DOCSLIB.ORG

Large-Scale Integration of Microarray Data: Investigating the Pathologies of Cancer and Infectious Diseases Noor Dawany Aydin Tozeren, Phd

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Large-Scale Integration of Microarray Data: Investigating the Pathologies of Cancer and Infectious Diseases Noor Dawany Aydin Tozeren, Phd Large-Scale Integration of Microarray Data: Investigating the Pathologies of Cancer and Infectious Diseases A Thesis Submitted to the Faculty of Drexel University by Noor Dawany in partial fulfillment of the requirements for the degree of Doctor of Philosophy June 2010 ii Dedications To my late grandmother, Dr. Siranoosh Raihani, for being a great inspiration… iii Acknowledgements First and foremost I would like to acknowledge my parents, Bassam and Nada, for their unconditional support throughout my educational life. I would not be here if it were not for their sacrifices and encouragement. I also want to thank my brother, Sahil, for telling me almost nine years ago to toughen up and continue; I have never looked back since then. I owe my sincerest gratitude to my advisor, Dr. Aydin Tozeren, for his patience, his support and for all the effort he has put into this accomplishment. He is responsible for where I am today and where I will be, for that I am forever grateful. I would also like to thank my colleagues at the Center for Integrated Bioinformatics: Mahdi Saramdy, Will Dampier, Yichuan Liu and Perry Evans, and my friends from the Biomedical Engineering Department for their help and for entertaining conversations that commemorate our lives as PhD students at Drexel University. Finally, a big thank you for the support of old friends, for always being there for me… iv Table of Contents List of Tables ................................................................................................................................ viii List of Figures .................................................................................................................................. x Abstract .......................................................................................................................................... xii Chapter 1: Introduction .................................................................................................................... 1 1.1 Motivation: ............................................................................................................................. 1 1.2 Transcription, Translation and Control of Gene Expression: ................................................ 1 1.3 Cancer Overview ................................................................................................................... 4 1.4 Viral Infections and Hijacking Cellular Functions ................................................................ 6 1.4.1 Human Immunodeficiency Virus .................................................................................... 7 1.4.2 Hepatitis C ...................................................................................................................... 9 1.4.3 Influenza A .................................................................................................................... 11 Chapter 2: Gene Expression Microarrays ...................................................................................... 15 2.1 Introduction .......................................................................................................................... 15 2.2 Microarray Normalization: .................................................................................................. 17 2.2.1 Robust Multichip Average Algorithm: ......................................................................... 18 2.2.2 Reference Robust Multichip Average ........................................................................... 19 2.2.3 Custom Chip Definition Files ....................................................................................... 20 2.3 Microarray Analysis and Differential Gene Expression ...................................................... 21 2.3.1 Significance Analysis of Microarrays ........................................................................... 22 2.3.2 Meta-Analysis ............................................................................................................... 23 2.4 Databases ............................................................................................................................. 25 2.4.1 Microarray Databases: .................................................................................................. 25 2.4.2 Functional Annotation Databases: ................................................................................ 26 v Chapter 3: Asymmetric integration of microarray data outperforms meta-analysis approach ...... 28 3.1 Summary .............................................................................................................................. 28 3.2 Background .......................................................................................................................... 29 3.3 Materials and Methods ......................................................................................................... 30 3.3.1 Microarray dataset selection ......................................................................................... 30 3.3.2 Normalization and differential expression .................................................................... 31 3.3.3 Common transcriptional profiles across all five tissue types ........................................ 33 3.3.4 Expanding IV analysis to cDNA data ........................................................................... 33 3.4 Results .................................................................................................................................. 35 3.4.1 Datasets and approaches ............................................................................................... 35 3.4.2 IV meta-analysis and merged SAM overlap significantly in results ............................. 37 3.4.3 Cell cycle pathway is commonly enriched in cancers .................................................. 38 3.4.4 Microarray results match cancer research literature with low p-values ........................ 42 3.5 Discussion ............................................................................................................................ 44 3.6 Conclusion ........................................................................................................................... 47 Chapter 4: Large-scale integration of microarray data reveals genes and pathways common to multiple cancer types ..................................................................................................................... 48 4.1 Summary .............................................................................................................................. 48 4.2 Background .......................................................................................................................... 49 4.3 Materials and Methods ......................................................................................................... 50 4.3.1 Microarray dataset selection and normalization ........................................................... 50 4.3.2 Differential gene expression ......................................................................................... 51 4.3.3 Functional annotation of top ranked and conserved genes ........................................... 52 4.3.4 Consistent differential expression across tissues .......................................................... 52 4.3.5 Cancer literature annotation of identified significant SAM genes ................................ 52 vi 4.4 Results .................................................................................................................................. 53 4.4.1 Dataset........................................................................................................................... 53 4.4.2 SAM genes and their match with research literature .................................................... 56 4.4.3 Cellular pathways enriched for top 400 SAM genes .................................................... 56 4.4.4 SAM genes in multiple gene lists ................................................................................. 59 4.5 Discussion ............................................................................................................................ 65 4.6 Conclusion ........................................................................................................................... 67 Chapter 5: Virus and host iron binding protein interactions .......................................................... 68 5.1 Summary .............................................................................................................................. 68 5.2 Background .......................................................................................................................... 69 5.3 Methods ............................................................................................................................... 71 5.3.1 Identification of iron-associated proteins ...................................................................... 71 5.3.2 Identifying direct HIV-1 iron binding protein targets ................................................... 72 5.3.3 Microarray dataset selection on viral infections ........................................................... 72 5.3.4 Microarray data normalization and differential gene expression .................................. 73 5.3.5 Distribution of gene expression levels of iron binding proteins ..................................
Load more

Recommended publications
  • Entrez Symbols Name Termid Termdesc 117553 Uba3,Ube1c
    Entrez Symbols Name TermID TermDesc 117553 Uba3,Ube1c ubiquitin-like modifier activating enzyme 3 GO:0016881 acid-amino acid ligase activity 299002 G2e3,RGD1310263 G2/M-phase specific E3 ubiquitin ligase GO:0016881 acid-amino acid ligase activity 303614 RGD1310067,Smurf2 SMAD specific E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 308669 Herc2 hect domain and RLD 2 GO:0016881 acid-amino acid ligase activity 309331 Uhrf2 ubiquitin-like with PHD and ring finger domains 2 GO:0016881 acid-amino acid ligase activity 316395 Hecw2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 361866 Hace1 HECT domain and ankyrin repeat containing, E3 ubiquitin protein ligase 1 GO:0016881 acid-amino acid ligase activity 117029 Ccr5,Ckr5,Cmkbr5 chemokine (C-C motif) receptor 5 GO:0003779 actin binding 117538 Waspip,Wip,Wipf1 WAS/WASL interacting protein family, member 1 GO:0003779 actin binding 117557 TM30nm,Tpm3,Tpm5 tropomyosin 3, gamma GO:0003779 actin binding 24779 MGC93554,Slc4a1 solute carrier family 4 (anion exchanger), member 1 GO:0003779 actin binding 24851 Alpha-tm,Tma2,Tmsa,Tpm1 tropomyosin 1, alpha GO:0003779 actin binding 25132 Myo5b,Myr6 myosin Vb GO:0003779 actin binding 25152 Map1a,Mtap1a microtubule-associated protein 1A GO:0003779 actin binding 25230 Add3 adducin 3 (gamma) GO:0003779 actin binding 25386 AQP-2,Aqp2,MGC156502,aquaporin-2aquaporin 2 (collecting duct) GO:0003779 actin binding 25484 MYR5,Myo1e,Myr3 myosin IE GO:0003779 actin binding 25576 14-3-3e1,MGC93547,Ywhah
    [Show full text]
  • Environmental Influences on Endothelial Gene Expression
    ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community.
    [Show full text]
  • Meta-Analyses of Expression Profiling Data in the Postmortem
    META-ANALYSES OF EXPRESSION PROFILING DATA IN THE POSTMORTEM HUMAN BRAIN by Meeta Mistry B.Sc., McMaster University, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2012 © Meeta Mistry, 2012 Abstract Schizophrenia is a severe psychiatric illness for which the precise etiology remains unknown. Studies using postmortem human brain have become increasingly important in schizophrenia research, providing an opportunity to directly investigate the diseased brain tissue. Gene expression profiling technologies have been used by a number of groups to explore the postmortem human brain and seek genes which show changes in expression correlated with schizophrenia. While this has been a valuable means of generating hypotheses, there is a general lack of consensus in the findings across studies. Expression profiling of postmortem human brain tissue is difficult due to the effect of various factors that can confound the data. The first aim of this thesis was to use control postmortem human cortex for identification of expression changes associated with several factors, specifically: age, sex, brain pH and postmortem interval. I conducted a meta-analysis across the control arm of eleven microarray datasets (representing over 400 subjects), and identified a signature of genes associated with each factor. These genes provide critical information towards the identification of problematic genes when investigating postmortem human brain in schizophrenia and other neuropsychiatric illnesses. The second aim of this thesis was to evaluate gene expression patterns in the prefrontal cortex associated with schizophrenia by exploring two methods of analysis: differential expression and coexpression.
    [Show full text]
  • (Ser739) Antibody-SL10016R
    SunLong Biotech Co.,LTD Tel: 0086-571- 56623320 Fax:0086-571- 56623318 E-mail:[email protected] www.sunlongbiotech.com Rabbit Anti-phospho-ZCWCC1 (Ser739) antibody SL10016R Product Name: phospho-ZCWCC1 (Ser739) Chinese Name: 磷酸化ZCWCC1抗体 AC004542.C22.1.; p-MORC2(Ser739); phospho-ZCWCC1(Ser739); CW type with coiled coil domain 1; KIAA0852; ZCW3; ZCWCC1; Zinc finger; Zinc finger CW type Alias: coiled coil domain protein 1; Zinc finger CW type with coiled coil domain 1; Zing finger CW type 3 zinc finger CW-type coiled-coil domain protein 1; MORC family CW-type zine finger 2; MORC2. Organism Species: Rabbit Clonality: Polyclonal React Species: Human,Mouse,Rat, WB=1:500-2000ELISA=1:500-1000IHC-P=1:400-800IHC-F=1:400-800ICC=1:100- 500IF=1:100-500（Paraffin sections need antigen repair） Applications: not yet tested in other applications. optimal dilutions/concentrations should be determined by the end user. Molecular weight: 114kDa Cellular localization: The nucleuscytoplasmic Form: Lyophilizedwww.sunlongbiotech.com or Liquid Concentration: 1mg/ml KLH conjugated synthesised phosphopeptide derived from human MORC2 around the immunogen: phosphorylation site of Ser739:KR(p-S)VA Lsotype: IgG Purification: affinity purified by Protein A Storage Buffer: 0.01M TBS(pH7.4) with 1% BSA, 0.03% Proclin300 and 50% Glycerol. Store at -20 °C for one year. Avoid repeated freeze/thaw cycles. The lyophilized antibody is stable at room temperature for at least one month and for greater than a year Storage: when kept at -20°C. When reconstituted in sterile pH 7.4 0.01M PBS or diluent of antibody the antibody is stable for at least two weeks at 2-4 °C.
    [Show full text]
  • 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
    [Show full text]
  • Downloaded the “Top Edge” Version
    bioRxiv preprint doi: https://doi.org/10.1101/855338; this version posted December 6, 2019. 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 4.0 International license. 1 Drosophila models of pathogenic copy-number variant genes show global and 2 non-neuronal defects during development 3 Short title: Non-neuronal defects of fly homologs of CNV genes 4 Tanzeen Yusuff1,4, Matthew Jensen1,4, Sneha Yennawar1,4, Lucilla Pizzo1, Siddharth 5 Karthikeyan1, Dagny J. Gould1, Avik Sarker1, Yurika Matsui1,2, Janani Iyer1, Zhi-Chun Lai1,2, 6 and Santhosh Girirajan1,3* 7 8 1. Department of Biochemistry and Molecular Biology, Pennsylvania State University, 9 University Park, PA 16802 10 2. Department of Biology, Pennsylvania State University, University Park, PA 16802 11 3. Department of Anthropology, Pennsylvania State University, University Park, PA 16802 12 4 contributed equally to work 13 14 *Correspondence: 15 Santhosh Girirajan, MBBS, PhD 16 205A Life Sciences Building 17 Pennsylvania State University 18 University Park, PA 16802 19 E-mail: [email protected] 20 Phone: 814-865-0674 21 1 bioRxiv preprint doi: https://doi.org/10.1101/855338; this version posted December 6, 2019. 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 4.0 International license. 22 ABSTRACT 23 While rare pathogenic copy-number variants (CNVs) are associated with both neuronal and non- 24 neuronal phenotypes, functional studies evaluating these regions have focused on the molecular 25 basis of neuronal defects.
    [Show full text]
  • Genome-Wide DNA Methylation Analysis on C-Reactive Protein Among Ghanaians Suggests Molecular Links to the Emerging Risk of Cardiovascular Diseases ✉ Felix P
    www.nature.com/npjgenmed ARTICLE OPEN Genome-wide DNA methylation analysis on C-reactive protein among Ghanaians suggests molecular links to the emerging risk of cardiovascular diseases ✉ Felix P. Chilunga 1 , Peter Henneman2, Andrea Venema2, Karlijn A. C. Meeks 3, Ana Requena-Méndez4,5, Erik Beune1, Frank P. Mockenhaupt6, Liam Smeeth7, Silver Bahendeka8, Ina Danquah9, Kerstin Klipstein-Grobusch10,11, Adebowale Adeyemo 3, Marcel M.A.M Mannens2 and Charles Agyemang1 Molecular mechanisms at the intersection of inflammation and cardiovascular diseases (CVD) among Africans are still unknown. We performed an epigenome-wide association study to identify loci associated with serum C-reactive protein (marker of inflammation) among Ghanaians and further assessed whether differentially methylated positions (DMPs) were linked to CVD in previous reports, or to estimated CVD risk in the same population. We used the Illumina Infinium® HumanMethylation450 BeadChip to obtain DNAm profiles of blood samples in 589 Ghanaians from the RODAM study (without acute infections, not taking anti-inflammatory medications, CRP levels < 40 mg/L). We then used linear models to identify DMPs associated with CRP concentrations. Post-hoc, we evaluated associations of identified DMPs with elevated CVD risk estimated via ASCVD risk score. We also performed subset analyses at CRP levels ≤10 mg/L and replication analyses on candidate probes. Finally, we assessed for biological relevance of our findings in public databases. We subsequently identified 14 novel DMPs associated with CRP. In post-hoc evaluations, we found 1234567890():,; that DMPs in PC, BTG4 and PADI1 showed trends of associations with estimated CVD risk, we identified a separate DMP in MORC2 that was associated with CRP levels ≤10 mg/L, and we successfully replicated 65 (24%) of previously reported DMPs.
    [Show full text]
  • Advances in Prognostic Methylation Biomarkers for Prostate Cancer
    cancers Review Advances in Prognostic Methylation Biomarkers for Prostate Cancer 1 1,2 1,2, 1,2, , Dilys Lam , Susan Clark , Clare Stirzaker y and Ruth Pidsley * y 1 Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia; [email protected] (D.L.); [email protected] (S.C.); [email protected] (C.S.) 2 St. Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales 2010, Australia * Correspondence: [email protected]; Tel.: +61-2-92958315 These authors have contributed equally. y Received: 22 September 2020; Accepted: 13 October 2020; Published: 15 October 2020 Simple Summary: Prostate cancer is a major cause of cancer-related death in men worldwide. There is an urgent clinical need for improved prognostic biomarkers to better predict the likely outcome and course of the disease and thus inform the clinical management of these patients. Currently, clinically recognised prognostic markers lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised, intermediate grade prostate cancer. Thus, there is major interest in identifying new molecular biomarkers to complement existing standard clinicopathological markers. DNA methylation is a frequent alteration in the cancer genome and offers potential as a reliable and robust biomarker. In this review, we provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis. We highlight advances in this field that have enabled the discovery of novel prognostic genes and discuss the potential of methylation biomarkers for noninvasive liquid-biopsy testing.
    [Show full text]
  • Regulates Target Gene Transcription Via Single-Stranded DNA Response Elements
    TLS/FUS (translocated in liposarcoma/fused in sarcoma) regulates target gene transcription via single-stranded DNA response elements Adelene Y. Tan1, Todd R. Riley, Tristan Coady, Harmen J. Bussemaker, and James L. Manley2 Department of Biological Sciences, Columbia University, New York, NY 10027 Contributed by James L. Manley, February 29, 2012 (sent for review December 9, 2011) TLS/FUS (TLS) is a multifunctional protein implicated in a wide range TLS has also been linked to splicing. It contains an RNP-type of cellular processes, including transcription and mRNA processing, RNA-binding domain and associates directly with SR protein as well as in both cancer and neurological disease. However, little is splicing factors (11). TET proteins have been detected in spliceo- currently known about TLS target genes and how they are recog- somes (12), and TLS was found associated with RNAP II and nized. Here, we used ChIP and promoter microarrays to identify snRNPs in a transcription and splicing complex in vitro (13). It is genes potentially regulated by TLS. Among these genes, we detected unclear whether and how TLS recruits splicing factors to sites of a number that correlate with previously known functions of TLS, active transcription, but one possibility is through its interaction and confirmed TLS occupancy at several of them by ChIP. We also with TBP and the TFIID complex. detected changes in mRNA levels of these target genes in cells where Here we provide insight into TLS regulation of RNAP II-tran- scribed genes. We used ChIP followed by promoter microarray TLS levels were altered, indicative of both activation and repression.
    [Show full text]
  • Shigella Sonnei and Shigella Flexneri
    THE GENETICS AND EPIDEMIOLOGY OF SHIGELLA SONNEI AND SHIGELLA FLEXNERI IN VIETNAM Benjamin Robert Sobkowiak A thesis submitted for the degree of Doctorate of Philosophy Department of Genetics, Evolution and Environment University College London 1 I, Benjamin Robert Sobkowiak, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. ………………………………………………………………………………… 2 Abstract Shigella sonnei is rapidly emerging as the primary agent of bacillary dysentery, or shigellosis, in many developing countries, replacing the historically more prevalent species, S. flexneri, in these regions. There have been various theories proposed to explain this phenomenon, including environmental changes and increased antimicrobial use, though the precise reasons for this shift are still uncertain. Here I present four studies investigating key ecological and genetic differences between S. sonnei and S. flexneri from a region that has undergone this pattern of species replacement, Vietnam. This work combines experimental and bioinformatics techniques with the aim of identifying the extent that differences in disinfectant sensitivity, chromosomal antimicrobial resistance profiles and gene content will contribute to the successful spread of S. sonnei over S. flexneri. Firstly, I conducted in vitro experimental work to characterise differences between species with respect to resistance to chlorine disinfection and tolerance to the detergent SDS. The mechanisms by which the bacteria respond to this treatment, in particular the role of efflux pumps, were then explored to determine whether any informative variation in these systems will explain any differences in disinfectant sensitivity. The availability of high quality whole genome sequences for ~150 of each Shigella species allowed for robust bioinformatics work to describe genomic variation between species.
    [Show full text]
  • Genetic Characterization of Caffeine Degradation by Bacteria and Its Potential Applications
    bs_bs_banner Minireview Genetic characterization of caffeine degradation by bacteria and its potential applications Ryan M. Summers,1† Sujit K. Mohanty,2† Sridhar production of chemical and fuels and development of Gopishetty3 and Mani Subramanian2,3* diagnostic tests have also been demonstrated. 1Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, Introduction USA. 2Department of Chemical and Biochemical Engineering Caffeine (1,3,7-trimethylxanthine) and related methylxan- and 3Center for Biocatalysis and Bioprocessing, The thines are natural purine alkaloids found in many plants University of Iowa, Coralville, IA 52241, USA. around the world (Ashihara and Crozier, 1999). These compounds are hypothesized to serve as natural insecti- cides, and have been shown to protect the plants Summary from insects and other predators (Nathanson, 1984; The ability of bacteria to grow on caffeine as sole Hollingsworth et al., 2002). Other possible reasons for carbon and nitrogen source has been known for over biosynthesis of caffeine include inhibition of plant matter 40 years. Extensive research into this subject has (Waller, 1989) and improved pollination (Wright et al., revealed two distinct pathways, N-demethylation and 2013). C-8 oxidation, for bacterial caffeine degradation. Methylxanthines are often consumed by humans in However, the enzymological and genetic basis for foods and beverages, including chocolate, coffee and tea. bacterial caffeine degradation has only recently been Coffee is a major worldwide agricultural commodity, with discovered. This review article discusses the recent millions of metric tons produced and distributed globally discoveries of the genes responsible for both each year (Summers et al., 2014). In addition to the N-demethylation and C-8 oxidation.
    [Show full text]
  • Supplementary Table 3 Gene Microarray Analysis: PRL+E2 Vs
    Supplementary Table 3 Gene microarray analysis: PRL+E2 vs. control ID1 Field1 ID Symbol Name M Fold P Value 69 15562 206115_at EGR3 early growth response 3 2,36 5,13 4,51E-06 56 41486 232231_at RUNX2 runt-related transcription factor 2 2,01 4,02 6,78E-07 41 36660 227404_s_at EGR1 early growth response 1 1,99 3,97 2,20E-04 396 54249 36711_at MAFF v-maf musculoaponeurotic fibrosarcoma oncogene homolog F 1,92 3,79 7,54E-04 (avian) 42 13670 204222_s_at GLIPR1 GLI pathogenesis-related 1 (glioma) 1,91 3,76 2,20E-04 65 11080 201631_s_at IER3 immediate early response 3 1,81 3,50 3,50E-06 101 36952 227697_at SOCS3 suppressor of cytokine signaling 3 1,76 3,38 4,71E-05 16 15514 206067_s_at WT1 Wilms tumor 1 1,74 3,34 1,87E-04 171 47873 238623_at NA NA 1,72 3,30 1,10E-04 600 14687 205239_at AREG amphiregulin (schwannoma-derived growth factor) 1,71 3,26 1,51E-03 256 36997 227742_at CLIC6 chloride intracellular channel 6 1,69 3,23 3,52E-04 14 15038 205590_at RASGRP1 RAS guanyl releasing protein 1 (calcium and DAG-regulated) 1,68 3,20 1,87E-04 55 33237 223961_s_at CISH cytokine inducible SH2-containing protein 1,67 3,19 6,49E-07 78 32152 222872_x_at OBFC2A oligonucleotide/oligosaccharide-binding fold containing 2A 1,66 3,15 1,23E-05 1969 32201 222921_s_at HEY2 hairy/enhancer-of-split related with YRPW motif 2 1,64 3,12 1,78E-02 122 13463 204015_s_at DUSP4 dual specificity phosphatase 4 1,61 3,06 5,97E-05 173 36466 227210_at NA NA 1,60 3,04 1,10E-04 117 40525 231270_at CA13 carbonic anhydrase XIII 1,59 3,02 5,62E-05 81 42339 233085_s_at OBFC2A oligonucleotide/oligosaccharide-binding
    [Show full text]