………………. 5 4. Creating indeXes………………………………………………………………………………………………………………………………………. 5 4.1. Genome IndeX for Bowtie / TopHat alignment……………………………………………………………………………….. 5 4.2. Transcriptome IndeX for Bowtie alignment……………………………………………………………………………………… 6 4.3. Genome IndeX for BWA alignment………………………………………………………………………………………………….. 7 4.4. Transcriptome IndeX for BWA alignment………………………………………………………………………………………….7 5. Read Alignment………………………………………………………………………………………………………………………………………… 8 5.1. Preliminaries…………………………………………………………………………………………………………………………………… 8 5.2. Genome read alignment with Bowtie……………………………………………………………………………………………… -
Supplementary Materials
Supplementary Materials Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR/Cas9 screening 1,† 1,† 2,3 1,3 Gabriele Picco , Elisabeth D Chen , Luz Garcia Alonso , Fiona M Behan , Emanuel 1 1 1 1 1 Gonçalves , Graham Bignell , Angela Matchan , Beiyuan Fu , Ruby Banerjee , Elizabeth 1 1 4 1,7 5,3 Anderson , Adam Butler , Cyril H Benes , Ultan McDermott , David Dow , Francesco 2,3 5,3 1 1 6,2,3 Iorio E uan Stronach , Fengtang Yang , Kosuke Yusa , Julio Saez-Rodriguez , Mathew 1,3,* J Garnett 1 Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK. 2 European Molecular Biology Laboratory – European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK. 3 Open Targets, Wellcome Genome Campus, Cambridge CB10 1SA, UK. 4 Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA. 5 GSK, Target Sciences, Gunnels Wood Road, Stevenage, SG1 2NY, UK and Collegeville, 25 Pennsylvania, 19426-0989, USA. 6 Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, Heidelberg 69120, Germany. 7 AstraZeneca, CRUK Cambridge Institute, Cambridge CB2 0RE * Correspondence to: [email protected] † Equally contributing authors Supplementary Tables Supplementary Table 1: Annotation of 1,034 human cancer cell lines used in our study and the source of RNA-seq data. All cell lines are part of the GDSC cancer cell line project (see COSMIC IDs). Supplementary Table 2: List and annotation of 10,514 fusion transcripts found in 1,011 cell lines. Supplementary Table 3: Significant results from differential gene expression for recurrent fusions. Supplementary Table 4: Annotation of gene fusions for aberrant expression of the 3 prime end gene. -
Creating a Roadmap for Building a Sustainable Genomics Facility in the Philippines
Philippine Journal of Science 148 (S1): 15-32, Special Issue on Genomics ISSN 0031 - 7683 Date Received: 19 Mar 2019 Creating a Roadmap for Building a Sustainable Genomics Facility in the Philippines Alexander T. Young* Philippine Genome Center University of the Philippines Diliman, Quezon City 1101 Philippines Genomics, bioinformatics, and high-throughput technologies provide a means to create breakthrough solutions for a remarkably wide diversity of fields – including medicine, agriculture, fisheries, livestock, and biodiversity. Significantly, the precision of genomics and DNA enable the creation of applications that are specifically optimized for the Philippines. Dissemination of these technologies throughout the country is accelerated by genomics facilities that provide access to core technologies. However, there are significant scientific and business challenges for creating a sustainable genomics facility in the Philippines. The historical successes and challenges of genomics and high-throughput technologies are reviewed, which point to potential solutions for creating a sustainable genomics facility in the Philippines. Keywords: bioinformatics, drug discovery, functional genomics, genomics, high-throughput DNA sequencing INTRODUCTION systems in order to find new applications that benefit society. In conjunction with the technological advances in biology New and exciting advances in genomics and high-throughput and engineering, advances in computational sciences have technologies are revolutionizing the application of biological enabled the daunting task of interpreting the massive amounts sciences. Advances in engineering, microfluidics, robotics, of data that these technologies generate (bioinformatics). and miniaturization have created machines that can rapidly The ability to profile thousands to millions of biological determine the DNA sequence of an organism’s genes or molecules in a massively parallel fashion promises to create the entire genome. -
Software List for Biology, Bioinformatics and Biostatistics CCT
Software List for biology, bioinformatics and biostatistics v CCT - Delta Software Version Application short read assembler and it works on both small and large (mammalian size) ALLPATHS-LG 52488 genomes provides a fast, flexible C++ API & toolkit for reading, writing, and manipulating BAMtools 2.4.0 BAM files a high level of alignment fidelity and is comparable to other mainstream Barracuda 0.7.107b alignment programs allows one to intersect, merge, count, complement, and shuffle genomic bedtools 2.25.0 intervals from multiple files Bfast 0.7.0a universal DNA sequence aligner tool analysis and comprehension of high-throughput genomic data using the R Bioconductor 3.2 statistical programming BioPython 1.66 tools for biological computation written in Python a fast approach to detecting gene-gene interactions in genome-wide case- Boost 1.54.0 control studies short read aligner geared toward quickly aligning large sets of short DNA Bowtie 1.1.2 sequences to large genomes Bowtie2 2.2.6 Bowtie + fully supports gapped alignment with affine gap penalties BWA 0.7.12 mapping low-divergent sequences against a large reference genome ClustalW 2.1 multiple sequence alignment program to align DNA and protein sequences assembles transcripts, estimates their abundances for differential expression Cufflinks 2.2.1 and regulation in RNA-Seq samples EBSEQ (R) 1.10.0 identifying genes and isoforms differentially expressed EMBOSS 6.5.7 a comprehensive set of sequence analysis programs FASTA 36.3.8b a DNA and protein sequence alignment software package FastQC -
Rnaseq2017 Course Salerno, September 27-29, 2017
RNASeq2017 Course Salerno, September 27-29, 2017 RNA-seq Hands on Exercise Fabrizio Ferrè, University of Bologna Alma Mater ([email protected]) Hands-on tutorial based on the EBI teaching materials from Myrto Kostadima and Remco Loos at https://www.ebi.ac.uk/training/online/ Index 1. Introduction.......................................2 1.1 RNA-Seq analysis tools.........................2 2. Your data..........................................3 3. Quality control....................................6 4. Trimming...........................................8 5. The tuxedo pipeline...............................12 5.1 Genome indexing for bowtie....................12 5.2 Insert size and mean inner distance...........13 5.3 Read mapping using tophat.....................16 5.4 Transcriptome reconstruction and expression using cufflinks...................................19 5.5 Differential Expression using cuffdiff........22 5.6 Transcriptome reconstruction without genome annotation........................................24 6. De novo assembly with Velvet......................27 7. Variant calling...................................29 8. Duplicate removal.................................31 9. Using STAR for read mapping.......................32 9.1 Genome indexing for STAR......................32 9.2 Read mapping using STAR.......................32 9.3 Using cuffdiff on STAR mapped reads...........34 10. Concluding remarks..............................35 11. Useful references...............................36 1 1. Introduction The goal of this -
The Green Revolution Shaped the Population Structure of the Rice Pathogen Xanthomonas Oryzae Pv
The ISME Journal (2020) 14:492–505 https://doi.org/10.1038/s41396-019-0545-2 ARTICLE The Green Revolution shaped the population structure of the rice pathogen Xanthomonas oryzae pv. oryzae 1 1,2 1 3 1 Ian Lorenzo Quibod ● Genelou Atieza-Grande ● Eula Gems Oreiro ● Denice Palmos ● Marian Hanna Nguyen ● 1 1 1,4 1 1 Sapphire Thea Coronejo ● Ei Ei Aung ● Cipto Nugroho ● Veronica Roman-Reyna ● Maria Ruby Burgos ● 1 1,5 1 3 1 Pauline Capistrano ● Sylvestre G. Dossa ● Geoffrey Onaga ● Cynthia Saloma ● Casiana Vera Cruz ● Ricardo Oliva 1 Received: 13 June 2019 / Revised: 15 October 2019 / Accepted: 17 October 2019 / Published online: 30 October 2019 © The Author(s) 2019. This article is published with open access Abstract The impact of modern agriculture on the evolutionary trajectory of plant pathogens is a central question for crop sustainability. The Green Revolution replaced traditional rice landraces with high-yielding varieties, creating a uniform selection pressure that allows measuring the effect of such intervention. In this study, we analyzed a unique historical pathogen record to assess the impact of a major resistance gene, Xa4, in the population structure of Xanthomonas oryzae pv. 1234567890();,: 1234567890();,: oryzae (Xoo) collected in the Philippines in a span of 40 years. After the deployment of Xa4 in the early 1960s, the emergence of virulent pathogen groups was associated with the increasing adoption of rice varieties carrying Xa4, which reached 80% of the total planted area. Whole genomes analysis of a representative sample suggested six major pathogen groups with distinctive signatures of selection in genes related to secretion system, cell-wall degradation, lipopolysaccharide production, and detoxification of host defense components.